An Acceptance and Commitment Therapy Prototype Mobile Program for Individuals With a Visible Difference: Mixed Methods Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Mobile apps may offer a valuable platform for delivering evidence-based psychological interventions for individuals with atypical appearances, or visible differences, who experience psychosocial appearance concerns such as appearance-based social anxiety and body dissatisfaction. Before this study, researchers and stakeholders collaboratively designed an app prototype based on acceptance and commitment therapy (ACT), an evidence-based form of cognitive behavioral therapy that uses strategies such as mindfulness, clarification of personal values, and value-based goal setting. The intervention also included social skills training, an established approach for increasing individuals' confidence in managing social interactions, which evoke appearance-based anxiety for many. OBJECTIVE: In this study, the authors aim to evaluate the feasibility of an ACT-based app prototype via the primary objectives of user engagement and acceptability and the secondary feasibility objective of clinical safety and preliminary effectiveness. METHODS: To address the feasibility objectives, the authors used a single-group intervention design with mixed methods in a group of 36 participants who have a range of visible differences. The authors collected quantitative data via measures of program use, satisfaction ratings, and changes over 3 time points spanning 12 weeks in outcomes, including selected ACT process measures (experiential avoidance, cognitive defusion, and valued action), scales of appearance concerns (appearance-based life disengagement, appearance-fixing behaviors, appearance self-evaluation, and fear of negative appearance evaluation), and clinical well-being (depression and anxiety). Semistructured exit interviews with a subsample of 12 participants provided qualitative data to give a more in-depth understanding of participants' views and experiences of the program. RESULTS: In terms of user engagement, adherence rates over 6 sessions aligned with the upper boundary of those reported across mobile mental health apps, with over one-third of participants completing all sessions over 12 weeks, during which a steady decline in adherence was observed. Time spent on sessions matched design intentions, and engagement frequencies highlighted semiregular mindfulness practice, mixed use of value-based goal setting, and high engagement with social skills training. The findings indicate a good overall level of program acceptability via satisfaction ratings, and qualitative interview findings offer positive feedback as well as valuable directions for revisions. Overall, testing for clinical safety and potential effectiveness showed encouraging changes over time, including favorable changes in appearance-related life disengagement, appearance-fixing behaviors, and selected ACT measures. No iatrogenic effects were indicated for depression or anxiety. CONCLUSIONS: An ACT-based mobile program for individuals struggling with visible differences shows promising proof of concept in addressing appearance concerns, although further revisions and development are required before further development and more rigorous evaluation.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".