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Audiovisual and Roleplay Education Improves Hand Washing Knowledge and Skills in School-Age Children Maulidya Aisyah Kusuma Dewi; Indriatie Indriatie; Sari Luthfiyah; Joko Suwito
International Journal of Advanced Health Science and Technology Vol. 5 No. 3 (2025): June
Publisher : Forum Ilmiah Teknologi dan Ilmu Kesehatan (FORITIKES)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijahst.v5i3.460

Abstract

Hand washing with soap is essential in preventing infections among school-aged children. Engaging educational methods such as audiovisual media and roleplay can improve children's knowledge and skills in proper hand hygiene. This study aimed to compare the effectiveness of audiovisual and roleplay-based education on hand washing knowledge and skills. A quasi-experimental design with a two-group pre-test post-test was conducted on 48 students at MINU Lumpur Gresik, selected by simple random sampling. Results showed significant improvement in both groups after the intervention (p < 0.05). Audiovisual education improved knowledge, while roleplay enhanced practical skills. No significant difference was found between the two methods overall. Both methods are effective in promoting hand washing behavior among children. This implies that either method can be integrated into school health programs to support hygiene education.
The Illness perception in patients with diabetes mellitus based on characteristics: cross-sectional study Anita Joeliantina; Indriatie Indriatie; Endang Soelistyowati; Nur Hasanah
International Journal of Advanced Health Science and Technology Vol. 5 No. 3 (2025): June
Publisher : Forum Ilmiah Teknologi dan Ilmu Kesehatan (FORITIKES)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijahst.v5i3.479r

Abstract

Disease perception is a factor that affects self-care practices, psychological distress, and other health outcomes in people living with diabetes mellitus (DM). A positive perception of the disease can affect the patient's self-care and health status. This study aims to determine the correlation between characteristic factors and illness perception in patients with DM. This study was correlational research with a cross-sectional approach. The sample size was 175 adult patients with DM recruited from 4 health centres in Surabaya in July–August 2024 using a simple random sampling technique. Data analysis uses linear regression with a significance level of 0.05. This study shows that most (59.42%) patients with DM have a high illness perception. The factors that had a positive correlation with illness perceptions were age (p=0.021), disease history (p=0.027), and comorbidity (p=0.023). Age, disease history, and comorbidities are factors that are positively correlated with illness perception. This must be vigilance for patients at risk of DM, so they are conditioned to perceive the disease positively. As part of health workers, nurses must provide continuous education and counselling about patients' diseases
Implications of Tuberculosis Prevention Smart Book in Elementary School Age Children on Parenting Social Support Approach Indriatie indriatie; Aida Novitasari; Anita Joeliantina
International Journal of Advanced Health Science and Technology Vol. 6 No. 3 (2026): June
Publisher : Forum Ilmiah Teknologi dan Ilmu Kesehatan (FORITIKES)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijahst.v6i3.571

Abstract

Tuberculosis (TB) remains a major public health challenge in Indonesia, particularly affecting vulnerable populations such as elementary school-aged children whose immune systems are not fully developed. Despite ongoing national efforts to mitigate the spread of TB, the incidence among children remains notably high, necessitating innovative prevention strategies. This study aims to evaluate the impact of a specifically designed Tuberculosis Prevention Smart Book on enhancing parental social support approaches towards childhood TB prevention. Employing a quantitative quasi-experimental design with a two-group pretest-posttest format, the research sampled 200 parents of children attending elementary schools in Surabaya. Data were collected through structured questionnaires measuring informational, instrumental, emotional, and reward dimensions of social support. Findings indicate a statistically significant improvement in all dimensions of parental support following the intervention, with p-values below 0.05 for informational, instrumental, and emotional support categories. Notably, the use of the Smart Book facilitated heightened parental awareness, increased proactive caregiving behaviors such as early symptom detection and maintaining hygienic environments, and strengthened emotional engagement with children. However, changes in the reward support dimension were not statistically significant. The study concludes that integrating educational Smart Books into health promotion initiatives markedly enhances parental involvement and social support mechanisms critical for childhood TB prevention. This approach offers a promising avenue for community-based TB control programs, emphasizing the pivotal role of parents in safeguarding child health and curbing TB transmission within educational settings
BIG DATA-BASED EPIDEMIOLOGICAL SURVEILLANCE MODELS FOR EARLY DETECTION OF FUTURE PANDEMICS Nur Hasanah; Dinarwiyata Dinarwiyata; Indriatie Indriatie; Heru Sulistijono; Khalil Zaman
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 5 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i5.4476

Abstract

Emerging infectious diseases continue to pose significant global health challenges due to increasing population mobility, environmental changes, and complex transmission patterns. Conventional epidemiological surveillance systems often experience limitations in detecting early outbreak signals because of delayed reporting and fragmented data sources. This study aims to develop a big data-based epidemiological surveillance model for improving early detection capabilities and strengthening future pandemic preparedness. The research employed an integrated computational approach combining big data analytics, artificial intelligence, machine learning algorithms, and heterogeneous data sources, including epidemiological records, mobility patterns, healthcare information, environmental indicators, and digital health signals. The results demonstrated that the proposed model achieved enhanced predictive performance by identifying early outbreak indicators before substantial increases in confirmed cases occurred. The integration of multidimensional datasets improved detection accuracy, responsiveness, and understanding of complex disease transmission dynamics. The study concludes that big data-based epidemiological surveillance provides a transformative framework for shifting pandemic management from reactive responses toward proactive prevention strategies.