title: Supporting self-management of obesity using a novel game architecture.
abstract: Obesity has commonly been addressed using a 'one size fits all' approach centred on a combination of diet and exercise. This has not succeeded in halting the obesity epidemic, as two-thirds of American adults are now obese or overweight. Practitioners are increasingly highlighting that one's weight is shaped by myriad factors, suggesting that interventions should be tailored to the specific needs of individuals. Health games have potential to provide such tailored approach. However, they currently tend to focus on communicating and/or reinforcing knowledge, in order to suscitate learning in the participants. We argue that it would be equally, if not more valuable, that games learn from participants using recommender systems. This would allow treatments to be comprehensive, as games can deduce from the participants' behaviour which factors seem to be most relevant to his or her weight and focus on them. We introduce a novel game architecture and discuss its implications on facilitating the self-management of obesity.
authors.affiliation: Simon Fraser University, Canada giabba@sfu.ca.
authors.forename: Philippe J
authors.initials: PJ
authors.lastname: Giabbanelli
authors.affiliation: Maastricht University, The Netherlands.
authors.forename: Rik
authors.initials: R
authors.lastname: Crutzen
mesh_terms: D055317:Evidence-Based Practice; D005717:Games, Experimental; D006801:Humans; D020407:Internet / Q000706:statistics & numerical data; D009765:Obesity / Q000628:therapy*; D012648:Self Care / Q000379:methods*; D012984:Software / Q000592:standards / Q000639:trends
keywords: health games; heterogeneity; patient empowerment; recommender systems