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Record W4417009191 · doi:10.2196/86290

A Haptic-Driven Serious Game for Cognitive Stimulation and Visual Impairment Mitigation in Older Adults Based on the Design-Play-Experience Framework: Cross-Sectional Mixed Methods Pilot Study

2025· article· en· W4417009191 on OpenAlexvenueno aff
Xin Huang, Nazlena Mohamad Ali, Shafrida Sahrani

Bibliographic record

VenueJMIR Serious Games · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsSerious gameBlueprintPsychological interventionCognitionVisual impairmentCognitive impairmentCognitive trainingCognitive load

Abstract

fetched live from OpenAlex

BACKGROUND: In the context of global aging, cognitive decline among older adults has become a prevalent issue, significantly impacting their daily lives. Serious games have demonstrated potential in enhancing cognitive abilities in this population. However, most existing serious games designed for older adults rely heavily on visual interfaces, which are often potentially detrimental for those with pre-existing visual impairments. OBJECTIVE: This study had two primary objectives: (1) to design a theoretical prototype for a haptic-driven serious game for older adults based on the Design-Play-Experience (DPE) framework, aiming to enhance cognitive abilities, including attention, logical reasoning, and decision-making while simultaneously mitigating challenges associated with visual impairment, and (2) to conduct a pilot study evaluating the prototype's usability, accessibility, and user experience within the target population. METHODS: We used a cross-sectional, mixed methods pilot study with a single-group observational design, comprising a theoretical design and a pilot user study. First, the DPE framework was systematically applied to develop a game prototype by integrating haptic feedback technology (using built-in smartphone vibration motors) across its 3 core dimensions: design (haptic symbol system, accessible interface), play (dynamic difficulty adjustment), and experience (emotional engagement). Subsequently, a pilot study was conducted with 10 older adults recruited via convenience sampling (mean age 62.9, SD 3.35 years; 5 male, 5 female; all with self-reported mild visual impairments, such as presbyopia). Following interaction with the prototype, data were collected remotely using the System Usability Scale (SUS) and semistructured interviews administered via videoconferencing. Quantitative data from the SUS were analyzed using descriptive statistics, while qualitative data from the interviews were processed using thematic analysis. RESULTS: Pilot user studies showed that the game prototype had good usability, with an average SUS score of 89.5 (SD 2.72; 95% CI 87.6-91.4), which is considered "excellent." Thematic analysis of the interviews revealed three significant themes. The first theme was intuitive haptic feedback, which reflected that participants were able to quickly grasp and value the vibrational cues used to identify cards. The second theme was based on reduced eye strain, in which the combination of large fonts, high-contrast interfaces, and haptic feedback was praised for its effectiveness in relieving eye strain. The third theme was simplicity, where the simplified card game mechanics were considered both fun and challenging. CONCLUSIONS: This study developed and validated a haptic, serious game for older adults. Its innovation lies in the systematic application of the DPE framework to achieve "haptic substitution for vision," which differs from previous research that focused on general immersive experiences. The main contribution of this study is providing a reusable design blueprint for creating easy-to-use cognitive training tools. These findings have practical implications in the real world, providing a feasible approach for deploying low visual load interventions in communities and care facilities.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.026
GPT teacher head0.406
Teacher spread0.380 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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