Face Detection-Enabled FAST Mobile Application for Stroke Risk Self-Screening in Hypertensive Patients
Year: 2026
Type: article
Study design: Clinical Trial
Abstract: Delayed pre-hospital recognition of stroke symptoms sustains high mortality and disability, especially where awareness and rapid access are limited. Objective: To evaluate a smartphone FAST application integrating face detection for self-screening among individuals with hypertension. Methods: This study was conducted from July to August 2025. Two expert raters assessed usability with the MAUQ; inter-rater reliability was assessed using Cohen’s kappa. A pre–post study (n=80) tested change in combined knowledge/behavior; normality was confirmed, and paired t-tests were applied. Post-intervention semi-structured interviews underwent thematic analysis. Results: Expert agreement was statistically significant (kappa = 0.400; p = 0.018), indicating fair-to-moderate reliability. Knowledge/behavior improved significantly from pre to post (t (79) = -21,334; p <0.001; mean difference −4.538; 95% CI -4,961 to -4,114). Themes included helpfulness (practical recognition, relevant education), internet dependence (limitations in low-coverage areas), perceived complexity (photo-capture burden), portability (use across settings), and targeted education (content aligned with needs). Conclusion: A smartphone-based FAST app with face detection is feasible and associated with significant gains in knowledge/behavior for early stroke recognition. Priorities include improving rater consistency, validating module accuracy against clinical standards, reducing dependence on connectivity, and streamlining image capture to enhance pre-hospital impact.
Source: Jurnal Kesehatan Masyarakat
Institutions: Universitas dr. Soebandi, Mahsa University, Universidade de Díli
Language: English
Subfield: General Health Professions
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $150
OpenAlex ID: W7214444496