JMIR Publications

ABSTRACT

Background:

Digital health technologies (DHT) have an ever-expanding role in health care management and delivery. Beyond their use as interventions, DHTs also serve as a vehicle for real-world data collection to characterize patients, their care journeys, and responses to other clinical interventions. There is a need to comprehensively map the evidence—across all conditions and technology types—on DHT use for measuring patient outcomes in the real world.

Objective:

To investigate the use of DHT for measuring real-world clinical outcomes using patient-generated data.

Methods:

We conducted this systematic scoping review in accordance with Joanna Briggs Institute methodology. Detailed eligibility criteria documented in a pre-registered protocol informed a search strategy for the included databases: MEDLINE (Ovid), CINAHL, Cochrane (CENTRAL), Embase, PsycINFO, ClinicalTrials.gov, and the EU Clinical Trials Register. We considered studies in which digital health data were collected, passively and/or actively, from patients with any specified health condition outside of clinical visits. Categories for key concepts, such as DHT type and analytic applications, were established where needed. Following screening and full-text review, data were extracted and analyzed using predefined fields, and findings were reported in accordance with the PRISMA-ScR.

Results:

The search strategy identified a total of 11,015 publications, with 7,308 records once duplicates and reviews were removed. After screening and full text review, 510 studies were included for extraction. These studies encompassed 169 different diseases and conditions over 20 therapeutic areas and 44 countries. DHTs used for mental health and addictions research (n = 111) were most prevalent. The most common type of DHT, smartphone/mobile apps, were reflected in approximately half of all studies (n = 250). Most studies used only one DHT (n = 346); however, the majority of digital technologies used were able to collect more than one type of data, with the most common being physiological (n = 189), clinical symptoms (n = 188), and behavioural (n = 171). Overall, there has been real growth in the depth and breadth of evidence, number of DHT types, and use of artificial intelligence and advanced analytics over time.

Conclusions:

This scoping review offers a comprehensive view of the variety of types of technology, data, collection methods, analytic approaches, and therapeutic applications within this growing body of evidence. To unlock the full potential of DHT for measuring health outcomes and capturing of digital biomarkers, there is a need for more rigorous research that goes beyond technology validation to demonstrate whether robust real-world data can be reliably captured from patients in their daily life, and whether its capture improves patient outcomes. The present study provides a valuable repository of DHT studies to inform subsequent research by health care providers, policymakers, and the life sciences industry.