Face Detection-Enabled FAST Mobile Application for Stroke Risk Self-Screening in Hypertensive Patients
- 1Nursing Department, Faculty of Health Science, Universitas dr. Soebandi, Indonesia
- 2Nursing Department, Mahsa University, Malaysia
- 3Faculty of Health Science, Universidade Dili, Timor-Leste
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.
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BibTeX
@article{fatarona2026face,
title = {{Face Detection-Enabled FAST Mobile Application for Stroke Risk Self-Screening in Hypertensive Patients}},
author = {Anita Fatarona and Hendra Cahyono and Zahra Saad and Fitri Kusumasa},
journal = {KEMAS: Jurnal Kesehatan Masyarakat},
year = {2026},
month = sep,
doi = {10.15294/kemas.v22i1.38954},
url = {https://doi.org/10.15294/kemas.v22i1.38954},
note = {CC BY},
}
RIS
TY - JOUR
TI - Face Detection-Enabled FAST Mobile Application for Stroke Risk Self-Screening in Hypertensive Patients
AU - Anita Fatarona
AU - Hendra Cahyono
AU - Zahra Saad
AU - Fitri Kusumasa
PY - 2026
DA - 2026/09/01
JO - KEMAS: Jurnal Kesehatan Masyarakat
DO - 10.15294/kemas.v22i1.38954
UR - https://doi.org/10.15294/kemas.v22i1.38954
AB - 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.
SN - 1858-1196
SN - 2355-3596
ER -