An mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study
Authors: Aguilera Adrian , Figueroa Caroline A. , Hernandez-Ramos Rosa , Sarkar Urmimala , Cemballi Anupama G , Gomez-Pathak Laura , Miramontes Jose , Tov Elad Yom , Chakraborty Bibhas , Yan Xiaoxi , Xu Jing , Modiri Arghavan , Aggarwal Jai , Williams Joseph Jay , Lyles Courtney R. 
Published: July 2020

ABSTRACTIntroductionDepression and diabetes are highly disabling diseases with a high prevalence and high rate of comorbidity, particularly in low-income ethnic minority patients. Though comorbidity increases the risk of adverse outcomes and mortality, most clinical interventions target these diseases separately. Increasing physical activity might be effective to simultaneously lower depressive symptoms and improve glycemic control. Self-management apps are a cost-effective, scalable and easy access treatment to increase physical activity. However, cutting-edge technological applications often do not reach vulnerable populations and are not tailored to an individual’s behavior and characteristics. Tailoring of interventions using machine learning methods likely increases the effectiveness of the intervention.Methods and analysisIn a three-arm randomized controlled trial we will examine the effect of a text-messaging smartphone application to encourage physical activity in low-income ethnic minority patients with comorbid diabetes and depression. The adaptive intervention group receives messages chosen from different messaging banks by a reinforcement learning algorithm. The uniform random intervention group receives the same messages, but chosen from the messaging banks with equal probabilities. The control group receives a weekly mood message. We aim to recruit 276 adults from primary care clinics aged 18 to 75 years who have been diagnosed with current diabetes and show elevated depressive symptoms (PHQ-8 >5). We will compare passively collected daily step counts, self-report PHQ-8 and most recent HbA1c from medical records at baseline and at intervention completion at 6-month follow-up.Ethics and disseminationThe Institutional Review Board at the University of California San Francisco approved this study (IRB: 17-22608). We plan to submit manuscripts describing our User Designed Methods and testing of the adaptive learning algorithm and will submit the results of the trial for publication in peer-reviewed journals and presentations at (inter)-national scientific meetings.Registrationclinicaltrials.gov: NCT03490253; pre-resultsArticle SummaryStrengths and LimitationsNovel approach of targeting diabetes and depressive symptoms using a smartphone applicationAbility to compare adaptive messaging for increasing physical activity to messages delivered with equal probabilitiesTesting of a smartphone application integrated within primary care settings in a low-income vulnerable patient populationLongitudinal design with 6-month follow-up enables assessing intervention effects over timeChallenges of this trial include supporting users in key behavior change in an automated manner with minimal in-person support