Marie Clark

Structured Type 1 Diabetes Education Delivered Within Routine Care Impact on glycemic control and diabetes-specific quality of life

OBJECTIVE To determine whether improvements in glycemic control and diabetes-specific quality of life (QoL) scores reported in research studies for the type 1 diabetes structured education program Dose Adjustment For Normal Eating (DAFNE) are also found when the intervention is delivered within routine U.K. health care. RESEARCH DESIGN AND METHODS Before and after evaluation of DAFNE to assess impact on glycemic control and QoL among 262 adults with type 1 diabetes. RESULTS There were significant improvements in HbA1c from baseline to 6 and 12 months (from 9.1 to 8.6 and 8.8%, respectively) in a subgroup with suboptimal control. QoL was significantly improved by 3 months and maintained at both follow-up points. CONCLUSIONS Longer-term improved glycemic control and QoL is achievable among adults with type 1 diabetes through delivery of structured education in routine care, albeit with smaller effect sizes than reported in trials.

Do people with diabetes understand their clinical marker of long-term glycemic control (HbA1c levels) and does this predict diabetes self-care behaviours and HbA1c?

OBJECTIVE Research demonstrates that patients have a poor understanding of glycosylated haemoglobin A1c (HbA1c) and that this impacts on effective diabetes self-management. This study attempted to replicate these findings in a UK outpatient sample of people with diabetes. METHOD 83 participants were recruited and asked to fill in a questionnaire assessing their understanding of HbA1c, diabetes self-care behaviours and diabetes-specific self-efficacy in relation to carrying out these self-care behaviours. RESULTS Only 26.5% of the participants were classified as having a good understanding of HbA1c. Correlational and univariate analyses indicated that this level of understanding was related to demographic variables, HbA1c levels and certain aspects of self-care and self-efficacy. A series of multiple regressions found that understanding was a significant predictor of HbA1c levels. CONCLUSION The majority of participants seemed to have a poor understanding of HbA1c and this was related to aspects of their diabetes management, self-efficacy and HbA1c levels. PRACTICAL IMPLICATIONS These findings provide support for the application of programmes and initiatives aimed at improving patients understanding of clinical disease markers.

How and Why do Patients with Type 1 Diabetes Sustain their Use of Flexible Intensive Insulin Therapy?: A Qualitative Longitudinal Investigation of Patients' Self-Management Practices Following Attendance at a Dose Adjustment for Normal Eating (DAFNE) Course

Diabet. Med. 28, 532–538 (2011)

Linguistic and Psychometric Validation of the Diabetes-Specific Quality-of-Life Scale in U.K. English for Adults With Type 1 Diabetes

OBJECTIVE To develop a linguistically and psychometrically validated U.K. English (U.K./Ireland) version of the Diabetes-Specific Quality-of-Life Scale (DSQOLS) for adults with type 1 diabetes. RESEARCH DESIGN AND METHODS We conducted independent forward and backward translation of the validated German DSQOLS. An iterative interview study with health professionals (n = 3) and adults with type 1 diabetes (n = 8) established linguistic validity. The DSQOLS was included in three Dose Adjustment for Normal Eating (DAFNE) studies (total N = 1,071). Exploratory factor analysis (EFA) was undertaken to examine questionnaire structure. Concurrent and discriminant validity, internal consistency, and reliability were assessed. RESULTS EFA indicated a six-factor structure for the DSQOLS (social aspects, fear of hypoglycemia, dietary restrictions, physical complaints, anxiety about the future, and daily hassles). High internal consistency reliability was found for these factors and the weighted treatment satisfaction scale (α = 0.85–0.94). All subscales were moderately, positively correlated with the Audit of Diabetes-Dependent Quality-of-Life (ADDQoL) measure, demonstrating evidence of concurrent validity. Lower DSQOLS subscale scores [indicating impaired quality of life (QoL)] were associated with the presence of diabetes-related complications. CONCLUSIONS The DSQOLS captures the impact of detailed aspects of modern type 1 diabetes management (e.g., carbohydrate counting and flexible insulin dose adjustment) that are now routine in many parts of the U.K. and Ireland. The U.K. English version of the DSQOLS offers a valuable tool for assessing the impact of treatment approaches on QoL in adults with type 1 diabetes.

Type 1 diabetes structured education: what are the core self-management behaviours?

Study aims were to (1) describe and compare the way diabetes structured education courses have evolved in the UK, (2) identify and agree components of course curricula perceived as core across courses and (3) identify and classify self‐care behaviours in order to develop a questionnaire assessment tool.

Incorporating Psychological Predictors of Treatment Response into Health Economic Simulation Models: A Case Study in Type 1 Diabetes

Background. Health economic modeling has paid limited attention to the effects that patients’ psychological characteristics have on the effectiveness of treatments. This case study tests 1) the feasibility of incorporating psychological prediction models of treatment response within an economic model of type 1 diabetes, 2) the potential value of providing treatment to a subgroup of patients, and 3) the cost-effectiveness of providing treatment to a subgroup of responders defined using 5 different algorithms. Methods. Multiple linear regressions were used to investigate relationships between patients’ psychological characteristics and treatment effectiveness. Two psychological prediction models were integrated with a patient-level simulation model of type 1 diabetes. Expected value of individualized care analysis was undertaken. Five different algorithms were used to provide treatment to a subgroup of predicted responders. A cost-effectiveness analysis compared using the algorithms to providing treatment to all patients. Results. The psychological prediction models had low predictive power for treatment effectiveness. Expected value of individualized care results suggested that targeting education at responders could be of value. The cost-effectiveness analysis suggested, for all 5 algorithms, that providing structured education to a subgroup of predicted responders would not be cost-effective. Limitations. The psychological prediction models tested did not have sufficient predictive power to make targeting treatment cost-effective. The psychological prediction models are simple linear models of psychological behavior. Collection of data on additional covariates could potentially increase statistical power. Conclusions. By collecting data on psychological variables before an intervention, we can construct predictive models of treatment response to interventions. These predictive models can be incorporated into health economic models to investigate more complex service delivery and reimbursement strategies.