Digital Biomarkers and Intelligent Health Monitoring: Integrating Wearable Technologies with Predictive Analytics

Chew Han Shi Jocelyn

Digital Biomarkers and Intelligent Health Monitoring: Integrating Wearable Technologies with Predictive Analytics

Author(s)	Dr. Chew Han Shi Jocelyn
Country	United States
Abstract	Digital biomarkers derived from wearable technologies are expanding the temporal and contextual range of health monitoring. Unlike conventional assessments conducted during occasional clinical visits, wearable devices can continuously capture heart-rate dynamics, physical activity, sleep patterns, skin temperature, peripheral oxygen saturation, electrodermal activity, gait, posture, and other physiological or behavioral signals in everyday settings. These measurements may support longitudinal health assessment, early recognition of meaningful deviations, treatment-response monitoring, and personalized care. Their usefulness, however, depends on whether raw signals can be converted into clinically interpretable and analytically reliable indicators. This simulation-based study develops a framework for integrating multimodal wearable sensing with predictive analytics. Eighteen hypothetical monitoring profiles were constructed with usable data completeness ranging from 42% to 97%. Predictive reliability was represented through an illustrative index combining signal quality, temporal continuity, multimodal corroboration, model calibration, and uncertainty management. The study compares single-signal monitoring, multimodal digital-biomarker integration, and clinician-supervised predictive monitoring. It also examines the influence of missing data, motion artifacts, device heterogeneity, behavioral context, algorithmic bias, and false alerts. The simulated analysis indicates that greater usable data completeness and multimodal coverage are associated with more stable predictive performance. Profiles with fragmented or low-quality measurements exhibit greater analytical uncertainty, while continuous multimodal monitoring supports improved differentiation between short-term physiological variation and persistent health changes. Nevertheless, wearable outputs should not be treated as autonomous diagnoses. Reliable use requires fit-for-purpose validation, transparent algorithms, clinically meaningful thresholds, privacy protection, equitable access, and qualified human review. The study concludes that digital biomarkers are most valuable when wearable technologies, predictive models, patient-reported information, and clinical expertise operate within an integrated and accountable health-monitoring system.
Keywords	: digital biomarkers, wearable technologies, predictive analytics, intelligent health monitoring, remote patient monitoring, machine learning, personalized healthcare, physiological sensing.
Field	Engineering
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-08-30

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American Journal of Advanced Multidisciplinary Research and Innovation

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Digital Biomarkers and Intelligent Health Monitoring: Integrating Wearable Technologies with Predictive Analytics

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