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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,035 | 0,047 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,005 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».