mHealth Interventions to Support Prescription Opioid Tapering in Patients With Chronic Pain: Qualitative Study of Patients’ Perspectives
Notice bibliographique
Résumé
BACKGROUND: Patients with chronic pain who are tapering prescription opioids report a need for greater support for coping with symptoms of pain and withdrawal. Mobile health (mHealth) technologies (SMS text messaging- or app-based) have the potential to provide patients with educational, emotional, and motivational support for opioid tapering beyond what is offered by their health care provider. However, it is not known whether patients with chronic pain who are tapering opioids would be willing or able to engage with technology-based support. OBJECTIVE: This study aims to examine patients' use of mobile technologies in health care, interest in using mHealth support, preferences for the form and content of mHealth support, and potential barriers to and facilitators of engagement with mHealth support for opioid tapering. METHODS: A total of 21 patients (11 women and 10 men; age range 29-83 years) with chronic noncancer pain on long-term opioid therapy who had recently initiated a voluntary opioid taper were recruited from primary and tertiary care clinics in metropolitan and regional Australia for a larger study of patients' experiences of opioid tapering. Participants had been taking prescription opioids for a mean duration of 13 (SD 9.6; range 0.25-30) years at the time of the study. Survey items characterized participants' typical mobile phone use and level of interest in mobile technology-based support for opioid tapering. Semistructured interviews further explored patients' use of mobile technologies and their interest in, preferences for, and perspectives on potential barriers to and facilitators of engagement with mHealth support for opioid tapering. Two researchers collaborated to conduct a thematic analysis of the interview data. RESULTS: All participants reported owning and using a mobile phone, and most (17/21, 81%) participants reported using mobile apps. The majority of participants expressed interest in SMS text messaging-based (17/21, 81%) and app-based (15/21, 71%) support for opioid tapering. Participants expected that messages delivering both informational and socioemotional support would be helpful. Participants expected that access to technology, mobile reception, internet connectivity, vision impairment, and low self-efficacy for using apps may be barriers to user engagement. Patients expected that continuity of care from their health care provider, flexible message dosing, responsivity, and familiarity with pain self-management strategies would increase user engagement. CONCLUSIONS: The results of this study indicate that patients with chronic noncancer pain may be willing to engage with SMS text messaging-based and app-based mHealth interventions to support opioid tapering. However, the feasibility and acceptability of these interventions may depend on how patients' preferences for functionality, content, and design are addressed.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».