The associations between sleep duration and sleep quality with overweight/obesity in professional drivers: evidence from an Iranian case-control study

Abstract

Objective

Sleep disturbances are increasingly recognized as important contributors to metabolic disorders, particularly in high-risk occupational groups such as professional drivers. This study aimed to examine the associations between sleep duration and sleep quality with overweight/obesity among Iranian professional drivers.

Methods

A case–control study was conducted among 1,200 male professional drivers undergoing annual occupational health examinations in Iran. Participants were classified into overweight/obese cases (body mass index (BMI) ≥ 25 kg/m²; n = 600) and non-overweight/obese controls (BMI < 25 kg/m²; n = 600). Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Logistic regression models were applied to estimate the odds ratios (OR) for overweight/obesity associated with sleep duration and sleep quality.

Results

A total of 45.1% of drivers had poor sleep quality, and 34.2% reported sleeping fewer than 7 h per 24-hour period. In the fully adjusted model, drivers who slept ≤ 7 h had higher odds of being overweight /obese compared with those who slept > 7 h (OR = 1.319; 95% CI: 1.011–1.722). Additionally, drivers with poor sleep quality had higher odds of being overweight /obese compared with drivers with good sleep quality (OR = 1.287; 95% CI: 1.014–1.625).

Conclusion

This study demonstrates that short sleep duration and poor sleep quality are associated with increased risk of overweight/obesity among professional drivers. These findings highlight sleep as an important and modifiable determinant of metabolic health in this occupational group. Incorporating sleep-focused strategies into occupational health programs may contribute to improved weight management and enhanced safety among professional drivers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-026-27923-y.

Introduction

Sleep quantity and quality are essential components of physiological regulation, cognitive performance, and sustained alertness—factors that are particularly critical for occupations such as professional driving, where continuous attention and rapid reaction are required. Insufficient or poor-quality sleep has been associated with impaired vigilance and a higher likelihood of motor vehicle crashes, emphasizing sleep as a major occupational safety determinant [1, 2]. Professional drivers face unique challenges—including irregular and extended work hours, circadian rhythm disruption, and high levels of fatigue—that make them especially vulnerable to chronic sleep deprivation [3]. Furthermore, sleep loss has been linked to metabolic dysregulation and increased risk of obesity, establishing a potential pathway through which inadequate sleep may negatively affect long-term health [4].

The interplay between sleep disturbances, metabolic dysfunction, and driving performance further amplifies the risk burden in this workforce. Short or fragmented sleep is known to alter appetite-regulating hormones—specifically by elevating ghrelin and reducing leptin—thereby increasing hunger and energy intake [5, 6]. Obesity itself can worsen sleep quality through mechanisms such as obstructive sleep apnea (OSA), reduced respiratory capacity, and chronic fatigue, ultimately impairing driving performance and elevating accident risk. The combination of inadequate sleep and obesity therefore represents a significant occupational hazard for drivers, where both conditions may independently and synergistically compromise safety [1].

Although numerous studies have evaluated associations between sleep characteristics and obesity, results remain inconsistent across populations. Some investigations report an inverse association between sleep duration and BMI [7, 8], while others describe U-shaped relationships [9]. Similar inconsistencies exist regarding sleep quality, with evidence alternately supporting positive, negative, or absent associations with overweight and obesity [10–12]. These disparities underscore the need for occupation-specific research in high-risk groups, where sleep patterns and lifestyle factors differ considerably from the general population.

Professional drivers, including those in Iran, often work long shifts, experience circadian misalignment, and face lifestyle constraints that increase susceptibility to both sleep disturbances and excess body weight (BW). Evidence from previous studies indicates that short sleep duration is associated with increased odds of obesity among truck drivers, and irregular work patterns further contribute to central adiposity [13, 14]. Given the high prevalence of sleep-related problems and obesity in this profession, understanding how sleep quantity and quality relate to overweight and obesity is essential for designing effective occupational health strategies. Therefore, this case–control study aims to investigate the relationship between sleep quantity and sleep quality with the risk of overweight and obesity among professional drivers in Iran, with the goal of providing evidence to support interventions that may improve both driver health and road safety.

Methods

Study design

This nested case–control study was conducted in 2018 as part of a research project (code: 9434) approved by the Ethics Committee of Shahroud University of Medical Sciences (approval number: IR.SHMU.REC.1395.20). All procedures were carried out in accordance with the principles of the Declaration of Helsinki and the institutional guidelines governing human research [15]. The study was nested within an established longitudinal cohort of professional drivers —defined as individuals whose primary occupation involves driving, including truck, bus, and taxi drivers, but excluding ordinary individuals who drive any type of vehicle without driving being their profession—in Iran, originally designed to investigate occupational and lifestyle-related health outcomes.

Inclusion criteria were: (1) male professional drivers aged 18–60 years; (2) holding a valid professional driving license (Base 1, Base 2, or Base 3); (3) active employment as a professional driver for at least one year prior to study enrollment; (4) undergoing the mandatory annual occupational health examination at the Kasra Occupational Health Center in Shahroud, Iran; and (5) providing written informed consent to participate in the study. Exclusion criteria were: (1) diagnosed sleep disorders other than OSA; (2) use of weight-affecting medications within the past three months; (3) following any weight-altering diet (for loss, gain, or maintenance) within the past three months; (4) bariatric surgery; (5) untreated endocrine disorders; (6) major psychiatric disorders; and (7) severe systemic illnesses.

From this cohort, drivers with overweight or obesity (BMI ≥ 25 kg/m²) were identified as cases (n = 600), while drivers with normal weight (BMI < 25 kg/m²) were selected as controls (n = 600). Controls were drawn from the same underlying cohort and were frequency-matched to cases by age and sex to enhance comparability between groups and reduce potential confounding and selection bias.

Measurements

Anthropometrics measurements

Anthropometric measurements were obtained by a trained examiner under standardized conditions. Height and BW were measured to the nearest 0.1 cm and 0.1 kg, respectively, using calibrated SECA instruments, with participants barefoot and wearing light clothing. BMI was calculated using the standard formula. Neck circumference and waist circumference (WC) were assessed with a non-elastic tape (precision 0.1 cm). NC was measured with the head in the Frankfort horizontal plane just below the laryngeal prominence, and WC was measured at the narrowest point between the tenth rib and the iliac crest. All measurements were performed three times, and the mean value was used for analysis to improve reliability.

Assessment of sleep quality index

Sleep quality was evaluated using PSQI, a self-reported instrument consisting of 19 items aggregated into seven components. Scoring procedures are provided in the Supplementary Material [13]. The Persian version of the PSQI has been validated and demonstrated acceptable reliability and construct validity in the Iranian population. Based on the global PSQI score, participants were categorized as having good sleep quality (PSQI ≤ 5) or poor sleep quality (PSQI > 5) [16].

Sleep duration measurement

Sleep duration was assessed using the Pittsburgh Sleep Quality Index (PSQI), which includes an item asking: ‘During the past month, how many hours of actual sleep did you get per night?’ Participants reported their average sleep duration in hours, and this continuous variable was then dichotomized using a cutoff of ≤ 7 h versus > 7 h. The cutoff of 7 h was selected a priori based on widely accepted guidelines and previous epidemiological studies investigating the association between sleep duration and obesity [17, 18].

STOP-BANG3 questionnaire

The STOP-BANG questionnaire is a widely validated screening tool for assessing the risk of OSA. It comprises eight yes/no items addressing key clinical and anthropometric factors, including snoring, daytime sleepiness, witnessed apnea, hypertension, BMI > 30 kg/m², age > 50 years, neck circumference > 40 cm, and male sex. Each affirmative response scores one point (total range: 0–8), with scores ≥ 3 indicating a high likelihood of OSA, while lower scores suggest reduced risk [19]. The Persian version of STOP-BANG has demonstrated acceptable validity and reliability against polysomnography in Iranian populations, supporting its use as a practical screening instrument for OSA assessment [20].

Clinical data

After a 12-hour overnight fast, venous blood samples were obtained from all participants and analyzed at the central laboratory of Razavi Clinic, Shahroud University of Medical Sciences. Serum concentrations of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides, and fasting blood glucose were measured using standard enzymatic methods with Pars Azmun kits (Karaj, Iran). Blood pressure was assessed in the seated position on the left arm using a calibrated sphygmomanometer under standardized conditions. Metabolic syndrome was identified according to ATP III criteria based on WC, triglycerides, HDL-C, fasting blood glucose, and blood pressure, with the presence of at least three abnormal components. Diabetes mellitus, dyslipidemia, and hypertension were defined in accordance with established American Diabetes Association (ADA), National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III), and Seventh Report of the Joint National Committee (JNC 7) guidelines, respectively [21].

Assessment of confounders and covariates

Diabetes was defined according to ADA criteria as FBG ≥ 126 mg/dL, 2-hour plasma glucose ≥ 200 mg/dL during an oral glucose tolerance test, HbA1c ≥ 6.5%, or current use of antidiabetic medication. Dyslipidemia was defined according to NCEP ATP III criteria as total cholesterol ≥ 240 mg/dL, LDL-C ≥ 160 mg/dL, TG ≥ 200 mg/dL, HDL-C < 40 mg/dL in men or < 50 mg/dL in women, or current use of lipid-lowering medication. Hypertension was defined according to JNC 7 criteria as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or current use of antihypertensive medication. Metabolic syndrome was defined according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria, requiring the presence of at least three of the following five components: waist circumference > 102 cm, triglycerides ≥ 150 mg/dL, high-density lipoprotein cholesterol < 40 mg/dL, blood pressure ≥ 130/85 mmHg or use of antihypertensive medication, and fasting blood glucose ≥ 110 mg/dL or use of antidiabetic medication [17]. Obstructive sleep apnea (OSA) risk was evaluated using the validated Persian version of the STOP-BANG questionnaire; scores ≥ 3 indicated high risk for OSA [15, 16]. Physical activity level was assessed using a validated self-report questionnaire, categorized as low, moderate, or high based on weekly frequency and duration, and converted to metabolic equivalent (MET) hours/day using the updated compendium of physical activities [22]. Shift work status and overnight driving were assessed using a self-report questionnaire. Participants were asked: (1) ‘Do you work in rotating or fixed night shifts?‘; and (2) ‘How many nights per week do you drive between 10:00 PM and 6:00 AM?’ (0, 1–2, 3–4, ≥ 5 nights per week). Overnight driving was defined as driving at least one night per week between 10:00 PM and 6:00 AM.

Statistical analysis

Continuous variables are reported as mean values with corresponding standard deviations, whereas categorical variables are expressed as counts and proportions. Between-group differences in continuous measures were evaluated using independent-samples t tests, and comparisons of categorical variables were performed using the chi-square test. To control for potential confounding influences, regression analyses were applied using three sequential models and all covariates were entered into the logistic regression models as categorical variables. The first model was unadjusted; the second model incorporated adjustments for educational levels, driving duration, and smoking status; shift work, and the fully adjusted third model additionally accounted for physical activity level, metabolic syndrome, and OSA. The outcome variable was categorical (BMI < 25 kg/m² as non-overweight/obese reference vs. BMI ≥ 25 kg/m² as overweight/obese. A two-sided p value of less than 0.05 was considered indicative of statistical significance. All analyses were conducted using IBM SPSS Statistics software, version 23 (IBM Corp., Armonk, NY, USA).

The required sample size for this case–control study was determined based on the expected difference in the prevalence of poor sleep quantity and/or quality between overweight/obese drivers and normal-weight controls. Previous epidemiological studies have reported a substantially higher prevalence of inadequate or poor-quality sleep among adults with overweight/obesity (approximately 40–55%) compared with normal-weight individuals (approximately 20–30%) [23]. Assuming a conservative prevalence of poor sleep of 45% in the overweight/obese (case) group and 30% in the control group, a two-sided significance level of 0.05 and a statistical power of 90%, the minimum required sample size was estimated using G*Power software (version 3.1) [24]. Under these assumptions, the calculated sample size was approximately 560 participants per group. To enhance statistical precision and account for potential exclusions or incomplete data, the final sample size was increased to 600 cases and 600 controls, resulting in a total study population of 1,200 male professional drivers.

Results

A total of 1,200 professional drivers were included in the analysis and classified into two groups according to BMI < 25 kg/m² (n = 600) and BMI ≥ 25 kg/m² (n = 600). Baseline demographic, occupational, lifestyle, and clinical characteristics of the participants are presented in Table 1. The distribution of driver’s license type differed significantly between the two BMI groups (χ² = 3.72, P = 0.027), with higher proportions of Base 1 and Base 2 licenses observed among drivers with BMI ≥ 25 kg/m². Daily driving duration also showed a significant difference between groups (χ² = 8.53, P < 0.001); Driving more than 8 h per day was more frequent among overweight/obese drivers (44.1%) than among normal-weight drivers (19.4%). Marital status differed significantly between groups (χ² = 4.81, P = 0.002), with a higher proportion of married participants in the overweight and obese group.

Table 1.

Baseline characteristics of the study participants

Variables	All Participants (N = 1200)	BMI < 25 N = 600	BMI > = 25 N = 600	χ²	P value
Driver’s license type (%)
Base 1	67.3%	21.3%	46.0%	3.72	0.027
Base 2	32.7%	12.5%	20.2%
Base 3	0.1%	0.1%	0
Driving time per day (%)
< 4 h	12.1%	5.7%	6.4%	8.53	< 0.001
4–8 h	24.5%	9.3%	15.2%
> 8 h	63.5%	19.4%	44.1%
Driving type (%)
Passenger cars	25.1%	12%	13.1%	0.009	0.124
Road vehicles	74.9%	35%	39.9%
Shift work (%)
Regular Shift	71.6%	34.1%	37.5%	0.031	< 0.001
Irregular Shift	14.9%	6.2%	8.7%
Split/Rotating Shift	13.5%	4.2%	9.3%
Overnight driving (%)
Daytime-only driving	47.7%	15.9%	31.8%	0.419	0.514
Overnight driving (≥ 1 night/week)	52.3%	18.3%	34%
Age (%)
=<30	17.1%	8.7%	8.4%	1.64	0.724
> 30	82.9	40.6	42.3%
Marital status (%)
Single	10.4%	4.8%	5.6%	4.81	0.002
Married	89.6%	29.4%	60.2%
Education level (%)
High school	77.2%	27.2%	50.0%	2.19	0.108
Diploma	6.4%	1.5%	4.9%
Bachelor	16.4%	5.6%	10.8%
Smoking (%)
No	25.6%	10.3%	15.3%	3.23	0.009
Yes	74.4%	24.0%	50.4%
Alcohol use (%)
No	98.1%	33.4%	64.7%	0.51	0.162
Yes	1.9%	0.9%	1.0%
Physical activity levels (%)
Low	69.3%	20.1%	49.2%	55.7	< 0.001
Moderate	26.7%	9.4%	17.3%
High	4%	0.9%	3.1%
Obstructive sleep apnea (%)
Low risk	90.6	33.8%	56.8%	54.26	< 0.001
High risk	9.4	0.4%	9%
Diabetes (%)
Yes	4.4%	0.4%	4.0%	7.79	< 0.001
No	95.6%	33.9%	61.7%
Dyslipidemia (%)
No	54.6%	22.3%	32.5%	12.41	< 0.001
Yes	45.4%	12.2%	33.2%
Hypertension (%)
No	96.4%	33.9%	62.5%	5.35	< 0.001
Yes	3.6%	0.4%	3.2%
Metabolic syndrome (%)
No	69.4%	31.5%	37.9%	74.58	< 0.001
Yes	30.4%	2.8%	27.9%

BMI Body mass index, FBG Fasting blood glucose, TG Triglycerides, HDL-C High density lipoprotein cholesterol, LDL-C Low density lipoprotein cholesterol, ISI Insomnia severity index, OSA Obstructive sleep apnea, ESS Epworth sleepiness scale, EDS Excessive daytime sleepiness

Physical activity level differed significantly between the two groups, with low physical activity being more common in the overweight/obese group compared to the non-overweight/obese group (49.2% vs. 20.1%; χ² = 55.7, P < 0.001). Metabolic syndrome was significantly more prevalent among drivers with BMI ≥ 25 kg/m² than among those with BMI < 25 kg/m² (27.9% vs. 2.8%; χ² = 74.58, P < 0.001). Obstructive sleep apnea risk also differed significantly between groups; high risk for OSA was more frequent in the overweight/obese group than in the non-overweight/obese group (9.0% vs. 0.4%; χ² = 54.26, P < 0.001). Regarding other metabolic conditions, the prevalence of diabetes mellitus (4.0% vs. 0.4%; χ² = 7.79, P < 0.001), dyslipidemia (33.2% vs. 12.2%; χ² = 12.41, P < 0.001), and hypertension (3.2% vs. 0.4%; χ² = 5.35, P < 0.001) was significantly higher among drivers with BMI ≥ 25 kg/m². With respect to lifestyle and demographic factors, smoking prevalence was significantly higher in the overweight/obese group compared to the non-overweight/obese group (50.4% vs. 24.0%; χ² = 3.23, P = 0.009). Furthermore, educational levels and driving duration also differed between the two groups; higher proportions of drivers with overweight/obesity had lower educational levels (high school: 50.0% vs. 27.2%) and longer driving duration (> 8 h: 44.1% vs. 19.4%) compared to non-overweight/obese drivers. Shift work status was significantly associated with overweight/obesity (χ² = 30.1, P < 0.001).The proportion of overweight/obese drivers was higher among those with irregular shifts (8.7% vs. 6.3%) and split/rotating shifts (9.8% vs. 4.2%) compared to non-overweight/obese drivers. While overnight driving (at least once per week) was more common in the overweight/obese group (34.0% vs. 18.3%), the difference was not statistically significant (χ² = 0.419, P = 0.514).

As presented in Table 2, the distribution of sleep quality, assessed using the PSQI, differed between the case (BMI ≥ 25 kg/m²) and control (BMI < 25 kg/m²) groups. A higher proportion of poor sleep quality was observed among participants with BMI ≥ 25 kg/m² compared with those with BMI < 25 kg/m² (30.7% vs. 14.4%); however, this difference was not statistically significant (χ² = 1.27, P = 0.104).

Table 2.

Comparison of sleep quantity and quality between case and control groups

Variables	BMI < 25 N = 600	BMI > = 25 N = 600	χ²	P value
PSQL score (%)
Poor sleep	14.4%	30.7%	1.27	0.104
Good sleep	19.9%	34.9%
Sleep duration
<=7	13.4%	20.8%	6.35	< 0.001
> 7	46.7%	19.1%

In contrast, a statistically significant difference was observed in sleep duration between the two BMI groups (χ² = 6.35, P < 0.001). Sleeping ≤ 7 h per night was more frequently reported among participants with BMI ≥ 25 kg/m² (20.8%) than among those with BMI < 25 kg/m² (13.4%). Conversely, sleeping more than 7 h was more common in the BMI < 25 kg/m² group (46.7%) compared with the BMI ≥ 25 kg/m² group (19.1%).

Analysis of individual PSQI components revealed significant differences between the two groups in several domains (Supplementary Table 1). Overweight/obese drivers had significantly higher rates of sleeping medication use (2.1% vs. 0.5%, P = 0.018), inefficient habitual sleep efficiency (59.1% vs. 29.6%, P = 0.033), sleep disturbances (10.3% vs. 1.8%, P < 0.001) compared to non-overweight/obese drivers. No significant differences were observed for subjective sleep quality, daytime dysfunction, or sleep latency (P > 0.05).

The associations between sleep characteristics and overweight/obesity were examined using logistic regression models (Table 3). In Model 1, short sleep duration (≤ 7 h) was associated with increased odds of overweight/obesity. This association remained significant after further adjustment in Models 2 and 3. In the fully adjusted model (Model 3), drivers who slept ≤ 7 h had higher odds of being overweight/obese compared with those who slept > 7 h (OR = 1.319; 95% CI: 1.011–1.722). Additionally, drivers with poor sleep quality had higher odds of being overweight or obese compared with drivers with good sleep quality (OR = 1.287; 95% CI: 1.014–1.639).

Table 3.

Results of logistic regression analysis for overweight/obesity among professional drivers

Study variables	Model 1	Model 2	Model 3
	OR (95% CI), P Value	OR (95% CI), P Value	OR (95% CI), P Value
Sleep quality
Good	reference	reference	reference
Poor	1.211 (0.961, 1.526), 0.105	1.270 (0.993, 1.604), 0.059	1.287 (1.014, 1.639), 0.041
Sleep duration
> 7 h	reference	reference	reference
<= 7 h	1.576 (1.239, 2.005), < 0.001	1.419 (1.114, 1.818), 0.006	1.319 (1.011, 1.722), 0.042

Overweight/obesity was defined as BMI ≥ 25 kg/m²

Model 1: unadjusted; model 2: adjustments for educational attainment, driving duration, smoking status; and shift work and model 3: additionally accounted for physical activity level, metabolic syndrome, and obstructive sleep apnea

Logistic regression analyses identified several PSQI components as independent correlates of overweight/obesity (BMI ≥ 25 kg/m²) among professional drivers. In the fully adjusted model, use of sleeping medication ≥ 1 time/week (OR = 2.326, p = 0.005), any sleep disturbance (OR = 3.098, p < 0.001), and sleep duration ≤ 7 h (OR = 1.319, p = 0.042) remained significantly associated with overweight/obesity. Inefficient sleep showed a modest association (OR = 1.021, p = 0.031), while subjective sleep quality and daytime dysfunction were not significant after adjustments (Supplementary Table 2).

Discussion

The present study examined the association between sleep duration and sleep quality with overweight/obesity in professional drivers. The findings indicate that short sleep duration and poor sleep quality are associated with an increased odds of overweight/obesity in this occupational group, after adjustment for potential confounders. These results highlight sleep as an important, yet often overlooked, factor in the development of excess BW among professional drivers.

Evidence from previous studies indicates that short sleep duration is associated with increased odds of obesity among truck drivers, and irregular work patterns further contribute to central adiposity [13, 14]. Some investigations report an inverse association between short sleep duration and increased BMI, while others describe U-shaped (i.e., both short and long sleep associated with obesity or null relationships. For example, a large cross-sectional study by Chen et al. found no significant association between sleep duration and overweight/obesity were found among boys [7]. In the study by Sa et al. (2020), shorter sleep duration was significantly associated with obesity among US college students; however, this association was not observed in South Korean college students [8]. Ren et al. (2022) reported gender-specific non-linear associations between sleep duration and BMI in rural Chinese adults. Among men, an inverse U-shaped relationship was observed, with BMI peaking at approximately 7 h of sleep. Among women, a U-shaped relationship was found, with both short and long sleep durations associated with higher BMI and the lowest BMI at around 9 h of sleep [9]. Fan et al. (2020) reported that short sleep duration was significantly associated with increased risk of overweight and obesity in a large sample of 35,414 Chinese children. Very short sleep showed an even stronger association with obesity (OR = 3.01). The inverted J-shaped relationship observed in their study supports our finding that insufficient sleep is a significant risk factor for excess body [25].

In the current study, short sleep duration (≤ 7 h) was consistently associated with higher odds of overweight /obesity across all regression models. These results are largely consistent with a substantial body of epidemiological evidence showing positive associations between inadequate sleep and increased adiposity. A recent large meta-analysis of prospective cohort studies reported that short sleep duration was significantly associated with central obesity, indicating that individuals with shorter sleep may have a slightly increased risk of excess abdominal fat accumulation compared with those with normal sleep duration [26]. A meta-analytic assessment conducted by Itani et al., encompassing 16 prospective cohort studies, demonstrated that short sleep duration (defined as less than 6 h per day) was significantly associated with an elevated risk of obesity. Specifically, individuals with insufficient sleep exhibited a 38% higher incidence of obesity (OR = 1.38, 95% CI 1.25–1.53) [27].

A meta-analysis study indicated that short sleep duration (OR: 1.412; 95% CI: 1.177–1.694) was significantly associated with the risk of future obesity [17]. Additionally, data from American adult population showed that adults with short nocturnal sleep had higher odds of overweight/ obesity than those with recommended sleep duration [18]. In a large-scale prospective investigation involving 14,800 participants, Patrick et al. reported that a decrease of 0.50 standard deviations in sleep duration was linked to a 1.45-fold elevation in the likelihood of obesity, along with an increase in WC (95% CI 1.03–2.04 and 1.02–2.06 respectively). These findings underscore the role of insufficient sleep as a significant contributor to obesity risk and are in agreement with the outcomes observed in the present study [28].

Insufficient sleep has been consistently associated with overweight/obesity through multiple interacting biological pathways. Key mechanisms include hormonal dysregulation, such as reduced leptin and elevated ghrelin, which increase appetite and caloric intake, as well as insulin resistance and metabolic disturbances that favor fat accumulation. Additionally, sleep deprivation can reduce physical activity due to fatigue and alter the gut microbiome and inflammatory responses, further promoting adiposity [29–31]. Furthermore, sleep deprivation may promote obesity through alterations in the gut microbiome and inflammatory pathways [32]. Reduced sleep is associated with increased intestinal permeability, a decline in beneficial microbial taxa (including Bacteroidetes, Actinobacteria, and Bifidobacteria), and elevated systemic inflammation [33, 34]. Evidence indicates a shared genetic architecture between obesity and sleep-related phenotypes, particularly involving circadian rhythm–regulating genes such as CLOCK and NR1D1, which have been robustly linked to BMI and obesity-associated traits. Moreover, findings from a Mendelian randomization analysis suggest that each additional hour of sleep duration is associated with approximately a 30% lower risk of central obesity (OR = 0.70, 95% CI: 0.64–0.77) [35]. Together, these pathways provide plausible biological explanations for the link between inadequate sleep and increased obesity risk.

Next, our results showed that poor sleep quality was associated with increased likelihood of being overweight/obese. A systematic review indicates that inadequate sleep, encompassing both short duration and poor sleep quality, is associated with an increased risk of overweight/obesity among young individuals (OR = 1.27, 95% CI: 1.05–1.53) [10]. In a large population-based study conducted among Chinese adults (N = 2,803), Hung et al. reported that individuals classified as overweight or obese exhibited a substantially greater likelihood of poor sleep quality, with risk increases of approximately 40% and 60%, respectively [11]. A study demonstrated that poor sleep quality is significantly associated with an increased likelihood of overweight and obesity [5]. The biological pathways linking sleep quality to obesity indicate that impaired sleep quality disrupts appetite regulation, which may promote unhealthy dietary choices and subsequently lead to increased energy intake [5].

Similarly, contradictory findings exist regarding sleep quality, with some studies reporting positive associations with overweight/obesity [10–12], while others found no independent association after adjustment for confounders such as depression or physical activity. These inconsistencies may be attributed to differences in study design, population characteristics, measurement tools, and adjustment for confounders. Therefore, the purpose of this study was not merely to replicate existing findings, but to specifically examine these associations in a high-risk and understudied occupational group—Iranian professional drivers—who face unique sleep challenges due to irregular work hours, shift work, and occupational stress, and in whom evidence remains scarce.

The present study demonstrates that specific sleep disturbances, namely any sleep disturbance (OR = 3.098), use of sleeping medication (OR = 2.326), and short sleep duration ≤ 7 h (OR = 1.319), are independently associated with overweight/obesity among professional drivers after full adjustment for confounders. Subjective sleep quality and daytime dysfunction were not significant in adjusted models, underscoring the importance of objective or behaviorally specific sleep measures. The persistence of these associations despite controlling for metabolic syndrome and obstructive sleep apnea suggests direct effects of sleep disruption on energy regulation, likely via circadian and neuroendocrine mechanisms. These findings support the integration of sleep disturbance screening into workplace obesity prevention strategies for professional drivers.

In present study, several lifestyle and demographic factors such as driving time, marital status, smoking, and physical activity levels contribute to overweight/ obesity among professional drivers. Longer driving hours are associated with prolonged sedentary behavior, irregular eating patterns, and reduced opportunities for physical activity, increasing the risk of weight gain [36]. Married individuals may encounter heightened risk owing to increased meal frequency and portion sizes. Tobacco smoking has been linked to altered metabolic regulation and central fat deposition [37], while low levels of physical activity further contribute to energy imbalance and the development of obesity [38]. Collectively, these factors underscore the multifactorial nature of obesity risk in professional driving populations. However, after adjustment for these variables, short sleep duration and poor sleep quality remained associated with an increased likelihood of being overweight or obese.

Strengths and limitations

This study has several strengths. The case-control design allowed efficient retrospective evaluation of poor sleep quality as a risk factor for overweight/obesity in drivers by comparing overweight/obese cases and non-overweight/obese controls from the same population. Matching cases and controls on sex and age minimized selection bias and enhanced comparability. The inclusion of the validated PSQI questionnaire and comprehensive adjustment for multiple confounders strengthened the internal validity of our findings. Furthermore, focusing on a large sample size of professional drivers, a high-risk occupational group with unique sleep and health challenges, adds important evidence to the literature on the relationship between sleep and obesity in understudied populations.

This study has several limitations. Sleep measures were self-reported (PSQI), which may introduce recall bias and misclassification, potentially attenuating true associations. We lacked objective sleep assessment (e.g., actigraphy or polysomnography), which limits measurement precision. Additionally, we did not collect information on daily energy intake or dietary patterns—known determinants of sleep quality and body weight—nor did we directly assess mental health conditions (e.g., depression, anxiety, stress) or use standardized instruments for occupational stress and fatigue. The absence of these data may have led to residual confounding. Although we analyzed individual PSQI components separately, we did not perform subgroup analyses based on specific sleep problem patterns (e.g., difficulty falling asleep versus maintaining sleep), as different patterns may have distinct metabolic effects. Finally, as our study focused specifically on Iranian professional drivers, caution is warranted when generalizing these findings to other populations or occupational contexts.

Suggestions for future research

Future studies should address these limitations by: (1) employing objective sleep measurement tools such as actigraphy or polysomnography; (2) collecting detailed dietary intake data using validated food frequency questionnaires or multiple 24-hour dietary recalls; (3) assessing mental health conditions, occupational stress and fatigue; (4) analyzing individual PSQI components separately to identify which specific domains of poor sleep quality are most strongly associated with overweight/obesity; (5) performing subgroup analyses based on specific sleep problem patterns to determine whether distinct sleep phenotypes have differential metabolic effects; and (7) conducting multicenter studies with diverse driver populations to enhance generalizability.

Conclusions

This study demonstrates that short sleep duration and poor sleep quality are associated with a higher likelihood of overweight/obesity among professional drivers. These findings highlight sleep as an important and modifiable determinant of metabolic health in this occupational group, consistent with previous evidence linking inadequate sleep to higher adiposity. Incorporating sleep-focused strategies into occupational health programs may contribute to improved weight management and enhanced safety among professional drivers. However, longitudinal and interventional studies are needed to clarify causal relationships and evaluate the potential effects of sleep improvement on weight regulation and overall health in this population.

Abbreviations

BMI

Body mass index

BW

Body weight

HDL-C

High density lipoprotein cholesterol

LDL-C

Low density lipoprotein cholesterol

OR

Odds ratios

OSA

Obstructive sleep apnea

PSQI

Pittsburgh Sleep Quality Index

WC

Waist circumference

Authors’ contributions

M.A. and M.H.E. made substantial contributions in the following roles: writing the original draft, conceptualization, funding acquisition, project administration, resources, supervision, investigation, methodology, validation, and visualization. In addition, M.A. contributed to the software and formal analysis. K.S.N. and M.H.E. contributed to conceptualization, project administration, and data acquisition.

Funding

This work was supported by Shahroud University of Medical Sciences [Grant number: 9434].

Data availability

The data supporting this study’s findings are available upon request. For data requests, please contact the corresponding author Dr. Mohammad Hossein Ebrahimi (Email: mohammadhosseinebrahimi2025@gmail.com).

Declarations

Ethics approval and consent to participate

This article is grounded in a research project designated by code 9434 and ethics code IR.SHMU.REC.1395.20, authorized by the Ethical Committees and Internal Review Boards under the auspices of the Vice Chancellor for Research at Shahroud University of Medical Sciences. All study protocols were thoroughly explained to the participants to ensure that they fully understood the nature, purpose, and potential risks of the study. Written informed consent was obtained from each participant before their inclusion in the study. The participants were assured that their participation was voluntary and that they could withdraw from the study at any time without any consequences.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.