ArticleJournal of diabetes science and technology2013
Evaluation of a blood glucose monitoring system with automatic high- and low-pattern recognition software in insulin-using patients: pattern detection and patient-reported insights.
Article in Journal of diabetes science and technology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Use of Blood Glucose Meters Featuring Color Range Indicators Improves Glycemic Control in Patients With Diabetes in Comparison to Blood Glucose Meters Without Color (ACCENTS Study).Journal of diabetes science and technology · 2018Trial
- Use of an Integrated Tool for Interpretation of Blood Glucose Data Improves Correctness of Glycemic Risk Assessment in Individuals With Type 1 and Type 2 Diabetes.Journal of diabetes science and technology · 2017Trial
- Remote Health Consultations Supported by a Diabetes Management Web Application With a New Glucose Meter Demonstrates Improved Glycemic Control.Journal of diabetes science and technology · 2016Trial
- Improved Glycemic Control Using a Bluetooth®-Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence From Over 144 000 People With Diabetes.Journal of diabetes science and technology · 2024Article
- Article
- Graphical user interface design to improve understanding of the patient-reported outcome symptom response.PloS one · 2023Article
- Establishing Expert, Multi-Disciplinary, Peer-Reviewed Consensus to Lead a Paradigm Shift in Optimal Blood Glucose Management.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2019Article
- A Review of Emerging Technologies in Diabetes Management for Multiple-Dose Insulin-Injecting Patients With Type 2 Diabetes Who Self-monitor Blood Glucose.The Journal of pharmacy technology : jPT : official publication of the Association of Pharmacy Technicians · 2019Review
- Evaluation of a New Digital Automated Glycemic Pattern Detection Tool.Diabetes technology & therapeutics · 2017Article
- Article
- Usability Evaluation of a Blood Glucose Monitoring System With a Spill-Resistant Vial, Easier Strip Handling, and Connectivity to a Mobile App: Improvement of Patient Convenience and Satisfaction.Journal of diabetes science and technology · 2016Article
- Evaluation of the utility of a glycemic pattern identification system.Journal of diabetes science and technology · 2014Article
- Penny wise and pound foolish: will shortsighted cost reduction measures compromise patient access to promising self-monitoring of blood glucose technology?Journal of diabetes science and technology · 2013Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to evaluate the performance of a glucose pattern recognition tool incorporated in a blood glucose monitoring system (BGMS) and its association with clinical measures, and to assess user perception and understanding of the pattern messages they receive.
methodsParticipants had type 1 or type 2 diabetes mellitus and were self-adjusting insulin doses for ≥1 year. During a 4-week home testing period, participants performed ≥6 daily self-tests, adjusted their insulin regimen based on BGMS results, and recorded pattern messages in the logbook. Participants reflected on usability of the pattern tool in a questionnaire.
resultsStudy participants (n = 101) received a mean ± standard deviation of 4.5 ± 1.9 pattern messages per week (3.6 ± 1.8 high glucose patterns and 0.9 ± 1.3 low glucose patterns). Most received ≥1 high (96.5%) and/or ≥1 low (46.0%) pattern message per week. The average number of high- and low-pattern messages per week was associated with higher and lower, respectively, baseline hemoglobin A1c (p < .01) and fasting plasma glucose (p < .05). Participants found high- and low-pattern messages clear and easy to understand (84.2% and 83.2%, respectively) and considered the frequency of low (82.0%) and high (63.4%) pattern messages about right. Overall, 71.3% of participants indicated they preferred to use a meter with pattern messages.
conclusionsThe on-device Pattern tool identified meaningful blood glucose patterns, highlighting potential opportunities for improving glycemic control in patients who self-adjust their insulin.
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