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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.
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
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
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.
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.
References
1. Chen R et al. Developing measures of cognitive impairment in the real world from consumer-grade multimodal sensor streams. KDD ’19. August 4–8, 2019 Aug 4-8.
2. The Heartline Study. https://www.heartline.com.
3. Foschini L. Privacy of Wearable and Sensors Data (or, the Lack Thereof?). Data Driven Investor, Medium. 2019.
4. Bradshaw B et al. Influenza surveillance using wearable mobile health devices. Online J Public Health Inform. 2019;11(1):e249.
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. doi: 10.1016/S2589-7500(19)30222-5.
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.
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
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
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.
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.
References
1. Chen R et al. Developing measures of cognitive impairment in the real world from consumer-grade multimodal sensor streams. KDD ’19. August 4–8, 2019 Aug 4-8.
2. The Heartline Study. https://www.heartline.com.
3. Foschini L. Privacy of Wearable and Sensors Data (or, the Lack Thereof?). Data Driven Investor, Medium. 2019.
4. Bradshaw B et al. Influenza surveillance using wearable mobile health devices. Online J Public Health Inform. 2019;11(1):e249.
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. doi: 10.1016/S2589-7500(19)30222-5.
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.
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
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
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.
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.
References
1. Chen R et al. Developing measures of cognitive impairment in the real world from consumer-grade multimodal sensor streams. KDD ’19. August 4–8, 2019 Aug 4-8.
2. The Heartline Study. https://www.heartline.com.
3. Foschini L. Privacy of Wearable and Sensors Data (or, the Lack Thereof?). Data Driven Investor, Medium. 2019.
4. Bradshaw B et al. Influenza surveillance using wearable mobile health devices. Online J Public Health Inform. 2019;11(1):e249.
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. doi: 10.1016/S2589-7500(19)30222-5.
The power and promise of person-generated health data – part 1
The time shared during clinical encounters provides small peeks into patients’ lives that get documented as episodic snapshots in electronic health records. But there is little information about how patients are doing outside of the office. With increasing emphasis on filling out mandatory parts of the EHR, there is less time available for in-depth, in-office conversations and phone follow-ups.
At the same time, it has become clear that it is not just the medicines we prescribe that affect our patients’ lives. Their behaviors outside of the office – being physically active, eating well, getting a good night’s rest, and adhering to medications – also impact their health outcomes.
The explosion of technology and personal data in our increasingly connected world provides powerful new sources of health and behavior information that generate new understanding of patients’ lives in their everyday settings.
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, has enabled us to establish a continuous connection with people who want to share information about their behavior and daily life.
Such rich, longitudinal information, known as person-generated health data (PGHD), can be searched for physiological and behavioral signatures that can be used in combination with traditional clinical information to predict, diagnose, and treat disease. It can also be used to understand the safety and effectiveness of medical interventions.
PGHD is defined as wellness and/or health-related data created, recorded, or gathered by individuals. It reflects events and interactions that occur during an person’s everyday life. Systematically gathering this information and organizing it to better understand patients’ approach to their health or their unique experience living with disease provides meaningful insights that complement the data traditionally collected as part of clinical trials or periodic office visits.
PGHD can produce a rich picture of a person’s health or symptom burden with disease. It allows the opportunity to measure the real human burden of a patient’s disease and how it changes over time, with an opportunity to detect changes in symptoms in real time.
PGHD can also enable participation in health research.
An example would be the work of Evidation Health in San Mateo, Calif. Evidation provides a platform to run research studies utilizing technology and systems to measure health in everyday life. Its app, Achievement, collects continuous behavior-related data from smartphones, wearables, connected devices, and apps. That provides opportunities for participants to join research studies that develop novel measures designed to quantify health outcomes in a way that more accurately reflects an individual’s day-to-day activities and experience. All data collected are at the direction of and with the permission of the individual.
“Achievers” are given points for taking health-related actions such as tracking steps or their sleep, which convert to cash that can be kept or donated to their favorite charities. Achievement’s 3.5 million diverse participants also receive offers to join research studies. This paradigm shift dramatically expands access to research to increase diversity, shortens the time to first data through rapid recruitment, and enhances retention rates by making it easier to engage. To date, more than 1 million users have chosen to participate in research studies. The technology is bringing new data and insights to health research; it supports important questions about quality of life, medical products’ real-world effectiveness, and the development of hyperpersonalized health care services.
This new type of data is transforming medical research by creating real-world studies of unprecedented size, such as the Apple Heart Study – a virtual study with more than 400,000 enrolled participants – which was designed to test the accuracy of Apple Watches in safely identifying atrial fibrillation. The FDA has cleared two features on the Apple Watch: the device’s ability to detect and notify the user of an irregular heart rhythm, and the ability to take a single-lead EKG feature that can provide a rhythm strip for a clinician to review.
The FDA clearance letters specify that the apps are “not intended to replace traditional methods of diagnosis or treatment.” They provide extra information, and that information might be helpful – but the apps won’t replace a doctor’s visit. It remains to be seen how these data will be used, but they have the potential to identify atrial fibrillation early, leading to treatment that may prevent devastating strokes.
Another example of home-generated health data is a tool that has obtained FDA clearance as a diagnostic device with insurance reimbursement: WatchPAT, a portable sleep apnea diagnostic device. WatchPAT is worn like a simple wristwatch, with no need for belts, wires, or nasal cannulas.
Over time, in-home tests like these that are of minimal inconvenience to the patient and reflect a real-world experience may eclipse traditional sleep studies that require patients to spend the night in a clinic while attached to wires and monitors.
Health data generated by connected populations will yield novel insights that may help us better predict, diagnose, and treat disease. These are examples of innovations that can extend clinicians’ abilities to remotely monitor or diagnose health conditions, and we can expect that more will continue to be integrated into the clinical and research settings in the near future.
In part 2 of this series, we will discuss novel digital measures and studies utilizing PGHD to impact population health.
Dr. Skolnik is professor of family and community medicine at Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, and associate director, family medicine residency program, Abington (Pa.) Jefferson Health. Dr. Foschini is cofounder and chief data scientist at Evidation Health in San Mateo, Calif. Bray Patrick-Lake is a patient thought leader and director, strategic partnerships, at Evidation Health.
References
Determining real-world data’s fitness for use and the role of reliability, September 2019. Duke-Margolis Center for Health Policy.
N Engl J Med. 2019 Nov 14;381(20):1909-17.
The time shared during clinical encounters provides small peeks into patients’ lives that get documented as episodic snapshots in electronic health records. But there is little information about how patients are doing outside of the office. With increasing emphasis on filling out mandatory parts of the EHR, there is less time available for in-depth, in-office conversations and phone follow-ups.
At the same time, it has become clear that it is not just the medicines we prescribe that affect our patients’ lives. Their behaviors outside of the office – being physically active, eating well, getting a good night’s rest, and adhering to medications – also impact their health outcomes.
The explosion of technology and personal data in our increasingly connected world provides powerful new sources of health and behavior information that generate new understanding of patients’ lives in their everyday settings.
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, has enabled us to establish a continuous connection with people who want to share information about their behavior and daily life.
Such rich, longitudinal information, known as person-generated health data (PGHD), can be searched for physiological and behavioral signatures that can be used in combination with traditional clinical information to predict, diagnose, and treat disease. It can also be used to understand the safety and effectiveness of medical interventions.
PGHD is defined as wellness and/or health-related data created, recorded, or gathered by individuals. It reflects events and interactions that occur during an person’s everyday life. Systematically gathering this information and organizing it to better understand patients’ approach to their health or their unique experience living with disease provides meaningful insights that complement the data traditionally collected as part of clinical trials or periodic office visits.
PGHD can produce a rich picture of a person’s health or symptom burden with disease. It allows the opportunity to measure the real human burden of a patient’s disease and how it changes over time, with an opportunity to detect changes in symptoms in real time.
PGHD can also enable participation in health research.
An example would be the work of Evidation Health in San Mateo, Calif. Evidation provides a platform to run research studies utilizing technology and systems to measure health in everyday life. Its app, Achievement, collects continuous behavior-related data from smartphones, wearables, connected devices, and apps. That provides opportunities for participants to join research studies that develop novel measures designed to quantify health outcomes in a way that more accurately reflects an individual’s day-to-day activities and experience. All data collected are at the direction of and with the permission of the individual.
“Achievers” are given points for taking health-related actions such as tracking steps or their sleep, which convert to cash that can be kept or donated to their favorite charities. Achievement’s 3.5 million diverse participants also receive offers to join research studies. This paradigm shift dramatically expands access to research to increase diversity, shortens the time to first data through rapid recruitment, and enhances retention rates by making it easier to engage. To date, more than 1 million users have chosen to participate in research studies. The technology is bringing new data and insights to health research; it supports important questions about quality of life, medical products’ real-world effectiveness, and the development of hyperpersonalized health care services.
This new type of data is transforming medical research by creating real-world studies of unprecedented size, such as the Apple Heart Study – a virtual study with more than 400,000 enrolled participants – which was designed to test the accuracy of Apple Watches in safely identifying atrial fibrillation. The FDA has cleared two features on the Apple Watch: the device’s ability to detect and notify the user of an irregular heart rhythm, and the ability to take a single-lead EKG feature that can provide a rhythm strip for a clinician to review.
The FDA clearance letters specify that the apps are “not intended to replace traditional methods of diagnosis or treatment.” They provide extra information, and that information might be helpful – but the apps won’t replace a doctor’s visit. It remains to be seen how these data will be used, but they have the potential to identify atrial fibrillation early, leading to treatment that may prevent devastating strokes.
Another example of home-generated health data is a tool that has obtained FDA clearance as a diagnostic device with insurance reimbursement: WatchPAT, a portable sleep apnea diagnostic device. WatchPAT is worn like a simple wristwatch, with no need for belts, wires, or nasal cannulas.
Over time, in-home tests like these that are of minimal inconvenience to the patient and reflect a real-world experience may eclipse traditional sleep studies that require patients to spend the night in a clinic while attached to wires and monitors.
Health data generated by connected populations will yield novel insights that may help us better predict, diagnose, and treat disease. These are examples of innovations that can extend clinicians’ abilities to remotely monitor or diagnose health conditions, and we can expect that more will continue to be integrated into the clinical and research settings in the near future.
In part 2 of this series, we will discuss novel digital measures and studies utilizing PGHD to impact population health.
Dr. Skolnik is professor of family and community medicine at Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, and associate director, family medicine residency program, Abington (Pa.) Jefferson Health. Dr. Foschini is cofounder and chief data scientist at Evidation Health in San Mateo, Calif. Bray Patrick-Lake is a patient thought leader and director, strategic partnerships, at Evidation Health.
References
Determining real-world data’s fitness for use and the role of reliability, September 2019. Duke-Margolis Center for Health Policy.
N Engl J Med. 2019 Nov 14;381(20):1909-17.
The time shared during clinical encounters provides small peeks into patients’ lives that get documented as episodic snapshots in electronic health records. But there is little information about how patients are doing outside of the office. With increasing emphasis on filling out mandatory parts of the EHR, there is less time available for in-depth, in-office conversations and phone follow-ups.
At the same time, it has become clear that it is not just the medicines we prescribe that affect our patients’ lives. Their behaviors outside of the office – being physically active, eating well, getting a good night’s rest, and adhering to medications – also impact their health outcomes.
The explosion of technology and personal data in our increasingly connected world provides powerful new sources of health and behavior information that generate new understanding of patients’ lives in their everyday settings.
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, has enabled us to establish a continuous connection with people who want to share information about their behavior and daily life.
Such rich, longitudinal information, known as person-generated health data (PGHD), can be searched for physiological and behavioral signatures that can be used in combination with traditional clinical information to predict, diagnose, and treat disease. It can also be used to understand the safety and effectiveness of medical interventions.
PGHD is defined as wellness and/or health-related data created, recorded, or gathered by individuals. It reflects events and interactions that occur during an person’s everyday life. Systematically gathering this information and organizing it to better understand patients’ approach to their health or their unique experience living with disease provides meaningful insights that complement the data traditionally collected as part of clinical trials or periodic office visits.
PGHD can produce a rich picture of a person’s health or symptom burden with disease. It allows the opportunity to measure the real human burden of a patient’s disease and how it changes over time, with an opportunity to detect changes in symptoms in real time.
PGHD can also enable participation in health research.
An example would be the work of Evidation Health in San Mateo, Calif. Evidation provides a platform to run research studies utilizing technology and systems to measure health in everyday life. Its app, Achievement, collects continuous behavior-related data from smartphones, wearables, connected devices, and apps. That provides opportunities for participants to join research studies that develop novel measures designed to quantify health outcomes in a way that more accurately reflects an individual’s day-to-day activities and experience. All data collected are at the direction of and with the permission of the individual.
“Achievers” are given points for taking health-related actions such as tracking steps or their sleep, which convert to cash that can be kept or donated to their favorite charities. Achievement’s 3.5 million diverse participants also receive offers to join research studies. This paradigm shift dramatically expands access to research to increase diversity, shortens the time to first data through rapid recruitment, and enhances retention rates by making it easier to engage. To date, more than 1 million users have chosen to participate in research studies. The technology is bringing new data and insights to health research; it supports important questions about quality of life, medical products’ real-world effectiveness, and the development of hyperpersonalized health care services.
This new type of data is transforming medical research by creating real-world studies of unprecedented size, such as the Apple Heart Study – a virtual study with more than 400,000 enrolled participants – which was designed to test the accuracy of Apple Watches in safely identifying atrial fibrillation. The FDA has cleared two features on the Apple Watch: the device’s ability to detect and notify the user of an irregular heart rhythm, and the ability to take a single-lead EKG feature that can provide a rhythm strip for a clinician to review.
The FDA clearance letters specify that the apps are “not intended to replace traditional methods of diagnosis or treatment.” They provide extra information, and that information might be helpful – but the apps won’t replace a doctor’s visit. It remains to be seen how these data will be used, but they have the potential to identify atrial fibrillation early, leading to treatment that may prevent devastating strokes.
Another example of home-generated health data is a tool that has obtained FDA clearance as a diagnostic device with insurance reimbursement: WatchPAT, a portable sleep apnea diagnostic device. WatchPAT is worn like a simple wristwatch, with no need for belts, wires, or nasal cannulas.
Over time, in-home tests like these that are of minimal inconvenience to the patient and reflect a real-world experience may eclipse traditional sleep studies that require patients to spend the night in a clinic while attached to wires and monitors.
Health data generated by connected populations will yield novel insights that may help us better predict, diagnose, and treat disease. These are examples of innovations that can extend clinicians’ abilities to remotely monitor or diagnose health conditions, and we can expect that more will continue to be integrated into the clinical and research settings in the near future.
In part 2 of this series, we will discuss novel digital measures and studies utilizing PGHD to impact population health.
Dr. Skolnik is professor of family and community medicine at Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, and associate director, family medicine residency program, Abington (Pa.) Jefferson Health. Dr. Foschini is cofounder and chief data scientist at Evidation Health in San Mateo, Calif. Bray Patrick-Lake is a patient thought leader and director, strategic partnerships, at Evidation Health.
References
Determining real-world data’s fitness for use and the role of reliability, September 2019. Duke-Margolis Center for Health Policy.
N Engl J Med. 2019 Nov 14;381(20):1909-17.