Caffeine Intake and Depressive Symptoms among Healthcare Workers: The Role of Stress and Sleep Quality

Caffeine Intake and Depressive Symptoms among Healthcare Workers: The Role of Stress and Sleep Quality | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Caffeine Intake and Depressive Symptoms among Healthcare Workers: The Role of Stress and Sleep Quality Sumia Enani, Salwa Albar, Muntaha Faisal Alsulaimani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9098832/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Caffeine is widely consumed among healthcare workers (HCWs) as a coping mechanism for occupational demands and may influence depressive symptoms. This study investigated this relationship after adjustment for perceived stress and sleep quality. In this cross-sectional study on licensed HCWs at a tertiary hospital in Jeddah, Saudi Arabia, habitual caffeine intake was assessed using a validated caffeine food frequency questionnaire. Depressive symptoms were assessed using Patient Health Questionnaire (PHQ-9), with clinically significant depressive symptoms defined as PHQ-9 ≥ 10. Perceived stress and sleep quality were measured. Logistic and linear regression models evaluated the associations with PHQ-9 ≥ 10 and PHQ-9 score respectively. Among 298 HCWs (mean age 37.5 years; 66.1% women), 18.5% had PHQ-9 ≥ 10. Mean caffeine intake was 216 mg/day (median 125 mg/day). HCWs with PHQ-9 ≥ 10 reported a higher caffeine intake than those without (Mean 282 vs. 201 mg/day and median 169 vs. 114 mg/day; p = 0.038). Caffeine intake was significantly associated with PHQ-9 score but not with PHQ-9 ≥ 10 after full adjustment (β = 0.331; p = 0.014 per twofold increase). Perceived stress and poorer sleep quality were independently associated with PHQ-9 ≥ 10. Higher caffeine intake may reflect response to occupational strains rather than a primary depression risk driver. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Biological sciences/Psychology Social science/Psychology Health sciences/Risk factors Caffeine depressive symptoms healthcare workers perceived stress sleep quality cross-sectional study Introduction Depression is a major contributor to the global burden of disease, affecting more than 300 million individuals worldwide, ranking as the single largest contributor to non-fatal health loss, and is among the top causes of disability-adjusted life years (DALYs) [ 1 ]. Healthcare workers (HCWs) are among the most vulnerable occupational groups for mental health challenges due to the unique nature and demands of their work [ 2 , 3 ]. Challenging work conditions, constant exposure to high patient care demands, emotional strain, rotating shifts and disturbed sleep patterns increase their susceptibility to depression, anxiety and burnout. Reviews on depression among HCWs in Saudi Arabia reported high prevalence reaching around 75% in some professional categories [ 4 ]. These mental health challenges impose concerns given the implication on health care delivery, patient care quality and healthcare system sustainability. Caffeine is the most widely consumed psychoactive substance [ 5 ], and therefore, its consumption is a behavioral factors that of a growing interest in the context of mental health. HCWs turn to caffeine, most commonly through coffee, tea and energy drinks, as a coping mechanism for occupational fatigue, to enhance short term alertness especially during long or irregular shifts and extended duty hours. A descriptive study of 600 healthcare providers in Saudi Arabia revealed that caffeine use was extremely common (94.3%) among them and that the average caffeine intake was 370 mg/day which is higher than adult average [ 6 ]. The primary neuropharmacological mechanism of caffeine is through antagonism of adenosine receptors in the nervous system which increases dopamine and noradrenaline activity, and in turn, increases arousal and wakefulness [ 7 ]. However, caffeine also might be harmful in the long term, contributing to disturbed sleep and increased stress levels, which are both contributors to depression, especially when consumed in high doses [ 8 ]. Habitual moderate caffeine intake has been linked to decreased risk of depression in meta-analyses [ 9 ]. However, studies included in this meta-analysis were mostly done on general Western population, limiting its generalizability to HCWs in non-Western populations or high-burden settings. Evidence from high-stress populations is limited and often lack detailed adjustment for psychological and lifestyle related factors. For example, the Nurses’ Health Study, a large cohort study, found that moderate caffeine intake, particularly from coffee, was associated with lower depression risk [ 10 ]. This previous study and other studies [ 11 , 12 ] mostly adjusted for lifestyle and medical conditions, but not always adjusted for stress physiology and sleep hygiene, which are well-established risk factors for depressive symptoms. While caffeine is widely used in HCWs, its relationship with depression remains unclear and might be confounded by chronic stress and poor sleep. To our knowledge, no prior studies in Saudi Arabia have explored the independent association between caffeine consumption and depressive symptoms in HCWs while accounting for psychological distress and poor sleep. Accordingly, this study seeks to investigate the cross-sectional association between habitual caffeine intake and depression in HCWs in one of the largest affiliated hospitals in the region, and to explore the confounding roles of various demographic, psychological and lifestyle factors including stress and sleep quality. The findings may help clarify whether the relationship between caffeine intake and depression is independent or reflects broader behavioral and psychological adaptation to the work environment, which will be helpful for designing healthcare staff wellness interventions. Results A total of 298 HCWs responded to the survey and participated in the study. The mean age of participants was 37.5 ± 7.8 years and 197 (66.1%) of them were women (Table 1 ). Most of the participants were Saudi (71.5%) and married (64.4%). The mean BMI based on self-reported height and weight was 26.1 ± 5.09, with 42.6% having normal weight, 37.1% overweight and 17.6% obese. Table 1 Descriptive characteristics of the study participants (N = 298 HCWs) Variable Total (N = 298) Without depressive symptoms (n = 243) With depressive symptoms (n = 55) P-value Age (years) mean ± SD 37.5 ± 7.8 38.1 ± 7.9 35.1 ± 6.9* 0.012 Sex n (%) Men 101 (33.9%) 87 (35.8%) 14 (25.5%) 0.143 Women 197 (66.1%) 156 (64.2%) 41 (74.5%) Nationality n (%) Saudi 213 (71.5%) 175 (72%) 38 (69.1%) 0.664 Non-Saudi 85 (28.5%) 68 (28%) 17 (30.9%) Marital status n (%) Single 88 (29.5%) 67 (27.6%) 21 (38.2%) 0.124 Married 192 (64.4%) 163 (67.1%) 29 (52.7%) Divorced or widowed 18 (6%) 13 (5.30%) 5 (9.10%) Educational level n (%) Diploma or less 44 (14.8%) 37 (15.2%) 7 (12.7%) 0.883 Bachelor 156 (52.3%) 127 (52.3%) 29 (52.7%) Postgraduate studies (Master or PhD) 98 (32.9%) 79 (32.5%) 19 (34.5%) Living status n (%) Alone 45 (15.1%) 32 (13.2%) 13 (23.6%) 0.160 With parents and/or sibling 105 (35.2%) 88 (36.2%) 17 (30.9%) Married couple with children 106 (35.6%) 90 (37%) 16 (29.1%) Married couple without children 30 (10.1%) 22 (9.10%) 8 (14.5%) Living alone with children 12 (4%) 11 (4.50%) 1 (1.80%) Household income (SAR) n (%) 30.000 70 (23.5%) 59 (24.3%) 11 (20%) Professional category n (%) Physician 48 (16.1%) 37 (15.2%) 11 (20%) 0.746 Nurse 111 (37.2%) 90 (37%) 21 (38.2%) Specialist/Allied health 114 (38.3%) 96 (39.5%) 18 (32.7%) Technical support 25 (8.4%) 20 (8.20%) 5 (9.10%) Work hours/week mean ± SD 43.7 ± 8.7 43.5 ± 8.9 44.3 ± 8 0.587 Work hours category n (%) ≤ 40 155 (52%) 131 (53.9%) 24 (43.6%) 0.168 > 40 143 (48%) 112 (46.1%) 31 (56.4%) Sleep duration (hours/day) mean ± SD 6.3 ± 1.18 6.4 ± 1.2 6.2 ± 1.2 0.484 Sleep duration category (hours/day) n (%) < 6 73 (24.6%) 57 (23.6%) 16 (29.1%) 0.584 6–7.5 180 (60.6%) 150 (62%) 30 (54.5%) ≥ 8 44 (14.8%) 35 (14.5%) 9 (16.4%) Physical activity n (%) Inactive 167 (56%) 129 (53.1%) 38 (69.1%) 0.066 Insufficiently active 86 (28.9%) 73 (30%) 13 (23.6%) Meets physical activity guidelines 45 (15.1%) 41 (16.9%) 4 (7.30%) Smoking status n (%) Non-smoker 220 (73.8%) 183 (75.3%) 37 (67.3%) 0.530 Cigarette smoker 29 (9.7%) 21 (8.60%) 8 (14.5%) Shisha smoker 26 (8.7%) 20 (8.20%) 6 (10.9%) Electronic cigarette smoker 23 (7.7%) 19 (7.80%) 4 (7.30%) BMI (kg/m2) mean ± SD 26.1 ± 5.1 26 ± 5.2 26.7 ± 4.7 0.374 BMI category n (%) Underweight 8 (2.7%) 7 (2.90%) 1 (1.80%) 0.117 Normal 126 (42.6%) 110 (45.6%) 16 (29.1%) Overweight 110 (37.2%) 83 (34.4%) 27 (49.1%) Obese 52 (17.6%) 41 (17%) 11 (20%) Data are mean ± SD for continuous and n (n%) for categorical variables. N = 298 HCWs. BMI was available for 296/298 participants as 2 did not report their height and weight. Percentages may not sum to 100% due to rounding. Insufficiently active: < 150 min/week (mod. activity) or < 75 min/week (vigorous), Meets physical activity guidelines: ≥ 150 min/week (mod. activity) or ≥ 75 min/week (vigorous) P-values from t-test for continuous variables and χ 2 for categorical variables. *p < 0.05 Among participating HCWs, 55 (18.5%) had clinically depressive symptoms (PHQ-9 ≥ 10) (Table 1 ). HCWs with depressive symptoms were significantly younger than those without (35.1 ± 6.9 vs 38.1 ± 7.9 years; p = 0.012). Sex, nationality, marital status, educational level, living status, household income, professional category, weekly work hours, sleep duration, smoking status and BMI did not differ significantly between those with and without depressive symptoms (all p > 0.05). Physical inactivity showed a non-significant trend toward higher prevalence of depressive symptoms, with higher proportion of inactivity among participants with depressive symptoms compared with those without (69.1% vs 53.1%; p = 0.066). Caffeine intake by depressive symptom status in HCWs Caffeine intake among HCWs is summarized in Table 2 . Among all participants, the mean caffeine intake was 216 ± 298 mg/day and the median was 125 mg/day (IQR 49–250). The majority of HCWs included had low caffeine intake (< 200 mg/day) accounting for 67.4%. The mean caffeine intake in this group was 82 ± 58 mg/day and the median was 78 mg/day (IQR 31–128). Moderate intake was reported by 50 (16.8%) HCWs. The mean caffeine intake in this group was 274 ± 56 mg/day and the median was 259 mg/day (IQR 229–322). High caffeine intake (> 400 mg/day) was reported by 47 HCWs (15.8%) with a mean of 722 ± 432 mg/day and a median of 561 mg/day (IQR 440–801). Table 2 Caffeine intake (total and by source) by depressive symptoms status (N = 298) Variable Total N = 298 Without depressive symptoms n = 243 With depressive symptoms n = 55 P-value Total caffeine intake, mg/day Mean ± SD 216 ± 291 201 ± 266 282 ± 378 0.038 Median (IQR) 125 (49–250) 114 (47–231) 169 (72–305) Caffeine intake by source, mg/day Coffee, mean ± SD 135 ± 213 123 ± 200 188 ± 259 0.037 Coffee, median (IQR) 67 (14–155) 61 (12.5–146) 104 (21–239) Tea, mean ± SD 57 ± 106 58 ± 108 53 ± 98 0.423 Tea, median (IQR) 18 (5–60) 18 (4–57) 23 (7–64) Chocolate, mean ± SD 7 ± 25 5 ± 14 18 ± 49 < 0.001 Chocolate, median (IQR) 1 (0–5) 0 (0–4) 3 (0–6) Energy drinks, mean ± SD 3 ± 10 3 ± 11 3 ± 7 0.016 Energy drinks, median (IQR) 0 (0–0) 0 (0–0) 0 (0–5) Soft drinks, mean ± SD 14 ± 30 12 ± 28 20 ± 40 0.032 Soft drinks, median (IQR) 3 (0–15) 3 (0–11) 6 (1–19) Caffeine intake category, n (n%) Low ( 400 mg/day) 47 (15.8%) 35 (14.4%) 12 (21.8%) Data are presented as mean ± SD and median (IQR) for continuous variables, and as frequency (%) for categorical variables. Depressive symptoms were defined as PHQ-9 ≥ 10. All continuous caffeine intake by depressive symptom status was analyzed using Mann-Whitney U test due to skewness. Categorical caffeine intake category association was analyzed using χ 2 test. Percentages may not sum to 100% due to rounding. Significant p-values are shown in bold. Caffeine intake differed by depressive symptoms status (Table 2 ). HCWs with clinically significant depressive symptoms (PHQ-9 ≥ 10) had a significantly higher total caffeine intake compared with those without depressive symptoms (Mean ± SD: 282 ± 378 vs 201 ± 266; median (IQR): 169 (72–305) vs 114 (47–231); p = 0.038). Higher coffee intake was the main contributor to this difference in caffeine intake (Mean ± SD: 188 ± 259 vs 123 ± 200; median (IQR): 104 (21–239) vs 61 (12.5–146); p = 0.037). Tea intake did not differ significantly with depressive symptom status. Categorical caffeine groups were not significantly associated with PHQ-9 ≥ 10. Psychological and sleep measures association with caffeine intake among HCWs Psychological and sleep measures among HCWs are summarized in Table 3 . The mean PHQ-9 score was 5.93 ± 5.31 and the median was 5 (IQR 2–9). The mean PSS-10 score was 16.9 ± 5.78 and the median was 17 (IQR 14–20) with 63 (21.1%) classified as having high perceived stress (PSS-10 > 20). The mean SQQ score was 16.1 ± 9.32 and the median was 17 (IQR 9–23) with 108 (36.6%) classified as having poor sleep quality (SQQ > 20). Higher caffeine intake was associated with higher PHQ-9 scores (p = 0.011), higher perceived stress PSS-10 means (p = 0.049) and poorer sleep quality SQQ scores (p = 0.013). The prevalence of high perceived stress and poor sleep quality were also associated with higher caffeine intake (p = 0.012 and p = 0.017 respectively). Table 3 Psychological and sleep measures among HCWs Measure Mean (SD) Median (IQR) Range n (%) above predefined cutoff PHQ-9 (Depression) 5.93 ± 5.31 5 (2–9) 0–24 55 (18.5%)* PSS-10 (Perceived stress) 16.9 ± 5.78 17 (14–20) 1–37 63 (21.1%) † SQQ (Sleep Quality Score) 16.1 ± 9.32 17 (9–23) 0–40 108 (36.6%) ‡ Data are presented as mean ± SD and median (IQR) for continuous data, and as frequency (%) for categorical data. *High depression defined as PHQ-9 ≥ 10 † High stress defined as PSS-10 > 20 ‡ Poor sleep defined as SQQ > 20 PHQ-9, Patient Health Questionnaire-9; PSS-10, Perceived Stress Scale-10; SQQ, Sleep Quality Questionnaire. Table 4 Psychological and sleep measures by caffeine groups among HCWs (N = 298) Measure Caffeine intake P-value Low ( 400 mg/day) n = 47 PHQ-9, mean ± SD 5.29 ± 4.88 7.22 ± 4.95 7.28 ± 6.81 0.011 PSS-10, mean ± SD 16.3 ± 5.72 18.4 ± 5.18 17.5 ± 6.34 0.049 SQQ score, mean ± SD 15 ± 9.19 a 18 ± 8.67 a,b 18.7 ± 9.9 b 0.013 High stress, n (n%) 34 (16.9%) 12 (24%) 17 (36.2%) 0.012 Poor sleep, n (n%) 64 (32%) 19 (38.8%) 25 (54.3%) 0.017 Data are presented as mean ± SD continuous data, and as frequency (%) for categorical data. High stress defined as PSS-10 > 20. Poor sleep defined as SQQ > 20 (higher SQQ = poorer sleep quality). Continuous outcomes were analyzed using one-way ANOVA and post-hoc pairwise comparisons were performed using the Bonferroni method; categorical outcomes were analyzed using χ 2 test. a and b are statistically significantly different in Bonferroni-adjusted pairwise comparisons ( p < 0.05). PHQ-9, Patient Health Questionnaire-9; PSS-10, Perceived Stress Scale-10; SQQ, Sleep Quality Questionnaire. Regression models for the association between caffeine intake and depressive symptoms in HCWs In crude logistic regression models, increased caffeine intake was associated with higher odds of having clinically significant depressive symptoms (PHQ-9 ≥ 10) as those consuming 200–400 mg/day of caffeine had OR of 1.481 (95% CI 0.687–3.193) and those consuming > 400 mg/day had OR of 1.853 (95% CI 0.867–3.958) compared with those consuming < 200 mg/day (Table 5 ); however, these associations were not statistically significant. After adjustment for potential demographic, sociodemographic, lifestyle, stress and sleep confounders, caffeine remained unassociated with PHQ-9 ≥ 10 (200–400 mg/day: OR 1.083, 95% CI (0.396–2.961); >400 mg/day: OR 0.975, 95% CI (0.338–2.81) Table 5 ). In contrast, higher perceived stress and poorer sleep quality scores were independently associated with higher odds of depression (stress: OR 1.211, 95% CI (1.106–1.325); sleep: OR 1.136, 95% CI (1.074–1.202); p < 0.001 for both). Table 5 Association between caffeine intake group and clinically significant depressive symptoms (PHQ-9 ≥ 10) Caffeine intake Model 1 (Crude) Model 2 (Adjusted) † Model 3 (Fully adjusted) § OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value 400 mg/day 1.853 (0.867–3.958) 0.111 1.561 (0.646–3.775) 0.323 0.975 (0.338–2.810) 0.962 Odds ratios (OR) from binary logistic regression. Outcome: PHQ-9 ≥ 10. N = 298; 55 participants had PHQ-9 ≥ 10. Model 1: Crude † Model 2: Adjusted for age (per year), sex, BMI (per 1 kg/m 2 ), smoking status, marital status, living status, nationality, and physical activity. § Model 3: Additionally adjusted for stress and sleep quality scales. In the crude linear regression model, each twofold increase in caffeine daily intake was associated with 0.69 (CI 0.376–0.998) point higher PHQ-9 score ( p < 0.001) (Table 6 ). After adjustment for demographic and lifestyle variables, the effect remained approximately similar (Model 2: β 0.622 (95% CI 0.297–0.947); p < 0.001). In the fully adjusted linear regression model, the association of caffeine intake and PHQ-9 was weakened but remained significant as each twofold increase in caffeine daily intake was associated with 0.331 (95% CI 0.067–0.595) point higher PHQ-9 score ( p = 0.014). Higher perceived stress and poorer sleep quality (higher SQQ) scores were also independently associated with higher odds of depression (stress: β = 0.253 (95% CI: 0.168–0.338); sleep: β = 0.246 (95% CI: 0.192–0.300); p < 0.001 for both). Table 6 Association between continuous caffeine intake and PHQ-9 score in linear regression models Caffeine intake β (95% CI) p-value Model 1 (Crude) 0.687 (0.376–0.998) < 0.001 Model 2 (Adjusted) † 0.622 (0.297–0.947) < 0.001 Model 3 (Fully adjusted) § 0.331 (0.067–0.595) 0.014 β represents the estimated change in PHQ-9 score per 1-unit increase in log2-transformed caffeine intake (2 fold increase) (N = 298 HCWs). † Adjusted for age (per year), sex, BMI (per 1 kg/m 2 ), smoking status, marital status, living status, nationality, and physical activity. § Additionally adjusted for stress and sleep quality scales. Discussion In this cross-sectional study of HCWs in a large tertiary hospital (KAUH) in Jeddah, 18.5% had clinically significant depressive symptoms (PHQ-9 ≥ 10). Average caffeine intake was 216 mg/day (median 125 mg/day) primarily driven by coffee intake, with 15.8% reporting high intake (> 400 mg/day). Total caffeine intake was significantly higher among HCWs with depressive symptoms. Higher total caffeine intake was also associated with higher depressive symptoms (PHQ-9 scores), higher perceived stress (PSS-10) and poorer sleep quality (SQQ). Moderate (200-400mg/day) and high (> 400mg/day) caffeine intake were not associated with higher odds of clinically significant depressive symptoms after adjustment, whereas stress and poorer sleep remained independently associated with the outcome. Together, this suggests that the association between caffeine intake and depression in this high-demand occupational group may largely be driven by stress burden and poor sleep hygiene rather than an independent caffeine-depression relationship. The observed prevalence of having depressive symptoms (18.5%) in HCWs is generally lower than other Saudi reports [ 4 , 13 , 14 ] but broadly consistent with studies that used the same depressive symptoms diagnostic tool (PHQ-9 ≥ 10) [ 15 , 27 , 28 ]. For example, in a multi-region cross-sectional study in the early COVID-19 outbreak that included > 15 hospitals in Saudi Arabia, significant depressive symptoms (PHQ-9 ≥ 10) was prevalent in 18.2% among HCWs with diverse professions [ 27 ]. Another multi-center cross-sectional study in Jeddah, Saudi Arabia that involved care- and non-care-related professions from 10 primary healthcare centers reported a substantially higher PHQ-9 ≥ 10 prevalence (36.3%) [ 14 ]. Differences in reported prevalence of depressive symptoms among HCWs in Saudi Arabia are attributable to variations in screening tools, pandemic phase, workload, role mix, and sampling strategies. Average daily intake of caffeine in the present KAUH sample was moderate (216 mg/day) but highly right-skewed (median 125 mg/day), with nearly sixth (15.8%) of participants reported high intake (> 400 mg/day). This distribution suggests that caffeine exposure in the majority of HCWs falls within the recommended safe caffeine limits for healthy adults [ 29 , 30 ]. However, a meaningful subset may have consumption levels that disrupt sleep and stress physiology. It is of a high importance that studies assess prevalence of higher than caffeine safe limits intake rather than just reporting mean mg/day. When compared with Saudi general-population data, the observed intake in this study is comparable with a previously reported estimate from a large survey with an average of 218 mg/day caffeine intake in adults [ 31 ]. However, it is considerably lower than a previously reported average of 446 mg/day among Saudi governmental healthcare providers [ 32 ]. The lower average intake in this study might be due to differences in sampling frame and participant mix as we sampled healthcare worker including care- and non-care providers from a single large academic university, whereas the previous study included healthcare providers in governmental hospitals. A key pattern in the finding of this study is that the daily caffeine exposure’s continuous association between with PHQ-9 score remained significant after full adjustment while the categorical association did not. This could be explained by the possibility of information loss when dichotomizing depression, especially when the number of cases is relatively low that could limit precision of adjusted logistic models. Another explanation could be the J-shaped coffee/caffeine relationship with depression reported in meta-analysis of observational studies with > 300,000 participant data [ 33 ]. In addition, stress and sleep attenuated the caffeine-depression relationship indicating that this relationship is partially explained by them. This is plausible as in a cross-sectional study, stress and sleep may act as confounders and/or potential mediating pathways, as caffeine can induce arousal and worsen sleep which increases depressive symptoms. However, temporal order cannot be established in cross-sectional designs. It would be more informative if prospective studies test mediation rather than treating stress and sleep purely as confounders. These studies also needed to establish directionality in caffeine-depression relationship, as depressive symptoms are often accompanied by fatigue and impaired concentration; HCWs may increase caffeine consumption as adaptive measures to meet work demands suggesting reverse causation. In contrast with our findings, systematic reviews and meta-analysis in general populations have consistently reported an inverse association between caffeine intake and depression, suggesting a protective effect [ 9 , 33 , 34 ]. This discrepancy could be due to differences in the context of the studies, as HCWs in tertiary hospitals may use caffeine to combat fatigue, which increases the likelihood that caffeine could be a marker of chronic under sleep rather than a luxury exposure. Another explanation is that many studies define the outcome as clinically diagnosed depression, but our study’s outcome is depressive symptoms that are not clinically assessed, which are more sensitive to current stressors. Unmeasured factors can also explain this consistency, as factors such as night shifts, workload intensity and trauma exposure were not measured in the current study but were corrected for in some studies that were included in meta-analysis studies [ 34 ]. Our finding were also in contrast with other large cohort studies [ 10 ]. The protective associations in cohort studies may reflect correlated healthy behaviors including coffee bioactive polyphenols rather than the possible reverse causation in cross-sectional designs. There are several strengths of this study. To the best of our knowledge, this was the first study to investigate the association among the HCWs population group in Saudi Arabia. In addition, validated questionnaires were used to detect the main association between caffeine consumption and depression, along with stress and sleep quality scales to control for confounders during analysis. Caffeine intake was assessed using a questionnaire that included multiple caffeine sources, covering both traditional beverages and trending drinks, including various types of coffee and tea. Participants were also asked about drink size to accurately estimate total daily caffeine intake. Moreover, sociodemographic and lifestyle characteristics, as well as stress levels and sleep quality, were adjusted for during analysis. This study has some limitations. First, the cross-sectional study design prevents the determination of directionality of the observed relationships. Second, the findings cannot be generalized to the entire population of Saudi Arabia as the study was conducted at a single center. Third, although the sample size met the calculated requirements, it remains relatively small for exploring associations in a cross-sectional analysis. Furthermore, convenience sampling may have promoted potential selection bias. Reliance on self-reported questionnaires also increases the likelihood of recall bias and resulting measurement inaccuracy. Finally, supplements containing caffeine and sleeping pills were not assessed for confounding control. Future research using well-designed longitudinal and interventional designs with large sample size is needed to explore the causal relationship between caffeine consumption and depression among HCWs. Potential confounders such as sleep, anxiety and stress needs to be investigated carefully with exploring their mediation effects. Controlling for sociodemographic characteristics and work burden factors is required to better understand the association between caffeine consumption and depression in HCWs. In this cross-sectional study of HCWs, higher total caffein intake was associated with greater depressive symptom severity but not with clinically significant symptoms after adjustment. In contrast, higher perceived stress and poorer sleep quality were independently associated with clinically significant depressive symptoms. These findings suggest that caffeine may function as a behavioral broader response to occupational stress and sleep disruption in HCWs, rather than a primary determinant of depression risk. Longitudinal studies are needed to clarify directionality and to determine whether stress and sleep quality mediate this relationship. Interventions aimed at improving HCWs mental health needs to prioritize stress reduction and sleep hygiene. Materials and Methods Study design and setting A cross-sectional, questionnaire-based study was conducted among HCWs at King Abdulaziz University Hospital (KAUH), Jeddah, Saudi Arabia, between January and July 2025, to investigate the association between habitual caffeine consumption and depression status, and to explore whether this relationship is independent or confounded by stress, sleep quality and lifestyle factors. Study population and sampling Following approval from KAUH administration and institutional ethics committee, participants were recruited using convenience sampling. An invitation containing the study information, consent form and study link was distributed via institutional email via Human Resources Unit. Participation was voluntary, and informed consents was obtained prior to enrollment. Eligible participants were licensed HCWs, including physicians, nurses, allied health professionals and support staff. Inclusion criteria were: age ≥ 22 years and current employment in KAUH for at least 6 months. Exclusion criteria included self-reported history of diagnosed psychiatric illness or chronic medical conditions requiring long-term medication use and pregnancy or recent childbirth (within the past 12 months) to reduce potential confounding by effects on mood, sleep or caffeine consumption. Sample size estimation Sample size was estimated using OpenEpi version 3.01 for cross-sectional studies with assuming two-sided 95% confidence level, 80% power, 1:1 ratio of unexposed to exposed. Based on an expected approximately 30% prevalence of moderate to severe depressive symptoms (Patient Health Questionnaire (PHQ-9) ≥ 10) among HCWs is Saudi and an odds ratio of 2.0, the minimum required sample size was around 295 participants [ 13 – 15 ]. Data collection Data were collected using a structured, self administered online questionnaire developed for this study and administered through the hospital’s secure platform. It consisted of six sections including eligibility screening and medical history; demographic, anthropometric, lifestyle and occupational characteristics; caffeine intake assessment; depression assessment using PHQ-9; sleep quality assessment using the Sleep Quality Questionnaire (SQQ); and perceived stress assessment using the Perceived Stress Scale (PSS-10). The questionnaire was available in English and Arabic versions. Arabic versions of standardized instruments were obtained from validated versions [ 16 – 18 ]. Prior to distribution, the questionnaire was reviewed by two experts to ensure clarity and validity. Screening and medical history To confirm inclusion and exclusion criteria, participants were asked whether they have any chronic diseases such as diabetes, hypertension, or asthma, or were previously diagnosed with depression or using any psychiatric medications. Female participants were asked about pregnancy and recent childbirth. Ineligible responders were automatically excluded by the online system. Demographics, anthropometric, lifestyle and occupational profile This section collected self-reported information on participants’ demographic, anthropometric, lifestyle and occupational characteristics. Participants reported their demographic data included age, sex, nationality, educational status, marital status, number of family members and income status. Self-reported anthropometric data included body weight (kg) and height (cm), which were used to calculate the body mass index (BMI). Occupational data included professional category (physician, nurse, allied health professionals and administrative/support staff) and continuous working hours. Working hours were then categorized to ≤ 40 and > 40 hours per week. Lifestyle data included: Smoking status: current smoker or non-smoker (never smoked or quit > 1 year) Physical activity: engaging in more than 30 min of moderate or vigorous-intensity exercise at least twice a week during work or leisure times Sleep duration: Continuous sleep duration (hours) per day. Sleep duration was then categorized to 8 hours per day. Caffeine intake assessment Habitual caffeine intake was measured using a validated semi-quantitative caffeine food-frequency questionnaire (C-FFQ) adopted for local dietary patterns and available in English and Arabic [ 16 ]. Items included in the C-FFQ were coffee, tea, chocolate, and energy and soft drinks with standard frequency options and serving sizes. Reported intake was converted into average daily consumption. Total caffeine intake was calculated as a sum of multiplied intake frequency and portion size by caffeine content (mg/serving) for each item. Caffeine content was obtained from the U.S. Department of Agriculture database [ 19 ] and local information [ 20 , 21 ]. Matcha tea was added to the questionnaire and its caffeine content was based on published estimates [ 22 ]. The Arabic version of the FFQ underwent forward-backward translation and pilot validation on 20 participants. Implausibly high intake (> 800 mg/day) was verified through recontacting participants for re-confirmation. Participants who did not respond (n = 2) were excluded from the analysis. Depression assessment Depression symptoms were assessed using PHQ-9 validated in English [ 23 ] and Arabic among Saudi populations [ 17 ]. Each of its 9 items is scored from 0 (‘not at all”) to 3 (“nearly every say”), yielding a total score of 0–27. A cutoff point of ≥ 10 was used to determine clinically relevant depression. Sleep quality assessment Sleep quality was assessed using the SQQ a self-reported validated tool was used to estimate participant's quality of sleep [ 24 ]. The questionnaire was translated to Arabic and its validity was tested. Participants rated their sleep quality over the past month using a 5-point Likert scale from 0 to 4 corresponding to strongly disagree, disagree, not sure, agree, or strongly agree. The questionnaire consists of ten items: six of them (3, 5, 6, 7, 8, 10) assess daytime sleepiness and four items (1, 2, 4, 9) evaluate sleep difficulty. Scores range from 0 to 40, with higher scores indicating poorer sleep quality. In the absence of a cut-off point for this scale, scores were analyzed continuously; for descriptive purposes, SQQ > 20 was used as an operational cutoff to additionally define poorer sleep quality, corresponding to an average item score of > 2 (above midpoint). Perceived stress assessment Perceived stress was assessed using the PSS-10 [ 25 , 26 ]. Responses for 10 items are recorded on a 5-point Likert scale from 0 (“never”) to 4 (“very often”) or its reverse score yielding a score that ranges from 0 to 40 with higher scores indicating greater stress. Six are negative (1, 2, 3, 6, 9, 10) assessing perceived helplessness and four are positive (4, 5, 7, 8) evaluating perceived self-efficacy. Both English [ 25 , 26 ] and previously validated Arabic [ 18 ] versions were used. For descriptive analysis, PSS-10 > 20 was used to additionally define having higher perceived stress. Ethical considerations Ethical approval was obtained from the Research Ethics Committee at King Abdulaziz University (KAU), reference No (HA-02-J-008). All methods were preformed in accordance with relevant guidelines and regulations and with the principles of the Declaration of Helsinki. Informed consent was obtained from all participants, and their confidentiality and privacy was strictly maintained. Statistical Analysis Statistical Package for Social Sciences (SPSS) version 28 was used to analyze the data. Distributional assumptions for continuous variables were assessed by the Shapiro-Wilk test and by using visual inspection of histograms and Q-Q plots. Continuous variables are presented as mean ± standard deviation SD and as median and interquartile range (IQR). Categorical variables are presented as frequency and percentages. Caffeine intake (mg/day) was examined both as a continuous intake and as intake categories (low ( 400 mg/day)). Bivariate comparisons between participants with and without depressive symptoms were performed using independent-sample t-test for normally distributed variables and Mann-Whitney U test for non-normally distributed variables. One way-ANOVA with Bonferroni post-hoc test was used to examine the differences in continuous psychological and sleep measures between coffee consumption categories. The chi-square test was used to examine association between categorical variables. The association between categorical caffeine intake and depressive symptoms was assessed using binary logistic regression. Odds ratios (OR) and 95% confidence intervals (CI) were estimated for moderate and high intake, with low intake being the reference category. Three models were fitted. Model 1 (crude); Model 2 adjusted for age (per year), sex, BMI (per 1 kg/m2), smoking status, marital status, living status, nationality, and physical activity; and Model 3 was additionally adjusted for stress and sleep quality scales (PSS-10 and SQQ scores). Model fit evaluation was conducted using the Hosmer-Lemeshow goodness-of-fit. The association between continuous caffeine intake and depressive symptoms was assessed linear regression models with PHQ-9 score as the outcome and log2-transformed caffeine daily intake as the independent variable. Three models were fitted. Model 1 (crude); Model 2 adjusted for age (per year), sex, BMI (per 1 kg/m2), smoking status, marital status, living status, nationality, and physical activity; and Model 3 was additionally adjusted for stress and sleep quality scales (PSS-10 and SQQ scores). All tests were two-sided, and a p value < 0.05 was considered statistically significant. Declarations Competing Interests Statement: The authors declare no competing interests. Funding Not applicable. Author Contribution Conceptualization, S.A., S.E., M.A.; methodology, S.A., S.E., M.A.; software, S.E., M.A.; validation, S.A., M.A.; formal analysis, S.E.; investigation, S.A., S.E., M.A.; resources, S.A., S.E., M.A.; data curation, S.A., S.E., M.A.; writing—original draft preparation, S.E.; writing—review and editing, S.A., S.E., M.A.; visualization, S.E; supervision, S.A., S.E.; project administration, S.E. All authors have read and agreed to the published version of the manuscript. Data Availability The data used and analyzed can be obtained from the corresponding author under a reasonable request. References World Health Organization. Depression and Other Common Mental Disorders: Global Health Estimates. (2017). World Health Organization. Our Duty of Care: A Global Call to Action to Protect the Mental Health of Health and Care Workers. (2022). Office of the Surgeon General. Addressing Health Worker Burnout: The U.S. Surgeon General’s Advisory on Building a Thriving Health Workforce (US Department of Health and Human Services (US Department of Health and Human Services, Washington, DC,, 2022). Ram, D. & Alharbi, H. Y. Mental Health Issues among Physicians in Saudi Arabia: A Scoping Review. Saudi J. Med. Med. Sci. 13 , 157–172 (2025). Temple, J. L. et al. The Safety of Ingested Caffeine: A Comprehensive Review. Front. Psychiatry . 8 , 257730 (2017). Amer, S. A. et al. Caffeine addiction and determinants of caffeine consumption among health care providers: a descriptive national study. Eur. Rev. Med. Pharmacol. Sci. 27 , 3230–3242 (2023). Fredholm, B. B., Bättig, K., Holmén, J., Nehlig, A. & Zvartau, E. E. Actions of Caffeine in the Brain with Special Reference to Factors That Contribute to Its Widespread Use. Pharmacol. Rev. 51 , 83–133 (1999). Unsal, S. & Sanlier, N. Longitudinal Effects of Lifetime Caffeine Consumption on Levels of Depression, Anxiety, and Stress: A Comprehensive Review. Curr. Nutr. Rep. 14 , 1–14 (2025). Grosso, G., Micek, A., Castellano, S., Pajak, A. & Galvano, F. Coffee, tea, caffeine and risk of depression: A systematic review and dose-response meta-analysis of observational studies. Mol. Nutr. Food Res. 60 , 223–234 (2016). Lucas, M. & Coffee Caffeine, and Risk of Depression Among Women. Arch. Intern. Med. 171 , 1571 (2011). Pham, N. M. et al. Green tea and coffee consumption is inversely associated with depressive symptoms in a Japanese working population. Public Health. Nutr. 17 , 625–633 (2014). Ruusunen, A. et al. Coffee, tea and caffeine intake and the risk of severe depression in middle-aged Finnish men: the Kuopio Ischaemic Heart Disease Risk Factor Study. Public Health. Nutr. 13 , 1215–1220 (2010). Aziz, G. A. M., ALghfari, S., Bogami, E., Abduljwad, K. & Bardisi, W. Prevalence and determinants of depression among primary healthcare workers in Jeddah, Saudi Arabia 2020. J. Family Med. Prim. Care . 11 , 3013 (2022). AlAteeq, D. A., Aljhani, S., Althiyabi, I. & Majzoub, S. Mental health among healthcare providers during coronavirus disease (COVID-19) outbreak in Saudi Arabia. J. Infect. Public Health . 13 , 1432–1437 (2020). Almarhapi, S. A. & Khalil, T. A. Depression among healthcare workers in North West Armed Forces hospital-Tabuk, Saudi Arabia: Prevalence and associated factors. Annals Med. Surg. 68 , 102681 (2021). Albar, S. A. et al. Caffeine sources and consumption among saudi adults living with diabetes and its potential effect on hba1c. Nutrients 13 , (2021). AlHadi, A. N. et al. An arabic translation, reliability, and validation of Patient Health Questionnaire in a Saudi sample. Annals Gen. Psychiatry 16 , (2017). Almadi, T., Cathers, I., Hamdan Mansour, A. M. & Chow, C. M. An Arabic version of the perceived stress scale: translation and validation study. Int. J. Nurs. Stud. 49 , 84–89 (2012). U.S. Department of Agriculture. FoodData Central. (2019). https://fdc.nal.usda.gov/ Naser, L. R., Sameh, A., Muzaffar, I., Omar, A. R. & Ahmed, M. A. Comparative evaluation of caffeine content in Arabian coffee with other caffeine beverages. Afr. J. Pharm. Pharmacol. 12 , 19–26 (2018). Latosińska, M. & Latosińska, J. N. Introductory Chapter: Caffeine, a Major Component of Nectar of the Gods and Favourite Beverage of Kings, Popes, Artists and Revolutionists, a Drug or a Poison? in The Question of Caffeine (InTech, 2017). 10.5772/intechopen.69693 Hori, K., Kurauchi, Y., Kotani, S. & Devkota, H. P. Analysis of Functional Compounds in Matcha by Quantitative Nuclear Magnetic Resonance Spectroscopy. Scientia Pharm. 2025, Vol. 93 , 93, (2025). Kroenke, K., Spitzer, R. L. & Williams, J. B. W. The PHQ-9: validity of a brief depression severity measure. J. Gen. Intern. Med. 16 , 606–613 (2001). Kato, T. Development of the Sleep Quality Questionnaire in healthy adults. J. Health Psychol. 19 , 977–986 (2014). Cohen, S. Perceived stress in a probability sample of the United States. in The social psychology of health 31–67 (Sage Publications, Inc., (1988). Cohen, S., Kamarck, T. & Mermelstein, R. A global measure of perceived stress. J. Health Soc. Behav. 24 , 385–396 (1983). Alghasab, N. S., Aljadani, A. H., Almesned, S. S. & Hersi, A. S. Depression among physicians and other medical employees involved in the COVID-19 outbreak: A cross-sectional study. Medicine 100 , e25290 (2021). Al Ammari, M., Sultana, K., Thomas, A. & Al Swaidan, L. Al Harthi, N. Mental Health Outcomes Amongst Health Care Workers During COVID 19 Pandemic in Saudi Arabia. Front. Psychiatry . 11 , 619540 (2021). Agostoni, C. et al. Scientific Opinion on the safety of caffeine. EFSA J. 13 , 4102 (2015). Wikoff, D. et al. Systematic review of the potential adverse effects of caffeine consumption in healthy adults, pregnant women, adolescents, and children. Food Chem. Toxicol. 109 , 585–648 (2017). Fallata, G. et al. Caffeine consumption and exposure in Saudi Arabia: a cross-sectional analysis. Front. Nutr. 12 , 1556001 (2025). Amer, S. A. et al. Caffeine addiction and determinants of caffeine consumption among health care providers: a descriptive national study. Eur. Rev. Med. Pharmacol. Sci. 27 , 3230–3242 (2023). Wang, L., Shen, X., Wu, Y. & Zhang, D. Coffee and caffeine consumption and depression: A meta-analysis of observational studies. Aust. N. Z. J. Psychiatry . 50 , 228–242 (2016). Torabynasab, K., Shahinfar, H., Payandeh, N. & Jazayeri, S. Association between dietary caffeine, coffee, and tea consumption and depressive symptoms in adults: A systematic review and dose-response meta-analysis of observational studies. Front. Nutr. 10 , 1051444 (2023). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9098832","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":611212203,"identity":"31e84d1a-2666-4f31-a887-99e2e75209e0","order_by":0,"name":"Sumia Enani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYNACAxsDMM1DgpY0krUwHCZBi3x778HPBQXnjflnJDA+eNvGYM/fQMhJZ84lS88wuG0mcSOB2XBuG0PijAOEtEjkGEjzGNy2YbiRwCbN28aQwEBIi/z8N8a/eQzO2cjfSGD/DdRiL09IC8MNHjOgLQfMDIC2MAO1MG4g6LAzOWbWPAbJxoZnHjZLzjknkbiRoMPazxjf5vljZzjvePLBD2/KbOzlCDoMARgbgIQE8epHwSgYBaNgFOAGACHrOoxAl42aAAAAAElFTkSuQmCC","orcid":"","institution":"King Abdulaziz University","correspondingAuthor":true,"prefix":"","firstName":"Sumia","middleName":"","lastName":"Enani","suffix":""},{"id":611212205,"identity":"2d06ce74-c3b5-4c72-ad3a-807c39048e96","order_by":1,"name":"Salwa Albar","email":"","orcid":"","institution":"King Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Salwa","middleName":"","lastName":"Albar","suffix":""},{"id":611212208,"identity":"d7d6314e-1d08-45b1-9c9e-beea3bcec3c2","order_by":2,"name":"Muntaha Faisal Alsulaimani","email":"","orcid":"","institution":"King Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Muntaha","middleName":"Faisal","lastName":"Alsulaimani","suffix":""}],"badges":[],"createdAt":"2026-03-12 01:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9098832/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9098832/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105904512,"identity":"81165abd-6aef-4813-8f17-6a96b99d70a7","added_by":"auto","created_at":"2026-04-01 10:09:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1528739,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9098832/v1/e49e13b9-fe1f-4ed7-82e6-936375e41301.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Caffeine Intake and Depressive Symptoms among Healthcare Workers: The Role of Stress and Sleep Quality","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDepression is a major contributor to the global burden of disease, affecting more than 300\u0026nbsp;million individuals worldwide, ranking as the single largest contributor to non-fatal health loss, and is among the top causes of disability-adjusted life years (DALYs) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Healthcare workers (HCWs) are among the most vulnerable occupational groups for mental health challenges due to the unique nature and demands of their work [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Challenging work conditions, constant exposure to high patient care demands, emotional strain, rotating shifts and disturbed sleep patterns increase their susceptibility to depression, anxiety and burnout. Reviews on depression among HCWs in Saudi Arabia reported high prevalence reaching around 75% in some professional categories [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These mental health challenges impose concerns given the implication on health care delivery, patient care quality and healthcare system sustainability.\u003c/p\u003e \u003cp\u003eCaffeine is the most widely consumed psychoactive substance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and therefore, its consumption is a behavioral factors that of a growing interest in the context of mental health. HCWs turn to caffeine, most commonly through coffee, tea and energy drinks, as a coping mechanism for occupational fatigue, to enhance short term alertness especially during long or irregular shifts and extended duty hours. A descriptive study of 600 healthcare providers in Saudi Arabia revealed that caffeine use was extremely common (94.3%) among them and that the average caffeine intake was 370 mg/day which is higher than adult average [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The primary neuropharmacological mechanism of caffeine is through antagonism of adenosine receptors in the nervous system which increases dopamine and noradrenaline activity, and in turn, increases arousal and wakefulness [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, caffeine also might be harmful in the long term, contributing to disturbed sleep and increased stress levels, which are both contributors to depression, especially when consumed in high doses [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHabitual moderate caffeine intake has been linked to decreased risk of depression in meta-analyses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, studies included in this meta-analysis were mostly done on general Western population, limiting its generalizability to HCWs in non-Western populations or high-burden settings. Evidence from high-stress populations is limited and often lack detailed adjustment for psychological and lifestyle related factors. For example, the Nurses\u0026rsquo; Health Study, a large cohort study, found that moderate caffeine intake, particularly from coffee, was associated with lower depression risk [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This previous study and other studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] mostly adjusted for lifestyle and medical conditions, but not always adjusted for stress physiology and sleep hygiene, which are well-established risk factors for depressive symptoms. While caffeine is widely used in HCWs, its relationship with depression remains unclear and might be confounded by chronic stress and poor sleep.\u003c/p\u003e \u003cp\u003eTo our knowledge, no prior studies in Saudi Arabia have explored the independent association between caffeine consumption and depressive symptoms in HCWs while accounting for psychological distress and poor sleep. Accordingly, this study seeks to investigate the cross-sectional association between habitual caffeine intake and depression in HCWs in one of the largest affiliated hospitals in the region, and to explore the confounding roles of various demographic, psychological and lifestyle factors including stress and sleep quality. The findings may help clarify whether the relationship between caffeine intake and depression is independent or reflects broader behavioral and psychological adaptation to the work environment, which will be helpful for designing healthcare staff wellness interventions.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 298 HCWs responded to the survey and participated in the study. The mean age of participants was 37.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8 years and 197 (66.1%) of them were women (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most of the participants were Saudi (71.5%) and married (64.4%). The mean BMI based on self-reported height and weight was 26.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.09, with 42.6% having normal weight, 37.1% overweight and 17.6% obese.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive characteristics of the study participants (N\u0026thinsp;=\u0026thinsp;298 HCWs)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;298)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout depressive symptoms\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;243)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWith depressive symptoms\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101 (33.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (35.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (25.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197 (66.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156 (64.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (74.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNationality\u003c/b\u003e n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaudi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213 (71.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (69.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Saudi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85 (28.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (30.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192 (64.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163 (67.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (52.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced or widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (5.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (9.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiploma or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156 (52.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (52.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (52.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostgraduate studies (Master or PhD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (32.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving status\u003c/b\u003e n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith parents and/or sibling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 (35.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (36.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (30.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried couple with children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106 (35.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried couple without children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (9.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone with children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (4.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold income (SAR)\u003c/b\u003e n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;8000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (22.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (21.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8000\u0026ndash;14.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (25.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15.000\u0026ndash;29.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (28.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (27.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (32.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (24.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProfessional category\u003c/b\u003e n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (37.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecialist/Allied health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114 (38.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (32.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnical support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (8.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (9.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWork hours/week\u003c/b\u003e mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWork hours category\u003c/b\u003e n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131 (53.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (43.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (46.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (56.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep duration (hours/day)\u003c/b\u003e mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep duration category (hours/day)\u003c/b\u003e n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (24.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180 (60.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (16.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity\u003c/b\u003e n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129 (53.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (69.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsufficiently active\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (28.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeets physical activity guidelines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (7.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220 (73.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183 (75.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (67.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCigarette smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (8.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShisha smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (8.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectronic cigarette smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (7.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (7.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m2)\u003c/b\u003e mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI category\u003c/b\u003e n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (2.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (45.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110 (37.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83 (34.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (49.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eData are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous and n (n%) for categorical variables. N\u0026thinsp;=\u0026thinsp;298 HCWs. BMI was available for 296/298 participants as 2 did not report their height and weight.\u003c/p\u003e \u003cp\u003ePercentages may not sum to 100% due to rounding.\u003c/p\u003e \u003cp\u003eInsufficiently active: \u0026lt; 150 min/week (mod. activity) or \u0026lt;\u0026thinsp;75 min/week (vigorous), Meets physical activity guidelines: \u0026ge; 150 min/week (mod. activity) or \u0026ge;\u0026thinsp;75 min/week (vigorous)\u003c/p\u003e \u003cp\u003eP-values from t-test for continuous variables and χ\u003csup\u003e2\u003c/sup\u003e for categorical variables.\u003c/p\u003e \u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong participating HCWs, 55 (18.5%) had clinically depressive symptoms (PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). HCWs with depressive symptoms were significantly younger than those without (35.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9 vs 38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9 years; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012). Sex, nationality, marital status, educational level, living status, household income, professional category, weekly work hours, sleep duration, smoking status and BMI did not differ significantly between those with and without depressive symptoms (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Physical inactivity showed a non-significant trend toward higher prevalence of depressive symptoms, with higher proportion of inactivity among participants with depressive symptoms compared with those without (69.1% vs 53.1%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.066).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCaffeine intake by depressive symptom status in HCWs\u003c/h2\u003e \u003cp\u003eCaffeine intake among HCWs is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Among all participants, the mean caffeine intake was 216\u0026thinsp;\u0026plusmn;\u0026thinsp;298 mg/day and the median was 125 mg/day (IQR 49\u0026ndash;250). The majority of HCWs included had low caffeine intake (\u0026lt;\u0026thinsp;200 mg/day) accounting for 67.4%. The mean caffeine intake in this group was 82\u0026thinsp;\u0026plusmn;\u0026thinsp;58 mg/day and the median was 78 mg/day (IQR 31\u0026ndash;128). Moderate intake was reported by 50 (16.8%) HCWs. The mean caffeine intake in this group was 274\u0026thinsp;\u0026plusmn;\u0026thinsp;56 mg/day and the median was 259 mg/day (IQR 229\u0026ndash;322). High caffeine intake (\u0026gt;\u0026thinsp;400 mg/day) was reported by 47 HCWs (15.8%) with a mean of 722\u0026thinsp;\u0026plusmn;\u0026thinsp;432 mg/day and a median of 561 mg/day (IQR 440\u0026ndash;801).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCaffeine intake (total and by source) by depressive symptoms status (N\u0026thinsp;=\u0026thinsp;298)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;298\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout depressive symptoms\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;243\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWith depressive symptoms\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;55\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal caffeine intake, mg/day\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216\u0026thinsp;\u0026plusmn;\u0026thinsp;291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201\u0026thinsp;\u0026plusmn;\u0026thinsp;266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e282\u0026thinsp;\u0026plusmn;\u0026thinsp;378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 (49\u0026ndash;250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (47\u0026ndash;231)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e169 (72\u0026ndash;305)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCaffeine intake by source, mg/day\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoffee, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135\u0026thinsp;\u0026plusmn;\u0026thinsp;213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123\u0026thinsp;\u0026plusmn;\u0026thinsp;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e188\u0026thinsp;\u0026plusmn;\u0026thinsp;259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoffee, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (14\u0026ndash;155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (12.5\u0026ndash;146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (21\u0026ndash;239)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTea, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u0026thinsp;\u0026plusmn;\u0026thinsp;106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u0026thinsp;\u0026plusmn;\u0026thinsp;108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53\u0026thinsp;\u0026plusmn;\u0026thinsp;98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTea, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (5\u0026ndash;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (4\u0026ndash;57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (7\u0026ndash;64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChocolate, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u0026thinsp;\u0026plusmn;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u0026thinsp;\u0026plusmn;\u0026thinsp;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChocolate, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy drinks, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy drinks, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoft drinks, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u0026thinsp;\u0026plusmn;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u0026thinsp;\u0026plusmn;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoft drinks, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (1\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCaffeine intake category, n (n%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow (\u0026lt;\u0026thinsp;200 mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201 (67.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169 (69.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (58.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate (200\u0026ndash;400 mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (16.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (20%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;400 mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (21.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and median (IQR) for continuous variables, and as frequency (%) for categorical variables. Depressive symptoms were defined as PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10. All continuous caffeine intake by depressive symptom status was analyzed using Mann-Whitney U test due to skewness. Categorical caffeine intake category association was analyzed using χ\u003csup\u003e2\u003c/sup\u003e test. Percentages may not sum to 100% due to rounding.\u003c/p\u003e \u003cp\u003eSignificant p-values are shown in bold.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCaffeine intake differed by depressive symptoms status (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). HCWs with clinically significant depressive symptoms (PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10) had a significantly higher total caffeine intake compared with those without depressive symptoms (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 282\u0026thinsp;\u0026plusmn;\u0026thinsp;378 vs 201\u0026thinsp;\u0026plusmn;\u0026thinsp;266; median (IQR): 169 (72\u0026ndash;305) vs 114 (47\u0026ndash;231); \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038). Higher coffee intake was the main contributor to this difference in caffeine intake (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 188\u0026thinsp;\u0026plusmn;\u0026thinsp;259 vs 123\u0026thinsp;\u0026plusmn;\u0026thinsp;200; median (IQR): 104 (21\u0026ndash;239) vs 61 (12.5\u0026ndash;146); \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037). Tea intake did not differ significantly with depressive symptom status. Categorical caffeine groups were not significantly associated with PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePsychological and sleep measures association with caffeine intake among HCWs\u003c/h3\u003e\n\u003cp\u003ePsychological and sleep measures among HCWs are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The mean PHQ-9 score was 5.93\u0026thinsp;\u0026plusmn;\u0026thinsp;5.31 and the median was 5 (IQR 2\u0026ndash;9). The mean PSS-10 score was 16.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.78 and the median was 17 (IQR 14\u0026ndash;20) with 63 (21.1%) classified as having high perceived stress (PSS-10\u0026thinsp;\u0026gt;\u0026thinsp;20). The mean SQQ score was 16.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.32 and the median was 17 (IQR 9\u0026ndash;23) with 108 (36.6%) classified as having poor sleep quality (SQQ\u0026thinsp;\u0026gt;\u0026thinsp;20). Higher caffeine intake was associated with higher PHQ-9 scores (p\u0026thinsp;=\u0026thinsp;0.011), higher perceived stress PSS-10 means (p\u0026thinsp;=\u0026thinsp;0.049) and poorer sleep quality SQQ scores (p\u0026thinsp;=\u0026thinsp;0.013). The prevalence of high perceived stress and poor sleep quality were also associated with higher caffeine intake (p\u0026thinsp;=\u0026thinsp;0.012 and p\u0026thinsp;=\u0026thinsp;0.017 respectively).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePsychological and sleep measures among HCWs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003en (%) above predefined cutoff\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ-9 (Depression)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.93\u0026thinsp;\u0026plusmn;\u0026thinsp;5.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (2\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55 (18.5%)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSS-10 (Perceived stress)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (14\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63 (21.1%)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSQQ (Sleep Quality Score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (9\u0026ndash;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108 (36.6%)\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and median (IQR) for continuous data, and as frequency (%) for categorical data.\u003c/p\u003e \u003cp\u003e*High depression defined as PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eHigh stress defined as PSS-10\u0026thinsp;\u0026gt;\u0026thinsp;20\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003ePoor sleep defined as SQQ\u0026thinsp;\u0026gt;\u0026thinsp;20\u003c/p\u003e \u003cp\u003ePHQ-9, Patient Health Questionnaire-9; PSS-10, Perceived Stress Scale-10; SQQ, Sleep Quality Questionnaire.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePsychological and sleep measures by caffeine groups among HCWs (N\u0026thinsp;=\u0026thinsp;298)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCaffeine intake\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLow\u003c/b\u003e \u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;200 mg/day)\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;201\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eModerate\u003c/b\u003e \u003c/p\u003e \u003cp\u003e(200\u0026ndash;400 mg/day)\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;50\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eHigh\u003c/b\u003e \u003c/p\u003e \u003cp\u003e(\u0026gt;\u0026thinsp;400 mg/day)\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;47\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ-9, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.29\u0026thinsp;\u0026plusmn;\u0026thinsp;4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.22\u0026thinsp;\u0026plusmn;\u0026thinsp;4.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.28\u0026thinsp;\u0026plusmn;\u0026thinsp;6.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSS-10, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSQQ score, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026thinsp;\u0026plusmn;\u0026thinsp;9.19 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u0026thinsp;\u0026plusmn;\u0026thinsp;8.67 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh stress, n (n%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (36.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor sleep, n (n%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (38.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (54.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD continuous data, and as frequency (%) for categorical data.\u003c/p\u003e \u003cp\u003eHigh stress defined as PSS-10\u0026thinsp;\u0026gt;\u0026thinsp;20. Poor sleep defined as SQQ\u0026thinsp;\u0026gt;\u0026thinsp;20 (higher SQQ\u0026thinsp;=\u0026thinsp;poorer sleep quality).\u003c/p\u003e \u003cp\u003eContinuous outcomes were analyzed using one-way ANOVA and post-hoc pairwise comparisons were performed using the Bonferroni method; categorical outcomes were analyzed using χ\u003csup\u003e2\u003c/sup\u003e test.\u003c/p\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e and \u003csup\u003eb\u003c/sup\u003e are statistically significantly different in Bonferroni-adjusted pairwise comparisons (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003ePHQ-9, Patient Health Questionnaire-9; PSS-10, Perceived Stress Scale-10; SQQ, Sleep Quality Questionnaire.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eRegression models for the association between caffeine intake and depressive symptoms in HCWs\u003c/h3\u003e\n\u003cp\u003eIn crude logistic regression models, increased caffeine intake was associated with higher odds of having clinically significant depressive symptoms (PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10) as those consuming 200\u0026ndash;400 mg/day of caffeine had OR of 1.481 (95% CI 0.687\u0026ndash;3.193) and those consuming\u0026thinsp;\u0026gt;\u0026thinsp;400 mg/day had OR of 1.853 (95% CI 0.867\u0026ndash;3.958) compared with those consuming\u0026thinsp;\u0026lt;\u0026thinsp;200 mg/day (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e); however, these associations were not statistically significant. After adjustment for potential demographic, sociodemographic, lifestyle, stress and sleep confounders, caffeine remained unassociated with PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10 (200\u0026ndash;400 mg/day: OR 1.083, 95% CI (0.396\u0026ndash;2.961); \u0026gt;400 mg/day: OR 0.975, 95% CI (0.338\u0026ndash;2.81) Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In contrast, higher perceived stress and poorer sleep quality scores were independently associated with higher odds of depression (stress: OR 1.211, 95% CI (1.106\u0026ndash;1.325); sleep: OR 1.136, 95% CI (1.074\u0026ndash;1.202); \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between caffeine intake group and clinically significant depressive symptoms (PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCaffeine intake\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1 (Crude)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2 (Adjusted) \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3 (Fully adjusted)\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;200 mg/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u0026ndash;400 mg/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.481 (0.687\u0026ndash;3.193)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.718 (0.724\u0026ndash;4.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.083 (0.396\u0026ndash;2.961)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;400 mg/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.853 (0.867\u0026ndash;3.958)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.561 (0.646\u0026ndash;3.775)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.975 (0.338\u0026ndash;2.810)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eOdds ratios (OR) from binary logistic regression. Outcome: PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10. N\u0026thinsp;=\u0026thinsp;298; 55 participants had PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10.\u003c/p\u003e \u003cp\u003eModel 1: Crude\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eModel 2: Adjusted for age (per year), sex, BMI (per 1 kg/m\u003csup\u003e2\u003c/sup\u003e), smoking status, marital status, living status, nationality, and physical activity.\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003eModel 3: Additionally adjusted for stress and sleep quality scales.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the crude linear regression model, each twofold increase in caffeine daily intake was associated with 0.69 (CI 0.376\u0026ndash;0.998) point higher PHQ-9 score (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). After adjustment for demographic and lifestyle variables, the effect remained approximately similar (Model 2: β 0.622 (95% CI 0.297\u0026ndash;0.947); \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the fully adjusted linear regression model, the association of caffeine intake and PHQ-9 was weakened but remained significant as each twofold increase in caffeine daily intake was associated with 0.331 (95% CI 0.067\u0026ndash;0.595) point higher PHQ-9 score (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014). Higher perceived stress and poorer sleep quality (higher SQQ) scores were also independently associated with higher odds of depression (stress: β\u0026thinsp;=\u0026thinsp;0.253 (95% CI: 0.168\u0026ndash;0.338); sleep: β\u0026thinsp;=\u0026thinsp;0.246 (95% CI: 0.192\u0026ndash;0.300); \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between continuous caffeine intake and PHQ-9 score in linear regression models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaffeine intake\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 1 (Crude)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.687 (0.376\u0026ndash;0.998)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2 (Adjusted)\u003c/b\u003e \u003csup\u003e\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.622 (0.297\u0026ndash;0.947)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 3 (Fully adjusted)\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026sect;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.331 (0.067\u0026ndash;0.595)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eβ represents the estimated change in PHQ-9 score per 1-unit increase in log2-transformed caffeine intake (2 fold increase) (N\u0026thinsp;=\u0026thinsp;298 HCWs).\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eAdjusted for age (per year), sex, BMI (per 1 kg/m\u003csup\u003e2\u003c/sup\u003e), smoking status, marital status, living status, nationality, and physical activity.\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003eAdditionally adjusted for stress and sleep quality scales.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cross-sectional study of HCWs in a large tertiary hospital (KAUH) in Jeddah, 18.5% had clinically significant depressive symptoms (PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10). Average caffeine intake was 216 mg/day (median 125 mg/day) primarily driven by coffee intake, with 15.8% reporting high intake (\u0026gt;\u0026thinsp;400 mg/day). Total caffeine intake was significantly higher among HCWs with depressive symptoms. Higher total caffeine intake was also associated with higher depressive symptoms (PHQ-9 scores), higher perceived stress (PSS-10) and poorer sleep quality (SQQ). Moderate (200-400mg/day) and high (\u0026gt;\u0026thinsp;400mg/day) caffeine intake were not associated with higher odds of clinically significant depressive symptoms after adjustment, whereas stress and poorer sleep remained independently associated with the outcome. Together, this suggests that the association between caffeine intake and depression in this high-demand occupational group may largely be driven by stress burden and poor sleep hygiene rather than an independent caffeine-depression relationship.\u003c/p\u003e \u003cp\u003eThe observed prevalence of having depressive symptoms (18.5%) in HCWs is generally lower than other Saudi reports [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] but broadly consistent with studies that used the same depressive symptoms diagnostic tool (PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For example, in a multi-region cross-sectional study in the early COVID-19 outbreak that included\u0026thinsp;\u0026gt;\u0026thinsp;15 hospitals in Saudi Arabia, significant depressive symptoms (PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10) was prevalent in 18.2% among HCWs with diverse professions [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Another multi-center cross-sectional study in Jeddah, Saudi Arabia that involved care- and non-care-related professions from 10 primary healthcare centers reported a substantially higher PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10 prevalence (36.3%) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Differences in reported prevalence of depressive symptoms among HCWs in Saudi Arabia are attributable to variations in screening tools, pandemic phase, workload, role mix, and sampling strategies.\u003c/p\u003e \u003cp\u003eAverage daily intake of caffeine in the present KAUH sample was moderate (216 mg/day) but highly right-skewed (median 125 mg/day), with nearly sixth (15.8%) of participants reported high intake (\u0026gt;\u0026thinsp;400 mg/day). This distribution suggests that caffeine exposure in the majority of HCWs falls within the recommended safe caffeine limits for healthy adults [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, a meaningful subset may have consumption levels that disrupt sleep and stress physiology. It is of a high importance that studies assess prevalence of higher than caffeine safe limits intake rather than just reporting mean mg/day.\u003c/p\u003e \u003cp\u003eWhen compared with Saudi general-population data, the observed intake in this study is comparable with a previously reported estimate from a large survey with an average of 218 mg/day caffeine intake in adults [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, it is considerably lower than a previously reported average of 446 mg/day among Saudi governmental healthcare providers [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The lower average intake in this study might be due to differences in sampling frame and participant mix as we sampled healthcare worker including care- and non-care providers from a single large academic university, whereas the previous study included healthcare providers in governmental hospitals.\u003c/p\u003e \u003cp\u003eA key pattern in the finding of this study is that the daily caffeine exposure\u0026rsquo;s continuous association between with PHQ-9 score remained significant after full adjustment while the categorical association did not. This could be explained by the possibility of information loss when dichotomizing depression, especially when the number of cases is relatively low that could limit precision of adjusted logistic models. Another explanation could be the J-shaped coffee/caffeine relationship with depression reported in meta-analysis of observational studies with \u0026gt;\u0026thinsp;300,000 participant data [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In addition, stress and sleep attenuated the caffeine-depression relationship indicating that this relationship is partially explained by them. This is plausible as in a cross-sectional study, stress and sleep may act as confounders and/or potential mediating pathways, as caffeine can induce arousal and worsen sleep which increases depressive symptoms. However, temporal order cannot be established in cross-sectional designs. It would be more informative if prospective studies test mediation rather than treating stress and sleep purely as confounders. These studies also needed to establish directionality in caffeine-depression relationship, as depressive symptoms are often accompanied by fatigue and impaired concentration; HCWs may increase caffeine consumption as adaptive measures to meet work demands suggesting reverse causation.\u003c/p\u003e \u003cp\u003eIn contrast with our findings, systematic reviews and meta-analysis in general populations have consistently reported an inverse association between caffeine intake and depression, suggesting a protective effect [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This discrepancy could be due to differences in the context of the studies, as HCWs in tertiary hospitals may use caffeine to combat fatigue, which increases the likelihood that caffeine could be a marker of chronic under sleep rather than a luxury exposure. Another explanation is that many studies define the outcome as clinically diagnosed depression, but our study\u0026rsquo;s outcome is depressive symptoms that are not clinically assessed, which are more sensitive to current stressors. Unmeasured factors can also explain this consistency, as factors such as night shifts, workload intensity and trauma exposure were not measured in the current study but were corrected for in some studies that were included in meta-analysis studies [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Our finding were also in contrast with other large cohort studies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The protective associations in cohort studies may reflect correlated healthy behaviors including coffee bioactive polyphenols rather than the possible reverse causation in cross-sectional designs.\u003c/p\u003e \u003cp\u003eThere are several strengths of this study. To the best of our knowledge, this was the first study to investigate the association among the HCWs population group in Saudi Arabia. In addition, validated questionnaires were used to detect the main association between caffeine consumption and depression, along with stress and sleep quality scales to control for confounders during analysis. Caffeine intake was assessed using a questionnaire that included multiple caffeine sources, covering both traditional beverages and trending drinks, including various types of coffee and tea. Participants were also asked about drink size to accurately estimate total daily caffeine intake. Moreover, sociodemographic and lifestyle characteristics, as well as stress levels and sleep quality, were adjusted for during analysis.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, the cross-sectional study design prevents the determination of directionality of the observed relationships. Second, the findings cannot be generalized to the entire population of Saudi Arabia as the study was conducted at a single center. Third, although the sample size met the calculated requirements, it remains relatively small for exploring associations in a cross-sectional analysis. Furthermore, convenience sampling may have promoted potential selection bias. Reliance on self-reported questionnaires also increases the likelihood of recall bias and resulting measurement inaccuracy. Finally, supplements containing caffeine and sleeping pills were not assessed for confounding control.\u003c/p\u003e \u003cp\u003eFuture research using well-designed longitudinal and interventional designs with large sample size is needed to explore the causal relationship between caffeine consumption and depression among HCWs. Potential confounders such as sleep, anxiety and stress needs to be investigated carefully with exploring their mediation effects. Controlling for sociodemographic characteristics and work burden factors is required to better understand the association between caffeine consumption and depression in HCWs.\u003c/p\u003e \u003cp\u003eIn this cross-sectional study of HCWs, higher total caffein intake was associated with greater depressive symptom severity but not with clinically significant symptoms after adjustment. In contrast, higher perceived stress and poorer sleep quality were independently associated with clinically significant depressive symptoms. These findings suggest that caffeine may function as a behavioral broader response to occupational stress and sleep disruption in HCWs, rather than a primary determinant of depression risk. Longitudinal studies are needed to clarify directionality and to determine whether stress and sleep quality mediate this relationship. Interventions aimed at improving HCWs mental health needs to prioritize stress reduction and sleep hygiene.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eA cross-sectional, questionnaire-based study was conducted among HCWs at King Abdulaziz University Hospital (KAUH), Jeddah, Saudi Arabia, between January and July 2025, to investigate the association between habitual caffeine consumption and depression status, and to explore whether this relationship is independent or confounded by stress, sleep quality and lifestyle factors.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population and sampling\u003c/h3\u003e\n\u003cp\u003e Following approval from KAUH administration and institutional ethics committee, participants were recruited using convenience sampling. An invitation containing the study information, consent form and study link was distributed via institutional email via Human Resources Unit. Participation was voluntary, and informed consents was obtained prior to enrollment. Eligible participants were licensed HCWs, including physicians, nurses, allied health professionals and support staff. Inclusion criteria were: age\u0026thinsp;\u0026ge;\u0026thinsp;22 years and current employment in KAUH for at least 6 months. Exclusion criteria included self-reported history of diagnosed psychiatric illness or chronic medical conditions requiring long-term medication use and pregnancy or recent childbirth (within the past 12 months) to reduce potential confounding by effects on mood, sleep or caffeine consumption.\u003c/p\u003e\n\u003ch3\u003eSample size estimation\u003c/h3\u003e\n\u003cp\u003eSample size was estimated using OpenEpi version 3.01 for cross-sectional studies with assuming two-sided 95% confidence level, 80% power, 1:1 ratio of unexposed to exposed. Based on an expected approximately 30% prevalence of moderate to severe depressive symptoms (Patient Health Questionnaire (PHQ-9)\u0026thinsp;\u0026ge;\u0026thinsp;10) among HCWs is Saudi and an odds ratio of 2.0, the minimum required sample size was around 295 participants [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eData were collected using a structured, self administered online questionnaire developed for this study and administered through the hospital\u0026rsquo;s secure platform. It consisted of six sections including eligibility screening and medical history; demographic, anthropometric, lifestyle and occupational characteristics; caffeine intake assessment; depression assessment using PHQ-9; sleep quality assessment using the Sleep Quality Questionnaire (SQQ); and perceived stress assessment using the Perceived Stress Scale (PSS-10).\u003c/p\u003e \u003cp\u003eThe questionnaire was available in English and Arabic versions. Arabic versions of standardized instruments were obtained from validated versions [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Prior to distribution, the questionnaire was reviewed by two experts to ensure clarity and validity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eScreening and medical history\u003c/h2\u003e \u003cp\u003eTo confirm inclusion and exclusion criteria, participants were asked whether they have any chronic diseases such as diabetes, hypertension, or asthma, or were previously diagnosed with depression or using any psychiatric medications. Female participants were asked about pregnancy and recent childbirth. Ineligible responders were automatically excluded by the online system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDemographics, anthropometric, lifestyle and occupational profile\u003c/h2\u003e \u003cp\u003eThis section collected self-reported information on participants\u0026rsquo; demographic, anthropometric, lifestyle and occupational characteristics. Participants reported their demographic data included age, sex, nationality, educational status, marital status, number of family members and income status. Self-reported anthropometric data included body weight (kg) and height (cm), which were used to calculate the body mass index (BMI). Occupational data included professional category (physician, nurse, allied health professionals and administrative/support staff) and continuous working hours. Working hours were then categorized to \u0026le;\u0026thinsp;40 and \u0026gt;\u0026thinsp;40 hours per week.\u003c/p\u003e \u003cp\u003eLifestyle data included:\u003c/p\u003e \u003cp\u003eSmoking status: current smoker or non-smoker (never smoked or quit\u0026thinsp;\u0026gt;\u0026thinsp;1 year)\u003c/p\u003e \u003cp\u003ePhysical activity: engaging in more than 30 min of moderate or vigorous-intensity exercise at least twice a week during work or leisure times\u003c/p\u003e \u003cp\u003eSleep duration: Continuous sleep duration (hours) per day. Sleep duration was then categorized to \u0026lt;\u0026thinsp;6, 6\u0026ndash;8 and \u0026gt;\u0026thinsp;8 hours per day.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCaffeine intake assessment\u003c/h2\u003e \u003cp\u003eHabitual caffeine intake was measured using a validated semi-quantitative caffeine food-frequency questionnaire (C-FFQ) adopted for local dietary patterns and available in English and Arabic [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Items included in the C-FFQ were coffee, tea, chocolate, and energy and soft drinks with standard frequency options and serving sizes. Reported intake was converted into average daily consumption. Total caffeine intake was calculated as a sum of multiplied intake frequency and portion size by caffeine content (mg/serving) for each item. Caffeine content was obtained from the U.S. Department of Agriculture database [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and local information [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Matcha tea was added to the questionnaire and its caffeine content was based on published estimates [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The Arabic version of the FFQ underwent forward-backward translation and pilot validation on 20 participants. Implausibly high intake (\u0026gt;\u0026thinsp;800 mg/day) was verified through recontacting participants for re-confirmation. Participants who did not respond (n\u0026thinsp;=\u0026thinsp;2) were excluded from the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDepression assessment\u003c/h2\u003e \u003cp\u003eDepression symptoms were assessed using PHQ-9 validated in English [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and Arabic among Saudi populations [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Each of its 9 items is scored from 0 (\u0026lsquo;not at all\u0026rdquo;) to 3 (\u0026ldquo;nearly every say\u0026rdquo;), yielding a total score of 0\u0026ndash;27. A cutoff point of \u0026ge;\u0026thinsp;10 was used to determine clinically relevant depression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSleep quality assessment\u003c/h2\u003e \u003cp\u003eSleep quality was assessed using the SQQ a self-reported validated tool was used to estimate participant's quality of sleep [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The questionnaire was translated to Arabic and its validity was tested. Participants rated their sleep quality over the past month using a 5-point Likert scale from 0 to 4 corresponding to strongly disagree, disagree, not sure, agree, or strongly agree. The questionnaire consists of ten items: six of them (3, 5, 6, 7, 8, 10) assess daytime sleepiness and four items (1, 2, 4, 9) evaluate sleep difficulty. Scores range from 0 to 40, with higher scores indicating poorer sleep quality. In the absence of a cut-off point for this scale, scores were analyzed continuously; for descriptive purposes, SQQ\u0026thinsp;\u0026gt;\u0026thinsp;20 was used as an operational cutoff to additionally define poorer sleep quality, corresponding to an average item score of \u0026gt;\u0026thinsp;2 (above midpoint).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePerceived stress assessment\u003c/h2\u003e \u003cp\u003ePerceived stress was assessed using the PSS-10 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Responses for 10 items are recorded on a 5-point Likert scale from 0 (\u0026ldquo;never\u0026rdquo;) to 4 (\u0026ldquo;very often\u0026rdquo;) or its reverse score yielding a score that ranges from 0 to 40 with higher scores indicating greater stress. Six are negative (1, 2, 3, 6, 9, 10) assessing perceived helplessness and four are positive (4, 5, 7, 8) evaluating perceived self-efficacy. Both English [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and previously validated Arabic [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] versions were used. For descriptive analysis, PSS-10\u0026thinsp;\u0026gt;\u0026thinsp;20 was used to additionally define having higher perceived stress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e was obtained from the Research Ethics Committee at King Abdulaziz University (KAU), reference No (HA-02-J-008). All methods were preformed in accordance with relevant guidelines and regulations and with the principles of the Declaration of Helsinki. Informed consent was obtained from all participants, and their confidentiality and privacy was strictly maintained.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical Package for Social Sciences (SPSS) version 28 was used to analyze the data. Distributional assumptions for continuous variables were assessed by the Shapiro-Wilk test and by using visual inspection of histograms and Q-Q plots. Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation SD and as median and interquartile range (IQR). Categorical variables are presented as frequency and percentages. Caffeine intake (mg/day) was examined both as a continuous intake and as intake categories (low (\u0026lt;\u0026thinsp;200 mg/day), moderate (200\u0026ndash;400 mg/day) and high (\u0026gt;\u0026thinsp;400 mg/day)). Bivariate comparisons between participants with and without depressive symptoms were performed using independent-sample t-test for normally distributed variables and Mann-Whitney U test for non-normally distributed variables. One way-ANOVA with Bonferroni post-hoc test was used to examine the differences in continuous psychological and sleep measures between coffee consumption categories. The chi-square test was used to examine association between categorical variables.\u003c/p\u003e \u003cp\u003eThe association between categorical caffeine intake and depressive symptoms was assessed using binary logistic regression. Odds ratios (OR) and 95% confidence intervals (CI) were estimated for moderate and high intake, with low intake being the reference category. Three models were fitted. Model 1 (crude); Model 2 adjusted for age (per year), sex, BMI (per 1 kg/m2), smoking status, marital status, living status, nationality, and physical activity; and Model 3 was additionally adjusted for stress and sleep quality scales (PSS-10 and SQQ scores). Model fit evaluation was conducted using the Hosmer-Lemeshow goodness-of-fit.\u003c/p\u003e \u003cp\u003eThe association between continuous caffeine intake and depressive symptoms was assessed linear regression models with PHQ-9 score as the outcome and log2-transformed caffeine daily intake as the independent variable. Three models were fitted. Model 1 (crude); Model 2 adjusted for age (per year), sex, BMI (per 1 kg/m2), smoking status, marital status, living status, nationality, and physical activity; and Model 3 was additionally adjusted for stress and sleep quality scales (PSS-10 and SQQ scores).\u003c/p\u003e \u003cp\u003eAll tests were two-sided, and a \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests Statement:\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, S.A., S.E., M.A.; methodology, S.A., S.E., M.A.; software, S.E., M.A.; validation, S.A., M.A.; formal analysis, S.E.; investigation, S.A., S.E., M.A.; resources, S.A., S.E., M.A.; data curation, S.A., S.E., M.A.; writing\u0026mdash;original draft preparation, S.E.; writing\u0026mdash;review and editing, S.A., S.E., M.A.; visualization, S.E; supervision, S.A., S.E.; project administration, S.E. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003e The data used and analyzed can be obtained from the corresponding author under a reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Depression and Other Common Mental Disorders: Global Health Estimates. (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Our Duty of Care: A Global Call to Action to Protect the Mental Health of Health and Care Workers. (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOffice of the Surgeon General. \u003cem\u003eAddressing Health Worker Burnout: The U.S. Surgeon General\u0026rsquo;s Advisory on Building a Thriving Health Workforce\u003c/em\u003e (US Department of Health and Human Services (US Department of Health and Human Services, Washington, DC,, 2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRam, D. \u0026amp; Alharbi, H. Y. Mental Health Issues among Physicians in Saudi Arabia: A Scoping Review. \u003cem\u003eSaudi J. Med. Med. Sci.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 157\u0026ndash;172 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTemple, J. L. et al. The Safety of Ingested Caffeine: A Comprehensive Review. \u003cem\u003eFront. Psychiatry\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, 257730 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmer, S. A. et al. Caffeine addiction and determinants of caffeine consumption among health care providers: a descriptive national study. \u003cem\u003eEur. Rev. Med. Pharmacol. Sci.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 3230\u0026ndash;3242 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFredholm, B. B., B\u0026auml;ttig, K., Holm\u0026eacute;n, J., Nehlig, A. \u0026amp; Zvartau, E. E. Actions of Caffeine in the Brain with Special Reference to Factors That Contribute to Its Widespread Use. \u003cem\u003ePharmacol. Rev.\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e, 83\u0026ndash;133 (1999).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnsal, S. \u0026amp; Sanlier, N. Longitudinal Effects of Lifetime Caffeine Consumption on Levels of Depression, Anxiety, and Stress: A Comprehensive Review. \u003cem\u003eCurr. Nutr. Rep.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 1\u0026ndash;14 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrosso, G., Micek, A., Castellano, S., Pajak, A. \u0026amp; Galvano, F. Coffee, tea, caffeine and risk of depression: A systematic review and dose-response meta-analysis of observational studies. \u003cem\u003eMol. Nutr. Food Res.\u003c/em\u003e \u003cb\u003e60\u003c/b\u003e, 223\u0026ndash;234 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLucas, M. \u0026amp; Coffee Caffeine, and Risk of Depression Among Women. \u003cem\u003eArch. Intern. Med.\u003c/em\u003e \u003cb\u003e171\u003c/b\u003e, 1571 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePham, N. M. et al. Green tea and coffee consumption is inversely associated with depressive symptoms in a Japanese working population. \u003cem\u003ePublic Health. Nutr.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 625\u0026ndash;633 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuusunen, A. et al. Coffee, tea and caffeine intake and the risk of severe depression in middle-aged Finnish men: the Kuopio Ischaemic Heart Disease Risk Factor Study. \u003cem\u003ePublic Health. Nutr.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 1215\u0026ndash;1220 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAziz, G. A. M., ALghfari, S., Bogami, E., Abduljwad, K. \u0026amp; Bardisi, W. Prevalence and determinants of depression among primary healthcare workers in Jeddah, Saudi Arabia 2020. \u003cem\u003eJ. Family Med. Prim. Care\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, 3013 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlAteeq, D. A., Aljhani, S., Althiyabi, I. \u0026amp; Majzoub, S. Mental health among healthcare providers during coronavirus disease (COVID-19) outbreak in Saudi Arabia. \u003cem\u003eJ. Infect. Public Health\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, 1432\u0026ndash;1437 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmarhapi, S. A. \u0026amp; Khalil, T. A. Depression among healthcare workers in North West Armed Forces hospital-Tabuk, Saudi Arabia: Prevalence and associated factors. \u003cem\u003eAnnals Med. Surg.\u003c/em\u003e \u003cb\u003e68\u003c/b\u003e, 102681 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbar, S. A. et al. Caffeine sources and consumption among saudi adults living with diabetes and its potential effect on hba1c. \u003cem\u003eNutrients\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlHadi, A. N. et al. An arabic translation, reliability, and validation of Patient Health Questionnaire in a Saudi sample. \u003cem\u003eAnnals Gen. Psychiatry\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmadi, T., Cathers, I., Hamdan Mansour, A. M. \u0026amp; Chow, C. M. An Arabic version of the perceived stress scale: translation and validation study. \u003cem\u003eInt. J. Nurs. Stud.\u003c/em\u003e \u003cb\u003e49\u003c/b\u003e, 84\u0026ndash;89 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Department of Agriculture. FoodData Central. (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fdc.nal.usda.gov/\u003c/span\u003e\u003cspan address=\"https://fdc.nal.usda.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaser, L. R., Sameh, A., Muzaffar, I., Omar, A. R. \u0026amp; Ahmed, M. A. Comparative evaluation of caffeine content in Arabian coffee with other caffeine beverages. \u003cem\u003eAfr. J. Pharm. Pharmacol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 19\u0026ndash;26 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLatosińska, M. \u0026amp; Latosińska, J. N. Introductory Chapter: Caffeine, a Major Component of Nectar of the Gods and Favourite Beverage of Kings, Popes, Artists and Revolutionists, a Drug or a Poison? in The Question of Caffeine (InTech, 2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5772/intechopen.69693\u003c/span\u003e\u003cspan address=\"10.5772/intechopen.69693\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHori, K., Kurauchi, Y., Kotani, S. \u0026amp; Devkota, H. P. Analysis of Functional Compounds in Matcha by Quantitative Nuclear Magnetic Resonance Spectroscopy. \u003cem\u003eScientia Pharm. 2025, Vol.\u003c/em\u003e \u003cb\u003e93\u003c/b\u003e, 93, (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKroenke, K., Spitzer, R. L. \u0026amp; Williams, J. B. W. The PHQ-9: validity of a brief depression severity measure. \u003cem\u003eJ. Gen. Intern. Med.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 606\u0026ndash;613 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKato, T. Development of the Sleep Quality Questionnaire in healthy adults. \u003cem\u003eJ. Health Psychol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 977\u0026ndash;986 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen, S. Perceived stress in a probability sample of the United States. in The social psychology of health 31\u0026ndash;67 (Sage Publications, Inc., (1988).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen, S., Kamarck, T. \u0026amp; Mermelstein, R. A global measure of perceived stress. \u003cem\u003eJ. Health Soc. Behav.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 385\u0026ndash;396 (1983).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlghasab, N. S., Aljadani, A. H., Almesned, S. S. \u0026amp; Hersi, A. S. Depression among physicians and other medical employees involved in the COVID-19 outbreak: A cross-sectional study. \u003cem\u003eMedicine\u003c/em\u003e \u003cb\u003e100\u003c/b\u003e, e25290 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl Ammari, M., Sultana, K., Thomas, A. \u0026amp; Al Swaidan, L. Al Harthi, N. Mental Health Outcomes Amongst Health Care Workers During COVID 19 Pandemic in Saudi Arabia. \u003cem\u003eFront. Psychiatry\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, 619540 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgostoni, C. et al. Scientific Opinion on the safety of caffeine. \u003cem\u003eEFSA J.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 4102 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWikoff, D. et al. Systematic review of the potential adverse effects of caffeine consumption in healthy adults, pregnant women, adolescents, and children. \u003cem\u003eFood Chem. Toxicol.\u003c/em\u003e \u003cb\u003e109\u003c/b\u003e, 585\u0026ndash;648 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFallata, G. et al. Caffeine consumption and exposure in Saudi Arabia: a cross-sectional analysis. \u003cem\u003eFront. Nutr.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 1556001 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmer, S. A. et al. Caffeine addiction and determinants of caffeine consumption among health care providers: a descriptive national study. \u003cem\u003eEur. Rev. Med. Pharmacol. Sci.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 3230\u0026ndash;3242 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, L., Shen, X., Wu, Y. \u0026amp; Zhang, D. Coffee and caffeine consumption and depression: A meta-analysis of observational studies. \u003cem\u003eAust. N. Z. J. Psychiatry\u003c/em\u003e. \u003cb\u003e50\u003c/b\u003e, 228\u0026ndash;242 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorabynasab, K., Shahinfar, H., Payandeh, N. \u0026amp; Jazayeri, S. Association between dietary caffeine, coffee, and tea consumption and depressive symptoms in adults: A systematic review and dose-response meta-analysis of observational studies. \u003cem\u003eFront. Nutr.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 1051444 (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Caffeine, depressive symptoms, healthcare workers, perceived stress, sleep quality, cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-9098832/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9098832/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCaffeine is widely consumed among healthcare workers (HCWs) as a coping mechanism for occupational demands and may influence depressive symptoms. This study investigated this relationship after adjustment for perceived stress and sleep quality. In this cross-sectional study on licensed HCWs at a tertiary hospital in Jeddah, Saudi Arabia, habitual caffeine intake was assessed using a validated caffeine food frequency questionnaire. Depressive symptoms were assessed using Patient Health Questionnaire (PHQ-9), with clinically significant depressive symptoms defined as PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10. Perceived stress and sleep quality were measured. Logistic and linear regression models evaluated the associations with PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10 and PHQ-9 score respectively. Among 298 HCWs (mean age 37.5 years; 66.1% women), 18.5% had PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10. Mean caffeine intake was 216 mg/day (median 125 mg/day). HCWs with PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10 reported a higher caffeine intake than those without (Mean 282 vs. 201 mg/day and median 169 vs. 114 mg/day; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038). Caffeine intake was significantly associated with PHQ-9 score but not with PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10 after full adjustment (β\u0026thinsp;=\u0026thinsp;0.331; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014 per twofold increase). Perceived stress and poorer sleep quality were independently associated with PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10. Higher caffeine intake may reflect response to occupational strains rather than a primary depression risk driver.\u003c/p\u003e","manuscriptTitle":"Caffeine Intake and Depressive Symptoms among Healthcare Workers: The Role of Stress and Sleep Quality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-31 08:33:40","doi":"10.21203/rs.3.rs-9098832/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"766530e0-36ed-4b5b-aec1-32d379c4f358","owner":[],"postedDate":"March 31st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65029473,"name":"Health sciences/Diseases"},{"id":65029474,"name":"Health sciences/Health care"},{"id":65029475,"name":"Health sciences/Medical research"},{"id":65029476,"name":"Biological sciences/Psychology"},{"id":65029477,"name":"Social science/Psychology"},{"id":65029478,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-03-31T08:33:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-31 08:33:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9098832","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9098832","identity":"rs-9098832","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}