Knowledge Hub

Diabetes Distress Reduces Over Time Among Adults with T2D Receiving Care in an Ambulatory Diabetes Clinic Setting.

Glooko investigated whether people with type 2 diabetes demonstrated improved glycemia after participating in remote patient monitoring programs.

In this randomized control trial, Glooko evaluated the short-term efficacy of mobile-enabled RPM implemented in an ambulatory diabetes clinic setting.

Glooko developed a machine learning algorithm that predicts risk for decreased sensor glucose time in range following a clinic appointment.

Glooko’s novel algorithm to assess glycemic control performs well compared to clinician assessment and helps clinicians make treatment decisions faster.

Results of a study using Glooko for remote management of an adult, Type 2 diabetes population experiencing glycemic control issues.

Glooko developed a machine learning model that predicts risk of declining engagement with its mHealth app that supports diabetes self-management.

In the study, Glooko evaluated the extent to which an mHealth app for long-acting insulin dose adjustment can help people with type 2 diabetes.

The purpose of this Glooko study is to evaluate the efficacy of remote patient monitoring pilot programs implemented in endocrinology clinics.

In the study, Glooko assesses whether the use of an in-app food logging feature corresponded with improved glycemic outcomes.

This retrospective Glooko study focuses on quantifying the clinical benefit of using a mobile health app to facilitate self-monitoring behavior.

In the study, Glooko assesses whether the use of reminders features corresponded with improved behavioral and glycemic outcomes in the real world.