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.

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.

BACKGROUND Following the need for prevention of non-communicable diseases, mobile health (mHealth) apps are increasingly used for promoting lifestyle behavior changes. While 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 The research question of this study concerns a large-scale, open access mHealth app focused on improving the lifestyle behaviors of its participants. This study examines whether periodic email prompts increase participant engagement with the mHealth app, and how this effect evolves over time. Points gained from activities in the app are used as an objective measure of participant engagement with the program. METHODS The data analyzed covers 22,797 participants over a period of 78 weeks. A Hidden Markov model (HMM) is employed for disentangling the over-time effects of periodic email prompts on participant engagement with the mHealth app, accounting for transitions between latent activity states. RESULTS The HMM indicates that participants are 70% of the time in the inactivity state – gaining zero points in total per week, 18% of the time in the average activity state – gaining 27 points per week, and 12% of the time in the high activity state – gaining 182 points per week. Receiving and opening a generic email is associated with a 3 percentage points increase in the likelihood of becoming active compared to the weeks when no email is received. 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 towards improving program effectiveness.

BACKGROUND Socioeconomic disparities in the adoption of preventive health programs represent a well-known challenge, with programs delivered online serving as a potential solution. The preventive health program examined in this study is a large-scale, open access online platform operating in the Netherlands, aimed at improving the health behaviors and wellness of its participants. OBJECTIVE This study examines differences in adoption of an online preventive health program across socioeconomic groups, whereby comparing between its two delivery means: website versus mobile phone app. METHODS The 83,466 participants in this cross-sectional, non-experimental study are individuals who have signed up for the health program between July 2012 and September 2019. The rate of program adoption per delivery means is estimated using the Prentice, Williams and Peterson Gap-Time model (PWP-GT), with the measure of neighborhood socio-economic status (NSES) employed to distinguish between population segments with different socioeconomic characteristics. Registration to the health program is voluntary and free, not within a controlled study setting, allowing for observing the true rate of adoption. RESULTS The estimation results indicate that program adoption across socioeconomic groups varies depending on program’s delivery means. For the website version, higher NSES groups have a higher likelihood of program adoption compared to the lowest NSES group (hazard ratio [HR]=1.03; 95% confidence interval [CI]=1.01, 1.05). For the mobile phone app version, the opposite holds: higher NSES groups have a lower likelihood of program adoption compared to the lowest NSES group (HR=0.94; 95% CI=0.91, 0.97). CONCLUSIONS Promoting preventive health programs using mobile phone apps can help increase program adoption among the lowest socioeconomic segment. Given the increasing usage of mobile phones among the disadvantaged population groups, structuring future health interventions to include mobile phone apps as means of delivery can support the stride towards diminishing health disparities.