Personalized message interventions did not show significant effects on overall step-count increase but were associated with a reduced risk of consecutive 2-week goal achievement failure events, suggesting personalized messages may play an important role in improving behavior change sustainability and failure resilience.
Key Findings
Results
Overall 12-week mean goal achievement rates were identical between groups, with no statistically significant difference.
The overall 12-week mean goal achievement rate was 87% in both the intervention group and the control group.
The odds ratio for goal achievement was 1.12 (95% CI 0.29–4.37; P=.87), indicating no significant between-group difference.
Primary outcome was weekly goal achievement rates for a 25,000-step weekly goal.
Analyses were conducted using generalized estimating equations (GEEs).
Results
Weekly step-count patterns showed no significant differences between the intervention and control groups over 12 weeks.
The group × time interaction estimate was -417 (SE 09; P=.31), indicating no statistically significant difference in step-count trajectories.
Step-count patterns were analyzed using linear mixed models (LMMs).
Both groups received incentives based on goal achievement and general health information.
The intervention group additionally received weekly personalized messages from research staff based on midweek step-count performance.
Results
The risk of consecutive 2-week goal achievement failure events was significantly lower in the intervention group compared to the control group.
The hazard ratio for consecutive 2-week goal achievement failure was 0.67 (95% CI 0.52–0.86; P=.002).
The intervention group showed an approximately 33% lower likelihood of consecutive 2-week goal achievement failure events than the control group.
This outcome was analyzed using Cox proportional hazards models.
Consecutive 2-week goal achievement failure events were a pre-specified primary outcome of the trial.
Methods
Of 174 randomized participants, 141 (81%) engaged in the intervention and were included in analyses, with 33 participants not participating after randomization.
The intervention group included 67 participants and the control group included 74 participants in the final analyses.
Participants were recruited offline through community advertisements containing a QR code linking to an online sign-up form.
The trial was conducted from April 1 to June 22, 2024, over 12 weeks in Mapo-gu, Seoul, Korea.
Screening was conducted using an online baseline questionnaire by research staff, while all intervention delivery and outcome assessments were conducted via the WalkOn mobile app.
Methods
The intervention was grounded in the Information-Motivation-Behavioral Skills (IMB) model and regulatory focus theory (RFT), with personalized messages delivered weekly based on midweek step-count performance.
Personalized messages were provided by research staff according to midweek step-count performance.
Messages were personalized using frameworks from both the IMB model and regulatory focus theory (RFT).
Both groups received incentives based on goal achievement and general health information; the intervention group received additional personalized messaging.
The trial was registered with the Clinical Research Information Service (CRIS), Republic of Korea, identifier KCT0010966.
Discussion
Personalized messaging was associated with improved behavior change sustainability and failure resilience, though not with overall increases in step counts.
Authors concluded that 'personalized messages may play an important role in improving behavior change sustainability and failure resilience.'
The significant finding was specifically in reducing the risk of prolonged failure streaks (consecutive 2-week non-achievement), not in aggregate step counts or overall goal achievement rates.
Authors suggest that 'incorporating personalized messaging strategies may enhance the effectiveness of digital health intervention design.'
The study highlights the importance of measuring failure resilience as a distinct outcome from average performance in digital health interventions.
What This Means
This research suggests that sending personalized messages to adults participating in a smartphone-based walking program did not increase their overall step counts or average goal achievement rates compared to a control group, but it did meaningfully reduce the chance of participants experiencing extended losing streaks — specifically, two or more consecutive weeks of failing to meet their step goals. Both groups in this 12-week Korean study used a mobile app called WalkOn to track steps toward a weekly 25,000-step target, and both received incentives for meeting goals and general health information. The only difference was that the intervention group also received weekly tailored messages from study staff based on how participants were doing midway through each week.
The key practical implication from this research is that personalized digital messaging may not be enough to boost overall physical activity levels, but it could help people 'bounce back' after setbacks rather than falling into prolonged periods of inactivity. This distinction matters because failure resilience — the ability to recover from a bad week rather than giving up entirely — is an important but often overlooked aspect of long-term behavior change. The intervention group was about 33% less likely to experience back-to-back weeks of failing to meet their step goals.
This research suggests that designers of digital health apps and wellness programs should consider tracking and targeting failure resilience as a distinct outcome, not just average performance or overall goal achievement. Personalized, timely feedback delivered through mobile technology may be a useful tool for keeping people engaged during difficult stretches, even if it does not dramatically increase average activity levels across a population.
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Jang S, Sohn A. (2026). Personalized Mobile App-Based Messaging and Step Tracking for Improving Walking Behavior in Adults: Randomized Controlled Trial.. JMIR mHealth and uHealth. https://doi.org/10.2196/84430