The Usage Effects, Effect Modifiers, and Experiences of a Web-Based App for Healthy Habit Formation in Adults: Exploratory Analysis of a Quasi-Experimental Real-World Intervention.
Rantala E, Valtanen M, et al. • JMIR mHealth and uHealth • 2026
An inexpensive, low-intensity digital support for healthy habits could foster small beneficial lifestyle changes, but effects require user engagement and may depend on the individual.
Key Findings
Results
A higher percentage of app use days was significantly associated with greater increases in physical activity.
A 10 percentage point higher percentage of use days was associated with a 3.09 (95% CI 0.79-5.39) metabolic equivalent of task (MET) hours per week greater increase in physical activity.
The association between app use days and physical activity was statistically significant.
Other associations between app use measures and physical activity outcomes were not reported as significant.
App use was measured as percentage of days with logins divided by total days of follow-up multiplied by 100.
Results
A higher number of reported habit performances was significantly associated with greater improvement in diet quality.
A 1-unit higher logarithmic number of reported habit performances was associated with a 0.51 (95% CI 0.07-0.95) Healthy Diet Index point greater improvement in diet quality.
Diet quality was measured using the Healthy Diet Index (HDI).
Habit performances included diet-related, physical activity-related, and BMI-related categories.
Other associations between app use and diet or BMI outcomes were nonsignificant.
Results
App engagement among completers was low, with a median of 5.9% of app use days and 22 reported habit performances over 90 days.
The median percentage of app use days was 5.9% (IQR 3.3%-11%) over the 90-day follow-up period.
The median total number of reported habit performances was 22 (IQR 3-68.5).
Of 6975 invitees, only 1282 (18.4%) accepted the invitation.
Only 382 participants (5.5% of invitees) completed the data collection required for assessment.
Completers had a mean age of 51 years (SD 15 y), and 263 of 382 (69%) were women.
Results
Greater baseline physical activity and a more positive attitude toward e-services appeared to enhance the effect of app use on physical activity.
Both greater physical activity at baseline and a more positive attitude to e-services at baseline were identified as effect modifiers for the association between app use and physical activity outcomes.
P values for these effect modification analyses were less than .05.
Effect modifiers examined included sociodemographics, health, lifestyle, and e-service use characteristics.
These findings suggest that the benefits of the app may depend on individual characteristics.
Results
The app received moderate acceptability and overall satisfaction ratings from users.
The median acceptability rating was 2.9 (IQR 2.4-3.3) on a scale of 1 to 4.
The median overall score was 7 (IQR 5-8) on a scale of 0 to 10.
Acceptability and satisfaction were assessed through questionnaires collected via the app.
Methods
The study used a 1-group pre-post quasi-experimental design with a web-based habit formation app offered to a subsample of a population-based survey.
Three-month app access was offered to participants aged 20-74 years from the Healthy Finland survey via SMS text messaging or mail.
The app provided personalized behavioral suggestions translated from evidence-based lifestyle guidelines into simple, repeatable actions called 'habits.'
Users could browse, select, and report habit performances, and monitor their progress.
Assessment used app log data, in-app questionnaires at baseline and at 45 and 90 days, and background information from the national population register and Healthy Finland Survey.
Linear mixed effects models were used to explore associations between app use and changes in self-reported outcomes.
Methods
Recruitment and retention were limited, with only 5.5% of invitees completing the required data collection.
6975 individuals were invited to participate.
1282 (18.4%) accepted the invitation.
Only 382 (5.5%) completed the data collection required for the assessment.
The low completion rate limits the generalizability and robustness of the findings.
The authors note findings warrant confirmation in more robust study designs.
What This Means
This research suggests that a low-cost, web-based app designed to help people build healthy lifestyle habits can produce small but meaningful improvements in physical activity and diet quality — but only among users who actually engage with it. In this study, people who logged into the app more frequently showed greater increases in physical activity, and people who recorded more habit completions showed greater improvements in diet quality. The app did not show significant effects on BMI. However, engagement with the app was quite low overall: on average, participants only used the app on about 6% of the days they had access to it, and many people who were invited to try the app never completed the study.
The research also found that who benefits from the app may matter as much as whether people use it. People who were already more physically active and those with a more positive attitude toward online services appeared to gain more physical activity benefit from using the app. This suggests that digital habit-support tools may work better for some people than others, and that simply offering access to an app is not enough — engagement is key to seeing results. The app received moderate satisfaction scores from users, suggesting it was reasonably well-received but not overwhelmingly so.
This research matters because it adds real-world evidence about how digital health tools perform when offered broadly to the public, rather than in tightly controlled research settings. The findings highlight both the potential and the limitations of low-intensity digital interventions: they may help motivated users make modest lifestyle improvements at low cost, but they are unlikely to reach or benefit everyone equally. The authors call for further research to better understand who is most likely to benefit from this type of digital health support, and for stronger study designs to confirm these preliminary findings.
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Rantala E, Valtanen M, Umer A, Parikka S, Pihlajamäki J, Ruotsalainen I, et al.. (2026). The Usage Effects, Effect Modifiers, and Experiences of a Web-Based App for Healthy Habit Formation in Adults: Exploratory Analysis of a Quasi-Experimental Real-World Intervention.. JMIR mHealth and uHealth. https://doi.org/10.2196/84076