Aim2Be mHealth intervention for children with overweight and obesity: study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: The prevalence of overweight and obesity remains high in Canada, and the current standard for the treatment of childhood obesity is in-person, family-based, multidisciplinary interventions that target lifestyle behaviors (e.g., diet, physical activity, and sedentary behaviors). These programs are costly to operate, have limited success, and report recruitment and retention challenges. With recent advances in technology, mobile health or mHealth has been presented as a viable alternative to in-person interventions for behavior change, especially with teens. PURPOSE: The primary aim of this study is to test the efficacy of Aim2Be, a gamified app based on behavior change theory with health coaching to improve weight outcomes (i.e., decrease in standardized body mass index (zBMI)) and lifestyle behaviors (i.e., improve dietary quality, increase fruit and vegetable intake, reduce sugar-sweetened beverage intake, increase physical activity, and reduce screen time) among children 10- to 17-years old with overweight or obesity versus their peers randomized into a waitlist control condition. The secondary aims of this study are to 1) test whether supplementing the Aim2Be program with health coaching increases adherence and 2) examine the mediators and moderators of adherence to the Aim2Be intervention. METHODS: We will employ a randomized controlled trial design and recruit 200 child and parent dyads to participate in the study (2019-2020). Participants will be recruited from Canadian pediatric weight management clinics and through online advertisements. Child participants must be between the ages of 10 and 17 years, have overweight or obesity, be able to read English at least at a grade 5 level, and have a mobile phone or home computer with internet access. Following baseline data collection, participants will be randomized into intervention and waitlist control groups. Intervention participants will receive access to Aim2Be, with access to health coaching. After having their data collected for 3 months, the control group will gain access to Aim2Be, with no access to health coaching. Participants will control their frequency and duration of app usage to promote autonomy. DISCUSSION: Findings from this study will determine the efficacy of using Aim2Be in improving child weight outcomes and lifestyle behaviors and guide future mHealth interventions for pediatric weight management. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03651284. Registered 29 August 2018.
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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 | 0.032 | 0.033 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.115 | 0.017 |
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".