Background: Despite the increased development and use of mobile health (mHealth) devices during the COVID-19 pandemic, there is little knowledge of willingness of the Chinese people to use mHealth devices and the key factors associated with their use in the post–COVID-19 era. Therefore, a more comprehensive and multiangle investigation is required. Objective: We aimed to probe Chinese attitudes regarding the use of mHealth and analyze possible associations between the attitude of willingness to use mHealth devices and some factors based on the socioecological model. Methods: A survey was conducted using quota sampling to recruit participants from 148 cities in China between June 20 and August 31, 2022. Data from the survey were analyzed using multiple stepwise regression to examine the factors associated with willingness to use mHealth devices. Standardized regression coefficients (β) and 95% CIs were calculated using multiple stepwise regression. Results: The survey contained a collection of 21,916 questionnaires and 21,897 were valid questionnaires, with a 99.91% effective response rate. The median score of willingness to use mHealth in the post–COVID-19 era was 70 points on a scale from 0 to 100. Multiple stepwise regression results showed that the female gender (β=.03, 95% CI 1.04-2.35), openness personality trait (β=.05, 95% CI 0.53-0.96), higher household per capita monthly income (β=.03, 95% CI 0.77-2.24), and commercial and multiple insurance (β=.04, 95% CI 1.77-3.47) were factors associated with the willingness to use mHealth devices. In addition, people with high scores of health literacy (β=.13, 95% CI 0.53-0.68), self-reported health rating (β=.22, 95% CI 0.24-0.27), social support (β=.08, 95% CI 0.40-0.61), family health (β=.03, 95% CI 0.03-0.16), neighbor relations (β=.12, 95% CI 2.09-2.63), and family social status (β=.07, 95% CI 1.19-1.69) were more likely to use mHealth devices. Conclusions: On the basis of the theoretical framework of socioecological model, this study identified factors specifically associated with willingness of the Chinese people to use mHealth devices in the post–COVID-19 era. These findings provide reference information for the research, development, promotion, and application of future mHealth devices.

Background: A healthy lifestyle is beneficial to individuals’ health. However, little is known about the associations of lifestyle factors with mental health and well-being. This study examined the associations of lifestyle factors with mental health (i.e., depression, anxiety, loneliness, perceived pressure, and self-rated health status) and well-being in Chinese adults.Methods: A nationally representative survey was conducted in China from 20 June 2022 to 31 August 2022. Data from the survey were analyzed using multiple linear regression to determine the associations of lifestyle with mental health and well-being in Chinese adults. Standardized regression coefficients (β) and 95% confidence intervals (CIs) were estimated using multiple linear regression.Results: The survey included 28,138 Chinese adults. Multiple linear regression results showed that there were significant negative associations of lifestyle scores with scores of depression (β = −0.93, 95% CI: −0.98, −0.88), anxiety (β = −0.71, 95% CI: −0.76, −0.67), loneliness (β = −0.23, 95% CI: −0.24, −0.21), and perceived pressure (β = −0.19, 95% CI: −0.22, −0.16). Moreover, there were significant positive associations of lifestyle with self-rated health status (β = 1.99, 95% CI: 1.79, 2.20) and well-being (β = 0.96, 95% CI: 0.91, 1.02).Conclusion: This study provides insight into the associations of lifestyle factors with mental health and well-being and highlights the importance of improving and maintaining healthy lifestyle behaviors for favorable mental health and well-being.

Background: The eating behaviors of older adults are associated with multiple factors. To promote older adults’ healthy diets, it is imperative to comprehensively study the factors associated with the eating behaviors of the aging population group. This study aimed to probe the associated factors of older adults’ eating behaviors from a socioecological model (SEM) perspective. Methods: In 2021, a cross-sectional survey was performed to recruit participants in China. The survey data were analyzed using a multivariate generalized linear model to identify the factors associated with eating behaviors in older adults. Standardized regression coefficients (β) and 95% confidence intervals (CIs) were estimated using a multivariate generalized linear model. Results: The survey contained 1147 valid older adult participants. Multivariate generalized linear model results showed that older adults with older age [aged 71–80 (β = -0.61), ≥ 81 (β = -1.12)], conscientiousness personality trait (β = -0.27), and higher family health levels (β = -0.23) were inclined to have better eating behaviors. The older adults with higher education levels [junior high school and high school (β = 1.03), junior college and above (β = 1.71)], higher general self-efficacy (β = 0.09), more severe depression symptoms (β = 0.24), and employment (β = 0.82) tended to have poorer eating behaviors. Conclusions: This study identified factors that are specifically associated with older adults’ eating behaviors from an SEM perspective. The comprehensive multiple-angle perspective consideration may be a valuable idea for studying healthy eating behaviors in older adults.

In today’s fast paced society, the phenomenon of rest intolerance has emerged as a prevalent psychological concern, which has been shown to pose potential risks to the mental and physical health of many people. However, rest intolerance remains underexplored, particularly among nursing students who face high academic pressure. This study aims to investigate rest intolerance and its associated factors among nursing students using the social ecological model, and to identify central indicators for intervention targets that alleviate this issue through network analysis. This research is a nationwide cross-sectional study using network analysis. From June 23 to September 29, 2024, the Rest Intolerance Scale-8 (RIS-8) was administered to evaluate rest intolerance among 1,878 nursing students in China. Data collection was conducted using stratified and quota sampling methods. Based on the social ecological model, factors potentially associated with rest intolerance were identified and subsequently investigated using random forest classification. In addition, to further elucidate factors associated with rest intolerance, univariate and multivariable generalized linear models were conducted. Finally, network analyses were undertaken to evaluate central indicators of rest intolerance and core factors among nursing students. Central indicators were identified based on their expected influence value. A higher expected influence value signifies a greater importance of the nodes in the network. The network’s stability was assessed using a case-dropping procedure. The self-reported level of rest intolerance indicated a median score of 24 points. The results of the multivariate generalized linear model analysis demonstrated that nursing students exhibiting higher scores of the study variables are associated with increased feelings of rest intolerance, including higher levels of online social networking addiction (β = 0.32; 95% CI = 0.26 to 0.38), depressive symptoms (β = 0.19; 95% CI = 0.09 to 0.28), perceived stress (β = 0.38; 95% CI = 0.24 to 0.51), and anxiety symptoms (β = 0.45; 95% CI = 0.17 to 0.74). Individuals with higher levels of health literacy (β = -0.17; 95% CI = -0.31 to -0.03) demonstrated a reduced likelihood of experiencing rest intolerance. In addition, the statement “When I am resting or having fun, I feel guilty” from the RIS-8 exhibited the greatest expected influence value within the network. Central indicators of anxiety and depressive symptoms were identified in the network configuration related to rest intolerance and its associated factors. Nursing students achieved a score of 60% of the total maximum on the Rest Intolerance Scale. Nursing educators and policymakers should fully consider the identified associated factors of this study when designing nursing education programs and relevant policies. Central indicators can be treated as intervention targets to effectively alleviate nursing students’ feelings of rest intolerance and promote their mental health. Not applicable.