Enhancing user wellbeing in augmented reality mHealth apps: The role of TAM and continuance
Bibliographic record
Abstract
The international health community strongly endorses public and patient involvement in healthcare (Charles & DeMaio, 1993; Department Health, 1999; Health Canada, 2000, Neuwelt, 2012). Advances in technology and evolving health legislation have driven the rise of health management applications, or mhealth (Zapata et al., 2015). These mhealth apps facilitate greater levels of patient empowerment by providing easy access to relevant health. However, augmented reality driven mhealth apps are under-researched. This study investigates the antecedents of AR-driven mhealth app continuance, drawing upon the TAM model (Davis, 1989) to highlight the relationship between perceived ease of use, perceived usefulness, continued use and how these ultimately impact user’s subjective wellbeing (Pyke et al., 2016; Smith & Diekmann, 2017).
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| 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".