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All Journal International Journal of Public Health Excellence (IJPHE)
Yan Deivita
Poltekkes Kemenkes Manado, Indonesia
Health Professions Medicine & Pharmacology Nursing Public Health

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Beyond BMI: The Synergistic Effect of Ultra-Processed Food Consumption and Sedentary Behavior on Gut Microbiota and Systemic Inflammation in Young Adults with Early Metabolic Syndrome – A Longitudinal Cohort Study Ian Astarina Mas'ud; Yeni Aryani; Restu Auliani; Erba Kalto Manik; Yan Deivita; Ani Laila; Yofa Sukmawati
International Journal of Public Health Excellence (IJPHE) Vol. 6 No. 1 (2026): June-December
Publisher : PT Inovasi Pratama Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55299/ijphe.v6i1.1955

Abstract

Background: Body mass index (BMI) inadequately captures metabolic risks driven by modern lifestyles, particularly the combination of ultra-processed food (UPF) intake and sedentary behavior. While each factor independently fosters low-grade systemic inflammation, their synergistic impact on the gut microbiota–inflammation axis in young adults with early metabolic syndrome (MetS) remains unexplored. Objective: This longitudinal cohort study examined the independent and synergistic effects of UPF consumption and sedentary behavior on gut microbiota composition and systemic inflammatory biomarkers in Indonesian young adults with early MetS over 12 months. Methods: We recruited 400 participants aged 20–30 years meeting 1–2 ATP III criteria for MetS. Dietary intake was assessed with a validated semi-quantitative FFQ with NOVA classification; sedentary time was measured via triaxial accelerometers. Gut microbiota was profiled using 16S rRNA sequencing (V3–V4 region), and hs-CRP, IL-6, and TNF-α were quantified at baseline and 12 months. Participants were stratified into four groups by median splits of UPF energy share (%kcal) and daily sedentary time. Generalized estimating equations tested synergistic interactions on inflammation, and mediation analyses evaluated microbiota diversity pathways. Results: The high-UPF/high-sedentary group exhibited the greatest increases in hs-CRP (+1.78 mg/L), IL-6 (+2.95 pg/mL), and TNF-α (+3.12 pg/mL), with a significant multiplicative interaction (p<0.001). Shannon diversity declined most sharply in the high-UPF/high-sedentary group (−0.32), paralleled by a rise in the Firmicutes/Bacteroidetes ratio (+0.47). Mediation analysis showed that 31.4% of the synergistic effect on hs-CRP was mediated by loss of diversity. Conclusion: Co-occurring high UPF consumption and sedentary behavior synergistically amplify systemic inflammation partly through gut dysbiosis in early MetS, underscoring the need to target both dietary and physical activity domains beyond BMI-centric strategies
Accuracy of A Deep Learning Model in Retinal Imaging Analysis for The Early Detection of Diabetic Retinopathy in A Southeast Asian Population: A Diagnostic Validation Study Yan Deivita; Bahmid Hasbullah; Aby Riestanti; Lita Umiputriani Gai; Nicholas Renaldo
International Journal of Public Health Excellence (IJPHE) Vol. 5 No. 1 (2025): June-December
Publisher : PT Inovasi Pratama Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55299/ijphe.v5i1.1625

Abstract

Background: Diabetic retinopathy (DR) represents a leading cause of preventable blindness in Southeast Asia, where diabetes prevalence continues rising dramatically. Deep learning models show promising diagnostic accuracy for DR detection, yet validation in Southeast Asian populations remains limited. Objective: To evaluate the accuracy and clinical applicability of a deep learning model for early DR detection through comprehensive qualitative analysis in Indonesian healthcare settings. Methods: A mixed-methods diagnostic validation study was conducted across three Indonesian provinces from January 2023 to December 2024. The study employed a convolutional neural network-based deep learning model trained on 15,000 retinal images for DR classification. Qualitative data collection included semi-structured interviews with 30 ophthalmologists, 20 primary care physicians, 15 healthcare administrators, and 40 patients. Thematic analysis explored stakeholder perspectives on diagnostic accuracy, implementation barriers, and clinical integration potential. Results: The deep learning model demonstrated 89.3% accuracy (95% CI: 86.7-92.1%), 91.7% sensitivity, and 87.1% specificity for detecting referable DR. Qualitative analysis revealed high stakeholder acceptance (87.5% patient trust, 90.0% physician interest) despite implementation concerns. Key themes included diagnostic accuracy validation needs, workflow integration challenges, infrastructure requirements, and cost-effectiveness potential. Primary barriers included image quality standardization, internet connectivity limitations, and regulatory approval processes. Conclusion: Deep learning models demonstrate promising diagnostic performance for DR screening in Southeast Asian populations, with strong stakeholder support for implementation. However, successful deployment requires addressing infrastructure limitations, regulatory frameworks, and clinician training needs. These findings support the potential for AI-enhanced DR screening to improve early detection outcomes in resource-constrained healthcare systems across Southeast Asia.
The Double-Edged Sword of Digital Health: A Mixed-Methods Study on the Efficacy and Health Equity Implications of AI-Powered Telemedicine Platforms in Post-Pandemic Rural Communities Yan Deivita; Fitriah Handayani; Ririn Rezky Ananda Sundun; Deisye Magdalena Dianne Pesik
International Journal of Public Health Excellence (IJPHE) Vol. 6 No. 1 (2026): June-December
Publisher : PT Inovasi Pratama Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55299/ijphe.v6i1.2172

Abstract

Artificial intelligence (AI)-supported telemedicine may extend clinical expertise to rural communities, yet its benefits can be offset by unequal connectivity, digital literacy, data representation, and institutional readiness. This study examined the efficacy and health-equity implications of AI-powered telemedicine in post-pandemic rural Indonesia. A qualitative-dominant, convergent mixed-methods secondary study integrated reflexive thematic analysis of peer-reviewed qualitative evidence and Indonesian policy documents with descriptive synthesis of verified national and clinical datasets. Evidence included 2023 national internet statistics, a 63,012-person chronic-disease survey, a 311-patient rheumatology survey, and a 15-interview qualitative study. Telemedicine was most effective for stable follow-up, medication management, specialist access, and travel-cost avoidance, but was less reliable for first diagnosis, unstable disease, and examination-dependent care. Rural internet use (59.33%) remained 17.47 percentage points below urban use (76.80%). Among people with hypertension and/or diabetes, 98.0% reported no telemedicine use, indicating a substantial implementation gap. Four integrated themes emerged: conditional clinical efficacy, redistributed rather than eliminated access burdens, algorithmic amplification of structural disadvantage, and the necessity of human-mediated hybrid care. Conclusion: AI-powered telemedicine is neither inherently equalizing nor intrinsically exclusionary. Its equity effect depends on assisted access, low-bandwidth design, representative validation, clinician oversight, interoperability, and disaggregated monitoring.
Co-Authors Aby Riestanti Ani Laila Bahmid Hasbullah Deisye Magdalena Dianne Pesik Erba Kalto Manik Fitriah Handayani Ian Astarina Mas'ud Lita Umiputriani Gai Nicholas Renaldo Restu Auliani Ririn Rezky Ananda Sundun Yeni Aryani Yofa Sukmawati