Experiences With a Multicomponent Digital Behavioral Pain Management Intervention for Adults With Sickle Cell Disease: Qualitative Analysis of the CaRISMA Trial
Notice bibliographique
Résumé
BACKGROUND: Chronic pain is prevalent among adults with sickle cell disease (SCD) and can be worsened by psychosocial factors such as depression and inadequate social support. Effective behavioral interventions (eg, cognitive behavioral therapy [CBT]) exist for chronic pain in various populations; however, few have been developed to address chronic pain in SCD. Several barriers have restricted the development and dissemination of CBT pain interventions in SCD, such as limited accessibility and time constraints. Digital interventions provide accessible and cost-effective pain management tools, offering self-management strategies, real-time monitoring, and personalized treatment options. Yet, there are limited data regarding patients' experiences with such interventions within the SCD population. The Cognitive Behavioral Therapy and Real-Time Pain Management Intervention for Sickle Cell Via Mobile Applications (CaRISMA) trial evaluated the effectiveness of a digital CBT intervention compared with a digital educational intervention for pain management in SCD. Evaluating participants' experiences can guide refinement of digital pain interventions in SCD. OBJECTIVE: This study aimed to gain a deeper understanding of the lived experiences of participants in the CaRISMA trial and to determine how to better adapt this intervention to the SCD population. The study examined individuals' overall experience with the trial and their perspectives of the trial components: a health coach, a chatbot-delivered digital CBT program, and an electronic pain diary. METHODS: Respondents were randomly selected to participate in semistructured interviews at (1) baseline, (2) the end of the intervention period at 3 months, and (3) the postintervention time point at 6 months or beyond. Interviews were audiotaped, transcribed verbatim, and analyzed using conventional content analysis. RESULTS: A total of 48 participants (women: 33/48, 69%) completed the interviews, with 24 and 19 completing midpoint and postintervention interviews, respectively. Participants generally had a positive experience in the trial. Many found value in learning about the connection between pain and mental health, considering it an important aspect of their well-being. The health coach played a key role in offering personalized support and guidance. Although the chatbot reinforced pain management strategies, its usefulness and engagement varied based on participants' prior knowledge of SCD. The pain diary helped increase self-awareness of pain patterns but was perceived as tedious and irrelevant by those without current pain episodes. CONCLUSIONS: This qualitative substudy of the CaRISMA trial showed that participants valued the personalized support of the health coach, education about the connection between stress and pain, and the self-reflection fostered by the pain diary. These findings highlight the potential of digital, patient-centered approaches to address the multifaceted needs of SCD care. For digital interventions, the inclusion of personalized support with ongoing communication appears to be a critical component that can influence treatment adherence and effectiveness. TRIAL REGISTRATION: ClinicalTrials.gov NCT04419168; https://clinicaltrials.gov/study/NCT04419168. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/29014.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,038 | 0,056 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».