mHealth Apps for Family Caregivers of Stroke Patients: A Systematic Review
Bibliographic record
Abstract
Background: Family caregivers of stroke patients play a critical role in post-stroke recovery, yet they often face significant challenges, including physical and emotional stress, lack of knowledge, and limited access to resources. Mobile health (mHealth) apps offer a promising solution to support caregivers by providing education, task management, and mental health resources. However, gaps remain in understanding the effectiveness, functionalities, and limitations of these apps. This study aimed to 1) identify and categorize existing mHealth apps for family caregivers of stroke patients, 2) evaluate the effectiveness of these apps in supporting caregivers, and 3) analyze the gaps in current mHealth offerings to inform future app development. Methods: A systematic literature review was conducted, analyzing 30 studies published between 2014 and 2024 from databases such as IEEE Xplore, Scopus, Web of Science, and PubMed. The studies were evaluated based on app functionality and effectiveness in caregiver support, and limitations were identified. Results: The findings revealed a diverse range of mHealth apps offering functionalities such as caregiver education, rehabilitation guidance, task management, communication tools, and health monitoring. However, notable gaps were identified, including limited multi-functionality, insufficient support for caregiver well-being, a lack of customization for diverse needs, and minimal validation through rigorous trials. Conclusion: While mHealth apps provide valuable tools for family caregivers of stroke patients, addressing the identified gaps is essential to maximize their impact. Future Development should focus on creating comprehensive, user-centered, and evidence-based apps that integrate education, mental health support, and task management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".