Cited by 93Diabetes Management and ResearchDiabetes Management and EducationMobile Health and mHealth Applications

Health Technologies for Monitoring and Managing Diabetes: A Systematic Review

Open access
Russell-Minda, Elizabeth, Jutai, Jeffrey, Speechley, Mark, Bradley, Kaitlin, Chudyk, Anna, Petrella, Robert·2009·Journal of Diabetes Science and Technology

Abstract

Background: The primary objective of this review was to determine the strength of evidence for the effectiveness of self-monitoring devices and technologies for individuals with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) based on specific health-related outcome measures. Self-monitoring devices included those that assist patients with managing diabetes and preventing cardiovascular complications (CVCs). A secondary objective was to explore issues of feasibility, usability, and compliance among patients and providers. Methods: Study criteria included individuals ≥14 years and youth (7–14 years) with T1DM or T2DM, intervention with a self-monitoring device, assessment of clinical outcomes with the device, literature in English, and ≥10 participants. Relevant published literature was searched from 1985 to 2008. Randomized controlled trials and observational studies were included. Data were extracted for clinical outcomes, feasibility and compliance methods, and results. Selected studies were independently evaluated with a validated instrument for assessing methodological quality. Results: Eighteen trials were selected. Predominant types of device interventions included self-monitoring of blood glucose, pedometers, and cell phone or wireless technologies. Feasibility and compliance were measured in the majority of studies. Conclusions: Self-monitoring of blood glucose continues to be an effective tool for the management of diabetes. Wireless technologies can improve diabetes self-care, and pedometers are effective lifestyle modification tools. The results of this review indicate a need for additional controlled trial research on existing and novel technologies for diabetes self-monitoring, on health outcomes associated with diabetes and CVCs, and device feasibility and compliance.

Integrity signals

Registry

Crossref · 10.1177/193229680900300628

Cites

57 of 57 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

1 of the 58 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Russell-Minda, E., Jutai, J., Speechley, M., Bradley, K., Chudyk, A., & Petrella, R.
Affiliations
  • Lawson Health Research Institute (CA)
  • University of Ottawa (CA)
  • Western University (CA)
Corresponding author
Petrella, Robert
Venue
Journal of Diabetes Science and Technology
Published
2009-11
Volume
3
Issue
6
Pages
1460-1471
ISSN
1932-2968 (ISSN-L)
OpenAlex
W2168602960
Cited by
92
References
58
Citations by year
2026: 2 · 2025: 5 · 2024: 2 · 2023: 2 · 2022: 6 · 2021: 4 · 2020: 3 · 2019: 6 · 2018: 6 · 2017: 8 · 2016: 5 · 2015: 8 · 2014: 11 · 2013: 13 · 2012: 7
FWCI
0.48
Type
journal article
Language
en
Open access
Locations
  • Journal of Diabetes Science and Technology (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
Canadian Institutes of Health Research (CCT- 83029)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Keywords
diabetes self-monitoring, type 1 diabetes, type 2 diabetes, blood glucose monitoring, pedometers, diabetes self-care, cardiovascular complications, lifestyle modification
MeSH terms
Adolescent, Blood Glucose, Child, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Equipment Design, Glycated Hemoglobin, Humans, Patient Compliance, Practice Patterns, Physicians', Predictive Value of Tests, Self Care, Blood Glucose Self-Monitoring, Treatment Outcome, Practice Guidelines as Topic, Evidence-Based Medicine, Diagnostic Equipment, Guideline Adherence, Biomedical Technology, Risk Reduction Behavior, Cell Phone, Actigraphy

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review