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Technology-Assisted Self-Monitoring of Lifestyle Behaviors and Health Indicators in Diabetes: Qualitative Study

Self-monitoring of blood glucose can lead to weight loss and better HbA1c levels through increased adherence to dietary recommendations [8,10,12,15].

Yan Du, Brittany Dennis, Shanae Lakel Rhodes, Michelle Sia, Jisook Ko, Rozmin Jiwani, Jing Wang

JMIR Diabetes 2020;5(3):e21183

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

Poor control of diabetes is shown to be related to a lack of knowledge around blood glucose (BG) monitoring, proper nutrition, and medication self-management [3].

Jennifer B Bollyky, Stephanie T Melton, Tong Xu, Stefanie L Painter, Brian Knox

JMIR Diabetes 2019;4(4):e14799

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Overcoming Clinical Inertia: A Randomized Clinical Trial of a Telehealth Remote Monitoring Intervention Using Paired Glucose Testing in Adults With Type 2 Diabetes

Participants were instructed to use paired glucose testing or a monthly, 3-day 7-point glucose profile until the 6-month A1c.

Deborah A Greenwood, Shelley A Blozis, Heather M Young, Thomas S Nesbitt, Charlene C Quinn

J Med Internet Res 2015;17(7):e178

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Prediction of Glucose Metabolism Disorder Risk Using a Machine Learning Algorithm: Pilot Study

At present, diabetes is diagnosed and predicted based on fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), and plasma glucose levels 2 hours after a 75-g oral glucose tolerance test (OGTT) [4].

Katsutoshi Maeta, Yu Nishiyama, Kazutoshi Fujibayashi, Toshiaki Gunji, Noriko Sasabe, Kimiko Iijima, Toshio Naito

JMIR Diabetes 2018;3(4):e10212

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Examining the Impact of a Novel Blood Glucose Monitor With Color Range Indicator on Decision-Making in Patients With Type 1 and Type 2 Diabetes and its Association With Patient Numeracy Level

IntroductionSelf-monitoring of blood glucose (SMBG) remains a cornerstone of diabetes management.

Mike Grady, Laurence Barry Katz, Christine Simone Strunk, Hilary Cameron, Brian Leonard Levy

JMIR Diabetes 2017;2(2):e24

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Transcription Errors of Blood Glucose Values and Insulin Errors in an Intensive Care Unit: Secondary Data Analysis Toward Electronic Medical Record-Glucometer Interoperability

audit of blood glucose tests and insulin data in a 20% stratified sample of all blood glucose tests available in the meters for patients admitted during 4 months (July to October 2016).

Azizeh Khaled Sowan, Ana Vera, Ashwin Malshe, Charles Reed

JMIR Med Inform 2019;7(1):e11873

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Use of a Connected Glucose Meter and Certified Diabetes Educator Coaching to Decrease the Likelihood of Abnormal Blood Glucose Excursions: The Livongo for Diabetes Program

Blood glucose (BG) excursions out of the normal range (70-180 mg/dL, or 3.9-10.0 mmol/L [4]) are important drivers of these extraordinary costs, often leading to unnecessary emergency department visits, hospitalization, urgent care visits, and office visits

Janelle Downing, Jenna Bollyky, Jennifer Schneider

J Med Internet Res 2017;19(7):e234

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Targeted Diabetes Education Text Messaging Program Increases Requests for Certified Diabetes Educator Coaching and Improves Blood Glucose Trends

Jodi Pulizzi, Jennifer Bollyky, Anastasia Toles, Michael Boulos, Jennifer Schneider

iproc 2016;2(1):e32

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Development of a Deep Learning Model for Dynamic Forecasting of Blood Glucose Level for Type 2 Diabetes Mellitus: Secondary Analysis of a Randomized Controlled Trial

in order to improve blood glucose control [2,17].

Syed Hasib Akhter Faruqui, Yan Du, Rajitha Meka, Adel Alaeddini, Chengdong Li, Sara Shirinkam, Jing Wang

JMIR Mhealth Uhealth 2019;7(11):e14452

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Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System

glucose dynamics [18].

Ashenafi Zebene Woldaregay, Ilkka Kalervo Launonen, Eirik Årsand, David Albers, Anna Holubová, Gunnar Hartvigsen

J Med Internet Res 2020;22(8):e18911

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