Encyclopedia of Behavioral Medicine

Living Edition
| Editors: Marc Gellman

eHealth/mHealth Trial Methodology

  • Fiona Louise HamiltonEmail author
  • Elizabeth MurrayEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-6439-6_102003-1

Synonyms

Definition

Trials and other methodologies used to evaluate the acceptability, effectiveness, and/or safety of eHealth/mHealth interventions.

Description

eHealth/mHealth, also known as digital health interventions (DHI), are interventions that deliver healthcare via digital technologies such as smartphones, websites, text messaging, or mobile applications (apps). They are complex interventions, typically incorporating a number of active components, and are used across a wide range of conditions, including health promotion, physiotherapy, and self-management of long-term conditions including mental health. They have the potential to increase access to effective, safe, highly cost-effective, convenient, and confidential care with less resource. Although not everyone will be able to use them, DHI may help reduce health inequalities for underserved populations if they increase virtual access to support and treatment and/or by using videos and...

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References and Further Reading

  1. Alkhaldi, G., et al. (2016). The effectiveness of prompts to promote engagement with digital interventions: A systematic review. Journal of Medical Internet Research, 18(1), e6.CrossRefGoogle Scholar
  2. Blandford, A., et al. (2018). Seven lessons for interdisciplinary research on interactive digital health interventions. Digital Health, 4, 205520761877032.CrossRefGoogle Scholar
  3. Brouwer, W., et al. (2011). Which intervention characteristics are related to more exposure to internet-delivered healthy lifestyle promotion interventions? A systematic review. Journal of Medical Internet Research, 13(1), e2.CrossRefGoogle Scholar
  4. Eccles, M. P., & Mittman, B. S. (2006). Welcome to implementation science. Implementation Science, 1, 1.CrossRefGoogle Scholar
  5. Eysenbach, G. (2005). The law of attrition. Journal of Medical Internet Research, 7(1), e11.CrossRefGoogle Scholar
  6. Lenarduzzi, V., & Taibi, D. (2016). MVP explained: A systematic mapping study on the definitions of minimal viable product. In 2016 42th Euromicro conference on software engineering and advanced applications (SEAA).Google Scholar
  7. Murray, E. (2013). Attrition revisited: Adherence and retention in a web-based alcohol trial. Journal of Medical Internet Research, 15, e162.CrossRefGoogle Scholar
  8. Murray, E., et al. (2016). Evaluating digital health interventions: Key questions and approaches. American Journal of Preventive Medicine, 51(5), 843–851.CrossRefGoogle Scholar
  9. Pham, Q., Wiljer, D., & Cafazzo, J. A. (2016). Beyond the randomized controlled trial: A review of alternatives in mHealth clinical trial methods. JMIR mHealth and uHealth, 4(3), e107.CrossRefGoogle Scholar
  10. Sekhon, M., Cartwright, M., & Francis, J. J. (2017). Acceptability of healthcare interventions: An overview of reviews and development of a theoretical framework. BMC Health Services Research, 17(1), 88.CrossRefGoogle Scholar
  11. Wickersham, K., et al. (2011). Assessing fidelity to an intervention in a randomized controlled trial to improve medication adherence. Nursing Research, 60(4), 264–269.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University College LondonLondonUK

Section editors and affiliations

  • Emily Lattie
    • 1
  1. 1.Center for Behavioral Intervention TechnologiesNorthwestern UniversityChicagoUSA