Feasibility and Acceptability of Smartwatches for Use by Nursing Home Residents
Bibliographic record
Abstract
Smartwatch wearables are a promising health information technology to monitor older adults with complex chronic care needs. Pilot and feasibility studies have assessed smartwatch use with community-dwelling older adults, but less is known about their use in nursing homes. The purpose of this study was to test the feasibility and acceptability of smartwatch technology in a real-world nursing home setting to generate initial evidence about potential use. Using a qualitative descriptive approach, we conducted a pilot feasibility and acceptability study of smartwatch technology: Phase 1, pretrial semistructured interviews and focus groups with nursing home leaders, staff, and residents/families; Phase 2, a 7-day smartwatch trial deployment with residents; and Phase 3, posttrial semistructured interviews and focus groups. Themes related to feasibility findings included a part of the workflow and making the technology work . Themes related to acceptability findings included it's everywhere anyway , how will you protect me , knowing how you really are , more information = more control , and knowing how they are doing . These findings have important implications for the design of technology-supported interventions incorporating these devices within the unique context of residential nursing homes to best meet the needs of older adult residents, families, and staff caretakers.
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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 | 0.025 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".