Behavioral weight-loss treatment plus motivational interviewing versus attention control: lessons learned from a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Studies evaluating the benefit of adding motivational interviewing (MI) to behavioral weight-loss programs (BWLPs) have yielded mixed findings. METHODS: ) enrolled in a 12-week BWLP and (2) explore levels of importance, confidence, and readiness for change ratings. RESULTS: Participants, who were randomized to receive two MI sessions or two attention control sessions, were assessed at baseline, the end of the BWLP, and 6 months post BWLP. Both groups decreased their weight from baseline to the end of the BWLP; however, there was no weight change in either group when measured between baseline and 6 months post BWLP. We observed no group differences in importance, confidence, and readiness for change after each session. CONCLUSIONS: We highlight some important lessons learned from the present trial that can be applied to MI + BWLP research. Participants may not have benefited from MI because they were already highly motivated to change, which highlights the importance of pretreatment assessment. Findings also suggest that treatment monitoring may help to enhance MI + BWLP efficacy by guiding a stepped-care approach that identifies individuals for whom additional MI sessions are needed, and when. A focus on refining elements of treatment remains an important direction. TRIAL REGISTRATION: ClinicalTrials.gov, Identifier: NCT02649634 . Retrospectively registered on 5 January 2016.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".