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Zeo, Inc. was one of the pioneer companies in the quantified self-industry offering a digital solution for individuals suffering from sleep problems. The company was founded by three undergraduate students who successfully raised capital, make strategic partnerships with major brands like the National Football League (NFL), and introduced their products in stores like Best Buy.

The collegiate founders recognized their lack of knowledge and skills from the beginning. To address this gap, they developed an advisory board to guide them on scientific matters. Later, they hired a seasoned executive with industry experience to run and grow the company. Zeo, Inc. also was getting favorable attention among leading sleep experts with publications in scientific journals about the promise of this innovative technology. Zeo, Inc. was positioned to potentially disrupt the technological and business model of the sleep industry at the time.

Suddenly, Zeo, Inc. announced it was shutting down the company. Technology pundits, the media and customers were surprised, “Why would a fast-rising company shut down?” Given the lack of board and senior leadership transparency, many speculated as to why Zeo, Inc. closed. The reasons ranged from a lack of a focus on the business model to not keeping up with new entrants into the digital health space such as FitBit. This case highlights for students the growth stages of startups with an emphasis of how a widely acclaimed startup can fail and the lessons to be learned from failure.

Partnership with Bayer Addressing Healthcare Needs.

Exploring the Factors behind Heart Failure Disease in Asia.

Tobacco Harm Reduction and Reduced-Risk Products.

MySIGN is a new wearable device for multiparametric remote monitoring developed by the Interdepartmental Centre for Industrial Research (CIRI) of the University of Bologna. The aim of this study is to assess the feasibility of data collection through MySIGN and the characterization of the signals and data recorded through the device. Healthy volunteers were asked to wear MySIGN and perform three tasks: Rest (R), Deep breathing test (DB), and a six-minute walking test (6MWT). During each task, the following parameters were collected via MySIGN: absolute bioimpedance value (BioZ), bioimpedance variation ($\mathrm{\Delta }$BioZ), respiratory rate, heart rate, ECG QT interval, blood oxygen saturation, and temperature. Fifteen volunteers were enrolled (nine females (60%), mean age $35.8\pm 9.3$ years). The parameters collected by the device were successfully recorded during the three tasks. BioZ values neither varied among the three tasks nor by stratifying the population according to age, whereas BioZ values were higher for subjects with a body mass index (BMI) $>23$kg/m². $\mathrm{\Delta }$BioZ remained unchanged regardless of the task, age, and BMI. MySIGN allows effective multiparametric data collection. The $\mathrm{\Delta }$BioZ might be a good value for estimating lung congestion because it is less influenced by other variables.

Data from digital health technologies (DHT), including wearable sensors like Apple Watch, Whoop, Oura Ring, and Fitbit, are increasingly being used in biomedical research. Research and development of DHT-related devices, platforms, and applications is happening rapidly and with significant private-sector involvement with new biotech companies and large tech companies (e.g. Google, Apple, Amazon, Uber) investing heavily in technologies to improve human health. Many academic institutions are building capabilities related to DHT research, often in cross-sector collaboration with technology companies and other organizations with the goal of generating clinically meaningful evidence to improve patient care, to identify users at an earlier stage of disease presentation, and to support health preservation and disease prevention. Large research consortia, cross-sector partnerships, and individual research labs are all represented in the current corpus of published studies. Some of the large research studies, like NIH’s All of Us Research Program, make data sets from wearable sensors available to the research community, while the vast majority of data from wearable sensors and other DHTs are held by private sector organizations and are not readily available to the research community. As data are unlocked from the private sector and made available to the academic research community, there is an opportunity to develop innovative analytics and methods through expanded access. This Session solicited research results leveraging digital health technologies, including wearable sensor data, describing novel analytical methods, and issues related to diversity, equity, inclusion (DEI) of both the underlying research data sets and the community of researchers working in this area. We particularly encouraged submissions describing opportunities for expanding and democratizing academic research using data from wearable sensors and related digital health technologies.

The National Institutes of Health’s (NIH) All of Us Research Program aims to enroll at least one million US participants from diverse backgrounds; collect electronic health record (EHR) data, survey data, physical measurements, biospecimens for genomics and other assays, and digital health data; and create a researcher database and tools to enable precision medicine research [1]. Since inception, digital health technologies (DHT) have been envisioned as essential to achieving the goals of the program [2]. A “bring your own device” (BYOD) study for collecting Fitbit data from participants’ devices was developed with integration of additional DHTs planned in the future [3]. Here we describe how participants can consent to share their digital health technology data, how the data are collected, how the data set is parsed, and how researchers can access the data.

The objective of this research was to build and assess the performance of a prediction model for post-operative recovery status measured by quality of life among individuals experiencing a variety of surgery types. In addition, we assessed the performance of the model for two subgroups (high and moderately consistent wearable device users). Study variables were derived from the electronic health records, questionnaires, and wearable devices of a cohort of individuals with one of 8 surgery types and that were part of the NIH All of Us research program. Through multivariable analysis, high frailty index (OR 1.69, 95% 1.05-7.22, p<0.006), and older age (OR 1.76, 95% 1.55-4.08, p<0.024) were found to be the driving risk factors of poor recovery post-surgery. Our logistic regression model included 15 variables, 5 of which included wearable device data. In wearable use subgroups, the model had better accuracy for high wearable users (81%). Findings demonstrate the potential for models that use wearable measures to assess frailty to inform clinicians of patients at risk for poor surgical outcomes. Our model performed with high accuracy across multiple surgery types and were robust to variable consistency in wearable use.

Data from digital health technologies (DHT), including wearable sensors like Apple Watch, Whoop, Oura Ring, and Fitbit, are increasingly being used in biomedical research. Research and development of DHT-related devices, platforms, and applications is happening rapidly and with significant private-sector involvement with new biotech companies and large tech companies (e.g. Google, Apple, Amazon, Uber) investing heavily in technologies to improve human health. Many academic institutions are building capabilities related to DHT research, often in cross-sector collaboration with technology companies and other organizations with the goal of generating clinically meaningful evidence to improve patient care, to identify users at an earlier stage of disease presentation, and to support health preservation and disease prevention. Large research consortia, cross-sector partnerships, and individual research labs are all represented in the current corpus of published studies. Some of the large research studies, like NIH’s All of Us Research Program, make data sets from wearable sensors available to the research community, while the vast majority of data from wearable sensors and other DHTs are held by private sector organizations and are not readily available to the research community. As data are unlocked from the private sector and made available to the academic research community, there is an opportunity to develop innovative analytics and methods through expanded access. This is the second year for this Session which solicited research results leveraging digital health technologies, including wearable sensor data, describing novel analytical methods, and issues related to diversity, equity, inclusion (DEI) of the research, data, and the community of researchers working in this area. We particularly encouraged submissions describing opportunities for expanding and democratizing academic research using data from wearable sensors and related digital health technologies.

Wearable silicone wristbands are a rapidly growing exposure assessment technology that offer researchers the ability to study previously inaccessible cohorts and have the potential to provide a more comprehensive picture of chemical exposure within diverse communities. However, there are no established best practices for analyzing the data within a study or across multiple studies, thereby limiting impact and access of these data for larger meta-analyses. We utilize data from three studies, from over 600 wristbands worn by participants in New York City and Eugene, Oregon, to present a first-of-its-kind manuscript detailing wristband data properties. We further discuss and provide concrete examples of key areas and considerations in common statistical modeling methods where best practices must be established to enable meta-analyses and integration of data from multiple studies. Finally, we detail important and challenging aspects of machine learning, meta-analysis, and data integration that researchers will face in order to extend beyond the limited scope of individual studies focused on specific populations.

Neuropsychiatric disorders are the leading cause of disability worldwide and there is no gold standard currently available for the measurement of mental health. This issue is exacerbated by the fact that the information physicians use to diagnose these disorders is episodic and often subjective. Current methods to monitor mental health involve the use of subjective DSM-5 guidelines, and advances in EEG and video monitoring technologies have not been widely adopted due to invasiveness and inconvenience. Wearable technologies have surfaced as a ubiquitous and unobtrusive method for providing continuous, quantitative data about a patient. Here, we introduce PRISM—Passive, Real-time Information for Sensing Mental Health. This platform integrates motion, light and heart rate data from a smart watch application with user interactions and text entries from a web application. We have demonstrated a proof of concept by collecting preliminary data through a pilot study of 13 subjects. We have engineered appropriate features and applied both unsupervised and supervised learning to develop models that are predictive of user-reported ratings of their emotional state, demonstrating that the data has the potential to be useful for evaluating mental health. This platform could allow patients and clinicians to leverage continuous streams of passive data for early and accurate diagnosis as well as constant monitoring of patients suffering from mental disorders.