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Diabetes Quality (diabetes + quality)
Selected AbstractsNot all roads lead to Rome,a review of quality of life measurement in adults with diabetesDIABETIC MEDICINE, Issue 4 2009J. Speight Abstract Aims Quality of life (QoL) is recognized widely as an important health outcome in diabetes, where the burden of self-management places great demands on the individual. However, the concept of QoL remains ambiguous and poorly defined. The aim of our review is to clarify the measurement of QoL in terms of conceptualization, terminology and psychometric properties, to review the instruments that have been used most frequently to assess QoL in diabetes research and make recommendations for how to select measures appropriately. Methods A systematic literature search was conducted to identify the ten measures most frequently used to assess QoL in diabetes research (including clinical trials) from 1995 to March 2008. Results Six thousand and eight-five abstracts were identified and screened for instrument names. Of the ten instruments most frequently used to assess ,QoL', only three actually do so [i.e. the generic World Health Organization Quality of Life (WHOQOL) and the diabetes-specific Diabetes Quality of Life (DQOL) and Audit of Diabetes-Dependent Quality of Life (ADDQoL)]. Seven instruments more accurately measure health status [Short-Form 36 (SF-36), EuroQoL 5-Dimension (EQ-5D)], treatment satisfaction [Diabetes Treatment Satisfaction Questionnaire (DTSQ)] and psychological well-being [Beck Depression Inventory (BDI), Hospital Anxiety and Depression Scale (HADS), Well-Being Questionnaire (W-BQ), Problem Areas in Diabetes (PAID)]. Conclusions No single measure can suit every purpose or application but, when measures are selected inappropriately and data misinterpreted, any conclusions drawn are fundamentally flawed. If we value QoL as a therapeutic goal, we must ensure that the instruments we use are both valid and reliable. QoL assessment has the proven potential to identify ways in which treatments can be tailored to reduce the burden of diabetes. With careful consideration, appropriate measures can be selected and truly robust assessments undertaken successfully. [source] Evaluation of a programme of group visits and computer-assisted consultations in the treatment of adolescents with Type 1 diabetesDIABETIC MEDICINE, Issue 11 2005M. Graue Abstract Aim To examine the effects of group visits and computer-assisted consultations on quality of life and glycaemic control in adolescents with Type 1 diabetes. Methods A total of 116 adolescents, aged 11,17 years, and their parents were randomly assigned to an intervention (n = 62) or a control group (n = 54). The intervention group was invited to a 15-month programme comprising group visits and computer-assisted consultations. The control group was offered traditional out-patient consultations. Outcomes included changes in HbA1c and the adolescents' assessment of generic and disease-specific health-related quality of life measured by the Child Health Questionnaire (CHQ-CF87) and the Diabetes Quality of Life Questionnaire (DQOL), respectively. Results One hundred and one adolescents (55/46) agreed to participate, mean age 14.2 years (sd 1.5), mean diabetes duration 6.5 years (sd 3.6, range 1,16 years), mean HbA1c 9.3% (sd 1.4, range 6.1,12.8%). Eighty-three (72%) completed the questionnaires at follow-up (intervention/control 45/38). There were significant age by randomization group interactions for diabetes-related impact (P = 0.018), diabetes-related worries (P = 0.004), mental health (P = 0.046) and general behaviour (P = 0.029), implying that the intervention was effective in older adolescents (above 13,14 years). No significant effects on mean HbA1c were identified. Conclusions Group visits and computer-assisted consultations had beneficial effects on health-related quality of life in older adolescents, the role of this intervention being questionable in younger adolescents. [source] Patient-assessed health outcome measures for diabetes: a structured reviewDIABETIC MEDICINE, Issue 1 2002A. M. Garratt Abstract Aims To identify available disease-specific measures of health-related quality of life (HRQL) for diabetes and to review evidence for the reliability, validity and responsiveness of instruments. Methods Systematic searches were used to identify instruments. Instruments were assessed against predefined inclusion and exclusion criteria. Letters were sent to authors requesting details of further instrument evaluation. Information relating to instrument content, patients, reliability, validity and responsiveness to change was extracted from published papers. Results The search produced 252 references. Nine instruments met the inclusion criteria: Appraisal of Diabetes Scale (ADS), Audit of Diabetes-Dependent Quality of Life (ADDQoL), Diabetes Health Profile (DHP-1, DHP-18), Diabetes Impact Measurement Scales (DIMS), Diabetes Quality of Life Measure (DQOL), Diabetes-Specific Quality of Life Scale (DSQOLS), Questionnaire on Stress in Diabetic Patients-Revised (QSD-R), Diabetes-39 (D-39) and Well-being Enquiry for Diabetics (WED). The shortest instrument (ADS) has seven items and the longest (WED) has 50 items. The ADS and ADDQoL are single-index measures. The seven multidimensional instruments have dimensions covering psychological well-being and social functioning but vary in the remainder of their content. The DHP-1 and DSQOLS are specific to Type 1 diabetes patients. The DHP-18 is specific to Type 2 diabetes patients. The DIMS and DQOL have weaker evidence for reliability and internal construct validity. Patients contributed to the content of the ADDQoL, DHP-1/18, DQOL, DSQOLS, D-39, QSD-R and WED. The authors of the ADDQoL, DHP-1/18, DQOL, DSQOLS gave explicit consideration to content validity. The construct validity of instruments was assessed through comparisons with instruments measuring related constructs and clinical and sociodemographic variables. None of the instruments has been formally assessed for responsiveness to changes in health. Conclusions Five of the diabetes-specific instruments have good evidence for reliability and internal and external construct validity: the ADDQoL, DHP-1/18, DSQOLS, D-39 and QSD-R. Instrument content should be assessed for relevance before application. The instruments should be evaluated concurrently for validity and responsiveness to important changes in health. [source] Associations between physical activity, sedentary behavior, and glycemic control in a large cohort of adolescents with type 1 diabetes: the Hvidoere Study Group on Childhood DiabetesPEDIATRIC DIABETES, Issue 4 2009J Åman Background:, The Hvidoere Study Group on Childhood Diabetes has demonstrated persistent differences in metabolic outcomes between pediatric diabetes centers. These differences cannot be accounted for by differences in demographic, medical, or treatment variables. Therefore, we sought to explore whether differences in physical activity or sedentary behavior could explain the variation in metabolic outcomes between centers. Methods:, An observational cross-sectional international study in 21 centers, with demographic and clinical data obtained by questionnaire from participants. Hemoglobin A1c (HbA1c) levels were assayed in one central laboratory. All individuals with diabetes aged 11,18 yr (49.4% female), with duration of diabetes of at least 1 yr, were invited to participate. Individuals completed a self-reported measure of quality of life (Diabetes Quality of Life - Short Form [DQOL-SF]), with well-being and leisure time activity assessed using measures developed by Health Behaviour in School Children WHO Project. Results:, Older participants (p < 0.001) and females (p < 0.001) reported less physical activity. Physical activity was associated with positive health perception (p < 0.001) but not with glycemic control, body mass index, frequency of hypoglycemia, or diabetic ketoacidosis. The more time spent on the computer (r = 0.06; p < 0.05) and less time spent doing school homework (r = ,0.09; p < 0.001) were associated with higher HbA1c. Between centers, there were significant differences in reported physical activity (p < 0.001) and sedentary behavior (p < 0.001), but these differences did not account for center differences in metabolic control. Conclusions:, Physical activity is strongly associated with psychological well-being but has weak associations with metabolic control. Leisure time activity is associated with individual differences in HbA1c but not with intercenter differences. [source] | ||||||||||