This paper is in the following e-collection/theme issue:

Web-based and Mobile Health Interventions (3114) Mobile Health (mhealth) (2765) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1309) Health Coaching for Diabetes Patients (142) Glucose Tracking and Self-Monitoring of Blood Glucose (150) Sensors for Glucose Monitoring and Diabetes Management (29)

Published on in Vol 19, No 7 (2017): July

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

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

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

Authors of this article:

Janelle Downing1 Author Orcid Image ;   Jenna Bollyky2, 3 Author Orcid Image ;   Jennifer Schneider2 Author Orcid Image
Article Authors Cited by (35) Tweetations (22) Metrics

Journals

  1. Lal R, Buckingham B, Maahs D. Advances in Care for Insulin-Requiring Patients Without Closed Loop. Diabetes Technology & Therapeutics 2018;20(S2):S2-85 View
  2. Wang J, Chu C, Li C, Hayes L, Siminerio L. Diabetes Educators’ Insights Regarding Connecting Mobile Phone– and Wearable Tracker–Collected Self-Monitoring Information to a Nationally-Used Electronic Health Record System for Diabetes Education: Descriptive Qualitative Study. JMIR mHealth and uHealth 2018;6(7):e10206 View
  3. Meinert E, Van Velthoven M, Brindley D, Alturkistani A, Foley K, Rees S, Wells G, de Pennington N. The Internet of Things in Health Care in Oxford: Protocol for Proof-of-Concept Projects. JMIR Research Protocols 2018;7(12):e12077 View
  4. Wang J, Cai C, Padhye N, Orlander P, Zare M. A Behavioral Lifestyle Intervention Enhanced With Multiple-Behavior Self-Monitoring Using Mobile and Connected Tools for Underserved Individuals With Type 2 Diabetes and Comorbid Overweight or Obesity: Pilot Comparative Effectiveness Trial. JMIR mHealth and uHealth 2018;6(4):e92 View
  5. Garg S, Hirsch I. Self-Monitoring of Blood Glucose. Diabetes Technology & Therapeutics 2019;21(S1):S-4 View
  6. Qu F, Yang Q, Wang B, You J. Aggregation-induced emission of copper nanoclusters triggered by synergistic effect of dual metal ions and the application in the detection of H2O2 and related biomolecules. Talanta 2020;207:120289 View
  7. Dixon R, Zisser H, Layne J, Barleen N, Miller D, Moloney D, Majithia A, Gabbay R, Riff J. A Virtual Type 2 Diabetes Clinic Using Continuous Glucose Monitoring and Endocrinology Visits. Journal of Diabetes Science and Technology 2020;14(5):908 View
  8. Alcántara-Aragón V. Improving patient self-care using diabetes technologies. Therapeutic Advances in Endocrinology and Metabolism 2019;10:204201881882421 View
  9. Bollyky J, Melton S, Xu T, Painter S, Knox B. The Effect of a Cellular-Enabled Glucose Meter on Glucose Control for Patients With Diabetes: Prospective Pre-Post Study. JMIR Diabetes 2019;4(4):e14799 View
  10. Marcotte L, Dugdale D. Prevention as a Population Health Strategy. Primary Care: Clinics in Office Practice 2019;46(4):493 View
  11. Silbert R, Salcido-Montenegro A, Rodriguez-Gutierrez R, Katabi A, McCoy R. Hypoglycemia Among Patients with Type 2 Diabetes: Epidemiology, Risk Factors, and Prevention Strategies. Current Diabetes Reports 2018;18(8) View
  12. Kaufman N, Ferrin C, Sugrue D. Using Digital Health Technology to Prevent and Treat Diabetes. Diabetes Technology & Therapeutics 2019;21(S1):S-79 View
  13. Bora A, Balasubramanian S, Babenko B, Virmani S, Venugopalan S, Mitani A, de Oliveira Marinho G, Cuadros J, Ruamviboonsuk P, Corrado G, Peng L, Webster D, Varadarajan A, Hammel N, Liu Y, Bavishi P. Predicting the risk of developing diabetic retinopathy using deep learning. The Lancet Digital Health 2021;3(1):e10 View
  14. Amante D, Harlan D, Lemon S, McManus D, Olaitan O, Pagoto S, Gerber B, Thompson M. Evaluation of a Diabetes Remote Monitoring Program Facilitated by Connected Glucose Meters for Patients With Poorly Controlled Type 2 Diabetes: Randomized Crossover Trial. JMIR Diabetes 2021;6(1):e25574 View
  15. Lindemer E, Jouni M, Nikolaev N, Reidy P, Mattie H, Rogers J, Giangreco L, Sherman M, Bartels M, Panch T. A pragmatic methodology for the evaluation of digital care management in the context of multimorbidity. Journal of Medical Economics 2021;24(1):373 View
  16. Fundoiano-Hershcovitz Y, Hirsch A, Dar S, Feniger E, Goldstein P. Role of Digital Engagement in Diabetes Care Beyond Measurement: Retrospective Cohort Study. JMIR Diabetes 2021;6(1):e24030 View
  17. Yu J, Xu T, James R, Lu W, Hoffman J. Relationship Between Diabetes, Stress, and Self-Management to Inform Chronic Disease Product Development: Retrospective Cross-Sectional Study. JMIR Diabetes 2020;5(4):e20888 View
  18. Boman N, Fernandez-Luque L, Koledova E, Kause M, Lapatto R. Connected health for growth hormone treatment research and clinical practice: learnings from different sources of real-world evidence (RWE)—large electronically collected datasets, surveillance studies and individual patients’ cases. BMC Medical Informatics and Decision Making 2021;21(1) View
  19. Shah N, Levy C. Emerging technologies for the management of type 2 diabetes mellitus. Journal of Diabetes 2021;13(9):713 View
  20. Yu J, Chiu C, Wang Y, Dzubur E, Lu W, Hoffman J. A Machine Learning Approach to Passively Informed Prediction of Mental Health Risk in People with Diabetes: Retrospective Case-Control Analysis. Journal of Medical Internet Research 2021;23(8):e27709 View
  21. Seixas A, Olaye I, Wall S, Dunn P. Optimizing Healthcare Through Digital Health and Wellness Solutions to Meet the Needs of Patients With Chronic Disease During the COVID-19 Era. Frontiers in Public Health 2021;9 View
  22. Crossen S, Romero C, Lewis C, Glaser N. Remote glucose monitoring is feasible for patients and providers using a commercially available population health platform. Frontiers in Endocrinology 2023;14 View
  23. Usoh C, Kilen K, Keyes C, Johnson C, Aloi J. Telehealth Technologies and Their Benefits to People With Diabetes. Diabetes Spectrum 2022;35(1):8 View
  24. de Oliveira C, Bolognese L, Balcells M, Aragon D, Zagury R, Nobrega C, Liu C, Dardari D. A data-driven approach to manage type 2 diabetes mellitus through digital health: The Klivo Intervention Program protocol (KIPDM). PLOS ONE 2023;18(2):e0281844 View
  25. Wang Y, Dzubur E, James R, Fakhouri T, Brunning S, Painter S, Madan A, Shah B. Association of physical activity on blood glucose in individuals with type 2 diabetes. Translational Behavioral Medicine 2022;12(3):448 View
  26. Li H, Dong L, Zhou W, Wu H, Zhang R, Li Y, Yu C, Wei W. Development and validation of medical record-based logistic regression and machine learning models to diagnose diabetic retinopathy. Graefe's Archive for Clinical and Experimental Ophthalmology 2023;261(3):681 View
  27. Lee U, Jung G, Ma E, Kim J, Kim H, Alikhanov J, Noh Y, Kim H. Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research Directions. IEEE/CAA Journal of Automatica Sinica 2023;10(1):42 View
  28. Grady M, Cameron H, Bhatiker A, Holt E, Schnell O. Real-World Evidence of Improved Glycemic Control in People with Diabetes Using a Bluetooth-Connected Blood Glucose Meter with a Mobile Diabetes Management App. Diabetes Technology & Therapeutics 2022;24(10):770 View
  29. Sabharwal M, Misra A, Ghosh A, Chopra G. Efficacy of Digitally Supported and Real-Time Self-Monitoring of Blood Glucose-Driven Counseling in Patients with Type 2 Diabetes Mellitus: A Real-World, Retrospective Study in North India. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy 2022;Volume 15:23 View
  30. Imrisek S, Lee M, Goldner D, Nagra H, Lavaysse L, Hoy-Rosas J, Dachis J, Sears L. Effects of a Novel Blood Glucose Forecasting Feature on Glycemic Management and Logging in Adults With Type 2 Diabetes Using One Drop: Retrospective Cohort Study. JMIR Diabetes 2022;7(2):e34624 View
  31. Majithia A, Erani D, Kusiak C, Layne J, Lee A, Colangelo F, Romanelli R, Robertson S, Brown S, Dixon R, Zisser H. Medication Optimization Among People With Type 2 Diabetes Participating in a Continuous Glucose Monitoring–Driven Virtual Care Program: Prospective Study. JMIR Formative Research 2022;6(4):e31629 View
  32. Vojtila L, Sherifali D, Dragonetti R, Ashfaq I, Veldhuizen S, Naeem F, Agarwal S, Melamed O, Crawford A, Gerretsen P, Hahn M, Hill S, Kidd S, Mulsant B, Serhal E, Tackaberry-Giddens L, Whitmore C, Marttila J, Tang F, Ramdass S, Lourido G, Sockalingam S, Selby P. Technology-Enabled Collaborative Care for Concurrent Diabetes and Distress Management During the COVID-19 Pandemic: Protocol for a Mixed Methods Feasibility Study. JMIR Research Protocols 2023;12:e39724 View
  33. Dai L, Sheng B, Chen T, Wu Q, Liu R, Cai C, Wu L, Yang D, Hamzah H, Liu Y, Wang X, Guan Z, Yu S, Li T, Tang Z, Ran A, Che H, Chen H, Zheng Y, Shu J, Huang S, Wu C, Lin S, Liu D, Li J, Wang Z, Meng Z, Shen J, Hou X, Deng C, Ruan L, Lu F, Chee M, Quek T, Srinivasan R, Raman R, Sun X, Wang Y, Wu J, Jin H, Dai R, Shen D, Yang X, Guo M, Zhang C, Cheung C, Tan G, Tham Y, Cheng C, Li H, Wong T, Jia W. A deep learning system for predicting time to progression of diabetic retinopathy. Nature Medicine 2024;30(2):584 View

Books/Policy Documents

  1. Hofer I, Figura M. Modern Monitoring in Anesthesiology and Perioperative Care. View