Interest in Digital Peer-Delivered Interventions and Preferences to Improve Pain Self-efficacy and Reduce Loneliness Among Patients With Chronic Pain: Mixed Methods Co-design Study
Bibliographic record
Abstract
BACKGROUND: Two important factors that prolong and exacerbate chronic noncancer pain (CNCP) and disability are low pain self-efficacy and loneliness. Yet, few interventions have shown long-term sustained improvements in pain self-efficacy, and there are no evidence-based treatments that target social connectedness in people living with CNCP. More effective and accessible interventions designed to target self-efficacy and social connectedness could ease the burden of CNCP. OBJECTIVE: To co-design accessible interventions to increase pain self-efficacy, social connection, pain-related outcomes, and quality of life, this study explored patients' interest and preferences for digital peer-delivered interventions for CNCP as well as implementation barriers and enablers. METHODS: This cross-sectional mixed methods study was part of a larger longitudinal cohort study. Adult Australian residents (N=186) with CNCP diagnosed by a medical professional or pain specialist were included. Participants were initially recruited through advertising on professional pain social media accounts and websites. Questions examined whether patients were interested in digital peer-delivered interventions and their preferences for specific features (eg, Newsfeed). Pain self-efficacy and loneliness were assessed using validated questionnaires, and the association between these factors and interest in digital peer-delivered support was explored. Open-ended questions explored implementation barriers, enablers, and suggestions for consideration in intervention design. RESULTS: There was interest in accessing digital peer-delivered interventions, with almost half of the sample indicating that they would access it if it was available. Those who indicated an interest in digital peer interventions reported both lower pain self-efficacy and greater loneliness than those who were not interested. Intervention content that incorporated education, links to health services and resources, and delivery of support by peer coaches were the most frequently preferred intervention features. Three potential benefits were identified: shared experience, social connection, and shared pain management solutions. Five potential barriers were identified: negative focus on pain, judgment, lack of engagement, negative impact on mental health, privacy and security concerns, and unmet personal preferences. Finally, there were 8 suggestions from participants: moderation of the group, interest subgroups, professional-led activities, psychological strategies, links to professional pain resources, newsletter, motivational content, live streaming, and online meetups. CONCLUSIONS: Digital peer-delivered interventions were of particular interest to those with CNCP who had lower levels of pain self-efficacy and higher levels of loneliness. Future co-design work could tailor digital peer-delivered interventions to these unmet needs. Intervention preferences and implementation barriers and enablers identified in this study could guide further co-design and the development of such interventions.
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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 | 0.035 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".