Telehealth and Diabetes Management

Kramer, Erik Seth; VanWyk, Jill; Holmstrom, Heather

doi:10.1016/j.pop.2022.04.007

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Telehealth interventions have been shown, and are encouraged, to support condition management and the reduction in diabetes-related complications [2]. With the current capabilities and advancements of telehealth in diabetes management, it is critical to help identify individuals with a higher risk of uncontrolled diabetes and provide them with targeted support and resources to help them manage their condition [3,4].…”

Section: Introductionmentioning

confidence: 99%

Proactive Identification of Patients with Diabetes at Risk of Uncontrolled Outcomes during a Diabetes Management Program: Conceptualization and Development Study Using Machine Learning

Khalilnejad,

Sun,

Kompala

et al. 2024

JMIR Form Res

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Background The growth in the capabilities of telehealth have made it possible to identify individuals with a higher risk of uncontrolled diabetes and provide them with targeted support and resources to help them manage their condition. Thus, predictive modeling has emerged as a valuable tool for the advancement of diabetes management. Objective This study aimed to conceptualize and develop a novel machine learning (ML) approach to proactively identify participants enrolled in a remote diabetes monitoring program (RDMP) who were at risk of uncontrolled diabetes at 12 months in the program. Methods Registry data from the Livongo for Diabetes RDMP were used to design separate dynamic predictive ML models to predict participant outcomes at each monthly checkpoint of the participants’ program journey (month-n models) from the first day of onboarding (month-0 model) up to the 11th month (month-11 model). A participant’s program journey began upon onboarding into the RDMP and monitoring their own blood glucose (BG) levels through the RDMP-provided BG meter. Each participant passed through 12 predicative models through their first year enrolled in the RDMP. Four categories of participant attributes (ie, survey data, BG data, medication fills, and health signals) were used for feature construction. The models were trained using the light gradient boosting machine and underwent hyperparameter tuning. The performance of the models was evaluated using standard metrics, including precision, recall, specificity, the area under the curve, the F1-score, and accuracy. Results The ML models exhibited strong performance, accurately identifying observable at-risk participants, with recall ranging from 70% to 94% and precision from 40% to 88% across the 12-month program journey. Unobservable at-risk participants also showed promising performance, with recall ranging from 61% to 82% and precision from 42% to 61%. Overall, model performance improved as participants progressed through their program journey, demonstrating the importance of engagement data in predicting long-term clinical outcomes. Conclusions This study explored the Livongo for Diabetes RDMP participants’ temporal and static attributes, identification of diabetes management patterns and characteristics, and their relationship to predict diabetes management outcomes. Proactive targeting ML models accurately identified participants at risk of uncontrolled diabetes with a high level of precision that was generalizable through future years within the RDMP. The ability to identify participants who are at risk at various time points throughout the program journey allows for personalized interventions to improve outcomes. This approach offers significant advancements in the feasibility of large-scale implementation in remote monitoring programs and can help prevent uncontrolled glycemic levels and diabetes-related complications. Future research should include the impact of significant changes that can affect a participant’s diabetes management.

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Section: Introductionmentioning

confidence: 99%

Proactive Identification of Patients with Diabetes at Risk of Uncontrolled Outcomes during a Diabetes Management Program: Conceptualization and Development Study Using Machine Learning

Khalilnejad,

Sun,

Kompala

et al. 2024

JMIR Form Res

View full text Add to dashboard Cite

show abstract

“…I also believe that these systems are, so to speak, also pioneers based on the introduction of wearable tools for mHealth-based self-assessment for PD. Moreover, this is very motivating because mHealth seemed to be more concentrated only on specific diseases such as diabetes, heart, and lung diseases [ 2 , 3 , 4 ].…”

mentioning

confidence: 99%

The Perspective of mHealth in the Self-Assessment of the Parkinson’s Disease. Comment on Kalafati et al. Testing of Motor Coordination in Degenerative Neurological Diseases. Healthcare 2022, 10, 1948

Giansanti¹

2023

Healthcare

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Proactive Identification of Patients with Diabetes at Risk of Uncontrolled Outcomes during a Diabetes Management Program: Conceptualization and Development Study Using Machine Learning (Preprint)

Khalilnejad,

Sun,

Kompala

et al. 2023

Preprint

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BACKGROUND The growth in the capabilities of telehealth have made it possible to identify individuals with a higher risk of uncontrolled diabetes and provide them with targeted support and resources to help them manage their condition. Thus, predictive modeling has emerged as a valuable tool for the advancement of diabetes management. OBJECTIVE This study aimed to conceptualize and develop a novel machine learning (ML) approach to proactively identify participants enrolled in a remote diabetes monitoring program (RDMP) who were at risk of uncontrolled diabetes at 12 months in the program. METHODS Registry data from the Livongo for Diabetes RDMP were used to design separate dynamic predictive ML models to predict participant outcomes at each monthly checkpoint of the participants’ program journey (month-n models) from the first day of onboarding (month-0 model) up to the 11th month (month-11 model). A participant’s program journey began upon onboarding into the RDMP and monitoring their own blood glucose (BG) levels through the RDMP-provided BG meter. Each participant passed through 12 predicative models through their first year enrolled in the RDMP. Four categories of participant attributes (ie, survey data, BG data, medication fills, and health signals) were used for feature construction. The models were trained using the light gradient boosting machine and underwent hyperparameter tuning. The performance of the models was evaluated using standard metrics, including precision, recall, specificity, the area under the curve, the <i>F</i><sub>1</sub>-score, and accuracy. RESULTS The ML models exhibited strong performance, accurately identifying observable at-risk participants, with recall ranging from 70% to 94% and precision from 40% to 88% across the 12-month program journey. Unobservable at-risk participants also showed promising performance, with recall ranging from 61% to 82% and precision from 42% to 61%. Overall, model performance improved as participants progressed through their program journey, demonstrating the importance of engagement data in predicting long-term clinical outcomes. CONCLUSIONS This study explored the Livongo for Diabetes RDMP participants’ temporal and static attributes, identification of diabetes management patterns and characteristics, and their relationship to predict diabetes management outcomes. Proactive targeting ML models accurately identified participants at risk of uncontrolled diabetes with a high level of precision that was generalizable through future years within the RDMP. The ability to identify participants who are at risk at various time points throughout the program journey allows for personalized interventions to improve outcomes. This approach offers significant advancements in the feasibility of large-scale implementation in remote monitoring programs and can help prevent uncontrolled glycemic levels and diabetes-related complications. Future research should include the impact of significant changes that can affect a participant’s diabetes management.

show abstract

Telehealth and Diabetes Management

Cited by 3 publications

References 9 publications

Proactive Identification of Patients with Diabetes at Risk of Uncontrolled Outcomes during a Diabetes Management Program: Conceptualization and Development Study Using Machine Learning

Proactive Identification of Patients with Diabetes at Risk of Uncontrolled Outcomes during a Diabetes Management Program: Conceptualization and Development Study Using Machine Learning

The Perspective of mHealth in the Self-Assessment of the Parkinson’s Disease. Comment on Kalafati et al. Testing of Motor Coordination in Degenerative Neurological Diseases. Healthcare 2022, 10, 1948

Proactive Identification of Patients with Diabetes at Risk of Uncontrolled Outcomes during a Diabetes Management Program: Conceptualization and Development Study Using Machine Learning (Preprint)

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