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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
Umeå University, Faculty of Social Sciences, Department of Psychology.ORCID iD: 0000-0001-5366-1169
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2018 (English)In: Journal of Medical Internet Research, ISSN 1438-8871, E-ISSN 1438-8871, Vol. 20, no 10, article id e10754Article in journal (Refereed) Published
Abstract [en]

Background: Low adherence to recommended treatments is a multifactorial problem for patients in rehabilitation after myocardial infarction (MI). In a nationwide trial of internet-delivered cognitive behavior therapy (iCBT) for the high-risk subgroup of patients with MI also reporting symptoms of anxiety, depression, or both (MI-ANXDEP), adherence was low. Since low adherence to psychotherapy leads to a waste of therapeutic resources and risky treatment abortion in MI-ANXDEP patients, identifying early predictors for adherence is potentially valuable for effective targeted care. Objectives: The goal of the research was to use supervised machine learning to investigate both established and novel predictors for iCBT adherence in MI-ANXDEP patients. Methods: Data were from 90 MI-ANXDEP patients recruited from 25 hospitals in Sweden and randomized to treatment in the iCBT trial Uppsala University Psychosocial Care Programme (U-CARE) Heart study. Time point of prediction was at completion of the first homework assignment. Adherence was defined as having completed more than 2 homework assignments within the 14-week treatment period. A supervised machine learning procedure was applied to identify the most potent predictors for adherence available at the first treatment session from a range of demographic, clinical, psychometric, and linguistic predictors. The internal binary classifier was a random forest model within a 3x10-fold cross-validated recursive feature elimination (RFE) resampling which selected the final predictor subset that best differentiated adherers versus nonadherers. Results: Patient mean age was 58.4 years (SD 9.4), 62% (56/90) were men, and 48% (43/90) were adherent. Out of the 34 potential predictors for adherence, RFE selected an optimal subset of 56% (19/34; Accuracy 0.64, 95% CI 0.61-0.68, P<.001). The strongest predictors for adherence were, in order of importance, (1) self-assessed cardiac-related fear, (2) sex, and (3) the number of words the patient used to answer the first homework assignment. Conclusions: For developing and testing effective iCBT interventions, investigating factors that predict adherence is important. Adherence to iCBT for MI-ANXDEP patients in the U-CARE Heart trial was best predicted by cardiac-related fear and sex, consistent with previous research, but also by novel linguistic predictors from written patient behavior which conceivably indicate verbal ability or therapeutic alliance. Future research should investigate potential causal mechanisms and seek to determine what underlying constructs the linguistic predictors tap into. Whether these findings replicate for other interventions outside of Sweden, in larger samples, and for patients with other conditions who are offered iCBT should also be investigated.

Place, publisher, year, edition, pages
JMIR Publications , 2018. Vol. 20, no 10, article id e10754
Keywords [en]
applied predictive modeling, cardiac rehabilitation, linguistics, supervised machine learning, recursive feature elimination, treatment adherence and compliance, Web-based interventions
National Category
Applied Psychology
Identifiers
URN: urn:nbn:se:umu:diva-152879DOI: 10.2196/10754ISI: 000446936700001PubMedID: 30305255OAI: oai:DiVA.org:umu-152879DiVA, id: diva2:1259925
Funder
Forte, Swedish Research Council for Health, Working Life and Welfare, 2014-4947Available from: 2018-10-31 Created: 2018-10-31 Last updated: 2018-10-31Bibliographically approved

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Madison, Guy

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