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Learn more: PMC Disclaimer | PMC Copyright Notice Am J Ind Med . 2026 Mar 12;69(5):372–381. doi: 10.1002/ajim.70072 Search in PMC Search in PubMed View in NLM Catalog Add to search The Association of Sleeping Duration and Sleep Problems With All‐Cause Mortality Among a Cohort of Industrial Workers Followed Up for 36 Years Gil Harari Gil Harari 1 School of Public Health, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel Find articles by Gil Harari 1, ✉ , Anat Gesser‐Edelsburg Anat Gesser‐Edelsburg 1 School of Public Health, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel Find articles by Anat Gesser‐Edelsburg 1 Author information Article notes Copyright and License information 1 School of Public Health, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel * Correspondence: Gil Harari ( [email protected] ) ✉ Corresponding author. Revised 2026 Feb 12; Received 2025 Jul 22; Accepted 2026 Feb 25; Issue date 2026 May. © 2026 The Author(s). American Journal of Industrial Medicine published by Wiley Periodicals LLC. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. PMC Copyright notice PMCID: PMC13070279 PMID: 41819810 ABSTRACT Background Sleep duration is a well‐established factor associated with all‐cause mortality and cardiovascular mortality. Poor sleep quality was also suggested to affect all‐cause mortality risk among adults. The Cardiovascular Occupational Risk Factor determination in Israel Study (CORDIS) is a prospective cohort study of industrial workers who entered the study during 1985–1990 and have been followed for 36 years. We examined the relationship between sleep duration, sleeping problems and difficulties, and all‐cause mortality in the CORDIS cohort. Method Self‐reported data, including sleep duration and sleeping problems, from 7287 participants were merged with data on all‐cause mortality obtained from the National Death Registry and the Central Bureau of Statistics. Results Over the 36‐year follow‐up, 2159 participants died: 445 were < 45 years and 1714 were ≥ 45 years. Sleep duration of ≤ 5 h significantly increased mortality risk (hazard ratio [HR] = 1.30, p = 0.0032), with a more pronounced effect in those < 45 years (HR = 1.55, p = 0.0028). Sleeping problems also increased mortality risk (HR = 1.30, p = 0.0088), with a stronger association among younger individuals (HR = 1.63, p = 0.0399). Conversely, difficulty sleeping when anticipating something unpleasant was linked to increased mortality only in those aged ≥ 45 years (HR = 1.17, p = 0.0440). Conclusions Our analysis showed that short sleep duration and sleeping problems are significant predictors for all‐cause mortality, particularly in younger individuals (< 45 years). These results emphasize the importance of addressing sleep problems among different age groups to potentially reduce mortality risk. Keywords: occupational health; prospective cohort; sleep duration; sleep problems, all‐cause mortality; sleeping hours 1. Introduction Sleep duration is a well‐established factor associated with all‐cause mortality and cardiovascular mortality. Many studies and meta‐analyses have found that both insufficient and excessive sleep can be linked to higher risks of various health outcomes, including cardiovascular diseases and overall mortality [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. The UMBRELLA systematic review of meta‐analyses, which examined the associations between sleep duration and health outcomes in population‐based studies, revealed a strong nonlinear U‐shaped or V‐shaped relationship between sleep duration and these medical conditions. Inappropriate sleep duration was found to be a risk factor for developing non‐cancer conditions with poor health outcomes, including cardiovascular diseases, coronary heart disease, cognitive decline, depression, falls, frailty, lung cancer, metabolic syndrome, and stroke. Dose–response analysis revealed that a 1‐h reduction in sleep per 24 h is associated with a 3%–11% increased risk for all‐cause mortality, coronary heart disease, osteoporosis, stroke, and type‐2 diabetes mellitus among short sleepers. Conversely, a 1‐h increment increase in long sleepers was associated with a 7%–17% higher risk for stroke mortality, coronary heart disease, stroke, and type‐2 diabetes mellitus in adults [ 9 ]. The National Health and Nutrition Examination Survey (NHANES), which included 26,977 participants aged ≥ 18 years followed from 2005 to 2014, confirmed the non‐linear, U‐shaped relationship between sleep duration and mortality risks. Both short and long sleep durations were associated with higher risks of all‐cause and cardiovascular mortality. The risk was minimized when sleep duration is approximately 7 h [ 10 ]. Some studies found no association between short sleep duration (< 6 h/day) and mortality [ 11 ]. In a review of 42 prospective studies on sleep duration and mortality, only 14 studies (25%) identified a U‐shaped relationship, while 23 studies (43%) found no association at all between sleep duration and risk for mortality. The remaining studies showed associations with either short or long sleep duration. The authors argued that the lack of a consistent U‐shaped relationship can be attributed to the variability in the ways sleep duration was measured and categorized, the different methods and variables used to adjust for potential confounders, and the variation in age ranges across studies [ 12 ]. In addition to sleep duration, poor sleep quality was also posited to affect all‐cause mortality risk among adults [ 13 , 14 , 15 ]. However, sleep quality was measured differently among studies and associations may differ among ethnic groups. The objective of the current study was to further elaborate on the association between sleep quality and all‐cause mortality in a cohort of industrial workers whose sleep quality was assessed at baseline and who were followed for mortality outcomes over a 36‐year period. This long follow‐up period allowed for the accrual of a large number of events, thereby increasing statistical power, and enabling more robust analysis. 2. Methods 2.1. Study Population The Cardiovascular Occupational Risk Factor determination in Israel Study (CORDIS) included industrial workers recruited from 21 industrial plants (metal work, textiles, light industry, electronics, food manufacturing and plywood production) across Israel for on‐site screening of cardiovascular risk factors. Data were collected from 8001 employees in two phases: 1985–1987 (6014 participants) and 1988–1990 (1987 participants) [ 16 , 17 , 18 ]. Employees with cancer and cardiovascular disease at baseline were excluded from participating in the study. The current analysis was restricted to Jewish participants aged 18–70 years at baseline. Follow‐up data for 297 Arab participants was not available. Data for 340 participants could not be merged with the data from the National Death Registry of the Israel Ministry of the Interior and the Central Bureau of Statistics. Additionally, 75 workers were aged < 18 or > 70 years and were therefore excluded. Two participants were excluded due to missing age values. Thus, a total of 7287 participants were included in the current analysis. The current analysis was approved by the University's Ethics Committee (approval number 409/24). The data were deidentified and contained no personal identifiable information. 2.2. Study Questionnaires and Data Collection Data collection, including completion of questionnaires, was carried out during 1985–1990 by trained technicians who interviewed the participants and performed physical examinations at the participants’ workplaces during work hours on the same day. Fasting blood samples for complete blood count, and blood chemistry were taken on a different day within several weeks of the interviews [ 19 ]. The study questionnaires included questions on demographics and socioeconomic status (sex, age, type of housing [flat/house], number of rooms, country of birth, the birth countries of each participant's parents and grandparents, year of immigration to Israel, education status, marital status), the occupational status and conditions at the plant (seniority, job description/responsibility, work schedule [shift work/hourly work], extent of physical effort during work), current and past physical activity (PA) frequency and type (leisure and work‐related), smoking status (smoking frequency, age at start and cessation, and type of tobacco products), and a nutrition questionnaire (type of diet [vegetarian/vegan, diabetic, low‐sodium, low‐fat or low‐calorie]; consumption of alcoholic beverages, coffee, tea and soft drinks). The medical questionnaire included questions on family and personal medical history and medications. An additional questionnaire was used for collecting information on sleep‐related variables. Sleep‐related variables included the number of sleep hours at night and the number of minutes or hours it took the participant to fall asleep. The participants were asked whether they have any sleep difficulties (1 = no, 2 = sometimes, 3 = often, 4 = very often). They were also asked to rate the statement “I cannot sleep when I expect something unpleasant” on a scale of 1 (completely untrue) to 6 (completely true). 2.3. Mortality Data Collection Data of the CORDIS cohort participants were merged in 2022 with data on mortality obtained from the National Death Registry of the Israel Ministry of the Interior and the Central Bureau of Statistics, using the participants' identification number and other details such as the date of birth. Coronary heart disease mortality was defined by the ICD‐9 codes 410‐414. 2.4. Statistical Analyses The data were analyzed using SAS ® version 9.4 (SAS Institute, Cary, NC, USA). Continuous variables were described as mean ± standard deviation (SD) and categorical data were described as number and frequency (%). Nightly hours of sleep were analyzed by two categories: ≤ 5 h and ≥ 6 h. The time it took the participants to fall asleep was analyzed by two categories: 1 h and ≥ 2 h. Having difficulties sleeping were categorized into two groups: “no” and “sometimes” were considered as not having sleeping problems, whereas “often” and “very often” were considered as having sleeping problem. Not being able to sleep when expecting something unpleasant was categorized into two groups: 1–3 were considered “False” and 4–6 were considered “True.” All‐cause mortality was compared by sleep duration categories and sleeping problems. The Kaplan‐Meier method was used to compare survival curves among study groups using the log‐rank test. Multivariate regression analysis was based on the Cox proportional hazard model for predicting mortality. Sleep duration and sleep problems were adjusted for the following potential confounders: age at screening, the interaction between sleep duration and age, father's country of origin, educational level, hypertension, diabetes, current smoking, alcohol consumption, coffee consumption, maintaining a special diet, blue collar status, body position at work, job scope, shift work, current leisure‐time physical activity (LTPA) status, socioeconomic status (number of people/room), body mass index (BMI), cholesterol, high‐density lipoprotein cholesterol (HDL), years in job, and anxiety score. The interactions between age category and sleep duration and sleep problems were tested (each interaction was tested separately). If the interaction was found statistically significant (≤ 0.15) it was included in the final model. This threshold of ≤ 0.15 was pre‐specified and is consistent with methodological recommendations for interaction testing, which is generally characterized by lower statistical power [ 20 , 21 ]. The Cox model was based on the incidence of the analyzed event (death due to any cause) and the time that had passed until the event occurred. The survival model links the time to event with the sleeping variable of interest and confounders that may be associated with that time to event. Confounders were selected based on baseline characteristics. All tests were two‐tailed, and a p value of < 0.05 was considered statistically significant. 3. Results 3.1. Characteristics of the Study Population and Baseline Conditions, Sleep Problems and Sleep Duration Overall, 7287 participants were included in the current analysis. The median follow‐up time of the cohort was 35.5 years. Table 1 presents the characteristics of the study population, and Supporting Information S1: Table 1 additionally reports the corresponding 95% confidence intervals (CIs). Most participants (4994, 68.5%) were male. The mean age of the study population at study enrollment was 41.5 ± 12.7 years and the mean number of years on the job was 9.1 ± 8.0. Most participants (7047, 96.7%) worked full time, were blue collar workers (5440, 74.7%) and worked only during regular working hours (4951, 67.9%). Table 1. Sleep duration by baseline demographics and characteristics of the study population. Sleep duration Sleep problems Parameter ( N = All) ≤ 5 h N = 737 ≥ 6 h N = 6550 No N = 6697 Yes N = 590 All N = 7287 Age, years ( N = 7287) 45.8 ± 12.4 41.1 ± 12.6 41.3 ± 12.7 44.5 ± 12.3 41.5 ± 12.7 Sex Male 523 (71.0) 4469 (68.2) 4593 (68.6) 399 (67.6) 4994 (68.5) Female 214 (29.0) 2081 (31.8) 2104 (31.4) 191 (32.4) 2295 (31.5) Father's origin Africa 173 (23.5) 1918 (29.3) 1912 (28.6) 179 (30.3) 2091 (28.7) Asia 135 (18.3) 1014 (15.5) 1062 (15.9) 87 (14.7) 1149 (15.8) Europe 369 (50.1) 2889 (44.1) 2993 (44.7) 265 (44.9) 3258 (44.7) Yemen 5 (0.7) 52 (0.8) 55 (0.8) 2 (0.3) 57 (0.8) Israel 53 (7.2) 655 (10.0) 654 (9.8) 54 (9.2) 708 (9.7) Education (years) < 12 464 (63.0) 3765 (57.5) 3839 (57.3) 390 (66.1) 4229 (58.0) 12 177 (24.0) 1706 (26.0) 1745 (26.1) 138 (23.4) 1883 (25.8) > 12 95 (12.9) 1070 (16.3) 1103 (16.5) 62 (10.5) 1165 (16.0) Persons/room ( N = 7284) 1.3 ± 0.7 1.3 ± 0.7 1.3 ± 0.7 1.3 ± 0.7 1.3 ± 0.7 Body mass index (kg/m 2 , N = 7197) 26.6 ± 4.2 25.6 ± 4.2 25.7 ± 4.1 26.3 ± 4.4 25.7 ± 4.2 Cholesterol (mg/dL, N = 7039) 206.4 ± 43.8 197.3 ± 43.9 197.7 ± 43.8 204.5 ± 45.9 198.3 ± 44.0 HDL Cholesterol (mg/dL, N = 7034) 44.6 ± 11.8 44.7 ± 12.0 44.7 ± 12.0 44.3 ± 11.5 44.7 ± 12.0 Hypertension 105 (14.2) 621 (9.5) 631 (9.4) 95 (16.1) 726 (10.0) Diabetes 35 (4.7) 197 (3.0) 197 (2.9) 35 (5.9) 232 (3.2) Current Smoking 261 (35.4) 2126 (32.5) 2203 (32.9) 184 (31.2) 2387 (32.8) Alcohol ≥ 3 times/week 72 (9.8) 467 (7.1) 487 (7.3) 52 (8.8) 539 (7.4) Coffee cups ≥ 3/day 261 (35.4) 2051 (31.3) 2146 (32.0) 166 (28.1) 2312 (31.7) Maintain special diet a 96 (13.0) 553 (8.4) 571 (8.5) 78 (13.2) 649 (8.9) Years in job ( N = 7274) 10.9 ± 8.9 8.9 ± 7.9 8.9 ± 8.0 10.8 ± 8.2 9.1 ± 8.0 Anxiety score ( N = 7287) 7.1 ± 3.1 7.1 ± 3.0 7.0 ± 2.9 8.3 ± 3.7 7.1 ± 3.0 Blue collar 590 (80.1) 4850 (74.0) 4969 (74.2) 471 (79.8) 5440 (74.7) Body Position at work Sitting 233 (31.6) 2592 (39.6) 2616 (39.1) 209 (35.4) 2825 (38.8) Standing 377 (51.2) 2978 (45.5) 3063 (45.7) 292 (49.5) 3355 (46.0) Walking/Climbing 126 (17.1) 966 (14.7) 1003 (15.0) 89 (15.1) 1092 (15.0) Job scope Full time job 710 (96.3) 6337 (96.7) 6472 (96.6) 575 (97.5) 7047 (96.7) Other 26 (3.5) 200 (3.1) 211 (3.2) 15 (2.5) 226 (3.1) Shift Work Day only 539 (73.1) 4412 (67.4) 4555 (68.0) 396 (67.1) 4951 (67.9) Day + extra hours 89 (12.1) 1201 (18.3) 1195 (17.8) 95 (16.1) 1290 (17.7) Other 108 (14.7) 926 (14.1) 935 (14.0) 99 (16.8) 1034 (14.2) Current LTPA 138 (18.7) 1398 (21.3) 1439 (21.5) 97 (16.4) 1536 (21.1) Open in a new tab Note: Categorical values are displayed as N (%) and continuous variables are displayed as mean ± standard deviation including total number of participants included in the calculations. Abbreviations: BMI, body mass index; HDL, high‐density lipoproteins; LTPA, leisure time physical activity. a Vegetarian/vegan, diabetic, low‐sodium, low‐fat or low‐calorie. 3.2. Distribution of Sleep Variables by Mortality Over the 36‐year follow up, 2159 participants (29.6%) died: 445 were younger than 45 and the rest (1714) were 45 and older. Among the deceased participants, 294 (13.6%) reported a nightly sleep duration of ≤ 5 h, 103 (5.0%) reported taking ≥1 h to fall asleep, 212 (9.8%) reported having sleep problems and 429 (19.9%) reported that they have difficulty sleeping when anticipating something unpleasant (Table 2 and Supporting Information S1: Table 2 ). Due to the small number of deaths in the categories related to the time it takes to fall asleep, further analyses were not performed for that variable. Table 2. Overall distribution of sleep variables by death. Sleep variables Deceased N = 2159 n (%) Alive N = 5128 n (%) All N = 7287 n (%) Number of nightly sleep hours ≤ 5 h 294 (13.6) 443 (8.6) 737 (10.1) ≥ 6 h 1865 (86.4) 4685 (91.4) 6550 (89.9) The time it takes to fall asleep No time 2052 (95.0) 4968 (96.9) 7020 (96.3) 1 h 77 (3.6) 117 (2.3) 194 (2.7) ≥ 2 h 30 (1.4) 43 (0.8) 73 (1.0) Sleep problems 212 (9.8) 378 (7.4) 590 (8.1) Cannot sleep when expecting something unpleasant 429 (19.9) 1341 (26.2) 1770 (24.3) Open in a new tab 3.3. The Association Between Sleep Duration and All‐Cause Mortality The multivariate analysis showed that sleep duration of ≤ 5 h was significantly associated with a higher risk for all‐cause mortality with a hazard ratio (HR) of 1.30 (95% CI 1.08–1.56; p = 0.0032) when adjusting for all potential confounders. This association remained significant when adjusting only for age at screening and the interaction between age and sleep duration (HR = 1.39 [95% CI 1.17–1.65]; p < 0.0001). Log‐Rank test used for the Kaplan‐Meier curve demonstrated a statistically significant higher all‐cause mortality for a sleep duration ≤ 5 h compared to ≥ 6 h ( p < 0.0001, Figure 1A ). A significant interaction between age and sleep duration (Table 3 , adjusted models 1 and 2) led us to perform the same analyses by age categories (< 45 years vs. ≥ 45 years). The multivariate analysis (adjusted for all potential confounders) demonstrated a very significant association for the young‐age category (< 45 years) versus the older age category (≥ 45 years), with an HR of 1.55 (95% CI 1.16–2.07; p = 0.0028) versus a HR of 1.0 (95% CI 0.87–1.15; p = 0.9726), respectively. The same direction was observed for the log‐rank test used for the Kaplan‐Meier curves ( p < 0.0001 vs. p = 0.4759 for < 45 years vs. age ≥ 45, respectively; Figure 1B,C ). Figure 1. Open in a new tab Kaplan‐Meier survival curves showing the association between nightly sleep duration (≤ 5 h vs. ≥ 6 h) and all‐cause mortality (A) The entire study population (B) Participants aged < 45 years (C) Participants aged ≥ 45 years. p value indicates the difference by log‐rank test. Table 3. Association between sleep variables and all‐cause mortality. All‐cause mortality – All ages N = 2159 All‐cause mortality –Age < 45 N = 445 All‐cause mortality –Age ≥ 45 N = 1714 Variable No. of deaths HR (95% CI) p value No. of deaths HR (95% CI) p value No. of deaths HR (95% CI) p value Association between sleep duration and all‐cause mortality Adjusted model 1 a Sleep duration (≤ 5 h vs. ≥ 6 h) 2028 1.30 (1.08, 1.56) 0.0032 423 1.55 (1.16, 2.07) 0.0028 1605 1.0 (0.87, 1.15) 0.9726 Adjusted Model 2 b Sleep duration (≤ 5 h vs. ≥ 6 h) 2117 1.39 (1.17, 1.65) < 0.0001 — — — — — — — — Association between sleeping problems and all‐cause mortality Adjusted model 1 a Sleeping problems (yes vs. no) 2028 1.296 (1.007, 1.667) 0.0088 423 1.63 (1.02, 2.60) 0.040 1605 0.97 (0.83, 1.14) 0.7283 Adjusted Model 2 c Sleep problems (yes vs. no) 2117 1.11 (0.88, 1.40) 0.064 — — — — — — — — Association between difficulty falling asleep when expecting something unpleasant and all‐cause mortality Adjusted model 1 a Sleeping problems (yes vs. no) 2028 1.04 (0.89, 1.21) 0.5875 423 0.89 (0.70, 1.14) 0.3550 1605 1.17 (1.00, 1.37) 0.0440 Adjusted Model 2 d Sleeping problems (yes vs. no) 2117 1.15 (1.01, 1.31) 0.5445 — — — — — — — — Open in a new tab Abbreviations: CI, confidence interval; HR, hazard ratio. a Cox proportional hazards regression model adjusted for age at screening, the interaction between sleep duration and age, father's country of origin, educational level, hypertension, diabetes, current smoking, alcohol consumption, coffee consumption, maintaining a special diet, blue collar status, body position at work, job scope, shift work, current leisure‐time physical activity (LTPA) status, socioeconomic status (number of people/room), body mass index (BMI), cholesterol, high‐density lipoprotein cholesterol (HDL), years in job and anxiety score. b Cox proportional hazards regression model adjusted for age at screening and the interaction between sleep duration and age. c Cox proportional hazards regression model adjusted for age at screening and the interaction between sleeping problems and age. d Cox proportional hazards regression model adjusted for age at screening and the interaction between difficulties falling asleep when expecting something unpleasant and age. We also attempted to categorize sleep duration differently; however, given the distribution of sleep duration at baseline, all alternative categorizations resulted in highly unbalanced group sizes, with very small numbers of participants in some categories. This limitation was particularly pronounced when analyses were stratified by mortality outcomes, leading to unstable estimates and reduced statistical power. We further explored modeling sleep duration as a continuous variable; however, this approach proved problematic due to a highly clustered and non‐normal distribution, concentrated around a narrow range of reported sleep hours, without sufficient variability to yield a meaningful mean or reflect a gradual change. Therefore, within the constraints of the available data, the dichotomous categorization represents the most methodologically robust and statistically stable approach. 3.4. The Association Between Sleep Problems and All‐Cause Mortality The multivariate analysis showed that having sleeping problems was significantly associated with a higher risk for all‐cause mortality with a HR of 1.30 (95% CI 1.01–1.67; p = 0.0088) when adjusting for all potential confounders. This association showed a trend for statistical significance when adjusting only for age at screening and the interaction between age and having sleeping problems (HR = 1.11 [95% CI 0.88–1.38]; p = 0.0638). Log‐rank test used for the Kaplan‐Meier curve demonstrated a statistically significant higher all‐cause mortality among participants who reported having sleeping problems compared to those with no sleeping problems ( p = 0.0006, Figure 2A ). Similar to the sleeping duration variable, a significant interaction between age and having sleeping problems (Table 3 , Adjusted models 1 and 2) led us to perform the same analyses by age categories. The multivariate analysis (adjusted for all potential confounders) demonstrated a significant association for the young‐age category (< 45 years) compared with the older‐age category (≥ 45 years) with a HR of 1.63 (95% CI 1.02–2.60; p = 0.0399) versus a HR of 0.97 (95% CI 0.83–1.14; p = 0.7283), respectively. The same direction was observed for the log‐rank test used for the Kaplan‐Meier curves ( p = 0.0781 vs. p = 0.3579 for < 45 years vs. ≥ 45 years, respectively, Figure 2B,C ). Figure 2. Open in a new tab Kaplan‐Meier survival curves showing the association between having sleeping problems (yes vs. no) and all‐cause mortality (A) The entire study population (B) Participants aged < 45 years (C) Participants aged ≥ 45 years. p value indicates the difference by log‐rank test. 3.5. The Association Between Difficulty Sleeping When Expecting Something Unpleasant and All‐Cause Mortality The log‐rank test used for the Kaplan‐Meier curve demonstrated a statistically significant higher all‐cause mortality among participants reporting sleeping difficulties when expecting something unpleasant ( p < 0.0001, Figure 3A ). However, according to the multivariate analysis, having this difficulty was not associated with a higher risk for all‐cause mortality when adjusting for all potential confounders or when adjusting for age and interaction between age and difficulty falling asleep (Table 3 , adjusted models 1 and 2). Multivariate analyses by age categories (adjusted for all potential confounders) demonstrated a statistically significant association for the older‐age category (≥ 45 years), as opposed to the younger‐age category (< 45 years) with a HR of 1.17 (95% CI 1.00–1.37; p = 0.0440) versus a HR of 0.89 (95% CI 0.70–1.14; p = 0.3550), respectively. The log‐rank test used for the Kaplan‐Meier curves showed the same direction ( p = 0.0006 vs. p = 0.2431 for ≥ 45 years vs. < 45 years, respectively, Figure 3B,C ). Figure 3. Open in a new tab Kaplan‐Meier survival curves showing the association between having difficulties falling asleep when expecting something unpleasant (yes vs. no) and all‐cause mortality (A) The entire study population (B) Participants aged < 45 years (C) Participants aged ≥ 45 years. p values indicate the difference by log‐rank test. 4. Discussion In this extensive 36‐year follow‐up study, we examined the relationship between sleep duration, sleeping problems, and the difficulty of sleeping when expecting something unpleasant, with all‐cause mortality. Our analysis included 7287 males and females, capturing a broad spectrum of baseline characteristics. Our analysis revealed a robust association between shorter sleep duration (≤ 5 h) and increased risk of all‐cause mortality. This association was consistently observed across various models, and specifically when the model was adjusted for a wide range of potential confounders. This association persisted also when only adjusting for age and the interaction between sleep duration and age. This suggests that younger individuals with short sleep duration may be at a greater relative risk. Our conclusion is consistent with the conclusion drawn by Åkerstedt et al. [ 22 ] who showed that the effect of sleep duration on mortality was highest among young individuals and decreased with increasing age. However, in that study, the participants were categorized by a cut‐off age of 65 years and demonstrated a U‐shaped relation between sleep duration and mortality. Other meta‐analyses and studies also found a clear effect of sleep duration on mortality among older participants [ 8 , 23 , 24 , 25 , 26 , 27 ]. It should be noted that different studies have varying proportions of older participants, a fact that could contribute to the variability in conclusions found in the literature. Our analyses suggest that the impact of short sleep duration on mortality might be more pronounced in younger individuals compared to older adults; young individuals who consistently get insufficient sleep may be at risk due to the cumulative effect of poor sleep on long‐term health, including cardiovascular, metabolic, and psychological conditions [ 28 , 29 ]. Mechanistically, short sleep duration has been linked to metabolic dysregulation, cardiovascular disease, and impaired immune function, which could collectively contribute to higher mortality rates [ 8 ]. Despite numerous studies and meta‐analyses indicating a U‐shaped relationship between sleep duration and health outcomes, our current study could not demonstrate this due to the limited number of participants reporting more than 9 h of sleep, resulting in insufficient statistical power. Additionally, the potential association between inadequate sleep and an increased risk of cardiovascular mortality could not be tested, as the number of cardiovascular deaths reported during the follow‐up period among workers with short sleep duration and sleeping problems, was very small. Having sleeping problems was also significantly associated with all‐cause mortality. Participants reporting sleep problems had a higher risk for all‐cause mortality after adjusting for multiple confounders. This association was borderline significant when only age and the interaction between age and sleep problems were considered. Similar to sleep duration, age stratification indicated that individuals younger than 45 years had a notably higher risk for all‐cause mortality compared to older ones. As sleeping problems in the CORDIS questionnaire were assessed through a general question that could be subjectively interpreted by the participant, a positive response to this question could encompass any sleep disorder or disturbance the participant considered relevant. Despite the general nature of this question, the results align with previous studies that identified a clear link between sleep disturbances and increased mortality [ 30 , 31 , 32 ]. According to the GAZEL Cohort Study [ 30 ], the effect of sleep disturbances was most pronounced for men under 45 years of age. This result is consistent with our finding that the younger group had a notably higher risk compared to older participants, although we did not analyze our findings by gender. Interestingly, multivariate analyses adjusting for all potential confounders did not show a statistically significant association between difficulty sleeping when expecting something unpleasant and all‐cause mortality for the overall population. However, age‐specific analyses revealed that older participants (≥ 45 years) with this difficulty had a higher mortality risk whereas no significant association was found in younger participants. In addition to the observed associations between sleep duration and mortality, it is important to consider the underlying biological and psychological mechanisms that may account for reverse causation. Although individuals with cardiovascular disease or cancer at baseline were excluded from participation, sleep disturbances may represent an early manifestation of underlying medical conditions, such as undiagnosed sleep disorders (e.g., obstructive sleep apnea), cardiovascular disease or malignancy, rather than an independent risk factor for mortality [ 33 , 34 ]. Short sleep duration has been linked to metabolic disruptions, such as insulin resistance and inflammation, which are associated with an increased risk of cardiovascular diseases and higher mortality rates [ 8 ]. Furthermore, insufficient sleep negatively impacts immune function, potentially leading to greater susceptibility to infections and other health complications [ 35 ]. On the psychological side, sleep disturbances are closely linked to stress and anxiety, which can exacerbate health issues and contribute to an elevated risk of mortality [ 36 ]. The bidirectional relationship between sleep disorders and mental health conditions, such as anxiety and depression, further highlights the need to address both the physiological and psychological aspects of sleep health in order to fully understand its impact on longevity [ 37 ]. 4.1. Strengths and Limitations The strengths of our study include its large sample size, long follow‐up period, and comprehensive adjustment for potential confounders. A key limitation of this study is the reliance on self‐reported sleep data, which may introduce recall bias and inaccuracies in the reporting of sleep patterns, and the lack of information on changes in sleep patterns over the 36‐year follow‐up among study participants. Participants may not have accurately remembered or reported their sleep behaviors, leading to potential misclassification. Additionally, the study may be subject to unmeasured confounding variables, such as unreported lifestyle factors or undiagnosed health conditions, which could influence the observed associations. Some sleep‐related items in the questionnaires may have reflected anxiety or stress rather than sleep problems. Indeed, these were intended as part of a broad subjective measure of sleep difficulties rather than for clinical diagnoses. Furthermore, the possibility of intervening factors, such as psychological stress or other health‐related behaviors, may have an impact on the outcomes and should be considered in the interpretation of the results. These limitations highlight the need for caution when generalizing the findings and suggest that future studies should incorporate more objective measures of sleep and account for a broader range of potential confounders. The study's findings highlight the critical role of sleep for overall health and longevity, especially among younger individuals. Short sleep duration and sleep problems are modifiable risk factors that could be targeted in public health interventions to potentially reduce mortality rates. The differential impact of sleep disturbances across age groups suggests that younger populations might benefit more from early interventions focused on improving sleep quality and duration. The implications of these findings are particularly critical for occupational health. Insufficient sleep or sleep problems among workers not only increases the risk of all‐cause mortality but also poses immediate dangers in the workplace. Short sleep duration and sleep problems have been associated with an increased risk of work accidents and injuries [ 38 , 39 , 40 , 41 ], as sleep‐deprived individuals often suffer from impaired cognitive function, reduced attention, and slower reaction times [ 42 ]. These effects can be catastrophic in high‐risk industrial environments where vigilance and quick reflexes are crucial for safety. Moreover, sleep problems can severely undermine employee productivity, leading to absenteeism, presenteeism, and decreased work quality [ 43 , 44 ]. For employers, addressing sleep health is not only a matter of improving employee well‐being but also of enhancing workplace safety, productivity, and overall organizational performance. Interventions such as flexible work hours, sleep education programs, and workplace wellness initiatives could play a crucial role in mitigating these risks. Employers should recognize the profound impact of sleep health on overall job performance and workplace safety and consider implementing workplace interventions to promote better sleep hygiene among their employees. Our study underscores the importance of considering a comprehensive set of confounders when examining sleep‐related mortality risks. Future research should explore the underlying mechanisms linking sleep parameters to mortality, also as a function of age, and investigate whether interventions aimed at improving sleep can directly influence longevity. 5. Conclusion Our study provides compelling evidence that short sleep duration and sleep problems are significant predictors of all‐cause mortality, particularly in younger (< 45 years) individuals. Addressing sleep issues through public health strategies could be a vital component in improving population health and reducing premature mortality. Further research is needed to confirm these findings, especially as a function of age, and develop effective interventions to enhance sleep health across different age groups. Author Contributions Gil Harari: conceptualization, methodology, investigation, formal analysis, writing – original draft, writing – review and editing. Anat Gesser‐Edelsburg: conceptualization, writing – review and editing. Funding The authors received no specific funding for this work. Ethics Statement The study was approved by the Ethics Committee of Haifa University, Haifa, Israel (approval number 409/24). The data were deidentified and contained no personal identifiable information. Conflicts of Interest The authors declare no conflicts of interest. Supporting information Supplementary Table 1: Sleep duration by baseline demographics and characteristics of the study population. Supplementary Table 2: Overall distribution of sleep variables by death. AJIM-69-372-s001.docx (26.4KB, docx) Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. References 1. Kim Y., Wilkens L. R., Schembre S. M., Henderson B. E., Kolonel L. N., and Goodman M. T., “Insufficient and Excessive Amounts of Sleep Increase the Risk of Premature Death From Cardiovascular and Other Diseases: The Multiethnic Cohort Study,” Preventive Medicine 57, no. 4 (2013): 377–385, 10.1016/j.ypmed.2013.06.017. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. García‐Perdomo H. A., Zapata‐Copete J., and Rojas‐Cerón C. A., “Sleep Duration and Risk of All‐Cause Mortality: A Systematic Review and Meta‐Analysis,” Epidemiology and Psychiatric Sciences 28, no. 5 (2019): 578–588, 10.1017/S2045796018000379. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. Gao C., Guo J., Gong T. T., et al., “Sleep Duration/Quality With Health Outcomes: An Umbrella Review of Meta‐Analyses of Prospective Studies,” Frontiers in Medicine 8 (2022): 813943, 10.3389/fmed.2021.813943. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. Yin J., Jin X., Shan Z., et al., “Relationship of Sleep Duration With All‐Cause Mortality and Cardiovascular Events: A Systematic Review and Dose‐Response Meta‐Analysis of Prospective Cohort Studies,” Journal of the American Heart Association 6, no. 9 (2017): e005947, 10.1161/jaha.117.005947. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. He M., Deng X., Zhu Y., Huan L., and Niu W., “The Relationship Between Sleep Duration and All‐Cause Mortality in the Older People: An Updated and Dose‐Response Meta‐Analysis,” BMC Public Health 20, no. 1 (2020): 1179, 10.1186/s12889-020-09275-3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. Kakizaki M., Kuriyama S., Nakaya N., et al., “Long Sleep Duration and Cause‐Specific Mortality According to Physical Function and Self‐Rated Health: The Ohsaki Cohort Study,” Journal of Sleep Research 22, no. 2 (2013): 209–216, 10.1111/j.1365-2869.2012.01053.x. [ DOI ] [ PubMed ] [ Google Scholar ] 7. Cappuccio F. P., Cooper D., D'Elia L., Strazzullo P., and Miller M. A., “Sleep Duration Predicts Cardiovascular Outcomes: A Systematic Review and Meta‐Analysis of Prospective Studies,” European Heart Journal 32, no. 12 (2011): 1484–1492, 10.1093/eurheartj/ehr007. [ DOI ] [ PubMed ] [ Google Scholar ] 8. Cappuccio F. P., D'Elia L., Strazzullo P., and Miller M. A., “Sleep Duration and All‐Cause Mortality: A Systematic Review and Meta‐Analysis of Prospective Studies,” Sleep 33, no. 5 (2010): 585–592, 10.1093/sleep/33.5.585. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Li J., Cao D., Huang Y., et al., “Sleep Duration and Health Outcomes: An Umbrella Review,” Sleep and Breathing 26, no. 3 (2022): 1479–1501, 10.1007/s11325-021-02458-1. [ DOI ] [ PubMed ] [ Google Scholar ] 10. Li J., Wu Q., Fan L., Yan Z., Shen D., and Zhang M., “Nonlinear Associations Between Sleep Duration and the Risks of All‐Cause and Cardiovascular Mortality Among the General Adult Population: A Long‐Term Cohort Study,” Frontiers in Cardiovascular Medicine 10 (2023): 1109225, 10.3389/fcvm.2023.1109225. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 11. Chen L.‐J., Hamer M., Lai Y.‐J., Huang B.‐H., Ku P.‐W., and Stamatakis E., “Can Physical Activity Eliminate the Mortality Risk Associated With Poor Sleep? A 15‐Year Follow‐Up of 341,248 MJ Cohort Participants,” Journal of Sport and Health Science 11, no. 5 (2022): 596–604, 10.1016/j.jshs.2021.03.001. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 12. Kurina L. M., McClintock M. K., Chen J.‐H., Waite L. J., Thisted R. A., and Lauderdale D. S., “Sleep Duration and All‐Cause Mortality: A Critical Review of Measurement and Associations,” Annals of Epidemiology 23, no. 6 (2013): 361–370, 10.1016/j.annepidem.2013.03.015. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. Del Brutto O. H., Mera R. M., Rumbea D. A., Sedler M. J., and Castillo P. R., “Poor Sleep Quality Increases Mortality Risk: A Population‐Based Longitudinal Prospective Study in Community‐Dwelling Middle‐Aged and Older Adults,” Sleep Health 10, no. 1 (2024): 144–148, 10.1016/j.sleh.2023.10.009. [ DOI ] [ PubMed ] [ Google Scholar ] 14. Garfield V., Joshi R., Garcia‐Hernandez J., Tillin T., and Chaturvedi N., “The Relationship Between Sleep Quality and All‐Cause, CVD and Cancer Mortality: The Southall and Brent REvisited study (SABRE),” Sleep Medicine 60 (2019): 230–235, 10.1016/j.sleep.2019.03.012. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Diao T., Zhou L., Yang L., et al., “Bedtime, Sleep Duration, and Sleep Quality and All‐Cause Mortality in Middle‐Aged and Older Chinese Adults: The Dongfeng‐Tongji Cohort Study,” Sleep Health 9, no. 5 (2023): 751–757, 10.1016/j.sleh.2023.07.004. [ DOI ] [ PubMed ] [ Google Scholar ] 16. Harari G., Green M. S., Magid A., and Zelber‐Sagi S., “Usefulness of Non‐High‐Density Lipoprotein Cholesterol as a Predictor of Cardiovascular Disease Mortality in Men in 22‐Year Follow‐Up,” American Journal of Cardiology 119, no. 8 (2017): 1193–1198, 10.1016/j.amjcard.2017.01.008. [ DOI ] [ PubMed ] [ Google Scholar ] 17. Harari G., Green M. S., and Zelber‐Sagi S., “Combined Association of Occupational and Leisure‐Time Physical Activity With All‐Cause and Coronary Heart Disease Mortality Among a Cohort of Men Followed‐Up for 22 Years,” Occupational and Environmental Medicine 72, no. 9 (2015): 617–624, 10.1136/oemed-2014-102613. [ DOI ] [ PubMed ] [ Google Scholar ] 18. Harari G., Green M. S., and Zelber‐Sagi S., “Estimation and Development of 10‐ and 20‐year Cardiovascular Mortality Risk Models in an Industrial Male Workers Database,” Preventive Medicine 103 (2017): 26–32, 10.1016/j.ypmed.2017.07.012. [ DOI ] [ PubMed ] [ Google Scholar ] 19. Froom P., Melamed S., Triber I., Ratson N. Z., and Hermoni D., “Predicting Self‐Reported Health: The CORDIS Study,” Preventive Medicine 39, no. 2 (2004): 419–423, 10.1016/j.ypmed.2004.02.006. [ DOI ] [ PubMed ] [ Google Scholar ] 20. D. W. L. Hosmer S. and Sturdivant R. X., Applied Logistic Regression (John Wiley & Sons, Inc, 2013). 3rd edition. [ Google Scholar ] 21. Selvin S., Statistical Analysis of Epidemiologic Data (Oxford University Press, 2004). 3rd Edition. [ Google Scholar ] 22. Åkerstedt T., Ghilotti F., Grotta A., Bellavia A., Lagerros Y. T., and Bellocco R., “Sleep Duration, Mortality and the Influence of Age,” European Journal of Epidemiology 32, no. 10 (2017): 881–891, 10.1007/s10654-017-0297-0. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 23. Silva A. A., Mello R. G. B., Schaan C. W., Fuchs F. D., Redline S., and Fuchs S. C., “Sleep Duration and Mortality in the Elderly: A Systematic Review With Meta‐Analysis,” BMJ Open 6, no. 2 (2016): e008119, 10.1136/bmjopen-2015-008119. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 24. Yeo Y., Ma S. H., Park S. K., et al., “A Prospective Cohort Study on the Relationship of Sleep Duration With All‐Cause and Disease‐Specific Mortality in the Korean Multi‐Center Cancer Cohort Study,” Journal of Preventive Medicine & Public Health 46, no. 5 (2013): 271–281, 10.3961/jpmph.2013.46.5.271. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Gangwisch J. E., Heymsfield S. B., Boden‐Albala B., et al., “Sleep Duration Associated With Mortality in Elderly, but Not Middle‐Aged, Adults in a Large US Sample,” Sleep 31, no. 8 (2008): 1087–1096. [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Hublin C., Partinen M., Koskenvuo M., and Kaprio J., “Sleep and Mortality: A Population‐Based 22‐Year Follow‐Up Study,” Sleep 30, no. 10 (2007): 1245–1253, 10.1093/sleep/30.10.1245. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Liu T. Z., Xu C., Rota M., et al., “Sleep Duration and Risk of All‐Cause Mortality: A Flexible, Non‐Linear, Meta‐Regression of 40 Prospective Cohort Studies,” Sleep Medicine Reviews 32 (2017): 28–36, 10.1016/j.smrv.2016.02.005. [ DOI ] [ PubMed ] [ Google Scholar ] 28. Steptoe A., “Sleep Duration and Health in Young Adults,” Archives of Internal Medicine 166, no. 16 (2006): 1689–1692, 10.1001/archinte.166.16.1689. [ DOI ] [ PubMed ] [ Google Scholar ] 29. Vestergaard C. L., Skogen J. C., Hysing M., Harvey A. G., Vedaa Ø., and Sivertsen B., “Sleep Duration and Mental Health in Young Adults,” Sleep Medicine 115 (2024): 30–38, 10.1016/j.sleep.2024.01.021. [ DOI ] [ PubMed ] [ Google Scholar ] 30. Rod N. H., Vahtera J., Westerlund H., et al., “Sleep Disturbances and Cause‐Specific Mortality: Results From the GAZEL Cohort Study,” American Journal of Epidemiology 173, no. 3 (2011): 300–309, 10.1093/aje/kwq371. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 31. Windred D. P., Burns A. C., Lane J. M., et al., “Sleep Regularity Is a Stronger Predictor of Mortality Risk Than Sleep Duration: A Prospective Cohort Study,” Sleep 47, no. 1 (2024): zsad253, 10.1093/sleep/zsad253. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 32. Zolfaghari S., Keil A., Pelletier A., and Postuma R. B., “Sleep Disorders and Mortality: A Prospective Study in the Canadian Longitudinal Study on Aging,” Sleep Medicine 114 (2024): 128–136, 10.1016/j.sleep.2023.12.023. [ DOI ] [ PubMed ] [ Google Scholar ] 33. Yeghiazarians Y., Jneid H., Tietjens J. R., et al., “Obstructive Sleep Apnea and Cardiovascular Disease: A Scientific Statement From the American Heart Association,” Circulation 144, no. 3 (2021): e56–e67, 10.1161/CIR.0000000000000988. [ DOI ] [ PubMed ] [ Google Scholar ] 34. Kuczyński W., Kudrycka A., Pierzchała K., et al., “Overall Mortality and Comorbidities in Obstructive Sleep Apnea in Poland,” Medical Science Monitor 31 (2025): e950826, 10.12659/msm.950826. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 35. Besedovsky L., Lange T., and Born J., “Sleep and Immune Function,” Pflügers Archiv ‐ European Journal of Physiology 463, no. 1 (2012): 121–137, 10.1007/s00424-011-1044-0. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 36. Baglioni C., Spiegelhalder K., Lombardo C., and Riemann D., “Sleep and Emotions: A Focus on Insomnia,” Sleep Medicine Reviews 14, no. 4 (2010): 227–238, 10.1016/j.smrv.2009.10.007. [ DOI ] [ PubMed ] [ Google Scholar ] 37. Alvaro P. K., Roberts R. M., and Harris J. K., “A Systematic Review Assessing Bidirectionality Between Sleep Disturbances, Anxiety, and Depression,” Sleep 36, no. 7 (2013): 1059–1068, 10.5665/sleep.2810. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Uehli K., Miedinger D., Bingisser R., et al., “Sleep Problems and Work Injury Types: A Study of 180 Patients in a Swiss Emergency Department,” Swiss Medical Weekly 143 (2013): w13902, 10.4414/smw.2013.13902. [ DOI ] [ PubMed ] [ Google Scholar ] 39. Alhainen M., Härmä M., Pentti J., et al., “Sleep Duration and Sleep Difficulties as Predictors of Occupational Injuries: A Cohort Study,” Occupational and Environmental Medicine 79, no. 4 (2022): 224–232, 10.1136/oemed-2021-107516. [ DOI ] [ PubMed ] [ Google Scholar ] 40. Colten H. R. and Altevogt B. M., ed., Institute of Medicine (US) Committee on Sleep Medicine and Research. Functional and Economic Impact of Sleep Loss and Sleep‐Related Disorders.” in Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem (US: National Academies Press, 2006), 137–172. [ PubMed ] [ Google Scholar ] 41. Léger D., Guilleminault C., Bader G., Lévy E., and Paillard M., “Medical and Socio‐Professional Impact of Insomnia,” Sleep 25, no. 6 (2002): 621–625. [ PubMed ] [ Google Scholar ] 42. Khan M. A. and Al‐Jahdali H., “The Consequences of Sleep Deprivation on Cognitive Performance,” Neurosciences 28, no. 2 (2023): 91–99, 10.17712/nsj.2023.2.20220108. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 43. Hui S. A. and Grandner M. A., “Trouble Sleeping Associated With Lower Work Performance and Greater Health Care Costs: Longitudinal Data From Kansas State Employee Wellness Program,” Journal of Occupational & Environmental Medicine 57, no. 10 (2015): 1031–1038, 10.1097/jom.0000000000000534. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 44. Kessler R. C., Berglund P. A., Coulouvrat C., et al., “Insomnia and the Performance of US Workers: Results From the America Insomnia Survey,” Sleep 34, no. 9 (2011): 1161–1171, 10.5665/sleep.1230. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplementary Table 1: Sleep duration by baseline demographics and characteristics of the study population. Supplementary Table 2: Overall distribution of sleep variables by death. AJIM-69-372-s001.docx (26.4KB, docx) Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. 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