Virtual Coach–Guided Online Acceptance and Commitment Therapy for Chronic Pain: Pilot Feasibility Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Veterans are disproportionately affected by chronic pain, with high rates of pain diagnoses (47%-56%) and a 40% higher rate of prevalence of severe pain than nonveterans. This is often accompanied by negative functional outcomes and higher mortality. Combined with research suggesting medical treatments for chronic pain are often insufficient, there is an urgent need for nonmedical pain self-management programs. An interactive online platform to deliver an efficacious treatment for chronic pain such as acceptance and commitment therapy (ACT) could be a valuable option to assist veterans with pain care at home. OBJECTIVE: This study aims to evaluate the virtual coach-guided Veteran ACT for Chronic Pain (VACT-CP) online program compared to a waitlist and treatment as usual (WL+TAU) control group through a small pilot feasibility randomized controlled trial. The primary aim was to evaluate the feasibility and acceptability of VACT-CP and study procedures, such as ease of recruitment, treatment receptivity, attrition and retention, sustained participation, system usability, and assessment of trial procedures. Secondary aims explored differences in the VACT-CP and WL+TAU groups on pre- and posttest (week 7) outcome measures for pain, mental health, functioning, and ACT processes. METHODS: Veterans with chronic pain were recruited and randomized to either the VACT-CP (n=20) or the WL+TAU (n=22) group in a parallel group trial design. Self-report surveys were administered to participants at baseline (week 0), at the intervention midpoint (week 3), immediately after the intervention (week 7), and at the 1-month follow-up (week 11). We used Wilcoxon signed rank tests with the intention-to-treat sample to describe changes in secondary outcomes from pre- to postintervention within each group. RESULTS: Study procedures showed good feasibility related to recruitment, enrollment, randomization, and study completion rates. Participants reported that VACT-CP was easy to use (System Usability Scale: mean 79.6, SD 12.8; median 82.5, IQR 70-87.5); they completed an average of 5 of the 7 total VACT-CP modules with high postintervention satisfaction rates. Qualitative feedback suggested a positive response to program usability, content tailoring, veteran centeredness, and perceived impact on pain management. Although the pilot feasibility trial was not powered to detect differences in clinical outcomes and significant findings should be interpreted with caution, the VACT-CP group experienced significant increases in chronic pain acceptance (P<.001) and decreases in depressive symptoms (P=.03). CONCLUSIONS: VACT-CP showed encouraging evidence of feasibility, usability, and acceptance, while also providing promising initial results in improving a key process in ACT for chronic pain-chronic pain acceptance-after online program use. A full-scale efficacy trial is needed to assess changes in clinical outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT03655132; http://clinicaltrials.gov/ct2/show/NCT03655132. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/45887.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".