Trends and determinants of time in bed in Geneva, Switzerland

Socioeconomic status and sleep health: a narrative synthesis of 3 decades of empirical research

STUDY OBJECTIVES

This review aims to assess the association between socioeconomic status (SES) and sleep health in the general population and the mediating effects of lifestyle and mental and physical health in this relationship.

METHODS

Observational studies testing the independent association between objective or subjective SES indicators and behavioral/physiological or clinical sleep health variables in the general population were included. PubMed/MEDLINE was searched for reports published from January 1990 to December 2019. The direction of effect was used as the primary effect measure, testing the hypothesis that low SES is associated with poor sleep health outcomes. Results are presented in the form of direction effect plots and synthesized as binomial proportions.

RESULTS

Overall, 336 studies were identified. A high proportion of effects at the expected direction was noted for measures of sleep continuity (100% for sleep latency, 50-100% for awakenings, 66.7-100% for sleep efficiency), symptoms of disturbed sleep (75-94.1% for insomnia, 66.7-100% for sleep-disordered breathing, 60-100% for hypersomnia), and general sleep satisfaction (62.5-100%), while the effect on sleep duration was inconsistent and depended on the specific SES variable (92.3% for subjective SES, 31.7% for employment status). Lifestyle habits, chronic illnesses, and psychological factors were identified as key mediators of the SES-sleep relationship.

CONCLUSIONS

Unhealthy behaviors, increased stress levels, and limited access to health care in low-SES individuals may explain the SES-sleep health gradient. However, the cross-sectional design of most studies and the high heterogeneity in employed measures of SES and sleep limit the quality of evidence. Further research is warranted due to important implications for health issues and policy changes.

CITATION

Papadopoulos D, Etindele Sosso FA. Socioeconomic status and sleep health: a narrative synthesis of 3 decades of empirical research. J Clin Sleep Med. 2023;19(3):605-620.

"How Do Greeks Sleep?" A Cross-Sectional Study among Indigenous and Minority Populations

Objective: To investigate sleep habits and their potential relationship with several sociodemographic, lifestyle and health related characteristics among indigenous and minority populations in Northeastern Greece. Materials and methods: Nine hundred fifty seven adults aged 19 to 86 years were enrolled in this cross-sectional study. Self-reported structured questionnaires were utilized. Results:The reported mean sleep duration on a weekly basis was 6:26±1:10 hours (range, 04:00 to 10:00 hours); sleep duration was 26 min longer on weekends (p < 0.001). In multivariate linear regression analysis, older age (β=-26.7 min, p=0.010), being divorced or widowed (β=-29.0 min, p < 0.001), high alcohol (β=-39.7 min, p < 0.001) or coffee (β=-36.9 min, p=0.006) consumption, screen exposure before bedtime for 1-2 hours (β=-18.9 min, p=0.004) or > one hour (β=-34.4 min, p < 0.001), having a child aged under six years (β=-62.3 min, p < 0.001), napping for > 30 min during the day (β=-35.2 min, p < 0.001) and morbidity (β=-21.5 min, p < 0.001) were independently associated with short sleep duration and lower sleep efficiency. Moreover, a tendency towards short sleep duration was associated with anxiety (β=-8.8 min, p=0.078) and depression (β=-12.8 min, p=0.029). Obesity (β=10.7 min, p=0.047), being a university student (β=41.0 min, p=0.002), high financial status (β=16.6 min, p=0.037) and high adherence to Mediterranean diet (β=15.4 min, p=0.002) were associated with long sleep duration. Conclusion:This study illustrates the association of sleep disturbances with several sociodemographic and health-related factors and dictates conduction of larger scale prospective studies to evaluate causality on the relationship between sleep patterns and lifestyle factors.

Are Physical Activities Associated With Perceived Stress? The Evidence From the China Health and Nutrition Survey

Psychological stress is a negative affective state. The association between physical activity and psychological stress was commonly reported in previous researches. Few published studies with large sample sizes have explored such an association in Chinese population. The current research aims to assess the association between perceived stress and physical activity preferences, as well as the association between risk of high perceived stress and physical activity behaviors (e.g., sports-, transport-, occupational-related physical activity, sedentary activities and time in bed). The data were collected from the China Health and Nutrition Survey (CHNS) in 2015. Generalized linear models and logistic regression models were used to explore the association between perceived stress and physical activity. In total, 11,066 participants were included in the current analysis. For physical activity preferences, we found that “like” preference of all six mentioned domains of activities contributed to lower perceived stress (i.e., lower perceived stress scale −14 score, all β < 0 and p < 0.05). For physical activity behaviors, the results indicated that none/low intensity physical activity behaviors (e.g., sports-, occupational- related, sedentary) were associated with risk of high perceived stress, except that low intensity of transport-related activities seemed to be protective from high stress. However, the association between intensity of physical activity behaviors and perceived stress was not simply the higher the better. High intensity of all these physical activity behaviors was also associated with high perceived stress. Our findings suggest that positive preferences and moderate physical activity behaviors were associated with low perceived stress. The findings herein highlight the effect of regulating physical activity on perceived stress, as well as inform potential strategies to reduce psychological stress.

Physical activity is associated with higher sleep efficiency in the general population: the CoLaus study

Study Objectives

To evaluate the association of objective physical activity (PA) and sedentary behavior (SB) with sleep duration and quality.

Methods

Cross-sectional study including 2649 adults (53.5% women, 45-86 years) from the general population. Proportions of time spent in PA and SB were measured using 14 day accelerometry. Low PA and high SB statuses were defined as the lowest and highest tertile of each behavior. "Inactive," "Weekend warrior," and "Regularly active" weekly patterns were also defined. Sleep parameters were derived from the accelerometer and validated questionnaires.

Results

High PA, relative to low PA, was associated with higher sleep efficiency (76.6 vs. 73.8%, p < 0.01) and lower likelihood of evening chronotype [relative-risk ratio (RR) and 95% CI: 0.71 (0.52; 0.97)]. Similar associations were found for low SB relative to high SB. "Weekend warriors" relative to "Inactives," had higher sleep efficiency [76.4 vs. 73.9%, p < 0.01] and lower likelihood of evening chronotype [RR: 0.63 (0.43; 0.93)]. "Regularly actives," relative to "Inactives," had higher sleep efficiency [76.7 vs. 73.9%, p < 0.01] and tended to have less frequently an evening chronotype [RR: 0.75 (0.54; 1.04), p = 0.09]. No associations were found for PA and SB with sleep duration, daytime sleepiness, insomnia, and risk of sleep apnea (after adjustment for body mass index).

Conclusions

High PA and low SB individuals, even if they do not sleep longer, have higher sleep efficiency and have less frequently an evening chronotype.

Bidirectional associations of accelerometer-determined sedentary behavior and physical activity with reported time in bed: Women's Health Study

OBJECTIVE

To examine the day-to-day, bidirectional associations of accelerometer-derived sedentary behavior and physical activity (PA) with reported time in bed in a large cohort of older women.

METHODS

Data are from 10086 Women's Health Study participants (aged 71.6 years; SD, 5.7) who agreed to wear an accelerometer and complete a diary for 7 consecutive days. Generalized linear (multilevel) models with repeated measures were used to examine the adjusted associations of the following: (1) reported time in bed with next-day accelerometer-determined counts and time spent sedentary and in light- and moderate-to-vigorous-intensity PA (MVPA) and (2) accelerometer estimates with reported time in bed that night, expressed as short (<7 hours), optimal (7-9 hours), and long (>9 hours) sleep.

RESULTS

Across days, short sleep was associated with an average of 5500 (SE, 1352) higher accelerometer counts the following day but was also related to higher average sedentary (46.5 [SE, 1.5] minutes) and light-intensity PA (11.9 [SE, 1.2] minutes) than optimal sleep (all P<.001). Long sleep was associated with lower accelerometer counts, time spent sedentary and in light-intensity PA, and a reduced likelihood of engaging in ≥20 minutes of MVPA (all P<.001) than optimal sleep. Higher PA during the day (higher accelerometer counts and ≥20 minutes of accumulated MVPA) was associated with a reduced likelihood of reporting short or long sleep that night (all P<.001).

CONCLUSIONS

Findings support the bidirectional associations of accelerometer-determined sedentary behavior and PA with reported time in bed in older women. Future studies are needed to confirm findings with sleep actigraphy in older women.

Has adult sleep duration declined over the last 50+ years?

The common assumption that population sleep duration has declined in the past few decades has not been supported by recent reviews, which have been limited to self-reported data. The aim of this review was to assess whether there has been a reduction in objectively recorded sleep duration over the last 50+ years. The literature was searched for studies published from 1960 to 2013, which assessed objective sleep duration (total sleep time (TST)) in healthy normal-sleeping adults. The search found 168 studies that met inclusion criteria, with 257 data points representing 6052 individuals ages 18-88 y. Data were assessed by comparing the regression lines of age vs. TST in studies conducted between 1960 and 1989 vs. 1990-2013. Weighted regression analyses assessed the association of year of study with age-adjusted TST across all data points. Regression analyses also assessed the association of year of study with TST separately for 10-y age categories (e.g., ages 18-27 y), and separately for polysomnographic and actigraphic data, and for studies involving a fixed sleep schedule and participants' customary sleep schedules. Analyses revealed no significant association of sleep duration with study year. The results are consistent with recent reviews of subjective data, which have challenged the notion of a modern epidemic of insufficient sleep.