Presenting new challenges
While PGHD has the potential to help people, it also presents new challenges. It is highly sensitive and personal – it can be as identifying as DNA.3
The vast amount of data that PGHD can collect from interaction with consumer wearable devices poses serious privacy risks if done improperly. To address those risks, companies like Evidation have built in protections. Evidation has an app, Achievement, that has enlisted a connected population of more than 3.5 million members who earn rewards for performing health-related actions, as tracked by wearables devices and apps. Through the Achievement app (See Figure 2.), members are provided opportunities to join research studies. As part of these studies, data collected from sensors and apps is used by permission of the member so that it is clear how their data are contributing to specific research questions or use cases.
This is a collaborative model of data collection built upon trust and permission and is substantially different than the collection of data from electronic health records (EHRs) – which is typically aggregated, deidentified, and commercialized, often without the patients’ knowledge or consent. Stringent protections, explicit permission, and transparency are absolutely imperative until privacy frameworks for data outside of HIPAA regulation catches up and protects patients from discrimination and unintended uses of their data.
Large connected cohorts can help advance our understanding of public health. In one study run on Achievement during the 2017-2018 flu season, a survey was sent to the Achievement population every week asking about symptoms of influenza-like illness and requesting permission to access historical data from their wearable around the influenza-like illness event.4 With the data, it was possible to analyze patterns of activity, sleep, and resting heart rate change around flu events. Resting heart rate, in particular, is shown to increase during fever and at the population level. In fact, through the use of PGHD, it is possible to use the fraction of people with resting heart rate above their usual baseline as a proxy to quantify the number of infected people in a region.5 This resting heart rate–informed flu surveillance method, if refined to increased accuracy, can work in near real time. This means it may be able detect influenza outbreaks days earlier than current epidemiological methods.
Health data generated by connected populations are in the early stages of development. It is clear that it will yield novel insights into health and disease. Only time will tell if it will be able to help clinicians and patients better predict, diagnose, and formulate treatment plans for disease.
Neil Skolnik, M.D. is a professor of family and community medicine at Sidney Kimmel Medical College, Thomas Jefferson University, and associate director of the Family Medicine Residency Program at Abington Jefferson Health. Luca Foschini PhD, is co-founder & chief data scientist at Evidation Health. Bray Patrick-Lake, MFS, is a patient thought leader and director of strategic partnerships at Evidation Health.
1. Chen R et al. Developing measures of cognitive impairment in the real world from consumer-grade multimodal sensor streams..
2. The Heartline Study..
3. Foschini L.. Data Driven Investor, Medium. 2019.
4. Bradshaw B et al. Influenza surveillance using wearable mobile health devices..
5. Radin JM et al. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. Lancet Digital Health. 2020..