February 4, 2022
Digital Health Platforms: Considerations for Study Design and the IRB
March 7, 2022
Patient assessments performed in the clinic are often reliant on patient memory of health events resulting in recall bias, and are limited in the information they provide in that data collection is done at a fixed time point. This allows little context to be associated with the data and longitudinal collection is either sparse or nonexistent. In addition, it has been suggested that even the act of being in a clinic can introduce biases in the data through what is known as the “white coat effect”—patients may be nervous seeing a doctor, thus impacting the information gathered during their visit. These limitations with clinical data collection can be overcome through digital health platforms, like CareEvolution’s MyDataHelps™, by enabling ecological momentary assessments (EMAs). EMAs are a powerful tool in clinical research as they reduce recall bias, enable longitudinal data collection, are ecologically valid, can be highly engaging, and may be individualized to each participant. EMAs are historically popular in psychological clinical research, but they also show huge potential for dietary assessment, self-report physical activity, and much more.
The utility of EMAs have been demonstrated in a number of high quality research studies. The Providing Mental Health Precision Treatment (PROMPT) study at the University of Michigan, for example, delivers EMAs to their participants through the MyDataHelps™ digital health platform. The ultimate goal of the PROMPT study is to broaden access to mental health care through mobile technology and accelerate recovery from mental illness through improved individualized treatment, thereby reducing the overall burden of depression. To achieve this goal, the study enrolls participants on the waitlist for traditional mental health care and collects a daily mental health symptom assessment. MyDataHelps™ then seamlessly combines EMA data with data collected through wearable devices, other surveys, and DNA kit facilitation to provide a complete digital phenotype of participants. The PROMPT study also returns daily mood to their participants in conjunction with their correlating controllable behaviors, including steps, sleep, meditation, and electronic cognitive behavioral therapy, so that patients can understand their own patterns and adjust their habits as appropriate.
The University of Michigan also utilizes EMAs in the Intern Health Study, a longitudinal cohort study that aims to improve the residency experience by investigating factors that impact stress and mood for medical interns in the United States and China. To understand the psychological factors that impact rates of depression, the Intern Health Study employs a daily mood assessment. These data are collected through MyDataHelps™ along with genetic information, residency program factors, and sensor data with the goal of facilitating meaningful improvements.
Expanding beyond EMAs to assess mental health symptoms and mood, the Prediction of Glycemic Response Study (PROGRESS) uses the MyDataHelps™ digital health platform for daily meal tracking for its participants. PROGRESS is a study run by the Scripps Digital Trials Center in the area of precision nutrition, evaluating how the body’s reactions to food impact different functions. Participants are enrolled, allow connections to their electronic health records and wearable devices, give a saliva sample through mobile kit facilitation, and provide information through surveys and meal tracking EMAs all through MyDataHelps™MyDataHelps Research & Wellness Platform.
Advancements in digital health platforms and the seamless integration of EMAs into digital clinical research has opened the door for further ecological patient interaction through ecological momentary interventions (EMIs). EMIs use incoming data to influence a participant’s behavior in the moment and are a demonstrated tool for intervention in a large number of fields, including: smoking cessation, weight loss, anxiety, diabetes management, eating disorders, alcohol use, and physical activity. Learn more about how remote patient monitoring and just-in-time adaptive interventions can be enabled with MyDataHelps™.
Heron, K. E., & Smyth, J. M. (2010). Ecological momentary interventions: Incorporating mobile technology into psychosocial and health behavior treatments. British Journal of Health Psychology, 15(1), 1–39. https://doi.org/10.1348/135910709×466063
Intern Health Study. (n.d.). Intern Health Study, University of Michigan. Retrieved February 28, 2022, from https://www.internhealthstudy.org/
Maugeri, A., & Barchitta, M. (2019). A Systematic Review of Ecological Momentary Assessment of Diet: Implications and Perspectives for Nutritional Epidemiology. Nutrients, 11(11), 2696. https://doi.org/10.3390/nu11112696
PROGRESS. (n.d.). PROGRESS Study, Scripps Research Digital Trials Center. Retrieved March 1, 2022, from https://progress.scripps.edu/
PROMPT Precision Health Study | University of Michigan Precision Health. (n.d.). Precision Health, University of Michigan. Retrieved March 1, 2022, from https://precisionhealth.umich.edu/our-research/prompt/
Trull, T. J., & Ebner-Priemer, U. W. (2009). Using experience sampling methods/ecological momentary assessment (ESM/EMA) in clinical assessment and clinical research: Introduction to the special section. Psychological Assessment, 21(4), 457–462. https://doi.org/10.1037/a0017653