U.S. consumers’ intentions to use wearable technology devices in the context of healthcare
Bibliographic record
Abstract
As the adoption of wearable technology devices increases, so does the abandonment of these devices. Today, an increasing number of health-conscious consumers use wearable technology devices (WTDs) to self-track their health. Large tech companies are trying to close the gap between consumer wearables and their use in healthcare. This study provides an insight into consumer intentions to use wearable technology in healthcare. A quantitative study was conducted to examine factors that affect behavioural intent to use WTDs. The researcher surveyed 277 participants. The results from statistical analysis of the data gathered through survey methodology showed that the research model’s constructs of performance expectancy, social influence, facilitating conditions, hedonic motivation, habit, and personalization were positively associated with the behavioral intention to use WTDs, while price value, privacy concerns, and health consciousness were not. The research findings contribute to the body of literature about consumer health information technology acceptance. Practitioners will also be able to use the results to increase the use of WTDs among consumers in the context of healthcare. Limitations of the study and recommendations for future research are discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".