Understanding the Usage of Content in a Mental Health Intervention for Depression: An Analysis of Log Data

Background Web-based interventions for the early treatment of depressive symptoms can be considered effective in reducing mental complaints. However, there is a limited understanding of which elements in an intervention contribute to effectiveness. For efficiency and effectiveness of interventions, insight is needed into the use of content and persuasive features. Objective The aims of this study were (1) to illustrate how log data can be used to understand the uptake of the content of a Web-based intervention that is based on the acceptance and commitment therapy (ACT) and (2) to discover how log data can be of value for improving the incorporation of content in Web-based interventions. Methods Data from 206 participants (out of the 239) who started the first nine lessons of the Web-based intervention, Living to the Full, were used for a secondary analysis of a subset of the log data of the parent study about adherence to the intervention. The log files used in this study were per lesson: login, start mindfulness, download mindfulness, view success story, view feedback message, start multimedia, turn on text-message coach, turn off text-message coach, and view text message. Differences in usage between lessons were explored with repeated measures ANOVAs (analysis of variance). Differences between groups were explored with one-way ANOVAs. To explore the possible predictive value of the login per lesson quartiles on the outcome measures, four linear regressions were used with login quartiles as predictor and with the outcome measures (Center for Epidemiologic Studies—Depression [CES-D] and the Hospital Anxiety and Depression Scale—Anxiety [HADS-A] on post-intervention and follow-up) as dependent variables. Results A significant decrease in logins and in the use of content and persuasive features over time was observed. The usage of features varied significantly during the treatment process. The usage of persuasive features increased during the third part of the ACT (commitment to value-based living), which might indicate that at that stage motivational support was relevant. Higher logins over time (9 weeks) corresponded with a higher usage of features (in most cases significant); when predicting depressive symptoms at post-intervention, the linear regression yielded a significant model with login quartile as a significant predictor (explained variance is 2.7%). Conclusions A better integration of content and persuasive features in the design of the intervention and a better intra-usability of features within the system are needed to identify which combination of features works best for whom. Pattern recognition can be used to tailor the intervention based on usage patterns from the earlier lessons and to support the uptake of content essential for therapy. An adaptable interface for a modular composition of therapy features supposes a dynamic approach for Web-based treatment; not a predefined path for all, but a flexible way to go through all features that have to be used.

[1]  S. Murphy,et al.  The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. , 2007, American journal of preventive medicine.

[2]  H. Trompetter,et al.  Measuring values and committed action with the Engaged Living Scale (ELS): psychometric evaluation in a nonclinical sample and a chronic pain sample. , 2013, Psychological assessment.

[3]  Bruce Neal,et al.  Rethinking the Dose-Response Relationship Between Usage and Outcome in an Online Intervention for Depression: Randomized Controlled Trial , 2013, Journal of medical Internet research.

[4]  G. Huston The Hospital Anxiety and Depression Scale. , 1987, The Journal of rheumatology.

[5]  S. Hayes,et al.  Acceptance and Commitment Therapy: An Experiential Approach to Behavior Change , 1999 .

[6]  Gregory T. Smith,et al.  Using Self-Report Assessment Methods to Explore Facets of Mindfulness , 2006, Assessment.

[7]  J. Kabat-Zinn,et al.  Full catastrophe living : using the wisdom of your body and mind to face stress, pain, and illness , 1990 .

[8]  Shlomo Berkovsky,et al.  Factors associated with persistent participation in an online diet intervention , 2012, CHI EA '12.

[9]  Saskia Marion Kelders,et al.  Understanding adherence to web-based interventions , 2007 .

[10]  S. Kelders,et al.  Persuasive System Design Does Matter: A Systematic Review of Adherence to Web-Based Interventions , 2012, Journal of medical Internet research.

[11]  Filip Smit,et al.  Mental health promotion as a new goal in public mental health care: a randomized controlled trial of an intervention enhancing psychological flexibility. , 2010, American journal of public health.

[12]  E. Bohlmeijer,et al.  Efficacy of an early intervention based on acceptance and commitment therapy for adults with depressive symptomatology: Evaluation in a randomized controlled trial. , 2011, Behaviour research and therapy.

[13]  S. Hayes,et al.  Comprar Acceptance And Commitment Therapy. The Process And Practice Of Mindful Change 2nd Ed. | Steven C. Hayes | 9781609189624 | Guilford Press , 2011 .

[14]  S. Hayes,et al.  Experiential avoidance and behavioral disorders: A functional dimensional approach to diagnosis and treatment. , 1996 .

[15]  J. Kabat-Zinn,et al.  Wherever you go, there you are : mindfulness meditation in everyday life , 1994 .

[16]  E. Bohlmeijer,et al.  Acceptance and commitment therapy as guided self-help for psychological distress and positive mental health: a randomized controlled trial , 2011, Psychological Medicine.

[17]  A. Barak,et al.  A Comprehensive Review and a Meta-Analysis of the Effectiveness of Internet-Based Psychotherapeutic Interventions , 2008 .

[18]  G. Andersson,et al.  Internet-Based and Other Computerized Psychological Treatments for Adult Depression: A Meta-Analysis , 2009, Cognitive behaviour therapy.

[19]  Steven C Hayes,et al.  ScholarWorks @ Georgia State University , 2018 .

[20]  Saskia M. Kelders,et al.  Using Log-Data as a Starting Point to Make eHealth More Persuasive , 2013, PERSUASIVE.

[21]  P. Spinhoven,et al.  The criterion validity of the Center for Epidemiological Studies Depression Scale (CES‐D) in a sample of self‐referred elders with depressive symptomatology , 2004, International journal of geriatric psychiatry.

[22]  P Sutcliffe,et al.  Health technology assessment. , 1986, Israel journal of medical sciences.

[23]  Harri Oinas-Kukkonen,et al.  Persuasive Systems Design: Key Issues, Process Model, and System Features , 2009, Commun. Assoc. Inf. Syst..

[24]  S. Hayes,et al.  Experimental avoidance and behavioral disorders: a functional dimensional approach to diagnosis and treatment. , 1996, Journal of consulting and clinical psychology.

[25]  David Castle,et al.  Using the Internet to enhance the treatment of depression. , 2006, Australasian psychiatry : bulletin of Royal Australian and New Zealand College of Psychiatrists.

[26]  J. Sharry,et al.  A Service-Based Evaluation of a Therapist-Supported Online Cognitive Behavioral Therapy Program for Depression , 2013, Journal of medical Internet research.

[27]  Derek Richards,et al.  Computer-based psychological treatments for depression: a systematic review and meta-analysis. , 2012, Clinical psychology review.

[28]  Per Carlbring,et al.  Internet-based behavioral activation and acceptance-based treatment for depression: a randomized controlled trial. , 2013, Journal of affective disorders.

[29]  Saskia M Kelders,et al.  Participants, Usage, and Use Patterns of a Web-Based Intervention for the Prevention of Depression Within a Randomized Controlled Trial , 2013, Journal of medical Internet research.

[30]  Jeong Yeob Han Transaction logfile analysis in health communication research: challenges and opportunities. , 2011, Patient education and counseling.

[31]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[32]  Nicol Nijland,et al.  A Holistic Framework to Improve the Uptake and Impact of eHealth Technologies , 2011, Journal of medical Internet research.

[33]  J. Ormel,et al.  A validation study of the Hospital Anxiety and Depression Scale (HADS) in different groups of Dutch subjects , 1997, Psychological Medicine.