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

Web-based and Mobile Health Interventions (2641) New Methods (800) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (403)

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 (46) Tweetations (16) Metrics

Journals

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  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
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  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
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  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
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  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
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  19. Thieme A, Belgrave D, Doherty G. Machine Learning in Mental Health. ACM Transactions on Computer-Human Interaction 2020;27(5):1 View
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  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
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  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;34(4):1945 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
  30. Dobashi K, Ho C, Fulford C, Grace Lin M, Higa C. Learning pattern classification using moodle logs and the visualization of browsing processes by time-series cross-section. Computers and Education: Artificial Intelligence 2022;3:100105 View
  31. Balaskas A, Schueller S, Cox A, Rashleigh C, Doherty G, Tizzoni M. Examining young adults daily perspectives on usage of anxiety apps: A user study. PLOS Digital Health 2023;2(1):e0000185 View
  32. Balaskas A, Schueller S, Cox A, Doherty G. Understanding users’ perspectives on mobile apps for anxiety management. Frontiers in Digital Health 2022;4 View
  33. Wakjira A, Bhattacharya S. Student Engagement Awareness in an Asynchronous E-Learning Environment. International Journal of Technology-Enabled Student Support Services 2023;12(1):1 View
  34. Jawad D, Cheng H, Wen L, Rissel C, Baur L, Mihrshahi S, Taki S. Interactivity, Quality, and Content of Websites Promoting Health Behaviors During Infancy: 6-Year Update of the Systematic Assessment. Journal of Medical Internet Research 2022;24(10):e38641 View
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Books/Policy Documents

  1. Yeager C, Bock J, Benight C. Secondary Trauma and Burnout in Military Behavioral Health Providers. View
  2. Dobashi K. Educational Data Science: Essentials, Approaches, and Tendencies. View