Thursday, May 19, 2022
5/19/2022
Project Description 1 Introduction More than any other phenomena in recent history, the COVID-19 pandemic has challenged how we approach patient-care due to the huge burden it has placed on hospitals, clinics, and health professionals. The health community has responded to this trend with research and technology leveraging data that goes beyond what is customarily thought of as “health data”, such as commu- nity and contextual data, social media, traffic, and mobility data. For example, Nsoesie et al.[84] analyzed hospital traffic and search engine data in Wuhan to infer early disease activity in Fall 2019. These new efforts, including our own work in utilizing mobility data to forecast COVID- 19’s transmission risk [94], uses what this NSF call-for-proposal refers to as “non-traditional health data”. In this proposal, we focus on one specific type of non-traditional health data, wearable data, which are also fast becoming an important source of health and disease data as they inform on a variety of personal, behavioral, social, contextual, and environmental health-relevant factors. Wearables have been primarily used for activity tracking [96, 15, 20, 80] and gained popularity with fitness applications; however, more recently, these devices have been used in an increasing number of health applications, including health monitoring, clinical-care, remote clinical-trials, drug delivery, and disease characterization to name a few. In fact, wearables have been found useful in a num- ber of applications and diseases (e.g., Parkinson’s disease, epilepsy and stroke [57], sleep disor- ders [12], cardiac disorders [90, 63] and cancer [75]). This trend is accelerating with the COVID-19 epidemic, e.g., smartphones have been proposed to track symptoms [64], monitor effectiveness of non-pharmaceutical interventions, assess potential spread, and support contact tracing [45]. Wearable measurements differ from traditional clinical measurements. When a patient visits a clinic, vitals and lab tests are collected in a “controlled” environment in a short duration of time using multiple devices. We define this monitoring in the controlled environment as Snapshot In-Clinic monitoring, abbreviated as SIC. Meanwhile, the recent growth and accessibility of the wearable devices such as smartphones and watches [97] with embedded activity and mobile sensors [114] enables the continuous monitoring of patients’ vital signs and other health indicators over a long duration of time. Patient monitoring using wearable devices typically happens in an “uncontrolled” setup at home or at work in a non-intrusive fashion with only a few sensors. This trend has also been encapsulated by the NIH mHealth’s initiatives, resulting in the evolution of new healthcare models such as “home healthcare” [9, 40] and “minute clinic” [125], which goes hand in hand with both ubiquitous sensors in smartphones and custom sensors like glucose monitors [62]. We define this monitoring in the uncontrolled environment as Longitudinal In-Field monitoring, abbreviated as LIFE. Clearly these are wordplay, i.e., SIC is for “sick” capturing patients’ state of mind when they visit a clinic/hospital vs. LIFE for when patients live their normal “life” at home and at work. LIFE monitoring makes up for greater than 99% of patients’ time, enabling outpatient monitoring of the effects of disease and its therapy on patient performance and quality of life. In fact, our preliminary data show that in some cases, such as assessment of performance status in cancer patients, LIFE data outperform in-office SIC assessments [82]. SIC monitoring is the current standard of care and is driven by improving outcomes in measurable Page 72
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