Home-Based Treatment for Chronic Pain Combining Neuromodulation, Computer-Assisted Training, and Telemonitoring in Patients With Breast Cancer: Protocol for a Rehabilitative Study
Notice bibliographique
Résumé
BACKGROUND: Chronic pain is a disabling symptom frequently reported in patients with breast cancer with a prevalence ranging from 25% to 60%, representing a major health issue. It has negative consequences on health status, causing psychological distress and affecting quality of life. Furthermore, the clinical management of chronic pain is often inadequate, and many patients do not benefit from the administration of pharmacological treatments. Alternative therapeutic options have been implemented to improve the psychophysical well-being of patients, including neuromodulation and complementary interventions. OBJECTIVE: We aimed to investigate the effectiveness of a home care strategy combining computerized rehabilitation, transcranial direct current stimulation (tDCS), and remote telemonitoring via a web-based platform in patients with breast cancer suffering for chronic pain. METHODS: A web-based structured survey aimed at monitoring chronic pain and its effect on psychological functions will be delivered to patients with breast cancer through social media and email. In total, 42 patients with breast cancer affected by chronic pain will be recruited during the medical screening visit. The patients will be randomly divided into 3 treatment groups that will carry out either tDCS only, exercise therapy only, or a combination of both over a 3-week period. All the treatments will be delivered at the patients' home through the use of a system including a tablet, wearable inertial sensors, and a tDCS programmable medical device. Using web-based questionnaires, the perception of pain (based on the pain self-efficacy questionnaire, visual analogue scale, pain catastrophizing scale, and brief pain inventory) and psychological variables (based on the hospital and anxiety depression scale and 12-item short form survey) will be assessed at the beginning of treatment, 1 week after the start of treatment, at the end of treatment, 1 month after the start of treatment, and 3 months after the start of treatment. The system's usability (based on the mobile app rating scale and system usability scale) and its involvement in the decision-making process (based on the 9-item shared decision-making questionnaire) will be also evaluated. Finally, at the end of the treatment, a digital focus group will be conducted with the 42 patients to explore their unexpressed needs and preferences concerning treatment. RESULTS: The study project is scheduled to start in June 2023, and it is expected to be completed by August 2025. CONCLUSIONS: We expect that the combination of tDCS and telemedicine programs will reduce pain perceived by patients with breast cancer and improve their mental well-being more effectively than single interventions. Furthermore, we assume that this home-based approach will also improve patients' participation in routine clinical care, reducing disparities in accessing health care processes. This integrated home care strategy could be useful for patients with breast cancer who cannot find relief from chronic pain with pharmacological treatments or for those who have limited access to care due to poor mobility or geographical barriers, thus increasing the patients' empowerment and reducing health care costs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/49508.
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 | 0,009 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,004 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,035 | 0,006 |
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 ».