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Enregistrement W4411369927 · doi:10.2196/53631

The Experience of and Needs for Exergames in Older Adults With Mild Cognitive Impairment: Qualitative Interview Study

2025· article· en· W4411369927 sur OpenAlexvenueaboutno aff
Xi Chen, Dian Jiang, Hongting Ning, Yifei Chen, Chi Zhang, Ruotong Peng, Yishu Zhu, Hui Feng

Notice bibliographique

RevueJMIR Serious Games · 2025
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueTechnology Use by Older Adults
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPsychologyQualitative researchCognitionCredibilityPopulationGerontologyMedicinePsychiatry

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: As a novel intervention method that combines exercise and games, exergames have demonstrated a positive impact on enhancing the cognitive and physical functions of older adults with mild cognitive impairment (MCI). However, there remains a dearth of knowledge and evidence regarding the experiences and needs of the older adult population in China with MCI about exergames. OBJECTIVE: This qualitative study aimed to investigate the experience of and needs for exergames among older adults with MCI. METHODS: We adopted a phenomenological methodology for this study, and conducted it at a community and nursing home in Changsha, Hunan Province, from June to August 2023. We used the purpose sampling method to conduct semistructured interviews with 21 older people with MCI. Older people with MCI were allowed to experience exergames using our preselected exergame device, the Nintendo Switch, and they were interviewed to understand their experience and needs for exergames. The interviews were recorded and transcribed verbatim, and the data were uploaded to NVivo 12 software for encoding. The corresponding text was then reviewed for data analysis. Data analysis was guided by the methodology proposed by Giorgi and was carried out simultaneously with data collection. This study's trustworthiness was evaluated according to credibility, dependability, confirmability, and transferability criteria. RESULTS: Overall, 21 participants (mean age 70.2, SD 7.6 y; n=17, 81% women; mean Montreal Cognitive Assessment score 18.8, SD 3.6) were interviewed. Moreover, 21 interviews were conducted. By the 18th interview, the data were saturated, and to make sure no new topics came up, we conducted 3 more interviews. The experience of older people with MCI with exergames includes five parts: their attitudes toward exergames vary, they are both entertaining and interesting, they promote physical activity and exercise, they pass the time and relieve loneliness, and their conditions of use are not restricted. The needs of older people with MCI for exergames include the desire to design older people-friendly exergames, ensure scientific validity and safety in the process of sports, provide a good gaming experience, exercise physical and cognitive function, and provide support and training. CONCLUSIONS: This study provides an interpretative understanding of the experiences and needs associated with exergames in older people with MCI, which could inform exergame development appropriate for this population and guide the implementation of exergame interventions in this population. Most older people with MCI expressed a positive attitude toward exergames, but not all were interested in them. Older people with MCI viewed exergames as both entertaining and fun, promoting physical activity and exercise, passing the time, relieving loneliness, and the conditions of use were not restricted. Exergames for older people with MCI should be older people-friendly, scientific, safe, provide a good play experience, exercise physical and cognitive function, and provide training and support. In the future, exergames should be tailored to meet the unique needs of older people with MCI, which is critical to improving their well-being.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,094
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,017
Tête enseignante GPT0,367
Écart entre enseignants0,350 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2025
Routes d'admission2
Résumé présentoui

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