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Trella AL, Zhang KW, Nahum-Shani I, Shetty V, Doshi-Velez F, Murphy SA. Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care. PROCEEDINGS OF THE ... INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE. INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE 2023; 37:15724-15730. [PMID: 37637073 PMCID: PMC10457015 DOI: 10.1609/aaai.v37i13.26866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
While dental disease is largely preventable, professional advice on optimal oral hygiene practices is often forgotten or abandoned by patients. Therefore patients may benefit from timely and personalized encouragement to engage in oral self-care behaviors. In this paper, we develop an online reinforcement learning (RL) algorithm for use in optimizing the delivery of mobile-based prompts to encourage oral hygiene behaviors. One of the main challenges in developing such an algorithm is ensuring that the algorithm considers the impact of current actions on the effectiveness of future actions (i.e., delayed effects), especially when the algorithm has been designed to run stably and autonomously in a constrained, real-world setting characterized by highly noisy, sparse data. We address this challenge by designing a quality reward that maximizes the desired health outcome (i.e., high-quality brushing) while minimizing user burden. We also highlight a procedure for optimizing the hyperparameters of the reward by building a simulation environment test bed and evaluating candidates using the test bed. The RL algorithm discussed in this paper will be deployed in Oralytics. To the best of our knowledge, Oralytics is the first mobile health study utilizing an RL algorithm designed to prevent dental disease by optimizing the delivery of motivational messages supporting oral self-care behaviors.
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Affiliation(s)
| | | | | | - Vivek Shetty
- Schools of Dentistry & Engineering, University of California, Los Angeles
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Bell L, Garnett C, Bao Y, Cheng Z, Qian T, Perski O, Potts HWW, Williamson E. How Notifications Affect Engagement With a Behavior Change App: Results From a Micro-Randomized Trial. JMIR Mhealth Uhealth 2023; 11:e38342. [PMID: 37294612 PMCID: PMC10337295 DOI: 10.2196/38342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 10/08/2022] [Accepted: 03/31/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND Drink Less is a behavior change app to help higher-risk drinkers in the United Kingdom reduce their alcohol consumption. The app includes a daily notification asking users to "Please complete your drinks and mood diary," yet we did not understand the causal effect of the notification on engagement nor how to improve this component of Drink Less. We developed a new bank of 30 new messages to increase users' reflective motivation to engage with Drink Less. This study aimed to determine how standard and new notifications affect engagement. OBJECTIVE Our objective was to estimate the causal effect of the notification on near-term engagement, to explore whether this effect changed over time, and to create an evidence base to further inform the optimization of the notification policy. METHODS We conducted a micro-randomized trial (MRT) with 2 additional parallel arms. Inclusion criteria were Drink Less users who consented to participate in the trial, self-reported a baseline Alcohol Use Disorders Identification Test score of ≥8, resided in the United Kingdom, were aged ≥18 years, and reported interest in drinking less alcohol. Our MRT randomized 350 new users to test whether receiving a notification, compared with receiving no notification, increased the probability of opening the app in the subsequent hour, over the first 30 days since downloading Drink Less. Each day at 8 PM, users were randomized with a 30% probability of receiving the standard message, a 30% probability of receiving a new message, or a 40% probability of receiving no message. We additionally explored time to disengagement, with the allocation of 60% of eligible users randomized to the MRT (n=350) and 40% of eligible users randomized in equal number to the 2 parallel arms, either receiving the no notification policy (n=98) or the standard notification policy (n=121). Ancillary analyses explored effect moderation by recent states of habituation and engagement. RESULTS Receiving a notification, compared with not receiving a notification, increased the probability of opening the app in the next hour by 3.5-fold (95% CI 2.91-4.25). Both types of messages were similarly effective. The effect of the notification did not change significantly over time. A user being in a state of already engaged lowered the new notification effect by 0.80 (95% CI 0.55-1.16), although not significantly. Across the 3 arms, time to disengagement was not significantly different. CONCLUSIONS We found a strong near-term effect of engagement on the notification, but no overall difference in time to disengagement between users receiving the standard fixed notification, no notification at all, or the random sequence of notifications within the MRT. The strong near-term effect of the notification presents an opportunity to target notifications to increase "in-the-moment" engagement. Further optimization is required to improve the long-term engagement. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.2196/18690.
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Affiliation(s)
- Lauren Bell
- Department of Medical Statistics, The London School of Hygiene and Tropical Medicine, London, United Kingdom
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Claire Garnett
- Research Department of Behavioural Science and Health, University College London, London, United Kingdom
| | - Yihan Bao
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
| | - Zhaoxi Cheng
- Department of Biostatistics, Harvard University, Cambridge, MA, United States
| | - Tianchen Qian
- Department of Statistics, University of California Irvine, Irvine, CA, United States
| | - Olga Perski
- Research Department of Behavioural Science and Health, University College London, London, United Kingdom
| | - Henry W W Potts
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Elizabeth Williamson
- Department of Medical Statistics, The London School of Hygiene and Tropical Medicine, London, United Kingdom
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Liu X, Deliu N, Chakraborty B. Microrandomized Trials: Developing Just-in-Time Adaptive Interventions for Better Public Health. Am J Public Health 2023; 113:60-69. [PMID: 36413704 PMCID: PMC9755932 DOI: 10.2105/ajph.2022.307150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Just-in-time adaptive interventions (JITAIs) represent an intervention design that adapts the provision and type of support over time to an individual's changing status and contexts, intending to deliver the right support on the right occasion. As a novel strategy for delivering mobile health interventions, JITAIs have the potential to improve access to quality care in underserved communities and, thus, alleviate health disparities, a significant public health concern. Valid experimental designs and analysis methods are required to inform the development of JITAIs. Here, we briefly review the cutting-edge design of microrandomized trials (MRTs), covering both the classical MRT design and its outcome-adaptive counterpart. Associated statistical challenges related to the design and analysis of MRTs are also discussed. Two case studies are provided to illustrate the aforementioned concepts and designs throughout the article. We hope our work leads to better design and application of JITAIs, advancing public health research and practice. (Am J Public Health. 2023;113(1):60-69. https://doi.org/10.2105/AJPH.2022.307150).
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Affiliation(s)
- Xueqing Liu
- Xueqing Liu is with the Centre for Quantitative Medicine, Duke-National University of Singapore (NUS) Medical School, Singapore. Nina Deliu is with the Medical Research Council Biostatistics Unit, University of Cambridge, UK, and the Department of Methods and Models for Economics, Territory and Finance, Sapienza University of Rome, Italy. Bibhas Chakraborty is with the Centre for Quantitative Medicine and Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore; the Department of Statistics and Data Science, NUS, Singapore; and the Department of Biostatistics and Bioinformatics, Duke University, Durham, NC
| | - Nina Deliu
- Xueqing Liu is with the Centre for Quantitative Medicine, Duke-National University of Singapore (NUS) Medical School, Singapore. Nina Deliu is with the Medical Research Council Biostatistics Unit, University of Cambridge, UK, and the Department of Methods and Models for Economics, Territory and Finance, Sapienza University of Rome, Italy. Bibhas Chakraborty is with the Centre for Quantitative Medicine and Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore; the Department of Statistics and Data Science, NUS, Singapore; and the Department of Biostatistics and Bioinformatics, Duke University, Durham, NC
| | - Bibhas Chakraborty
- Xueqing Liu is with the Centre for Quantitative Medicine, Duke-National University of Singapore (NUS) Medical School, Singapore. Nina Deliu is with the Medical Research Council Biostatistics Unit, University of Cambridge, UK, and the Department of Methods and Models for Economics, Territory and Finance, Sapienza University of Rome, Italy. Bibhas Chakraborty is with the Centre for Quantitative Medicine and Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore; the Department of Statistics and Data Science, NUS, Singapore; and the Department of Biostatistics and Bioinformatics, Duke University, Durham, NC
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Trella AL, Zhang KW, Nahum-Shani I, Shetty V, Doshi-Velez F, Murphy SA. Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines. ALGORITHMS 2022; 15:255. [PMID: 36713810 PMCID: PMC9881427 DOI: 10.3390/a15080255] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under real-time constraints, and accounting for the complexity of the environment, e.g., a lack of accurate mechanistic models for the user dynamics. To guide how one can tackle these challenges, we extend the PCS (predictability, computability, stability) framework, a data science framework that incorporates best practices from machine learning and statistics in supervised learning to the design of RL algorithms for the digital interventions setting. Furthermore, we provide guidelines on how to design simulation environments, a crucial tool for evaluating RL candidate algorithms using the PCS framework. We show how we used the PCS framework to design an RL algorithm for Oralytics, a mobile health study aiming to improve users' tooth-brushing behaviors through the personalized delivery of intervention messages. Oralytics will go into the field in late 2022.
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Affiliation(s)
- Anna L. Trella
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02420, USA
- Correspondence:
| | - Kelly W. Zhang
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02420, USA
| | - Inbal Nahum-Shani
- Institute for Social Research, University of Michigan, Ann Arbor, MI 48109, USA
| | - Vivek Shetty
- Schools of Dentistry & Engineering, University of California, Los Angeles, CA 90095, USA
| | - Finale Doshi-Velez
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02420, USA
| | - Susan A. Murphy
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02420, USA
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Figueroa CA, Luo TC, Jacobo A, Munoz A, Manuel M, Chan D, Canny J, Aguilera A. Conversational Physical Activity Coaches for Spanish and English Speaking Women: A User Design Study. Front Digit Health 2021; 3:747153. [PMID: 34713207 PMCID: PMC8531260 DOI: 10.3389/fdgth.2021.747153] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 09/06/2021] [Indexed: 11/17/2022] Open
Abstract
Introduction: Digital technologies, including text messaging and mobile phone apps, can be leveraged to increase people's physical activity and manage health. Chatbots, powered by artificial intelligence, can automatically interact with individuals through natural conversation. They may be more engaging than one-way messaging interventions. To our knowledge, physical activity chatbots have not been developed with low-income participants, nor in Spanish-the second most dominant language in the U.S. We recommend best practices for physical activity chatbots in English and Spanish for low-income women. Methods: We designed a prototype physical activity text-message based conversational agent based on various psychotherapeutic techniques. We recruited participants through SNAP-Ed (Supplemental Nutrition Assistance Program Education) in California (Alameda County) and Tennessee (Shelby County). We conducted qualitative interviews with participants during testing of our prototype chatbot, held a Wizard of Oz study, and facilitated a co-design workshop in Spanish with a subset of our participants. Results: We included 10 Spanish- and 8 English-speaking women between 27 and 41 years old. The majority was Hispanic/Latina (n = 14), 2 were White and 2 were Black/African American. More than half were monolingual Spanish speakers, and the majority was born outside the US (>50% in Mexico). Most participants were unfamiliar with chatbots and were initially skeptical. After testing our prototype, most users felt positively about health chatbots. They desired a personalized chatbot that addresses their concerns about privacy, and stressed the need for a comprehensive system to also aid with nutrition, health information, stress, and involve family members. Differences between English and monolingual Spanish speakers were found mostly in exercise app use, digital literacy, and the wish for family inclusion. Conclusion: Low-income Spanish- and English-speaking women are interested in using chatbots to improve their physical activity and other health related aspects. Researchers developing health chatbots for this population should focus on issues of digital literacy, app familiarity, linguistic and cultural issues, privacy concerns, and personalization. Designing and testing this intervention for and with this group using co-creation techniques and involving community partners will increase the probability that it will ultimately be effective.
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Affiliation(s)
- Caroline A. Figueroa
- School of Social Welfare, University of California, Berkeley, Berkeley, CA, United States
| | - Tiffany C. Luo
- School of Social Welfare, University of California, Berkeley, Berkeley, CA, United States
| | - Andrea Jacobo
- School of Public Health, University of California, Berkeley, Berkeley, CA, United States
| | - Alan Munoz
- School of Social Welfare, University of California, Berkeley, Berkeley, CA, United States
| | - Minx Manuel
- School of Public Health, University of California, Berkeley, Berkeley, CA, United States
| | - David Chan
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
| | - John Canny
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
| | - Adrian Aguilera
- School of Social Welfare, University of California, Berkeley, Berkeley, CA, United States
- Department of Psychiatry and Behavioral Sciences, Zuckerberg San Francisco General Hospital, University of California, San Francisco, San Francisco, CA, United States
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