Designing a Digital Twin for the Management of Noncommunicable Diseases: Protocol for a Pilot Study and Methodology...

Noncommunicable diseases (NCDs) have become the leading cause of mortality worldwide. NCDs account for 89% of all deaths in the United States and cost the US economy more than US $47 trillion in direct and indirect expenses. NCDs also account for the main cause of disability worldwide, and the incidence is increasing. The leading NCDs include diabetes, cancer, cardiovascular disease, chronic respiratory disease, and mental health conditions. Outside of aging, NCDs are caused by modifiable behavioral risk factors that include smoking, drug and alcohol abuse, unhealthy diet, obesity, and inadequate physical activity, and treatment must be directed to all of these domains. We hypothesize that a digital twin concept can be used to personalize treatment regimens through analysis of data that allows for artificial intelligence-based decision making.

This study aims to present a methodology to validate this concept, which would provide a new clinical approach toward addressing the leading cause of disability and mortality worldwide today.

This study will use delta scores between treatment arms to ascertain whether that distribution was normal for each of the study variables. Parametric (eg, analysis of covariance) or nonparametric analyses will be used to examine the variables to determine the impact of digital twin efficacy over normal treatment paradigms.

Recruitment of participants is expected to begin 6 months after study funding has been awarded and the needed approvals have been obtained. The expected results will show that digital twin modeling using the biopsychosocial characteristics of each participant will be statistically significant, supporting using this approach for personalized medical care.

This study can help to identify significant clinical characteristics to help mitigate the impact of NCDs through biopsychosocial treatment paradigms. This paper proposes a statistical framework to evaluate the validity of the platform's modeling in support of clinical decision making.