Feasibility and acceptability of gamified cycling exercise for residents in a long-term care home: A qualitative study
Bibliographic record
Abstract
Gamification can motivate older adults to exercise by transforming physical activities into enjoyable experiences. Incorporating gaming elements in cycling exercises can foster a sense of interest and achievement, potentially improving health outcomes. This study investigated the acceptability and feasibility of motivating residents living in a long-term care (LTC) home with a gamified cycling exercise. Fourteen residents completed a 4-week gamified cycling exercise twice a week. Safety during exercise was addressed by assessing heart rate and observation. With an interpretive description approach, we conducted observations and interviews with residents and family members and focus groups with staff and leadership. The thematic analysis identifies three themes representing the feasibility and acceptability of gamified cycling exercise among LTC residents: ease of use and accessibility, physical and mental health benefits, fun engagement and community building. Future research should explore dementia-friendly design, culture-related game content, family orientation and engagement, group exercise and organization support. This study showed the promising acceptability and feasibility of gamified cycling exercise in an LTC home. Successful implementation relies on tailoring interventions to meet residents' specific needs and preferences while acquiring rapport with interdisciplinary staff in the care home.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".