This paper is in the following e-collection/theme issue:

Web-based and Mobile Health Interventions (2037) New Methods (649) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (357)

Published on in Vol 16 , No 11 (2014) :November

Analyzing Engagement in a Web-Based Intervention Platform Through Visualizing Log-Data

Analyzing Engagement in a Web-Based Intervention Platform Through Visualizing Log-Data

Analyzing Engagement in a Web-Based Intervention Platform Through Visualizing Log-Data

Authors of this article:

Cecily Morrison 1, 2 Author Orcid Image ;   Gavin Doherty 3 Author Orcid Image
Article Authors Cited by (38) Tweetations (16) Metrics

Journals

  1. Gao P, Guan L, Liu Y, Liu F, Yu W, Li X, Liu S, Lu Y, Li H, Xiang H. Cultivating global health professionals: evaluation of a training course to develop international consulting service competence in China. Global Health Journal 2020;4(2):51 View
  2. Ainsworth B, Steele M, Stuart B, Joseph J, Miller S, Morrison L, Little P, Yardley L. Using an Analysis of Behavior Change to Inform Effective Digital Intervention Design: How Did the PRIMIT Website Change Hand Hygiene Behavior Across 8993 Users?. Annals of Behavioral Medicine 2017;51(3):423 View
  3. Enrique A, Palacios J, Ryan H, Richards D. Exploring the Relationship Between Usage and Outcomes of an Internet-Based Intervention for Individuals With Depressive Symptoms: Secondary Analysis of Data From a Randomized Controlled Trial. Journal of Medical Internet Research 2019;21(8):e12775 View
  4. Usher-Smith J, Winther L, Shefer G, Silarova B, Payne R, Griffin S. Factors Associated With Engagement With a Web-Based Lifestyle Intervention Following Provision of Coronary Heart Disease Risk: Mixed Methods Study. Journal of Medical Internet Research 2017;19(10):e351 View
  5. Meyer D, Jayawardana M, Muir S, Ho D, Sackett O. Promoting Psychological Well-Being at Work by Reducing Stress and Improving Sleep: Mixed-Methods Analysis. Journal of Medical Internet Research 2018;20(10):e267 View
  6. Perski O, Blandford A, West R, Michie S. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Translational Behavioral Medicine 2017;7(2):254 View
  7. Bauermeister J, Golinkoff J, Muessig K, Horvath K, Hightow-Weidman L. Addressing engagement in technology-based behavioural HIV interventions through paradata metrics. Current Opinion in HIV and AIDS 2017;12(5):442 View
  8. Mihuta M, Green H. The implementation of web-based cognitive rehabilitation in adult cancer survivors: examining participant engagement, attrition and treatment fidelity. Supportive Care in Cancer 2018;26(2):499 View
  9. Usher-Smith J, Masson G, Mills K, Sharp S, Sutton S, Klein W, Griffin S. A randomised controlled trial of the effect of providing online risk information and lifestyle advice for the most common preventable cancers: study protocol. BMC Public Health 2018;18(1) View
  10. Brown M, O'Neill N, van Woerden H, Eslambolchilar P, Jones M, John A. Gamification and Adherence to Web-Based Mental Health Interventions: A Systematic Review. JMIR Mental Health 2016;3(3):e39 View
  11. Pham Q, Graham G, Carrion C, Morita P, Seto E, Stinson J, Cafazzo J. A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review. JMIR mHealth and uHealth 2019;7(1):e11941 View
  12. Kemmeren L, van Schaik A, Smit J, Ruwaard J, Rocha A, Henriques M, Ebert D, Titzler I, Hazo J, Dorsey M, Zukowska K, Riper H. Unraveling the Black Box: Exploring Usage Patterns of a Blended Treatment for Depression in a Multicenter Study. JMIR Mental Health 2019;6(7):e12707 View
  13. Stassen G, Grieben C, Froböse I, Schaller A. Engagement with a Web-Based Health Promotion Intervention among Vocational School Students: A Secondary User and Usage Analysis. International Journal of Environmental Research and Public Health 2020;17(7):2180 View
  14. Lim B, Kay J, Liu W. How Does a Nation Walk?. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019;3(2):1 View
  15. Myneni S, Sridharan V, Cobb N, Cohen T. Content-Sensitive Characterization of Peer Interactions of Highly Engaged Users in an Online Community for Smoking Cessation: Mixed-Methods Approach for Modeling User Engagement in Health Promotion Interventions. Journal of Participatory Medicine 2018;10(3):e9 View
  16. Milward J, Deluca P, Drummond C, Kimergård A. Developing Typologies of User Engagement With the BRANCH Alcohol-Harm Reduction Smartphone App: Qualitative Study. JMIR mHealth and uHealth 2018;6(12):e11692 View
  17. Morrison L, Geraghty A, Lloyd S, Goodman N, Michaelides D, Hargood C, Weal M, Yardley L. Comparing usage of a web and app stress management intervention: An observational study. Internet Interventions 2018;12:74 View
  18. Bond R, Moorhead A, Mulvenna M, O'Neill S, Potts C, Murphy N. Exploring temporal behaviour of app users completing ecological momentary assessments using mental health scales and mood logs. Behaviour & Information Technology 2019;38(10):1016 View
  19. Thieme A, Belgrave D, Doherty G. Machine Learning in Mental Health. ACM Transactions on Computer-Human Interaction 2020;27(5):1 View
  20. Richmond H, Hall A, Hansen Z, Williamson E, Davies D, Lamb S. Using mixed methods evaluation to assess the feasibility of online clinical training in evidence based interventions: a case study of cognitive behavioural treatment for low back pain. BMC Medical Education 2016;16(1) View
  21. Morrison C, D'Souza M, Huckvale K, Dorn J, Burggraaff J, Kamm C, Steinheimer S, Kontschieder P, Criminisi A, Uitdehaag B, Dahlke F, Kappos L, Sellen A. Usability and Acceptability of ASSESS MS: Assessment of Motor Dysfunction in Multiple Sclerosis Using Depth-Sensing Computer Vision. JMIR Human Factors 2015;2(1):e11 View
  22. Hermes E, Lyon A, Schueller S, Glass J. Measuring the Implementation of Behavioral Intervention Technologies: Recharacterization of Established Outcomes. Journal of Medical Internet Research 2019;21(1):e11752 View
  23. Lederman R, Gleeson J, Wadley G, D’alfonso S, Rice S, Santesteban-Echarri O, Alvarez-Jimenez M. Support for Carers of Young People with Mental Illness. ACM Transactions on Computer-Human Interaction 2019;26(1):1 View
  24. Miller S, Ainsworth B, Yardley L, Milton A, Weal M, Smith P, Morrison L. A Framework for Analyzing and Measuring Usage and Engagement Data (AMUsED) in Digital Interventions: Viewpoint. Journal of Medical Internet Research 2019;21(2):e10966 View
  25. Arden-Close E, Smith E, Bradbury K, Morrison L, Dennison L, Michaelides D, Yardley L. A Visualization Tool to Analyse Usage of Web-Based Interventions: The Example of Positive Online Weight Reduction (POWeR). JMIR Human Factors 2015;2(1):e8 View
  26. Bell L, Garnett C, Qian T, Perski O, Williamson E, Potts H. Engagement With a Behavior Change App for Alcohol Reduction: Data Visualization for Longitudinal Observational Study. Journal of Medical Internet Research 2020;22(12):e23369 View
  27. Mulvenna M, Bond R, Delaney J, Dawoodbhoy F, Boger J, Potts C, Turkington R. Ethical Issues in Democratizing Digital Phenotypes and Machine Learning in the Next Generation of Digital Health Technologies. Philosophy & Technology 2021 View
  28. Chekroud A, Bondar J, Delgadillo J, Doherty G, Wasil A, Fokkema M, Cohen Z, Belgrave D, DeRubeis R, Iniesta R, Dwyer D, Choi K. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry 2021;20(2):154 View
  29. Scarpa M, Prilletensky I, McMahon A, Myers N, Prilleltensky O, Lee S, Pfeiffer K, Bateman A, Brincks A. Is Fun For Wellness Engaging? Evaluation of User Experience of an Online Intervention to Promote Well-Being and Physical Activity. Frontiers in Computer Science 2021;3 View

Books/Policy Documents

  1. Yeager C, Bock J, Benight C. Secondary Trauma and Burnout in Military Behavioral Health Providers. View