Adherence to a caloric budget and body weight change vary by season, gender, and BMI: An observational study of daily users of a mobile health app
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Objective Self‐monitoring, one of the most important behaviors for successful weight loss, can be facilitated through mobile health applications (mHealth apps). Therefore, it is of interest to determine whether consistent users of these apps succeed in achieving their weight goals. This study used data from an mHealth app that enabled tracking of caloric intake, body weight, and physical activity and provided a caloric budget depending on weight goal. The primary objective was to evaluate adherence to caloric budget and body weight change among the most consistent (i.e., daily) trackers of caloric intake over a calendar year ( n = 9372, 50% male). Methods Gender‐stratified linear mixed models were conducted to examine the effects of quarter of year (Q1–Q4 as season proxies) and body mass index (BMI) group (normal weight, overweight, obesity) on adherence to a caloric budget (kcal/day). Change in body weight was analyzed using a subset of users ( n = 5808) who entered their weight in the app at least once per week, once per month, or once in Q1 and Q4. Physical activity entries were evaluated in exploratory analyses. Results Only users with obesity met their caloric budget in Q1. Deviation from budget increased for all groups from Q1 to Q2 (mean change[±standard error of the mean]: +23.7[±1.8] and +39.7[±2.2] kcal/day for female and male users, p < 0.001), was stable between Q2 and Q3, and fluctuated thereafter depending on gender and BMI, with greater deviation among males with overweight. Users with obesity with weight entries at least once per month lost the most weight (−6.1[±0.3] and −4.5[±0.3] kg for females and males, p < 0.001). Physical activity was highest in the summer months. Conclusions Among consistent calorie trackers, adherence to a caloric budget and body weight vary by season, gender, and BMI. Self‐monitoring of body weight in addition to calorie tracking may lead to improved weight loss outcomes.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 it