@article{lam_using_2021,
	title = {Using wearable activity trackers to predict {Type}-2 {Diabetes}: {A} machine learning-based cross-sectional study of the {UK} {Biobank} accelerometer cohort},
	url = {https://preprints.jmir.org/preprint/23364},
	doi = {10.2196/23364},
	journal = {JMIR Diabetes},
	author = {Lam, B and Catt, M and Cassidy, S and Bacardit, J and Darke, P and Butterfield, S and Alshabrawy, O and Trenell, M and Missier, P},
	month = jan,
	year = {2021},
}

{"_id":"BcJE8r7bh4nycjFLr","bibbaseid":"lam-catt-cassidy-bacardit-darke-butterfield-alshabrawy-trenell-etal-usingwearableactivitytrackerstopredicttype2diabetesamachinelearningbasedcrosssectionalstudyoftheukbiobankaccelerometercohort-2021","authorIDs":[],"author_short":["Lam, B","Catt, M","Cassidy, S","Bacardit, J","Darke, P","Butterfield, S","Alshabrawy, O","Trenell, M","Missier, P"],"bibdata":{"bibtype":"article","type":"article","title":"Using wearable activity trackers to predict Type-2 Diabetes: A machine learning-based cross-sectional study of the UK Biobank accelerometer cohort","url":"https://preprints.jmir.org/preprint/23364","doi":"10.2196/23364","journal":"JMIR Diabetes","author":[{"propositions":[],"lastnames":["Lam"],"firstnames":["B"],"suffixes":[]},{"propositions":[],"lastnames":["Catt"],"firstnames":["M"],"suffixes":[]},{"propositions":[],"lastnames":["Cassidy"],"firstnames":["S"],"suffixes":[]},{"propositions":[],"lastnames":["Bacardit"],"firstnames":["J"],"suffixes":[]},{"propositions":[],"lastnames":["Darke"],"firstnames":["P"],"suffixes":[]},{"propositions":[],"lastnames":["Butterfield"],"firstnames":["S"],"suffixes":[]},{"propositions":[],"lastnames":["Alshabrawy"],"firstnames":["O"],"suffixes":[]},{"propositions":[],"lastnames":["Trenell"],"firstnames":["M"],"suffixes":[]},{"propositions":[],"lastnames":["Missier"],"firstnames":["P"],"suffixes":[]}],"month":"January","year":"2021","bibtex":"@article{lam_using_2021,\n\ttitle = {Using wearable activity trackers to predict {Type}-2 {Diabetes}: {A} machine learning-based cross-sectional study of the {UK} {Biobank} accelerometer cohort},\n\turl = {https://preprints.jmir.org/preprint/23364},\n\tdoi = {10.2196/23364},\n\tjournal = {JMIR Diabetes},\n\tauthor = {Lam, B and Catt, M and Cassidy, S and Bacardit, J and Darke, P and Butterfield, S and Alshabrawy, O and Trenell, M and Missier, P},\n\tmonth = jan,\n\tyear = {2021},\n}\n\n","author_short":["Lam, B","Catt, M","Cassidy, S","Bacardit, J","Darke, P","Butterfield, S","Alshabrawy, O","Trenell, M","Missier, P"],"key":"lam_using_2021","id":"lam_using_2021","bibbaseid":"lam-catt-cassidy-bacardit-darke-butterfield-alshabrawy-trenell-etal-usingwearableactivitytrackerstopredicttype2diabetesamachinelearningbasedcrosssectionalstudyoftheukbiobankaccelerometercohort-2021","role":"author","urls":{"Paper":"https://preprints.jmir.org/preprint/23364"},"metadata":{"authorlinks":{}},"downloads":2},"bibtype":"article","biburl":"https://bibbase.org/network/files/tHMs8ic86gSWoTp44","creationDate":"2021-02-02T16:13:41.607Z","downloads":2,"keywords":[],"search_terms":["using","wearable","activity","trackers","predict","type","diabetes","machine","learning","based","cross","sectional","study","biobank","accelerometer","cohort","lam","catt","cassidy","bacardit","darke","butterfield","alshabrawy","trenell","missier"],"title":"Using wearable activity trackers to predict Type-2 Diabetes: A machine learning-based cross-sectional study of the UK Biobank accelerometer cohort","year":2021,"dataSources":["ner3YxPo3mvD9E5ym","BDjqJntjXzyBmLxhv","oiWqtmpFQ6ZtiMEK2","k75vCTghu54BjX5qH","j9tnaL2u4rifwAc2v","NCorZq2vkXK6BnhLF","ze2X9uz8Dcv2oGipf","afppXLgSuddAzAL9e","wJE4ynGem9MRsXBRn","9zrgMZfGdRkdkNXfZ","qTQGxWDYeue2pHBus"]}