Self‐Weighing Increases Weight Loss in Free‐Living Adults: A Double‐Blind Randomized Field Trial among 200,000 Health App Users
Bibliographic record
Abstract
Self‐weighing is closely associated with sustained weight loss, but randomized trials to test causality have been limited to small samples in controlled settings subject to Hawthorne effects. This study employs a simple behavioral nudge to induce more frequent self‐weighing among new users of the Lose It! app, randomly assigned among all individuals who enrolled over a four week period in 2014. The study population is similar in age, sex and socio‐economic status to other groups seeking weight‐loss assistance in the United States and Canada. There were no differences between treatment (T) and control (C) other than the nudge, and subjects were not told about differences between T and C. Among the 184,955 users who recorded at least one plausible data point, we find mean self‐weighing frequency (days) was T=4.27±0.07 and C=3.85±0.07, and mean weight loss (lbs) was T=1.95±0.04 and C=1.90±0.04, for a difference in weight loss of 0.054±0.052 over the first three months after enrollment. Using two‐stage least squares to estimate the causal effect of self‐monitoring, we find that each additional weigh‐in led to an average increase in weight loss of 0.131±0.119 lbs. These results suggest that encouraging frequent self‐weighing can improve the efficacy of weight loss programs at a national level, at very low additional cost.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".