A Refined Mobile Health Intervention (SMARTFAMILY2.0) to Promote Physical Activity and Healthy Eating in a Family Setting: Randomized Controlled Trial
Bibliographic record
Abstract
Background Many mobile health (mHealth) apps focus on promoting physical activity (PA) and healthy eating (HE). However, there is limited empirical evidence regarding their effectiveness in initiating and sustaining behavior change, particularly among children and adolescents. Considering that behavior is influenced by social contexts, it is essential to take core settings like family dynamics into account when designing mHealth apps. Objective The purpose of this study was to further develop and refine the SMARTFAMILY (SF) app targeting PA and HE in a collective family-based setting by enhancing design and usability, as well as by adding gamification aspects, health literacy, and just-in-time adaptive interventions to the first version of the app. Methods The SF2.0 app, based on behavior change theories and techniques, was developed, implemented, and evaluated. The app was used in a collective family setting, with family members using it individually and cooperatively. In a cluster-randomized controlled trial, the intervention group (IG) used the app for 3 consecutive weeks, while the control group (CG) received no treatment. Primary outcomes included PA measured through self-reports and accelerometry, as well as self-reported fruit and vegetable intake (FVI) for HE. Secondary outcomes included intrinsic motivation, behavior-specific self-efficacy, and the Family Health Climate. A follow-up assessment (T2) was conducted 4 weeks after the postmeasurement (T1) to assess intervention effects. Multilevel analyses were performed in R (R Foundation for Statistical Computing), considering the hierarchical structure of individuals (level 1) within families (level 2). Results Overall, 55 families (28 CG, 105/209; 27 IG, 104/209 participants) were recruited for the study. In total, 3 families (3 CG, n=12) chose to drop out of the study due to personal reasons before T0. Overall, no evidence for meaningful and statistically significant increases in PA was observed in favor of the IG of our physically active sample. However, the app elucidated positive effects in favor of the IG for FVI diary (T0-T1; P=.03), joint PA (T0-T1; P=.02 and T0-T2; P<.001), and joint family meals (T0-T1; P=.004). Conclusions The SF2.0 trial evaluated an mHealth intervention designed to promote PA and HE within families. Despite incorporating a theoretical foundation, several behavior change techniques based on family life, and gamification and just-in-time adaptive intervention features, the intervention did not significantly increase PA levels among physically active participants. FVI, joint PA, and joint meals were improved within the IG. Previous studies on digital health interventions have produced mixed results, and family-based mHealth interventions remain rare, with limited focus on whole-family behavior and randomized controlled trials. To enhance intervention effectiveness, future app development could consider incorporating even more advanced features and should focus on inactive participants. Further research is needed to better understand intervention engagement and tailor mHealth approaches for primary prevention efforts. Trial Registration German Clinical Trials Register DRKS00010415; https://www.drks.de/search/en/trial/DRKS00010415 International Registered Report Identifier (IRRID) RR2-10.2196/20534
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".