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

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.