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

A Self-Report Measure of Diabetes Self-Management for Type 1 and Type 2 Diabetes : The Diabetes Self-Management Questionnaire-Revised (DSMQ-R) – Clinimetric Evidence From Five Studies

Aims: Measurement tools to evaluate self-management behavior are useful for diabetes research and clinical practice. The Diabetes Self-Management Questionnaire (DSMQ) was introduced in 2013 and has become a widely used tool. This article presents a revised and updated version, DSMQ-R, and evaluates its properties in assessing self-management practices in type 1 diabetes (T1D) and type 2 diabetes (T2D). Methods: The DSMQ-R is a multidimensional questionnaire with 27 items regarding essential self-management practices for T1D and T2D (including diabetes-adjusted eating, glucose testing/monitoring, medication taking, physical activity and cooperation with the diabetes team). For the revised form, the original items were partially amended and the wording was updated; eleven items were newly added. The tool was applied as part of health-related surveys in five clinical studies (two cross-sectional, three prospective) including a total of 1,447 people with T1D and T2D. Using this data base, clinimetric properties were rigorously tested. Results: The analyses showed high internal and retest reliability coefficients for the total scale and moderate to high coefficients for the subscales. Reliability coefficients for scales including the new items were consistently higher. Correlations with convergent criteria and related variables supported validity. Responsiveness was supported by significant short to medium term changes in prospective studies. Significant associations with glycemic outcomes were observed for DSMQ-R-assessed medication taking, glucose monitoring and eating behaviors. Conclusions: The results support good clinimetric properties of the DSMQ-R. The tool can be useful for research and clinical practice and may facilitate the identification of improvable self-management practices in individuals

Comparison of Satisfaction with Their Glucose Monitoring Device in Patients Using Flash Glucose Monitoring vs. Patients Using SMBG

Flash Glucose Monitoring offers several benefits to patients such as ease of use and no additional finger pricks. However, there are also some limitations that are being discussed such as perceived lack of accuracy and skin irritations. We analyzed different aspects of patients’ satisfaction with Flash Glucose Monitoring (Flash) by comparing Flash users to patients who use SMBG. We used the Glucose Monitoring Satisfaction Survey that consists of 15 items with a scale range of 1 to 5. 133 patients who were using Flash for 7.9 ± 9.5 months were compared with 83 patients who used SMBG. As a measure of between-group effect size, Cohens d was used. In general, Flash users were significantly more satisfied with how things were going with their diabetes (d = 0.31; p = 0.024). SMBG users indicated that measurement takes more time to use (d = 0.67; p < 0.001), makes them worry more (d = 0.32; p = 0.024), is more of a hassle to use (d = 0.45; p = 0.002), and is more painful to use (d = 0.55; p < 0.001) when compared to Flash users. Feeling frustrated with diabetes (d = 0.31; p = 0.027) as well as being depressed (d = 0.40; p = 0.002) were scored lower by Flash users. In addition, Flash users felt less restricted by diabetes (d = 0.52; p < 0.001) and more spontaneous (d = 0.67; p < 0.001). Interestingly, there were no significant differences regarding the rating of accuracy (d = 0.19; p = 0.157), trusting the numbers (d = 0.01; p = 0.934), and skin irritations (d = 0.26; p = 0.053). The comparison demonstrated that there are several perceived benefits associated with using Flash. Ease of use and gaining more flexibility or feeling less restricted were important topics for Flash users. Possible limitations of Flash were not perceived as such by the users

Comparison of Glycemic Control between Experienced Users of Flash Glucose Monitoring vs. Flash-Naïve Patients

We conducted a randomized controlled trial to evaluate the efficacy of a newly developed education program for Flash Glucose Monitoring (Flash). Eligible participants were all patients with intensified insulin therapy who either had experience with Flash or no experience. In this observational analysis, we used baseline-date of our study to analyze whether patients who were experienced with Flash (Flash-experienced) achieved better glycemic control than patients who newly received Flash (Flash-naïve). A total of 216 patients were recruited. All patients received Flash at baseline and used it for 2 weeks before being randomized to either receive the education program or using Flash without education. 133 patients who indicated at the beginning of the 2-week period that they have used Flash in the last 6 months were compared to 83 patients who had no prior experience with Flash. HbA1c at the beginning of the 2 weeks was comparable between groups (8.4 ± 1.0 vs. 8.4 ± 0.9; p = .89). At the end of the 2 weeks, Flash-naïve patients achieved lower mean glucose values than Flash-experienced patients (179.6 ± 25.9 vs. 192.3 ± 39.2 mg/dl, p = 0.005). While time spent in hypoglycemia (≤ 70 mg/dl) was not different between the groups (69.3 ± 52.0 vs. 67.9 ± 52.9 min/day, p = .85), Flash-naïve patients had a higher time in range (71-180 mg/dl) (716.5 ± 174.1 vs. 660.2 ± 209.2 min/day, p = .036) and spent less time in hyperglycemia (> 180 mg/dl) (656.0 ± 194.0 vs. 715.0 ± 232.8 min/day, p = .048). Interestingly, Flash-experienced patients had no better glycemic control than patients previously using SMBG. Thus, experienced as well as naïve patients could benefit from the education program. During the 2 weeks, Flash-naïve patients achieved a better glycemic profile than Flash-experienced patients. Bearing in mind the limitations of the observational analysis, this could be due to the introduction of a new technology and a higher motivation in patients newly switched to Flash

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 HbA(1c) 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 HbA(1c) was compared to the SDSCA. RESULTS: The items showed appropriate characteristics (mean item-total-correlation: 0.46 ± 0.12; mean correlation with HbA(1c): -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 HbA(1c) (GM: -0.39; DC: -0.30; PA: -0.15; HU: -0.22; SS: -0.40). All correlations with HbA(1c) 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

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 (β = −0.34, 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 (β = 0.41, 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

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