A look at engagement profiles and behavior change: A profile analysis examining engagement with the Aim2Be lifestyle behavior modification app for teens and their families
Bibliographic record
Abstract
Mobile-Health is increasingly used to deliver lifestyle modification interventions; however, little is known about how users engage with these apps. This study aims to profile how teens engage with Aim2Be– a lifestyles behavior modification app), characterize engagement profiles, and examine which engagement profiles support changes in behaviors (diet, physical activity , screen time and sleep) and changes in the mediators targeted by the app. Data were collected from 301 teens (14.8 years, 49% boys, 68% Caucasian) living in Canada, from March to October 2018, who utilized the Aim2Be app for 4.5 months. App-analytics tracked teen engagement with the app features (selecting aims, completing tasks and quick wins, using the knowledge center and social wall, and accessing the virtual coach). Factor mixture modeling identified the following engagement profiles: Uninvolved (32%) did not use most app features; Dabblers (25%) minimally used the app features; Engaged (24%) had moderate-to-high use of app features; and Keeners (19%) had the highest use of all app features. Regression models showed that teens were more engaged with Aim2Be if their parents were involved and if they participated with their mothers and/or an educated parent. Finally, Keeners significantly improved on most mediators of behavior change and increased their fruit and vegetable intake. The findings suggest that parental engagement supported teen engagement of the Aim2Be app and high engagement was needed to support behavior change among teens. Gaining a greater understanding of the features that appeal to teens is necessary to support behavior change.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".