An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms Linardon, Jake, Fuller-Tyszkiewicz, Matthew, Shatte, A and Greenwood, Christopher 2022, An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms, International Journal of Eating Disorders, vol. 55, no. 6, pp. 845-850, doi: 10.1002/eat.23733. Attached Files Name Description MIMEType Size Downloads Title An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms Author(s) Linardon, Jake orcid.org/0000-0003-4475-7139 Fuller-Tyszkiewicz, Matthew orcid.org/0000-0003-1145-6057 Shatte, A Greenwood, Christopher orcid.org/0000-0002-9211-6312 Journal name International Journal of Eating Disorders Volume number 55 Issue number 6 Start page 845 End page 850 Total pages 6 Publisher Wiley Place of publication Chichester, Eng. Publication date 2022-06 ISSN 0276-3478 1098-108X Keyword(s) adherence digital eating disorders e-health engagement intervention machine learning prediction randomized controlled trial uptake Social Sciences Science & Technology Life Sciences & Biomedicine Psychology, Clinical Nutrition & Dietetics Psychiatry Psychology OUTCOMES Language eng DOI 10.1002/eat.23733 Field of Research 11 Medical and Health Sciences 17 Psychology and Cognitive Sciences HERDC Research category C1 Refereed article in a scholarly journal Persistent URL http://hdl.handle.net/10536/DRO/DU:30168552 Document type: Journal Article Collections: Faculty of Health School of Psychology Open Access Collection Related Links Link Description Link to full-text (open access)   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Thu, 19 May 2022, 09:15:12 EST Fri, 20 May 2022, 08:29:47 EST Tue, 05 Jul 2022, 16:08:49 EST Wed, 20 Jul 2022, 15:00:50 EST Wed, 20 Jul 2022, 16:07:17 EST Wed, 20 Jul 2022, 16:11:25 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 14 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Thu, 19 May 2022, 09:15:12 EST