Perceived glucose levels matter more than CGM-based data in predicting diabetes distress in type 1 or type 2 diabetes: a precision mental health approach using n-of-1 analyses.

AIMS/HYPOTHESIS: Diabetes distress is one of the most frequent mental health issues identified in people with type 1 and type 2 diabetes. Little is known about the role of glucose control as a potential contributor to diabetes distress and whether the subjective perception of glucose control or the objective glycaemic parameters are more important for the experience. With the emergence of continuous glucose monitoring (CGM), this is a relevant question as glucose values are now visible in real-time. We employed a precision monitoring approach to analyse the independent associations of perceived and measured glucose control with diabetes distress on a daily basis. By using n-of-1 analyses, we aimed to identify individual contributors to diabetes distress per person and analyse the associations of these individual contributors with mental health at a 3 month follow-up. METHODS: In this prospective, observational study, perceived (hypoglycaemia/hyperglycaemia/glucose variability burden) and measured glucose control (time in hypoglycaemia and hyperglycaemia, CV) were assessed daily for 17 days using an ecological momentary assessment (EMA) approach with a special EMA app and CGM, respectively. Mixed-effect regression analysis was performed, with daily diabetes distress as the dependent variable and daily perceived and CGM-measured metrics of glucose control as random factors. Individual regression coefficients of daily distress with perceived and CGM-measured metrics were correlated with levels of psychosocial well-being at a 3 month follow-up. RESULTS: Data from 379 participants were analysed (50.9% type 1 diabetes; 49.6% female). Perceived glucose variability (t=14.360; p<0.0001) and perceived hyperglycaemia (t=13.637; p<0.0001) were the strongest predictors of daily diabetes distress, while CGM-based glucose variability was not significantly associated (t=1.070; p=0.285). There was great heterogeneity between individuals in the associations of perceived and measured glucose parameters with diabetes distress. Individuals with a stronger association between perceived glucose control and daily distress had more depressive symptoms (ß=0.32), diabetes distress (ß=0.39) and hypoglycaemia fear (ß=0.34) at follow-up (all p<0.001). Individuals with a stronger association between CGM-measured glucose control and daily distress had higher levels of psychosocial well-being at follow-up (depressive symptoms: ß=-0.31; diabetes distress: ß=-0.33; hypoglycaemia fear: ß=-0.27; all p<0.001) but also higher HbA1c (ß=0.12; p<0.05). CONCLUSIONS/INTERPRETATION: Overall, subjective perceptions of glucose seem to be more influential on diabetes distress than objective CGM parameters of glycaemic control. N-of-1 analyses showed that CGM-measured and perceived glucose control had differential associations with diabetes distress and psychosocial well-being 3 months later. The results highlight the need to understand the individual drivers of diabetes distress to develop personalised interventions within a precision mental health approach.

Person-reported outcomes in diabetes care: What are they and why are they so important?

In this review, we aim to show how person-reported outcomes (PROs) and person-reported experiences (PREs) can significantly contribute to the way diabetes care is delivered, the involvement of people with diabetes in diabetes care, and the collaboration between health care professionals and people with diabetes. This review focuses on the definition and measurement of PROs and PREs, the importance of PROs and PREs for person-centred diabetes care, and integrating the perspectives of people with diabetes in the evaluation of medical, psychological and technological interventions. PROs have been increasingly accepted by Health Technology Assessment bodies and are therefore valued in the context of reimbursement decisions and consequently by regulators and other health care stakeholders for the allocation of health care resources. Furthermore, the review identified current challenges to the assessment and use of PROs and PREs in clinical care and research. These challenges relate to the combination of questionnaires and ecological momentary assessment for measuring PROs and PREs, lack of consensus on a core outcome set, limited sensitivity to change within many measures and insufficient standardization of what can be considered a minimal clinically important difference. Another issue that has not been sufficiently addressed is the involvement of people with diabetes in the design and development of measures to assess PROs and PREs.

[Cognition and depression in older people with diabetes]. / Kognition und Depression bei älteren Menschen mit Diabetes.

Cognitive dysfunction, dementia and depression are typical comorbidities of diabetes and are more common in older people with diabetes than in the general population. Both comorbid diseases have a substantial impact on the therapy of diabetes and lead to a reduction of the functional level in everyday life, the quality of life and are associated with increased mortality. This article summarizes the most important results on the etiology of cognitive dysfunction and depression and their consequences for clinical practice.

Reduced heart rate variability in people with type 1 diabetes and elevated diabetes distress: Results from the longitudinal observational DIA-LINK1 study.

AIMS: People with type 1 diabetes have a higher risk for cardiovascular disease (CVD). Reduced heart rate variability (HRV) is a clinical marker for CVD. In this observational study using continuous HRV measurement across 26 days, we investigated whether psychological stressors (diabetes distress, depressive symptoms) and glycaemic parameters (hypo- and hyperglycaemic exposure, glycaemic variability and HbA1c ) are associated with lower HRV in people with type 1 diabetes. METHODS: Data from the non-interventional prospective DIA-LINK1 study were analysed. At baseline, depressive symptoms and diabetes distress were assessed. Glucose values and HRV were recorded daily for 26 days using continuous glucose monitoring (CGM) and a wrist-worn health tracker respectively. Multilevel modelling with participant as nesting factor was used to analyse associations between day-to-day HRV and diabetes distress, depressive symptoms and CGM-derived parameters. RESULTS: Data from 149 participants were analysed (age: 38.3 ± 13.1 years, HbA1c : 8.6 ± 1.9%). Participants with elevated diabetes distress had a significantly lower HRV across the 26 days compared to participants without elevated distress (ß = -0.28; p = 0.004). Elevated depressive symptoms were not significantly associated with HRV (ß = -0.18; p = 0.074). Higher daily exposure to hyperglycaemia (ß = -0.44; p = 0.044), higher average exposure to hypoglycaemia (ß = -0.18; p = 0.042) and higher HbA1c (ß = -0.20; p = 0.018) were associated with reduced HRV across the 26 days. Sensitivity analysis with HRV averaged across all days corroborated these results. CONCLUSIONS: Diabetes distress is a clinically meaningful psychosocial stressor that could play a role in the cardiovascular health of people with type 1 diabetes. These findings highlight the need for integrated psychosocial care in diabetes management.

Coordination of glucose monitoring, self-care behaviour and mental health: achieving precision monitoring in diabetes.

Monitoring of glucose plays an essential role in the management of diabetes. However, to fully understand and meaningfully interpret glucose levels, additional information on context is necessary. Important contextual factors include data on behaviours such as eating, exercise, medication-taking and sleep, as well as data on mental health aspects such as stress, affect, diabetes distress and depressive symptoms. This narrative review provides an overview of the current state and future directions of precision monitoring in diabetes. Precision monitoring of glucose has made great progress over the last 5 years with the emergence of continuous glucose monitoring (CGM), automated analysis of new glucose variables and visualisation of CGM data via the ambulatory glucose profile. Interestingly, there has been little progress in the identification of subgroups of people with diabetes based on their glycaemic profile. The integration of behavioural and mental health data could enrich such identification of subgroups to stimulate precision medicine. There are a handful of studies that have used innovative methodology such as ecological momentary assessment to monitor behaviour and mental health in people's everyday life. These studies indicate the importance of the interplay between behaviour, mental health and glucose. However, automated integration and intelligent interpretation of these data sources are currently not available. Automated integration of behaviour, mental health and glucose could lead to the identification of certain subgroups that, for example, show a strong association between mental health and glucose in contrast to subgroups that show independence of mental health and glucose. This could inform precision diagnostics and precision therapeutics. We identified just-in-time adaptive interventions as a potential means by which precision monitoring could lead to precision therapeutics. Just-in-time adaptive interventions consist of micro-interventions that are triggered in people's everyday lives when a certain problem is identified using monitored behaviour, mental health and glucose variables. Thus, these micro-interventions are responsive to real-life circumstances and are adaptive to the specific needs of an individual with diabetes. We conclude that, with current developments in big data analysis, there is a huge potential for precision monitoring in diabetes.

Evaluation of a Stepped Care Approach to Manage Depression and Diabetes Distress in Patients with Type 1 Diabetes and Type 2 Diabetes: Results of a Randomized Controlled Trial (ECCE HOMO Study).

INTRODUCTION: Depression is a common and serious complication of diabetes. Treatment approaches addressing the specific demands of affected patients are scarce. OBJECTIVE: The aim of this work was to test whether a stepped care approach for patients with diabetes and depression and/or diabetes distress yields greater depression reduction than treatment-as-usual. METHODS: Two-hundred and sixty patients with diabetes and elevated depressive symptoms (CES-D ≥16) and/or elevated diabetes distress (PAID ≥40) were randomized to stepped care for depression or diabetes treatment-as-usual. The primary outcome was the rate of meaningful depression reduction at the 12-month follow-up according to the HAMD (score <9 or reduction by ≥50%). Secondary outcomes were changes in depression scores (HAMD/CES-D), diabetes distress (PAID), diabetes acceptance (AADQ), well-being (WHO-5), quality of life (EQ-5D/SF-36), self-care behavior (SDSCA/DSMQ), HbA1c, and biomarkers of inflammation. RESULTS: One-hundred and thirty-one individuals were assigned to stepped care and 129 to treatment-as-usual. Overall, 15.4% were lost to follow-up. Meaningful depression reduction was observed in 80.2 versus 51.2% in stepped care versus treatment-as-usual (p < 0.001, intention-to-treat analysis). Of the secondary measures, the HAMD (∆ -3.2, p < 0.001), WHO-5 (∆ 1.5, p = 0.007), and AADQ (∆ -1.0, p = 0.008) displayed significant treatment effects, while effects on CES-D (∆ -2.3, p = 0.065), PAID (∆ -3.5, p = 0.109), and SDSCA (∆ 0.20, p = 0.081) were not significantly different. Both groups showed comparable changes in EQ-5D/SF-36, DSMQ, HbA1c, and biomarkers of inflammation (all p ≥ 0.19). CONCLUSIONS: The stepped care approach improved depression, well-being, and acceptance. The results support that increasing treatment intensity on demand is effective and can help provide more optimal treatment. The inclusion of diabetes-specific interventions may be beneficial for patients with diabetes and elevated depression.

[Physical and psychological long-term consequences of diabetes mellitus]. / Körperliche und psychische Folgeerkrankungen bei Diabetes mellitus.

Despite improvements in the treatment of diabetes and better care conditions, those affected still have a significantly increased risk of physical and psychological complications and a reduced quality of life compared to the general population. About 21% of all deaths in Germany are due to diabetes and its complications, and the mortality risk for people with diabetes is more than 1.5 times higher than for people without diabetes. This review describes the prevalence and risk factors for the most common physical and psychological complications of diabetes as well as their influences on patients' quality of life. Associations between the complications and increased mortality are shown.In large intervention studies, the importance of good glucose control - especially at the beginning of the disease - has been shown regarding a reduction in the mortality rate. Other important influencing factors include blood pressure, blood lipids, and smoking. Worldwide study results indicate a stable trend about improved life expectancy of people with diabetes in recent years. In the future, the positive trend could be continued through better care structures and new technologies as well as digital devices in research and therapy. With the help of precision medicine, individual risk factors and protective factors could be identified to even better prevent the development of complications.

Associations of Time in Range and Other Continuous Glucose Monitoring-Derived Metrics With Well-Being and Patient-Reported Outcomes: Overview and Trends.

Time in glucose ranges is increasingly relevant for research and clinical practice. Whereas the clinical validity of these metrics has been demonstrated with regard to long-term complications, their associations with patient-reported outcomes such as well-being, diabetes distress, and fear of hypoglycemia remain an open research question. This article reviews existing evidence on links between times in glycemic ranges and patient-reported outcomes. It also describes a novel research approach of using ecological momentary assessment to analyze on a more granular level in real time possible associations of these parameters of glycemic control and patient-reported outcomes. Such an approach could further our understanding of how glucose and patient-reported outcomes may be interconnected.

National public health system responses to diabetes and other important noncommunicable diseases : Background, goals, and results of an international workshop at the Robert Koch Institute.

Diabetes mellitus and other noncommunicable diseases (NCDs) represent an emerging global public health challenge. In Germany, about 6.7 million adults are affected by diabetes according to national health surveys, including 1.3 million with undiagnosed diabetes. Complications of diabetes result in an increasing burden for individuals and society as well as enormous costs for the health care system. In response, the Federal Ministry of Health commissioned the Robert Koch Institute (RKI) to implement a diabetes surveillance system and the Federal Center for Health Education (BZgA) to develop a diabetes prevention strategy. In a two-day workshop jointly organized by the RKI and the BZgA, representatives from public health institutes in seven countries shared their expertise and knowledge on diabetes prevention and surveillance. Day one focused on NCD surveillance systems and emphasized both the strengthening of sustainable data sources and the timely and targeted dissemination of results using innovative formats. The second day focused on diabetes prevention strategies and highlighted the importance of involving all relevant stakeholders in the development process to facilitate its acceptance and implementation. Furthermore, the effective translation of prevention measures into real-world settings requires data from surveillance systems to identify high-risk groups and evaluate the effect of measures at the population level based on analyses of time trends in risk factors and disease outcomes. Overall, the workshop highlighted the close link between diabetes prevention strategies and surveillance systems. It was generally stated that only robust data enables effective prevention measures to encounter the increasing burden from diabetes and other NCDs.

Negative association between depression and diabetes control only when accompanied by diabetes-specific distress.

Evidence of the negative impact of depression on glycaemic control is equivocal, and diabetes-related distress has been proposed as potential mediator. 466 diabetes patients were cross-sectionally assessed for depression (Center for Epidemiologic Studies Depression Scale), diabetes-related distress (Diabetes Distress Scale), and glycaemic control (HbA1c). We distinguished the associations of depression and diabetes distress with glycaemic control using analysis of variance and multiple regression. Neither patients with depression only nor diabetes distress only differed significantly from controls regarding HbA1c. However, HbA1c was substantially increased when both conditions were present (9.2 vs. 8.6 %, P = 0.01). As in previous studies, we observed a significant association between depression and hyperglycaemia (P < 0.01). However, a mediation analysis revealed that this association in fact depended on the presence of diabetes distress (P < 0.01). Depression seems to be associated with hyperglycaemia particularly when accompanied by diabetes distress, suggesting that adjusting clinical procedures regarding diabetes distress may facilitate the identification and care of high-risk patients.

Predicting Glucose Values: A New Era for Continuous Glucose Monitoring.

The last 25 years of CGM have been characterized above all by providing better and more accurate glucose values in real time and analyzing the measured glucose values. Trend arrows are the only way to look into the future, but they are often too imprecise for therapy adjustment. While AID systems provide algorithms to use glucose values for glucose control, this has not been possible with stand-alone CGM systems, which are most used by people with diabetes. By analyzing the measured values with algorithms, often supported by AI, this should be possible in the future. This provides the user with important information about the further course of the glucose level, such as during the night. Predictive approaches can be used by next-generation CGM systems. These systems can proactively prevent glucose events such as hypo- or hyperglycemia. With the Accu-Chek® SmartGuide Predict app, an integral part of a novel CGM system, and the Glucose Predict (GP) feature, people with diabetes have the first commercially available CGM system with predictive algorithms. It characterizes the CGM systems of the future, which not only analyze past values and current glucose values in the future, but also use these values to predict future glucose progression.

Explaining improvement in diabetes distress: a longitudinal analysis of the predictive relevance of resilience and acceptance in people with type 1 diabetes.

AIMS: To analyze if midterm improvement in diabetes distress can be explained by resilience, diabetes acceptance, and patient characteristics. METHODS: N = 179 adults with type 1 diabetes were enrolled during their stay at a tertiary diabetes center (monocentric enrolment) and followed up over three months in a prospective, observational study ('DIA-LINK1'). Improvement in diabetes distress was assessed as reduction in the Problem Areas in Diabetes Scale score from baseline to follow-up. Resilience (Resilience Scale-13), acceptance (Diabetes Acceptance Scale), and patient characteristics were analyzed as predictors of improvement in diabetes distress using hierarchical multiple regression. RESULTS: Greater reductions in diabetes distress were significantly explained by lower diabetes acceptance at baseline (ß = -0.34, p < 0.01), while resilience, diabetes complications, and other person-related variables were not significantly related to changes in diabetes distress (all p > 0.05). When change in diabetes acceptance from baseline to follow-up was added to the model, improved diabetes distress was explained by increasing diabetes acceptance (ß = 0.41, p < 0.01) and a shorter duration of diabetes (ß = -0.18, p = 0.03), while baseline diabetes acceptance was no longer significantly associated (ß = -0.14, p > 0.05). CONCLUSIONS: Diabetes acceptance is inversely related to diabetes distress, and increasing acceptance explained greater improvement in diabetes distress. These findings suggest that increasing diabetes acceptance may facilitate the reduction of diabetes distress. Treatment approaches targeting acceptance might be useful for the mental healthcare of people with type 1 diabetes and clinically elevated diabetes distress.

Nocturnal hypoglycemia in the era of continuous glucose monitoring.

Nocturnal hypoglycemia is a common acute complication of people with diabetes on insulin therapy. In particular, the inability to control glucose levels during sleep, the impact of external factors such as exercise, or alcohol and the influence of hormones are the main causes. Nocturnal hypoglycemia has several negative somatic, psychological, and social effects for people with diabetes, which are summarized in this article. With the advent of continuous glucose monitoring (CGM), it has been shown that the number of nocturnal hypoglycemic events was significantly underestimated when traditional blood glucose monitoring was used. The CGM can reduce the number of nocturnal hypoglycemia episodes with the help of alarms, trend arrows, and evaluation routines. In combination with CGM with an insulin pump and an algorithm, automatic glucose adjustment (AID) systems have their particular strength in nocturnal glucose regulation and the prevention of nocturnal hypoglycemia. Nevertheless, the problem of nocturnal hypoglycemia has not yet been solved completely with the technologies currently available. The CGM systems that use predictive models to warn of hypoglycemia, improved AID systems that recognize hypoglycemia patterns even better, and the increasing integration of artificial intelligence methods are promising approaches in the future to significantly minimize the risk of a side effect of insulin therapy that is burdensome for people with diabetes.

More Frequent Use of Glucose Alarms Is Associated with Continuous Glucose Monitoring-Specific Diabetes Education: Findings from the Dia·Link Diabetes Panel.

The associations of continuous glucose monitoring (CGM)-specific diabetes education with real-world utilization of glucose alerts and alarms were assessed in current CGM-users with type 1 or type 2 diabetes. A cross-sectional online survey was conducted in Germany assessing utilization (use and responses) of different alerts and alarms. Ordinal logistic regression analyses were conducted to analyze associations between utilization and participation in CGM-specific education. Data from 453 participants were analyzed (86.2% type 1 diabetes). Participants who received CGM-specific education were more likely to regularly use low-glucose alerts (odds ratio [OR] = 5.43, P < 0.001), low-glucose alarms (OR = 2.03, P = 0.027), and rate of change alerts (OR = 4.20, P = 0.009), and were more likely to immediately react to low-glucose alerts (OR = 5.23, P < 0.001) and rate of change alerts (OR = 3.75, P = 0.018). CGM-specific education has the potential to increase utilization of and response to alerts and alarms. This may help to implement more preventive elements regarding glucose management in everyday life.

How to Use Continuous Glucose Monitoring Efficiently in Diabetes Management: Opinions and Recommendations by German Experts on the Status and Open Questions.

Today, continuous glucose monitoring (CGM) is a standard diagnostic option for patients with diabetes, at least for those with type 1 diabetes and those with type 2 diabetes on insulin therapy, according to international guidelines. The switch from spot capillary blood glucose measurement to CGM was driven by the extensive and immediate support and facilitation of diabetes management CGM offers. In patients not using insulin, the benefits of CGM are not so well studied/obvious. In such patients, factors like well-being and biofeedback are driving CGM uptake and outcome. Apps can combine CGM data with data about physical activity and meal consumption for therapy adjustments. Personalized data management and coaching is also more feasible with CGM data. The same holds true for digitalization and telemedicine intervention ("virtual diabetes clinic"). Combining CGM data with Smart Pens ("patient decision support") helps to avoid missing insulin boluses or insulin miscalculation. Continuous glucose monitoring is a major pillar of all automated insulin delivery systems, which helps substantially to avoid acute complications and achieve more time in the glycemic target range. These options were discussed by a group of German experts to identify concrete gaps in the care structure, with a view to the necessary structural adjustments of the health care system.

Preferences of people with diabetes for diabetes care in Germany: a discrete choice experiment.

OBJECTIVES: The objective of this study is to elicit health care preferences of people with diabetes and identify classes of people with different preferences. METHODS: A discrete choice experiment was conducted among people with diabetes in Germany comprising attributes of role division in daily diabetes care planning, type of lifestyle education, support for correct medication intake, consultation frequency, emotional support, and time spent on self-management. A conditional logit model and a latent class model were used to elicit preferences toward diabetes care and analyze preference heterogeneity. RESULTS: A total of 76 people with diabetes, recruited in two specialized diabetes care centers in Germany (mean age 51.9 years, 37.3% women, 49.1% type 2 diabetes mellitus, 50.9% type 1 diabetes mellitus), completed the discrete choice experiment. The most important attributes were consultation frequency, division in daily diabetes care planning, and correct medication intake. The latent class model detected preference heterogeneity by identifying two latent classes which differ mainly with respect to lifestyle education and medication intake. CONCLUSION: While the majority of people with diabetes showed preferences in line with current health care provision in Germany, a relevant subgroup wished to strengthen lifestyle education and medication intake support with an aid or website.

Efficacy of a Digital Diabetes Logbook for People With Type 1, Type 2, and Gestational Diabetes: Results From a Multicenter, Open-Label, Parallel-Group, Randomized Controlled Trial.

BACKGROUND: In a randomized controlled trial, the efficacy of a digital diabetes diary regarding a reduction of diabetes distress was evaluated. METHODS: A randomized controlled trial with a 12-week follow-up was conducted in 41 study sites across Germany. Key eligibility criteria were a diagnosis of type 1, type 2, or gestational diabetes and regular self-monitoring of blood glucose. Participants were randomly assigned (2:1 ratio) to either use the digital diabetes logbook (mySugr PRO), or to the control group without app use. The primary outcome was the reduction in diabetes distress at the 12-week follow-up. All analyses were based on the intention-to-treat population with all randomized participants. The trial was registered at the German Register for Clinical Studies (DRKS00022923). RESULTS: Between February 11, 2021, and June 24, 2022, 424 participants (50% female, 50% male) were included, with 282 being randomized to the intervention group (66.5%) and 142 to the control group (33.5%). A total of 397 participants completed the trial (drop-out rate: 6.4%). The median reduction in diabetes distress was 2.41 (interquartile range [IQR]: -2.50 to 8.11) in the intervention group and 1.25 (IQR: -5.00 to 7.50) in the control group. The model-based adjusted between-group difference was significant (-2.20, IQR: -4.02 to -0.38, P = .0182) favoring the intervention group. There were 27 adverse events, 17 (6.0%) in the intervention group, and 10 (7.0%) in the control group. CONCLUSIONS: The efficacy of the digital diabetes logbook was demonstrated regarding improvements in mental health in people with type 1, type 2, and gestational diabetes.

Relationship Between Lipohypertrophy, Glycemic Control, and Insulin Dosing: A Systematic Meta-Analysis.

Background: Lipohypertrophy is a common complication in patients with diabetes receiving insulin therapy. There is a lack of consensus regarding how much lipohypertrophy affects diabetes management. Our study aimed to assess the potential correlation between lipohypertrophy and glycemic control, as well as insulin dosing in patients with diabetes. Methods: We performed a systematic review followed by a meta-analysis to collect data about glycemic control and insulin dosing in diabetic patients with and without lipohypertrophy. To identify relevant studies published in English, we searched medical databases (MEDLINE/PubMed, Embase, and CENTRAL) from 1990 to January 20, 2023. An additional hand-search of references was performed to retrieve publications not indexed in medical databases. Results of meta-analyses were presented either as prevalence odds ratios (pORs) or mean differences (MDs) with 95% confidence intervals (95% CIs). This study was registered on PROSPERO (CRD42023393103). Results: Of the 5540 records and 240 full-text articles screened, 37 studies fulfilled the prespecified inclusion criteria. Performed meta-analyses showed that patients with lipohypertrophy compared with those without lipohypertrophy were more likely to experience unexplained hypoglycemia (pOR [95% CI] = 6.98 [3.30-14.77]), overall hypoglycemia (pOR [95% CI] = 6.65 [1.37-32.36]), and glycemic variability (pOR [95% CI] = 5.24 [2.68-10.23]). Patients with lipohypertrophy also had higher HbA1c (MD [95% CI] = 0.55 [0.23-0.87] %), and increased daily insulin consumption (MD [95% CI] = 7.68 IU [5.31-10.06]). Conclusions: These results suggest that overall glycemic control is worse in patients with lipohypertrophy than in those without this condition.

The Diabetes Self-Management Questionnaire (DSMQ): development and evaluation of an instrument to assess diabetes self-care activities associated with glycaemic control.

BACKGROUND: Though several questionnaires on self-care and regimen adherence have been introduced, the evaluations do not always report consistent and substantial correlations with measures of glycaemic control. Small ability to explain variance in HbA1c constitutes a significant limitation of an instrument's use for scientific purposes as well as clinical practice. In order to assess self-care activities which can predict glycaemic control, the Diabetes Self-Management Questionnaire (DSMQ) was designed. METHODS: A 16 item questionnaire to assess self-care activities associated with glycaemic control was developed, based on theoretical considerations and a process of empirical improvements. Four subscales, 'Glucose Management' (GM), 'Dietary Control' (DC), 'Physical Activity' (PA), and 'Health-Care Use' (HU), as well as a 'Sum Scale' (SS) as a global measure of self-care were derived. To evaluate its psychometric quality, 261 patients with type 1 or 2 diabetes were assessed with the DSMQ and an established analogous scale, the Summary of Diabetes Self-Care Activities Measure (SDSCA). The DSMQ's item and scale characteristics as well as factorial and convergent validity were analysed, and its convergence with HbA1c was compared to the SDSCA. RESULTS: The items showed appropriate characteristics (mean item-total-correlation: 0.46 ± 0.12; mean correlation with HbA1c: -0.23 ± 0.09). Overall internal consistency (Cronbach's alpha) was good (0.84), consistencies of the subscales were acceptable (GM: 0.77; DC: 0.77; PA: 0.76; HU: 0.60). Principal component analysis indicated a four factor structure and confirmed the designed scale structure. Confirmatory factor analysis indicated appropriate fit of the four factor model. The DSMQ scales showed significant convergent correlations with their parallel SDSCA scales (GM: 0.57; DC: 0.52; PA: 0.58; HU: n/a; SS: 0.57) and HbA1c (GM: -0.39; DC: -0.30; PA: -0.15; HU: -0.22; SS: -0.40). All correlations with HbA1c were significantly stronger than those obtained with the SDSCA. CONCLUSIONS: This study provides preliminary evidence that the DSMQ is a reliable and valid instrument and enables an efficient assessment of self-care behaviours associated with glycaemic control. The questionnaire should be valuable for scientific analyses as well as clinical use in both type 1 and type 2 diabetes patients.