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Analytical Research: What is it, Importance + Examples

Finding knowledge is a loose translation of the word “research.” It’s a systematic and scientific way of researching a particular subject. As a result, research is a form of scientific investigation that seeks to learn more. Analytical research is one of them.

Any kind of research is a way to learn new things. In this research, data and other pertinent information about a project are assembled; after the information is gathered and assessed, the sources are used to support a notion or prove a hypothesis.

An individual can successfully draw out minor facts to make more significant conclusions about the subject matter by using critical thinking abilities (a technique of thinking that entails identifying a claim or assumption and determining whether it is accurate or untrue).

What is analytical research?

This particular kind of research calls for using critical thinking abilities and assessing data and information pertinent to the project at hand.

Determines the causal connections between two or more variables. The analytical study aims to identify the causes and mechanisms underlying the trade deficit’s movement throughout a given period.

It is used by various professionals, including psychologists, doctors, and students, to identify the most pertinent material during investigations. One learns crucial information from analytical research that helps them contribute fresh concepts to the work they are producing.

Some researchers perform it to uncover information that supports ongoing research to strengthen the validity of their findings. Other scholars engage in analytical research to generate fresh perspectives on the subject.

Various approaches to performing research include literary analysis, Gap analysis , general public surveys, clinical trials, and meta-analysis.

Importance of analytical research

The goal of analytical research is to develop new ideas that are more believable by combining numerous minute details.

The analytical investigation is what explains why a claim should be trusted. Finding out why something occurs is complex. You need to be able to evaluate information critically and think critically. 

This kind of information aids in proving the validity of a theory or supporting a hypothesis. It assists in recognizing a claim and determining whether it is true.

Analytical kind of research is valuable to many people, including students, psychologists, marketers, and others. It aids in determining which advertising initiatives within a firm perform best. In the meantime, medical research and research design determine how well a particular treatment does.

Thus, analytical research can help people achieve their goals while saving lives and money.

Methods of Conducting Analytical Research

Analytical research is the process of gathering, analyzing, and interpreting information to make inferences and reach conclusions. Depending on the purpose of the research and the data you have access to, you can conduct analytical research using a variety of methods. Here are a few typical approaches:

Quantitative research

Numerical data are gathered and analyzed using this method. Statistical methods are then used to analyze the information, which is often collected using surveys, experiments, or pre-existing datasets. Results from quantitative research can be measured, compared, and generalized numerically.

Qualitative research

In contrast to quantitative research, qualitative research focuses on collecting non-numerical information. It gathers detailed information using techniques like interviews, focus groups, observations, or content research. Understanding social phenomena, exploring experiences, and revealing underlying meanings and motivations are all goals of qualitative research.

Mixed methods research

This strategy combines quantitative and qualitative methodologies to grasp a research problem thoroughly. Mixed methods research often entails gathering and evaluating both numerical and non-numerical data, integrating the results, and offering a more comprehensive viewpoint on the research issue.

Experimental research

Experimental research is frequently employed in scientific trials and investigations to establish causal links between variables. This approach entails modifying variables in a controlled environment to identify cause-and-effect connections. Researchers randomly divide volunteers into several groups, provide various interventions or treatments, and track the results.

Observational research

With this approach, behaviors or occurrences are observed and methodically recorded without any outside interference or variable data manipulation . Both controlled surroundings and naturalistic settings can be used for observational research . It offers useful insights into behaviors that occur in the actual world and enables researchers to explore events as they naturally occur.

Case study research

This approach entails thorough research of a single case or a small group of related cases. Case-control studies frequently include a variety of information sources, including observations, records, and interviews. They offer rich, in-depth insights and are particularly helpful for researching complex phenomena in practical settings.

Secondary data analysis

Examining secondary information is time and money-efficient, enabling researchers to explore new research issues or confirm prior findings. With this approach, researchers examine previously gathered information for a different reason. Information from earlier cohort studies, accessible databases, or corporate documents may be included in this.

Content analysis

Content research is frequently employed in social sciences, media observational studies, and cross-sectional studies. This approach systematically examines the content of texts, including media, speeches, and written documents. Themes, patterns, or keywords are found and categorized by researchers to make inferences about the content.

Depending on your research objectives, the resources at your disposal, and the type of data you wish to analyze, selecting the most appropriate approach or combination of methodologies is crucial to conducting analytical research.

Examples of analytical research

Analytical research takes a unique measurement. Instead, you would consider the causes and changes to the trade imbalance. Detailed statistics and statistical checks help guarantee that the results are significant.

For example, it can look into why the value of the Japanese Yen has decreased. This is so that an analytical study can consider “how” and “why” questions.

Another example is that someone might conduct analytical research to identify a study’s gap. It presents a fresh perspective on your data. Therefore, it aids in supporting or refuting notions.

Descriptive vs analytical research

Here are the key differences between descriptive research and analytical research:

The study of cause and effect makes extensive use of analytical research. It benefits from numerous academic disciplines, including marketing, health, and psychology, because it offers more conclusive information for addressing research issues.

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A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining

Md saiful islam.

1 Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA; [email protected] (M.S.I.); [email protected] (M.M.H.); [email protected] (X.W.); [email protected] (H.D.G.)

Md Mahmudul Hasan

Xiaoyi wang, hayley d. germack.

2 National Clinician Scholars Program, Yale University School of Medicine, New Haven, CT 06511, USA

3 Bouvé College of Health Sciences, Northeastern University, Boston, MA 02115, USA

Md Noor-E-Alam

Associated data.

The growing healthcare industry is generating a large volume of useful data on patient demographics, treatment plans, payment, and insurance coverage—attracting the attention of clinicians and scientists alike. In recent years, a number of peer-reviewed articles have addressed different dimensions of data mining application in healthcare. However, the lack of a comprehensive and systematic narrative motivated us to construct a literature review on this topic. In this paper, we present a review of the literature on healthcare analytics using data mining and big data. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a database search between 2005 and 2016. Critical elements of the selected studies—healthcare sub-areas, data mining techniques, types of analytics, data, and data sources—were extracted to provide a systematic view of development in this field and possible future directions. We found that the existing literature mostly examines analytics in clinical and administrative decision-making. Use of human-generated data is predominant considering the wide adoption of Electronic Medical Record in clinical care. However, analytics based on website and social media data has been increasing in recent years. Lack of prescriptive analytics in practice and integration of domain expert knowledge in the decision-making process emphasizes the necessity of future research.

1. Introduction

Healthcare is a booming sector of the economy in many countries [ 1 ]. With its growth, come challenges including rising costs, inefficiencies, poor quality, and increasing complexity [ 2 ]. U.S. healthcare expenditures increased by 123% between 2010 and 2015—from $2.6 trillion to $3.2 trillion [ 3 ]. Inefficient—non-value added tasks (e.g., readmissions, inappropriate use of antibiotics, and fraud)—constitutes 21–47% of this enormous expenditure [ 4 ]. Some of these costs were associated with low quality care—researchers found that approximately 251,454 patients in the U.S. die each year due to medical errors [ 5 ]. Better decision-making based on available information could mitigate these challenges and facilitate the transition to a value-based healthcare industry [ 4 ]. Healthcare institutions are adopting information technology in their management system [ 6 ]. A large volume of data is collected through this system on a regular basis. Analytics provides tools and techniques to extract information from this complex and voluminous data [ 2 ] and translate it into information to assist decision-making in healthcare.

Analytics is the way of developing insights through the efficient use of data and application of quantitative and qualitative analysis [ 7 ]. It can generate fact-based decisions for “planning, management, measurement, and learning” purposes [ 2 ]. For instance, the Centers for Medicare and Medicaid Services (CMS) used analytics to reduce hospital readmission rates and avert $115 million in fraudulent payment [ 8 ]. Use of analytics—including data mining, text mining, and big data analytics—is assisting healthcare professionals in disease prediction, diagnosis, and treatment, resulting in an improvement in service quality and reduction in cost [ 9 ]. According to some estimates, application of data mining can save $450 billion each year from the U.S. healthcare system [ 10 ]. In the past ten years, researchers have studied data mining and big data analytics from both applied (e.g., applied to pharmacovigilance or mental health) and theoretical (e.g., reflecting on the methodological or philosophical challenges of data mining) perspectives.

In this review, we systematically organize and summarize the published peer-reviewed literature related to the applied and theoretical perspectives of data mining. We classify the literature by types of analytics (e.g., descriptive, predictive, prescriptive), healthcare application areas (i.e., clinical decision support, mental health), and data mining techniques (i.e., classification, sequential pattern mining); and we report the data source used in each review paper which, to our best knowledge, has never done before.

Motivation and Scope

There is a large body of recently published review/conceptual studies on healthcare and data mining. We outline the characteristics of these studies—e.g., scope/healthcare sub-area, timeframe, and number of papers reviewed—in Table 1 . For example, one study reviewed awareness effect in type 2 diabetes published between 2001 and 2005, identifying 18 papers [ 11 ]. This current review literature is limited—most of the papers listed in Table 1 did not report the timeframe and/or number of papers reviewed (expressed as N/A).

Characteristics of existing review/conceptual studies on the related topics.

N/A represents Not Reported.

There is no comprehensive review available which presents the complete picture of data mining application in the healthcare industry. The existing reviews (16 out of 21) are either focused on a specific area of healthcare, such as clinical medicine (three reviews) [ 16 , 17 , 19 ], adverse drug reaction signal detection (two reviews) [ 25 , 26 ], big data analytics (four reviews) [ 8 , 10 , 22 , 24 ], or the application and performance of data mining algorithms (five reviews) [ 9 , 13 , 14 , 20 , 21 ]. Two studies focused on specific diseases (diabetes [ 11 ], skin diseases [ 18 ]). To the best of our knowledge, none of these studies present the universe of research that has been done in this field. These studies are also limited in the rigor of their methodology except for four articles [ 11 , 16 , 22 , 25 ], which provide key insights including the timeframe covered in the study, database search, and literature inclusion or exclusion criteria, but they are limited in their scope of topics covered (see Table 1 ).

Beyond condensing the applied literature, our review also adds to the body of theoretical reviews in the analytics literature. Current theoretical reviews are limited to methodological challenges and techniques to overcome those challenges [ 15 , 16 , 27 ] and application and impact of big data analytics in healthcare [ 23 ]. In summary, the current reviews listed in Table 1 lacks in (1) width of coverage in terms of application areas, (2) breadth of data mining techniques, (3) assessment of literature quality, and (4) systematic selection and analysis of papers. In this review, we aim to fill the above-mentioned gaps. We add to this literature by covering the applied and theoretical perspective of data mining and big data analytics in healthcare with a more comprehensive and systematic approach.

2. Methodology

The methodology of our review followed the checklist proposed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 28 ]. We assessed the quality of the selected articles using JBI Critical Appraisal Checklist for analytical cross sectional studies [ 29 ] and Critical Appraisal Skills Programme (CASP) qualitative research checklist [ 30 ].

2.1. Input Literature

Selected literature and their selection process for the review are described in this section. Initially a two phase advance keyword search was conducted on the database Web of Science and one phase (Phase 2) search in PubMed and Google Scholar with time filter 1 January 2005 to 31 December 2016 in “All Fields”. Journal articles written in English was added as additional filters. Keywords listed in Table 2 were used in different phases. The complete search procedure was conducted using the following procedure:

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart [ 28 ] illustrating the literature search process.

• Exclusion criteria: This included articles reporting on results of: qualitative study, survey, focus group study, feasibility study, monitoring device, team relationship measurement, job satisfaction, work environment, “what-if” analysis, data collection technique, editorials or short report, merely mention data mining, and articles not published in international journals . Duplicates were removed (33 articles). Finally, 117 articles were retained for the review. Figure 1 provides a PRISMA [ 28 ] flow diagram of the review process and Supplementary Information File S1 (Table S1) provides the PRISMA checklist.

Keywords for database search.

1 A logical operator used between the keywords during database search. 2 Cancer was listed independently because other dominant associations have the word “disease” associated with them (i.e., heart disease, skin disease, mental disease etc.).

2.2. Quality Assessment and Processing Steps

The full text of each of the 117 articles was reviewed separately by two researchers to eliminate bias [ 28 ]. To assess the quality of the cross sectional studies, we applied the JBI Critical Appraisal Checklist for Analytical Cross Sectional Studies [ 29 ]. For theoretical papers, we applied the Critical Appraisal Skills Programme (CASP) qualitative research checklist [ 30 ]. We modified the checklist items, as not all items specified in the JBI or CASP checklists were applicable to studies on healthcare analytics ( Supplementary Materials Table S2 ). We evaluated each article’s quality based on inclusion of: (1) clear objective and inclusion criteria; (2) detailed description of sample population and variables; (3) data source (e.g., hospital, database, survey) and format (e.g., structured Electronic Medical Record (EMR), International Classification of Diseases code, unstructured text, survey response); (4) valid and reliable data collection; (5) consideration of ethical issues; (6) detailed discussion of findings and implications; (7) valid and reliable measurement of outcomes; and (8) use of an appropriate data mining tool for cross-sectional studies and (1) clear statement of aims; (2) appropriateness of qualitative methodology; (3) appropriateness of research design; (4) clearly stated findings; and (5) value of research for the theoretical papers. Summary characteristics from any study fulfilling these criteria were included in the final data aggregation ( Supplementary Materials Table S3 ).

To summarize the body of knowledge, we adopted the three-step processing methodology outlined by Levy and Ellis [ 31 ] and Webster and Watson [ 32 ] ( Figure 2 ). During the review process, information was extracted by identifying and defining the problem, understanding the solution process and listing the important findings (“Know the literature”). We summarized and compared each article with the articles associated with the similar problems (“Comprehend the literature”). This simultaneously ensured that any irrelevant information was not considered for the analysis. The summarized information was stored in a spreadsheet in the form of a concept matrix as described by Webster and Watson [ 32 ]. We updated the concept matrix periodically, after completing every 20% of the articles which is approximately 23 articles, to include new findings (“Apply”). Based on the concept matrix, we developed a classification scheme (see Figure 3 ) for further comparison and contrast. We established an operational definition (see Table 3 ) for each class and same class articles were separated from the pool (“Analyze and Synthesis”). We compared classifications between researchers and we resolved disagreements (on six articles) by discussion. The final classification provided distinguished groups of articles with summary, facts, and remarks made by the reviewers (“Evaluate”).

Three stages of effective literature review process, adapted from Levy and Ellis [ 31 ].

Classification scheme of the literature.

Operational definition of the classes.

* Most of the definitions listed in this table are well established in literature and well know. Therefore, we did not use any specific reference. However, for some classes, specifically for types of analytics and data, varying definitions are available in the literature. We cited the sources of those definitions.

2.3. Results

The network diagram of selected articles and the keywords listed by authors in Figure 4 represents the outcome of the methodological review process. We elaborate on the resulting output in the subsequent sections using the structure of the developed classification scheme ( Figure 3 ). We also report the potential future research areas.

Visualization of high-frequency keywords of the reviewed papers. The white circles symbolize the articles and the blue circles represent keywords. The keywords that occurred only once are eliminated as well as the corresponding articles. The size of the blue circles and the texts represent how often that keyword is found. The size of the white circles is proportional to the number of keywords used in that article. The links represents the connections between the keywords and the articles. For example, if a blue circle has three links (e.g., Decision-Making) that means that keyword was used in three articles. The diagram is created with the open source software Gephi [ 34 ].

2.3.1. Methodological Quality of the Studies

Out of 117 papers included in this review, 92 applied analytics and 25 were qualitative/conceptual. The methodological quality of the analytical studies (92 out of 117) were evaluated by a modified version of 8 yes/no questions suggested in JBI Critical Appraisal Checklist for Analytical Cross Sectional Studies [ 29 ]. Each question contains 1 point (1 if the answer is Yes or 0 for No). The score achieved by each paper is provided in the final column of Supplementary Materials Table S3 . On average, each paper applying analytics scored 7.6 out of 8, with a range of 6–8 points. Major drawbacks were the absence of data source and performance measure of data mining algorithms. Out of 92 papers, 23 did not evaluate or mention the performance of the applied algorithms and eight did not mention the source of the data. However, all the papers in healthcare analytics had a clear objective and a detailed discussion of sample population and variables. Data used in each paper was either de-identified/anonymized or approved by institute’s ethical committee to ensure patient confidentiality.

We applied the Critical Appraisal Skills Programme (CASP) qualitative research checklist [ 30 ] to evaluate the quality of the 25 theoretical papers. Five questions (out of ten) in that checklist were not applicable to the theoretical studies. Therefore, we evaluated the papers in this section in a five-point scale (1 if the answer is Yes or 0 for No). Papers included in this review showed high methodological quality as 21 papers (out of 25) scored 5. The last column in the Supplementary Materials Table S3 provides the score achieved by individual papers.

2.3.2. Distribution by Publication Year

The distribution of articles published related to data mining and big data analytics in healthcare across the timeline of the study (2005–2016) is presented in Figure 5 . The distribution shows an upward trend with at least two articles in each year and more than ten articles in the last four years. Additionally, this trend represents the growing interest of government agencies, healthcare practitioners, and academicians in this interdisciplinary field of research. We anticipate that the use of analytics will continue in the coming years to address rising healthcare costs and need of improved quality of care.

Distribution of publication by year (117 articles).

2.3.3. Distribution by Journal

Articles published in 74 different journals were included in this study. Table 4 lists the top ten journals in terms of number of papers published. Expert System with Application was the dominant source of literature on data mining application in healthcare with 7 of the 117 articles. Journals were interdisciplinary in nature and spanned computational journals like IEEE Transection on Information Technology in Biomedicine to policy focused journal like Health Affairs . Articles published in Expert System with Application, Journal of Medical Systems, Journal of the American Medical Informatics Association, Healthcare Informatics Research were mostly related to analytics applied in clinical decision-making and healthcare administration. On the other hand, articles published in Health Affairs were predominantly conceptual in nature addressing policy issues, challenges, and potential of this field.

Top 10 journals on application of data mining in healthcare.

3. Healthcare Analytics

Out of 117 articles, 92 applied analytics for decision-making in healthcare. We discuss the types of analytics, the application area, the data, and the data mining techniques used in these articles and summarize them in Supplementary Materials Table S4 .

3.1. Types of Analytics

We identified three types of analytics in the literature: descriptive (i.e., exploration and discovery of information in the dataset), predictive (i.e., prediction of upcoming events based on historical data) and prescriptive (i.e., utilization of scenarios to provide decision support). Five of the 92 studies employed both descriptive and predictive analytics. In Figure 6 , which displays the percentage of healthcare articles using each analytics type, we show that descriptive analytics is the most commonly used in healthcare (48%). Descriptive analytics was dominant in all the application areas except in clinical decision support. Among the application areas, pharmacovigilance studies only used descriptive analytics as this application area is focused on identifying an association between adverse drug effects with medication. Predictive analytics was used in 43% articles. Among application areas, clinical decision support had the highest application of predictive analytics as many studies in this area are involved in risk and morbidity prediction of chest pain, heart attack, and other diseases. In contrast, use of prescriptive analytics was very uncommon (only 9%) as most of these studies were focused on either a specific population base or a specific disease scenario. However, some evidence of prescriptive analytics was found in public healthcare, administration, and mental health (see Supplementary Materials Table S4 ). These studies create a data repository and/or analytical platform to facilitate decision-making for different scenarios.

Types of analytics used in literature. ( a ) Percentage of analytics type; ( b ) Analytics type by application area.

3.2. Types of Data

To identify types of data, we adopted the classification scheme identified by Raghupathi and Raghupathi [ 23 ] which takes into account the nature (i.e., text, image, number, electronic signal), source, and collection method of data together. Table 3 provides the operational definitions of taxonomy adopted in this paper. Figure 7 a presents the percentage of data type used and Figure 7 b, the number of usage by application area. As expected, human generated (HG) data, including EMR, Electronic Health Record (HER), and Electronic Patient Record (EPR), is the most commonly (77%) used form. Web or Social media (WS) data is the second dominant (11%) type of data, as increasingly more people are using social media now and ongoing digital revolution in the healthcare sector [ 35 ]. In addition, recent development in Natural Language Processing (NLP) techniques is making the use of WS data easier than before [ 36 ]. The other three types of data (SD, BT, and BM) consist of only about 12% of total data usage, but popularity and market growth of wearable personal health tracking devices [ 37 ] may increase the use of SD and BM data.

Percentage of data type used ( a ) and type of data used by application area ( b ).

3.3. Data Mining Techniques

Data mining techniques used in the articles reviewed include classification, clustering, association, anomaly detection, sequential pattern mining, regression, and data warehousing. While elaborate description of each technique and available algorithms is out of scope of this review, we report the frequency of each technique and its sector wise distribution in Figure 8 a,b, respectively. Among the articles included in the review, 57 used classification techniques to analyze data. Association and clustering were used in 21 and 18 articles, respectively. Use of other techniques was less frequent.

Utilization of data mining techniques, ( a ) by percentage and ( b ) by application area.

A high proportion (8 out of 9) of pharmacovigilance papers used association. Use of classification was dominant in every sector except pharmacovigilance ( Figure 8 b). Data warehousing was mostly used in healthcare administration ( Figure 8 b).

We delved deeper into classification as it was utilized in the majority (57 out of 92) of the papers. There are a number of algorithms used for classification, which we present in a word cloud in Figure 9 . Support Vector Machine (SVM), Artificial Neural Network (ANN), Logistic Regression (LR), Decision Tree (DT), and DT based algorithms were the most commonly used. Random Forest (RF), Bayesian Network and Fuzzy-based algorithms were also often used. Some papers (three papers) introduced novel algorithms for specific applications. For example, Yeh et al. [ 38 ] developed discrete particle swarm optimization based classification algorithm to classify breast cancer patients from a pool of general population. Self-organizing maps and K-means were the most commonly used clustering algorithm in healthcare. Performance (e.g., accuracy, sensitivity, specificity, area under the ROC curve, positive predictive value, negative predictive value etc.) of each of these algorithms varied by application and data type. We recommend applying multiple algorithms and choosing the one which achieves the best accuracy.

Word cloud [ 39 ] with classification algorithms.

4. Application of Analytics in Healthcare

Table 3 provides the operational definitions of the six application areas (i.e., clinical decision support, healthcare administration, privacy and fraud detection, mental health, public health, and pharmacovigilance) identified in this review. Figure 10 shows the percentage of articles in each area. Among different classes in healthcare analytics, data mining application is mostly applied in clinical decision support (42%) and administrative purposes (32%). This section discusses the application of data mining in these areas and identifies the main aims of these studies, performance gaps, and key features.

Percentage of papers utilized healthcare analytics by application area (92 articles out of 117).

4.1. Clinical Decision Support

Clinical decision support consists of descriptive and/or predictive analysis mostly related to cardiovascular disease (CVD), cancer, diabetes, and emergency/critical care unit patients. Some studies developed novel data mining algorithms which we review. Table 5 describes the topics investigated and data sources used by papers using clinical decision-making, organized by major diseases category.

Topics and data sources of papers using clinical decision-making, organized by major disease category.

4.1.1. Cardiovascular Disease (CVD)

CVD is one of the most common causes of death globally [ 45 , 77 ]. Its public health relevance is reflected in the literature—it was addressed by seven articles (18% of articles in clinical decision support).

Risk factors related to Coronary Heart Disease (CHD) were distilled into a decision tree based classification system by researchers [ 40 ]. The authors investigated three events: Coronary Artery Bypass Graft Surgery (CABG), Percutaneous Coronary Intervention (PCI), and Myocardial Infarction (MI). They developed three models: CABG vs. non-CABG, PCI vs. non-PCI, and MI VS non-MI. The risk factors for each event were divided into four groups in two stages. The risk factors were separated into before and after the event at the 1st stage and modifiable (e.g., smoking habit or blood pressure) and non-modifiable (e.g., age or sex) at the 2nd stage for each group. After classification, the most important risk factors were identified by extracting the classification rules. The Framingham equation [ 78 ]—which is widely used to calculate global risk for CHD was used to calculate the risk for each event. The most important risk factors identified were age, smoking habit, history of hypertension, family history, and history of diabetes. Other studies on CHD show similar results [ 79 , 80 , 81 ]. This study had implications for healthcare providers and patients by identifying risk factors to specifically target, identify and in the case of modifiable factors, reduce CHD risk [ 40 ].

Data mining has also been applied to diagnose Coronary Artery Disease (CAD) [ 41 ]. Researchers showed that in lieu of existing diagnostic methods (i.e., Coronary Angiography (CA))—which are costly and require high technical skill—data mining using existing data like demographics, medical history, simple physical examination, blood tests, and noninvasive simple investigations (e.g., heart rate, glucose level, body mass index, creatinine level, cholesterol level, arterial stiffness) is simple, less costly, and can be used to achieve a similar level of accuracy. Researchers used a four-step classification process: (1) Decision tree was used to classify the data; (2) Crisp classification rules were generated; (3) A fuzzy model was created by fuzzifying the crisp classifier rules; and (4) Fuzzy model parameters were optimized and the final classification was made. The proposed optimized fuzzy model achieved 73% of prediction accuracy and improved upon an existing Artificial Neural Network (ANN) by providing better interpretability.

Traditional data mining and machine learning algorithms (e.g., probabilistic neural networks and SVM) may not be advanced enough to handle the data used for CVD diagnosis, which is often uncertain and highly dimensional in nature. To tackle this issue, researchers [ 42 ] proposed a Fuzzy standard additive model (SAM) for classification. They used adaptive vector quantization clustering to generate unsupervised fuzzy rules which were later optimized (minimized the number of rules) by Genetic Algorithm (GA). They then used the incremental form of a supervised technique, Gradient Descent, to fine tune the rules. Considering the highly time consuming process of the fuzzy system given large number of features in the data, the number of features was reduced with wavelet transformation. The proposed algorithm achieved better accuracy (78.78%) than the probabilistic neural network (73.80%), SVM (74.27%), fuzzy ARTMAP (63.46%), and adaptive neuro-fuzzy inference system (74.90%). Another common issue in cardiovascular event risk prediction is the censorship of data (i.e., the patient’s condition is not followed up after they leave hospital and until a new event occurs; the available data becomes right-censored). Elimination and exclusion of the censored data create bias in prediction results. To address the censorship of the data in their study on CVD event risk prediction after time, two studies [ 43 , 44 ] used Inverse Probability Censoring Weighting (IPCW). IPCW is a pre-processing step used to calculate the weights on data which are later classified using Bayesian Network. One of these studies [ 43 ] provided an IPCW based system which is compatible with any machine learning algorithm.

Electrocardiography (ECG)—non-invasive measurement of the electrical activity of the heartbeat—is the most commonly used medical studies in the assessment of CVD. Machine learning offers potential optimization of traditional ECG assessment which requires decompressing before making any diagnosis. This process takes time and large space in computers. In one study, researchers [ 45 ] developed a framework for real-time diagnosis of cardiovascular abnormalities based on compressed ECG. To reduce diagnosis time—which is critical for clinical decision-making regarding appropriate and timely treatment—they proposed and tested a mobile based framework and applied it to wireless monitoring of the patient. The ECG was sent to the hospital server where the ECG signals were divided into normal and abnormal clusters. The system detected cardiac abnormality with 97% accuracy. The cluster information was sent to patient’s mobile phone; and if any life-threatening abnormality was detected, the mobile phone alerted the hospital or the emergency personnel.

Data analytics have also been applied to more rare CVDs. One study [ 46 ] developed an intervention prediction model for Hypoplastic Left Heart Syndrome (HLHS). HLHS is a rare form of fatal heart disease in infants, which requires surgery. Post-surgical evaluation is critical as patient condition can shift very quickly. Indicators of wellness of the patients are not easily or directly measurable, but inferences can be made based on measurable physiological parameters including pulse, heart rhythm, systemic blood pressure, common atrial filling pressure, urine output, physical exam, and systemic and mixed venous oxygen saturations. A subtle physiological shift can cause death if not noticed and intervened upon. To help healthcare providers in decision-making, the researchers developed a prediction model by identifying the correlation between physiological parameters and interventions. They collected 19,134 records of 17 patients in Pediatric Intensive Care Units (PICU). Each record contained different physiological parameters measured by devices and noted by nurses. For each record, a wellness score was calculated by the domain experts. After classifying the data using a rough set algorithm, decision rules were extracted for each wellness score to aid in making intervention plans. A new measure for feature selection—Combined Classification Quality (CCQ)—was developed by considering the effect of variations in a feature values and distinct outcome each feature value leads to. Authors showed that higher value of CCQ leads to higher classification accuracy which is not always true for commonly used measure classification quality (CQ). For example, two features with CQ value of 1 leads to very different classification accuracy—35.5% and 75%. Same two features had CCQ value 0.25 and 0.40, features with 0.40 CCQ produced 75% classification accuracy. By using CCQ instead of CQ, researchers can avoid such inconsistency.

4.1.2. Diabetes

The disease burden related to diabetes is high and rising in every country. According to the World Health Organization’s (WHO) prediction, it will become the seventh leading cause of death by 2030 [ 82 ]. Data mining has been applied to identify rare forms of diabetes, identify the important factors to control diabetes, and explore patient history to extract knowledge. We reviewed 7 studies that applied healthcare analytics to diabetes.

Researchers extracted knowledge about diabetes treatment pathways and identified rare forms and complications of diabetes using a three level clustering framework from examination history of diabetic patients [ 48 ]. In this three-level clustering framework, the first level clustered patients who went through regular tests for monitoring purposes (e.g., checkup visit, glucose level, urine test) or to diagnose diabetes-related complications (e.g., eye tests for diabetic retinopathy). The second level explored patients who went through diagnosis for specific or different diabetic complications only (e.g., cardiovascular, eye, liver, and kidney related complications). These two level produced 2939 outliers out of 6380 patients. At the third level, authors clustered these outlier patients to gain insight about rare form of diabetes or rare complications. A density based clustering algorithm, DBSCAN, was used for clustering as it doesn’t require to specify the number of clusters apriori and is less sensitive to noise and outliers. This framework for grouping patients by treatment pathway can be utilized to evaluate treatment plans and costs. Another group of researchers [ 49 ] investigated the important factors related to type 2 diabetes control. They used feature selection via supervised model construction (FSSMC) to select the important factors with rank/order. They applied naïve bayes, IB1 and C4.5 algorithm with FSSMC technique to classify patients having poor or good diabetes control and evaluate the classification efficiency for different subsets of features. Experiments performed with physiological and laboratory information collected from 3857 patients showed that the classifier algorithms performed best (1–3% increase in accuracy) with the features selected by FSSMC. Age, diagnosis duration, and Insulin treatment were the top three important factors.

Data analytics have also been applied to identify patients with type 2 diabetes. In one study [ 52 ], using fragmented data from two different healthcare centers, researchers evaluated the effect of data fragmentation on a high throughput clinical phenotyping (HTCP) algorithm to identify patients at risk of developing type 2 diabetes. When a patient visits multiple healthcare centers during a study period, his/her data is stored in different EMRs and is called fragmented. In such cases, using HTPC algorithm can lead to improper classification. An experiment performed in a rural setting showed that using data from two healthcare centers instead of one decreased the false negative rate from 32.9% to 0%. In another study, researchers [ 51 ] utilized sparse logistic regression to predict type 2 diabetes risk from insurance claims data. They developed a model that outperformed the traditional risk prediction methods for large data sets and data sets with missing value cases by increasing the AUC value from 0.75 to 0.80. The dataset contained more than 500 features including demography, specific medical conditions, and comorbidity. And in another study, researchers [ 53 ] developed prediction and risk diagnosis model using a hybrid system with SVM. Using features like blood pressure, fasting blood sugar, two-hour post-glucose tolerance, cholesterol level along with other demographic and anthropometric features, the SVM algorithm was able to predict diabetes risk with 97% accuracy. One reason for achieving high accuracy compared to the study using insurance claims data [ 51 ] is the structured nature of the data which came from a cross-sectional survey on diabetes.

Different statistical and machine learning algorithms are available for classification purposes. Researchers [ 50 ] compared the performance of two statistical method (LR and Fisher linear discriminant analysis) and four machine learning algorithms (SVM (using radial basis function kernel), ANN, Random Forest, and Fuzzy C-mean) for predicting diabetes diagnosis. Ten features (age, gender, BMI, waist circumference, smoking, job, hypertension, residential region (rural/urban), physical activity, and family history of diabetes) were used to test the classification performance (diabetes or no diabetes). Parameters for ANN and SVM were optimized through Greedy search. SVM showed best performance in all performance measures. SVM was at least 5% more accurate than other classification techniques. Statistical methods performed similar to the other machine learning algorithms. This study was limited by a low prevalence of diabetes in the dataset, however, which can cause poor classification performance. Researchers [ 47 ] also proposed a novel pattern recognition algorithm by using convolutional nonnegative matrix factorization. They considered a patient as an entity and each of patients’ visit to the doctor, prescriptions, test result, and diagnosis are considered as an event over time. Finding such patterns can be helpful to group similar patients, identify their treatment pathway as well as patient management. Though they did not compare the pattern recognition accuracy with existing methods like single value decomposition (SVD), the matrix-like representation makes it intuitive.

4.1.3. Cancer

Cancer is another major threat to public health [ 83 ]. Machine learning has been applied to cancer patients to predict survival, and diagnosis. We reviewed five studies that applied healthcare analytics to cancer.

Despite many advances in treatment, accurate prediction of survival in patients with cancer remains challenging considering the heterogeneity of cancer complexity, treatment options, and patient population. Survival of prostate cancer patients has been predicted using a classification model [ 54 ]. The model used a public database-SEER (Surveillance, Epidemiology, and End Result) and applied a stratified ten-fold sampling approach. Survival prediction among prostate cancer patients was made using DT, ANN and SVM algorithm. SVM outperformed other algorithms with 92.85% classification accuracy wherein DT and ANN achieved 90% and 91.07% accuracy respectively. This same database has been used to predict survival of lung cancer patients [ 56 ]. After preprocessing the 11 features available in the data set, authors identified two features (1. removed and examined regional lymph node count and 2. malignant/in-situ tumor count) which had the strongest predictive power. They used several supervised classification methods on the preprocessed data; ensemble voting of five decision tree based classifiers and meta-classifiers (J48 DT, RF, LogitBoost, Random Subspace, and Alternating DT) provided the best performance—74% for 6 months, 75% for 9 months, 77% for 1 year, 86% for 2 years, and 92% for 5 years survival. Using this technique, they developed an online lung cancer outcome calculator to estimate the risk of mortality after 6 months, 9 months, 1 year, 2 years and 5 years of diagnosis.

In addition to predicting survival, machine learning techniques have also been used to identify patients with cancer. Among patients with breast cancer, researchers [ 38 ] have proposed a new hybrid algorithm to classify breast cancer patient from patients who do not have breast cancer. They used correlation and regression to select the significant features at the first stage. Then, at the second stage, they used discrete Particle Swarm Optimization (PSO) to classify the data. This hybrid algorithm was applied to Wisconsin Breast Cancer Data set available at UCI machine learning repository. It achieved better accuracy (98.71%) compared to a genetic algorithm (GA) (96.14%) [ 84 ] and another PSO-based algorithm (93.4%) [ 85 ].

Machine learning has also been used to identify the nature of cancer (benign or malignant) and to understand demographics related to cancer. Among patients with breast cancer, researchers [ 42 ] applied the Fuzzy standard additive model (SAM) with GA (discussed earlier in relation to CVD)-predicting the nature of breast cancer (benign or malignant). They used a UCI machine learning repository which was capable of classifying uncertain and high dimensional data with greater accuracy (by 1–2%). Researchers have also used big data [ 55 ] to create a visualization tool to provide a dynamic view of cancer statistics (e.g., trend, association with other diseases), and how they are associated with different demographic variables (e.g., age, sex) and other diseases (e.g., diabetes, kidney infection). Use of data mining provided a better understanding of cancer patients both at demographic and outcome level which in terms provides an opportunity of early identification and intervention.

4.1.4. Emergency Care

The Emergency department (ED) is the primary route to hospital admission [ 58 ]. In 2011, 20% of US population had at least one or more visits to the ED [ 86 ]. EDs are experiencing significant financial pressure to increase efficiency and throughput of patients. Discrete event simulation (i.e., modeling system operations with sequence of isolated events) is a useful tool to understand and improve ED operations by simulating the behavior and performance of EDs. Certain features of the ED (e.g., different types of patients, treatments, urgency, and uncertainty) can complicate simulation. One way to handle the complexity is to group the patients according to required treatment. Previously, the “casemix” principle, which was developed by expert clinicians to groups of similar patients in case-specific settings (e.g., telemetry or nephrology units), was used, but it has limitations in the ED setting [ 58 ]. Researchers applied [ 58 ] data mining (clustering) to the ED setting to group the patients based on treatment pattern (e.g., full ward test, head injury observation, ECG, blood glucose, CT scan, X-ray). The clustering model was verified and validated by ED clinicians. These grouping data were then used in discrete event simulation to understand and improve ED operations (mainly length of stay) and process flows for each group.

Chest pain admissions to the ED have also been examined using decision-making framework. Researchers [ 57 ] proposed a three stage decision-making framework for classifying severity of chest pain as: AMI, angina pectoris, or other. At the first stage, lab tests and diagnoses were collected and the association between them were extracted. In the second stage, experts developed association rules between lab tests diagnosis to help physicians make quick diagnostic decisions based diagnostic tests and avoid further unnecessary lab tests. In the third stage, authors developed a classification tree to classify the chest pain diagnosis based on selected lab test, diagnosis and medical record. This hybrid model was applied to the emergency department at one hospital. They developed the classification system using 327 association rules to selected lab tests using C5.0, Neural Network (NN) and SVM. C5.0 algorithm achieved 94.18% accuracy whereas NN and SVM achieved 88.89% and 85.19% accuracy respectively.

4.1.5. Intensive Care

Intensive care units cater to patients with severe and life-threatening illness and injury which require constant, close monitoring and support to ensure normal bodily function. Death is a much more common event in an ICU compared to a general medical unit—one study showed that 22.4% of total death in hospitals occurred in the ICU [ 87 ]. Survival predictions and identification of important factors related to mortality can help healthcare providers plan care. We identified two papers [ 59 , 60 ] that developed prediction models for ICU mortality rate prediction. Using a large amount of ICU patient data (specifically from the first 24 h of the stay) collected from University of Kentucky Hospital from 1998 to 2007 (38,474 admissions), one group of researchers identified 15 out of 40 significant features using Pearson’s Chi-square test (for categorical variables) and Student-t test (for continuous variable) [ 59 ]. The mortality rate was predicted by DT, ANN, SVM and APACHE III, a logistic regression based approach. Compared to the other methods applied, DT’s AUC value was higher by 0.02. The study was limited, however, by only considering the first 24 h of admission to the ICU, which may not be enough to make prediction on mortality rate. Another team of researchers [ 60 ] applied a similarity metric to predict 30-day mortality prediction in 17,152 ICU admissions data extracted from MIMIC-II database [ 88 ]. Their analysis concluded that a large group of similar patient data (e.g., vital sign, laboratory test result) instead of all patient data would lead to slightly better prediction accuracy. The logistic regression model for mortality prediction achieved 0.83 AUC value when 5000 similar patients were used for training but, its performance declined to 0.81 AUC when all the available patient data were used.

4.1.6. Other Applications

In addition to CVD, diabetes, cancer, emergency care, and ICU care, data mining has been applied to various clinical decision-making problems like pressure ulcer risk prediction, general problem lists, and personalized medical care. To predict pressure ulcer formation (localized skin and tissue damage because of shear, friction, pressure or any combination of these factors), researchers [ 62 ] developed two classification-based predictive models. One included all 14 features (including age, sex, course, Anesthesia, body position during operation, and skin status) and another, reduced model, including significant features only (5 in DT model, 7 in SVM, LR and Mahalanobis Taguchi System model). Mahalanobis Taguchi System (MTS), SVM, DT, and LR were used for both classification and feature selection (in the second model only) purposes. LR and SVM performed slightly better when all the features were included, but MTS achieved better sensitivity and specificity in the reduced model (+10% to +15%). These machine learning techniques can provide better assistance in pressure ulcer risk prediction than the traditional Norton and Braden medical scale [ 62 ]. Though the study provides the advantages of using data mining algorithms, the data set used here was imbalanced as it only had 8 cases of pressure ulcer in 168 patients. Also among patients with pressure ulcers, another team of researchers [ 63 ] recommended a data mining based alternative to the Braden scale for prediction. They applied data mining algorithms to four years of longitudinal patient data to identify the most important factors related to pressure ulcer prediction (i.e., days of stay in the hospital, serum albumin, and age). In terms of C-statistics, RF (0.83) provided highest predictive accuracy over DT (0.63), LR (0.82), and multivariate adaptive regression splines (0.78).

For data mining algorithms, which often show poor performance with imbalanced (i.e., low occurrence of one class compared to other classes) data, researchers [ 70 ] developed a sub-sampling technique. They designed two experiments, one considered sub-sampling technique and another one did not. For a highly imbalanced data set, Random Forest (RF), SVM, and Bagging and Boosting achieved better classification accuracy with this sub-sampling technique in classifying eight diseases (male genital disease, testis cancer, encephalitis, aneurysm, breast cancer, peripheral atherosclerosis, and diabetes mellitus) that had less than 5% occurrences in the National Inpatient Sample (NIS) data of Healthcare Cost and Utilization Project (HCUP). Surprisingly, possibly due to balancing the dataset through sub-sampling, RF slightly outperformed (+0.01 AUC) the other two methods.

The patient problem list is a vital component of clinical medicine. It enables decision support and quality measurement. But, it is often incomplete. Researchers have [ 64 ] suggested that a complete list of problems leads to better quality treatment in terms of final outcome [ 64 ]. Complete problem lists enable clinicians to get a better understanding of the issue and influence diagnostic reasoning. One group of researchers proposed a data mining model to find an association between patient problems and prescribed medications and laboratory tests which can act as a support to clinical decision-making [ 64 ]. Currently, domain experts spend a large amount of time for this purpose but, association rule mining can save both time and other resources. Additionally, consideration of unstructured data like doctor’s and/or nurse’s written comments and notes can provide additional information. These association rules can aid clinicians in preventing errors in diagnosis and reduce treatment complexity. For example, a set of problems and medications can co-occur frequently. If a clinician has knowledge about this relation, he/she can prescribe similar medications when faced with a similar set of problems. One group of researchers [ 61 ] developed an approach which achieved 90% accuracy in finding association between medications and problems, and 55% accuracy between laboratory tests and problems. Among outpatients diagnosed with respiratory infection, 92.79% were treated with drugs. Physicians could choose any of the 100,013 drugs available in the inventory. Moreover, in an attempt to examine the treatment plan patterns, they identified the 78 most commonly used drugs which could be prescribed, regardless of patient’s complaints and demography. The classification model used to identify the most common drugs achieved 74.73% accuracy and most importantly found variables like age, race, gender, and complaints of patients were insignificant.

Personalized medicine—tailored treatment based on a patient’s predicted response or risk of disease—is another venue for data mining algorithms. One group of researchers [ 66 ] used a big data framework to create personalized care system. One patient’s medical history is compared with other available patient data. Based on that comparison, possibility of a disease of an individual was calculated. All the possible diseases were ranked from high risk to low risk diseases. This approach is very similar to how online giants Netflix and Amazon suggest movies and books to the customer [ 66 ]. Another group of researchers [ 67 ] used the Electronic Patient Records (EPR), which contains structured data (e.g., disease code) and unstructured data (e.g., notes and comments made by doctors and nurses at different stages of treatment) to develop personalized care. From the unstructured text data, the researchers extracted clinical terms and mapped them to an ontology. Using this mapped codes and existing structured data (disease code), they created a phenotypic profile for each patient. The patients were divided into different clusters (with 87.78% precision) based on the similarity of their phenotypic profile. Correlation of diseases were captured by counting the occurrences of two or more diseases in patient phenotype. Then, the protein/gene structure associated with the diseases was identified and a protein network was created. From the sharing of specific protein structure by the diseases, correlation was identified.

Among patients with asthma, researchers [ 65 ] used environmental and patient physiological data to develop a prediction model for asthma attack to give doctors and patients a chance for prevention. They used data from a home-care institute where patients input their physical condition online; and environmental data (air pollutant and weather data). Their data mining model involved feature selection through sequential pattern mining and risk prediction using DT and association rule mining. This model can make asthma attack risk prediction with 86.89% accuracy. Real implementation showed that patients found risk prediction helpful to avoid severe asthma attacks.

Among patients with Parkinson’s disease, researchers [ 73 ] introduced a comprehensive end-to-end protocol for complex and heterogeneous data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, the researchers used a Synthetic Minority Over-sampling Technique (SMOTE) to rebalance the data set. Rebalancing the dataset using SMOTE improved SVM’s classification accuracy from 76% to 96% and AdaBoost’s classification accuracy from 96% to 99%. Moreover, the study found that traditional statistical classification approaches (e.g., generalized linear model) failed to generate reliable predictions but machine learning-based classification methods performed very well in terms of predictive precision and reliability.

Among patients with kidney disease, researchers [ 71 ] developed a prediction model to forecast survival. Data collected from four facilities of University of Iowa Hospital and Clinics contains 188 patients with over 707 visits and features like blood pressure measures, demographic variables, and dialysis solution contents. Data was transformed using functional relation (i.e., the similarity between two or more features when two features have same values for a set of patients, they are combined to form a single feature) between the features. The data set was randomly divided into eight sub-sets. Sixteen classification rules were generated for the eight sub-sets using two classification algorithms—Rough Set (RS) and DT. Classes represented survival beyond three years, less than three years and undetermined. To make predictions, each classification rule (out of 16) had one vote and the majority vote decided the final predictive class. Transformed data increased predictive accuracy by 11% than raw data and DT (67% accuracy) performed better than RS (56% accuracy). The researchers suggested that this type of predictive analysis can be helpful in personalized treatment selection, resource allocation for patients, and designing clinical study. Among patients on kidney dialysis, another group of researchers [ 74 ] applied temporal pattern mining to predict hospitalization using biochemical data. Their result showed that amount of albumin—a type of protein float in blood—is the most important predictor of hospitalization due to kidney disease.

Among patients over 50 years of age, researchers [ 75 ] developed a data mining model to predict five years mortality using the EHR of 7463 patients. They used Ensemble Rotating Forest algorithm with alternating decision tree to classify the patients into two classes of life expectancy: (1) less than five years and (2) equal or greater than five years. Age, comorbidity count, previous record of hospitalization record, and blood urea nitrogen were a few of the significant features selected by correlation feature selection along with greedy stepwise search method. Accuracy achieved by this approach (AUC 0.86) was greater than the standard modified Charlson Index (AUC 0.81) and modified Walter Index (AUC 0.78). Their study showed that age, hospitalization prior the visit, and highest blood urea nitrogen were the most important factors for predicting five years morbidity. This five-year morbidity prediction model can be very helpful to optimally use resources like cancer screening for those patients who are more likely to be benefit from the resources.

Another group of researchers [ 76 ] addressed the limitations of existing software technology for disease diagnosis and prognosis, such as inability to handle data stream (DT), impractical for complex and large systems (Bayesian Network), exhaustive training process (NN). To overcome these restriction, authors proposed a decision tree based algorithm called “Very Fast Decision Tree (VFDT)”. Comparison with a similar system developed by IBM showed that VFDT utilizes lesser amount of system resources and it can perform real time classification.

Researchers have also used data mining to optimize the glaucoma diagnosis process [ 68 ]. Traditional approaches including Optical Coherence Tomography, Scanning Laser Polarimetry (SLP), and Heidelberg Retina Tomography (HRT) scanning methods are costly. This group used Fundus image data which is less costly and classified patient as either normal or glaucoma patient using SVM classifier. Before classification, authors selected significant features by using Higher Order Spectra (HOS) and Discrete Wavelet Transform (DWT) method combined and separately. Several kernel functions for SVM—all delivering similar levels of accuracy—were applied. Their approach produced 95% accuracy in glaucoma prediction. For diagnostic evaluation of chest imaging for suspicion for malignancy, researchers [ 69 ] designed trigger criteria to identify potential follow-up delays. The developed trigger predicted the patients who didn’t require follow-up evaluation. The analysis of the experiment result indicated that the algorithm to identify patients’ delays in follow-up of abnormal imaging is effective with 99% sensitivity and 38% specificity.

Data mining has also been applied to [ 72 ] compare three metrics to identify health care associated infections—Catheter Associated Bloodstream Infections, Catheter Associated Urinary Tract Infections and Ventilator Associated Pneumonia. Researchers compared traditional surveillance using National Healthcare Safety Network methodology to data mining using MedMined Data Mining Surveillance (CareFusion Corporation, San Diego, CA, USA), and administrative coding using ICD-9-CM. Traditional surveillance proved to be superior than data mining in terms of sensitivity, positive predictive value and rate estimation.

Data mining has been used in 38 studies of clinical decision-making CVD (7 articles), diabetes (seven articles), cancer (five articles), emergency care (two articles), intensive care (two articles), and other applications (16 articles). Most of the studies developed predictive models to facilitate decision-making and some developed decision support system or tools. Authors often tested their models with multiple algorithms; SVM was at the top of that list and often outperformed other algorithms. However, 15 [ 38 , 40 , 42 , 45 , 47 , 51 , 54 , 56 , 58 , 60 , 61 , 66 , 73 , 74 , 76 ] of the studies did not incorporate expert opinion from doctors, clinician, or appropriate healthcare personals in building models and interpreting results (see the study characteristics in Supplementary Materials Table S3 ). We also noted that there is an absence of follow-up studies on the predictive models, and specifically, how the models performed in dynamic decision-making situations, if doctors and healthcare professionals comfortable in using these predictive models, and what are the challenges in implementing the models if any exist? Existing literature does not focus on these salient issues.

4.2. Healthcare Administration

Data mining was applied to administrative purposes in healthcare in 32% (29 articles) of the articles reviewed. Researchers have applied data mining to: data warehousing and cloud computing; quality improvement; cost reduction; resource utilization; patient management; and other areas. Table 6 provides a list of these articles with major focus areas, problems analyzed and the data source.

Problem analyzed and data sources in healthcare administration.

4.2.1. Data Warehousing and Cloud Computing

Data warehousing [ 90 ] and cloud computing are used to securely and cost-effectively store the growing volume of electronic patient data [ 1 ] and to improve hospital outcomes including readmissions. To identify cause of readmission, researchers [ 89 ] developed an open source software—Analytic Information Warehouse (AIW). Users can design a virtual data model (VDM) using this software. Required data to test the model can be extracted in terms of a temporal ontology from the data warehouse and analysis can be performed using any standard analyzing tool. Another group of researchers took a similar approach to develop a Clinical Data Warehouse (CDW) for traditional Chinese medicine (TCM). The warehouse contains clinical information (e.g., symptoms, disease, and treatment) for 20,000 inpatients and 20,000 outpatients. Data was collected in a structured way using pre-specified ontology in electronic form. CDW provides an interface for online data mining, online analytical processing (OLAP) and network analysis to discover knowledge and provide clinical decision support. Using these tools, classification, association and network analysis between symptoms, diseases and medications (i.e., herbs) can be performed.

Apart from clinical purposes, data warehouses can be used for research, training, education, and quality control purposes. Such a data repository was created using the basic idea of Google search engine [ 92 ]. Users can pull the radiology report files by searching keywords like a simple google search following the predefined patient privacy protocol. Another data repository was created as a part of collaborative study between IBM and University of Virginia and its partner, Virginia Commonwealth University Health System was created [ 93 ]. The repository contains 667,000 patient record with 208 attributes. HealthMiner—a data mining package for healthcare created by IBM—was used to perform unsupervised analysis like finding associations, pattern and knowledge discovery. This study also showed the research benefits of this type of large data repository. Researchers [ 91 ] proposed a framework based on cloud computing and big data to unify data collected from different sources like public databases and personal health devices. The architecture was divided into 3 layers. The first layer unified heterogeneous data from different sources, the second layer provided storage support and facilitated data processing and analytics access, and the third layer provided result of analysis and platform for professionals to develop analytical tools. Some researchers [ 94 ] used mobile devices to collect personal health data. Users took part in a survey on their mobile devices and got a diagnosis report based on their health parameters input in the survey. Each survey data were saved in a cloud-based interface for effective storage and management. From user input stored in cloud, interactive geo-spatial maps were developed to provide effective data visualization facility.

4.2.2. Healthcare Cost, Quality and Resource Utilization

Ten articles applied data mining to cost reduction, quality improvement and resource utilization issues. One group of researchers predicted healthcare costs using an algorithmic approach [ 96 ]. They used medical claim data of 800,000 people collected by an insurance company over the period of 2004–2007. The data included diagnoses, procedures, and drugs. They used classification and clustering algorithms and found that these data mining algorithms improve the absolute prediction error more than 16%. Two prediction models were developed, one using both cost and medical information and the other used only cost information. Both models had similar accuracy on predicting healthcare costs but performed better than traditional regression methods. The study also showed that including medical information does not improve cost prediction accuracy. Risk-adjusted health care cost predictions, with diagnostic groups and demographic variables as inputs, have also been assessed using regression tree boosting [ 100 ]. Boosted regression tree and main effects linear models were used and fitted to predict current (2001) and prospective (2002) total health care costs per patient. The authors concluded that the combination of regression tree boosting and a diagnostic grouping scheme are a competitive alternative to commonly used risk-adjustment systems.

A sizable amount ($37.6 billion) of healthcare costs is attributable to medical errors, 45% of which stems from preventable errors [ 95 ]. To aid in physician decision-making and reduce medical errors, researchers [ 95 ] proposed a data mining-based framework-Sequential Clustering Algorithm. They identified patterns of treatment plans, tests, medication types and dosages prescribed for specific diseases, and other services provided to treat a patient throughout his/her stay in the hospital. The proposed framework was based on cloud computing so that the knowledge extracted from the data could be shared among hospitals without sharing the actual record. They proposed to share models using Virtual Machine (VM) images to facilitate collaboration among international institutions and prevent the threat of data leakage. This model was implemented in two hospitals, one in Taiwan and another in Mongolia. To identify best practices for specific diseases and prevent medical errors, another group of researchers [ 101 ] proposed a decision support system using information extraction from online documents through text and data mining. They focused on evidence based management, quality control, and best practice recommendations for medical prescriptions.

Length of Stay (LOS) is another important indicator of cost and quality of care. Accurate prediction of LOS can lead to efficient management of hospital beds and resources. To predict LOS for CAD patients, researchers [ 98 ] compared multiple models—SVM, ANN, DT and an ensemble algorithm, combing SVM, C5.0, and ANN. Ensemble algorithm and SVM produced highest accuracy, 95.9% and 96.4% respectively. In contrast, ANN was least accurate with 53.9% accuracy wherein DT achieved 83.5% accuracy. Anticoagulant drugs, nitrate drugs, and diagnosis were the top three predictors along with diastolic blood pressure, marital status, sex, presence of comorbidity, and insurance status.

To predict healthcare quality, researchers [ 104 ] used sentiment analysis (computationally categorizing opinions into categories like positive, negative and neutral) on patients’ online comments about their experience. They found above 80% agreement between sentiment analysis from online forums and traditional paper based surveys on quality prediction (e.g., cleanliness, good behavior, recommendation). Proposed approach can be an inexpensive alternative to traditional surveys and reports to measure healthcare quality.

Identification of influential factors in insurance coverage using data mining can aid insurance providers and regulators to design targeted service, additional service or proper allocation of resources to increase coverage rates. To develop a classification model to identify health insurance coverage, researchers [ 103 ] used data mining techniques. Based on 23 socio-economic, lifestyle and demographic factors, they developed a classification model with two classes, Insured and uninsured. The model was solved by ANN and DT. ANN provided 4% more accuracy than DT in predicting health insurance coverage. Among the factors, income, employment status, education, and marital status were the most important predictive factors of insurance coverage.

Among patients with lung cancer, researchers [ 97 ] investigated healthcare resource utilization (i.e., the number of visits to the medical oncologists) characteristics. They used DT, ANN and LR separately and an ensemble algorithm combining DT and ANN which resulted in the greatest accuracy (60% predictive accuracy). DT was employed to identify the important predictive features (among demographics, diagnosis, and other medical information) and ANN for classification. Data mining revealed that the utilization of healthcare resources by lung cancer patients is “supply-sensitive and patient sensitive” where supply represents availability of resources in certain region and patient represents patient preference and comorbidity. A resource allocation monitoring model for better management of primary healthcare network has also been developed [ 99 ]. Researchers considered the primary-care network as a collection of hierarchically connected modules given that patients could visit multiple physicians and physicians could have multiple care location, which is an indication of imbalanced resource distribution (e.g., number of physicians, care locations). The first level of the hierarchy consisted of three modules: health activities, population, and health resources. The second level monitored the healthcare provider availability and dispersion. The third level considered the actual visits, physicians and their availability, accessibility, and unlisted (i.e., without any assigned physician) patients. The top level of this network conducted an overall assessment of the network and made allocation accordingly. This hierarchical model was developed for a specific region in Slovenia, however, it could be easily adapted for any other region.

Overuse of screening and tests by physicians also contributes to inefficiencies and excess costs [ 102 ]. Current practice in pathology diagnosis is limited by disease focus. As an alternative to disease based system, researchers [ 102 ] used data mining in cooperation with case-based reasoning to develop an evidence based decision support system to decrease the use of unnecessary tests and reduce costs.

4.2.3. Patient Management

Patient management involves activities related to efficient scheduling and providing care to patients during their stay in a healthcare institute. Researchers [ 105 ] developed an efficient scheduling system for a rural free clinic in the United States. They proposed a hybrid system where data mining was used to classify the patients and association rule mining was used to assign a “no-show” probability. Results obtained from data mining were used to simulate and evaluate different scheduling techniques. On the other hand, these schedules could be divided into visits with administrative purposes and medical purposes. Researchers [ 108 ] suggested that patients who visit the health center for administrative purposes take less time than the patients with medical reasons. They proposed a predictive model to forecast the number of visits for administrative purposes. Their model improved the scheduling system with time saving of 21.73% (660,538 min). In contrast to administrative information/task seeking patients, some patients come for medical care very frequently and consume a large percentage of clinical workload [ 107 ]. Identifying the risk factors for frequent visit to health centers can help in reducing cost and resource utilization. A study among 85 working age “frequent attenders” identified the primary risk factors using Bayesian classification technique. The risk factors are, “high body mass index, alcohol abstinence, irritable bowel syndrome, low patient satisfaction, and fear of death” [ 107 ].

Improving publicly reported patient safety outcomes is also critical to healthcare institutions. Falls are one such outcome and are the most common and costly source of injury during hospitalization [ 110 ]. Researchers [ 109 ] analyzed the important factors related to patient falls during hospitalization. First, the authors selected significant features by Chi-square test (10 features out of 72 fall related variables were selected) and then applied ANN to develop a predictive model which achieves 0.77 AUC value. Stepwise logistic regression achieved 0.42 AUC value with 3 important variables. Both models showed that the fall assessment by nurses and use of anti-psychotic medication are associated with a lower risk of falls, and the use of diuretics is associated with an increased risk of falls. Another group of researchers [ 110 ] used fall related injury data to validate the structured information in EMR from clinical notes with the help of text mining. A group of nurses manually reviewed the electronic records to separate the correct documents from the erroneous ones which was considered as the basis of comparison. Authors employed both supervised (using a portion of manually labeled files as training set) and unsupervised technique (without considering the file labels) to classify and cluster the records. The unsupervised technique failed to separate the fare documents from the erroneous ones, wherein supervised technique performed better with 86% of fare documents in one cluster. This method can be applicable to semi-automate the EMR entry system.

4.2.4. Other Applications

Data mining has beed applied [ 111 ] to investigate the relationship between physician’s training at specific schools, procedures performed, and costs of the procedure. Researchers explored this relationship at three level: (1) they explored the distribution of procedures performed; (2) the relationship between procedures performed by physician and their alma mater—the institute that a doctor attended or got his/her degree from; and (3) geographic distribution of amount billed and payment received. This study suggested that medical school training does relate to practice in terms of procedures performed and bill charged. Patients can also provide useful information about physicians and their performance. Another group of researchers [ 112 ] used topic modeling algorithm—Latent Dirichlet Allocation (LDA)—to understand patients’ review of physicians and their concerns.

Data mining has also been applied [ 115 ] to analyze the information seeking behavior of health care professionals, and to assess the feasibility of measuring drug safety alert response from the usage logs of online medical information resources. Researchers analyzed two years of user log-in data in UpToDate website to measure the volume of searches associated with medical conditions and the seasonal distribution of those searches. In addition, they used a large collection of online media articles and web log posts as they characterized food and drug alert through the changes in UpToDate search activity compared to the general media activity. Some researchers [ 113 ] examined changes of key performance indicators (KPIs) and clinical workload indicators in Greek National Health System (NHS) hospitals with the help of data mining. They found significant changes in KPIs when necessary adjustments (e.g., workload) were made according to the diagnostic related group. The results remained for general hospitals like cancer hospitals, cardiac surgery as well as small health centers and regional hospitals. Their findings suggested that the assessment methodology of Greek NHS hospitals should be re-evaluated in order to identify the weaknesses in the system, and improve overall performance. And in home healthcare, another group of researchers [ 116 ] reviewed why traditional statistical analysis fails to evaluate the performance of home healthcare agencies. The authors proposed to use data mining to identify the drivers of home healthcare service among patients with heart failure, hip replacement, and chronic obstructive pulmonary disease using length of stay and discharge destination.

The relationship between epidemiological and genetic evidence and post market medical device performance has been evaluated using HCUPNet data [ 114 ]. This feasibility study explored the potential of using publicly accessible data for identifying genetic evidence (e.g., comorbidity of genetic factors like race, sex, body structure, and pneumothorax or fibrosis) related to devices. It focused on the ventilation-associated iatrogenic pneumothorax outcome in discharge of mechanical ventilation and continuous positive airway pressure (CPAP). The results demonstrated that genetic evidence-based epidemiologic analysis could lead to both cost and time efficient identification of predictive features. The literature of data mining applications in healthcare administration encompasses efficient patient management, healthcare cost reduction, quality of care, and data warehousing to facilitate analytics. We identified four studies that used cloud-based computing and analytical platforms. Most of the research proposed promising ideas, however, they do not provide the results and/or challenges during and after implementation. An ideal example of implementation could be the study of efficient appointment scheduling of patients [ 108 ].

4.3. Healthcare Privacy and Fraud Detection

Health data privacy and medical fraud are issues of prominent importance [ 118 ]. We reviewed four articles—displayed and described in Table 7 —that discussed healthcare privacy and fraud detection.

List of papers in healthcare privacy and fraud detection.

The challenges of privacy protection have been addressed by a group of researchers [ 122 ] who proposed a new anonymization algorithm for both distributed and centralized anonymization. Their proposed model performed better than K-anonymization model in terms of retaining data utility without losing much data privacy (for K = 20, the discernibility ratio—a normalized measure of data quality—of the proposed approach and traditional K-anonymization method were 0.1 and 0.4 respectively). Moreover, their proposed algorithm could handle large scale, high dimensional datasets. To address the limitations of today’s healthcare information systems—EHR data systems limited by lack of inter-operability, data size, and security—a mobile cloud computing-based big data framework has been proposed [ 119 ]. This novel cloud-based framework proposed storing EHR data from different healthcare providers in an Internet provider’s facility, offering providers and patients different levels of access and authority. Security would be ensured by using encryption algorithms, one-time passwords, or 2-factor authentication. Big data analytics would be handled using Google big query or MapReduce software. This framework could reduce cost, increase efficiency, and ensure security compared to the traditional technique which uses de-identification or anonymization technique. This traditional technique leaves healthcare data vulnerable to re-identification. In a case study, researchers demonstrated that hackers can make association between small pieces of information and can identify patients [ 120 ]. The case study made use of personal information provided in two Medicare social networking sites, MedHelp and Mp and Th1 to identify an individual.

Detection of fraud and abuse (i.e., suspicious care activity, intentional misrepresentation of information, and unnecessary repetitive visits) uses big data analytics. Using gynecological hospital data, researchers [ 121 ] developed a framework from two domain experts manually identifying features of fraudulent cases from a data pool of treatment plans doctors frequently follow. They applied this framework to Bureau of National Health Insurance (BNHI) data from Taiwan; their proposed framework detected 69% of the fraudulent cases, which improved the existing model that detected 63% of the fraudulent cases.

In summary, patient data privacy and fraud detection are of major concern given increasing use of social media and people’s tendency to put personal information on social media. Existing data anonymization or de-identification techniques can become less effective if they are not designed considering the fact that a large portion of our personal information is now available on social media.

4.4. Mental Health

Mental illness is a global and national concern [ 123 ]. According to the National Survey on Drug Use and Health (NSDUH) data from 2010 to 2012, 52.2% of U.S. population had either mental illness, or substance abuse/dependence [ 124 ]. Additionally, nearly 30 million people in the U.S. suffer from anxiety disorders [ 125 ]. Table 8 summarizes the four articles we reviewed that apply data mining in analyzing, diagnosing, and treating mental health issues.

List of data mining application in mental health with data sources.

To classify developmental delays of children based on illness, researchers [ 126 ] examined the association between illness diagnosis and delays by building a decision tree and finding association between cognitive, language, motor, and social emotional developmental delays. This study has implications for healthcare professionals to identify and intervene on delays at an early stage. To assist physicians in monitoring anxiety disorder, another group of researchers [ 125 ] developed a data mining based personalized treatment. The researchers used Context Awareness Information including static (personal information like, age, sex, family status etc.) and dynamic (stress, environmental, and symptoms context) information to build static and dynamic user models. The static model contained personal information and the dynamic model contained four treatment-supportive services (i.e., lifestyle and habits pattern detection service, context and stress level pattern detection service, symptoms and stress level pattern detection service, and stress level prediction service). Relations between different dynamic parameters were identified in first three services and the last service was used for stress level prediction under different scenarios. The model was validated using data from 27 volunteers who were selected by anxiety measuring test.

To predict early diagnosis for mental disorders (e.g., insomnia, dementia), researchers developed a model detecting abnormal physical activity recorded by a wearable device [ 127 ]. They performed two experiments to compare the development of a reference model using historical user physical movement data. In the first experiment, users wore the watch for one day and based on that day, a reference behavior model was developed. After 22 days, the same user used it again for a day and abnormality was detected if the user’s activities were significantly different from the reference model. In the second experiment, users used the watch regularly for one month. Abnormality was detected with a fuzzy valuation function and validated with user’s reported activity level. In both experiments, users manually reported their activity level, which was used as a validating point, only two out of 26 abnormal events were undetected. Through these two experiments, the researchers claimed that their model could be useful for both online and offline abnormal behavior detection as the model was able to detect 92% of the unusual events.

To classify schizophrenia, another study [ 128 ] used free speech (transcribed text) written or verbalized by psychiatric patients. In a pool of patients with schizophrenia and control subjects, using supervised algorithms (SVM and DT), they discriminated between patients with schizophrenia and normal control patients. SVM achieved 77% classification accuracy whereas DT achieved 78% accuracy. When they added patients with mania to the pool, they were unable to differentiate patients with schizophrenia.

Use of data analytics in diagnosing, analyzing, or treating mental health patients is quite different than applying analytics to predict cancer or diabetes. Context of data (static, dynamic, or unobservable environment) seemed more important than volume in this case [ 125 ], however, this is not always adopted in literature. A model without situational awareness (a context independent model) may lose predictive accuracy due to the confounding effect of surrounding environment [ 129 ].

4.5. Public Health

Seven articles addressed issues that were not limited to any specific disease or a demographic group, which we classified as public health problems. Table 9 contains the list of papers considering public health problems with data sources.

List of data mining application in public health with data sources.

To make data mining accessible to non-expert users, specifically public health decision makers who manage public cancer treatment programs in Brazil, researchers [ 134 ] developed a framework for an automated data mining system. This system performed a descriptive analysis (i.e., identifying relationships between demography, expenditure, and tumor or cancer type) for public decision makers with little or no technical knowledge. The automation process was done by creating pre-processed database, ontology, analytical platform and user interface.

Analysis of disease outbreaks has also applied data analytics. [ 131 , 133 ] Influenza, a highly contagious disease, is associated with seasonal outbreaks. The ability to predict peak outbreaks in advance would allow for anticipatory public health planning and interventions to lessen the effect of the outbreaks. To predict peak influenza visits to U.S. military health centers, researchers [ 131 ] developed a method to create models using environmental and epidemiological data. They compared six classification algorithms—One-Classifier 1, One-Classifier 2 [ 137 ], a fusion of the One-Classifiers, DT, RF, and SVM. Among them, One-Classifier 1 was the most efficient with F-score 0.672 and SVM was second best with F-score 0.652. To examine the factors that drive public and professional search patterns for infectious disease outbreaks another group of researchers [ 133 ] used online behavior records and media coverage. They identified distinct factors that drive professional and layperson search patterns with implications for tailored messaging during outbreaks and emergencies for public health agencies.

To store and integrate multidimensional and heterogeneous data (e.g., diabetes, food, nutrients) applied to diabetes management, but generalizable to other diseases researchers [ 130 ] proposed an intelligent information management framework. Their proposed methodology is a robust back-end application for web-based patient-doctor consultation and e-Health care management systems with implications for cost savings.

A real-time medical emergency response system using the Internet of Things (networking of devices to facilitate data flow) based body area networks (BANs)—a wireless network of wearable computing devices was proposed by researchers [ 136 ]. The system consists of “Intelligent Building”—a data analysis model which processes the data collected from the sensors for analysis and decision. Though the author claims that the proposed system had the capability of efficiently processing wireless BAN data from millions of users to provide real-time response for emergencies, they did not provide any comparison with the state-of-the-art methods.

Decision support tools for regional health institutes in Slovenia [ 135 ] have been developed using descriptive data mining methods and visualization techniques. These visualization methods could analyze resource availability, utilization and aid to assist in future planning of public health service.

To build better customer relations management at an Iranian hospital, researchers [ 132 ] applied data mining techniques on demographic and transactions information. The authors extended the traditional Recency, Frequency, and Monetary (RFM) model by adapting a new parameter “Length” to estimate the customer life time value (CLV) of each patient. Patients were separated into classes according to estimated CLV with a combination of clustering and classification algorithms. Both DT and ANN performed similarly in classification with approximately 90% accuracy. This type of stratification of patient groups with CLV values would help hospitals to introduce new marketing strategies to attract new customers and retain existing ones.

The application of data mining to public health decision-making has become increasingly common. Researchers utilized data mining to design healthcare programs and emergency response, to identify resource utilization, patient satisfaction as well as to develop automated analytics tool for non-expert users. Continuation of this effort could lead to a patient-centered, robust healthcare system.

4.6. Pharmacovigilance

Pharmacovigilance involves post-marketing monitoring and detection of adverse drug reactions (ADRs) to ensure patient safety [ 138 ]. The estimated annual social cost of ADR events exceeds one billion dollars, making it an important part of healthcare system [ 139 ]. Characteristics of the nine papers addressing pharmacovigilance are displayed in Table 10 .

List of data mining application in pharmacovigilance with data sources.

Researchers considered muscular and renal AEs caused by pravastatin, simvastatin, atorvastatin, and rosuvastatin by applying data mining techniques to the FDA’s Adverse Event Reporting System (FAERS) database reports from 2004 to 2009 [ 143 ]. They found that all statins except simvastatin were associated with muscular AE; rosuvastatin had the strongest association. All statins, besides atorvastatin, were associated with acute renal failure. The criteria used to identify significant association were: proportional reporting ratio (PRR), reporting odds ratio (ROR), information component (IC), and empirical Bayes geometric mean (EBGM). In another study of AEs related to statin family, researchers used a Korean claims database [ 145 ] and showed that a relative risk-based data-mining approach successfully detected signals for rosuvastatin.

Three more studies used the FDA’s AERS report database. In an examination of ADR “hypersensitivity” to six anticancer agents [ 142 ] data mining results showed that Paclitaxel is associated with mild to lethal reaction wherein Docetaxel is associated to lethal reaction, and the other four drugs were not associated to hypersensitivity [ 142 ]. Another researcher [ 139 ] argued that AEs can be caused not only by a single drug, but also by a combination of drugs [ 140 ]. They showed that that 84% of the AERs reports contain an association between at least one drug and two AEs or two drugs and one AE. Another group [ 138 ] increased precision in detecting ADRs by considering multiple data sources together. They achieved 31% (on average) improvement in identification by using publicly available EHRs in combination with the FDA’s AERS reports.

Furthermore, dose-dependent ADRs have been identified by researchers using models developed from structured and unstructured EHR data [ 141 ]. Among the top five drugs associated with ADRs, four were found to be related to dose [ 141 ]. Pharmacovigilance activity has also been prioritized using unstructured text data in EHRs [ 144 ]. In traditional pharmacovigilance, ADRs are unknown. While looking for association between a drug and any possible ADR, it is possible to get false signals. Such false signals can be avoided if a list of possible ADRs is already known. Researchers [ 144 ] developed an ordered list of 23 ADRs which can be very helpful for future pharmacovigilance activities. To detect unexpected and rare ADRs in real-world healthcare administrative databases, another group of researchers [ 146 ] designed an algorithm—Unexpected Temporal Association Rules (UTARs)—that performs more effectively than existing techniques.

We identified one study that used data outside of adverse event reports or HER data. For early detection of ADR, one group of researchers used online forums [ 140 ]. They identified the side effect of a specific drug called “Erlotinib” used for lung cancer. Sentiment analysis—a technique of categorizing opinions—on data collected from different cancer discussion forums showed that 70% of users had a positive experience after using this drug. Users most frequently reported were acne and rash. Apart from pharmacovigilance, this type of analysis can be very helpful for the pharmaceutical companies to analyze customer feedback. Researchers can take advantage of the popularity of social media and online forums for identifying adverse events. These sources can provide signals of AEs quicker than FDA database as it takes time to update the database. By the time AE reports are available in the FDA database, there could already be significant damage to patient and society. Moreover, it can help to avoid the limitations of FDA AERS database like biased reporting and underreporting [ 141 ].

5. Theoretical Study

Twenty-five of the articles we reviewed focus on the theoretical aspects of the application of data mining in healthcare including designing the database framework, data collection, and management to algorithmic development. These intellectual contributions extend beyond the analytical perspective of data—descriptive, predictive or prescriptive analytics—to the sectors and problems highlighted in Table 11 .

Problem analyzed in theoretical studies.

The existing theoretical literature on disease control highlighted the current state of epidemics, cancer and mental health. To help physicians make real-time decisions about patient care, one group of researchers [ 147 ] proposed a real-time EMR data mining based clinical decision support system. They emphasized the need to have an anonymized EMR database which can be explored by using a search engine similar to web search engine. In addition, they focused on designing a framework for next generation EMR-based database that can facilitate the clinical decision-making process, and is also capable of updating a central population database once patients’ recent (new) clinical records are available. Another researcher [ 148 ] forecasted future challenges in infection control that entails the importance of having timely surveillance system and prevention programs in place. To that end, they necessitate the formation, control and utilization of fully computerized patient record and data-mining-derived epidemiology. Finally, they recommended performance feedback to caregivers, wide accessibility of infection prevention tools, and access to documents like lessons learned and evidence-based best practices to strengthen the infection control, surveillance, and prevention scheme. Authors in [ 150 ] addressed the activities executed by national Institute of Mental Health (NIMH) in collaboration with other state organizations (e.g., Substance Abuse and Mental Health Service Administration (SAMSHSA), Center for Mental Health Service (CMHS) to promote optimal collection, pooling/aggregation, and use of big data to support ongoing and future researches of mental health practices. The outcome summary showcased that effective pooling/aggregation of state-level data from different sources can be used as a dashboard to set priorities to improve service qualities, measure system performance and to gain specific context-based insights that are generalizable and scalable across other systems, leading to a successful learning-based mental health care system. Another group of researchers [ 150 ] outlined the barriers and potential benefits of using big data from CancerLinQ (a quality and measurement reporting system as an initiative of the American Society of Clinical Oncology (ASCO) that collects information from EHRs of cancer patients for oncologists to improve the outcome and quality of care they provide to their patients). However, the authors also mentioned that these benefits are contingent upon the confidence of the patients, encouraging them to share their data out of the belief that their health records would be used appropriately as a knowledge base to improve the quality of the health care of others, as it is for themselves. This motivated ASCO to ensure that proper policies and procedures are in place to deal with the data quality, data security and data access, and adopt a comprehensive regulatory framework to ensure patients’ data privacy and security.

Another group of researchers [ 151 ] data quality and database management to quantify, and consequentially understand the inherent uncertainty originating from radiology reporting system. They discussed the necessity of having a structured reporting system and emphasized the use of standardize language, leading to Natural Language Processing (NLP). Furthermore, they also indicated the need for creating a Knowledge Discovery Database (KDD) which will be consistent to facilitate the data-driven and automated decision support technologies to help improving the care provided to patients based on enhanced diagnosis quality and clinical outcome. A group of authors in [ 152 ] pointed that the success derived from the current trend of big-data analytics largely depends on how better the quality of the data collected from variety of sources are ensured. Their findings imply that the data quality should be assessed across the entire lifecycle of health data by considering the errors and inaccuracies stemmed from multiple of sources, and should also quantify the impact that data collection purpose on the knowledge and insights derived from the big data analytics. For that to ensure, they recommend that enterprises who deal with healthcare big data should develop a systematic framework including custom software or data quality rule engines, leading to an effective management of specific data-quality related problems. Researchers in [ 155 ] uncovered the lack of connection between phenomenological and mechanistic models in computational biomedicines. They emphasized the importance of big data which, when successfully extracted and analyzed, followed by the combination with Virtual Physiological Human (VPH)—an initiative to encourage personalized healthcare—can afford with effective and robust medicine solutions. In order for that to happen, they mentioned some challenges (e.g., confidentiality, volume and complexity of big data; integration of bioinformatics, systems biology and phenomics data; efficient storage of partial or complete data within organization to maximize the performance of overall predictive analytics) and concluded that these need to be addressed for successful development of big data technologies in computational medicines, enabling their adoption in clinical settings. Even though big data can generate significant value in modern healthcare system, researchers in [ 154 ] stated that without a set of proper IT infrastructures, analytical and visualization tools, and interactive interfaces to represent the work flows, the insights generated from big data will not be able to reach its full potential. To overcome this, they recommended that health care organizations engaging in data sharing devise new policies to protect patients’ data against potential data breaches.

Three papers [ 155 , 156 , 157 ] considered health care policies and ethical and legal issues. One [ 155 ] outlined a national action plan to incorporate sharable and comparable nursing data beyond documentation of care into quality reporting and translational research. The plan advocates for standardized nursing terminologies, common data models, and information structures within EHRs. Another paper [ 157 ] analyzed the major policy, ethical, and legal challenges of performing predictive analytics on health care big data. Their proposed recommendations for overcoming challenges raised in the four-phase life cycle of a predictive analytics model (i.e., data acquisition, model formulation and validation, testing in real-world setting and implementation and use in broader scale) included developing a governance structure at the earliest phase of model development to guide patients and participating stakeholders across the process (from data acquisition to model implementation). They also recommended that model developers strictly comply with the federal laws and regulations in concert with human subject research and patients information privacy when using patients’ data. And another paper [ 156 ] explored four central questions regarding: (i) aspects of big-data most relevant to health care, (ii) policy implications, (iii) potential obstacles in achieving policy objectives, and (iv) availability of policy levers, particularly for policy makers to consider when developing public policy for using big data in healthcare. They discussed barriers (including ensuring transparency among patients and health care providers during data collection) to achieve policy objectives based on a recent UK policy experiment, and argued for providing real-life examples of ways in which data sharing can improve healthcare.

Three papers [ 158 , 159 , 160 ] offered examples of realistic ways such as establishing policy leadership and risk management framework combining commercial and health care entities to recognize existing privacy related problem and devise pragmatic and actionable strategies of maintaining patient privacy in big data analytics. One paper [ 158 ] provided a policy overview of health care and data analytics, outlined the utility of health care data from a policy perspective, reviewed a variety of methods for data collection from public and private sources, mobile devices and social media, examined laws and regulations that protect data and patients’ privacy, and discussed a dynamic interplay among three aspects of today’s big data driven personal health care—policy goals to tackle both cost, population health problem and eliminate disparity in patient care while maintaining their privacy. Another study [ 159 ] proposed a Secure and Privacy Preserving Opportunistic Computing (SPOC) framework to be used in healthcare emergencies focused on collecting intensive personal health information (through mobile devices like smart phone or wireless sensors) with minimal privacy disclosure. The premise of this framework is that when a user of this system (called medical user) faces any emergency, other users in the vicinity with similar disease or symptom (if available) can come to help that user before professional help arrives. It is assumed that two persons with similar disease are skilled enough to help each other and the threshold of similarity is controlled by the user. And in physician prescribing—another paper [ 160 ] identified strategies for data mining from physicians’ prescriptions while maintaining patient privacy.

Theoretical research on personalized-health care services—treatment plans designed for someone based on the susceptibility of his/her genomic structure to a disease—also emerged from the literature review. One study [ 161 ] highlighted the potential of powerful analytical tools to open an avenue for predictive, preventive, participatory, and personalized (P4) medicine. They suggested a more nuanced understanding of the human systems to design an accurate computational model for P4 medicine. Reviewing the research paradgims of current person-centered approaches and traditions, another study [ 162 ] advocated a transdisciplinary and complex systems approach to improve the field. They synthesized the emerging aproaches and methodologies and highlighted the gaps between academic research and accessibility of evaluation, informatics, and big data from health information systems. Another paper [ 163 ] reviewed the availability of big data and the role of biomedical informatics in personalized medicine, emphasizing the ethical concerns related to personalized medicines and health equity. Personalized medicine has a potential to reduce healthcare cost, however, the researchers think it can create race, income, and educational disparity. Certain socioeconomic and demographic groups currently have less or no access to healthcare and data driven personalized medicine will exclude those groups, increasing disparities. They also highlighted the impact of EHRs and CDWs on the field of personalized medicine through acclerated research and decreased the delivery time of new technologies.

A myriad of extant theoretical points has also been identified in the literature. These topics range from exploiting big data to: study the paradigm shift in healthcare policy and management from prioritizing volume to value [ 164 , 167 ]; aid medical device consumers in their decision-making [ 166 ]; improve emergency departments [ 169 ]; perform command surveillance and policy analysis for Army leadership [ 170 ]; to comparing different simulation methods (i.e., systems dynamics, discrete event simulation and agent based modeling) for specific health care system problems like resource allocation, length of stay [ 165 ]; to the ethical challenges of security, management, and ownership [ 170 ]. Another researcher outlined the challenges the E.U. is facing in data mining given numerous historical, technical, legal, and political barriers [ 168 ].

6. Future Research and Challenges

Data mining has been applied in many fields including finance, marketing, and manufacturing [ 172 ]. Its application in healthcare is becoming increasingly popular [ 173 ]. A growing literature addresses the challenges of data mining including noisy data, heterogeneity, high dimensionality, dynamic nature, computational time. In this section, we focus on future research applications including personalized care, information loss in preprocessing, collecting healthcare data for research purposes, automation for non-experts, interdisciplinarity of study and domain expert knowledge, integration into the healthcare system, and prediction-specific to data mining application and integration in healthcare.

• Personalized care

The EMR is increasingly used to document demographic and clinician patient information [ 1 ]. EMR data can be utilized to develop personalized care plans, enhancing patient experience [ 162 ] and improving care quality.

• Loss of information in pre-processing

Pre-processing of data, including handling missing data, is the most time-consuming and costly part of data mining. The most common method used in the papers reviewed was deletion or elimination of missing data. In one study, approximately 46.5% of the data and 363 of 410 features were eliminated due to missing values [ 49 ]. In another, researchers [ 98 ] were only able to use 2064 of 4948 observations (42%) [ 98 ]. By eliminating missing value cases and outliers, we are losing a significant amount of information. Future research should focus on finding a better method of missing value estimation than elimination. Moreover, data collection techniques should be developed or modified to avoid this issue.

Similar to missing data, deletion or elimination is a common way to handle outliers [ 174 ]. However, as illustrated in one of the studies we reviewed [ 48 ], outliers can be used to gain information about rare forms of diseases. Instead of neglecting the outliers, future research should analyze them to gain insight.

• Collecting healthcare data for research purpose

Traditionally, the primary objective of data collection in healthcare is documentation of patient condition and care planning [ 109 ]. Including research objectives in the data collection process through structured fields could yield more structured data with fewer cases of error and missing values [ 64 ]. A successful example of data collection for research purpose is the Study of Health in Pomerania (SHIP) [ 175 ]. The objective of SHIP was to identify common diseases, population level risk factors, and overall health of people living in the north-east region of Germany. This study only suffered from one “mistake” for every 1000 data entries [ 175 ] which ensures a structured form of data with high reliability, less noise and fewer missing values. We can take advantage of current documentation processes (EMR or EHR) by modifying them to collect more reliable and structured data. Long-term vision and planning is required to introduce research purpose in healthcare data collection.

• Automation of data mining process for non-expert users

The end users of data mining in healthcare are doctors, nurses, and healthcare professionals with limited training in analytics. One solution for this problem is to develop an automated (i.e., without human supervision) system for the end users [ 134 ]. A cloud-based automated structure to prevent medical errors could also be developed [ 95 ]; but the task would be challenging as it involves different application areas and one algorithm will not have similar accuracy for all applications [ 134 ].

• Interdisciplinary nature of study and domain expert knowledge

Healthcare analytics is an interdisciplinary research field [ 134 ]. As a form of analytics, data mining should be used in combination with expert opinion from specific domains—healthcare and problem specific (i.e., oncologist for cancer study, cardiologist for CVD) [ 106 ]. Approximately 32% of the articles in analytics did not utilize expert opinion in any form. Future research should include members from different disciplines including healthcare.

• Integration in healthcare system

Very few articles reviewed made an effort to integrate the data mining process into the actual decision-making framework. The impact of knowledge discovery through data mining on healthcare professional’s workload and time is unclear. Future studies should consider the integration of the developed system and explore the effect on work environments.

• Prediction error and “The Black Swan” effect

In healthcare, it is better not to predict than making an erroneous prediction [ 46 ]. A little under half of the literature we identified in analytics is dedicated to prediction but, none of the articles discussed the consequence of a prediction error. High prediction accuracy for cancer or any other disease does not ensure an accurate application to decision-making.

Moreover, prediction models may be better at predicting commonplace events than rare ones [ 176 ]. Researchers should develop more sophisticated models to address the unpredictable, “The Black Swan” [ 176 ]. One study [ 101 ] addressed a similar issue in evidence based recommendations for medical prescriptions. Their concern was, how much evidence should be sufficient to make a recommendation. Many of the studies in this review do not address these salient issues. Future research should address the implementation challenges of predictive models, especially how the decision-making process should adapt in case of errors and unpredictable incidents.

7. Conclusions

The development of an informed decision-making framework stems from the growing concern of ensuring a high value and patient-focused health care system. Concurrently, the availability of big data has created a promising research avenue for academicians and practitioners. As highlighted in our review, the increased number of publications in recent years corroborates the importance of health care analytics to build improved health care systems world-wide. The ultimate goal is to facilitate coordinated and well-informed health care systems capable of ensuring maximum patient satisfaction.

This paper adds to the literature on healthcare and data mining ( Table 1 ) as it is the first, to our knowledge, to take a comprehensive review approach and offer a holistic picture of health care analytics and data mining. The comprehensive and methodologically rigorous approach we took covers the application and theoretical perspective of analytics and data mining in healthcare. Our systematic approach starting with the review process and categorizing the output as analytics or theoretical provides readers with a more widespread review with reference to specific fields.

We also shed light on some promising recommendations for future areas of research including integration of domain-expert knowledge, approaches to decrease prediction error, and integration of predictive models in actual work environments. Future research should recommend ways so that the analytic decision can effectively adapt with the predictive model subject to errors and unpredictable incidents. Regardless of these insightful outcomes, we are not constrained to mention some limitations of our proposed review approach. The sole consideration of academic journals and exclusion of conference papers, which may have some good coverage in this sector is the prime limitation of this review. In addition to this, the search span was narrowed to three databases for 12 years which may have ignored some prior works in this area, albeit the increasing trend since 2005 and less number of publications before 2008 can minimize this limitation. The omission of articles published in languages other than English can also restrict the scope of this review as related papers written in other languages might be evident in the literature. Moreover, we did not conduct forward (reviewing the papers which cited the selected paper) and backward (reviewing the references in the selected paper and authors’ prior works) search as suggested by Levy and Ellis [ 31 ].

Despite these limitations, the systematic methodology followed in this review can be used in the universe of healthcare areas.

Supplementary Materials

The following are available online at http://www.mdpi.com/2227-9032/6/2/54/s1 , Table S1: PRISMA checklist, Table S2: Modified checklists and comparison, Table S3: Study characteristics, Table S4: Classification of reviewed papers by analytics type, application area, data type, and data mining techniques.

Author Contributions

Contribution of the authors can be summarized in following manner. Conceptualization: M.S.I., M.N.-E.-A.; Formal analysis: M.S.I., M.M.H., X.W.; Investigation: M.S.I., M.M.H., X.W.; Methodology: M.S.I.; Project administration: M.S.I., M.N.-E.-A.; Supervision: M.N.-E.-A.; Visualization: M.S.I., X.W.; Writing—draft: M.S.I., M.M.H., H.D.G.; Writing—review and editing: M.S.I., M.M.H., H.D.G., M.N.-E.-A.

Germack is supported by CTSA Grant Number TL1 TR001864 from the National Center for Advancing Translational Science (NCATS), a component of the National Institutes of Health (NIH). The content is solely the responsibility of the authors and does not necessarily represent the official views of this organization.

Conflicts of Interest

The authors declare no conflict of interest.

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Research published in Nature shows that surface-enhanced Raman spectroscopy carried out with colloids can quantify a range of molecules down to concentrations at the femtomolar level.

• Daniel M. Czajkowsky

Rapid room-temperature phosphorescence chiral recognition of natural amino acids

Chiral recognition of amino acids with luminescence, despite its advantages, is usually slow and lacks generality. Here, the authors demonstrate that L-phenylalanine derived benzamide can manifest the structural difference between the natural, left-handed amino acid and its right-handed counterpart via the difference in room-temperature phosphorescence, irrespective of the specific chemical structure.

• Xiaoyu Chen
• Renlong Zhu
• Guoqing Zhang

Performance and robustness of small molecule retention time prediction with molecular graph neural networks in industrial drug discovery campaigns

• Aleksejs Kontijevskis

Label-free separation of peripheral blood mononuclear cells from whole blood by gradient acoustic focusing

• Julia Alsved
• Mahdi Rezayati Charan
• Per Augustsson

News and Comment

Stress monitoring with wearable technology and AI

Physicochemical-sensing electronic skins — combined with artificial intelligence — could be used to develop personalized stress management systems.

• H. Ceren Ates

And yet it rotates!

Electrocatalysis would not be the same without the rotating disk electrode. Its invention in the mid-twentieth century enabled immense developments, which rendered it a classic technique in electrochemistry. The rotating disk electrode will remain a cornerstone of electrocatalysis with further advances that bridge the gap with real systems.

• Serhiy Cherevko
• Ioannis Katsounaros

Discovering cryptic natural products by substrate manipulation

Cryptic halogenation reactions result in natural products with diverse structural motifs and bioactivities. However, these halogenated species are difficult to detect with current analytical methods because the final products are often not halogenated. An approach to identify products of cryptic halogenation using halide depletion has now been discovered, opening up space for more effective natural product discovery.

• Ludek Sehnal
• Libera Lo Presti
• Nadine Ziemert

Improving reproducibility of photocatalytic reactions—how to facilitate broad application of new methods

Photocatalysis, as a powerful tool for synthetic transformations, has the potential to impact industrial applications. In this commentary, we discuss the challenges and requirements with respect to reproducing published photocatalytic reactions in the (life science) industry.

• Santiago Cañellas
• Manuel Nuño
• Elisabeth Speckmeier

Spectrum of the past

Thirty-four years ago, Curry and Rumelhart described a neural network-based approach to annotate tandem mass spectra. Their ideas foreshadowed several important developments in computational mass spectrometry over the past decade, but many of the challenges they discuss remain relevant today.

• Michael A. Skinnider

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

8.5 Writing Process: Creating an Analytical Report

Learning outcomes.

By the end of this section, you will be able to:

• Identify the elements of the rhetorical situation for your report.
• Find and focus a topic to write about.
• Gather and analyze information from appropriate sources.
• Distinguish among different kinds of evidence.
• Draft a thesis and create an organizational plan.
• Compose a report that develops ideas and integrates evidence from sources.
• Give and act on productive feedback to works in progress.

You might think that writing comes easily to experienced writers—that they draft stories and college papers all at once, sitting down at the computer and having sentences flow from their fingers like water from a faucet. In reality, most writers engage in a recursive process, pushing forward, stepping back, and repeating steps multiple times as their ideas develop and change. In broad strokes, the steps most writers go through are these:

• Planning and Organization . You will have an easier time drafting if you devote time at the beginning to consider the rhetorical situation for your report, understand your assignment, gather ideas and information, draft a thesis statement, and create an organizational plan.
• Drafting . When you have an idea of what you want to say and the order in which you want to say it, you’re ready to draft. As much as possible, keep going until you have a complete first draft of your report, resisting the urge to go back and rewrite. Save that for after you have completed a first draft.
• Review . Now is the time to get feedback from others, whether from your instructor, your classmates, a tutor in the writing center, your roommate, someone in your family, or someone else you trust to read your writing critically and give you honest feedback.
• Revising . With feedback on your draft, you are ready to revise. You may need to return to an earlier step and make large-scale revisions that involve planning, organizing, and rewriting, or you may need to work mostly on ensuring that your sentences are clear and correct.

Considering the Rhetorical Situation

Like other kinds of writing projects, a report starts with assessing the rhetorical situation —the circumstance in which a writer communicates with an audience of readers about a subject. As the writer of a report, you make choices based on the purpose of your writing, the audience who will read it, the genre of the report, and the expectations of the community and culture in which you are working. A graphic organizer like Table 8.1 can help you begin.

Summary of Assignment

Write an analytical report on a topic that interests you and that you want to know more about. The topic can be contemporary or historical, but it must be one that you can analyze and support with evidence from sources.

The following questions can help you think about a topic suitable for analysis:

• Why or how did ________ happen?
• What are the results or effects of ________?
• Is ________ a problem? If so, why?
• What are examples of ________ or reasons for ________?
• How does ________ compare to or contrast with other issues, concerns, or things?

Consult and cite three to five reliable sources. The sources do not have to be scholarly for this assignment, but they must be credible, trustworthy, and unbiased. Possible sources include academic journals, newspapers, magazines, reputable websites, government publications or agency websites, and visual sources such as TED Talks. You may also use the results of an experiment or survey, and you may want to conduct interviews.

Consider whether visuals and media will enhance your report. Can you present data you collect visually? Would a map, photograph, chart, or other graphic provide interesting and relevant support? Would video or audio allow you to present evidence that you would otherwise need to describe in words?

Another Lens. To gain another analytic view on the topic of your report, consider different people affected by it. Say, for example, that you have decided to report on recent high school graduates and the effect of the COVID-19 pandemic on the final months of their senior year. If you are a recent high school graduate, you might naturally gravitate toward writing about yourself and your peers. But you might also consider the adults in the lives of recent high school graduates—for example, teachers, parents, or grandparents—and how they view the same period. Or you might consider the same topic from the perspective of a college admissions department looking at their incoming freshman class.

Quick Launch: Finding and Focusing a Topic

Coming up with a topic for a report can be daunting because you can report on nearly anything. The topic can easily get too broad, trapping you in the realm of generalizations. The trick is to find a topic that interests you and focus on an angle you can analyze in order to say something significant about it. You can use a graphic organizer to generate ideas, or you can use a concept map similar to the one featured in Writing Process: Thinking Critically About a “Text.”

Asking the Journalist’s Questions

One way to generate ideas about a topic is to ask the five W (and one H) questions, also called the journalist’s questions : Who? What? When? Where? Why? How? Try answering the following questions to explore a topic:

Who was or is involved in ________?

What happened/is happening with ________? What were/are the results of ________?

When did ________ happen? Is ________ happening now?

Where did ________ happen, or where is ________ happening?

Why did ________ happen, or why is ________ happening now?

How did ________ happen?

For example, imagine that you have decided to write your analytical report on the effect of the COVID-19 shutdown on high-school students by interviewing students on your college campus. Your questions and answers might look something like those in Table 8.2 :

Asking Focused Questions

Another way to find a topic is to ask focused questions about it. For example, you might ask the following questions about the effect of the 2020 pandemic shutdown on recent high school graduates:

• How did the shutdown change students’ feelings about their senior year?
• How did the shutdown affect their decisions about post-graduation plans, such as work or going to college?
• How did the shutdown affect their academic performance in high school or in college?
• How did/do they feel about continuing their education?
• How did the shutdown affect their social relationships?

Any of these questions might be developed into a thesis for an analytical report. Table 8.3 shows more examples of broad topics and focusing questions.

Gathering Information

Because they are based on information and evidence, most analytical reports require you to do at least some research. Depending on your assignment, you may be able to find reliable information online, or you may need to do primary research by conducting an experiment, a survey, or interviews. For example, if you live among students in their late teens and early twenties, consider what they can tell you about their lives that you might be able to analyze. Returning to or graduating from high school, starting college, or returning to college in the midst of a global pandemic has provided them, for better or worse, with educational and social experiences that are shared widely by people their age and very different from the experiences older adults had at the same age.

Some report assignments will require you to do formal research, an activity that involves finding sources and evaluating them for reliability, reading them carefully, taking notes, and citing all words you quote and ideas you borrow. See Research Process: Accessing and Recording Information and Annotated Bibliography: Gathering, Evaluating, and Documenting Sources for detailed instruction on conducting research.

Whether you conduct in-depth research or not, keep track of the ideas that come to you and the information you learn. You can write or dictate notes using an app on your phone or computer, or you can jot notes in a journal if you prefer pen and paper. Then, when you are ready to begin organizing your report, you will have a record of your thoughts and information. Always track the sources of information you gather, whether from printed or digital material or from a person you interviewed, so that you can return to the sources if you need more information. And always credit the sources in your report.

Kinds of Evidence

Depending on your assignment and the topic of your report, certain kinds of evidence may be more effective than others. Other kinds of evidence may even be required. As a general rule, choose evidence that is rooted in verifiable facts and experience. In addition, select the evidence that best supports the topic and your approach to the topic, be sure the evidence meets your instructor’s requirements, and cite any evidence you use that comes from a source. The following list contains different kinds of frequently used evidence and an example of each.

Definition : An explanation of a key word, idea, or concept.

The U.S. Census Bureau refers to a “young adult” as a person between 18 and 34 years old.

Example : An illustration of an idea or concept.

The college experience in the fall of 2020 was starkly different from that of previous years. Students who lived in residence halls were assigned to small pods. On-campus dining services were limited. Classes were small and physically distanced or conducted online. Parties were banned.

Expert opinion : A statement by a professional in the field whose opinion is respected.

According to Louise Aronson, MD, geriatrician and author of Elderhood , people over the age of 65 are the happiest of any age group, reporting “less stress, depression, worry, and anger, and more enjoyment, happiness, and satisfaction” (255).

Fact : Information that can be proven correct or accurate.

According to data collected by the NCAA, the academic success of Division I college athletes between 2015 and 2019 was consistently high (Hosick).

Interview : An in-person, phone, or remote conversation that involves an interviewer posing questions to another person or people.

During our interview, I asked Betty about living without a cell phone during the pandemic. She said that before the pandemic, she hadn’t needed a cell phone in her daily activities, but she soon realized that she, and people like her, were increasingly at a disadvantage.

Quotation : The exact words of an author or a speaker.

In response to whether she thought she needed a cell phone, Betty said, “I got along just fine without a cell phone when I could go everywhere in person. The shift to needing a phone came suddenly, and I don’t have extra money in my budget to get one.”

Statistics : A numerical fact or item of data.

The Pew Research Center reported that approximately 25 percent of Hispanic Americans and 17 percent of Black Americans relied on smartphones for online access, compared with 12 percent of White people.

Survey : A structured interview in which respondents (the people who answer the survey questions) are all asked the same questions, either in person or through print or electronic means, and their answers tabulated and interpreted. Surveys discover attitudes, beliefs, or habits of the general public or segments of the population.

A survey of 3,000 mobile phone users in October 2020 showed that 54 percent of respondents used their phones for messaging, while 40 percent used their phones for calls (Steele).

• Visuals : Graphs, figures, tables, photographs and other images, diagrams, charts, maps, videos, and audio recordings, among others.

Thesis and Organization

Drafting a thesis.

When you have a grasp of your topic, move on to the next phase: drafting a thesis. The thesis is the central idea that you will explore and support in your report; all paragraphs in your report should relate to it. In an essay-style analytical report, you will likely express this main idea in a thesis statement of one or two sentences toward the end of the introduction.

For example, if you found that the academic performance of student athletes was higher than that of non-athletes, you might write the following thesis statement:

student sample text Although a common stereotype is that college athletes barely pass their classes, an analysis of athletes’ academic performance indicates that athletes drop fewer classes, earn higher grades, and are more likely to be on track to graduate in four years when compared with their non-athlete peers. end student sample text

The thesis statement often previews the organization of your writing. For example, in his report on the U.S. response to the COVID-19 pandemic in 2020, Trevor Garcia wrote the following thesis statement, which detailed the central idea of his report:

student sample text An examination of the U.S. response shows that a reduction of experts in key positions and programs, inaction that led to equipment shortages, and inconsistent policies were three major causes of the spread of the virus and the resulting deaths. end student sample text

After you draft a thesis statement, ask these questions, and examine your thesis as you answer them. Revise your draft as needed.

• Is it interesting? A thesis for a report should answer a question that is worth asking and piques curiosity.
• Is it precise and specific? If you are interested in reducing pollution in a nearby lake, explain how to stop the zebra mussel infestation or reduce the frequent algae blooms.
• Is it manageable? Try to split the difference between having too much information and not having enough.

Organizing Your Ideas

As a next step, organize the points you want to make in your report and the evidence to support them. Use an outline, a diagram, or another organizational tool, such as Table 8.4 .

Drafting an Analytical Report

With a tentative thesis, an organization plan, and evidence, you are ready to begin drafting. For this assignment, you will report information, analyze it, and draw conclusions about the cause of something, the effect of something, or the similarities and differences between two different things.

Introduction

Some students write the introduction first; others save it for last. Whenever you choose to write the introduction, use it to draw readers into your report. Make the topic of your report clear, and be concise and sincere. End the introduction with your thesis statement. Depending on your topic and the type of report, you can write an effective introduction in several ways. Opening a report with an overview is a tried-and-true strategy, as shown in the following example on the U.S. response to COVID-19 by Trevor Garcia. Notice how he opens the introduction with statistics and a comparison and follows it with a question that leads to the thesis statement (underlined).

student sample text With more than 83 million cases and 1.8 million deaths at the end of 2020, COVID-19 has turned the world upside down. By the end of 2020, the United States led the world in the number of cases, at more than 20 million infections and nearly 350,000 deaths. In comparison, the second-highest number of cases was in India, which at the end of 2020 had less than half the number of COVID-19 cases despite having a population four times greater than the U.S. (“COVID-19 Coronavirus Pandemic,” 2021). How did the United States come to have the world’s worst record in this pandemic? underline An examination of the U.S. response shows that a reduction of experts in key positions and programs, inaction that led to equipment shortages, and inconsistent policies were three major causes of the spread of the virus and the resulting deaths end underline . end student sample text

For a less formal report, you might want to open with a question, quotation, or brief story. The following example opens with an anecdote that leads to the thesis statement (underlined).

student sample text Betty stood outside the salon, wondering how to get in. It was June of 2020, and the door was locked. A sign posted on the door provided a phone number for her to call to be let in, but at 81, Betty had lived her life without a cell phone. Betty’s day-to-day life had been hard during the pandemic, but she had planned for this haircut and was looking forward to it; she had a mask on and hand sanitizer in her car. Now she couldn’t get in the door, and she was discouraged. In that moment, Betty realized how much Americans’ dependence on cell phones had grown in the months since the pandemic began. underline Betty and thousands of other senior citizens who could not afford cell phones or did not have the technological skills and support they needed were being left behind in a society that was increasingly reliant on technology end underline . end student sample text

Body Paragraphs: Point, Evidence, Analysis

Use the body paragraphs of your report to present evidence that supports your thesis. A reliable pattern to keep in mind for developing the body paragraphs of a report is point , evidence , and analysis :

• The point is the central idea of the paragraph, usually given in a topic sentence stated in your own words at or toward the beginning of the paragraph. Each topic sentence should relate to the thesis.
• The evidence you provide develops the paragraph and supports the point made in the topic sentence. Include details, examples, quotations, paraphrases, and summaries from sources if you conducted formal research. Synthesize the evidence you include by showing in your sentences the connections between sources.
• The analysis comes at the end of the paragraph. In your own words, draw a conclusion about the evidence you have provided and how it relates to the topic sentence.

The paragraph below illustrates the point, evidence, and analysis pattern. Drawn from a report about concussions among football players, the paragraph opens with a topic sentence about the NCAA and NFL and their responses to studies about concussions. The paragraph is developed with evidence from three sources. It concludes with a statement about helmets and players’ safety.

student sample text The NCAA and NFL have taken steps forward and backward to respond to studies about the danger of concussions among players. Responding to the deaths of athletes, documented brain damage, lawsuits, and public outcry (Buckley et al., 2017), the NCAA instituted protocols to reduce potentially dangerous hits during football games and to diagnose traumatic head injuries more quickly and effectively. Still, it has allowed players to wear more than one style of helmet during a season, raising the risk of injury because of imperfect fit. At the professional level, the NFL developed a helmet-rating system in 2011 in an effort to reduce concussions, but it continued to allow players to wear helmets with a wide range of safety ratings. The NFL’s decision created an opportunity for researchers to look at the relationship between helmet safety ratings and concussions. Cocello et al. (2016) reported that players who wore helmets with a lower safety rating had more concussions than players who wore helmets with a higher safety rating, and they concluded that safer helmets are a key factor in reducing concussions. end student sample text

Developing Paragraph Content

In the body paragraphs of your report, you will likely use examples, draw comparisons, show contrasts, or analyze causes and effects to develop your topic.

Paragraphs developed with Example are common in reports. The paragraph below, adapted from a report by student John Zwick on the mental health of soldiers deployed during wartime, draws examples from three sources.

student sample text Throughout the Vietnam War, military leaders claimed that the mental health of soldiers was stable and that men who suffered from combat fatigue, now known as PTSD, were getting the help they needed. For example, the New York Times (1966) quoted military leaders who claimed that mental fatigue among enlisted men had “virtually ceased to be a problem,” occurring at a rate far below that of World War II. Ayres (1969) reported that Brigadier General Spurgeon Neel, chief American medical officer in Vietnam, explained that soldiers experiencing combat fatigue were admitted to the psychiatric ward, sedated for up to 36 hours, and given a counseling session with a doctor who reassured them that the rest was well deserved and that they were ready to return to their units. Although experts outside the military saw profound damage to soldiers’ psyches when they returned home (Halloran, 1970), the military stayed the course, treating acute cases expediently and showing little concern for the cumulative effect of combat stress on individual soldiers. end student sample text

When you analyze causes and effects , you explain the reasons that certain things happened and/or their results. The report by Trevor Garcia on the U.S. response to the COVID-19 pandemic in 2020 is an example: his report examines the reasons the United States failed to control the coronavirus. The paragraph below, adapted from another student’s report written for an environmental policy course, explains the effect of white settlers’ views of forest management on New England.

student sample text The early colonists’ European ideas about forest management dramatically changed the New England landscape. White settlers saw the New World as virgin, unused land, even though indigenous people had been drawing on its resources for generations by using fire subtly to improve hunting, employing construction techniques that left ancient trees intact, and farming small, efficient fields that left the surrounding landscape largely unaltered. White settlers’ desire to develop wood-built and wood-burning homesteads surrounded by large farm fields led to forestry practices and techniques that resulted in the removal of old-growth trees. These practices defined the way the forests look today. end student sample text

Compare and contrast paragraphs are useful when you wish to examine similarities and differences. You can use both comparison and contrast in a single paragraph, or you can use one or the other. The paragraph below, adapted from a student report on the rise of populist politicians, compares the rhetorical styles of populist politicians Huey Long and Donald Trump.

student sample text A key similarity among populist politicians is their rejection of carefully crafted sound bites and erudite vocabulary typically associated with candidates for high office. Huey Long and Donald Trump are two examples. When he ran for president, Long captured attention through his wild gesticulations on almost every word, dramatically varying volume, and heavily accented, folksy expressions, such as “The only way to be able to feed the balance of the people is to make that man come back and bring back some of that grub that he ain’t got no business with!” In addition, Long’s down-home persona made him a credible voice to represent the common people against the country’s rich, and his buffoonish style allowed him to express his radical ideas without sounding anti-communist alarm bells. Similarly, Donald Trump chose to speak informally in his campaign appearances, but the persona he projected was that of a fast-talking, domineering salesman. His frequent use of personal anecdotes, rhetorical questions, brief asides, jokes, personal attacks, and false claims made his speeches disjointed, but they gave the feeling of a running conversation between him and his audience. For example, in a 2015 speech, Trump said, “They just built a hotel in Syria. Can you believe this? They built a hotel. When I have to build a hotel, I pay interest. They don’t have to pay interest, because they took the oil that, when we left Iraq, I said we should’ve taken” (“Our Country Needs” 2020). While very different in substance, Long and Trump adopted similar styles that positioned them as the antithesis of typical politicians and their worldviews. end student sample text

The conclusion should draw the threads of your report together and make its significance clear to readers. You may wish to review the introduction, restate the thesis, recommend a course of action, point to the future, or use some combination of these. Whichever way you approach it, the conclusion should not head in a new direction. The following example is the conclusion from a student’s report on the effect of a book about environmental movements in the United States.

student sample text Since its publication in 1949, environmental activists of various movements have found wisdom and inspiration in Aldo Leopold’s A Sand County Almanac . These audiences included Leopold’s conservationist contemporaries, environmentalists of the 1960s and 1970s, and the environmental justice activists who rose in the 1980s and continue to make their voices heard today. These audiences have read the work differently: conservationists looked to the author as a leader, environmentalists applied his wisdom to their movement, and environmental justice advocates have pointed out the flaws in Leopold’s thinking. Even so, like those before them, environmental justice activists recognize the book’s value as a testament to taking the long view and eliminating biases that may cloud an objective assessment of humanity’s interdependent relationship with the environment. end student sample text

Citing Sources

You must cite the sources of information and data included in your report. Citations must appear in both the text and a bibliography at the end of the report.

The sample paragraphs in the previous section include examples of in-text citation using APA documentation style. Trevor Garcia’s report on the U.S. response to COVID-19 in 2020 also uses APA documentation style for citations in the text of the report and the list of references at the end. Your instructor may require another documentation style, such as MLA or Chicago.

Peer Review: Getting Feedback from Readers

You will likely engage in peer review with other students in your class by sharing drafts and providing feedback to help spot strengths and weaknesses in your reports. For peer review within a class, your instructor may provide assignment-specific questions or a form for you to complete as you work together.

If you have a writing center on your campus, it is well worth your time to make an online or in-person appointment with a tutor. You’ll receive valuable feedback and improve your ability to review not only your report but your overall writing.

Another way to receive feedback on your report is to ask a friend or family member to read your draft. Provide a list of questions or a form such as the one in Table 8.5 for them to complete as they read.

Revising: Using Reviewers’ Responses to Revise your Work

When you receive comments from readers, including your instructor, read each comment carefully to understand what is being asked. Try not to get defensive, even though this response is completely natural. Remember that readers are like coaches who want you to succeed. They are looking at your writing from outside your own head, and they can identify strengths and weaknesses that you may not have noticed. Keep track of the strengths and weaknesses your readers point out. Pay special attention to those that more than one reader identifies, and use this information to improve your report and later assignments.

As you analyze each response, be open to suggestions for improvement, and be willing to make significant revisions to improve your writing. Perhaps you need to revise your thesis statement to better reflect the content of your draft. Maybe you need to return to your sources to better understand a point you’re trying to make in order to develop a paragraph more fully. Perhaps you need to rethink the organization, move paragraphs around, and add transition sentences.

Below is an early draft of part of Trevor Garcia’s report with comments from a peer reviewer:

student sample text To truly understand what happened, it’s important first to look back to the years leading up to the pandemic. Epidemiologists and public health officials had long known that a global pandemic was possible. In 2016, the U.S. National Security Council (NSC) published a 69-page document with the intimidating title Playbook for Early Response to High-Consequence Emerging Infectious Disease Threats and Biological Incidents . The document’s two sections address responses to “emerging disease threats that start or are circulating in another country but not yet confirmed within U.S. territorial borders” and to “emerging disease threats within our nation’s borders.” On 13 January 2017, the joint Obama-Trump transition teams performed a pandemic preparedness exercise; however, the playbook was never adopted by the incoming administration. end student sample text

annotated text Peer Review Comment: Do the words in quotation marks need to be a direct quotation? It seems like a paraphrase would work here. end annotated text

annotated text Peer Review Comment: I’m getting lost in the details about the playbook. What’s the Obama-Trump transition team? end annotated text

student sample text In February 2018, the administration began to cut funding for the Prevention and Public Health Fund at the Centers for Disease Control and Prevention; cuts to other health agencies continued throughout 2018, with funds diverted to unrelated projects such as housing for detained immigrant children. end student sample text

annotated text Peer Review Comment: This paragraph has only one sentence, and it’s more like an example. It needs a topic sentence and more development. end annotated text

student sample text Three months later, Luciana Borio, director of medical and biodefense preparedness at the NSC, spoke at a symposium marking the centennial of the 1918 influenza pandemic. “The threat of pandemic flu is the number one health security concern,” she said. “Are we ready to respond? I fear the answer is no.” end student sample text

annotated text Peer Review Comment: This paragraph is very short and a lot like the previous paragraph in that it’s a single example. It needs a topic sentence. Maybe you can combine them? end annotated text

annotated text Peer Review Comment: Be sure to cite the quotation. end annotated text

Reading these comments and those of others, Trevor decided to combine the three short paragraphs into one paragraph focusing on the fact that the United States knew a pandemic was possible but was unprepared for it. He developed the paragraph, using the short paragraphs as evidence and connecting the sentences and evidence with transitional words and phrases. Finally, he added in-text citations in APA documentation style to credit his sources. The revised paragraph is below:

student sample text Epidemiologists and public health officials in the United States had long known that a global pandemic was possible. In 2016, the National Security Council (NSC) published Playbook for Early Response to High-Consequence Emerging Infectious Disease Threats and Biological Incidents , a 69-page document on responding to diseases spreading within and outside of the United States. On January 13, 2017, the joint transition teams of outgoing president Barack Obama and then president-elect Donald Trump performed a pandemic preparedness exercise based on the playbook; however, it was never adopted by the incoming administration (Goodman & Schulkin, 2020). A year later, in February 2018, the Trump administration began to cut funding for the Prevention and Public Health Fund at the Centers for Disease Control and Prevention, leaving key positions unfilled. Other individuals who were fired or resigned in 2018 were the homeland security adviser, whose portfolio included global pandemics; the director for medical and biodefense preparedness; and the top official in charge of a pandemic response. None of them were replaced, leaving the White House with no senior person who had experience in public health (Goodman & Schulkin, 2020). Experts voiced concerns, among them Luciana Borio, director of medical and biodefense preparedness at the NSC, who spoke at a symposium marking the centennial of the 1918 influenza pandemic in May 2018: “The threat of pandemic flu is the number one health security concern,” she said. “Are we ready to respond? I fear the answer is no” (Sun, 2018, final para.). end student sample text

A final word on working with reviewers’ comments: as you consider your readers’ suggestions, remember, too, that you remain the author. You are free to disregard suggestions that you think will not improve your writing. If you choose to disregard comments from your instructor, consider submitting a note explaining your reasons with the final draft of your report.

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Although research paper assignments may vary widely, there are essentially two basic types of research papers. These are argumentative and analytical .

Argumentative

In an argumentative research paper, a student both states the topic they will be exploring and immediately establishes the position they will argue regarding that topic in a thesis statement . This type of paper hopes to persuade its reader to adopt the view presented.

 Example : a paper that argues the merits of early exposure to reading for children would be an argumentative essay.

An analytical research paper states the topic that the writer will be exploring, usually in the form of a question, initially taking a neutral stance. The body of the paper will present multifaceted information and, ultimately, the writer will state their conclusion, based on the information that has unfolded throughout the course of the essay. This type of paper hopes to offer a well-supported critical analysis without necessarily persuading the reader to any particular way of thinking.

Example : a paper that explores the use of metaphor in one of Shakespeare's sonnets would be an example of an analytical essay.

*Please note that this LibGuide will primarily be concerning itself with argumentative or rhetorical research papers.

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Research Paper Analysis: How to Analyze a Research Article + Example

Why might you need to analyze research? First of all, when you analyze a research article, you begin to understand your assigned reading better. It is also the first step toward learning how to write your own research articles and literature reviews. However, if you have never written a research paper before, it may be difficult for you to analyze one. After all, you may not know what criteria to use to evaluate it. But don’t panic! We will help you figure it out!

In this article, our team has explained how to analyze research papers quickly and effectively. At the end, you will also find a research analysis paper example to see how everything works in practice.

• 🔤 Research Analysis Definition

📊 How to Analyze a Research Article

✍️ how to write a research analysis.

• 📝 Analysis Example
• 🔎 More Examples

🔗 References

🔤 research paper analysis: what is it.

A research paper analysis is an academic writing assignment in which you analyze a scholarly article’s methodology, data, and findings. In essence, “to analyze” means to break something down into components and assess each of them individually and in relation to each other. The goal of an analysis is to gain a deeper understanding of a subject. So, when you analyze a research article, you dissect it into elements like data sources , research methods, and results and evaluate how they contribute to the study’s strengths and weaknesses.

📋 Research Analysis Format

A research analysis paper has a pretty straightforward structure. Check it out below!

Research articles usually include the following sections: introduction, methods, results, and discussion. In the following paragraphs, we will discuss how to analyze a scientific article with a focus on each of its parts.

How to Analyze a Research Paper: Purpose

The purpose of the study is usually outlined in the introductory section of the article. Analyzing the research paper’s objectives is critical to establish the context for the rest of your analysis.

When analyzing the research aim, you should evaluate whether it was justified for the researchers to conduct the study. In other words, you should assess whether their research question was significant and whether it arose from existing literature on the topic.

Here are some questions that may help you analyze a research paper’s purpose:

• Why was the research carried out?
• What gaps does it try to fill, or what controversies to settle?
• How does the study contribute to its field?
• Do you agree with the author’s justification for approaching this particular question in this way?

How to Analyze a Paper: Methods

When analyzing the methodology section , you should indicate the study’s research design (qualitative, quantitative, or mixed) and methods used (for example, experiment, case study, correlational research, survey, etc.). After that, you should assess whether these methods suit the research purpose. In other words, do the chosen methods allow scholars to answer their research questions within the scope of their study?

For example, if scholars wanted to study US students’ average satisfaction with their higher education experience, they could conduct a quantitative survey . However, if they wanted to gain an in-depth understanding of the factors influencing US students’ satisfaction with higher education, qualitative interviews would be more appropriate.

When analyzing methods, you should also look at the research sample . Did the scholars use randomization to select study participants? Was the sample big enough for the results to be generalizable to a larger population?

You can also answer the following questions in your methodology analysis:

• Is the methodology valid? In other words, did the researchers use methods that accurately measure the variables of interest?
• Is the research methodology reliable? A research method is reliable if it can produce stable and consistent results under the same circumstances.
• Is the study biased in any way?
• What are the limitations of the chosen methodology?

How to Analyze Research Articles’ Results

You should start the analysis of the article results by carefully reading the tables, figures, and text. Check whether the findings correspond to the initial research purpose. See whether the results answered the author’s research questions or supported the hypotheses stated in the introduction.

To analyze the results section effectively, answer the following questions:

• What are the major findings of the study?
• Did the author present the results clearly and unambiguously?
• Are the findings statistically significant ?
• Does the author provide sufficient information on the validity and reliability of the results?
• Have you noticed any trends or patterns in the data that the author did not mention?

How to Analyze Research: Discussion

Finally, you should analyze the authors’ interpretation of results and its connection with research objectives. Examine what conclusions the authors drew from their study and whether these conclusions answer the original question.

You should also pay attention to how the authors used findings to support their conclusions. For example, you can reflect on why their findings support that particular inference and not another one. Moreover, more than one conclusion can sometimes be made based on the same set of results. If that’s the case with your article, you should analyze whether the authors addressed other interpretations of their findings .

Here are some useful questions you can use to analyze the discussion section:

• What findings did the authors use to support their conclusions?
• How do the researchers’ conclusions compare to other studies’ findings?
• How does this study contribute to its field?
• What future research directions do the authors suggest?
• What additional insights can you share regarding this article? For example, do you agree with the results? What other questions could the researchers have answered?

Now, you know how to analyze an article that presents research findings. However, it’s just a part of the work you have to do to complete your paper. So, it’s time to learn how to write research analysis! Check out the steps below!

1. Introduce the Article

As with most academic assignments, you should start your research article analysis with an introduction. Here’s what it should include:

• The article’s publication details . Specify the title of the scholarly work you are analyzing, its authors, and publication date. Remember to enclose the article’s title in quotation marks and write it in title case .
• The article’s main point . State what the paper is about. What did the authors study, and what was their major finding?
• Your thesis statement . End your introduction with a strong claim summarizing your evaluation of the article. Consider briefly outlining the research paper’s strengths, weaknesses, and significance in your thesis.

Keep your introduction brief. Save the word count for the “meat” of your paper — that is, for the analysis.

2. Summarize the Article

Now, you should write a brief and focused summary of the scientific article. It should be shorter than your analysis section and contain all the relevant details about the research paper.

Here’s what you should include in your summary:

• The research purpose . Briefly explain why the research was done. Identify the authors’ purpose and research questions or hypotheses .
• Methods and results . Summarize what happened in the study. State only facts, without the authors’ interpretations of them. Avoid using too many numbers and details; instead, include only the information that will help readers understand what happened.
• The authors’ conclusions . Outline what conclusions the researchers made from their study. In other words, describe how the authors explained the meaning of their findings.

If you need help summarizing an article, you can use our free summary generator .

3. Write Your Research Analysis

The analysis of the study is the most crucial part of this assignment type. Its key goal is to evaluate the article critically and demonstrate your understanding of it.

We’ve already covered how to analyze a research article in the section above. Here’s a quick recap:

• Analyze whether the study’s purpose is significant and relevant.
• Examine whether the chosen methodology allows for answering the research questions.
• Evaluate how the authors presented the results.
• Assess whether the authors’ conclusions are grounded in findings and answer the original research questions.

Although you should analyze the article critically, it doesn’t mean you only should criticize it. If the authors did a good job designing and conducting their study, be sure to explain why you think their work is well done. Also, it is a great idea to provide examples from the article to support your analysis.

4. Conclude Your Analysis of Research Paper

A conclusion is your chance to reflect on the study’s relevance and importance. Explain how the analyzed paper can contribute to the existing knowledge or lead to future research. Also, you need to summarize your thoughts on the article as a whole. Avoid making value judgments — saying that the paper is “good” or “bad.” Instead, use more descriptive words and phrases such as “This paper effectively showed…”

Need help writing a compelling conclusion? Try our free essay conclusion generator !

5. Revise and Proofread

Last but not least, you should carefully proofread your paper to find any punctuation, grammar, and spelling mistakes. Start by reading your work out loud to ensure that your sentences fit together and sound cohesive. Also, it can be helpful to ask your professor or peer to read your work and highlight possible weaknesses or typos.

📝 Research Paper Analysis Example

We have prepared an analysis of a research paper example to show how everything works in practice.

No Homework Policy: Research Article Analysis Example

This paper aims to analyze the research article entitled “No Assignment: A Boon or a Bane?” by Cordova, Pagtulon-an, and Tan (2019). This study examined the effects of having and not having assignments on weekends on high school students’ performance and transmuted mean scores. This article effectively shows the value of homework for students, but larger studies are needed to support its findings.

Cordova et al. (2019) conducted a descriptive quantitative study using a sample of 115 Grade 11 students of the Central Mindanao University Laboratory High School in the Philippines. The sample was divided into two groups: the first received homework on weekends, while the second didn’t. The researchers compared students’ performance records made by teachers and found that students who received assignments performed better than their counterparts without homework.

The purpose of this study is highly relevant and justified as this research was conducted in response to the debates about the “No Homework Policy” in the Philippines. Although the descriptive research design used by the authors allows to answer the research question, the study could benefit from an experimental design. This way, the authors would have firm control over variables. Additionally, the study’s sample size was not large enough for the findings to be generalized to a larger population.

The study results are presented clearly, logically, and comprehensively and correspond to the research objectives. The researchers found that students’ mean grades decreased in the group without homework and increased in the group with homework. Based on these findings, the authors concluded that homework positively affected students’ performance. This conclusion is logical and grounded in data.

This research effectively showed the importance of homework for students’ performance. Yet, since the sample size was relatively small, larger studies are needed to ensure the authors’ conclusions can be generalized to a larger population.

🔎 More Research Analysis Paper Examples

Do you want another research analysis example? Check out the best analysis research paper samples below:

• Gracious Leadership Principles for Nurses: Article Analysis
• Effective Mental Health Interventions: Analysis of an Article
• Nursing Turnover: Article Analysis
• Nursing Practice Issue: Qualitative Research Article Analysis
• Quantitative Article Critique in Nursing
• LIVE Program: Quantitative Article Critique
• Evidence-Based Practice Beliefs and Implementation: Article Critique
• “Differential Effectiveness of Placebo Treatments”: Research Paper Analysis
• “Family-Based Childhood Obesity Prevention Interventions”: Analysis Research Paper Example
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• “Fostering Early Breast Cancer Detection” Article Analysis
• Lesson Planning for Diversity: Analysis of an Article
• Journal Article Review: Correlates of Physical Violence at School
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• “Democracy and Collective Identity in the EU and the USA”: Article Analysis
• China’s Hegemonic Prospects: Article Review
• Article Analysis: Fear of Missing Out
• Article Analysis: “Perceptions of ADHD Among Diagnosed Children and Their Parents”
• Codependence, Narcissism, and Childhood Trauma: Analysis of the Article
• Relationship Between Work Intensity, Workaholism, Burnout, and MSC: Article Review

We hope that our article on research paper analysis has been helpful. If you liked it, please share this article with your friends!

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Home » Research Paper – Structure, Examples and Writing Guide

Research Paper – Structure, Examples and Writing Guide

Table of Contents

Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

• Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
• Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
• Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
• Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
• Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
• Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
• Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
• Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
• Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

• Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
• Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
• Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

• How often do you use social media per day?
• Less than 30 minutes
• 30 minutes to 1 hour
• 1 to 2 hours
• 2 to 4 hours
• More than 4 hours
• Which social media platforms do you use?
• Others (Please specify)
• How often do you experience the following on social media?
• Social comparison (comparing yourself to others)
• Cyberbullying
• Fear of Missing Out (FOMO)
• Have you ever experienced any of the following mental health problems in the past month?
• Do you think social media use has a positive or negative impact on your mental health?
• Very positive
• Somewhat positive
• Somewhat negative
• Very negative
• In your opinion, which factors contribute to the negative impact of social media on mental health?
• Social comparison
• In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
• Education on healthy social media use
• Counseling for mental health problems caused by social media
• Social media detox programs
• Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

• Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
• Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
• Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
• Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
• Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

• For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
• For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
• To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
• To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

• To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
• To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
• To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
• To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

• Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
• Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
• Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
• Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
• Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
• Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

• Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
• Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
• Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
• Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
• Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
• Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

• Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
• Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
• Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
• Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
• Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
• Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to Write an Analytical Research Paper

A research paper is one of the most frequently assigned papers along with different types of essays. Research papers can be classified into types as well as essays. There are seven common types of research papers: analytical, argumentative, experimental, definition, problem-solution, cause and effect, and research reports.

Each of these types has particular features and purposes. We have decided to provide you with an informative guide on how to write an analytical research paper. Therefore, we will give you exhaustive writing instructions, analyze general traits of this paper, and share a few helpful tips with you. Our well-educated writer has also created a great analytical research paper sample with comments. You can use it as an example for your own work.

What Is an Analytical Research Paper?

An analytical research paper is an academic piece of writing that is aimed at analyzing different points of view from multiple sources on a particular topic. In contrast to an argumentative research paper, you don’t have to persuade your readers that your personal point of view is correct and others are wrong. The main purpose of the analytical research paper is to present a few different opinions and to draw logical conclusions. Although your topic may be controversial and debatable, you don’t have to choose one side. You may evaluate the works of researchers on the chosen topic, but you should always remain objective.

Don’t confuse analytical and definition research papers. Definition papers don’t require deep analysis, as they simply gather and categorize data on a certain topic to inform readers. All information left unanalyzed may serve as an excellent framework for future analytical or argumentative papers.

As well as a problem-solution research paper, the analytical paper may include a few recommendations in the concluding section. However, the search for solutions is not the main goal of the analytical paper, and this function is optional.

Analytical Research Paper: Special Features

We hope that you won’t confuse an analytical research paper with any other type of writing after our explanation. Now, let’s figure out what characteristics are necessary for a high-quality analytical research paper! You are welcome to use this list both to evaluate yours or other authors’ works.

1. Complex informative basis

Good sources are necessary for any research, especially for an analytical research paper. You have to gather the opinions on your topic from different perspectives. However, we don’t recommend you to use commercial websites and personal blogs as sources for your research paper. They are unreliable and frequently present unverified information.

2. Proper formatting style

As you know, there are different formatting styles: APA, MLA, Chicago, etc. Their guides specify the rules of citation, paper structure, the format of footnotes, and other formalities. You should always ask your instructor about proper formatting style as this factor may influence your grade.

3. An acute and interesting topic

The first characteristic works for all analytical research papers but doesn’t apply to historical ones. However, even research about ancient events should be interesting for your readers. No doubt, you are not creating your paper for the general public, so you are allowed to use terminology and complex words.

4. Logical structure

There is no universal template for an analytical research paper. Still, you should be logical and consistent: put an abstract at the beginning of your paper, an introduction before the research study, and conclusion at the end. Remember that readers should easily follow your thoughts without any difficulties. If your analytical research paper is rather long, compose a table of contents.

5. Critical interpretation of sources

You shouldn’t simply say that you agree or disagree with a particular author. Analyze his or her argumentation, and only after that will you have the opportunity to evaluate the conclusions objectively. Remain critical, realistic, and unbiased. Don’t allow external factors to influence your judgment.

6. No logical fallacies

Logical mistakes are common for those who begin their path in the world of science and research. Look at the topic from different angles in order to get a full picture. The more you know, the higher the chances that you will avoid fallacies. You may also find a list of logical mistakes on the internet and check your work.

7. A fresh perspective

Gathering others’ opinions is not your only goal. You should also present your own thoughts and offer a new point of view on the topic. Try to be original and creative, as nobody likes plagiarists. Even if your topic is not extremely unique, you can always find an aspect that hasn’t been covered by previous researchers yet.

8. A relevant thesis statement

Put your thesis statement in the introduction. Remember that it should reflect your research question and establish the structure of the whole paper. Surprisingly, we recommend you to compose your introduction along with the thesis statement second to last. This method will help you to create the most relevant introduction.

9. A formal writing style

No jargon, no colloquial language, no exclamation marks. You probably know all these “NOs.” Still, we will list a few things that you should avoid in your analytical research paper:

• contractions
• figures of speech and metaphors
• ambiguous meanings
• texting-style words
• first-person narration
• first and second person pronouns

10. Immaculate objectiveness

Analytical Research Paper Outline: Proper Structure

The appropriate structure for an analytical research paper may vary. It depends on your topic and discipline. We highly recommend you to ask your instructor about the required sections, otherwise, you risk getting a low grade. However, the main sections remain the same. Let’s take a closer look at each part of the paper.

Title page. You may think that the only function of the title page is to identify the author and inform the reader about the topic of the paper. You are totally right. The title page is not a part of your analytical research paper which you should worry about. If you are able to compose a serious scientific paper, we are sure that you also can write your name, title of your work, date, and affiliation. When it comes to titling your work, don’t forget about keywords and conciseness.

Abstract. Every guide usually claims an abstract to be an optional section of your analytical research paper. However, it plays such an important role that it would be a huge mistake to ignore this section. An abstract is like a nice dress for a girl: it attracts and intrigues. You should present the whole work in 150-200 words, so readers will be able to decide if they need to spend some time to read your paper. No doubt, your abstract must be perfect! Write it last, when your paper will be prepared. In such a way, an abstract will reflect all main aspects of your work: thesis statement, sources, findings, and conclusions.

Introduction. The introductory part has three main functions. First of all, you provide your readers with background information related to your topic. You shouldn’t start with the story about Adam and Eve, but make sure that your readers will be informed. Next, state the problem clearly and concisely. Your problem statement establishes the goals and structure of your paper, so be attentive and accurate. Finally, describe the previous studies related to your topic. Mention how they correlate with your methods and what aspects of the chosen theme they cover.

Literature review. As we mentioned above, an analytical research paper requires a good informative basis consisting of relevant, authoritative, and reliable sources. In this section, you should provide your readers with the findings on your topic made by previous researchers. Cite all sources properly in order to avoid plagiarism. A literature review proves that your analytical research paper is not baseless and that readers should take it seriously.

Research design. This section has no strict structure, as its content depends on the work that you have done to create your paper. Mention all aspects of the working process: problem statement, objectives, sources of data, data collection, methods, etc. The purpose of the research design is to explain to readers how exactly you have gathered information to get your results.

Conclusion. The concluding part of your analytical research paper can be presented as a whole or can be divided into subsections: summary, discussion, and recommendations. The choice depends on you, but we highly recommend you to pick the second option. When you divide the text into a few subsections, you facilitate the reading process a lot. In the summary, you display all your findings and observations in a brief and understandable manner. Consider the use of diagrams, charts, and tables. The discussion section demonstrates how the results of your work are relevant to previous researches on this topic and their importance for further investigations. In your recommendations, give some instructions for the researchers who want to continue your study.

Steps to Write an Analytical Research Paper

Surprisingly, the writing stage is not the only thing you need to do to get a perfect analytical research paper. Let’s figure out what steps might be important to start your work and to bring your paper to perfection at the end.

1. Choose a topic

Sometimes, fortune smiles on students, and they get an opportunity of free choice. That doesn’t happen every day, but you should always be ready to face this occurrence. The chosen topic should satisfy three criteria: it should be interesting, narrow, and acute. Check out our list of amazing analytical research paper topics !

2. Specify all details

Nobody will accuse you of extreme curiosity if you ask your instructor for additional attention to your questions. Specify all unclear aspects. Pay special attention to the formatting style and requirements related to word limit and structure.

3. Find reliable sources

A complex informative basis is the key to a high-quality analytical research paper. Make sure that your sources are reliable and modern. Personal blogs and ancient manuscripts are not allowed!

4. Make notes

Memory is not perfect. Even geniuses have to make notes from time to time. Moreover, it is hard to memorize all the sources that you use during your working process. If you don’t mention all studies that serve as a basis for your paper, you will be accused as a plagiarist. That would not look good on your resume.

5. Brainstorm ideas

Drink a cup of tea and eat your favorite sweets. You have to be inspired and relaxed. Your brain should be full of energy to generate a few creative ideas. New approaches, unusual methods, interesting details – all these things will increase the value of your analytical research paper.

6. Make an outline

Planning is the root of everything. A good outline will let you structure your disjointed thoughts and ideas and imagine a future structure of your paper. Try to make a list of all sections and mention key phrases and words that you are planning to include in the text. You may also draw a pyramid, circle, or any other figure that helps you to create a starting point for your work.

7. Write the first draft

Don’t expect to get a perfect draft in the first try. Some writers edit their novels for years! We know that you don’t have years to accomplish your assignment. However, try to do your best.

8. Edit your paper

This step may seem boring. Actually, it is rather boring to read and reread your own paper over and over again. Actually, we recommend you to have some rest when your analytical research paper will be finished. Your brain will be able to focus on spelling and formatting only after a good nap.

9. Proofread the text

If you are sure that the main parts of your paper can not be better, then it is the right moment to focus on smaller details: spelling, wording, and punctuation. Even the most insignificant mistakes will spoil the overall impression. Be accurate and attentive!

10. Get feedback

A fresh perspective never hurts. Apply to your peers, colleagues, roommates, parents, distant cousins, or imaginary friends. In short, apply to anybody who is able to read your paper and say a few words about its quality.

Analytical Research Paper: Example Analyzed

The best way to learn more about writing an analytical research paper is to read good examples provided by experienced authors. Luckily for you, we have one great sample right here. Moreover, we have asked our writer to leave some comments on her analytical research paper. In such a manner, you will be informed about the functions of each section in this analytical research paper example . Don’t hesitate to use our text as a template for your own work. Please, don’t commit plagiarism, as you can be accused of academic dishonesty.

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We are 100% sure that you now know enough about how to write an analytical research paper. At least, we have done everything possible to teach you. Now, it is up to you. We strongly believe in your talents and writing skills. After reading this article attentively, you can firmly say: “I know everything to write my research paper without stress!” You have no chances to compose a bad analytical research paper!

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Types of research papers

Analytical research paper

Argumentative or persuasive paper, definition paper, compare and contrast paper, cause and effect paper, interpretative paper, experimental research paper, survey research paper, frequently asked questions about the different types of research papers, related articles.

There are multiple different types of research papers. It is important to know which type of research paper is required for your assignment, as each type of research paper requires different preparation. Below is a list of the most common types of research papers.

➡️ Read more:  What is a research paper?

In an analytical research paper you:

• pose a question
• collect relevant data from other researchers
• analyze their different viewpoints

You focus on the findings and conclusions of other researchers and then make a personal conclusion about the topic. It is important to stay neutral and not show your own negative or positive position on the matter.

The argumentative paper presents two sides of a controversial issue in one paper. It is aimed at getting the reader on the side of your point of view.

You should include and cite findings and arguments of different researchers on both sides of the issue, but then favor one side over the other and try to persuade the reader of your side. Your arguments should not be too emotional though, they still need to be supported with logical facts and statistical data.

Tip: Avoid expressing too much emotion in a persuasive paper.

The definition paper solely describes facts or objective arguments without using any personal emotion or opinion of the author. Its only purpose is to provide information. You should include facts from a variety of sources, but leave those facts unanalyzed.

Compare and contrast papers are used to analyze the difference between two:

Make sure to sufficiently describe both sides in the paper, and then move on to comparing and contrasting both thesis and supporting one.

Cause and effect papers are usually the first types of research papers that high school and college students write. They trace probable or expected results from a specific action and answer the main questions "Why?" and "What?", which reflect effects and causes.

In business and education fields, cause and effect papers will help trace a range of results that could arise from a particular action or situation.

An interpretative paper requires you to use knowledge that you have gained from a particular case study, for example a legal situation in law studies. You need to write the paper based on an established theoretical framework and use valid supporting data to back up your statement and conclusion.

This type of research paper basically describes a particular experiment in detail. It is common in fields like:

Experiments are aimed to explain a certain outcome or phenomenon with certain actions. You need to describe your experiment with supporting data and then analyze it sufficiently.

This research paper demands the conduction of a survey that includes asking questions to respondents. The conductor of the survey then collects all the information from the survey and analyzes it to present it in the research paper.

➡️ Ready to start your research paper? Take a look at our guide on how to start a research paper .

In an analytical research paper, you pose a question and then collect relevant data from other researchers to analyze their different viewpoints. You focus on the findings and conclusions of other researchers and then make a personal conclusion about the topic.

The definition paper solely describes facts or objective arguments without using any personal emotion or opinion of the author. Its only purpose is to provide information.

Cause and effect papers are usually the first types of research papers that high school and college students are confronted with. The answer questions like "Why?" and "What?", which reflect effects and causes. In business and education fields, cause and effect papers will help trace a range of results that could arise from a particular action or situation.

This type of research paper describes a particular experiment in detail. It is common in fields like biology, chemistry or physics. Experiments are aimed to explain a certain outcome or phenomenon with certain actions.

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Writing a Research Paper

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The Research Paper

There will come a time in most students' careers when they are assigned a research paper. Such an assignment often creates a great deal of unneeded anxiety in the student, which may result in procrastination and a feeling of confusion and inadequacy. This anxiety frequently stems from the fact that many students are unfamiliar and inexperienced with this genre of writing. Never fear—inexperience and unfamiliarity are situations you can change through practice! Writing a research paper is an essential aspect of academics and should not be avoided on account of one's anxiety. In fact, the process of writing a research paper can be one of the more rewarding experiences one may encounter in academics. What is more, many students will continue to do research throughout their careers, which is one of the reasons this topic is so important.

Becoming an experienced researcher and writer in any field or discipline takes a great deal of practice. There are few individuals for whom this process comes naturally. Remember, even the most seasoned academic veterans have had to learn how to write a research paper at some point in their career. Therefore, with diligence, organization, practice, a willingness to learn (and to make mistakes!), and, perhaps most important of all, patience, students will find that they can achieve great things through their research and writing.

The pages in this section cover the following topic areas related to the process of writing a research paper:

• Genre - This section will provide an overview for understanding the difference between an analytical and argumentative research paper.
• Choosing a Topic - This section will guide the student through the process of choosing topics, whether the topic be one that is assigned or one that the student chooses themselves.
• Identifying an Audience - This section will help the student understand the often times confusing topic of audience by offering some basic guidelines for the process.
• Where Do I Begin - This section concludes the handout by offering several links to resources at Purdue, and also provides an overview of the final stages of writing a research paper.

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• Knowledge Base

Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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This paper is in the following e-collection/theme issue:

Published on 17.4.2024 in Vol 26 (2024)

Digital Interventions for Recreational Cannabis Use Among Young Adults: Systematic Review, Meta-Analysis, and Behavior Change Technique Analysis of Randomized Controlled Studies

Authors of this article:

• José Côté 1, 2, 3 , RN, PhD   ; 
• Gabrielle Chicoine 3, 4 , RN, PhD   ; 
• Billy Vinette 1, 3 , RN, MSN   ; 
• Patricia Auger 2, 3 , MSc   ; 
• Geneviève Rouleau 3, 5, 6 , RN, PhD   ; 
• Guillaume Fontaine 7, 8, 9 , RN, PhD   ; 
• Didier Jutras-Aswad 2, 10 , MSc, MD  

1 Faculty of Nursing, Université de Montréal, Montreal, QC, Canada

2 Research Centre of the Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada

3 Research Chair in Innovative Nursing Practices, Montreal, QC, Canada

4 Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON, Canada

5 Department of Nursing, Université du Québec en Outaouais, Saint-Jérôme, QC, Canada

6 Women's College Hospital Institute for Health System Solutions and Virtual Care, Women's College Hospital, Toronto, ON, Canada

7 Ingram School of Nursing, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada

8 Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Sir Mortimer B. Davis Jewish General Hospital, Montreal, QC, Canada

9 Kirby Institute, University of New South Wales, Sydney, Australia

10 Department of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada

Corresponding Author:

José Côté, RN, PhD

Research Centre of the Centre Hospitalier de l’Université de Montréal

850 Saint-Denis

Montreal, QC, H2X 0A9

Phone: 1 514 890 8000

Email: [email protected]

Background: The high prevalence of cannabis use among young adults poses substantial global health concerns due to the associated acute and long-term health and psychosocial risks. Digital modalities, including websites, digital platforms, and mobile apps, have emerged as promising tools to enhance the accessibility and availability of evidence-based interventions for young adults for cannabis use. However, existing reviews do not consider young adults specifically, combine cannabis-related outcomes with those of many other substances in their meta-analytical results, and do not solely target interventions for cannabis use.

Objective: We aimed to evaluate the effectiveness and active ingredients of digital interventions designed specifically for cannabis use among young adults living in the community.

Methods: We conducted a systematic search of 7 databases for empirical studies published between database inception and February 13, 2023, assessing the following outcomes: cannabis use (frequency, quantity, or both) and cannabis-related negative consequences. The reference lists of included studies were consulted, and forward citation searching was also conducted. We included randomized studies assessing web- or mobile-based interventions that included a comparator or control group. Studies were excluded if they targeted other substance use (eg, alcohol), did not report cannabis use separately as an outcome, did not include young adults (aged 16-35 y), had unpublished data, were delivered via teleconference through mobile phones and computers or in a hospital-based setting, or involved people with mental health disorders or substance use disorders or dependence. Data were independently extracted by 2 reviewers using a pilot-tested extraction form. Authors were contacted to clarify study details and obtain additional data. The characteristics of the included studies, study participants, digital interventions, and their comparators were summarized. Meta-analysis results were combined using a random-effects model and pooled as standardized mean differences.

Results: Of 6606 unique records, 19 (0.29%) were included (n=6710 participants). Half (9/19, 47%) of these articles reported an intervention effect on cannabis use frequency. The digital interventions included in the review were mostly web-based. A total of 184 behavior change techniques were identified across the interventions (range 5-19), and feedback on behavior was the most frequently used (17/19, 89%). Digital interventions for young adults reduced cannabis use frequency at the 3-month follow-up compared to control conditions (including passive and active controls) by −6.79 days of use in the previous month (95% CI −9.59 to −4.00; P <.001).

Conclusions: Our results indicate the potential of digital interventions to reduce cannabis use in young adults but raise important questions about what optimal exposure dose could be more effective, both in terms of intervention duration and frequency. Further high-quality research is still needed to investigate the effects of digital interventions on cannabis use among young adults.

Trial Registration: PROSPERO CRD42020196959; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=196959

Introduction

Cannabis use among young adults is recognized as a public health concern.

Young adulthood (typically the ages of 18-30 y) is a critical developmental stage characterized by a peak prevalence of substance use [ 1 , 2 ]. Worldwide, cannabis is a substance frequently used for nonmedical purposes due in part to its high availability in some regions and enhanced product variety and potency [ 3 , 4 ]. The prevalence of cannabis use (CU) among young adults is high [ 5 , 6 ], and its rates have risen in recent decades [ 7 ]. In North America and Oceania, the estimated past-year prevalence of CU is ≥25% among young adults [ 8 , 9 ].

While the vast majority of cannabis users do not experience severe problems from their use [ 4 ], the high prevalence of CU among young adults poses substantial global health concerns due to the associated acute and long-term health and psychosocial risks [ 10 , 11 ]. These include impairment of cognitive function, memory, and psychomotor skills during acute intoxication; increased engagement in behaviors with a potential for injury and fatality (eg, driving under the influence); socioeconomic problems; and diminished social functioning [ 4 , 12 - 14 ]. Importantly, an extensive body of literature reveals that subgroups engaging in higher-risk use, such as intensive or repeated use, are more prone to severe and chronic consequences, including physical ailments (eg, respiratory illness and reproductive dysfunction), mental health disorders (eg, psychosis, depression, and suicidal ideation or attempts), and the potential development of CU disorder [ 4 , 15 - 17 ].

Interventions to Reduce Public Health Impact of Young Adult CU

Given the increased prevalence of lifetime and daily CU among young adults and the potential negative impact of higher-risk CU, various prevention and intervention programs have been implemented to help users reduce or cease their CU. These programs primarily target young adults regardless of their CU status [ 2 , 18 ]. In this context, many health care organizations and international expert panels have developed evidence-based lower-risk CU guidelines to promote safer CU and intervention options to help reduce risks of adverse health outcomes from nonmedical CU [ 4 , 16 , 17 , 19 ]. Lower-risk guidance-oriented interventions for CU are based on concepts of health promotion [ 20 - 22 ] and health behavior change [ 23 - 26 ] and on other similar harm reduction interventions implemented in other areas of population health (eg, lower-risk drinking guidelines, supervised consumption sites and services, and sexual health) [ 27 , 28 ]. These interventions primarily aim to raise awareness of negative mental, physical, and social cannabis-related consequences to modify individual-level behavior-related risk factors.

Meta-analyses have shown that face-to-face prevention and treatment interventions are generally effective in reducing CU in young adults [ 18 , 29 - 32 ]. However, as the proportion of professional help seeking for CU concerns among young adults remains low (approximately 15%) [ 33 , 34 ], alternative strategies that consider the limited capacities and access-related barriers of traditional face-to-face prevention and treatment facilities are needed. Digital interventions, including websites, digital platforms, and mobile apps, have emerged as promising tools to enhance the accessibility and availability of evidence-based programs for young adult cannabis users. These interventions address barriers such as long-distance travel, concerns about confidentiality, stigma associated with seeking treatment, and the cost of traditional treatments [ 35 - 37 ]. By overcoming these barriers, digital interventions have the potential to have a stronger public health impact [ 18 , 38 ].

State of Knowledge of Digital Interventions for CU and Young Adults

The literature regarding digital interventions for substance use has grown rapidly in the past decade, as evidenced by several systematic reviews and meta-analyses of randomized controlled trial (RCT) studies on the efficacy or effectiveness of these interventions in preventing or reducing harmful substance use [ 2 , 39 - 41 ]. However, these reviews do not focus on young adults specifically. In addition, they combine CU-related outcomes with those of many other substances in their meta-analytical results. Finally, they do not target CU interventions exclusively.

In total, 4 systematic reviews and meta-analyses of digital interventions for CU among young people have reported mixed results [ 42 - 45 ]. In their systematic review (10 studies of 5 prevention and 5 treatment interventions up to 2012), Tait et al [ 44 ] concluded that digital interventions effectively reduced CU among adolescents and adults at the posttreatment time point. Olmos et al [ 43 ] reached a similar conclusion in their meta-analysis of 9 RCT studies (2 prevention and 7 treatment interventions). In their review, Hoch et al [ 42 ] reported evidence of small effects at the 3-month follow-up based on 4 RCTs of brief motivational interventions and cognitive behavioral therapy (CBT) delivered on the web. In another systematic review and meta-analysis, Beneria et al [ 45 ] found that web-based CU interventions did not significantly reduce consumption. However, these authors indicated that the programs tested varied significantly across the studies considered and that statistical heterogeneity was attributable to the inclusion of studies of programs targeting more than one substance (eg, alcohol and cannabis) and both adolescents and young adults. Beneria et al [ 45 ] recommend that future work “establish the effectiveness of the newer generation of interventions as well as the key ingredients” of effective digital interventions addressing CU by young people. This is of particular importance because behavior change interventions tend to be complex as they consist of multiple interactive components [ 46 ].

Behavior change interventions refer to “coordinated sets of activities designed to change specified behavior patterns” [ 47 ]. Their interacting active ingredients can be conceptualized as behavior change techniques (BCTs) [ 48 ]. BCTs are specific and irreducible. Each BCT has its own individual label and definition, which can be used when designing and reporting complex interventions and as a nomenclature system when coding interventions for their content [ 47 ]. The Behavior Change Technique Taxonomy version 1 (BCTTv1) [ 48 , 49 ] was developed to provide a shared, standardized terminology for characterizing complex behavior change interventions and their active ingredients. Several systematic reviews with meta-regressions that used the BCTTv1 have found interventions with certain BCTs to be more effective than those without [ 50 - 53 ]. A better understanding of the BCTs used in digital interventions for young adult cannabis users would help not only to establish the key ingredients of such interventions but also develop and evaluate effective interventions.

In the absence of any systematic review of the effectiveness and active ingredients of digital interventions designed specifically for CU among community-living young adults, we set out to achieve the following:

• conduct a comprehensive review of digital interventions for preventing, reducing, or ceasing CU among community-living young adults,
• describe the active ingredients (ie, BCTs) in these interventions from the perspective of behavior change science, and
• analyze the effectiveness of these interventions on CU outcomes.

Protocol Registration

We followed the Cochrane Handbook for Systematic Reviews of Interventions [ 54 ] in designing this systematic review and meta-analysis and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines in reporting our findings (see Multimedia Appendix 1 [ 55 ] for the complete PRISMA checklist). This review was registered in PROSPERO (CRD42020196959).

Search Strategy

The search strategy was designed by a health information specialist together with the research team and peer reviewed by another senior information specialist before execution using Peer Review of Electronic Search Strategies for systematic reviews [ 56 ]. The search strategy revolved around three concepts:

• CU (eg, “cannabis,” “marijuana,” and “hashish”)
• Digital interventions (eg, “telehealth,” “website,” “mobile applications,” and “computer”)
• Young adults (eg, “emerging adults” and “students”)

The strategy was initially implemented on March 18, 2020, and again on October 13, 2021, and February 13, 2023. The full, detailed search strategies for each database are presented in Multimedia Appendix 2 .

Information Sources

We searched 7 electronic databases of published literature: CINAHL Complete, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Embase, MEDLINE, PubMed, and PsycINFO. No publication date filters or language restrictions were applied. A combination of free-text keywords and Medical Subject Headings was tailored to the conventions of each database for optimal electronic searching. The research team also manually screened the reference lists of the included articles and the bibliographies of existing systematic reviews [ 18 , 31 , 42 - 45 ] to identify additional relevant studies (snowballing). Finally, a forward citation tracking procedure (ie, searching for articles that cited the included studies) was carried out in Google Scholar.

Inclusion Criteria

The population, intervention, comparison, outcome, and study design process is presented in Multimedia Appendix 3 . The inclusion criteria were as follows: (1) original research articles published in peer-reviewed journals; (2) use of an experimental study design (eg, RCT, cluster RCT, or pilot RCT); (3) studies evaluating the effectiveness (or efficacy) of digital interventions designed specifically to prevent, reduce, or cease CU as well as promote CU self-management or address cannabis-related harm and having CU as an outcome measure; (4) studies targeting young adults, including active and nonactive cannabis users; (5) cannabis users and nonusers not under substance use treatment used as controls in comparator, waitlist, or delayed-treatment groups offered another type of intervention (eg, pharmacotherapy or psychosocial) different from the one being investigated or participants assessed only for CU; and (6) quantitative CU outcomes (frequency and quantity) or cannabis abstinence. Given the availability of numerous CU screening and assessment tools with adequate psychometric properties and the absence of a gold standard in this regard [ 57 ], any instrument capturing aspects of CU was considered. CU outcome measures could be subjective (eg, self-reported number of CU days or joints in the previous 3 months) or objective (eg, drug screening test). CU had to be measured before the intervention (baseline) and at least once after.

Digital CU interventions were defined as web- or mobile-based interventions that included one or more activities (eg, self-directed or interactive psychoeducation or therapy, personalized feedback, peer-to-peer contact, and patient-to-expert communication) aimed at changing CU [ 58 ]. Mobile-based interventions were defined as interventions delivered via mobile phone through SMS text message, multimedia messaging service (ie, SMS text messages that include　multimedia content, such as pictures, videos, or emojis), or mobile apps, whereas web-based interventions (eg, websites and digital platforms) were defined as interventions designed to be accessed on the web (ie, the internet), mainly via computers. Interventions could include self-directed and web-based interventions with human support. We defined young adults as aged 16 to 35 years and included students and nonstudents. While young adulthood is typically defined as covering the ages of 18 to 30 years [ 59 ], we broadened the range given that the age of majority and legal age to purchase cannabis differs across countries and jurisdictions. This was also in line with the age range targeted by several digital CU interventions (college or university students or emerging adults aged 15-24 years) [ 31 , 45 ]. Given the language expertise of the research team members and the available resources, only English- and French-language articles were retained.

Exclusion Criteria

Knowledge synthesis articles, study protocols, and discussion papers or editorials were excluded, as were articles with cross-sectional, cohort, case study or report, pretest-posttest, quasi-experimental, or qualitative designs. Mixed methods designs were included only if the quantitative component was an RCT. We excluded studies if (1) use of substances other than cannabis (eg, alcohol, opioids, or stimulants) was the focus of the digital intervention (though studies that included polysubstance users were retained if CU was assessed and reported separately); (2) CU was not reported separately as an outcome or only attitudes or beliefs regarding, knowledge of, intention to reduce, or readiness or motivation to change CU was measured; and (3) the data reported were unpublished (eg, conferences and dissertations). Studies of traditional face-to-face therapy delivered via teleconference on mobile phones and computers or in a hospital-based setting and informational campaigns (eg, web-based poster presentations or pamphlets) were excluded as well. Studies with samples with a maximum age of <15 years and a minimum age of >35 years were also excluded. Finally, we excluded studies that focused exclusively on people with a mental health disorder or substance use disorder or dependence or on adolescents owing to the particular health care needs of these populations, which may differ from those of young adults [ 1 ].

Data Collection

Selection of studies.

Duplicates were removed from the literature search results in EndNote (version X9.3.3; Clarivate Analytics) using the Bramer method for deduplication of database search results for systematic reviews [ 60 ]. The remaining records were uploaded to Covidence (Veritas Health Innovation), a web-based systematic review management system. A reviewer guide was developed that included screening questions and a detailed description of each inclusion and exclusion criterion based on PICO (population, intervention, comparator, and outcome), and a calibration exercise was performed before each stage of the selection process to maximize consistency between reviewers. Titles and abstracts of studies flagged for possible inclusion were screened first by 2 independent reviewers (GC, BV, PA, and GR; 2 per article) against the eligibility criteria (stage 1). Articles deemed eligible for full-text review were then retrieved and screened for inclusion (stage 2). Full texts were assessed in detail against the eligibility criteria again by 2 reviewers independently. Disagreements between reviewers were resolved through consensus or by consulting a third reviewer.

Data Extraction Process

In total, 2 reviewers (GC, BV, PA, GR, and GF; 2 per article) independently extracted relevant data (or informal evidence) using a data extraction form developed specifically for this review and integrated into Covidence. The form was pilot-tested on 2 randomly selected studies and refined accordingly. Data pertaining to the following domains were extracted from the included studies: (1) Study characteristics included information on the first and corresponding authors, publication year, country of origin, aims and hypotheses, study period, design (including details on randomization and blinding), follow-up times, data collection methods, and types of statistical analysis. (2) Participant characteristics included study target population, participant inclusion and exclusion criteria, sex or gender, mean age, and sample sizes at each data collection time point. (3) Intervention characteristics, for which the research team developed a matrix inspired by the template for intervention description and replication 12-item checklist [ 61 ] to extract informal evidence (ie, intervention descriptions) from the included studies under the headings name of intervention, purpose, underpinning theory of design elements, treatment approach, type of technology (ie, web or mobile) and software used, delivery format (ie, self-directed, human involvement, or both), provider characteristics (if applicable), intervention duration (ie, length of treatment and number of sessions or modules), material and procedures (ie, tools or activities offered, resources provided, and psychoeducational content), tailoring, and unplanned modifications. (4) Comparator characteristics were details of the control or comparison group or groups, including nature (passive vs active), number of groups or clusters (if applicable), type and length of the intervention (if applicable), and number of participants at each data collection time point. (5) Outcome variables, including the primary outcome variable examined in this systematic review, that is, the mean difference in CU frequency before and after the intervention and between the experimental and control or comparison groups. When possible, we examined continuous variables, including CU frequency means and SDs at the baseline and follow-up time points, and standardized regression coefficients (ie, β coefficients and associated 95% CIs). The secondary outcomes examined included other CU outcome variables (eg, quantity of cannabis used and abstinence) and cannabis-related negative consequences (or problems). Details on outcome variables (ie, definition, data time points, and missing data) and measurements (ie, instruments, measurement units, and scales) were also extracted.

In addition, data on user engagement and use of the digital intervention and study attrition rates (ie, dropouts and loss to follow-up) were extracted. When articles had missing data, we contacted the corresponding authors via email (2 attempts were made over a 2-month period) to obtain missing information. Disagreements over the extracted data were limited and resolved through discussion.

Data Synthesis Methods

Descriptive synthesis.

The characteristics of the included studies, study participants, interventions, and comparators were summarized in narrative and table formats. The template for intervention description and replication 12-item checklist [ 61 ] was used to summarize and organize intervention characteristics and assess to what extent the interventions were appropriately described in the included articles. As not all studies had usable data for meta-analysis purposes and because of heterogeneity, we summarized the main findings (ie, intervention effects) of the included studies in narrative and table formats for each outcome of interest in this review.

The BCTs used in the digital interventions were identified from the descriptions of the interventions (ie, experimental groups) provided in the articles as well as any supplementary material and previously published research protocols. A BCT was defined as “an observable, replicable, and irreducible component of an intervention designed to alter or redirect causal processes that regulate behavior” [ 48 ]. The target behavior in this review was the cessation or reduction of CU by young adults. BCTs were identified and coded using the BCTTv1 [ 48 , 49 ], a taxonomy of 93 BCTs organized into 16 hierarchical thematic clusters or categories. Applying the BCTTv1 in a systematic review allows for the comparison and synthesis of evidence across studies in a structured manner. This analysis allows for the identification of the explicit mechanisms underlying the reported behavior change induced by interventions, successful or not, and, thus, avoids making implicit assumptions about what works [ 62 ].

BCT coding was performed by 2 reviewers independently—BV coded all studies, and GC and GF coded a subset of the studies. All reviewers completed web-based training on the BCTTv1, and GF is an experienced implementation scientist who had used the BCTTv1 in prior work [ 63 - 65 ]. The descriptions of the interventions in the articles were read line by line and analyzed for the clear presence of BCTs using the guidelines developed by Michie et al [ 48 ]. For each article, the BCTs identified were documented and categorized using supporting textual evidence. They were coded only once per article regardless of how many times they came up in the text. Disagreements about including a BCT were resolved through discussion. If there was uncertainty about whether a BCT was present, it was coded as absent. Excel (Microsoft Corp) was used to compare the reviewers’ independent BCT coding and generate an overall descriptive synthesis of the BCTs identified. The BCTs were summarized by study and BCT cluster.

Statistical Analysis

Meta-analyses were conducted to estimate the size of the effect of the digital interventions for young adult CU on outcomes of interest at the posttreatment and follow-up assessments compared with control or alternative intervention conditions. The outcome variables considered were (1) CU frequency and other CU outcome variables (eg, quantity of cannabis used and abstinence) at baseline and the posttreatment time point or follow-up measured using standardized instruments of self-reported CU (eg, the timeline followback [TLFB] method) [ 66 ] and (2) cannabis-related negative consequences measured using standardized instruments (eg, the Marijuana Problems Scale) [ 67 ].

Under our systematic review protocol, ≥2 studies were needed for a meta-analysis. On the basis of previous systematic reviews and meta-analyses in the field of digital CU interventions [ 31 , 42 - 45 ], we expected between-study heterogeneity regarding outcome assessment. To minimize heterogeneity, we chose to pool studies with similar outcomes of interest based on four criteria: (1) definition of outcome (eg, CU frequency, quantity consumed, and abstinence), (2) type of outcome variable (eg, days of CU in the previous 90 days, days high per week in the previous 30 days, and number of CU events in the previous month) and measure (ie, instruments or scales), (3) use of validated instruments, and (4) posttreatment or follow-up time points (eg, 2 weeks or 1 month after the baseline or 3, 6, and 12 months after the baseline).

Only articles that reported sufficient statistics to compute a valid effect size with 95% CIs were included in the meta-analyses. In the case of articles that were not independent (ie, more than one published article reporting data from the same clinical trial), only 1 was included, and it was represented only once in the meta-analysis for a given outcome variable regardless of whether the data used to compute the effect size were extracted from the original paper or a secondary analysis paper. We made sure that the independence of the studies included in the meta-analysis of each outcome was respected. In the case of studies that had more than one comparator, we used the effect size for each comparison between the intervention and control groups.

Meta-analyses were conducted only for mean differences based on the change from baseline in CU frequency at 3 months after the baseline as measured using the number of self-reported days of use in the previous month. As the true value of the estimated effect size for outcome variables might vary across different trials and samples, we used a random-effects model given that the studies retained did not have identical target populations. The random-effects model incorporates between-study variation in the study weights and estimated effect size [ 68 ]. In addition, statistical heterogeneity across studies was assessed using I 2 , which measures the proportion of heterogeneity to the total observed dispersion; 25% was considered low, 50% was considered moderate, and 75% was considered high [ 69 ]. Because only 3 studies were included in the meta-analysis [ 70 - 72 ], publication bias could not be assessed. All analyses were completed using Stata (version 18; StataCorp) [ 73 ].

Risk-of-Bias Assessment

The risk of bias (RoB) of the included RCTs was assessed using the Cochrane RoB 2 tool at the outcome level [ 74 ]. Each distinct risk domain (ie, randomization process, deviations from the intended intervention, missing outcome data, measurement of the outcome, and selection of the reported results) was assessed as “low,” “some concerns,” or “high” based on the RoB 2 criteria. In total, 2 reviewers (GC and BV) conducted the assessments independently. Disagreements were discussed, and if not resolved consensually by the 2, the matter was left for a third reviewer (GF) to settle. The assessments were summarized by risk domain and outcome and converted into figures using the RoB visualization tool robvis [ 75 ].

Search Results

The database search generated a total of 13,232 citations, of which 7822 (59.11%) were from the initial search on March 18, 2020, and 2805 (21.2%) and 2605 (19.69%) were from the updates on October 13, 2021, and February 13, 2023, respectively. Figure 1 presents the PRISMA study flow diagram [ 76 ]. Of the 6606 unique records, 6484 (98.15%) were excluded based on title and abstract screening. Full texts of the remaining 1.85% (122/6606) of the records were examined, as were those of 25 more reports found through hand searching. Of these 147 records, 128 (87.1%) were excluded after 3 rounds of full-text screening. Of these 128 records, 39 (30.5%) were excluded for not being empirical research articles (eg, research protocols). Another 28.1% (36/128) were excluded for not meeting our definition of digital CU intervention. The remaining records were excluded for reasons that occurred with a frequency of ≤14%, including young adults not being the target population and the study not meeting our study design criteria (ie, RCT, cluster RCT, or pilot RCT). Excluded studies and reasons for exclusion are listed in Multimedia Appendix 4 . Finally, 19 articles detailing the results of 19 original studies were included.

Description of Studies

Study characteristics.

Multimedia Appendix 5 [ 70 - 72 , 77 - 92 ] describes the general characteristics of the 19 included studies. The studies were published between 2010 and 2023, with 58% (11/19) published in 2018 or later. A total of 53% (10/19) of the studies were conducted in the United States [ 77 - 86 ], 11% (2/19) were conducted in Canada [ 87 , 88 ], 11% (2/19) were conducted in Australia [ 71 , 89 ], 11% (2/19) were conducted in Germany [ 72 , 90 ], 11% (2/19) were conducted in Switzerland [ 70 , 91 ], and 5% (1/19) were conducted in Sweden [ 92 ]. A total of 79% (15/19) were RCTs [ 70 - 72 , 77 , 79 , 81 - 83 , 86 - 92 ], and 21% (4/19) were pilot RCTs [ 78 , 80 , 84 , 85 ].

Participant Characteristics

The studies enrolled a total of 6710 participants—3229 (48.1%) in the experimental groups, 3358 (50%) in the control groups, and the remaining 123 (1.8%) from 1 study [ 82 ] where participant allocation to the intervention condition was not reported. Baseline sample sizes ranged from 49 [ 81 ] to 1292 [ 72 ] (mean 352.89, SD 289.50), as shown in Multimedia Appendix 5 . Participant mean ages ranged from 18.03 (SD 0.31) [ 79 ] to 35.3 (SD 12.6) years [ 88 ], and the proportion of participants who identified as female ranged from 24.7% [ 91 ] to 84.1% [ 80 ].

Of the 19 included studies, 10 (53%) targeted adults aged ≥18 years, of which 7 (70%) studies focused on adults who had engaged in past-month CU [ 70 , 71 , 80 , 84 , 85 , 90 , 91 ], 2 (20%) studies included adults who wished to reduce or cease CU [ 72 , 89 ], and 1 (10%) study focused on noncollege adults with a moderate risk associated with CU [ 88 ]. Sinadinovic et al [ 92 ] targeted young adults aged ≥16 years who had used cannabis at least once a week in the previous 6 months. The remaining 8 studies targeted college or university students (aged ≥17 y) specifically, of which 7 (88%) studies focused solely on students who reported using cannabis [ 78 , 79 , 81 - 83 , 86 , 87 ] and 1 (12%) study focused solely on students who did not report past-month CU (ie, abstainers) [ 77 ].

Intervention Characteristics

The 19 included studies assessed nine different digital interventions: (1) 5 (26%) evaluated Marijuana eCHECKUP TO GO (e-TOKE), a commercially available electronic intervention used at colleges throughout the United States and Canada [ 77 , 78 , 81 - 83 ]; (2) 2 (11%) examined the internationally known CANreduce program [ 70 , 91 ]; (3) 2 (11%) evaluated the German Quit the Shit program [ 72 , 90 ]; (4) 2 (11%) assessed a social media–delivered, physical activity–focused cannabis intervention [ 84 , 85 ]; (5) 1 (5%) investigated the Swedish Cannabishjälpen intervention [ 92 ]; (6) 1 (5%) evaluated the Australian Grassessment: Evaluate Your Use of Cannabis website program [ 89 ]; (7) 1 (5%) assessed the Canadian Ma réussite, mon choix intervention [ 87 ]; (8) 1 (5%) examined the Australian Reduce Your Use: How to Break the Cannabis Habit program [ 71 ]; and (9) 4 (21%) each evaluated a unique no-name intervention described as a personalized feedback intervention (PFI) [ 79 , 80 , 86 , 88 ]. Detailed information regarding the characteristics of all interventions as reported in each included study is provided in Multimedia Appendix 6 [ 70 - 72 , 77 - 113 ] and summarized in the following paragraphs.

In several studies (8/19, 42%), the interventions were designed to support cannabis users in reducing or ceasing their consumption [ 70 , 72 , 80 , 87 , 89 - 92 ]. In 37% (7/19) of the studies, the interventions aimed at reducing both CU and cannabis-related consequences [ 79 , 81 - 85 , 88 ]. Other interventions focused on helping college students think carefully about the decision to use cannabis [ 77 , 78 ] and on reducing either cannabis-related problems among undergraduate students [ 86 ] or symptoms associated with CU disorder in young adults [ 71 ].

In 26% (5/19) of the studies, theory was used to inform intervention design along with a clear rationale for theory use. Of these 5 articles, only 1 (20%) [ 87 ] reported using a single theory of behavior change, the theory of planned behavior [ 114 ]. A total of 21% (4/19) of the studies selected only constructs of theories (or models) for their intervention design. Of these 4 studies, 2 (50%) evaluated the same intervention [ 72 , 90 ], which focused on principles of self-regulation and self-control theory [ 93 ]; 1 (25%) [ 70 ] used the concept of adherence-focused guidance enhancement based on the supportive accountability model of guidance [ 94 ]; and 1 (25%) [ 71 ] reported that intervention design was guided by the concept of self-behavioral management.

The strategies (or approaches) used in the delivery of the digital interventions were discussed in greater detail in 84% (16/19) of the articles [ 70 - 72 , 79 - 81 , 83 - 92 ]. Many of these articles (9/19, 47%) reported using a combination of approaches based on CBT or motivational interviewing (MI) [ 70 , 71 , 79 , 83 - 85 , 90 - 92 ]. PFIs were also often mentioned as an approach to inform intervention delivery [ 7 , 71 , 79 , 86 - 88 ].

More than half (13/19, 68%) of all the digital interventions were asynchronous and based on a self-guided approach without support from a counselor or therapist. The study by Côté et al [ 87 ] evaluated the efficacy of a web-based tailored intervention focused on reinforcing a positive attitude toward and a sense of control over cannabis abstinence through psychoeducational messages delivered by a credible character in short video clips and personalized reinforcement messages. Lee et al [ 79 ] evaluated a brief, web-based personalized feedback selective intervention based on the PFI approach pioneered by Marlatt et al [ 95 ] for alcohol use prevention and on the MI approach described by Miller and Rollnick [ 96 ]. Similarly, Rooke et al [ 71 ] combined principles of MI and CBT to develop a web-based intervention delivered via web modules, which were informed by previous automated feedback interventions targeting substance use. The study by Copeland et al [ 89 ] assessed the short-term effectiveness of Grassessment: Evaluate Your Use of Cannabis, a brief web-based, self-complete intervention based on motivational enhancement therapy that included personalized feedback messages and psychoeducational material. In the studies by Buckner et al [ 80 ], Cunningham et al [ 88 ], and Walukevich-Dienst et al [ 86 ], experimental groups received a brief web-based PFI available via a computer. A total of 16% (3/19) of the studies [ 77 , 78 , 82 ] applied a program called the Marijuana eCHECKUP TO GO (e-TOKE) for Universities and Colleges, which was presented as a web-based, norm-correcting, brief preventive and intervention education program designed to prompt self-reflection on consequences and consideration of decreasing CU among students. Riggs et al [ 83 ] developed and evaluated an adapted version of e-TOKE that provided participants with university-specific personalized feedback and normative information based on protective behavioral strategies for CU [ 97 ]. Similarly, Goodness and Palfai [ 81 ] tested the efficacy of eCHECKUP TO GO-cannabis, a modified version of e-TOKE combining personalized feedback, norm correction, and a harm and frequency reduction strategy where a “booster” session was provided at 3 months to allow participants to receive repeated exposure to the intervention.

In the remaining 32% (6/19) of the studies, which examined 4 different interventions, the presence of a therapist guide was reported. The intervention evaluated by Sinadinovic et al [ 92 ] combined principles of psychoeducation, MI, and CBT organized into 13 web-based modules and a calendar involving therapist guidance, recommendations, and personal feedback. In total, 33% (2/6) of these studies evaluated a social media–delivered intervention with e-coaches that combined principles of MI and CBT and a harm reduction approach for risky CU [ 84 , 85 ]. Schaub et al [ 91 ] evaluated the efficacy of CANreduce, a web-based self-help intervention based on both MI and CBT approaches, using automated motivational and feedback emails, chat with a counselor, and web-based psychoeducational modules. Similarly, Baumgartner et al [ 70 ] investigated the effectiveness of CANreduce 2.0, a modified version of CANreduce, using semiautomated motivational and adherence-focused guidance-based email feedback with or without a personal online coach. The studies by Tossman et al [ 72 ] and Jonas et al [ 90 ] used a solution-focused approach and MI to evaluate the effectiveness of the German Quit the Shit web-based program that involves weekly feedback provided by counselors.

In addition to using different intervention strategies or approaches, the interventions were diverse in terms of the duration and frequency of the program (eg, web-based activities, sessions, or modules). Of the 12 articles that provided details in this regard, 2 (17%) on the same intervention described it as a brief 20- to 45-minute web-based program [ 77 , 78 ], 2 (17%) on 2 different interventions reported including 1 or 2 modules per week for a duration of 6 weeks [ 71 , 92 ], and 7 (58%) on 4 different interventions described them as being available over a longer period ranging from 6 weeks to 3 months [ 70 , 72 , 79 , 84 , 85 , 87 , 90 , 91 ].

Comparator Types

A total of 42% (8/19) of the studies [ 72 , 77 - 80 , 85 , 87 , 92 ] used a passive comparator only, namely, a waitlist control group ( Multimedia Appendix 5 ). A total of 26% (5/19) of the studies used an active comparator only where participants were provided with minimal general health feedback regarding recommended guidelines for sleep, exercise, and nutrition [ 81 , 82 ]; strategies for healthy stress management [ 83 ]; educational materials about risky CU [ 88 ]; or access to a website containing information about cannabis [ 71 ]. In another 21% (4/19) of the studies, which used an active comparator, participants received the same digital intervention minus a specific component: a personal web-based coach [ 70 ], extended personalized feedback [ 89 ], web-based chat counseling [ 91 ], or information on risks associated with CU [ 86 ]. A total of 21% (4/19) of the studies had more than one control group [ 70 , 84 , 90 , 91 ].

Outcome Variable Assessment and Summary of Main Findings of the Studies

The methodological characteristics and major findings of the included studies (N=19) are presented in Multimedia Appendix 7 [ 67 , 70 - 72 , 77 - 92 , 115 - 120 ] and summarized in the following sections for each outcome of interest in this review (ie, CU and cannabis-related consequences). Of the 19 studies, 11 (58%) were reported as efficacy trials [ 7 , 77 , 79 , 81 - 83 , 86 - 88 , 91 , 92 ], and 8 (42%) were reported as effectiveness trials [ 70 - 72 , 78 , 84 , 85 , 89 , 90 ].

Across all the included studies (19/19, 100%), participant attrition rates ranged from 1.6% at 1 month after the baseline [ 77 , 78 ] to 75.1% at the 3-month follow-up [ 70 ]. A total of 37% (7/19) of the studies assessed and reported results regarding user engagement [ 71 , 78 , 84 , 85 , 90 - 92 ] using different types of metrics. In one article on the Marijuana eCHECKUP TO GO (e-TOKE) web-based program [ 78 ], the authors briefly reported that participation was confirmed for 98.1% (158/161) of participants in the intervention group. In 11% (2/19) of the studies, which were on a similar social media–delivered intervention [ 84 , 85 ], user engagement was quantified by tallying the number of comments or posts and reactions (eg, likes and hearts) left by participants. In both studies [ 84 , 85 ], the intervention group, which involved a CU-related Facebook page, displayed greater interactions than the control groups, which involved a Facebook page unrelated to CU. One article [ 84 ] reported that 80% of participants in the intervention group posted at least once (range 0-60) and 50% posted at least weekly. In the other study [ 85 ], the results showed that intervention participants engaged (ie, posting or commenting or clicking reactions) on average 47.9 times each over 8 weeks. In total, 11% (2/19) of the studies [ 90 , 91 ] on 2 different web-based intervention programs, both consisting of web documentation accompanied by chat-based counseling, measured user engagement either by average duration or average number of chat sessions. Finally, 16% (3/19) of the studies [ 71 , 91 , 92 ], which involved 3 different web-based intervention programs, characterized user engagement by the mean number of web modules completed per participant. Overall, the mean number of web modules completed reported in these articles was quite similar: 3.9 out of 13 [ 92 ] and 3.2 [ 91 ] and 3.5 [ 71 ] out of 6.

Assessment of CU

As presented in Multimedia Appendix 7 , the included studies differed in terms of how they assessed CU, although all used at least one self-reported measure of frequency. Most studies (16/19, 84%) measured frequency by days of use, including days of use in the preceding week [ 91 ] or 2 [ 80 ], days of use in the previous 30 [ 70 - 72 , 78 , 84 - 86 , 88 - 90 ] or 90 days [ 79 , 81 , 82 ], and days high per week [ 83 ]. Other self-reported measures of CU frequency included (1) number of CU events in the previous month [ 87 , 90 ], (2) cannabis initiation or use in the previous month (ie, yes or no) [ 77 ], and (3) days without CU in the previous 7 days [ 92 ]. In addition to measuring CU frequency, 42% (8/19) of the studies also assessed CU via self-reported measures of quantity used, including estimated grams consumed in the previous week [ 92 ] or 30 days [ 72 , 85 , 90 ] and the number of standard-sized joints consumed in the previous 7 days [ 91 ] or the previous month [ 70 , 71 , 89 ].

Of the 19 articles included, 10 (53%) [ 70 - 72 , 80 , 84 - 86 , 89 , 90 , 92 ] reported using a validated instrument to measure CU frequency or quantity, including the TLFB instrument [ 66 ] (n=9, 90% of the studies) and the Marijuana Use Form (n=1, 10% of the studies); 1 (10%) [ 79 ] reported using CU-related questions from an adaptation of the Global Appraisal of Individual Needs–Initial instrument [ 115 ]; and 30% (3/10) [ 81 , 82 , 91 ] reported using a questionnaire accompanied by a calendar or a diary of consumption. The 19 studies also differed with regard to their follow-up time measurements for assessing CU, ranging from 2 weeks after the baseline [ 80 ] to 12 months after randomization [ 90 ], although 12 (63%) of the studies included a 3-month follow-up assessment [ 70 - 72 , 79 , 81 , 82 , 84 , 85 , 88 , 90 - 92 ].

Of all studies assessing and reporting change in CU frequency from baseline to follow-up assessments (19/19, 100%), 47% (9/19) found statistically significant differences between the experimental and control groups [ 70 - 72 , 80 , 81 , 83 , 85 , 87 , 91 ]. Importantly, 67% (6/9) of these studies showed that participants in the experimental groups exhibited greater decreases in CU frequency 3 months following the baseline assessment compared with participants in the control groups [ 70 - 72 , 81 , 85 , 91 ], 22% (2/9) of the studies showed greater decreases in CU frequency at 6 weeks after the baseline assessment [ 71 , 83 ], 22% (2/9) of the studies showed greater decreases in CU frequency at 6 months following the baseline assessment [ 81 , 85 ], 11% (1/9) of the studies showed greater decreases in CU frequency at 2 weeks after the baseline [ 80 ], and 11% (1/9) of the studies showed greater decreases in CU frequency at 2 months after treatment [ 87 ].

In the study by Baumgartner et al [ 70 ], a reduction in CU days was observed in all groups, but the authors reported that the difference was statistically significant only between the intervention group with the service team and the control group (the reduction in the intervention group with social presence was not significant). In the study by Bonar et al [ 85 ], the only statistically significant difference between the intervention and control groups at the 3- and 6-month follow-ups involved total days of cannabis vaping in the previous 30 days. Finally, in the study by Buckner et al [ 80 ], the intervention group had less CU than the control group 2 weeks after the baseline; however, this was statistically significant only for participants with moderate or high levels of social anxiety.

Assessment of Cannabis-Related Negative Consequences

A total of 53% (10/19) of the studies also assessed cannabis-related negative consequences [ 78 - 84 , 86 , 88 , 92 ]. Of these 10 articles, 8 (80%) reported using a validated self-report instrument: 4 (50%) [ 81 , 82 , 86 , 88 ] used the 19-item Marijuana Problems Scale [ 67 ], 2 (25%) [ 78 , 79 ] used the 18-item Rutgers Marijuana Problem Index [ 121 , 122 ], and 2 (25%) [ 80 , 84 ] used the Brief Marijuana Consequences Questionnaire [ 116 ]. Only 10% (1/10) of the studies [ 92 ] used a screening tool, the Cannabis Abuse Screening Test [ 117 , 118 ]. None of these 10 studies demonstrated a statistically significant difference between the intervention and control groups. Of note, Walukevich-Dienst et al [ 86 ] found that women (but not men) who received an web-based PFI with additional information on CU risks reported significantly fewer cannabis-related problems than did women in the control group at 1 month after the intervention ( B =−1.941; P =.01).

Descriptive Summary of BCTs Used in Intervention Groups

After the 19 studies included in this review were coded, a total of 184 individual BCTs targeting CU in young adults were identified. Of these 184 BCTs, 133 (72.3% ) were deemed to be present beyond a reasonable doubt, and 51 (27.7%) were deemed to be present in all probability. Multimedia Appendix 8 [ 48 , 70 - 72 , 77 - 92 ] presents all the BCTs coded for each included study summarized by individual BCT and BCT cluster.

The 184 individual BCTs coded covered 38% (35/93) of the BCTs listed in the BCTTv1 [ 48 ]. The number of individual BCTs identified per study ranged from 5 to 19, with two-thirds of the 19 studies (12/19, 63%) using ≤9 BCTs (mean 9.68). As Multimedia Appendix 8 shows, at least one BCT fell into 13 of the 16 possible BCT clusters. The most frequent clusters were feedback monitoring , natural consequences , goal planning , and comparison of outcomes .

The most frequently coded BCTs were (1) feedback on behavior (BCT 2.2; 17/19, 89% of the studies; eg, “Once a week, participants receive detailed feedback by their counselor on their entries in diary and exercises. Depending on the involvement of each participant, up to seven feedbacks are given” [ 90 ]), (2) social support (unspecified) (BCT 3.1; 15/19, 79% of the studies; eg, “The website also features [...] blogs from former cannabis users, quick assist links, and weekly automatically generated encouragement emails” [ 71 ]), and (3) pros and cons (BCT 9.2; 14/19, 74% of the studies; eg, “participants are encouraged to state their personal reasons for and against their cannabis consumption, which they can review at any time, so they may reflect on what they could gain by successfully completing the program” [ 70 ]). Other commonly identified BCTs included social comparison (BCT 6.2; 12/19, 63% of the studies) and information about social and environmental consequences (BCT 5.3; 11/19, 58% of the studies), followed by problem solving (BCT 2.1; 10/19, 53% of the studies) and information about health consequences (BCT 5.1; 10/19, 53% of the studies).

RoB Assessment

Figure 2 presents the overall assessment of risk in each domain for all the included studies, whereas Figure 3 [ 70 - 72 , 77 - 92 ] summarizes the assessment of each study at the outcome level for each domain in the Cochrane RoB 2 [ 74 ].

Figure 2 shows that, of the included studies, 93% (27/29) were rated as having a “low” RoB arising from the randomization process (ie, selection bias) and 83% (24/29) were rated as having a “low” RoB due to missing data (ie, attrition bias). For bias due to deviations from the intended intervention (ie, performance bias), 72% (21/29) were rated as having a “low” risk, and for selective reporting of results, 59% (17/29) were rated as having a “low” risk. In the remaining domain regarding bias in measurement of the outcome (ie, detection bias), 48% (14/29) of the studies were deemed to present “some concerns,” mainly owing to the outcome assessment not being blinded (eg, self-reported outcome measure of CU). Finally, 79% (15/19) of the included studies were deemed to present “some concerns” or were rated as having a “high” RoB at the outcome level ( Figure 3 [ 70 - 72 , 77 - 92 ]). The RoB assessment for CU and cannabis consequences of each included study is presented in Multimedia Appendix 9 [ 70 - 72 , 77 - 92 ].

Meta-Analysis Results

Due to several missing data points and despite contacting the authors, we were able to carry out only 1 meta-analysis of our primary outcome, CU frequency. Usable data were retrieved from only 16% (3/19) [ 70 - 72 ] of the studies included in this review. These 3 studies provided sufficient information to calculate an effect size, including mean differences based on change-from-baseline measurements and associated 95% CIs (or SE of the mean difference) and sample sizes per intervention and comparison conditions. The reasons for excluding the other 84% (16/19) of the studies included heterogeneity in outcome variables or measurements, inconsistent results, and missing data ( Multimedia Appendix 10 [ 77 - 92 ]).

Figure 4 [ 70 - 72 ] illustrates the mean differences and associated 95% CIs of 3 unique RCTs [ 70 - 72 ] that provided sufficient information to allow for the measurement of CU frequency at 3 months after the baseline relative to a comparison condition in terms of the number of self-reported days of use in the previous month using the TLFB method. Overall, the synthesized effect of digital interventions for young adult cannabis users on CU frequency, as measured using days of use in the previous month, was −6.79 (95% CI −9.59 to −4.00). This suggests that digital CU interventions had a statistically significant effect ( P <.001) on reducing CU frequency at the 3-month follow-up compared with the control conditions (both passive and active controls). The results of the meta-analysis also showed low between-study heterogeneity ( I 2 =48.3%; P =.12) across the 3 included studies.

The samples of the 3 studies included in the meta-analysis varied in size from 225 to 1292 participants (mean 697.33, SD 444.11), and the mean age ranged from 24.7 to 31.88 years (mean 26.38, SD 3.58 years). These studies involved 3 different digital interventions and used different design approaches to assess intervention effectiveness. One study assessed the effectiveness of a web-based counseling program (ie, Quit the Shit) against a waitlist control [ 72 ], another examined the effectiveness of a fully self-guided web-based treatment program for CU and related problems (ie, Reduce Your Use: How to Break the Cannabis Habit) against a control condition website consisting of basic educational information on cannabis [ 71 ], and the third used a 3-arm RCT design to investigate whether the effectiveness of a minimally guided internet-based self-help intervention (ie, CANreduce 2.0) might be enhanced by implementing adherence-focused guidance and emphasizing the social presence factor of a personal e-coach [ 70 ].

Summary of Principal Findings

The primary aim of this systematic review was to evaluate the effectiveness of digital interventions in addressing CU among community-living young adults. We included 19 randomized controlled studies representing 9 unique digital interventions aimed at preventing, reducing, or ceasing CU and evaluated the effects of 3 different digital interventions on CU. In summary, the 3 digital interventions included in the meta-analysis proved superior to control conditions in reducing the number of days of CU in the previous month at the 3-month follow-up.

Our findings are consistent with those of 2 previous meta-analyses by Olmos et al [ 43 ] and Tait et al [ 44 ] and with the findings of a recently published umbrella review of systematic reviews and meta-analyses of RCTs [ 123 ], all of which revealed a positive effect of internet- and computer-based interventions on CU. However, a recent systematic review and meta-analysis by Beneria et al [ 45 ] found that web-based CU interventions did not significantly reduce CU. Beneria et al [ 45 ] included studies with different intervention programs that targeted diverse population groups (both adolescents and young adults) and use of more than one substance (eg, alcohol and cannabis). In our systematic review, a more conservative approach was taken—we focused specifically on young adults and considered interventions targeting CU only. Although our results indicate that digital interventions hold great promise in terms of effectiveness, an important question that remains unresolved is whether there is an optimal exposure dose in terms of both duration and frequency that might be more effective. Among the studies included in this systematic review, interventions varied considerably in terms of the number of psychoeducational modules offered (from 2 to 13), time spent reviewing the material, and duration (from a single session to a 12-week spread period). Our results suggest that an intervention duration of at least 6 weeks yields better results.

Another important finding of this review is that, although almost half (9/19, 47%) of the included studies observed an intervention effect on CU frequency, none reported a statistically significant improvement in cannabis-related negative consequences, which may be considered a more distal indicator. More than half (10/19, 53%) of the included studies investigated this outcome. It seems normal to expect to find an effect on CU frequency given that reducing CU is often the primary objective of interventions and because the motivation of users’ is generally focused on changing consumption behavior. It is plausible to think that the change in behavior at the consumption level must be maintained over time before an effect on cannabis-related negative consequences can be observed. However, our results showed that, in all the included studies, cannabis-related negative consequences and change in behavior (CU frequency) were measured at the same time point, namely, 3 months after the baseline. Moreover, Grigsby et al [ 124 ] conducted a scoping review of risk and protective factors for CU and suggested that interventions to reduce negative CU consequences should prioritize multilevel methods or strategies “to attenuate the cumulative risk from a combination of psychological, contextual, and social influences.”

A secondary objective of this systematic review was to describe the active ingredients used in digital interventions for CU among young adults. The vast majority of the interventions were based on either a theory or an intervention approach derived from theories such as CBT, MI, and personalized feedback. From these theories and approaches stem behavior change strategies or techniques, commonly known as BCTs. Feedback on behavior , included in the feedback monitoring BCT cluster, was the most common BCT used in the included studies. This specific BCT appears to be a core strategy in behavior change interventions [ 125 , 126 ]. In their systematic review of remotely delivered alcohol or substance misuse interventions for adults, Howlett et al [ 53 ] found that feedback on behavior , problem solving , and goal setting were the most frequently used BCTs in the included studies. In addition, this research group noted that the most promising BCTs for alcohol misuse were avoidance/reducing exposure to cues for behavior , pros and cons , and self-monitoring of behavior, whereas 2 very promising strategies for substance misuse in general were problem solving and self-monitoring of behavior . In our systematic review, in addition to feedback on behavior , the 6 most frequently used BCTs in the included studies were social support , pros and cons , social comparison , problem solving , information about social and environmental consequences , and information about health consequences . Although pros and cons and problem solving were present in all 3 studies of digital interventions included in our meta-analysis, avoidance/reducing exposure to cues for behavior was reported in only 5% (1/19) of the articles, and feedback on behavior was more frequently used than self-monitoring of behavior. However, it should be noted that the review by Howlett et al [ 53 ] examined digital interventions for participants with alcohol or substance misuse problems, whereas in this review, we focused on interventions that targeted CU from a harm reduction perspective. In this light, avoidance/reducing exposure to cues for behavior may be a BCT better suited to populations with substance misuse problems. Lending support to this, a meta-regression by Garnett et al [ 127 ] and a Cochrane systematic review by Kaner et al [ 128 ] both found interventions that used behavior substitution and credible source to be associated with greater reduction in excessive alcohol consumption compared with interventions that used other BCTs.

Beyond the number and types of BCTs used, reflecting on the extent to which each BCT in a given intervention suits (or does not suit) the targeted determinants (ie, behavioral and environmental causes) is crucial for planning intervention programs [ 26 ]. It is important when designing digital CU interventions not merely to pick a combination of BCTs that have been associated with effectiveness. Rather, the active ingredients must fit the determinants that the interventionists seek to influence. For example, action planning would be more relevant as a BCT for young adults highly motivated and ready to take action on their CU than would pros and cons , which aims instead to bolster motivation. Given that more than half of all digital interventions are asynchronous and based on a self-guided approach and do not offer counselor or therapist support, a great deal of motivation is required to engage in intervention and behavior change. Therefore, it is essential that developers consider the needs and characteristics of the targeted population to tailor intervention strategies (ie, BCTs) for successful behavior change (eg, tailored to the participant’s stage of change). In most of the digital interventions included in this systematic review, personalization was achieved through feedback messages about CU regarding descriptive norms, motives, risks and consequences, and costs, among other things.

Despite the high number of recent studies conducted in the field of digital CU interventions, most of the included articles in our review (17/19, 89%) reported on the development and evaluation of web-based intervention programs. A new generation of health intervention modalities such as mobile apps and social media has drawn the attention of researchers in the past decade and is currently being evaluated. In this regard, the results from a recently published scoping review [ 129 ], which included 5 studies of mobile apps for nonmedical CU, suggested that these novel modes of intervention delivery demonstrated adequate feasibility and acceptability. Nevertheless, the internet remains a powerful and convenient medium for reaching young adults with digital interventions intended to support safe CU behaviors [ 123 , 130 ].

Quality of Evidence

The GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach [ 131 - 133 ] was used to assess the quality of the evidence reviewed. It was deemed to be moderate for the primary outcome of this review, that is, CU frequency in terms of days of use in the previous month (see the summary of evidence in Multimedia Appendix 11 [ 70 , 72 ]). The direction of evidence was broadly consistent—in all 3 RCT studies [ 70 - 72 ] included in the meta-analysis, participants who received digital CU interventions reduced their consumption compared with those who received no or minimal interventions. The 3 RCTs were similar in that they all involved a web-based, multicomponent intervention program aimed at reducing or ceasing CU. However, the interventions did differ or vary in terms of several characteristics, including the strategies used, content, frequency, and duration. Given the small number of studies included in the meta-analysis, we could not conclude with certainty which intervention components, if any, contributed to the effect estimate observed.

Although inconsistency, indirectness, and imprecision were not major issues in the body of evidence, we downgraded the evidence from high to moderate quality on account of RoB assessments at the outcome level. The 3 RCT studies included in the meta-analysis were rated as having “some concerns” of RoB, mainly due to lack of blinding, which significantly reduced our certainty relative to subjective outcomes (ie, self-reported measures of CU frequency). A positive feature of these digital intervention trials is that most procedures are fully automated, and so there was typically a low RoB regarding randomization procedures, allocation to different conditions, and intervention delivery. It is impossible to blind participants to these types of behavior change interventions, and although some researchers have made attempts to counter the impact of this risk, performance bias is an inescapable issue in RCT studies of this kind. Blinding of intervention providers was not an issue in the 3 RCTs included in the meta-analysis because outcome data collection was automated. However, this same automated procedure made it very difficult to ensure follow‐up. Consequently, attrition was another source of bias in these RCT studies [ 70 - 72 ]. The participants lost to follow-up likely stopped using the intervention. However, there is no way of determining whether these people would have benefited more or less than the completers if they had seen the trial through.

The 3 RCTs included in the meta-analysis relied on subjective self-reported measures of CU at baseline and follow‐up, which are subject to recall and social desirability bias. However, all 3 studies used a well-validated instrument of measurement to determine frequency of CU, the TLFB [ 66 ]. This is a widely used, subjective self-report tool for measuring frequency (or quantity) of substance use (or abstinence). It is considered a reliable measure of CU [ 134 , 135 ]. Finally, it should be pointed out that any potential bias related to self‐reported CU frequency would have affected both the intervention and control groups (particularly in cases in which control groups received cannabis‐related information), and thus, it was unlikely to account for differential intervention effects. Moreover, we found RoB due to selective reporting in some studies owing mainly to the absence of any reference to a protocol. Ultimately, these limitations may have biased the results of the meta-analysis. Consequently, future research is likely to further undermine our confidence in the effect estimate we observed and report considerably different estimates.

Strengths and Limitations

Our systematic review and meta-analysis has a number of strengths: (1) we included only randomized controlled studies to ensure that the included studies possessed a rigorous research design, (2) we focused specifically on cannabis (rather than combining multiple substances), (3) we assessed the effectiveness of 3 different digital interventions on CU frequency among community-living young adults, and (4) we performed an exhaustive synthesis and comparison of the BCTs used in the 9 digital interventions examined in the 19 studies included in our review based on the BCTTv1.

Admittedly, this systematic review and meta-analysis has limitations that should be recognized. First, although we searched a range of bibliographic databases, the review was limited to articles published in peer-reviewed journals in English or French. This may have introduced publication bias given that articles reporting positive effects are more likely to be published than those with negative or equivocal results. Consequently, the studies included in this review may have overrepresented the statistically significant effects of digital CU interventions.

Second, only a small number of studies were included in the meta-analyses because many studies did not provide adequate statistical information for calculating and synthesizing effect sizes, although significant efforts were made to contact the authors in case of missing data. Because of the small sample size used in the meta-analysis, the effect size estimates may not be highly reflective of the true effects of digital interventions on CU frequency among young adults. Furthermore, synthesizing findings across studies that evaluated different modalities of web-based intervention programs (eg, fully self-guided vs with therapist guidance) and types of intervention approaches (eg, CBT, MI, and personalized feedback) may have introduced bias in the meta-analytical results due to the heterogeneity of the included studies, although heterogeneity was controlled for using a random-effects model and our results indicated low between-study heterogeneity.

Third, we took various measures to ensure that BCT coding was carried out rigorously throughout the data extraction and analysis procedures: (1) all coders received training on how to use the BCTTv1; (2) all the included articles were read line by line so that coders became familiar with intervention descriptions before initiating BCT coding; (3) the intervention description of each included article was double coded after a pilot calibration exercise with all coders, and any disagreements regarding the presence or absence of a BCT were discussed and resolved with a third party; and (4) we contacted the article authors when necessary and possible for further details on the BCTs they used. However, incomplete reporting of intervention content is a recognized issue [ 136 ], which may have resulted in our coding BCTs incorrectly as present or absent. Reliably specifying the BCTs used in interventions allows their active ingredients to be identified, their evidence to be synthesized, and interventions to be replicated, thereby providing tangible guidance to programmers and researchers to develop more effective interventions.

Finally, although this review identified the BCTs used in digital interventions, our approach did not allow us to draw conclusions regarding their effectiveness. Coding BCTs simply as present or absent does not consider the frequency, intensity, and quality with which they were delivered. For example, it is unclear how many individuals should self‐monitor their CU. In addition, the quality of BCT implementation may be critical in digital interventions where different graphics and interface designs and the usability of the BCTs used can have considerable influence on the level of user engagement [ 137 ]. In the future, it may be necessary to develop new methods to evaluate the dosage of individual BCTs in digital health interventions and characterize their implementation quality to assess their effectiveness [ 128 , 138 ]. Despite its limitations, this review suggests that digital interventions represent a promising avenue for preventing, reducing, or ceasing CU among community-living young adults.

Conclusions

The results of this systematic review and meta-analysis lend support to the promise of digital interventions as an effective means of reducing recreational CU frequency among young adults. Despite the advent and popularity of smartphones, web-based interventions remain the most common mode of delivery for digital interventions. The active ingredients of digital interventions are varied and encompass a number of clusters of the BCTTv1, but a significant number of BCTs remain underused. Additional research is needed to further investigate the effectiveness of these interventions on CU and key outcomes at later time points. Finally, a detailed assessment of user engagement with digital interventions for CU and understanding which intervention components are the most effective remain important research gaps.

Acknowledgments

The authors would like to thank Bénédicte Nauche, Miguel Chagnon, and Paul Di Biase for their valuable support with the search strategy development, statistical analysis, and linguistic revision, respectively. This work was supported by the Ministère de la Santé et des Services sociaux du Québec as part of a broader study aimed at developing and evaluating a digital intervention for young adult cannabis users. Additional funding was provided by the Research Chair in Innovative Nursing Practices. The views and opinions expressed in this manuscript do not necessarily reflect those of these funding entities.

Data Availability

The data sets generated and analyzed during this study are available from the corresponding author on reasonable request.

Authors' Contributions

JC contributed to conceptualization, methodology, formal analysis, writing—original draft, supervision, and funding acquisition. GC contributed to conceptualization, methodology, formal analysis, investigation, data curation, writing—original draft, visualization, and project administration. BV contributed to conceptualization, methodology, formal analysis, investigation, data curation, writing—original draft, and visualization. PA contributed to conceptualization, methodology, formal analysis, investigation, data curation, writing—original draft, visualization, and project administration. GR contributed to conceptualization, methodology, formal analysis, investigation, data curation, and writing—review and editing. GF contributed to conceptualization, methodology, formal analysis, investigation, data curation, and writing—review and editing. DJA contributed to conceptualization, methodology, formal analysis, writing—review and editing, and funding acquisition.

Conflicts of Interest

None declared.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

Detailed search strategies for each database.

Population, intervention, comparison, outcome, and study design strategy.

Excluded studies and reasons for exclusion.

Study and participant characteristics.

Description of intervention characteristics in the included articles.

Summary of methodological characteristics and major findings of the included studies categorized by intervention name.

Behavior change techniques (BCTs) coded in each included study summarized by individual BCT and BCT cluster.

Risk-of-bias assessment of each included study for cannabis use and cannabis consequences.

Excluded studies and reasons for exclusion from the meta-analysis.

Summary of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation tool.

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Abbreviations

Edited by T Leung, G Eysenbach; submitted 30.11.23; peer-reviewed by H Sedrati; comments to author 02.01.24; revised version received 09.01.24; accepted 08.03.24; published 17.04.24.

©José Côté, Gabrielle Chicoine, Billy Vinette, Patricia Auger, Geneviève Rouleau, Guillaume Fontaine, Didier Jutras-Aswad. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Fatal Traffic Risks With a Total Solar Eclipse in the US

• 1 Department of Medicine, University of Toronto, Toronto, Ontario, Canada
• 2 Evaluative Clinical Science Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
• 3 Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
• 4 Division of General Internal Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
• 5 Center for Leading Injury Prevention Practice Education & Research, Toronto, Ontario, Canada
• 6 Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
• 7 Centre for Clinical Epidemiology & Evaluation, University of British Columbia, Vancouver, British Columbia, Canada

A total solar eclipse occurs when the moon temporarily obscures the sun and casts a dark shadow across the earth. This astronomical spectacle has been described for more than 3 millennia and can be predicted with high precision. Eclipse-related solar retinopathy (vision loss from staring at the sun) is an established medical complication; however, other medical outcomes have received little attention. 1

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Redelmeier DA , Staples JA. Fatal Traffic Risks With a Total Solar Eclipse in the US. JAMA Intern Med. Published online March 25, 2024. doi:10.1001/jamainternmed.2023.5234

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First evidence of ancient human occupation found in giant lava tube cave in Saudi Arabia

Mathew Stewart , Griffith University ; Huw Groucutt , University of Malta , and Michael Petraglia , Griffith University

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For 600 years the Voynich manuscript has remained a mystery. Now we think it’s partly about sex

Keagan Brewer , Macquarie University

We found three new species of extinct giant kangaroo – and we don’t know why they died out when their cousins survived

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Friday essay: ‘too many Aboriginal babies’ – Australia’s secret history of Aboriginal population control in the 1960s

Laura Rademaker , Australian National University ; Jakelin Troy , University of Sydney , and Julia Hurst , The University of Melbourne

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Aboriginal people made pottery and sailed to distant offshore islands thousands of years before Europeans arrived

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Bitcoin Price Dynamics Before and After Halving Events: Bitwise Analysis

In their detailed research paper titled “The Bitcoin Halving: A Programmatic Monetary Policy,” Juan Leon and Matt Hougan of Bitwise Asset Management explore the nuanced impacts of Bitcoin’s halving events on the cryptocurrency’s price dynamics and mining ecosystem. This scholarly work delves into the programmed reductions in Bitcoin’s block rewards, a feature embedded within its blockchain technology, designed to halve the number of bitcoins awarded to miners approximately every four years.

The authors begin by contextualizing the significance of halving as a cornerstone of Bitcoin’s monetary design, which aims to reduce the rate of new coin creation over time, thereby instilling scarcity and potentially boosting the asset’s value. This design mirrors the principles of precious metals like gold, where reduced supply can lead to price appreciation under stable or increasing demand conditions.

Leon and Hougan meticulously analyze past halving events to forecast potential market reactions. They highlight that despite the predictable nature of these events—dates for halvings are known years in advance—the market’s response often incorporates both rational and speculative elements. Their research indicates that while there is a common expectation of price increases post-halving, the actual market reaction can vary substantially. Short-term analyses suggest a “sell the news” phenomenon, where price increases leading up to a halving are followed by stagnation or declines immediately afterwards. However, long-term data reveals a different story, with substantial price appreciations occurring in the year following past halvings.

Bitwise @BitwiseInvest For more insights, check out our recent paper, “The Bitcoin Halving: A Programmatic Monetary Policy.” https://t.co/Ih1Yg8nVcd Apr 16, 2024

The Bitwise paper also addresses the critical topic of mining economics affected by halvings. Mining, the process through which transactions are verified and new bitcoins are created, becomes less profitable immediately after a halving due to the reduced rewards per block. This situation poses significant challenges for miners, impacting their operational decisions and the overall network security. The authors point out that while halvings decrease revenue streams from block rewards, they also incentivize miners to seek efficiency improvements and cost reductions, which can lead to technological innovations within the sector.

Moreover, the paper discusses the broader implications of halvings on Bitcoin’s market structure, including changes in market liquidity, trader behavior, and the role of institutional investors. The authors argue that each halving serves as a test of the Bitcoin market’s maturity, highlighting shifts from speculative trading towards more stable, long-term investment strategies as the market evolves.

The analysis extends to the psychological and speculative aspects surrounding halvings. The authors note that the anticipation of price increases can lead to speculative bubbles, which may distort the fundamental understanding and analysis of Bitcoin’s market dynamics. They emphasize the importance of distinguishing between speculative hype and genuine growth drivers, such as adoption rates, technological advancements, and regulatory developments.

Bitwise’s research also touches on the interaction between halvings and regulatory environments, noting that regulatory clarity and advancements can significantly impact the effectiveness and market response to these events. As such, they call for a balanced regulatory approach that fosters innovation while providing adequate protection against potential market manipulations and other risks.

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