Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System

Yom-Tov, Elad; Feraru, Guy; Kozdoba, Mark; Mannor, Shie; Tennenholtz, Moshe; Hochberg, Irit

doi:10.2196/jmir.7994

Cited by 149 publications

(176 citation statements)

References 22 publications

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“…Due to the short duration of the study, we did not observe any change in HbA 1c levels after initiation of the program. However, as observed previously [ 38 , 39 ], a longer program might result in weight, BMI, and HbA 1c reduction. Although we observed a 100-odd increase in steps from week 1 to week 2, a beneficial effect in T2DM patients would be visible only if rigorous physical activity is performed for a long duration.…”

Section: Discussionsupporting

confidence: 63%

Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes

Weatherall¹,

Paprocki²,

Meyer³

et al. 2018

JMIR Mhealth Uhealth

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BackgroundFew studies assessing the correlation between patient-reported outcomes and patient-generated health data from wearable devices exist.ObjectiveThe aim of this study was to determine the direction and magnitude of associations between patient-generated health data (from the Fitbit Charge HR) and patient-reported outcomes for sleep patterns and physical activity in patients with type 2 diabetes mellitus (T2DM).MethodsThis was a pilot study conducted with adults diagnosed with T2DM (n=86). All participants wore a Fitbit Charge HR for 14 consecutive days and completed internet-based surveys at 3 time points: day 1, day 7, and day 14. Patient-generated health data included minutes asleep and number of steps taken. Questionnaires assessed the number of days of exercise and nights of sleep problems per week. Means and SDs were calculated for all data, and Pearson correlations were used to examine associations between patient-reported outcomes and patient-generated health data. All respondents provided informed consent before participating.ResultsThe participants were predominantly middle-aged (mean 54.3, SD 13.3 years), white (80/86, 93%), and female (50/86, 58%). Use of oral T2DM medication correlated with the number of mean steps taken (r=.35, P=.001), whereas being unaware of the glycated hemoglobin level correlated with the number of minutes asleep (r=−.24, P=.04). On the basis of the Fitbit data, participants walked an average of 4955 steps and slept 6.7 hours per day. They self-reported an average of 2.0 days of exercise and 2.3 nights of sleep problems per week. The association between the number of days exercised and steps walked was strong (r=.60, P<.001), whereas the association between the number of troubled sleep nights and minutes asleep was weaker (r=.28, P=.02).ConclusionsFitbit and patient-reported data were positively associated for physical activity as well as sleep, with the former more strongly correlated than the latter. As extensive patient monitoring can guide clinical decisions regarding T2DM therapy, passive, objective data collection through wearables could potentially enhance patient care, resulting in better patient-reported outcomes.

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Section: Discussionsupporting

confidence: 63%

Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes

Weatherall¹,

Paprocki²,

Meyer³

et al. 2018

JMIR Mhealth Uhealth

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show abstract

“…Authors validated the system with 55 patients with prediabetes. Previously, Yom-Tov et al proposed a DSS based on a RL algorithm that automatically sends messages to patients who are following a personalized plan for physical exercise [ 131 ]. The approach was validated in 27 sedentary T2D patients.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Contreras¹,

Vehı́²

2018

J Med Internet Res

364

207

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BackgroundArtificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis.ObjectiveThe objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges.MethodsA review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review.ResultsWe propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results.ConclusionsWe obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.

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“…Reinforcement learning is only in its infancy in applications outside of computer games 5 , but there is clearly an opportunity for this approach to greatly improve on clinical decision-making. A number of investigators have recently used this approach to enhance decision-making in clinical care 19 , including in the intensive care unit 20 .…”

Section: Discussionmentioning

confidence: 99%

Applications of Machine Learning in Decision Analysis for Dose Management for Dofetilide

Levy

Biswas

Weber

et al. 2019

Preprint

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Initiation of the antiarrhythmic medication dofetilide requires an FDA-mandated 3 days of telemetry monitoring due to heightened risk of toxicity within this time period. Although a recommended dose management algorithm for dofetilide exists, there is a range of real-world approaches to dosing the medication. In this multicenter investigation, we examined the decision process for dose adjustment of dofetilide during the observation period using machine-learning approaches, including supervised, unsupervised, and reinforcement learning applications. Logistic regression approaches identified any dose-adjustment as a strong negative predictor of successful loading (i.e., discharged on dofetilide) of the medication (OR 0.19, 95%CI 0.12 -0.31, p < 0.001 for discharge on dofetilide), indicating that these adjustments are strong determinants of whether patients "tolerate" the medication. Using multiple supervised approaches, including regularized logistic regression, random forest, boosted gradient decision trees, and neural networks, we were unable to identify any model that predicted dose adjustments better than a naïve approach. A reinforcement-learning algorithm, in contrast, predicted which patient characteristics and dosing decisions that resulted in the lowest risk of failure to be discharged on the medication. Future studies could apply this algorithm prospectively to examine improvement over standard approaches.

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Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System

Cited by 149 publications

References 22 publications

Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes

Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Applications of Machine Learning in Decision Analysis for Dose Management for Dofetilide

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