Effective use of information technologies by seniors: the case of wearable device use
Bibliographic record
Abstract
Healthcare is an area that has benefitted from the developments in wearable device technology. Seniors, who usually suffer from multiple comorbidities, are among the target users of these devices, and research has shown potential health benefits for seniors when they use these devices effectively. However, the adoption rate of wearable devices is low, especially among seniors, preventing the full utilisation of their data in healthcare. In this study, we interviewed forty-four seniors across North America and collected data from their wearable devices to develop a theoretical affordance network-based model to explain seniors’ effective use of wearable devices. Our model indicates that despite the apparent simplicity of wearable devices, they have multiple affordances that help seniors achieve several goals, including activity monitoring, activity planning, and activity improvement. Furthermore, we identified factors that enable seniors to actualise the affordances of wearable devices and achieve their goals. The results of this study suggest a strong relationship between seniors’ mental and physical capabilities and their willingness to use and benefit from wearable devices. We join other researchers in their call for a contextual study on consumer technology use.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".