The health potential of neighborhoods: A population-wide study in the Netherlands

Neighborhood effects on the health of elderly persons: evidence from China

BACKGROUND

Presently, global aging has become increasingly serious, whereas the health concerns brought by aging have become a public issue that warrants an urgent solution from all countries across the world. Therefore, this research paper discusses the influence of neighborhood health on elderly individuals' health, and extending a realistic basis for the other economies to improve the neighborhood environment and promote the health of the elderly.

METHODS

Based on the data of CHARLS2018, this research paper adopts the samples that fulfill the study requirements (N = 7326). we constructed a comprehensive research framework integrating oprobit regression model, heterogeneity analysis, conditional mixed process(CMP)robustness testing, Furthermore, the KHB decomposition method is implemented to ascertain the influential mechanism of NMH and NPH on the mental- and physical health of elderly persons.

RESULTS

The oprobit regression model analysis indicates that NMH 0.434 and NPH 0.550 exert positive influences on the elderly's mental- and physical health. Meanwhile, the effects of conditional mixed process on NMH and NPH stand at 0.381 and4.372, which are different from the oprobit regression results; thereby, indicating the existence of endogeneity. Afterward, KHB mediating effect confirms that Internet use, gift reciprocity, and charity activity contribute 30.21% and 16.83% to mental- and physical health, respectively.

CONCLUSIONS

Firstly, the NMH and NPH demonstrate a positive influence on the mental- and physical health of the elder population. However, there exist heterogeneous differences. Secondly, the conditional mixed process deals with the endogeneity of NMH and NPH. Thirdly, social integration, social interaction, and social engagement serve as significant transmission mechanisms for the influences of NMH and NPH on the health of elderly persons.

The impact of neighborhood mental health on the mental health of older adults

BACKGROUND

In this article, we use cross-sectional data obtained from the 2018 China Health and Aging Tracking Survey (CHARLS) to examine the impact of neighborhood mental health at the community level on the mental health of older adults aged 60 years and older.

METHODS

NMH is the average mental health of older adults in the same community, excluding the older adults themselves. The explained variable mental health in this paper was measured using the simple CES-D depression scale. The mediating variables were social connectedness, social participation and social inclusion, and the instrumental variables were physical exercise and amusement. regression analysis was conducted using OLS regression models, two-stage least squares (IV-2SLS) instrumental variables to address the two-way causality of NMH and MH, and KHB decomposition was used to investigate the effect mechanism.

RESULTS

Baseline regressions showed that the neighborhood mental health effect positively influenced the mental health of older adults (Coef. = 0.356, 95% CI 0.315,0.397). The neighborhood mental health effect estimated by IV-2SLS (Coef. = 0.251, 95% CI 0.096,0.405) was higher than the OLS regression, indicating endogeneity. The mediated effects of KHB showed total (Coef. = 0.356, 95% CI 0.314,0.398), direct (Coef. = 0.281, 95% CI 0.232,0.330), and indirect effects (Coef. = 0.075, 95% CI 0.049,0.101). While the total effect was 1.266 times higher than the direct effect, 21.03% of the total effect came from mediating variables.

CONCLUSIONS

First, the neighborhood mental health effect has a positive impact on the mental health of older adults, but there are heterogeneous differences based on gender, age, and place of residence. Second, the results of the IV-2SLS estimation showed that the effect of the neighborhood mental health effect was underestimated if endogenous problems were not controlled for. Third, the effect of neighborhood mental health on older adults' mental health was tested to be stable. Moreover, social connectedness, social participation, and social interaction are important mediating mechanisms for the effect of neighborhood mental health on older adults' mental health. This study provides new perspectives and ideas for an in-depth understanding of the mental health of older adults in the context of social transformation in China.

The Effect of Periodic Email Prompts on Participant Engagement with a Behavior Change mHealth App: Longitudinal Study (Preprint)

BACKGROUND

Following the need for the prevention of noncommunicable diseases, mobile health (mHealth) apps are increasingly used for promoting lifestyle behavior changes. Although mHealth apps have the potential to reach all population segments, providing accessible and personalized services, their effectiveness is often limited by low participant engagement and high attrition rates.

OBJECTIVE

This study concerns a large-scale, open-access mHealth app, based in the Netherlands, focused on improving the lifestyle behaviors of its participants. The study examines whether periodic email prompts increased participant engagement with the mHealth app and how this effect evolved over time. Points gained from the activities in the app were used as an objective measure of participant engagement with the program. The activities considered were physical workouts tracked through the mHealth app and interactions with the web-based coach.

METHODS

The data analyzed covered 22,797 unique participants over a period of 78 weeks. A hidden Markov model (HMM) was used for disentangling the overtime effects of periodic email prompts on participant engagement with the mHealth app. The HMM accounted for transitions between latent activity states, which generated the observed measure of points received in a week.

RESULTS

The HMM indicated that, on average, 70% (15,958/22,797) of the participants were in the inactivity state, gaining 0 points in total per week; 18% (4103/22,797) of the participants were in the average activity state, gaining 27 points per week; and 12% (2736/22,797) of the participants were in the high activity state, gaining 182 points per week. Receiving and opening a generic email was associated with a 3 percentage point increase in the likelihood of becoming active in that week, compared with the weeks when no email was received. Examining detailed email categories revealed that the participants were more likely to increase their activity level following emails that were in line with the program's goal, such as emails regarding health campaigns, while being resistant to emails that deviated from the program's goal, such as emails regarding special deals.

CONCLUSIONS

Participant engagement with a behavior change mHealth app can be positively influenced by email prompts, albeit to a limited extent. Given the relatively low costs associated with emails and the high population reach that mHealth apps can achieve, such instruments can be a cost-effective means of increasing participant engagement in the stride toward improving program effectiveness.

Adoption of the Website and Mobile App of a Preventive Health Program Across Neighborhoods With Different Socioeconomic Conditions in the Netherlands: Longitudinal Study

Background

Socioeconomic disparities in the adoption of preventive health programs represent a well-known challenge, with programs delivered via the web serving as a potential solution. The preventive health program examined in this study is a large-scale, open-access web-based platform operating in the Netherlands, which aims to improve the health behaviors and wellness of its participants.

Objective

This study aims to examine the differences in the adoption of the website and mobile app of a web-based preventive health program across socioeconomic groups.

Methods

The 83,466 participants in this longitudinal, nonexperimental study were individuals who had signed up for the health program between July 2012 and September 2019. The rate of program adoption per delivery means was estimated using the Prentice, Williams, and Peterson Gap–Time model, with the measure of neighborhood socioeconomic status (NSES) used to distinguish between population segments with different socioeconomic characteristics. Registration to the health program was voluntary and free, and not within a controlled study setting, allowing the observation of the true rate of adoption.

Results

The estimation results indicate that program adoption across socioeconomic groups varies depending on the program’s delivery means. For the website, higher NSES groups have a higher likelihood of program adoption compared with the lowest NSES group (hazard ratio 1.03, 95% CI 1.01-1.05). For the mobile app, the opposite holds: higher NSES groups have a lower likelihood of program adoption compared with the lowest NSES group (hazard ratio 0.94, 95% CI 0.91-0.97).

Conclusions

Promoting preventive health programs using mobile apps can help to increase program adoption among the lowest socioeconomic segments. Given the increasing use of mobile phones among disadvantaged population groups, structuring future health interventions to include mobile apps as means of delivery can support the stride toward diminishing health disparities.