Associations Between Occupational and Leisure-Time Physical Activity With Employee Stress,Burnout and Well-Being Among Healthcare Industry Workers

Abstract

Purpose:

Examine the associations of occupational and leisure-time physical activity with job stress, burnout, and well-being among healthcare industry workers.

Design:

Quantitative; cross-sectional.

Setting:

Healthcare Industry.

Sample:

US Amazon Mechanical Turk participants (n = 550) employed in the healthcare industry, worked 35 hours or more per week, had ≥ 1 supervisor and ≥ 1 co-worker, and were ≥ 18 years old.

Measures:

Self-reported measures of occupational physical activity (OPA) and leisure-time physical activity (LTPA), employee well-being, job stress, and burnout operationalized as exhaustion and disengagement.

Analysis:

Associations between OPA and LTPA with employee well-being, job stress, exhaustion and disengagement were assessed with separate multiple linear regression models.

Results:

OPA had positive significant associations with job stress (β = 0.10, P value = .003) and exhaustion (β = 0.21, P value < .0001). No significant associations were found between OPA with other psychological outcomes. A significant inverse association was found between LTPA and exhaustion (β = −0.04, P value = .007).

Conclusion:

In a sample of U.S. health care workers, and consistent with prior epidemiological studies, greater LTPA was associated with lower feelings of exhaustion. In contrast, health care workers with greater OPA reported higher perceptions of job stress and exhaustion. The findings underscore the need for more research aimed at understanding relationships between OPA and psychological health among healthcare workers.

Keywords

workplace health employee physical activity burnout healthcare

Purpose

Adults in the United States spend most of their waking time at work, averaging approximately 8.5 hours per day.¹ Nearly two-thirds of adults say work is a significant source of stress.² Work stressors can include fear of layoffs, challenging performance expectations, and increased workload.

Healthcare workers are particularly at risk for stress and burnout due to responsibilities to meet customer demands and ensure patient safety. Long working hours and a high prevalence of shift work also increases the risk of stress, burnout, and emotional exhaustion.^3,4 Between 2011 and 2014, physician burnout has increased.⁵ Clinician burnout has been found to be associated with suboptimal patient care practices, medical errors, decreased team work and longer than normal patient recovery times.^5,6

There is a need to better understand the consequences of stress among healthcare workers and provide them with tools to adequately manage stress and burnout. Unfortunately, many people ineffectively manage their stress levels.² Results of the Workplace Health in America Survey indicated the number of stress management programs remained unchanged between 2004 and 2017 while nutrition and physical activity programs have increased in workplaces.⁷ For health workers, use of stress management techniques is associated with better job satisfaction and productivity, as well as improved patient outcomes.^5,6

Physical activity is one coping mechanism known to mitigate stress and burnout effects.^8,9 Increasing physical activity is consistently related to improved well-being and lower perceptions of job stress.¹⁰ For example, persistently inactive employees have 4 times higher job stress than persistently active employees.¹⁰ Although there have been studies illustrating the benefits of physical activity on mental health outcomes, the importance of the domain of activity has been less studied. The majority of studies measure leisure-time physical activity only.¹¹ Leisure-time activity is defined as exercise and physically active hobbies done in one’s free-time.¹² Leisure-time physical activity can be a structured program, participation in sports, or lifestyle tasks such as gardening.¹² Occupational physical activity, however, is completed during the performance of a job.¹² Occupational physical activity can range from walking performed by restaurant wait staff to heavy lifting and other types of manual labor such as farm work.¹² Healthcare workers also perform a range of physical activities from patient transport to walking from a work station to patient rooms.

The health effects of occupational activity are mixed. Gay and colleagues found increased occupational physical activity was associated with a decrease in body fat percentage and waist circumference.¹³ Conversely, increased occupational physical activity also has been linked with long-term sickness and job absence among workers.¹⁴ However, this later finding typically stems from samples with blue collar jobs or those with low socioeconomic status who report relatively little leisure-time physical activity.^15,16 These conflicting results highlight the importance of exploring occupational physical activity in more detail, especially among white collar workers.

This study examined if healthcare workers’ physical activity, both leisure-time and from work, have associations with job stress, employee well-being, and burnout. It was hypothesized that both occupational and leisure-time physical activity would show associations with job stress, employee well-being, and burnout suggesting psychological benefits of being physically active.

Methods

Design

The study design was cross-sectional and included 557 Amazon Mechanical Turk participants who reported working in the healthcare industry. The final sample with complete data included 550 participants due to 7 participants failing an attention-checking item. Amazon.com’s Mechanical Turk, an internet-based service where human intelligence tasks (HITs) are completed by workers for compensation, was used to collect survey responses.¹⁷ Individuals sign up to complete tasks such as taking surveys, image filtering, and categorization (https://www.mturk.com/worker/help). Interested individuals were recruited via the MTurk site (www.MTurk.com). Amazon MTurk Master Workers is a type of MTurk worker who has consistently demonstrated a high degree of success in performing a wide range of HITs through the platform (https://www.mturk.com/worker/help). However, Amazon MTurk Master Workers are more likely to be female, older, and more experienced in their assignments than regular workers, and were excluded to minimize potential sampling bias.¹⁸

Sample

MTurk users were screened for inclusion criteria prior to taking the survey. Participants were workers who reported being employed in the United States in the healthcare industry, worked 35 hours or more per week (considered full-time in this study),¹⁹ had ≥ 1 supervisor and ≥ 1 co-worker, and were ≥ 18 years old.

Power calculation

The analyses presented here are from a larger study where the sample size was determined a priori for a different aim. A post-hoc sample size calculation, using GPower 3.1,²⁰ indicates 0.998 power to detect a 0.05 R² effect size with alpha set to 0.05.

Measures

Demographics

Participants self-reported common demographic, socioeconomic, and anthropometric determinants of physical activity, ^21,22 age in years, sex, race/ethnicity, education level (eg, Bachelor’s Degree, Postgraduate work or degree), household income, marital status, supervisor responsibility, and job title. Job title was self-reported by participants and classified by 2 independent coders into 44 different codes using the US Census Bureau 2017 occupational code list.²³ Discrepancies were assessed by a third coder. Participants were further classified as direct patient care and non-direct patient care workers. Direct patient care workers included physicians, nurses, technologists, whereas non-direct patient care workers included research assistants, coders, billers, and analysts. Participants also self-reported height (inches) and weight (pounds) to calculate Body Mass Index (BMI)

Physical activity

Leisure-time activity

Leisure-time activity is defined as exercise, sports, and physically active hobbies done in one’s leisure-time.¹² The Godin Leisure-Time Exercise Questionnaire was used to measure leisure-time physical activity of the employee.²⁴ Respondents indicated how many times per week they engaged in strenuous, moderate, and mild inducing activity and then reported the average number of minutes exercised at that intensity.²⁵ Weekly frequencies and minutes of strenuous, moderate, and light activities were multiplied by 9, 5, and 3, respectively to estimate total weekly MET-hours of activity (Metabolic Equivalent of Task). Moderate-to-vigorous intensity activity (MVPA) was calculated using the same formula but omitting light-intensity activities. Total weekly leisure activity and MVPA were transformed to meet the assumption of normality.

Occupational activity

The work index subscale from the Baecke Physical Activity Questionnaire was used to measure occupational physical activity²⁶ with 8 items that include self-reported work intensity value. The first item asks, “What level of physical activity does your job involve?” with the following point value: Low activity = 1 point, Moderate activity = 3 points, High activity = 5 points.²⁶ Items 2 to 7 included questions regarding work-related sitting, standing, walking, heavy lifting, tiredness, and sweating with response options on a 5-point scale ranging from “Never” to “Always.” The last question included one-item comparison with others of the same age ranging from “Much Heavier” to “Much Lighter.” The calculation for the mean work activity score was: (Item 1 + (6 − Item 2) + Item 3 + Item 4 + Item 5 + Item 6 + Item 7 + Item 8)/8.²⁶ In the current study, the Cronbach α coefficient was 0.88.

Missing values

For occupational physical activity, the sample had 56 missing data on one item for the work index subscale from the Baecke Physical Activity questionnaire due to survey formatting. A dummy variable was created that classified participants as either having complete data (all 8 items of the Baecke Physical Activity Questionnaire) or missing the one item (7 of 8 items). An independent samples t test was performed on the mean work activity score to compare participants with complete Baecke Physical Activity Questionnaire data with those who had the 7 of 8 items. Participants who had missing data for the one item did not differ significantly in their mean work activity score relative to participants responded to all 8 items (P value = .5566). For the 56 participants who were missing the one item, the mean score for the 7 items they did have were calculated.

Job stress

Job stress can be conceptualized as employees’ perceptions and reactions to stressors at work. Cohen’s Perceived Stress Scale²⁷ is widely used as an index of perceptions of stress, assessing the degree to which situations are stressful or uncontrollable as well as current levels of stress. Wilson et al adapted the scale for the workplace setting when testing a model of healthy work organization in retail employees.²⁸ A total of 13 items on past month stressors were included in this job stress scale, with response options scored from 0 to 4 (Never, Almost Never, Sometimes, Fairly Often, Very Often). The mean score was used for analyses. In the current study, the Cronbach α coefficient was 0.87.

Job burnout—Exhaustion and disengagement

Burnout can be conceptualized an employees’ work-related mental exhaustion and disengagement. The exhaustion subscale and disengagement subscale from the Oldenburg Burnout Inventory (OLBI) was used to measure burnout over the past month.²⁹ There are 8 items each for the exhaustion and disengagement subscales. Items were scored from 1 to 6 (Strongly Disagree, Disagree, Somewhat Disagree, Somewhat Agree, Agree, Strongly Agree). Separate mean scores were calculated for exhaustion and disengagement and used for analysis. Cronbach α coefficients were 0.85 for exhaustion and 0.84 for disengagement.

Employee well-being

For employee well-being, the six-item subscale specific to workplace well-being was used from the Employee Well-Being scale.³⁰ The Life Well-Being and Psychological Well-Being subscales were not pertinent to the study and therefore were excluded. Items were scored from 1 to 6 (Strongly Disagree, Disagree, Somewhat Disagree, Somewhat Agree, Agree, Strongly Agree). The mean score for employee well-being was calculated. In the current study, the Cronbach alpha coefficient for employee well-being subscale was 0.94.

Analysis

Statistical analyses were conducted with SAS v 9.4 (Cary, North Carolina). Job Stress, Employee Well-being, Exhaustion, Disengagement, Age, and BMI variables approximated normal distributions. Total weekly leisure activity and MVPA leisure activity scores were transformed (square root) to meet the assumption of normality.

Frequencies and proportions were calculated for sex, race/ethnicity, education, income, marital status, supervisory status, and job title. Means and standard deviations (SDs) were calculated for age, BMI, leisure-time physical activity, occupational physical activity, job stress, burnout, and employee well-being. Pearson correlations were calculated for occupational and leisure-time physical activity with job stress, exhaustion and disengagement, and employee well-being. Independent samples t tests or separate one-way ANOVAs were conducted to assess differences in employee outcomes by job title, supervisory status, sex, income, marital status, education, and race/ethnicity, potential covariates associated with employee outcomes.^31

-35 Separate multiple linear regression models were conducted to assess the associations of occupational and leisure-time physical activity with job stress, exhaustion and disengagement, and employee well-being adjusting for covariates identified in bivariate analyses. Although each covariate was not consistently associated with each outcome, the group of covariates was used for all outcomes for consistency. Previous studies have controlled for different covariates related to physical activity.³⁶ The interaction of occupational physical activity and leisure-time physical activity was also tested in the regression models as a post hoc consideration for the purpose of exploring how leisure-time physical activity and occupational physical activity were interrelated with employee outcomes based on recent evidence suggesting compensation effects of high amounts of occupational physical activity on leisure-time physical activity.^37
-39 Interaction effects were analyzed post hoc by comparing adjusted mean outcomes computed from the regression equations using ± 1 SD on the leisure-time physical activity and occupational physical activity measures creating 4 subgroups: low leisure-time physical activity and low occupational physical activity; low leisure-time physical activity and high occupational physical activity; high leisure-time physical activity and low occupational physical activity; and high leisure-time physical activity and high occupational physical activity.

Results

The study sample included a total of 550 participants from the healthcare labor sector with most of the sample being female (76.2%). Sample characteristics can be found in Table 1. The mean BMI for participants was 28.48 (SD = 7.26), and mean age was 35.72 (SD = 9.56) years. Mean levels for occupational physical activity were 2.59 (SD = 0.86) and total leisure-time physical activity (MET hours/week [Metabolic Equivalent of Task]) (square-root transformed mean) was 4.74 (SD = 2.78) (Table 1).

Table 1.

Participant Characteristics.

	Mean	SD	Range
Age, years	35.72	9.56	21.00-74.00
Body Mass Index (BMI; kg/m²) (n = 548)	28.48	7.26	16.64-60.40
Employee Well-Being	4.37	1.16	1.00-6.00
Job stress (n = 542)	1.58	0.60	0.08-3.92
Exhaustion	3.23	0.93	1.00-5.63
Disengagement (n = 547)	3.31	0.98	1.00-6.00
Occupational Physical Activity	2.59	0.86	1.00-5.00
Total Leisure-Time Physical Activity (MET hrs/week; square root) (n = 544)	4.74	2.78	0.00-18.28
MVPA Leisure-Time Physical Activity (MET hrs/week; square root) (n = 545)	3.93	2.83	0.00-16.73
Total Leisure-Time Physical Activity (MET hrs/week) (n = 544)	30.14	34.80	0.00-334.00
MVPA Leisure-Time Physical Activity (MET hrs/week) (n = 545)	23.45	29.43	0.00-280.00
	n	%
Sex
Female	419	76.2
Male	131	23.8
Race/ethnicity
White/Caucasian	422	77.0
Black or African American	53	9.7
Asian	26	4.7
Hispanic	19	3.5
Other (Native American, Multi-racial, Other)	28	5.1
Education level
Some High School or High school graduate/GED	23	4.2
Associate Degree, some college, or technical/vocational training	205	37.3
Bachelor’s Degree	172	31.3
Postgraduate work or Postgraduate Degree	150	27.3
Income level (household)
$0-20,000	13	2.4
$20,000-40,000	104	18.9
$40,000-60,000	135	24.6
$60,000-80,000	119	21.6
$80,000+	179	32.6
	n	%
Marital Status
Divorce/Separated	62	11.3
Married/Living with Partner	355	64.7
Single	132	24.0
Job Title
Non-Direct Patient Care Workers	306	55.6
Direct Patient Care Workers	244	44.4
Supervisory Status
No	358	65.21
Yes	191	34.79

Unless otherwise stated, n = 550.

MET = Metabolic Equivalent of Task.

Table 2 shows Pearson correlation coefficients for age, BMI, job stress, employee well-being, exhaustion, disengagement, and leisure-time physical activity variables. Occupational physical activity was found to be significantly and positively associated with employee well-being (r = 0.08, P < .05), exhaustion (r = 0.18, P < .0001), and job stress (r = 0.13, P < .01). Total leisure-time physical activity was inversely associated with exhaustion (r = −0.12, P < .01), but not other worker health outcomes. Results of the independent samples t tests and one-way ANOVAs between possible covariates and employee outcomes are provided in supplemental files. Briefly, there were statistically significant associations between job title, sex, income, and marital status with one or more employee outcomes.

Table 2.

Pearson Correlation Analysis Among Continuous Study Variables.

	1.	2.	3.	4.	5.	6.	7.	8.	9.
1. Employee Well-Being	1.00
2. Disengagement	−0.81^d	1.00
3. Exhaustion	−0.54^d	0.67^d	1.00
4. Job Stress	−0.46^d	0.57^d	0.74^d	1.00
5. Total Leisure-Time Physical Activity (square root)	0.04	−0.02	−0.12^b	−0.03	1.00
6. MVPA Leisure-Time Physical Activity (MET hrs/week) (square root)	0.06	−0.05	−0.15^c	−0.06	0.93^d	1.00
7. Occupational Physical Activity	0.08^a	0.01	0.18^d	0.13^b	0.08	0.03	1.00
8. BMI	0.05	−0.02	0.03	−0.03	−0.21^d	−0.22 ^d	−0.06	1.00
9. Age	0.02	−0.06	−0.09^a	−0.07	−0.02	−0.01	−0.05	0.17^c	1.00

^a P < .05; ^b P < .01; ^c P < .001; ^d P < .0001.

BMI = Body Mass Index; MET = Metabolic Equivalent of Task.

The interaction of occupational physical activity and leisure-time physical activity was also tested in the regression models (Table 3). There was a statistically significant interaction for the stress and exhaustion outcomes. Using ± one standard deviation, employees with low occupational physical activity (−1 SD) and high leisure-time physical activity (+1 SD) have a stress score of 1.76; employees with high occupational physical activity and high leisure-time physical activity have a stress score of 2.02; employees with low leisure-time physical activity and low occupational physical activity have a stress score of 1.90 and employees with low leisure-time physical activity and high occupational physical activity have a stress score of 1.94. The differences between groups were small given the potential range of scores. For exhaustion, employees with low occupational physical activity and high leisure-time physical activity have an exhaustion score of 3.16; employees with high occupational physical activity and high leisure-time physical activity have an exhaustion score of 3.63; employees with low leisure-time physical activity and low occupational physical activity have an exhaustion score of 3.51 and employees with low leisure-time physical activity and high occupational physical activity have an exhaustion score of 3.72. The differences between groups were also relatively small given the possible range of exhaustion scores. The interaction term was not statistically significant for wellbeing or disengagement.

Table 3.

Regression Analysis Summary for Employee Well-Being, Job Stress, Exhaustion and Disengagement.

Variable		Employeewell-being(R² = 0.04)				Job stress(R² = 0.06)				Exhaustion(R² = 0.11)				Disengagement(R² = 0.04)
Variable		B	SE	t	P-Value	B	SE	t	P-Value	B	SE	T	P-Value	B	SE	t	P-Value
Occupational Physical Activity		0.07	0.07	1.08	0.282	0.10	0.03	2.99	0.003	0.21	0.05	4.10	<.0001	0.05	0.06	0.95	0.341
Total Leisure-Time Physical Activity (square root)		0.02	0.02	1.22	0.223	0.00	0.01	−0.47	0.641	−0.04	0.01	−2.73	0.007	0.00	0.02	−0.49	0.624
Occupational Physical Activity * Total Leisure-Time Physical Activity (square root)		0.00	0.02	−0.06	0.9551	0.02	0.01	2.58	0.010	0.03	0.01	2.02	0.0436	0.00	0.02	0.31	0.759
Age, years		0.00	0.01	−0.11	0.910	0.00	0.00	−1.16	0.246	−0.01	0.00	−1.25	0.211	0.00	0.00	−0.45	0.651
BMI, kg/m²		0.01	0.01	1.45	0.149	0.00	0.00	−0.65	0.515	0.00	0.01	0.27	0.789	0.00	0.01	−0.48	0.631
Sexref = male	(Female vs Male)	0.19	0.12	1.61	0.108	0.15	0.06	2.46	0.014	0.22	0.09	2.40	0.017	−0.13	0.10	−1.29	0.197
Income, ref >$80,000	($0-20,000)	0.14	0.34	0.40	0.693	−0.05	0.18	−0.28	0.777	0.39	0.27	1.48	0.139	0.23	0.29	0.79	0.430
	($20,000-40,000)	−0.11	0.16	−0.71	0.478	−0.01	0.08	−0.15	0.878	0.06	0.12	0.48	0.633	0.14	0.14	1.06	0.290
	($40,000-60,000)	0.04	0.14	0.31	0.755	0.05	0.07	0.70	0.487	0.09	0.11	0.83	0.407	0.09	0.12	0.72	0.470
	($60,000-80,000)	0.23	0.14	1.65	0.100	−0.20	0.07	−2.75	0.006	−0.24	0.11	−2.25	0.025	−0.23	0.12	−1.91	0.056
Marital Status, ref = Single	(Divorced/Separated/Widowed)	0.11	0.20	0.56	0.576	−0.04	0.10	−0.39	0.693	−0.11	0.16	−0.69	0.489	−0.13	0.17	−0.76	0.445
	Married/Living with Partner	0.20	0.13	1.55	0.122	−0.01	0.07	−0.18	0.861	−0.17	0.10	−1.67	0.095	−0.13	0.11	−1.19	0.233
Non-Direct Patient Care Workers vs Direct Care Workers		−0.21	0.11	−1.90	0.058	0.07	0.06	1.18	0.239	0.14	0.09	1.63	0.103	0.18	0.09	1.96	0.051

Models were adjusted for age, sex, income, marital status, BMI, and job title.

BMI = Body Mass Index.

Discussion

This study examined occupational and leisure-time physical activity associations with employee well-being, job stress, exhaustion, and disengagement among workers in the healthcare industry. Greater total leisure-time physical activity was correlated with less reported exhaustion. Whether feeling less exhausted prompted more leisure-time physical activity or vice-versa is unknown based on our study design. Regardless, there is a substantial body of epidemiological literature documenting a consistent inverse association between leisure-time physical activity and feelings of fatigue.^40
-42 The leisure-time activity finding is similar to known evidence linking leisure-time physical activity with positive mental health outcomes related to exhaustion such as anxiety, depression and sleep.^8,9 In this study, the hypothesis was that higher occupational activity would be associated with better worker psychological outcomes because healthcare workers engage in primarily light-intensity physical activity on the job,⁴³ not the types of activity (eg, heavy industrial work) associated with poorer health.^14,15 However, contrary to the hypothesis, greater occupational physical activity was associated with greater job stress and exhaustion. The present occupational physical activity results are similar to past research, showing associations between occupational physical activity with poorer health outcomes.^14,36,44

The interaction of occupational physical activity and leisure-time physical activity were also tested in the regression models which resulted in a statistically significant interaction for the stress and exhaustion outcomes. These results suggest occupational physical activity is moderating the effect of leisure-time physical activity, such that there is a consistent main effect of leisure-time physical activity being associated with significantly less stress and exhaustion, but the associations between leisure-time physical activity and stress and exhaustion disappear when employees engage in a lot of occupational physical activity. Previous studies have shown greater total volume of activity is associated with greater stress and exhaustion.⁴⁵ The differences in scores across high and low occupational physical activity were not large, and the interaction was not significant for disengagement or wellbeing. Further study is warranted to elucidate the intersection of occupational and leisure-time physical activity in addition to understanding interactions between other covariates.

Duration of occupational activity is not measured in the Baecke scale, nor in other self-reported measures of occupational activity.^14,46 There are clear associations between duration of leisure-time physical activity and exhaustion, where greater activity participation is correlated with lower perceived exhaustion or fatigue.^40
-42 This lack of detail about occupational physical activity duration may create less variation in occupational physical activity, relative to the variation in leisure-time activity (eg, Table 1), leading to the mixed results. Alternatively, expectations of the health effects of exercise can have powerful effects on health outcomes, thus the different findings for occupational versus leisure-time physical activity may have been the result of different expectations.⁴⁷ Many people expect leisure-time physical activity to improve psychological well-being and other health outcomes,⁴⁸ but there is evidence that people lack those same expectations about occupational physical activity.⁴⁹

Also, as conceptualized in work stress models, employees with low control and high demands at work engage in less leisure-time physical activity than employees that report high control and low demands at work.^50,51 These studies support that employees with lower control and high demands may have a different association between activity and health outcomes. A study of shift-work nurses found that lower fatigue was associated with greater perceived rewards and control over work and not associated with physical demands or energy expended.⁵² Occupational characteristics in the work environment such as mental and physical demands are typical for healthcare workers that provide direct patient care. These factors related to demand and control within job roles and responsibilities, inherent to the occupational activity measure, may be confounders of the associations found between occupational physical activity and employee outcomes in the sample.

Lastly, there are other factors in the work environment that may contribute to the psychological health of workers, evidenced by the small amount of variance explained in the regression models. Factors may include co-worker interactions, job roles and duties, workplace culture and practices, autonomy, hierarchies within the hospital system, perceived and actual workplace support, organizational structures, and workflow.^14,53 Important work environmental factors, such as participation in shift work, predisposes workers to increased cardiometabolic risk and psychological strain.^6,52

Limitations

There are several limitations in the present study. First, the study design was cross-sectional indicating only associated effects and not causal implications between our independent and dependent variables. The surveys were self-report and recall bias may have taken place among the participants when reporting frequency, intensity or duration of physical activity or the intensity of the perceptions of psychological status. Additionally, MTurk respondents are typically younger and more educated, and therefore may not be representative of the US working population.⁵⁴

Conclusion

In a sample of U.S. health care workers, and consistent with prior epidemiological studies, greater leisure-time physical activity was associated with lower feelings of exhaustion. In contrast, health care workers with greater occupational activity reported higher perceptions of job stress and exhaustion. The findings underscore the need for more research aimed at understanding relationships between occupational physical activity and psychological health among healthcare workers.

So What?

What is already known on this topic?

Existing literature has examined the associations between physical activity and employee outcomes, but less is known about domain of physical activity.

What does this article add?

Among healthcare industry workers, occupational physical activity was shown to be associated with job stress and exhaustion. Leisure-time physical activity was inversely related to exhaustion.

What are the implications for health promotion practice or research?

Workplace interventions should consider other environmental factors in future analyses as well as incorporate leisure-time physical activity interventions that can help mitigate job stress and burnout in this population.

Supplemental Material

Supplemental Material, sj-docx-1-ahp-10.1177_08901171211011372 - Associations Between Occupational and Leisure-Time Physical Activity With Employee Stress, Burnout and Well-Being Among Healthcare Industry Workers

Supplemental Material, sj-docx-1-ahp-10.1177_08901171211011372 for Associations Between Occupational and Leisure-Time Physical Activity With Employee Stress, Burnout and Well-Being Among Healthcare Industry Workers by Marilyn Batan Wolff, Patrick J. O’Connor, Mark G. Wilson and Jennifer L. Gay in American Journal of Health Promotion

Supplemental Material

Supplemental Material, sj-docx-2-ahp-10.1177_08901171211011372 - Associations Between Occupational and Leisure-Time Physical Activity With Employee Stress, Burnout and Well-Being Among Healthcare Industry Workers

Supplemental Material, sj-docx-2-ahp-10.1177_08901171211011372 for Associations Between Occupational and Leisure-Time Physical Activity With Employee Stress, Burnout and Well-Being Among Healthcare Industry Workers by Marilyn Batan Wolff, Patrick J. O’Connor, Mark G. Wilson and Jennifer L. Gay in American Journal of Health Promotion

Footnotes

Authors’ Note

Marilyn Batan Wolff conceived of the study and was responsible for data collection. Marilyn Batan Wolff and Jennifer L. Gay conducted the analyses. Marilyn Batan Wolff, Jennifer L. Gay, Patrick J. O’Connor, and Mark G. Wilson contributed to the design, results interpretation, and writing and revision of the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was funded in part by a Ramsey Award from the University of Georgia. The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Marilyn Batan Wolff

Mark G. Wilson

Jennifer L. Gay

Supplemental Material

Supplemental material for this article is available online.

References

U.S. Department of Labor. Bureau of Labor Statistics. American Time Use Survey. Average hours employed people spent working on days worked by day of week, 2019 annual averages (Full-Time workers). Accessed November 20, 2019. https://www.bls.gov/news.release/pdf/atus.pdf

American Psychological Association. Stress in America 2020 : A National Mental Health Crisis. 2020. Accessed April 19, 2021. https://www.apa.org/news/press/releases/stress/2019/stress-america-2019.pdf

Dall’Ora

Griffiths

Ball

Simo

Aiken

. Association of 12 h shifts and nurses’ job satisfaction, burnout and intention to leave: findings from a cross-sectional study of 12 European countries. BMJ Open. 2015;5(9):e008331.

Spence Laschinger

Leiter

. The impact of nursing work environments on patient safety outcomes: the mediating role of burnout/engagement. J Nurs Adm. 2006;36(5):259–267.

Shanafelt

Hasan

Dyrbye

, et al. Changes in burnout and satisfaction with work-life balance in physicians and the general US working population between 2011 and 2014. Mayo Clin Proc. 2015;90(12):1600–1613.

Hall

Johnson

Watt

Tsipa

O’Connor

. Healthcare staff wellbeing, burnout, and patient safety: a systematic review. PLoS One. 2016;11(7):e0159015–e0159015.

Linnan

Cluff

Lang

Penne

Leff

. Results of the workplace health in America survey. Am J Health Promot. 2019;33(5):652–665.

Lindwall

Gerber

Jonsdottir

Borjesson

Ahlborg

Jr . The relationships of change in physical activity with change in depression, anxiety, and burnout: a longitudinal study of Swedish healthcare workers. Health Psychol. 2014;33(11):1309–1318.

Toker

Biron

. Job burnout and depression: unraveling their temporal relationship and considering the role of physical activity. J App Psychol. 2012;97(3):699–710.

10.

Yang

Telama

Hirvensalo

, et al. The benefits of sustained leisure-time physical activity on job strain. Occup Med. 2010;60(5):369–375.

11.

Hamer

Chida

Active commuting and cardiovascular risk: a meta-analytic review. Prev Med. 2008;46(1):9–13.

12.

Centers for Disease Control and Prevention. National Center for Health Statistics. National Health Interview Survey. Glossary. Updated February 17, 2017. Accessed June 2, 2019. https://www.cdc.gov/nchs/nhis/physical_activity/pa_glossary.htm

13.

Gay

Buchner

Smith

Occupational physical activity opposes obesity: a cross-sectional modern replication of the Morris 1953 London busmen study. J Occup Environ Med. 2019;61(3):177–182.

14.

Holtermann

Hansen

Burr

Sogaard

Sjogaard

. The health paradox of occupational and leisure-time physical activity. Br J Sports Med. 2012;46(4):291–295.

15.

Shephard

. Is there a ‘recent occupational paradox’ where highly active physically active workers die early? Or are there failures in some study methods? Br J Sports Med. 2019;53(24):1557–1559.

16.

Oppert

Thomas

Charles

Benetos

Basdevant

Simon

. Leisure-time and occupational physical activity in relation to cardiovascular risk factors and eating habits in French adults. Public Health Nutr. 2006;9(6):746–754.

17.

Buhrmester

Kwang

Gosling

Amazon’s mechanical turk: a new source of inexpensive, yet high-quality, data?

Perspect Psychol Sci. 2011;6(1) 3–5.

18.

Loepp

Kelly

. Distinction without a difference? An assessment of MTurk Worker types. Res Politics. 2020;7(1):1–8.

19.

U.S. Bureau of Labor Statistics. Labor force statistics from the current population survey. Full- or part-time status. Updated April 12, 2021. Accessed April 19, 2021. https://www.bls.gov/cps/lfcharacteristics.htm

20.

Faul

Erdfelder

Buchner

Lang

. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods. 2009;41(4):1149–1160.

21.

O’Donoghue

Kennedy

Puggina

Aleksovska

Buck

, et al. Socio-economic determinants of physical activity across the life course: a “determinants of diet and Physical activity” (DEDIPAC) umbrella literature review. PLoS One. 2018;13(1):e0190737.

22.

Aleksovska

Puggina

Giraldi

, et al. Biological determinants of physical activity across the life course: a “determinants of diet and physical activity” (DEDIPAC) umbrella systematic literature review. Sports Med Open. 2019;5(1):2. doi:10.1186/s40798-018-0173-9

23.

U.S. Census Bureau. Code Lists, Definitions, and Accuracy. American Community Survey and Puerto Rico Community Survey, 2017 Code List. 2017. Accessed November 1, 2018. https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.2017.html

24.

Godin

Shephard

. A simple method to assess exercise behavior in the community. Can J Appl Sport Sci. 1985;10(3):141–146.

25.

Amireault

Godin

The Godin-Shephard leisure-time physical activity questionnaire: validity evidence supporting its use for classifying healthy adults into active and insufficiently active categories. Percept Motor skills. 2015;120(2):604–622.

26.

Baecke

Burema

Frijters

. A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr. 1982;36(5):936–942.

27.

Cohen

Kamarck

Mermelstein

. A global measure of perceived stress. J Health Soc Behav. 1983;24(4):385–396.

28.

Wilson

Dejoy

Vandenberg

Richardson

McGrath

. Work characteristics and employee health and well-being: test of a model of healthy work organization. J Occup Organ Psychol. 2004;77(4):565–588.

29.

Demerouti

Bakker

Vardakou

Kantas

The convergent validity of two burnout instruments: a multitrait-multimethod analysis. Eur J Psychol Assess. 2003;19(1):12–23.

30.

Zheng

Zhu

Zhao

Zhang

. Employee well-being in organizations: theoretical model, scale development, and cross-cultural validation. J Organ Behav. 2015;36(5):621–644.

31.

Wong

Laschinger

HKS

. The influence of frontline manager job strain on burnout, commitment and turnover intention: a cross-sectional study. Int J Nurs Stud. 2015;52(12):1824–1833.

32.

Job stress and burnout in hospital employees. Comparisons of different medical professions in a regional hospital in Taiwan. BMJ Open. 2014;4:e004185. doi:10.1136/bmjopen-2013-004185

33.

Templeton

Bernstein

Sukhera

. Gender-Based Differences in Burnout: Issues Faced by Women Physicians. NAM Perspectives. Discussion Paper, National Academy of Medicine, 2019. doi:10.31478/201905a

34.

Ortega

Ramirez-Baena

la Fuente-Solana

Emilia

Vargas

Gómez-Urquiza

. Gender, marital status, and children as risk factors for burnout in nurses: a meta-analytic study. Int J Environ Res Public Health. 2018;15(10):2102.

35.

Lunau

Siegrist

Dragano

Wahrendorf

. The association between education and work stress: does the policy context matter? PLoS One. 2015;10(3):e0121573. Published 2015 Mar 26. doi:10.1371/journal.pone.0121573

36.

Coenen

Huysmans

Holtermann

, et al. Towards a better understanding of the ‘physical activity paradox’: the need for a research agenda. Br J Sports Med 2020;54(17):1055–1057.

37.

Gay

Buchner

Smith

. An examination of compensation effects in accelerometer-measured occupational and non-occupational physical activity. Prev Med Rep. 2017;8:55–59.

38.

Hylkema

Brouwer

Kooijman

, et al. Accelerometer Measured Sedentary and Physical Activity Behaviors of working patients after Total Knee Arthroplasty, and their Compensation Between Occupational and Leisure Time. J Occup Rehabil. 2020;1–10. https://doi.org/10.1007/s10926-020-09924-9

39.

Nooijen

Del Pozo-Cruz

Nyberg

Sanders

Galanti

Forsell

. Are changes in occupational physical activity level compensated by changes in exercise behavior? Eur J Public Health. 2018;28(5):940–943.

40.

Loy

Cameron

O’Connor

. Perceived fatigue and energy are independent unipolar states: supporting evidence. Med Hypotheses. 2018;113:46–51.

41.

O’Connor

Puetz

. Chronic physical activity and feelings of energy and fatigue. Med Sci Sports Exerc. 2005;37(2):299–305.

42.

Puetz

. Physical activity and feelings of energy and fatigue: epidemiological evidence. Sports Med. 2006;36(9):767–780.

43.

Hendrich

Chow

Skierczynski

. A 36-hospital time and motion study: how do medical-surgical nurses spend their time? Perm J. 2008;12(3):25–34.

44.

Coenen

Huysmans

Holtermann

, et al. Do highly physically active workers die early? A systematic review with meta-analysis of data from 193 696 participants. Br J Sports Med. 2018;52(20):1320–1326.

45.

Hall

Heck

Sandler

Ritz

Chen

Krause

. Occupational and leisure-time physical activity differentially predict 6-year incidence of stroke and transient ischemic attack in women. Scand J Work Environ Health. 2019;45(3):267–279.

46.

Ukkola

Gagnon

Rankinen

, et al. Age, body mass index, race and other determinants of steroid hormone variability: the HERITAGE Family Study. Eur J Endocrinol. 2001;145(1):1–9. doi:10.1530/eje.0.1450001

47.

Colloca

Miller

. Role of expectations in health. Curr Opin Psychiatry. 2011;24(2):149–155.

48.

Lindheimer

Szabo

Raglin

Beedie

Carmichael

O’Connor

. Reconceptualizing the measurement of expectations to better understand placebo and nocebo effects in psychological responses to exercise. Eur J Sport Sci. 2020;20(3):338–346.

49.

Crum

Langer

. Mind-set matters: exercise and the placebo effect. Psychol Sci. 2007;18(2):165–171.

50.

Kouvonen

Kivimäki

Elovainio

Virtanen

Linna

Vahtera

. Job strain and leisure-time physical activity in female and male public sector employees. Prev Med. 2005;41(2):532–539.

51.

Fransson

Heikkilä

Nyberg

, et al. Job strain as a risk factor for leisure-time physical inactivity: an individual-participant meta-analysis of up to 170,000 men and women. The IPD-Work Consortium. Am J Epidemiol. 2012;176(12):1078–1089.

52.

Johnston

Allan

Powell

DJH

, et al. Why does work cause fatigue? A real-time investigation of fatigue, and determinants of fatigue in nurses working 12-hour shifts. Ann Behav Med. 2019;53(6):551–562.

53.

Linzer

Poplau

Grossman

, et al. A cluster randomized trial of interventions to improve work conditions and clinician burnout in primary care: results from the Healthy Work Place (HWP) study. J Gen Intern Med. 2015;30(8):1105–1111.

54.

Litman

Robinson

Abberbock

. TurkPrime.com: a versatile crowdsourcing data acquisition platform for the behavioral sciences. Behav Res Methods. 2017;49(2):433–442.

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