Please use this identifier to cite or link to this item: https://doi.org/10.1136/bmjopen-2019-034723
Title: MHealth app using machine learning to increase physical activity in diabetes and depression: Clinical trial protocol for the DIAMANTE Study
Authors: Aguilera, A.
Figueroa, C.A.
Hernandez-Ramos, R.
Sarkar, U.
Cemballi, A.
Gomez-Pathak, L.
Miramontes, J.
Yom-Tov, E.
Chakraborty, B. 
Yan, X.
Xu, J. 
Modiri, A.
Aggarwal, J.
Jay Williams, J.
Lyles, C.R.
Keywords: Depression & mood disorders
Diabetes & endocrinology
Health informatics
Telemedicine
Issue Date: 2020
Publisher: BMJ Publishing Group
Citation: Aguilera, A., Figueroa, C.A., Hernandez-Ramos, R., Sarkar, U., Cemballi, A., Gomez-Pathak, L., Miramontes, J., Yom-Tov, E., Chakraborty, B., Yan, X., Xu, J., Modiri, A., Aggarwal, J., Jay Williams, J., Lyles, C.R. (2020). MHealth app using machine learning to increase physical activity in diabetes and depression: Clinical trial protocol for the DIAMANTE Study. BMJ Open 10 (8) : e034723. ScholarBank@NUS Repository. https://doi.org/10.1136/bmjopen-2019-034723
Rights: Attribution-NonCommercial 4.0 International
Abstract: Introduction Depression 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 glycaemic 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 behaviour and characteristics. Tailoring of interventions using machine learning methods likely increases the effectiveness of the intervention. Methods and analysis In a three-arm randomised 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 18-75 years who have been diagnosed with current diabetes and show elevated depressive symptoms (Patient Health Questionnaire depression scale-8 (PHQ-8) >5). We will compare passively collected daily step counts, self-report PHQ-8 and most recent haemoglobin A1c from medical records at baseline and at intervention completion at 6-month follow-up. Ethics and dissemination The 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. Trial registration number NCT03490253; pre-results. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
Source Title: BMJ Open
URI: https://scholarbank.nus.edu.sg/handle/10635/197594
ISSN: 20446055
DOI: 10.1136/bmjopen-2019-034723
Rights: Attribution-NonCommercial 4.0 International
Appears in Collections:Staff Publications
Elements

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1136_bmjopen_2019_034723.pdf485.07 kBAdobe PDF

OPEN

NoneView/Download

SCOPUSTM   
Citations

27
checked on Dec 2, 2022

Page view(s)

105
checked on Dec 1, 2022

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons