• 1 Freemasons Foundation Centre for Men's Health, School of Medicine, University of Adelaide, Adelaide, Australia
  • 2 Department of Movement and Sports Sciences, Ghent University, Brussels, Belgium
  • 3 Health Research Institute, Centre for Physical Activity and Health, Department of Physical Education and Sport Sciences, University of Limerick, Limerick, Ireland
  • 4 Physical Activity Research Group, Appleton Institute, School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, Australia
  • 5 Alliance for Research in Exercise, Nutrition and Activity, Sansom Institute, School of Health Sciences, University of South Australia, Adelaide, Australia
  • 6 Department of Rheumatology, Erasmus Medical Center, Rotterdam, Netherlands
  • 7 Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
  • 8 Centre for Innovative Research Across the Life Course, Faculty of Health and Life Sciences, Coventry University, Coventry, United Kingdom
  • 9 Department of Nutritional Sciences, College of Agriculture & Life Sciences, University of Arizona, Tucson, AZ, United States
  • 10 Department of Health Promotion, Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
J Med Internet Res, 2018 11 16;20(11):e292.
PMID: 30446482 DOI: 10.2196/jmir.9397


Engagement in electronic health (eHealth) and mobile health (mHealth) behavior change interventions is thought to be important for intervention effectiveness, though what constitutes engagement and how it enhances efficacy has been somewhat unclear in the literature. Recently published detailed definitions and conceptual models of engagement have helped to build consensus around a definition of engagement and improve our understanding of how engagement may influence effectiveness. This work has helped to establish a clearer research agenda. However, to test the hypotheses generated by the conceptual modules, we need to know how to measure engagement in a valid and reliable way. The aim of this viewpoint is to provide an overview of engagement measurement options that can be employed in eHealth and mHealth behavior change intervention evaluations, discuss methodological considerations, and provide direction for future research. To identify measures, we used snowball sampling, starting from systematic reviews of engagement research as well as those utilized in studies known to the authors. A wide range of methods to measure engagement were identified, including qualitative measures, self-report questionnaires, ecological momentary assessments, system usage data, sensor data, social media data, and psychophysiological measures. Each measurement method is appraised and examples are provided to illustrate possible use in eHealth and mHealth behavior change research. Recommendations for future research are provided, based on the limitations of current methods and the heavy reliance on system usage data as the sole assessment of engagement. The validation and adoption of a wider range of engagement measurements and their thoughtful application to the study of engagement are encouraged.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.