The power and promise of person-generated health data (Part II)

In Part I of our discussion we introduced the concept of person-generated health data (PGHD), defined as wellness and/or health-related data created, recorded, or gathered by individuals. The ubiquity and remarkable technological progress of personal computing devices, including wearables, smartphones, and tablets, along with the multitude of sensor modalities embedded within these devices, enables a continuous connection with individuals wanting to share information about their behavior and daily life.

Bray Patrick-Lake

Such rich, longitudinal information is now being used in combination with traditional clinical information to predict, diagnose, and formulate treatment plans for diseases, as well as understand the safety and effectiveness of medical interventions.

Identifying a disease early

One novel example of digital technologies being used for early identification of disease was a promising 2019 study by Eli Lilly (in collaboration with Apple and Evidation Health) called the Lilly Exploratory Digital Assessment Study.

In this study, the feasibility of using PGHD for identifying physiological and behavioral signatures of cognitive impairment was examined for the purpose of seeking new methods to detect mild cognitive impairment (MCI) in a timely and cost-effective manner. The study enrolled 31 study participants with cognitive impairment and 82 without cognitive impairment. It used consumer-grade sensor technologies (the iPhone, Apple Watch, iPad, and Beddit sleep monitor) to continuously and unobtrusively collect data. Among the information the researchers collected were interaction with the phone keyboard, accelerometer data from the Apple Watch, volume of messages sent/received, and sleep cycles.¹

^{Courtesy of Evidation Health, Inc.}

Figure 1. Behaviorgram is shown.

A total of 16 terabytes of data were collected over the course of 12 weeks. Data were organized into a behaviorgram (See Figure 1) that gives a holistic picture of a day in a patient’s life. A machine learning model was used to distinguish between behaviorgrams of symptomatic versus healthy controls, identifying typing speed, circadian rhythm shifts, and reliance on helper apps, among other things, as differentiating cognitively impaired from healthy controls. These behaviorgrams may someday serve as “fingerprints” of different diseases, with specific diseases displaying predictable patterns. In the near future, digital measures like the ones investigated in this study are likely to be used to help clinicians predict and diagnose disease, as well as to better understand disease progression and treatment response.

Leading to better health outcomes

Dr. Luca Foschini

The potential of PGHD to detect diseases early and lead to better health outcomes is being investigated in the Heartline study, a collaboration between Johnson & Johnson and Apple, which is supported by Evidation.²

This study aims to enroll 150,000 adults age 65 years and over to analyze the impact of Apple Watch–based early detection of irregular heart rhythms consistent with atrial fibrillation (AFib). The researchers’ hypothesis is that jointly detecting atrial fibrillation early and providing cardiovascular health programs to new AFib patients, will lead to patients being treated by a medical provider for AFib that otherwise would not have been detected. This, in turn, would lead to these AFib patients decreasing their risks of stroke and other serious cardiovascular events, including death, the study authors speculated.