Literature DB >> 28872765

Digital health technology and diabetes management.

Avivit Cahn1,2, Amit Akirov3,4, Itamar Raz1.   

Abstract

Diabetes care is largely dependent on patient self-management and empowerment, given that patients with diabetes must make numerous daily decisions as to what to eat, when to exercise, and determine their insulin dose and timing if required. In addition, patients and providers are generating vast amounts of data from many sources, including electronic medical records, insulin pumps, sensors, glucometers, and other wearables, as well as evolving genomic, proteomic, metabolomics, and microbiomic data. Multiple digital tools and apps have been developed to assist patients to choose wisely, and to enhance their compliance by using motivational tools and incorporating incentives from social media and gaming techniques. Healthcare teams (HCTs) and health administrators benefit from digital developments that sift through the enormous amounts of patient-generated data. Data are acquired, integrated, analyzed, and presented in a self-explanatory manner, highlighting important trends and items that require attention. The use of decision support systems may propose data-driven actions that, for the most, require final approval by the patient or physician before execution and, once implemented, may improve patient outcomes. The digital diabetes clinic aims to incorporate all digital patient data and provide individually tailored virtual or face-to-face visits to those persons who need them most. Digital diabetes care has demonstrated only modest HbA1c reduction in multiple studies and borderline cost-effectiveness, although patient satisfaction appears to be increased. Better understanding of the barriers to digital diabetes care and identification of unmet needs may yield improved utilization of this evolving technology in a safe, effective, and cost-saving manner.
© 2017 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd.

Entities:  

Keywords:  big data; decision support; diabetes; digital therapy; mobile app; 决策支持; 大数据; 手机应用程序; 数字化治疗; 糖尿病

Mesh:

Year:  2017        PMID: 28872765     DOI: 10.1111/1753-0407.12606

Source DB:  PubMed          Journal:  J Diabetes        ISSN: 1753-0407            Impact factor:   4.006


  24 in total

1.  New Paradigm of Personalized Glycemic Control Using Glucose Temporal Density Histograms.

Authors:  Uriel Trahtemberg; Tova Hallas; Yehonatan Segman; Ella Sheiman; Michal Shasha; Kobi Nissim; Yosef Joseph Segman
Journal:  J Diabetes Sci Technol       Date:  2019-01-08

2.  Supporting Good Intentions With Good Evidence: How to Increase the Benefits of Diabetes Social Media.

Authors:  Claire Reidy; David C Klonoff; Katharine D Barnard-Kelly
Journal:  J Diabetes Sci Technol       Date:  2019-05-16

Review 3.  The role of digital health technology in rural cancer care delivery: A systematic review.

Authors:  Bonny B Morris; Brianna Rossi; Bernard Fuemmeler
Journal:  J Rural Health       Date:  2021-09-04       Impact factor: 5.667

4.  Validation of a Lower Back "Wearable"-Based Sit-to-Stand and Stand-to-Sit Algorithm for Patients With Parkinson's Disease and Older Adults in a Home-Like Environment.

Authors:  Minh H Pham; Elke Warmerdam; Morad Elshehabi; Christian Schlenstedt; Lu-Marie Bergeest; Maren Heller; Linda Haertner; Joaquim J Ferreira; Daniela Berg; Gerhard Schmidt; Clint Hansen; Walter Maetzler
Journal:  Front Neurol       Date:  2018-08-10       Impact factor: 4.003

5.  Wearables for gait and balance assessment in the neurological ward - study design and first results of a prospective cross-sectional feasibility study with 384 inpatients.

Authors:  Felix P Bernhard; Jennifer Sartor; Kristina Bettecken; Markus A Hobert; Carina Arnold; Yvonne G Weber; Sven Poli; Nils G Margraf; Christian Schlenstedt; Clint Hansen; Walter Maetzler
Journal:  BMC Neurol       Date:  2018-08-16       Impact factor: 2.474

6.  A Smartphone App to Improve Medication Adherence in Patients With Type 2 Diabetes in Asia: Feasibility Randomized Controlled Trial.

Authors:  Zhilian Huang; Eberta Tan; Elaine Lum; Peter Sloot; Bernhard Otto Boehm; Josip Car
Journal:  JMIR Mhealth Uhealth       Date:  2019-09-12       Impact factor: 4.773

7.  Digital Diabetes Care System Observations from a Pilot Evaluation Study in Vietnam.

Authors:  Tran Quang Khanh; Pham Nhu Hao; Eytan Roitman; Itamar Raz; Baruch Marganitt; Avivit Cahn
Journal:  Int J Environ Res Public Health       Date:  2020-02-03       Impact factor: 3.390

8.  Preferences of people with type 2 diabetes for telemedical lifestyle programmes in Germany: protocol of a discrete choice experiment.

Authors:  Jana Sommer; Jan Dyczmons; Sandra Grobosch; Veronika Gontscharuk; Markus Vomhof; Michael Roden; Andrea Icks
Journal:  BMJ Open       Date:  2020-09-09       Impact factor: 2.692

9.  Toward Integration of mHealth in Primary Care in the Netherlands: A Qualitative Analysis of Stakeholder Perspectives.

Authors:  Esmee L S Bally; Tomris Cesuroglu
Journal:  Front Public Health       Date:  2020-01-15

Review 10.  New and Emerging Technologies in Type 1 Diabetes.

Authors:  Jordan S Sherwood; Steven J Russell; Melissa S Putman
Journal:  Endocrinol Metab Clin North Am       Date:  2020-10-14       Impact factor: 4.741

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