Effectiveness of a worksite‐based weight loss randomized controlled trial: The worksite study
Bibliographic record
Abstract
OBJECTIVE: To determine the effectiveness of an individually targeted Internet-based intervention with monetary incentives (INCENT) at reducing weight of overweight and obese employees when compared with a less-intensive intervention (Livin' My Weigh [LMW]) 6 months after program initiation. METHODS: Twenty-eight worksites were randomly assigned to either INCENT or LMW conditions. Both programs used evidence-based strategies to support weight loss. INCENT was delivered via daily e-mails over 12 months while LMW was delivered quarterly via both newsletters and on-site educational sessions. Generalized linear mixed models were conducted for weight change from baseline to 6 months post-program and using an intention-to-treat analysis to include all participants with baseline weight measurements. RESULTS: Across 28 worksites, 1,790 employees (M = 47 years of age; 79% Caucasian; 74% women) participated. Participants lost an average of 2.27 lbs (P < 0.001) with a BMI decrease of 0.36 kg/m(2) (P < 0.001) and 1.30 lbs (P < 0.01) with a BMI decrease of 0.20 kg/m(2) (P < 0.01) in INCENT and LMW, respectively. The differences between INCENT and LMW in weight loss and BMI reduction were not statistically significant. CONCLUSIONS: This study suggests that INCENT and a minimal intervention alternative may be effective approaches to help decrease the overall obesity burden within worksites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".