Apple’s ResearchKit: smart data collection for the smartphone era?

Jardine, Jennifer; Fisher, Jonathan; Carrick, Benjamin

doi:10.1177/0141076815600673

Cited by 58 publications

(43 citation statements)

References 8 publications

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“…Baseline users had a higher rate of hospitalization (6%) and emergency department visits (11%) in the 6 months before enrollment than CDC rates (2% and 8%, respectively; Table 1) 3 . Of note, 13% of Baseline users reported a history of intubation and 37% reported the use of oral steroids (in the 6 months before enrollment) to treat an asthma exacerbation ( Supplementary Table 2 ).…”

Section: Resultsmentioning

confidence: 97%

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

et al. 2017

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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.

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Section: Resultsmentioning

confidence: 97%

“…The AMHS is one of the few studies to examine the value and validity of the novel mobile health research platform, ResearchKit 3 . We conducted a prospective, observational study focused on the feasibility of conducting research remotely via this platform and observed certain strengths as well as limitations to this methodology.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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“…In their assessment of Apple's ResearchKit, Jardine et al (2015) point out that '[t]he potential for bias is significant' (294). For researchers, this also implies that demographic data need to be collected and possible bias accounted for.…”

Section: Algorithmic Biasmentioning

confidence: 99%

Big Data-Driven Health Surveillance

2018

The Big Data Agenda: Data Ethics and Critical Data Studies

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“…Of these, the most well-known are Apple's HealthKit and ResearchKit services [13, 25] where the former provides a smartphone based storage engine optimized for collection and querying of a broad spectrum of health-related sensor data, and the latter provides organizers of research studies, that make use of such data, support for study management functions such as subject recruitment. However, unlike mCerebrum, the HealthKit targets low-rate data and has no native support for other critical functions such as data quality assessment, biomarker computation, privacy management, and context-triggered user data collection, notifications, and interventions.…”

Section: Related Workmentioning

confidence: 99%

mCerebrum

Hossain

Hnat

Saleheen

et al. 2017

Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems

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The development and validation studies of new multisensory biomarkers and sensor-triggered interventions requires collecting raw sensor data with associated labels in the natural field environment. Unlike platforms for traditional mHealth apps, a software platform for such studies needs to not only support high-rate data ingestion, but also share raw high-rate sensor data with researchers, while supporting high-rate sense-analyze-act functionality in real-time. We present mCerebrum, a realization of such a platform, which supports high-rate data collections from multiple sensors with realtime assessment of data quality. A scalable storage architecture (with near optimal performance) ensures quick response despite rapidly growing data volume. Micro-batching and efficient sharing of data among multiple source and sink apps allows reuse of computations to enable real-time computation of multiple biomarkers without saturating the CPU or memory. Finally, it has a reconfigurable scheduler which manages all prompts to participants that is burden- and context-aware. With a modular design currently spanning 23+ apps, mCerebrum provides a comprehensive ecosystem of system services and utility apps. The design of mCerebrum has evolved during its concurrent use in scientific field studies at ten sites spanning 106,806 person days. Evaluations show that compared with other platforms, mCerebrum's architecture and design choices support 1.5 times higher data rates and 4.3 times higher storage throughput, while causing 8.4 times lower CPU usage. CCS Concepts • Human-centered computing → Ubiquitous and mobile computing; Ubiquitous and mobile computing systems and tools; • Computer systems organization → Embedded and cyber-physical systems; ACM Reference format Syed Monowar Hossain, Timothy Hnat, Nazir Saleheen, Nusrat Jahan Nasrin, Joseph Noor, Bo-Jhang Ho, Tyson Condie, Mani Srivastava, and Santosh Kumar. 2017. mCerebrum: A Mobile Sensing Software Platform for Development and Validation of Digital Biomarkers and Interventions. In Proceedings of SenSys ′17, Delft, Netherlands, November 6–8, 2017, 14 pages.

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Apple’s ResearchKit: smart data collection for the smartphone era?

Cited by 58 publications

References 8 publications

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

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

Big Data-Driven Health Surveillance

mCerebrum

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