Feasibility and acceptability of an mHealth app integrating pain-cognitive behavioral therapy and pharmacologic support for patients with advanced cancer.
Notice bibliographique
Résumé
275 Background: Up to 60% of patients with advanced cancer and chronic pain experience inadequate relief from analgesics. We developed an mHealth app that augments pharmacologic therapy by integrating CBT-based psychological support for patients with cancer pain, and evaluated its feasibility and acceptability. Methods: Patients were recruited from the Dana-Farber Cancer Institute (Boston, MA) and Stephenson Cancer Center (Oklahoma City, OK). Patients were ≥21 years old, diagnosed with an advanced solid tumor or hematologic malignancy, had moderate-to-severe pain (4 or greater on 0-10 scale) that was related to cancer or their cancer treatment, and were using opioids. Exclusion criteria were cognitive impairment, history of opioid use disorder, enrollment in hospice, and acute pain from a recent surgery. App content integrated pain-CBT treatment with pain education and opioid support, and utilized just-in-time adaptive interventions (e.g. algorithm-based advice for pain, stress, and sleep) in response to once daily symptom surveys, including once daily psychoeducational content to support pain self-management. Patients used the app for 2-4 weeks, after which they completed the E-Acceptability scale and semi-structured debriefing interview. Results: Fifteen patients (female=9, mean age=50.3) enrolled on the study and 93% opted to test the app for 28 days (1 patient chose to test the app for 14 days). Patients who tested the app for 28 days completed a median of 18 of the 28 daily surveys (54% total surveys completed). Within patients who tested the app, 11 patients (73%) completed ≥50% of their daily surveys, with 4 (27%) completing ≥75%, and 4 (27%) completing ≤25% of surveys. Overall satisfaction with the app was high (4.43/5-pt Likert scale). Patients also rated the app as understandable (4.93/5) and easy to use (4.57/5-pt Likert scale). In study interviews, many patients reported that the app improved their ability to self-manage their cancer-related pain by facilitating the creation of more intentional pain management routines, and by helping to modify maladaptive thinking patterns allowing them to better cope with their pain. Patients emphasized how salient, clear, and succinct the information was, particularly in the context of coping with stress and pain. Conclusions: The STAMP+CBT app was an acceptable and feasible method to integrate and deliver psychological/behavioral treatment with opioid support to support cancer pain management. Compliance may have been influenced by the app’s notification structure limiting survey engagement to a 45-minute period each day. A future, larger pilot study will further evaluate feasibility and acceptability in patients with advanced cancer. In addition to additional content, the updated version of the app retains notifications but allows patients to return to missed content for 24 hours.
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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 | 0,000 | 0,000 |
| 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 ».