Engagement With Daily Symptom Reporting, Passive Smartphone Sensing, and Wearable Device Data Collection During Chemotherapy: Longitudinal Observational Study
Notice bibliographique
Résumé
BACKGROUND: Chemotherapy can cause symptoms that impair quality of life and functioning. Remote monitoring of daily symptoms and activity during outpatient treatment may enable earlier detection and management of emerging toxicities but requires patients, including older and acutely ill patients, to engage with technology to report symptoms through smartphones and to charge and wear mobile devices. OBJECTIVE: This study aimed to identify factors associated with participant engagement with collecting 3 data streams (ie, daily patient-reported symptom surveys, passive smartphone sensing, and a wearable Fitbit device [Google]) during chemotherapy. METHODS: We enrolled 162 patients receiving outpatient chemotherapy into a 90-day prospective study. Patients were asked to install apps on their smartphones to rate daily symptoms and to collect passive sensor data and to wear a Fitbit device for the duration of the study. Participants completed baseline demographic and quality of life questionnaires, and clinical information was extracted from the electronic medical record. We fit a series of logistic generalized estimating equations to evaluate the association between demographic and clinical factors and daily engagement with each data stream. RESULTS: Participants completed daily surveys on 61% (SD 27%) of days and collected sufficient smartphone data and wearable sensor data on 73% (SD 35%) and 70% (SD 33%) of enrolled days, respectively, on average. Relative to White participants, non-White patients demonstrated lower odds of engagement with both symptom surveys (odds ratio [OR] 0.49, 95% CI 0.29-0.81; P=.006) and wearable data collection (OR 0.35, 95% CI 0.17-0.73; P=.005). Patients with stage 4 cancer also exhibited lower odds of engagement with symptom reporting than those with earlier stage disease (OR 0.69, 95% CI 0.48-1.00; P=.048), and patients were less likely to complete symptom ratings on the weekend (OR 0.90, 95% CI 0.83-0.97; P=.008). Older patients (OR 1.03, 95% CI 1.01-1.06; P=.01) and those who reported better cognitive functioning at study entry (OR 1.18, 95% CI 1.03-1.34; P=.02) were more likely to engage with Fitbit data collection, and patients who reported higher levels of depressive symptoms were less likely to engage with smartphone data collection (OR 1.18, 95% CI 1.03-1.36; P=.02). CONCLUSIONS: Remote patient monitoring during chemotherapy has the potential to improve clinical management, but only if patients engage with these systems. Our results suggest significant associations between demographic and clinical factors and long-term engagement with smartphone and wearable device assessments during chemotherapy. Non-White participants, those with metastatic cancer, or those with existing cognitive impairment may benefit from additional resources to optimize engagement. Contrary to hypotheses, older adults were more likely than younger adults to engage consistently with wearable device assessments.
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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 | 0,000 | 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 ».