{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Nursing Department, Faculty of Health Science, Universitas dr. Soebandi, Indonesia",
    "Nursing Department, Mahsa University, Malaysia",
    "Faculty of Health Science, Universidade Dili, Timor-Leste"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Anita Fatarona"
    },
    {
      "affs": [
        0
      ],
      "name": "Hendra Cahyono"
    },
    {
      "affs": [
        1
      ],
      "name": "Zahra Saad"
    },
    {
      "affs": [
        2
      ],
      "name": "Fitri Kusumasa"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0pg4jsdzg4x82.bib",
  "cited_by": 0,
  "doi": "10.15294/kemas.v22i1.38954",
  "fulltext_url": "https://journal.unnes.ac.id/journals/kemas/article/view/38954",
  "id": "0pg4jsdzg4x82",
  "issns": [
    "1858-1196",
    "2355-3596"
  ],
  "landing_url": "https://doi.org/10.15294/kemas.v22i1.38954",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "R"
    ],
    [
      "lcc",
      "R5-920"
    ],
    [
      "lcc",
      "RA1-1270"
    ],
    [
      "openalex",
      "3600|Mobile Health and mHealth Applications"
    ]
  ],
  "openalex": "W7214444496",
  "pdf_url": "https://journal.unnes.ac.id/journals/kemas/article/download/38954/11508",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": "Universitas Negeri Semarang",
  "ris": "https://research.draconitic.com/paper/0pg4jsdzg4x82.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:24Z",
      "native_id": "742d0f0f2dc84bc58e997b2f2465295e",
      "source": "doaj",
      "url": "https://doi.org/10.15294/kemas.v22i1.38954"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:RA1-1270",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Face Detection-Enabled FAST Mobile Application for Stroke Risk Self-Screening in Hypertensive Patients",
  "title_text": "Face Detection-Enabled FAST Mobile Application for Stroke Risk Self-Screening in Hypertensive Patients",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:24Z",
  "url": "https://research.draconitic.com/paper/0pg4jsdzg4x82",
  "venue": "KEMAS: Jurnal Kesehatan Masyarakat",
  "versions": []
}