Clinical evidence downloads

  • Glooko And Cecelia Health’s Results Suggested Strong Patient Engagement And Clinically Meaningful Reductions In A1C And Above Average Time-In-Range.

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  • Using secondary analyses to examine whether the comprehensive Glooko remote patient monitoring diabetes management program improved diabetes-related distress compared to usual care in a sample of adults with type 2 diabetes.

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

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

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

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

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

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

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  • 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.

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

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

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

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

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