Motion-Based Technology for People With Dementia Training at Home: Three-Phase Pilot Study Assessing Feasibility and Efficacy
Notice bibliographique
Résumé
BACKGROUND: Persons with dementia tend to be vulnerable to mobility challenges and hence face a greater risk of fall and subsequent fractures, morbidity, and mortality. Motion-based technologies (MBTs), also called sensor-based technologies or virtual reality, have the potential for assisting physical exercise and training as a part of a disease management and rehabilitation program, but little is known about its' use for people with dementia. OBJECTIVE: The purpose of this pilot study was to investigate the feasibility and efficacy of MBT physical training at home for people with dementia. METHODS: A 3-phase pilot study: (1) baseline start-up, (2) 15 weeks of group training at a local care center twice a week, and (3) 12 weeks of group training reduced to once a week, supplemented with individual MBT training twice a week at home. A total of 26 people with dementia from a municipality in Southern Denmark were eligible and agreed to participate in this study. Three withdrew from the study, leaving 23 participants for the final analysis. Feasibility was measured by the percentage of participants who trained with MBT at home, and their completion rate of total scheduled MBT sessions. Efficacy was evaluated by physical function, measured by Sit-to-Stand (STS), Timed-Up-and-Go (TUG), 6-minute Walk Test (6MW), and 10-meter Dual-task Walking Test (10MDW); cognitive function was measured by Mini-Mental State Examination (MMSE) and Neuropsychiatric Inventory-Questionnaire (NPI-Q); and European Quality of Life 5 dimensions questionnaire (EQOL5) was used for measuring quality of life. Descriptive statistics were applied accordingly. Wilcoxon signed-rank and rank-sum tests were applied to explore significant differences within and between the groups. RESULTS: As much as 12 of 23 participants (52%) used the supplemental MBT training at home. Among them, 6 (50%) completed 75% or more scheduled sessions, 3 completed 25% or less, and 3 completed between 25% and 75% of scheduled sessions. For physical and cognitive function tests, supplementing with MBT training at home showed a tendency of overall stabilization of scores among the group of participants who actively trained with MBT; especially, the 10MDW test even showed a significant improvement from 9.2 to 7.1 seconds (P=.03). We found no positive effect on EQOL5 tests. CONCLUSIONS: More than half of the study population with dementia used MBT training at home, and among them, half had an overall high adherence to the home training activity. Physical function tended to remain stable or even improved among high-adherence MBT individuals. We conclude that MBT training at home may be feasible for some individuals with dementia. Further research is warranted.
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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 | 0,007 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».