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

Abstract

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.

Clinical review of user engagement with mental health smartphone apps: evidence, theory and improvements

“…For instance, a recent population-level study of an iPhone app to track asthma symptoms successfully enrolled nearly 8000 participants. However, by 6 months, only 175 (ie, just 2%) of those participants had engaged enough to take a survey 10. Even one of the world’s most engaging and popular apps, Pokémon GO, which encourages users to be active and walk in order to collect virtual items spread throughout their community, suffered from limited engagement in terms of health outcomes.…”

Towards Personal Exposures: How Technology Is Changing Air Pollution and Health Research

“…The AMHS app was downloaded nearly 50,000 times in the 6 months after its launch. The study was able to demonstrate the utility of conducting a new study entirely through a smart phone app, successfully linking asthma symptoms to changes in heat, pollen and air pollution, including the 2015 wildfires in Washington State [33]. However, this study also documented several challenges that can inform future air pollution studies using smartphones for epidemiology.…”

“…These included selection bias, retention, reporting bias, and privacy concerns. Of the 50,000 downloads of the AMHS app, 8,524 individuals completed the consent process and only 2,317 individuals were classified as robust users [33]. Not surprisingly, these individuals tended to be younger, whiter, wealthier and more educated when compared to the CDC asthma registry [33].…”

“…Of the 50,000 downloads of the AMHS app, 8,524 individuals completed the consent process and only 2,317 individuals were classified as robust users [33]. Not surprisingly, these individuals tended to be younger, whiter, wealthier and more educated when compared to the CDC asthma registry [33]. Based on their experience with the AMHS app, Chan et al [33] concluded that studies conducted entirely through smartphone applications are best suited for studies requiring rapid enrollment, pose minimal risks, examine hypothesis with short time-frames, require frequent data collecting, use passive data collection (e.g.…”

Big Data-Driven Health Surveillance

“…As long as demographic limitations, e.g. with regards to generalisability, are taken into account and acknowledged, these are not necessarily problematic sample features (Chan, Yu-Feng Yvonne et al 2017). But one should not forget that demographic characteristics are just the tip of the iceberg when it comes to potential bias.…”