Exercise Management Using a Mobile App in Patients With Parkinsonism: Prospective, Open-Label, Single-Arm Pilot Study
Notice bibliographique
Résumé
BACKGROUND: Patients with parkinsonism have higher inactivity levels than the general population, and this results in increased comorbidities. Although exercise has benefits for motor function and quality of life (QOL) in patients with parkinsonism, these patients face many barriers to exercise participation, such as lack of motivation, fatigue, depression, and time constraints. Recently, the use of mobile apps has been highlighted as a remote exercise management strategy for patients with chronic diseases. OBJECTIVE: This study aimed to evaluate the effects of home-based exercise management with a customized mobile app on the exercise amount, physical activity, and QOL of patients with parkinsonism. METHODS: This was a prospective, open-label, single-arm pilot study. The therapist installed the app in the smartphones of the participants and educated them on how to use the app. The therapist developed an individualized multimodal exercise program that consisted of stretching, strengthening, aerobic, balance and coordination, and oral-motor and vocal exercises. Participants were encouraged to engage in an 8-week home-based exercise program delivered through a customized app. The alarm notifications of the app provided reminders to exercise regularly at home. The primary outcome was the exercise amount. The secondary outcomes were assessed using the International Physical Activity Questionnaire (IPAQ), Parkinson's Disease Questionnaire-39 (PDQ-39), and Geriatric Depression Scale (GDS). The usability of the customized app was assessed using a self-report questionnaire. RESULTS: A total of 21 participants with parkinsonism completed the intervention and assessment between September and December 2020 (mean age: 72 years; women: 17/21, 81%; men: 4/21, 19%). The participants reported a significant increase in the total amount of exercise (baseline: mean 343.33, SD 206.70 min/week; 8-week follow-up: mean 693.10, SD 373.45 min/week; P<.001) and in the amount of each exercise component, including stretching, strengthening, balance and coordination, and oral-motor and vocal exercise after 8 weeks. Analysis of the secondary outcomes revealed significant improvements in the IPAQ (P=.006), PDQ-39 (P=.02), and GDS (P=.04) scores. The usability of the program with the mobile app was verified based on the positive responses such as "intention to use" and "role expectation for rehabilitation." CONCLUSIONS: Exercise management with a customized mobile app may be beneficial for improving exercise adherence, physical activity levels, depression management, and QOL in patients with parkinsonism. This remotely supervised technology-based, reinforcing, and multimodal exercise management strategy is recommended for use in patients with parkinsonism. In addition, this program proved useful as an alternative exercise management strategy during the COVID-19 pandemic when patients with Parkinson disease were less physically active than before and showed aggravation of symptoms. However, additional clinical trials are needed to evaluate the efficacy of this exercise program in a large population and to confirm its disease-modifying effects.
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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 | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».