Targeted prescription of cognitive–behavioral therapy versus person-centered counseling for depression using a machine learning approach.

BACKGROUND The digital healthcare community has been urged to enhance engagement and clinical outcomes by analyzing multidimensional digital phenotypes. OBJECTIVE This study aimed to investigate the performance of multivariate phenotypes predicting the engagement rate and health outcomes of digital cognitive behavioral therapy (dCBT) using a machine learning approach. METHODS We leveraged both conventional phenotypes assessed by validated psychological questionnaires and multidimensional digital phenotypes within time-series data from a mobile app of 45 participants undergoing digital cognitive behavioral therapy (dCBT) for eight weeks. To discriminate the important characteristics, we conducted a machine-learning analysis. RESULTS A higher engagement rate was associated with higher weight loss at 8 weeks (r = -0.59, p < 0001) and 24 weeks (r = -0.52, p = 0001). The machine learning approach revealed distinct multivariate profiles associated with varying impacts on the outcomes. Lower self-esteem on the conventional phenotype and higher in-app motivational measures on digital phenotypes commonly accounted for both engagement and health outcomes. In addition, eight types of digital phenotypes predicted engagement rates (mean R2 = 0416, SD = 0006). The prediction of short-term weight change (mean R2 = 0382, SD = 0015) was associated with six different digital phenotypes. Lastly, two behavioral measures of digital phenotypes were associated with a long-term weight change (mean R2 = 0590, SD = 0011). CONCLUSIONS Our findings successfully demonstrated how multiple psychological constructs, such as emotional, cognitive, behavioral, and motivational phenotypes, elucidate the mechanisms and clinical efficacy of digital intervention with the machine learning method. Our results also highlight the importance of assessing multiple aspects of motivation before and during the intervention to improve both engagement rate and clinical outcomes. This line of research may shed light on the development of advanced prevention and personalized digital therapeutics. CLINICALTRIAL ClinicalTrials.gov NCT03465306 (Retrieved September 18, 2017, https://register.clinicaltrials.gov/NCT03465306)

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Supplemental Material for Targeted Prescription of Cognitive–Behavioral Therapy Versus Person-Centered Counseling for Depression Using a Machine Learning Approach

Journal of Consulting and Clinical Psychology ◽

10.1037/ccp0000476.supp ◽

2020 ◽

Keyword(s):

Machine Learning ◽

Cognitive Behavioral Therapy ◽

Behavioral Therapy ◽

Cognitive Behavioral ◽

Learning Approach ◽

Machine Learning Approach

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Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

10.31234/osf.io/eqcdx ◽

2019 ◽

Author(s):

Oskar Flygare ◽

Jesper Enander ◽

Erik Andersson ◽

Brjánn Ljótsson ◽

Volen Z Ivanov ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forests ◽

Clinical Utility ◽

Body Dysmorphic Disorder ◽

Prediction Models ◽

Behavioral Therapy ◽

Learning Approach ◽

Learning Approaches ◽

Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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Application of machine learning to structural connectome to predict symptom reduction in depressed adolescents with cognitive behavioral therapy (CBT)

NeuroImage Clinical ◽

10.1016/j.nicl.2019.101914 ◽

2019 ◽

Vol 23 ◽

pp. 101914 ◽

Cited By ~ 1

Author(s):

Olga Tymofiyeva ◽

Justin P. Yuan ◽

Chiung-Yu Huang ◽

Colm G. Connolly ◽

Eva Henje Blom ◽

...

Keyword(s):

Machine Learning ◽

Cognitive Behavioral Therapy ◽

Behavioral Therapy ◽

Cognitive Behavioral ◽

Symptom Reduction ◽

Structural Connectome

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Internet-based Individualized Cognitive Behavioral Therapy for Shift Work Sleep Disorder (Empowered by Wellbeing Prediction): A Pilot Study Protocol (Preprint)

10.2196/preprints.24799 ◽

2020 ◽

Author(s):

Asami Ito-Masui ◽

Eiji Kawamoto ◽

Ryota Sakamoto ◽

Akane Sano ◽

Eishi Motomura ◽

...

Keyword(s):

Machine Learning ◽

Cognitive Behavioral Therapy ◽

Sleep Disorder ◽

Shift Work ◽

Behavioral Therapy ◽

Phase 1 ◽

Cognitive Behavioral ◽

Biometric Data ◽

Shift Workers ◽

Shift Work Sleep Disorder

BACKGROUND Shift work sleep disorders (SWSDs) are associated with the high turnover rates of nurses, and are considered a major medical safety issue. However, initial management can be hampered by insufficient awareness. In recent years, it has become possible to visualize, collect and analyze the work-life balance of healthcare workers with irregular sleeping and working habits by using wearable sensors that can continuously monitor biometric data under real life settings. In addition, internet-based cognitive behavioral therapy for psychiatric disorders has been shown to be effective. Application of wearable sensors and machine learning may potentially enhance the beneficial effects of internet-based cognitive behavioral therapy. OBJECTIVE In this study, we aim to develop and evaluate the effect of a new Internet-based cognitive behavioral therapy for shift work sleep disorder (iCBTS). This system includes current methods, such as medical sleep advice, as well as machine learning wellbeing prediction to improve sleep durations of shift workers and prevent declines in their wellbeing. METHODS This study consists of two phases: (1) preliminary data collection and machine learning for wellbeing prediction; (2) intervention and evaluation of iCBTS for shift work sleep disorder. Shift workers in the ICU at Mie University will wear a wearable sensor that collects biometric data and answer daily questionnaires regarding their wellbeing. Next, they will be provided with an iCBTS app for 4 weeks. Sleep and wellbeing measurements between baseline and the intervention period will then be compared. RESULTS Recruitment for phase 1 ended in October 2019. Recruitment for phase 2 is scheduled to start in October 2020. Preliminary results are expected to be available by summer 2021. CONCLUSIONS iCBTS empowered with wellbeing prediction is expected to improve the sleep durations of shift workers, thereby enhancing their overall well-being. Findings of this study will reveal the potential of this system for improving sleep disorders among shift workers. CLINICALTRIAL UMIN clinical trials registry (phase 1: UMIN 000036122, phase 2: UMIN000040547)

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