china the new normal case study

The “new normal” in US-China relations: Hardening competition and deep interdependence

Subscribe to the china bulletin, ryan hass ryan hass senior fellow - foreign policy , center for asia policy studies , john l. thornton china center , chen-fu and cecilia yen koo chair in taiwan studies, the michael h. armacost chair.

August 12, 2021

The intensification of U.S.-China competition has captured significant attention in recent years. American attitudes toward China have become more negative during this period, as anger has built over disruptions resulting from the COVID-19 pandemic, Beijing’s trampling of Hong Kong’s autonomy, human rights violations in Xinjiang, and job losses to China.

Amidst this focus on great power competition, two broader trends in the U.S.-China relationship have commanded relatively less attention. The first has been the widening gap in America’s and China’s overall national power relative to every other country in the world. The second has been the continuing thick interdependence between the United States and China, even amidst their growing rivalry. Even on economic issues, where rhetoric and actions around decoupling command the most attention, trade and investment data continue to point stubbornly in the direction of deep interdependence. These trends will impact how competition is conducted between the U.S. and China in the coming years.

Separating from the pack

As America’s unipolarity in the international system has waned, there has been renewed focus on the role of major powers in the international system, including the European Union, Russia, India, and Japan. Each of these powers has a major population and substantial economic weight or military heft, but as my Brookings colleague Bruce Jones has observed , none have all. Only the United States and China possess all these attributes.

The U.S. and China are likely to continue amassing disproportionate weight in the international system going forward. Their growing role in the global economy is fueled largely by both countries’ technology sectors . These two countries have unique traits. These include world-class research expertise, deep capital pools, data abundance, and highly competitive innovation ecosystems. Both are benefitting disproportionately from a clustering effect around technology hubs. For example, of the roughly 4,500 artificial intelligence-involved companies in the world, about half operate in the U.S. and one-third operate in China. According to a widely cited study by PricewaterhouseCoopers, the U.S. and China are set to capture 70% of the $15.7 trillion windfall that AI is expected to add to the global economy by 2030.

The United States and China have been reinvesting their economic gains to varying degrees into research and development for new and emerging technologies that will continue to propel them forward. While it is not foregone that the U.S. and China will remain at the frontier of innovation indefinitely, it also is not clear which other countries might displace them or on what timeline. Overall, China’s economy likely will cool in the coming years relative to its blistering pace of growth in recent decades, but it is not likely to collapse.

Deep interdependence

At the same time, bilateral competition between the United States and China also is intensifying. Even so, rising bilateral friction has not – at least not yet – undone the deep interdependencies that have built up between the two powers over decades.

In the economic realm, trade and investment ties remain significant, even as both countries continue to take steps to limit vulnerabilities from the other. For example, Chinese regulators have been asserting greater control over when and where Chinese companies raise capital; Beijing’s recent probe of ride-hailing app Didi Chuxing provides but the latest example. China’s top leaders have been emphasizing the need for greater technology “self-sufficiency” and have been pouring billions of dollars of state capital into this drive. Meanwhile, U.S. officials have been seeking to limit American investments from going to Chinese companies linked to the military or surveillance sectors. The Security and Exchange Commission’s scrutiny of initial public offerings for Chinese companies and its focus on ensuring Chinese companies meet American accounting standards could result in some currently listed Chinese companies being removed from U.S. exchanges. Both countries have sought to disentangle supply chains around sensitive technologies with national security, and in the American case, human rights dimensions. U.S. officials have sought to raise awareness of the risks for American firms of doing business in Hong Kong and Xinjiang .

Even so, U.S.-China trade and investment ties remain robust. In 2020, China was America’s largest goods trading partner, third largest export market, and largest source of imports. Exports to China supported an estimated 1.2 million jobs in the United States in 2019. Most U.S. companies operating in China report being committed to the China market for the long term.

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U.S. investment firms have been increasing their positions in China, following a global trend . BlackRock , J.P. Morgan Chase, Goldman Sachs, and Morgan Stanley have all increased their exposure in China, matching similar efforts by UBS , Nomura Holdings , Credit Suisse , and AXA . The Rhodium Group estimates that U.S. investors held $1.1 trillion in equities issued by Chinese companies, and that there was as much as $3.3 trillion in U.S.-China two-way equity and bond holdings at the end of 2020.

One leg of the U.S.-China economic relationship that has atrophied in recent years has been China’s flow of investment into the United States. This has largely been a product of tightened capital controls in China, growing Chinese government scrutiny of its companies’ offshore investments, and enhanced U.S. screening of Chinese investments for national security concerns.

Another area of U.S.-China interdependence has been knowledge production. As U.S.-China technology expert Matt Sheehan has observed , “With the rise of Chinese talent and capital, the exchange of technological know-how between the United States and China now takes place among private businesses and between individuals.” Leading technology companies in both countries have been building research centers in the other. Alibaba , Baidu , and Tencent have all opened research centers in the United States, just as Apple , Microsoft , Tesla , and other major American technology companies rely upon engineering talent in China.

In science collaboration, The Nature Index ranks the joint research between the two countries as the world’s most academically fertile. U.S.-China scientific collaboration grew by more than 10% each year on average between 2015 and 2019. Even following the global spread of COVID-19, American and Chinese experts collaborated more during the past year than over the previous five years combined . This has led to over 100 co-authored articles in leading scientific journals and frequent joint appearances in science-focused workshops and webinars.

China also is the largest source of international students in the United States. In the 2019-20 year, there were over 370,000 Chinese students in the U.S., representing 34% of international students in colleges and universities. Up until now, many of the top Chinese students have stayed in the United States following graduation and contributed to America’s scientific, technological, and economic development. It remains to be seen whether this trend will continue.

Competitive interdependence

The scale of American and Chinese interests implicated will likely induce sobriety over time in Washington and Beijing as to how the relationship is managed. The U.S. policy focus for the foreseeable future is not likely to be seeking to “defeat” China or compel the collapse of the Chinese Communist Party. Rather, the focus will be on taking steps at home and with partners abroad to strengthen America’s long-term competitiveness vis-à-vis China. At the same time, American leaders will continue to push their Chinese counterparts to improve the treatment of their citizens. Such efforts are definitional to America’s self-identity as a champion of values.

The dense webs formed by trade, financial, scientific, and academic links between the United States and China will make it difficult for one side to inflict harm on the other without hurting itself in the process. As Joe Nye has written , “America can decouple security risks like Huawei from its 5G telecommunications network, but trying to curtail all trade with China would be too costly. And even if breaking apart economic interdependence were possible, we cannot decouple the ecological interdependence that obeys the laws of biology and physics, not politics.”

President Joe Biden likely will use the challenges posed by China as a spur for his domestic resilience agenda. He is not an ideologue, though, and is unlikely to limit his own flexibility by painting the world with permanent black and white dividing lines. The Biden team knows it will be harder to realize progress on serious global challenges like climate change, pandemics, and inclusive global economic recovery without pragmatic dealings with non-democratic states.

Major near-term improvements to the U.S.-China relationship are unlikely, barring an unexpected moderation in Beijing’s behavior. At the same time, the relationship is also unlikely to tip into outright hostility, barring an unforeseen dramatic event, such as a Chinese act of aggression against an American security partner.

U.S.-China relations are going to be hard-nosed and tense. Neither side is likely to offer concessions in service of smoother relations. At the same time, the balance of interests on both sides likely will control hostile impulses, placing the relationship in a state of hardening competition that coexists alongside a mutual awareness that both sides will be impacted — for good or ill — by their capacity to address common challenges.

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Home > Books > Proceedings of the 2nd Czech-China Scientific Conference 2016

China’s “New Normal” and Its Quality of Development

Reviewed: 09 November 2016 Published: 01 February 2017

DOI: 10.5772/66791

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China’s new normal means a new higher stage of development, when an alternative is to improve the quality of economic development instead of accelerating growth rate by expansion policies. And the quality of development is the quality of living of most people. This study is to examine the current situations of China’s quality of development by comparing China’s human development index, inequality indices (Gini, quintile, and Palma), and development potential (human capital index) with the developed countries in Europe, North America, and Oceania, as well as countries with typical traits, such as the Latin American countries, Japan, and Czech Republic; further to put forward China’s policy focuses in the new normal stage according to the concluded research results.

• China’s new normal
• human development index (HDI)
• human capital index (HCI)
• quality of life

Author Information

• Faculty of Economics, Hebei GEO University, Shijiazhuang, Hebei, China

Haochen Guo

Mengnan zhang.

*Address all correspondence to: [email protected]

1. China’s new normal–a new higher stage

China’s new normal is original from the slowdown of the GDP growth rate in recent years. Graph 1 shows three obvious slowdowns since 1979. The three slowdowns are all accompanying with economic upheavals and big inflations, only the last and current one induces a new concept, “New Normal.”

Graph 1.

Per capita GDP growth China 1979–2014 (1978 constant). Data source: Chinese statistics yearbook 2015: 3–1, 3–5.

In May 2014, President Xi Jinping put forward the “new normal of China’s economy,” and described a series of new performances of China’s economy. On December 5, 2014, the Politburo meeting of the Communist Party of China formally advocated to “take the initiative to adapt to the economic development of the new normal.” Since then, the Chinese economy has entered a “new normal” stage.

Generally, the “new normal” has two characteristics: the first is about the slowdown from high-speed growth to high-middle-speed growth; the second is about the transformation of growth pattern from scale extensive growth to quality and intensive growth [ 1 ]. For the future strategy of Chinese government, there seem also two main streams: one is focusing on the growth speed, while thinking the transformation of growth pattern is given, for they think China need to sustain a growth speed to cross the middle-income trap that is the first priority of China [ 2 – 5 ]; another is to focus on the transformation of growth pattern and growth quality, while keeping the high-middle-speed growth even middle-speed growth [ 1 , 6 , 7 ]. We stand for the second view.

The speed slowdown of China’s economic growth is not a bad thing. First, the growth rate from the high-speed down to high-middle-speed is suitable for China. China’s GDP growth rate of 6.9% and per capita GDP growth rate of 6.3% in 2015, are still high enough in the context of the world (the world average of GDP growth rate is 2.5%, 2015). Second, the slowdown is beneficial from the consideration of the limit of natural resources and serious environmental problems of China, as the environment could no longer sustain the long lasting high-speed growth, even if it is further lower; after all, the ecological environment is the precondition of a country’s sustainable development. Third, as a common sense, high-speed growth is apt to bring economic upheaval, and destroy the stability of development. Hence, in long run, keeping a high-middle-speed is better than high-speed for the sake of stable sustainable development.

Moreover, the speed slowdown is a good signal that indicates China has been entering a new stage of development, when an alternative is to improve the quality of economic development instead of accelerating growth rate by expansion policies. And the quality of development is the quality of living of most people; i.e., we can pay more attentions to most people’s quality of life, as like a developed country’s performances.

In brief, China’s new normal means a new higher stage of development with the pursuit of a developed country. This study is to examine the current situations of China’s quality of development by comparing China’s human development index, inequality indices (Gini, quintile, and Palma), and development potential (human capital index) with the developed countries in Europe, North America, and Oceania, as well as countries with typical traits, such as the Latin American countries, Japan and Czech Republic; further to put forward China’s policy focuses in the new normal stage, so to catch up with the developed countries in quality of development.

2. Material and methods

For comparing the quality of development, we arrange here with representative countries, comparable indicators and methodologies.

2.1. Countries considered

China is a large developing country with the largest population and large land mass in the world, and with socialist nature as its Constitution expressed. The countries as comparing counterparts, we choose mainly concerning: (1) well developed (at least its HDI higher than China’s); (2) relative competent size of territory and population; and (3) representative in different regions and social models. By data testing, 14 countries have been selected as reference countries as follows.

The four countries, Norway, Denmark, Sweden, and Finland, are all Nordic countries, well developed with long-term stable sustainable qualified development, as generally accepted model of ideal society on the globe currently, the “Nordic model,” which have more socialist component, such as generous social welfare and equal opportunity for public services to each family and individual all over the country.

These two countries, Germany and Switzerland, are high developed market economies with more socialist-natures in the “Rhine model,” as major roles in mainland Europe with long-term stable qualified development and good performance in equality aspect.

The two countries, USA and UK, are well-developed market economies, natured as typical capitalist market in the “Anglo-Saxon model,” and once the super powers in different ages.

The country of Australia is on the Oceania, tightly related with China in commercial intercourse; well-developed capitalist economy with sound social welfare as well.

The country of Japan is the next neighbor of China, the first and most developed economy in Asia, and has good performance generally but in depression for a long time in recent years.

The country of Czech Republic is a former socialist country located in central-eastern Europe, with the history of a member of former Soviet Union alliance, and keeps the most equal society record; not well developed but with very high value of human development index (Rank 28 in 2014 in nearly 200 countries).

The three countries, Argentina, Mexico, and Brazil, are also developing countries but capitalist natured in Latin America, ranking forefront of the world in inequality.

2.2. Indicators and methods

The chapter is to examine China’s “new normal” state by comparing related indicators with 14 other countries typically scattered in the world (except Africa). Considering the paper’s international angle, we make comparability and internationalism as the prime principles when selecting indicators utilized. Therefore, all indicators and data as follows are from UNDP, (http://hdr.undp.org) [ 8 ], the exception sources will be marked in addition at the right point.

2.2.1. Human development index (HDI)

The HDI represents a broader definition of well-being and provides a composite measure of three basic dimensions of human development: health (a long and healthy life), education (knowledge), and income (a decent standard of living) [ 9 ]. HDI is the most comparable and available indicator for measuring quality of life among countries.

2.2.2. Inequality indices (Gini, quintile, and Palma)

The World bank emphasizes, “To begin to understand what life is like in a country–to know, for example, how many of its inhabitants are poor–it is not enough to know that country’s per capita income. The number of poor people in a country and the average quality of life also depend on how equally–or unequally–income is distributed” [ 10 ]. The Gini Coefficient is the most frequently used inequality index as “the mean difference from all observed quantities” [ 11 ]. However, the Gini does not capture where in the distribution the inequality occurs. For this reason, other two indicators, quintile ratio, and Palma ratio, are also chosen in the paper, which are more clearly reflect the high income and low income gap, successfully excluding the influence of middle income people.

The quintile ratio (20:20 or 20/20 ratio) compares how much richer the top 20% of populations are to the bottom 20% of a given population, which is actually a part of the Gini Coefficient that prevents the middle 60% statistically obscuring inequality, meanwhile highlighting the difference between two poles.

The Palma Ratio, meaning the ratio of the top 10% of population’s share of gross national income (GNI), divided by the poorest 40% of the population’s share of GNI–could provide a more policy-relevant indicator of the extent of inequality in each country, and may be particularly relevant to poverty reduction policy. It is based on the work of Chilean economist Jose Gabriel Palma who found that the “middle classes” tend to capture around 50% of national income, while the other half is split between the richest 10% and poorest 40% [ 12 ].

2.2.3. Human capital index (HCI)

“A nation’s human capital endowment–the skills and capacities that reside in people and that are put to productive use–can be a more important determinant of its long-term economic success than virtually any other resource. This resource must be invested in and leveraged efficiently in order for it to generate returns–for the individuals involved as well as an economy as a whole” [ 13 ].

Graph 2 is drawn to show the relations among human development index and its three components, human, capital, and equality. Here, we emphasize that the HDI includes HCI, which account for two-thirds of HDI, even though education and health are not the whole HCI, but at least the major aspects; education and health are both capabilities residing in people, which is directly related to a person’s income and in social level to both quantity and quality of economic development; Equalization and justice are important complement of HDI, which also have promoting effects on people’s education and health by its benefiting mostly to the general public. That is, HDI, HCI, and equality are interrelated and tend to promote along the arrow directions, which constitute and cooperate the quality of development/quality of life.

Graph 2.

The promoting relations of equality, human capital, and human development.

All data used are registered in official sources. The international data for comparing among countries are from international organizations, UNDP. The method used in the chapter is mostly comparative analysis approaches with statistical graphs and tables.

3. Experimental

Here, we examine for comparing China’s quality of development with the representative countries by using the three serials indicators; and conduct comprehensive comparative analysis and evaluation.

3.1. Human development and living quality

3.1.1. hdi overall status.

Graph 3 shows the level of human development index of the 15 countries selected with various colors, which implies the overall quality of development and quality of life of different country groups. China is at the bottom of the row, ranked 90th in the world, and approximately accounts for 77% of the highest valued country, Norway; 79% of the United States, the typical capitalist country; and 82% of Japan, Asia’s most developed country. That means we have a long distance to go in quality of life.

Graph 3.

HDI in world context 2014.

Table 1 shows the overall level of HDI of four level groups, and the world and the developing countries. China, the second biggest economy in the world, is nearly 20% less than the level of the first 50 countries, and just at the average level of the world in quality of life.

Table 1.

Overall level of human development in different groups 2014.

3.1.2. HDI components

In Annex Table 1 , we make HDI and its component indicators in order respectively and make a sum rank in order to see the influence of each component. From Annex Table 1 and Graph 4 , we notice first that the general pattern does not change: (1) the upper ranked 8 countries are still upper but with changed ranks; (2) the lower seven countries are lower by the same rank with HDI order; (3) China retains at its bottom position by reordering, including total rank and almost all component cases (life expectancy of China is the only factor that does not row at the extreme bottom, which might somehow show off the medical condition or Chinese traditional medicine).

Graph 4.

Components of HDI by GNI order 2014.

Moreover, we find some prominent features in Annex Table 1 and Graph 4 : (1) Both Germany and UK’s re-ranks are upper by the same factor, “mean years of schooling” showing social sustainability, which imply the labor force and the civilized residents endowed by education; UK in Anglo-Saxon model with capitalist nature, has the similar pattern (8:1:8) with Germany (6:1:6) in “Rhine model,” but far from the pattern of USA (10:4:3); Czech Republic (with similar pattern 11:8:11) rows upper also by its “mean years of schooling,” which means education gains much attention in Czech as well. (2) Australia (3:3:7) has almost the opposite pattern with USA, but with better momentum of development in practical economy than USA. (3) The life expectancy order of Japan is at the first, which might reflect Japanese life style is very healthy.

3.2. Inequality

Equalization and justice are important complement of HDI, so we here analyze income inequality standing for measuring social equality and justice, although which is far from comprehensive but essential and quantitative. According to the data of the National Bureau of Statistics, China’s Gini coefficient has ever peaked to 49.1 in 2008, began to decline since 2010, to 46.9 in 2014, along with policy’s functioning.

Graph 5 shows that, in the Gini coefficient case, China (2014) performs better than the three Latin countries and the two typical capitalist countries, USA and UK. However, the quintile ratio that shows the polarization in income distribution by the top 20% to the bottom 20%, has different performance: China’ s value of quintile ratio is only better than that of the three Latin countries but worse than USA and UK, and far worse than other countries included; The Palma ratio, the richest 10% of population’s share of gross national income divided by the poorest 40%’s share, provides support to the quintile’s case.

From the computing results in Table 2 , we can see more clearly that China’s polarization in income distribution, i.e., the highest income group to the lowest, excluding the influence of middle income people is conspicuous worse than the Gini performance with the influence of middle income populations included, by observing the deviations from the average of the 15 countries considered.

Graph 5.

Income inequalities by Gini order 2014.

Table 2.

Fifteen countries’ comparison of income inequality by Gini Order 2014.

Of course, the income inequality in three Latin countries show much worse cases than in China; and their polarization is even much worse than their Gini case as well. That is probably the reason why the Latin countries could not performance better with so much endowment of natural resources. Therefore, equality and social justice in China as institutional environment given by the government should improve continuously for the sake of promoting the living quality of the people.

In addition, China is a socialist country as its Constitution expressed, and in case any adverse effect happens, it is very necessary for China to have higher pursuit in equality and social justice, e.g., reach to 35/7/1.5 (Gini/quintile/Palma), equivalently the average level of listed 15 countries, close to the level of UK (38/7.6/1.7) or Australia (34/5.9/1.3), as the minimum pursuits in 5–10 year, from 37/10/2, the currently level of China by the inequality index.

3.3. Human capital

Generally observing the history and experiences of all developed countries, it is common nature that every country pays enough attention to two factors: labor force and ecological environment, which are two bases of a human society. We here focus on labor force only for which is the most active factor for social economic development, though ecological environment is a big problem in China.

A group of American economists, such as Gary S. Becker, T. W. Schultz, George J. Stigler, Milton Friedman, etc., advocate the concept “human capital” to describe the quality of labor force [ 14 ]. Now, that the concept of human capital has been widely spread and accepted, and for the sake of comparing the quality of labor force internationally, we take the advantage of data availability to use it, even though we are a bit shy to treat labors as capital.

3.3.1. Human capital index and its aging structure

From Graph 6 , we can see that China’s human capital level rows at the lowest position in the other 14 countries, and upper than Brazil. In aging structure, it seems a common problem currently for all other 14 countries but China. In fact, the aging issue in China is becoming a problem because of China’s one-child policy which lasted 35 years. So, it becomes urgent to promote the quality of labors, if given the labor force participation and employment rate.

Graph 6.

Human capital index and its structure by overall order 2015.

3.3.2. Labor force participation and employment

China has no doubt the best performance both in labor force participation and employment ( Graph 7 ). Then, we see the quality of labor, for “education and training are the most important investments in human capital” [ 14 ].

Graph 7.

Employment and labour force paticipationparticipation by unemployment order

3.3.3. Education efficiency

From 15-year-old students’ performance in 2012, we find that the quality of labor force in China is worth optimistic for the future. But on second thought, Chinese is so diligent and smart that China should have the highest quality of development, but China’s HDI is at the 90th position, just at the middle level of the world. Why? There might be many reasons involved, may we have another paper to discuss the issue for the limit of article length.

4. Results and conclusions

From what has been discussed above, we conclude the following results:

Equalization and justice are important complement of HDI; The HDI includes HCI; The two major parts of HCI, education and health, are both capabilities residing in people, which directly related to a person’s income and in social level to both quantity and quality of economic development, and directly benefited from equalization and justice; Hence, HDI, HCI, and equality are inter relatedly constitute and cooperate the quality of development/quality of life. ( Graph 2 ) The economy (income) is the business of market, while the education and health of labors and the income distribution should be supervised and guaranteed by the government; that is to say that the quality of life should be achieved by the combination of government and market.

The overall level of HDI in China is nearly 20% less than the level of the first 50 countries, and just at the average level of the world in quality of life. Among the selected 15 countries, China is at bottom of the row, ranked 90th in the world, and approximately accounts for 77% of the highest valued country, Norway; 79% of the United States, the typical capitalist country; and 82% of Japan, the Asian most developed country. That means we have a long way to go in quality of life ( Table 1 , Graph 3 ).

Both Germany and UK have best performance in “Mean years of schooling,” which implying the labor force and the civilized residents endowed by education; UK in Anglo-Saxon model with capitalist nature, has the similar pattern (8:1:8, means rank of health/education/economy) with Germany (6:1:6) in “Rhine model,” but far from the pattern of USA (10:4:3); Czech Republic (with similar pattern 11:8:11) rows upper also by its “Mean years of schooling,” which means education gains much attention in Czech as well. Australia (3:3:7) has almost the opposite pattern with USA, but with better momentum of development in practical economy than USA. China should not take the model of USA, but learn more from Germany, UK and Australia, and Czech, that is, pay more attention to education for a civilized society in the future (Annex Table 1 ).

In the Gini coefficient case, China (2014) performs better than the three Latin countries and the two typical capitalist countries, USA and UK; China’ s quintile ratio is only better than that of the three Latin countries but worse than USA and UK; The Palma ratio provides support to the quintile’s case. That is, China’s polarization in income distribution is conspicuous worse than the Gini performance with the influence of middle income populations included. Hence, we should concern more of the low income groups ( Graph 5 , Table 1 ).

The income inequality of three Latin countries shows much worse cases than in China, and their polarization is even much worse than their Gini case as well. Serious inequality cannot bring a developed economy from the lesson of Latin countries. Therefore, equality and social justice in China as institutional environment given by the government should improve continuously for the sake of promoting the living quality of the people ( Table 1 ).

China is a socialist country as its constitution expressed, and in case any adverse effect happens, it is very necessary for China to have higher pursuit in equality and social justice, e.g., reach to 35/7/1.5 (Gini/quintile/Palma), equivalently the average level of listed 15 countries, close to the level of UK (38/7.6/1.7) or Australia (34/5.9/1.3), as the minimum pursuits in 5–10 years, from 37/10/2, the currently level of China by the inequality index ( Table 1 ).

China’s human capital Index row at the lowest position among the countries, only better than Brazil’s ( Graph 6 ). But as the positive factor of HCI, China has the best performance in all 15 countries both in labor force participation and employment ( Graph 7 ). From 15-year-old students’ performance in education efficiency in 2012, the quality of labor force in China is worth optimistic for the future ( Graph 8 ). Therefore, China has its advantages in human capital, and furtherly in the potential of development.

It is possible to achieve better growth speed while we are focusing on the quality of development.

Graph 8.

Education quality by order of science 2012 (Pperformance of 15-year-old student).

Acknowledgments

The authors would like to thank Dr. Tomáš Wroblowský, VSB, Czech Republic, for his feedback and suggestions regarding data and the quantitative methodologies used in the chapter.

We would also like to thank anonymous referees for their valuable comments and corrections to our English writing.

We would like to express our gratitude to both Social Science Foundation (Serial No: HB15LJ002), funded by Hebei Programming Office for Philosophy and Social Science, China, and Soft Science Foundation (Serial No: 16457699D), funded by Hebei Bureau of Science and Technology, China, for providing us with research funds.

The research is supported by the SGS project of VŠB-TU Ostrava Czech Republic under No. SP2016/11.

JEL classification: E6, F5, F6, O15, O5

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Suspected silent pituitary somatotroph neuroendocrine tumor associated with acromegaly-like bone disorders: a case report

• Tongxin Xiao 1 ,
• Xinxin Mao 2 ,
• Ou Wang 1 ,
• Yong Yao 3 ,
• Kan Deng 3 ,
• Huijuan Zhu 1 &
• Lian Duan   ORCID: orcid.org/0000-0002-4213-474X 1  

BMC Endocrine Disorders volume  24 , Article number:  121 ( 2024 ) Cite this article

Metrics details

Growth hormone (GH) positive pituitary neuroendocrine tumors do not always cause acromegaly. Approximately one-third of GH-positive pituitary tumors are classified as non-functioning pituitary tumors in clinical practice. They typically have GH and serum insulin-like growth factor 1 (IGF-1) levels in the reference range and no acromegaly-like symptoms. However, normal hormone levels might not exclude the underlying hypersecretion of GH. This is a rare and paradoxical case of pituitary tumor causing acromegaly-associated symptoms despite normal GH and IGF-1 levels.

Case presentation

We report a case of a 35-year-old woman with suspicious acromegaly-associated presentations, including facial changes, headache, oligomenorrhea, and new-onset diabetes mellitus and dyslipidemia. Imaging found a 19 × 12 × 8 mm pituitary tumor, but her serum IGF-1 was within the reference, and nadir GH was 0.7ng/ml after glucose load at diagnosis. A thickened skull base, increased uptake in cranial bones in bone scan, and elevated bone turnover markers indicated abnormal bone metabolism. We considered the pituitary tumor, possibly a rare subtype in subtle or clinically silent GH pituitary tumor, likely contributed to her discomforts. After the transsphenoidal surgery, the IGF-1 and nadir GH decreased immediately. A GH and prolactin-positive pituitary neuroendocrine tumor was confirmed in the histopathologic study. No tumor remnant was observed three months after the operation, and her discomforts, glucose, and bone metabolism were partially relieved.

Conclusions

GH-positive pituitary neuroendocrine tumors with hormonal tests that do not meet the diagnostic criteria for acromegaly may also cause GH hypersecretion presentations. Patients with pituitary tumors and suspicious acromegaly symptoms may require more proactive treatment than non-functioning tumors of similar size and invasiveness.

Peer Review reports

Acromegaly is mainly associated with growth hormone (GH) hypersecretion caused by GH/somatotroph pituitary neuroendocrine tumors (PitNETs) [ 1 ]. Symptoms like facial changes and other acromegaly-related metabolic abnormalities are clues for the suspicion of acromegaly, but they are not mandatory for diagnosis [ 2 , 3 ]. Biochemical tests, including serum insulin-like growth factor-1 (IGF-1) and nadir GH during oral glucose tolerant test (OGTT), are critical to diagnosing acromegaly. Patients with normal IGF-1 levels are usually considered acromegaly unlikely [ 1 ]. The cut-off of nadir GH in the OGTT is traditionally 1.0ng/ml, while increasing clinicians consider 0.4ng/ml as a better diagnostic cut point when using assays capable of detecting lower GH levels [ 1 , 2 ]. However, these diagnostic criteria still have limitations. For example, mild acromegaly could exhibit nadir GH < 0.4ng/ml [ 4 ], and the levels of IGF-1 could also be affected by physiological and non-physiological factors [ 1 ].

Up to 30% of patients with PitNETs synthesizing GH show normal IGF-1 and nadir GH levels [ 5 , 6 , 7 ]. Accordingly, silent GH PitNET is commonly used to describe clinical and biochemical non-functioning GH immunostaining-positive PIT-1 (pituitary specific transcription factor 1) lineage PitNETs. [ 7 , 8 ] Silent GH PitNET is a rare entity (about 2–4% of resected PitNETs), and they might have a younger onset age and a higher risk of recurrence compared with common non-functioning PitNETs [ 7 , 9 , 10 ]. A continuous spectrum may describe the PitNETs with different clinical presentations, serum hormone levels, and pathologic characteristics: functioning (typical symptoms and elevated hormones), whispering or subtle (subtle symptoms with elevated hormones), clinically silent (no symptom with elevated hormones), and silent (no symptom with normal serum hormones) tumors [ 6 , 10 ]. In addition to typical acromegaly, the classification and surveillance strategy of cases with mild hormone elevation or associated symptoms is still ambiguous. Clinically silent cases with elevated GH and IGF-1 were occasionally reported [ 3 , 11 , 12 ], but no reports of cases with acromegaly-related symptoms and normal hormone levels have been published to our knowledge.

Here, we report a paradoxical case with suspicious acromegaly symptoms, associated complications, and pituitary macroadenoma. The serum IGF-1 was normal, and nadir GH was 0.7ng/ml. A GH-positive PIT-1 lineage pituitary neuroendocrine tumor was confirmed after surgery, with IGF-1 and nadir GH decreasing significantly. We consider this case an untraditional subtype within the spectrum of GH PitNETs, as it does not align with the classic definitions of either acromegaly or silent GH-PitNETs. We aim to highlight that acromegaly-associated presentations might occur in clinically subtle or silent somatotroph PitNETs that cannot be diagnosed as acromegaly.

A 35-year-old woman complained of oligomenorrhea and acromegaly-like facial changes of 13 years duration. She delivered a healthy baby following natural conception during this period, and her last menstruation was one year previously, after induced abortion for two consecutive pregnancy losses at eight weeks. She occasionally had headaches, excessive perspiration, and lower back discomfort, while she denied galactorrhea, taking contraceptives or any medications that may contain estrogen, and a familial history of pituitary disease. Her height and weight were 163 cm and 78 kg (with an increase of 23 kg since the onset of symptoms). She underwent surgery for a left occipital bone fracture because of a car accident at the age of 19, after which she gradually developed strabismus. The patient had late-stage pregnancy-induced hypertension, which resolved after delivery. She was diagnosed with diabetes mellitus one year ago but refused treatment. On physical examination, she showed widened nasal alae, thickened lip, and an enlarged tongue without enlargement of the hands and feet, suspected as acromegaly. Dynamic pituitary MRI found a 19 × 12 × 8 mm lesion in the pituitary gland without stalk deviation or extension into the cavernous sinus (Fig.  1 ). IGF-1 was measured twice with a 15-day interval, showing 212 and 133ng/ml (reference: 63–223ng/ml). The nadir GH after 75 g glucose load was 0.7ng/ml (Table  1 ). Other pituitary hormones stayed normal. Meanwhile, a thickened skull base was observed in CT (Fig.  2 ), and the whole-body bone scan indicated diffusely increased uptake in cranial bones. Bone turnover markers were elevated, with β-CTX and TP1NP measuring 2.06ng/ml and 135ng/ml, respectively. T-25OHD was 15.8ng/ml, and ALP was 213U/L. Her serum calcium, phosphate, and bone mineral density (Z-score at lumbar spine L1-L4: -0.7) were within the normal range (Table  1 ). These raised suspicion for metabolic bone disease. In addition, she had confirmed diabetes mellitus with Hb1Ac of 10.8% and hypertriglyceridemia with triglyceride rising to 3.96mmol/L. Hepatic steatosis was confirmed, with a mild impaired liver function (ALT ranged from 28 to 61U/L). A thorough body inspection did not find other suspected tumors. No pathogenic variants were identified by whole exome sequencing (WES), while two variants of uncertain significance were detected in the FOXA2 and LEPR genes (Supplementary Table 1 ).

MRI image of the pituitary tumor. A , T1-weighted image (T1-WI) contrast-enhanced sagittal plane reveals a hypointense nodule, B , T1-WI contrast-enhanced coronal plane shows a macroadenoma across the right and left of the pituitary, without extension into the cavernous sinus or optical chiasm. Arrows indicated the location of the lesion

Head CT image showing a thickened skull base. A , Head CT image showed a thickened skull base; B, Head CT image showed a thickened skull base and the skull lesion caused by a previous traffic accident

In light of these suspicious symptoms, complications, and borderline nadir GH, we consider the possibility of a silent GH-secreting pituitary neuroendocrine tumor in this patient. Then, she received endoscopic transsphenoidal tumor resection surgery, with the soft pituitary tumor removed. In the histopathological examination of the resected specimen (Fig.  3 ), a PIT-1 lineage PitNET without other positive-staining transcription factors was confirmed. It was partially positive for GH while more weakly staining in PRL. Its Ki-67 proliferation index was 1%. CAM5.2 staining indicated a sparsely granulated pattern, and SSTR2 was positive. The staining of other pituitary hormones was negative. After the surgery, the IGF-1 dropped to 77ng/mL in 3 days and remained at 125ng/mL three months post-operation. Similarly, β-CTX and P1NP decreased significantly to 1.20ng/mL and 86.0ng/mL 3 months after surgery. (Table  1 ) She now takes one tablet of metformin sitagliptin twice daily and vitamin D3 1000U daily regularly. No new-onset discomfort was reported. 3 months after surgery, her fast blood glucose, Hb1Ac, and triglyceride were 7.9 mmol/L, 7.0%, and 3.41 mmol/L, respectively. No remnant of pituitary tumor was observed at the last visit.

Pathologic and immunohistochemical image of the pituitary neuroendocrine tumor. A , H&E staining shows typical pituitary neuroendocrine tumor cells (×200). B , GH was moderately positive. C , PIT-1 was strongly positive. D , PRL was scattered and focal positive. E , ki-67 proliferative activity was approximately 1%. F , CAM5.2 was in a sparsely granulated pattern

Discussion and conclusions

We report a paradoxical PitNET case showing several likely acromegaly-associated presentations but no elevation of IGF-1 or nadir GH after glucose load was confirmed. This patient experienced partial relief of discomforts after the pituitary surgery, and immunostaining showed a PIT-1 lineage tumor with moderately positive GH and a much weaker staining of PRL. We consider this case possibly an untraditional clinically silent GH pituitary PitNET.

In our case, both two random IGF-1 tests taken two weeks apart were in the normal range, with a borderline nadir GH. We observed a two-fold decrease in IGF-1 levels in tests taken two weeks apart, but this difference might simply be due to normal sampling and testing variations. Despite the limitations in biochemical diagnostic criteria for acromegaly and a possible weak impact of the metabolism of IGF-1 because of hepatic steatosis, it is the fact that these results could not distinguish the case from patients without GH PitNETs. However, this PitNET might still have a capacity for GH hypersecretion, even if it is relatively moderate. According to the theory of the continuous spectrum of PitNETs ranging from silent to functioning [ 10 ], the rising hormones may not cause obvious clinical symptoms [ 12 , 13 ]. Meanwhile, it is widely accepted that clinical symptoms develop after the elevation of corresponding hormones. Still, our case showed a rare scenario in which a young woman had several acromegaly-like presentations with a normal IGF-1 and 0.7ng/ml nadir GH. Although her amenorrhea was likely due to uterine lesions, other symptoms like facial changes and comorbidities (including diabetes mellitus, dyslipidemia, and metabolic bone disease) were all possibly acromegaly-associated [ 1 ]. For example, acromegaly is associated with abnormal skeletal metabolism, leading to elevation of bone turnover markers, lower bone quality, and increased risks of fractures [ 14 , 15 ]. In our case, decreasing bone turnover markers after pituitary tumor resection supported that the GH-positive PitNETs had at least a partial influence on her abnormal bone metabolism. Likewise, metabolic complications in this case, including diabetes, dyslipidemia, and hepatic steatosis, are also diseases recommended for screening in acromegaly [ 1 ]. The concurrence of these diseases is less likely to be a coincidence without an underlying hypersecretion of GH.

Meanwhile, differential diagnoses of a thickened skull base, increased radioactive uptake in a bone scan, and elevated bone turnover markers should also be considered. These differential diagnoses mainly included other metabolic bone diseases, like Paget disease of bone, osteopetrosis, fibrous dysplasia of bone, and pachydermoperiostosis [ 16 ]. However, this patient did not meet the clinical diagnosis criteria for most of these diseases. She had no enlargement of hands or hypertrophic skin changes, while WES did not identify any relevant gene mutation. Considering the rapid decline of IGF-1 and nadir GH after the surgery, together with her early-onset diabetes mellitus and hypertriglyceridemia without relevant familial history, from a monistic perspective, we suppose that it is highly likely that her GH-positive PitNET, which may cause a relatively moderate but long-lasting GH secretion, contributed most to the abnormal condition. Still, given the atypical and rare combination of active clinical symptoms and silent test results in this case, we conservatively considered that other comorbidities would not be completely ruled out at this stage. A long-term follow-up to observe whether her presentations and bone turnover markers could be relieved well without recurrence of the PitNET could assist in the final confirmation of the role of GH-PitNETs in this case.

As for pathologic classification, because of the predominant GH staining, a weaker PRL staining, and no TSH staining, this PIT-1 lineage PitNET was classified as a mammosomatotroph tumor. GH and PIT-1 positive PitNETs span a wide range of heterogeneous tumors with different clinical characteristics, like aggressiveness and secretion activity [ 8 , 17 ]. Silent or subtle GH PitNETs share the pathologic classification with typical acromegaly, but their distribution of specific pathological subtypes differs. More tumors are likely expressing multiple pituitary hormones or even multiple transcription factors in silent PitNETs [ 7 , 10 ]. For example, over half of silent GH PitNETs could have co-positive staining of PRL, as shown in our case, which is much more than those causing acromegaly [ 9 ]. Although the tumor, in this case, did not find invasion on imaging and its Ki-67 proliferation index was 1%, a close follow-up is recommended because it also exhibited several high-risk features.

For PitNETs that can express hormones like GH or ACTH without inducing noticeable hormone level elevations and symptoms, the underlying mechanisms remained unclear. An intriguing hypothesis is that some of these PitNETs might display a more primitive stage of differentiation [ 5 ]. Therefore, they may tend to retain the ability to express more pituitary hormones or even transcription factors, while hormone synthesis or secretion functions are less developed. This may also explain why PitNETs with higher aggressiveness are more common in silent GH or ACTH tumors [ 10 , 18 ]. Another common assumption is that a short disease duration causes a lack of clinical change, especially when the secretion capacity of somatotroph PitNETs is moderate. However, a short disease duration could not explain our case since her clinical presentations seemed more apparent than the rechecked hormone levels at diagnosis.

Additionally, we propose another potential explanation for biochemically silent cases with GH hypersecretion symptoms: GH-positive PitNETs may also secret GH cyclically, similar to cyclic Cushing disease. There are reports of ‘silent’ corticotroph PitNETs showing associated manifestations without remarkable biochemical tests [ 19 ], but no similar case has yet been reported in somatotroph PitNETs. It is possible that our patient was tested at her trough in GH concentration, but the long-lasting effect of cyclic hypersecretion of GH and IGF-1 may cause associated symptoms. The mechanisms of cyclic Cushing disease are also undetermined. Hypotheses include hypothalamic dysfunction, the infarction or bleeding of pituitary tumors, and uncommon sensitivity to positive and negative feedback in specific patients [ 20 , 21 ]. These assumptions may also be applied to GH-positive tumors. Since the half-life of IGF-1 is longer than serum cortisol in Cushing’s disease, a distinct fluctuation caused by ‘cyclic acromegaly’ would be more challenging to detect if such tumors indeed exist.

In summary, a normal IGF-1 and nadir GH at diagnosis cannot exclude the possibility of underlying GH hypersecretion from pituitary tumors in patients with suspicious acromegaly presentations. Tumors resection might improve acromegaly-like symptoms, and silent GH PitNETs have a higher risk of invasiveness and recurrence. Therefore, a more proactive surgical treatment should be considered in suspicious GH PitNETs than non-functioning tumors of similar size.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Growth hormone

Insulin-like growth factor 1

Pituitary neuroendocrine tumors

Oral glucose tolerant test

Pituitary specific transcription factor 1

Carboxy-terminal cross-linked telopeptide of type 1 collagen

Total procollagen type 1 N-terminal propeptide

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Acknowledgements

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This research was funded by the National High-Level Hospital Clinical Research Funding (2022-PUMCH-A-155, 2022-PUMCH-B-016).

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T.X.: writing the original draft and collecting data. X.M.: pathologic data and discussion. O.W.: participate in bone abnormality discussions. Y.Y. and K.D.: reviewing and editing. H.Z.: supervision and reviewing. L.D.: collecting data, supervision, and reviewing.

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Xiao, T., Mao, X., Wang, O. et al. Suspected silent pituitary somatotroph neuroendocrine tumor associated with acromegaly-like bone disorders: a case report. BMC Endocr Disord 24 , 121 (2024). https://doi.org/10.1186/s12902-024-01657-7

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“This is a very, very uncomfortable illustration of the fragility of the world’s core internet infrastructure,” said Ciaran Martin, the former chief executive of Britain’s National Cyber Security Center and a professor at the Blavatnik School of Government at Oxford University.

A cyberattack did not cause the widespread outage, but the effects on Friday showed how devastating the damage can be when a main artery of the global technology system is disrupted. It raised broader questions about CrowdStrike’s testing processes and what repercussions such software firms should face when flaws in their code cause major disruptions.

How a Software Update Crashed Computers Around the World

Here’s a visual explanation for how a faulty software update crippled machines.

How the airline cancellations rippled around the world (and across time zones)

Share of canceled flights at 25 airports on Friday

50% of flights

Ai r po r t

Bengalu r u K empeg o wda

Dhaka Shahjalal

Minneapolis-Saint P aul

Stuttga r t

Melbou r ne

Be r lin B r anden b urg

London City

Amsterdam Schiphol

Chicago O'Hare

Raleigh−Durham

B r adl e y

Cha r lotte

Reagan National

Philadelphia

1:20 a.m. ET

CrowdStrike’s stock price so far this year

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ASM is a therapeutic target in dermatomyositis by regulating the differentiation of naive CD4 + T cells into Th17 and Treg subsets

• Yuehong Chen 1 ,
• Huan Liu 1 ,
• Zhongling Luo 1 ,
• Jiaqian Zhang 1 ,
• Min Dong 1 ,
• Geng Yin 2 &
• Qibing Xie 1  

Skeletal Muscle volume  14 , Article number:  16 ( 2024 ) Cite this article

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This study aims to investigate the involvement of acid sphingomyelinase (ASM) in the pathology of dermatomyositis (DM), making it a potential therapeutic target for DM.

Patients with DM and healthy controls (HCs) were included to assess the serum level and activity of ASM, and to explore the associations between ASM and clinical indicators. Subsequently, a myositis mouse model was established using ASM gene knockout and wild-type mice to study the significant role of ASM in the pathology and to assess the treatment effect of amitriptyline, an ASM inhibitor. Additionally, we investigated the potential treatment mechanism by targeting ASM both in vivo and in vitro.

A total of 58 DM patients along with 30 HCs were included. The ASM levels were found to be significantly higher in DM patients compared to HCs, with median (quartile) values of 2.63 (1.80–4.94) ng/mL and 1.64 (1.47–1.96) ng/mL respectively. The activity of ASM in the serum of DM patients was significantly higher than that in HCs. Furthermore, the serum levels of ASM showed correlations with disease activity and muscle enzyme levels. Knockout of ASM or treatment with amitriptyline improved the severity of the disease, rebalanced the CD4 T cell subsets Th17 and Treg, and reduced the production of their secreted cytokines. Subsequent investigations revealed that targeting ASM could regulate the expression of relevant transcription factors and key regulatory proteins.

ASM is involved in the pathology of DM by regulating the differentiation of naive CD4 + T cells and can be a potential treatment target.

Introduction

Dermatomyositis (DM) is a subtype of idiopathic inflammatory myopathy characterized by skin rashes like heliotrope rash or Gottron sign, symmetric proximal limb muscle weakness, and the presence of myositis-specific antibodies including anti-MDA5, Anti-Mi2, antiNXP2, anti-TIF1, among others. These antibodies aid in classifying clinical features and guiding management [ 1 , 2 ]. The reported prevalence of DM ranges from 1 to 6 per 100,000 in the United States and 3.8 per 100,000 in Sweden, with a higher prevalence among individuals aged 50 to 79 years and a two-fold higher occurrence in females compared to males [ 2 , 3 ]. Apart from affecting muscles and skin, DM can also involve in the immune system, lungs, heart, gastrointestinal tract, endocrine system and other tissues, which greatly affect the diagnosis and treatment as well as intensify the financial burden [ 2 ]. Following a DM diagnosis, screening for malignancies and interstitial lung disease is recommend due to the high risk of developing malignancies with a prevalence of 20% and interstitial lung disease with a prevalence of 42% [ 1 , 4 ]. Pathologically, DM is characterized by perifascicular atrophy and the infiltration of various immune cells like macrophages, B cells, T cells, plasma cells, and plasmacytoid dendritic cells [ 2 ].Currently, the primary treatment options for DM involve a combination of glucocorticoids and immunosuppressive drugs like methotrexate, azathioprine, calcineurin inhibitors, and mycophenolate mofetil, among others. However, these treatments are associated with adverse effects such as an increased risk of infection, bone marrow suppression, liver toxicity, osteoporosis, and a high recurrence rate, which limits their use [ 5 , 6 , 7 ]. Recent advancements in understanding the pathological mechanism of DM have identified potential targets like T cells, B cells, cytokines such as tumor necrosis factor-α (TNF-α), and Janus kinase (JAK) for the treatment. Although targeted treatments have shown promise, the evidence supporting their effectiveness primarily comes from case reports or case series, highlighting the need for more high-quality studies [ 8 , 9 , 10 , 11 , 12 , 13 ]. The current immunosuppressive drugs have limited efficacy, and addressing the key pathological molecules involved in DM still falls short of meeting therapeutic demands. Therefore, the identification of new therapeutic targets has become a pressing matter. While the exact mechanism behind DM remains unclear, the search for novel key regulatory molecules offers hope for future treatments.

Acid sphingomyelinase (ASM) is an enzyme that breaks down lipids present in cell membrane and lysosomes. It also interacts with various proteins distributed throughout the cell membrane [ 14 ]. ASM can hydrolyze the phosphodiester bonds of sphingomyelin on the plasma membrane, leading to the generation of ceramides. This enzymatic function of ASM is crucial for controlling cellular immune responses, as well as cellular processes like differentiation, proliferation, and apoptosis [ 15 , 16 ]. It is worth noting that the sphingomyelin/ceramide signaling pathway, regulated by ASM, has been implicated in various diseases such as cancer [ 17 ], cardiovascular diseases like atherosclerosis [ 18 ], neurological disorders including Alzheimer’s disease [ 19 ], and respiratory conditions like chronic obstructive pulmonary disease [ 20 ]. Consequently, this pathway represents a promising target for therapeutic intervention in these conditions. However, the specific role of ASM in the pathology of DM remains incompletely understood. This study presents evidence linking ASM to the pathology of DM using a well-established mouse model, experimental autoimmune myositis (EAM) mouse model to study myositis. Additionally, it demonstrates that ASM plays a crucial role in regulating the differentiation of naive CD4 + T cells into distinct T cell subsets, suggesting ASM as a potential therapeutic target for myositis treatment.

RPMI-1640 media (abs9468) was purchased from univ bio-technology CO., Ltd (Shanghai, China) and fetal bovine serum (FBS, 10099-141) was purchased from Gibco (Grand Island, NY, USA). Complete Freund’s adjuvant (7027) and incomplete Freund’s Adjuvant (7002) was purchased from Chondrex (Chondrex Inc, WA, USA). Amitriptyline hydrochloride (AMI, HY-B0527A) was purchased from MedChemExpress (Monmouth Junction, NJ, USA). Plastic feeding tubes (TFEP-001, 2.25 × 50 mm) were purchased from Shanghai Yuyan Instruments Company (Shanghai, China). The RNeasy ® Mini kit (74,104) was purchased from Qiagen (Hilden, Germany). The anti-GAPDH antibody (EPR16891, ab181602), anti-Acid sphingomyelinase antibody (ab83354), acidic Sphingomyelinase Assay Kit (Fluorometric) (ab190554), and human Acid sphingomyelinase ELISA Kit (SMPD1) (ab277075) were purchased from Abcam (Cambridge, UK). ChamQ SYBR qPCR Master Mix (Q311-02) and HiScript ® III RT SuperMix for qPCR (+ gDNA wiper, R323-01) were purchased from Vazyme (Nanjing, China). The anti-STAT3 antibody (AF6294), anti-STAT3 (phospho Y705) antibody (AF3293), and enhanced chemiluminescence kit (KF005) were purchased from Affinity Biosciences (Cincinnati, OH, USA). The Immobilon ® -P transfer membrane, 0.45 μm (IPVH00010), was purchased from Merck Millipore (Billerica, MA, USA). The anti-STAT5 Antibody (381,427) and phospho-STAT5 (Tyr694, 381,125 ) were bought from ZEN BIO (Shanghai, China). Mouse IL-6 ELISA kit (MM-1011M1), mouse IL-10 ELISA kit (MM-0176M1), mouse IL-17 A ELISA kit (MM-0759M1), mouse ASM ELISA kit (MM-46150M1) were bought from Meimian (Jiangsu, China). Cell lysis buffer (P0013) and the BCA protein quantitation assay (P0010) were purchased from Beyotime (Shanghai, China). The protease inhibitor cocktail (GK10014), phosphatase inhibitor cocktail I (GK10011), phosphatase inhibitor cocktail II (GK10012), and bordetella pertussis toxin (GC17532) were purchased from Glpbio (Montclair, CA, USA). PBS (1×, G4202) and environmentally friendly GD fixing solution (G1111-100mL) were purchased from Servicebio (Wuhan, China). Foxp3 / Transcription Factor Staining Buffer Set (00-5523-00), ic fixation buffer (00-822-49), fixable Viability Dye 780 APC-cy7 (50-169-66), and permeabilization Buffer (00-8333-56) were bought from ThermoFisher Scientific (Waltham, MA, USA). EasySep™ Mouse Naïve CD4 + T Cell Isolation Kit (19,765) were bought from Stemcell Technologies (Canada). Mouse Th17 Cell Differentiation Kit (CDK017) was bought from R&D Systems (minneapolis, minnesota, USA). Anti-Mouse CD3 BV510 (740,147), anti-Mouse CD25 BB515 (564,424), anti-Mouse FOXP3 APC (560,401), and GolgiStop™ Protein Transport Inhibitor (554,724) were bought from BD Biosciences (New Jersey, USA). Anti-Mouse CD4 Percp-cy5.5 (E-AB-F1097J) and anti-Mouse IL-17 A PE (E-AB-F1199D) were bought from Elabscience (Wuhan, China). Plant hemagglutinin (48–68) (115721-95-4) was bought from AbMole (Chicago, USA). Grip Strength Meter (yls-13 A) was bought from Jinan Yiyan Technology Development Co., LTD (Jinan, China).

Study population

Patients who met the EULAR/ACR criteria for the diagnosis of DM [ 21 ] Were recruited tetween December 2019 and November 2020 at the Department of Rheumatology, West China Hospital of Sichuan University. The exclusion criteria were as follows: other lung diseases such as idiopathic pulmonary fibrosis, pulmonary sarcoidosis, pulmonary infection, and chronic obstructive pulmonary disease; autoimmune diseases other than DM; malignant diseases; pregnancy; and overall poor health. A total of 58 DM patients and 30 healthy controls (HCs) matched for age and gender were included. The detailed information regarding the enrolled DM patients and HCs is provided in Table  1 . This study was conducted in compliance with the Declaration of Helsinki and approved by the ethics committee of West China Hospital (No. 246 in 2019). Written informed consent was obtained from all participants, and all methods followed relevant guidelines and regulations.

Myositis disease activity assessment

During the clinical evaluation, two experienced rheumatologists collected data on the patients’ medical history and conducted physical examinations. The myositis disease activity assessment tool [ 22 ] was used to measure disease activity, specifically utilizing the myositis intention to treat activity index (MITAX). The global disease activity score derived from MITAX is computed by summing as the total of the worst category scores for each of the seven individual organ systems (constitutional, cutaneous, skeletal, gastrointestinal, pulmonary, cardiac, and muscle). This score is divided by the maximum possible score, which ranges from 1 to 63. Each organ system is categorized into five categories: A (active), B (beware), C (contentment), D (discount), and E (no evidence). These categories correspond to values of 9, 3, 1, 0 (indicating no current activity but previously active), and 0 (indicating no current or previous activity), respectively. Therefore, a higher score indicates increased disease activity. The physician assessed disease activity at the time of enrollment.

Cutaneous damage assessment

The cutaneous assessment tool-binary method (CAT-BM) [ 23 ] was utilized to assess cutaneous damage with disease activity evaluate based on 7 indicators: Gottron papule, Heliotrope rash, erythema on the zygomatic or facial regions, linear erythema on the limbs’ extension side, V-zone erythema on the front of the neck, erythema on the back of the neck and shoulder (known as the Shawl sign), and erythema on non-exposed areas. Disease activity scores ranged from 0 to 17 while lesion severity was scored on a 0–11 scale, accounting for atrophy or pigmentation at the lesion site. The total score, ranging from 0 to 28, was calculated by combining the disease activity and lesion severity scores.

Detection of serum ASM levels

Each patient and HC provided a 4mL venous blood sample, which was collected into a coagulating agent-containing blood collection tube. The samples were then allowed to clot at room temperature for about 2 h. After clot formation, the samples were centrifuged at 2000 g for 10 min. Subsequently, the sera were collected and stored at a temperature of − 80° C. Human ASM ELISA kit was utilized to assess serum ASM levels following the kit’s provided specifications.

Female and male C57BL/6J-smpd1 heterozygous mice, aged four to six weeks, were obtained from Cyagen Bioscience (Guangzhou, China) and were then bred to generate SMPD1 −/− mice. Wild-type C57BL/6 female mice, aged seven weeks, were obtained from the Beijing Huafukang Biotechnology Company (Beijing, China). The mice were randomly housed in cages with five mice per cage at the Animal Facility of Chengdu Frontier Medical Center, West China Hospital, Sichuan University, under pathogen-free conditions. They were provided with adequate food and water and allowed to acclimate for one week before any experiments were conducted. The mice were maintained on a 12-hour light and 12-hour dark cycle at a consistent temperature ranging from 22 to 24 ◦C. Seven-week-old female wild-type guinea-pigs were purchased from Byrness Weil Biotech Ltd. (Chongqing, China). All animal experiments performed in this study were approved by the Animal Ethics Committee of West China Hospital, Sichuan University (approval number: 2,020,243 A).

Genotyping strategy

Genotyping of SMPD1 −/− mice was conducted following protocols provided by Cyagen Bioscience. In brief, tails from 3-week-old mice were digested in a buffer composed of 50 mM KCl, 10 mM Tris-HCl (pH 9.0), 0.1% Triton X-100, and 0.4 mg/mL Proteinase K. Then, the genomic DNA were amplified with the following components: 7.9 µL of double-distilled water, 0.8 µL of forward primer, 0.8 µL of reverse primer, 10 µL of 2 ×Mouse Direct PCR Mix and 0.5 µL of DNA. This amplification process involved 35 cycles. The PCR conditions consisted of denaturation at 94 ◦C for 5 min, annealing at 58 ◦C for 30 s, extension at 72 ◦C for 30 s per kilobase pair, and an additional extension at 72 ◦C for 7 min. Then, PCR products were visualized by agarose gel electrophoresis. The primer sequences employed are listed below: PCR primer set 1, F1: 5′ -GCA AAG TCT TAT TCA CTG CTC T-3′, R1:5′ -AGA GAT GTT CCA AGT CGA AAA GAT-3′, product size: 641 bp; PCR primer set 2, F1: 5′ -TAA AGT TAG GGA GAG TAA AGT CAG C-3′, R2: 5′ -CCA TCT ATT TGG TAA ACT CGG TAG-3′, product size: 550 bp.

EAM mouse model

After a week of housing and acclimatization at the animal facility, mice were randomly assigned to each group using a random number table, with six mice in each group, to establish the EAM model. The model was induced by 1.5 mg of myosin extracted from wild-type guinea pigs, along with complete Freund’s adjuvant containing 10 mg/ml of Mycobacterium tuberculosis. This method was based on previously published protocols [ 24 ]. Drugs were administered daily through oral gavage two days prior to model establishment and continued until the end of the experiment. The model group received a drug dilution buffer composed of water, ethanol, and 2% acetic acid in a ratio of 8:3:1 by volume, with less than 5% of DMSO included. The drug groups were administrated the same concentrations of DMSO and dilution buffer, along with varying concentrations of the tested drugs (AMI 1 mg/kg or 10 mg/kg).

At the conclusion of the study, mice were subjected to muscle strength testing using a mouse grip tester (YLS-13 A, Jinan Yiyan Technology Development Co., LTD, China). The test was repeated three times, and the average values were recorded. Subsequently, the mice were sacrificed and blood samples were collected to obtain sera. The spleens were weighed and used for flow cytometry analysis to detect the CD4 T cell subsets Th17 and Treg. Moreover, samples of the gastrocnemius muscle were collected and either preserved as fresh specimens in liquid nitrogen or fixed in the GD fixing solution for pathological analysis.

Detection of serum CK

Mouse serum samples were sent to Wuhan Servicebio Technology Co., Ltd (Wuhan, China) for the detection of serum CK levels. The analysis was performed using an automatic biochemistry analyzer (Chemray 800) manufactured by Rayto Life and Analytical Sciences Co., Ltd ( Shenzhen, China ).

Flow cytometry

To detect the percentages of CD4 T subsets in spleens, we utilized the following fluorescent-labeled antibodies: APC-Cy7-FVS780 (1:2000) for cell death, Anti-Mouse CD3 BV510 (1:50), Anti-Mouse CD4 Percp-cy5.5 (1:50), Anti-Mouse CD25 BB515 (1:50), Anti-Mouse IL-17 A PE (1:50), and Anti-Mouse FOXP3 APC (1:50) for Th17 or Tregs. The stained samples were analyzed using a flow cytometer (Cytoflex, Beckmam, USA).

Muscle homogenate

To prepare the muscle homogenates, the muscle was removed from liquid nitrogen and placed on ice for thawing. The muscle tissues were then fragmented into small sections and transferred to tissue grinding tubes, each containing 1 large steel ball (4 mm in diameter) and 2 small steel balls (3 mm in diameter) for grinding. Samples were obtained by adding PBS with a protease inhibitor (at a ratio of 1:100) to the tubes With each tube having 9 µL of PBS per 1 mg of tissue. The low temperature grinder (KZ-III-FP, Servicebio) was pre-chilled before use. The muscle pieces were ground for 10 s at of 70 Hz with three cycles of grinding at -10 °C and a 20 s pause between each cycle. After grinding, the samples were stored at -20 °C overnight, then thawed twice in liquid nitrogen and centrifuged at 5000 g for 10 min at 4 °C. The resulting supernatants were collected and the protein concentrations were determined using a BCA protein concentration assay kit.

H&E staining

Muscle tissues were fixed in an environmentally friendly solution, GD fixing solution, for 24 h. Subsequently, the tissues underwent paraffin embedding and sectioning to produce 5 μm sections. The hematoxylin and eosin (H&E) staining procedure was conducted following the manufacturer’s instructions. Neutral gum was applied to seal the slides which were subsequently stored at room temperature. An automated quantitative pathology imaging system was employed to scan the stained sections (Vectra Polaris, United States).

Quantitative analysis of H&E staining

Pathological qualitative scores were assessed based on the infiltration of inflammatory cells in the H&E stained sections of mouse muscles [ 25 ]. The scoring system ranged from 0 to 4.5, with scores of 1 indicating less than 5 muscle fibers affected, scores of 2 indicating 5 to 30 muscle fibers involved, scores of 3 indicating the involvement of a muscle bundle, and scores of 4 representing diffused widespread lesions. Additionally, a score of 0.5 was added when multiple lesions were found in a muscle segment.

IHC staining

To analyze the levels of crucial proteins, we employed immunohistochemical (IHC) staining.The process began with dewaxing the slides using xylene followed by hydration with gradient alcohol. Subsequently, antigen retrieval was performed, along with blocking of endogenous peroxidase and non-specific binding sites. The target protein was then detected using a primary antibody (diluted at 1:100) and the slides were left to incubate overnight at 4 ℃. The next day, thorough washing of the slides was carried out, followed by the addition of a secondary antibody labeled with horse radish peroxidase (diluted at 1:500) and an hour-long incubation at room temperature. A 3,3’-diaminobenzidine (DAB) kit was used to develop a brown color,, cell nuclei were counterstained, and the slides were sealed. Finally, an automatic quantitative pathology imaging system was employed to automatically scan the slides (Vectra Polaris, USA).

Enzyme-linked immunosorbent assay (ELISA) kits were employed according to the manufacturer’s instructions to measure the levels of ASM, IL-6, IL-10, and IL-17 A in the sera or tissue homogenates of mice. The optical density at 450 nm was measured using a microplate reader (CLARIOstar, BMG LABTECH, Germany), and protein concentrations were determined via a standard curve.

To analyze relative target gene expressions, the method of quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) was employed. The expressions of target genes were normalized with the internal reference GAPDH. The collection of RNAs from either cells or tissues was carried out following the instructions of the RNA extraction kit. The extracted RNA was then reverse transcribed into complementary DNA (cDNA), which served as the template for synthesizing the target gene using the specific primers. The quantification of gene expression levels was determined using the formula 2 −ΔΔCq . The following set of primers was utilized for the experiment: GAPDH primers for human (hGAPDH): F 5′-3′: CAC ATG GCC TCC AAG GAG TAA, R 5′-3′: TGA GGG TCT CTC TCT TCC TCT TGT; hASM: F 5′-3′: CTG TCT GAC TCT CGG GTT CTC, R 5′-3′: CTA TGC GAT GTA ACC TGGCAG; GAPDH primers for mouse (mGAPDH): F 5′-3′: AGG TCG GTG TGA ACG GAT TTG, 5′-3′ R: GGG GTC GTT GAT GGC AAC A; mIL-6: F 5′-3′: TTC CAT CCA GTT GCC TTC TTG, R 5′-3′: AGG TCT GTT GGG AGT GGT ATC; mIL-10: F 5′-3′: CTT ACT GAC TGG CAT GAG GAT CA, R 5′-3′: GCA GCT CTA GGA GCA TGT GG; mIL-17: 5′-3′ F: TCA GCG TGT CCA AAC ACT GAG, 5′-3′ R: CGC CAA GGG AGT TAA AGA CTT; mFOXP3: 5′-3′ F: AGT GGC AGG GAA GGA GTG TCA G, 5′-3′ R: AGG CTG GAT AAC GGC AGA GGA G; mRORγT: 5′-3′ F: AAG GTG GTA CTG GGT ATG GC, 5′-3′ R: CTC TTG GGC CTT GCA GTC TT; mASM: 5′-3′ F: ACT CCA CGG TTC TTT GGG TTC, 5′-3′ R: CGG CGC TAT GGC ACT GAA T.

Western blotting

Jurkat T cells were cultivated in RPMI-1640 medium supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin, incubated at 37 ℃ with 5% CO2. Cell passages were performed if the density exceeded 1.0 × 10 6 cells per milliliter. For key proteins analysis in the cell signaling pathway, Jurkat T cells were seeded in 6-well plates at a density of 5 × 10 4 cells per milliliter. AMI at 1µM or 10 µM was added, followed by overnight incubation. The next day, cells were stimulated with plant hemagglutinin at 5 µg/mL for 30 min before proteins extraction.

Protein expression levels were detected using Western blotting (WB). Protein extraction was carried out from stimulated cells or protein supernatants from muscle homogenates. Following determination of the protein concentrations, denaturation was achieved by adding loading buffer and heating at 95 °C for 10 min. Subsequently, 20 µg of proteins were loaded onto a gel for separation via gel electrophoresis. Protein transfer onto a nitrocellulose membrane was employed using a wet transfer system. The membrane was then blocked at room temperature for 30 min using 5% (w/v) non-fat milk in 1×Tris Buffered Saline containing 1‰ Tween 20 (TBST). Primary antibody was added to the membrane and left overnight at 4 °C. After three washes with TBST, the membrane was exposed to the secondary antibody for 1 h at ambient temperature. Following additional washes, the bands on the membrane were visualized by gel scanner (ChemiDoc XRS, BIO-RAD, USA) with an enhanced chemiluminescent substrate.

Spleen naïve CD4 + T cell differentiation

The differentiation of naïve CD4 + T cells from the spleen into T cell subsets was conducted using established methodologies [ 26 , 27 ]. For Th17 differentiation, a 24-well plate was coated with 1 mL of PBS containing anti-CD3 (2 µg/mL) and anti-CD28 (1 µg/mL) and incubated overnight at 4 °C. The following day, spleens from 8-week-old WT C57BL/6 mice were harvested. Spleen cells were isolated by grinding the spleen on a 70-µm mesh and collected in PBS supplemented with 1% FBS. The collected cells were centrifuged at 1000 rpm for 5 min, repeated twice, and red blood cells were lysed with ACK lysing buffer for 5 min on ice. The CD4 + T cells were then isolated using a CD4 + T cell isolation kit following the manufacturer’s instructions. The coating buffer was removed, and the plate was washed with PBS before seeding the CD4 + T cells in the Th17 differentiation buffer. Additionally, AMI (1µM) or AMI (10µM) was added. After four days, the cells were haevested for flow cytometry analysis. To assess differentiation, 1 µL of Golgi stop buffer was added to each well and incubated for 4 h. Subsequently, the cells were stained. Cell death was assessed using APC-Cy7-FVS780 (1:2000), and Th17 cells were stained with Anti-Mouse CD3 BV510 (1:50), Anti-Mouse CD4 Percp-cy5.5 (1:50), and Anti-Mouse IL-17 A PE (1:50). Data acquisition was performed using a flow cytometry instrument (Cytoflex, Beckmam, USA).

Statistical analysis

The data analysis software SPSS version 22.0 (SPSS, Inc., Chicago, IL, USA) or GraphPad Prism version 6.0 (GraphPad, Inc., La Jolla, CA, USA) was utilized for organizing all data. The normal distribution of continuous variables was assessed using the Kolmogorov-Smirnov test. Data were presented as either the mean ± standard deviation (SD), median (quartile), or number (percentage). The Mann-Whitney U-test was employed to compare differencies between the two groups of continuous variables with non-normal distribution. The t-test was utilized to compare two means, while one-way ANOVA was employed for comparisons among more than two means. Bonferroni corrections were applied for pairwise comparisons among multiple groups. Spearman’s correlation coefficient (r) was employed to analyze the relationships between serum ASM levels and myositis disease activity, activity and damage of cutaneous manifestations, muscle enzymes including CK, LDH, HBDH, AST, and other clinical indicators. Statistical significance was set at a p value of less than 0.05. Any result with a p value less than 0.05 was considered statistically significant, denoted by asterisks(* < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001).

ASM is highly expressed in DM and associates with disease activity

A total of 58 DM patients were included in the study, with an average age of 48.16 ± 9.52 years, along with 30 HCs with an average age of 47.73 ± 7.64 years. Among the DM patients, 72.41% were female, whereas among the HCs, 83.33% were female. There were no statistically differences in age ( p  = 0.834) and gender ( p  = 0.130) between the DM patients and HCs (Table  1 ). To assess the expression levels of ASM in DM, the serum ASM levels were measures. The results indicated a significant increase of ASM levels in DM patients compared to HCs, with median values of 2.63 (1.80–4.94) ng/mL and 1.64 (1.47–1.96) ng/mL, respectively (Fig.  1 A). Subgroup analysis by gender revealed that gender did not have a significant impact on serum ASM levels in both DM patients, female 2.51 (1.79–3.95) ng/mL versus male 2.73 (2.01–5.50) ng/mL, p  = 0.3121, and HCs, female 1.71 (1.47-2.00) ng/mL versus male 1.53 (1.52–1.55) ng/mL, p  = 0.209. Subsequently, the expression levels of ASM in muscle tissue were examined using qRT-PCR and IHC, showing increased levels in DM patients compared to HCs. Particularly, ASM was predominantly expressed in infiltrated nucleated cells at the perimysium, both in the cytoplasm and nuclei, as well as on plasma membrane (Fig.  1 B-C). Additionally, the activity of ASM in the serum of DM patients was found to be significantly higher than in HCs (Fig.  1 D). Thus, both the activity and levels of ASM are notably elevated in patients with DM.

ASM exhibits high expression in DM patients and associates with disease activity. ( A ) serum ASM levels detected using ELISA ( B ) mRNA expression level of ASM in muscle tissues of patients ( C ) protein levels of ASM in patients’ muscle tissues, scale bar 20 μm ( D ) serum ASM activity ( E ) Correlation analysis between serum ASM levels and overall disease activity ( F ) Correlation analysis between serum ASM levels and cutaneous severity score. * P  < 0.05, ** P  < 0.01, *** P  < 0.001,  n  = 30 in healthy controls, n  = 58 in patients with DM

To investigate the relationship between serum levels of ASM and clinical disease activity as well as clinical laboratory indicators, we conducted correlation analysis. The results revealed a correlation between ASM levels and both the global disease activity score (R 2  = 0.1367, p  = 0.0043) and cutaneous severity score (R 2  = 0.2142, p  = 0.0003) (Fig.  1 E-F). Additionally, we observed associations between ASM levels and muscle enzymes, such as LDH (R 2  = 0.1663, p  = 0.0016), HBDH (R 2  = 0.1546, p  = 0.0025), and AST (R 2  = 0.3529, p  < 0.0001) (sFig. 1 A-D). While there was a marginal correlation with CK (R 2  = 0.0745, p  = 0.0418), this association was not significant after removing outliers (R 2  = 0.0339, p  = 0.1872). Subgroup analysis by gender revealed a significant association between CK and ASM in female DM patients (R 2  = 0.1342, p  = 0.0185) but not in male DM patients (R 2  = 0.0013, p  = 0.8927) (sFig. 1 E-F). However, no significant correlations were found between serum levels of ASM and CKBM, CRP, HRCT score, CD4, CD8, or KL6 (data not shown). Overall, these findings suggest a positive correlation between serum levels of ASM and disease activity in DM patients.

The EAM mouse model is frequently used to replicate the disease phenotype of polymyositis and DM in human. This model is commonly utilized to study the underlying pathological mechanisms and potential treatment targets of DM. In this study, we successfully established the EAM model and observed several key indicators of the successful model creation. When compared to the control mice, the EAM mice exhibited reduced muscle strength, increased serum CK levels, enlarged spleens, aggregated inflammatory cell infiltration, and higher histological scores on H&E staining (sFig. 2 A-F). These findings suggest the successful establishment of the EAM model. Additionally, we assessed the expression levels of ASM in mouse muscle using qRT-PCR and IHC, as well as ASM activity. The results revealed that EAM model mice had higher mRNA and protein levels of ASM compared to control mice, along with enhanced ASM activity in the muscle (sFig. 3 A-E). In summary, our findings demonstrate increased levels of ASM in the serum and muscle tissue of DM patients, accompanied by enhanced enzymatic activity. Furthermore, these elevated levels were positively correlated with disease activity.

ASM involves in the pathology of EAM mice

Levels and activity of ASM were found to be elevated in both DM patients and EAM model mice. To investigate the significance of ASM in the pathology of the EAM mouse model, ASM gene knockout mice (SMPD1 −/− ) were utilized.The results demonstrated that SMPD1 −/− EAM mice exhibited higher higher muscle strength, reduced serum CK levels, decreased spleen size and weight, as well as reduced inflammatory cell infiltration and pathological score (Fig.  2 A-F). These findings indicate that the disease severity in SMPD1 −/− EAM mice was alleviated, suggesting a potential role of ASM in the pathology of EAM.

ASM plays a crucial role in the pathology of EAM mice. Establishment of EAM mouse model in ASM knockout (SMPD1 −/− ) mice. ( A ) muscle strength ( B ) serum CK level ( C ) spleen ( D ) spleen weight ( E ) H&E staining of muscle section ( F ) quantitative analysis of H&E staining. * P  < 0.05, ** P  < 0.01, *** P  < 0.001, n  = 6 in SMPD1 +/+ mice group, n  = 5 SMPD1 −/− mice group

Amitriptyline is therapeutic against EAM mice model

Our previous results have highlighted the role of ASM in the pathology of the EAM mice model. To investigate the potential therapeutic effect of small molecule drugs targeting ASM on EAM mouse model, we selected AMI, a widely used ASM small molecule inhibitor. AMI was orally administrated at both higher (10 mg/kg) and lower (1 mg/kg) doses, starting two days before model induction. Results showed that compared to the control group, mice in the EAM model group exhibited decreased muscle strength, elevated serum CK levels, increased spleen weight, and increased muscle histopathology scores, indicating successful EAM model establishment (Fig.  3 A-F). Treatment with both 1 mg/kg and 10 mg/kg doses of AMI significantly increased muscle strength, decreased serum CK levels, reduced spleen weight, and lowered muscle histopathology scores compared to the EAM model group (Fig.  3 A-F). Furthermore, analysis of ASM mRNA expression via qRT-PCR and ASM activity in muscle tissue confirmed that AMI effectively suppressed both ASM expression and activity (Fig.  3 G-H). These findings suggest that targeting ASM with the inhibitor AMI can alleviate disease severity in EAM model mice.

Amitriptyline is therapeutic against EAM mice model. To establish the EAM mouse model, wild type mice were subject to induction and subsequently treated with amitriptyline (AMI) at dosage levels of 1 mg/kg or 10 mg/kg. ( A ) muscle strength ( B ) serum CK level ( C ) spleen ( D ) spleen weight ( E ) H&E staining of muscle section ( F ) quantitative analysis of H&E staining. ( G ) mRNA expression level of ASM in muscle tissues ( H ) serum ASM activity. * P  < 0.05, ** P  < 0.01, *** P  < 0.001, n ranges from 3 to 7 in each analysis group

The proportion of CD4 T cell subsets was imbalanced in EAM mice model

According to our study, ASM plays a critical role in the pathology of myositis. Inhibiting ASM activity with AMI has shown therapeutic effects in an EAM mouse model, prompting further investigation into the involvement mechanism of ASM in myositis pathology. Previous research has reported imbalances in CD4 T cell subsets in patients with DM, including a decrease in Treg cells and an increase in Th17 cells [ 28 ]. Restoring the balance of these CD4 T cell subsets may have therapeutic benefits. ASM has been identified as a key regulator in the differentiation of naive CD4 T cells [ 15 ]. Our findings in the EAM mouse model revealed similar results, showing an increase in Th17 cells and a decrease in Treg cells in the spleen compared to the control group (Fig.  4 A-B). To investigate the role of ASM in regulating of naive CD4 T cell differentiation, we used SMPD1 −/− mice to establish the EAM model. The results demonstrated that knocking out ASM could rebalance the CD4 T cell subsets, increasing Treg cells and decreasing Th17 cells (Fig.  4 C-D). To confirm whether the therapeutic effects of AMI are also achieved through regulating CD4 T cell subsets by affecting ASM, we performed flow cytometry analysis on spleen cells. The results indicated that AMI could reduce the increased Th17 cells and increase the reduced Treg cells (Fig.  4 E-F). Furthermore, we analyzed the Treg ratio of CD25 + Foxp3 + versus CD25-Foxp3 + in EAM models, the data indicated a decreasing trend in the Treg ratio after knocking out SMPD1 or inhibiting ASM activity with amitriptyline (Fig.  4 G-H).

The proportion of CD4 T cell subsets was imbalanced in EAM mice model. Flow cytometry was applied to test the CD4 T cell subsets Treg and Th17 in spleen. ( A ) Treg in the wild type EAM model mice ( B ) Th17 in the wild type EAM model mice ( C ) Treg in the SMPD1 −/− EAM model mice ( D ) Th17 in the SMPD1 −/− EAM model mice ( E ) Th17 in the wild type EAM model mice treated with amitriptyline ( F ) Treg in the wild type EAM model mice treated with amitriptyline ( G ) Treg ratio of CD25 + Foxp3 + versus CD25-Foxp3 + in SMPD1 −/− EAM model mice ( H ) Treg ratio of CD25 + Foxp3 + versus CD25-Foxp3 + in EAM model mice treated with amitriptyline. * P  < 0.05, ** P  < 0.01, *** P  < 0.001, n ranges from 5 to 7 in each analysis group

Further, we examined the levels of cytokines IL-17 A, IL-10, and IL-6, which are known to impact the differentiation of Th17 cells [ 29 ]. Out results showed a significant increase in both mRNA expression and secretion levels of IL-17 A in the muscle tissue, spleen tissue, and serum of the EAM mouse model. Treatment with AMI was found to reduce the production of IL-17 A (sFig. 4 A-D). Conversely, both mRNA expression and secretion levels of IL-10 were elevated in the muscle tissue of EAM mouse, with further enhancement upon AMI treatment (sFig. 4 E-H). Similarly, both mRNA expression and secretion levels of IL-6 were elevated in the muscle tissue and serum of EAM mice, but decreased with AMI treatment(sFig. 4 I-K). These findings indicate an imbalance in CD4 T cell subsets in the EAM mouse model, suggesting that targeting ASM could potentially rebalance these subsets.

AMI regulates the CD4 T cells differentiation in EAM mouse model

The differentiation of naive CD4 + T cells into subsets is regulated by the relative transcription factors, RAR-related orphan receptor (RORγT) and FOXP3, which respectively regulate Th17 and Treg differentiation. Phospho-signal transducer and activator of transcription 3 (p-STAT3) is the key regulator for Th17, while phospho-signal transducer and activator of transcription 5 (p-STAT5) is the key regulator for Treg [ 29 ]. To evaluate these mechanisms, we analyzed the mRNA expression levels of transcription factors RORγT and FOXP3 in muscle and spleen tissues, as well as the protein levels of p-STAT3 and p-STAT5 in muscle tissues. The results showed an increase in both RORγT and FOXP3 mRNA expression in the muscle and spleen tissues of the EAM mouse group. However, AMI treatment only reduced the mRNA expression levels of RORγT (Fig.  5 A-D). Additionally, the ratio of p-STAT3 to STAT3 was significantly higher in the EAM mouse group, but AMI reduced this ratio. Whereas, the ratio of p-STAT5 to STAT5 was not elevated in the EAM mouse group, but AMI increased this ratio (Fig.  5 E-H). In summary, RORγT transcription factor and p-STAT3 protein level were increased in the EAM mouse group, and AMI effectively decreased their levels.

AMI regulates the CD4 + T cells differentiation in EAM mouse model. ( A ) mRNA expression level of RORγT in muscle tissues detected by qRT-PCR ( B ) mRNA expression level of RORγT in spleen detected by qRT-PCR ( C ) mRNA expression level of FOXP3 in muscle tissues detected by qRT-PCR ( D ) mRNA expression level of FOXP3 in spleen detected by qRT-PCR. ( E ) protein level of p-STAT3 and STAT3 in muscle tissue detected by WB ( F ) the quantitative anaysis of p-STAT3/STAT3 ( G ) protein level of p-STAT5 and STAT5 in muscle tissue detected by WB ( H ) the quantitative anaysis of p-STAT5/STAT5. * P  < 0.05, ** P  < 0.01. *** P  < 0.001, n ranges from 4 to 7 in each analysis group

AMI regulates the naive CD4 + T cell differentiation into Th17 in vitro

Our in vivo study demonstrated that ASM can affect the differentiation of CD4 + T cells. To further validate this finding, we isolated naive CD4 + T cells from the spleen and induced them to differentiate into Th17 subsets. Subsequently, we performed flow cytometry, mRNA analysis, and WB to assess the outcomes. The results showed that naive CD4 + T cells were effectively induced into Th17 subsets, showing a significant increase in IL-17 A mRNA expression level and an increase in p-STAT3 protein level in vitro. The treatment with AMI reduced the proportion of Th17 cells, decreased IL-17 A mRNA expression level, and lowered p-STAT3 protein levels (Fig.  6 A-C). In summary, AMI can regulate the differentiation of naive CD4 + T cells into Th17 cells in vitro.

AMI regulates the naive CD4 + T cell differentiation into Th17 in vitro. Naive CD4 + T cells were isolated from spleen and then were induced to differentiate into Th17 under the Th17 differentiation condition. ( A ) Th17 subset detected by flow cytometry ( B ) mRNA expression level of IL-17 A detected by qRT-PCR ( C ) protein level of p-STAT3 detected by WB. * P  < 0.05, ** P  < 0.01. *** P  < 0.001, experiments were repeated twice

The pathology of DM is complex and remains incompletely understood, with current evidence suggesting the presence of immune cell abnormalities in tissues and circulation, involving various cell types such as T cells, macrophages, dendritic cells, B cells, mast cells and neutrophil [ 30 , 31 ]. Immune imbalance is believed to be a key factor in the disease progression [ 32 ]. Both innate immunity and adaptive immunity are involved in the development of DM, with lymphocytes and their secreted cytokines playing a central role in the pathological mechanism. T lymphocytes, particularly the CD4 + T cells presented by MHC Class II peptide are the predominant cell types in the inflammatory infiltration of DM [ 33 ], contributing to its pathogenesis. This includes helper T cells Th1, Th2, and Th17, which secrete multiple cytokines to coordinate the immune response and provide co-stimulatory signals for antibody production. Additionally, Tregs inhibit the lytic activity of myoreactive CD8 + T cells and play an anti-inflammatory role [ 34 ].

In the context of DM, there is an imbalance in T cell subpopulations. Various studies have demonstrated that DM patients exhibit higher levels of Th17 cells and the cytokine IL-17 A in their muscle tissue [ 35 , 36 ], as well as elevated serum levels of IL-17 A compared to healthy individuals. Moreover, the level of IL-17 A is positively correlated with disease activity [ 37 ]. Conversely, the number of Treg cells in the peripheral blood of DM patients is decreased, along with reduced levels of serum IL-10 [ 28 , 38 ]. Th17 cells play a crucial role in maintaining the immune response and act as major regulators in the inflammatory microenvironment of muscle tissue [ 28 ]. On one hand, IL-17 produced by Th17 cells stimulates the production of IL-6 and chemokine 20, while also regulating the survival and differentiation of B lymphocytes [ 35 ]. On the other hand, IL-17 induces NF-κB activation, which inhibits myocyte migration and myogenic differentiation [ 39 ]. IL-6, a proinflammatory molecule in the inflamed microenvironment, is initially produced by innate immune responses such as macrophages. It then plays a role in the adaptive immune response by promoting CD4 + T differentiation into Th17 cells. IL-17, produced by Th17 cells, in turn, stimulates the production of IL-6, creating a mutual promotion effect [ 40 , 41 ]. Tregs are a heterogeneous population, with those residing in muscle tissue capable of suppressing the inflammatory response in affected muscles, promoting the conversion of pro-inflammatory macrophages into an anti-inflammatory phenotype, and regulating the differentiation of muscle stem cells through the secretion of amphiregulin [ 42 ]. The delicate balance between pro-inflammatory T cell subsets and regulatory T cells is crucial for maintaining peripheral tolerance [ 28 ]. Therefore, correcting the immune imbalance of T cell subsets may represent a crucial therapeutic approach for managing DM. In our study, we observed an imbalance of T cell subsets in EAM model mice, specifically an increase in Th17 cells and their secretion of IL-17 A and the inflammatory cytokine IL-6, alongside a decrease in Treg cells and their secretion of IL-10.

According to previous reports, ASM plays a crucial role in regulating the differentiation of naive CD4 + T cells into T cell subsets. ASM accomplishes this by mediating CD3 and CD28 signal transduction through ceramide production, thereby controlling the activation and proliferation of naive CD4 + T cells. This process facilitates the differentiation of Th1 and Th17 cells, while simultaneously impeding the quantity and function of Treg cells [ 15 , 43 ]. In the differentiation process of Th17 cells, the key molecule is STAT3, whereas STAT5 is important in Treg differentiation. Upon exposure to TGF-β and IL-6, STAT3 is activated in conjunction with TCR-co-stimulative signals. The phosphorylation of STAT3 triggers the expression of the transcription factor RORγt, which fosters the differentiation of CD4 + T cells into Th17 cell subpopulations. Simultaneously, the differentiation of Treg cells is hindered by the down-regulation of TGF-β-induced FOXP3 [ 44 ]. Conversely, IL-2 signaling triggers the phosphorylation of STAT5, leading to its binding to the FOXP3 promoter and subsequent induction of FOXP3 expression [ 45 ]. Furthermore, TGF-β can activate of Sma- and Mad-related protein (SMAD) 2 and SMAD3 [ 46 ], which in turn stimulate the transcription factor FOXP3 and facilitate the differentiation of Treg cells from naive CD4 + T cells.

In our study, we examined the activity and levels of ASM in both DM patients and EAM model mice. We observed elevated ASM activity and serum levels in DM patients. Subgroup analysis based on gender did not show a statistical difference in serum ASM levels, but a correlation between serum ASM levels and CK levels was found in female DM patients rather than male patients, possibly due to gender difference [ 47 ]. However, further research is needed due to the limted sample size of enrolled population. In EAM model mice, there was an increase in ASM activity and protein levels inmuscle tissues. The CD4 + T cell subsets Th17 showed an increase, while Treg levels were reduced. Interestingly, in SMPD1 −/− knockout EAM model mice, we observed a reversal of the imbalance between Th17 and Tregs, leading to a reduction in disease severity. Moreover, treatment with the ASM inhibitor amitriptyline in the EAM model mice resulted in a therapeutic effect by balancing Th17 and Treg subsets. These findings suggest that ASM plays a crucial role in regulating the differentiation of naive CD4 + T cells into T cell subsets, and targeting ASM could potentially reduce EAM severity. Further investigations revealed that ASM can regulate the phosphorylation levels of STAT3 and STAT5 in vitro, indicating its involvement in regulating the differentiation of CD4 + T cells into Th17 and Treg subsets by controlling key protein molecules’ phosphorylation Levels.

Compared to the EAM model group, treatment with AMI resulted in a reduction in IL-6 expression. The varying levels of IL-6 played a crucial role in creating inflammatory environments that influenced the differentiation of naive CD4 + T cells into Th17 cells or Treg cells. The administration of AMI in EAM mouse models led to decreased expression of RORγT in the spleen and muscle, as well as reduced levels of phosphorylated STAT3 protein in muscle tissue. This suggested that targeting ASM could inhibit the differentiation of CD4 + T cells into Th17 cells. However, the levels of IL-10 and the transcription factor FOXP3 did not consistently change across different tissues. In the spleen of the EAM model group, the expressions of IL-10 and FOXP3 were lower compared to the control group, while in muscle tissue, IL-10 and FOXP3 levels were higher than in serum. Although AMI treatment increased IL-10 levels in EAM mice, it did not increase the level of FOXP3. Burzyn et al. proposed that Treg cells in injured muscle tissue represent a distinct subtype of CD4 + CD25 + FOXP3 + Treg cell population, exhibiting a unique transcriptome and TCR pool compared to those in lymphoid organs [ 48 ]. Previous studies have shown that Treg cells are more abundant in the muscles of adolescents with DM compared to normal muscles [ 49 , 50 ]. Similarly, adult DM muscle biopsies have also shown a high presence of Treg cells [ 51 ], indicating that the increase in Treg cells is a response to local muscle inflammation. While the specific mechanism behind the enrichment of Treg cells in inflammatory muscles remains incompletely understood, previous studies [ 52 , 53 ] have demonstrated that the increased migration of Treg cells to inflamed muscles can mitigate muscle injury and promote the repair of chronically inflamed muscles. This beneficial effect is linked to effector molecules produced by Treg cells, such as IL-10, which has been associated with a protective role in muscle dystrophy [ 54 , 55 ] and the promotion of muscle repair following loss injury [ 56 ]. Therefore, the enrichment of Treg cells and their effector molecules, including IL-10, in local muscles plays a crucial role in facilitating the repair of injured muscles. In line with these findings, our study observed a significant increase in levels of the anti-inflammatory factor IL-10 after treating EAM mouse models with the ASM inhibitor AMI, leading to a marked reduction in disease severity and muscle damage.

The Treg cells present at the inflamed sites exhibit instability and plasticity [ 57 ]. Alongside the CD4 + CD25 + Foxp3 + T cells, the subgroup of CD4 + CD25-Foxp3 + T cells has also been implicated in various immune dysregulatory diseases, such as systemic lupus erythematous. In this context, the proportions of CD4 + CD25-Foxp3 + T cells have shown a positive association with disease activity markers like dsDNA, complements, proteinuria and renal involvement. Conversely, these proportions tend to decrease as the disease transitions into an inactive state [ 58 ]. Our analysis of the Treg ratio between CD25 + Foxp3 + and CD25-Foxp3 + in EAM model revealed a decreasing trend in the Treg ratio following the knockout of SMPD1 or inhibition of ASM activity. The potential role of CD25-Foxp3 + T cells in EAM pathology and the involvement of ASM in regulating CD25 expression and Treg subsets warrant further investigation.

ASM plays a crucial role in the pathology of DM by regulating the transformation of naive CD4 + T cells into Th17 and Treg subsets. This not only offers a promising therapeutic target for DM but also enhances comprehension of how amitriptyline functions as a treatment. Furthermore, it introduces novel opportunities for utilizing amitriptyline in conditions linked to increased ASM activity.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

We express our gratitude Wentong Meng and Qiaorong Huang (Laboratory of Stem Cell Biology, West China Hospital, Sichuan University) for their valuable support in performing flow cytometry analysis. Additionally, we extend our thanks to Li Zhou from the Core Facilities at West China Hospital, Sichuan University for providing assistance in the scanning process of H&E staining slides.

This study is funded by post-doctoral research and development fund of West China Hospital of Sichuan University (2019HXBH090), and National Natural Science Foundation of China (No. 82201985).

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Yuehong Chen, Huan Liu, Zhongling Luo, Jiaqian Zhang, Min Dong & Qibing Xie

Department of General Practice, West China Hospital, General Practice Medical Center, Sichuan University, 37 Guoxue lane, Chengdu, 610041, China

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YC and HL designed and performed experiments, collected and analyzed data and wrote and revised the paper. ZL and JZ designed the experiments, analyzed the data and revised the paper. MD assisted experiments, collected and analyzed data and assisted in editing the manuscript. GY and QX designed and supervised this study, analyzed data and wrote and edited the manuscript. All authors reviewed the manuscript. Yuehong Chen and Huan Liu contributed equally to this study.

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Correspondence to Geng Yin or Qibing Xie .

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Chen, Y., Liu, H., Luo, Z. et al. ASM is a therapeutic target in dermatomyositis by regulating the differentiation of naive CD4 + T cells into Th17 and Treg subsets. Skeletal Muscle 14 , 16 (2024). https://doi.org/10.1186/s13395-024-00347-1

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SWOT Analysis of China: The New Normal

SWOT analysis stands for – Strengths, Weaknesses, Opportunities and Threats. Strengths and Weaknesses are result of Normal Casename internal factors, while opportunities and threats arise from developments in external environment in which Normal Casename operates. SWOT analysis will help us in not only getting a better insight into Normal Casename present competitive advantage but also help us in how things have to evolve to maintain and consolidate the competitive advantage.

- Streamlined processes and efficient operation management – Normal Casename is one of the most efficient firms in its segment. The credit for the performance goes to successful execution and efficient operations management.

- Experienced and successful leadership team – Normal Casename management team has been a success over last decade by successfully predicting trends in the industry.

- Low profitability which can hamper new project investment – Even though Normal Casename financial statement is stable, but going forward Normal Casename 5-7% profitability can lead to shortage of funds to invest into new projects.

- Little experience of international market – Even though it is a major player in local market, Normal Casename has little experience in international market. According to Richard H.K. Vietor, Haviland Sheldahl-Thomason , Normal Casename needs international talent to penetrate into developing markets.

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- Lucrative Opportunities in International Markets – Globalization has led to opportunities in the international market. Normal Casename is in prime position to tap on those opportunities and grow the market share.

- Growing dominance of digital players such as Amazon, Google, Microsoft etc can reduce the manoeuvring space for Normal Casename and put upward pressure on marketing budget.

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Political Factors

- Little dangers of armed conflict – Based on the research done by international foreign policy institutions, it is safe to conclude that there is very little probability of country entering into an armed conflict with another state.

- Political consensus among various parties regarding taxation rate and investment policies. Over the years the country has progressively worked to lower the entry of barrier and streamline the tax structure.

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- According to Richard H.K. Vietor, Haviland Sheldahl-Thomason . Normal Casename should closely monitor consumer disposable income level, household debt level, and level of efficiency of local financial markets.

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Competition among existing players, bargaining power of suppliers, bargaining power of buyers, threat of new entrants, and threat of substitutes.

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Richard H.K. Vietor, Haviland Sheldahl-Thomason (2018) , "China: The New Normal Harvard Business Review Case Study. Published by HBR Publications.

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ORIGINAL RESEARCH article

Why and when does multitasking impair flow and subjective performance a daily diary study on the role of task appraisals and work engagement.

• 1 Department of Business Studies, Institute of Tax Law and Economics, Leiden University, Leiden, Netherlands
• 2 Department of Management and Organization, School of Business and Economics, VU Amsterdam, Amsterdam, Netherlands
• 3 Department of Social, Health and Organizational Psychology, Utrecht University, Utrecht, Netherlands

In this diary study, we contribute to research on day-level multitasking in organizations by investigating why and when multitasking impairs employees’ work-related flow and subjective job performance on a daily basis. Drawing on Lazarus and Folkman’s transactional model of stress and coping, we propose that employees’ appraisal of their daily tasks (i.e., less challenging and more hindering) may explain why multitasking has negative implications for flow and job performance. Moreover, we expect that daily work engagement can buffer the detrimental effects of multitasking on flow and job performance. A total of 33 professional workers in the food industry participated in our study and were asked to respond to 10 daily surveys at work across 4 weeks. In line with our expectations, results showed that on days when employees’ working time was highly fragmented across a high number of tasks, they experienced less flow and, in turn, their job performance was lower on that particular day. Moreover, appraisal of daily tasks as less challenging – though not more hindering – explained why multitasking impairs flow. Finally, daily work engagement buffered the detrimental impact of multitasking on flow. The results presented in this paper offer novel and ecologically valid insights into why and when multitasking may backfire for employees.

1 Introduction

People believe that doing several things at the same time will help them get more done, and by switching among tasks, they feel productive as more tasks are performed in a single day ( Adler and Benbunan-Fich, 2013 ; Peifer and Zipp, 2019 ). Yet this might simply be an illusion, also referred to as the myth of multitasking ( Rosen, 2008 ) or multitasking paradox ( Appelbaum et al., 2008 ). Concerns about productivity loss due to multitasking are rampant, especially with the alarming new heights that it has reached. In a field study by Mark et al. (2005) , almost a quarter of interrupted work was not resumed on the same day. If work was resumed, it took more than 25 min and employees had worked in more than two other working spheres before resuming interrupted work. Wajcman and Rose’s (2011) study documented an average of 88 work episodes per day, with not even half of the workday being spent on activities that last for more than 10 min. The COVID-19 pandemic posed additional challenges, as employees working from home reported an increase in multitasking behaviors ( Leroy et al., 2021 ). As Leroy et al. (2021) put it: “work time has never been so fragmented” (p. 1457).

Scholars are yet to fully understand the implications of multitasking for employees and organizations. If detailed comprehension is lacking, society runs the risk that multitasking remains common organizational practice without proper management of its complexities. To increase organizations’ willingness and ability to address these concerns, it is imperative to study how multitasking behavior relates to employee effectiveness. After having been dominated by studies conducted in laboratory settings (see Baethge et al., 2015 ), the field has witnessed a rise in diary studies, both quantitative (e.g., Aitken et al., 2023 ) and qualitative (e.g., Feldman and Greenway, 2021 ). Importantly, a few diary studies suggest that daily flow – a short-term peak experience that individuals experience when fully immersed in an activity ( Csikszentmihalyi, 1997 ) – may be a key mechanism in the day-level multitasking–job performance relationship ( Peifer and Zipp, 2019 ; Aitken et al., 2023 ). Yet it remains elusive what are the psychological processes underlying employees’ responses to multitasking.

Against this background, we develop and test a conceptual model that specifies a pathway (i.e., why ) and condition under which (i.e., when ) day-level multitasking behavior impairs work-related flow and ultimately hampers the subjective performance of employees. In building our conceptual model, we draw on the transactional model of stress and coping ( Lazarus and Folkman, 1984 ). This theoretical model posits that reactions to work stressors, such as fragmentation of work time due to multitasking, depend on (1) how we appraise those stressors (i.e., as opportunity or threat) and (2) the resources we possess to cope with those stressors. Recent research on interruptions has indeed shown that the (positive or negative) appraisals of employees shape reactions ( Hunter et al., 2019 ; Darouei et al., 2024 ). We go one step further and argue that a work stressor can also shape the appraisal of other, more neutral job characteristics. Specifically, we propose that multitasking influences employees’ appraisal of their daily tasks (i.e., as less challenging and more hindering) and this explains why multitasking behaviors would impair flow. Moreover, we explore whether day-specific work engagement – a positive and high arousal affective-motivational state characterized by energy and involvement ( Bakker et al., 2011 ) – as a resource can help employees cope with multitasking.

Our study contributes to the literature on multitasking, stress, and flow in at least three notable ways. First, we put forward flow as a key mediator that can explain the relationship between multitasking and job performance. The multitasking reality of modern organizations suggests that interruptions to workflow are an accepted part of organizational life ( Jett and George, 2003 ). Yet, surprisingly, the concept of flow has received hardly any attention in research on multitasking and work interruptions (see the review by Puranik et al., 2020 ). Our study is among the first to examine the day-to-day relationship between multitasking and flow [for related work, see Peifer and Zipp’s (2019) study on multitasking behaviors, and Aitken et al.’s (2023) on intrusions while teleworking]. Second, we propose that appraisal is key to understanding the psychological processes that may explain how multitasking influences employees’ well-being and job performance. We link daily fluctuations in multitasking behaviors to meaningful variations in task appraisals in order to illuminate the stressor–well-being relationship (see also Smith et al., 2022 ). Our third contribution relates to the examination of work engagement as a day-level variable that explains why individuals may experience multitasking differently across days. No prior studies have yet investigated whether and how work engagement can assist employees in handling daily job demands, even though it is a source of energy and concerns the investment of personal resources ( Christian et al., 2011 ). While work engagement is typically studied as an outcome in and of itself ( Puranik et al., 2020 ) or as a mediator in the association between job characteristics and performance ( Bakker et al., 2023 ), we examine work engagement as a day-specific resource that can buffer the detrimental effects of multitasking behaviors on flow and job performance.

2 Theoretical framework

We draw on the two-dimensional typology of multitasking behaviors to conceptualize multitasking along the lines of share of resources allocated to the execution of work activities and share of time in which work activities are observed ( Circella et al., 2012 ). With regard to the time dimension, our focus is on multitasking within the time frame of a single working day; that is, all tasks performed during the day will be considered to have happened sequentially or simultaneously (see Kirchberg et al., 2015 ). Monotasking refers to the situation in which a single activity occupies a person’s full resources for a particular period of time. Within the time frame of a full working day, monotasking is very uncommon nowadays, since many – if not most – employees are forced to shift attention between a high number of tasks on a daily basis. When employees execute multiple tasks, their behavior can be classified as switching, interleaving, or overlaying, depending on a person’s allocation of (e.g., mental) resources among the tasks of a particular workday ( Circella et al., 2012 ). Switching is alternating between activities in such a way that one fully interrupts one task with another. Interleaving involves partial alternation to a second task, while another activity remains in the background. Hence, in the case of interleaving, the main activity claims most but not all resources, for instance due to attention residue ( Leroy, 2009 ). Overlaying refers to the simultaneous execution of tasks: both activities are carried out at the same time with a parallel allocation of resources. Whether it happens sequentially or simultaneously, multitasking can lead to fragmentation of the work day ( Appelbaum et al., 2008 ), and the question central to our study is “at what cost?”

In this diary study, we integrate key theoretical models of stress with the literature on multitasking and flow in order to elucidate the process by which day-level multitasking impedes job performance. Carrying out a task can be seen as goal-directed behavior, and doing more than one thing at a time (be it sequentially or simultaneously) is associated with regulation hindrances that act as stressors ( Frese and Zapf, 1994 ). In the transactional model of stress and coping (TSC), Lazarus and Folkman (1984) viewed stress as an individual outcome generated through the person’s appraisal of stressors in the environment. When we encounter a stressor, we first assess how stressful it is through primary appraisal. We simultaneously engage in secondary appraisal, evaluating whether we have the necessary resources to cope with the stressful situation. Building on the TSC, the challenge-hindrance stressor framework (CHSF; LePine et al., 2005 ) postulates that stressors are conceptually distinct from each other, such that some stressors tend to be appraised as challenges (i.e., potential for achievement and personal growth), while other stressors tend to be appraised as hindrances (i.e., may thwart personal development). Interestingly, Peifer and Zipp (2019) relied on a model that integrates flow into the transactional model of stress, which they refer to as the transactional model of stress and flow (TMSF). This adapted model explains the connection between stressors and flow. When individuals perceive stressors as challenges (primary appraisal) and possess adequate coping resources (secondary appraisal), they are more likely to experience flow as an alternative state to stress.

The concept of flow finds it origins in the work of Mihaly Csikszentmihalyi. He studied the subjective experiences of creative painters, chess players, rock climbers, and many others, and he was intrigued when these people became fully absorbed in their activity and found that activity intrinsically motivating. People “in flow” are in a state of consciousness where they become totally immersed in an activity and operate at full capacity ( Nakamura and Csikszentmihalyi, 2002 ). People can find flow in almost any activity, but research shows it is mainly experienced in the work environment ( Csikszentmihalyi and LeFevre, 1989 ), where flow refers to peak experiences of fluent, uninterrupted work ( Gerpott et al., 2022 ).

We draw on Lazarus and Folkman’s (1984) TSC and its extensions (CHSF and TMSF) to propose that day-level multitasking is a hindrance stressor that negatively impacts the primary appraisal of one’s daily tasks, ultimately influencing flow experiences and daily performance. We also propose that multitasking interferes with the experience of flow less strongly when employees feel engaged at work. Work engagement is a motivational construct that refers to the investment of personal resources toward the tasks associated with one’s work role ( Christian et al., 2011 ). In contrast to flow, which is more closely related to a specific activity as it is an optimal experience of fluent work (i.e., experiential well-being; Ilies et al., 2017 ), work engagement represents a psychological connection with one’s work in general ( Christian et al., 2011 ; Gerpott et al., 2022 ) (see also Yan and Donaldson, 2023 , on the differences between flow and engagement). While engagement is a relatively enduring state of mind, it also ebbs and flows, showing day-to-day fluctuations around an employee’s average level ( Sonnentag et al., 2010 ). Applying the TMSF, we argue that state work engagement will allow for a more positive secondary appraisal in stressful situations of high multitasking such that on days when employees are engaged, they come to experience flow as an alternative experience to stress. The overall conceptual model guiding this research is depicted in Figure 1 .

Figure 1 . Conceptual model.

2.1 Hypotheses

Multitasking may be an impediment to flow. Task-related preconditions for the experience of flow are a balance between challenges and skills, clear and specific goals, and immediate feedback on one’s performance (see Fullagar and Kelloway, 2013 ). Under these conditions, experience naturally unfolds from moment to moment and one enters a flow state ( Nakamura and Csikszentmihalyi, 2002 ). However, the cognitive costs of executing multiple tasks in parallel and alternating between tasks may add considerably to the demands and challenges of a particular workday. Challenge stressors may turn into hindrance stressors when interruptions accumulate ( Baethge et al., 2015 ). Moreover, alternations imply that employees are shifting goals, and any feedback from the work itself is suspended until the moment of resumption and completion of the main task ( Monk et al., 2008 ). Thus, multitasking interferes with the preconditions of flow.

In addition, the very nature of multitasking is contradictory to how the flow experience is characterized. Flow is a subjective state with the following characteristics: (a) intense and focused concentration, (b) loss of self-consciousness, (c) a sense of control, (d) distortion of time, and (e) intrinsic rewards ( Nakamura and Csikszentmihalyi, 2002 ; Fullagar and Kelloway, 2013 ). When work is fragmented due to multitasking, it becomes difficult to get fully absorbed in one’s work. After all, deep involvement in the task at hand is interrupted and disrupted. Multitasking may also limit one’s sense of control over the processes and outcomes of the task because many alternations are uncontrollable and require the expenditure of self-regulatory resources ( Freeman and Muraven, 2010 ). Ready-to-resume interventions may work ( Leroy and Glomb, 2018 ), however, as Fullagar and Kelloway (2013) noted, “as soon as attention shifts to try to maintain control, flow dissipates” (p. 44). Furthermore, when people multitask, it is very unlikely that the notion of time disappears and that employees will feel time is flying by. Instead, multitaskers are very much aware of the notion of time, as indicated by their higher sense of time pressure ( Baethge and Rigotti, 2013 ; Leroy and Glomb, 2018 ).

To experience the peak state of flow at work, an initial investment of self-regulatory resources is required ( Csikszentmihalyi, 2000 ). Interestingly, research shows that people sometimes voluntarily switch between tasks when they are unable to achieve the state of flow in an ongoing task (e.g., when they are stuck; Adler and Benbunan-Fich, 2013 ). Although we acknowledge that a switch can enhance flow, based on the aforementioned, we posit that cumulative switches during the day will make it difficult to enter or stay in a flow state. Thus, we hypothesize that on days when employees experience higher levels of multitasking, they will report reduced flow compared with days when they multitask to a lesser extent (see also Peifer and Zipp, 2019 ).

Hypothesis 1 : Within individuals (across days), multitasking is negatively related to flow experienced at work.

We also propose a mediating mechanism for the within-individual relationship between multitasking behaviors and flow, namely appraisal of the daily tasks as challenging or hindering. No single day is the same; that is, employees work on a different set of tasks every day. Importantly, how employees appraise these tasks may vary across days. Although tasks are stressors, “many everyday stressors are not clearly positive nor negative and so are most likely to be open to personal appraisal” ( Hobfoll, 1989 , p. 519). In line with this notion, scholars have distinguished between challenge and hindrance stressors ( LePine et al., 2005 ).

We believe that primary appraisal of the daily tasks may explain why multitasking behavior has negative implications for flow. Doing more than one thing at a time is not harmful per se , but employees may find it difficult to interpret their tasks, on average, as opportunities to grow either personally or professionally when they have to perform multiple tasks in parallel (i.e., overlaying) or alternate between tasks (i.e., switching and interleaving). Any single task that would under normal circumstances be considered a challenge stressor may become a hindrance stressor when an employee has limited opportunity to work on it with undivided attention. Thus, we expect that the higher the level of multitasking on a particular day, the less likely it is that an individual will appraise the sum of daily tasks as a positive challenge and the more likely it is that the daily tasks are jointly appraised as a hindrance.

These appraisals, in turn, should be related to flow. The TMSF posits that flow is experienced during a task that is appraised as challenging ( Peifer and Zipp, 2019 ). Previous between-individual research has found that challenge demands are positively related to flow, while hindrance demands are negatively related to flow ( Van Oortmerssen et al., 2020 ). If employees see the potential for personal growth and gain in executing their tasks, they may come to experience “eustress” ( Selye, 1983 ), an experience of being totally focused in a mindful state of challenge and a healthy state of aroused attention on the task ( Hargrove et al., 2013 ). A difficult-yet-manageable set of tasks will help employees savor their work, find the work experience of that day rewarding, and stay immersed in the tasks at hand. In contrast, looking upon one’s work as stressful may seriously undermine a flow state. Thus, we hypothesize that task appraisal mediates the relationship between multitasking and flow in such a way that multitasking is negatively associated with challenge appraisal, which in turn is positively associated with flow (H2a), while multitasking is positively associated with hindrance appraisal, which in turn is negatively associated with flow (H2b).

Hypothesis 2a : Challenge appraisals of the daily tasks will mediate the within-individual effect of multitasking on flow.

Hypothesis 2b : Hindrance appraisals of the daily tasks will mediate the within-individual effect of multitasking on flow.

Much research has been devoted to understanding when and for whom multitasking inhibits well-being and performance ( Puranik et al., 2020 ). Day-specific moderators, however, seem a neglected focus. We argue that people’s ability to deal with multitasking can be higher on some days than on other days, dependent on one’s daily level of work engagement. On days when employees are engaged, they feel energetic and are in a positive, fulfilling work-related state of mind ( Schaufeli et al., 2002 ). When they are in this state, they are better able to channel physical, emotional, and cognitive energies into their work tasks such that they are not easily fatigued and can show persistence in the face of difficulties ( Christian et al., 2011 ). A workday full of multitasking is cognitively and emotionally demanding and likely to deplete self-regulatory resources, with little opportunity for recovery and replenishment ( Leroy, 2009 ; Freeman and Muraven, 2010 ; Baethge and Rigotti, 2013 ; Baethge et al., 2015 ). The state of work engagement offers substitute personal resources that should make it easier to deal with the stressful nature of multitasking. Applying insights from the TMSF ( Peifer and Zipp, 2019 ) to our model, we posit that work engagement offers the coping resources necessary to experience flow as an alternative experience to stress. Thus, we expect that on days when employees feel energetic and dedicated at work, they are rather well-equipped to retain a high level of concentration and absorption in the face of multitasking.

Hypothesis 3 : Work engagement moderates the within-individual effect of multitasking on flow such that on days when employees are highly engaged, the negative effect of multitasking on flow is weaker compared with days when employees are less engaged.

When employees become totally immersed in their work and enjoy it intensely, they are more likely to excel at what they do. During flow, performance is automatic, and one has a sense of confidence and ease ( Harris et al., 2017 ). Flow is a peak experience that often coincides with optimal performance ( Csikszentmihalyi, 1997 ). Bakker and Van Woerkom (2017) argued that flow is a desirable state not only for task performance but also for creativity, productivity, and service quality. In a cross-sectional field study, Demerouti (2006) showed that work-related flow was beneficial for both in-role and extra-role performance, as rated by colleagues, but only for conscientious employees, who apparently are better at directing their attention toward achieving crucial tasks that are in line with the goals of the organization. The flow–performance relationship has been established also at the within-individual level, with a number of daily diary studies linking the flow state to both in-role and extra-role performance ( Peifer and Zipp, 2019 ; Soriano et al., 2021 ; Weintraub et al., 2021 ; Gerpott et al., 2022 ; Aitken et al., 2023 ). These findings have been further corroborated in a recent meta-analysis by Liu et al. (2023) , who demonstrated that flow is positively related to job performance.

Based on the logic above, we hypothesize that on days when employees experience high levels of flow, they will feel they are performing better compared with days when they are not in a state of flow. As indicated in previously formulated hypotheses, we expect that multitasking will negatively influence flow through the mediating mechanism of task appraisal. Therefore, we also hypothesize a serial mediation such that multitasking has an indirect effect on job performance via challenge and hindrance appraisals of the daily set of tasks and subsequently flow.

Hypothesis 4 : Within individuals (across days), flow is positively related to job performance.

Hypothesis 5 : Challenge and hindrance appraisals of the daily tasks and flow will serially mediate the within-individual relationship between multitasking and job performance.

3 Materials and methods

3.1 sample and procedure.

To empirically test the proposed model, we designed a diary study of work activities aimed to capture fragmentation of work time due to multitasking as it appears in everyday organizational life. The data for this study were collected at a multinational company that is world leading in the food industry. We collected data among employees of a business unit situated in the Netherlands. To get a representative view of employees’ multitasking, flow experience, work engagement, appraisal of daily tasks, and performance, daily measurements were repeated for a total of 10 days spread evenly across 4 weeks of data collection. Daily surveys were sent out to 65 employees who agreed to participate in this study. A response rate of 75.4% resulted in 49 employees completing the daily surveys, capturing a total of 189 daily records. Participants were instructed to complete the survey toward the end of their workday. Records that were not completed at the designated time were removed for further analyses. Moreover, as we aimed to study daily fluctuations in multitasking and other constructs, we also had to remove respondents with only a single daily record. Our final sample consisted of 158 daily records from 33 employees.

Only 27 of the 33 employees completed a one-time questionnaire at the end of the study, which contained questions on demographic variables. We have missing data for three of these participants, thus resulting in descriptive information for 24 respondents, which consisted of 11 women (45.8%) and 13 men (54.2%), with a mean age of 35.58 years ( SD  = 7.73). Analysis of this descriptive information revealed that, on average, participants had worked 4.96 years within this organization ( SD  = 2.16). Our respondents can be characterized as highly educated, as all had finished higher education, with 91.7% having a master’s degree. The majority of respondents (91.7%) had a fixed contract. The sample included respondents from a variety of different countries, with the majority being French (58.3%) and 16.7% being Dutch. Other nationalities were Afghan, British, Finnish, Greek, Mexican, Polish, and Russian.

3.2 Measures

3.2.1 multitasking.

Respondents were asked to provide a list of all their work activities that day and to indicate for each of the activities how much time they spent working on it. In line with the two-dimensional conceptualization of multitasking ( Circella et al., 2012 ), this strategy enabled us to operationalize day-level multitasking as the share of resources (here: time) allocated to work activities within the time frame of a single working day. Focusing on the workday as the unit of time is too coarse to assess the types of multitasking behavior (i.e., switching, interleaving, or overlaying), but our goal here is to get an indication of how fragmented the workday has become due to the execution of multiple and different activities, irrespective of whether tasks are executed in parallel or whether a task is fully or only partly left behind when alternating. We therefore used a diversity measure that captures fragmentation of time across multiple tasks, which was computed using Simpson’s (1949) diversity formula:

Here, i represents a particular task, R is the total number of tasks, n i is the proportion of time spent on the i th task, and N is the total amount of time spent across all tasks. In this sample, 15 was the highest number of tasks on a day, and an average working day consisted of 6.4 tasks. The value of D ranges between 0 (all working time is devoted to a single task – that is, monotasking) and 1 (multitasking in a highly fragmented manner). Simpson’s index captures the level of multitasking for an individual respondent on a given day.

To measure employees’ daily flow experiences, we used the Flow State Scale (FSS) developed by Jackson and Marsh (1996) . This scale consists of a total of 36 items on nine dimensions of the flow state. When conducting diary studies, Ohly et al. (2010) recommend using short scales or even single-item measures. We therefore focused on the subscales ‘concentration’ and ‘autotelic experience’ and selected three items based on factor loadings and face validity. Example items are “I had total concentration today” and “I really enjoyed today’s work experience.” We asked respondents to indicate their agreement on a five-point Likert scale ranging from 1 =  strongly disagree to 5 =  strongly agree . Across days, the average internal consistency was 0.79.

3.2.3 Job performance

We evaluated employees’ daily performance at work using a single-item self-report measure. Respondents were asked to indicate their agreement with the following statement: “Today, I was able to carry out the core parts of my job.” Answers were recorded on a five-point Likert scale ranging from 1 =  strongly disagree to 5 =  strongly agree .

3.2.4 Work engagement

To assess employees’ daily engagement, we relied on Breevaart et al.’s (2012) validated scale for measuring state work engagement and selected only those items that are part of the ultra-short version of the Utrecht Work Engagement Scale (UWES-3; Schaufeli et al., 2019 ). The UWES consists of vigor, dedication, and absorption as dimensions of engagement. We used one item for each dimension, namely “Today, I felt bursting with energy” (vigor), “Today, I was enthusiastic about my job” (dedication), and “Today, I was immersed in my work” (absorption). We asked respondents to indicate their agreement with these statements on a five-point Likert scale ranging from 1 =  strongly disagree to 5 =  strongly agree . Given the conceptual overlap between absorption and flow ( Schaufeli et al., 2002 ), particularly on a daily level, we decided to drop the third item for the specific purposes of our study. The two-item measure of daily engagement had an average Cronbach’s alpha of 0.54 across days.

3.2.5 Appraisal of daily tasks

As mentioned earlier, we asked respondents to provide a list of all their work activities on a particular day. Respondents were also asked how they appraised each of their work activities. We built on prior work on stress appraisal by Searle and Auton (2015) , who proposed that appraisal scales can be framed in different ways, amongst others in relation to a task the respondent is currently performing. We then used the response scale of the Valencia Eustress-Distress Appraisal Scale (VEDAS) developed by Rodríguez et al. (2013) to measure task appraisals. Specifically, each task had two corresponding six-point Likert scales that enabled respondents to indicate their positive and negative appraisals of their daily tasks. The response scale for challenge appraisal referred to a task as 1 =  very definitely is NOT a source of opportunity/challenge or 6 =  very definitely IS a source of opportunity/challenge , while the response scale for hindrance appraisal referred to a task as 1 =  very definitely is NOT a source of pressure or 6 =  very definitely IS a source of pressure . To obtain scores on the degree of challenge and hindrance employees perceived in the total of tasks performed during the workday, we aggregated the task-level challenge and hindrance appraisals of a particular day.

3.3 Analyses

The use of repeated measurements resulted in a nested data structure, where days (Level 1; n  = 158) are nested within individuals (Level 2; n  = 33). For each variable, we estimated a two-level null model (i.e., no predictors) that partitions the total variance into between-individual and within-individual variance components. Table 1 shows that the percentages of variance due to within-individual variation in construct scores varied between 53.0% (hindrance appraisal) and 94.6% (multitasking). Thus, our constructs show high day-to-day fluctuations, and within-individual analyses are thus appropriate. We therefore use hierarchical linear modeling (HLM; Bryk and Raudenbush, 1992 ).

Table 1 . Variance components of null models for level-1 variables.

To avoid an overly piecemeal analysis of our model, we used the multilevel modeling approach outlined by Bauer et al. (2006) to test our mediation hypotheses. This methodology estimates simultaneously the distinct paths in a mediation model. In all HLM analyses, we specified random intercepts – random slopes for the models at level 2 to account for differences in slopes across individuals. We centered each level-1 predictor variable relative to the individuals’ means across days on that variable. As such, the scores represent deviations from the respondent’s respective mean, and “the subject serves as his or her own control” ( DeLongis et al., 1988 , p. 487).

Table 2 presents the descriptive statistics for all study variables as well as the between- and within-individual correlations.

Table 2 . Correlation matrix.

To test Hypothesis 1, we regressed flow on multitasking. We found that on days characterized by high levels of multitasking, employees experienced less flow compared with days on which they had to multitask to a lesser extent ( B  = −1.09, p  < 0.001, β  = −0.23). We then used the multilevel procedures of Bauer et al. (2006) to holistically test a 1–1–1 mediation model in which multitasking influences flow via appraisal of daily tasks. We observed that multitasking was negatively associated with appraisal of the sum of daily tasks as challenging ( B  = −1.40, p  < 0.001, β  = −0.34), and challenge appraisals were positively associated with flow ( B  = 0.28, p  = 0.017, β  = 0.24). Thus, both paths of the mediation were significantly different from zero (see also Model 1 in Table 3 ). Yet, to test our mediation hypothesis directly, we used a package called ‘RMediation’ ( Tofighi and MacKinnon, 2011 ), which provides an estimate of the indirect effect and a confidence interval around this effect on the basis of the distribution-of-the-product method. RMediation estimated the indirect effect at −0.396 with a 95% CI [−0.821, −0.075], which supports Hypothesis 2a; on days when employees multitasked, they were less likely to appraise their daily tasks as challenging, which interfered with their flow experience. To test Hypothesis 2b, we specified an alternative model with hindrance appraisal as the mediator (Model 2 in Table 3 ). Within individuals, multitasking was unrelated to appraisal of the sum of daily tasks as hindering ( B  = −0.66, p  = 0.182, β  = −0.17), and hindrance appraisals were not linked to flow ( B  = 0.11, p  = 0.353, β  = 0.09). We can conclude that the association between multitasking and flow was not mediated by appraisal of daily tasks as hindering, and we, therefore, reject Hypothesis 2b.

Table 3 . HLM results for testing mediation.

The next step involved testing a model that incorporates daily work engagement as a moderator of the multitasking–flow relationship. In this moderation model, both multitasking ( B  = −0.84, p  < 0.001, β  = −0.17) and engagement ( B  = 0.53, p  < 0.001, β  = 0.59) had significant main effects on flow. In addition, the interaction between multitasking and work engagement was significant in predicting flow ( B  = 1.25, p  = 0.027). This result lends support to Hypothesis 3. The interactive effect is shown in Figure 2 , further explored using the simple slopes procedure described by Preacher et al. (2006) . Simple slopes were calculated for conditional values of the moderator at ±1 SD . Tests of simple slopes indicated that the effect of multitasking on flow was significant at lower (−1 SD ) levels of work engagement (simple slope = −1.69, p  = 0.003) and at average levels of work engagement (simple slope = −0.84, p  = 0.017). At higher (+1 SD ) levels of work engagement, multitasking did not significantly reduce flow (simple slope = 0.01, p  = 0.985). We also calculated the region of significance of the simple slopes, which defines the specific values of the moderator at which the slope is statistically significant. We found that the simple slope of flow regressed on multitasking was significant for any value of work engagement below 0.11, and centered scores ranged from −1.79 to 2.00. In other words, moderate to high levels of daily work engagement buffered the detrimental effect of multitasking on flow.

Figure 2 . Interaction of work engagement with multitasking in predicting flow.

Finally, we regressed performance on flow. In support of Hypothesis 4, we found that on days when employees experienced more flow, they reported higher performance compared with days on which they experienced less flow ( B  = 0.65, p  < 0.001, β  = 0.46). We again used the procedures of Bauer et al. (2006) , this time to test a model in which flow mediates the association between multitasking and daily performance (Model 3 in Table 3 ). We observed that multitasking was negatively associated with flow ( B  = −1.19, p  = 0.001, β  = −0.25) and flow was positively associated with daily performance ( B  = 0.69, p  < 0.001, β  = 0.49). Thus, on days when employees multitasked more, they experienced less flow and in turn reported lower performance, compared with days on which they multitasked less. This indirect effect was estimated at −0.82 with a 95% CI [−1.38, −0.34]. Together with the mediating mechanism of challenge appraisals of daily tasks that we found earlier, this result supports Hypothesis 5.

5 Discussion

Today, many – if not most – employees are forced to shift attention between a high number of tasks on a daily basis, perhaps even get addicted to multitasking ( Dean and Webb, 2011 ), yet at what cost? This paper aimed to investigate the implications of multitasking for individuals and organizations. We have argued that a better understanding of the consequences of multitasking requires (a) an ecologically valid examination of the way in which daily tasks unfold, (b) an integration of scholarly work on multitasking and work-related flow, and (c) identification of mediators and moderators that contextualize employees’ responses to multitasking. Hence, the present diary study developed and tested a comprehensive model of day-level multitasking that aimed to shed light on why and when performance of multiple tasks relates to flow and, consequently, to subjective performance at work. Our results provide support for most of the hypotheses advanced herein.

The results of our study have important implications for research on the consequences of multitasking. In a sample of professional workers, we observed that on days when employees’ working time was highly fragmented across a high number of tasks, they experienced less flow and, in turn, their self-reported performance was lower for that particular day. Adler and Benbunan-Fich (2013) posited that people who are in flow are totally focused on a single task and unlikely to multitask. Our results speak to this notion as they indicate that day-level multitasking indeed impairs flow.

We further contribute to Peifer and Zipp’s (2019) line of research and theoretical model (i.e., transactional model of stress and flow) in three important ways. First, we have put forward (and empirically tested) a mediating mechanism that underlies the relationship between multitasking and flow, focusing on appraisal of daily tasks as challenging or hindering. We found that an explanation for this relationship is that a working day full of multitasking impairs the perception of one’s daily tasks as challenges or opportunities. On days when employees’ time for tasks was highly fragmented, those tasks were appraised as less challenging, which resulted in less flow experiences. This result is in line with previous research on how flow experiences are particularly observed in challenging tasks (see Peifer and Zipp, 2019 ). For instance, Csikszentmihalyi (2000) described how surgeons find flow during difficult surgeries. We add to previous research the insight that multitasking as a stressor makes it more difficult to appraise one’s tasks as challenging tasks. In other words, on days characterized by multitasking, employees are less inclined to appraise their daily tasks as challenging.

Importantly, we found no such effect for the hindrance appraisal of daily tasks. While interruptions as such are typically appraised as hindrances ( Smith et al., 2022 ), this does not necessarily mean that the interrupted and interrupting tasks are considered hindrances. Our result suggests that the hindrance of performing multiple tasks does not add to the pressure associated with a particular task or turn these tasks into hassles. Rather, multitasking is detrimental only in the sense that it interferes with the potential for achievement and personal growth of certain daily tasks (i.e., challenging tasks are not perceived as such). Hindrance appraisal of daily tasks was not related to flow, which is in line with the meta-analytic result of Liu et al. (2023) that hindrance demands were not significantly related to flow or any of its subdimensions. Conceptually, flow is more closely linked to challenges ( Peifer and Zipp, 2019 ), and it indeed seems that only challenge appraisals can link multitasking to flow.

A second way in which we build on Peifer and Zipp’s (2019) study is that we have illuminated the ‘when’ of the multitasking–flow relationship by empirically testing daily engagement as a buffer for this relationship. It is not impossible to experience flow while multitasking. Our data indicated that employees are not equally engaged at work across days; there are days when employees feel more energetic and dedicated than on other days. Importantly, Sonnentag et al. (2010) noted that “in many work settings there are specific times and periods when it is necessary that employees are particularly engaged at work” (p. 27). Work engagement may buffer flow-inhibitors and may build resources that could activate flow-enablers ( Yan and Donaldson, 2023 ). Our results speak to this notion, showing that on days when employees were highly engaged, the detrimental impact of multitasking on flow was weakened. It appears that daily engagement offers employees substitute resources to cope with day-level multitasking and remain in a flow state despite the demands of multitasking. Given that work engagement is a positive and high-activation state ( Bakker et al., 2011 ), in a multitasking environment, engaged employees should be more willing to put in the effort (e.g., to navigate attention back to a task) and have abundant energy to do so (e.g., in fact navigating back to a task), much like conscientious individuals are likely to do ( Demerouti, 2006 ). Thus, even though engaged employees have to multitask as much as their less engaged counterparts, they have the capacity and willpower to create conditions under which flow can be attained and maintained.

Third, we have addressed Peifer and Zipp’s (2019) call for studying objective multitasking demands. We consider it a key strength of our study that we relied on a fine-grained measure of multitasking that is based on daily diaries. We have investigated this phenomenon in an organizational setting, focusing on the daily working life of employees and examining how they feel they are performing in light of the multitasking reality of their organization. Thus, from a methodological point of view, we believe our results offer ecologically valid insights into why and when day-level multitasking hinders flow and subjective performance on a given day.

Our study contributes to the broad field of multitasking, as we believe the theorizing and empirical results presented in this paper may be useful in extending frameworks for the study of cumulative interruptions and task switches (see Baethge et al., 2015 ; Puranik et al., 2020 ). By first demonstrating that impaired flow explains the effect of multitasking on daily job performance, and then illuminating why (i.e., reduced challenge appraisals) and when (i.e., on days when employees are less engaged) day-level multitasking is detrimental to flow, we contribute to a more comprehensive understanding of employees’ short-term responses to juggling multiple tasks. The ‘why’ and ‘when’ of the multitasking–flow relationship relate to the primary and secondary appraisal processes in the transactional model of stress and flow ( Peifer and Zipp, 2019 ). With regard to primary appraisal, we found that performing multiple tasks reduces the challenges one perceives throughout the workday, thereby hindering flow because flow tends to be experienced in challenging tasks. In accordance with the challenge-hindrance stressor framework ( LePine et al., 2005 ), we argue that multitasking can be categorized as a hindrance stressor, for which employees need to adopt a coping strategy if they want to experience flow. Our study suggests that daily work engagement may provide the necessary coping resources, allowing for a more positive secondary appraisal. We believe these are insights that help further specify the transactional model of stress and flow.

Finally, the present study advances our understanding of the antecedents and consequences of flow. We have shown that flow, as an experiential well-being state ( Ilies et al., 2017 ), has immediate implications for how employees perform. While research has established the importance of flow for predicting performance, our study adds to a small but growing body of research that examines flow as a within-person performance process ( Peifer and Zipp, 2019 ; Soriano et al., 2021 ; Weintraub et al., 2021 ; Gerpott et al., 2022 ; Aitken et al., 2023 ). Importantly, we have identified multitasking as a major obstacle to work-related flow, at least for those employees who do not feel particularly engaged. Our study thus provides empirical support for the notion that entering flow requires initial energy (which might get lost when multitasking), but once reached it is a state in which people can recover and build resources (see also Gerpott et al., 2022 ).

5.1 Practical implications

The results from this study suggest that multitasking poses serious concerns for employee effectiveness. Notably, we observed almost no between-individual variance in day-level multitasking (see Table 1 ). Thus, the level of multitasking cannot be explained by any individual-level differences (such as personality or work style) but rather is determined by situational, day-level variables. In other words, multitasking is not a given or stable work feature; some days are characterized by more alternations between activities and will require more (simultaneous or sequential) multitasking behaviors than other days. In a way, this suggests that employees and managers are left with little opportunity to be proactive and optimize the daily work environment. However, Bakker and Van Woerkom (2017) claimed that flow should not be seen as a passively determined state but can be shaped by individual behaviors (see also Liu et al., 2023 ). In a similar vein, we believe that the level of multitasking should not be seen as something employees and managers have no control over. Employees need to realize they are active agents; they can develop strategies for managing cumulative alternations and opt for a flow-conducive strategy to cope with multiple tasks (which multitasking is not; Peifer and Zipp, 2019 ). In fact, optimizing job demands (i.e., simplifying the job and bypassing inefficient work processes) may be more fruitful than minimizing job demands ( Demerouti and Peeters, 2018 ). For managers, it is important to realize that if employees are spread too thin, this limits the learning potential of challenging tasks. They should therefore be mindful of daily variations in multitasking and assist employees on those days when they have to juggle many tasks.

Such assistance could be reflected in promoting strategies for time and attention management. Specifically, organizations are recommended to enable employees to monotask more frequently and for longer stretches of time, for instance by creating spaces designated for focused, uninterrupted work. Moreover, anecdotal evidence from our study suggests that organizations should try to limit the number of meetings, as these prompted many of the task switches. Evitable work switches may be reduced also by implementing new ways of working that enable employees to work whenever and wherever they want, in particular when they need time to work on solitary projects, thereby distancing themselves physically from colleagues and clients. Such organizational interventions could enhance the restorativeness of the work environment (see Bellini et al., 2023 ).

Working from home may help in shaping more engaged subsequent workdays ( Darouei and Pluut, 2021 ), which will assist employees in coping with multitasking, as our results suggest. That being said, teleworkers may struggle with homeplace intrusions and social isolation as impediments to flow ( Aitken et al., 2023 ). Personal resources (such as discipline and resilience) seem critical in this respect. Therefore, we recommend that organizations offer personal resources interventions and mindfulness trainings for employees who are regularly faced with the demands of multitasking. An intervention study found that personal resources had a positive impact on work engagement ( Bakker and van Wingerden, 2021 ), while mindfulness has been found to result in improved self-regulation ( Glomb et al., 2011 ) and work engagement ( Leroy et al., 2013 ). Mindfulness seems to be fundamentally connected to workplace functioning, especially in light of the multitasking reality of many organizations. The growing body of research on mindfulness interventions suggests that working mindfully is associated with attentional stability (sustaining attention), attentional control (directing attention amid competing demands), and attentional efficiency (economical use of attentional resources) ( Good et al., 2016 ). When the workday involves multiple tasks, those benefits of working mindfully should assist employees in alternating effectively between activities, finding focus in their work, and determining which tasks should be completed with undivided attention.

5.2 Limitations and directions for future research

We should note several limitations of our study. Although multitasking was measured based on detailed reports of work activities ( cf . perceptual measures; e.g., Kirchberg et al., 2015 ; Peifer and Zipp, 2019 ), we did not use in-the-moment data collection. We did not have information on the share of resources other than time allocated to activities, how often employees were interrupted, if and when they resumed their tasks, or the reasons for multitasking. Thus, we are somewhat limited in our understanding of the nature of employees’ multitasking behaviors and it remains unclear whether flow is hindered by the performance of multiple tasks simultaneously, the interruptions associated with multitasking, or the task-switching aspects. We therefore recommend that future studies distinguish between the switching, interleaving, and overlaying types of multitasking behavior ( Circella et al., 2012 ). Higher levels of granularity would allow for examining whether challenge and hindrance appraisals differ across types and what are the consequences for flow and job performance. Future studies are also recommended to use observation methods (see Wajcman and Rose, 2011 ) or a fixed format in the survey by which employees log the start time, end time, and resumption time of all daily tasks (see Feldman and Greenway, 2021 ), if feasible.

Despite this limitation, our study design and data collection method were still intensive and demanding, which has come at the cost of the number of participants in the sample and the number of daily records in the analysis. Given the small sample size, it is important to interpret the magnitude of the effect sizes reported herein with caution. Also, inter-item correlations can be unstable if the sample size is small ( Kennedy, 2022 ) and the internal consistency of a scale can decrease if items are dropped ( Schaufeli et al., 2019 ). We indeed observed in our study that day-specific Cronbach’s alphas are quite sensitive to small sample sizes and shortened scales. Hence, we spur researchers to investigate the relationships proposed herein and replicate our results using a large sample, ideally with multiple measurements throughout the day. That is, our results may be subject to concerns regarding the possibility of reversed causation and common method bias ( Siemsen et al., 2010 ), as our design did not involve temporal separation of the daily measures and we relied merely on employees’ self-reports for the assessment of job performance.

Our daily diary design covered 4 weeks and we were therefore not able to take a long-term perspective on the consequences of multitasking. Notably, multitasking is considered a poor strategy for learning ( Rosen, 2008 ), and the results of the current study on challenge appraisal speak to this notion. Our suggestion is that researchers adopt longitudinal designs and examine the implications of day-level multitasking and flow for longer-term outcomes such as workplace learning. Moreover, we have exclusively focused on outcomes in the work domain. It would be interesting for future researchers to examine multitasking as a hindrance demand that depletes (personal) resources, thereby spilling over to the home domain and potentially leading to work–family conflict. We further believe that research in the field of job crafting can make a contribution to the literature on multitasking by identifying crafting behaviors of individuals (mostly likely in the domains of task and relationship crafting, see Wrzesniewski and Dutton, 2001 ) that serve as responses to organizational realities of multitasking. Finally, while we focused on state work engagement, trait work engagement may also prove promising to study as a person-level resource that buffers the detrimental impact of multitasking (see Puranik et al., 2020 ) as well as its relationship with job crafting in this context (see Bakker et al., 2023 ).

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation upon request.

Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the patients/participants or patients/participants legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.

Author contributions

HP: Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. MD: Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing. MZ: Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: multitasking, flow, engagement, stress appraisal, experience-sampling methodology

Citation: Pluut H, Darouei M and Zeijen MEL (2024) Why and when does multitasking impair flow and subjective performance? A daily diary study on the role of task appraisals and work engagement. Front. Psychol . 15:1384453. doi: 10.3389/fpsyg.2024.1384453

Received: 09 February 2024; Accepted: 15 July 2024; Published: 25 July 2024.

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Copyright © 2024 Pluut, Darouei and Zeijen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Helen Pluut, [email protected]

† These authors have contributed equally to this work

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• Frank Cerabino
• Nation & World
• Fort Lauderdale

Who is Kai Trump? What we know about Trump’s oldest grandchild who lives in Florida

Donald Trump’s last campaign for president in 2016 was filled with frequent appearances and speeches from his children , who have been some of the most vocal supporters of his campaigns for president.

And on the third day of the Republican National Convention in Milwaukee this week, Trump’s oldest granddaughter, Kai Trump, became the first of his grandchildren to talk about what it’s like to have one of the most famous people in the world as a grandfather.

In her speech, Kai mostly focused on the side of Trump that no one sees, calling him a “normal grandpa.”

"I'm speaking today to share the side of my grandpa that people don't often see," she said. 

"To me, he's just a normal grandpa. He gives us candy and soda when our parents aren't looking. He always wants to know how we're doing in school."

Here’s what we know about New York-born Floridian Kai Trump.

How old is Kai Trump, Trump's granddaughter?

Kai Trump is the oldest of Donald Trump’s 10 grandchildren and the oldest child of Donald Trump Jr.’s five children. She just celebrated her 17th birthday a little over two months ago, on May 12.

She “has been in the public eye from a young age, attending high-profile events such as her grandfather's inauguration and the White House Easter Egg Roll,” USA TODAY reported.

“Kai's speech at the RNC marks her first significant campaign role, highlighting her growing involvement in the Trump family's political activities.”

Who are Kai Trump's parents?

Kai Madison Trump is the oldest child of Donald Trump Jr. and former model Vanessa Kay Trump, formerly Vanessa Kay Pergolizzi.

Vanessa and Donald Jr. married in 2005 and had five children together before they eventually divorced, after 13 years of marriage, in 2018.

Will Melania, Barron be at the RNC? Here's which Trumps have appeared

Is Kai Trump a golfer?

Yes, Kai Trump shares her grandfather’s love of golf . She plays competitively and earlier this year, won a ladies' club championship at Trump's golf course in West Palm Beach, Florida .

Her Instagram page is mostly golf content, down to her username, which is @kaitrumpgolfer .

According to her SportsRecruits profile, Kai plays on the varsity golf team for her school, The Benjamin School in Jupiter, Florida. Charlie Woods, Tiger Woods' son, also goes to school and plays golf there.

"Golf has always been a massive part of my life and is my biggest passion. I always aspire to be a leader and a positive figure on and off the golf course. I hope to continue my athletic career at the college level," Kai's SportsRecruits biography says. Kai will graduate in 2026.

Tonight's Florida speakers: Here's when Trump, Hulk Hogan, Tucker Carlson speak at RNC tonight. Kid Rock to perform

Where is Kai Trump from?

Kai was born and mostly raised in New York, but now lives in Jupiter with her mother. According to Kai's SportsRecruits profile , she moved to south Florida about four years ago, when she was 13 years old.

China – The New Normal Case Solution & Answer

Home » Case Study Analysis Solutions » China – The New Normal

Question: How has China performed since its accession to the World Trade Organization?

China, under the leadership of the Moo Zedong, faced many complications domestically, and its ideology was based on Marxism-Leninism alongside Chinese revolution.On the other hand, the approach of Mao structured the country into a government controlled production and prices. However, Mao’s leadership faced many negative campaigns which led to the Great Leap Forward and resulted in millions of deaths in the country. Hence, Mao backed from his responsibilities and handed over to the senior leader, Deng Xiaoping.

Similarly, following the death of Mao, Deng became thenewleader of the People’s Republic of China, and he immediately took the export-led strategy to grow in the market. On the other hand, Deng the initiated the agriculture support program to feed its growing population on priority basis through the household responsibility system. In this system, thegovernment leased the land to the farmers and asked them to sell products in the market. It brought many incentives and doubled the agriculture output in the country, and through this way, China becameself-sufficient to feed its population.

Furthermore, the government performed well to adhere to its export-led country, and it further directed the Township and village enterprises (TVE’s) to produce simple products for the exports.Due to these reasons, TVE’s grew annually by 9% between 1978 and 1996, and it employed 135 million people.Apart from that, after 15 years of negotiations with the WTO authorities, China entered WTO, but at some agreed reforms in the country to facilitate foreign enterprise, free trade, and increased transparency of the Chinese law.

Similarly, Chinaincreased its sales of goods and products in the market to facilitate the foreign companies, and it also provided national treatments to the foreign banks, as well as it increased the transparency in laws and regulations. However, these restrictions did not prevent China from becoming the world’s largest exporting country in 2009. Similarly, China went on to follow as imposed by WTO, as well as it focused on the theft of intellectual property. Additionally,a study identified that China is the largest producer of replicated products, and that it copies foreign patents. Moreover, China was athird largest country in the world for aninternational patent filer in 2014.

China has the unconventional approach towards export growth rather than free trade, openness to trade, and planned economy.Nonetheless,its exports were three times larger than per-capita income, which included electronic items and auto parts. Moreover, since 2001 to 2014, the exports of Chinaincreased by 15%, which made Chinathe world’s largest trade surplus of $595 billion in 2015, however that surplus was not because of export, but it was also due to the decrease in the imports.Aside from the growth of the country, China controls all the capital account transactions including the capital market securities, commercial and financial credits, real estate transactions, and direct investment as well. This resulted in capital inflow in the country, however there was aninsignificant capital outflow. Consequently, China’s foreign exchange reserves increasedsignificantly due to which, China is one of the largest US Treasury bill holders.Apart from that, after its entry into the world trade organization, it performed well in the trade and FDI in the country…………….

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China – The New Normal Harvard Case Solution & Analysis

Home >> Harvard Case Study Analysis Solutions >> China – The New Normal

Introduction

Chinawas raised as the communist state under the leadership of the Mao Zedong in 1949. He declared the state as the People’s Republic of China, however, China’s central ideology was based on Marxism-Leninism along with the Chinese Revolution practices. Meanwhile, Moa put China in the structure of the collectiveorganizations, and he also ruled the production quotas and prices in the country.Furthermore, Mao’s approaches to the economy and social structural reforms marked by the differentcampaigns and unsuccessful revolutions that led death of millions of peoples. Following the house arrest of Mao, and his death in 1976 brought anew leader in power in 1978, Deng Xiaoping.

On the other hand, Chinese people’sdistrust of the government in their policies and structural was on social, economic, and political. Meanwhile, the government was considering more consultative oriented governing process through the “seeking truth from fact.”Apart from this, China adopted the export-led development strategy, due to which the economy of China improved.Moreover, the Chinese government also considered the agriculture industry seriously to feed its growing populationsas well as it increased the resources into the township and village enterprises (TVE’s). Meanwhile, the governmentforced on bringing the foreign investment in the country to grow in the market.

However, due to its interventions in the market, laws and enforcement became hurdles for the foreign companies to operate in the free market environment that Chinese market lacked in that position. Given the numerous reforms in the country, foreign investment increased to $250 billion since the death of Mao, theex-leader of China.On contrary, China itself was one ofthe attractive places for the multinational companies to shift their production facilities in order to benefit from cheap labor force and newly provided infrastructure and tax relief to the foreign companies. This foreign investment in the country was highly supported by the government of China, and China’s main aim of expanding its footprints around the world through export worldwide, and attracting foreign investment in the country.

China – The New Normal Harvard Case Solution & Analysis

Lastly,the country’s effort to enter the world trade center was the second step to generate benefit from globalcommerce communications of the different countries. However, China was forced to follow WTO’s strict guidelines that were stricter than the entry rules over another country, due to its economic policies, and political, and governmental supply-side intervention in the market, and also being a communist country. Meanwhile, the country becameone of the leading export countries in the world. Its growth was the fruit of its efforts towards the foreign investment, and exports as well.

Apart from that, the government of China has been the most significant problem regarding addressing the issues identified that have long term consequences on the Chinese economy as awhole. However, its presence in the global market does not guarantee its growth to sustain or increase because of the lack of structural issues in the governing bodies in China. Meanwhile, the structural reforms involved reducing the industrial capacity, developing domestic consumption and services, and fixing the state-owned enterprises, also managing the debt and opening capital markets..................

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