A context-aware adaptive feedback agent for activity monitoring and coaching

A focus in treatment of chronic diseases is optimizing levels of physical activity. At Roessingh Research and Development, a system was developed, consisting of a Smartphone and an activity sensor, that can measure a patient’s daily activity behavior and provide motivational feedback messages. We are currently looking into ways of increasing the effectiveness of motivational messages that aim to stimulate sustainable behavioral change, by adapting its timing and content to individual patients in their current context of use

Predicting feedback compliance in a teletreatment application

Health care provision is facing resourcing challenges which will further increase in the 21st century. Health care mediated by technology is widely seen as one important element in the struggle to maintain existing standards of care. Personal health monitoring and treatment systems with a high degree of autonomic operation will be required to support self-care. Such systems must provide many services and in most cases must incorporate feedback to patients to advise them how to manage the daily details of their treatment and lifestyle changes. As in many other areas of healthcare, patient compliance is however an issue. In this experiment we apply machine learning techniques to three corpora containing data from trials of body worn systems for activity monitoring and feedback. The overall objective is to investigate how to improve feedback compliance in patients using personal monitoring and treatment systems, by taking into account various contextual features associated with the feedback instances. In this article we describe our first machine learning experiments. The goal of the experiments is twofold: to determine a suitable classification algorithm and to find an optimal set of contextual features to improve the performance of the classifier. The optimal feature set was constructed using genetic algorithms. We report initial results which demonstrate the viability of this approach

The Smart technology to Manage type 2 diabetes and Achieve health goals through Record keeping and Tailored feedback (SMART) Study

Lifestyle interventions reduce the risk of diabetic complications; however, many of these interventions are difficult to implement in everyday practice. This study investigated whether a smartphone application and data mining system, GlucoGuide™, could be a functional and effective tool to supplement a lifestyle intervention in order to enhance patient care for people with prediabetes or type 2 diabetes (T2D). Using a quasi-experimental design, seventeen participants diagnosed with prediabetes or T2D were given the STEP™ test and a lifestyle prescription, and either a paper journal or the GlucoGuide™ system to record important health markers. The primary analysis compared clinical fasting blood glucose, blood pressure and step count at baseline, 1, 2 and 3 months. A significant decrease in diastolic blood pressure was seen over the 12 week study (F=3.009,

F.: Personality Diagnosis for Personalized eHealth Services

Abstract. In this paper we present two different approaches to personality diagnosis, for the provision of innovative personalized services, as used in a case study where diabetic patients were supported in the improvement of physical activity in their daily life. The first approach presented relies on a static clustering of the population, with a specific motivation strategy designed for each cluster. The second approach relies on a dynamic population clustering, making use of recommendation systems and algorithms, like Collaborative Filtering. We discuss pro and cons of each approach and a possible combination of the two, as the most promising solution for this and other personalization services in eHealth