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Record W4413624662 · doi:10.2196/73221

The Impact of Active Augmented Reality Games on Physical Activity and Cognition Among Older Adults: Feasibility Study

2025· article· en· W4413624662 on OpenAlexvenueno aff
Amy Shirong Lu, Bhagyashree Parkar, Susan E. Hall, Dominika M. Pindus, Arthur F. Kramer

Bibliographic record

VenueJMIR Serious Games · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsPreprintCognitionPhysical activityPsychologyAugmented realityComputer scienceHuman–computer interactionPhysical medicine and rehabilitationMedicineWorld Wide WebNeuroscience

Abstract

fetched live from OpenAlex

Background: Physical activity (PA) enhances physical health as well as cognitive and brain health, yet motivating older adults to initiate and sustain PA remains challenging, a difficulty exacerbated by the COVID-19 pandemic. Active augmented reality (AAR) games, integrating digital gameplay with real-world physical movement, provide an enjoyable and accessible means for PA promotion among older adults in independent living environments, mitigating barriers such as poor weather and unfavorable neighborhood environments. However, limited research has explored the feasibility and impact of AAR interventions in this population. Objective: This feasibility study examines the acceptability, safety, and preliminary effects of AAR games to enhance PA levels and cognitive functions among older adults, as well as their user experiences. We also examined the practicality of home-based AAR gameplay using minimal equipment and constrained physical space. Methods: Sixteen independent-living older adults aged 65-85 years (mean 74.6, SD 3.73) participated in a single-session AAR intervention using the Active Arcade game set by playing four 10-minute AAR games. PA levels were assessed using ActiGraph wGT3x-bt accelerometers and Polar H10 heart rate monitors. Cognitive function was evaluated pre- and post-gameplay using NIH Toolbox's visual reasoning test and Flanker inhibitory control and attention tests. Surveys of PA intention and motivation as well as the gaming experience questionnaire, along with semistructured interviews, were conducted afterwards, providing both quantitative and qualitative insights into the feasibility and appeal of AAR gameplay from the target population. Results: All participants completed the study protocol without adverse events, demonstrating high feasibility and acceptability. Participants engaged in moderate-to-vigorous PA during 20%-30% of the gameplay, as measured by accelerometers and heart rate monitors. Of the 16 participants, 7 were taking beta blockers. The mean values of average %HRMax suggest that those not on beta blockers generally met the moderate-intensity threshold, whereas those on beta blockers tended to fall slightly below it. Cognitive assessments revealed significant improvements in visual reasoning postintervention, with the effect sustained after adjustment for age and education (P=.03), suggesting potential cognitive benefits from a single bout of AAR gameplay. Survey responses indicated high levels of PA intention (mean 4.15/5, SD 0.59), motivation (mean 5.67/7, SD 1.24), high positive affect (mean 4.35/5, SD 0.80), and low negative affect (mean 1.30/5, SD 0.46) associated with AAR gameplay. Around 75% of gameplay occurred within a 4×4 ft area (mean 29.77/40 min, SD 2.46), indicating suitability for home environments. Thematically analyzed interview feedback emphasized participants' enjoyment, ease of use, desire for progressive difficulty, and the need to cater to diverse physical abilities and individual preferences. Conclusions: AAR games are a feasible, accessible, and enjoyable alternative for PA and cognitive engagement among older adults. Future research should investigate the long-term effects, sustainability, and broader applicability of AAR interventions to fully realize their potential in aging populations.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.014
GPT teacher head0.365
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 designObservational
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

Citations2
Published2025
Admission routes1
Has abstractyes

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