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

mHealth for Wellness, Behavior Change and Prevention (2728) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1301) Diabetes Prevention (77) Glucose Tracking and Self-Monitoring of Blood Glucose (150)

Published on in Vol 7, No 10 (2019): October

Examining the Use of Glucose and Physical Activity Self-Monitoring Technologies in Individuals at Moderate to High Risk of Developing Type 2 Diabetes: Randomized Trial

Examining the Use of Glucose and Physical Activity Self-Monitoring Technologies in Individuals at Moderate to High Risk of Developing Type 2 Diabetes: Randomized Trial

Examining the Use of Glucose and Physical Activity Self-Monitoring Technologies in Individuals at Moderate to High Risk of Developing Type 2 Diabetes: Randomized Trial

Authors of this article:

Maxine E Whelan1, 2, 3 Author Orcid Image ;   Mark W Orme2, 3, 4, 5 Author Orcid Image ;   Andrew P Kingsnorth2, 3 Author Orcid Image ;   Lauren B Sherar2, 3, 6 Author Orcid Image ;   Francesca L Denton2, 3 Author Orcid Image ;   Dale W Esliger2, 3, 6 Author Orcid Image
Article Authors Cited by (21) Tweetations (11) Metrics

Journals

  1. Balducci S. Prevention of type 2 diabetes by physical activity: What has history taught us?. Diabetes/Metabolism Research and Reviews 2020;36(5) View
  2. Rodríguez-Rodríguez I, Rodríguez J, Molina-García-Pardo J, Zamora-Izquierdo M, Martínez-Inglés M. A Comparison of Different Models of Glycemia Dynamics for Improved Type 1 Diabetes Mellitus Management with Advanced Intelligent Analysis in an Internet of Things Context. Applied Sciences 2020;10(12):4381 View
  3. Kingsnorth A, Whelan M, Orme M, Routen A, Sherar L, Esliger D. Resistance to data loss from the Freestyle Libre: impact on glucose variability indices and recommendations for data analysis. Applied Physiology, Nutrition, and Metabolism 2021;46(2):148 View
  4. Minen M, Corner S, Berk T, Levitan V, Friedman S, Adhikari S, Seng E. Heartrate variability biofeedback for migraine using a smartphone application and sensor: A randomized controlled trial. General Hospital Psychiatry 2021;69:41 View
  5. Oikonomidi T, Ravaud P, James A, Cosson E, Montori V, Tran V. An International, Mixed-Methods Study of the Perceived Intrusiveness of Remote Digital Diabetes Monitoring. Mayo Clinic Proceedings 2021;96(5):1236 View
  6. Minen M, Adhikari S, Padikkala J, Tasneem S, Bagheri A, Goldberg E, Powers S, Lipton R. Smartphone‐Delivered Progressive Muscle Relaxation for the Treatment of Migraine in Primary Care: A Randomized Controlled Trial. Headache: The Journal of Head and Face Pain 2020;60(10):2232 View
  7. Lam B, Catt M, Cassidy S, Bacardit J, Darke P, Butterfield S, Alshabrawy O, Trenell M, Missier P. Using Wearable Activity Trackers to Predict Type 2 Diabetes: Machine Learning–Based Cross-sectional Study of the UK Biobank Accelerometer Cohort. JMIR Diabetes 2021;6(1):e23364 View
  8. Rodríguez-Rodríguez I, Rodríguez J, Woo W, Wei B, Pardo-Quiles D. A Comparison of Feature Selection and Forecasting Machine Learning Algorithms for Predicting Glycaemia in Type 1 Diabetes Mellitus. Applied Sciences 2021;11(4):1742 View
  9. Rodriguez-León C, Villalonga C, Munoz-Torres M, Ruiz J, Banos O. Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review. JMIR mHealth and uHealth 2021;9(6):e25138 View
  10. Ahmad E, Lim S, Lamptey R, Webb D, Davies M. Type 2 diabetes. The Lancet 2022;400(10365):1803 View
  11. Stecher C, Berardi V, Fowers R, Christ J, Chung Y, Huberty J. Identifying App-Based Meditation Habits and the Associated Mental Health Benefits: Longitudinal Observational Study. Journal of Medical Internet Research 2021;23(11):e27282 View
  12. Fowers R, Berardi V, Huberty J, Stecher C. Using mobile meditation app data to predict future app engagement: an observational study. Journal of the American Medical Informatics Association 2022;29(12):2057 View
  13. Larsen R, Wagner V, Korfitsen C, Keller C, Juhl C, Langberg H, Christensen J. Effectiveness of physical activity monitors in adults: systematic review and meta-analysis. BMJ 2022:e068047 View
  14. Utesch T, Piesch L, Busch L, Strauss B, Geukes K. Self-tracking of daily physical activity using a fitness tracker and the effect of the 10,000 steps goal. German Journal of Exercise and Sport Research 2022;52(2):300 View
  15. Shih H, Quinn L, Morgan-Jones P, Long K, Schreier A, Friel C. Wearable Activity Monitors to Support Physical Activity Interventions in Neurodegenerative Disease: A Feasibility Study. Neurodegenerative Disease Management 2023;13(3):177 View
  16. Mortensen S, Skou S, Brønd J, Ried-Larsen M, Petersen T, Jørgensen L, Jepsen R, Tang L, Bruun-Rasmussen N, Grøntved A. Detailed descriptions of physical activity patterns among individuals with diabetes and prediabetes: the Lolland-Falster Health Study. BMJ Open Diabetes Research & Care 2023;11(5):e003493 View
  17. Lee M, Song M, Bessette H, Roberts Davis M, Tyner T, Reid A. Use of wearables for monitoring cardiometabolic health: A systematic review. International Journal of Medical Informatics 2023;179:105218 View
  18. Kitazawa M, Takeda Y, Hatta M, Horikawa C, Sato T, Osawa T, Ishizawa M, Suzuki H, Matsubayashi Y, Fujihara K, Yamada T, Sone H. Lifestyle Intervention With Smartphone App and isCGM for People at High Risk of Type 2 Diabetes: Randomized Trial. The Journal of Clinical Endocrinology & Metabolism 2024;109(4):1060 View
  19. Hiemstra M, Reichert S, Mitchell M. Examining a Remote Group-Based Type 2 Diabetes Self-Management Education Program in the COVID-19 Era Using the ORBIT Model: Small 6-Week Feasibility Study. JMIR Formative Research 2024;8:e46418 View

Books/Policy Documents

  1. Schembre S, Liao Y, Jospe M. Obesity and Diabetes. View
  2. Ruiz J, Guerrero J, Cruz C, Jimenez D, Lama J, Espinilla M. Proceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023). View