Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of other conditions. We hope that releasing data contributed by engaged research participants will seed a new community of analysts working collaboratively on understanding mobile health data to advance human health.

Objective COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. Methods The Clinical and Translational Science Award (CTSA) Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. Organized in inclusive workstreams, in two months we created: legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. Discussion The N3C has demonstrated that a multi-site collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multi-organizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19. LAY SUMMARY COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though medical records are abundant, they are largely inaccessible to outside researchers. Statistical, machine learning, and causal research are most successful with large datasets beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many clinical centers to reveal patterns in COVID-19 patients. To create N3C, the community had to overcome technical, regulatory, policy, and governance barriers to sharing patient-level clinical data. In less than 2 months, we developed solutions to acquire and harmonize data across organizations and created a secure data environment to enable transparent and reproducible collaborative research. We expect the N3C to help save lives by enabling collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care needs and thereby reduce the immediate and long-term impacts of COVID-19.

Digital technologies such as smartphones are transforming the way scientists conduct biomedical research using real-world data. Several remotely-conducted studies have recruited thousands of participants over a span of a few months. Unfortunately, these studies are hampered by substantial participant attrition, calling into question the representativeness of the collected data including generalizability of findings from these studies. We report the challenges in retention and recruitment in eight remote digital health studies comprising over 100,000 participants who participated for more than 850,000 days, completing close to 3.5 million remote health evaluations. Survival modeling surfaced several factors significantly associated(P < 1e-16) with increase in median retention time i) Clinician referral(increase of 40 days), ii) Effect of compensation (22 days), iii) Clinical conditions of interest to the study (7 days) and iv)Older adults(4 days). Additionally, four distinct patterns of daily app usage behavior that were also associated(P < 1e-10) with participant demographics were identified. Most studies were not able to recruit a representative sample, either demographically or regionally. Combined together these findings can help inform recruitment and retention strategies to enable equitable participation of populations in future digital health research.

Citizen science models of public participation in scientific research represent a growing area of opportunity for health and biomedical research, as well as new impetus for more collaborative forms of engagement in large-scale research. However, this also surfaces a variety of ethical issues that both fall outside of and build upon the standard human subjects concerns in bioethics. This article provides background on citizen science, examples of current projects in the field, and discussion of established and emerging ethical issues for citizen science in health and biomedical research.

Data sharing, and the good annotation practices it depends on, must become part of the fabric of daily research for researchers and funders.