mHealth for research

Ipek Ensari, Noémie Elhadad
Digital health · 2020 · pp. 79–102 · doi:10.1016/b978-0-12-820077-3.00005-5 · W3110745293
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⚙ AI-generated summary by gemini-2.5-flash-lite, 2026-06-10 ⓘ

This paper provides best practices for designing engaging mHealth applications for observational biomedical research, using endometriosis as a case study for patient symptom tracking and disease understanding.

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Abstract

There have been several research- and commercial-based mHealth solutions for individuals to monitor and manage their chronic conditions. As such, engagement strategies have been primarily investigated and proposed in the context of promoting behavior change. However, there is little research on the use of self-tracking applications for observational, biomedical research where an individual self-tracks not for their own care but rather to contribute their experience of disease. This chapter aims to provide the reader with recommendations and best practices for designing user-relevant mHealth applications to promote engagement and retention, which subsequently allows for generation of meaningful insights about the disease population of interest. We focus on endometriosis as a case study, an understudied and poorly understood women’s health condition with chronic, fluctuating symptoms that result in heterogeneity in experiences of patients. As such, it presents as an example where direct patient input with regard to disease symptoms and progression over time can contribute to a better biopsychosocial understanding of the disease.

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Condition tags

endometriosis

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Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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License: CC0 · commercial use OK