Article Text

Download PDFPDF

mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study
  1. Adrian Aguilera1,2,
  2. Caroline A Figueroa1,
  3. Rosa Hernandez-Ramos1,
  4. Urmimala Sarkar2,
  5. Anupama Cemballi2,
  6. Laura Gomez-Pathak1,
  7. Jose Miramontes2,
  8. Elad Yom-Tov3,
  9. Bibhas Chakraborty4,5,6,
  10. Xiaoxi Yan4,
  11. Jing Xu4,
  12. Arghavan Modiri7,
  13. Jai Aggarwal7,
  14. Joseph Jay Williams7,
  15. Courtney R Lyles2
  1. 1School of Social Welfare, University of California Berkeley, Berkeley, California, USA
  2. 2UCSF Center for Vulnerable Populations in the Division of General Internal Medicine San Francisco, Zuckerberg San Francisco General Hospital, San Francisco, California, USA
  3. 3Microsoft Research, Herzeliya, Israel
  4. 4Centre for Quantitative Medicine, Duke-National University of Singapore Medical School, Singapore
  5. 5Department of Statistics and Applied Probability, National University of Singapore, Singapore
  6. 6Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA
  7. 7Computer Science, University of Toronto, Toronto, Ontario, Canada
  1. Correspondence to Dr Caroline A Figueroa; c.a.figueroa{at}


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.

  • telemedicine
  • health informatics
  • depression & mood disorders
  • diabetes & endocrinology

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:

View Full Text

Statistics from


  • AA and CAF are joint first authors.

  • Contributors AA and CRL designed the overall study. RH-R, LG-P, JM, AC, US and CF were involved in the design and implementation of the user-centred design phase and RCT. JJW, JM, JA, CF and EY-T were involved in the design of the reinforcement learning algorithm. CF wrote the first draft of the paper. BC, XY and JX revised the statistical analyses and power calculation sections of the paper. All authors revised the manuscript for relevant scientific content and approved the final version of the manuscript.

  • Funding This trial is funded by an R01 to Dr. Adrian Aguilera and Dr. Lyles, 1R01 HS25429-01 from the Agency for Healthcare Research and Quality.

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.