title: Identifying functional discrepancies in self monitoring intake applications.
abstract: Self Monitoring Intake (SMI) has been shown to be an effective method to motivate adherence to a healthy diet. The key function in SMI applications is to document daily dietary intake, which requires users to select intake from a long pick list of items. Current approaches for food item selection are poor in usability because of insufficient representation of knowledge behind the work domain. This poster presents a new approach to discover and model the underlying knowledge.
authors.affiliation: University of Texas at Houston, TX, USA.
authors.forename: Min
authors.initials: M
authors.lastname: Zhu
authors.forename: Zhe
authors.initials: Z
authors.lastname: Li
authors.forename: Muhammad F
authors.initials: MF
authors.lastname: Walji
authors.forename: Jiajie
authors.initials: J
authors.lastname: Zhang
mesh_terms: D004032:Diet; D015930:Diet Records; D004435:Eating; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D012648:Self Care / Q000379:methods*; D013781:Texas