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Barriers to Remote Health Interventions for Type 2 Diabetes: A Systematic Review and Proposed Classification Scheme.

Michelle M Alvarado1, Hye-Chung Kum1,2, Karla Gonzalez Coronado1

  • 1Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States.

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|February 15, 2017
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Summary

Remote health interventions for type 2 diabetes management face adoption barriers, primarily data accuracy issues, scalability limitations, and technology illiteracy, significantly impacting patient engagement and leading to high dropout rates.

Keywords:
biomedical technologydiabetes mellitus, type 2early medical interventionremote sensing technologyterminology as topic

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Area of Science:

  • Diabetes management and remote health technologies.
  • Health informatics and patient engagement strategies.

Background:

  • Diabetes self-management requires adherence to daily habits like glucose monitoring, medication, exercise, and diet.
  • Remote interventions are emerging to support diabetes self-management between clinic visits.
  • Widespread adoption of remote health for diabetes faces significant barriers.

Purpose of the Study:

  • To identify and classify barriers to the adoption of remote health interventions for type 2 diabetes management.

Main Methods:

  • Systematic literature search of 6 electronic databases (2010-2015).
  • Inclusion of studies on remote technologies for type 2 diabetes self-management.
  • Data extraction on study characteristics, outcomes, dropout rates, technologies, and identified barriers.

Main Results:

  • Lack of data accuracy (32%), scalability limitations (24%), and technology illiteracy (24%) were the most common barriers.
  • Technology illiteracy affected low-income populations; scalability issues were more prevalent in mid-income groups.
  • Barriers significantly impeded patient engagement (40.5%), correlating with high dropout rates (up to 57%).

Conclusions:

  • Identified barriers pose major challenges for remote diabetes health intervention design.
  • Breakthrough technologies are needed to improve patient engagement and data reliability.
  • Further research on dropout causes, risk identification, and cost-benefit analysis is crucial for sustainability.