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Enregistrement W4404692731 · doi:10.2196/preprints.68755

Evaluating the Clinical Effectiveness of an Exergame-Based Training Program Using “WarioWare: Move It!” to Enhance Physical and Cognitive Function in Older Adults with Mild Cognitive Impairment and Dementia in Rural Long-Term Care Facilities: A Randomized Controlled Trial (Preprint)

2024· preprint· en· W4404692731 sur OpenAlexaboutno aff
Aoyu Li, Jingwen Li, Yan Geng, Yan Qiang, Juanjuan Zhao

Notice bibliographique

Revuenon disponible
Typepreprint
Langueen
DomaineMedicine
ThématiqueFrailty in Older Adults
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDementiaCognitionCognitive impairmentGerontologyPhysical medicine and rehabilitationPhysical therapyTraining (meteorology)MedicinePhysical educationPsychologyMedical educationPsychiatryGeography

Résumé

récupéré en direct d'OpenAlex

BACKGROUND Cognitive impairment is prevalent among older adults and frequently misdiagnosed or diagnosed late, increasingly drawing attention as a significant health issue in aging populations. Compared to community-dwelling individuals, cognitive impairments are more common among residents of long-term care facilities (LTCFs). These facilities face challenges implementing organized exercise programs due to a shortage of professional caregivers and limited resources. Additionally, older adults may lose interest in repetitive interventions over time. “WarioWare: Move It!” by Nintendo, a novel exergame that combines aerobic exercise, body coordination, balance training, and cognitive tasks, provides an immersive experience to enhance motivation and reduce staff intervention, presenting a potential solution. OBJECTIVE This study aims to assess the clinical effectiveness of an exergame-based training program delivered via “WarioWare: Move It!” in improving physical flexibility, joint range of motion, motor coordination, hand dexterity, and cognitive function in elderly residents of LTCFs. METHODS The randomized controlled trial was conducted across multiple rural LTCFs in Shanxi Province, involving participants aged 65 and older. Participants were randomly assigned to either the intervention group (receiving the “WarioWare: Move It!” intervention) or the control group (receiving standard care). The intervention involved motion-sensing actions and postures (such as waving, jumping, arm swinging, rotating, and mimicking object movements) using the Joy-Con controllers for 60 minutes twice a week over 12 weeks. Primary outcome measures were derived from a battery of clinical tests, including the Sit and Reach test (the distance between the hands and toes when reaching forward), Shoulder Flexibility test (the distance between hands clasped behind the back), Trunk Rotation Flexibility test (the angle of the waist rotation to each side), Shoulder Range of Motion test (the angles of shoulder flexion, extension, abduction, and adduction), Elbow Range of Motion test (the angle of elbow flexion), Figure-of-Eight Walk test (completion time), Standing Balance test (balance duration), Hand Dexterity test (the number of blocks moved by the dominant hand in one minute), and Cognitive Function tests (e.g., Cognitive Abilities Screening Instrument, the Chinese version of the Mini-Mental State Examination, and the Montreal Cognitive Assessment). Statistical analysis was performed using mixed ANOVA, with time as the within-subject factor and intervention group as the between-subject factor, to assess the training effects on the various outcome measures. RESULTS A total of 232 participants were recruited and randomly assigned to the intervention group, including 18 (56%) with mild dementia, 9 (50%) with moderate dementia, and 89 (49%) with mild cognitive impairment. The mixed ANOVA results revealed significant group × time interactions across several physical flexibility assessments. Specifically, the remaining distance between the hands and toes during the forward bend showed a significant interaction (F = 8.484, P < 0.001, η² = 0.098), as did the distance between the hands clasped behind the back (F = 3.666, P = 0.035, η² = 0.045) and the angle formed by the trunk during left and right waist rotation (F = 17.353, P < 0.001, η² = 0.182). Significant group × time interactions were also observed for forward flexion (F = 17.655, P < 0.001, η² = 0.185) and abduction (F = 6.281, P = 0.004, η² = 0.075) of the shoulder joint, as well as for elbow flexion (F = 17.353, P < 0.001, η² = 0.041). Similarly, a significant group × time interaction was found for the time taken to complete the Figure of Eight Walk test (F = 11.846, P < 0.001, η² = 0.132). Additionally, a significant group × time interaction in the number of blocks moved within one minute (F = 4.016, P = 0.022, η² = 0.049). Lastly, all scale scores exhibited significant group × time interactions (all P < 0.001), with effect sizes of 0.145 for the Cognitive Abilities Screening Instrument, 0.406 for the Mini-Mental State Examination, and 0.169 for the Montreal Cognitive Assessment. CONCLUSIONS The “WarioWare: Move It!” intervention significantly improved physical flexibility, joint range of motion, motor coordination, hand dexterity, and cognitive function in older adults with mild cognitive impairment or dementia residing in rural LTCFs. The intervention offers an innovative and feasible approach for promoting elderly health in resource-limited settings, demonstrating potential for widespread application in similar environments. CLINICALTRIAL The study was registered with the Chinese Clinical Trial Registry under Registration No.ChiCTR2400092790.

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 machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Essai randomisé · Signal consensuel: Essai randomisé
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,029

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,003
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0030,004
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0090,001

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.

Tête enseignante Opus0,037
Tête enseignante GPT0,406
Écart entre enseignants0,369 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeEssai randomisé
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

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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