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Record W4404534955 · doi:10.2196/57352

The Effect of Young People–Assisted, Individualized, Motion-Based Video Games on Physical, Cognitive, and Social Frailty Among Community-Dwelling Older Adults With Frailty: Randomized Controlled Trial

2024· article· en· W4404534955 on OpenAlexvenueno aff
Arkers Kwan Ching Wong, Melissa Qian Zhang, Jonathan Bayuo, Karen Kit Sum Chow, Bonnie Po Wong, B. Liu, David Chi Ho Lau, Tobias Kowatsch

Bibliographic record

VenueJMIR Serious Games · 2024
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsGerontologyRandomized controlled trialCognitionPhysical medicine and rehabilitationAging in placePhysical therapyMotion (physics)MedicinePsychologyComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Background: The aging population highlights the need to maintain both physical and psychological well-being. Frailty, a multidimensional syndrome, increases vulnerability to adverse outcomes. Although physical exercise is effective, adherence among older adults with frailty is often low due to barriers. Motion-based video games (MBVGs) may enhance motivation and engagement. Objective: This study aims to evaluate the effect of individualized exercise programs that combine MBVGs, intergenerational support, and therapeutic frameworks on physical, cognitive, and social frailty outcomes in community-dwelling older adults. Methods: This randomized controlled trial was conducted from March 2022 to October 2023 across 6 community centers in Hong Kong. Participants aged 60 years and above with mild neurocognitive disorder were recruited, screened, and randomly assigned to either an intervention (n=101) or control group (n=101). The intervention included an 18-week program with 12 supervised exercise sessions utilizing motion-based technology, led by occupational therapists and assisted by youth volunteers. Data were collected at baseline (T1) and postintervention (T2), focusing on physical, cognitive, and social frailty outcomes, as well as client-related metrics. Statistical analyses were performed using SPSS, with significance set at P<.05. Results: A total of 202 participants were recruited, with a mean age of 78.8 years (SD 7.8). Both groups showed improvements in balance from T1 to T2, with a significant time effect (β=-0.63, P=.03). The intervention group demonstrated enhancements in hand strength and BMI, but no statistically significant between-group differences were observed. The intervention group also exhibited significant improvements in cognitive function (β=2.43, P<.001), while the control group's scores declined. Short-term memory improved for both groups, with no significant differences noted. Both groups experienced a reduction in depression levels, with a significant within-group effect at T2 (β=-1.16, P=.001). Improvements in social connectedness and eHealth literacy were observed in both groups, with the latter showing a significant within-group effect at T2 (β=3.56, P=.002). No significant effects were found for social isolation, physical activities, or quality of life. Conclusions: The growing aging population necessitates innovative strategies to support aging in place. Results indicated statistically significant improvements only in BMI and cognition, while other outcomes such as loneliness, balance, and eHealth literacy showed positive trends but lacked significance. Despite the limitations observed, particularly regarding the role of volunteer support and the diverse needs of community-dwelling older adults, the findings contribute to the foundation for future research aimed at enhancing biopsychosocial outcomes. Future studies should explore tailored interventions that consider individual preferences and abilities, as well as evaluate specific components of motion-based video games to optimize their effectiveness.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.291
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2024
Admission routes1
Has abstractyes

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