Understanding Senior Adults’ Needs, Preferences, and Experiences of Commercial Exergames for Health: Usability Study
Bibliographic record
Abstract
BACKGROUND: Many senior adults are at risk of mental and physical disorders due to a lack of sufficient exercise. Therefore, adherent exercise should be urgently promoted to improve senior adults' muscle strength, preventing falls and conditions caused by physical and cognitive decline. However, off-the-shelf exercise games, so-called exergames, are mainly targeted at the younger generation or children, while senior adults are neglected, when this age group strongly needs exercise. Exergames could serve as a health intervention for promoting exercise. OBJECTIVE: This study aimed to investigate senior adults' experience, perceptions, and acceptance of game technology to promote exercise in order to suggest game design guidelines. METHODS: In this usability study, participants engaged in playing Nintendo Switch and Xbox Kinect games, after which semistructured interviews were conducted. Before the gameplay, the participants provided their background information, exercise habits, and use of technology products. Next, all participants completed a workshop including 3 activities (brief instructions on how to play the games: 20 minutes; playing the selected exergames: 80 minutes; semistructured interviews: 20 minutes) for 2 hours a day for 3 days each. The participants played the latest Nintendo Switch games (eg, Just Dance, Boxing, Ring Fit Adventure) and Xbox Kinect games (eg, Kinect Adventures!, Mini Games). Just Dance, Zumba, and Boxing were played in activity 1; Ring Fit Adventure and Mini Games in activity 2; and Kinect Adventures! in activity 3. Reflexive thematic analysis was applied to identify the relative themes generated from the interviews. RESULTS: In total, 22 participants (mean age 70.4, SD 6.1 years) were enrolled in the workshop in May 2021. The results of the generated themes included incomprehension of game instructions, psychological perception of game technology, and game art preferences. The subthemes generated from game art preferences included favorite game genres, characters, and scenes. CONCLUSIONS: There is a significant need for customized game tutorials considering senior adults' cognitive and physical aging. Furthermore, the adventure game genre is preferable to other games. Humanlike game characters are preferable, especially those with a fit and healthy body shape. Nature scenes are more enjoyable than indoor stages or rooms. Furthermore, the game intensity design and playing time should be carefully planned to meet the World Health Organization's criteria for physical activity in older adults. Intelligent recommendation systems might be helpful to support older adults with various health conditions. The guidelines suggested in this study might be beneficial for game design, exercise training, and game technology adoption of exergames for older adults to improve health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".