Comparing Self-Monitoring Strategies for Weight Loss in a Smartphone App: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Self-monitoring of dietary intake is a valuable component of behavioral weight loss treatment; however, it declines quickly, thereby resulting in suboptimal treatment outcomes. OBJECTIVE: This study aimed to examine a novel behavioral weight loss intervention that aims to attenuate the decline in dietary self-monitoring engagement. METHODS: ) and were randomized to a 12-week stand-alone weight loss intervention using the MyFitnessPal smartphone app for daily self-monitoring of either (1) both weight and diet, with weekly lessons, action plans, and feedback (Simultaneous); (2) weight through week 4, then added diet, with the same behavioral components (Sequential); or (3) only diet (App-Only). All groups received a goal to lose 5% of initial weight by 12 weeks, a tailored calorie goal, and automated in-app reminders. Participants were recruited via online and offline methods. Weight was collected in-person at baseline, 1 month, and 3 months using calibrated scales and via self-report at 6 months. We retrieved objective self-monitoring engagement data from MyFitnessPal using an application programming interface. Engagement was defined as the number of days per week in which tracking occurred, with diet entries counted if ≥800 kcal per day. Other assessment data were collected in-person via online self-report questionnaires. RESULTS: . One-third (33/100) were from racial or ethnic minority groups. During the trial, 5 participants became ineligible. Of the remaining 100 participants, 84% (84/100) and 76% (76/100) completed the 1-month and 3-month visits, respectively. In intent-to-treat analyses, there was no difference in weight change at 3 months between the Sequential arm (mean -2.7 kg, 95% CI -3.9 to -1.5) and either the App-Only arm (-2.4 kg, -3.7 to -1.2; P=.78) or the Simultaneous arm (-2.8 kg, -4.0 to -1.5; P=.72). The median number of days of self-monitoring diet per week was 1.9 (interquartile range [IQR] 0.3-5.5) in Sequential (once began), 5.3 (IQR 1.8-6.7) in Simultaneous, and 2.9 (IQR 1.2-5.2) in App-Only. Weight was tracked 4.8 (IQR 1.9-6.3) days per week in Sequential and 5.1 (IQR 1.8-6.3) days per week in Simultaneous. Engagement in neither diet nor weight tracking differed between arms. CONCLUSIONS: Regardless of the order in which diet is tracked, using tailored goals and a commercial mobile app can produce clinically significant weight loss. Stand-alone digital health treatments may be a viable option for those looking for a lower intensity approach. TRIAL REGISTRATION: ClinicalTrials.gov NCT03254953; https://clinicaltrials.gov/ct2/show/NCT03254953 (Archived by WebCite at http://www.webcitation.org/72PyQrFjn).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".