Physiotherapists' and Physiotherapy Students' Perspectives on the Use of Mobile or Wearable Technology in Their Practice
Bibliographic record
Abstract
Purpose: Although extensive research has been carried out on the determinants of mobile or wearable health care technology (mHealth), as well as on its acceptance by patients and other health care providers, very little research has been done on physiotherapists' perspectives on the use of mHealth in their current or future practice. The aims of this study were to (1) explore the attitudes of physiotherapists toward mHealth using a modified technology acceptance model questionnaire, (2) understand the applications and delivery paradigms that are most desirable, and (3) assess the content validity of the questionnaire. Method: The questionnaire was administered online. Participants (n=76) were recruited using snowball and convenience sampling. Data were analyzed using factor analysis and partial least-squares path modelling. Results: Results indicate that perceived usefulness and perceived ease of use were related to early adoptive behaviour among participants. We found no evidence that age, gender, experience, or practice setting influenced early adoptive behaviour. Participants demonstrated favourable attitudes toward mHealth tools in clinical practice. Conclusions: This article provides initial insights into factors that are likely to be significant determinants of adoption of mHealth among physiotherapists. Further work, including qualitative research, will help to identify personal and institutional factors that will improve the acceptance of mHealth.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".