Real World Evidence: Mobile Health (mHealth)

Section 1



Christopher E. Knoepke, PhD, MSW

Jennifer D. Portz, PhD, MSW

Sheana Bull, PhD, MPH

Lisa Sandy, MPH

Thomas Glorioso, MS

Joy Wachtal, MPH

Phat Luong, MS

Adrian Hernandez, MD

Michael Ho, MD, PhD


Contributing Editor

Karen Staman, MS

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 virtually limitless ways 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).

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).




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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.

Version History

Published March 16, 2020


Knoepke CE, Portz JD, Bull S, et al. Real World Evidence: Mobile Health (mHealth): Introduction. In: Rethinking Clinical Trials: A Living Textbook of Pragmatic Clinical Trials. Bethesda, MD: NIH Health Care Systems Research Collaboratory. Available at: Updated March 26, 2020. DOI: 10.28929/121.