Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Diabetes: Management and Pharmacotherapy

Diabetes: Management and Pharmacotherapy

The therapy for diabetes aims to alleviate hyperglycemia-related symptoms, prevent acute metabolic decompensation, and reduce chronic end-organ complications. Glycemic control is evaluated through short-term (self-monitoring, continuous glucose monitoring) and long-term (A1c, fructosamine) metrics, enabling near real-time tracking of blood glucose levels and reflecting glycemic control over specific time frames.
Insulin remains the cornerstone of treatment for most patients with type 1 and many...

Errors occurring during blood pressure monitoring

Errors occurring during blood pressure monitoring

Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
Several factors...

Pulse rhythm

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

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

JMIR formative research·2024

Same author

The Effect of a Digital Mental Health Program on Anxiety and Depression Symptoms: Retrospective Analysis of Clinical Severity.

JMIR formative research·2023

Same author

Association of physical activity on blood glucose in individuals with type 2 diabetes.

Translational behavioral medicine·2021

Same author

Figure Correction: The Effect of a Cellular-Enabled Glucose Meter on Glucose Control for Patients With Diabetes: Prospective Pre-Post Study.

JMIR diabetes·2020

Same author

Drivers of weight loss in a CDC-recognized digital diabetes prevention program.

BMJ open diabetes research & care·2020

Same author

The Effect of a Cellular-Enabled Glucose Meter on Glucose Control for Patients With Diabetes: Prospective Pre-Post Study.

JMIR diabetes·2019

Search research articles

Related Experiment Video

Updated: Sep 29, 2025

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study.

Sowmya Kamath¹, Karthik Kappaganthu¹, Stefanie Painter¹

¹Teladoc Health, Purchase, NY, United States.

JMIR Formative Research

|March 21, 2022

Summary

Personalized machine learning models can predict how specific actions improve glycemic control for individuals with diabetes. Engaging with recommended actions leads to greater A1c reduction, enhancing diabetes management programs.

Keywords:

causal engagement glycemic control mHealth machine learning mobile health observational personalization recommendation recommender systems type 2 diabetes

More Related Videos

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Published on: April 12, 2021

Improving IV Insulin Administration in a Community Hospital

Improving IV Insulin Administration in a Community Hospital

Published on: June 11, 2012

Related Experiment Videos

Last Updated: Sep 29, 2025

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Published on: April 12, 2021

Improving IV Insulin Administration in a Community Hospital

Improving IV Insulin Administration in a Community Hospital

Published on: June 11, 2012

Area of Science:

Digital health
Machine learning in healthcare
Diabetes self-management

Background:

Personalization enhances engagement and outcomes in diabetes management.
Many individuals with diabetes struggle to achieve glycemic control.
Machine learning offers potential for personalized diabetes management.

Purpose of the Study:

Evaluate machine learning models for predicting A1c improvement.
Develop personalized recommendations for diabetes self-management actions.
Enhance clinical outcomes in remote diabetes monitoring.

Main Methods:

Retrospective analysis of member engagement data.
Building member-level models to predict A1c improvement.
Utilizing doubly robust learning for heterogeneous treatment effects.

Main Results:

Coach interaction and glucose monitoring most effective for many.
Personalized recommendations improved A1c by 0.8% more than non-recommended actions.
46% of members benefited from non-traditional interventions.

Conclusions:

Personalized action recommendations reduce A1c effectively.
Machine learning models can guide members toward beneficial actions.
Heterogeneous treatment effects are key for optimizing diabetes care.