Acceptance and Commitment Therapy for Body Dissatisfaction: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
INTRODUCTION: Body dissatisfaction (BD) is increasing internationally and is associated with a range of physical and psychological negative outcomes, including eating disorders (ED). As a result, there is a great need to identify treatments that reduce BD to intervene and prevent these outcomes. Acceptance and commitment therapy (ACT) has been employed for body image and weight concerns. However, there is limited data on the efficacy of ACT in reducing BD. OBJECTIVE: To evaluate the efficacy of ACT on measures of BD in those with or without a diagnosable ED. METHODS: A systematic review and meta-analysis were conducted, including 12 studies that utilized individual, group, or online self-help ACT interventions targeting BD (N = 825; analyzed at the end of interventions N = 741). RESULTS: = 45.61). The strongest effect sizes were produced by subgroups of participants who were at high risk for EDs (k = 6, g = 0.631, p < 0.001), and those who received ACT via online self-help forums (k = 3, g = 0.563, p < 0.005). A majority of studies (75%) had low to medium risk of bias. The mean dropout rate across ACT interventions was 23.51%. DISCUSSION: These results demonstrate that ACT is efficacious in BD reduction, particularly in those with high BD, at risk for developing EDs, and in the form of online self-help. ACT, therefore, shows potential as a prevention program, in addition to augmenting existing multidisciplinary treatments for ED patients. Due to limitations of this meta-analysis, further research should investigate a greater volume of large-scale trials from diverse populations across the globe.
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.024 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".