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

Personalized glucose forecasting for type 2 diabetes using data assimilation

Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual’s blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. We conclude by examining ways to present predictions as forecast-derived range quantities and evaluate the comparative advantages of these ranges

Automatic Generation of Personalized Recommendations in eCoaching

Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

Machine Learning of Lifestyle Data for Diabetes

Self-Monitoring of Blood Glucose (SMBG) for Type-2 Diabetes (T2D) remains highly challenging for both patients and doctors due to the complexities of diabetic lifestyle data logging and insufficient short-term and personalized recommendations/advice. The recent mobile diabetes management systems have been proved clinically effective to facilitate self-management. However, most such systems have poor usability and are limited in data analytic functionalities. These two challenges are connected and affected by each other. The ease of data recording brings better data for applicable data analytic algorithms. On the other hand, the irrelevant or inaccurate data input will certainly commit errors and noises. The output of data analysis, as potentially valuable patterns or knowledge, could be the incentives for users to contribute more data. We believe that the incorporation of machine learning technologies in mobile diabetes management could tackle these challenge simultaneously. In this thesis, we propose, build, and evaluate an intelligent mobile diabetes management system, called GlucoGuide for T2D patients. GlucoGuide conveniently aggregates varieties of lifestyle data collected via mobile devices, analyzes the data with machine learning models, and outputs recommendations. The most complicated part of SMBG is diet management. GlucoGuide aims to address this crucial issue using classification models and camera-based automatic data logging. The proposed model classifies each food item into three recommendation classes using its nutrient and textual features. Empirical studies show that the food classification task is effective. A lifestyle-data-driven recommendations framework in GlucoGuide can output short-term and personalized recommendations of lifestyle changes to help patients stabilize their blood glucose level. To evaluate performance and clinical effectiveness of this framework, we conduct a three-month clinical trial on human subjects, in collaboration with Dr. Petrella (MD). Due to the high cost and complexity of trials on humans, a small but representative subject group is involved. Two standard laboratory blood tests for diabetes are used before and after the trial. The results are quite remarkable. Generally speaking, GlucoGuide amounted to turning an early diabetic patient to be pre-diabetic, and pre-diabetic to non-diabetic, in only 3-months, depending on their before-trial diabetic conditions. cThis clinical dataset has also been expanded and enhanced to generate scientifically controlled artificial datasets. Such datasets can be used for varieties of machine learning empirical studies, as our on-going and future research works. GlucoGuide now is a university spin-off, allowing us to collect a large scale of practical diabetic lifestyle data and make potential impact on diabetes treatment and management

Enabling Automated, Conversational Health Coaching with Human-Centered Artificial Intelligence

Health coaching is a promising approach to support self-management of chronic conditions like type 2 diabetes; however, there aren’t enough coaching practitioners to support those in need. Advances in Artificial Intelligence (AI) and Machine Learning (ML) have the potential to enable innovative, automated health coaching interventions, but important gaps remain in applying AI and ML to coaching interventions. This thesis aims to identify computational approaches and interactive technologies that enable automated health coaching systems. First, I utilized computational approaches that leverage individuals’ self-tracking and health data and used an expert system to translate ML inferences into personalized nutrition goal recommendations. The system, GlucoGoalie, was evaluated in multiple studies including a 4-week deployment study which demonstrated the feasibility of the approach. Second, I compared human-powered and automated/chatbot approaches to health coaching in a 3-week study which found that t2.coach — a scripted, theoretically-grounded chatbot designed through an iterative, user-centered process — cultivated a coach-like experience that had many similarities to the experience of messaging with actual health coaches, and outlined directions for automated, conversational coaching interventions. Third, I examined multiple AI approaches to enable micro-coaching dialogs — brief coaching conversations related to specific meals, to support achievement of nutrition goals — including a knowledge-based system for natural language understanding, and a data-driven, reinforcement learning approach for dialog management. Together, the results of these studies contribute methods and insights that take steps towards more intelligent conversational coaching systems, with resonance to research in informatics, human-computer interaction, and health coaching

The Age of Artificial Intelligence: Use of Digital Technology in Clinical Nutrition

Purpose of review Computing advances over the decades have catalyzed the pervasive integration of digital technology in the medical industry, now followed by similar applications for clinical nutrition. This review discusses the implementation of such technologies for nutrition, ranging from the use of mobile apps and wearable technologies to the development of decision support tools for parenteral nutrition and use of telehealth for remote assessment of nutrition. Recent findings Mobile applications and wearable technologies have provided opportunities for real-time collection of granular nutrition-related data. Machine learning has allowed for more complex analyses of the increasing volume of data collected. The combination of these tools has also translated into practical clinical applications, such as decision support tools, risk prediction, and diet optimization. Summary The state of digital technology for clinical nutrition is still young, although there is much promise for growth and disruption in the future

Human Coaching Methodologies for Automatic Electronic Coaching (eCoaching) as Behavioral Interventions with Information and Communication Technology: Systematic Review

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Smart Sensing Technologies for Personalised Coaching

People living in both developed and developing countries face serious health challenges related to sedentary lifestyles. It is therefore essential to find new ways to improve health so that people can live longer and can age well. With an ever-growing number of smart sensing systems developed and deployed across the globe, experts are primed to help coach people toward healthier behaviors. The increasing accountability associated with app- and device-based behavior tracking not only provides timely and personalized information and support but also gives us an incentive to set goals and to do more. This book presents some of the recent efforts made towards automatic and autonomous identification and coaching of troublesome behaviors to procure lasting, beneficial behavioral changes