JMIR Publications

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 the background characteristics of users affect their tendency to adopt well-being promoting digital apps and how actively they use them.

Objective:

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 research data were collected as a part of the Feeling Good North Savo program in Finland. The BitHabit app, originally developed in the Stop Diabetes study, was available for a two-month trial in spring 2023 after completing a short digital questionnaire with questions about well-being, life satisfaction, and lifestyle. We used univariate assessments and logistic regression analysis to assess how individual characteristics associated with the initiation of BitHabit app use. To assess user archetypes among those who initiated app use, we conducted k-means cluster analysis and multinomial logistic regression analysis.

Results:

1647 individuals responded to the digital survey and 863 of them initiated BitHabit app use. Lower odds for initiating app use were detected among males (OR = 0.66; p < 0.001), unemployed (0.64; 0.04), higher general life satisfaction (0.94; 0.04) and a lower number of reported life challenges (1.13; 0.02). Three distinct user archetypes were detected based on app use activity, life satisfaction, and reported life challenges: (1) Thriving non-active users, (2) Struggling non-active users and (3) Active users. Based on the multinomial logistic regression, older participants had lower odds for being thriving non-active (0.96; 0.1) or struggling non-active users (0.93; < 0.001) than active users. Retired participants had higher odds for being struggling non-active than active users (4.06; 0.01). Unemployed participants had lower odds for being thriving non-active than active users (0.2; < 0.001). Physically more active had higher odds for being thriving non-active than active users (2.71; 0.05). Participants with higher alcohol consumption has higher odds for being struggling non-active users than active users (3.22; 0.03).

Conclusions:

While lower general life satisfaction and less favorable health behavior appeared to increase the likelihood of trying the BitHabit app, those who eventually actively used the app were more satisfied with their lives at baseline. Additionally, among non-active users, there were recognizable user profiles of thriving and struggling non-active users. Individual characteristics such as gender, age, and employment appear to be variously associated with both the initiation of app use and actively using the app. 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.