Automate, Illuminate, Predict: A Universal Framework for Integrating Wearable Sensors in Healthcare

Megan K O'Brien; Kristen Hohl; Richard L Lieber; Arun Jayaraman

doi:10.1159/000540492

Automate, Illuminate, Predict: A Universal Framework for Integrating Wearable Sensors in Healthcare

Digit Biomark. 2024 Aug 26;8(1):149-158. doi: 10.1159/000540492. eCollection 2024 Jan-Dec.

Authors

Megan K O'Brien^{1

2}, Kristen Hohl¹, Richard L Lieber^{1

2

3}, Arun Jayaraman^{1

2}

Affiliations

¹ Shirley Ryan AbilityLab, Chicago, IL, USA.
² Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA.
³ Edward Hines Jr, VA Hospital, Maywood, IL, USA.

Abstract

Background: Wearable sensors have been heralded as revolutionary tools for healthcare. However, while data are easily acquired from sensors, users still grapple with questions about how sensors can meaningfully inform everyday clinical practice and research.

Summary: We propose a simple, comprehensive framework for utilizing sensor data in healthcare. The framework includes three key processes that are applied together or separately to (1) automate traditional clinical measures, (2) illuminate novel correlates of disease and impairment, and (3) predict current and future outcomes. We demonstrate applications of the Automate-Illuminate-Predict framework using examples from rehabilitation medicine.

Key messages: Automate-Illuminate-Predict provides a universal approach to extract clinically meaningful data from wearable sensors. This framework can be applied across the care continuum to enhance patient care and inform personalized medicine through accessible, noninvasive technology.

Keywords: Algorithms; Clinical study; Digital biomarkers; Influence and/or predict health-related outcomes; Monitoring; Quantifiable physiological and behavioral data; Wearable sensors.

Grants and funding

This work was supported by the Shirley Ryan AbilityLab and the National Institutes of Health (NIH Grant No. P2CHD101899 to establish the Center for Smart Use of Technology to Assess Real-world Outcomes [C-STAR]). This work was also supported in part by a Research Career Scientist Award (Award No. IK6 RX003351) from the United States Department of Veterans Affairs Rehabilitation R&D (Rehab RD) Service. The funders had no role in the design, data collection, data analysis, and reporting of this study.