Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint

Abstract

The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities.

Putting the usability of wearable technology in forensic psychiatry to the test: a randomized crossover trial

“…The use of wearable technology (e.g., chest straps, ear lobe sensors, wristbands, patches) provides exiting opportunities for delivering continuous (as opposed to episodic) feedback and for interventions on outcomes relevant to the individual. Several systematic reviews showed that wearable technology can have a positive impact on sleep, stress, physical activity, depression, emotional regulation, and cardiovascular and metabolic functioning ( 14 , 20 , 24 – 30 ). Sleep, stress and physical activity are considered transdiagnostic markers of psychiatric problems ( 18 – 20 ), and can be monitored relatively easy with wearable technology, assuming that the algorithms are accurate and robust ( 35 ).…”

“…Based on these biomarkers, recommender systems might provide recommendations for personalized interventions, which will have significant impact on the use of the technology in healthcare and the relationship between patients and their healthcare professional ( 21 – 23 ). Recent meta-analyses have resulted in small to medium effect sizes of wearable technology, including fitness trackers, activity trackers and biofeedback devices on stress, sleep, physical activity, depression, emotional and behavioral self-regulation, cardiovascular functioning, and metabolic syndrome ( 14 , 16 , 20 , 24 – 31 ). However, the implementation of wearable technology in forensic psychiatry faces challenges, including limited technology readiness, acceptance, usability of the devices, continuous use of the devices, privacy concerns and data management ( 14 , 32 – 35 ).…”

Editorial: Multimodal digital approaches to personalized medicine

“…Digital health is rapidly evolving and expanding, powering a paradigm shift in evidence generation for clinical development and care delivery (Marra et al, 2020 ). Driven by advances in cutting-edge biosensors and multi-sensor wearable devices, increasing maturity and acceptance of remote clinical trial models (Izmailova et al, 2021 ) and digital therapeutics (Stern et al, 2022 ) and huge growth in computational approaches to processing and interpreting this digital health data, there is great excitement in the field about the possible applications of multimodal digital approaches (Clay et al, 2022 ). Multimodal data has particular relevance to addressing complex measurement concepts and to development of better personalized treatment, incorporating digital and behavioral phenotyping with molecular endotyping.…”