Technology Acceptance of a Mobile Application to Support Family Caregivers in a Long-Term Care Facility
Bibliographic record
Abstract
Abstract Background Family caregivers are unpaid individuals who provide care to people with chronic conditions or disabilities. Family caregivers generally do not have formal care-related training. However, they are an essential source of care. Mobile technologies can benefit family caregivers by strengthening communication with care staff and supporting the monitoring of care recipients. Objective We conducted a mixed-method study to evaluate the acceptance and usability of a mobile technology called the Smart Care System. Methods Using convenience sampling, we recruited 27 family caregivers to evaluate the mobile Smart Care System (mSCS). In the quantitative phase, we administered initial and exit questionnaires based on the Unified Theory of Acceptance and Use of Technology. In the qualitative phase, we conducted focus groups to explore family caregivers' perspectives and opinions on the usability of the mSCS. With the quantitative data, we employed univariate, bivariate, and partial least squares analyses, and we used content analysis with the qualitative data. Results We observed a high level of comfort using digital technologies among participants. On average, participants were caregivers for an average of 6.08 years (standard deviation [SD] = 6.63), and their mean age was 56.65 years (SD = 11.62). We observed a high level of technology acceptance among family caregivers (7.69, SD = 2.11). Behavioral intention (β = 0.509, p-value = 0.004) and facilitating conditions (β = 0.310, p-value = 0.049) were statistically significant and related to usage behavior. In terms of qualitative results, participants reported that the mobile application supported care coordination and communication with staff and provided peace of mind to family caregivers. Conclusion The technology showed high technology acceptance and intention to use among family caregivers in a long-term care setting. Facilitating conditions influenced acceptance. Therefore, it would be important to identify and optimize these conditions to ensure technology uptake.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".