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Machine Learning Analysis of Engagement Behaviors in Older Adults With Dementia Playing Mobile Games: Exploratory Study
Melika Torabgar, Mathieu Figeys, Shaniff Esmail, Eleni Stroulia, Adriana Maria Rios Rincon. JMIR Serious Games, 2025-03-03
Topic: Risk, prevention and care in population studies
Significant differences in 64% of engagement-related behaviors were found between groups, notably in torso movements, voice modulation, facial expressions, and concentration. Including engagement-related behaviors, environmental disturbances, technical issues, and personal characteristics resulted in the best model for classifying cases of dementia correctly, achieving an F1-score of 0.91 (95% CI 0.851-0.963) and an area under the receiver operating curve of 0.99 (95% CI 0.984-1.000). Key features distinguishing between older adults with and without dementia during serious gameplay included torso, voice, facial, and concentration behaviors, as well as age. The best performing machine learning model identified included features of engagement-related behavios, environmental disturbances, technical challenges, and personal attributes. Engagement-related behaviors observed during serious gaming offer crucial markers for identifying dementia. Machine learning models that incorporate these unique behavioral markers present a promising, noninvasive approach for early dementia screening in a variety of settings.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Torabgar, M., Figeys, M., Esmail, S., Stroulia, E., & Rincon, A. M. R. (2025). Machine Learning Analysis of Engagement Behaviors in Older Adults With Dementia Playing Mobile Games: Exploratory Study. JMIR Serious Games. https://doi.org/10.2196/54797
Vancouver
Torabgar M, Figeys M, Esmail S, Stroulia E, Rincon AMR. Machine Learning Analysis of Engagement Behaviors in Older Adults With Dementia Playing Mobile Games: Exploratory Study. JMIR Serious Games. 2025. doi:10.2196/54797. PMID: 40029608.
BibTeX
@article{torabgar2025-40029608,
author = {Torabgar, Melika and Figeys, Mathieu and Esmail, Shaniff and Stroulia, Eleni and Rincon, Adriana Maria Rios},
title = {{Machine Learning Analysis of Engagement Behaviors in Older Adults With Dementia Playing Mobile Games: Exploratory Study}},
journal = {JMIR Serious Games},
year = {2025},
doi = {10.2196/54797},
pmid = {40029608},
url = {https://doi.org/10.2196/54797}
}
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Key findings
- Significant differences in 64% of engagement-related behaviors were found between groups, notably in torso movements, voice modulation, facial expressions, and concentration.
- Including engagement-related behaviors, environmental disturbances, technical issues, and personal characteristics resulted in the best model for classifying cases of dementia correctly, achieving an F1-score of 0.91 (95% CI 0.851-0.963) and an area under the receiver operating curve of 0.99 (95% CI 0.984-1.000).
- Key features distinguishing between older adults with and without dementia during serious gameplay included torso, voice, facial, and concentration behaviors, as well as age.
- The best performing machine learning model identified included features of engagement-related behavios, environmental disturbances, technical challenges, and personal attributes.
- Engagement-related behaviors observed during serious gaming offer crucial markers for identifying dementia.
Why it matters
In an exploratory study, machine learning models using engagement behaviors during mobile games—such as torso movements, voice, facial expressions, and concentration—classified dementia cases with high accuracy. This is a promising noninvasive screening approach, not a treatment, cure, or prevention for Alzheimer's disease.
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Study details
- Participants15
- Evidence strengthLow
How this study scored 37/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomized / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (JMIR Serious Games) |
| Journal's citation record | 0 / 4 | JMIR Serious Games: journal h-index 68 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 1 / 4 | 15 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 3 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 1.9× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 3 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Adriana Maria Rios Rincon: h-index 17 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Alberta: institution h-index 1,174 (OpenAlex) |
| Multicenter collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40029608) |
| Free to read | 2 / 2 | Free to read |
| Total | 37 / 100 | — |
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