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Face Detection-Enabled FAST Mobile Application for Stroke Risk Self-Screening in Hypertensive Patients

Jurnal Kesehatan Masyarakat · Vol. 22 No. 1 (2026) · hlm. 37–45

DOI
10.15294/kemas.v22i1.38954
ISSN
2355-3596, 1858-1196
Terbit
2026-09-26
Penerbit
Universitas Negeri Semarang in collaboration with Ikatan Ahli Kesehatan Masyarakat Indonesia (IAKMI Tingkat Pusat) and Jejaring Nasional Pendidikan Kesehatan (JNPK)
Bahasa
eng

Abstrak

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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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/kemas/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Face Detection-Enabled FAST Mobile Application for Stroke Risk Self-Screening in Hypertensive Patients</dc:title>
	<dc:creator xml:lang="en">Fatarona, Anita</dc:creator>
	<dc:creator xml:lang="en">Cahyono, Hendra</dc:creator>
	<dc:creator xml:lang="en">Saad, Zahra</dc:creator>
	<dc:creator xml:lang="en">Kusumasa, Fitri</dc:creator>
	<dc:subject xml:lang="en">Stroke</dc:subject>
	<dc:subject xml:lang="en">Hypertension</dc:subject>
	<dc:subject xml:lang="en">Face Detection</dc:subject>
	<dc:subject xml:lang="en">FAST</dc:subject>
	<dc:subject xml:lang="en">Mobile Health Application</dc:subject>
	<dc:description xml:lang="en">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 &amp;lt;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.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang in collaboration with Ikatan Ahli Kesehatan Masyarakat Indonesia (IAKMI Tingkat Pusat) and Jejaring Nasional Pendidikan Kesehatan (JNPK)</dc:publisher>
	<dc:date>2026-09-26</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/kemas/article/view/38954</dc:identifier>
	<dc:identifier>10.15294/kemas.v22i1.38954</dc:identifier>
	<dc:source xml:lang="en">Jurnal Kesehatan Masyarakat; Vol. 22 No. 1 (2026); 37 - 45</dc:source>
	<dc:source>2355-3596</dc:source>
	<dc:source>1858-1196</dc:source>
	<dc:source>10.15294/kemas.v22i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/kemas/article/view/38954/11508</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Jurnal Kesehatan Masyarakat</dc:rights>
</oai_dc:dc>