Exposure to “Exergames” Increases Older Adults’ Perception of the Usefulness of Technology for Improving Health and Physical Activity: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: High rates of sedentary behaviors in older adults can lead to poor health outcomes. However, new technologies, namely exercise-based videogames ("exergames"), may provide ways of stimulating uptake and ongoing participation in physical activities. Older adults' perceptions of the use of technology to improve health are not known. OBJECTIVE: The study aimed to determine use and perceptions of technology before and after using a 5-week exergame. METHODS: Focus groups determined habitual use of technology and the participant's perceptions of technology to assist with health and physical activity. Surveys were developed to quantitatively measure these perceptions and were administered before and after a 5-week intervention. The intervention was an exergame that focused on postural balance ("Your Shape Fitness Evolved 2012"). Games scores, rates of game participation, and enjoyment were also recorded. RESULTS: A total of 24 healthy participants aged between 55 and 82 years (mean 70, SD 6 years) indicated that after the intervention there was an increased awareness that technology (in the form of exergames) can assist with maintaining physical activity (P<.001). High levels of enjoyment (Physical Activity Enjoyment Scale [PACES-8] score mean 53.0, SE 0.7) and participation rates over the whole study (83%-100%) were recorded. CONCLUSIONS: Older adults' have low perception of the use of technology for improving health outcomes until after exposure to exergames. Technology, in the form of enjoyable exergames, may be useful for improving participation in physical activity that is relevant for older adults.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".