scholarly journals Machine-learning analysis identifies digital behavioral phenotypes for engagement and health outcome efficacy of mHealth interventions for obesity: post-hoc analyses of a randomized trial (Preprint)

2021 ◽  
Author(s):  
Meelim Kim ◽  
Jaeyeong Yang ◽  
Woo-Young Ahn ◽  
Hyung Jin Choi
Keyword(s):  
Machine Learning ◽  
Cognitive Behavioral Therapy ◽  
Health Outcomes ◽  
Clinical Outcomes ◽  
Weight Change ◽  
Behavioral Therapy ◽  
Cognitive Behavioral ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Learning Analysis

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)

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

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.

Weight loss trajectories and related factors in a 16-week mobile obesity intervention: A retrospective observational study (Preprint)

2021 ◽  
Author(s):  
Ho Heon Kim ◽  
Young In Kim ◽  
Andreas Michaelides ◽  
Yu Rang Park
Keyword(s):  
Machine Learning ◽  
Weight Loss ◽  
Effect Size ◽  
Weight Change ◽  
Average Weight ◽  
Learning Approach ◽  
Obesity Management ◽  
Significant Difference ◽  
Machine Learning Approach ◽  
Cluster 2

BACKGROUND In obesity management, whether patients lose 5% or more of their initial weight is a critical factor in their clinical outcome. However, evaluations that only take this approach cannot identify and distinguish between individuals whose weight change varies and those who steadily lose weight. Evaluation of weight loss considering the volatility of weight change through a mobile-based intervention for obesity can facilitate the understanding of individuals’ behavior and weight changes from a longitudinal perspective. OBJECTIVE With machine learning approach, we examined weight loss trajectories and explored the factors related to behavioral and app usage characteristics that induce weight loss. METHODS We used the lifelog data of 19,784 individuals who enrolled in a 16-week obesity management program on the healthcare app Noom in the US during August 8, 2013 to August 8, 2019. We performed K-means clustering with dynamic time warping to cluster the weight loss time series and inspected the quality of clusters with the total sum of distance within the clusters. To identify the usage factors to determine clustering assignment, we longitudinally compared weekly usage statistics with effect size on a weekly basis. RESULTS Initial Body Mass Index (BMI) of participants was 33.9±5.9 kg/m2, and ultimately reached an average BMI of 32.0±5.7 kg/m2. In their weight log, 5 Clusters were identified: Cluster 1 (sharp decrease) showed a high proportion of weight reduction class between 10% and 15%—the highest among the five clusters (n=2,364/12,796, 18.9%)—followed by Cluster 2 (moderate decrease), Cluster 3 (increase), Cluster 4 (yoyo), Cluster 5 (other). In comparison between cluster 2 and cluster 4, although the effect size of difference in the average meal input adherence and average weight input adherence did not show a significant difference in the first week, it increased continuously for 7 weeks (Cohen’s d=0.408; 0.38). CONCLUSIONS With machine learning approach clustering shape-based timeseries similarity, this study identified 5 weight loss trajectories in mobile weight management app. Overall adherence and early adherence related to self-monitoring emerged as a potential predictor of these trajectories.

scholarly journals The Implementation and Effectiveness of Digital Cognitive Behavioral Therapy for Insomnia in Primary Care: A Pilot Study

2021 ◽  
Vol 2 ◽  
pp. 263348952110536
Author(s):  
Eric D.A. Hermes ◽  
Robert A. Rosenheck ◽  
Laura Burrone ◽  
Greg Dante ◽  
Carrie Lukens ◽  
...  
Keyword(s):  
Primary Care ◽  
Pilot Study ◽  
Cognitive Behavioral Therapy ◽  
Clinical Outcomes ◽  
Implementation Strategy ◽  
Behavioral Therapy ◽  
Cognitive Behavioral ◽  
Low Intensity ◽  
Phone Calls ◽  
Insomnia Severity

Background Digital interventions delivering Cognitive Behavioral Therapy for insomnia (Digital CBTi) may increase utilization of effective care for a common and serious condition. A low-intensity implementation strategy may facilitate digital CBTi use in healthcare settings. This pilot study assessed the feasibility of implementing a digital CBTi in Veterans Health Administration (VA) primary care through iterative modifications to a low-intensity implementation strategy, while evaluating clinical outcomes of a specific digital CBTi program. Methods A self-directed digital CBTi was implemented in the primary care clinics of a single VA facility using a cohort trial design that iteratively modified an implementation strategy over three 8-month phases. The phase 1 implementation strategy included (1) provider education; (2) point-of-care information via pamphlets; and (3) provider referral to digital CBTi through phone calls or messages. Phases 2 and 3 maintained these activities, while (1) adding a clinic-based coach who performed initial patient education and follow-up support contacts, (2) providing additional recruitment pathways, and (3) integrating the referral mechanism into provider workflow. Implementation outcomes included provider adoption, patient adoption, and acceptability. Clinical outcomes (insomnia severity, depression severity, and sedative hypnotic use) were compared among enrollees at baseline and 10 weeks. Results Across all phases 66 providers (48.9%) made 153 referrals, representing 0.38% of unique clinic patients. Of referrals, 77 (50.3%) enrolled in the study, 45 (29.4%) engaged in the program, and 24 (15.7%) completed it. Provider and patient adoption did not differ meaningfully across phases. Among enrollees, digital CBTi was acceptable and the Insomnia Severity Index decreased by 4.3 points (t = 6.41, p < 0.001) and 13 (18.6%) reached remission. The mean number of weakly sedative-hypnotic doses decreased by 2.2 (35.5%) (t = 2.39, p < 0.02). Conclusions Digital CBTi implementation in VA primary care is feasible using low-intensity implementation strategy, resulting in improved clinical outcomes for users. However, iterative implementation strategy modifications did not improve adoption. The trial was registered at clinicaltrials.gov (NCT03151083).

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