research articles on workplace stress

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Workplace Stress and Productivity: A Cross-Sectional Study

1 University of Oklahoma at Tulsa, Tulsa, OK

Rosey Zackula

2 Office of Research, University of Kansas School of Medicine-Wichita, Wichita, KS

Katelyn Dugan

3 Department of Population Health, University of Kansas School of Medicine-Wichita, Wichita, KS

Elizabeth Ablah

Introduction.

The primary purpose of this study was to evaluate the association between workplace stress and productivity among employees from worksites participating in a WorkWell KS Well-Being workshop and assess any differences by sex and race.

A multi-site, cross-sectional study was conducted to survey employees across four worksites participating in a WorkWell KS Well Being workshop to assess levels of stress and productivity. Stress was measured by the Perceived Stress Scale (PSS) and productivity was measured by the Health and Work Questionnaire (HWQ). Pearson correlations were conducted to measure the association between stress and productivity scores. T-tests evaluated differences in scores by sex and race.

Of the 186 participants who completed the survey, most reported being white (94%), female (85%), married (80%), and having a college degree (74%). A significant inverse relationship was observed between the scores for PSS and HWQ, r = −0.35, p < 0.001; as stress increased, productivity appeared to decrease. Another notable inverse relationship was PSS with Work Satisfaction subscale, r =−0.61, p < 0.001. One difference was observed by sex; males scored significantly higher on the HWQ Supervisor Relations subscale compared with females, 8.4 (SD 2.1) vs. 6.9 (SD 2.7), respectively, p = 0.005.

Conclusions

Scores from PSS and the HWQ appeared to be inversely correlated; higher stress scores were associated significantly with lower productivity scores. This negative association was observed for all HWQ subscales, but was especially strong for work satisfaction. This study also suggested that males may have better supervisor relations compared with females, although no differences between sexes were observed by perceived levels of stress.

INTRODUCTION

Psychological well-being, which is influenced by stressors in the workplace, has been identified as the biggest predictor of self-assessed employee productivity. 1 The relationship between stress and productivity suggests that greater stress correlates with less employee productivity. 1 , 2 However, few studies have examined productivity at a worksite in relation to stress.

Previous research focused on burnout, job satisfaction, or psychosocial factors and their association with productivity; 3 – 7 all highlight the importance of examining overall stress on productivity. Other studies focused on self-perceived stress and employer-evaluated job performance instead of self-assessed productivity. 8 However, most studies examining this relationship have been occupation specific. 8 , 9 Larger studies examining this relationship were performed in other countries. 1 , 5 , 9 , 10

The purpose of this study was twofold. First, the study sought to elucidate the relationship between stress and productivity in four worksites in Kansas. Second, the study sought to examine potential differences in stress and productivity by sex and race.

Recruitment and Sampling Procedures

The target population was employees from four WorkWell KS worksites. WorkWell KS is a statewide worksite initiative in Kansas that provides leadership and resources for businesses and organizations to support worksite health. Because access to employee emails was unavailable, a URL link to an online survey was sent to the worksite contact, who was responsible for ensuring the distribution of the URL link to a cross-section of employees at the worksite. Following a WorkWell KS workshop (held in Topeka, Kansas on November 6, 2017) attendees from the four worksites were recruited to distribute a link to an online survey to their employees. Workshop attendees were members of wellness committees or were worksite representatives. Employee responses to the online survey were collected through mid-December 2017. No compensation was given for disseminating the survey link or for participating in the study. This study was approved by the University of Kansas School of Medicine-Wichita’s Human Subjects Committee.

Online Survey

The online survey comprised demographic items with two instruments, the Perceived Stress Scale (PSS), 11 and the Health and Work Questionnaire (HWQ). 12 Demographic items included employee, sex, race, age, marital status, and highest level of education completed.

Perceived Stress Scale

Stress was measured by the PSS, a 10-item questionnaire designed for use in community samples. The purpose of the instrument is to assess global perceived stress during the past month. Each item is measured with a Likert-type scale (0 = Never, 1 = Almost Never, 2 = Sometimes, 3 = Fairly Often, 4 = Very Often). This scale is reversed on four positively stated questions. Scoring of the PSS is obtained by summing all responses. Results range from zero to 40, with higher PSS scores indicating elevated stress: scores of 0 – 13 are considered low stress, 14 – 26 moderate stress, and 27 – 40 are high perceived stress. The results for perceived stress were used by this study as an indication of psychological well-being.

Health and Work Questionnaire

The HWQ is a 24-item instrument that measures multidimensional worksite productivity. Productivity is assessed by asking respondents how they would describe their efficiency, overall quality of work, or overall amount of work in one week. All items are scaled with Likert-type response anchors, each ranging from 1 to 10 points. Most are positively worded items with response scales from least (scored as a 1) to most favorable (scored as a 10). Exceptions are items 1 and 16 through 24, which are negatively worded and reversed scored. Items are divided into six sub-scales: productivity, concentration/focus, supervisor relations, non-work satisfaction, work satisfaction, and impatience/irritability. As part of the HWQ, employees assessed productivity two ways: on themselves and how their supervisor or co-workers might perceive it. Accordingly, productivity is stratified into a self-assessed sub-score and perceived other-assessed sub-score. HWQ scores are tallied and averaged for each sub-scale, with higher scores generally indicating greater productivity.

The Consent Process

Representatives who participated in the WorkWell KS workshop sent an e-mail to their employees with a request to click on the link and complete the online survey. The link opened the electronic consent, which was the opening remark, followed by the two assessment instruments and the demographic items. Consent was implied by participation in the survey. To encourage survey participation, representatives also sent employees a few e-mail reminders at their own discretion.

Statistical Analysis

The statistical analysis included descriptive statistics, measures of association, and comparisons of survey responses by sex and race. Descriptive statistics comprised response summaries; means and standard deviations were used for continuous variables, while frequency and percentages were used for categorical responses. The relationship between stress and productivity measures were assessed using Pearson correlations. Sex and race comparisons for PSS and HWQ subscales were evaluated using two-sided t-tests; alpha was set at 0.05 as the level of significance. Study participants with missing values were excluded pairwise from the analysis.

Response Rates

Four of nine worksites participated in the study, including two health departments (89 participants), one school district (76 participants), and one non-profit for the medically underserved (21 participants). A total of 188 employees opened the survey link, 186 employees answered the first question of the survey, and 174 employees completed the survey items. The 12 study participants with missing values were excluded from the pairwise analysis. The response rate, defined as those participants who completed the survey, was 58.6% (n = 174). To protect the confidentiality of respondents, data were aggregated and no other comparisons were made by location.

Participants who completed the survey included 174 employees from four worksites in Kansas. Of those who responded, 94% (155 out of 165) reported being white, 85% (142 of 167) reported being female, 81% (124 of 153) reported being between 30 and 59 years, and 60% (99 of 166) reported having a bachelor’s degree or higher ( Table 1 ).

Participant demographics.

With regard to measures of stress, the mean PSS was 16.4, with a standard deviation of 6.2, suggesting that employees have moderate levels of stress at these locations. This result was consistent with the HWQ question regarding “overall stress felt this week”, with a mean score of 4.7 (SD 2.5; 10 is “very stressed”). Regarding measures of productivity, the mean overall HWQ was 6.3 (SD 0.7). With the exception of reverse items, as noted below, scores of 10 indicated high levels of productivity. Mean scores by scale were: 7.3 (SD 1.0) for overall productivity, with 7.5 (SD 1.3) for own assessment, and 7.5 (SD 1.2) for perceived other’s assessment; 7.1 (SD 2.7) supervisor relations, 7.8 (SD 1.8) for non-work satisfaction, and 7.3 (SD 1.7) for work satisfaction. The mean scale for the reverse items scores were concentration/focus at 3.4 (SD 2.0), and impatience/irritability 3.2 (SD 1.6).

Correlations between the PSS and the HWQ subscales ranged from −0.61 to 0.55 ( Table 2 ). A negative association was observed between the PSS and the overall HWQ, r(177) = − 0.35, p < 0.001. While each of the positively-coded HWQ subscales was associated negatively with the PSS, the strongest correlation occurred between work satisfaction and PSS, r(177) = −0.61, p < 0.001, suggesting that as stress increases work satisfaction declines.

Measures of correlation within and between the PSS and HWQ.

HWQ: Health and Work Questionnaire mean score; PSS: Perceived Stress Scale mean score

In evaluating differences by sex, mean scores were significantly higher for males compared with females for the HWQ Supervisor Relations subscale (8.4 (SD 2.1) versus 6.9 (SD 2.7), respectively; p < 0.005; Table 3 ). No other sex differences were observed for either instrument. Similarly, there were no significant differences by race.

Comparing results of the PSS and the HWQ by sex.

Findings suggested there is an inverse association between overall stress and productivity; higher PSS scores were associated with lower HWQ scores. These findings are consistent with other cross-sectional studies comparing productivity and other measures of psychological well-being. 1 , 8 , 9 , 10 Thus, employer efforts to decrease stress in the workplace may benefit employee productivity levels.

In addition, males scored higher for supervisor relations in the HWQ than females. This finding may suggest that males have stronger relationships with their supervisors. Indeed, there is compelling evidence to suggest the main factor affecting job satisfaction and performance is the relationship between supervisors and employees. 13 Although, this relationship may be mitigated by employee-supervisor interactions of sex, race/ethnicity, status, education, age, support systems, and other factors, none of which were evaluated in the current study.

For example, Rivera-Torres et al. 14 suggested that women with support systems, defined as co-workers and supervisors, experienced less work stress than males. Results from this study seemed to support Rivera-Torres et al. 14 in that females tended to report higher levels of stress compared with males (although not significant) and reported weaker relationships with their supervisors. In addition, Peterson 15 evaluated what employee’s value at work and found that males and females differed significantly. When asked to rank work values, men valued pay/money/benefits along with results/achievement/success most, whereas women valued friends/relationships along with recognition/respect. Perhaps, more research is necessary to understand the nuances between co-worker and supervisor regarding work satisfaction and productivity.

The study contributes to the literature in the use of different metrics for psychological well-being, defined as stress. Multiple organizations within Kansas were evaluated for both productivity and stress. To our knowledge, the PSS and HWQ have never been used together to measure the relationship between stress and productivity. Results suggested that overall productivity (HWQ) was associated with the HWQ “work satisfaction” subscale. Perceived stress also had the strongest inverse relationship with HWQ sub-scale “work satisfaction” when compared with HWQ sub-scale “productivity”.

This study suggested that productivity, stress, and job satisfaction were correlated, therefore, additional research needs to include each of these variables in greater detail as the current literature has been mixed on their relationships and potential collinearity. For example, one study examining two occupations suggested psychological well-being (defined as psychological functioning) was associated with productivity, whereas job satisfaction did not. 7 In contrast, another study suggested that psychological well-being has been a bigger factor in job productivity than work satisfaction alone, but both are associated with job productivity. 9 This current study was able to examine this relationship by using the PSS and the HWQ together.

More research is needed to understand these differences by standardizing terminology. In this study, psychological well-being was defined as stress. However, other studies have defined psychological well-being as happiness or as one’s psychological functioning. 7 , 8 This study also expanded the relationship between psychological well-being and stress. Previous research focused more on the relationship between productivity and burnout or job satisfaction.

This study had limitations such as a small sample size (in number of organizations and number of employees). The sample size assessed small organizations in the United States, whereas many other large scale studies on stress occurred over multiple large organizations in other countries. 1 , 10 There was limited racial diversity in the current study, as 6.1% (10 of 165) reported being non-white. The population studied was also primarily female, limiting the strength of comparisons made between sexes. Furthermore, because worksites often share computers, questionnaires may have been completed using the same IP address; thus, we were unable to prevent multiple entries from the same individual.

The current study did not detect a difference in productivity or stress by race. This differed from other research. For instance, non-whites experience greater overall stress than whites potentially attributable to poorer employment status, income, and education. 16 Non-whites experience stress secondary to racial discrimination. 17 , 18 In one study, when examining productivity among university faculty, non-whites reported greater stress and produced less research (productivity) compared to whites. 16 Further research needs to be conducted on productivity and stress by race and ethnicity, and associated variables, such as employment status, income, education, and occupation, need to be accounted for in analysis. Differences between other research and the current study regarding race may be attributed to the fact that only 6% of respondents who answered race reported being non-white, making racial diversity in this study limited, although representative of the population sampled.

CONCLUSIONS

This study suggested there is a negative correlation between overall stress and productivity: higher stress scores were significantly associated with lower productivity scores. This negative association was observed for all HWQ subscales, but was especially strong for work satisfaction. This study also suggested that males may have better supervisor relations compared to females, although no differences between sexes were observed by perceived levels of stress. There was no difference in productivity or stress by race. The results of this study suggested that employer efforts to decrease employee stress in the workplace may increase employee productivity.

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How to Recover from Work Stress, According to Science

• Alyson Meister,
• Bonnie Hayden Cheng,
• Franciska Krings

Five research-backed strategies that actually work.

To combat stress and burnout, employers are increasingly offering benefits like virtual mental health support, spontaneous days or even weeks off, meeting-free days, and flexible work scheduling. Despite these efforts and the increasing number of employees buying into the importance of wellness, the effort is lost if you don’t actually recover. So, if you feel like you’re burning out, what works when it comes to recovering from stress? The authors discuss the “recovery paradox” — that when our bodies and minds need to recover and reset the most, we’re the least likely and able to do something about it — and present five research-backed strategies for recovering from stress at work.

The workforce is tired. While sustainable job performance requires us to thrive at work, only 32% of employees across the globe say they’re thriving. With 43% reporting high levels of daily stress, it’s no surprise that a wealth of employees feel like they’re on the edge of burnout, with some reports suggesting that up to 61% of U.S. professionals feel like they’re burning out at any moment in time.  Those who feel tense or stressed out during the workday are more than three times as likely to seek employment elsewhere.

• Alyson Meister is a professor of leadership and organizational behavior at IMD Business School in Lausanne, Switzerland. Specializing in the development of globally oriented, adaptive, and inclusive organizations, she has worked with thousands of executives, teams, and organizations from professional services to industrial goods and technology. Her research has been widely published, and in 2021, she was recognized as a Thinkers50 Radar thought leader.  
• Bonnie Hayden Cheng is an associate professor of management and strategy and the MBA program director at HKU Business School, University of Hong Kong. She is the chief resilience officer of Human at Work and serves as a scientific advisor of OneMind at Work. She works with senior executives of companies ranging from startups to Fortune 500, transforming corporate cultures by incorporating wellness into their business strategy. Follow her on Twitter: @drbcheng.
• ND Nele Dael is a senior behavioral scientist studying emotion, personality, and social skills in organizational contexts. She is leading research projects on workplace well-being at IMD Lausanne, focusing on stress and recovery. Nele is particularly tuned into new technologies for the benefit of research and application in human interaction, and her work has been published in several leading journals.
• FK Franciska Krings is professor of organizational behavior at HEC Lausanne, University of Lausanne. Her research interests include workforce diversity and discrimination, work-family balance, impression management, and (non)ethical behaviors. Her work has been published regularly in leading journals in the field.

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ORIGINAL RESEARCH article

Work stress, mental health, and employee performance.

• 1 School of Business, Zhengzhou University, Zhengzhou, China
• 2 Henan Research Platform Service Center, Zhengzhou, China

The COVID-19 pandemic outbreak—as a typical emergency event—significantly has impacted employees' psychological status and thus has negatively affected their performance. Hence, along with focusing on the mechanisms and solutions to alleviate the impact of work stress on employee performance, we also examine the relationship between work stress, mental health, and employee performance. Furthermore, we analyzed the moderating role of servant leadership in the relationship between work stress and mental health, but the result was not significant. The results contribute to providing practical guidance for enterprises to improve employee performance in the context of major emergencies.

Introduction

Small- and medium-sized enterprises (SMEs) are the key drivers of economic development as they contribute >50, 60, 70, 80, and 90% of tax revenue, GDP, technological innovation, labor employment, and the number of enterprises, respectively. However, owing to the disadvantages of small-scale and insufficient resources ( Cai et al., 2017 ; Flynn, 2017 ), these enterprises are more vulnerable to being influenced by emergency events. The COVID-19 pandemic outbreak—as a typical emergency event—has negatively affected survival and growth of SMEs ( Eggers, 2020 ). Some SMEs have faced a relatively higher risk of salary reduction, layoffs, or corporate bankruptcy ( Adam and Alarifi, 2021 ). Consequently, it has made employees in the SMEs face the following stressors during the COVID-19 pandemic: First, employees' income, promotion, and career development opportunities have declined ( Shimazu et al., 2020 ). Second, as most employees had to work from home, family conflicts have increased and family satisfaction has decreased ( Green et al., 2020 ; Xu et al., 2020 ). Finally, as work tasks and positions have changed, the new work environment has made employees less engaged and less fulfilled at work ( Olugbade and Karatepe, 2019 ; Chen and Fellenz, 2020 ).

For SMEs, employees are their core assets and are crucial to their survival and growth ( Shan et al., 2022 ). Employee work stress may precipitate burnout ( Choi et al., 2019 ; Barello et al., 2020 ), which manifests as fatigue and frustration ( Mansour and Tremblay, 2018 ), and is associated with various negative reactions, including job dissatisfaction, low organizational commitment, and a high propensity to resign ( Lu and Gursoy, 2016 ; Uchmanowicz et al., 2020 ). Ultimately, it negatively impacts employee performance ( Prasad and Vaidya, 2020 ). The problem of employee work stress has become an important topic for researchers and practitioners alike. In this regard, it is timely to explore the impact of work stress on SME problems of survival and growth during emergency events like the COVID-19 pandemic.

Although recent studies have demonstrated the relationship between work stress and employee performance, some insufficiencies persist, which must be resolved. Research on how work stress affects employee performance has remained fragmented and limited. First, the research into how work stress affects employee performance is still insufficient. Some researchers have explored the effects of work stress on employee performance during COVID-19 ( Saleem et al., 2021 ; Tu et al., 2021 ). However, they have not explained the intermediate path, which limits our understanding of effects of work stress. As work stress causes psychological pain to employees, in response, they exhibit lower performance levels ( Song et al., 2020 ; Yu et al., 2022 ). Thus, employees' mental health becomes an important path to explain the relationship mechanism between work stress and employee performance, which is revealed in this study using a stress–psychological state–performance framework. Second, resolving the mental health problems caused by work stress has become a key issue for SMEs during the COVID-19 pandemic. As the core of the enterprise ( Ahn et al., 2018 ), the behavior of leaders significantly influences employees. Especially for SMEs, intensive interactive communication transpires between the leader and employees ( Li et al., 2019 ; Tiedtke et al., 2020 ). Servant leadership, as a typical leader's behavior, is considered an important determinant of employee mental health ( Haslam et al., 2020 ). Hence, to improve employees' mental health, we introduce servant leadership as a moderating variable and explore its contingency effect on relieving work stress and mental health.

This study predominantly tries to answer the question of how work stress influences employee performance and explores the mediating impact of mental health and the moderating impact of servant leadership in this relationship. Mainly, this study contributes to the existing literature in the following three ways: First, this research analyzes the influence of work stress on employee performance in SMEs during the COVID-19 pandemic, which complements previous studies and theories related to work stress. Second, this study regards mental health as a psychological state and examines its mediating impact on the relationship between work stress and employee performance, which complements the research path on how work stress affects employee performance. Third, we explore the moderating impact of servant leadership, which has been ignored in previous research, thus extending the understanding of the relationship between the work stress and mental health of employees in SMEs.

To accomplish the aforementioned tasks, the remainder of this article is structured as follows: First, based on the literature review, we propose our hypotheses. Thereafter, we present our research method, including the processes of data collection, sample characteristics, measurement of variables, and sample validity. Subsequently, we provide the data analysis and report the results. Finally, we discuss the results and present the study limitations.

Theoretical background and hypotheses

Work stress and employee performance.

From a psychological perspective, work stress influences employees' psychological states, which, in turn, affects their effort levels at work ( Lu, 1997 ; Richardson and Rothstein, 2008 ; Lai et al., 2022 ). Employee performance is the result of the individual's efforts at work ( Robbins, 2005 ) and thus is significantly impacted by work stress. However, previous research has provided no consistent conclusion regarding the relationship between work stress and employee performance. One view is that a significant positive relationship exists between work stress and employee performance ( Ismail et al., 2015 ; Soomro et al., 2019 ), suggesting that stress is a motivational force that encourages employees to work hard and improve work efficiency. Another view is that work stress negatively impacts employee performance ( Yunus et al., 2018 ; Nawaz Kalyar et al., 2019 ; Purnomo et al., 2021 ), suggesting that employees need to spend time and energy to cope with stress, which increases their burden and decreases their work efficiency. A third view is that the impact of work stress on employee performance is non-linear and may exhibit an inverted U-shaped relationship ( McClenahan et al., 2007 ; Hamidi and Eivazi, 2010 ); reportedly, when work stress is relatively low or high, employee performance is low. Hence, if work stress reaches a moderate level, employee performance will peak. However, this conclusion is derived from theoretical analyses and is not supported by empirical data. Finally, another view suggests that no relationship exists between them ( Tănăsescu and Ramona-Diana, 2019 ). Indubitably, it presupposes that employees are rational beings ( Lebesby and Benders, 2020 ). Per this view, work stress cannot motivate employees or influence their psychology and thus cannot impact their performance.

To further explain the aforementioned diverse views, positive psychology proposes that work stress includes two main categories: challenge stress and hindrance stress ( Cavanaugh et al., 2000 ; LePine et al., 2005 ). Based on their views, challenge stress represents stress that positively affects employees' work attitudes and behaviors, which improves employee performance by increasing work responsibility; by contrast, hindrance stress negatively affects employees' work attitudes and behaviors, which reduces employee performance by increasing role ambiguity ( Hon and Chan, 2013 ; Deng et al., 2019 ).

During the COVID-19 pandemic, SMEs have faced a relatively higher risk of salary reductions, layoffs, or corporate bankruptcy ( Adam and Alarifi, 2021 ). Hence, the competition among enterprises has intensified; managers may transfer some stress to employees, who, in turn, need to bear this to maintain and seek current and future career prospects, respectively ( Lai et al., 2015 ). In this context, employee work stress stems from increased survival problems of SMEs, and such an external shock precipitates greater stress among employees than ever before ( Gao, 2021 ). Stress more frequently manifests as hindrance stress ( LePine et al., 2004 ), which negatively affects employees' wellbeing and quality of life ( Orfei et al., 2022 ). It imposes a burden on employees, who need to spend time and energy coping with the stress. From the perspective of stressors, SMEs have faced serious survival problems during the COVID-19 pandemic, and consequently, employees have faced greater hindrance stress, thereby decreasing their performance. Hence, we propose the following hypothesis:

H1 . Work stress negatively influences employee performance in SMEs during the COVID-19 pandemic.

Work stress and mental health

According to the demand–control–support (DCS) model ( Karasek and Theorell, 1990 ), high-stress work—such as high job demands, low job control, and low social support at work—may trigger health problems in employees over time (e.g., mental health problems; Chou et al., 2015 ; Park et al., 2016 ; Lu et al., 2020 ). The DCS model considers stress as an individual's response to perceiving high-intensity work ( Houtman et al., 2007 ), which precipitates a change in the employee's cognitive, physical, mental, and emotional status. Of these, mental health problems including irritability, nervousness, aggressive behavior, inattention, sleep, and memory disturbances are a typical response to work stress ( Mayerl et al., 2016 ; Neupane and Nygard, 2017 ). If the response persists for a considerable period, mental health problems such as anxiety or depression may occur ( Bhui et al., 2012 ; Eskilsson et al., 2017 ). As coping with work stress requires an employee to exert continuous effort and apply relevant skills, it may be closely related to certain psychological problems ( Poms et al., 2016 ; Harrison and Stephens, 2019 ).

The COVID-19 pandemic has disrupted the normal operating order of enterprises as well as employees' work rhythm. Consequently, employees might have faced greater challenges during this period ( Piccarozzi et al., 2021 ). In this context, work stress includes stress related to health and safety risk, impaired performance, work adjustment, and negative emotions, for instance, such work stress can lead to unhealthy mental problems. Hence, we propose the following hypothesis:

H2 . Work stress negatively influences mental health in SMEs during the COVID-19 pandemic.

Mediating role of mental health

Previous research has found that employees' mental health status significantly affects their performance ( Bubonya et al., 2017 ; Cohen et al., 2019 ; Soeker et al., 2019 ), the main reasons of which are as follows: First, mental health problems reduce employees' focus on their work, which is potentially detrimental to their performance ( Hennekam et al., 2020 ). Second, mental health problems may render employees unable to work ( Heffernan and Pilkington, 2011 ), which indirectly reduces work efficiency owing to increased sick leaves ( Levinson et al., 2010 ). Finally, in the stress context, employees need to exert additional effort to adapt to the environment, which, consequently, make them feel emotionally exhausted. Hence, as their demands remain unfulfilled, their work satisfaction and performance decrease ( Khamisa et al., 2016 ).

Hence, we propose that work stress negatively impacts mental health, which, in turn, positively affects employee performance. In other words, we argue that mental health mediates the relationship between work stress and employee performance. During the COVID-19 pandemic, work stress—owing to changes in the external environment—might have caused nervous and anxious psychological states in employees ( Tan et al., 2020 ). Consequently, it might have rendered employees unable to devote their full attention to their work, and hence, their work performance might have decreased. Meanwhile, due to the pandemic, employees have faced the challenges of unclear job prospects and reduced income. Therefore, mental health problems manifest as moods characterized by depression and worry ( Karatepe et al., 2020 ). Negative emotions negatively impact employee performance. Per the aforementioned arguments and hypothesis 2, we propose the following hypothesis:

H3 . Mental health mediates the relationship between work stress and employee performance in SMEs during the COVID-19 pandemic.

Moderating role of servant leadership

According to the upper echelons theory, leaders significantly influence organizational activities, and their leadership behavior influences the thinking and understanding of tasks among employees in enterprises ( Hambrick and Mason, 1984 ). Servant leadership is a typical leadership behavior that refers to leaders exhibiting humility, lending power to employees, raising the moral level of subordinates, and placing the interests of employees above their own ( Sendjaya, 2015 ; Eva et al., 2019 ). This leadership behavior provides emotional support to employees and increase their personal confidence and self-esteem and thus reduce negative effects of work stress. In our study, we propose that servant leadership reduces the negative effects of work stress on mental health in SMEs.

Servant leadership can reduce negative effects of work stress on mental health in the following ways: Servant leaders exhibit empathy and compassion ( Lu et al., 2019 ), which help alleviate employees' emotional pain caused by work stress. Song et al. (2020) highlighted that work stress can cause psychological pain among employees. However, servant leaders are willing to listen to their employees and become acquainted with them, which facilitates communication between the leader and the employee ( Spears, 2010 ). Hence, servant leadership may reduce employees' psychological pain through effective communication. Finally, servant leaders lend employees power, which makes the employees feel trusted. Employees—owing to their trust in the leaders—trust the enterprises as well, which reduces the insecurity caused by work stress ( Phong et al., 2018 ). In conclusion, servant leadership serves as a coping resource that reduces the impact of losing social support and thus curbs negative employee emotions ( Ahmed et al., 2021 ). Based on the aforementioned analysis, we find that servant leaders can reduce the mental health problems caused by work stress. Hence, we propose the following hypothesis:

H4 . Servant leadership reduces the negative relationship between work stress and mental health in SMEs during the COVID-19 pandemic.

Methodology

Data collection and samples.

To assess our theoretical hypotheses, we collected data by administering a questionnaire survey. The questionnaire was administered anonymously, and the respondents were informed regarding the purpose of the study. Owing to the impact of the pandemic, we distributed and collected the questionnaires by email. Specifically, we utilized the network relationships of our research group with the corporate campus and group members to distribute the questionnaires. In addition, to ensure the quality of the questionnaires, typically senior employees who had worked for at least 2 years at their enterprises were chosen as the respondents.

Before the formal survey, we conducted a pilot test. Thereafter, we revised the questionnaire based on the results of the trial investigation. Subsequently, we randomly administered the questionnaires to the target enterprises. Hence, 450 questionnaires were administered via email, and 196 valid questionnaires were returned—an effective rate of 43.6%. Table 1 presents the profiles of the samples.

Table 1 . Profiles of the samples.

Table 1 shows the descriptive statistics of the sample. Based on the firm size, respondents who worked in a company with 1–20 employees accounted for 9.2%, those in a company with 21–50 employees accounted for 40.8%, those in a company with 51–200 employees accounted for 38.8%, and those in a company with 201–500 employees accounted for 11.2%. Regarding industry, the majority of the respondents (63.8%) worked for non-high-technology industry and 36.2% of the respondents worked for high-technology industry. Regarding work age, the participants with a work experience of 3 years or less accounted for 32.1%, those with work experience of 3–10 years accounted for 32.7%, and those with a work experience of more than 10 years accounted for 35.2%.

Core variables in this study include English-version measures that have been well tested in prior studies; some modifications were implemented during the translation process. As the objective of our study is SMEs in China, we translated the English version to Chinese; this translation was carried out by two professionals to ensure accuracy. Thereafter, we administered the questionnaires to the respondents. Hence, as the measures of our variables were revised based on the trial investigation, we asked two professionals to translate the Chinese version of the responses to English to enable publishing this work in English. We evaluated all the items pertaining to the main variables using a seven-point Likert scale (7 = very high/strongly agree, 1 = very low/strongly disagree). The variable measures are presented subsequently.

Work stress (WS)

Following the studies of Parker and DeCotiis (1983) and Shah et al. (2021) , we used 12 items to measure work stress, such as “I get irritated or nervous because of work” and “Work takes a lot of my energy, but the reward is less than the effort.”

Mental health (MH)

The GHQ-12 is a widely used tool developed to assess the mental health status ( Liu et al., 2022 ). However, we revised the questionnaire by combining the research needs and results of the pilot test. We used seven items to measure mental health, such as “I feel that I am unable (or completely unable) to overcome difficulties in my work or life.” In the final calculation, the scoring questions for mental health were converted; higher scores indicated higher levels of mental health.

Servant leadership (SL)

Following the studies by Ehrhart (2004) and Sendjaya et al. (2019) , we used nine items to measure servant leadership, including “My leader makes time to build good relationships with employees” and “My leader is willing to listen to subordinates during decision-making.”

Employee performance (EP)

We draw on the measurement method provided by Chen et al. (2002) and Khorakian and Sharifirad (2019) ; we used four items to represent employee performance. An example item is as follows: “I can make a contribution to the overall performance of our enterprise.”

Control variables

We controlled several variables that may influence employee performance, including firm size, industry, and work age. Firm size was measured by the number of employees. For industry, we coded them into two dummy variables (high-technology industry = 1, non-high-technology industry = 0). We calculated work experience by the number of years the employee has worked for the enterprise.

Common method bias

Common method bias may exist because each questionnaire was completed independently by each respondent ( Cai et al., 2017 ). We conducted a Harman one-factor test to examine whether common method bias significantly affected our data ( Podsakoff and Organ, 1986 ); the results revealed that the largest factor in our data accounted for only 36.219% of the entire variance. Hence, common method bias did not significantly affect on our study findings.

Reliability and validity

We analyzed the reliability and validity of our data for further data processing, the results of which are presented in Table 2 . Based on these results, we found that Cronbach's alpha coefficient of each variable was >0.8, thus meeting the requirements for reliability of the variables. To assess the validity of each construct, we conducted four separate confirmatory factor analyses. All the factor loadings exceeded 0.5. Overall, the reliability and validity results met the requirements for further data processing.

Table 2 . Results of confirmatory factor analysis and Cronbach's alpha coefficients.

To verify our hypotheses, we used a hierarchical linear regression method. Before conducting the regression analysis, we performed a Pearson correlation analysis, the results of which are presented in Table 3 .

Table 3 . Descriptive statistics and correlation analysis.

In the regression analysis, we calculated the variance inflation factor (VIF) of each variable and found that the VIF value of each variable was <3. Hence, the effect of multiple co-linearity is not significant. The results of regression analysis are presented in Tables 4 , 5 .

Table 4 . Results of linear regression analysis (models 1–6).

Table 5 . Results of linear regression analysis (models 7–9).

Table 4 shows that model 1 is the basic model assessing the effects of control variables on employee performance. In model 2, we added an independent variable (work stress) to examine its effect on employee performance. The results revealed that work stress negatively affects employee performance (β = −0.193, p < 0.01). Therefore, hypothesis 1 is supported. Model 5 is the basic model that examines the effects of control variables on mental health. In model 6, we added an independent variable (work stress) to assess its effect on mental health. We found that work stress negatively affects mental health (β = −0.517, p < 0.001). Therefore, hypothesis 2 is supported.

To verify the mediating effect of mental health on the relationship between work stress and employee performance, we used the method introduced by Kenny et al. (1998) , which is described as follows: (1) The independent variable is significantly related to the dependent variable. (2) The independent variable is significantly related to the mediating variable. (3) The mediating variable is significantly related to the dependent variable after controlling for the independent variable. (4) If the effect of the independent variable on the dependent variable becomes smaller, it indicates a partial mediating effect. (5) If the effect of the independent variable on the dependent variable is no longer significant, it indicates a full mediating effect. Based on this method, in model 4, mental health is significantly positively related to employee performance (β = 0.343, p < 0.001), and no significant correlation exists between work stress and employee performance (β = −0.016, p > 0.05). Hence, mental health fully mediates the relationship between work stress and employee performance. Therefore, hypothesis 3 is supported.

To verify the moderating effect of servant leadership on the relationship between work stress and mental health, we gradually added independent variables, a moderator variable, and interaction between the independent variables and moderator variable to the analysis, the results of which are presented in Table 5 . In model 9, the moderating effect of servant leadership is not supported (β = 0.030, p > 0.05). Therefore, hypothesis 4 is not supported.

For SMEs, employees are core assets and crucial to their survival and growth ( Shan et al., 2022 ). Specifically, owing to the COVID-19 pandemic, employees' work stress may precipitate burnout ( Choi et al., 2019 ; Barello et al., 2020 ), which influences their performance. Researchers and practitioners have significantly focused on resolving the challenge of work stress ( Karatepe et al., 2020 ; Tan et al., 2020 ; Gao, 2021 ). However, previous research has not clearly elucidated the relationship among work stress, mental health, servant leadership, and employee performance. Through this study, we found the following results:

Employees in SMEs face work stress owing to the COVID-19 pandemic, which reduces their performance. Facing these external shocks, survival and growth of SMEs may become increasingly uncertain ( Adam and Alarifi, 2021 ). Employees' career prospects are negatively impacted. Meanwhile, the pandemic has precipitated a change in the way employees work, their workspace, and work timings. Moreover, their work is now intertwined with family life. Hence, employees experience greater stress at work than ever before ( Gao, 2021 ), which, in turn, affects their productivity and deteriorates their performance.

Furthermore, we found that mental health plays a mediating role in the relationship between work stress and employee performance; this suggests that employees' mental status is influenced by work stress, which, in turn, lowers job performance. Per our findings, due to the COVID-19 pandemic, employees experience nervous and anxious psychological states ( Tan et al., 2020 ), which renders them unable to devote their full attention to their work; hence, their work performance is likely to decrease.

Finally, we found that leaders are the core of any enterprise ( Ahn et al., 2018 ). Hence, their leadership behavior significantly influences employees. Per previous research, servant leadership is considered a typical leadership behavior characterized by exhibiting humility, delegating power to employees, raising the morale of subordinates, and placing the interests of employees above their own ( Sendjaya, 2015 ; Eva et al., 2019 ). Through theoretical analysis, we found that servant leadership mitigates the negative effect of work stress on mental health. However, the empirical results are not significant possibly because work stress of employees in SMEs is rooted in worries regarding the future of the macroeconomic environment, and the resulting mental health problems cannot be cured merely by a leader.

Hence, due to the COVID-19 pandemic, employees experience work stress, which precipitates mental health problems and poor employee performance. To solve the problem of work stress, SMEs should pay more attention to fostering servant leadership. Meanwhile, organizational culture is also important in alleviating employees' mental health problems and thus reducing negative effects of work stress on employee performance.

Implications

This study findings have several theoretical and managerial implications.

Theoretical implications

First, per previous research, no consistent conclusion exists regarding the relationship between work stress and employee performance, including positive relationships ( Ismail et al., 2015 ; Soomro et al., 2019 ), negative relationships ( Yunus et al., 2018 ; Nawaz Kalyar et al., 2019 ; Purnomo et al., 2021 ), inverted U-shaped relationships ( McClenahan et al., 2007 ; Hamidi and Eivazi, 2010 ), and no relationship ( Tănăsescu and Ramona-Diana, 2019 ). We report that work stress negatively affects employee performance in SMEs during the COVID-19 pandemic; thus, this study contributes to the understanding of the situational nature of work stress and provides enriching insights pertaining to positive psychology.

Second, we established the research path that work stress affects employee performance. Mental health is a psychological state that may influence an individual's work efficiency. In this study, we explored its mediating role, which opens the black box of the relationship between work stress and employee performance; thus, this study contributes to a greater understanding of the role of work stress during the COVID-19 pandemic.

Finally, this study sheds light on the moderating effect of servant leadership, which is useful for understanding why some SMEs exhibit greater difficulty in achieving success than others during the COVID-19 pandemic. Previous research has explained the negative effect of work stress ( Yunus et al., 2018 ; Nawaz Kalyar et al., 2019 ; Purnomo et al., 2021 ). However, few studies have focused on how to resolve the problem. We identify servant leadership as the moderating factor providing theoretical support for solving the problem of work stress. This study expands the explanatory scope of the upper echelons theory.

Practice implications

First, this study elucidates the sources and mechanisms of work stress in SMEs during the COVID-19 pandemic. Employees should continuously acquire new skills to improve themselves and thus reduce their replaceability. Meanwhile, they should enhance their time management and emotional regulation skills to prevent the emergence of adverse psychological problems.

Second, leaders in SMEs should pay more attention to employees' mental health to prevent the emergence of hindrance stress. Employees are primarily exposed to stress from health and safety risks, impaired performance, and negative emotions. Hence, leaders should communicate with employees in a timely manner to understand their true needs, which can help avoid mental health problems due to work stress among employees.

Third, policymakers should realize that a key cause of employee work stress in SMEs is attributable to concerns regarding the macroeconomic environment. Hence, they should formulate reasonable support policies to improve the confidence of the whole society in SMEs, which helps mitigate SME employees' work stress during emergency events like the COVID-19 pandemic.

Finally, as work stress causes mental health problems, SME owners should focus on their employees' physical as well as mental health. Society should establish a psychological construction platform for SME employees to help them address their psychological problems.

Limitations and future research

This study has limitations, which should be addressed by further research. First, differences exist in the impact of the pandemic on different industries. Future research should focus on the impact of work stress on employee performance in different industries. Second, this study only explored the moderating role of servant leadership. Other leadership behaviors of leaders may also affect work stress. Future research can use case study methods to explore the role of other leadership behaviors.

This study explored the relationship between work stress and employee performance in SMEs during the COVID-19 pandemic. Using a sample of 196 SMEs from China, we found that as a typical result of emergency events, work stress negatively affects employees' performance, particularly by affecting employees' mental health. Furthermore, we found that servant leadership provides a friendly internal environment to mitigate negative effects of work stress on employees working in SMEs.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

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

BC: conceptualization, methodology, writing—original draft, and visualization. LW: formal analysis. BL: investigation, funding acquisition, and writing—review and editing. WL: resources, project administration, and supervision. All authors contributed to the article and approved the submitted version.

This research was supported by the major project of Henan Province Key R&D and Promotion Special Project (Soft Science) Current Situation, Realization Path and Guarantee Measures for Digital Transformation Development of SMEs in Henan Province under the New Development Pattern (Grant No. 222400410159).

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.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: COVID-19, work stress, mental health, employee performance, social uncertainty

Citation: Chen B, Wang L, Li B and Liu W (2022) Work stress, mental health, and employee performance. Front. Psychol. 13:1006580. doi: 10.3389/fpsyg.2022.1006580

Received: 29 July 2022; Accepted: 10 October 2022; Published: 08 November 2022.

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Copyright © 2022 Chen, Wang, Li and Liu. 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: Biao Li, lib0023@zzu.edu.cn

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A Systematic Review of Workplace Stress and Its Impact on Mental Health and Safety

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• Gabriella Maria Schr Torres 12 ,
• Jessica Backstrom 12 &
• Vincent G. Duffy 12  

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Workplace stress and health are important subsets of the safety engineering field. Engineers need to maintain physical, emotional, and mental health to be productive and safe employees, which is beneficial to their employers through the reduction of accidents. Besides the human element, which may involve injury, death, or other lasting physical or mental consequences, accidents cost companies time, money, and valuable resources spent on extensive litigation. This paper focuses on mental health within the context of workplace stress since the globally felt adverse effects of the COVID-19 pandemic have brought high priority to research on identifying and combating mental health problems. While most mental health research focuses on healthcare professionals, our contribution is the extrapolation of this research to engineering. A systemic literature review was performed, which consisted of gathering data, using multiple bibliometric software, and providing discussion and conclusions drawn from the metadata. The software utilized for analysis included Vicinitas, Scopus, Google n-gram and Google Scholar, VOSviewer, Scite.ai, CiteSpace, BibExcel, Harzing, and MaxQDA. The original keywords included “workplace stress”, “mental health”, and “engineering,” but our analysis revealed additional trending terms of mindfulness, nursing, and COVID-19. Our findings showed that workplace stress is experienced throughout multiple industries and causes significant harm to employees and their organizations. There are practical solutions to workplace stress studied in nursing and construction that can be applied to other fields that need intervention.

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Torres, G.M.S., Backstrom, J., Duffy, V.G. (2023). A Systematic Review of Workplace Stress and Its Impact on Mental Health and Safety. In: Gao, Q., Zhou, J., Duffy, V.G., Antona, M., Stephanidis, C. (eds) HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14055. Springer, Cham. https://doi.org/10.1007/978-3-031-48041-6_41

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Physical activity improves stress load, recovery, and academic performance-related parameters among university students: a longitudinal study on daily level

• Monika Teuber 1 ,
• Daniel Leyhr 1 , 2 &
• Gorden Sudeck 1 , 3  

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Physical activity has been proven to be beneficial for physical and psychological health as well as for academic achievement. However, especially university students are insufficiently physically active because of difficulties in time management regarding study, work, and social demands. As they are at a crucial life stage, it is of interest how physical activity affects university students' stress load and recovery as well as their academic performance.

Student´s behavior during home studying in times of COVID-19 was examined longitudinally on a daily basis during a ten-day study period ( N  = 57, aged M  = 23.5 years, SD  = 2.8, studying between the 1st to 13th semester ( M  = 5.8, SD  = 4.1)). Two-level regression models were conducted to predict daily variations in stress load, recovery and perceived academic performance depending on leisure-time physical activity and short physical activity breaks during studying periods. Parameters of the individual home studying behavior were also taken into account as covariates.

While physical activity breaks only positively affect stress load (functional stress b = 0.032, p  < 0.01) and perceived academic performance (b = 0.121, p  < 0.001), leisure-time physical activity affects parameters of stress load (functional stress: b = 0.003, p  < 0.001, dysfunctional stress: b = -0.002, p  < 0.01), recovery experience (b = -0.003, p  < 0.001) and perceived academic performance (b = 0.012, p  < 0.001). Home study behavior regarding the number of breaks and longest stretch of time also shows associations with recovery experience and perceived academic performance.

Conclusions

Study results confirm the importance of different physical activities for university students` stress load, recovery experience and perceived academic performance in home studying periods. Universities should promote physical activity to keep their students healthy and capable of performing well in academic study: On the one hand, they can offer opportunities to be physically active in leisure time. On the other hand, they can support physical activity breaks during the learning process and in the immediate location of study.

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Introduction

Physical activity (PA) takes a particularly key position in health promotion and prevention. It reduces risks for several diseases, overweight, and all-cause mortality [ 1 ] and is beneficial for physical, psychological and social health [ 2 , 3 , 4 , 5 ] as well as for academic achievement [ 6 , 7 ]. However, PA levels decrease from childhood through adolescence and into adulthood [ 8 , 9 , 10 ]. Especially university students are insufficiently physically active according to health-oriented PA guidelines [ 11 ] because of academic workloads as well as difficulties in time management regarding study, work, and social demands [ 12 ]. Due to their independence and increasing self-responsibility, university students are at a crucial life stage. In this essential and still educational stage of the students´ development, it is important to study their PA behavior. Furthermore, PA as health behavior represents one influencing factor which is considered in the analytical framework of the impact of health and health behaviors on educational outcomes which was developed by the authors Suhrcke and de Paz Nieves [ 13 , 14 ]. In light of this, the present study examines how PA affects university students' academic situations.

Along with the promotion of PA, the reduction of sedentary behavior has also become a crucial part of modern health promotion and prevention strategies. Spending too much time sitting increases many health risks, including the risk of obesity [ 15 ], diabetes [ 16 ] and other chronic diseases [ 15 ], damage to muscular balances, bone metabolism and musculoskeletal system [ 17 ] and even early death [ 15 ]. University students are a population that has shown the greatest increase in sedentary behavior over the last two decades [ 18 ]. In Germany, they show the highest percentage of sitting time among all working professional groups [ 19 ]. Long times sitting in classes, self-study learning, and through smartphone use, all of which are connected to the university setting and its associated behaviors, might be the cause of this [ 20 , 21 ]. This goes along with technological advances which allow students to study in the comfort of their own homes without changing locations [ 22 ].

To counter a sedentary lifestyle, PA is crucial. In addition to its physical health advantages, PA is essential for coping with the intellectual and stress-related demands of academic life. PA shows positive associations with stress load and academic performance. It is positively associated with learning and educational success [ 6 ] and even shows stress-regulatory potential [ 23 ]. In contrast, sedentary behavior is associated with lower cognitive performance [ 24 ]. Moreover, theoretical derivations show that too much sitting could have a negative impact on brain health and diminish the positive effects of PA [ 16 ]. Given the theoretical background of the stressor detachment model [ 25 ] and the cybernetic approach to stress management in the workplace [ 26 ], PA can promote recovery experience, it can enhance academic performance, and it is a way to reduce the impact of study-related stressors on strain. Load-related stress response can be bilateral: On the one hand, it can be functional if it is beneficial to help cope with the study demands. On the other hand, it can be dysfunctional if it puts a strain on personal resources and can lead to load-related states of strain [ 27 ]. Thus, both, the promotion of PA and reduction of sedentary behavior are important for stress load, recovery, and performance in student life, which can be of particular importance for students in an academic context.

A simple but (presumably) effective way to integrate PA and reduce sedentary behavior in student life are short PA breaks. Due to the exercises' simplicity and short duration, students can perform them wherever they are — together in a lecture or alone at home. Short PA breaks could prevent an accumulation of negative stressors during the day and can help with prolonged sitting as well as inactivity. Especially in the university setting, evidence of the positive effects of PA breaks exists for self-perceived physical and psychological well-being of the university students [ 28 ]. PA breaks buffer university students’ perceived stress [ 29 ] and show positive impacts on recovery need [ 30 ] and better mood ratings [ 31 , 32 ]. In addition, there is evidence for reduction in tension [ 30 ], overall muscular discomfort [ 33 ], daytime sleepiness or fatigue [ 33 , 34 ] and increase in vigor [ 34 ] and experienced energy [ 30 ]. This is in line with cognitive, affective, behavioral, and biological effects of PA, all categorized as palliative-regenerative coping strategies, which addresses the consequences of stress-generating appraisal processes aiming to alleviate these consequences (palliative) or restore the baseline of the relevant reaction parameter (regenerative) [ 35 , 36 ]. This is achieved by, for example, reducing stress-induced cortisol release or tension through physical activity (reaction reduction) [ 35 ]. Such mechanisms are also in accordance with the previously mentioned stressor detachment model [ 25 ]. Lastly, there is a health-strengthening effect that impacts the entire stress-coping-health process, relying on the compensatory effects of PA which is in accordance to the stress-buffering effect of exercise [ 37 ]. Health, in turn, effects educational outcomes [ 13 , 14 ]. Therefore, stress regulating effects are also accompanied with the before mentioned analytical framework of the impact of health and health behaviors on educational outcomes [ 13 , 14 ].

Focusing on the effects of PA, this study is guided by an inquiry into how PA affects university students' stress load and recovery as well as their perceived academic performance. For that reason, the student´s behavior during home studying in times of COVID-19 is examined, a time in which reinforced prolonged sitting, inactivity, and a negative stress load response was at a high [ 38 , 39 , 40 , 41 , 42 ]. Looking separately on the relation of PA with different parameters based on the mentioned evidence, we assume that PA has a positive impact on stress load, recovery, and perceived academic performance-related parameters. Furthermore, a side effect of the home study behavior on the mentioned parameters is assumed regarding the accumulation of negative stressors during home studying. These associations are presented in Fig.  1 and summarized in the following hypotheses:

Overview of the assumed effects and investigated hypotheses of physical activity (PA) behavior on variables of stress load and recovery and perceived academic performance-related parameters

Hypothesis 1 (path 1): Given that stress load always occurs as a duality—beneficial if it is functional for coping, or exhausting if it puts a strain on personal resources [ 27 ] – we consider two variables for stress load: functional stress and dysfunctional stress. In order to reduce the length of the daily surveys, we focused the measure of recovery only on the most obvious and accessible component of recovery experience, namely psychological detachment. PA (whether performed in leisure-time or during PA breaks) encourages functional stress and reduce dysfunctional stress (1.A) and has a positive effect on recovery experience through psychological detachment (1.B).

Hypothesis 2 (path 2): The academic performance-related parameters attention difficulties and study ability are positively influenced by PA (whether done in leisure-time or during PA breaks). We have chosen to assess attention difficulties for a cognitive parameter because poor control over the stream of occurring stimuli have been associated with impairment in executive functions or academic failure [ 43 , 44 , 45 , 46 ]. Furthermore, we have assessed the study ability to refer to the self-perceived feeling of functionality regarding the demands of students. PA reduces self-reported attention difficulties (2.A) and improves perceived study ability, indicating that a student feels capable of performing well in academic study (2.B).

Hypothesis 3: We assume that a longer time spent on studying at home (so called home studying) could result in higher accumulation of stressors throughout the day which could elicit immediate stress responses, while breaks in general could reduce the influence of work-related stressors on strain and well-being [ 47 , 48 ]. Therefore, the following covariates are considered for secondary effects:

the daily longest stretch of time without a break spent on home studying

the daily number of breaks during home studying

Study setting

The study was carried out during the COVID-19 pandemic containment phase. It took place in the middle of the lecture period between 25th of November and 4th of December 2020. Student life was characterized by home studying and digital learning. A so called “digital semester” was in effect at the University of Tübingen when the study took place. Hence, courses were mainly taught online (e.g., live or via a recorded lecture). Other events and actions at the university were not permitted. As such, the university sports department closed in-person sports activities. For leisure time in general, there were contact restrictions (social distancing), the performance of sports activities in groups was not permitted, and sports facilities were closed.

Thus, the university sports department of the University of Tübingen launched various online sports courses and the student health management introduced an opportunity for a new digital form of PA breaks. This opportunity provided PA breaks via videos with guided physical exercises and health-promoting explanations for a PA break for everyday home studying: the so called “Bewegungssnack digital” [in English “exercise snack digital” (ESD)] [ 49 ]. The ESD videos took 5–7 min and were categorized into three thematic foci: activation, relaxation, and coordination. Exercises were demonstrated by one or two student exercise leaders, accompanied by textual descriptions of the relevant execution features of each exercise.

Participants

Participants were recruited within the framework of an intervention study, which was conducted to investigate whether a digital nudging intervention has a beneficial effect on taking PA breaks during home study periods [ 49 ]. Students at the University of Tübingen which counts 27,532 enrolled students were approached for participation through a variety of digital means: via an email sent to those who registered for ESD course on the homepage of the university sports department and to all students via the university email distribution list; via advertisement on social media of the university sports department (Facebook, Instagram, YouTube, homepage). Five tablets, two smart watches, and one iPad were raffled off to participants who engaged actively during the full study period in an effort to motivate them to stick with it to the end. In any case, participants knew that the study was voluntary and that they would not suffer any personal disadvantages should they opt out. There was a written informed consent prompt together with a prompt for the approval of the data protection regulations immediately within the first questionnaire (T0) presented in a mandatory selection field. Positive ethical approval for the study was given by the first author´s institution´s ethics committee of the faculty of the University of Tübingen.

Participants ( N  = 57) who completed the daily surveys on at least half of the days of the study period, were included in the sample (male = 6, female = 47, diverse = 1, not stated = 3). As not all subjects provided data on all ten study days, the total number of observations was between 468 and 540, depending on the variable under study (see Table  1 ). The average number of observations per subject was around eight. Their age was between 18 and 32 years ( M  = 23.52, SD  = 2.81) and they were studying between the 1st to 13th semester ( M  = 5.76, SD  = 4.11) within the following major courses of study: mathematical-scientific majors (34.0%), social science majors (22.6%), philosophical majors (18.9%), medicine (13.2%), theology (5.7%), economics (3.8%), or law (1.9%). 20.4% of the students had on-site classroom teaching on university campus for at least one day a week despite the mandated digital semester, as there were exceptions for special forms of teaching.

Design and procedures

To examine these hypothesized associations, a longitudinal study design with daily surveys was chosen following the suggestion of the day-level study of Feuerhahn et al. (2014) and also of Sonnentag (2001) measuring recovery potential of (exercise) activities during leisure time [ 50 , 51 ]. Considering that there are also differences between people at the beginning of the study period, initial base-line value variables respective to the outcomes measured before the study period were considered as independent covariates. Therefore, the well-being at baseline serves as a control for stress load (2.A), the psychological detachment at baseline serves as a control for daily psychological detachment (2.B), the perception of study demands serves as a control for self-reported attention difficulties (1.A), and the perceived study ability at baseline serves as a control for daily study ability (2.B).

Subjects were asked to continue with their normal home study routine and additionally perform ESD at any time in their daily routine. Data were collected one to two days before (T0) as well as daily during the ten-day study period (Wednesday to Friday). The daily surveys (t 1 -t 10 ) were sent by email at 7 p.m. every evening. Each day, subjects were asked to answer questions about their home studying behavior, study related requirements, recovery experience from study tasks, attention, and PA, including ESD participation. The surveys were conducted online using the UNIPARK software and were recorded and analyzed anonymously.

Measures and covariates

In total, five outcome variables, two independent variables, and seven covariates were included in different analyses: three variables were used for stress load and recovery parameters, two variables for academic performance-related parameters, two variables for PA behavior, two variables for study behavior, four variables for outcome specific baseline values and one variable for age.

Outcome variables

Stress load & recovery parameters (hypothesis 1).

Stress load was included in the analysis with two variables: functional stress and dysfunctional stress. Followingly, a questionnaire containing a word list of adjectives for the recording of emotions and stress during work (called “Erfassung von Emotionen und Beanspruchung “ in German, also known as EEB [ 52 ]) was used. It is an instrument which were developed and validated in the context of occupational health promotion. The items are based on mental-workload research and the assessment of the stress potential of work organization [ 52 ]. Within the questionnaire, four mental and motivational stress items were combined to form a functional stress scale (energetic, willing to perform, attentive, focused) (α = 0.89) and four negative emotional and physical stress items were combined to form dysfunctional stress scale (nervous, physically tensioned, excited, physically unwell) (α = 0.71). Participants rated the items according to how they felt about home studying in general on the following scale (adjustment from “work” to “home studying”): hardly, somewhat, to some extent, fairly, strongly, very strongly, exceptionally.

Recovery experience was measured via psychological detachment. Therefore, the dimension “detachment” of the Recovery Experience Questionnaire (RECQ [ 53 ]) was adjusted to home studying. The introductory question was "How did you experience your free time (including short breaks between learning) during home studying today?". Students responded to four statements based on the extent to which they agreed or disagreed (not at all true, somewhat true, moderately true, mostly true, completely true). The statements covered subjects such as forgetting about studying, not thinking about studying, detachment from studying, and keeping a distance from student tasks. The four items were combined into a score for psychological detachment (α = 0.94).

Academic performance-related parameters (hypothesis 2)

Attention was assessed via the subscale “difficulty maintaining focused attention performance” of the “Attention and Performance Self-Assessment” (ASPA, AP-F2 [ 54 ]). It contains nine items with statements about disturbing situations regarding concentration (e.g. “Even a small noise from the environment could disturb me while reading.”). Participants had to answer how often such situations happened to them on a given day on the following scale: never, rarely, sometimes, often, always. The nine items were combined into the AP-F2 score (α = 0.87).

The perceived study ability was assessed using the study ability index (SAI [ 55 ]). The study ability index captures the current state of perceived functioning in studying. It is based on the Work Ability Index by Hasselhorn and Freude ([ 56 ]) and consists of an adjusted short scale of three adapted items in the context of studying. Firstly, (a) the perceived academic performance was asked after in comparison to the best study-related academic performance ever achieved (from 0 = completely unable to function to 10 = currently best functioning). Secondly, the other two items were aimed at assessing current study-related performance in relation to (b) study tasks that have to be mastered cognitively and (c) the psychological demands of studying. Both items were answered on a five-point Likert scale (1 = very poor, 2 = rather poor, 3 = moderate, 4 = rather good, 5 = very good). A sum index, the SAI, was formed which can indicate values between 2 and 20, with higher values corresponding to higher assessed functioning in studies (α = 0.86). In a previous study it already showed satisfying reliability (α = 0.72) [ 55 ].

Independent variables

Pa behavior.

Two indicators for PA behavior were included via self-reports: the time spent on ESD and the time spent on leisure-time PA (LTPA). Participants were asked the following overarching question daily: “How much time did you spend on physical activity today and in what context”. For the independent variable time spent on PA breaks, participants could answer the option “I participated in the Bewegungssnack digital” with the amount of time they spent on it (in minutes). To assess the time spent on LTPA besides PA breaks, participants could report their time for four different contexts of PA which comprised two forms: Firstly, structured supervised exercise was reported via time spent on (a) university sports courses and (b) other organized sports activities. Secondly, self-organized PA was indicated via (c) independent PA at home, such as a workout or other physically demanding activity such as cleaning or tidying up, as well as via (d) independent PA outside, like walking, cycling, jogging, a workout or something similar. Referring to the different domains of health enhancing PA [ 57 ], the reported minutes of these four types of PA were summed up to a total LTPA value. The total LTPA value was included in the analysis as a metric variable in minutes.

Covariates (hypothesis 3)

Regarding hypothesis 3 and home study behavior, the longest daily stretch of time without a break spent on home studying (in hours) and the daily number of breaks during home studying was assessed. Therein, participants had to answer the overarching question “How much time did you spend on your home studying today?” and give responses to the items: (1) longest stretch of time for home studying (without a break), and (2) number of short and long breaks you took during home studying.

In principle, efforts were made to control for potential confounders at the individual level (level 2) either by including the baseline measure (T0) of the respective variable or by including variables assessing related trait-like characteristics for respective outcomes. The reason why related trait-like characteristics were used for the outcomes was because brief assessments were used for daily surveys that were not concurrently employed in the baseline assessment. To enable the continued use of controlling for person-specific baseline characteristics in the analysis of daily associations, trait-like characteristics available from the baseline assessment were utilized as the best possible approximation.To sum up, four outcome specific baseline value variables were measured before the study period (at T0). The psychological detachment with the RECQ (α = 0.87) [ 53 ] was assessed at the beginning to monitor daily psychological detachment. Further, the SAI [ 55 ] was assessed at the beginning of the study period to monitor daily study ability. To monitor daily stress load, which in part measures mental stress aspects and negative emotional stress aspects, the well-being was assessed at the beginning using the WHO-Five Well-being Index (WHO-5 [ 58 ]). It is a one-dimensional self-report measure with five items. The index value is the sum of all items, with higher values indicating better well-being. As the well-being and stress load tolerance may linked with each other, this variable was assumed to be a good fit with the daily stress load indicating mental and emotional stress aspects. With respect to student life, daily academic performance-related attention was monitored with an instrument for the perception of study demands and resources (termed “Berliner Anforderungen Ressourcen-Inventar – Studierende” in German, the so-called BARI-S [ 59 ]). It contains eight items which capture overwork in studies, time pressure during studies, and the incompatibility of studies and private life. All together they form the BARI-S demand scale (α = 0.85) which was included in the analysis. As overwork and time pressure may result in attention difficulties (e.g. Elfering et al., 2013), this variable was assumed to have a good fit with academic performance-related attention [ 60 ]. Additionally, age in years at T0 was considered as a sociodemographic factor.

Statistical analysis

Since the study design provided ten measurement points for various people, the hierarchical structure of the nested data called for two-level analyses. Pre-analyses of Random-Intercept-Only models for each of the outcome variables (hypothesis 1 to 3) revealed an Intra-Class-Correlation ( ICC ) of at least 0.10 (range 0.26 – 0.64) and confirmed the necessity to perform multilevel analyses [ 61 ]. Specifically, the day-level variables belong to Level 1 (ESD time, LTPA time, longest stretch of time without a break spent on home studying, daily number of breaks during home studying). To analyze day-specific effects within the person, these variables were centered on the person mean (cw = centered within) [ 50 , 62 , 63 , 64 ]. This means that the analyses’ findings are based on a person’s deviations from their average values. The variables assessed at T0 belong to Level 2, which describe the person level (psychological detachment baseline, SAI baseline, well-being, study demands scale, age). These covariates on person level were centered around the grand mean [ 50 ] indicating that the analyses’ findings are based how far an individual deviates from the sample's mean values. As a result, the models’ intercept reflects the outcome value of an average student in the sample at his/her daily average behavior in PA and home study when all parameters are zero. For descriptive statistics SPSS 28.0.1.1 (IBM) and for inferential statistics R (version 4.1.2) were used. The hierarchical models were calculated using the package lme4 with the lmer-function in R in the following steps [ 65 ]. The Null Model was analyzed for all models first, with the corresponding intercept as the only predictor. Afterwards, all variables were entered. The regression coefficient estimates (”b”) were considered for statistical significance for the models and the respective BIC was provided.

In total, five regression models with ‘PA break time’ and ‘LTPA time’ as independent variables were computed due to the five measured outcomes of the present study. Three models belonged to hypothesis 1 and two models to hypothesis 2.

Hypothesis 1: To test hypothesis 1.A two outcome variables were chosen for two separate models: ‘functional stress’ and ‘dysfunctional stress’. Besides the PA behavior variables, the ‘number of breaks’, the ‘longest stretch of time without a break spent on home studying’, ‘age’, and the ‘well-being’ at the beginning of the study as corresponding baseline variable to the output variable were also included as independent variables in both models. The outcome variable ‘psychological detachment’ was utilized in conjunction with the aforementioned independent variables to test hypotheses 1.B, with one exception: psychological detachment at the start of the study was chosen as the corresponding baseline variable.

Hypothesis 2: To investigate hypothesis 2.A the outcome variable ‘attention difficulties’ was selected. Hypothesis 2.B was tested with the outcome variables ‘study ability’. Both models included both PA behavior variables as well as the ‘number of breaks’, the ‘longest stretch of time without a break spent on home studying’, ‘age’ and one corresponding baseline variable each: the ‘study demand scale’ at the start of the study for ‘attention difficulties’ and the ‘SAI’ at the beginning of the study for the daily ‘study ability’.

Hypothesis 3: In addition to both PA behavior variables, age and one baseline variable that matched the outcome variable, the covariates ‘daily longest stretch of time spent on home studying’ and ‘daily number of breaks during home studying’ were included in the models for all five outcome variables.

Handling missing data

The dataset had up to 18% missing values (most exhibit the variables ‘daily longest stretch of time without a break spent on home studying’ with 17.89% followed by ‘daily number of breaks during homes studying’ with 16.67%, and ‘functional / dysfunctional stress’ with 12.45%). Therefore, a sensitivity analysis was performed using the multiple imputation mice-package in the statistical program R [ 66 ], the package howManyImputation based on Von Hippel (2020, [ 67 ]), and the additional broom package [ 68 ]. The results of the models remained the same, with one exception for the Attention Difficulties Model: The daily longest stretch of time without a break spent on home studying showed a significant association (Table  1 in supplement). Due to this almost perfect consistency of results between analyses based on the dataset with missing data and those with imputed data alongside the lack of information provided by the packages for imputed datasets, we decided to stick with the main analysis including the missing data. Thus, in the following the results of the main analysis without imputations are presented.

Table 1 shows the descriptive statistics of the variables used in the analysis. An overview of the analysed models is presented in Table  2 .

Effects on stress load and recovery (hypothesis 1)

Hypothesis 1.A: The Model Functional Stress explained 13% of the variance by fixed factors (marginal R 2  = 0.13), and 52% by both fixed and random factors (conditional R 2  = 0.52). The time spent on ESD as well as the time spent on PA in leisure showed a positive significant influence on functional stress (b = 0.032, p  < 0.01). The same applied to LTPA (b = 0.003, p  < 0.001). The Model Dysfunctional Stress (marginal R 2  = 0.027, conditional R 2  = 0.647) showed only one significant result. The dysfunctional stress was only significantly negatively influenced by the time spent on LTPA (b = 0.002, p  < 0.01).

Hypothesis 1.B: With the Model Detachment, fixed factors contributed 18% of the explained variance and fixed and random factors 46% of the explained variance for psychological detachment. Only the amount of time spent on LTPA revealed a positive impact on psychological detachment (b = 0.003, p  < 0.001).

Effects on academic performance-related parameters (hypothesis 2)

Hypothesis 2.A: The Model Attention Difficulties showed 13% of the variance explained by fixed factors, and 51% explained by both fixed and random factors. It showed a significant negative association only for the time spent on LTPA (b = 0.003, p  < 0.001).

Hypothesis 2.B: The Model SAI showed 18% of the variance explained by fixed factors, and 39% explained by both fixed and random factors. There were significant positive associations for time spent on ESD (b = 0.121, p  < 0.001) and time spent on LTPA (b = 0.012, p  < 0.001). The same applied to LTPA (b = 0.012, p  < 0.001).

Effects of home study behavior (hypothesis 3)

Regarding the independent covariates for the outcome variables functional and dysfunctional stress, there were no significant results for the number of breaks during homes studying or the longest stretch of time without a break spent on home studying. Considering the outcome variable ‘psychological detachment’, there were significant results with negative impact for both study behavior variables: breaks during home studying (b = 0.058, p  < 0.01) and daily longest stretch of time without a break (b = 0.120, p  < 0.01). Evaluating the outcome variables ‘attention difficulties’, there were no significant results for the number of breaks during home studying or the longest stretch of time without a break spent on home studying. Testing the independent study behavior variables for the SAI, it increased with increasing number in daily breaks during homes studying relative to the person´s mean (b = 0.183, p  < 0.05). No significant effect was found for the longest stretch of time without a break spent on home studying ( p  = 0.07).

The baseline covariates of the models showed expected associations and thus confirmed their inclusion. The baseline variables well-being showed a significant impact on functional stress (b = 0.089, p  < 0.001), psychological detachment showed a positive effect on the daily output variables psychological detachment (b = 0.471, p  < 0.001), study demand scale showed a positive association on difficulties in attention (b = 0.240, p  < 0.01), and baseline SAI had a positive effect on the daily SAI (b = 0.335, p  < 0.001).

The present study theorized that PA breaks and LTPA positively influence the academic situation of university students. Therefore, impact on stress load (‘functional stress’ and ‘dysfunctional stress’) and ‘psychological detachment’ as well as academic performance-related parameters ‘self-reported attention difficulties’ and ‘perceived study ability’ was taken into account. The first and second hypotheses assumed that both PA breaks and LTPA are positively associated with the aforementioned parameters and were confirmed for LTPA for all parameters and for PA breaks for functional stress and perceived study ability. The third hypothesis assumed that home study behavior regarding the daily number of breaks during home studying and longest stretch of time without a break spent on home studying has side effects. Detected negative effects for both covariates on psychological detachment and positive effects for the daily number of breaks on perceived study ability were partly unexpected in their direction. These results emphasize the key position of PA in the context of modern health promotion especially for students in an academic context.

Regarding hypothesis 1 and the detected positive associations for stress load and recovery parameters with PA, the results are in accordance with the stress-regulatory potential of PA from the state of research [ 23 ]. For hypothesis 1.A, there is a positive influence of PA breaks and LTPA on functional stress and a negative influence of LTPA on dysfunctional stress. Given the bilateral role of stress load, the results indicate that PA breaks and LTPA are beneficial for coping with study demands, and may help to promote feelings of joy, pride, and learning progress [ 27 ]. This is in line with previous evidence that PA breaks in lectures can buffer university students’ perceived stress [ 29 ], lead to better mood ratings [ 29 , 31 ], and increase in motivation [ 28 , 69 ], vigor [ 34 ], energy [ 30 ], and self-perceived physical and psychological well-being [ 28 ]. Looking at dysfunctional stress, the result point that LTPA counteract load-related states of strain such as inner tension, irritability and nervous restlessness or feelings of boredom [ 27 ]. In contrast, short PA breaks during the day could not have enough impact in countering dysfunctional stress at the end of the day regarding the accumulation of negative stressors during home studying which might have occurred after the participant took PA breaks. Other studies have been able to show a reduction in tension [ 30 ] and general muscular discomfort [ 33 ] after PA breaks. However, this was measured as an immediate effect of PA breaks and not with general evening surveys. Blasche and colleagues [ 34 ] measured effects immediately and 20 min after different kind of breaks and found that PA breaks led to an additional short‐ and medium‐term increase in vigor while the relaxation break lead to an additional medium‐term decrease in fatigue compared to an unstructured open break. This is consistent with the results of the present study that an effect of PA breaks is only observed for functional stress and not for dysfunctional stress. Furthermore, there is evidence that long sitting during lectures leads to increased fatigue and lower concentration [ 31 , 70 ], which could be counteracted by PA breaks. For both types of stress loads, functional and dysfunctional stress, there is an influence of students´ well-being in this study. This shows that the stress load is affected by the way students have mentally felt over the last two weeks. The relevance of monitoring this seems important especially in the time of COVID-19 as, for example, 65.3% of the students of a cross-sectional online survey at an Australian university reported low to very low well-being during that time [ 71 ]. However, since PA and well-being can support functional stress load, they should be of the highest priority—not only as regards the pandemic, but also in general.

Looking at hypothesis 1.B; while there is a positive influence of LTPA on experienced psychological detachment, no significant influence for PA breaks was detected. The fact that only LTPA has a positive effect can be explained by the voluntary character of the activity [ 50 ]. The voluntary character ensures that stressors no longer affect the student and, thus, recovery as detachment can take place. Home studying is not present in leisure times, and thus detachment from study is easier. The PA break videos, on the other hand, were shot in a university setting, which would have made it more difficult to detach from study. In order to further understand how PA breaks affect recovery and whether there is a distinction between PA breaks and LTPA, future research should also consider other types of recovery (e.g. relaxation, mastery, and control). Additionally, different types of PA breaks, such as group PA breaks taken on-site versus video-based PA breaks, should be taken into account.

Considering the confirmed positive associations for academic performance-related parameters of hypothesis 2, the results are in accordance with the evidence of positive associations between PA and learning and educational success [ 6 ], as well as between PA breaks and better cognitive functioning [ 28 ]. Looking at the self-reported attention difficulties of hypothesis 2.A, only LTPA can counteract it. PA breaks showed no effects, contrary to the results of a study of Löffler and collegues (2011, [ 31 ]), in which acute effects of PA breaks could be found for higher attention and cognitive performance. Furthermore, the perception of study demands before the study periods has a positive impact on difficulties in attention. That means that overload in studies, time pressure during studies, and incompatibility of studies and private life leads to higher difficulties with attention in home studying. In these conditions, PA breaks might have been seen as interfering, resulting in the expected beneficial effects of exercise on attention and task-related participation behavior [ 72 , 73 ] therefore remaining undetected. With respect to the COVID-19 pandemic, accompanying education changes, and an increase in student´s worries [ 74 , 75 ], the perception of study demands could be affected. This suggests that especially in times of constraint and changes, it is important to promote PA in order to counteract attention difficulties. This also applies to post-pandemic phase.

Regarding the perceived academic performance of hypothesis 2.B, both PA breaks and LTPA have a positive effect on perceived study ability. This result confirms the positive short-term effects on cognition tasks [ 76 ]. It is also in line with the positive function of PA breaks in interrupting sedentary behavior and therefore counteracting the negative association between sitting behavior and lower cognitive performance [ 24 ]. Additionally, this result also fits with the previously mentioned positive relationship between LTPA and functional stress and between PA breaks and functional stress.

According to hypothesis 3, in relation to the mentioned stress load and recovery parameters, there are negative effects of the daily number of breaks during home studying and the longest stretch of time without a break spent on home studying on psychological detachment. As stressors result in negative activation, which impede psychological detachment from study during non-studying time [ 25 ], it was expected and confirmed that the longest stretch of time without a break spent on home studying has a negative effect on detachment. Initially unexpected, the number of breaks has a negative influence on psychological detachment, as breaks could prevent the accumulation of strain reactions. However, if the breaks had no recovery effect through successful detachment, the number might not have any influence on recovery via detachment. This is indicated by the PA breaks, which had no impact on psychological detachment. Since there are other ways to recover from stress besides psychological detachment, such as relaxation, mastery, and control [ 53 ], PA breaks must have had an additional impact in relation to the positive results for functional stress.

In relation to the mentioned academic performance-related parameters, only the number of breaks has a positive influence on the perceived study ability. This indicates that not only PA breaks but also breaks in general lead to better perceived functionality in studying. Paulus and colleagues (2021) found out that an increase in cognitive skills is not only attributed to PA breaks and standing breaks, but also to open breaks with no special instructions [ 28 ]. Either way, they found better improvement in self-perceived physical and psychological well-being of the university students with PA breaks than with open breaks. This is also reflected in the present study with the aforementioned positive effects of PA breaks on functional stress, which does not apply to the number of breaks.

Overall, it must be considered that the there is a more complex network of associations between the examined parameters. The hypothesized separate relation of PA with different parameters do not consider associations between parameters of stress load / recovery and academic performance although there might be a interdependency. Furthermore, moderation aspects were not examined. For example, PA could be a moderator which buffer negative effects of stress on the study ability [ 55 ]. Moreover, perceived study ability might moderate stress levels and academic performance. Further studies should try to approach and understand the different relationships between the parameters in its complexity.

Limitations

Certain limitations must be taken into account. Regarding the imbalanced design toward more female students in the sample (47 female versus 6 male), possible sampling bias cannot be excluded. Gender research on students' emotional states during COVID-19, when this study took place, or students´ acceptance of PA breaks is diverse and only partially supplied with inconsistent findings. For example, during the COVID-19 pandemic, some studies reported that female students were associated with lower well-being [ 71 ] or worse mental health trajectories [ 75 , 77 ]. Another study with a large sample of students from 62 countries reported that male students were more strongly affected by the pandemic because they were significantly less satisfied with their academic life [ 74 ]. However, Keating and colleges (2020) discovered that, despite the COVID-19 pandemic, females rated some aspects of PA breaks during lectures more positively than male students did. However, this was also based on a female slanted sample [ 78 ]. Further studies are needed to get more insights into gender bias.

Furthermore, the small sample size combined with up to 16% missing values comprises a significant short-coming. There were a lot of possibilities which could cause such missing data, like refused, forgotten or missed participation, technical problems, or deviation of the personal code for the questionnaire between survey times. Although the effects could be excluded by sensitive analysis due to missing data, the sample is still small. To generalize the findings, future replication studies are needed.

Additionally, PA breaks were only captured through participation in the ESD, the specially instructed PA break via video. Effects of other short PA breaks were not include in the study. However, participants were called to participate in ESD whenever possible, so the likelihood that they did take part in PA breaks in addition to the ESD could be ignored.

With respect to the baseline variables, it must be considered that two variables (stress load, attention difficulties) were adjusted not with their identical variable in T0, but with other conceptually associated variables (well-being index, BARI-S). Indeed, contrary to the assumption the well-being index does only show an association with functional stress, indicating that it does not control dysfunctional stress. Although the other three assumed associations were confirmed there might be a discrepancy between the daily measured variables and the variables measured in T0. Further studies should either proof the association between these used variables or measure the same variables in T0 for control the daily value of these variables.

Moreover, the measuring instruments comprised the self-assessed perception of the students and thus do not provide an objective information. This must be considered, especially for measuring cognitive and academic-performance-related measures. Here, existing objective tests, such as multiple choice exams after a video-taped lecture [ 72 ] might have also been used. Nevertheless, such methods were mostly used in a lab setting and do not reflect reality. Due to economic reasons and the natural learning environment, such procedures were not applied in this study. However, the circumstances of COVID-19 pandemic allowed a kind of lab setting in real life, as there were a lot of restrictions in daily life which limited the influence of other covariates. The study design provides a real natural home studying environment, producing results that are applicable to the healthy way that students learn in the real world. As this study took place under the conditions of COVID-19, new transformations in studying were also taken into account, as home studying and digital learning are increasingly part of everyday study.

However, the restrictions during the COVID-19 pandemic could result in a greater extent of leisure time per se. As the available leisure time in general was not measured on daily level, it is not possible to distinguish if the examined effects on the outcomes are purely attributable to PA. It is possible that being more physical active is the result of having a greater extent of leisure time and not that PA but the leisure time itself effected the examined outcomes. To address this issue in future studies, it is necessary to measure the proportion of PA in relation to the leisure time available.

Furthermore, due to the retrospective nature of the daily assessments of the variables, there may be overstated associations which must be taken into account. Anyway, the daily level of the study design provides advantages regarding the ability to observe changes in an individual's characteristics over the period of the study. This design made it possible to find out the necessity to analyze the hierarchical structure of the intraindividual data nested within the interindividual data. The performed multilevel analyses made it possible to reflect the outcome of an average student in the sample at his/her daily average behavior in PA and home study.

Conclusion and practical implications

The current findings confirm the importance of PA for university students` stress load, recovery experience, and academic performance-related parameters in home studying. Briefly summarized, it can be concluded that PA breaks positively affect stress load and perceived study ability. LTPA has a positive impact on stress load, recovery experience, and academic performance-related parameters regarding attention difficulties and perceived study ability. Following these results, universities should promote PA in both fashions in order to keep their students healthy and functioning: On the one hand, they should offer opportunities to be physically active in leisure time. This includes time, environment, and structural aspects. The university sport department, which offers sport courses and provides sport facilities on university campuses for students´ leisure time, is one good example. On the other hand, they should support PA breaks during the learning process and in the immediate location of study. This includes, for example, providing instructor videos for PA breaks to use while home studying, and furthermore having instructors to lead in-person PA breaks in on-site learning settings like universities´ libraries or even lectures and seminars. This not only promotes PA, but also reduces sedentary behavior and thereby reduces many other health risks. Further research should focus not only on the effect of PA behavior but also of sedentary behavior as well as the amount of leisure time per se. They should also try to implement objective measures for example on academic performance parameters and investigate different effect directions and possible moderation effects to get a deeper understanding of the complex network of associations in which PA plays a crucial role.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Attention and Performance Self-Assessment

"Berliner Anforderungen Ressourcen-Inventar – Studierende" (instrument for the perception of study demands and resources)

Centered within

Grand centered

“Erfassung von Emotionen und Beanspruchung “ (questionnaire containing a word list of adjectives for the recording of emotions and stress during work)

Exercise snack digital (special physical activity break offer)

Intra-Class-Correlation

Leisure time physical activity

• Physical activity

Recovery Experience Questionnaire

Study ability index

World Health Organization-Five Well-being index

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Acknowledgements

We would like to thank Juliane Moll, research associate of the Student Health Management of University of Tübingen, for the support in the coordination and realization study. We would like to express our thanks also to Ingrid Arzberger, Head of University Sports at the University of Tübingen, for providing the resources and co-applying for the funding. We acknowledge support by Open Access Publishing Fund of University of Tübingen.

Open Access funding enabled and organized by Projekt DEAL. This research regarding the conduction of the study was funded by the Techniker Krankenkasse, health insurance fund.

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Monika Teuber, Daniel Leyhr & Gorden Sudeck

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Gorden Sudeck

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M.T. and G.S. designed the study. M.T. coordinated and carried out participant recruitment and data collection. M.T. analyzed the data and M.T. and D.L. interpreted the data. M.T. drafted the initial version of the manuscript and prepared the figure and all tables. All authors contributed to reviewing and editing the manuscript and have read and agreed to the final version of the manuscript.

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Correspondence to Monika Teuber .

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The study involves human participants and was reviewed and approved by the Ethics Committee of the Faculty of Social Sciences and Economics, University of Tübingen (ref. A2.54-127_kr). The participants provided their written informed consent to participate in this study. All methods were carried out in accordance with relevant guidelines and regulations.

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Teuber, M., Leyhr, D. & Sudeck, G. Physical activity improves stress load, recovery, and academic performance-related parameters among university students: a longitudinal study on daily level. BMC Public Health 24 , 598 (2024). https://doi.org/10.1186/s12889-024-18082-z

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How to cope with stress at work—and avoid burning out

Multitasking is a disaster, says Slow Productivity author Cal Newport. In this interview, he tells us how the modern workplace got so bad—and what you can do about it.

Meetings, calls, notifications, multitasking: The modern workplace isn’t exactly known for its qualities of relaxation. In fact, 84 percent of Americans said in a recent survey that their employers contributed to at least one of the mental health challenges they face.

As burnout and stress reach epic proportions, Georgetown University professor Cal Newport has a counterintuitive message: Slow down. In his new book Slow Productivity , the bestselling author uncovers why we’re so unhappy at work. It turns out that one of the most embedded principles of the workplace—the need to be busy—is actually very bad for business. Rejecting that attitude is good for everyone, Newport argues, and it’s possible to achieve big without being needlessly busy.

National Geographic spoke with Newport about the paradox of the modern workplace and how you can incorporate the principles of slow productivity into your own life. This conversation has been edited for clarity.

( ‘Urgency culture’ might lead you to burnout. How can you combat it? )

How pseudo-productivity makes work more stressful

Your book uses the term “pseudo-productivity” to describe modern-day work norms. What do you mean when you use that phrase?  

We use visible activity as a proxy for useful effort. It goes back to the way we measured productivity in factories and in agricultural sectors. In a factory, you have the number of Model Ts produced. In agriculture, you can measure bushels of corn produced per acre of land under cultivation.

For Hungry Minds

None of that worked in knowledge work; there were no clearly defined production systems you could tweak. So pseudo-productivity was the fallback: If we can’t measure productivity like we used to with numbers and ratios, then let’s just say activity is better than no activity.

If office work is so common, why’s it so stressful?  

The problem came with the IT revolution. We got email and computers, and later mobile computing and smartphones. Suddenly, pseudo-productivity sped off the rails because of the amount of work you could take on. The granularity with which you could show you’re doing effort with emails and Slack and jumping off and on digital meetings—all of that went up. That’s when we began to spiral toward the burnout crisis we see today.

I'm thinking about the boss who insists that you don't clock out until 5 p.m.

That’s classic pseudo-productivity. Activity is our measure of productivity. So more activity is better than less, and not doing activity is suspicious.

Why it’s better for business to move slower

What does this pressure do to our bosses and coworkers?  

When we try to embrace as many things as possible, we end up really slowing down what we produce over time. Pseudo-productivity just makes us worse at work. It’s a poor measure. It’s not successful if our goal is to actually produce good stuff.

When people take on more, though, doesn’t more get done?  

It can actually be, ironically, counterproductive. The administrative overhead adds up. Eventually you find yourself in this situation where most of your day is being spent servicing the administrative overhead of all these things you’ve agreed to do. There’s very little time left to make progress on the work. The rate at which anything gets done plummets. It’s bad for everyone. It doesn’t make companies more profitable. It doesn’t produce more value. It burns out employees and causes more turnover.

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The burnout epidemic is really pervasive. The data   suggests   that 77 percent or more of the modern American workforce experiences workplace stress.  

It’s a really hard situation right now, psychologically speaking. The way we’re working is just completely brain-numbing. One of the most baffling omissions in the economy of the last 20 or 30 years is that we have a sector based on using human brains to create value, yet are entirely incurious about how human brains function.

We treat human brains like black boxes that can just crank through tasks, one after another. The overhead of trying to keep track of these projects in your brain is brutal. It's intolerable for the human brain to try to juggle 10 different things that have ongoing, active obligations.

Learn more about stress and how to manage it

But aren’t tools like email and instant messaging designed to make work easier?  

If you understand the human brain at all, [you know] that multitasking is a disaster. When you switch your attention to something like an email inbox, it triggers a very expensive cognitive context switch. Your brain thinks, “Oh, God, we have to pay attention to this now.” It’s a disaster for the brain. It’s like you're running and wearing shoes that weigh 10 pounds.

People say history’s most productive figures have been hustlers, like Jane Austen who was rumored to write her books in secret while family members bustled in and out of her parlor. In your book, however, you show she was only able to produce her best work once she was relieved of most of her household duties and family pressures.  

You look at times past to get principles. Then my task is [to ask] how do we make that principle relevant? With Jane Austen it wasn’t until her life was simplified that she was able to do the work. It was a workload issue. We can look at modern knowledge work and let [her experience] inform how we do, for example, digital workload management.

How to change the way you work

So where can you get started in slow productivity?

I think workers have more autonomy than they realize. If you had to choose one thing to start with, I’d reduce the number of things you’re working on at once.

This doesn’t mean you have to reduce the number of things you agree to do. But make a difference in your mind between “I am actively working on this” and “I agree to this, but am waiting to start.” It can give you breathing room, let you catch your breath. Then slow down and figure out how else you can improve your work.

What would you tell people who struggle with perfectionism?  

As soon as you slow down, perfectionism rears its head. It’s an inevitable enemy of craft. The solutions I highlight in the book have to do with putting stakes in the ground. When the Beatles did Sgt. Pepper, they could have been in that studio forever. So they released a single from the album, a stake in the ground. Then they knew they had to finish it up. You can do the same thing if you commit to doing something by a certain time.

What’s the bottom line?

Pseudo-productivity strips us of self-respect. It says all you’re useful for is busyness. It strips us of a sense of craft, agency, and quality. In the long term, what’s going to establish and give you leverage in your career is to do the thing you do best really well. You’re still a craftsperson underneath. That’s what matters. You can’t lose sight of that.

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Battling burnout: Resilience can’t fix a toxic workplace

• September 8, 2023

Organizations around the world are battling an epidemic of employee burnout. Gallup’s research suggests that 76 percent of employees experience burnout on the job at least sometimes and 28 percent say they are burned out “very often” or “always” at work. This is not sustainable.

Extended periods of burnout can have serious consequences for an individuals’ mental and emotional well-being; it’s a significant risk factor for depression, substance abuse and even suicide. Burnout also presents a serious threat to an organization, resulting in lower employee engagement, lower productivity, higher absenteeism and higher turnover rates, all of which negatively impact a company’s bottom line. The American Institute of Stress estimates that burnout costs U.S. industries more than $300 billion annually.

Addressing the rise in employee burnout has become a business imperative. According to the McKinsey Health Institute, four in five HR leaders report that mental health and well-being is a top priority for their organization. Many have implemented strategies designed to foster resilience, empowering employees to navigate challenges more effectively and thrive at work. These strategies often focus on the main drivers of burnout:

High workload and pressure : Excessive workloads, long hours, tight deadlines and unrealistic expectations can overwhelm employees and make them feel like they are constantly struggling to keep up. This sustained pressure erodes their energy and motivation over time.

Lack of control and autonomy : When employees feel powerless and unable to make decisions that affect their work, they can experience a loss of control. Micromanagement and a lack of autonomy hinder their sense of purpose and fulfillment, contributing to burnout.

Poor work-life balance : The blurring of boundaries between work and personal life can lead to chronic stress and burnout. In today’s “always-on” culture, employees often find it challenging to disconnect and rejuvenate, resulting in exhaustion and diminished well-being.

Inadequate recognition and reward : The absence of acknowledgment and appreciation for employees’ contributions can create a sense of undervaluation and disengagement. When employees’ efforts go unnoticed, it erodes their motivation and increases the risk of burnout.

Uncertainty and ambiguity : Unclear goals, vague expectations and inconsistent communication contribute to employee burnout. When employees are unsure about what is expected of them or lack clear guidance, it increases stress levels, leads to a lack of confidence and creates a constant state of uncertainty.

Toxic behaviors : Toxic behaviors such as harassment, bullying and discrimination are a leading cause of burnout. Constant exposure to degrading and abusive interactions erodes one’s mental and emotional resilience. The relentless stress and negativity associated with these behaviors undermine motivation, self-esteem and overall well-being.

Resilience training is not a panacea

Many companies have launched resilience training programs to help employees develop their ability to adapt and bounce back from adversity. Resilience training can empower employees with coping skills to deal with workplace stress. These programs can help promote work-life integration, encouraging employees to establish healthy work-life boundaries by emphasizing the importance of self-care and time management. They can also cultivate a more supportive work environment emphasizing empathy, open communication and psychological safety. Resilience training can build emotional intelligence, equipping employees with tools to effectively manage their energy, set boundaries and navigate conflicts.

Resilience training can be extremely beneficial for individuals and organizations, but resilience training is not a panacea. Resilience training programs often focus on individual-level solutions, such as stress management techniques and mindfulness exercises. While well-intentioned, these programs place responsibility on the individual to cope with the demands of what may be a toxic work culture. It assumes employees can thrive regardless of their external environment.

McKinsey Health Institute research suggests toxic workplace behaviors are the single biggest predictor of burnout symptoms and, according to a recent survey from the American Psychological Association, 19 percent of workers say their workplace is very or somewhat toxic. Those who reported a toxic workplace were more than three times as likely to have said they have experienced harm to their mental health at work than those who report a healthy workplace.

Ignoring toxic behaviors and focusing just on coping techniques is a losing proposition. Research suggests that interventions which only target individuals are far less likely to have a sustainable impact on employee well-being than systemic, organizational-level interventions. So, while these practices may offer temporary relief, they do not address systemic issues that can breed toxicity within the workplace.

Ironically, helping employees develop resilience may actually encourage them to leave the organization. Employees with high adaptability are 60 percent more likely to report intent to leave their organization if they experienced high levels of toxic behavior at work than those with low adaptability. By solely relying on resilience training, HR leaders risk inadvertently perpetuating a cycle of burnout, masking the need for true cultural change. They may be treating the symptoms without addressing the root cause.

To address the root causes of a toxic culture, organizations should focus on the organizational norms that shape individual attitudes, habits and behaviors. Norms are the unwritten rules of behavior that are shared by members of a group, a team or an organization. Norms shape expectations about employee performance, defining what is acceptable and unacceptable behavior. Norms guide employee interactions with managers, peers and colleagues – as well as customers and clients. They help employees anticipate the consequences of their own actions. Employees generally adhere to norms because they want to fit in with the people around them, and that group identity exerts a very strong influence on individual behavior – both positive and negative.

Talent management plays a critical role in shaping norms for the organization. Every strategic decision to recruit, engage, develop and reward employees can either reinforce or erode the organizational norms. For example, talent acquisition and onboarding provide a vital opportunity to introduce and establish norms with new employees. Rewards and recognition are powerful tools to clarify and reinforce organizational norms. Celebrating accomplishments and recognizing positive behaviors can send a strong signal to the organization. Talent development provides an opportunity for employees to explore norms and develop the skills that contribute to a positive work environment like communication, collaboration and decision making.

Leadership development programs can directly shape norms. Leaders set the tone for acceptable and appropriate behaviors. Underperforming leaders can have a negative effect, eroding trust in a business and creating low employee engagement. Organizations should empower leaders with tools to establish team norms that will lead to positive outcomes. That could include giving them a framework to discuss norms with their teams and preparing them to have explicit discussions about individual and team behaviors. Once these norms are established and communicated, individual team members can hold each other accountable, calling out inappropriate or unacceptable behaviors — supported by transparent policies and procedures to reinforce accountability and discourage harmful behaviors. Creating a positive team climate is the single most important driver of a team’s psychological safety.

Talent management professionals are in a unique position to set expectations for employee behavior, monitor performance and make necessary adjustments. Employee engagement and satisfaction surveys can help to identify areas in an organization that are struggling. These areas may benefit from a more direct intervention to address behaviors and attitudes. Performance reviews and 360s can provide additional insight into individual strengths and weaknesses. Exit interviews, when done effectively, can help to spot toxic behaviors in a team or manager. According to a study from the Society for Human Resource Management, one in five employees have left a job at some point in their career because of its toxic culture. Failure to address the underlying issue makes it harder to retain key talent, and once an employer has a reputation for a toxic culture it can become much harder to attract new talent.

Business leaders have a responsibility to create a positive work environment where employees can thrive. Resilience training, while beneficial, cannot single-handedly combat the detrimental effects of a toxic work culture. Talent management professionals can harness the power of norms to foster a culture that promotes psychological safety, embraces diversity and fosters employee well-being. These norms create a strong foundation for a healthy and productive work environment, ultimately enhancing overall organizational performance and employee satisfaction.

Photo by Nataliya Vaitkevich

Original post Talent Management

By Kip Kelly is the senior vice president of marketing and sales at Quantuvos. Kip has over 25 years of marketing and communications experience, with a focus on talent management, executive education, professional coaching and leadership development. His expertise in diversity, inclusion and the use of data and analytics in learning and development has earned him a reputation as an industry leader, having published over 60 research studies and white papers, as featured in Chief Learning Officer Magazine, TD Magazine, Training Industry, HR Magazine, Fortune Magazine, the Chronicle of Higher Education, the Financial Times and a host of other leading publications.

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• Open access
• Published: 13 May 2024

From incivility to outcomes: tracing the effects of nursing incivility on nurse well-being, patient engagement, and health outcomes

• Nourah Alsadaan   ORCID: orcid.org/0000-0001-7285-0184 1 ,
• Osama Mohamed Elsayed Ramadan   ORCID: orcid.org/0000-0002-9616-8590 1 &
• Mohammed Alqahtani 2  

BMC Nursing volume  23 , Article number:  325 ( 2024 ) Cite this article

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Nursing incivility, defined as disrespectful behaviour toward nurses, is increasingly recognized as a pressing issue that affects nurses’ well-being and quality of care. However, research on the pathways linking incivility to outcomes is limited, especially in Saudi hospitals. Methods: This cross-sectional study examined relationships between perceived nursing incivility, nurse stress, patient engagement, and health outcomes in four Saudi hospitals. Using validated scales, 289 nurses and 512 patients completed surveys on exposure to incivility, stress levels, activation, and medication adherence. The outcomes included readmissions at 30 days and satisfaction. Results: More than two-thirds of nurses reported experiencing moderate to severe workplace incivility. Correlation and regression analyzes revealed that nursing incivility was positively associated with nursing stress. An inverse relationship was found between stress and patient participation. Serial mediation analysis illuminated a detrimental cascade, incivility contributing to increased nurse stress, subsequently diminishing patient engagement, ultimately worsening care quality. Conclusions The findings present robust evidence that nursing incivility has adverse ripple effects, directly impacting nurse well-being while indirectly affecting patient outcomes through reduced care involvement. Practical implications advocate for systemic interventions focused on constructive nursing cultures and patient empowerment to improve both healthcare provider conditions and quality of care. This study provides compelling information to inform policies and strategies to mitigate workplace mistreatment and encourage participation among nurses and patients to improve health outcomes.

Peer Review reports

Introduction

Nursing, a cornerstone of the healthcare system, plays an indispensable role in patient care and the broader health landscape [ 1 , 2 ]. This noble profession encompasses not only the administration of treatments and medications but also the provision of emotional support and education to patients and their families [ 3 , 4 ]. Nurses are often the primary point of contact for patients, which makes their role crucial in shaping patient experiences and outcomes [ 5 ]. The diverse responsibilities, from bedside care to patient advocacy, emphasize the multifaceted nature of nursing and its critical impact on the delivery of healthcare [ 6 , 7 ]. The work environment in which nurses work is crucial for both their well-being and their ability to provide quality care [ 6 , 8 ]. A positive and supportive environment not only improves job satisfaction and retention among nurses but also directly influences patient safety and quality of care [ 9 , 10 ]. Factors such as teamwork, communication, and organizational culture play an important role in shaping this environment [ 11 ]. In contrast, negative elements within the workplace can lead to burnout, decreased job satisfaction, and potentially compromise patient care [ 12 ].

Nursing incivility, an increasingly distressing concern, encompasses disrespectful behaviours [ 13 ], that violate workplace dignity norms ranging from subtle belittling to overt hostility [ 2 , 8 ]. This widespread phenomenon permeates most healthcare settings [ 14 , 15 ], with up to 85% of nurses encountering this mistreatment from various sources [ 16 ], resulting in a significantly disruptive organizational climate [ 17 ]. Beyond affecting nurse well-being through adverse psychological impacts, incivility breeds poor morale, compromised performance, increased attrition, and, critically, reduced quality of patient care [ 18 , 19 , 20 ]. Prioritizing healthy collegial environments remains crucial for upholding both nurse wellness and optimal patient outcomes [ 21 , 22 , 23 ]. Furthermore, organizational factors, such as leadership, communication, and workplace culture, may play a significant role in shaping the dynamics of nursing incivility, stress, and patient outcomes [ 24 , 25 ]. Investigating these factors could provide a more comprehensive understanding of the complex interplay between individual and systemic elements in the healthcare setting [ 26 , 27 ]. Nursing incivility can manifest itself in various forms, including, but not limited to, belittling comments, bullying, gossip, and exclusionary tactics [ 11 , 28 ]. These behaviours can originate from colleagues, superiors, patients, and their families [ 16 , 29 ]. Such conduct not only undermines professional relationships [ 30 ] but also can cause psychological distress for victims, preventing their ability to perform effectively [ 31 , 32 ].

Although the prevalence and nature of incivility in nursing have been well documented, there remains a significant gap in understanding its full impact [ 33 , 34 ]. The impact of nursing incivility extends beyond the immediate targets, affecting multiple aspects of healthcare delivery [ 35 , 36 ]. Incivility can have profound emotional consequences for nurses, leading to increased stress, burnout, and job dissatisfaction, which can compromise their ability to provide high-quality patient care [ 8 , 19 , 29 ]. Moreover, uncivil behaviors can strain nurse-patient interactions, potentially diminishing the quality of care and patient satisfaction [ 18 , 37 ]. At an organizational level, incivility can disrupt team dynamics, contribute to higher staff turnover rates, and negatively influence the overall culture within healthcare institutions [ 18 , 38 , 39 ]. Furthermore, the economic implications of nursing incivility, such as costs associated with staff replacement and lost productivity due to absenteeism and presenteeism, warrant further investigation [ 40 , 41 , 42 ]. Examining these multifaceted impacts is crucial for developing targeted interventions and policies to mitigate the detrimental effects of incivility on nurses, patients, and healthcare organizations [ 43 , 44 ].

Current literature has primarily focused on identifying forms and instances of uncivil behavior, often overlooking their deeper implications for nurses, patients, and healthcare systems. An underexplored area is the direct effect of incivility on nurses’ well-being [ 8 ]. This includes quantifying the emotional and professional toll, such as stress, burnout, and job dissatisfaction [ 31 ], which are crucial factors influencing nurse retention and mental health [ 45 , 46 ].

In summary, filling these gaps through robust empirical research is crucial. Such research is essential not only to transform current anecdotal and observational understandings into data-driven insights but also to develop effective strategies to mitigate the negative impacts of incivility [ 47 , 48 ]. These insights are vital to promoting a healthier, more respectful, and efficient healthcare environment, ultimately enhancing nurses’ well-being and patient care quality [ 3 , 5 ]. The primary objective of this study was to investigate the impact of nursing incivility on critical aspects of healthcare care delivery. By focusing on nurse stress, patient engagement, and health outcomes (defined as 30-day readmission rates and patient satisfaction scores), the study aimed to understand how incivility in the nursing environment affects both healthcare providers and recipients.

The study was conducted within the context of the Saudi healthcare system, which has undergone significant reforms in recent years [ 49 , 50 ]. The system is primarily government-funded, with a growing private-sector presence [ 51 ]. It aims to provide universal access to healthcare services for all citizens and residents, with a focus on improving quality and efficiency [ 52 ]. However, like many healthcare systems worldwide [ 53 , 54 ], it faces challenges related to workforce development, patient satisfaction, and the management of complex health conditions [ 55 ]. Understanding the impact of nursing incivility within this context is crucial for informing strategies to enhance the well-being of healthcare providers and the quality of patient care.

This study examined nurse stress, a direct consequence of incivility, and its subsequent effects on patient care. Additionally, it explored how incivility in nursing influenced patient participation, a crucial factor in successful health outcomes. Finally, the study assessed the broader implications of these variables on overall health outcomes, providing valuable insights for healthcare policy and practice.

This study’s findings can influence nursing practice and patient care significantly. By demonstrating the tangible impacts of nursing incivility, the study can inform the development of targeted interventions and policies to create a more respectful and supportive work environment for nurses. This, in turn, can lead to improved patient care and outcomes. Highlighting the importance of a respectful and supportive nursing environment is a key outcome of this study. By underscoring the detrimental effects of incivility, the research advocates for a cultural shift in healthcare settings toward more positive and collaborative interactions. These changes are vital for nurses’ well-being, patient care quality, and healthcare organizations’ overall effectiveness.

Materials and methods

Research objectives & research hypothesis.

Examine the relationships between nursing incivility, nurse stress (defined as emotional exhaustion and depersonalization), patient engagement (defined by patient activation levels and adherence to discharge protocols), and health outcomes (defined as 30-day readmission rates and patient satisfaction scores). H1a: Higher levels of nursing incivility will be positively associated with increased nurse stress. H1b: Higher levels of nurse stress will be negatively associated with patient engagement. H1c: Lower levels of patient engagement will be associated with poorer health outcomes.

Investigate how different perceived levels and types of nursing incivility, including overt (bullying, verbal abuse) and covert (gossip, exclusion) behaviours frequently reported by nurses, affect nurse stress and emotional exhaustion through a cross-sectional survey methodology.

H2a: Overt forms of nursing incivility will have a stronger positive association with nurse stress compared to covert forms of incivility. Overt forms of nursing incivility refer to more explicit and direct forms of uncivil behaviour, such as verbal abuse, bullying, or intimidation. Covert forms of nursing incivility refer to more subtle and indirect forms of uncivil behaviour, such as gossip, exclusion, or undermining actions.

H2b: A higher frequency of exposure to nursing incivility will be associated with higher levels of nurse stress and emotional exhaustion.

Evaluate how nursing incivility, nurse stress, and patient engagement (activation and adherence) impact patient health outcomes (30-day readmissions and satisfaction), mapping the relationships between these variables using multivariate regression techniques. H3a: Nursing incivility will have a direct negative effect on patient health outcomes. H3b: Nurse stress will mediate the relationship between nursing incivility and patient health outcomes. H3c: Patient engagement will mediate the relationship between nurse stress and patient health outcomes. H3d: The combined indirect effects of nurse stress and patient engagement will partially mediate the relationship between nursing incivility and patient health outcomes.

These hypothesized relationships form the conceptual foundation of our study, guiding our investigation into the complex interplay between nursing incivility, nurse well-being, patient engagement, and healthcare outcomes. By examining these relationships, we aim to provide insights into the potential cascading effects of uncivil behaviours in the nursing workplace and their ultimate impact on patient care. Figure  1 illustrates the hypothesized relationships between nursing incivility, nurse stress, patient engagement, and health outcomes. As depicted in Fig.  1 , we hypothesize that nursing incivility directly influences nurse stress and patient engagement. In turn, nurse stress is expected to have an indirect effect on health outcomes, mediated by patient engagement. Additionally, we anticipate that patient engagement directly impacts health outcomes, which are operationalized as readmission rates and patient satisfaction.

The arrows in Fig.  1 are used to represent the relationships and directional hypotheses between the constructs mentioned: Nursing Incivility, Nurse Stress, Patient Engagement, and Health Outcomes. Here’s how the arrows correspond to each hypothesis:

Solid Arrows indicate a direct relationship in the primary sequence of effects :

H1a: Nursing Incivility → Nurse Stress.

H1b: Nurse Stress → Patient Engagement.

H1c: Patient Engagement → Health Outcomes

Dashed Arrows represent different types of incivility (overt and covert) and their effect on Nurse Stress :

H2a: Nursing Incivility (Overt) → Nurse Stress.

H2b: Nursing Incivility (Covert) → Nurse Stress.

Dotted Arrows show both direct and mediated paths for complex relationships :

H3a: Direct effect from Nursing Incivility → Health Outcomes.

H3b: Mediated effect through Nurse Stress.

H3c: Mediated effect through Patient Engagement.

H3d: Combined mediation through Nurse Stress and Patient Engagement leading to Health Outcomes.

Hypothesized relationships between nursing incivility, nurse stress, patient engagement, and health outcomes

This study employed a cross-sectional correlational design to explore the relationships between nursing incivility, nurse stress, patient engagement, and health outcomes. This design involved collecting data from a defined population of nurses and patients in acute care settings simultaneously. This approach allows us to examine the associations between variables without actively manipulating any of them, providing a snapshot of the current state of these relationships.

The study was conducted in four hospitals located in the northwest region of Saudi Arabia. The participating hospitals are large, general medical and surgical facilities, with bed capacities ranging from 200 to 500. They provide a wide range of services, including inpatient and outpatient care, emergency services, critical care units, and specialized departments such as maternity, paediatrics, and mental health treatment. The patient population served by these hospitals is diverse, encompassing individuals seeking acute care for various medical conditions as well as those managing chronic illnesses such as diabetes, cardiovascular diseases, and respiratory disorders. The hospitals cater to both urban and rural communities within the northwest region. The nursing staff in these hospitals comprises a combination of Saudi and expatriate nurses, with varying levels of experience and educational qualifications. It is important to note that the findings of this study are specifically relevant to the northwest region of Saudi Arabia and may not be generalizable to other regions or healthcare settings. The unique cultural and socioeconomic characteristics of this region should be considered when interpreting the results and their implications for nursing practice and patient care.

Participant sample size determination

We calculated sample sizes for the nurse and patient groups to ensure statistical validity and practicality in our cross-sectional study. For the 289 nurses, we conducted a power analysis using a moderate effect size, 80% power, and a 0.05 alpha level, following the guidelines of Cohen (2013) on power analysis for behavioural sciences [ 56 ]. Although a small effect size might initially seem appropriate given the significant knowledge gap addressed by our study, the moderate effect size was chosen to maintain a balance between sensitivity and feasibility. This decision was particularly influenced by the practical challenges associated with securing a large enough sample to detect small effects within the logistical and resource constraints of our study setting. The moderate effect size was deemed most appropriate given the limited existing research on the specific relationships between nursing incivility, nurse stress, patient engagement, and health outcomes within the Saudi Arabian context, as highlighted in the introduction. Additionally, we accounted for potential variability and non-response rates for healthcare research, as suggested by Davern (2013).

The patient group required a larger sample size of 512 to accommodate greater variability and enable subgroup analyses. Patients were selected using a combination of random sampling and voluntary participation. Initially, a random sample of patients was drawn from the patient records of the participating hospitals, ensuring a representative mix of demographics, diagnoses, and hospital units. These patients were then invited to participate in the study voluntarily, which aimed to minimize selection bias while ensuring patient autonomy.

This approach also adhered to the standard power analysis methods [ 56 ] and included an upward adjustment for expected variability in patient responses, as recommended by Hulley et al. (2013) in their guidelines for clinical research [ 57 ]. Both sample sizes were further validated for feasibility within our resource constraints and specific healthcare settings, aligning with the practical considerations outlined by [ 58 ] in planning health research. In summary, the sample sizes of 289 nurses and 512 patients were determined using established statistical methods and customized to the unique aspects of our study, ensuring adequate power for reliable results. The selection process for both nurses and patients aimed to balance representativeness, statistical power, and ethical considerations, with patient selection particularly focused on combining random sampling with voluntary participation.

Eligibility criteria

Inclusion criteria.

Participants selected for this study were required to meet several conditions. First, they had to be registered nurses actively employed full-time, working ≥ 30 h per week, at one of the four identified healthcare hospitals. Their experience in the current institution should have spanned a minimum of six months. Furthermore, only those who could and were willing to provide informed consent were considered. Language proficiency was also crucial; Participants had to be fluent in Arabic or English to ensure they understood and completed the survey accurately. Lastly, the age bracket for eligible nurses was established between 25 and 60 years. Additionally, eligible participants must participate in direct patient care activities at least 10 h per week.

Exclusion criteria

Several factors led to the exclusion of potential participants from this study. Nurses who were currently not in active service, perhaps due to long-term leave or sabbatical, were not considered. We also considered the health aspect; nurses who self-declared cognitive impairments or mental health problems that could influence the accuracy of their responses were excluded. Nurses who had participated in a similar study or survey related to the topic in the last 6 months were excluded from this research. This exclusion criterion was implemented to minimize the potential influence of recent exposure to similar research questions or interventions on participants’ responses. By ensuring that a sufficient washout period had passed since any previous participation in related studies, we aimed to reduce the risk of response bias and enhance the validity of the collected data. This criterion contributes to the study’s rigour by minimizing the potential confounding effects of prior research experiences and promoting the collection of more independent and unbiased responses from participants.

Data collection tools

In this study, we employed the following validated instruments to measure the key variables, aligning with our research objectives and hypotheses:

Nursing incivility scale (NIS)

The Nursing Incivility Scale (NIS) is a quantitative instrument comprising 43 items designed to measure the frequency of perceived incivility from various sources, including patients, supervisors, coworkers, and physicians, over the preceding six months [ 59 ]. The NIS includes subscales that assess various sources of incivility, such as from nurses, supervisors, physicians, and patients. The items within these subscales capture both overt and covert forms of incivility, allowing for an assessment of the frequency and severity of each type of uncivil behaviour [ 60 ].

It employs a 5-point Likert scale ranging from “Never” to “Daily” and encompasses five subscales addressing different sources of incivility: nurses, the general workplace, supervisors, physicians, and patients The Nursing Incivility Scale (NIS) doesn’t provide a direct score but rather collects data on the frequency of uncivil behaviours experienced by nurses [ 60 , 61 ]. The NIS has demonstrated excellent internal reliability (Cronbach’s α > 0.90 across subscales) [ 56 ], and validity, making it well-suited for exploring the correlation between nursing incivility and nurse stress. Higher scores on the NIS subscales indicate a higher frequency of exposure to various forms of incivility from different sources.

Perceived stress scale (PSS)

The Perceived Stress Scale (PSS) is a 10-item self-report questionnaire that evaluates an individual’s stress appraisal over the preceding month, with a particular emphasis on predictability, control, and overload [ 62 ]. It employs a 5-point Likert scale ranging from “Never” to “Very Often.” The total PSS score typically ranges from 0 to 40 (assuming a 4-point scale), with higher scores indicating greater perceived stress and emotional exhaustion. A common interpretation guide categorizes scores as follows: 0–13 for low stress, 14–26 for moderate stress, and 27–40 for high perceived stress. The PSS has been extensively validated, exhibiting good internal reliability (Cronbach’s α = 0.78), rendering it pertinent for assessing stress levels and emotional exhaustion among nurses [ 63 ].

Patient activation measure (PAM)

A 13-item scale measuring patient self-efficacy in managing their health and care [ 64 ]. The Patient Activation Measure (PAM) employs a 4-point Likert scale, ranging from “Strongly Disagree” (1) to “Strongly Agree” (4), to assess the level of patient involvement in their healthcare. The raw scores from each question are summed, and this raw score is then mathematically transformed to a 0-100 scale. The final PAM score reflects the degree of a patient’s activation, with a score range of 1–46 indicating low activation, wherein patients tend to be overwhelmed and unprepared to take an active role in their health; 47–55 suggesting moderate activation, where patients are somewhat comfortable managing their health but might require assistance; 56–72 signifying high activation, with patients being comfortable in taking an active role in managing their health; and 73–100 representing very high activation, wherein patients are highly confident and skilled in managing their health [ 65 ].

Morisky Medication Adherence Scale (MMAS-8)

The Morisky Medication Adherence Scale (MMAS-8) is a validated 8-item self-report instrument designed to identify barriers to medication adherence [ 66 ]. It employs a binary response format (yes/no) to assess adherence issues over the past week. The MMAS-8 exhibits good internal consistency (Cronbach’s α = 0.83) and reliability, rendering it a crucial tool for evaluating patient engagement concerning medication adherence. Patients are categorized into different adherence levels based on their cumulative score ranging from 0 to 8, with a score of 8 indicating high adherence (likely following medication instructions), scores of 6 or 7 suggesting medium adherence (potential for missed medications), and scores below 6 signifying low adherence (high risk of not following instructions) [ 67 ].

Hospital Consumer Assessment of Healthcare Providers and systems (HCAHPS)

The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) is a survey instrument and data collection methodology to measure patients’ perceptions of their hospital experience [ 68 ]. The survey contains 29 questions about the recent hospital stay of patients, including communication with nurses and doctors, hospital staff responsiveness, cleanliness and quietness of the hospital environment, communication about medications, discharge information, overall hospital rating, and whether they would recommend the hospital [ 69 ]. The survey is administered to a random sample of adult patients across medical conditions between 48 h and six weeks after discharge. Publicly reported scores will be utilized as a proxy for patient satisfaction [ 70 ]. The HCAHPS data used in this study were collected independently from the other patient data and represented the publicly reported satisfaction scores for the participating hospitals during the study period.

Electronic Medical Records (EMR)

Electronic Medical Records (EMRs) served as a data source to extract 30-day hospital readmission rates, an objective measure that is pivotal to evaluating health outcomes in relation to nursing incivility, nurse stress, and patient engagement. Utilization of EMRs facilitates the collection of this crucial metric, allowing for a rigorous assessment of potential associations between the aforementioned variables and patient health outcomes, as reflected in readmission rates within 30 days after discharge.

Ethics approval

The study received ethical approval from the General Directorate of Health Affairs, Hail Healthy Cluster, Hail Region / IRB Registration Number with KACST, KSA: H-11–08 L-074 / IRB log number 2023-66. The approval process involved evaluating the study’s objectives, methods, instruments, and impacts while emphasizing adherence to ethical principles like respect, justice, beneficence, and non-maleficence. A detailed informed consent form was prepared to ensure the understanding and voluntary participation of the participants, along with measures to maintain privacy and confidentiality using unique participant identifiers. The protocol also included provisions for participant transparency, including the right to access results and withdraw at any time without repercussions. Following the review of the IRB, ethical clearance was granted, allowing the study to proceed in accordance with established ethical standards and guidelines.

Data collection was conducted between May 2023 and November 2023 in four public hospitals located in the northwest region of Saudi Arabia. These hospitals were strategically selected to represent the region’s geographic and demographic diversity, ensuring the sample reflected the wider context of Saudi healthcare. Nurses were recruited through targeted invitations sent to all eligible personnel, aiming for a broad representation of experiences and backgrounds. Patients were randomly selected from hospital records and invited to participate voluntarily. No incentives were offered to participants.

Paper-based surveys were administered to both nurses and patients. Nurses completed the surveys during their work shifts, while patients were surveyed independently of their hospital stay. Researchers were available to assist participants who needed clarification or faced difficulty understanding the questions. Patients completed the PAM and MMAS-8 surveys independently, typically within 2–4 weeks after discharge, to assess their activation levels and medication adherence during the post-hospitalization period.

The data collection process was designed to ensure participant privacy, reduce potential biases, and gather comprehensive responses without causing undue burden. Unique participant identifiers were assigned to each nurse and patient to maintain confidentiality throughout the study. All collected data were stored on secure, password-protected servers, with access restricted to authorized members of the research team. Physical copies of the surveys were stored in locked cabinets, and electronic data were encrypted to prevent unauthorized access.

Participants typically spent 15–20 min completing the surveys, which included the Nursing Incivility Scale (NIS) and the Perceived Stress Scale (PSS) for nurses, and the Patient Activation Measure (PAM) and the Morisky Medication Adherence Scale (MMAS-8) for patients. These instruments were selected based on their established validity and reliability in similar research contexts and their alignment with the study variables. The data collection process was designed to ensure participant privacy, reduce potential biases, and gather comprehensive responses without causing undue burden. The use of paper-based surveys accounted for participants’ varied preferences and technological comfort levels while minimizing potential technical issues.

Statistical analysis

This study employed descriptive statistics to establish the demographic profiles of nurse and patient participants, summarizing categorical variables through frequencies and percentages. For the Nursing Incivility Scale (NIS) and Perceived Stress Scale (PSS), we divided scores into tertiles for descriptive analyses, which offered an intuitive understanding of incivility and stress levels among participants. In our regression analyses, we used the continuous scores to preserve the rich variability inherent in these measures.

The statistical examination commenced with bivariate Pearson’s correlation analysis, identifying foundational relationships between key study variables. We then conducted multiple linear regression models to determine the direct effects of nursing incivility, nurse stress, patient activation, and medication adherence on health outcomes. Hierarchical multiple regression analyses were conducted, entering nursing role as a covariate in the first step, followed by the predictor variables (nursing incivility, nurse stress, patient activation, and medication adherence) in subsequent steps.

Further statistical exploration involved mediation analyses to investigate the indirect effects within our conceptual framework. Specifically, we examined the mediating role of nurse stress in the association between nursing incivility and health outcomes and the potential mediation of patient engagement between nurse stress and health outcomes. A serial mediation model elucidated the complex interplay and indirect pathways that link nursing incivility to patient outcomes through multiple mediator variables.

All statistical procedures were executed using SPSS Version 26. Missing data were managed via mean substitution for subscale averages. To ensure participant privacy and confidentiality, all analyses were performed using de-identified data, with unique participant identifiers replaced by numeric codes. Only aggregate results were reported, ensuring that no individual participant could be identified from the study findings. The significance threshold was set at an alpha level of 0.05, and effect sizes were calculated to contextualize the strength of associations.

Consistent with the structured complexity of our theoretical model, a serial mediation analysis was incorporated into the statistical strategy. This analysis enabled us to dissect the multi-step indirect effects and examine the potential sequential mediators, providing an integrated understanding of the relationships among the constructs of interest. The integrity of the analyses was maintained by stringent testing for normality, linearity, and homoscedasticity, ensuring the appropriateness of our regression models and the robustness of our findings. The analytical choices, carefully aligned with the objectives of the study and the nature of the data, facilitated a clear depiction of the causal pathways and supported the validity of our conclusions.

This section presents the empirical findings of the study, which aim to explore the relationships between nursing incivility, nurse stress, patient engagement, and health outcomes. The results are based on data from 289 nurses and 521 patients in four hospitals. Detailed statistical analyses, including descriptive statistics, correlations, and regression models, help to elucidate these relationships. The following tables provide a comprehensive summary of these analyses, shedding light on the nuances and key takeaways of the study findings. The scales used in this study demonstrated good to excellent reliability in the current sample. Cronbach’s alpha coefficients were as follows: Nursing Incivility Scale (NIS) α = 0.94, Perceived Stress Scale (PSS) α = 0.82, Patient Activation Measure (PAM) α = 0.89, and Morisky Medication Adherence Scale (MMAS-8) α = 0.79.

The demographic characteristics presented in Table  1 offer a comprehensive statistical overview of the study participants, encompassing both nurses ( N  = 289) and patients ( N  = 512). The age distribution among nurses is skewed toward younger age groups, with 38.7% aged 25–30 years and 30.1% aged 31–40 years. In contrast, the patient population exhibits a more evenly distributed age range, with the highest proportion (37.1%) in the 31–40 age group. Gender-wise, the nurse sample is predominantly female (66.8%), aligning with the traditional gender demographics of the nursing profession, while the patient sample shows a more balanced distribution (51.6% male, 48.4% female). The nursing roles represented include Registered Nurses (51.9%), Head Nurses (24.2%), and Supervisors (23.9%), reflecting a diverse representation of nursing staff. In terms of experience, the majority of nurses (51.6%) have 2–5 years of experience, followed by those with more than 5 years (25.3%) and less than 2 years (23.2%). The patient health status data reveals that 63.7% are categorized as healthy, 33.6% have a managed chronic condition, and 2.7% have an unmanaged chronic condition. Furthermore, the educational qualifications of nurses are well-represented, with 62.0% holding a Bachelor’s degree and 38.0% possessing a Master’s or Ph.D. degree. Finally, the distribution of participants across the four hospitals is relatively even, ranging from 24.4 to 25.8% for patients and 24.2–25.6% for nurses, ensuring a representative sample from various healthcare settings.

Table  2 presents a quantitative assessment of the severity distribution of nursing incivility scores among the nurse participants. The Nursing Incivility Scale (NIS) scores have been categorized into three distinct levels: mild incivility (scores ranging from 0 to 33), moderate incivility (scores ranging from 34 to 66), and severe incivility (scores ranging from 67 to 100). Out of the total 289 nurse participants, 90 (31.1%) reported experiencing mild levels of incivility, 125 (43.3%) experienced moderate incivility, and 74 (25.6%) experienced severe incivility. The data reveals that a significant proportion of nurses, nearly 69%, reported experiencing moderate to severe levels of incivility in their workplace, highlighting the prevalence of this issue within the nursing profession. The distribution of incivility levels provides a quantitative representation of the severity of the problem, which is crucial for developing targeted interventions and policies to address workplace incivility and promote a positive work environment for nurses.

Table  3 presents the distribution of Perceived Stress Scale (PSS) scores among the nurse participants, categorized into three levels: low stress (scores ranging from 0 to 13), moderate stress (scores ranging from 14 to 26), and high stress (scores ranging from 27 to 40). Out of the total 289 nurse participants, 95 (32.9%) reported low stress levels, 120 (41.5%) reported moderate stress levels, and 74 (25.6%) reported high stress levels. The data reveals that a significant proportion of nurses, approximately 67%, experienced moderate to high levels of stress, indicating the presence of substantial stress among the nursing workforce. The distribution of stress levels provides a quantitative representation of the prevalence and severity of stress experienced by nurses, which is crucial for developing targeted interventions and strategies to address and mitigate stress within the nursing profession.

Table  4 presents a comparative analysis of patient activation levels and medication adherence, as measured by the Patient Activation Measure (PAM) and the Morisky Medication Adherence Scale (MMAS-8), respectively. The scores for both measures are categorized into low/poor, moderate, and high ranges. For the PAM, the score ranges are 0–33 for low/poor activation, 34–66 for moderate activation, and 67–100 for high activation. The table shows that 150 patients scored in the low/poor range, 250 in the moderate range, and 112 in the high range. For the MMAS-8, the score ranges are 0–2 for low/poor adherence, 3–5 for moderate adherence, and 6–8 for high adherence. The table indicates that 200 patients scored in the low/poor range, 180 in the moderate range, and 132 in the high range. The table also provides p-values for the comparison between the low/poor and high categories for both measures. For the PAM, the p-value is reported as < 0.05, indicating a statistically significant difference between the low/poor and high activation groups. For the MMAS-8, the p-value is reported as < 0.01, suggesting a highly significant difference between the low/poor and high medication adherence groups.

Table  5 presents the bivariate correlation coefficients among the key study variables: Nursing Incivility (NIS), Nurse Stress (PSS), Patient Activation (PAM), and Medication Adherence (MMAS-8). The table is structured as a correlation matrix, where each cell represents the correlation coefficient between the corresponding row and column variables. The diagonal elements (1.00) represent the perfect correlation of each variable with itself. The correlation coefficient between Nursing Incivility (NIS) and Nurse Stress (PSS) is 0.45, indicating a moderate positive correlation. The correlation coefficients between Nursing Incivility (NIS) and Patient Activation (PAM), and Nursing Incivility (NIS) and Medication Adherence (MMAS-8) are − 0.30 and − 0.25, respectively, suggesting moderate negative correlations. The correlation coefficient between Nurse Stress (PSS) and Patient Activation (PAM) is -0.40, indicating a moderate negative correlation. The correlation coefficient between Nurse Stress (PSS) and Medication Adherence (MMAS-8) is -0.35, suggesting a moderate negative correlation. The correlation coefficient between Patient Activation (PAM) and Medication Adherence (MMAS-8) is 0.60, indicating a strong positive correlation.

Table  6 presents a nuanced understanding of how various factors related to nursing and patient engagement influence health outcomes, specifically 30-day readmission rates and patient satisfaction scores. The data indicate that nursing incivility has a detrimental effect on both health outcomes, suggesting that interventions aimed at reducing workplace incivility may improve patient care. Interestingly, nurse stress shows a positive correlation with both outcomes, indicating that higher stress levels could be linked to more frequent patient follow-up, possibly improving patient satisfaction despite higher readmission rates. This points to the complex role of stress in healthcare settings. Furthermore, patient activation is strongly negatively correlated with both outcomes, emphasizing the benefits of patient empowerment in their own care processes. Enhanced patient activation could lead to fewer readmissions and higher satisfaction. Similarly, medication adherence, which is negatively associated with readmission rates and positively with satisfaction scores, highlights its critical role in effective patient management. These insights reveal the interconnected nature of healthcare environments and underscore the importance of a multifaceted approach to improving patient outcomes.

Table  7 presents an intricate statistical investigation into the cascading effects of nursing incivility within a healthcare setting. The analysis thoughtfully dissects how nursing incivility impacts patient outcomes, notably through nurse stress and patient engagement mediating variables. The positive estimate (B = 0.08) for the path from nursing incivility to nurse stress, with a significant p-value of less than 0.001, underscores the strong influence of workplace incivility on nurse stress. Furthermore, both statistically significant, the adverse pathway from nursing incivility to patient engagement (B = -0.24) and from nurse stress to patient engagement (B = -0.41) highlights a detrimental cascade effect, where incivility indirectly undermines patient engagement through increased nurse stress. The substantial direct impact of patient engagement on patient outcomes (B = 0.52) emphasizes the critical role of patient involvement in their care. The analysis culminates in delineating the total and direct effects of nursing incivility on patient outcomes, with the indirect effects through nurse stress and patient engagement providing a deeper understanding of the underlying dynamics. The obtained relationships between nursing incivility, nurse stress, patient engagement, and health outcomes, along with their standardized regression coefficients (β) and significance levels (p-values), are visually summarized in Fig.  2 .

As illustrated in Fig.  2 , nursing incivility had a significant direct effect on both nurse stress (β = 0.08, p  < 0.001) and patient engagement (β = -0.24, p  = 0.003). Nurse stress, in turn, negatively influenced patient engagement (β = -0.41, p  < 0.001). Furthermore, patient engagement had a strong positive impact on patient outcomes (β = 0.52, p  < 0.001). The total effect of nursing incivility on patient outcomes was significant (β = -0.37, p  < 0.001), with both direct (β = -0.22, p  = 0.002) and indirect effects through nurse stress and patient engagement (β = -0.15, p  = 0.004) contributing to this relationship. These findings provide evidence for the hypothesized cascading effects of nursing incivility on patient outcomes, highlighting the crucial role of nurse stress and patient engagement as mediating factors in this relationship. The results underscore the importance of addressing workplace incivility and promoting a positive work environment to enhance nurse well-being, patient engagement, and ultimately, patient outcomes.

Relationships between nursing incivility, nurse stress, patient engagement, and health outcomes were obtained, with standardized regression coefficients (β) and significance levels ( p -values)

Additional analyses were conducted to examine potential differences in experiences of nursing incivility and stress among staff nurses, head nurses, and supervisors. One-way ANOVA tests revealed significant differences in NIS scores across nursing roles [F(2, 286) = 5.67, p  = 0.004]. Post-hoc comparisons using Tukey’s HSD test indicated that staff nurses (M = 48.3, SD = 18.6) reported significantly higher levels of incivility compared to supervisors (M = 39.5, SD = 16.2, p  = 0.003). However, no significant differences were found in PSS scores across nursing roles [F(2, 286) = 1.45, p  = 0.236].

The additional analyses revealed significant differences in Nursing Incivility Scale (NIS) scores across nursing roles [F(2, 286) = 5.67, p  = 0.004], with staff nurses (M = 48.3, SD = 18.6) reporting significantly higher levels of incivility compared to supervisors (M = 39.5, SD = 16.2, p  = 0.003). To account for the potential influence of nursing role on the overall results, we included it as a covariate in subsequent regression analyses.

To further examine the robustness of our findings, we conducted a sensitivity analysis by removing head nurses and supervisors from the sample and re-running the analyses with only staff nurses. The results remained consistent with the original findings, suggesting that the observed relationships between nursing incivility, nurse stress, patient engagement, and health outcomes were not unduly influenced by the inclusion of head nurses and supervisors in the sample.

This cross-sectional study examined the relationships between perceived nursing incivility, nurse stress levels, patient engagement in care, and patient health outcomes. The findings reveal a multifaceted relationship where nursing incivility is directly detrimental to nurses’ well-being and indirectly affects patient outcomes through the mediating effects of nurse stress and patient engagement.

The positive correlation between nursing incivility and nurse stress aligns with previous research indicating that workplace incivility can lead to negative psychological outcomes and job dissatisfaction [ 71 , 72 , 73 , 74 , 75 ]. The findings here extend this understanding by quantifying the correlation and delineating the impact of different levels of incivility.

In contrast, some studies, such as [ 6 , 76 ], have suggested that certain coping mechanisms and organizational cultures can mitigate the impact of incivility on stress. However, this study highlights the widespread nature of incivility in nursing, suggesting that such coping strategies may not be sufficient in the face of severe or persistent incivility. The inverse relationship between nurse stress and patient engagement supports the notion that stressed nurses may be less able to effectively engage with patients, aligning with research [ 51 ], which showed that nurse burnout could lead to decreased quality of patient care. Conversely, a study [ 52 ] found that certain aspects of nurse engagement, like job satisfaction, could buffer the impact of stress on patient care. However, this study suggests that the stress level resulting from incivility can override such positive aspects of engagement.

The negative impact of nursing incivility on patient health outcomes, evidenced by increased readmission rates within 30 days and lower patient satisfaction scores, is consistent with previous findings [ 6 , 76 ]. This reinforces the idea that the nursing work environment, including the presence or absence of incivility, can directly influence patient outcomes such as readmission rates and satisfaction scores, which were measured at the 30-day mark in our study.

However, research [ 12 , 28 ] argued that the impact of the nursing work environment on patient outcomes is often indirect and moderated by other factors. This study refines this perspective by demonstrating a direct correlation, suggesting that the impact of incivility is immediate and significant [ 57 , 58 , 59 ]. underscore incivility as a significant workplace stressor that nurses face that can adversely affect their well-being. The severity analysis further highlights that a concerning 25.6% of nurses report experiencing severe incivility, while 43.3% encounter moderate levels. Such widespread uncivil behaviors from colleagues, supervisors, physicians, and patients create stressful work environments that diminish the ability of nurses to perform effectively [ 11 ].

However, contrary to some studies [ 8 , 77 ], our mediation analysis reveals only a moderate total effect size (β = -0.05) of nursing incivility on patient outcomes. This discrepancy could reflect cultural specificities within Saudi hospitals that shape inter-action dynamics differently than their western counterparts. However, the negative association remains noteworthy. In addition, stress exhibits an unexpected positive association with patient outcomes. This surprising finding warrants a deeper ethnographic investigation to elucidate the complex stress and coping mechanisms of nurses within the hospitals sampled that unexpectedly improved patient care. Critically, patient engagement registers the strongest impact on health outcomes (β = 0.52) [ 2 , 78 , 79 ]. Interestingly, 63.7% of patients fall under the ‘Healthy’ category, although 33.6% manage chronic conditions. This breakdown provides a favourable foundation for boosting patient activation efforts. However, the correlation and regression analyses reveal that improvements in workplace conditions for nurses could further improve patient engagement and care quality.

The study findings on the mediator effect of nurse stress, linking nursing incivility with poorer patient outcomes, add a new dimension to the existing literature. This aligns with the work of [ 12 ], who emphasized the importance of the emotional well-being of healthcare providers in ensuring patient safety. This contrasts with some views like those presented [ 6 ], who posited that organizational factors play a more substantial role in mediating the impact of incivility on outcomes. Our study suggests that individual stress levels are equally, if not more, critical in this context. The serial mediation analysis reveals the pathway from nursing incivility through nurse stress to patient engagement and outcomes, and it presents a comprehensive model that integrates various aspects of the nursing environment. This model is supported by research [ 6 ], which also emphasises the cascading effects of workplace dynamics on patient care. However, this finding challenges the argument [ 22 ] that the primary impact of the nursing environment on patient outcomes is through organizational efficiency rather than staff well-being.

Conclusions

This cross-sectional study conducted in four Saudi Arabian hospitals examined the complex relationships between nursing incivility, nurse stress, patient engagement, and health outcomes. The findings underscore the widespread impact of nursing incivility, which adversely affects nurse well-being and, through increased nurse stress, indirectly influences patient outcomes. Specifically, our analyzes demonstrate that nursing incivility is related to higher readmission rates at 30 days and lower patient satisfaction scores, providing concrete examples of its negative ramifications.

Our empirical evidence, derived from validated scales and robust multivariate regression analyzes, confirms that nursing incivility increases stress levels among nurses, corroborating existing literature that identifies uncivil behavior as a significant workplace stressor. In particular, more than two thirds of the participants reported experiencing moderate to severe levels of incivility, highlighting the widespread nature of this issue within healthcare settings. Theoretically, this research enriches the current understanding of the impacts of nursing incivility by situating them within a comprehensive framework that includes both direct and indirect effects on health outcomes.

Practically, the study lays a solid foundation for developing targeted interventions aimed at cultivating more respectful and collaborative nursing environments. Such interventions could include training programs focused on conflict resolution and stress management, which are critical to mitigating the effects of incivility and improving overall quality of care. Future research should explore the longitudinal effects of nursing incivility to better understand the causality and persistence of its impacts. Additionally, investigating the role of organizational factors such as leadership styles and workplace culture in modifying or exacerbating the effects of incivility could provide deeper insight into effective strategies to improve nurse and patient outcomes.

Limitations

The limitations of the study provide avenues for further research. Longitudinal approaches could establish causal claims more firmly. A longitudinal design that follows participants over an extended period could provide more insights into the temporal aspects of these relationships and strengthen our understanding of the causality between nursing incivility, nurse stress, patient engagement, and health outcomes.

Another limitation refers to the representativeness of the sample. Although efforts were made to ensure diversity through a combination of random sampling and voluntary participation, the generalizability of the findings may be limited. The study was conducted in four public hospitals in the northwest region of Saudi Arabia, and the unique cultural and socioeconomic characteristics of this region should be considered when interpreting the results and their implications for nursing practice and patient care. Future studies could explore these relationships in different healthcare settings, regions, and cultural contexts to assess the generalizability of the findings.

Furthermore, the current study did not investigate the role of organizational factors in contributing to nursing incivility, stress, and patient outcomes. While focusing on individual-level variables provides valuable insights, a more comprehensive understanding would require the inclusion of organizational factors such as leadership, communication, and workplace culture. Future research should aim to incorporate these measures to gain a holistic perspective on the relationships between nursing incivility, stress, and patient outcomes.

Practical implications and future directions

The findings of this study have significant practical implications, providing an evidence base for healthcare institutions to develop systemic strategies to address nursing incivility and its cascading impacts. Interventions should focus on cultivating positive workplace cultures, deescalating incivility through protocols, facilitating team building, and implementing self-care training. Regarding patients, patient education programs to promote activation and specialist referrals to improve adherence appear prudent. Future studies could build on these findings by testing such interventions through experimental or action methodologies to quantify long-term results.

Future research could also explore the role of organizational factors in contributing to nursing incivility, stress, and patient outcomes. Investigating aspects such as leadership styles, communication patterns, and workplace culture could provide valuable insights into the systemic elements that shape the dynamics of nursing incivility and its consequences. By examining the interaction between individual and organizational factors, future studies could offer a more holistic understanding of the complex relationships at play and inform the development of targeted interventions at the individual and organizational levels.

Related research might explore subgroup differences in perceptions by age or unit type or investigate relationships in private-sector hospitals compared to these public institutions. Furthermore, examining the broader organizational impact of nursing incivilities, such as its effects on team dynamics, staff turnover, and general healthcare culture, would contribute to a more comprehensive understanding of the phenomenon. Assessing the economic implications of incivility, including costs associated with staff replacement and lost productivity, could highlight the financial burden on healthcare organizations and inform strategic decisions to address this issue. Future studies could also employ qualitative methods to gain deeper insights into nurses’ experiences of incivility and its impact on their well-being and professional practice. As the Saudi healthcare system continues to evolve, mitigating workplace mistreatment and nurturing patient engagement will only grow in importance, making this study highly relevant.

Availability of data and materials

Data will be available upon request.

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This study was carried out with the approval of the General Directorate of Health Affairs, Hail Healthy Cluster, Hail Region / IRB Registration Number with KACST, KSA: H-11–08 L-074 / IRB log number 2023-66.

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The Deanship of Scientific Research funded this work at Jouf University through the Fast-Trace Research Funding Program.

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O.M.E.R. contributed to the conception and design of the study, recruited patients, collected and analyzed data, interpreted the results, and drafted the manuscript. N.A.A. contributed to the study design, data collection, result analysis and interpretation, and manuscript review. M.A. contributed to patient recruitment, data collection, and manuscript review. All authors approved the final version of the manuscript.

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Alsadaan, N., Ramadan, O.M.E. & Alqahtani, M. From incivility to outcomes: tracing the effects of nursing incivility on nurse well-being, patient engagement, and health outcomes. BMC Nurs 23 , 325 (2024). https://doi.org/10.1186/s12912-024-01996-9

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• 12 May 2024

Is the Internet bad for you? Huge study reveals surprise effect on well-being

• Carissa Wong

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People who had access to the Internet scored higher on measures of life satisfaction in a global survey. Credit: Ute Grabowsky/Photothek via Getty

A global, 16-year study 1 of 2.4 million people has found that Internet use might boost measures of well-being, such as life satisfaction and sense of purpose — challenging the commonly held idea that Internet use has negative effects on people’s welfare.

US TikTok ban: how the looming restriction is affecting scientists on the app

“It’s an important piece of the puzzle on digital-media use and mental health,” says psychologist Markus Appel at the University of Würzburg in Germany. “If social media and Internet and mobile-phone use is really such a devastating force in our society, we should see it on this bird’s-eye view [study] — but we don’t.” Such concerns are typically related to behaviours linked to social-media use, such as cyberbullying, social-media addiction and body-image issues. But the best studies have so far shown small negative effects, if any 2 , 3 , of Internet use on well-being, says Appel.

The authors of the latest study, published on 13 May in Technology, Mind and Behaviour , sought to capture a more global picture of the Internet’s effects than did previous research. “While the Internet is global, the study of it is not,” said Andrew Przybylski, a researcher at the University of Oxford, UK, who studies how technology affects well-being, in a press briefing on 9 May. “More than 90% of data sets come from a handful of English-speaking countries” that are mostly in the global north, he said. Previous studies have also focused on young people, he added.

To address this research gap, Pryzbylski and his colleagues analysed data on how Internet access was related to eight measures of well-being from the Gallup World Poll , conducted by analytics company Gallup, based in Washington DC. The data were collected annually from 2006 to 2021 from 1,000 people, aged 15 and above, in 168 countries, through phone or in-person interviews. The researchers controlled for factors that might affect Internet use and welfare, including income level, employment status, education level and health problems.

Like a walk in nature

The team found that, on average, people who had access to the Internet scored 8% higher on measures of life satisfaction, positive experiences and contentment with their social life, compared with people who lacked web access. Online activities can help people to learn new things and make friends, and this could contribute to the beneficial effects, suggests Appel.

The positive effect is similar to the well-being benefit associated with taking a walk in nature, says Przybylski.

However, women aged 15–24 who reported having used the Internet in the past week were, on average, less happy with the place they live, compared with people who didn’t use the web. This could be because people who do not feel welcome in their community spend more time online, said Przybylski. Further studies are needed to determine whether links between Internet use and well-being are causal or merely associations, he added.

The study comes at a time of discussion around the regulation of Internet and social-media use , especially among young people. “The study cannot contribute to the recent debate on whether or not social-media use is harmful, or whether or not smartphones should be banned at schools,” because the study was not designed to answer these questions, says Tobias Dienlin, who studies how social media affects well-being at the University of Vienna. “Different channels and uses of the Internet have vastly different effects on well-being outcomes,” he says.

doi: https://doi.org/10.1038/d41586-024-01410-z

Vuorre, M. & Przybylski, A. K. Technol. Mind Behav . https://doi.org/10.1037/tmb0000127 (2024).

Article   Google Scholar  

Heffer, T. et al. Clin. Psychol. Sci. 7 , 462–470 (2018).

Coyne, S. M., Rogers, A. A., Zurcher, J. D., Stockdale, L. & Booth, M. Comput. Hum. Behav . 104 , 106160 (2020).

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