Aim: To examine the nature and prevalence of Cambodian nurses' work hours and overtime and related factors Background: The chronic shortage of nursing workforce is a major cause of overtime among nurses. Introduction: Nursing shortage and working overtime among nurses negatively affect nurse and patient outcomes, but nurses' work hours and overtime in Cambodia have not been comprehensively examined. Methods: A multicenter cross-sectional study was conducted in four Cambodian hospitals. Data were collected from 253 nurses providing direct nursing care using a questionnaire. The STROBE checklist was used for reporting this study. Results: More than a fifth of staff nurses worked more than 48 h, which is the legal work hour limit in Cambodia. Two major reasons for working mandatory or voluntary overtime, on-call or 24-h on-call were (a) not wanting to let down colleagues and (b) able to get all work done. The number of patients cared for was related to whether or not nurses worked 48 h or more. Conclusion: Overtime work and adverse nurse scheduling are common in Cambodia. Implications for nursing and health policy: Nurse managers and healthcare institutes in Cambodia need to monitor Cambodian nurses' work hours, which are often beyond the legal work hour limit. Moreover, it is important to understand why nurses work overtime and develop health policies, strategies, and programs that can help promote patient and nurse safety and retain qualified nursing staff. The 24-h on-call practice needs to be regulated according to the labor policy in healthcare institutes to prevent adverse nurse and patient outcomes.

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In the Republic of Korea, social distancing policies relied on voluntary participation by citizens and exhibited short-term changes. In this situation, the effects of such policies varied depending on each community’s capacity to comply. Here, we collected subway ridership data for 294 stations on nine Seoul Metro lines and aggregated the data for each station to the 184 smallest administrative areas. We found that the mean percent change in subway ridership was fitted by an additive model of the log-transformed percent ratio of the restaurant industry (estimated degrees of freedom (EDF) = 3.24, P < 0.001), the Deprivation Index (DI) (EDF = 3.66, P = 0.015), and the proportion of essential workers (β =  − 0.10 (95% confidence interval − 0.15 to − 0.05, P < 0.001). We found a distinct decrease in subway ridership only in the least deprived areas, suggesting that social distancing is costly.

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BACKGROUND Prevention of the risk factors of metabolic syndrome (MetS) in middle-aged individuals is an important public health issue. Technology-mediated interventions, such as wearable health devices, can aid in lifestyle modification, but they require habitual use to sustain healthy behavior. However, the underlying mechanisms and predictors of habitual use of wearable health devices among middle-aged individuals remain unclear. OBJECTIVE We investigated the predictors of habitual use of wearable health devices among middle-aged individuals with risk factors for MetS. METHODS We proposed a combined theoretical model based on the Health Belief Model, Unified Technology of the Acceptance and use of Technology 2, and perceived risk. We conducted an online survey of 300 middle-aged individuals with MetS between September 3 and 7, 2021. We validated the model using structural equation modeling. RESULTS The model explained 86.6% of the variance in the habitual use of wearable health devices. The goodness-of-fit indices revealed that the proposed model has a desirable fit with the data. Performance expectancy was the core variable influencing the habitual use of wearable devices (β=.537, P<.001) and intention to continue the use of wearable devices (β=.848, P<.001). Habitual use was positively influenced by intention to continue the use of wearable devices (β=.439, P<.001). The indirect effects of performance expectancy were partially mediated by the intention to continue the use of wearable devices (β=.372, P=.03). Performance expectancy was influenced by health motivation (β=0.497, P<.001) and effort expectancy (β=.558, P<.001). Perceived vulnerability contributed more to health motivation (β=.562, P<.001) than perceived severity (β=.243, P=.008). CONCLUSIONS The habitual use of wearable devices was influenced by users’ expectation that the device productivity exceeded its required effort. Additionally, health motivation positively influenced the performance expectancy. The healthcare needs of middle-aged individuals with MetS risk factors should be explored to enhance the performance expectancy of managing health with wearable health devices and to increase their habitual use.