A Digital Home-Based Health Care Center for Remote Monitoring of Side Effects During Breast Cancer Therapy: Prospective, Single-Arm, Monocentric Feasibility Study
Notice bibliographique
Résumé
Background: The introduction of oral anticancer therapies has, at least partially, shifted treatment from clinician-supervised hospital care to patient-managed home regimens. However, patients with breast cancer receiving oral cyclin-dependent kinase 4/6 inhibitor therapy still require regular hospital visits to monitor side effects. Telemonitoring has the potential to reduce hospital visits while maintaining quality care. Objective: This study aims to develop a digital home-based health care center (DHHC) for acquiring electrocardiograms (ECGs), white blood cell (WBC) counts, side effect photo documentation, and patient-reported quality of life (QoL) data. Methods: The DHHC was set up using an Apple Watch Series 6 (ECG measurements), a HemoCue WBC DIFF Analyzer (WBC counts), an iPhone SE (QoL assessments and photo documentation), a TP-Link M7350-4G Wi-Fi router, and a Raspberry Pi 4 Model B. A custom-built app stored and synchronized remotely collected data with the clinic. The feasibility and acceptance of the DHHC among patients with breast cancer undergoing cyclin-dependent kinase 4/6 inhibitor therapy were evaluated in a prospective, single-arm, monocentric study. Patients (n=76) monitored side effects-ECGs, WBC counts, photo documentation, and QoL-at 3 predefined time points: study inclusion (on-site), day 14 (remote), and day 28 (remote). After the study completion, patients completed a comprehensive questionnaire on user perception and feasibility. Adherence to scheduled visits, the success rate of the data transfer, user perception and feasibility, and the clinical relevance of remote measurements were evaluated. Results: Mean adherence to the planned remote visits was 63% on day 14 and 37% on day 28. ECG measurements were performed most frequently (day 14: 57/76, 75%; day 28: 31/76, 41%). The primary patient-reported reason for nonadherence was device malfunction. The expected versus the received data transfer per patient was as follows: ECGs: 3 versus 3.04 (SD 1.9); WBC counts: 3 versus 2.14 (SD 1.14); QoL questionnaires: 3 versus 2.5 (SD 1.14); and photo documentation: 6 versus 4.4 (SD 3.36). Among patients, 81% (55/68) found ECG measurements easy, 82% (55/67) found photo documentation easy, and 48% (33/69) found WBC measurements easy. Additionally, 61% (40/66) of patients felt comfortable with self-monitoring and 79% (54/68) were willing to integrate remote monitoring into their future cancer care. Therapy-induced decreased neutrophil count was successfully detected (P<.001; mean baseline: 4.3, SD 2.2, ×109/L; on-treatment: 1.8, SD 0.8, ×109/L). All-grade neutropenia and corrected QT interval prolongations were detected in 80% (55/68) and 2% (1/42) of patients, respectively. Conclusions: Adherence to scheduled remote visits was moderate, with nonadherence primarily attributed to device-related complications, which may have also affected the success rate of data transfer. Overall, patients considered remote monitoring useful and feasible. The prevalence of reported adverse events was comparable to existing literature, suggesting clinical potential. This initial feasibility study highlights the potential of the DHHC.
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 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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 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 ».