About 80,000 Americans are diagnosed annually with brain or central nervous system tumors, and these cancers are some of the most difficult to treat. While methods for detection, early management, and long-term follow-up continue to improve, physicians base their decisions about patient care on limited data collected mostly when patients are in the clinic for treatment rather than on a day-to-day basis. Further, treatment typically is not initiated until tumors have progressed to late stages when they cause debilitating, noticeable symptoms. To address these limitations, Dr. Stephen Friend of 4YouandMe and his collaborators will test the feasibility of using smart devices and health monitoring apps to track brain cancer patients’ symptoms in real time and on a more continuous basis. Toward this goal, they will conduct an observational study of 100 high-risk patients that will generate comprehensive data that can be analyzed by machine learning to develop models of disease progression. Using each patient’s own smartphone and wearable devices such as watches and rings in conjunction with health apps that collect, compile, and transmit data directly to clinicians, the team will be able to monitor daily changes in everything from gait to mental health to sleep. The analysis of these data will reveal patterns of symptoms that are missed by traditional patient monitoring during clinical visits. These insights could enable doctors to detect tumors early, before symptoms become noticeably severe, and to closely monitor and adjust treatments in patients with advanced disease.
Goodday SM, Karlin E, Alfarano A, Brooks A, Chapman C, Desille R, Karlin DR, Emami H, Woods N, Boch A, Foschini L, Wildman M, Cormack F, Taptiklis N, Pratap A, Ghassemi M, Goldenberg A, Nagaraj S, Walsh E, Friend S. An alternative to the ‘light touch’ digital health remote study: The Stress and Recovery in Frontline COVID-19 Healthcare Workers Study. JMIR Form Res. 2021.