Comparative Assessment of Multimodal Sensor Data Quality Collected Using Android and iOS Smartphones in Real-World Settings.

Healthcare researchers are increasingly utilizing smartphone sensor data as a scalable and cost-effective approach to studying individualized health-related behaviors in real-world settings. However, to develop reliable and robust digital behavioral signatures that may help in the early prediction of the individualized disease trajectory and future prognosis, there is a critical need to quantify the potential variability that may be present in the underlying sensor data due to variations in the smartphone hardware and software used by large population. Using sensor data collected in real-world settings from 3000 participants' smartphones for up to 84 days, we compared differences in the completeness, correctness, and consistency of the three most common smartphone sensors-the accelerometer, gyroscope, and GPS- within and across Android and iOS devices.

Young people's data governance preferences for their mental health data: MindKind Study findings from India, South Africa, and the United Kingdom.

Mobile devices offer a scalable opportunity to collect longitudinal data that facilitate advances in mental health treatment to address the burden of mental health conditions in young people. Sharing these data with the research community is critical to gaining maximal value from rich data of this nature. However, the highly personal nature of the data necessitates understanding the conditions under which young people are willing to share them.

Smartphone-Based VO2max Measurement With Heart Snapshot in Clinical and Real-world Settings With a Diverse Population: Validation Study.

Background: Maximal oxygen consumption (VOmax) is one of the most predictive biometrics for cardiovascular health and overall mortality. However, VOmax is rarely measured in large-scale research studies or routine clinical care because of the high cost, participant burden, and requirement for specialized equipment and staff.

Objective: To overcome the limitations of clinical VOmax measurement, we aim to develop a digital VOmax estimation protocol that can be self-administered remotely using only the sensors within a smartphone.

Evaluating the Utility of Smartphone-Based Sensor Assessments in Persons With Multiple Sclerosis in the Real-World Using an App (elevateMS): Observational, Prospective Pilot Digital Health Study.

Background: Multiple sclerosis (MS) is a chronic neurodegenerative disease. Current monitoring practices predominantly rely on brief and infrequent assessments, which may not be representative of the real-world patient experience. Smartphone technology provides an opportunity to assess people's daily-lived experience of MS on a frequent, regular basis outside of episodic clinical evaluations.

Detecting the impact of subject characteristics on machine learning-based diagnostic applications.

Collection of high-dimensional, longitudinal digital health data has the potential to support a wide-variety of research and clinical applications including diagnostics and longitudinal health tracking. Algorithms that process these data and inform digital diagnostics are typically developed using training and test sets generated from multiple repeated measures collected across a set of individuals. However, the inclusion of repeated measurements is not always appropriately taken into account in the analytical evaluations of predictive performance.

Toward artifact-free data in Hadamard transform-based double multiplexing of ion mobility-Orbitrap mass spectrometry.

The integration of ion mobility spectrometry (IMS) with a trap-based mass spectrometer (MS) such as Orbitrap using the dual gate approach suffers from low duty cycle. Efforts to improve the duty cycle involve the utilization of Hadamard transform-based double multiplexing which significantly improves the signal-to-noise ratio and duty cycle of the ion mobility-Orbitrap mass spectrometry (IM-Orbitrap MS) platform. However, artifacts and noise in the demultiplexed data significantly reduce the data quality and negate the benefits of multiplexing.