Enhancing Elderly Engagement: A Comprehensive Study on the Positive Impact of Robot‐Assisted Activities in Nursing Homes
Notice bibliographique
Résumé
BACKGROUND: The integration of robots in nursing homes marks a transformative shift in elderly care, serving as both functional aids and sources of engagement and entertainment. Amid challenges tied to aging populations and limited resources, robots contribute to residents' well-being by facilitating social interactions, providing cognitive stimulation, and offering recreational activities in nursing homes, representing a promising frontier in the evolution of care for aging populations. METHOD: In this study, a diverse group of residents aged 65 and older across multiple nursing homes engaged with a humanoid robot specially programmed for diverse activities, including joke-telling, singing, dancing, playing games, and aiding with daily tasks. Utilizing a pre-post design, baseline assessments were conducted before the robot's introduction, followed by regular post-implementation evaluations using the Brief Introspection Mood Scale (BMIS), Montreal Cognitive Assessment (MOCA), and electrodermal activity (EDA) recorded through wearable sensors. The study involved thorough training for nursing home staff on the robot's functionalities, and residents were gradually introduced to the robot through supervised interactive sessions, ensuring a smooth integration into the nursing home environment. Quantitative data from BMIS and MOCA underwent statistical analyses to discern patterns and changes over time, while EDA data were scrutinized for correlations with mood and cognitive assessments. Qualitative insights derived from resident and staff interviews, using thematic analysis, captured nuanced experiences. RESULTS: The study confirmed the robot's effectiveness in engaging and entertaining residents, showcasing overwhelmingly positive outcomes. Residents consistently enjoyed enhanced mood and emotional well-being, as indicated by substantial increases in positive affect according to BMIS scores. Quantitative analysis of MOCA scores revealed positive trends in cognitive functionality. Wearable sensors measuring EDA demonstrated heightened physiological arousal and positive emotional responses during residents' interactive sessions with the robot. Staff reported improved resident morale and observed the robot's effectiveness in creating a lively and interactive atmosphere within the nursing home. CONCLUSION: The results of this study affirm the multifunctional entertainment robot's positive impact on residents in nursing homes. From bolstering emotional well-being to enhancing cognitive functions, the robot emerges as a promising tool for enriching the lives of elderly individuals in care settings.
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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) | 0,001 | 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 ».