Optimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven Behavioral Simulator

Wang, Shihan; Zhang, Chao; Kröse, B.J.A.; Hoof, Herke van

doi:10.1007/s10916-021-01773-0

Cited by 24 publications

(11 citation statements)

References 28 publications

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“…Various forms of ‘meaningful’ feedback about a patient’s health status can provide a sense of support, guidance and information, as well as giving reassurance to patients [ 41 ]. However, feedback by itself may also be an intrinsic motivator to sustained HM beyond any ‘clinically meaningful’ relevance of change in performance scores [ 42 , 43 , 44 ].…”

Section: Discussionmentioning

confidence: 99%

Patient Acceptability of Home Monitoring for Neovascular Age-Related Macular Degeneration Reactivation: A Qualitative Study

O’Connor

Treanor

Ward

et al. 2022

IJERPH

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Neovascular age-related macular degeneration (nAMD) is a chronic, progressive condition and the commonest cause of visual disability in older adults. This study formed part of a diagnostic test accuracy study to quantify the ability of three index home monitoring (HM) tests (one paper-based and two digital tests) to identify reactivation in nAMD. The aim of this qualitative research was to investigate patients’ or participants’ views about acceptability and explore adherence to weekly HM. Semi-structured interviews were held with 78/297 participants (26%), with close family members (n = 11) and with healthcare professionals involved in training participants in HM procedures (n = 9) (n = 98 in total). A directed thematic analytical approach was applied to the data using a deductive and inductive coding framework informed by theories of technology acceptance. Five themes emerged related to: 1. The role of HM; 2. Suitability of procedures and instruments; 3. Experience of HM; 4. Feasibility of HM in usual practice; and 5. Impediments to patient acceptability of HM. Various factors influenced acceptability including a patient’s understanding about the purpose of monitoring. While initial training and ongoing support were regarded as essential for overcoming unfamiliarity with use of digital technology, patients viewed HM as relatively straightforward and non-burdensome. There is a need for further research about how use of performance feedback, level of support and nature of tailoring might facilitate further the implementation of routinely conducted HM. Home monitoring was acceptable to patients and they recognised its potential to reduce clinic visits during non-active treatment phases. Findings have implications for implementation of digital HM in the care of older people with nAMD and other long-term conditions.

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Section: Discussionmentioning

confidence: 99%

Patient Acceptability of Home Monitoring for Neovascular Age-Related Macular Degeneration Reactivation: A Qualitative Study

O’Connor

Treanor

Ward

et al. 2022

IJERPH

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“…The Smart PAUL app differs from the Basic PAUL app in that it can optimize the timing of reminders with a self-learning module [ 36 ]. The self-learning algorithm has the opportunity to learn right times (ie, JITAI) to send reminders based on the time of the day, the day of the week, previous PA behavior, and agenda availability [ 36 ]. Although the timing differed between the Basic and Smart PAUL apps, the content of the reminders was equal.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, we set out to investigate 2 novel ways of JIT prompting for PA behaviors with an mHealth app, the Playful Active Urban Living (PAUL) app. First, to initiate running or walking behavior, the app sends JITAI reminder messages based on a reinforcement learning algorithm [ 36 ]. Second, during a PA session (outdoor running or walking), the individual receives JIT location-based strength exercise prompts containing instructional videos for performing strength exercises.…”

Section: Introductionmentioning

confidence: 99%

Just-in-Time Prompts for Running, Walking, and Performing Strength Exercises in the Built Environment: 4-Week Randomized Feasibility Study

Sporrel¹,

Wang²,

Ettema³

et al. 2022

JMIR Form Res

Self Cite

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Background App-based mobile health exercise interventions can motivate individuals to engage in more physical activity (PA). According to the Fogg Behavior Model, it is important that the individual receive prompts at the right time to be successfully persuaded into PA. These are referred to as just-in-time (JIT) interventions. The Playful Active Urban Living (PAUL) app is among the first to include 2 types of JIT prompts: JIT adaptive reminder messages to initiate a run or walk and JIT strength exercise prompts during a walk or run (containing location-based instruction videos). This paper reports on the feasibility of the PAUL app and its JIT prompts. Objective The main objective of this study was to examine user experience, app engagement, and users’ perceptions and opinions regarding the PAUL app and its JIT prompts and to explore changes in the PA behavior, intrinsic motivation, and the perceived capability of the PA behavior of the participants. Methods In total, 2 versions of the closed-beta version of the PAUL app were evaluated: a basic version (Basic PAUL) and a JIT adaptive version (Smart PAUL). Both apps send JIT exercise prompts, but the versions differ in that the Smart PAUL app sends JIT adaptive reminder messages to initiate running or walking behavior, whereas the Basic PAUL app sends reminder messages at randomized times. A total of 23 participants were randomized into 1 of the 2 intervention arms. PA behavior (accelerometer-measured), intrinsic motivation, and the perceived capability of PA behavior were measured before and after the intervention. After the intervention, participants were also asked to complete a questionnaire on user experience, and they were invited for an exit interview to assess user perceptions and opinions of the app in depth. Results No differences in PA behavior were observed (Z=−1.433; P=.08), but intrinsic motivation for running and walking and for performing strength exercises significantly increased (Z=−3.342; P<.001 and Z=−1.821; P=.04, respectively). Furthermore, participants increased their perceived capability to perform strength exercises (Z=2.231; P=.01) but not to walk or run (Z=−1.221; P=.12). The interviews indicated that the participants were enthusiastic about the strength exercise prompts. These were perceived as personal, fun, and relevant to their health. The reminders were perceived as important initiators for PA, but participants from both app groups explained that the reminder messages were often not sent at times they could exercise. Although the participants were enthusiastic about the functionalities of the app, technical issues resulted in a low user experience. Conclusions The preliminary findings suggest that the PAUL apps are promising and innovative interventions for promoting PA. Users perceived the strength exercise prompts as a valuable addition to exercise apps. However, to be a feasible intervention, the app must be more stable.

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“…Asiimwe et al (2020),Helman et al (2022),Posthuma et al (2020),Kellett and Sebat (2017), Garca-del Valle et al (2021), Vinegar and Kwong (2021), Downey et al (2018), Alshwaheen et al (2021), da Silva et al (2021), Lauritsen et al Enable personalized monitoring with decentralized learning • Overcome data privacy issues G. Chen, Xiao, et al (2021), Mukherjee et al (2020), Zheng et al (2021), Nguyen et al (2023), Wu et al (2022), Y. Chen et al (2020), Shaik, Tao, Higgins, Gururajan, et al (2022); Shaik, Tao, Higgins, Xie, et al (2022) Adaptive learning DRL, A2C, DQN • Learn patient behavior patterns • Dynamic treatment regimes • Just-in-time-adaptiveinterventions • Sequential decision making tasks C. Yu et al (2023), Laber et al (2014), I. Y. Chen, Joshi, et al (2021), Watts et al (2020), Naeem et al (2021), Nahum-Shani et al (2017),Wang, Zhang, et al (2021),Gönül et al (2021) …”

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confidence: 99%

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Shaik

Tao

Higgins

et al. 2023

WIREs Data Min & Knowl

139

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The adoption of artificial intelligence (AI) in healthcare is growing rapidly.Remote patient monitoring (RPM) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in-home care, and even hospitalized patients. The reliability of manual patient monitoring systems depends on staff time management which is dependent on their workload. Conventional patient monitoring involves invasive approaches which require skin contact to monitor health status. This study aims to do a comprehensive review of RPM systems including adopted advanced technologies, AI impact on RPM, challenges and trends in AI-enabled RPM. This review explores the benefits and challenges of patient-centric RPM architectures enabled with Internet of Things wearable devices and sensors using the cloud, fog, edge, and blockchain technologies. The role of AI in RPM ranges from physical activity classification to chronic disease monitoring and vital signs monitoring in emergency settings. This review results show that AI-enabled RPM architectures have transformed healthcare monitoring applications because of their ability to detect early deterioration in patients' health, personalize individual patient health parameter monitoring using federated learning, and learn human behavior patterns using techniques such as reinforcement learning. This review discusses the challenges and trends to adopt AI to RPM systems and implementation issues. The future directions of AI in RPM applications are analyzed based on the challenges and trends.

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Optimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven Behavioral Simulator

Cited by 24 publications

References 28 publications

Patient Acceptability of Home Monitoring for Neovascular Age-Related Macular Degeneration Reactivation: A Qualitative Study

Patient Acceptability of Home Monitoring for Neovascular Age-Related Macular Degeneration Reactivation: A Qualitative Study

Just-in-Time Prompts for Running, Walking, and Performing Strength Exercises in the Built Environment: 4-Week Randomized Feasibility Study

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

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