{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T16:23:33Z","timestamp":1787675013173,"version":"build-2784847793"},"reference-count":95,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:00:00Z","timestamp":1787616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>The integration of digital therapeutics (DTx), wearable electronic devices, and artificial intelligence (AI) represents a significant advancement in personalized healthcare. The primary purpose of this structured narrative review is to evaluate the convergence of these technologies, providing a consolidated framework that bridges the gap between raw biometric data acquisition and actionable, AI-driven clinical insights. This paper synthesizes the latest literature on the intersection of mobile health (mHealth), machine learning (ML), and physiological tracking, with a primary focus on heart rate variability (HRV) and associated biochemical markers, such as cortisol, salivary alpha-amylase, and interleukins. Instead of viewing wearable outputs simply as raw data, we critically evaluate the technical verification and clinical validation required to define them as true \u201cdigital biomarkers.\u201d By evaluating multimodal sensor technologies and advanced predictive algorithms, this paper outlines the clinical utility of digital biomarkers in diagnosing and proactively managing cardiovascular, neurological, metabolic, and psychiatric conditions, noting classification accuracies frequently exceeding 85% in controlled settings. However, we strongly caution that internally validated performance in controlled settings does not inherently demonstrate external clinical utility. The clinical relevance of this study lies in its holistic approach to identifying how continuous monitoring can broaden healthcare accessibility while improving precision medicine. Furthermore, it deeply addresses the technical challenges of highly variable ambulatory data quality, the necessity for robust artifact reduction (e.g., via LSTM and GAN architectures), and the limitations of small, homogeneous training datasets. We highlight the essential need for demographic-aware algorithmic models, external validation, and decentralized privacy-preserving models (e.g., federated learning) in diverse populations to ensure the safe, equitable clinical translation of DTx, mHealth, ML, and AI technologies.<\/jats:p>","DOI":"10.3390\/bioengineering13090977","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T15:55:06Z","timestamp":1787673306000},"page":"977","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Reactive to Proactive Healthcare: Synergizing Wearable Biomarkers and Machine Learning in Digital Therapeutics"],"prefix":"10.3390","volume":"13","author":[{"given":"Kwanjoon","family":"Park","sequence":"first","affiliation":[{"name":"Department of Software Engineering, College of Engineering, Chonnam National University, Gwangju 61186, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3710-9850","authenticated-orcid":false,"given":"Eunice Kwan Chae","family":"Park","sequence":"additional","affiliation":[{"name":"Creative Technology Management, Humanities, Arts, and Social Sciences Division, Underwood International College, Yonsei University, Seoul 03722, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Woo Hyun","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Physiology, Medical School, Jeonbuk National University, Jeonju 54896, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2295-5524","authenticated-orcid":false,"given":"Eun-Young","family":"Jeon","sequence":"additional","affiliation":[{"name":"Institute of Humanities, Yonsei University, Seoul 03722, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"eady6497","DOI":"10.1126\/science.ady6497","article-title":"From reactive to proactive: Continuous protein monitoring for preventive health care","volume":"389","author":"Donnelly","year":"2025","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1038\/s41574-025-01175-z","article-title":"Challenges and opportunities of wearable molecular sensors in endocrinology and metabolism","volume":"22","author":"Gerber","year":"2026","journal-title":"Nat. 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