Literature DB >> 30269984

Patient-Driven Diabetes Care of the Future in the Technology Era.

Sahar Ashrafzadeh1, Osama Hamdy2.   

Abstract

The growing burden of diabetes is fueled by obesity-inducing lifestyle behaviors including high-calorie diets and lack of physical activity. Challenges in access to diabetes specialists and educators, low adherence to medications, and inadequate motivational support for proper disease self-management contribute to poor glycemic control in patients with diabetes. Simultaneously, high patient volumes and low reimbursement rates limit physicians' time spent on lifestyle behavior counseling. These barriers to efficient diabetes care lead to high rates of diabetes-related complications, driving healthcare costs up and reducing the quality of patients' lives. Considering recent advancements in healthcare delivery technologies such as smartphone applications, telemedicine, m-health, device connectivity, machine-learning technology, and artificial intelligence, there is significant opportunity to achieve better efficiency in diabetes care and increase patient involvement in diabetes self-management, which ultimately may put an end to soaring diabetes-related healthcare expenditures. This review explores the patient-driven diabetes care of the future in the technology era.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  artificial intelligence; diabetes mellitus; healthcare access; lifestyle intervention; m-health; mobile health; patient-driven care; smartphone applications; telehealth; telemedicine

Mesh:

Year:  2018        PMID: 30269984     DOI: 10.1016/j.cmet.2018.09.005

Source DB:  PubMed          Journal:  Cell Metab        ISSN: 1550-4131            Impact factor:   27.287


  9 in total

Review 1.  Type 1 diabetes mellitus: much progress, many opportunities.

Authors:  Alvin C Powers
Journal:  J Clin Invest       Date:  2021-04-15       Impact factor: 14.808

Review 2.  Diabetes clinic reinvented: will technology change the future of diabetes care?

Authors:  Marwa Al-Badri; Osama Hamdy
Journal:  Ther Adv Endocrinol Metab       Date:  2021-03-23       Impact factor: 3.565

3.  Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices.

Authors:  Paola Stolfi; Ilaria Valentini; Maria Concetta Palumbo; Paolo Tieri; Andrea Grignolio; Filippo Castiglione
Journal:  BMC Bioinformatics       Date:  2020-12-14       Impact factor: 3.169

Review 4.  Effectiveness of Digital Counseling Environments on Anxiety, Depression, and Adherence to Treatment Among Patients Who Are Chronically Ill: Systematic Review.

Authors:  Karoliina Paalimäki-Paakki; Mari Virtanen; Anja Henner; Miika T Nieminen; Maria Kääriäinen
Journal:  J Med Internet Res       Date:  2022-01-06       Impact factor: 5.428

5.  In-person and virtual multidisciplinary intensive lifestyle interventions are equally effective in patients with type 2 diabetes and obesity.

Authors:  Marwa Al-Badri; Cara L Kilroy; Jacqueline Ifat Shahar; Shaheen Tomah; Hannah Gardner; Mallory Sin; Jennie Votta; Aliza Phillips-Stoll; Aaron Price; Joan Beaton; Chandra Davis; Jo-Anne Rizzotto; Shilton Dhaver; Osama Hamdy
Journal:  Ther Adv Endocrinol Metab       Date:  2022-04-18       Impact factor: 4.435

6.  Factors Influencing the Desirability, Acceptability, and Adherence of Patients with Diabetes to Telemedicine.

Authors:  Raul Patrascu; Alin Albai; Adina Braha; Laura Gaita; Sandra Lazar; Ovidiu Potre; Bogdan Timar
Journal:  Medicina (Kaunas)       Date:  2022-07-26       Impact factor: 2.948

7.  What Intervention Elements Drive Weight Loss in Blended-Care Behavior Change Interventions? A Real-World Data Analysis with 25,706 Patients.

Authors:  Felix Schirmann; Philipp Kanehl; Lucy Jones
Journal:  Nutrients       Date:  2022-07-21       Impact factor: 6.706

8.  The Effects of a Lifestyle Intervention Supported by the InterWalk Smartphone App on Increasing Physical Activity Among Persons With Type 2 Diabetes: Parallel-Group, Randomized Trial.

Authors:  Ida Kær Thorsen; Yanxiang Yang; Laura Staun Valentiner; Charlotte Glümer; Kristian Karstoft; Jan Christian Brønd; Rasmus Oestergaard Nielsen; Charlotte Brøns; Robin Christensen; Jens Steen Nielsen; Allan Arthur Vaag; Bente Klarlund Pedersen; Henning Langberg; Mathias Ried-Larsen
Journal:  JMIR Mhealth Uhealth       Date:  2022-09-28       Impact factor: 4.947

9.  Health Technology Readiness Profiles Among Danish Individuals With Type 2 Diabetes: Cross-Sectional Study.

Authors:  Ida Kær Thorsen; Sine Rossen; Charlotte Glümer; Julie Midtgaard; Mathias Ried-Larsen; Lars Kayser
Journal:  J Med Internet Res       Date:  2020-09-15       Impact factor: 5.428

  9 in total

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