Identification of key factors related to digital health observational study adherence and retention by data-driven approaches: an exploratory secondary analysis of two prospective longitudinal studies.

Background: Longitudinal digital health studies combine passively collected information from digital devices, such as commercial wearable devices, and actively contributed data, such as surveys, from participants. Although the use of smartphones and access to the internet supports the development of these studies, challenges exist in collecting representative data due to low adherence and retention. We aimed to identify key factors related to adherence and retention in digital health studies and develop a methodology to identify factors that are associated with and might affect study participant engagement.

A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19.

Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants and emerging diseases like monkeypox, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing toward individuals who are most likely to be infected and, thus, increasing the testing positivity rate (i.

Demographic Imbalances Resulting From the Bring-Your-Own-Device Study Design.

Digital health technologies, such as smartphones and wearable devices, promise to revolutionize disease prevention, detection, and treatment. Recently, there has been a surge of digital health studies where data are collected through a bring-your-own-device (BYOD) approach, in which participants who already own a specific technology may voluntarily sign up for the study and provide their digital health data. BYOD study design accelerates the collection of data from a larger number of participants than cohort design; this is possible because researchers are not limited in the study population size based on the number of devices afforded by their budget or the number of people familiar with the technology.

Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches.

Prediabetes affects one in three people and has a 10% annual conversion rate to type 2 diabetes without lifestyle or medical interventions. Management of glycemic health is essential to prevent progression to type 2 diabetes. However, there is currently no commercially-available and noninvasive method for monitoring glycemic health to aid in self-management of prediabetes.

Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept.

Introduction: Diabetes prevalence continues to grow and there remains a significant diagnostic gap in one-third of the US population that has pre-diabetes. Innovative, practical strategies to improve monitoring of glycemic health are desperately needed. In this proof-of-concept study, we explore the relationship between non-invasive wearables and glycemic metrics and demonstrate the feasibility of using non-invasive wearables to estimate glycemic metrics, including hemoglobin A1c (HbA1c) and glucose variability metrics.