Older Adults’ Attitudes Toward Ambulatory Technology to Support Monitoring and Coaching of Healthy Behaviors: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Prevention of functional decline demands a holistic perspective of health management. Older adults are becoming avid users of technology; however, technology is not yet largely used in supporting self-management of health in daily life. Previous research suggests that the low adherence to these technologies is likely to be associated with the fact that opinions and wishes of the older population are not always taken into consideration when designing new technology. OBJECTIVE: The aim of this study was to investigate the attitudes of older adults living independently regarding technology to support healthy behaviors, addressing nutrition, physical and cognitive function, and well-being. METHODS: In-depth semistructured interviews were performed with 12 older adults addressing 4 themes: (1) current practices in health management, (2) attitudes toward using technology to support health management, (3) wishes from technology, and (4) change in attitudes after actual use of technology. The fourth theme was investigated with a follow-up interview after participants had used a step counter, a smart scale, and a mobile app for 1 month. Data collected were analyzed using inductive thematic analysis. RESULTS: Participants were active in self-managing their health and foresaw an added value on using technology to support them in adopting healthier behaviors in everyday life. Attitudes and wishes differed considerably per health domain, with cognitive function being the most sensitive topic. Fears from technology mentioned were attention theft, replacement of human touch, and disuse of existing abilities. Poststudy interviews suggest that attitudes toward technology improve after a short period of use. CONCLUSIONS: Technology to support aging in place must target health literacy, allow personalization in the design but also in the use of the technology, and tackle existing fears concerning technology. Further research should investigate the effect of these strategies on the adherence to technology to be used in daily life. We outline a set of recommendations of interest to those involved in developing and implementing technology to support aging in place, focusing on acceptance, barriers, and ethical concerns.
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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 | 0.008 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".