A Bowling Exergame to Improve Functional Capacity in Older Adults: Co-Design, Development, and Testing to Compare the Progress of Playing Alone Versus Playing With Peers
Bibliographic record
Abstract
BACKGROUND: Older people often do not meet the recommended levels of exercise required to reduce functional decline. Social interaction is mentioned by this cohort as a reason for joining group-based exercises, which does not occur when exercising alone. This perspective shows that exergames can be used as motivational resources. However, most available exergames are generic, obtained from commercial sources, and usually not specifically designed or adapted for older people. OBJECTIVE: In this study, we aim to co-design and develop a new exergame alongside older participants to (1) tailor the game mechanics and optimize participants' adherence to and enjoyment of exercise; (2) test the participants' functional capacity, motivation, and adherence to the exergaming program; and (3) compare these scores between those who played alone and those who played with peers. METHODS: We conducted a co-design process to develop a new exergame adapted to older people. For user testing, 23 participants were divided into 2 groups to play individually (alone group) or to compete in pairs (with peers group). They played the game twice a week, resulting in 21 exergaming sessions. We assessed the participants' General Physical Fitness Index (GPFI) before and after the user testing. We also administered questionnaires about the gaming experience and exercise adherence with its motivators and barriers. RESULTS: We introduced a new bowling exergame for Xbox with a Kinect motion sensor that can be played in single or multiplayer mode. For the GPFI measurements, the sample was homogeneous in the pretest (with peers group: mean 40.5 [SD 9.6], alone group: mean 33.9 [SD 7.8]; P=.11). After the exergame testing sessions, both groups had significant gains (with peers group: mean 57.5 [SD 8.7], P=.005; alone group: mean 44.7 [SD 10.6]; P=.02). Comparing the posttest between groups, it was found that the group in which participants played with peers had better outcomes than the group in which participants played alone (P=.02). Regarding the gaming experience and exercise adherence, both groups recognized the benefits and expressed enthusiasm toward the exergame. CONCLUSIONS: The findings suggest that the developed exergame helps in improving the functional capacity and adherence to physical exercise among older people, with even better results for those who played with peers. In addition to leading to more appropriate products, a co-design approach may positively influence the motivation and adherence of participants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".