Presentation
Incorporating Patient-Generated Health Data in Clinical Quality Improvement Projects and Research Studies: Challenges, Concerns and Considerations
DescriptionPromoting self-management practices is an integral part of any chronic condition care plan. Therefore, it is essential that we invest in empowering individuals with knowledge and information to become more engaged and responsible for their own health and healthcare. Providing individuals with education and tools to engage in self-management practices has been shown to reduce preventable hospitalizations, increase self-efficacy, and improve health-related quality of life. Decisions that are made on a day-to-day basis often have far greater implications on one’s health than decisions originating from one’s care team or healthcare system. For example, people with diabetes receive guidelines like insulin ratios at clinical appointments but spend hours daily making self-management decisions based on these guidelines. Individuals with sleep apnea receive treatment devices but adherence to them varies from 66% to 74%, depending on study and population.
More and more individuals have turned to consumer-grade wearable devices to track and monitor everything from physical activity to sleep to heart rate. Consumer-grade wearable device manufacturers have taken the lead in creating platforms to present the data collected from these devices. Some have taken further steps to visualize and interpret the data to help users understand their own data. However, these features are often based on proprietary formulas that may not always align with the standard of care or peer-reviewed evidence (e.g., Fitbit sleep score is derived from “duration”, “quality”, and “restoration” data). Thus, consumers need help to soundly interpret the data available and apply the information to make decisions about their own care.
With decreasing unit costs and increasing acceptability of these devices, wearables have been integrated into interventions, both as a data collection tool (e.g., step count) and/or the mechanism to encourage health behavior change (e.g., vibration to “nudge” user to stand up or to exercise). An important part of any intervention utilizing a wearable is how patient-generated health data (PGHD) are collected, accessed, and translated into actionable information for the individual.
This panel will 1) discuss how we’ve addressed challenges related to technology acceptance and/or digital health literacy levels with a tailored onboarding experience for participants; 2) describe efforts to visually integrate disparate streams of data so that individuals can draw connections among their health behaviors and biometrics; and 3) discuss best practices and other considerations when cultivating partnerships between researchers and industry.
More and more individuals have turned to consumer-grade wearable devices to track and monitor everything from physical activity to sleep to heart rate. Consumer-grade wearable device manufacturers have taken the lead in creating platforms to present the data collected from these devices. Some have taken further steps to visualize and interpret the data to help users understand their own data. However, these features are often based on proprietary formulas that may not always align with the standard of care or peer-reviewed evidence (e.g., Fitbit sleep score is derived from “duration”, “quality”, and “restoration” data). Thus, consumers need help to soundly interpret the data available and apply the information to make decisions about their own care.
With decreasing unit costs and increasing acceptability of these devices, wearables have been integrated into interventions, both as a data collection tool (e.g., step count) and/or the mechanism to encourage health behavior change (e.g., vibration to “nudge” user to stand up or to exercise). An important part of any intervention utilizing a wearable is how patient-generated health data (PGHD) are collected, accessed, and translated into actionable information for the individual.
This panel will 1) discuss how we’ve addressed challenges related to technology acceptance and/or digital health literacy levels with a tailored onboarding experience for participants; 2) describe efforts to visually integrate disparate streams of data so that individuals can draw connections among their health behaviors and biometrics; and 3) discuss best practices and other considerations when cultivating partnerships between researchers and industry.
Moderator
Event Type
Discussion Panel
TimeTuesday, April 13:30pm - 4:30pm EDT
LocationPier 2/3
Digital Health (DH)