User Archetypes of a Well-Being-Promoting Mobile App Among Adults: Cross-Sectional Study and Cluster Analysis of Usage Patterns.

Abstract

Background: A healthy lifestyle is associated with mental well-being, and digital lifestyle interventions can be effective in promoting a healthy lifestyle. However, they do not appear to work for all, and we have limited knowledge of how users' background characteristics affect their tendency to adopt well-being-promoting digital apps and actively use them.

Objective: This study aimed to explore the association of the study participants' characteristics and current well-being with their likelihood of using a well-being-promoting mobile app.

Methods: The BitHabit web app (Wellpro Impact Solutions Ltd) was available for a 2-month trial in spring 2023 after completing a short cross-sectional digital questionnaire with questions about well-being, life satisfaction, and lifestyle. Individuals aged 15 years or younger were excluded from the analysis. We used logistic regression to assess how individual characteristics were associated with the initiation of BitHabit app use. To assess user archetypes among those who initiated app use, and k-means clustering analysis and multinomial logistic regression to assess user archetypes among those who initiated app use.

Results: A total of 1646 eligible individuals responded to the questionnaire, and 863 initiated app use. Lower odds of initiating app use were detected among males (odds ratio [OR] 0.66, 95% CI 0.51-0.85; P<.001), the unemployed (OR 0.68, 95% CI 0.48-0.97; P=.03), those with higher general life satisfaction (OR 0.94, 95% CI 0.89-1.00; P=.04), and those reporting fewer life challenges (OR 1.13, 95% CI 1.02-1.24; P=.02). We identified (1) thriving non-active users, (2) struggling non-active users, and (3) active users as archetypes based on app use activity, life satisfaction, and reported life challenges. Older participants had lower odds of being thriving nonactive (OR 0.96, 95% CI 0.94-0.99; P=.01) or struggling nonactive users (OR 0.93, 95% CI 0.90-0.96; P<.001) than active users. Retired participants had higher odds of being struggling nonactive than active users (OR 4.06, 95% CI 1.44-11.42; P=.01) and unemployed lower odds of being thriving nonactive than active users (OR 0.2, 95% CI 0.08-0.51; P<.001). Those who were physically more active had higher odds of being thriving nonactive than active users (OR 2.71, 95% CI 1.00-7.32; P=.05). Participants with higher alcohol consumption had higher odds of being struggling nonactive users than active users (OR 3.22, 95% CI 1.16-8.99; P=.03).

Conclusions: While lower general life satisfaction and less favorable health behavior appeared to increase the likelihood of trying the app, those who eventually actively used the app were more satisfied with their lives at baseline. In addition, among nonactive users, there were recognizable user profiles of thriving and struggling nonactive users, which were associated with various individual characteristics. Further research is needed to develop digital apps to attract more potential users and meet the needs of those with an unhealthy lifestyle and poor mental health.