| Title |
Predictors of adherence with self-care guidelines among persons with type 2 diabetes: results from a logistic regression tree analysis
|
|---|---|
| Published in |
Journal of Behavioral Medicine, December 2011
|
| DOI | 10.1007/s10865-011-9392-y |
| Pubmed ID | |
| Authors |
Takashi Yamashita, Cary S. Kart, Douglas A. Noe |
| Abstract |
Type 2 diabetes is known to contribute to health disparities in the U.S. and failure to adhere to recommended self-care behaviors is a contributing factor. Intervention programs face difficulties as a result of patient diversity and limited resources. With data from the 2005 Behavioral Risk Factor Surveillance System, this study employs a logistic regression tree algorithm to identify characteristics of sub-populations with type 2 diabetes according to their reported frequency of adherence to four recommended diabetes self-care behaviors including blood glucose monitoring, foot examination, eye examination and HbA1c testing. Using Andersen's health behavior model, need factors appear to dominate the definition of which sub-groups were at greatest risk for low as well as high adherence. Findings demonstrate the utility of easily interpreted tree diagrams to design specific culturally appropriate intervention programs targeting sub-populations of diabetes patients who need to improve their self-care behaviors. Limitations and contributions of the study are discussed. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 50% |
| United States | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 58 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 9 | 16% |
| Student > Ph. D. Student | 8 | 14% |
| Student > Doctoral Student | 6 | 10% |
| Researcher | 6 | 10% |
| Lecturer | 5 | 9% |
| Other | 12 | 21% |
| Unknown | 12 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Nursing and Health Professions | 15 | 26% |
| Medicine and Dentistry | 9 | 16% |
| Social Sciences | 7 | 12% |
| Psychology | 4 | 7% |
| Computer Science | 2 | 3% |
| Other | 9 | 16% |
| Unknown | 12 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 April 2014.
All research outputs
#14,141,030
of 22,659,164 outputs
Outputs from Journal of Behavioral Medicine
#763
of 1,069 outputs
Outputs of similar age
#153,957
of 242,398 outputs
Outputs of similar age from Journal of Behavioral Medicine
#5
of 7 outputs
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