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Record 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 on OpenAlexvenueaboutno aff
Xi Chen, Dian Jiang, Hongting Ning, Yifei Chen, Chi Zhang, Ruotong Peng, Yishu Zhu, Hui Feng

Bibliographic record

VenueJMIR Serious Games · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyQualitative researchCognitionCredibilityPopulationGerontologyMedicinePsychiatry

Abstract

fetched live from 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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.367
Teacher spread0.350 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2025
Admission routes2
Has abstractyes

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