Triggering Weight Management Using Digital Avatars: Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: ) digital and personalized images of their future selves with reduced body weight will likely trigger them to achieve that new body weight. OBJECTIVE: The purpose of this study is to assess whether digital avatars can trigger weight management action and identify some of the measurable factors that distinguish those who may be triggered. METHODS: A prospective cohort study followed participants for 12 weeks through 5 recorded interviews. Participants were screened for suitability for the study using the Cosmetic Procedure Screening Questionnaire as a measure of body dysmorphia. At interview 1, participants were shown 10 images from a "Food-pics" database and invited to estimate their calorie value. The intervention, the FutureMe app, delivered at interview 2, provided each participant an opportunity to see and take away a soft copy of an avatar of themselves as they might appear in the future depending on their calorie consumption and exercise regimen. Participants completed the readiness for change (S-Weight) survey based on Prochaska Stages of Change Model and the processes of change (P-Weight) survey. Any changes in diet, exercise, or weight were self-reported. RESULTS: or lose on average 10.5 kg within 13 weeks (-0.8 kg per week). Most participants stated that they would achieve these results by limiting their calorie intake to 1500 calories per day and taking the equivalent of 1 hour of bicycling per day. At interview 1, more participants were in the preparation stage of behavior change than in subsequent interviews. By interview 5, most of the participants were at the maintenance stage. Participants who overestimated the recommended number of calories were more likely to be in the contemplation stage (P=.03). CONCLUSIONS: Volunteers who participated in the study were mainly women older than 40 years and beyond the contemplation stage of change for weight management, and those who took weight management action were demonstrated to have a more accurate idea of the calorie content of different foods. Most participants set ambitious targets for weight loss, but few, if any, achieve these goals. However, most people who completed this study were actively taking action to manage their weight. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12619001481167; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378055&isReview=true.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".