User adoption of mobile apps for chronic disease management: A case study based on myFitnessCompanion®

Leijdekkers, P; Gay, V

User adoption of mobile apps for chronic disease management: A case study based on myFitnessCompanion®

Leijdekkers, P

Gay, V

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7251 LNCS pp. 42 - 49
Issue Date:: 2012-06-18

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Field	Value	Language
dc.contributor.author	Leijdekkers, P https://orcid.org/0000-0003-3579-1175	en_US
dc.contributor.author	Gay, V https://orcid.org/0000-0002-4301-1995	en_US
dc.date.issued	2012-06-18	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7251 LNCS pp. 42 - 49	en_US
dc.identifier.isbn	9783642307782	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22239
dc.description.abstract	Health and Fitness apps for smart phones and tablets are changing the way we look after our health. This paper analyses the usage of such an app called myFitnessCompanion for chronic disease management. The analysis is based on data collected from 5000 ∈+∈ users over a period of 7 months. Highlights of the study show that blood glucose, weight and blood pressure are the main physiological data being monitored. Americans and Germans are the front-runners in adopting mobile health apps and are willing to pay for it. Most users choose to enter data manually instead of using automated wireless Bluetooth sensors. Users prefer to store the collected data on the phone rather than exporting it to Personal Health Record Systems. © 2012 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-30779-9_6	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	User adoption of mobile apps for chronic disease management: A case study based on myFitnessCompanion®	en_US
dc.type	Conference Proceeding
utslib.citation.volume	7251 LNCS	en_US
utslib.for	0805 Distributed Computing	en_US
dc.location.activity	Artimino, Italy	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	7251 LNCS	en_US

Abstract:

Health and Fitness apps for smart phones and tablets are changing the way we look after our health. This paper analyses the usage of such an app called myFitnessCompanion for chronic disease management. The analysis is based on data collected from 5000 ∈+∈ users over a period of 7 months. Highlights of the study show that blood glucose, weight and blood pressure are the main physiological data being monitored. Americans and Germans are the front-runners in adopting mobile health apps and are willing to pay for it. Most users choose to enter data manually instead of using automated wireless Bluetooth sensors. Users prefer to store the collected data on the phone rather than exporting it to Personal Health Record Systems. © 2012 Springer-Verlag.

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http://hdl.handle.net/10453/22239