The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit

• Published in: Nature biotechnology Vol. 35; no. 4; pp. 354 - 362
• Main Authors: Chan, Yu-Feng Yvonne, Wang, Pei, Rogers, Linda, Tignor, Nicole, Zweig, Micol, Hershman, Steven G, Genes, Nicholas, Scott, Erick R, Krock, Eric, Badgeley, Marcus, Edgar, Ron, Violante, Samantha, Wright, Rosalind, Powell, Charles A, Dudley, Joel T, Schadt, Eric E
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.04.2017; Nature Publishing Group
• Subjects: 631/250; 692/699/1785; Adolescent; Adult; Aged; Agriculture; Air quality; Asthma; Asthma - diagnosis; Asthma - epidemiology; Bioinformatics; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Clinical trials; Computer based research; Feasibility studies; Female; Health Services Research - organization & administration; Health Surveys - methods; Health Surveys - statistics & numerical data; Humans; Life Sciences; Male; Methods; Middle Aged; Mobile applications; New York - epidemiology; Observational studies; Observational Studies as Topic - methods; Patient Selection; Pollen; Population Surveillance - methods; Prevalence; Research Design; Risk Factors; Telemedicine - utilization; Wildfires; Young Adult; United States
• ISSN: 1087-0156; 1546-1696
• DOI: 10.1038/nbt.3826

Use of the ResearchKit platform to track symptoms of a large cohort of asthma sufferers over time demonstrates the pros and cons of mobile health applications in large-scale observational studies. The feasibility of using mobile health applications to conduct observational clinical studies requires rigorous validation. Here, we report initial findings from the Asthma Mobile Health Study, a research study, including recruitment, consent, and enrollment, conducted entirely remotely by smartphone. We achieved secure bidirectional data flow between investigators and 7,593 participants from across the United States, including many with severe asthma. Our platform enabled prospective collection of longitudinal, multidimensional data (e.g., surveys, devices, geolocation, and air quality) in a subset of users over the 6-month study period. Consistent trending and correlation of interrelated variables support the quality of data obtained via this method. We detected increased reporting of asthma symptoms in regions affected by heat, pollen, and wildfires. Potential challenges with this technology include selection bias, low retention rates, reporting bias, and data security. These issues require attention to realize the full potential of mobile platforms in research and patient care.