Development and Usability Testing of a Scalable Digital Acceptance and Commitment Therapy Intervention for Chronic Postsurgical Pain
Bibliographic record
Abstract
Background: The Transitional Pain Service (TPS) at Toronto General Hospital is a multidisciplinary program focused on preventing chronic postsurgical pain (CPSP). While the program is effective in terms of patient outcomes, the scarcity of specialized pain psychologists limits its broader implementation. Aims: Acceptance and Commitment Therapy (ACT) is an evidence-based psychology intervention used at the TPS. We aimed to develop a fully self-guided, digital psychology intervention for individuals at risk for CPSP.Methods: The digital self-guided intervention was modeled after a 3-hour ACT group workshop at the TPS. It includes psychoeducation about ACT and pain that revolves around the ACT Matrix, a tool for evaluating coping strategies and enhancing quality of life. The intervention was developed on the digital health application, Manage My Pain (MMP), as an interactive, self-guided program. Development followed an iterative process, with qualitative feedback from TPS patients, clinicians, research staff and app developers. Results: The online intervention was implemented as four sequential 15 min parts plus a fifth optional summary and reference part. Each part includes didactic modules interspersed with interactive activities, experiential exercises, and optional learning checks. The MMP platform’s existing navigation features helped streamline development. Patient and clinician feedback from “think-aloud” and retrospective interviews helped refine key usability factors, including engagement, navigation, and content clarity. Conclusions: The self-guided digital ACT intervention is scalable and can be easily implemented at clinics without specialized pain psychologists. Further testing in a randomized controlled trial will assess its acceptability and efficacy as a substitute for psychologist-led group workshops.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".