User-Driven Development of a Digital Behavioral Intervention for Chronic Pain: Multimethod Multiphase Study
Bibliographic record
Abstract
BACKGROUND: Recent research shows that chronic pain affects 27% of the adult population. For many, pain significantly impairs quality of life and everyday functioning. Behavioral interventions have shown utility, but access remains limited. Digital health solutions can increase reach, but there is a need for user-friendly, feasible, and evidence-based digital interventions. OBJECTIVE: This study aimed to clarify how a digital behavioral intervention for people with chronic pain can be developed through a user-centered approach to address the needs and preferences of the target population. METHODS: This study used a multimethod approach involving end users, namely, patients with chronic pain and therapists, to develop prototypes for a digital behavioral intervention across 3 phases. In the preparation phase (phase 0), fictional patient personas (n=3) were created to represent the diversity of the target population while emphasizing transdiagnostic features across people with chronic pain. In the design phase (phase 1), qualitative data from focus groups with patients (n=5; aged 37-51 years; 4/5, 80% women; 2/5, 40% diagnosed with Ehlers-Danlos syndrome; 3/5, 60% either undiagnosed or uncertain about their diagnosis) and therapists (n=12 licensed psychologists; aged 29-64 years; 9/12, 75% women) were collected to explore end-user preferences for the intervention design and content. In the testing phase (phase 2), the initial full prototype of the digital intervention was piloted with patients (n=11; aged 36-58 years; 9/11, 82% women; with diverse diagnoses, including migraine, arthritis, fibromyalgia, complex regional pain syndrome, hypermobile Ehlers-Danlos syndrome, herniated disc, chronic fatigue syndrome, and 1/11, 9% cases of undiagnosed pain) and therapists (n=3 licensed psychologists; aged 36-58 y; 3/3, 100% women). The Consolidated Framework for Implementation Research was used to structure analyses of end-user feedback. RESULTS: On the basis of end-user input, a 6-week digital behavioral intervention for chronic pain was created. Focus groups highlighted the importance of accessibility and adaptability of the digital intervention, emphasizing the need for tailored content, flexibility (eg, contact with the therapist via asynchronous messaging, telephone, or video calls), and user-friendly design (eg, easy navigation between modules, short microsessions, and visualizations). Average weekly ratings (scale from 1=not at all to 7=very much) by patients during pilot-testing indicated that the intervention was helpful (mean range 4.27-5.45, SD range 1.20-2.20), enjoyable (mean range 3.81-4.81, SD range 1.12-2.08), and understandable (mean range 4.45-6, SD range 1.30-1.86), suggesting initial acceptability and usability of the intervention. CONCLUSIONS: The results illustrated the utility of the patient personas when preparing, of the focus groups when designing, and of the end-user feedback when testing this new digital intervention for people with chronic pain. The findings indicated that the intervention is promising while also providing relevant end-user suggestions (eg, video content, text-to-speech function, and add-on modules) to guide further improvements.
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 | 0.041 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".