Older Adults’ Perceptions of the Usefulness of Technologies for Engaging in Physical Activity: Using Focus Groups to Explore Physical Literacy
Bibliographic record
Abstract
Insufficient physical activity (PA) levels observed among older adults remain extremely high and pose a danger to developing and maintaining their physical literacy (PL). Each person’s level of PL partly depends on their physical and cognitive skills, confidence level, and degree of motivation to practice PA daily. New technologies, such as exergames and wearable fitness trackers, may enable older adults to increase their PL, stimulating uptake and ongoing PA participation. Objective: This focus group study aims to describe older adults’ perceptions of the use of technologies to engage in physical exercise programs. Methods: Fifteen participants were randomly selected from a sample of 40 older adults who completed a randomized controlled trial that investigated the benefits of using technology in the context of group-based exercise programs. Separate post-intervention focus groups were performed with an exergaming group, a conventional physical training group, and a no training group (control). Data were mapped onto constructs from the four domains of PL: affective, physical, cognitive, and behavioral. Results: Generally, participants expressed positive perceptions about the benefits of using technology to engage in PA. These positive feelings outweighed the costs and the lack of familiarization with technology. Common themes for the three groups emerged from the discussions and included familiarization with technology, using fitness tracker to monitor PA, previous exposure to technology, and interaction with peers, staff members, and relatives. In particular, participants from the exergaming group explored the ideas of training their cognitive skills while using the exergame accessories, exercising in an alternative way, competitive versus cooperative play, changes in sense of humor, skill transferability from game to real environment, progressions of the exercise intensities, and the potential use of exergames for rehabilitation. Conclusions: Participants in this study reported positive perceptions about implementing technology into exercise. Emphasizing the benefits of using technology in group-based exercise programs may increase older adults’ PL levels and their future technology adoption. The potential implementation of technology into conventional exercise programs should focus on older adults’ lifelong values, biopsychosocial conditions, and the possibility of reducing age-related risk of injuries and chronic diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".