We constructed a retrospective cohort of participants drawn from a randomized comparative effectiveness study to evaluate the relationship between change in HbA1c and Diabetes-39 quality of life. HbA1c at one-year follow-up was significantly associated with overall quality of life on the Diabetes-39. Our multiple linear regression models suggest that improvements in HbA1c among patients completing diabetes self-management interventions are significantly associated with increased quality of life on the diabetes control and sexual functioning subscales of the Diabetes-39. No association was established between changes in HbA1c and the anxiety and worry, social burden, and energy and mobility subscales. Baseline burden of illness, a proxy for baseline quality of life, predicted overall quality of life as well as all subscales of the Diabetes-39, as expected.
This study firmly establishes the relationship between improved HbA1c
, a critical clinical biomarker in diabetes, and the Diabetes-39, a patient-centered diabetes-specific quality of life measure among patients completing a self-management education program. Several previous studies have attempted to explore the relationship between clinical indicators, such as HbA1c
, and a variety of diabetes-specific quality of life measures [31
]. Unfortunately, these associations have been weak [41
] or nonexistent [42
], present for only very few of a scale’s domains [36
], or are specific to type 1 diabetes only [34
]. Further, prior studies report on measures that have poor evidence for validity and reliability [32
], focus on singular aspects of quality of life (e.g., distress [37
]), ignore key components of quality of life such as physical and social functioning [9
], or include several items that are not diabetes-specific [9
]. Additionally, several reviews of diabetes-specific quality of life measures [9
] have recognized the lack of empirical evidence on the responsiveness of these scales to changes in health status.
This analysis of HbA1c
and diabetes-specific quality of life addresses many of the limitations of prior studies. The Diabetes-39 diabetes-specific quality of life measure has been recommended for use in research and clinical settings by all of the aforementioned reviews of diabetes-specific quality of life measures [9
]. The instrument has good evidence for validity and reliability, includes several domains that cover many aspects of quality of life, and is applicable to a wide population of patients [9
]. The Diabetes-39 is one of few diabetes-specific quality of life measures that have been shown to be responsive to changes in health status [39
]. Further, this instrument does not impose a definition of quality of life upon respondents, but instead allows patients to frame responses in the context of their own personal conceptualization of quality of life. Also, patients were directly involved in the selection of items for the questionnaire [33
]. These attributes make the instrument highly patient-centered, one of the most critical components to any patient-assessed quality of life measure. Thus, our study focuses on a diabetes-specific quality of life measure that is a prime candidate for analysis.
Our statistical methods also address several prior studies’ shortcomings. While most previous attempts to examine the relationship between HbA1c
and quality of life used simple linear correlations [34
], our analyses included predictive linear regression models. This allows for a more robust analysis and provides a quantification of the impact of HbA1c
on quality of life. To our knowledge, two prior studies have employed linear regression models to assess this relationship [37
]. However, one study [38
] grouped continuous HbA1c
data into two groups. This reduces a model’s ability to quantify the effect of changes in HbA1c
on quality of life, and diminishes the overall robustness of the model. A second study [37
] modeled HbA1c
as the primary dependent variable. This is not in line with the Institute of Medicine’s vision [2
] in which patient-centered measures, such as quality of life, are the ultimate outcomes of care. Our analysis included a regression of continuous HbA1c
data with quality of life as the primary outcome.
Few prior studies have examined the relationship between clinical indicators and diabetes-specific quality of life measures among participants who all completed diabetes self-management programs. These programs were deeply embedded in primary care. One program was led by a primary care physician, while the other was led by nurse educators and registered dieticians. The latter model represents the type of delivery system redesign that is characteristic to many primary care innovations [3
]. Our examination of the relationship between clinical indicators and quality of life outcomes in the context of patient-centered diabetes self-management programs demonstrates that HbA1c
improvements among participants in these programs are associated with better quality of life. Previous studies have included diabetes-specific quality of life among outcome measures [40
]. These studies approach both quality of life and HbA1c
as distinct outcomes, and do not explore the association between the two variables. Unlike prior studies, our study examines the relationship between changes in HbA1c
and diabetes-specific quality of life. In the post-ACCORD era, there has been reduced emphasis on intensive HbA1c
]. However, the current study suggests that improved HbA1c
resulting from diabetes self-management interventions is associated with better diabetes-specific quality of life. Thus, HbA1c
control is relevant to patient-centered outcomes and should remain a valuable goal in diabetes care.
There were limitations to our study. A sample size of 75 limited the range of analytic strategies that could be employed. The sample size may also have affected the power of our analyses, which may account for the weak association between changes in HbA1c
and some of the Diabetes-39 subscales. The generalizability of our study may also be limited. Our sample is reflective of the United States Veterans Administration patient population, consisting largely of older patients who are predominantly male, of older age, and have significant co-morbidities. Further, all of the participants in our cohort participated in at least one diabetes self-management program. Thus, we were unable to assess the impact of participation in these programs on quality of life as compared with patients who did not participate in any self-management programs. Additionally, the lack of Diabetes-39 data at baseline precluded an examination of the responsiveness of this diabetes-specific quality of life measure over time. However, our analysis does include HbA1c
data from multiple time points and includes a measure of burden of illness at baseline. Many previous studies used cross-sectional data from one time point [34
]. Our analyses included HbA1c
data from both before and after participation in diabetes self-management programs.
Future studies should be certain to collect quality of life data both before and after diabetes self-management programs so that the responsiveness of quality of life measures can be assessed. Subsequent studies should also include larger, more diverse samples to ensure adequate power and generalizability. The inclusion of a control group that does not receive any programs beyond routine care may also allow for future examinations of the impact of diabetes-self management programs on quality of life.