Differences in Sleep Patterns and Mental Health Problems During Different Periods of COVID-19 Outbreak Among Community-Dwelling Older Men in Hong Kong

Effects of sleep quality on the risk of various long COVID symptoms among older adults following infection: an observational study

BACKGROUND

The long-term sequelae of coronavirus disease 2019 (COVID-19) and its recovery have becoming significant public health concerns. Therefore, this study aimed to enhance the limited evidence regarding the relationship between sleep quality on long COVID among the older population aged 60 years or old.

METHODS

Our study included 4,781 COVID-19 patients enrolled from April to May 2023, based on the Peking University Health Cohort. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) scale. Long COVID was evaluated by well-trained health professionals through patients' self-reported symptoms. Binary logistic regression models were employed to calculate odds ratios (OR) and 95% confidence intervals (95% CI).

RESULTS

The prevalence of long COVID among older adults was 57.4% (2,743/4,781). Specifically, the prevalence of general symptoms, cardiovascular symptoms, respiratory symptoms, gastrointestinal symptoms, and neurological and psychiatric symptoms was 47.7% (2,282/4,781), 3.4% (163/4,781), 35.2% (1683/4,781), 8.7% (416/4,781) and 5.8% (279/4,781), respectively. For each one-point increase in PSQI scores, the risk of long COVID, general symptoms, cardiovascular symptoms, gastrointestinal symptoms, and neurological and psychiatric symptoms increased by 3% (95% CI: 1.01, 1.06), 3% (95% CI: 1.01, 1.06), 7% (95% CI: 1.01, 1.13), 11% (95% CI: 1.07, 1.15), and 20% (95% CI: 1.15, 1.25), respectively. In multivariate models, compared with good sleepers, COVID-19 patients with poor sleep quality exhibited an increased risk of general symptoms (aOR = 1.17; 95% CI: 1.03, 1.33), cardiovascular symptoms (aOR = 1.50; 95% CI: 1.06, 2.14), gastrointestinal symptoms (aOR = 2.03; 95% CI: 1.61, 2.54), and neurological and psychiatric symptoms (aOR = 2.57; 95% CI = 1.96, 3.37).

CONCLUSIONS

Our findings indicate that poor sleep quality is related to various manifestations of long COVID in older populations. A comprehensive assessment and multidisciplinary management of sleep health and long COVID may be essential to ensure healthy aging in the future.

Impact of National Lockdown Measures on the Association Between Social Media Use and Sleep Disturbance During COVID-19: A Meta-Analysis of 21 Nations

During the COVID-19 pandemic, the extensive lockdown measures implemented for disease mitigation triggered a surge in round-the-clock social media use, giving rise to widespread concerns regarding its impact on sleep health. This meta-analysis examined the association between social media use and sleep disturbance during the pandemic, along with potential moderators. The dataset included 43 independent samples comprising 68,247 residents of 21 countries across 7 world regions. The three-level mixed-effects meta-analysis revealed a weak, positive overall effect size (r = 0.1296, 95% confidence interval: 0.0764-0.1828, k = 90). The magnitude of the effect size varied by the type of social media use: compulsive use exhibited a moderately strong effect size, whereas information-focused use showed marginal significance. The effect size was more pronounced in countries imposing stricter (vs. less strict) lockdown measures. Lockdown status also moderated this association, with a marginally significant effect size observed during lockdowns but a significant effect size after lockdowns. For demographics, samples involving emerging adults demonstrated moderately strong effect sizes, whereas those involving the general population had modest effect sizes. Notably, the interaction between the type of social media use and lockdown status was significant. Specifically, the positive association with information-focused use was significant only during lockdowns, whereas that with general use was significant after, but not during, lockdowns. However, compulsive use showed a moderately strong effect size both during and after lockdowns. These findings underscored the importance of considering multiple factors-such as the type of social media use, context, and demographics-when studying social media use and sleep health.

Assessing Neurobehavioral Alterations Among E-waste Recycling Workers in Hong Kong

Background

E-waste workers in Hong Kong are handling an unprecedented amount of e-waste, which contains various neurotoxic chemicals. However, no study has been conducted to evaluate the neurological health status of e-waste workers in Hong Kong. This study aimed to evaluate the prevalence of neurobehavioral alterations and to identify the vulnerable groups among Hong Kong e-waste workers.

Methods

We recruited 109 Hong Kong e-waste workers from June 2021 to September 2022. Participants completed standard questionnaires and wore a GENEActiv accelerometer for seven days. Pittsburgh Sleep Quality Index and Questionnaire 16/18 (Q16/18) were used to assess subjective neurobehavioral alterations. The GENEActiv data generated objective sleep and circadian rhythm variables. Workers were grouped based on job designation and entity type according to the presumed hazardous level. Unconditional logistic regression models measured the associations of occupational characteristics with neurobehavioral alterations after adjusting for confounders.

Results

While dismantlers/repairers and the workers in entities not funded by the government were more likely to suffer from neurotoxic symptoms in Q18 (adjusted odds ratio: 3.18 [1.18-9.39] and 2.77 [1.10-7.46], respectively), the workers from self-sustained recycling facilities also have poor performances in circadian rhythm. Results also showed that the dismantlers/repairers working in entities not funded by the government had the highest risk of neurotoxic symptoms compared to the lowest-risk group (i.e., workers in government-funded companies with other job designations).

Conclusion

This timely and valuable study emphasizes the importance of improving the working conditions for high-risk e-waste workers, especially the dismantlers or repairers working in facilities not funded by the government.

Sleep Quality in Older People: The Impact of Age, Professional Activity, Financial Situation, and Chronic Diseases During the SARS-CoV-2 Pandemic

BACKGROUND The SARS-CoV-2 pandemic negatively affected health and social life, notably deteriorating sleep quality in older adults. Studies report inconsistent findings on sleep disturbances during this period, influenced by various physiological, emotional, and sociodemographic factors. This study aimed to identify these determining factors. MATERIAL AND METHODS The study was conducted among 342 people 60 years of age or older participating in online classes of randomly selected Senior Clubs and the University of the Third Age in the southern regions of Poland. RESULTS Sleep problems (PSQI >5 points) were diagnosed in 250 subjects (83.6%). Logistic regression analysis showed that the quality of sleep significantly depends on: age, as people aged 66-70 were more likely to have better sleep quality than people aged 60-65 (OR=3.07), and those over 70 scored better than people aged 60-65 (OR=2.87); current job - employed people have a better chance of better sleep quality (OR=3.08) than unemployed people; financial situation, people assessing their financial situation as very good/good had a better chance of better sleep quality (OR=2.00) compared to people assessing their financial situation as very bad, bad/average; chronic diseases, people without chronic diseases had a chance of better sleep quality (OR=2.45) than people with chronic diseases. CONCLUSIONS Age, financial situation, current job, and chronic disease were the most important factors determining sleep quality in older people. The identification of factors affecting sleep quality can be used as important data to develop interventions and programs to improve sleep quality.

Development and validation of prediction models for poor sleep quality among older adults in the post-COVID-19 pandemic era

BACKGROUND

Corona Virus Disease 2019 (COVID-19) has a significant impact on sleep quality. However, the effects on sleep quality in the post-COVID-19 pandemic era remain unclear, and there is a lack of a screening tool for Chinese older adults. This study aimed to understand the prevalence of poor sleep quality and determine sensitive variables to develop an effective prediction model for screening sleep problems during infectious diseases outbreaks.

MATERIALS AND METHODS

The Peking University Health Cohort included 10,156 participants enrolled from April to May 2023. The Pittsburgh Sleep Quality Index (PSQI) scale was used to assess sleep quality. The data were randomly divided into a training-testing cohort (n = 7109, 70%) and an independent validation cohort (n = 3027, 30%). Five prediction models with 10-fold cross validation including the Least Absolute Shrinkage and Selection Operator (LASSO), Stochastic Volatility Model (SVM), Random Forest (RF), Artificial Neural Network (ANN), and XGBoost model based on the area under curve (AUC) were used to develop and validate predictors.

RESULTS

The prevalence of poor sleep quality (PSQI >7) was 30.69% (3117/10,156). Among the generated models, the LASSO model outperformed SVM (AUC 0.579), RF (AUC 0.626), ANN (AUC 0.615) and XGBoost (AUC 0.606), with an AUC of 0.7. Finally, a total of 12 variables related to sleep quality were used as parameters in the prediction models. These variables included age, gender, ethnicity, educational level, residence, marital status, history of chronic diseases, SARS-CoV-2 infection, COVID-19 vaccination, social support, depressive symptoms, and cognitive impairment among older adults during the post-COVID-19 pandemic. The nomogram illustrated that depressive symptoms contributed the most to the prediction of poor sleep quality, followed by age and residence.

CONCLUSIONS

This nomogram, based on twelve-variable, could potentially serve as a practical and reliable tool for early identification of poor sleep quality among older adults during the post-pandemic period.