Personalizable intervention systems to promote healthy behavior change

Adopting healthy behaviors can prevent the onset of many adverse health conditions. However, behavior changes are difficult to make, and often, people who like to improve their behaviors do not know how to do that. Personalizable intervention systems could assist them to achieve healthy behavior change. These systems decide what would be the optimal intervention for the target user based on his or her characteristics, including current and past behavior patterns. In this thesis, we propose novel solutions that address the main challenges in building a personalizable intervention system to promote healthy behavior change. First, we propose a system based on a Bayesian mixture model to identify subpopulations with different behavior changes from longitudinal data. This system is especially suitable when the amount of data is limited, and when there are unobserved factors that might affect behavior change. Second, we propose CLINT, a system based on a latent-variable model, to discover and predict behavior change patterns from fine-grained sensor data. The novelty of this system is that it produces interpretable patterns that could be used to suggest successful behavior change strategies from the existing users similar to the target user. Third, we propose a personalizable intervention system to improve the physical activeness of senior adults. The main novelty of this system is that it uses historical time series fitness data to decide which intervention to recommend. Finally, we propose ACFR, an adversarial approach to reduce intervention bias in observational data. This approach learns a balanced representation of the covariates that allows personalizable intervention systems to make a better estimate of the intervention effect. Our solutions turn existing human behavior data into actionable insights for future users who may have unhealthy lifestyles

Analysis of the recommendation algorithm in Cohesy

Pervasive health care takes steps to design, develop, and evaluate computer technologies that help citizens participate more closely in their own healthcare, on one hand, and on the other to provide flexibility in the life of patient who lead an active everyday life with work, family and friends. This paper presents a novel collaborative algorithm that generates recommendations and suggestions for preventive intervention. The main purpose of this algorithm is to find the dependency of the users’ health condition and physical activities he/she performs. The recommendation algorithm, presented in this paper, is part of the Collaborative health care system model called COHESY. COHESY improves quality of care and life to its users, by offering freedom to enjoy life with the confidence that a medical professional is monitoring theirs health condition

Recommendation algorithm based on collaborative filtering and his application in health care

This paper presents a novel recommendation algorithm that generates recommendations and suggestions for preventive intervention. Presented algorithm is part of the Collaborative health care system model called COHESY. The purpose of recommendation algorithm is to give a recommendation for performing a specific activity that will improve user’s health, based on his given health condition and set of knowledge derived from the history of the user and users like him. The aim of the recommendation algorithm is to discover which activities affect change in the value of each health parameter individually. Once revealed, algorithm can use that information in situations it recognizes as same or similar to previous health conditions of a same or another user with similar medical condition. If there is evidence in users’ history, that the execution of a certain physical activity has improved user health parameters and condition, it can be concluded that the activity can help him or other users with similar health issues and improve their health condition. In this paper we also evaluate the proposed algorithm by using generic data

Validation of the Collaborative Health Care System Model COHESY

Collaborative health care system model COHESY allows monitoring of users’ health parameters and theirs physical activities. This system model helps its users to actively participate in their health care and prevention, thereby providing an active life in accordance with their daily responsibilities at work, family and friends. Recommendation algorithm, which is part of the social network of the proposed model, gives recommendations to the users for performing a specific activity that will improve their health. These recommendations are based on the users’ health condition, prior knowledge derived from users’ health history, and the knowledge derived from the medical histories of users with similar characteristics. In this paper we give validation of the proposed model by using simulations on generic data

Collaborative system for prevention of increased blood sugar level

Today there is a growing interest towards the adoption of novel technology in the field of medical monitoring and personal health care systems in general. In this paper we present algorithms that generate recommendations and suggestions for preventive intervention instead of emergency care and hospital admissions, used in the model of the collaborative health care system (Cohesy). Use of data sets from the health and physical activities history of users can be very important for improving the health of users because the history of the diagnoses can detect which physical activities contributed to the improvement of the health of the user. Step further, we use these data sets to generate recommendation for activity in cases where the user health has deteriorated

Use of collaboration techniques and classification algorithms in personal healthcare

Adoption of mobile devices and technology in the field of medical monitoring and personal health care systems is very important nowadays, especially when it comes to certain categories of people with chronicle diseases who need 24 hour access to medical care. The collaborative Information system model we present in this paper, gives a new dimension in the usage of novel technologies in healthcare. Using mobile, web and broadband technologies enable the citizens to have ubiquity of support services where ever they may be. The model incorporates collaboration techniques and classification algorithms in order to generate recommendations and suggestions for preventive intervention. In addition, the system enables the patient (system user) to contact other people with similar condition and exchange their experience. This system improves the terms of home care treatment of the patient and allows the user to adapt his/her physical activities to improve own health condition. 2012 IUPESM and Springer-Verlag

Recommender System for Responsive Engagement of Senior Adults in Daily Activities

Understanding and predicting how people change their behavior after an intervention from time series data is an important task for health recommender systems. This task is especially challenging when the time series data is frequently sampled. In this paper, we develop and propose a novel recommender system that aims to promote physical activeness in elderly people. The main novelty of our recommender system is that it learns how senior adults with different lifestyle change their activeness after a digital health intervention from minute-by-minute fitness data in an automated way. We trained the system and validated the recommendations using data from senior adults. We demonstrated that the low-level information contained in time series data is an important predictor of behavior change. The insights generated by our recommender system could help senior adults to engage more in daily activities

General Assisted Living System Architecture Model

Novel information and communication technologies create possibilities to change the future of health care and support. Ambient Assisted Living (AAL) is seen as a promising alternative to the current care models so a number of researchers have developed AAL systems with promising results. The main goal of AAL solutions is to apply ambient intelligence technologies to enable people with specific needs to continue to live in their preferred environments. In this paper, we are presenting a general architecture of system for assisted living that supports most of the use cases for such system

Evaluation of Health Care System Model Based on Collaborative Algorithms

The rapid development and use of information and communication technologies in the last two decades has influenced a dramatic transformation of public health and health care, changing the roles of the health care support systems and services. Recent trends in health care support systems are focused on developing patient-centric pervasive environments and the use of mobile devices and technologies in medical monitoring and health care systems [1]

Collaborative Health-Care System (COHESY) Model

Recent trends in health-care support systems are focused on developing patient-centric pervasive environments and the use of mobile devices and technologies in medical monitoring and health-care systems (Ballegaard et al. 2008). This is of particular importance for patients with chronic diseases who require 24 h medical care. The COllaborative HEalth-care SYstem (COHESY) model, presented in this chapter, allows monitoring of users’ health parameters and their physical activities. The proposed model uses a social network that allows communication between users with the same or similar conditions and exchange of their experiences. This model creates an opportunity for increasing users’ health care within their homes—24 h medical monitoring on one hand and increased capacity of health institutions on the other hand—resulting in reduction of overall costs for consumers and health-care institutions (Zimmerman and Chang 2008). The proposed model helps its users to actively participate in their health care and prevention, thereby providing an active life in accordance with their daily responsibilities at work and with family and friends