research on diabetes support groups

Recent Findings on the Effectiveness of Peer Support for Patients with Type 2 Diabetes

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Published: 21 May 2024
Volume 18 , pages 65–79, ( 2024 )

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James J. Werner 1 , 2 , 3 , 4 , 5 ,
Kelsey Ufholz 1 , 2 &
Prashant Yamajala 1 , 2

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Purpose of Review

To review randomized controlled trials (RCTs) published from 2021–2023 that reported the effects of peer support interventions on outcomes in patients with type 2 diabetes (T2DM).

Recent Findings

Literature searches yielded 137 articles and nine RCTs were ultimately reviewed. The reviewed trials involved in-person support groups, peer coach/mentor support, cultural peer support by community health workers, peer support during shared medical appointments (SMAs) including virtual reality-based SMAs, telehealth-facilitated programs, and telephone peer support. Most interventions combined two or more peer support strategies.

Peer support was associated with significant decreases in HbA1c in 6 of the 9 reviewed studies. The largest statistically significant improvements in HbA1c were reported in a study of community health workers in Asia (-2.7% at 12 months) and a Canadian study in which trained volunteer peer coaches with T2DM met with participants once and subsequently made weekly or biweekly phone calls to them (-1.35% at 12 months). Systolic blood pressure was significantly improved in 3 of 9 studies.

The findings suggest that peer support can be beneficial to glycemic control and blood pressure in T2DM patients. Studies of peer support embedded within SMAs resulted in significant reductions in HbA1c and suggest that linkages between healthcare systems, providers, and peer support programs may enhance T2DM outcomes.

Enhancing Peer Support Interventions in Diabetes Care

Mobile-Enhanced Peer Support for African Americans with Type 2 Diabetes: a Randomized Controlled Trial

Peer support of complex health behaviors in prevention and disease management with special reference to diabetes: systematic reviews.

Avoid common mistakes on your manuscript.

Introduction

Diabetes is one of the fastest growing global health crises and is a major cause of blindness, kidney failure, heart attacks, stroke, lower limb amputation, and mortality [ 1 ]. In 2021, approximately 537 million people had diabetes worldwide and the number is projected to reach 643 million by 2030 and 783 million by 2045 [ 2 ].

Type 2 diabetes (T2DM) accounts for more than 90% of diabetes cases. Increasing rates are driven by socio-economic, demographic, environmental, and genetic factors [ 3 ]. Much of diabetes care needs to be carried out by patients, including monitoring blood glucose, taking medication, getting regular physical activity and maintaining a healthy diet [ 4 ••, 5 ]. Although complications are often preventable through improved metabolic control and implementation of self-care and lifestyle behaviors, patients face numerous self-management barriers [ 6 , 7 ]. These include inadequate diabetes knowledge, lack of resources for exercise and healthy eating, discomfort from diabetes-related health problems, negative emotions, and lack of social support [ 8 ].

Clinical guidelines recommend that individuals with T2DM participate in a lifestyle management program that includes diabetes self-management education and support (DSMES) comprising medical nutrition therapy, physical activity, smoking cessation counseling, and psychosocial care [ 4 ••, 9 ••]. Peer support is not a specified component of DSMES programs, although diabetes education is often conducted in groups of peers with T2DM [ 10 ]. The utilization, reach, and effectiveness of DSMES programs can be improved through peer support, which may help to overcome barriers and compensate for limited resources [ 11 ]. DSMES programs have proven to be effective in lowering HbA1c, reducing diabetes complications, lowering rates of all-cause mortality, increasing self-efficacy, improving coping skills, and reducing diabetes distress [ 10 ].

Ongoing support from family and peers can be beneficial as patients endeavor to implement and sustain the behaviors needed to effectively self-manage T2DM [ 12 ]. Peer support has been defined as “the provision of emotional, appraisal and informational assistance by a created social network member who possesses experiential knowledge of a specific behavior or stressor and similar characteristics as the target population”. [ 13 ] Peer support typically involves emotional support and informational support. When both peers have diabetes, mutual reciprocity takes place when individuals take turns sharing their personal experiences and providing emotional and informational support to the other person. Through these mechanisms, peer support can improve understanding, motivation, self-efficacy, and mood, and reduce perceived barriers in individuals with T2DM [ 14 ]. Diabetes distress, which is the negative emotional experience resulting from the challenge of living with the demands of diabetes, can also be reduced through peer support. [ 15 ] A classification of diabetes peer support models based on the work of Heisler [ 5 ] is provided in Table 1 titled, Types of Peer Support Models Used in Type 2 Diabetes.

Different types of peers are used across the spectrum of interventions used in diabetes peer support programs. Fellow T2DM patients are peers on the basis of sharing the experience of living with diabetes. Peer support specialists employed by health clinics are peers because they too have been diagnosed with T2DM and live with the condition. Community health workers supporting patients with T2DM are considered to be “cultural peers” because they also live in the same communities, share patients’ racial and ethnic identities and cultural backgrounds, and may have similar life situations including caring for family members with T2DM, although they do not necessarily have a diagnosis of T2DM [ 16 ].

During the COVID-19 pandemic, patients with T2DM were at increased risk of severe infections and mortality and many experienced disruptions to their social support networks [ 12 ]. These were significant psychological stressors and increased diabetes self-management challenges [ 11 ]. The findings of several diabetes peer support interventions conducted during the pandemic are reported in this review.

Peer support interventions have the potential to positively affect outcomes for individuals with T2DM [ 17 •, 18 ]. Though they have shown promise, the type and overall effectiveness of peer support programs is highly variable. Our purpose was to identify peer support studies to better understand the breadth of strategies used and their relative effectiveness on diabetes self-management and cardiometabolic outcomes.

PubMed, the Cochrane Library, and the PsycInfo and PsycArticles databases were searched for articles published between January 1, 2021 and December 31, 2023. This timeframe was selected based on T2DM patients’ increased needs for social and emotional support around the time of the COVID-19 pandemic [ 19 , 20 ]. The included search terms were type 2 diabetes, diabetes mellitus, peer support, social support, support group, self-help group, group support, community support, group education, group visits, and shared medical appointments. Search terms for outcome variables included HbA1c, glycemic control, obesity, body mass index, and weight loss. Studies reporting only survey data or qualitative findings, or that otherwise lacked physiologic outcomes were excluded. Studies of other forms of diabetes (e.g., type 1, gestational) were excluded as were studies of children and non-English language articles.

The searches yielded 137 articles for which 129 abstracts were reviewed against inclusion and exclusion criteria, yielding 41 articles that were examined in full text format. After the exclusion of non-RCTs, pilot studies, and studies with incomplete data or low rates of completion, 9 articles were utilized in this review. A flowchart based on PRISMA guidelines is provided in Fig. 1 , titled Flow Diagram for Review [ 21 ].

Flow diagram for review

Key findings from the review are discussed in this section and are summarized in Table 2 titled, Characteristics and Key Outcomes of Reviewed Studies.

In-Person Peer Support

There is significant evidence for the effectiveness of in-person (IP) support in diabetes education programs [ 17 •, 18 , 22 ]. IP support allows direct verbal and non-verbal communication, enables social interactions and facilitates the exchange of peer-to-peer information, support, and goal setting.

Shared Medical Appointments

Shared medical appointments (SMAs) are typically IP doctor-patient visits in which groups of patients are seen by one or more healthcare providers. Patients interact with both providers and with peers during SMAs [ 23 ]. Heisler and colleagues compared the effectiveness of diabetes SMAs to usual care in improving HbA1c [ 24 •]. Patients with T2DM and elevated HbA1c who had been prescribed medication were recruited from 5 U.S. Veterans Affairs (VA) health systems. Participants engaged in IP SMAs of 60–120 min totaling 6–8 h over 12 months where they received education, engaged in goal setting, and shared their experiences with peers. A SMA plus reciprocal peer support intervention arm was available in which participants had the option to be connected to another participant for mutual support, but uptake was low resulting in data being combined with the SMA-only group data. The control group received usual care.

At 6 months, the intervention group’s mean HbA1c was reduced by 0.35% compared to usual care ( p = 0.001) and was lower than usual care by 0.53% in participants attending at least half of the sessions ( p = 0.001), however, these differences were not significant at 12 months. Insulin starts were significantly greater in the intervention group at both 6 and 12 months. Significant mean differences were not observed for systolic blood pressure, statin starts, or HTN med class changes.

Health Clinic Staff-facilitated Support Groups

Mexico is among countries with the highest prevalence of diabetes, with more than 24% of adults over age 50 being diagnosed with T2DM [ 25 ]. The Mexican Ministry of Health’s primary care services include diabetes support groups known as Grupos de Ayuda Mutua (GAM), which provide a supportive group environment, glucose self-monitoring and health information via monthly meetings. Rosales and colleagues sought to complement the GAM program with Meta Salud Diabetes (MSD), a secondary prevention curriculum designed to operate within the framework of the GAM and evaluated its effects on Framingham CVD risk in a cluster-randomized trial [ 26 ]. The objective was for the MSD to improve the existing GAM by providing educational information, group discussions, and interactive workshops to promote a healthier diet, increased physical activity and reduced disease complications.

The GAM + MSD intervention consisted of 2-h IP participatory educational sessions and group-based interactive activities facilitated by trained health center staff that engaged GAM support groups at 22 government health clinics over 13 consecutive weeks. The following items were on the agenda at each session: blood pressure and glucose monitoring; readings, discussions and games related to the day’s topic of focus; implementation of a physical activity, and a follow-up goal setting exercise. The usual care group participated in standard GAM support groups.

Mean Framingham CVD risk score was significantly reduced by 3.17% points in the MSD arm versus GAM usual care at 3 months ( p = 0.013), however, the between groups difference was not significant at 12 months. Greater reduction in mean Framingham CVD risk was observed in men than women and in those whose HbA1c was < 8% at baseline. No significant effects were observed for HbA1c, blood pressure, or blood lipids. Compared to baseline, diabetes distress in the GAM + MSD group was significantly lower at the 3- and 12-month follow-up assessments.

IP Diet & Lifestyle Intervention with Telephone Support

Sampson and colleagues implemented an IP diet and lifestyle intervention with and without additional telephone support provided by trained peers in the UK for individuals with newly diagnosed “screen detected” type 2 diabetes [ 27 ]. Participants were randomized into three arms. Those in the intervention arm (INT) received six 2-h group educational sessions over 12 weeks and up to fifteen 2.5-h group maintenance sessions 8 weeks apart; maximum possible contact time was 49.5 h over 30 months. Individuals randomized to the second arm received the INT intervention plus monthly telephone support from peer volunteers who themselves had T2DM and who had received training as diabetes peer mentors (INT-DPM). Telephone contacts were monthly for 3 months and then every 2 months thereafter for up to 46 months. Those in the control arm (CON) received no intervention.

At 12 months, mean HbA1c significantly decreased in the telephone peer support INT-DPM group compared to the control group (CON) (-0.3%; p = 0.007), however, differences were not significant between the INT-DPM and INT groups, or between the INT and control groups. Days of resistance exercise per week significantly increased compared to the control group in both the INT and INT-DPM groups (INT: 4.22 days, p = 0.01; INT-DPM: 3.32 days, p = 0.02). Glycemic control was significantly lower in INT-DPM group vs. the CON group for participants < 65 years of age compared to those > 65 years (INT-DPM -2.7% vs. CON -2.2%; interaction p = 0.007). No significant differences were observed between any of the groups for body weight, body fat %, or waist circumference.

Family- and Friend-focused Peer Intervention

Positive support by family and friends of individuals with T2DM is strongly associated with more consistent self-care and lower glycemic control [ 28 ]. However, it is possible for both helpful and harmful interactions to co-occur within families and friendships. As cultural peers, family members and friends can support or undermine an individual’s diabetes self-care behaviors, which affects the likelihood of them continuing to implement the behaviors [ 29 ].

Nelson and colleagues implemented an intervention entitled, Family/friend Activation to Motivate Self-care (FAMS) to assist patients with T2DM in pursuing self-care goals and in effectively responding to both helpful and harmful input from family and friends [ 30 ]. In this RCT, FAMS provided monthly health coaching phone calls by behavioral health providers that were subsequently reinforced by text messaging over the 9-month intervention period. Each FAMS participant had the option to designate a cultural peer support person (family member or friend) and invite them to receive the text messages sent by their health coach, potentially prompting peer interactions about the participant’s health behaviors and diabetes self-management goals. Monthly coaching calls to FAMS participants focused on building the following skills: improving the ability to recognize and manage both helpful and harmful messages from friends or family members, SMART goal setting, assertive communication, obtaining social support, collaborative problem-solving, and cognitive behavioral coping skills. Participants in the control group received usual care. Participants and support persons in both study arms received print materials on managing diabetes and providing support to individuals with diabetes, and participants received text messages advising how to access their HbA1c results.

At 6 months, mean HbA1c decreased by 0.25% compared to controls but did not reach statistical significance ( p = 0.09). Effects on HbA1c vs. controls were also non-significant at 9 months (-0.05%; p = 0.9) and at 12 months (-0.02%; p = 0.9). Interestingly, participants who were not cohabitating with their support person had significantly lower HbA1c at 6 months (-0.64%; p = 0.033) but not at other time points. The investigators suggested that out-of-home support is likely to strengthen one’s support network, especially for those living alone, and may result in fewer instances of harmful social interactions than can occur with a cohabitating support person. At both 6 and 9 months, significant improvements were observed in intermediate targets of the FAMS intervention compared to controls including diabetes self-efficacy, problem eating behaviors, summative physical activity, helpful family/friend involvement, and autonomy support, although the study did not attempt to measure the relative contributions made by health coaches and cultural peer support persons to these outcomes. Another recent trial of a family and friend cultural peer support intervention for patients with diabetes yielded similar findings, with improvements in intermediate targets but non-significant effects on HbA1c [ 31 ].

Peer Coaches and Peer Mentors

Peer leader supported self-care intervention.

Although large numbers of patients with T2DM receive diabetes care in primary care settings, patients at the highest risk are often managed in subspecialty settings. Tang and colleagues conducted a peer-led intervention for adult T2DM patients presenting to endocrinology and diabetes education centers in British Columbia, Canada [ 32 ] and compared it to a usual care control group. Peer leaders completed 30 h of knowledge and skills training in T2DM self-management, providing emotional support, and linking patients to clinical resources. It is important to note that the peer leaders themselves had T2DM and a mean self-reported HbA1c ≤ 8.0% [ 33 ]. Individuals in the usual care control group received all services normally available to them at the diabetes education centers, including access to a nurse, dietician and social worker.

Intervention group participants received 30 h of T2DM self-management education and were subsequently matched with a peer leader based on gender and geographic proximity. After an initial IP educational meeting between peer leader and participant, peer leaders made 12 weekly telephone contacts with their partners over the first 3 months of the 48-month intervention to discuss self-management challenges, solve problems, and set self-management goals. During the final 36 weeks, peer leaders made 18 biweekly telephone support calls to intervention group participants.

At 3 months and 12 months post-intervention, HbA1c was not significantly different either from baseline within the intervention group or between the intervention and control groups. Significant between group improvements in systolic blood pressure were observed in the intervention group compared to controls at 12 months (-4.1 points; p = 0.023). Overall distress and emotional distress scores in the intervention group were significantly lower at 3 months and 12 months than at baseline but neither were significantly different than controls at any time point.

Community Health Workers

Community health workers (CHWs) are cultural peers in that they typically share cultural, racial, ethnic, community, and socioeconomic backgrounds with the individuals they serve and they may have direct experience with target health conditions as a patient or in support of a family member [ 34 ]. With education and training in diabetes self-management and health behavior change skills, CHWs have the potential to positively affect diabetes outcomes [ 35 ].

Home-based Diabetes Education and Counseling by Trained Volunteers

In Nepal, the healthcare system has traditionally focused on communicable diseases rather than chronic conditions and has struggled to adapt to rapidly increasing rates of T2DM [ 36 ]. A national initiative to address this need involves the mobilization of more than 50,000 Female Community Health Volunteers (FCHVs) in communities across the country. In this study, these locally-based cultural peers delivered a 12-month IP community-based diabetes intervention to adults in semi-urban settings in Western Nepal with the goal of reducing mean blood glucose [ 37 ]. This cluster randomized trial enrolled community residents with a current diagnosis of T2DM or mean blood glucose levels of 126 mg/dL or higher. FCHVs provided home-based diabetes education and counseling, measured blood glucose, blood pressure, height, and weight, and referred patients for clinic visits as needed. Home visits by FCHVs took place once every 4 months (3 visits) for one year. Participants randomized to the control group received usual care in which they managed their T2DM as usual and were placed on a wait list with the option to receive the intervention after 12 months.

Mean fasting blood glucose in the intervention group significantly decreased by 2.7% in the FCHV intervention group while it increased by 2.2% in the usual care group ( p < 0.001). Systolic blood pressure significantly decreased (-7.86 points; p = 0.002) in the intervention group vs. controls. Significant improvements were not observed in diastolic blood pressure, physical activity, BMI, alcohol consumption, smoking, fruit and vegetable intake, or medication adherence.

CHW-led Diabetes Education and Support

In a Houston, TX-based study (USA), Spanish-fluent CHWs served as cultural peers to Spanish-speaking low-income Latino(a) adults with T2DM [ 38 ]. In the intervention group, CHWs led monthly IP diabetes education group visits for 6 months and engaged in weekly m-Health communication (phone call or text) with study participants. To increase the CHWs’ knowledge of T2DM, they received quarterly diabetes training from physicians via telehealth . Participants randomized to the control group received usual care and placed on a wait list to receive the intervention after 6 months.

At 6 months, mean HbA1c in the intervention group was significantly lower (-0.98%) than controls ( p = 0.002) and decreased to a significantly greater extent among intervention group participants with uncontrolled diabetes (-1.31%) compared to control group participants with uncontrolled diabetes ( p = 0.007). Mean systolic blood pressure decreased significantly more in the intervention group than control group (-6.89 vs. + 0.03 points; p = 0.023) and the intervention group was also had a significantly greater reduction in mean diastolic blood pressure vs. the control group (-3.36 vs. + 0.2 points; p = 0.046). No significant between group differences were observed for body weight. Significantly more patients in the intervention group started insulin and statin medications compared to controls. Preventive care measures (B12 screening, foot and eye exams, immunizations, urine microalbumin) occurred significantly more often in the intervention group than the control group ( p < 0.001).

Peer Coaching Supported DVD Education

The personal stories of peers with T2DM were leveraged in an intervention based on social cognitive theory that aimed to improve medication adherence among low-income African American adults with T2DM [ 39 ]. The study took place in a rural Alabama region predominantly populated by African Americans that includes some of the poorest counties in the US, with a prevalence of diabetes of roughly twice the national average [ 40 ]. Medical resources are scarce and age-adjusted mortality rates are 39% higher for African Americans compared to the US average. Study participants were taking diabetes oral medications and who reported medication non-adherence or wanted help taking their medications. Peer coaches were community residents who had diabetes or took care of family members with diabetes.

Intervention participants were provided with DVDs that featured diabetes education and personal stories about how community members accepted their diagnosis of T2DM and overcame barriers to medication adherence during a six-month, 11-session behavioral diabetes self-care program delivered by peer coaches over the phone. During a 6-week intensive phase, intervention participants watched a 15–30 min DVD each week about the challenges and successes experienced by patients with diabetes and afterwards discussed the video’s content with a peer coach in a 30–60 min telephone call. After 6 weeks, peer coaches made biweekly or monthly telephone calls over the remaining 4.5 months to monitor participants’ progress, review information, provide education, and develop self-management strategies. Control participants received a self-paced general health program on topics that were independent of study outcomes.

After 6 months, the study’s primary outcome, medication adherence, significantly improved in the intervention group vs. controls (-0.25; p < 0.0001), however the study arms did not differ in mean HbA1c, blood pressure, body weight, or LDL cholesterol. Intervention participants’ beliefs in the necessity of medications, their medication use self-efficacy, and their diabetes self-efficacy significantly increased, although participants’ self-rated quality of life was not significantly improved. The authors speculated that improvements in medication adherence may not have been substantial enough to affect the physiological measures after 6 months. A limitation that potentially attenuated outcomes was that follow-up blood draws for some participants were delayed for a significant period of time after completion of the intervention.

SMAs Utilizing Telehealth and Virtual Environments

Telehealth became heavily-utilized during the COVID-19 pandemic and has proven comparable to IP consultations in clinical effectiveness [ 41 ]. In Boston, MA (USA) a two-arm trial was implemented to compare conventional IP diabetes SMAs to virtual reality-augmented diabetes SMAs conducted via telehealth for African American or Black and Hispanic or Latina adult women with uncontrolled HbA1c (≥ 8.0%) [ 42 ].

A 3D computer-based simulated environment was used to engage study participants in SMAs and DSME using a telehealth platform with “immersive, experiential learning with animated educational content” [ 42 ]. Virtual world (VW) participants used personal avatars to represent themselves and engage with peers to carry out behavioral changes. They used the avatars to engage in SMAs and to practice the use of positive health behaviors such as dance and social support. Those in the VW group were provided with laptops and wireless internet access.

Both IP and virtual SMAs lasted 120 min including taking vital signs, documenting acute and chronic illness symptoms, health system visits, and self-management activities. All participants received the same 8-module DSME curriculum on topics including diabetes self-monitoring, preventive care, healthy eating, exercise, and stress management.

English- and Spanish-language cohorts of 6–12 individuals met in IP or VW settings for 8 weekly diabetes SMAs of 2-h duration. A healthcare provider met individually with each participant in a separate physical space or via a private telehealth session or telephone call for a one-on-one consult during every SMA. After 8 weeks, participants entered a 16-week maintenance phase when no formal SMAs occurred.

An intention-to-treat (ITT) analysis showed significant mean improvements in HbA1c from baseline to 6 months in the IP group (-0.8%; 10.2% to 9.4%) and in the VW group (-0.5%; 9.7% to 9.2%); between group differences were not statistically significant suggesting that the VW-based SMAs were no less effective than the traditional IP SMAs. Changes in HbA1c in a subset of participants completing at least 6 SMAs per protocol (PP) were similar to the results of the ITT analysis in that between group means were not significantly different.

Significant improvements in levels of physical activity were not observed in either the ITT or PP analyses. The investigators reported that participants had difficulties using the equipment to measure physical activity. Changes in depression scores within the IP and VM groups were non-significant, although substantial within group improvements were observed for diabetes distress in both groups.

Mechanisms of Peer Support

Peer support can play an important role in improving patients’ glycemic outcomes in T2DM [ 43 ]. Conceptually, peer support can be grounded in social cognitive theory [ 44 ] and self-determination theory [ 45 ]. Social cognitive theory proposes that learning can occur by observing the behavior of others and its consequences. Without the necessity of direct personal experience for learning, the volume of knowledge and skills that can be quickly learned by patients with T2DM is greatly expanded compared to experiential learning. Further, by observing peers as “social models,” self-efficacy and mastery of diabetes self-care behaviors may increase [ 44 ].

Self-determination theory posits that individuals are influenced by three factors: their capacity to exert control over their life and their future, their sense of competence (which increases with positive peer feedback which in turn increases motivation), and their connectedness to others [ 46 ]. Peer support can reinforce all 3 of these factors, particularly by providing positive feedback on behaviors, increasing self-efficacy, and leading to satisfying interpersonal connections [ 45 ].

The mechanisms of peer support relationships include having a shared lived experience, the identification of individual strengths, the provision of social and practical support, and the unique position of the peer as fellow patient, peer support specialist, or CHW cultural peer rather than a medical provider [ 47 ]. The mutual sharing of personal difficulties and experiences of suffering have been shown to increase feelings of normalization, connectedness and shared humanity [ 48 , 49 ]. Because peers have faced many of the same problems, peer support is a practical way to learn ways to overcome barriers to diabetes self-management [ 6 ].

Diabetes peer support interventions were associated with significant decreases in HbA1c in 6 of the 9 reviewed studies, and these findings align with those of older reviews. A 2021 systematic review and meta-analysis of 12 studies showed that diabetes self-management education with integrated peer support reduced HbA1c levels, (SMD, -0.41; 95% confidence interval [ 50 ], -0.69 to -0.13; p < 0.001) [ 17 •]. A scoping review by Lu et. al. found that a wide range of peer support models used as complements to primary care were associated with favorable improvements in HbA1c [ 51 ]. Further, a meta-analysis by Liang and colleagues found that peer support significantly improved self-efficacy compared with controls receiving usual care or usual diabetes education [SMD = 0.41, 95% CI = (0.20, 0.62), p = 0.0001], and self-management behaviors were significantly improved versus controls [SMD = 1.21, 95% CI = (0.58, 1.84), p = 0.0002] [ 18 ].

A sizeable decrease in HbA1c (-2.7%) was observed in a Nepal-based intervention study in which trained FCHVs held IP visits with the households of participants to provide diabetes education once every 4 months [ 37 ]. Although intervention contacts were infrequent and the FCHV interventionists were volunteers, it should be noted that FCHVs served as extenders of healthcare providers with whom they work closely at local clinics. As cultural peers to study participants, they were often regarded as integral, trusted member of their communities with whom a relationship had been established, often over many years. In addition, this was the only reviewed study that featured a household-based intervention, involving families in participants’ adoption of diet and lifestyle behaviors, attendance at clinic appointments, and modifications to medication adherence practices.

Mobile phones are nearly ubiquitous and can increase the number of contacts among peers. Six of the nine reviewed studies used mobile phones to support the intervention and four of these studies resulted in significant improvements in glycemic control. Several studies used both telephone calls and text messaging between peers and participants, providing more frequent opportunities for diabetes education, goal setting, problem-solving, trust-building, and strengthening of connections.

Healthcare system-sponsored diabetes education and peer support programs may have advantages compared to stand-alone peer support programs. Study participants were recruited at health clinics for 7 of the reviewed studies, 5 of which resulted in significant reductions in HbA1c. All three of the reviewed studies involving SMAs resulted in significant decreases in HbA1c, suggesting that directly integrating primary care providers and diabetes clinical services into diabetes education and peer support programs yields impactful benefits.

A study comparing VW SMAs to IP SMAs to found them to be comparable in effectiveness. The significant health risks posed by the COVID-19 pandemic to individuals with T2DM were reduced in the VW group as participants’ engaged in the intervention from their homes in comparison. In contrast, IP group visits were implemented in healthcare clinics in proximity to other patients and clinic staff. The VW intervention presents a potentially safer and more convenient way for T2DM patients to participate in SMAs and peer support programs while receiving benefits that are on par with IP SMAs.

Limitations

There are several limitations to this review. The review focuses on diabetes peer support interventions published over a relatively short 3-year time period. The review captured only studies published from 2021–2023 and does not include research published outside of this time frame.

Two of the included studies had modest sample sizes. Small samples have numerous disadvantages including increased risk of bias, increased variability, limited generalizability, and reduced statistical power, all of which can threaten the validity and reliability of a study’s findings.

The follow-up periods were only 6 months in duration for 2 of the 9 studies, which does not permit an evaluation of the longer-term durability of the treatment effect in diabetes, a long-term chronic disease. Due to the short time frames involved, caution should be exercised in drawing conclusions from these studies.

This review was intended to include a number of peer support interventions conducted during the period of the COVID-19 pandemic, which is recognized by the World Health Organization (WHO) to have started in March 2020 and ended in May 2023 [ 52 ]. However, it was difficult to determine the temporal overlap of the study periods with the pandemic. Although most of the included articles specified a date for the initiation of recruitment, they did not indicate an end date for the peer support intervention, which would have allowed it to be determined if the intervention extended into the pandemic period.

The pandemic was an event unrelated to study interventions but that may have influenced outcomes, and is a type of history bias. Interestingly, none of the articles discussed the possible influence of the pandemic on study participants’ experiences and study outcomes. Because there was no assessment of the effects of the pandemic on participants’ experiences in the reviewed studies, it is not possible to estimate its influence on outcomes. Additional research is needed to more closely examine the influence of the pandemic and other types of widespread disruptive events on patients with T2DM and the effects on peer support and outcomes.

It is important to note that diabetes peer support interventions are rarely studied in isolation. In the studies in the present review, peer support was part of a multifaceted intervention involving diabetes self-management education and sometimes clinical care. This makes it difficult to separate and isolate the effects of peer support from other aspects of the interventions. More research is needed to study the effects of peer support both alone and as part of multidimensional diabetes interventions.

Conclusions

Our findings suggest that diabetes education and peer support interventions can be effective for improving control of HbA1c and blood pressure, however, they were not associated with significant improvements in body weight. Studies of peer support groups embedded within SMAs resulted in significant reductions in HbA1c, suggesting that the engagement of clinicians in diabetes peer interventions enhances outcomes. In the design of peer support programs, it may be advantageous to engage households, healthcare providers, and clinics in program implementation.

Data Availability

No datasets were generated or analysed during the current study.

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Werner, J.J., Ufholz, K. & Yamajala, P. Recent Findings on the Effectiveness of Peer Support for Patients with Type 2 Diabetes. Curr Cardiovasc Risk Rep 18 , 65–79 (2024). https://doi.org/10.1007/s12170-024-00737-6

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Augmenting Traditional Support Groups for Adolescents With Type 1 Diabetes Using Instagram: Mixed Methods Feasibility Study

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1 Department of Pediatrics, University of Washington, Seattle, WA, United States.
2 Seattle Children's Research Institute, Seattle, WA, United States.
PMID: 34673527
PMCID: PMC8569531
DOI: 10.2196/21405

Background: In-person support groups have been shown to benefit adolescents with type 1 diabetes (T1D) by helping to decrease perceived diabetes burden and improving knowledge related to chronic disease management. However, barriers exist to participation in traditional support groups, including the timing and location of meetings and resources needed to attend. Adolescents are increasingly utilizing online support groups, which may provide solutions to some of the challenges faced when implementing in-person support groups.

Objective: The purpose of this study was to assess the feasibility and acceptability of a hybrid support group model where traditional in-person support groups were augmented with Instagram participation between monthly support group sessions for adolescents with T1D.

Methods: Participants (13-18 years old with T1D for ≥6 months) were asked to post photos each week for 3 months based on predetermined topics related to diabetes management. At the end of each month, participants attended an in-person support group to discuss their photos using the Photovoice method. Feasibility was assessed through enrollment and retention, number of Instagram posts, poststudy questionnaire, and a template analysis of the focus groups.

Results: Of 24 eligible participants, 16 (67%) enrolled in the study, with 3 dropping out prior to support group participation. The number of photos posted over 3 months ranged from 14 to 41. Among the 11 participants who completed a follow-up questionnaire, the majority of participants (6/11, 55%) reported that they very much enjoyed participating in the hybrid support group, and more than three-quarters (9/11, 82%) of participants reported that they "related to the photos posted." Over half of participants (8/11, 73%) reported "learning something new from the photos posted," which arose from sharing knowledge and experiences related to navigating the common challenges of diabetes management. Additionally, the use of Instagram posts helped facilitate peer discussions during the in-person support groups.

Conclusions: The novel combination of using Instagram to augment traditional in-person support groups was feasible and acceptable to adolescents with T1D. The overall satisfaction with the hybrid support group model, combined with the observed engagement with peers between support group sessions over social media, suggests that a hybrid support group model may have the potential to provide more pronounced benefits to adolescents than in-person meetings alone. Future research should investigate the use of social media as part of the support group model and examine the potential improvement of self-esteem, benefit-finding, and social support using validated tools in adolescents with diabetes.

Keywords: adolescent; diabetes mellitus, type 1; self-help groups; social media.

©Faisal S Malik, Cara Lind, Sarah Duncan, Connor Mitrovich, Michael Pascual, Joyce P Yi-Frazier. Originally published in JMIR Diabetes (https://diabetes.jmir.org), 21.10.2021.

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Findings from a Diabetes Support Group—A Pilot Study

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LORI MCLEER MALONEY , JEREMY FLOOD , NAJI ALAMUDDIN , MONA AL MUKADDAM; Findings from a Diabetes Support Group—A Pilot Study. Diabetes 1 July 2018; 67 (Supplement_1): 720–P. https://doi.org/10.2337/db18-720-P

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Purpose: Diabetes Self-Management Education (DSME) is a crucial aspect of treating type 2 diabetes mellitus (T2DM). Daily individual decisions affect patient health, well-being, and long term outcomes. This pilot study evaluated what best educates and motivates patients to improve glucose control.

Method: 17 participants with T2DM were recruited from Penn Rodebaugh Diabetes Center to attend 3 monthly diabetes support group meetings, and receive American Association of Diabetes Educators education. Weight and hemoglobin A1C (A1C) were measured at baseline and completion. Fitbit Activity Trackers were provided, as well as bi-weekly communication to reinforce behavior change. Pre and post-study surveys assessed nutrition, activity, monitoring blood glucose, and taking medication. Two sided paired t-test was performed to compare change in A1C and weight. A type I error rate of 0.was used for statistical significance.

Results: 15 participants completed the study. DSME interventions resulted in a statistically significant decrease in mean A1C from 8.5% to 7.7% with a mean A1C reduction of 0.8% in 3 months (p-value=0.01). Mean weight decreased 4.80 lb (p-value=0.0001). 11 remained on same medications, and 1 required reduced insulin. 3 added a glucagon-like peptide receptor agonist (GLP-1RA), with 1 adding a sodium-glucose cotransporter-2 inhibitor. 7 increased daily steps wearing Fitbit, while 8 reported no motivation. 10 increased weekly exercise, and 11 increased daily activity. All benefited from bi-weekly reinforcement, and reported improved nutrition. Food replica models aided visual reinforcement.

Conclusion: DSME in a group setting can motivate self-care and reduce both A1C and weight, however, the study met challenges. Time per patient spent coordinating was substantial. Food models and phone communication to reinforce lifestyle modification were useful. Research is needed to determine what provides long-term sustainability in a busy clinical practice.

L. McLeer Maloney: None. J. Flood: None. N. Alamuddin: Consultant; Self; Novo Nordisk Inc.. M. Al Mukaddam: None.

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Diabetes Support provides ongoing support to people with diabetes to maintain and reinforce upon the skills knowledge, and lifestyle changes gained from Recognized Diabetes Self-Management Education and Support (DSMES) services . Diabetes Support is a great way for patients to connect with resources in their community. We hope the resources below will be useful to both you and your patients/clients.

Education Recognition Programs

The ADA’s recognized DSMES programs help individuals gain the knowledge, skills, and confidence to thrive with diabetes. The program’s certified educators are ready to work with you to find practical solutions that fit your personal needs. Visit www.diabetes.org/findaprogram to find a location near you.

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Through this free year-long program available in English and Spanish, participants receive guidance on emotional well-being, healthy eating, getting active, and more. Participants receive six informational digital packages, a monthly e-newsletter with tips and resources and opportunities to find support from others living with diabetes.

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Our Patient Education Library has more than 170 free downloadable or printable PDF materials on a range of diabetes-related topics. We encourage you to share these with your patients/clients. Materials are available in Arabic, Chinese, English, French, Haitian Creole, Korean, Portuguese, Russian, Spanish, Tagalog, and Vietnamese.

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Diabetes Support Groups

Defeat diabetes foundation, american diabetes association, beyond type 2, carenity: type 2 diabetes community, diabetessisters, diabetes daily, frequently asked questions.

By 2030, it is estimated that globally over 500 million people will be living with type 2 diabetes .

Receiving a diagnosis of type 2 diabetes and learning to manage your condition can be overwhelming and confusing. People with diabetes spend an estimated 8,000 hours per year self-managing their condition in addition to the time they spend within a medical setting. Support groups can be a helpful addition to your diabetes management team, along with guidance and care from your healthcare providers.

Read on to find information on select online and in-person support groups.

photography by Kate Hiscock / Getty Images

Support groups for diabetes are not a replacement for professional medical care. It is important to research groups before joining them and always verify information received from support groups and their members.

Membership fee : Free
Structure : Search tool
Details : Allows you to look for contact information by state
Things to consider : It isn't a support group itself, but rather a tool to find a support group

Defeat Diabetes Foundation (DDF) has been in existence for more than 30 years. Its mission is to find sustainable solutions to help prevent , identify , and manage type 2 diabetes. It raises awareness of diabetes by providing research-based information to communities and individuals. DDF offers action-oriented steps everyone can take to help prevent the development of type 2 diabetes, including providing information on nutrition and exercise and sharing comprehensive resources on their website.

DDF provides outreach and helps people connect with community-based impact programs. In addition to information found on their website, they offer a sign-up newsletter and a search tool to find support groups.

Membership fee : Varies by program
Structure : Individual programs, online information, diabetes education
Details : Extensive resource offering the opportunity for connecting with professionals and with peers
Things to consider : Covers a broad scope of aspects surrounding diabetes, including education, research, legal rights, and more

The American Diabetes Association (ADA) is a large organization consisting of a network of over 565,000 volunteers (as well as families and caregivers ), more than 250 staff members, and a professional society of almost 16,000 healthcare providers.

ADA aims to prevent diabetes and improve the lives of those who are affected by diabetes. They fund research, provide services to communities, offer credible information, and advocate for the rights of those with diabetes.

The ADA runs a recognized diabetes education program facilitated by certified educators, which helps people with diabetes learn practical information about their condition and gain skills and confidence as they navigate living with diabetes. A referral from the healthcare provider managing your diabetes is needed to enter the program.

The ADA also offers an afterschool program for kids ages 5 to 12 called Project Power . The program promotes healthy lifestyle choices that can help prevent type 2 diabetes and empower children to develop healthy lifelong habits .

The program is free and offered in person or virtually. Check the website for available dates and times.

Structure : Online community
Details : An online platform that allows you to connect with other people affected by type 2 diabetes, find resources, ask questions, have discussions, etc. Available in English and Spanish.
Things to consider : You request to join by answering two questions (why you want to join and where you heard about them), then wait for approval

Beyond Type 2 is a program that stems from the nonprofit organization Beyond Type 1. Beyond Type 1 was founded in 2015 and launched Beyond Type 2 in 2019.

Beyond Type 2 provides a platform for people with type 2 diabetes to connect online with a community in which they can ask questions, share experiences, exchange ideas and resources, and more. The program is available in both English and Spanish.

Beyond Type 2 is supported by a partnership between The American Diabetes Association and Beyond Type 1.

Structure : Online forum
Details : An online forum where members can ask for advice, seek and give support, and discuss topics surrounding diagnosis, treatment, and diet
Things to consider : Carenity is a general site for many health conditions. The type 2 diabetes forum is one community within the greater site

Carenity is an online community that offers a social network through a newsfeed, discussion forums, private messaging, and more.

Under the Carenity umbrella, there is a section specifically for people with type 2 diabetes .

Structure : Online forum, online and in-person meet-ups
Details : Peer support and education for individuals with all forms of diabetes
Things to consider : This resource is for women only

Founded in 2008, DiabetesSisters is an organization managed by women with diabetes that offers education and support services to help women living with diabetes thrive. Peer support is a core component of the organization.

DiabetesSisters has an online forum through which women with diabetes can connect with each other.

DiabetesSisters also has a program called Part of DiabetesSisters (PODS) Meetups for women age 18 and up who are living with any type of diabetes, including prediabetes . You can look at the spotlight list on their meetups page, check their event calendar , or complete a PODS interest form to connect with a meetup.

Details : A collection of discussion boards separated by category
Things to consider : This site consists of discussions between members only, not expert advice

TuDiabetes is an online community for people with all types of diabetes . It is a program through the nonprofit organization Beyond Type 1 and is sponsored by the Diabetes Hands Foundation. As of 2014, it had more than 35,000 registered members.

Content on the message boards is curated by members and should not be used as a primary source of information but as a place to start your search or as a complement to expert advice.

Details : Discussion boards separated by topic
Things to consider : The website and its forums address all types of diabetes, so you will need to look specifically for topics and information that pertain to type 2 diabetes

Diabetes Daily was founded in 2005 after one of its founders was diagnosed with type 1 diabetes and saw a need for community-based support for people living with diabetes.

Diabetes Daily has online forums through its website and also helps people connect through Facebook, Twitter, and email. Their online community is made up of almost 1 million members.

The Diabetes Daily website posts information about diabetes , including recipes .

Online vs. Local Groups

Both online and in-person local diabetes groups foster a community that includes aspects such as:

Peer support
Self-expression
Sharing information
Seeking and giving advice

Some benefits of an online support group include:

Easier access
More flexibility in terms of when and how the community is accessed
An opportunity for anonymity for those who want it
The ability to quietly observe without participating for those not ready to share
Cost-effectiveness

There can be downsides to online support groups as well, such as:

High access to misinformation, some of which can be harmful
Potential privacy and security risks
Less personal
Greater potential for harassment and bad behavior due to “the online disinhibition effect"

In-person support groups can be more difficult to plan as they require a venue (such as places of worship, classrooms, or community centers) and occur on a schedule that must be worked into the participants' calendars.

While less convenient, some people may prefer in-person groups as they allow for a more personal face-to-face connection. They can also foster relationships with local people who may have common interests outside of their condition. In-person groups also provide the opportunity for social activities and guest speakers.

Support groups can help people with type 2 diabetes connect with others who have the same condition. These groups allow for peer support, sharing information and resources, venting frustration, offering encouragement, and more.

Support groups can exist in-person or online (such as on online forums or social media).

Information found in support groups is not always accurate and should be verified. Support groups are not a substitute for professional medical care.

A Word From Verywell

Whether you have just received a diagnosis, or have been living with type 2 diabetes for a while, you may find a support group beneficial.

Do your research before joining an in-person or online group to make sure they are a credible resource, and talk to your healthcare provider before applying the medical advice you receive from other members.

Looking at credible diabetes organizations such as the American Diabetes Association and Beyond Type 1/Beyond Type 2 is a good start. Check the community links on these websites for online forums or links to support groups.

There are a number of ways you can help support someone with diabetes, including:

Educating yourself on their condition
Seeing them as an individual with unique needs
Attending their appointments (if they want you to)
Asking what they need and listening to what they say
Recognizing that managing diabetes can be time-consuming and allowing time for checking blood sugar and other necessary procedures during your plans together
Respecting their boundaries and not expecting them to share everything about their condition or experience with you
Understanding that changes in blood sugar can cause mood shifts
Engaging in the same lifestyle choices and activities they are, such as a healthful diet and physical activity
Knowing how to recognize the signs of low blood sugar and what to do about it

Beyond Type 2. Home .

Litchman ML, Walker HR, Ng AH, et al. State of the science: a scoping review and gap analysis of diabetes online communities . J Diabetes Sci Technol . 2019;13(3):466-492. doi:10.1177/1932296819831042

Hilliard ME, Sparling KM, Hitchcock J, Oser TK, Hood KK. The emerging diabetes online community . Curr Diabetes Rev. 2015;11(4):261-272. doi:10.2174/1573399811666150421123448

Defeat Diabetes Foundation. The tools you need to win the fight to defeat diabetes .

American Diabetes Association. Home .

Carenity. Diabetes (type 2) forum .

DiabetesSisters. Home .

TuDiabetes. Home .

Diabetes Daily. Welcome to Diabetes Daily!

Herrero N, Guerrero-Solé F, Mas-Manchón L. Participation of patients with type 2 diabetes in online support groups is correlated to lower levels of diabetes self-management . J Diabetes Sci Technol . 2021;15(1):121-126. doi:10.1177/1932296820909830

diaTribe. How and where to find in-person support groups and social activities for young adults with diabetes .

Centers For Disease Control and Prevention. Friends, family & diabetes .

By Heather Jones Jones is a freelance writer with a strong focus on health, parenting, disability, and feminism.

Defeat Diabetes Foundation

Diabetes Support Groups

Facing a diabetes diagnosis, whether it’s you, a cherished individual, or a family member, can lead to feelings of being swamped. Engaging in a diabetes support group represents a proactive strategy for discovering assistance, guidance, insights, and resources, forming an integral component of managing diabetes holistically.

If you’re seeking a diabetes support group in the United States to assist you in handling your diabetes, we encourage you to commence your search with the details provided below. Just click one of the buttons to locate a diabetes support group within your state.

Have a diabetes support group you would like to list on our directory? Please send your information to: [email protected]

Knowledge is power, and action taken from that knowledge produces results. We invite you to explore our site and arm yourself with the important knowledge and support you need to prevent diabetes, manage the disease, and better understand the connection diabetes has to the health of our planet.

Understanding Diabetes

Diabetes risk factors, healthful eating, active and mindful lifestyle, global awareness and responsibility, research explained.

Overview & Types
Symptoms & Diagnosis
Understanding Blood Sugar
Diet & Exercise
Better Living
Complications
Related Conditions
Symptoms & Causes
Diagnosis & Tests
Prevention & Treatment
Living & Managing
Complications & Related Diseases
Treating & Managing
Complications & Related Conditions
Gestational Diabetes
Appointment Prep
View Full Guide

Where to Find More Diabetes Resources

Looking for organizations that provide information that can help you manage your diabetes? You may want to start with these groups.

Health Organizations

Academy of Nutrition and Dietetics (formerly the American Dietetic Association) 120 South Riverside Plaza, Suite 2000 Chicago, IL 60606-6995 800-877-1600 www.eatright.org

American Association of Clinical Endocrinologists (AACE) 245 Riverside Avenue, Suite 200 Jacksonville, FL 32202 (904) 353-7878 www.aace.com

American Diabetes Association (ADA) 2451 Crystal Drive, Suite 900 Arlington, VA 22202 800-342-2383 www.diabetes.org

American Association of Diabetes Educators (AADE) 125 S. Wacker, Suite 600 Chicago, IL 60606 800-338-3633 www.diabeteseducator.org

Juvenile Diabetes Research Foundation International (JDRF) 200 Vesey St New York, NY 10281 800-533-2873 www.jdrf.org

National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) 9000 Rockville Pike Bethesda, MD 20892 (301) 496-3583 https://www2.niddk.nih.gov/

Diabetes Journals/Newsletters/Social Networks

TypeOneNation

Juvenile Diabetes Research Foundation International

typeonenation.org

Diabetes Forecast: The Healthy Living Magazine

from the American Diabetes Association https://www.diabetesforecast.org/

MyFoodAdvisor: Recipes for Healthy Living

from the American Diabetes Association

https://www.diabetes.org/food-and-fitness/food/myfoodadvisor.html

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Lawrence Mandarino, PhD , director of the Center for Disparities in Diabetes, Obesity and Metabolism at the University of Arizona Health Sciences , has been researching this condition for decades. The mission of the Center for Disparities in Diabetes, Obesity and Metabolism is to improve the health and well-being of the Hispanic and Latino population while advancing research into diabetes and other metabolic conditions

The center’s Wellness Bank, or El Banco por Salud , is central to this mission.

Using data to improve health

Type 2 diabetes involves unusually high blood sugar levels and an inability of the body to use that sugar for fuel. If left untreated, complications can include heart disease, vision loss and kidney disease.

Risk factors for Type 2 diabetes include being overweight and inactive, in addition to socio-economic, demographic, environmental and genetic influences. That’s where the Wellness Bank comes in. Its biorepository of blood, DNA specimens and patient data serves as a resource to connect academic researchers and the community.

[A Hispanic family in a kitchen preparing a healthy meal together]

Teran was one of those patients.

“I was not the first person to ignore their health in my family. My mom has a very similar story to how she found out she was diabetic. I lost my sister three years ago, and she had previously suffered a heart attack before finding out she was diabetic,” she said. “I decided right there that I needed to do this. I needed to be an example for nieces, nephews, children and grandchildren. They've lost too much already.”

Teran underwent gastric bypass surgery and lost 155 pounds. She started eating a healthy diet and being more active. When she signed up for a study through the Wellness Bank, she learned another potentially lifesaving piece of information about her health.

“They would monitor my blood pressure, blood sugar and heart rate before and after walking on the treadmill. That’s where I found out I also have an irregular heartbeat,” Teran said. “I had to get a procedure done to make sure I didn’t have any blood clots. I take medication for it now.”

For people with Type 2 diabetes, or metabolic conditions that frequently lead to Type 2 diabetes, participating in the Wellness Bank offers a unique opportunity to explore their health at zero cost to them.

“Participants get all of their lab tests for free, including things like their dietary sugar control, cholesterol control, kidney function, liver function and electrolyte levels,” said Dr. Soltani. “I review each of these lab tests and it is not infrequent that I find that they do have diabetes, or out-of-control blood pressure or kidney failure. This partnership allows us to immediately follow up with them.”

Researchers and doctors benefit from increased knowledge of social and biological factors leading to poor glucose control with an emphasis on the implementation of pharmacogenomics clinical decision support.

Tracking diabetes through time

Dr. Mandarino, who also is a professor and chief of the Division Endocrinology, Diabetes and Metabolism in the Department of Medicine at the UArizona College of Medicine – Tucson , created the Arizona Insulin Resistance Registry, a precursor to the Wellness Bank, in the Phoenix area in the early 2000s.

“We were able to collect biospecimens and data from hundreds of people in the community that are still being used by researchers today, but we also learned some valuable lessons about things we wanted to do differently,” Dr. Mandarino said. “Many of our participants did not have health insurance or a primary care doctor. We realized we needed a community partner that could explain the results of different lab tests and follow up with a treatment plan if needed.”

[Portrait images of Drs. Lisa Soltani and Lawrence Mandarino]

The Wellness Bank and its stores of data have helped facilitate dozens of studies. Researchers can access participant blood samples and also retrieve details about lifestyle and eating habits. Study objectives range from searching for a link between mental health and diabetes, to assessing the association between breastfeeding and metabolic syndrome, to exploring diet interventions for food insecure adults.

“In one study, we asked participants on three different days if they had eaten any fruit or vegetables that day and scored them on a healthy-eating index,” Dr. Mandarino said. “We found that low scores were associated with people who also noted that they sometimes did not have enough money to buy food. This is one example of how someone could have a genetic risk factor for Type 2 diabetes, and then socio-economic factors can have an additive effect.”

Teran says the Wellness Bank was a key part of her path to better health, and she is happy her participation might advance science and research geared to help her community.

“I am so proud of my heritage, and I have always stood up for my family and community,” she said. “I am honored to do this for them.”

Experts Lawrence Mandarino, PhD Director, Center for Disparities in Diabetes, Obesity and Metabolism, University of Arizona Health Sciences Professor, Department of Medicine, College of Medicine – Tucson Chief, Division of Endocrinology, Diabetes and Metabolism , College of Medicine – Tucson

Contact: Brian Brennan UArizona Health Sciences Office of Communications 520-621-3510, [email protected]

Original story link .

ALSO SEE: “Dr. Lawrence Mandarino Recognized with College of Medicine – Tucson Mentoring Award” | Posted July 21, 2020 “UA Health Sciences Centers Collaborate to Present Discovering New Medicines at Arizona Summit” | Posted Sept. 30, 2019 “DOM Continues to Lead U.S. News ‘Best Hospital’ List for Banner – UMC Tucson” | Posted Aug. 15, 2018 “DOM Faculty Turn Out in Force for 50th Anniversary UA College of Medicine Research Fair” | Posted Oct. 23, 2017 “UA-Led Study on How Insulin Controls Glucose Uptake Picked for Cover of Proteomics Journal” | Posted Oct. 3, 2017 “Dr. Lawrence J. Mandarino to Head new UA Center for Disparities in Diabetes, Obesity and Metabolism” | Posted April 12, 2016

Study protocol
Open access
Published: 03 July 2024

A holistic lifestyle mobile health intervention for the prevention of type 2 diabetes and common mental disorders in Asian women with a history of gestational diabetes: a randomised control trial with 3-year follow-up protocol

Alicia Salamanca-Sanabria ORCID: orcid.org/0000-0002-2756-5592 1 ,
Seaw Jia Liew 2 ,
Jacqueline Mair 3 , 4 , 5 ,
Maria De Iorio 1 , 6 ,
Young Doris Yee Ling 7 ,
Mya Thway Tint 1 , 2 ,
Yew Tong Wei 6 , 8 ,
Karen Lim 9 ,
Desmond Ong 10 ,
Yu Chung Chooi 1 ,
Vicky Tay 1 &
Johan Gunnar Eriksson 1 , 2 , 11 , 12

Trials volume 25 , Article number: 443 ( 2024 ) Cite this article

121 Accesses

1 Altmetric

Metrics details

Women with a history of gestational diabetes mellitus (GDM) are 12-fold more likely to develop type 2 diabetes (T2D) 4–6 years after delivery than women without GDM. Similarly, GDM is associated with the development of common mental disorders (CMDs) (e.g. anxiety and depression). Evidence shows that holistic lifestyle interventions focusing on physical activity (PA), dietary intake, sleep, and mental well-being strategies can prevent T2D and CMDs. This study aims to assess the effectiveness of a holistic lifestyle mobile health intervention (mHealth) with post-GDM women in preventing T2D and CMDs in a community setting in Singapore.

The study consists of a 1-year randomised controlled trial (RCT) with a 3-year follow-up period. Post-GDM women with no current diabetes diagnosis and not planning to become pregnant will be eligible for the study. In addition, participants will complete mental well-being questionnaires (e.g. depression, anxiety, sleep) and their child’s socio-emotional and cognitive development. The participants will be randomised to either Group 1 (Intervention) or Group 2 (comparison). The intervention group will receive the “LVL UP App”, a smartphone-based, conversational agent-delivered holistic lifestyle intervention focused on three pillars: Move More (PA), Eat Well (Diet), and Stress Less (mental wellbeing). The intervention consists of health literacy and psychoeducational coaching sessions, daily “Life Hacks” (healthy activity suggestions), slow-paced breathing exercises, a step tracker (including brisk steps), a low-burden food diary, and a journaling tool. Women from both groups will be provided with an Oura ring for tracking physical activity, sleep, and heart rate variability (a proxy for stress), and the “HAPPY App”, a mHealth app which provides health promotion information about PA, diet, sleep, and mental wellbeing, as well as display body mass index, blood pressure, and results from the oral glucose tolerance tests. Short-term aggregate effects will be assessed at 26/27 weeks (midpoint) and a 1-year visit, followed by a 2, 3, and 4-year follow-up period.

High rates of progression of T2D and CMDs in women with post-GDM suggest an urgent need to promote a healthy lifestyle, including diet, PA, sleep, and mental well-being. Preventive interventions through a holistic, healthy lifestyle may be the solution, considering the inextricable relationship between physical and psychological health. We expect that holistic lifestyle mHealth may effectively support behavioural changes among women with a history of GDM to prevent T2D and CMDs.

Trial status

The protocol study was approved by the National Healthcare Group in Singapore, Domain Specific Review Board (DSRB) [2023/00178]; June 2023. Recruitment began on October 18, 2023.

Trial registration

ClinicalTrials.gov NCT05949957. The first submission date is June 08, 2023.

Peer Review reports

Introduction

Background and rationale {6a}.

Individuals, families, and societies worldwide are affected by diabetes. According to recent reports, one in ten adults has diabetes worldwide, accounting for 10.5% of the global population [ 1 , 2 ]. Globally, it is projected to be 700 million people with diabetes, with women being 343 million by 2045 [ 2 ]. Diabetes ranks ninth for women and eighth for men in the global ranking of disability-adjusted life years (DALYs) [ 3 , 4 ]. There is a parallel increase in obesity and type 2 diabetes (T2D) in the general population, as well as a rise in gestational diabetes mellitus (GDM) incidence [ 5 ]. GDM affects between 1 and 30% of pregnancies worldwide [ 6 ] and is particularly prevalent among Asian women [ 7 ], showing distribution in the Middle East (8.8–20.0%), Southeast (9.6–18.3) and Western Pacific (4.5–20.3%), respectively [ 6 ]. A recent report in Singapore shows that GDM prevalence is 23.5% [ 8 ]. GDM can cause pregnancy and birth complications and long-term chronic conditions such as cardiometabolic disorders and T2D in both mother and offspring [ 9 , 10 ]. Compared to women with a normoglycemic pregnancy, women with a history of GDM are more likely to develop T2D [ 11 ]. The prospective birth cohort study in Singapore, Growing Up in Singapore Towards Healthy Outcomes (GUSTO), showed that the risk of developing T2D is 12-fold higher in Singaporean women 4–6 years after their GDM diagnosis [ 12 ].

Risk factors for T2D among those with a history of GDM included greater pre-pregnancy Body Mass Index (BMI), excessive weight gain, unhealthy dietary patterns, physical inactivity, and a short period of lactation [ 13 ]. Post-natal risk, such as missing medical assistance in the continuum of GDM care after delivery, could be another risk for progression to T2D among Asian mothers with a history of GDM [ 7 ]. In terms of the pathophysiology, GDM has several similarities to T2D, namely impaired insulin sensitivity and dysfunction of the β-cell caused by the metabolic stress of pregnancy [ 5 , 14 ].

Besides T2D and cardiometabolic diseases, current literature suggests that women with a history of GDM also have a higher risk of developing common mental disorders (CMDs) (depression, anxiety) [ 15 , 16 ]. CMDs are the most common morbidity in the peripartum (during pregnancy and up to 1 year following delivery). Approximately one in five women develop CMDs during pregnancy or in the year following delivery [ 17 ], which is associated with adverse maternal and foetal outcomes as well as emotional and behavioural difficulties in the offspring [ 18 ]. Likewise, poor quality of sleep associated with gestational diabetes can pose significant health risks to both mothers and their newborns [ 19 , 20 , 21 ]. Previous studies have shown that women with GDM have shorter sleep time, less efficient sleep, and more sleep disorders and daytime dysfunction [ 19 ]. The evidence has been demonstrated that sleep disturbances can impair glucose metabolism by affecting insulin sensitivity and β-cell function, which is also associated with obesity and depression [ 20 ]. Thus, women with a history of GDM also may present poor sleep quality and CMDs. However, effective interventions that aim to improve these women’s mental well-being and sleep quality remain scarce.

A systematic review and meta-analysis about the prevention of T2D in women with previous GDM has shown that lifestyle interventions produced only a borderline reduction in T2D risk (RR 0.75, 95% CI: 0.55–1.03) [ 22 ]. The interventions did not focus on modifiable risk factors like sleep and mental well-being, which could have been addressed to improve the outcome. Observational studies suggest that approximately 45% of GDM cases might be preventable by the adoption of a healthy diet and increased physical activity (PA), including psychosocial factors (stress, anxiety, depression interventions) [ 6 , 23 , 24 , 25 ]. However, most preventative studies are undertaken in Western countries [ 21 ], and it is challenging to directly apply these strategies in Asian countries due to differences in demographics, socioeconomics, ethnicity, and cultural factors [ 7 ]. A recent systematic review study concluded that in most of the studies, the evidence-based intervention might not be culturally appropriate for the prevention of T2D in women with a history of GDM [ 13 ].

Postpartum women often report difficulty engaging with lifestyle management interventions due to several barriers [ 26 , 27 ], including multiple face-to-face sessions, travel distances, time constraints, and cost are some factors [ 26 , 28 ] that may be alleviated by technological advancements [ 29 ]. Thus, lifestyle interventions delivered through mobile health (mHealth) interventions represent a feasible, affordable and scalable solution for the prevention of T2D and CMDs among women with a history of GDM [ 30 ]. While several mHealth lifestyle intervention studies have been conducted to prevent NCDs [ 31 , 32 ] and CMDs [ 33 ] few have taken a holistic approach to improving health and mental wellbeing. The inextricable link between physical and psychological health [ 2 , 4 , 22 , 23 , 24 ] highlights the combined effects of holistic interventions integrating body and mind may have a greater total effect than any single intervention in preventing T2D and CMD [ 34 ]. The LvL UP is a scalable holistic mHealth intervention to prevent NCDs and CMDs in Asian populations [ 35 ]. The intervention has been informed by leading evidence- and theory-based frameworks in mental health [ 36 ] and behaviour change [ 36 ] to deliver motivational interviewing-inspired digital coaching via conversational agent and behavioural tools centred around three core pillars: Move More, Eat Well, Stress Less. The intervention has undergone rigorous development and refinement, including feasibility and user-centred design studies (unpublished data) to assess its technical feasibility and acceptability, but has not yet been tested for effectiveness.

Objectives {7}

This study aims to assess the effectiveness of a scalable smartphone-delivered holistic lifestyle coaching intervention for the prevention of T2D and CMDs with post-GDM Asian women from the community setting in Singapore. We have five specific objectives:

Objective 1: To carry out a 1-year randomised control trial (RCT) followed by a 3-year follow-up period with post-GDM women from the community setting in Singapore.

Objective 2: To examine the potential impacts of the proposed intervention on the health and mental well-being of women and their children.

Objective 3: To determine the diabetes risk of the participants over a 3-year follow-up period.

Objective 4: To explore the potential economic impacts of the proposed intervention (e.g. healthcare expenditures)

Objective 5: To study the importance of gut microbiota and epigenetic factors in changes in glucose metabolism.

Trial design {8}

This study is a 1-year randomised controlled trial (RCT) with 3 years of follow-up. We will conduct a two-arm single parallel assignment individually randomised equal allocation control trial on n = 400 women with a history of GDM. It will compare the aggregate effects of the Intervention group (receiving a holistic lifestyle mHealth intervention (LvLUP App), Sleep tracker, Oura ring/Oura App and psychoeducation to promote healthy lifestyle (Happy App)) and (comparison group (Happy App (psychoeducation only), Oura ring and Oura App) at 26/27 weeks, and 1-year visit. After completing the 1-year RCT period, both groups will be followed up in years 2, 3, and 4. We hypothesise that the intervention will reduce the risk of T2D/pre-diabetes and CMDs in post-GDM women.

Methods: participants, Interventions, and outcomes

Study settings {9}.

Participants are women with a history of GDM from a large community setting in Singapore.

Eligibility criteria {10}

All eligible females in this study will be (a) women between 21 and 45 years old; (b) with a history of GDM (at least 1 year and no more than 10 years); (c) Chinese, Malay, or Indian ethnicity; (d) BMI between 18.5 and 35 kg/m 2 ; (e) not planning to conceive in the next 1 year; (f) not performing exclusive breastfeeding during the study period; (g) owners of a smartphone compatible with the study mobile Apps; (h) proficient in the English language; (j)willing to comply with study protocol; and (k) able to provide written informed consent.

Exclusion criteria include (a) current or previous diagnosis of diabetes (type 1 or 2), except GDM; (b) currently pregnant; (c)given birth within the last 12 weeks; (d) severely limited mobility (e.g. wheelchair-bound, require long-term walking aid, etc.); (e) diagnosed with malnutrition or eating disorder; (f) diagnosed with cancers, unstable heart diseases, severe kidney disease, severe liver disease; (g) diagnosed with severe insomnia, unstable mental conditions, dementia, or cognitive impairment; (h) experienced alcohol or drug abuse; (i) currently having medications known to influence glucose metabolism (e.g. peroral corticosteroids); and (j) currently participating in a concurrent clinical trial or lifestyle intervention study.

Who will solicit informed consent? {26a}

Consent procedures were approved by the Domain Specific Review Board (DSRB) of the National Healthcare Group in Singapore. All potential subjects are required to sign the informed consent in order to participate in the study.

Additional consent provisions for collection and use of participant data and biological specimens in ancillary studies {26b}

There are no additional consent provisions for collecting or using biological samples. If we decide to do an ancillary study in the future, we will seek prior consent of participants to be re-contacted and re-consent for the ancillary study following DSRB approval.

Interventions

Explanation for choice of comparators {6b}.

mHealth may provide practical and scalable support to women adopting and maintaining a healthy lifestyle. The effectiveness of mHealth interventions targeting lifestyle behaviours has been demonstrated [ 31 , 34 , 37 ]. More recently, studies have supported that prevention and promotion request more than one pillar, meaning that holistic lifestyle behaviours, such as PA, diet, sleep, and mental well-being, are more effective in parallel [ 34 ]. Studies have shown that education alone is insufficient to support individuals in changing their lifestyles, and it is important to incorporate techniques and behavioural strategies that facilitate and measure the effectiveness of the interventions [ 31 , 34 , 37 ]. Therefore, we propose to compare two mHealth interventions, the intervention group (LvL UP App, Happy App, and Oura Ring) and the comparison group (HAPPY App and Oura Ring). The LvL UP App is based on evidence-based strategies and interactive tools developed in Singapore [ 35 ]. The HAPPY App is available to every eligible participant (both groups) in the trial; it provides public holistic health recommendations and displays participants’ health data measured during study visits over time. Also, the Oura ring is a sleep tracker that includes heart rate variability, and a step tracker will be provided to both groups.

Intervention description {11a}

The comparison and intervention group will be assigned to holistic lifestyle mHealth interventions. The comparison group (Happy App and Oura ring) will receive only psychoeducation to promote a healthy lifestyle through the HAPPY App and sleep tracking using an Oura ring and associated App. The Happy App was developed by the Singapore Institute for Clinical Sciences (SICS); it encompasses only educational content on lifestyles based on resources from the Singapore Health Promotion Board, including information about diet intake, physical activity, sleep, and mental well-being. The HAPPY app also displays health outcome data collected during the study visits (e.g. body weight, blood pressure, fasting glucose, and 2-h 75 g OGTT). The Oura Ring is an activity-tracking wearable that monitors physical activity, sleep, and heart rate variability, which can be synced with the Oura App.

The intervention group (LvL UP App, Happy App, and Oura Ring) will receive the same Happy App, Oura Ring and Oura App, with the additional LvL UP App version 2.0. The LvL UP App has been developed by the Future Health Technologies Programme at the Singapore ETH-Centre (SEC) for iOS and Android. It is designed to be culturally relevant in Singapore and is based on evidence-based behavioural strategies [ 35 ]. A separate paper[ 35 ] describes the conceptual model, development process, and characteristics of LvLUP version 1.0. In this study, we will use an updated 2.0. version. The LvL UP App 2.0 is a smartphone-delivered holistic lifestyle intervention focusing on three core pillars: eat well (diet), move more (PA), and stress less (mental well-being), including sleep content in each pillar delivered through a coaching health and tools paradigm.

LvL UP App: coaching health

The participants assigned to LvL UP will receive weekly motivational interview-inspired coaching sessions from a digital coach (conversational agent; CA) on topics related to the core intervention pillars. Coaching sessions are text-based dialogues between the participant and the CA based on predefined rules, and each session lasts between pillars, which are recommended in a predefined order, although participants can choose the order and topic if they wish. There are eight coaching sessions per pillar, which are recommended in a predefined order, although participants can choose the order and topic if they wish. The participant starts the intervention with a welcome dialogue, which introduces the LvL UP App and assesses the participant’s needs in each of the core pillars using the Patient Health Questionnaire-4 (PHQ-4) [ 38 ], a Modified Food Frequency Questionnaire (FFQ) based on dietary recommendations, and the International Physical Activity Questionnaire-Short Form (IPAQ-SF) [ 39 ]. Based on the participant’s needs, a starting pillar is recommended, but the participant can still choose the pillar they wish to focus on. Following the welcome dialogue, the participants complete two short sessions to identify their perceived values and strengths, which are later reflected to the participant by the CA during coaching sessions.

LvL UP App: tools

Additionally, the LvL UP App includes different tools that complement each pillar, including a set of self-regulatory behaviour change techniques, actionable habit-forming suggestions, and a slow-paced breathing training mini-game. The self-regulatory tools include a low-burden image tracking food diary ( MakanMemo ), a step tracker ( StepLah! ) that displays steps collected by Apple Health (iOS) or GoogleFit (Android) and allows users to set and track step goals, including brisk steps (120 steps/min or higher), and a Journal with different templates inspired by cognitive behavioural therapy to support mental well-being. Life Hacks are actionable tips for improving healthy habits and are designed to be easily executed during the daily routine. They operate on the premise that taking small, concrete actions can accumulate and lead to noticeable changes over time. Finally, Breeze is a slow-paced breathing training mini-game that uses breathing data collected by the smartphone microphone to move a sailboat along a river in real time [ 40 ].

The intended use of the LvL UP intervention is to complete weekly coaching sessions and use the tools daily. Gamification is built into the App whereby a puzzle piece is awarded for completed activities to fill up the LvL UP Shield. Once the shield is filled, the participant progresses to the next level and the shield resets. There are three levels in total. The LvL UP App delivers reminders and prompts (push notifications) related to intervention actions to encourage the participant to complete the intervention as intended.

Criteria for discontinuing or modifying allocated interventions {11b}

The participant will be withdrawn from the study if she becomes pregnant during the 1-year intervention period. If a participant becomes pregnant during the 3-year follow-up period, she will skip the follow-up visit for the year and return to the subsequent follow-up visit(s) post-pregnancy. In the same way, if a participant is diagnosed with T2D by her doctor during the 1-year intervention period, she will be invited to complete the endpoint study visit measures before being withdrawn from the study. If the participant is diagnosed with T2D by the study team during the follow-up visits, she will be removed after completing that visit.

Strategies to assess and improve adherence to interventions {11c}

User adherence in mHealth interventions varied based on user-related, content-related, and technology (personalise remainders, user-friendly, technically stable App, personal support, and gamification features) related barriers and facilitators of engagement [ 41 , 42 ]. The intervention group [LvL UP] will receive notifications and reminders about the coaching session’s date and time, daily random lifehack, and motivational messages to engage with the LvL UP App. Additionally, a dashboard link to monitor the participant’s live activity regarding last-time interaction, tools used, and the current level is considered to reach up to support adherence with the participants. Thus, the following three strategies will be implemented: calling participants who have not interacted for 2 weeks with the LvL UP App after baseline; texting the participants who have low interaction (e.g. using the App once/twice for 2 weeks), and sending automatic SMS 3, 5 and 7 weeks after the baseline visit to encourage them to use the App.

The HAPPY App will detect the number of logged entries and the last interaction with the app. A manual schedule reminder will be sent by the App if the participant has not read the suggested articles or documents.

Relevant concomitant care permitted or prohibited during the trial {11d}

The participants will be excluded if they have enrolled in a concurrent lifestyle intervention research study during the study. However, trial participants can receive concomitant treatment (e.g. psychotherapy, pharmacology), which will be assessed during the follow-up period.

Provisions for post-trial care {30}

Access to the Happy App will continue for the 3-year follow-up period after the trial for all the participants.

Outcomes {12}

Table 1 shows the outcomes and timeline of the study. The primary outcome will be the onset of T2D/confirmed by a 2-h 75 g oral glucose tolerance test (OGTT) over a 4-year study period. OGTT will be done at the screening visit, year 1 visit, and follow-up visits in years 2, 3, and 4. Secondary outcomes include (a) onset of impaired fasting glucose and impaired glucose tolerance; (b) changes in cardiometabolic variables (body weight, HbA 1c , insulin, blood lipids, blood pressure), and (c) changes in women’s body composition as assessed by bioelectrical impedance analysis (BIA). Secondary measures will be taken at baseline visits, middle point, year 1, and 2, 3, and 4-year follow-up period.

Additionally, we will assess changes in women’s mental well-being by completing the following questionnaires at all study visits:

Beck Depression Inventory (BDI-II): A 21-item self-reported rating inventory using a 3-point Likert scale that measures symptoms and severity of depression, classified as follows: minimal (0–13); mild (14–19); moderate (20–28); and severe (29–63). The BDI-II internal consistency as measured by Cronbach’s alpha ranges from 0.87 to 0.93 [ 43 ].

State-Trait Anxiety Inventory (STAI): A 40-item self-report questionnaire using a 4-point Likert scale. The STAI measures two types of anxiety — state anxiety and trait anxiety, which are classified as no or low anxiety (20–37), moderate anxiety (38–44), and high anxiety (45–80). Internal consistency coefficients for the scale have ranged from 0.86 to 0.95 [ 44 ].

Cognitive Emotion Regulation Questionnaire ( CERQ ): A 36-item self-report measure designed to identify the cognitive emotion regulation strategies (or cognitive coping strategies) someone uses after having experienced adverse events or situations. It is commonly used to assess individual differences in the cognitive regulation of emotions in response to stressful, threatening, or traumatic life events. Internal consistency ranges from 0.72 and 0.83 [ 45 ].

Subjective Happiness Scale (SHS): A 4-item self-report measure to assess an individual’s overall happiness. Internal consistency ranges from 0.79 to 0.94 [ 46 ].

WHO-5 Wellbeing Index ( WHO-5 ): A 5-item self-reported questionnaire that aims to assess mental wellbeing over the last 2 weeks. The WHO-5 shows reliability across countries between 0.86 and 0.96 [ 47 ].

Perceived Stress Scale ( PSS-4 ): A brief self-report measure of the extent to which recent life events are considered stressful. The PSS-4 has demonstrated acceptable criterion validity and internal consistency ( α = 0.72) [ 48 ].

The short Form Health Survey ( SF-36 ): Indicates overall health status; it aims to assess the impact of clinical and social interventions on quality of life in several domains. The SF-36 has shown a reliability of 0.90 [ 49 ].

Health Literacy Questionnaire ( HLQ ): The HLQ examines an individual’s understanding, access and engagement with health information and health services. The HLQ Cronbach’s alpha has shown an internal consistency of 0.87 [ 50 ].

Moreover, changes in women’s sleep will be assessed using the following measurements:

Morningness-Eveningness Questionnaire ( MEQ ): A 19-item MEQ aims to assess morningness and eveningness where questions are asked preferentially, i.e. asking respondents to indicate when, for example, they would prefer to wake up or start sleep, rather than when they do. MEQ internal consistency is 0.82 [ 51 ].

Pittsburgh Sleep Quality Index ( PSQI ): Measures the quality and patterns of sleep-in adults. It differentiates “poor” from “good” sleep quality by measuring seven areas (components): subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medications, and daytime dysfunction over the last month. The PSQI Cronbach’s alpha has shown good internal consistency ranging from 0.64 to 0.83 [ 52 ].

Furthermore, changes in the health and well-being of the women’s children will be collected. Only children born during a GDM pregnancy will be considered in this study. The following measurements will be used:

Cognitive Home Environment Questionnaire ( StimQ ): The StimQ-toddler (age 12–35 months)/StimQ-pre-school (for age 36 – 72 months) is designed to identify the different types of toys and games that children have in the home and the kinds of activities that mother and child do together [ 53 ]. The StimQ has demonstrated good internal and external reliability ( α = 0.85) [ 54 ].

Ages & Stages Questionnaires-3 ( ASQ-3 ) (1–66 months): The ASQ-3 development screening tool assesses children’s communication, autonomy, compliance, adaptive functioning, affect, and interaction with people. ASQ-3 is a reliable and valid instrument with a test–retest reliability of 92%, a sensitivity of 87.4% and a specificity of 95.7% [ 55 ].

Behaviour Rating Inventory of Executive Functions-2 (BRIEF-2) parent form : BRIEF-2 is designed for use in medical and educational settings to estimate the global executive function of children/youth and determine whether a comprehensive assessment is appropriate (age 5–18 years). BRIEF-2 has shown reliability coefficients ranging between 0.8 and 0.9 [ 56 ].

Behavioural and Emotional Screening System (BASC-3-BESS) Parent Form : This screens children and adolescents’ behavioural and emotional strengths and weaknesses (age 3–18 years 11 months). BASC-3 BESS has high internal consistency at 0.80 [ 57 ].

Changes in the child’s sleep will be assessed at the baseline visit and the first-year visit. The following is a description of child sleep questionnaires:

The Brief Infant Sleep Questionnaire-Revised Short Form ( BISQ-R-SF ): A parent-reported toddler (0–3 years) sleep over the prior 1 week, assessing sleep patterns, ecology, and parental perceptions of sleep. The BISQ-R indicates excellent reliability at 0.80 [ 58 ].

Children’s Sleep Habits Questionnaire (CSHQ) Short Form (SF)version : The SF-CSHQ assesses the frequency of children’s behaviours associated with common paediatric sleep difficulties for children between 48 and 72 months. The SF-CSHQ has been validated, showing Internal consistency coefficients ranging from 0.90 to 0.94 [ 59 ].

After completing the 1-year RCT period, both groups will be followed up for 3 years. During the follow-up visits, body measurements, OGTT, bio-sampling, and data collection will be conducted, and the questionnaires listed above.

Additionally, the intervention group will also complete the following questionnaires through the LvL UP App:

Patient Health Questionnaire-4 ( PHQ-4 ): A 4-item self-report measure that assesses anxiety and depression symptoms. Each item is scored on a 4-point scale (0–3), and scores range from 0 to 27. The score can be used to describe a patient’s symptoms in one of the five categories: none (0–2), mild (3–5), moderate (6–8), and severe (9–12), The PHQ-4 has been shown to have good internal reliability (Cronbach alpha = 0.82) [ 38 ].

Modified Food Frequency Questionnaire ( MFFQ ): A 7-item self-report checklist of food and beverages with a frequency response section based on My Healthy Plate Singapore[ 60 ].

The International Physical Activity Questionnaire-Short Form ( IPAQ-SF ): A self-report measure assesses the type of intensity of physical activity and sitting time[ 39 ]. The IPAQ-SF has shown a moderate internal consistency of 0.647 [ 61 , 62 ].

Screening survey overview and follow-up

The screening surveys will include questions of 8 types: (i) socio-demographics, (ii) GDM and family history of diabetes, (iii) medical history, (iv) lifestyle, (v) dietary, (vi) diabetes awareness, (vii) paternal demographics and health and (viii) child demographics and health (see Table 2 ). Those surveys will also be completed in baseline and 1-year visits (see Table 1 ).

Participants timeline {13}

The participants’ flow for the trial is outlined in Fig. 1 . Once participants are identified through one of the recruiting methods, they will be invited to the SICS clinic for screening, in which a consent form will be explained and signed, and OGTT test, height, weight, blood pressure measures, HbA1c (finger proc test) and sociodemographic survey will be completed. Those who are eligible will be randomised to one of the two arms (comparison or intervention group) and will subsequently be asked to take follow-up height, weight and blood pressure measurements, BIA, report the child’s height and weight, fasting blood draw, collection of the buccal swab, saliva, and stool sample, and complete questionnaires for the mother and their child at midpoint (26/27 weeks) visit, and year one visit after randomisation. After the midpoint assessments, additional interventions will be delivered via the HAPPY App to subjects in the intervention group who require further recommendations to improve their BMI or manage their body weight. During the 3 year-followed-up periods, participants will be asked to complete the following: (a) OGTT test; (b) height, weight and blood pressure measurements; (c) BIA; (d) fasting blood draw; (e) collection of the buccal swab, saliva and stool samples; (f) reported child’s height and weight; (g) complete mother and child questionnaires; (h) issue of Oura Ring (8 weeks); and (i) 14-day e-Diary recording in year 2, 3 and 4 visit. The schedule of enrolment, intervention, and assessments for the trial is outlined in Fig. 2 .

Participant flow consort

Schedule for enrolment, intervention, and assessments for the trial. OGTT, 2-h 75 g Oral Glucose Tolerance Test; BIA, Bioelectrical impedance analysis, SF-36, short-form health survey; STAI, The Stare-Trait Anxiety Inventory; BDI-II, Beck Depression Inventory; CERQ, Cognitive Emotion Regulation Questionnaire; MEQ, Morningness-Eveningness Questionnaire; PSQI, The Pittsburgh Sleep Quality Index; SHS, Subjective Happiness Scale; HLQ, Health Literacy Questionnaire; PSS-4, Perceived Stress Scale 4; WHO-5, World Health Organization Wellbeing Index; BISQ-R-SF, Brief Infant Sleep Questionnaire-Revised Short Form; CSHQ, Children’s Sleep Habits Questionnaire; StimQ toddler/preschool, Cognitive Home Environment Questionnaire; ASQ-3, Ages & Stages Questionnaires-3; BRIEF-2, Behaviour Rating Inventory of Execute Functions-2; BASC-3-BESS Parent Form, Behavioral and Emotional Screening System; PHQ-4, Patient Health Questionnaire; IPAQ-SF, The International Physical Activity Questionnaire- Short Form; MFFQ, Modified Food Frequency Questionnaire

Sample size {14}

Expected number of participants.

The sample size is estimated based on the cumulative diabetes risk identified from the GUSTO study [ 12 ]. We assume groups of equal size, with a significance level of 0.05% and a power of 80%. Additionally, we consider a median survival time of 6 years for the non-intervention group (based on GUSTO data), a follow-up period of 3 years, and an expected 50% reduction in the risk of developing T2D for the intervention group (hazard ratio = 0.5). These assumptions yield a cumulative event rate of 0.22 for the comparison group and 0.12 for the intervention group. Considering a 20% censoring rate, we calculate a sample size of 189 for the intervention group and 188 for the comparison group, aiming for a total of 65 events over the study period[ 63 ].

Recruitment {15}

The study protocol was approved by the National Healthcare Group in Singapore, Domain Specific Review Board. There are four avenues of recruitment. The first is contacting participants from a previous pilot study conducted by the same research team in this protocol [ 64 ]. The second avenue is recruiting participants using snowball sampling (e.g. friends, family), the third is via electronic flyer invitation through corporate e-mails (National University Healthcare System (NUHS) and Agency for Science, Technology and Research (A*STAR)), and the fourth is advertising on social media (e.g. LinkedIn, Instagram, Facebook advertisements). Interested individuals will contact the study team via phone or e-mail. The study team will explain the study and answer any questions raised . Potential participants will be invited to attend a screening visit for informed consent and screening procedures. Those who are screened passed will be scheduled to attend baseline and midpoint visits (26/27 weeks), year 1, and follow-up visit periods in years 2, 3, and 4. All the participants will be able to provide written informed consent.

Assignment intervention: allocation

Sequence generation {16a}.

A total of 400 women will be stratified and randomised based on prognosis factors to either group 1 (intervention) or group 2 (control) based on 1:1 allocation using R statistical software.

Concealment mechanism {16b}

The allocation is concealed in that the allocation sequence is automatically generated in R software and simultaneous with the assignment.

Implementation {16c}

The allocation sequence will automatically be generated in the Block Rand R software at the end of the baseline, and a screen will show participants to which group she has been assigned.

Assignment of interventions: blinding

Who will be blinded {17a}.

Given the nature of the study design, blinding of participants and research personnel will not be possible. The study team will instruct the participants in the intervention and comparison groups to download the Apps, and each participant will be provided with a unique identification code.

Procedure for unblinding if needed {17b}

In the case of high depression scores on the BDI, including item 9 (suicidal ideation) and anxiety on the STAI, the participant will be flagged and reported to research coordinators, who will contact the participant to provide additional referral and helpline resources in Singapore. Similarly, scores that reveal a potential developmental delay in the child measured by the ASQ-3 will be notified to the mother, and a referral will be made for further evaluation.

Data collection and management

Plans for assessment and collection of outcomes{18a}.

The baseline, midpoint visit (26/27 weeks), 1-year visit, and 3-year follow-up assessments will be administered on-site through the Recap platform assisted by research coordinators. The REDCap will be used for all the measurements and will follow complex skip logic developed for this study, based on participants’ responses to reduce respondent burden and increase the completeness and accuracy of responses.

The intervention group randomised to the LvL UP App will collect objective and self-reported scales from the mHealth App throughout the intervention.

Plans to promote participant retention and complete follow-up {18b}

Participants will be reimbursed for their time, inconvenience and transportation costs as follows: (a) screening visit — S$20; (b) completion of baseline study visit — S$80; (c) completion of midpoint study visit — S$40; (d) completion of year 1 study visit — S$80; (e) completion of level 1 to level 3 digital coaching sessions via LvL UP App (applicable to participants in intervention group) will be a total of S$120 and S$40 per level of completion; (f) completions for all three study visits, participants will be given an additional S$80 as bonus; and (g) completion of year 2, 3 and 4 yearly visit the participants will receive S$80 per visit. The research team will call the participants to remind them to attend the visits.

Additionally, the intervention group will receive reminders from the LvL UP App regarding the day and time of the coaching session and daily random lifehack from any of the pillars.

Data management {19}

The Singapore Institute for Clinical Sciences (SICS) team will maintain a master dataset for all participants who were referred to the project for recruitment, along with a record of whether they withdrew before or after completing the screening and baseline measurements or after initiating the intervention, were considered ineligible for participation, or were terminated from the study or conducted the study. Protection of participant privacy concerning biological samples and questionnaire data will be achieved by assigning each participant a study identification number and then creating a separate de-identified file. Anonymised data from the participants will be shared between SEC and SICS through a secure AWS server. All data analyses will be carried out with the de-identified data file.

Confidentiality {27}

Identifiable individual participant information obtained from this study is confidential, and disclosure to non-relevant third parties is prohibited. Participant confidentiality will be further ensured by assigning each participant a unique participant identification code for anonymised data entry and sample identification. All computer entry and networking programs will be done with coded numbers only.

Statistical methods

Statistical methods for primary and secondary outcomes {20a}, analysis of aggregate intervention effects.

The primary continuous outcome for the onset of diabetes will be analyzed with linear mixed models (LMM) with outcome measurement (at the three follow-up time points) as the dependent variable and based on the intention-to-treatment (ITT) principle.

Multiple linear regressions will determine the relationship between outcome measures and predictors, adjusting for potential confounders. Multiple linear regression analyses will estimate the effects between groups at the end point follow-up. Generalised estimating equations and mixed effect models will be used for longitudinal data analysis. A p -value of < 0.05 will be considered statistically significant. Correction for multiple tests will be performed. All statistical analyses will be conducted using the R program or STATA for Windows (Stata Corporation). The treatment effect within and between the two groups will be assessed using the Cohen d statistics [ 65 ].

Interim analyses {21b}

Interim analyses will be conducted to examine initial trial efficacy and safety outcomes. Frequencies (percentages), means (standard deviations), or medians (interquartile ranges) will be used to summarise the data collected from questionnaires and surveys. Statistical tests will be performed, e.g. Chi-square or Fisher’s exact test for categorical variables, and two independent sample t -tests or ANOVA for continuous variables. Nonparametric tests will be used as deemed fit. For binary outcomes, the strength of associations between independent and dependent variables will be assessed by odds ratio estimated in logistic regression models. Crude associations will be evaluated using univariate models. Subsequently, associations will be evaluated using multivariable models adjusted for potential confounders.

Methods for additional analyses (e.g. subgroup analyses) {20b}

We will carry out additional analyses that evaluate the effect of the intervention on the children born to the women during the GDM pregnancy with a focus on cognitive and socio-emotional development (ASQ-3, BASC-3-BESS, BRIEF, StimQ) and sleep (BISQ-R-SF, CSHQ) using linear mixed models (LMM) with outcome measurements (baseline and the three follow-up time points) based on intention-to-treat (ITT).

Descriptive statistics will be used to establish the intervention response rate based on the number of coaching sessions completed, logins into the App, and estimated time spent.

Methods in analysis to handle protocol non-adherence and any statistical methods to handle missing data {20c}

Intention-to-treat analysis will be used to examine trial efficacy. Total scores will be modelled using fixed effects of time, intervention group, interaction between time and intervention group, and a random effect of individual.

Plans to give access to the full protocol, participant-level data and statistical code {31c}

The anonymized datasets analysed during the current study and statistical code are available from the corresponding author on reasonable request, as is the full protocol.

Oversight and monitoring

Composition of the coordinating centre and trial steering committee {5d}.

The study team will have overall responsibility for monitoring the integrity of the study and participant safety. The study team will monitor consent procedures, and safety plans prior to study initiation, amendments thereafter, and monitoring study progress, in terms of recruitment and retention of participants, adverse events, and protocol deviations.

Composition of the data monitoring committee, its role and reporting structure {21a}

The committee comprises research coordinators (RCs), a data manager, co-investigators, and the principal investigator (JGE). RCs are responsible for electronic data capture following the standard operating procedure, and the data manager monitors data collection. The data manager will raise queries with RCs during data monitoring. The data manager prepares data exports for monthly review and reports to the principal investigator and co-investigators.

Adverse event reporting and harms {22}

The Principal Investigator (PI) (JGE) will manage the data and safety monitoring after participant enrolment. In the event of any adverse events or safety issues, appropriate reporting will be made to DSRB accordingly. Regarding the questionnaires, we have the following monitoring plan: (a)BDI-II: participants who exhibit suicidality ideation (item 9) or have scored 29 or above (severe depression) will be offered a clinical referral. If the participant declines the referral, she will be provided with local helpline contact numbers (Samaritans of Singapore and Institute of Mental Health); (b) STAI: for participants who exhibit high anxiety with a score of 45 or above in the STAI, the study team will provide a local helpline contact number (Singapore Association for Mental Health). Additionally, the PHQ-4 will be completed by the LvL UP App participants (intervention group) with a score of 9 or higher (severe anxiety depression); the LvL UP App will provide information regarding mental health services in Singapore, including local helplines. The study team will follow up with the participant if she wishes to get a clinical referral. Finally, based on the ASQ-3, if a child shows scores suggesting developmental delay, we will discuss with the participant if she has any concerns and would like a referral for clinical follow-up.

Biological samples will be collected through blood tests, which will be analyzed by an accredited lab providing the normal reference range of the lab results. The PI (JGE) will review blood test results. For all abnormal blood test results and incidental findings discovered during the study period, the study PI will determine if the findings are clinically significant and actionable. The study PI will sign a referral letter to seek primary healthcare. Subsequently, a delegated study team member will contact the participant to relay the internal findings on behalf of the study PI. Participants who agree to be re-identified and notified of the internal findings will be contacted at their primary contact number provided in the consent form. The referral letter will be emailed to her. If the participant is uncontactable on her direct contact number, her next of kin listed on the consent form will be contacted. If the participant and next of kin are not contactable after 1 month, the referral letter will be sent directly to the participant’s home address. In the case incidental findings meet the study exclusion criteria, the affected participant will be informed and withdrawn from the study.

For the rest of the study measures/questionnaires, there is no clinical validation available. Hence no applicable management plan will be carried out.

Frequency and plans for monitoring trial conduct {23}

Study activities, including those related to consent, randomisation, data collection, and intervention, will be monitored on an ongoing basis . The data management team will monitor the data on the Amazon Web Services (AWS) server and REDCap platform. The study team will upload outcome data by the participants at the AWS server monthly.

Plans for communicating necessary protocol amendments to relevant parties (e.g. trial participants, ethical committees) {25}

Changes to the study protocol will be communicated to the DSRB when required. Deviations from the protocol will be fully documented using a breach report form and it will be updated to the protocol in the clinical trial registry.

Dissemination plans {31a}

Study results will be disseminated through webinars, written documents, conferences, and research meetings at SICS. We will also prepare scientific reports of the study results for publication in peer-reviewed journals.

This study aims to identify post-GDM women from community settings in Singapore and assess the effectiveness of a holistic lifestyle mHealth intervention for the prevention of T2D and CMDs. It is essential to develop preventative interventions to encourage healthy lifestyle habits, given the high incidence rates of T2D and CMD among women following GDM. This study may provide further support for the effectiveness of preventative interventions that consider the close relationship between physical and psychological health. Holistic lifestyle interventions that promote healthy habits such as maintaining a balanced healthier diet, engaging in regular physical activity, getting adequate sleep, and prioritising mental well-being can work together to produce more effective results [ 34 , 66 , 67 ]. mHealth also offers practical, affordable, and scalable intervention support for individuals adopting and maintaining healthy lifestyles. The evidence has shown that holistic mHealth targeting lifestyle behaviours are available and have shown to be effective [ 31 , 34 , 37 ]. However, few studies have been tested using multicompetent lifestyle using mHealth [ 34 ]. This RCT study represents a pioneer attempt to investigate the effectiveness of a holistic lifestyle mHealth intervention focused on the prevention of T2D and CMDs in women with a history of GDM.

mHealth has the potential to transform preventive healthcare, and particularly support post-GDM women who encounter challenges due to fatigue, child-caring responsibilities, and motivation to change their lifestyles. Additionally, we expect that the holistic mHealth proposed will permit to build capacities to disseminate interventions and could have enormous public health implications for the region and serve as a model for implementing preventative interventions in Asia and globally.

DSRB Approval of Protocol Version 1.0; 1/6/2023. Recruitment began 20/10/2023. Recruitment is tentatively scheduled to be completed on 30/10/2024.

Availability of data and materials {29}

Final de-identified data and trial materials will be publicly available after the trial is concluded.

Abbreviations

Agency for Science, Technology and Research

Amazon Web Services

Behavioral and Emotional Screening System

Behavioural change techniques

Brief Infant Sleep Questionnaire-Revised Short Form

Beck Depression Inventory

Behaviour Rating Inventory of Execute Functions-2

Cognitive behavioural therapy

Cognitive Emotion Regulation Questionnaire

Common mental disorders

Children’s Sleep Habits Questionnaire

Domain-Specific Review Board

Diabetes Prevention Program

Health Literacy Questionnaire

Gestational diabetes mellitus

Morningness-Eveningness Questionnaire

National University Health System

National University of Singapore

Physical activity

Patient health questionnaire, 4-item

Principal investigator

The Pittsburgh Sleep Quality Index

Perceived Stress Scale 4

Positive psychology

Randomised control trial

Singapore ETH-Centre

Subjective Happiness Scale

Singapore Institute for Clinical Sciences

Short-form health curve

The State-Trait Anxiety Inventory

Cognitive Home Environment Questionnaire toddler/preschool

Type 2 diabetes

World Health Organization-5 Wellbeing Index

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Acknowledgements

We would like to express our gratitude and acknowledge the research coordinators, biobank team, and data management team at the Singapore Institute for Clinical Sciences (SICS) and National University of Singapore (NUS) who support this study, as well as the Singapore ETH-Centre (SEC) members who support the LvL UP App intervention for this study.

This study is supported by Human Potential- FY21 Prenatal/Early Childhood grant call 2023 (Grant number: H22P0M0004) from the Agency for Science, Technology and Research (A*STAR) Singapore, and Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.

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SL and JGE contribute to the study’s conception, design, and development of the study protocol. JM support the intervention design and study protocol. AS-S wrote the initial draft with contributions and revisions from all the authors who read and approved the final manuscript.

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Salamanca-Sanabria, A., Liew, S.J., Mair, J. et al. A holistic lifestyle mobile health intervention for the prevention of type 2 diabetes and common mental disorders in Asian women with a history of gestational diabetes: a randomised control trial with 3-year follow-up protocol. Trials 25 , 443 (2024). https://doi.org/10.1186/s13063-024-08247-x

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Ann Fam Med
v.13(Suppl 1); 2015 Aug

Effects of Providing Peer Support on Diabetes Management in People With Type 2 Diabetes

1 Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong

Rebecca Wong

2 Diabetes and Endocrine Centre, Prince of Wales Hospital, Shatin, Hong Kong

3 Ruttonjee Hospital, Hong Kong

Harriet Chung

4 Asia Diabetes Foundation, Prince of Wales Hospital, Shatin, Hong Kong

5 Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, Hong Kong

6 Alice Ho Mui Ling Nethersole Hospital, Tai Po, Hong Kong

7 North District Hospital, Fanling, Hong Kong

Chiuchi Tsang

Roseanne yeung.

8 Division of Endocrinology, University of British Columbia, Canada

Juliana C. N. Chan

We examined the effects of participating in a “train-the-trainer” program and being a peer supporter on metabolic and cognitive/psychological/behavioral parameters in Chinese patients with type 2 diabetes.

In response to our invitation, 79 patients with fair glycemic control (HbA 1c <8%) agreed to participate in a “train-the-trainer” program to become peer supporters. Of the 59 who completed the program successfully, 33 agreed to be peer supporters (“agreed trainees”) and were each assigned to support 10 patients for 1 year, with a voluntary extension period of 3 additional years, while 26 trainees declined to be supporters (“refused trainees”). A group of 60 patients with fair glycemic control who did not attend the training program and were under usual care were selected as a comparison group. The primary outcome was the change in average HbA 1c levels for the 3 groups from baseline to 6 months.

At 6 months, HbA 1c was unchanged in the trainees (at baseline, 7.1 ± 0.3%; at 6 months, 7.1 ± 1.1%) but increased in the comparison group (at baseline, 7.1 ± 0.5%; at 6 months, 7.3 ± 1.1%. P = .02 for between-group comparison). Self-reported self-care activities including diet adherence and foot care improved in the trainees but not the comparison group. After 4 years, HbA 1c remained stable among the agreed trainees (at baseline, 7.0 ± 0.2%; at 4 years: 7.2 ± 0.6%), compared with increases in the refused trainees (at baseline, 7.1 ± 0.4%; at 4 years, 7.8 ± 0.8%) and comparison group (at baseline, 7.1 ± 0.5%; at 4 years, 8.1 ± 0.6%. P = .001 for between-group comparison).

CONCLUSIONS

Patients with diabetes who engaged in providing ongoing peer support to other patients with diabetes improved their self-care while maintaining glycemic control over 4 years.

INTRODUCTION

Diabetes self-management is often emotionally and physically taxing, demanding lifelong commitment to medication adherence and lifestyle modification. 1 Health care professionals such as diabetes nurses can effectively deliver diabetes self-management education (DSME), especially for the initial acquisition of knowledge and skills. 2 , 3 In the United States, the Medicare system provides for 10 hours of initial diabetes education in the first year for patients who have diabetes, with 2 hours of follow-up education for each subsequent year. 4 In a meta-analysis of 31 randomized controlled trials, DSME programs decreased HbA 1c by 0.76% more than in the comparison groups at immediate follow-up, and by 0.26% at 1 to 3 months, with a 1% reduction in HbA 1c associated with every additional 23.6 hours of contact. 2 Given the importance of contact time to maintain learned behaviors, peer support has been recommended as a means to improve long-term self-management. 5

Peer support refers to the transfer of experiential knowledge of a specific behavior or coping strategy for a stressor between people who share a particular characteristic. 6 , 7 Thus, people with a common illness can share knowledge and experience in a less hierarchical and more reciprocal relationship than that between patients and health care professionals. 8 , 9 Recent studies support the use of expert patients as peer supporters for patients with chronic diseases. 7 , 10 , 11 To date, most studies have focused on the effects of peer support on the recipients, 12 – 14 while the effects of being a peer supporter have been systematically examined in only a few studies. These studies, which have involved conditions other than diabetes, have reported improvements in health behaviors and self-efficacy, 15 , 16 depression, 16 and even mortality risk among peer supporters. 17 , 18

We previously reported a randomized trial conducted to evaluate the effect of receiving peer support in patients with type 2 diabetes. 19 In an integrated care setting that incorporated specialized diabetes clinics in Hong Kong, receiving peer support did not further improve cardiometabolic well-being within 1 year, but a subgroup of patients with negative emotions benefited from peer support to the extent of having improved psychological health and reduced hospitalization. In this part of the same study, we prospectively evaluated the effects of providing peer support on metabolic, cognitive, and psychological parameters in peer supporters themselves.

Participant Recruitment and Selection

Between February 1 and May 31 of 2009, participants from 3 hospitals (Ruttonjee Hospital, Alice Ho Miu Ling Nethersole Hospital, and Prince of Wales Hospital, all in Hong Kong) were identified and recruited by their nurses during routine medical visits. Patients with type 2 diabetes aged 18 to 75 years with fair glycemic control (HbA 1c <8%), good understanding of living with diabetes, clear communication skills, and a desire to serve were invited to attend a “train-the-trainer” program as potential peer supporters. Exclusion criteria included illiteracy, physical impairment, and mental illness impairing communication with others. Those who completed the training program and passed assessments were invited to be peer supporters. Those who agreed (“agreed trainees”) were compared with those who declined (“refused trainees”). A group of patients from the same sites under usual care who had similar glycemic control but did not attend the training program were selected as comparison group subjects. All patients gave written informed consent for research and publication purposes. The study was approved by the Chinese University of Hong Kong New Territories East Cluster Clinical Research Ethics Committee.

The “Train-the-Trainer” Program

The train-the-trainer program was designed to empower trainees to provide basic knowledge and emotional support to their peers with type 2 diabetes. The program consisted of 4 monthly workshops, each lasting 8 hours, for a total of 32 hours. Health care experts led the workshops, which included both didactic components and interactive components such as role playing and group sharing. The main components of the syllabus were these:

Effective communication, focusing on positive thinking, empathetic listening, and appropriate questioning, taught by a neurolinguistic programming expert
Diabetes diet review, with cooking tips, education on common misconceptions of the diabetic diet, and suggestions for weight management, taught by an accredited dietician
Physical activity training, including precautions to take during exercise, stretching exercises, and sustaining motivation for daily physical activity, delivered by a nurse qualified in fitness training
Behavioral psychology, with emphasis on positive thinking, goal setting, decision making, and coping with negative emotions, delivered by a qualified psychologist At the end of the training program, all trainees underwent formal evaluation using case scenarios and questionnaires to assess their competency as potential peer supporters.

Peer Support Delivery

Each agreed trainee was assigned 10 patients of the same gender to support. Agreed trainees were introduced to their patient groups in several meetings where the rationale, purpose, and expectations for this study were explained. The meeting was hosted by 1 attending doctor, 1 nurse, and the project coordinator. The peer supporters were asked to provide structured peer support for at least 1 year, with provision for a voluntary extension of 3 more years.

We have described elsewhere how peer support was delivered during the 1-year structured program. 19 Briefly, the peer supporters were asked to give each of their assigned patients a 15- to 20-minute telephone call biweekly for the first 3 months, monthly for the second 3 months, and every 2 months for the last 6 months. Peer supporters were given a checklist to use in reviewing specific self-management skills, including medication adherence, healthy diet, regular exercise, sick day management, foot care, and glucose monitoring. They were also encouraged to provide psychological support based on their own experiences. Peer supporters submitted their phone call checklists every 3 months for documentation of their discussion items, duration of each call, and relevant remarks. Additional electronic communication and group gatherings were left to the discretion of the participants. During the voluntary extension period, the peer supporters were asked to maintain contact with their assigned patients every 1 to 2 months for another 3 years. They were also required to document the calls and return the checklists to the project coordinator every year.

Providing Ongoing Support to Peer Supporters

In the 1-year structured program, peer supporters were reviewed by the doctor-nurse team and a project coordinator in 3 half-day debriefing meetings to share experiences, troubleshoot, and provide mutual support. Peer supporters were given opportunities to express their feelings and frustrations with their patient groups and to develop follow-up actions. Peer supporters anxious about their performance were reminded that, as nonprofessionals, they should not expect themselves to perform at the professional level, and they were reminded to encourage their patients to seek medical advice for uncertain issues. They were also asked to share sensitive information only with the medical team.

In the voluntary 3-year extension period, peer supporters met every 6 months with the project team, including at least 1 nurse, in a less formal group setting, such as hiking or having lunch together. They were encouraged to build a community among themselves and contact each other to share experiences and provide mutual support. They were also reminded to seek help from the project coordinator or the nurses if they encountered any problems.

Outcome Measurements

The primary outcome was change in HbA 1c at 6 months. Secondary outcomes included changes in blood pressure, lipid profile, and cognitive/psychological/behavioral measures. Changes in the latter were assessed using validated instruments in Chinese, including the Depression Anxiety and Stress Scale (DASS) for emotional health, 20 the EuroQol-5D (EQ-5D) for health-related quality of life, 21 the Diabetes Empowerment Scale (DES) for self-efficacy, 22 the Patient Health Questionnaire (PHQ) for depression, 23 the General Health Questionnaire (GHQ) for psychological health in general population, 24 and the Summary of Diabetes Self Care Activities (SDSCA) for self-care activities. 25 At the end of the 3-year extension period, metabolic parameters were retrieved from the Hong Kong Hospital Authority Clinical Management System, which is shared by all public hospitals.

Clinical Care

All 3 groups were managed in the usual care setting of their hospital or community-based clinic. Hong Kong has a heavily subsidized health care system, and all patients have access to medications, investigations, and consultations for a nominal fee (US $10 per clinic visit, US $1.50 per drug for a 3–4–month supply). All individuals had access to professional diabetes education at their hospital and were usually followed up every 3–4 months at their clinics.

Sample Size Estimation and Statistical Analysis

As we said earlier, this training program was part of a trial to evaluate the effect of receiving peer support on glycemic control in patients with type 2 diabetes. 19 Based on power calculations for the main study, we needed 30 peer supporters for a 1:10 ratio of peer supporters to peers. Expecting that one-half of the qualified trainees might agree to become peer supporters, we needed to train 60 subjects. Assuming a 25% attrition rate, we enrolled 79 patients in the training program.

All data were expressed as mean ± SD, median (interquartile range [IQR]), or percentage, as appropriate. Paired t -tests for continuous variables and McNemar tests for categorical variables were used for within-group comparisons. For between-group comparisons at baseline, independent t -tests and chi-square tests were used, while analysis of covariance was used for comparing the change from baseline to the 6th month between groups. We adjusted for gender when comparing the trainees and comparison group. For comparison of metabolic control after 4 years, we compared the entire group of agreed trainees, refused trainees and comparison group members using one-way ANOVA. All statistical analysis was performed using SPSS Statistics, version 20.0 (IBM).

From February through May 2009, 79 eligible patients with fair glycemic control, age 55.6 ± 11.5 years, with disease duration of 11.0 ± 6.7 years, 35% male, consented to attend the train-the-trainer program, and another group of 60 patients with similar HbA 1c levels who did not obtain the training were selected as the comparison group. Of the 59 trainees who completed the training program and passed their assessments, 33 agreed to be peer supporters and 26 refused. Two-thirds (21/33) of the peer supporters continued the 3-year voluntary extension and 17 completed the entire extension period ( Figure 1 ).

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Recruitment and assessments of participants.

Comparison Between the Trainees and Comparison Group

At baseline, while the comparison group was more heavily male than the trainee group (65% vs 35%), the 2 groups did not differ in age, disease duration, education, or risk factor control. The trainees had higher SDSCA scores in the domains of glucose monitoring and medication adherence than the comparison group ( Table 1 ).

Clinical, Psychological, and Behavioral Characteristics at Baseline and After 6 Months for Patients With Type 2 Diabetes Who Joined the Training Program (Trainee Group) and Those Under Usual Care (Comparison Group)

	Trainee Group n = 79		Comparison Group n = 60		Between Groups Value

	Baseline	Month 6	Baseline	Month 6

Age, y, mean ± SD	55.6 ± 11.5		56.5 ± 10.9
Male (%)	35		65
High school or above (%)	12.5		20.6
Disease duration, y, mean ± SD	11.0 ± 6.7		8.3 ± 6.6
Non-smoker (%)	93.6		87.9

HbA , mean ± SD	7.1 ± 0.3	7.1 ± 1.1	7.1 ± 0.5	7.3 ± 1.1
Fasting plasma glucose, mg/dL, mean ± SD	137 ± 43	137 ± 20	142 ± 42	144 ± 43	.56
Systolic blood pressure, mmHg, mean ± SD	127 ± 13.6	123 ± 14.8	129 ± 29.5	122 ± 54.0	.28
Diastolic blood pressure, mmHg, mean ± SD	71 ± 7.0	72 ± 12.5	77 ± 17.0	79 ± 11.2	.15
Total cholesterol, mg/dL, mean ± SD	180 ± 30	170 ± 39	170 ± 25	170 ± 20
Triglyceride, mg/dL, mean (IQR)	124 (70–168)	124 (53–160)	124 (62–186)	132 (88–204)	.39
HDL-cholesterol, mg/dL, mean (IQR)	58 (31–77)	58 (35–81)	46 (31–77	46 (31–77)	.20
LDL-cholesterol, mg/dL, mean ± SD	100 ± 27	93 ± 31	97 ± 23	93 ± 23	.15

DES total score, mean ± SD	37.9 ± 6.71	37.0 ± 7.38	41.0 ± 8.02	40.4 ± 9.77	.42
GHQ total score, mean ± SD	44.4 ± 3.93	43.3 ± 3.67	45.5 ± 4.63	45.1 ± 4.06	.09
DASS total score, mean (IQR)	4 (2–9)	3 (0–7)	6 (2–9)	5 (1–8)	.68
PHQ total score, mean (IQR)	2 (1–3)	1 (0–2)c	2 (0–3)	1 (0–3)	.21
EQ-5D index, mean (IQR)	1 (0.80–1)	1 (0.82–1)	1 (0.80–1)	1 (0.81–1)	.34
EQ-5D VAS, mean ± SD	81.6 ± 11.5	81.8 ± 10.7	81.6 ± 12.4	79.0 ± 11.5	.25

General diet, mean ± SD	4.73 ± 1.63	5.57 ± 1.32c	4.6 ± 1.8	4.62 ± 1.88
Special diet, mean ± SD	4.55 ± 1.27	5.32 ± 1.3	4.42 ± 1.7	4.46 ± 1.5
Exercise, mean ± SD	4.55 ± 2.15	4.63 ± 2.07	3.83 ± 2.32	3.65 ± 2.07	.07
Glucose monitoring, mean ± SD	2.58 ± 1.81	2.89 ± 1.77	2.33 ± 2.08	2.52 ± 2.21	.47
Foot care, mean ± SD	4.97 ± 1.78	5.66 ± 1.44	4.31 ± 2.18	4.27 ± 1.95
Medication adherence, mean ± SD	6.81 ± 1.06	6.59 ± 1.41	5.95 ± 1.84	6.3 ± 1.6	.75

Note: These are the score ranges for assessment tools:

DES: 20-item Diabetes Empowerment Scale, range 20–100; higher score means better self-efficacy.

GHQ: 12-item General Health Questionnaire, range 0–36; higher score means poorer psychological health.

DASS: 21-item Depression Anxiety Stress Scale, range 0–63; higher score means more depression, anxiety and stress.

PHQ: 9-item Patient Health Questionnaire, range 0–27; higher score means more depression.

EQ-5D index score: 5-item Euroqol; UK traffic was used; range -0.594 to 1; higher score means better health-related quality of life.

EQ-5D VAS: Visual Analogue Scale of EQ-5D, range 0–100; higher score means better self-rated health status.

SDSCA: (14-item Summary of Diabetes Self Care Assessment, range: 0–98; higher score means better self-care.

After 6 months, HbA 1c had increased in the comparison group from 7.1 ± 0.3% to 7.3 ± 1.1% ( P = .19) while remaining unchanged in the trainee group (7.1 ± 0.3% to 7.1 ± 1.1%, P = .81; between-group P = .02). The trainee group also had reductions in total cholesterol (180 ± 30 mg/dL to 170 ± 39 mg/Dl [4.7 ± 0.9 mmol/L to 4.3 ± 1.0 mmol/L], P = .01), low density-lipoprotein cholesterol (LDL-C) (100 ± 27 mg/dL to 93 ± 31 mg/dL, P = .03), and improvements in self-reported self-care activities, which were not seen in the comparison group.

Comparison Between the Agreed Group and Refused Group

Of the 59 qualified trainees, 26 refused to be peer supporters due to lack of time (50%), lack of interest in the program (19%), feeling unprepared (15%), and other reasons (16%). Among the 33 agreed trainees, 9 (28%) were housewives, 15 (47%) were retirees, and 8 (25%) were non-manual workers. The two groups had similar clinical profiles at baseline except that the agreed trainees had a better self-rated health status based on GHQ and EQ-5D VAS ( Table 2 ).

Clinical, Psychological, and Behavioral Characteristics at Baseline and 6 Months of Patients With Type 2 Diabetes Who Agreed to Become Peer Supporters (Agreed Trainees) And Patients Who Refused (Refused Trainees) After Attending The Training Program

	Agreed Trainees (n = 33)		Refused Trainees (n = 26)		Between Groups Value

	Baseline	Month 6	Baseline	Month 6

Age, y, mean ± SD	55.6 ± 11.5		53.8 ± 14.8
Male (%)	35		20
High school or above (%)	20.6		12.5
Disease duration, y, mean ± SD	11.3 ± 6.7		12.6 ± 6.4
Non-smoker (%)	93.8		92.9
				125.1
HbA , mean ± SD	7.0 ±2	7.0 ± 0.6	7.1 ± 0.4	7.1 ± 0.5	.38
Fasting plasma glucose, mg/dL	135 ± 41	117 ± 31	140 ± 61	137 ± 43	.04
Systolic blood pressure, mmHg, mean ± SD	125 ± 12.3	123 ± 10. 7	127 ± 14.4	125 ± 19.9	.17
Diastolic blood pressure, mmHg, mean ± SD	72 ± 9.5	73 ± 9.9	69 ± 6.2	72 ± 12.4	.27
Total cholesterol, mg/dL, mean ± SD	174 ± 35	155 ± 50	182 ± 35	170 ± 39	.81
Triglyceride, mg/dL, mean (IQR)	124 (97–160)	142 (97–186)	124 (53–160)	124 (53–142)	.16
HDL-cholesterol, mg/dL, mean (IQR)	50 (42–58)	46 (38–54)	58 (46–66)	58 (6–77)	.41
LDL-cholesterol, mg/dL, mean ± SD	97 ± 31	89 ± 35	104 ± 22	100 ± 23	.69

DES mean score, mean ± SD	4.18 ± 0.35	4.13 ± 0.27	4.02 ± 0.35	3.97 ± 0.15	.20
GHQ total score, mean ± SD	43.5 ± 3.8	43 ± 3.82	46.2 ± 4.2	44.3 ± 2.76	.45
DASS total score, mean (IQR)	4 (2–9)	3 (0–8)	6 (3–14)	3.5 (1.8–11.8)	.67
PHQ total score, mean (IQR)	2 (1–3)	0 (0–2)	2 (0–4)	2 (1–3)	.33
EQ-5D index, mean (IQR)	1 (0.80–1)	1 (0.82–1)	1 (0.80–1)	0.80 (0.15–1)	.009
EQ-5D VAS, mean ± SD	84.6 ± 7.7	88.1 ± 8.9	74.5 ± 14.2	76.1 ± 15.2	.95

General diet, mean ± SD	4.59 ± 1.62	5.76 ± 0.95	4.11 ± 2.01	5.3 ± 2.11	.52
Special diet, mean ± SD	4.62 ± 1.28	5.78 ± 1.30	4.39 ± 1.39	5.17 ± 1.28	.39
Exercise, mean ± SD	4.48 ± 1.95	4.76 ± 2.06	3.68 ± 2.28	4.4 ± 2.12	.56
Glucose monitoring, mean ± SD	2.82 ± 1.94	3.17 ± 1.89	3.23 ± 2.47	2.5 ± 1.78	.63
Foot care, mean ± SD	4.85 ± 1.82	5.79 ± 1.45	4.33 ± 1.87	5.4 ± 1.43	.09
Medication adherence, mean ± SD	6.79 ± 1.11	6.54 ± 1.5	6.45 ± 1.48	7 ± 0.85	.31

After 6 months, fasting plasma glucose had decreased from 135 ± 41 mg/dL to 117 ± 31 mg/dL, ( P = .033) in the agreed trainees while not changing in the refused trainees (140 ± 61 mg/dL to 137 ± 43 mg/dL, P = .61; between-group P = .04). Health-related quality of life decreased in the refused trainees (1[0.8–1.0] vs 0.8[0.5–1.0], P = .35) but remained stable in the agreed trainees (1[0.8–1.0] vs 1[0.8–1.0], P = .57) (between-group P = .009) who also had improvements in total cholesterol, LDL-C, depressive symptoms, and self-care compared with baseline ( Table 2 ).

Metabolic Control After 4 years

HbA 1c remained unchanged in the agreed trainees after they had provided structured peer support for 4 years (baseline, 7.0 ± 0.2%; at 4 years, 7.2 ± 0.6%, P = .07) but increased in both the refused trainees (7.1 ± 0.4% to 7.8 ± 0.8%, P = .02) and the comparison group (7.1 ± 0.5% to 8.1 ± 0.6%, P = .01; among groups, P <.001) ( Figure 2 ). No significant changes in blood pressure or LDL-C were observed in any group during the 4 years (data not shown).

An external file that holds a picture, illustration, etc.
Object name is 42yinfig2.jpg

Comparison of glycemic control in agreed trainees, refused trainees, and the comparison group during a 4-year observational period.

Data were presented as mean ± SE.

P = .001: Comparison of HbA1c at extension year 2013 among the accepted trainees, refused trainees, and the comparison group.

Patients with type 2 diabetes and fair glycemic control who attended a peer support train-the-trainer program focusing on diet, exercise, psychology, and communication improved their own self-care behaviors and metabolic control. Those who agreed to become peer supporters had higher self-rated health status at baseline with further improvements in glycemic and lipid control as well as self-care behaviors at 6 months. After 4 years, the agreed peer supporters maintained their glycemic control while control deteriorated in the refused and comparison groups.

In a recent meta-analysis of quality improvement strategies within diabetes care, promotion of self-management had the largest positive effect on metabolic control. 26 In our study, people who joined the train-the-trainer program emphasizing DSME improved their self-care behaviors and glycemic control compared with the comparison group at 6 months, lending further support to the effectiveness of self-management training in type 2 diabetes. 3 Moreover, by attending the train-the-trainer program, these patients were empowered with coping skills to address the chronicity of their condition with a focus on positive thinking, goal setting, and stress management, which were associated with reduced depressive symptoms. These findings echo the importance of incorporating coping strategies for dealing with negative emotions in addition to providing medical knowledge and technical skills in a well-designed DSME program. 27 , 28

Compared with patients who attended the train-the-trainer program but refused to be peer supporters, those who agreed to be peer supporters had very similar characteristics at baseline except higher self-rated health status, perhaps reflecting a happier and more optimistic group of individuals. 29 Interestingly, the improvement in self-care behaviors and metabolic control after 6 months appeared to be limited to those who became peer supporters despite the fact thatthe refused group had completed the same training program. Thus, beyond the benefits of the education included in the training, subsequent differences between the agreed and refused trainees suggest benefits of actually providing peer support, not just being trained to do so. Moreover, the volunteer effect and willingness to help among peer supporters might have self-perpetuating and positive benefits. 30 The intention to support others might have engaged the peer supporters to improve their own self-care behaviors and maintain good glycemic control over the long-term. 31 , 32 Additionally, voluntary work has been associated with higher self-esteem, 33 lower psychological distress, better quality of life, 34 and reduced mortality, 18 all findings consistent with the positive changes on emotions and self-management seen in our peer supporters.

Of note, although the refused trainees had a 0.2% lower HbA 1c at 6 months after the training program than the comparison group, HbA 1c had increased by 1% in both these groups after 4 years, in contrast to the observed maintenance of glycemic control of the agreed trainees. These findings underscore the importance of ongoing reinforcement to maintain the short-term improvement after a typical DSME program. 2 , 35 , 36 The mutual learning and ongoing support among peers, peer supporters, and healthcare professionals might have further improved the ability of peer supporters to control their diabetes, solve problems, and cope with negative emotions associated with living with diabetes. 37 , 38

This study had several limitations. Participants in the study group and comparison group were not randomized, but selected based on their having similar glycemic control. The selection process resulted in a mismatch in gender distribution between the groups, for which we adjusted in our statistical comparisons. A self-selection bias in the makeup of the agreed and refused trainee groups is likely. Although we collected reasons for refusal, we did not capture factors like employment status that may have affected patients’ decisions.

Both groups had similar clinical profiles, however, and both received similar clinical care in the same settings. The substantial difference in glycemic control after 4 years supports the hypothesis that engagement as peer supporters, not merely self-selection bias, contributed to the improvements seen in the agreed trainees.

The small sample size in each group precluded more refined analyses such as repeated-measures analysis. We also acknowledge that error may have resulted from multiple comparisons. We did not measure psychosocial status at the end of the 4-year period and were not able to evaluate the longitudinal impact of providing peer support on emotional status. Further, we did not capture detailed information about treatments, such as antidiabetic drug dosage changes, the addition of insulin, etc. Lastly, this study was conducted in hospital-based diabetes clinics and enlisted a multidisciplinary team to train the peer supporters, which might not be generalizable to primary care settings.

This study prospectively reported the long-term effects of providing ongoing peer support to others on patients with type 2 diabetes. It captured multidimensional outcomes and provides longitudinal evidence that by providing ongoing help to others, patients with diabetes benefited in regards to self-care, psychological health, and glycemic control over 4 years. Engaging patients to become peer supporters may be a useful strategy for long-term diabetes management.

Acknowledgments

Special thanks to all staff at the Asia Diabetes Foundation for their support.

Conflicts of interest: authors report none.

Funding support: Funding for this research was provided by the American Academy of Family Physicians Foundation through the Peers for Progress program with support from the Eli Lilly and Company Foundation and by the Asia Diabetes Foundation. The funders had no role in the study design, data collection, data analysis, or preparation of the manuscript.

Previous presentation: The preliminary results of this study were accepted as a poster presentation at the World Diabetes Congress 2011; December 8, 2011; Dubai, United Arab Emirates.

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Published: 05 July 2024

Strategic use of resources to enhance colorectal cancer screening for patients with diabetes (SURE: CRC4D) in federally qualified health centers: a protocol for hybrid type ii effectiveness-implementation trial

Denalee M. O’Malley 1 , 2 ,
Benjamin F. Crabtree 1 , 2 ,
Srivarsha Kaloth 1 ,
Pamela Ohman-Strickland 1 , 2 , 3 ,
Jeanne Ferrante 1 , 2 ,
Shawna V. Hudson 1 , 2 &
Anita Y. Kinney 2 , 3

BMC Primary Care volume 25 , Article number: 242 ( 2024 ) Cite this article

Metrics details

Persons with diabetes have 27% elevated risk of developing colorectal cancer (CRC) and are disproportionately from priority health disparities populations. Federally qualified health centers (FQHCs) struggle to implement CRC screening programs for average risk patients. Strategies to effectively prioritize and optimize CRC screening for patients with diabetes in the primary care safety-net are needed.

Guided by the Exploration, Preparation, Implementation and Sustainment Framework, we conducted a stakeholder-engaged process to identify multi-level change objectives for implementing optimized CRC screening for patients with diabetes in FQHCs. To identify change objectives, an implementation planning group of stakeholders from FQHCs, safety-net screening programs, and policy implementers were assembled and met over a 7-month period. Depth interviews ( n = 18–20) with key implementation actors were conducted to identify and refine the materials, methods and strategies needed to support an implementation plan across different FQHC contexts. The planning group endorsed the following multi-component implementation strategies: identifying clinic champions, development/distribution of patient educational materials, developing and implementing quality monitoring systems, and convening clinical meetings. To support clinic champions during the initial implementation phase, two learning collaboratives and bi-weekly virtual facilitation will be provided. In single group, hybrid type 2 effectiveness-implementation trial, we will implement and evaluate these strategies in a in six safety net clinics ( n = 30 patients with diabetes per site). The primary clinical outcomes are: (1) clinic-level colonoscopy uptake and (2) overall CRC screening rates for patients with diabetes assessed at baseline and 12-months post-implementation. Implementation outcomes include provider and staff fidelity to the implementation plan, patient acceptability, and feasibility will be assessed at baseline and 12-months post-implementation.

Study findings are poised to inform development of evidence-based implementation strategies to be tested for scalability and sustainability in a future hybrid 2 effectiveness-implementation clinical trial. The research protocol can be adapted as a model to investigate the development of targeted cancer prevention strategies in additional chronically ill priority populations.

Trial registration

This study was registered in ClinicalTrials.gov (NCT05785780) on March 27, 2023 (last updated October 21, 2023).

Patients with diabetes mellitus have an estimated 27% elevated lifetime risk of developing colorectal cancer (CRC), and are disproportionately from priority health disparities populations (e.g., low-income, Non-Hispanic Black and Hispanic) [ 1 , 2 ]. Nationally, guideline concordant receipt of CRC screening for patients with diabetes is not significantly different for women with diabetes (57% vs. patients without diabetes 58%) and is significantly higher among men with diabetes (63% vs. patients with diabetes 58%) [ 3 ]. CRC screening for patients with diabetes, who do not have other indications of high risk (e.g., family history of CRC, polyp removal during colonoscopy, personal history of CRC, inflammatory bowel disease) are advised to follow the average risk screening recommendations [ 4 ]. Federally qualified health centers (FQHCs) primarily serve as primary care for priority health disparities populations and struggle to sustainably implement CRC screening programs for average-risk patients which includes patients with diabetes. CRC screening uptake in FQHCs populations has been consistently lower (44.1%) than the national average for average risk, age-eligible adults (67.3%) [ 5 ].

Persons receiving diabetes care in FQHCs have elevated health risks overall and higher rates of poverty and low-income status than the general population [ 6 ]. Ten percent of FQHC patients have a diabetes diagnosis and more than a third within this group have uncontrolled diabetes (HbA1c > 9%). Failure to implement preventive CRC screenings translates to an average of 6.5 years of lost life for patients subsequently diagnosed with CRC [ 7 ]. Moreover, this contributes to greater burden for patients with diabetes who are diagnosed with CRC who suffer greater morbidity, all-cause mortality, and cancer-specific mortality compared to CRC patients [ 8 , 9 , 10 ]. Therefore, efforts to prioritize CRC screening for patients with diabetes are needed in primary care safety-net settings.

Multiple evidence-based CRC screening tests are available which complicates implementation. The U.S. Preventive Services Taskforce (USPSTF) recommends CRC screening in adults aged 45–75, with multiple screening options available including non-invasive stool based testing: high sensitivity guaiac fecal occult blood tests (gFOBT), fecal immunochemical test (FIT), FIT plus stool DNA testing (FIT-DNA); and direct visualization tests: colonoscopy, computed tomography (CT) colography, and flexible sigmoidoscopy (FS) (with or without FIT) (see Table 1 for intervals) [ 4 ]. Colonoscopy and FS, have been shown to reduce mortality by (68% and 28%, respectively). FIT and FOBT are associated with 13–33% mortality reductions. Stool-based testing mortality reductions require sustained annual adherence. [ 11 , 12 , 13 , 14 ]. Research has shown that failures to screen at all, to screen at appropriate intervals, and to follow-up on abnormal results are associated with risk of CRC death [ 15 ].

Given major differences in mortality reduction benefits, temporal intervals for retesting, costs, and patient burden, controversies have emerged surrounding the pros and cons of testing methods [ 16 , 17 ]. Colonoscopy and FS allow for polypectomies, which can prevent CRC [ 18 , 19 ]; however, FS is not widely used in the U.S, because colonoscopy evaluates the entire colon, can be done every 10 years, and is associated with a greater mortality reduction [ 20 ]. A re-analysis of the USPSTF data suggest that prevention, through the removal of polyps during colonoscopy, is the sole mechanism of CRC mortality reductions [ 19 ]. Colonoscopy is thus the “gold standard,” despite critiques about the rigor of this evidence (e.g., indirect and observational). [ 21 , 22 , 23 , 24 ]. In FQHCs, non-invasive tests are emphasized and colonoscopies are often a second line-screening based on abnormal gFOBT/FIT findings. [ 25 ]. Non-invasive tests are emphasized because these are less costly, require less time (and time off of work), less complicated to complete, do not require transportation, and are guideline concordant [ 26 ]. Despite stool based testing’s acceptability, US-based trials in FQHCs designed to increase annual adherence to stool-based testing have reported low screening adherence over three years (10.4–16.4%) [ 27 , 28 , 29 ].

Prioritizing colonoscopy with longer testing intervals in under-resourced FQHCs for patients with diabetes introduces fewer opportunities for care breakdowns, is guideline concordant, and prevents CRC by removing premalignant colonic polyps. Guided by the Exploration, Preparation, Implementation and Sustainment [ 30 ]. (EPIS) framework, this research study will develop and evaluate targeted CRCs screening strategies for patients with diabetes in safety-net settings. This study addresses known implementation challenges using a “designing for dissemination” approach [ 31 , 32 , 33 ] that attends to important contextual, organizational capacity and patient complexity factors that impact CRC screening program implementation in clinics and uptake among patients with diabetes.

Conceptual framework

The design of this study was guided by the EPIS framework. EPIS is an evidence-based practice (EBP) implementation framework that includes four defined phases for assessment of inner and outer contextual factors that influence EBP implementation (see Table 2 ). For this study, the EBP is CRC screening uptake among age eligible patients with diabetes. Exploration is the act of identifying patient needs and the availability of EBPs to address identified needs, and the decision to adopt evidence into practice based on fit within the inner clinical context. During this phase, the adaptations to the evidence are based on system, organization, and individual patient factors. Preparation includes planning implementation, inventorying proposed challenges, and developing strategies to overcome anticipated barriers. A critical component of this phase is the planning of implementation strategies to support EBP utilization in the next two phases and to address organizational climate to ensure that EBPs will be supported, expected, and rewarded. During the clinical trial, this study focuses on implementation, the process of assuring and balancing fidelity to the EBP delivered with adaptations needed to assure program success. Sustainment focuses on maintenance and program and factors impacting implementation over the long haul. EPIS considers innovation factors, which are the characteristics of the EBP being implemented. The innovation-EBP fit considers if the EBP fits the patient, provider, and organizational needs. Innovation factors are assessed and can be adapted to maximize the fit of an EBP while maintaining the core elements of the intervention to retain fidelity.

Methods and design

Identifying multi-level change levers: a multi-method stakeholder informed approach.

Earlier phases of this research focused on the Exploration and Preparation phases, while the current protocol describes the intervention implementation and its evaluation. During the exploration phase, a secondary analysis was conducted of a nationally representative data set to identify patient level determinants of CRC screening uptake overall (i.e., with any test) and test-specific uptake among individuals with diabetes. We explored disparities in uptake overall and testing type based on race, ethnicity, income, and educational status. Additionally, a scoping literature review was performed to identify evidence-based interventions and implementation strategies for CRC screening and diabetes management in FQHCs. Based on this scoping review, we identified additional interventions and implementation strategies, using the Expert Recommendations for Implementing Change (ERIC) taxonomy [ 34 ]. A list of interventions and implementation strategies was compiled related to diabetes management processes to expand an existing measure that was developed and used to evaluate the use of evidence-based intervention and implementation for CRC in FQHCs [ 35 ].

For the preparation phase of the formative research, we used implementation mapping, an iterative process that incorporates community based participatory research principles [ 36 , 37 ]. An Implementation Planning Group (IPG) was assembled to represent a diversity of implementation actors (e.g., clinicians, state-level decision makers, screening safety-net programs) who work in and with FQHCs. The goal of the IPG, which met 5 times over a six-month period, was to develop shared understandings of the research problem based on empirical knowledge from the national survey analysis, the scoping review of the literature, and local knowledge of the IPG members about patient population and clinic system capacities. The IPG group identified and prioritized the selection of implementation strategies to improve CRC screening uptake for patients with diabetes. The IPG and research team iterated an implementation plan specifying multi-level change objectives and implementation determinants to develop supports to help prioritize CRC screening implementation for patients with diabetes.

Guided by the insights of the exploration and preparation phases, we developed the Strategic Use of Resources for Enhanced ColoRectal Cancer Screening in Patients with Diabetes (SURE: CRC4D) implementation toolkit, which includes tailorable materials and protocols that will be tested in a single arm, hybrid type 2 effectiveness-implementation single arm clinical trial. The objectives of this trial are to:

Determine the effectiveness of the SURE: CRC4D multi-component implementation strategies to increase CRC screening uptake among patients with diabetes.

Evaluate the fidelity, feasibility, and acceptability of SURE: CRC4D implementation.

Refine the SURE: CRC4D toolkit based on multi-level user feedback and conduct an evaluation to promote scalability and sustainable use.

Study participants and setting

This single arm trial will be conducted in six FQHC clinical sites in New Jersey. Eligibility criteria for the FQHC clinics include: (1) provide care to at least 450 patients aged 50–74 years; (2) 10% of patient population previously diagnosed with diabetes; (3) located in New Jersey; and, 3) clinical and administrative leadership willing to engage in the intervention and research requirements (interviews, data validation, process evaluation). Implementation outcomes will be assessed using mixed methods guided by the EPIS constructs (see Table 2 ). The methods of this study have been reported using Standard Protocol Items: Recommendation for Interventional Trials (SPIRIT) guidelines (Supplemental file 1 ).

During the implementation a clinic-based registry of patients eligible for CRC screening will be developed for each clinic at baseline and updated at six and 12 months post-baseline. Patient eligibility criteria will include: (1) patients not up-to-date or due for CRC screening [ 4 ]. based on electronic health record (EHR) documentation (e.g. FOBT/FIT test in last year, flexible sigmoidoscopy within 4 years, or colonoscopy within 9 years), (2) previous diagnosis of type II diabetes, (3) age-eligible for CRC screening (45–74 years of age) and (4) ) FIT/FOBT that has been ordered for more than 6 months that has not been completed or a sigmoidoscopy or colonoscopy referral that has not been completed for 12 or more months. Patients are excluded if they have EHR documentation medical conditions not concordant with standard CRC screening intervals (e.g. prior CRC diagnosis, inflammatory bowel disease, renal failure, etc.) [ 4 ].

The NJ Primary Care Research Network (NJPCRN) will recruit eligible clinics for participation. The NJPCRN is an Agency for Healthcare Research and Quality recognized practice-based research in primary care practices. The NJPRN will contact FQHCs that participated in previous research and ask IPG members to make introductions with their FQHC leadership networks. Emails with study flyers will be sent to the FQHC with follow-up telephone outreach. This protocol has been approved by the Rutgers University Institutional Review Board (Pro2020002075). All study participants will be asked to provide informed consent prior to participation in all phase of this research. We expect the distribution of patient participants to reflect the racial/ethnic diversity of the FQHCs recruited, who predominantly serve low-income, racial, and ethnic minority populations.

Implementation strategies

The goal of SURE: CRC4D is to enable FQHC clinics to adopt strategies to optimize the use of evidence based colorectal cancer screenings (See Table 1 ) uptake for patients with type II diabetes. To accomplish this, multi-level, multi-component implementation strategies (see Table 3 ) will be utilized. The core components of this implementation effort includes the identification and engagement of 2–3 clinic change champions, who will participate in two virtual learning collaborative events [ 38 , 39 , 40 , 41 ] and lead the change effort in the clinic aided by bi-weekly virtual practice facilitator support [ 38 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ]. The SURE: CRC4D toolkit will include guidance on pulling data to develop and implement quality monitoring systems to provide regular audit/feedback to the clinic, patient educational materials in English and Spanish and dissemination materials for clinical meetings to orient other clinic members to the change process being implemented to optimize CRC screening for patients with diabetes [ 50 , 51 ]. Clinic champions will tailor toolkit resources as clinics may have different electronic medical records, type and composition of staff, clinical workflows, and standing clinical team meetings.

The implementation will be rolled out over a 12-week period. Initially, clinics will be asked to identify 1–3 clinic champions, with at least one clinician (i.e., physician, advanced practice nurse, physician assistant) per team. Each team will meet with the external practice change facilitator approximately two weeks prior to the initial learning collaborative. This initial facilitation meeting is introductory, with the goal of encouraging clinic champions to reflect about the current clinic CRC screening strategies and diabetes care management processes prior to the 1st virtual learning collaborative. Clinical champions will attend the 1- hour virtual learning collaborative, where the materials in the SURE: CRC4D toolkit will be provided and reviewed, and each clinic team will formulate practice change goals. Teams will decide on how to deploy the toolkit strategies at their FQHC sites over the course of the next ten weeks. The practice facilitator will support the clinic champion team in the development, implementation, and refinement of the local practice change plan. The champions will meet with the practice facilitator every two weeks for 8 weeks (4 times). During this time, the plan will be refined and adjusted based on feedback from clinic leaders and practice staff members and identified strengths and barriers that are encountered during the implementation effort. At week 10, a second learning collaborative will be virtually convened, providing a forum where the different clinic teams can share their successes and obstacles during the development and execution of their plan. This forum will foster cross-team learning and idea generation that can inform the refinement of the SURE: CRC4D toolkit and sustainability of practice change efforts. Two weeks after the second learning collaborative, a final virtual facilitation meeting will be held to reflect and refine the practice plan to support sustainability.

Evaluation of the effectiveness and implementation of SURE: CRC4D

The effectiveness and implementation of SURE: CRC4D will be evaluated using a mixed method learning evaluation strategy, where ongoing data collection and analysis are used to refine implementation to optimize adoption of CRC screening for patients with diabetes [ 52 , 53 ]. This evaluation is designed to address two research questions: (1) are the adapted implementation strategies clinically effective in increasing CRC screening rates for patients with diabetes; and, (2) are the implementation strategies feasible and acceptable to implementers (e.g., clinicians and staff) and patients in FQHCs? This evaluation builds an evidence base about the effectiveness of the implementation strategies in a real-world context and allows for the collection of data that can be used to refine the implementation toolkit for a larger scale, definitive cluster randomized controlled trial. Guided by EPIS, contextual factors were selected based on suggestions from clinical stakeholders, community partners, and previous literature suggesting they may influence implementation success [ 54 , 55 , 56 ] (see Table 4 ). The following assessments and measures will be collected to evaluate the trial:

Organizational assessments

Guided by EPIS, contextual factors will be evaluated at baseline and 1 year-post implementation. Medical Directors or the Chief Operating Officer of each clinic will be asked to complete a web-based survey called the Clinic Organizational Information Form (COIF). This survey assesses Implementation Climate and History of Implementation related to CRC screening and diabetes management [ 35 ]. Additionally, patient demographics, management strategies, and payor mix are collected using this survey for each clinic.

Clinic staff measures and assessments

The Clinic Staff Questionnaire (CSQ) will be administered to all practice clinicians and staff members at baseline and 12-month post-implementation. The clinic team measures include Medical Provider and Staff Background and history with the organization. Additionally, Change Process Capability will be measured, specifically “previous history of change,” and “ability to initiate and sustain change.” [ 57 ]. These two measures have been identified these as key mechanisms for successful organizational change and its wide use in cardiovascular care implementation [ 58 , 59 , 60 ]. Additional practice-based measures will include: Adaptive Reserve a feature of resilient organizations shown to be associated with practice-level implementation of CRC screening, will be measured in the CSQ with the validated 23-item scale [ 57 , 61 ]. The CSQ will also include the Implementation Leadership Scale (ILS), a brief psychometrically strong measure that contains 12-items with four subscales of proactive, knowledgeable, supportive, and perseverant leadership [ 62 ].

Process data outcomes

Learning collaborative and facilitation phone calls will be audio recorded and transcribed to document issues that arose during the implementation process. Additionally, qualitative interviews will be conducted at baseline and beginning at 6 months post implementation. We will select key implementers (3–4 individuals per site) to assess perceptions of organizational readiness to change, leadership style and additional facility characteristics (e.g., assets and deficits of location, satisfaction with ease of access to facility, etc.). Staff and clinician perceptions of the SURE: CRC4D implementations’ feasibility and acceptability will also assessed asking providers and staff to describe their implementation experiences. The interviews will probe stakeholder perceptions of change in their organizations, systems, and factors that they think impacted implementation. Staff or provider fidelity will be assessed based on the clinic-level proportion of eligible patients who were (1) contacted based on implementation protocol and (2) completed any CRC screening at 1 year.

Patient level: clinical effectiveness outcome and implementation assessment

The primary outcome variables to assess clinical effectiveness will be the clinic level proportion of patients with diabetes who: (a) receive any CRC screening and (b) complete a colonoscopy at 12 months from baseline. An exploratory analysis will assess clinic-level CRC screening completion by glucose control (controlled vs. uncontrolled, i.e., HbA1c > 9 at 12 months). Patient level data is collected in aggregate and will include no identified personal health information.

Patient acceptability will be assessed through the assessment of patient rates of opting-out and non-adherence of CRC screening. This rate will be based on the proportion of CRC screening among patients with diabetes compared to overall eligible patient population (without diabetes) in each clinic.

Data analyses

Qualitative analysis.

On a quarterly basis, we will analyze data from each clinic site using a comparative case analysis [ 63 ]. Organizational level data and interview transcripts will be organized, read and coded in ATLAS.ti. Data will be analyzed on an ongoing basis, and a working summary of emergent findings will be updated as incoming data is added. As a validity check of qualitative results, we will check relevant data interpretations against all new data using a constant comparison approach [ 64 ]. We will note similarities and differences of implementation feasibility between practice sites based on clinic characteristics and from data provided in interviews. Each quarter all quantitative and qualitative results will be summarized in brief reports to be shared with the research team for reflections on any changes needed. These analyses represent ongoing monitoring and feedback to inform refinements of the implementation strategy and clinical trial procedures to refine implementation strategy to better fit local needs and contexts.

Quantitative analysis

Descriptive statistics will be used to summarize patient and clinic characteristics. We will declare our intervention a success if at least 25% of those unscreened are screened at 12-month follow-up in this difficult to reach population. We will declare the optimization of screening a success if 15% of those unscreened are screened with a colonoscopy or flexible sigmoidoscopy at 12-months. Overall improvement metrics are comparable to improvements in previous CRC screening implementation studies in FQHCs [ 65 , 66 ]. At baseline, we will calculate average screening rates and their confidence intervals across all practice sites in intent to treat analyses and at 12-months we will assess screening rates and their confidence intervals for all sites. The confidence intervals will be compared to 25%. We will compare differences in CRC screening by glucose control, sex, and race/ethnicity.

Power calculations

The value of information method [ 67 ] was utilized to select a sample size balancing the costs and feasibility goals of the trial. This sample size (e.g., six clinic sites, assuming at least n = 30 patient in each) is sufficient to generate preliminary estimates of the estimated effect (80% confidence interval) of the implementation strategy on CRC screeningrates [ 68 , 69 ]. In developing the power calculation, we assume equal numbers of patients ( n = 50) per clinic (the anticipated number of eligible patients, n = 450 CRC screening eligible, with > 10% diabetes diagnosis). Of those with diabetes, we expect 40% to be up-to-date with screening guidelines based on the average rate of CRC screening in FQHCs [ 70 ]. Thus, the target sample size is n = 30 patients in each FQHC.

This study aims to optimize CRC screening using the engagement of multi-level stakeholders (patients, clinicians, staff in FQHCs) and using an implementation mapping during the exploration and preparation phases prior to implementation [ 37 ]. This project is innovative in several key ways. Regarding conceptual innovation, few studies have included CRC screening as a component of diabetes care prior to CRC diagnosis [ 71 , 72 ], while many have focused on improving CRC screening for average risk adults in FQHCs [ 65 , 66 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ].An EPIS framework systematic review concluded that attention to planning EBP use is “infrequent though critical [ 80 ].” FQHC implementation of CRC screening programs focus on achieving the Uniform Data System (UDS) targets, which do not distinguish patients at greater risk for CRC in the “average-risk” patient population [ 70 , 81 ]. Metrics for UDS CRC screening program are also cross-sectional and collected as separate metrics unrelated to diabetes care or annual stool-based testing adherence. For FIT and FOBT stool-based CRC screening strategies to be clinically effective and for their mortality reductions to be realized sustained annual adherence is required, which has been proven difficult to accomplish in safety-net primary care settings [ 12 , 13 ]. Additionally, few FQHCs formally assess factors related screening prior to implementing improvement interventions [ 82 ]. This study aims to optimize CRC prevention using the engagement of multi-level stakeholders (patients, clinicians, staff in FQHCs) and using an implementation mapping during the exploration and preparation phases prior to implementation [ 37 ].

Despite being the most studied evidence-based cancer screening in the National Institutes of Health implementation science portfolio, no systematic studies have integrated CRC screening and diabetes evidence-based approaches to prioritize preventive care for patients with diabetes in the primary care safety-net. To date, research has focused on overall CRC guideline adherence, relying on an ‘all boats rise’ approach despite the failures of such strategies to achieve improvements in chronic disease targets [ 83 ]. In contrast, this study focuses on optimizing CRC screening using targeted implementation strategies to address disparities among individuals with diabetes to promote health equity.

Study findings are poised to inform the develop scalable, equitable approaches to CRC screening in safety-net primary care settings. If successful, next steps will include testing the scalability and sustainability in federally qualified health centers nationally. Further, this approach can be adapted as a model to investigate the development of targeted cancer prevention strategies in additional chronically ill priority populations.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Colorectal cancer

Computed tomography

Fecal immunochemical tests

Fecal immunochemical test stool DNA testing

Federally qualified health centers

Flexible sigmoidoscopy

Guaiac fecal occult blood tests

Implementation Planning Group

Electronic health record

Evidence-based practice

Expert Recommendations for Implementing Change

Exploration, Preparation, Implementation, Sustainment

New Jersey Primary Care Research Network

CRC4D: Strategic Use of Resources for Enhanced ColoRectal Cancer Screening in Patients with Diabetes

Standard Protocol Items: Recommendation for Interventional Trials

Uniform Data System

U.S. Preventive Services Taskforce

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We would like to thank our FQHC participants for informing the intervention and their contributions to this work.

This research is supported by the National Cancer Institute (K99 CA256043/R00CA256043) The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement, by the funders.

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O’Malley, D.M., Crabtree, B.F., Kaloth, S. et al. Strategic use of resources to enhance colorectal cancer screening for patients with diabetes (SURE: CRC4D) in federally qualified health centers: a protocol for hybrid type ii effectiveness-implementation trial. BMC Prim. Care 25 , 242 (2024). https://doi.org/10.1186/s12875-024-02496-0

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Sars-cov-2 infection positively correlates with hyperglycemia and inflammatory markers in covid-19 patients: a clinical research study.

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Click here to enlarge figure

Clinical Characteristics	Total	Normoglycemic (RBS < 140 mg/dL)	Hyperglycemic (RBS > 140 mg/dL)
Total number of subjects	n = 550	n = 202	n = 348
Gender
Male	354 (64.36)	127 (62.87)	227 (65.22)
Female	196 (35.63)	75 (37.12)	121 (34.77)
Age distribution
<30	57 (10.36)	28 (13.86)	29 (8.33)
30–60	368 (66.90)	136 (67.32)	232 (66.66)
>60	125 (22.72)	38 (18.81)	87 (35.08)
Vaccination status
Unvaccinated	442 (80.36)	159 (78.71)	283 (81.32)
Vaccinated	108 (19.63)	43 (21.28)	65 (18.67)
Single Dose vaccination	75 (69.44)	31 (72.09)	44 (67.69)
Double Dose vaccination	33 (30.55)	12 (27.90)	21 (32.30)
Clinical features
Fever	379 (68.90)	131 (64.85)	248 (71.26)
Cough	417 (75.81)	149 (73.76)	268 (77.01)
Breathlessness	249 (45.27)	83 (41.08)	166 (47.70)
Preexisting diabetes	158 (28.72)	32 (15.84)	126 (36.20)
New onset hyperglycemia	225 (40.90)	None	225 (64.65)

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Chikkahonnaiah, P.; Dallavalasa, S.; Tulimilli, S.V.; Dubey, M.; Byrappa, S.H.; Amachawadi, R.G.; Madhunapantula, S.V.; Veeranna, R.P. SARS-CoV-2 Infection Positively Correlates with Hyperglycemia and Inflammatory Markers in COVID-19 Patients: A Clinical Research Study. Diseases 2024 , 12 , 143. https://doi.org/10.3390/diseases12070143

Chikkahonnaiah P, Dallavalasa S, Tulimilli SV, Dubey M, Byrappa SH, Amachawadi RG, Madhunapantula SV, Veeranna RP. SARS-CoV-2 Infection Positively Correlates with Hyperglycemia and Inflammatory Markers in COVID-19 Patients: A Clinical Research Study. Diseases . 2024; 12(7):143. https://doi.org/10.3390/diseases12070143

Chikkahonnaiah, Prashanth, Siva Dallavalasa, SubbaRao V. Tulimilli, Muskan Dubey, Shashidhar H. Byrappa, Raghavendra G. Amachawadi, SubbaRao V. Madhunapantula, and Ravindra P. Veeranna. 2024. "SARS-CoV-2 Infection Positively Correlates with Hyperglycemia and Inflammatory Markers in COVID-19 Patients: A Clinical Research Study" Diseases 12, no. 7: 143. https://doi.org/10.3390/diseases12070143

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Diabetes Education, Prevention and Support
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Diabetes Support Groups

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Overview of Peer Support Models to Improve Diabetes Self-Management and
Higher levels of social support—especially illness-specific or regimen-specific support—are associated with better diabetes and other illness self-management. 1,4-8 Moreover, observational studies suggest that providing social support to others may result in health benefits comparable to—or even greater than—receiving support. Individuals who provide social support through ...
Participation of Patients With Type 2 Diabetes in Online Support Groups
Diabetes is one of the most prevalent long-term diseases (LTDs) of this age. Nowadays, people affected by an LTD 1 are increasingly participating in digital communities, 2 often referred to as online support groups (OSGs), to share feelings, gain emotional support, and obtain information about the disease. Diabetes care requires both disease understanding and disease education, as well as ...
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Recent Findings on the Effectiveness of Peer Support for ...
Purpose of Review To review randomized controlled trials (RCTs) published from 2021-2023 that reported the effects of peer support interventions on outcomes in patients with type 2 diabetes (T2DM). Recent Findings Literature searches yielded 137 articles and nine RCTs were ultimately reviewed. The reviewed trials involved in-person support groups, peer coach/mentor support, cultural peer ...
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Peer support in diabetes self-management allows patients to engage in mutual knowledge-sharing, collaborative problem-solving, and emotional support for the stresses of dealing with type 2 diabetes. ... This study has significant implications for clinical research and practice in diabetes or public health. ... weekly meetings, and closer group ...
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Digitalising diabetes support groups in response to the coronavirus
Peer support groups are groups of people who share something in common and use their experiences to help each other. As the pandemic persists and health care teams are adjusting to the changing circumstances, facilitating access to peer support groups that can hold meetings via a digital platform can provide a unique source of additional help for diabetes self-management and mental wellbeing.
Augmenting Traditional Support Groups for Adolescents With ...
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Background: social support is important for adaptation in chronic diseases, such as diabetes and depression, because it favors recovery and adherence to treatment. Introducing its evaluation in the follow-up of diabetic patients can reduce complications derived from secondary non-adherence. Aims: to establish social support in diabetic patients and its correlation with depressive symptoms.
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This pilot study evaluated what best educates and motivates patients to improve glucose control. Method: 17 participants with T2DM were recruited from Penn Rodebaugh Diabetes Center to attend 3 monthly diabetes support group meetings, and receive American Association of Diabetes Educators education. Weight and hemoglobin A1C (A1C) were measured ...
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Diabetes support groups can help serve as an invaluable resource for people with diabetes. Peer support groups are readily available both in-person and online. You can tap into the resources and relationships through websites and on social media. You also may want to connect with people and groups in your community.
Adult Support Groups
Like all of Diabetes Foundation's services, our support groups on diabetes-related topics are completely free of charge. Diabetes management is a learning process. If you or a loved one living with diabetes is struggling to create a diabetes healthcare plan, call the Diabetes Foundation at 201-444-0337 or email [email protected].
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To locate these groups, just enter "diabetes" in the search field on the platform. You can also start by checking out the Facebook support groups below. Facebook group name and link. Number of members. Type 1 Diabetes Support Group. Over 60,000. Diabetes Support Group. Around 40,000. Diabetes Support.
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Diabetes mellitus (DM) is a common comorbidity in COVID-19 subjects. Hyperglycemia at hospital admission identified as a major risk factor and is responsible for poor prognosis. Hematological and inflammatory parameters have been recognized as predictive markers of severity in COVID-19. In this clinical study, we aimed to assess the impact of hyperglycemia at hospital admission on ...

Recent Findings on the Effectiveness of Peer Support for Patients with Type 2 Diabetes

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IP Diet & Lifestyle Intervention with Telephone Support

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A holistic lifestyle mobile health intervention for the prevention of type 2 diabetes and common mental disorders in Asian women with a history of gestational diabetes: a randomised control trial with 3-year follow-up protocol

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Objectives {7}

Trial design {8}

Methods: participants, Interventions, and outcomes

Eligibility criteria {10}

Who will solicit informed consent? {26a}

Additional consent provisions for collection and use of participant data and biological specimens in ancillary studies {26b}

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LvL UP App: coaching health

LvL UP App: tools

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