Applying machine learning to predict future adherence to physical activity programs.

BackgroundIdentifying individuals who are unlikely to adhere to a physical exercise regime has potential to improve physical activity interventions. The aim of this paper is to develop and test adherence prediction models using objectively measured physical activity data in the Mobile Phone-Based Physical Activity Education program (mPED) trial. To the best of our knowledge, this is the first to apply Machine Learning methods to predict exercise relapse using accelerometer-recorded physical activity data.MethodsWe use logistic regression and support vector machine methods to design two versions of a Discontinuation Prediction Score (DiPS), which uses objectively measured past data (e.g., steps and goal achievement) to provide a numerical quantity indicating the likelihood of exercise relapse in the upcoming week. The respective prediction accuracy of these two versions of DiPS are compared, and then numerical simulation is performed to explore the potential of using DiPS to selectively allocate financial incentives to participants to encourage them to increase physical activity.Resultswe had access to a physical activity trial data that were continuously collected every 60 sec every day for 9 months in 210 participants. By using the first 15 weeks of data as training and test on weeks 16-30, we show that both versions of DiPS have a test AUC of 0.9 with high sensitivity and specificity in predicting the probability of exercise adherence. Simulation results assuming different intervention regimes suggest the potential benefit of using DiPS as a score to allocate resources in physical activity intervention programs in reducing costs over other allocation schemes.ConclusionsDiPS is capable of making accurate and robust predictions for future weeks. The most predictive features are steps and physical activity intensity. Furthermore, the use of DiPS scores can be a promising approach to determine when or if to provide just-in-time messages and step goal adjustments to improve compliance. Further studies on the use of DiPS in the design of physical activity promotion programs are warranted.Trial registrationClinicalTrials.gov NCT01280812 Registered on January 21, 2011

a randomized controlled trial

학위논문(박사) -- 서울대학교대학원 : 의과대학 의과학과, 2021.8. 최형진.Background - Since lifestyle modification is the cornerstone of the obesity treatment, digital therapeutics (DTx) became one of the compelling and easily accessible treatment modalities. Objective - This research proposes to validate the treatment efficacy, understand behavioral changes by eating behavioral analysis, identify the predictive digital phenotypes for engagement and clinical outcomes, and examine genetic precision medicine of a novel digital therapeutic for obesity (dCBT-O). Method – This was an open-label, active-comparator, randomized controlled trial. Seventy female participants with body mass index (BMI) scores above 24kg/m² and no clinical problems besides obesity were randomized into experimental and control groups. The experimental group (dCBT-O group; 45 participants) was connected with a therapist intervention using a digital healthcare service that provided daily feedback and assignments for 8 weeks. The control group (25 participants) also used the digital healthcare service but practiced self-care without therapist intervention. Regarding the validating treatment efficacy, the primary outcomes of this study were objectively measured: weight in kg as well as other body compositions at 0, 8, and 24 weeks. Also, several eating behavioral phenotypes were assessed by buffet test-meal and food diary in app to examine the healthy behavioral change. Regarding the predictors for treatment efficacy, multidimensional digital phenotypes within time-series data were analyzed by elastic net regression method and obesity-related SNPs were genotyped from dCBT-O group. Result – Both weight (–3.1%, SD 4.5, vs –0.7%, SD 3.4; p = 0.036) and fat mass (–6.3%, SD 8.8, vs –0.8%, SD 8.1; p = 0.021) reduction at 8 weeks in the dCBT-O group were significantly higher than in the control group. Applying the machine learning approach, sixteen types of digital phenotypes (i.e., lower intake of high calorie food and evening snack, higher interaction frequency with mentors) predicted engagement rates, thirteen different digital phenotypes (i.e., lower intake of high calorie food and carb, higher intake of low calorie food) predicted the short-term weight change, and eight measures of digital phenotypes (i.e., lower intake of carb and evening snack, higher motivation) predicted the long-term weight change. The dCBT-O was also successful in promoting healthy eating behaviors that led to physiological and psychological adjustment for the metabolic mechanisms and consequences of healthy eating behavior. Lastly, CETP and APOA2 SNPs were significantly associated with the change in BMI (p = 0.028 and p = 0.005, respectively) at 24 weeks and eating behavioral phenotypes (p = 0.007 for healthy diet diversity and p = 0.036 for healthy diet proportion, respectively), the clinical efficacy markers of this study. Conclusion – These findings confirm that the multidisciplinary approach via digital modalities enhances the clinical efficacy of digital-based interventions for obesity. Moreover, it contributes to better understand the mechanisms of human eating behavior related to weight control. This line of research may shed light on the development of advanced prevention and personalized digital therapeutics.비만은 대표적인 생활습관 질병으로 알려져 있다. 따라서, 효과적인 비만 치료를 위해서는 다차원적인 치료적 접근이 중요시되는데, 디지털 치료제(Digital Therapeutics; DTx)는 이러한 접근에 최적화 되어있다. 본 연구의 목적은 새로 개발한 비만 디지털 치료제의 효과를 임상적 지표들과 섭식 행동 표현형들의 변화를 기반으로 검증하며, 치료적 순응도와 효과성을 예측할 수 있는 디지털 표현형들과 유전형들을 탐색하는 것이다. 본 연구에서는 BMI 24 이상, 기타 임상적인 증상을 보이지 않는 70명의 2-30대 여성들을 대상으로 대조군 대비 비만 디지털 치료제군(Digital Therapeutic for Obesity; dCBT-O군)에 1:2 비율의 무작위배정 임상시험을 시행하였다. dCBT-O군의 비만 치료는 임상심리학 전공 및 디지털 헬스케어 전문가가 8주 동안 진행하였으며, 24주차에는 치료 후 경과에 대한 평가를 실시하였다. 비만 디지털 치료제 효과 검증의 주요 지표는 체중을 비롯한 다양한 신체 계측 지표들의 변화이다. 이차 지표는 뷔폐실험과 모바일 어플리케이션 내 식단기록에서 수집된 섭식행동 표현형들을 기반으로 건강한 섭식행동 변화이다. 치료 순응도 및 효과 예측 인자들을 발굴하기 위해서는 다차원적인 시계열 디지털 표현형들을 머신러닝 기법으로 분석하였다. 그리고, 치료 반응 수준을 예측하는 유전형들을 찾기 위해 단일염기다형(Single Nucleotide Polymorphisms; SNP) 분석을 시행하였다. 본 연구의 주요 결과로 첫째, 8주간 치료 직후 dCBT-O군의 체중 변화가 대조군의 체중 변화에 비해 유의미하게 감량하였으며, 치료 종료 후 24주차도 체중이 감량 및 유지되었다. 둘째, dCBT-O군의 섭식행동이 대조군의 섭식행동에 비해 유의미하게 건강한 섭식행동으로 증진되었다. 셋째, 머신러닝 분석의 결과 16가지 디지털 표현형들이 치료적 순응도를 예측하고, 13가지 디지털 표현형들이 단기적인 치료효과를 예측하며, 8가지 디지털 표현형들이 장기적인 치료효과를 예측하였다. 마지막으로, CETP와 APOA2 SNP 유전형들이 신체계측 변화와 섭식행동변화와 유의미한 상관을 보였다. 본 연구는 디지털 기술을 활용한 다학제적인 접근이 비만 디지털 치료제의 임상 효과를 향상시킨다는 것을 보여준다. 또한 다차원적인 분석을 통해 체중 조절과 관련된 인간의 섭식 행동의 메커니즘을 더 잘 이해하는 데 기여한다. 본 연구는 첨단 예방의학과 정밀의학을 위한 디지털 치료제 개발에 중요한 패러다임을 제시할 것이다.Chapter 1. Introduction 1 Part I. Validating the treatment efficacy and finding its predictive markers: development of a dCBT-O 6 Part II. Eating behavioral analysis using buffet test-meal and food diary in app: understanding human eating behavior change by dCBT-O 8 Part III. Digital phenotyping using machine-learning analysis: identifying a predictive model for engagement in application and clinical outcomes of dCBT-O 11 Part IV. Genetic analysis for predicting the clinical responses: genetic precision medicine of dCBT-O 14 Chapter 2. Method 19 Chapter 3. Results 40 Chapter 4. Discussion 75 Perspectives A. Main issues related to DTx for obesity and eating behavior problems 91 Perspectives B. Limitations of DTx being applied in the clinics 96 Perspectives C. Future perspectives and recommendations 96 Chapter 5. Conclusion 99 Bibliography 100 Abstract in Korean 118 Acknowledgement 120박

Addressing data accuracy and information integrity in mHealth using ML

The aim of the study was finding a way in which Machine Learning can be applied in mHealth Solutions to detect inaccurate data that can potentially harm patients. The result was an algorithm that classified accurate and inaccurate data