Intention to Adopt mHealth Apps Among Informal Caregivers: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Caregiving responsibility can change caregivers' lives; modify their emotions; and make them feel frustrated, fearful, and nervous, thereby imposing physical and mental stress. Caregiving-related mobile apps provide a platform for obtaining valuable and trusted information, connecting more easily with other caregivers, monitoring medications, and managing appointments, and assessing health requirements and conditions of care receivers. Such apps also incorporate valuable resources that address care for the caregivers. Despite the potential benefits of caregiving-related apps, only a limited number of caregivers have adopted and used them. OBJECTIVE: The aim of this study is to explore the important factors that affect caregivers' intentions to integrate related mobile apps into their routine caregiving responsibilities. METHODS: Using the protection motivation theory, we conducted a cross-sectional study among 249 participants. Purposive sampling was used to target participants who met 4 inclusion criteria: US residents, owning and using a smartphone, informal caregivers (individuals who give care to a friend or family member without payment) who provided at least 8 hours of care per week in the past year, and those currently not using any mobile app for caregiving purposes. We created a survey using Qualtrics and posted it on Amazon's Mechanical Turk website. Participants received monetary compensation after successful completion of the survey. RESULTS: We found that capabilities and skills of caregivers to use mobile apps, the app's effectiveness in responding to the needs of caregivers, the degree of control of caregivers over their responsibilities, and the decisions they make for their care receivers can predict their willingness to adopt caregiving-related apps. In addition, the severity of health status and vulnerability of care receivers to unexpected health changes indirectly shape their caregivers' decisions to adopt and use mobile apps for caregiving purposes. CONCLUSIONS: This study explores the important factors that affect informal caregivers' intentions to adopt related mobile apps into their routine caregiving responsibilities. The results contribute to both mobile health adoption and the caregiving literature, and they offer significant implications for developers, health care practitioners, and policy makers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".