Evidence mapPaperPMID 20144288Full record

ArticleJournal of diabetes science and technology2009

Value of self-monitoring blood glucose pattern analysis in improving diabetes outcomes.

Christopher G Parkin, Jaime A Davidson

Open access · bronzeAbstract read
In one paragraph

Article in Journal of diabetes science and technology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
2.0field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed, 95 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Observational
  4. Article
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  7. Self-Monitoring of Blood Glucose as an Integral Part in the Management of People with Type 2 Diabetes Mellitus.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2022
    Article
  8. Article
  9. Structured Blood Glucose Monitoring in Primary Care: A Practical, Evidence-Based Approach.Clinical diabetes : a publication of the American Diabetes Association · 2020
    Article
  10. Article
  11. Article
  12. 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 · 2019
    Review
  13. Article
  14. A visual analytics approach for pattern-recognition in patient-generated data.Journal of the American Medical Informatics Association : JAMIA · 2018
    Article
  15. Article
  16. Article
  17. Article
  18. Clinical Use of Professional Continuous Glucose Monitoring.Diabetes technology & therapeutics · 2017
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 1 country.

Christopher G ParkinCGParkin Communications, Inc., Carmel, Indiana 46032, USA. cgparkin@aol.com
Jaime A Davidson
Rolls-Royce (United States) · USThe University of Texas Southwestern Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Self-monitoring of blood glucose (SMBG) is an important adjunct to hemoglobin A1c (HbA1c) testing. This action can distinguish between fasting, preprandial, and postprandial hyperglycemia; detect glycemic excursions; identify and monitor resolution of hypoglycemia; and provide immediate feedback to patients about the effect of food choices, activity, and medication on glycemic control. Pattern analysis is a systematic approach to identifying glycemic patterns within SMBG data and then taking appropriate action based upon those results. The use of pattern analysis involves: (1) establishing pre- and postprandial glucose targets; (2) obtaining data on glucose levels, carbohydrate intake, medication administration (type, dosages, timing), activity levels and physical/emotional stress; (3) analyzing data to identify patterns of glycemic excursions, assessing any influential factors, and implementing appropriate action(s); and (4) performing ongoing SMBG to assess the impact of any therapeutic changes made. Computer-based and paper-based data collection and management tools can be developed to perform pattern analysis for identifying patterns in SMBG data. This approach to interpreting SMBG data facilitates rational therapeutic adjustments in response to this information. Pattern analysis of SMBG data can be of equal or greater value than measurement of HbA1c levels.

Indexed as

Blood Glucose Self-MonitoringPatient ComplianceBlood GlucoseDecision Making, Computer-AssistedDiabetes MellitusFastingGlycated HemoglobinHumansHypoglycemic AgentsPostprandial PeriodStress, PsychologicalTreatment OutcomeBlood GlucoseGlycated HemoglobinHypoglycemic Agents

Identifiers

PMID20144288
PMCPMC2769875
OpenAlexW2097959326

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.