@Article{info:doi/10.2196/50508,
author="Eaton, Cyd
and Vallejo, Natalie
and McDonald, Xiomara
and Wu, Jasmine
and Rodr{\'i}guez, Rosa
and Muthusamy, Nishanth
and Mathioudakis, Nestoras
and Riekert, Kristin A",
title="User Engagement With mHealth Interventions to Promote Treatment Adherence and Self-Management in People With Chronic Health Conditions: Systematic Review",
journal="J Med Internet Res",
year="2024",
month="Sep",
day="24",
volume="26",
pages="e50508",
keywords="mobile health; mHealth; digital health; treatment adherence; self-management; user engagement; chronic health conditions; mobile phone",
abstract="Background: There are numerous mobile health (mHealth) interventions for treatment adherence and self-management; yet, little is known about user engagement or interaction with these technologies. Objective: This systematic review aimed to answer the following questions: (1) How is user engagement defined and measured in studies of mHealth interventions to promote adherence to prescribed medical or health regimens or self-management among people living with a health condition? (2) To what degree are patients engaging with these mHealth interventions? (3) What is the association between user engagement with mHealth interventions and adherence or self-management outcomes? (4) How often is user engagement a research end point? Methods: Scientific database (Ovid MEDLINE, Embase, Web of Science, PsycINFO, and CINAHL) search results (2016-2021) were screened for inclusion and exclusion criteria. Data were extracted in a standardized electronic form. No risk-of-bias assessment was conducted because this review aimed to characterize user engagement measurement rather than certainty in primary study results. The results were synthesized descriptively and thematically. Results: A total of 292 studies were included for data extraction. The median number of participants per study was 77 (IQR 34-164). Most of the mHealth interventions were evaluated in nonrandomized studies (157/292, 53.8{\%}), involved people with diabetes (51/292, 17.5{\%}), targeted medication adherence (98/292, 33.6{\%}), and comprised apps (220/292, 75.3{\%}). The principal findings were as follows: (1) >60 unique terms were used to define user engagement; ``use'' (102/292, 34.9{\%}) and ``engagement'' (94/292, 32.2{\%}) were the most common; (2) a total of 11 distinct user engagement measurement approaches were identified; the use of objective user log-in data from an app or web portal (160/292, 54.8{\%}) was the most common; (3) although engagement was inconsistently evaluated, most of the studies (99/195, 50.8{\%}) reported >1 level of engagement due to the use of multiple measurement methods or analyses, decreased engagement across time (76/99, 77{\%}), and results and conclusions suggesting that higher engagement was associated with positive adherence or self-management (60/103, 58.3{\%}); and (4) user engagement was a research end point in only 19.2{\%} (56/292) of the studies. Conclusions: The results revealed major limitations in the literature reviewed, including significant variability in how user engagement is defined, a tendency to rely on user log-in data over other measurements, and critical gaps in how user engagement is evaluated (infrequently evaluated over time or in relation to adherence or self-management outcomes and rarely considered a research end point). Recommendations are outlined in response to our findings with the goal of improving research rigor in this area. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42022289693; https://www.crd.york.ac.uk/prospero/display{\_}record.php?ID=CRD42022289693 ",
issn="1438-8871",
doi="10.2196/50508",
url="https://www.jmir.org/2024/1/e50508",
url="https://doi.org/10.2196/50508",
url="http://www.ncbi.nlm.nih.gov/pubmed/39316431"
}