Measuring the quality of life of patients with diabetes: Validating the tool and determining the effects on quality of life

There are several tools for measuring quality of life (QoL) and specifically, health-related quality of life (HRQoL) for persons with diabetes. A commonly-used measure, the Diabetes Quality of Life (DQOL) Survey, developed for the Diabetes Control and Complications Trial (DCCT), has been used in several experimental settings, and its reliability and validity are well-established. However, it is considered too long to be of practical use in clinical settings. Because of this, a shortened version of the tool was used recently in the Community Diabetes Education (CoDE) Project in Dallas, Texas, a clinic-based patient education program that uses a specially-trained community healthcare worker to provide patient education. However, the modified scale has never been tested for reliability and validity. Thus, one goal of this thesis was to measure these psychometric properties of the scale. After establishing the reliability and validity, the results of the scale were analyzed to determine the effects of the intervention on the subjects’ quality of life. The changes in QoL scales were compared with changes in physiologic measures which are most closely allied with diabetes, including blood glucose levels, weight/BMI, co-morbidities and health beliefs in order to determine if there is a relationship between such measures and quality of life. The results of the reliability and validity testing were not conclusive. Measures of reliability and criterion validity were established, but these contrasted with poor measures of repeatability and content validity. The effect of the intervention on quality of life, however, was more significant, particularly regarding the impact of diabetes. Those who received the counseling had significantly higher scores on the Impact scale than those who did not, and the former group had much greater improvement in scores over the twelve month period than the latter group. ^

A systematic review and meta-analysis of diabetes disease management programs

Context. Despite the rapid growth of disease management programs, there are still questions about their efficacy and effectiveness for improving patient outcomes and their ability to reduce costs associated with chronic disease. ^ Objective. To determine the effectiveness of disease management programs on improving the results of HbA1c tests, lipid profiles and systolic blood pressure (SBP) readings among diabetics. These three quantitative measures are widely accepted methods of determining the quality of a patient's diabetes management and the potential for future complications. ^ Data Sources. MEDLINE and CINAHL were searched from 1950 to June 2008 using MeSH terms designed to capture all relevant studies. Scopus pearling and hand searching were also done. Only English language articles were selected. ^ Study Selection. Titles and abstracts for the 2347 articles were screened against predetermined inclusion and exclusion criteria, yielding 217 articles for full screening. After full article screening, 29 studies were selected for inclusion in the review. ^ Data Extraction. From the selected studies, data extraction included sample size, mean change over baseline, and standard deviation for each control and experimental arm. ^ Results. The pooled results show a mean HbA1c reduction of 0.64%, 95% CI (-0.83 to -0.44), mean SBP reduction of 7.39 mmHg (95% CI to -11.58 to -3.2), mean total cholesterol reduction of 5.74 mg/dL (95% CI, -10.01 to -1.43), and mean LDL cholesterol reduction of 3.74 mg/dL (95% CI, -8.34 to 0.87). Results for HbA1c, SBP and total cholesterol were statistically significant, while the results for LDL cholesterol were not. ^ Conclusions. The findings suggest that disease management programs utilizing five hallmarks of care can be effective at improving intermediate outcomes among diabetics. However, given the significant heterogeneity present, there may be fundamental differences with respect to study-specific interventions and populations that render them inappropriate for meta-analysis. ^