Precision Prevention of Cancer at Scale: Using Digital Phenotyping to Monitor and Target Modifiable Mental Health Risk Factors via a Smartphone App

Dartmouth Cancer Center, Precision Prevention strategic priority

10/1/22 – 9/30/23

Nicholas C. Jacobson (Geisel School of Medicine at Dartmouth), Lisa A. Marsch (Geisel School of Medicine at Dartmouth)

Lisa Oh

Occurring in approximately 1 in 5 persons globally in any given year, depressive and anxiety disorders significantly increase the risk of the development of cancer, increase cancer specific mortality, and increase all-cause mortality in cancer patients. Although behavioral interventions have shown efficacy in treating depressive and anxiety disorders, most persons with a depressive and/or anxiety disorder do not receive any form of mental health treatment. This suggests that behavioral treatments for mental health may be a modifiable risk factor for prevention interventions of cancer development and mortality, but the current mental health care system is unable to scale to the large number of persons needing treatment and is unable to adequately personalize care to enhance treatment efficacy. Advances in technology provide an unprecedented opportunity for rapid personalized assessment and tailored intervention of depressive and anxiety disorders at scale. Smartphones can continuously and passively capture a variety of depressive and anxiety symptoms that are also associated with cancer risk, including sleep disturbances, decreased energy expenditure, lack of social contact, low exposure to natural light, and disturbances in heart rate. Smartphone applications can passively and continuously assess depressive and anxiety symptoms that are also are predictive of cancer risk and can deliver scalable personalized interventions. As such, the current work will examine the early efficacy of a personalized digital intervention targeting anxiety and depressive symptoms in reducing cancer risk and examine whether the intervention may work to reduce cancer risk by reducing sleep disturbances and increasing energy expenditure, social contact, exposure to natural light, and/or heart rate flexibility.

The current study proposes to test a novel personalized prevention which is capable of being scaled to deliver treatment to population-levels. Furthermore, the present work may elucidate novel digital biomarkers of cancer risk from data collected passively and continuously within persons’ daily lives.