Qualitative feasibility study of the mobile app Destroke for clinical stroke monitoring based on the NIH Stroke Scale
Bibliographic record
Abstract
Abstract Background Stroke is a leading cause of severe disability in the United States, but there is no effective method for patients to accurately detect the signs of stroke at home. We developed a mobile app, Destroke, that allows remote performance of a modified NIH stroke scale (NIHSS) by patients. Aims To assess the feasibility of a mobile app for stroke monitoring and education by patients with a history of stroke Materials and Methods We enrolled 25 patients with a history of stroke in a prospective open-label study to evaluate the feasibility of the Destroke app in patients with stroke. Nineteen patients completed all study assessments, with a median time from stroke onset to enrollment of 5.6 years (range 0.1-12 years). We designed a modified NIHSS that assessed 12 out of 16 tasks on the NIHSS. Patients completed this test eight times over a 28-day period. We conducted pre-study surveys that assessed demographic information, stroke and cardiovascular history, baseline NIHSS, and experience using mobile technologies, and mid- and post-study surveys that assessed patient satisfaction on app usage and confidence in stroke detection. Results Ten men and nine women participated in this study (median age of 64 (33-76)), representing ten US states and Washington D.C. Median baseline NIHSS was 0 (0-4). 15 patients reported using health apps. On a 5-point Likert scale, patients rated the app as 4.2 on being able to understand and use the app and 4.3 on using the app when instructed by their doctor. For eight patients with poor confidence in detecting the signs of a stroke before the study, six showed higher confidence after the study. Conclusions The use of an at-home stroke monitoring app is feasible by patients with a history of stroke and improves confidence in detecting the signs of stroke.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".