@inproceedings{91772306acc6498389545eca9b8ccb8e,
title = "Optimizing Decision Support for Tailored Health Behavior Change Applications",
abstract = "The Tailored Lifestyle Change Decision Aid (TLC DA) system was designed to provide support for a person to make an informed choice about which behavior change to work on when multiple unhealthy behaviors are present. TLC DA can be delivered via web, smartphones and tablets. The system collects a significant amount of information that is used to generate tailored messages to consumers to persuade them in certain healthy lifestyles. One limitation is the necessity to collect vast amounts of information from users who manually enter. By identifying an optimal set of self-reported parameters we will be able to minimize the data entry burden of the app users. The study was to identify primary determinants of health behavior choices made by patients after using the system. Using discriminant analysis an optimal set of predictors was identified. The resulting set included smoking status, smoking cessation success estimate, self-efficacy, body mass index and diet status. Predicting smoking cessation choice was the most accurate, followed by weight management. Physical activity and diet choices were better identified in a combined cluster.",
keywords = "Consumer Behaviour, Decision Support Systems, Self-Management, Statistical Analysis",
author = "Rita Kukafka and Jeong, {In Cheol} and Joseph Finkelstein",
note = "Publisher Copyright: {\textcopyright} 2015 IMIA and IOS Press.; 15th World Congress on Health and Biomedical Informatics, MEDINFO 2015 ; Conference date: 19-08-2015 Through 23-08-2015",
year = "2015",
doi = "10.3233/978-1-61499-564-7-108",
language = "English (US)",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "108--112",
editor = "Andrew Georgiou and Sarkar, {Indra Neil} and {de Azevedo Marques}, {Paulo Mazzoncini}",
booktitle = "MEDINFO 2015",
}