Game on: Staff Insights into Gamified Exercise for Long-Term Care Residents Living with Dementia—A Pilot Study
Bibliographic record
Abstract
Background/Objectives: The aging population presents significant challenges to healthcare systems, with conditions like dementia severely affecting the quality of life for older adults, especially those in long-term care. Gamification has the potential to motivate older adults to engage in 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 pilot study aims to explore interdisciplinary staff perspectives on using a digital game to motivate cycling exercise among residents living with dementia in long-term care (LTC). Methods: This study applied a qualitative description design. Using an interpretive description approach, we conducted focus groups with 29 staff members, including recreational therapists, rehabilitation therapists, nurses, care aides, and leadership in an LTC home. The consolidated framework for implementation research (CFIR) guided the data analysis to identify barriers and facilitators to adopting the digital game. Results: Engaging LTC residents living with dementia presents various challenges. Identified potential barriers to implementing the cycling game include cognitive and physical limitations, resistance to change, and intervention complexity. Frontline staff strategies include flexible invitations, social groups, making it fun, and building rapport. Success relies heavily on its cultural and individual relevance, along with strong support from leadership, peers, and family. Conclusions: This pilot study explored staff perspectives on the potential integration of a gamified cycling intervention for older adults with dementia in long-term care settings. Staff emphasized that successful implementation would depend on addressing key barriers and identifying enabling strategies. Based on these findings, practice implications were provided to support effective integration. Further research, including resident input and long-term evaluations, is needed to assess the feasibility, acceptance, and effectiveness of gamification in promoting health outcomes for this population. This study adhered to the COREQ Checklist.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".