Cargando…
REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial
INTRODUCTION: Achieving optimal diabetes control requires several daily self-management behaviours, especially adherence to medication. Evidence supports the use of text messages to support adherence, but there remains much opportunity to improve their effectiveness. One key limitation is that messa...
Autores principales: | , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8647547/ https://www.ncbi.nlm.nih.gov/pubmed/34862289 http://dx.doi.org/10.1136/bmjopen-2021-052091 |
_version_ | 1784610626593619968 |
---|---|
author | Lauffenburger, Julie C Yom-Tov, Elad Keller, Punam A McDonnell, Marie E Bessette, Lily G Fontanet, Constance P Sears, Ellen S Kim, Erin Hanken, Kaitlin Buckley, J Joseph Barlev, Renee A Haff, Nancy Choudhry, Niteesh K |
author_facet | Lauffenburger, Julie C Yom-Tov, Elad Keller, Punam A McDonnell, Marie E Bessette, Lily G Fontanet, Constance P Sears, Ellen S Kim, Erin Hanken, Kaitlin Buckley, J Joseph Barlev, Renee A Haff, Nancy Choudhry, Niteesh K |
author_sort | Lauffenburger, Julie C |
collection | PubMed |
description | INTRODUCTION: Achieving optimal diabetes control requires several daily self-management behaviours, especially adherence to medication. Evidence supports the use of text messages to support adherence, but there remains much opportunity to improve their effectiveness. One key limitation is that message content has been generic. By contrast, reinforcement learning is a machine learning method that can be used to identify individuals’ patterns of responsiveness by observing their response to cues and then optimising them accordingly. Despite its demonstrated benefits outside of healthcare, its application to tailoring communication for patients has received limited attention. The objective of this trial is to test the impact of a reinforcement learning-based text messaging programme on adherence to medication for patients with type 2 diabetes. METHODS AND ANALYSIS: In the REinforcement learning to Improve Non-adherence For diabetes treatments by Optimising Response and Customising Engagement (REINFORCE) trial, we are randomising 60 patients with suboptimal diabetes control treated with oral diabetes medications to receive a reinforcement learning intervention or control. Subjects in both arms will receive electronic pill bottles to use, and those in the intervention arm will receive up to daily text messages. The messages will be individually adapted using a reinforcement learning prediction algorithm based on daily adherence measurements from the pill bottles. The trial’s primary outcome is average adherence to medication over the 6-month follow-up period. Secondary outcomes include diabetes control, measured by glycated haemoglobin A1c, and self-reported adherence. In sum, the REINFORCE trial will evaluate the effect of personalising the framing of text messages for patients to support medication adherence and provide insight into how this could be adapted at scale to improve other self-management interventions. ETHICS AND DISSEMINATION: This study was approved by the Mass General Brigham Institutional Review Board (IRB) (USA). Findings will be disseminated through peer-reviewed journals, clinicaltrials.gov reporting and conferences. TRIAL REGISTRATION NUMBER: Clinicaltrials.gov (NCT04473326). |
format | Online Article Text |
id | pubmed-8647547 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-86475472021-12-17 REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial Lauffenburger, Julie C Yom-Tov, Elad Keller, Punam A McDonnell, Marie E Bessette, Lily G Fontanet, Constance P Sears, Ellen S Kim, Erin Hanken, Kaitlin Buckley, J Joseph Barlev, Renee A Haff, Nancy Choudhry, Niteesh K BMJ Open Public Health INTRODUCTION: Achieving optimal diabetes control requires several daily self-management behaviours, especially adherence to medication. Evidence supports the use of text messages to support adherence, but there remains much opportunity to improve their effectiveness. One key limitation is that message content has been generic. By contrast, reinforcement learning is a machine learning method that can be used to identify individuals’ patterns of responsiveness by observing their response to cues and then optimising them accordingly. Despite its demonstrated benefits outside of healthcare, its application to tailoring communication for patients has received limited attention. The objective of this trial is to test the impact of a reinforcement learning-based text messaging programme on adherence to medication for patients with type 2 diabetes. METHODS AND ANALYSIS: In the REinforcement learning to Improve Non-adherence For diabetes treatments by Optimising Response and Customising Engagement (REINFORCE) trial, we are randomising 60 patients with suboptimal diabetes control treated with oral diabetes medications to receive a reinforcement learning intervention or control. Subjects in both arms will receive electronic pill bottles to use, and those in the intervention arm will receive up to daily text messages. The messages will be individually adapted using a reinforcement learning prediction algorithm based on daily adherence measurements from the pill bottles. The trial’s primary outcome is average adherence to medication over the 6-month follow-up period. Secondary outcomes include diabetes control, measured by glycated haemoglobin A1c, and self-reported adherence. In sum, the REINFORCE trial will evaluate the effect of personalising the framing of text messages for patients to support medication adherence and provide insight into how this could be adapted at scale to improve other self-management interventions. ETHICS AND DISSEMINATION: This study was approved by the Mass General Brigham Institutional Review Board (IRB) (USA). Findings will be disseminated through peer-reviewed journals, clinicaltrials.gov reporting and conferences. TRIAL REGISTRATION NUMBER: Clinicaltrials.gov (NCT04473326). BMJ Publishing Group 2021-12-03 /pmc/articles/PMC8647547/ /pubmed/34862289 http://dx.doi.org/10.1136/bmjopen-2021-052091 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/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: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Public Health Lauffenburger, Julie C Yom-Tov, Elad Keller, Punam A McDonnell, Marie E Bessette, Lily G Fontanet, Constance P Sears, Ellen S Kim, Erin Hanken, Kaitlin Buckley, J Joseph Barlev, Renee A Haff, Nancy Choudhry, Niteesh K REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial |
title | REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial |
title_full | REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial |
title_fullStr | REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial |
title_full_unstemmed | REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial |
title_short | REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial |
title_sort | reinforcement learning to improve non-adherence for diabetes treatments by optimising response and customising engagement (reinforce): study protocol of a pragmatic randomised trial |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8647547/ https://www.ncbi.nlm.nih.gov/pubmed/34862289 http://dx.doi.org/10.1136/bmjopen-2021-052091 |
work_keys_str_mv | AT lauffenburgerjuliec reinforcementlearningtoimprovenonadherencefordiabetestreatmentsbyoptimisingresponseandcustomisingengagementreinforcestudyprotocolofapragmaticrandomisedtrial AT yomtovelad reinforcementlearningtoimprovenonadherencefordiabetestreatmentsbyoptimisingresponseandcustomisingengagementreinforcestudyprotocolofapragmaticrandomisedtrial AT kellerpunama reinforcementlearningtoimprovenonadherencefordiabetestreatmentsbyoptimisingresponseandcustomisingengagementreinforcestudyprotocolofapragmaticrandomisedtrial AT mcdonnellmariee reinforcementlearningtoimprovenonadherencefordiabetestreatmentsbyoptimisingresponseandcustomisingengagementreinforcestudyprotocolofapragmaticrandomisedtrial AT bessettelilyg reinforcementlearningtoimprovenonadherencefordiabetestreatmentsbyoptimisingresponseandcustomisingengagementreinforcestudyprotocolofapragmaticrandomisedtrial AT fontanetconstancep reinforcementlearningtoimprovenonadherencefordiabetestreatmentsbyoptimisingresponseandcustomisingengagementreinforcestudyprotocolofapragmaticrandomisedtrial AT searsellens reinforcementlearningtoimprovenonadherencefordiabetestreatmentsbyoptimisingresponseandcustomisingengagementreinforcestudyprotocolofapragmaticrandomisedtrial AT kimerin reinforcementlearningtoimprovenonadherencefordiabetestreatmentsbyoptimisingresponseandcustomisingengagementreinforcestudyprotocolofapragmaticrandomisedtrial AT hankenkaitlin reinforcementlearningtoimprovenonadherencefordiabetestreatmentsbyoptimisingresponseandcustomisingengagementreinforcestudyprotocolofapragmaticrandomisedtrial AT buckleyjjoseph reinforcementlearningtoimprovenonadherencefordiabetestreatmentsbyoptimisingresponseandcustomisingengagementreinforcestudyprotocolofapragmaticrandomisedtrial AT barlevreneea reinforcementlearningtoimprovenonadherencefordiabetestreatmentsbyoptimisingresponseandcustomisingengagementreinforcestudyprotocolofapragmaticrandomisedtrial AT haffnancy reinforcementlearningtoimprovenonadherencefordiabetestreatmentsbyoptimisingresponseandcustomisingengagementreinforcestudyprotocolofapragmaticrandomisedtrial AT choudhryniteeshk reinforcementlearningtoimprovenonadherencefordiabetestreatmentsbyoptimisingresponseandcustomisingengagementreinforcestudyprotocolofapragmaticrandomisedtrial |