Identifying Optimal Wearable Devices for Monitoring Mobility in Hospitalized Older Adults: Feasibility, Acceptability, and Validity Study
Notice bibliographique
Résumé
Background: Hospitalized, frail older adults have an increased risk of developing hospital-acquired disability associated with hospital practices of restricted physical activity and immobilization. The use of activity tracking wearable devices may allow identification and prevention of mobility decline, reducing hospital-acquired disability. Objective: This study aimed to identify the optimal wearable device and wear location for monitoring mobility in older hospitalized patients. Specific objectives included (1) comparison of the feasibility and acceptability of ActiGraph wGT3X-BT (ActiGraph LLC), MOX1 (Maastricht Instruments), MetaMotionC (mBientLab), and Fitbit Versa (Google) for continuous mobility monitoring and (2) determination of the concurrent validity of the selected device for detecting body posture and step count. Methods: Participants were recruited for this observational study in the acute medical care unit of an academic hospital in Hamilton, Ontario, Canada. Eligible patients were aged 60 years and older, able to undertake the mobility protocol, and had an anticipated length of stay greater than 4 days. The study was divided into 2 experiments. Experiment 1 evaluated the feasibility of 4 wearable devices and validated the derived data for body posture and step count. Experiment 2 involved a mobility assessment session and a 24-hour monitoring and feasibility period with the selected device from experiment 1. Results: The ActiGraph wGT3X-BT emerged as the most feasible device, demonstrating superior usability, data acquisition, and management. The thigh-worn ActiGraph accurately detected sedentary behavior, while the ankle-worn device provided detailed information on step counts and body postures. Bland-Altman plots and intraclass correlation coefficients indicated that the ankle-worn ActiGraph showed excellent reliability for step counting, with minimal bias and narrow limits of agreement. Patients expressed a high willingness to wear a continuous mobility tracking device at the hospital and at home. Conclusions: Thigh- and ankle-worn ActiGraph are optimal for assessing and monitoring mobility in older hospitalized patients. Challenges such as discomfort and device removal observed during the 24-hour monitoring period highlight areas for future studies. Overall, our findings support the integration of wearable technology in hospital settings to enhance mobility monitoring and early intervention strategies. Further research is warranted to evaluate the long-term use of wearable data for predicting health outcomes post hospitalization and informing clinical decision-making to promote early mobility.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 ».