Predicting Adherence to Internet-Delivered Psychotherapy for Symptoms of Depression and Anxiety After Myocardial Infarction: Machine Learning Insights From the U-CARE Heart Randomized Controlled Trial

Wallert, John; Gustafson, Emelie; Held, Claes; Madison, Guy; Norlund, Fredrika; Essén, Louise von; Olsson, Erik

doi:10.2196/10754

Cited by 59 publications

(47 citation statements)

References 44 publications

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“…Several studies have shown that certain covariates at the participant level, such as adherence to the intervention, participant age, or gender [ 41 - 43 ], can influence dropout rates and intervention success. It is also of interest whether the use of the intervention or the amount of contact with the study team influenced the changes in the outcome measures.…”

Section: Methodsmentioning

confidence: 99%

Efficacy of a Guided Web-Based Self-Management Intervention for Depression or Dysthymia: Randomized Controlled Trial With a 12-Month Follow-Up Using an Active Control Condition

Oehler¹,

Görges²,

Rogalla³

et al. 2020

J Med Internet Res

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Background An increasing number of studies suggest that web-based interventions for patients with depression can reduce their symptoms and are expected to fill currently existing treatment gaps. However, evidence for their efficacy has mainly been derived from comparisons with wait-list or treatment as usual controls. In particular, designs using wait-list controls are unlikely to induce hope and may even have nocebo effects, making it difficult to draw conclusions about the intervention’s efficacy. Studies using active controls are rare and have not yielded conclusive results. Objective The main objective of this study is to assess the acute and long-term antidepressant efficacy of a 6-week, guided, web-based self-management intervention building on the principles of cognitive behavioral therapy (iFightDepression tool) for patients with depression compared with web-based progressive muscle relaxation as an active control condition. Methods A total of 348 patients with mild-to-moderate depressive symptoms or dysthymia (according to the Mini International Neuropsychiatric Interview) were recruited online and randomly assigned to 1 of the 2 intervention arms. Acute antidepressant effects after 6 weeks and long-term effects at 3-, 6-, and 12-month follow-up were studied using the Inventory of Depressive Symptomatology–self-rating as a primary outcome parameter and change in quality of life (Short Form 12) and user satisfaction (client satisfaction questionnaire) as secondary outcome parameters. Treatment effects were assessed using mixed model analyses. Results Over the entire observation period, a greater reduction in symptoms of depression (P=.01) and a greater improvement of life quality (P<.001) was found in the intervention group compared with the active control group. Separate tests for each time point revealed significant effects on depressive symptoms at the 3-month follow-up (d=0.281; 95% CI 0.069 to 0.493), but not after 6 weeks (main outcome:d=0.192; 95% CI −0.020 to 0.404) and 6 and 12 months. The intervention was significantly superior to the control condition with respect to user satisfaction (25.31 vs 21.97; t259=5.804; P<.01). Conclusions The fact that antidepressant effects have been found for a guided self-management tool in comparison with an active control strengthens the evidence base for the efficacy of web-based interventions. The antidepressant effect became most prominent at the 3-month follow-up. After 6 weeks of intervention, significant positive effects were observed on life quality but not on depressive symptoms. Although the effect size of such web-based interventions on symptoms of depression might be smaller than that suggested by earlier studies using wait-list control conditions, they can be a cost-effective addition to antidepressants and face-to-face psychotherapy. Trial Registration International Clinical Trials Registry Platform ICTRP080-15-09032015; https://apps.who.int/trialsearch/Trial2.aspx?TrialID=DRKS00009323

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Section: Methodsmentioning

confidence: 99%

Efficacy of a Guided Web-Based Self-Management Intervention for Depression or Dysthymia: Randomized Controlled Trial With a 12-Month Follow-Up Using an Active Control Condition

Oehler¹,

Görges²,

Rogalla³

et al. 2020

J Med Internet Res

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“…A study was done by Garcia et al [59] outlines several studies done about mental health monitoring system that uses sensors and machine learning. Similarly, ML-based algorithms have been used to predict user adherence to IDPT for depression and anxiety after myocardial infarction [74]. The use of MLbased predictive algorithms can be used to increase adaptation in IDPT systems, and the overall process is illustrated in Figure 6.…”

Section: Adaptation Through Predictive Algorithmsmentioning

confidence: 99%

Adaptive Systems for Internet-Delivered Psychological Treatments

et al. 2020

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Internet-Delivered Psychological Treatments (IDPT) are based on evidence-based psychological treatment models adjusted for interaction through the Internet. The use of Internet technologies has the potential to increase the availability of evidence-based mental health services for a far-reaching population with the use of fewer resources. Despite evidence that Internet Interventions can be effective means in mental health morbidities, most current IDPT systems are tunnel-based, inflexible, and non-interoperable. Hence it becomes essential to understand which elements of an Internet intervention contribute to effectiveness and treatment outcomes. By analogy, adaptation is a central aspect of successful face-to-face mental health therapy. Adaptability to patient needs can be regarded as an essential outcome factor in online systems for mental health interventions as well. While some aspects of rule-based and machine-learning-based adaptation have attracted attention in recent IDPT development, systematic reporting of core components, dimensions of adaptiveness, information architecture, and strategies for adaptation in the IDPT system are still lacking. To bridge this gap, we propose a model that shows how adaptive systems are represented in classical control theory and discuss how the model can be used to specify adaptive IDPT systems. Concerning the reference model, we outline the core components of adaptive IDPT systems, the main adaptive elements, dimensions of adaptiveness, information architecture applied to adaptive systems, and strategies used in the adaptation process. We also provide comprehensive guidelines on how to develop an adaptive IDPT system based on the Person-Based Approach.

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“…The same study concluded that pattern recognition can be a useful tool to tailor interventions based on usage patterns from earlier lessons [ 56 ]. With the hype of data science, several studies [ 12 , 40 , 44 , 49 , 50 , 58 , 60 - 62 ] attempted to use some form of predictive algorithm to adapt the intervention. While some studies did not report the outcome of the overall study [ 40 , 49 ], most of them reported that the use of predictive algorithms had a positive effect on the adaptation of interventions [ 12 , 44 , 50 , 58 , 60 - 62 ].…”

Section: Resultsmentioning

confidence: 99%

Adaptive Elements in Internet-Delivered Psychological Treatment Systems: Systematic Review

Mukhiya¹,

Wake²,

İnal³

et al. 2020

J Med Internet Res

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Background Internet-delivered psychological treatments (IDPTs) are built on evidence-based psychological treatment models, such as cognitive behavioral therapy, and are adjusted for internet use. The use of internet technologies has the potential to increase access to evidence-based mental health services for a larger proportion of the population with the use of fewer resources. However, despite extensive evidence that internet interventions can be effective in the treatment of mental health disorders, user adherence to such internet intervention is suboptimal. Objective This review aimed to (1) inspect and identify the adaptive elements of IDPT for mental health disorders, (2) examine how system adaptation influences the efficacy of IDPT on mental health treatments, (3) identify the information architecture, adaptive dimensions, and strategies for implementing these interventions for mental illness, and (4) use the findings to create a conceptual framework that provides better user adherence and adaptiveness in IDPT for mental health issues. Methods The review followed the guidelines from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The research databases Medline (PubMed), ACM Digital Library, PsycINFO, CINAHL, and Cochrane were searched for studies dating from January 2000 to January 2020. Based on predetermined selection criteria, data from eligible studies were analyzed. Results A total of 3341 studies were initially identified based on the inclusion criteria. Following a review of the title, abstract, and full text, 31 studies that fulfilled the inclusion criteria were selected, most of which described attempts to tailor interventions for mental health disorders. The most common adaptive elements were feedback messages to patients from therapists and intervention content. However, how these elements contribute to the efficacy of IDPT in mental health were not reported. The most common information architecture used by studies was tunnel-based, although a number of studies did not report the choice of information architecture used. Rule-based strategies were the most common adaptive strategies used by these studies. All of the studies were broadly grouped into two adaptive dimensions based on user preferences or using performance measures, such as psychometric tests. Conclusions Several studies suggest that adaptive IDPT has the potential to enhance intervention outcomes and increase user adherence. There is a lack of studies reporting design elements, adaptive elements, and adaptive strategies in IDPT systems. Hence, focused research on adaptive IDPT systems and clinical trials to assess their effectiveness are needed.

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Predicting Adherence to Internet-Delivered Psychotherapy for Symptoms of Depression and Anxiety After Myocardial Infarction: Machine Learning Insights From the U-CARE Heart Randomized Controlled Trial

Cited by 59 publications

References 44 publications

Efficacy of a Guided Web-Based Self-Management Intervention for Depression or Dysthymia: Randomized Controlled Trial With a 12-Month Follow-Up Using an Active Control Condition

Efficacy of a Guided Web-Based Self-Management Intervention for Depression or Dysthymia: Randomized Controlled Trial With a 12-Month Follow-Up Using an Active Control Condition

Adaptive Systems for Internet-Delivered Psychological Treatments

Adaptive Elements in Internet-Delivered Psychological Treatment Systems: Systematic Review

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