Digital Health Technology for Real-World Clinical Outcome Measurement Using Patient-Generated Data: Systematic Scoping Review

Abstract

Background: Digital health technologies (DHTs) play 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 their responses to other clinical interventions. There is a need to comprehensively map the evidence-across all conditions and technology types-on DHT measurement of patient outcomes in the real world.

Objective: We aimed to investigate the use of DHTs to measure real-world clinical outcomes using patient-generated data.

Methods: We conducted this systematic scoping review in accordance with the Joanna Briggs Institute methodology. Detailed eligibility criteria documented in a preregistered protocol informed a search strategy for the following databases: MEDLINE (Ovid), CINAHL, Cochrane (CENTRAL), Embase, PsycINFO, ClinicalTrials.gov, and the EU Clinical Trials Register. We considered studies published between 2000 and 2022 wherein digital health data were collected, passively or actively, from patients with any specified health condition outside of clinical visits. Categories for key concepts, such as DHT type and analytical 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 established guidelines.

Results: The search strategy identified 11,015 publications, with 7308 records after duplicates and reviews were removed. After screening and full-text review, 510 studies were included for extraction. These studies encompassed 169 different conditions in over 20 therapeutic areas and 44 countries. The DHTs used for mental health and addictions research (111/510, 21.8%) were the most prevalent. The most common type of DHT, mobile apps, was observed in approximately half of the studies (250/510, 49%). Most studies used only 1 DHT (346/510, 67.8%); however, the majority of technologies used were able to collect more than 1 type of data, with the most common being physiological data (189/510, 37.1%), clinical symptoms data (188/510, 36.9%), and behavioral data (171/510, 33.5%). 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, analytical approaches, and therapeutic applications within this growing body of evidence. To unlock the full potential of DHT for measuring health outcomes and capturing 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. This study provides a valuable repository of DHT studies to inform subsequent research by health care providers, policy makers, and the life sciences industry.

Trial registration: Open Science Framework 5TMKY; https://osf.io/5tmky/.

Keywords: clinical intervention; digital biomarkers; digital health; digital health management; digital tools; electronic health record; health outcomes; mHealth; mobile health; mobile phone; patient-generated health data; real-world data; real-world evidence; wearables.

Figure 1

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram. DHT: digital health technology.

Figure 2

Number of included studies and those involving artificial intelligence (AI) by year.

Figure 3

Types of digital health technologies used in the included studies by year.

Figure 4

Included studies by therapeutic area and disease or condition. ALS: amyotrophic lateral sclerosis; CNS: central nervous system.

Figure 5

Method of data entry by therapeutic area. CNS: central nervous system.

Figure 6

Analytic applications by type of data. QoL: quality of life.

Figure 7

Collection of multiple types of patient-generated data by number of studies. QoL: quality of life.