Real World Evidence: Mobile Health (mHealth)
Section 1
Introduction
Mobile and digital technologies hold an intriguing theoretic benefit to health, particularly in terms of supporting patient self-management of common, chronic conditions. The combined ubiquity of consumer electronic devices (cell phones, tablets, computers, etc.) with a growing body of knowledge surrounding the use of administrative and patient-reported outcome data to identify, target, and tailor interventions to specific patients, provides increasing opportunities in which to improve how patients manage their health. These possibilities, coupled with the profitability of many digital health applications, has led to explosive development of these programs and associated interventions (Griffiths et al. 2006).
Unfortunately, little is known about either the efficacy or effectiveness of these applications. Less is known, arguably, about the logistic and practical issues of identifying and targeting individual patients and healthcare providers for digital health interventions using administrative data, either in the context of care delivery or for the purposes of research. While a small number of digital health interventions have been subjected to traditional efficacy trials, even fewer have been evaluated using pragmatic methods (more closely mirroring the manner in which these programs are used by typical patients in the context of their care), and most are not evaluated at all (Eysenbach et al. 2011; Larsen et al. 2019).
A Pivot to Virtual Technologies During the COVID-19 Pandemic
“The COVID-19 pandemic has considerably disrupted nearly all aspects of daily life, including healthcare delivery and clinical research. Because pragmatic clinical trials are often embedded within healthcare delivery systems, they may be at high risk of disruption due to the dual impacts on the conduct of both clinical care and research” (O'Brien et al, 2022).
Beginning in 2020, the widespread disruptions caused by the COVID-19 pandemic included delays or pauses in research site activation and in-person staff training, challenges to data collection strategies, travel restrictions, and the need to adapt the delivery of the intervention. Social distancing also affected operations, research teams, and patients during the shift from in-person to virtual interactions.
A recent analysis of the pandemic’s effect on several of the NIH Collaboratory pragmatic trials found that teams needed to carefully consider whether the study intervention would be effective when delivered virtually. However, the studies “least affected by healthcare operations-related disruptions were those with enrollment systems already in place and those relying heavily on automated data collection through the electronic health record and/or mobile technologies.”
Key benefits of pandemic-related modifications included expanded outreach capabilities and greater inclusiveness when using virtual interventions. The authors highlighted a need in the post-COVID era for research identifying the impacts of virtual interventions and data collection on study populations, completeness of data, and participant engagement.
In this chapter, we will outline many of the possibilities, advantages, and challenges associated with mobile health (mHealth) interventions, with a particular focus on design and evaluation of these programs using pragmatic trial methodologies. We will illustrate many design and evaluation challenges, culminating with a discussion of how these considerations influence the ongoing development of the “Personalized Patient Data and Behavioral Nudges to Improve Adherence to Chronic Cardiovascular Medications (Nudge)” project: an NIH Collaboratory-funded pragmatic clinical trial of a targeted, cell phone-based medication adherence intervention specifically for patients with common cardiovascular conditions (hypertension, hypercholesterolemia, diabetes, atrial fibrillation, and coronary artery disease; NCT03973931).
SECTIONS
Resources
Grand Rounds
Introducing the Digital Medicine Society (Andy Coravos, MBA, Jen Goldsack, MS, MBA)
Advancing the Use of Mobile Technologies for Data Capture & Improved Clinical Trials (John Hubbard, PhD, Barry Peterson, PhD, Cheryl Grandinetti, PharmD)
Using Nudges to Improve the Delivery of Health Care (Mitesh S. Patel, MD, MBA)
The Democratization of Medicine: Open Access, Social Media, AI, Apps, and Empowering the Patient as the Future of Clinical Research
REFERENCES
Eysenbach G, CONSORT-EHEALTH Group. 2011. CONSORT-EHEALTH: improving and standardizing evaluation reports of Web-based and mobile health interventions. J Med Internet Res. 13(4):e126. doi:10.2196/jmir.1923. PMID: 22209829.
Griffiths F, Lindenmeyer A, Powell J, Lowe P, Thorogood M. 2006. Why are health care interventions delivered over the Internet? A systematic review of the published literature. J Med Internet Res. 8(2):e10. doi:10.2196/jmir.8.2.e10. PMID: 16867965.
Larsen ME, Huckvale K, Nicholas J, et al. 2019. Using science to sell apps: evaluation of mental health app store quality claims. Npj Digit Med. 2(1):1–6. doi:10.1038/s41746-019-0093-1. PMID: 31304366.
O'Brien EC, Sugarman J, Weinfurt KP, et al. 2022. The impact of COVID-19 on pragmatic clinical trials: lessons learned from the NIH Health Care Systems Research Collaboratory. Trials. 21;23(1):424. doi: 10.1186/s13063-022-06385-8. PMID: 35597988.