Adrián Aguilera|Caroline A. Figueroa|Rosa Hernandez-Ramos|Urmimala Sarkar|Anupama Gunshekar Cemballi|Laura Elizabeth Pathak|Jose Miramontes|Elad Yom?Tov|Bibhas Chakraborty|Xiaoxi Yan|Jing Xu|Arghavan Modiri|Jai Aggarwal|Joseph Jay Williams|Courtney R. Lyles
mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study
San Francisco General Hospital|University of California, Berkeley|University of California, Berkeley|University of California, Berkeley|San Francisco General Hospital|San Francisco General Hospital|University of California, Berkeley|San Francisco General Hospital|Microsoft Research (United Kingdom)|National University of Singapore|Quantitative BioSciences|Duke University|Quantitative BioSciences|National University of Singapore|Quantitative BioSciences|National University of Singapore|University of Toronto|University of Toronto|University of Toronto|San Francisco General Hospital
depression diabetes highly disabling diseases high prevalence high rate comorbidity particularly lowincome ethnic minority patients though comorbidity increases risk adverse outcomes mortality clinical interventions target diseases separately increasing physical activity might effective simultaneously lower depressive symptoms improve glycaemic control selfmanagement apps costeffective scalable easy access treatment increase physical activity however cuttingedge technological applications often reach vulnerable populations tailored individuals behaviour characteristics tailoring interventions using machine learning methods likely increases effectiveness interventionin threearm randomised controlled trial examine effect textmessaging smartphone application encourage physical activity lowincome ethnic minority patients comorbid diabetes depression adaptive intervention group receives messages chosen different messaging banks reinforcement learning algorithm uniform random intervention group receives messages chosen messaging banks equal probabilities control group receives weekly mood message aim recruit 276 adults primary care clinics aged 1875 years diagnosed current diabetes show elevated depressive symptoms patient health questionnaire depression scale8 phq8 5 compare passively collected daily step counts selfreport phq8 recent haemoglobin a1c medical records baseline intervention completion 6month followupthe institutional review board university california san francisco approved study irb 1722608 plan submit manuscripts describing userdesigned methods testing adaptive learning algorithm submit results trial publication peerreviewed journals presentations international scientific meetingsnct03490253 preresults