Mindfulness‐based interventions for weight loss: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: An increasing number of studies are investigating the efficacy of mindfulness-based interventions (MBIs) for weight loss and obesity-related eating behaviours. However, the results of past reviews are inconsistent. OBJECTIVE: To clarify these inconsistencies, we conducted a comprehensive effect-size analysis to evaluate the efficacy of MBIs on weight loss and eating behaviours. DATA SOURCE: Data sources were identified through a systematic review of studies published in journals or as dissertations in PsychINFO, PubMed, CINAHL, Web of Science, Medline and Scopus, ProQuest or OATD from the first available date to March 10, 2017. REVIEW METHODS: A total of 18 publications (19 studies, n = 1,160) were included. RESULTS: Mean weight loss for MBIs at post-treatment was 6.8 and 7.5 lb at follow-up. In pre-post comparisons, effect-size estimates suggest that MBIs are moderately effective for weight loss (n = 16; Hedge's g = .42; 95% CI [.26, .59], p < .000001) and largely effective in reducing obesity-related eating behaviours (n = 10; Hedge's g = .70; CI 95% [.36, 1.04], p < .00005). Larger effects on weight loss were found in studies that used a combination of informal and formal meditation practice (n = 6; Hedge's g = .55; CI 95% [.32, .77], p < .00001) compared with formal meditation practice alone (n = 4; Hedge's g = .46; CI [.10, .83], p < .05). CONCLUSION: Results suggest that MBIs are effective in reducing weight and improving obesity-related eating behaviours among individuals with overweight and obesity. Further research is needed to examine their efficacy for weight loss maintenance.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".