Abstract
BACKGROUND:
The study aimed to find out the association between sleep duration and psychological resilience in a population-based survey.
MATERIALS AND METHODS:
A cross-sectional survey was conducted in August 2022, employing a cluster random sampling method to recruit community residents at Futian District in Shenzhen, China. A total of 2,445 participants aged 18 years and over were included in the study. The Brief Resilience Scale (BRS) was utilized to measure psychological resilience, and sleep duration was classified according to the American Heart Association’s sleep duration categories. Multivariable linear regression was used to analyze the relationship between psychological resilience and sleep duration after adjusting for gender, age, smoking status, physical exercise frequency, body mass index (BMI), and education level.
RESULTS:
The participants displayed moderate levels of psychological resilience, with a mean resilience score of 3.46 (standard deviation [SD] = 0.62) and a mean sleep duration of 7.04 h (SD = 1.10). After adjusting for covariates, longer sleep duration was associated with higher psychological resilience (β = 0.047, P < 0.05), indicating that participants with a long sleep duration had higher resilience scores than those with a short sleep duration.
CONCLUSION:
Longer sleep duration is positively associated with higher psychological resilience in community residents. These findings suggest that improving sleep duration may be a promising approach to enhancing psychological resilience, preventing psychological problems, and promoting overall physical and mental health development.
Keywords: Cross-sectional studies, epidemiology, psychological resilience, sleep duration
Introduction
With the rapid development of the economy and culture, people’s stress levels are constantly increasing. The negative emotions brought by stress can seriously affect physical and mental health. Especially in recent major public health crises,[1,2] a mental health crisis has emerged globally.[3,4] The detection rate of stress, depression, or anxiety symptoms has sharply increased, posing a threat to people’s lives, health, and social stability.[5] Psychological resilience, also known as psychological elasticity or resilience, refers to the ability to bounce back after exposure to stress.[6] The protective effect of psychological resilience has a positive role in helping individuals recover psychological balance in the face of setbacks or stressful environments.[7,8] Effectively utilizing psychological resilience can reduce the negative effects of stress. Therefore, improving the level of psychological resilience of residents can help them effectively cope with psychological stress, improve their ability to cope with stress and adversity, thereby reducing the occurrence of psychological problems, and promoting overall physical and mental health development.
Sleep behavior is a natural physiological phenomenon that occurs periodically, and the synthesis and release of melatonin is a fundamental regulatory mechanism that makes sleep behavior have a cyclic rhythm of day and night alternation.[9] However, with the increasing popularity and use of electronic smart devices, the improvement of education levels, and the use of artificial light, among other environmental factors, sleep behavior, such as sleep duration and quality, has been affected.[10] Epidemiological surveys and clinical studies have shown that about one-third of adults have sleep problems,[11,12] and the sleep duration of children, adults, and the elderly has not reached the recommended sleep duration of the International Sleep Organization.[13,14,15,16] Sleep disorders are associated with psychological disorders and mental factors.[17] A previous study reported that sleep disorders were found to reduce psychological resilience.[18] While good-quality sleep and good sleep habits may result in better hormone regulation, especially of cortisol, which was associated with stress and resilience, the association between sleep and resilience is less investigated in the Chinese population.
This study, therefore, aims to find out the associations between sleep duration and psychological resilience in a community-based population in Shenzhen, China. We hypothesized that longer sleep duration was associated with a higher level of psychological resilience.
Materials and Methods
Study design and setting
In August 2022, our study employed a cluster random sampling method to recruit community residents in Futian District in Shenzhen, China. In this district, there are 104 administrative communities that were regarded as natural clusters for sampling. We then randomly selected 10% of the clusters (ten communities) and administrative officers from eight communities agreed to participate.
Study participants and sampling
Citizens or local permanent residents (people who are registered as Shenzhen citizens, but not those who lived outside of Shenzhen for no less than six months, and non-registered Shenzhen citizens who have temporary residence permits and have lived in Shenzhen for no less than six months) who were 18 years or older in the selected communities were randomly recruited. Only one adult member of a total of 3014 households in the selected communities was invited to participate. In total, 3014 people were invited and 2445 participants aged 18 years and over completed the questionnaires with no missing data.
Data collection tool and technique
Our study utilized the Brief Resilience Scale (BRS) developed by Smith et al., which is a unidimensional assessment tool consisting of six items. The scale includes three positively worded items (items 1, 3, and 5) and three negatively worded items (items 2, 4, and 6), scored on a five-point Likert scale: strongly disagree, disagree, neutral, agree, and strongly agree. Various modified versions of the BRS have been well-validated.[19,20,21,22,23] The resilience score is calculated as the mean value of the six items, with higher scores indicating greater levels of resilience.[24,25,26,27]
Sleep duration was collected through a self-reported questionnaire and classified according to the American Heart Association’s sleep duration categories.[28] Under this classification, a sleep duration of fewer than 7 h was defined as a short sleep duration, while a sleep duration of more than 9 h was defined as a long sleep duration. Sleep duration of 7 to 9 h was considered normal.[28]
Body mass index (BMI) was calculated based on height and weight, using the formula weight (in kilograms) divided by height (in meters) squared. According to the Guidelines for Prevention and Control of Overweight and Obesity in Chinese Adults, BMI is categorized into four classes: underweight, normal weight, overweight, and obesity.[27] The following definitions of covariates were adopted and modified slightly from China Nutrition and Health Surveys.[29] Educational attainment was classified as limited literacy, primary school, junior high school, senior high school, junior college, undergraduate, and master’s degree and higher. Smoking status was coded as never smoker (who never smoked), former smoker (who smoked before and has quitted smoking), occasional smoker (who smoked less than ten cigarettes per week on a nonregular basis), or frequent smoker (who smoked more than ten cigarettes per week on a regular basis). Physical exercise frequency was recorded as at least 5 times per week with at least 30 minutes each time, at least 4 times per week with at least 30 minutes each time, at least 3 times per week with at least 30 minutes each time, at least 2 times per week with at least 30 minutes each time, at least 1 time per week with at least 30 minutes each time, once a month with at least 30 minutes but less than once a week, and less than once a month.
Ethical consideration
This study was approved by the Ethical Review Board of the School of Public Health (Shenzhen), Sun Yat-Sen University (No. 2022-049).
Statistical analysis
Count and frequency were used to describe categorical data, while mean ± standard deviation was used to describe continuous variables. We used Z or χ2 statistics to compare the differences between men and women and used T statistics in the multivariable regression models. A confirmatory factor analysis was conducted to validate the two-factor model of the scale. Multivariable linear regression was used to analyze the relationship between psychological resilience and sleep duration after adjusting for gender, age, smoking status, physical exercise frequency, BMI category, and education level. We additionally defined high resilience as a resilience score >4 according to the literature that suggested defining high resilience as one standard deviation above the mean (3.4 + 0.6 in our study)[30] and performed multivariable logistic regression models. A significance level of P ≤ 0.05 was used to indicate statistical significance in this study. All analysis was performed using R version 4.2.1 (R Core Team, Vienna, Austria).
Results
Basic characteristics of the study participants
As given in Table 1 and Supplementary Figure 1 (465.7KB, tif) , 2445 participants were recruited for this study, of which 846 (34.6%) were men and 1599 (65.4%) were women. The mean age of the participants was 41.09 years (SD = 13.72), and the mean BMI was 22.59 kg/m2 (SD = 3.35). The participants had a mean sleep duration of 7.04 h (SD = 1.10) and a mean resilience score of 3.46 (SD = 0.62). The level of psychological resilience in our participants was moderate, with men scoring an average of 3.50 (SD = 0.63) and women scoring an average of 3.45 (SD = 0.61).
Table 1.
Basic characteristic of study participants
| Variables | Men | Women | Total | Z or –χ2 | P |
|---|---|---|---|---|---|
| Gender | 846 (34.60%) | 1599 (65.40%) | 2445 (100%) | ||
| Age | 40.96±13.79 | 41.16±13.68 | 41.09±13.72 | -0.29 | 0.773 |
| Height (m) | 1.72±0.06 | 1.60±0.05 | 1.64±0.08 | -35.76 | <0.01 |
| Weight (kg) | 69.98±9.52 | 56.65±8.90 | 61.26±11.10 | -29.49 | <0.01 |
| BMI (kg/m2) | 23.61±2.96 | 22.05±3.42 | 22.59±3.35 | -14.06 | <0.01 |
| Sleep duration (h) | 7.07±1.07 | 7.03±1.11 | 7.04±1.10 | -0.66 | 0.513 |
| Resilience score | 3.50±0.63 | 3.45±0.61 | 3.46±0.62 | -1.25 | 0.211 |
| BMI category | 168.37 | ||||
| Underweight | 26 (3.07%) | 147 (9.19%) | 173 (7.08%) | <0.01 | |
| Normal | 441 (52.13%) | 1114 (69.67%) | 1555 (63.60%) | ||
| Overweight | 322 (38.06%) | 263 (16.45%) | 585 (23.93%) | ||
| Obesity | 57 (6.74%) | 75 (4.69%) | 132 (5.40%) | ||
| Sleep duration category | 0.16 | ||||
| Short sleep duration | 252 (29.79%) | 482 (30.14%) | 734 (30.02%) | 0.926 | |
| Normal sleep duration | 578 (68.32%) | 1090 (68.17%) | 1668 (68.22%) | ||
| Long sleep duration | 16 (1.89%) | 27 (1.69%) | 43 (1.76%) | ||
| Education level | 11.72 | ||||
| Illiterate/limited literacy | 2 (0.13%) | 3 (0.35%) | 5 (0.20%) | 0.069 | |
| Primary school | 104 (6.50%) | 55 (6.50%) | 159 (6.50%) | ||
| Junior high school | 437 (27.33%) | 208 (24.59%) | 645 (26.38%) | ||
| Senior high school | 16 (1.00%) | 8 (0.95%) | 24 (0.98%) | ||
| Junior college | 664 (41.53%) | 331 (39.13%) | 995 (40.70%) | ||
| Undergraduate | 103 (6.44%) | 80 (9.46%) | 183 (7.48%) | ||
| Master’s degree or higher | 273 (17.07%) | 161 (19.03%) | 434 (17.75%) | ||
| Smoking status | 584.91 | ||||
| Never smoker | 1551 (97.00%) | 503 (59.46%) | 2054 (84.01%) | <0.01 | |
| Former smoker | 26 (1.63%) | 117 (13.83%) | 143 (5.85%) | ||
| Occasional smoker | 10 (0.63%) | 64 (7.57%) | 74 (3.03%) | ||
| Frequent smoker | 12 (0.75%) | 162 (19.15%) | 174 (7.12%) | ||
| Physical exercise frequency | 12.48 | ||||
| At least 5 times per week | 314 (19.64%) | 128 (15.13%) | 442 (18.08%) | 0.052 | |
| At least 4 times per week | 245 (15.32%) | 117 (13.83%) | 362 (14.81%) | ||
| At least 3 times per week | 281 (17.57%) | 165 (19.50%) | 446 (18.24%) | ||
| At least 2 times per week | 113 (7.07%) | 52 (6.15%) | 165 (6.75%) | ||
| At least 1 time per week | 170 (10.63%) | 95 (11.23%) | 265 (10.84%) | ||
| Once a month but less than once a week | 212 (13.26%) | 130 (15.37%) | 342 (13.99%) | ||
| Less than once a month | 264 (16.51%) | 159 (18.79%) | 423 (17.30%) |
There was a statistically significant difference in BMI between men and women (P < 0.05), with men having a higher mean BMI of 23.95 kg/m2 (SD = 3.09) compared to women’s mean BMI of 21.90 kg/m2 (SD = 2.87). However, there was no statistically significant difference in age, sleep duration, or resilience score between men and women (P > 0.1). Regarding educational attainment, most participants (67.08%) had completed high school or above, with the highest proportion (40.7%) having a junior college education, followed by junior high school (26.38%). We found no statistically significant difference in education level between men and women (P = 0.069). In terms of physical exercise frequency, most participants (36.32%) engaged in physical exercise at least 2–3 times per week, followed by those who exercised at least five times per week (18.08%). The smallest proportion of participants (6.75%) exercised at least two times per week. After stratifying by gender, we found no statistically significant difference in physical exercise frequency between men and women (P = 0.052). In total, 23.93% of participants were classified as overweight and 5.40% were classified as obese. Furthermore, the proportion of underweight women was significantly higher than that of men (P < 0.05). The proportion of men who had never smoked was significantly higher than that of women (P < 0.05), while the proportion of women who had quit smoking, occasionally smoked, or smoked daily was significantly higher than that of men (P < 0.05).
Association between resilience score and sleep duration
Table 2 and Supplementary Table 1 present the association between resilience score and sleep duration. We constructed three models: Model 1 included only sleep duration, Model 2 included sleep duration, age, and gender, and Model 3 included sleep duration, age, gender, smoking status, frequency of physical exercise, BMI, and educational level. The independent variables were chosen based on both stepwise selection and the directed acyclic graph, which was used to select confounders in epidemiological research. In the final model with covariates adjustment (model 3), F-statistics was 4.7 (P < 0.001), and adjusted R2 was 0.135. The regression coefficients for all models were statistically significant (β = 0.047, 95% CI: 0.024, 0.069) after adjusting for multiple confounders. Further analysis by gender yielded comparable results [Table 3, P < 0.05]. The positive regression coefficients indicate that longer sleep duration is associated with a higher level of psychological resilience score. Table 4 shows that longer sleep duration was associated with higher odds of high resilience (OR = 1.32, 95% CI: 1.17, 1.50, P < 0.05). The AUC statistics from the multivariable adjusted logistic regression model was 0.72 (95% CI: 0.68, 0.75), and positive predictive value was 0.83.
Table 2.
Association between sleep duration and resilience score in all participants
| Model | Sleep duration | T-statistics | P |
|---|---|---|---|
| Model 1a (β and 95% CI) | 0.046 (0.024~0.068) | 4.10 | <0.01 |
| Model 2b (β and 95% CI) | 0.048 (0.025~0.070) | 4.18 | <0.01 |
| Model 3c (β and 95% CI) | 0.047 (0.024~0.069) | 4.09 | <0.01 |
CI=Confidence interval. aModel 1 included only sleep duration. bModel 2 included sleep duration, age, and gender. cModel 3 included sleep duration, age, gender, smoking status, physical exercise frequency, BMI category, and educational level
Supplementary Table 1.
Multivariable linear regression analysis of sleep duration and psychological resilience, β (95%CI)
| Model | β | T-Statistics | P |
|---|---|---|---|
| Sleep duration | 0.047 | 4.09 | <0.01 |
| Age | 0.01 | 2.28 | <0.05 |
| Gender | 0.02 | 0.76 | >0.05 |
| Smoking status | |||
| Never smoker | Reference | ||
| Former smoker | 0.06 | 1.16 | >0.05 |
| Occasional smoker | -0.03 | -0.37 | >0.05 |
| Frequent smoker | -0.02 | -0.32 | >0.05 |
| Physical exercise frequency | |||
| At least 5 times per week | Reference | ||
| At least 4 times per week | -0.06 | -1.04 | >0.05 |
| At least 3 times per week | -0.04 | -0.76 | >0.05 |
| At least 2 times per week | -0.11 | -2.30 | <0.05 |
| At least 1 time per week | -0.09 | -2.07 | <0.05 |
| Once a month but less than once a week | -0.11 | -2.39 | <0.05 |
| Less than once a month | -0.20 | -4.57 | <0.05 |
| BMI category | |||
| Underweight | -0.13 | -2.64 | <0.05 |
| Normal | Reference | ||
| Overweight | -0.025 | -0.39 | >0.05 |
| Obesity | 0.10 | 1.87 | >0.05 |
| Educational level | |||
| Junior high school or below | Reference | ||
| Senior high school | 0.18 | 4.22 | <0.05 |
| Junior college | 0.15 | 2.75 | <0.05 |
| Undergraduate | 0.25 | 4.67 | <0.05 |
| Master’s degree or higher | 0.33 | 4.85 | <0.05 |
Table 3.
Association between sleep duration and resilience score by gender
| Model | Gender | Sleep duration | T-statistics | P |
|---|---|---|---|---|
| Model 1a (β and 95% CI) | Men | 0.048 (0.009~0.088) | 2.38 | 0.017 |
| Women | 0.044 (0.018~0.071) | 3.25 | 0.001 | |
| Model 2b (β and 95% CI) | Men | 0.049 (0.009~0.088) | 2.43 | 0.016 |
| Women | 0.048 (0.020~0.075) | 3.42 | 0.001 | |
| Model 2b (β and 95% CI) | Men | 0.049 (0.020~0.075) | 3.49 | 0.017 |
| Women | 0.045 (0.017~0.072) | 3.21 | 0.002 |
CI=Confidence interval. aModel 1 included only sleep duration. bModel 2 included sleep duration and age. cModel 3 included sleep duration, age, smoking status, physical exercise frequency, BMI category, and educational level
Table 4.
Association between sleep duration and higher resilience score using logistic regression models, OR (95% CI)
| Groups | OR (95% CI) | T-statistics | P |
|---|---|---|---|
| Total | 1.32 (1.17, 1.50) | 4.36 | <0.05 |
| Men | 1.43 (1.17, 1.76) | 3.42 | <0.05 |
| Women | 1.27 (1.08, 1.50) | 2.90 | 0.003 |
CI=Confidence interval. Model included sleep duration, age, smoking status, physical exercise frequency, BMI category, and educational level as covariates
Discussion
In this study, we investigated the daily average sleep duration and psychological resilience score of non-institutionalized residents in Shenzhen and explored the associations between sleep duration and psychological resilience score in this population. We found that longer sleep duration was associated with higher levels of psychological resilience, implying that people could gain benefits from enhancing psychological resilience by promoting a healthy sleep pattern.
Our results that the psychological resilience of residents was found to be at a moderate level, with an average score of 39.24 (±8.02) points (total score 0–50), which was higher than that of 367 residents in a community in Yichang in 2020,[31] but comparable to that of residents in Wuhan in 2020.[32] No gender differences were observed in the magnitudes of psychological resilience scores in our study. Although previous studies have reported inconsistent results in this regard, with some studies similar to ours[33] and others reporting gender differences in psychological resilience scores.[34,35,36] The discrepancies may lie in the fact that the study population, sample size, and ethnicity were different. Further, we found a significant positive association between sleep duration and psychological resilience score, which was consistent even after stratifying by gender. These findings suggest that longer sleep duration is associated with higher psychological resilience scores. This finding is consistent with most studies, including a meta-analysis that demonstrated a positive correlation between sleep duration and resilience score.[37] In addition, a cohort study of 55,021 members of the US military found a significant association between insomnia symptoms and a decline in self-rated health,[38] and individuals with short sleep duration also exhibited similar manifestations, which is consistent with our research findings.
The biological mechanisms underlying the impact of sleep duration on psychological resilience are not fully understood. However, some studies suggested that the effect of sleep on psychological resilience may be related to neural plasticity in the brain.[39] Neural plasticity refers to the ability of neurons to form new connections or strengthen existing ones, which is crucial for learning and memory formation, as well as adaptation to environmental changes.[40] Sleep can help consolidate connections between existing neurons and establish new connections between neurons in the brain, thereby enhancing neural plasticity. Prolonged sleep deprivation may lead to a reduction in neural plasticity, which can affect psychological resilience.[41] In addition, sleep can also affect hormone levels in the body. Some studies suggest that sleep deprivation may lead to an increase in stress hormone levels,[42] such as cortisol, which may lower psychological resilience.[43] Moreover, in terms of sociological mechanism, sleep deprivation may reduce an individual’s willingness to engage in social interactions,[44] and social interaction is considered an important factor in enhancing psychological resilience.[45] Sleep deprivation may also affect an individual’s coping strategies,[46] reducing their ability to cope with challenges and increasing the likelihood of failure. Further studies are needed to explore the pathways in depth.
The study has several limitations, including the use of self-reported data to measure sleep duration, which may be subject to recall bias. Additionally, the study was limited to the residents in Shenzhen, China, and may not be generalizable to other regions or populations. The study design is cross-sectional, which precludes the establishment of causal relationships between sleep duration and psychological resilience. Despite these limitations, this study has several strengths. Firstly, it is the first study to investigate the correlation between sleep duration and resilience score in residents of Futian District, Shenzhen, and the data obtained are representative of this region. Secondly, our sample size was large (sample size of 2,445 participants) and enabled us to conduct gender-specific analyses. This sample size enhances the statistical power and generalizability of the findings to similar populations. Thirdly, the study utilized validated assessment tools, such as the Brief Resilience Scale (BRS), to measure psychological resilience and sleep duration, respectively. The use of these objective tools reduces measurement bias and increases the reliability and validity of the findings. Finally, the study employed multiple linear regression to analyze the relationship between sleep duration and psychological resilience, after controlling for potential confounding factors such as age, smoking status, physical exercise frequency, BMI, and education level and could minimize bias due to confounding.
Conclusions
In conclusion, our study provides evidence of a significant positive correlation between sleep duration and psychological resilience in community residents. These findings suggest that improving sleep duration may be a useful strategy for enhancing psychological resilience and promoting overall physical and mental health development, particularly in the post-pandemic era. Future policies and interventions aimed at promoting psychological resilience could focus on sleep duration.
Financial support and sponsorship
The study was supported by Sun Yat-Sen University and Futian Health Commission (FTWS2023088).
Conflicts of interest
There are no conflicts of interest.
Distribution of Continuous Variables
Acknowledgment
The authors would like to sincerely thank all the participants and research team members as well as staff for their coordination in the community study.
References
- 1.Barzilay R, Moore TM, Greenberg DM, DiDomenico GE, Brown LA, White LK, et al. Resilience, COVID–19-related stress, anxiety and depression during the pandemic in a large population enriched for healthcare providers. Transl Psychiatry. 2020;10:291.. doi: 10.1038/s41398-020-00982-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Polizzi C, Lynn SJ, Perry A. Stress and coping in the time of Covid-19: Pathways to resilience and recovery. Clin Neuropsychiatry. 2020;17:59–62. doi: 10.36131/CN20200204. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Masten AS, Motti-Stefanidi F. Multisystem resilience for children and youth in disaster: Reflections in the context of COVID-19. Advers Resil Sci. 2020;1:95–106. doi: 10.1007/s42844-020-00010-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Chen S, Bonanno GA. Psychological adjustment during the global outbreak of COVID-19: A resilience perspective. Psychol Trauma. 2020;12:S51–4. doi: 10.1037/tra0000685. [DOI] [PubMed] [Google Scholar]
- 5.Walsh F. Loss and resilience in the time of COVID-19: Meaning making, hope, and transcendence. Fam Process. 2020;59:898–911. doi: 10.1111/famp.12588. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Yıldırım M, Solmaz F. COVID-19 burnout, COVID–19 stress and resilience: Initial psychometric properties of COVID-19 burnout scale. Death Stud. 2022;46:524–32. doi: 10.1080/07481187.2020.1818885. [DOI] [PubMed] [Google Scholar]
- 7.Ruhabadi F, Assarroudi A, Mahdavifar N, Rad M. Correlations of resilience with coping strategies, and the underlying factors in the nurses working in COVID-19 hospitals. J Educ Health Promot. 2022;11:398.. doi: 10.4103/jehp.jehp_1634_21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Verma AK, Ayub A, Singh GP, Kumar A. Resilience related to novel coronavirus among doctors and undergraduate medical students-A study from India. J Educ Health Promot. 2022;11:350.. doi: 10.4103/jehp.jehp_60_22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Holst SC, Werth E, Landolt HP. Pharmacotherapy of sleep-wake disorders. Praxis (Bern 1994) 2019;108:131–8. doi: 10.1024/1661-8157/a003189. [DOI] [PubMed] [Google Scholar]
- 10.Felder-Schmittbuhl MP, Buhr ED, Dkhissi-Benyahya O, Hicks D, Peirson SN, Ribelayga CP, et al. Ocular clocks: Adapting mechanisms for eye functions and health. Invest Ophthalmol Vis Sci. 2018;59:4856–70. doi: 10.1167/iovs.18-24957. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Wilson S, Anderson K, Baldwin D, Dijk DJ, Espie A, Espie C, et al. British Association for Psychopharmacology consensus statement on evidence-based treatment of insomnia, parasomnias and circadian rhythm disorders: An update. J Psychopharmacol. 2019;33:923–47. doi: 10.1177/0269881119855343. [DOI] [PubMed] [Google Scholar]
- 12.Chung KF, Yeung WF, Ho FY, Yung KP, Yu YM, Kwok CW. Cross-cultural and comparative epidemiology of insomnia: The Diagnostic and statistical manual (DSM), International classification of diseases (ICD) and International classification of sleep disorders (ICSD) Sleep Med. 2015;16:477–82. doi: 10.1016/j.sleep.2014.10.018. [DOI] [PubMed] [Google Scholar]
- 13.Li S, Zhu S, Jin X, Yan C, Wu S, Jiang F, et al. Risk factors associated with short sleep duration among Chinese school-aged children. Sleep Med. 2010;11:907–16. doi: 10.1016/j.sleep.2010.03.018. [DOI] [PubMed] [Google Scholar]
- 14.Cespedes EM, Gillman MW, Kleinman K, Rifas-Shiman SL, Redline S, Taveras EM. Television viewing, bedroom television, and sleep duration from infancy to mid-childhood. Pediatrics. 2014;133:e1163–71.. doi: 10.1542/peds.2013-3998. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Hale L, Guan S. Screen time and sleep among school-aged children and adolescents: A systematic literature review. Sleep Med Rev. 2015;21:50–8. doi: 10.1016/j.smrv.2014.07.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Yadav PK, Kalyani VC, Narayan D, Kataria N. A descriptive study to assess knowledge related to hypertension and its impact upon exercises and sleep pattern among adults from communities of Uttarakhand. J Educ Health Promot. 2023;12:85.. doi: 10.4103/jehp.jehp_1175_22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Baglioni C, Battagliese G, Feige B, Spiegelhalder K, Nissen C, Voderholzer U, et al. Insomnia as a predictor of depression: A meta-analytic evaluation of longitudinal epidemiological studies. J Affect Disord. 2011;135:10–9. doi: 10.1016/j.jad.2011.01.011. [DOI] [PubMed] [Google Scholar]
- 18.Chatburn A, Coussens S, Kohler MJ. Resiliency as a mediator of the impact of sleep on child and adolescent behavior. Nat Sci Sleep. 2013;6:1–9. doi: 10.2147/NSS.S54913. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Soer R, Six Dijkstra MWMC, Bieleman HJ, Stewart RE, Reneman MF, Oosterveld FGJ, et al. Measurement properties and implications of the Brief Resilience Scale in healthy workers. J Occup Health. 2019;61:242–50. doi: 10.1002/1348-9585.12041. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Chmitorz A, Wenzel M, Stieglitz RD, Kunzler A, Bagusat C, Helmreich I, et al. Population-based validation of a German version of the Brief Resilience Scale. PLoS One. 2018;13:e0192761.. doi: 10.1371/journal.pone.0192761. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Kunzler AM, Chmitorz A, Bagusat C, Kaluza AJ, Hoffmann I, Schäfer M, et al. Construct validity and population-based norms of the German Brief Resilience Scale (BRS) Eur J Health Psychol. 2018;25:107–17. doi: 10.1027/2512-8442/a000016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Kyriazos TA, Stalikas A, Prassa K, Galanakis M, Yotsidi V, Lakioti A. Psychometric evidence of the Brief Resilience Scale (BRS) and Modeling distinctiveness of resilience from depression and stress. Psychology. 2018;9:1828–57. [Google Scholar]
- 23.Rodríguez-Rey R, Alonso-Tapia J, Hernansaiz-Garrido H. Reliability and validity of the Brief Resilience Scale (BRS) Spanish version. Psychol Assess. 2016;28:e101–10.. doi: 10.1037/pas0000191. [DOI] [PubMed] [Google Scholar]
- 24.da Silva-Sauer L, de la Torre-Luque A, Smith BW, C M C Lins M, Andrade S, Fernández-Calvo B. Brief Resilience Scale (BRS) Portuguese version: Validity and metrics for the older adult population. Aging Ment Health. 2021;25:1554–63. doi: 10.1080/13607863.2020.1753015. [DOI] [PubMed] [Google Scholar]
- 25.Sánchez J, Estrada-Hernández N, Booth J, Pan D. Factor structure, internal reliability, and construct validity of the Brief Resilience Scale (BRS): A study on persons with serious mental illness living in the community. Psychol Psychother. 2021;94:620–45. doi: 10.1111/papt.12336. [DOI] [PubMed] [Google Scholar]
- 26.Konaszewski K, Niesiobędzka M, Surzykiewicz J. Validation of the Polish version of the Brief Resilience Scale (BRS) PLoS One. 2020;15:e0237038.. doi: 10.1371/journal.pone.0237038. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Chunming Chen, Frank C Lu. The guidelines for prevention and control of overweight and obesity in Chinese adults. Biomed Environ Sci. 2004;17(Suppl):1–36. [PubMed] [Google Scholar]
- 28.Haack M, Serrador J, Cohen D, Simpson N, Meier-Ewert H, Mullington JM. Increasing sleep duration to lower beat-to-beat blood pressure: A pilot study. J Sleep Res. 2013;22:295–304. doi: 10.1111/jsr.12011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Yu D, Zhao L, Zhang J, Yang Z, Yang L, Huang J, et al. China nutrition and health surveys (1982-2017) China CDC Wkly. 2021;3:193–5. doi: 10.46234/ccdcw2021.058. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Smith BW, Epstein EE, Oritz JA, Christopher PK, Tooley EM. The Foundations of Resilience: What are the critical resources for bouncing back from stress? In: Prince-Embury S, . and Saklofske D.H., (Eds.), editors. Resilience in children, adolescents, and adults: Translating research into practice, The Springer series on human exceptionality. New York, NY: Springer; 2013. pp. 167–187. [Google Scholar]
- 31.Caiyun Chang, Zuyang Xi, Zifeng Li, Yufeng Tian, Xiaoqian Zeng. Anxiety and Resilience in the lockdown regions. Hainan Medicine [Chinese] 2021;32:128–131. [Google Scholar]
- 32.Xiaoguang Li, Xiagang Wu. Gender, Social Class, and Mental Health. Population and Development [Chinese] 2021;27:95–105. [Google Scholar]
- 33.Nicholas PK. Hardiness, self-care practices and perceived health status in older adults. J Adv Nurs. 1993;18:1085–94. doi: 10.1046/j.1365-2648.1993.18071085.x. [DOI] [PubMed] [Google Scholar]
- 34.Consedine NS, Magai C, Krivoshekova YS. Sex and age cohort differences in patterns of socioemotional functioning in older adults and their links to physical resilience. Ageing Int. 2005;30:209–44. [Google Scholar]
- 35.Masood A, Masud Y, Mazahir S. Gender differences in resilience and psychological distress of patients with burns. Burns. 2016;42:300–6. doi: 10.1016/j.burns.2015.10.006. [DOI] [PubMed] [Google Scholar]
- 36.Nygren B, Aléx L, Jonsén E, Gustafson Y, Norberg A, Lundman B. Resilience, sense of coherence, purpose in life and self-transcendence in relation to perceived physical and mental health among the oldest old. Aging Ment Health. 2005;9:354–62. doi: 10.1080/1360500114415. [DOI] [PubMed] [Google Scholar]
- 37.Arora T, Grey I, Östlundh L, Alamoodi A, Omar OM, Hubert Lam KB, et al. A systematic review and meta-analysis to assess the relationship between sleep duration/quality, mental toughness and resilience amongst healthy individuals. Sleep Med Rev. 2022;62:101593.. doi: 10.1016/j.smrv.2022.101593. [DOI] [PubMed] [Google Scholar]
- 38.Seelig AD, Jacobson IG, Donoho CJ, Trone DW, Crum-Cianflone NF, Balkin TJ. Sleep and health resilience metrics in a large military cohort. Sleep. 2016;39:1111–20. doi: 10.5665/sleep.5766. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Weiss JT, Donlea JM. Roles for sleep in neural and behavioral plasticity: Reviewing variation in the consequences of sleep loss. Front Behav Neurosci. 2021;15:777799.. doi: 10.3389/fnbeh.2021.777799. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Albert PR. Adult neuroplasticity: A new “cure” for major depression? J Psychiatry Neurosci. 2019;44:147–50. doi: 10.1503/jpn.190072. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Krause AJ, Simon EB, Mander BA, Greer SM, Saletin JM, Goldstein-Piekarski AN, et al. The sleep-deprived human brain. Nat Rev Neurosci. 2017;18:404–18. doi: 10.1038/nrn.2017.55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Nollet M, Wisden W, Franks NP. Sleep deprivation and stress: A reciprocal relationship. Interface Focus. 2020;10:20190092.. doi: 10.1098/rsfs.2019.0092. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Nishimi K, Koenen KC, Coull BA, Segerstrom SC, Austin SB, Kubzansky LD. Psychological resilience and diurnal salivary cortisol in young adulthood. Psychoneuroendocrinology. 2022;140:105736.. doi: 10.1016/j.psyneuen.2022.105736. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Ben Simon E, Walker MP. Sleep loss causes social withdrawal and loneliness. Nat Commun. 2018;9:3146.. doi: 10.1038/s41467-018-05377-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Liao Z, Zhou H, He Z. The mediating role of psychological resilience between social participation and life satisfaction among older adults in China. BMC Geriatr. 2022;22:948.. doi: 10.1186/s12877-022-03635-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Alhola P, Polo-Kantola P. Sleep deprivation: Impact on cognitive performance. Neuropsychiatr Dis Treat. 2007;3:553–67. [PMC free article] [PubMed] [Google Scholar]
Associated Data
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Supplementary Materials
Distribution of Continuous Variables