Effort-reward imbalance and self-rated health with the mediating role of sleep quality and physical activity among healthcare workers

Abstract

Beyond its personal importance, the health of healthcare workers (HCWs) is a critical factor influencing healthcare system performance and patient care quality due to their direct involvement in patient outcomes and the operational efficiency of healthcare services. This study investigates the association between effort-reward imbalance (ERI) and self-rated health (SRH) among HCWs, exploring the potential mediating roles of sleep quality and physical activity. This cross-sectional study used baseline data from a convenience sample of the Qazvin Employee Health Cohort Study (2021–2022, northwestern, Iran). SRH was assessed using a single-item instrument. In the cohort study, trained interviewers administered the validated Persian versions of the 22-item ERI questionnaire, the Pittsburgh Sleep Quality Index (PSQI), and the International Physical Activity Questionnaire (IPAQ). We used structural equation modeling as our main analytical approach. Analysis of 1250 medical university employees revealed that 43.39% reported suboptimal SRH. Those with suboptimal SRH were older, more likely to be female, and had a significantly higher prevalence of ERI (66.5% vs. 56.1%), poor sleep quality (63.5% vs. 37.8%), and low physical activity (33.3% vs. 26.6%) compared to those with optimal SRH (all p < 0.01). In correlation analysis, SRH was negatively associated with effort, the effort-reward ratio, and poor sleep, and positively associated with reward, and physical activity. Structural equation modeling demonstrated that ERI had a significant direct negative association with SRH (β = -0.07, p = 0.017) and a significant indirect negative association via worsened sleep quality (β = -0.08, p < 0.001) and slightly better physical activity (β = 0.01, p = 0.015). Although more than half of the participants reported optimal SRH and physical activity levels, ERI and poor sleep quality were still prevalent among HCWs. Among HCWs, the SRH of those perceiving their work effort exceeded their rewards was affected by poor sleep quality. Policymakers and health system managers must account for the complex relationship between health and behavioral constructs in occupational settings. 

Introduction

The occupational health of healthcare workers (HCWs) represents a critically neglected area, particularly in developing countries^1,2. According to the International Occupational Hygiene Association (IOHA), protecting worker health and well-being in sectors like hospitals, national government, and public utilities necessitates a systematic process of anticipating, recognizing, evaluating, and controlling occupational hazards³. The health and well-being of HCWs, including nurses and physicians, directly impact their personal lives, families, and professional performance. Prevalent issues such as job stress, burnout, depression, and suicidal ideation can significantly impair HCWs functioning and, consequently, the quality of patient care^1,4. Evidence suggests a significantly higher prevalence of occupational health problems among HCWs in developing countries compared to high-income nations. A systematic review indicated an average prevalence rate of 60.17%, with figures as high as 83% reported for Ethiopia^5,6.

Self-rated health (SRH) demonstrates a strong and significant association with health-related behaviors and total morbidity⁷. SRH provides a simple, applicable measure of both general health status and personal health perception⁴. The SRH of HCWs is critically important, as it is directly linked to their professional performance and capacity to fulfill duties. Consequently, factors such as physical and mental health disorders, diverse workplace stressors, and unfair working conditions do not only affect HCWs individually; they significantly compromise overall healthcare system functionality, most notably through a decline in the quality of patient care^7,8.

The ERI model is a key theoretical framework for understanding the impact of work stress on the health of HCWs. This model posits that work-related stress arises from a perceived imbalance between high efforts expended and low reward received. When HCWs consistently contribute more effort than they are rewarded for, the resulting effort-reward imbalance (ERI) can lead to significant adverse health outcomes⁹. Evidence from systematic reviews indicates that ERI is both highly prevalent and globally widespread among HCWs, for example, 57% in Japan, 32.3% in Vietnam, and 80.7% in Greece. This widespread imbalance constitutes a significant risk factor for mental health disorders in this workforce^10–12. A study conducted in Tabriz, Iran, found that HCWs with a higher effort–reward (ER) ratio experienced significantly more physical and mental health complaints, with nurses being especially affected¹³. This finding aligns with prior research, including multiple studies among Danish nurses and Chinese physicians which have shown a direct association between ERI and SRH^14,15. Consequently, ERI has been recognized as an influencing factor on SRH, particularly among healthcare professionals¹⁶.

Sleep disorders constitute a prevalent health issue among HCWs^17–19. Poor sleep quality is highly prevalent among HCWs^20–23. Notably, 66.7% of physicians report poor sleep quality¹⁸. This issue may be linked to factors in the work environment, including job complexity, high workload, and lack of support, which affect HCWs across various professional groups¹⁷. Poor sleep quality contributes to numerous adverse outcomes among HCWs, including unhealthy lifestyle factors, a diminished quality of life, physiological dysfunction, ERI, and certain self-rated morbidities^20,22–24. Research assessing psychological factors in the Norwegian workforce found that improved social support, better work-family balance, and a reduction in ERI can mitigate sleep problems among HCWs²⁵. Given that both ERI and sleep problems are dynamic and exhibit a bidirectional relationship, longitudinal studies may yield additional insights²⁶.

Physical activity is also a key determinant of SRH²⁷. For healthcare workers, engagement in regular physical activity contributes both to improved health outcomes and to prolonged workforce participation²⁸. Although physical activity is crucial for maintaining health, research from multiple countries including Nigeria, Ghana, Malaysia, and Denmark, suggests that HCWs generally engage in low levels of physical activity, thereby increasing their vulnerability to non-communicable diseases (NCDs)^27–33. Complementing these findings, an occupational cohort study in Japan demonstrated that workers reporting high ERI have an 8–28% increased likelihood of physical inactivity³⁴.

The health of HCWs is critically important for maintaining effective patient care and overall healthcare system functionality. A review of the literature indicates that among Iranian HCWs, the prevalence of ERI is high³⁵. Previous studies have reported a direct association between ERI and physical activity, SRH, and sleep quality^14,16,25,35. In addition, a direct relationship between physical activity and sleep quality with SRH has also been documented^15,22–24. Moreover, bidirectional relationships among these variables have been proposed³⁶. In light of such interconnections and the possible mediating pathways involved, further investigation is warranted^37,38. While numerous studies have examined the health of HCWs, most have typically focused on the association of only one or two variables with health outcomes.

Specifically, while the relationship between ERI and SRH has been investigated, few studies have considered the role of intermediary factors. This study had two primary aims: (1) to determine the direct association between ERI and SRH, and (2) to assess the role of physical activity and sleep quality as the mediators in the indirect and total associations between ERI and SRH among Qazvin University of Medical Sciences employees (Fig. 1). This is the first study to investigate the combined relationships among these four constructs within a single structural model, illustrating how ERI, physical activity, and sleep quality collectively influence SRH among HCWs. These findings can inform policymakers in developing targeted interventions to improve HCW health and well-being.

Fig. 1.

Conceptual model of this study.

Method

The present descriptive-analytical cross-sectional study utilized baseline data from the Qazvin Employees’ Health Cohort Study (QEHCS), comprising 1250 employees aged 18 years and older. The QEHCS is a component of the Prospective Epidemiological Research Studies in Iran (PERSIAN), a national cohort focusing on employees at medical universities³⁹. This study, conducted between 2021 and 2022, aimed to determine the prevalence of various risk factors and their roles in the incidence of NCDs among this workforce. Initially, all employees across all departments at Qazvin University of Medical Sciences (QUMS) were invited to participate. Subsequently, those who agreed and met the inclusion criteria were enrolled, yielding a response rate of 61%. Eligible employees were recruited using convenience sampling. The inclusion criteria were: (1) permanent employment at any unit of the university; (2) a minimum of two years of service at QUMS; (3) age ≥ 18 years; and (4) non-pregnancy status for female workers. Participants were enrolled at the QEHCS after providing written informed consent. The sample size was determined proportionally to the number of employees in each university sector: educational, clinical, administrative, services, and health. Data on demographics, lifestyle, biochemistry, psychology, and physical health were collected through face-to-face interviews conducted by trained staff. Blood and urine samples were also taken. For this cross-sectional analysis, we utilized all available data on QUMS workers, including demographic characteristics (sex, age, educational level, body mass index (BMI), marital status), lifestyle factors (sleep quality, physical activity), SRH, and work-related factors (ERI). The present study, has been approved by the Research Ethics Committee of QUMS to use secondary data from the Qazvin Employees’ Health Cohort Study (Ethics Code: IR.QUMS.REC.1405.010).

Measurements

Effort-reward imbalance (ERI)

We used the validated Persian version of the ERI scale to assess job stress among healthcare workers⁴⁰. This internationally validated questionnaire is composed of three domains: effort, reward, and over-commitment. The first five questions measured the effort required to fulfill work duties. The next eleven questions assessed workplace rewards, and the final six questions evaluated over-commitment. The ER ratio was derived from the standard formula: Effort/(Reward × c), with ‘c’ serving as a correction factor for the unequal number of items in each subscale⁴¹. An ERI is indicated by a ratio greater than 1.0. The internal consistency of the effort and reward subscales, as measured by Cronbach’s alpha, was 0.6 and 0.74, respectively. Although the effort subscale was somewhat low, it was still supported by previous research within an Iranian sample⁴⁰.

Pittsburgh sleep quality index (PSQI)

Sleep patterns were assessed using the validated Persian version of the Pittsburgh Sleep Quality Index (PSQI). This 19-item instrument consists of seven components: subjective sleep quality, sleep disturbances, sleep latency, sleep duration, habitual sleep efficiency, use of sleep medications, and daytime dysfunction. Each component is scored from 0 to 3, with higher scores indicating poorer sleep quality in that domain. These subscale scores are summed to produce a global PSQI score ranging from 0 to 21. In line with established evidence, a global score greater than 5 was used to define poor sleep quality in the present study^42,43. The internal consistency of the PSQI in our sample was acceptable, with a Cronbach’s alpha of 0.73.

Self-rated health

To assess SRH, we employed a single-item measure derived from the World Health Organization Quality of Life-BREF (WHOQOL-BREF) instrument. This validated item asks participants: “How is your health in general?”⁴⁴. This single-item measure uses a 5-point Likert scale ranging from “very bad” to “very good” to assess SRH. Higher scores correspond with better SRH. For analysis, responses were categorized into two groups: an “optimal SRH” group (“very good” and “good”) and a “suboptimal SRH” group (“moderate,” “bad,” and “very bad”)^45–47. The psychometric properties of the SRH questionnaire have been evaluated across diverse populations, including validation studies within the Persian community^47,48. The intraclass correlation coefficient (ICC) for the questionnaire was 0.83 (95% CI: 0.72, 0.90), indicating acceptable reliability^49,50.

Physical activity

Physical activity was assessed using the validated Persian version of the long-form International Physical Activity Questionnaire (IPAQ), a standard instrument that categorizes activity levels as poor, moderate, or high. Using the International Physical Activity Questionnaire (IPAQ) long form categorical scoring, participants were classified into three activity levels: high, moderate, and low. High activity was defined as either: a) vigorous activity on ≥ 3 days accumulating ≥ 1500 MET-minutes/week, or b) any combination of activities on ≥ 7 days accumulating ≥ 3000 MET-minutes/week. Moderate activity was defined as meeting any of three criteria: a) vigorous activity on ≥ 3 days for ≥ 20 min/day, b) moderate activity or walking on ≥ 5 days for ≥ 30 min/day, or c) any combination on ≥ 5 days accumulating ≥ 600 MET-minutes/week. Participants not meeting criteria for ‘moderate’ or ‘high’ were classified as having low activity^51,52.

Statistical analysis

Data were analyzed using descriptive and inferential statistics. Continuous variables are presented as mean ± standard deviation (SD); categorical variables as n (%). Spearman’s correlation was used for bivariate analysis. Structural equation modeling (SEM) was performed to examine the direct, indirect, and total associations of the ER ratio with SRH status, with sleep quality and physical activity specified as mediators. The model was estimated using the WLSMV (weighted least squares mean and variance adjusted) estimator. The model’s goodness of fit was evaluated using the Tucker–Lewis index (TLI > 0.90), Comparative Fit Index (CFI > 0.90), Standardized Root Mean Squared Residual (SRMR < 0.06), and Root Mean Square Error of Approximation (RMSEA < 0.05)⁵³. The data were analyzed using R (version 4.4.3) for all statistical computations.

Result

A total of 1250 medical university employees were enrolled in the study. The mean age of participants was 40.57 ± 8.01 years, with 543 (43.44%) females and 707 (56.56%) males. Based on SRH status, 708 participants (56.61%) were classified in the “optimal” SRH group (SRH > 3), while 542 (43.39%) were in the “suboptimal” SRH group (SRH ≤ 3). The prevalence of ERI, poor sleep quality, and low physical activity were 60.61%, 48.87%, and 29.84%, respectively.

As detailed in Table 1, participants in the optimal SRH group were significantly younger than those in the suboptimal SRH group (40.00 ± 8.06 vs. 41.33 ± 7.91 years, p = 0.004). The proportion of males was significantly higher in the optimal SRH group (63.68%) compared to the suboptimal SRH group (47.4%, p < 0.001). Furthermore, a significantly higher prevalence of ERI (66.54% vs. 56.06%, p < 0.001), poor sleep quality (63.5% vs. 37.79%, p < 0.001), and low physical activity levels (33.27% vs. 26.64%, p = 0.001) was observed in the suboptimal SRH group.

Table 1.

Sociodemographic, and occupational characteristics of participants by self-rated health status.

Variable			Self-rated health status
	level	Total	Suboptimal	Optimal	P-value
Age		40.57 ± 8.01	41.33 ± 7.91	40.00 ± 8.06	0.004
BMI		26.82 ± 4.28	27.24 ± 4.55	26.49 ± 4.04	0.003
Height (cm)		168.71 ± 10.19	166.96 ± 10.15	170.07 ± 10.01	0.000
Weight (kg)		76.61 ± 15.40	76.08 ± 15.35	77.01 ± 15.43	0.300
Gender	Female	543 (43.44%)	283 (52.6%)	255 (36.32%)	< 0.001
	Male	707 (56.56%)	255 (47.4%)	447 (63.68%)
Education	Under diploma/diploma	359 (28.72%)	166 (30.86%)	191 (27.21%)	0.164
	Bachelor’s degree or higher	891 (71.28%)	372 (69.14%)	511 (72.79%)
Job status	Non-clinical	897 (72.46%)	387 (72.2%)	507 (72.74%)	0.852
	Clinical	341 (27.54%)	149 (27.8%)	190 (27.26%)
Type of shift	Regular	677 (54.73%)	321 (60%)	356 (50.71%)	< 0.001
	Rotating schedule	538 (43.49%)	199 (37.2%)	339 (48.29%)
	Irregular	22 (1.78%)	15 (2.8%)	7 (1%)
Marital status	Divorce/Widow	43 (3.44%)	21 (3.9%)	22 (3.13%)	0.150
	Married	1066 (85.28%)	467 (86.8%)	592 (84.33%)
	Single	141 (11.28%)	50 (9.29%)	88 (12.54%)
ERI		1.15 ± 0.35	1.20 ± 0.35	1.11 ± 0.34	< 0.001
	Balance	488 (39.39%)	180 (33.46%)	308 (43.94%)	< 0.001
	Imbalance	751 (60.61%)	358 (66.54%)	393 (56.06%)
Effort		18.67 ± 3.76	18.93 ± 3.80	18.46 ± 3.71	0.030
Reward		37.06 ± 5.68	35.98 ± 5.64	37.90 ± 5.57	< 0.001
Physical activity
	Low	373 (29.84%)	179 (33.27%)	187 (26.64%)	0.001
	Moderate	499 (39.92%)	225 (41.82%)	274 (39.03%)
	High	378 (30.24%)	134 (24.91%)	241 (34.33%)
PSQI		6.30 ± 3.69	7.60 ± 3.95	5.32 ± 3.13	< 0.001
	Good sleeper	633 (51.13%)	196 (36.5%)	433 (62.21%)	< 0.001
	Poor sleeper	605 (48.87%)	341 (63.5%)	263 (37.79%)
Self-rated health		3.66 ± 0.96	2.74 ± 0.57	4.36 ± 0.48	< 0.001

Data are presented as mean ± standard deviation for continuous variables and n (%) for categorical variables.

BMI = Body Mass Index (kg/m²); ERI = Effort-Reward Imbalance; PSQI = Pittsburgh Sleep Quality Index.

Bolded P-value s indicate statistical significance (p < 0.05).

Bivariate correlations between key study variables are presented in Table 2. SRH status was positively correlated with physical activity (r = 0.12, p < 0.001) and reward (r = 0.17, p < 0.001), and negatively correlated with effort (r =−0.06, p = 0.043), the effort-reward imbalance (ERI) (r= −0.13, p < 0.001), and sleep quality (PSQI) (r = −0.30, p < 0.001). Given that higher PSQI scores indicate poorer sleep quality, this negative correlation indicates that individuals with optimal self-rated health reported better sleep quality.

Table 2.

Correlations among self-rated health status, effort-reward imbalance, sleep quality, and physical activity.

Physical activity	Physical activity	Self-rated health	Effort	Reward	ERI	PSQI
	1 (< 0.001)
Self-rated health	0.12 (< 0.001)	1 (< 0.001)
Effort	0.18 (< 0.001)	−0.06 (0.04)	1 (< 0.001)
Reward	−0.01 (0.661)	0.17 (< 0.001)	−0.24 (< 0.001)	1 (< 0.001)
ERI	0.13 (< 0.001)	−0.13 (< 0.001)	0.84 (< 0.001)	−0.70 (< 0.001)	1 (< 0.001)
PSQI	−0.03 (0.251)	−0.30 (< 0.001)	0.13 (< 0.001)	−0.22 (< 0.001)	0.21 (< 0.001)	1 (< 0.001)

Bolded P-values indicate statistically significant correlations (p < 0.05).

The structural equation model (SEM) tested the direct and indirect effects of ERI on binary SRH status, with sleep quality and physical activity (log-transformed) as mediators (Table 3; Fig. 2). The model demonstrated an excellent fit to the data (CFI = 0.964, TLI = 0.961, RMSEA = 0.043, SRMR = 0.059). ERI had a significant direct negative association with SRH status (B = −0.15, β = −0.07, p = 0.04), indicating that higher ERI was associated with lower odds of reporting good health. Furthermore, ERI demonstrated a significant indirect negative association with SRH status through sleep quality (β = −0.08, p < 0.001), indicating that higher ERI was associated with worse sleep quality, which in turn was associated with lower odds of good SRH. The indirect pathway through physical activity was statistically significant (β = 0.01, p = 0.015). The total indirect association of ERI with SRH was significant (, β = −0.06, p < 0.001).

Table 3.

Direct, indirect, and total associations of effort-reward imbalance and self-rated health from structural equation modeling.

Direct associations		Unstandardized estimate	95% CI	Standardized estimate	p_value
	ERI → sleep quality	0.24	(0.15, 0.32)	0.18	< 0.001
	ERI → Log(physical activity)	0.35	(0.10, 0.60)	0.08	0.006
	sleep quality → self-rated status	−0.66	(−0.77, −0.55)	−0.43	< 0.001
	Log(physical activity) → self-rated status	0.08	(0.05, 0.10)	0.16	< 0.001
	ERI → self-rated status	−0.15	(−0.29, −0.01)	−0.07	0.040
Indirect associations	ERI →Log(physical activity) → self-rated status	0.03	(0.01, 0.05)	0.01	0.015
	ERI → sleep quality → self-rated status	−0.16	(−0.22, −0.10)	−0.08	< 0.001
	Total indirect	−0.13	(−0.19, −0.07)	−0.06	< 0.001
CI = Confidence Interval; Log(physical activity) = logarithmically transformed physical activity measured in MET-minutes/week. Model fit indices: CFI = 0.964, TLI = 0.961, RMSEA = 0.043, SRMR = 0.059. Bolded P-value indicate statistical significance at p < 0.05.

Fig. 2.

Structural equation model of effort-reward imbalance, sleep quality, physical activity, and self-rated health.

Discussion

In the present study, we explored the association between ERI and SRH among employees at Qazvin University of Medical Sciences. Furthermore, we assessed the mediating role of physical activity and sleep quality in the relationship between ERI and SRH. The results of this study showed that 56.61% of participants had optimal SRH, while the prevalence of ERI was 60.61%. Employees with poor SRH had experienced a significantly higher prevalence of ERI than those with optimal SRH. Slightly fewer than half of the participants reported poor sleep quality, and 29.84% reported low physical activity. Both physical activity and sleep quality differed significantly between employees with optimal and suboptimal SRH. The analysis revealed a significant negative direct association between ERI and SRH. In assessing potential mediators, sleep quality and physical activity were found to mediate the relationship between ERI and SRH.

The results indicated that over half of the participants rated their SRH as optimal (good or very good). This prevalence is higher than that reported among HCWs in China (40.1%), environmental health officers in The Gambia (20%), and general workers in Brazil (26.7%), though it is lower than the rate observed among nurses in Brazil (77.6%)^54–56.

The present findings suggest that the prevalence of optimal SRH among HCWs appears elevated when compared with rates reported in the general populations of Iran and Sweden^57,58. This is notable given that occupational factors, including work-related stress and employment status, are established as determinants of HCW well-being. Since the health of this group directly influences the quality of patient and community care, protecting it is of paramount importance⁵⁹. This urgency is underscored by rising psychosocial problems linked to stressful working conditions In response, the World Health Organization (WHO) has, since the onset of the COVID-19 pandemic, prioritized HCW health through targeted interventions⁵⁹.

A high prevalence of ERI (60.61%) was found in our study, indicating that HCWs who exerted high effort received low rewards. This suggests a significant mismatch between the effort expended by HCWs and the rewards received in the work environment. This issue was particularly pronounced among clinical HCWs involved in direct patient care⁵⁴. Previous research in Tabriz, Iran, found a high prevalence of ERI (54.8%) and attributed it to the working conditions within the hospital environment⁶⁰. This imbalance is a significant determinant of health, with HCWs experiencing ERI facing a twofold risk of depression and psychological distress⁵⁶. More broadly, across various occupational groups, high ERI scores correlate with odds of poor health ranging from 2.4 to 5.2⁴¹. Together, this evidence underscores the importance of addressing ERI and its sequelae; targeted interventions to improve HCWs mental health could thus enhance professional performance, elevate the quality of patient care, and positively influence SRH⁶¹.

This study further identified a notable prevalence of poor sleep quality among nearly half of the HCWs. Although this rate is lower than the 58.9% reported for healthcare professionals in Northwest Ethiopia, it remains a significant concern⁶². This is particularly evident given that demanding work environments marked by high workloads and inadequate support, are established contributors to compromised sleep. In turn, poor sleep quality elevates the risk for NCDs, underscoring the broader health implications of occupational stressors in healthcare settings^17,24. In a study by Nordentoft et al., it was shown that working conditions characterized by ERI are associated with a threefold higher risk of sleep disorders. Psychological work stressors are similarly recognized as significant contributors to poor sleep quality^25,56.

In the present study, an association between ERI, sleep quality, and SRH was observed, which is consistent with previous research^{18,20,22,23,26,63,64}. Given the complex, multi-faceted nature of these variables across work, family, and environmental domains, elucidating the mechanisms that link them is essential^25,26. This is particularly salient for relationships such as that between sleep problems and ERI, which evidence suggests is bidirectional. Consequently, longitudinal research is needed to unpack the dynamic interplay between these factors over time and yield deeper causal insight. In a study by Zhu et al. in China, sleep quality was shown to affect SRH indirectly through the mediating roles of depression and anxiety²¹. In our study, sleep quality mediated the association between ERI and SRH. Structural equation modeling revealed that ERI directly and negatively predicted SRH, with sleep quality mediating this association, a result that aligns with prior research involving HCWs^13,34. Sleep quality is influenced by occupational factors such as job type and shift work, which are associated with ERI. This imbalance can generate chronic stress, potentially leading to unhealthy lifestyle behaviors, diminished mental and social well-being, and increased self-reported morbidities. Ultimately, this cascade of effects contributes to impaired performance in both personal and social activities^13,23.

This study also found that physical activity among HCWs varies from moderate to high. This result is inconsistent with studies from Malaysia, Nigeria, and Ghana, the latter specifically among physicians, which reported low physical activity levels and a significant association with SRH^27,29,31. Furthermore, research in Malaysia indicates that HCWs with poorer health status demonstrate lower motivation to engage in physical activity. In line with this, our study identified a direct and significant association between physical activity and better SRH, a finding consistent with prior literature^27,31. Given that the current study found physical activity to significantly mediate the association between ERI and SRH, its considerable role in enhancing quality of life and promoting overall healthy living continues to be of importance Although previous studies have noted an association between ERI and physical inactivity^28,65,66. a systematic review on the effects of stress on physical activity and exercise found that stress can influence exercise adoption, maintenance, and relapse in different ways. According to this systematic review, 18.2% of the prospective studies found positive effects of stress on physical activity. This suggests that some individuals are able to cope with stress by exercising. Additionally, these studies highlighted the importance of habitual behavior: people who are habitually active are more likely to exercise during stressful situations compared to others⁶⁷. Consequently, promoting physical activity is widely advised as a strategy to prevent premature exit from the labor market.

The strength of this study lies in its novel examination of both the direct and indirect (via sleep quality and physical activity) associations between ERI and SRH among HCWs. However, because prior research on the individual links within this model exists, the findings invite both comparison and potential debate regarding the integrated pathways. In addition to its strengths, this study has several limitations. Due to its cross-sectional design, causal inferences cannot be made, as the temporal sequence of the variables cannot be established. Although healthcare workers were invited to participate, most were unable to do so because of their demanding workloads and busy schedules, resulting in a response rate of 61%. Furthermore, the use of self-administered questionnaires may introduce information bias, and the convenience sampling method limits the balance and generalizability of the sample across groups.

Conclusion

Overall, we found that while more than half of the participants reported optimal SRH, a majority also experienced ERI. Furthermore, nearly half reported poor sleep quality, and approximately one-third reported low physical activity. Analysis of the association between ERI and SRH among HCWs with sleep quality and physical activity as potential mediators, revealed several major findings. First, our analysis confirmed significant negative associations between optimal HCWs SRH and three factors: higher ERI, poorer sleep quality, and lower physical activity levels. Second, our results affirmed that high ERI at work is directly associated with SRH, as well as indirectly associated through the mediation of sleep quality and physical activity. This ultimately impacts both their personal well-being and their ability to provide high-quality services. Therefore, the healthcare system should implement not only psychosocial support programs, such as promoting respect, collegial support, career promotion prospects, and acknowledgment of personal and family challenges, but also adequate financial incentives (e.g., competitive salaries) to safeguard the health of HCWs. Conducting further longitudinal studies would help clarify these relationships and provide more comprehensive information.

Abbreviations

HCW

Healthcare worker

SRH

Self-rated health

ERI

Effort-reward imbalance

PSQI

Pittsburgh sleep quality index

IPAQ

International physical activity questionnaire

IOHA

International occupational hygiene association

ER ratio

Effort-reward ratio

NCD

Non-communicable disease

QEHCS

Qazvin employees’ health cohort study

QUMS

Qazvin university of medical sciences

BMI

Body mass index

WHOQOL-BREF

World health organization quality of life–BREF

ICC

Intraclass correlation coefficient

MET

Metabolic equivalent of task

M

Mean

SD

Standard deviation

SEM

Structural equation modeling

CFI

Comparative fit index

GFI

Goodness of fit index

TLI

Tucker-lewis index

RMSEA

Root mean square error of approximation

SRMR

Standardized root mean square residual

B

Beta regression coefficient

CI

Confidence interval

SB

Standardized beta regression coefficient

Author contributions

The study was conceived and designed by HT and ZH. Investigation was performed by Seyed Mahyar M, MM and SGH. Formal analysis was conducted by NM and AM. The first draft of manuscript was written by ZH, HT, and Seyyed Mehdi M. manuscript review and editing were conducted by HH and AM. SHG, Seyyed Mehdi M, MM and HT supervised the project. ZH provided essential resources. All authors interpreted the results and approved the final manuscript.

Data availability

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

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This study was approved by the Research Ethics Committee of Qazvin University of Medical Sciences (Ethics Code: IR.QUMS.REC.1405.010). All participants provided written informed consent prior to participation. Data were anonymized and handled in accordance with the ethical standards of the Declaration of Helsinki.

Consent for publication

All participants in the Qazvin Employee Health Cohort study provided written informed consent prior to their inclusion. This consent explicitly included permission for the use of their anonymized data in scientific publications.