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Machine Learning for Predicting Adherence to Internet-Delivered Psychotherapy for Symptoms of Depression and Anxiety after Myocardial Infarction: Insights from the U-CARE Heart Trial (Preprint)
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BACKGROUND: BACKGROUND: Low adherence to recommended treatments is a multifactorial problem in rehabilitation for patients with 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. OBJECTIVE: Applied predictive modelling with 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 U-CARE Heart. Time-point of prediction was at completion of the first homework assignment. Adherence was defined as having completed at least the first two 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 which best differentiated adherers versus non-adherers. RESULTS: RESULTS: Patient mean age was 58.4 (9.4) years, 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 < 0.01). 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: CONCLUSIONS: Adherence to iCBT for MI-ANXDEP patients 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, seek to determine what underlying constructs the linguistic predictors tap into, and whether these findings replicate for other interventions, outside of Sweden, in larger samples, and for patients with other conditions whom are offered iCBT. CLINICALTRIAL: TRIAL REGISTRATION: ClinicalTrials.gov NCT01504191; https://clinicaltrials.gov/ct2/show/NCT01504191 (Archived at Webcite at http://www.webcitation.org/6xWWSEQ22)
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Wallert et al. (2018) reported that for patients participating in an eCBT program for post-myocardial infarction depression and anxiety, self-assessed cardiac-related fear, female sex, number of words used to complete the first homework, and self-rated depression were predictors of adherence with 64% accuracy.
“Wallert et al. (2018) reported that for patients participating in an eCBT program for post-myocardial infarction depression and anxiety, self-assessed cardiac-related fear, female sex, number of words used to complete the first homework, and self-rated depression were predictors of adherence with 64% accuracy.”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [94]
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Experimental — accuracy not yet measuredWallert et al. (2018) noted that contrary to the literature, education level and age did not show a strong predictive power in this study.
“They noted that contrary to the literature, education level and age did not show a strong predictive power in this study (94).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [94]
Wallert et al. (2018) noted that education level is a commonly reported predictive factor of treatment response in the literature regarding patients participating in eCBT.
“Similarly, Wallert et al. (2018) noted that education level is a commonly reported predictive factor of treatment response, in the literature regarding patients participating in eCBT (94).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [94]
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Metadata
- Authors
- Wallert, J., Gustafson, E., Held, C., Madison, G., Norlund, F., von Essen, L., & Olsson, E. M. G.
- Affiliations
- Uppsala University (SE)
- Umeå University (SE)
- Corresponding author
- Wallert, John
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2018-04-11
- Volume
- 20
- Issue
- 10
- Pages
- e10754-e10754
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC6234350
- OpenAlex
- W2875602397
- Cited by
- 63
- References
- 43
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 11 · 2023: 14 · 2022: 12 · 2021: 7 · 2020: 6 · 2019: 3
- FWCI
- 5.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DiVA at Umeå University (Umeå University) (submittedVersion, other-oa)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac Health and Mental Health
- Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
- Keywords
- online therapy, medication adherence, internet-based cognitive behavioral therapy, myocardial infarction, depression, anxiety, predictors of adherence, homework completion, sex differences, post-infarction rehabilitation, random forest, recursive feature elimination
- MeSH terms
- Machine Learning, Anxiety, Depression, Female, Humans, Male, Middle Aged, Myocardial Infarction, Psychotherapy, Internet
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