A mobile application-based post-stroke care strategy for survivors and their caregivers for prevention and management of post-stroke complications – “Stroke Home Care:” Development and feasibility
Bibliographic record
Abstract
Objectives: In developing nations such as India, a disparity exists between the available resources for stroke rehabilitation and the substantial burden of stroke cases. Consequently, the provision of cost-effective and multidisciplinary post-stroke rehabilitation care to stroke survivors becomes of paramount importance. The utilization of mobile applications (apps) for stroke care has been on the rise, offering a personalized and pragmatic solution with the potential for wider reach in settings constrained by limited resources. To address the unmet needs in the prevention and management of post-stroke complications, we conceptualized a strategy known as a mobile application-based post-stroke care strategy for both survivors and their caregivers. Materials and Methods: The scope of the app's focus was determined based on the incidence of post-stroke complications within a prospective cohort of stroke patients, in conjunction with existing literature. An initial "web-based mobile app" prototype was crafted to align with the identified focus area. Before the development of the final app version, a feasibility study was conducted involving 30 participant dyads (comprising a patient and a caregiver). Content validity was evaluated by a panel of 20 stroke experts encompassing neurologists, nurses, physiotherapists, and psychologists. Results: The "Stroke Home Care" (SHC) mobile app was conceived as a web-based educational tool aimed at preventing and managing post-stroke complications. It seeks to train caregivers of immobile stroke patients in the administration of preventive and therapeutic care procedures, thereby potentially enhancing survivors' quality of life and alleviating caregivers' burden. The feasibility and validity studies indicated "high satisfaction" levels among most caregivers and experts (>75%), with the remainder expressing "satisfaction" and no "dissatisfaction" regarding app utilities. Stroke experts unanimously deemed the app "appropriate", with consensus on contents, video quality, video length, and voice clarity. Caregivers reported "satisfactory" user experiences, encountering no issues during app installation or operation. Suggestions from both caregivers and experts were integrated into the final app version. Conclusion: The "SHC" app represents a feasible and well-received innovation tailored for the use by caregivers of stroke survivors. Consequently, the initial feasibility of the developed app serves as a precursor to a randomized controlled clinical trial aimed at substantiating its effectiveness within the post-stroke survivor and caregiver population. Notably, within resource-constrained contexts, this app has the potential to be a pivotal tool for post-stroke care.
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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 | 0.000 | 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".