Wearable vital-sign sensors for early detection of severe dengue and sepsis: Clinical applications and implementation in resource-limited settings.

Introduction: Severe dengue and sepsis account for tens of millions of cases and deaths annually in low-resource settings, where spot-check vital sign monitoring misses early physiological deterioration. Affordable wearable sensors that stream continuous data and feed machine learning algorithms promise earlier detection and improved outcomes. Methods: We conducted a structured narrative literature review of human studies on PubMed, Embase, Scopus, and Google Scholar that evaluated wearable vital-sign devices for predicting severe dengue or sepsis in resource-limited areas. Two reviewers independently screened, extracted, and synthesized data on study design, devices, vital streams, predictive performance, and implementation barriers. Results: Seven eligible studies (2019-2024) from Vietnam, Rwanda, Bangladesh, and South Korea tested photoplethysmography or single-lead electrocardiography wearables in intensive care units and emergency departments of hospitals. The algorithms achieved an area under the receiver operating characteristic curve of 0.83-0.86 for forecasting dengue shock up to 2 hours and sepsis deterioration up to 5-48 hours before conventional recognition. The precision reached 0.79 for the three-level dengue severity classification. Continuous monitoring reduces the time to intervention and matches or exceeds the accuracy of manual charting. Conclusion: Early evidence shows that low-cost wearable sensors can deliver clinically meaningful lead times for severe dengue and sepsis in developing countries' hospitals. However, generalizability, artifact suppression, power autonomy, and economic sustainability remain unproven. Multicentre pragmatic trials, edge-optimized algorithms, and full cost-effectiveness analyses are needed before routine adoption.