PREWELL Digital Well-being Promotion Intervention With the BitHabit App

Mental wellbeing is a major public health concern, and digital interventions have emerged as a scalable approach for promoting mental well-being and healthy lifestyle behaviors. However, evidence regarding long-term effectiveness and the role of user engagement remains limited. This study evaluates the effectiveness of the BitHabit digital wellbeing application in promoting mental wellbeing and healthy lifestyle among adults living in North Savo, Finland.

The BitHabit application is a digital behavior change intervention designed to support mental wellbeing through the adoption of small, self-selected wellbeing-promoting habits. The application incorporates principles of self-determination theory, habit formation, self-monitoring, and behavior change techniques. Participants select habits from a library of well-being-promoting activities and track their implementation over time.

The intervention study uses a stepped-wedge randomized controlled design in which all participants eventually receive access to the intervention, while participants awaiting access serve as controls. Adults aged 18 to 64 years living in North Savo were recruited between 2024 and 2025 through population registry sampling and open public recruitment. Following informed consent and baseline assessment, participants were randomly allocated to receive access to the intervention at different time points during the study.

Power calculations using Monte Carlo simulations indicated that approximately 1,600 participants would provide 80% power to detect a standardized mean difference of 0.30 in mental wellbeing (SWEMWBS), assuming four intervention sequences, five measurement occasions, a within-person intraclass correlation coefficient of 0.70, and 30% attrition.

The primary outcome mental wellbeing is assessed using the Short Warwick-Edinburgh Mental Wellbeing Scale (SWEMWBS). Secondary outcomes include depressive symptoms measured with the Patient Health Questionnaire (PHQ-9), self-reported capabilities, loneliness measured with the UCLA 3-item Loneliness Scale (UCLA-3), life satisfaction measured on a 0-10 scale, and self-reported health behaviours. Outcomes are measured at baseline and repeated follow-up assessments throughout the study. We hypothesize that participants receiving the intervention will show greater improvement in all primary and secondary outcome measures compared with the control condition. App usage data are used to evaluate intervention adoption and engagement and to examine whether effectiveness differs according to actual intervention use.

Statistical analyses will be conducted using mixed-effects regression models that account for the stepped-wedge design and repeated measurements within participants. Models will include fixed effects for intervention exposure and time, and a random intercept for participants. Estimates will be reported with 95% confidence intervals and a two-sided significance level of 0.05.

In addition to quantitative outcome assessments, participant experiences and acceptability of the intervention will be evaluated with user satisfaction questionnaires and qualitative interviews conducted among a subset of participants. These data will be used to assess perceived usefulness, user experiences, and opportunities for further development of the intervention.

In addition to questionnaire and app usage data, participants will be linked to regional and national administrative registers. Register-based follow-up covers the period from one year before study enrolment until up to ten years after study completion. Data sources include health and social care registers, prescription and reimbursement registers, employment-related registers, and population registers. These data will be used to evaluate the long-term effectiveness and cost-effectiveness of the intervention, including health service use, medication use, social outcomes, and associated costs.