Authors
Mayuri, Bidisha Bhattacharya, Kunal Kar, Pravin Pawar, Inderbir Singh, Tanikan Sangnim, Kampanart Huanbutta
Published in
Journal of obesity. Volume 2026. Issue 1. Pages e9482093.
Abstract
Obesity remains a complex global public health crisis driven by environmental, biochemical, and behavioral factors, leading to severe metabolic comorbidities. Digital therapeutics (DTx) has emerged as a scalable, data-driven intervention, utilizing wearable sensors, mobile applications, and artificial intelligence (AI) to deliver personalized weight-management strategies. Rather than relying solely on traditional approaches, true DTx distinguishes it through rigorous clinical validation and targeted behavioral modification. Current evidence from clinical trials and real-world data indicates that while DTx effectively enhances patient engagement and promotes short-term behavioral change, sustaining long-term adherence and significant weight loss remains a critical challenge. Furthermore, the integration of AI models offers advanced personalization, though their efficacy must be balanced against potential algorithmic biases and traditional statistical approaches. Successful implementation of DTx into standard obesity care requires overcoming substantial barriers, particularly digital divide, data privacy concerns, and fragmented regulatory frameworks. Ultimately, realizing the full potential of DTx necessitates collaborative efforts to establish standardized regulations, ensure equitable access, and design evidence-based interventions that prioritize long-term patient engagement and clinical efficacy.
PMID:
42895797
Bibliographic data and abstract were imported from PubMed on 20 Oct 2026.
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