Perceptions of patients and nurses regarding the use of wearables in inpatient settings: a mixed methods study
Bibliographic record
Abstract
Wearable devices for hospitalized patients could help improve care. The purpose of this study was to highlight key barriers and facilitators involved in adopting wearable technology in acute care settings using patient and clinician feedback. Hospitalized patients, 18 years or older, were recruited at the General Medicine inpatient units in Toronto, Ontario to wear the Fitbit® Charge 2 or Charge 3. Fifty General Medicine adult inpatients were recruited. Patients and nurses provided feedback on structured questionnaires. Key themes from open-ended questions were analyzed. Primary outcomes of interest included the exploring patient and nurse perceptions of their experiences with wearable devices as well as their feasibility in clinical settings. Overall, both patients (n = 39) and nurses (n = 28) valued the information provided by Fitbits and shared concerns about device functionality and wearable design. Specifically, patients were interested in using wearables to enhance their self-monitoring, while nurses questioned data validity, as well as ease of incorporating wearables into their workflow. We found that patients wanted improved device design and functionality and valued the opportunity to improve their self-efficacy and to work in partnership with the medical team using wearable technology. Nurses wanted more device functionality and validation and easier ways to incorporate them into their workflow. To achieve the potential benefits of using wearable devices for enhanced monitoring, this study identifies challenges that must first be addressed in order for this technology to be widely adopted in clinical settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".