Predictors of glycemic control in insulin-using adults with type 2 diabetes.
Abstract
OBJECTIVE: To determine the characteristics that influence glycemic control among insulin-using adults with type 2 diabetes. RESEARCH DESIGN AND METHODS: We studied all 1,333 eligible members of a large not-for-profit health maintenance organization who responded to a 1997 survey. We tested associations among demographic, treatment, and psychometric variables with mean 1997 HbA1c values. The Problem Areas in Diabetes (PAID) instrument was used to assess the emotional effect of living with diabetes, and the Short Form 12 Physical Function Scale was used to assess the effect of physical limitations on daily activities. Based on differences between and within treatment groups, we built models to predict glycemic control for subgroups of subjects who were using insulin alone and those who were using insulin in combination with an oral hypoglycemic agent. RESULTS: Younger age, lower BMI, and increased emotional distress about diabetes (according to the PAID scale) were all significant predictors (P < 0.05) of worse glycemic control. However, except among individuals with an HbA1c level of >8.0 who were receiving combination therapy, only approximately 10% of the variance in glycemic control could be predicted by demographic, treatment, or psychometric characteristics. CONCLUSIONS: Personal characteristics explain little of the variation in glycemic control in insulin-using adults with type 2 diabetes. Possible explanations are that the reduced complexity of control in type 2 diabetes makes the disease less sensitive to personal factors than control in type 1 diabetes, that health-related behavior is less driven by personal and environmental characteristics among older individuals, or that, in populations exposed to aggressive glycemic control with oral hypoglycemic agents and nurse care managers, personal differences become largely irrelevant.
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Depression, poor general health, high stress, and obesity result in poor glycemic control.
“risk factors such as depression, poor general health, high-stress levels, and obesity result in poor glycemic control.”
From Factors Associated with Long-Term Control of Type 2 Diabetes Mellitus (2016) · cites this paper as [18]
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Lee, S.-M., Song, I., Suh, D., Chang, C., & Suh, D.-C. (2018) · Journal of Obesity & Metabolic Syndrome · doi:10.7570/jomes.2018.27.4.238 · Cited by 6 · see citing works
- Authors
- Nichols, G. A., Hillier, T. A., Javor, K., & Brown, J. B.
- Affiliations
- Kaiser Permanente (US)
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2000-03-01
- Volume
- 23
- Issue
- 3
- Pages
- 273-277
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- OpenAlex
- W2024618354
- Cited by
- 130
- References
- 0
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 1 · 2023: 1 · 2022: 3 · 2021: 2 · 2020: 6 · 2019: 5 · 2018: 7 · 2017: 6 · 2016: 10 · 2015: 8 · 2014: 10 · 2013: 3 · 2012: 14
- FWCI
- 3.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Diabetes Care (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- glycemic control, type 2 diabetes, insulin therapy, HbA1c, predictors of glycemic control, emotional distress, diabetes distress, physical function, oral antidiabetic drugs, combination therapy, body mass index, age, health maintenance organizations
- MeSH terms
- Activities of Daily Living, Adult, Diabetes Mellitus, Type 2, Emotions, Female, Health Maintenance Organizations, Glycated Hemoglobin, Humans, Insulin, Male, Middle Aged, Oregon, Regression Analysis, Biomarkers, Health Behavior, Body Mass Index
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.
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