Continuous glucose monitoring in older adults with diabetes: Data from the diabetes prospective follow-up (DPV) registry.

AIMS: To analyse predictors for continuous glucose monitoring (CGM) use in people with diabetes aged ≥60 years using insulin therapy and to assess the rates of CGM use during recent years (2019-2021). RESEARCH DESIGN AND METHODS: Prospective study including 6849 individuals with diabetes and insulin therapy (type 2 diabetes: n = 5320; type 1 diabetes: n = 1529) aged ≥60 years. Data from 129 treatment centres were retrieved from the Diabetes Prospective Follow-up Registry (DPV) in March 2023. RESULTS: Sensor use in individuals aged ≥60 years has increased in type 1 (2019: 28%, 2020: 39%, 2021: 45%) and type 2 diabetes (2019: 10%, 2020: 16%, 2021: 18%). Predictors for sensor use in older individuals with type 1 diabetes are younger age and CSII use (p < 0.001). Predictors in older individuals with type 2 diabetes are younger age, longer diabetes duration, higher BMI and CSII use (p < 0.001). CONCLUSIONS: CGM has become more common in older adults with diabetes and will presumably increase further. Age is a predictor for sensor use in older adults with diabetes. Age-related physical barriers and insufficient usability of devices, lack of interest in technologies, but possibly also effects of prejudice on the grounds of age may contribute to this finding.

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.

Fear of Hypoglycemia and Diabetes Distress: Expected Reduction by Glucose Prediction.

BACKGROUND: Extended glucose predictions are novel in diabetes management. Currently, there is no solution widely available. People with diabetes mellitus (DM) are offered features like trend arrows and limited predictions linked to predefined situations. Thus, the impact of extended glucose predictions on the burden of diabetes and person-reported outcomes (PROs) is unclear. METHODS: In this online survey, 206 people with type 1 and type 2 diabetes (T1D and T2D), 70.9% and 29.1%, respectively, who participated in the dia·link online panel and were current continuous glucose monitoring (CGM) users, were presented with different scenarios of hypothetical extended glucose predictions. They were asked to imagine how low glucose predictions of 30 minutes and overnight as well as glucose predictions up to 2 hours would influence their diabetes management. Subsequently, they completed the Hypoglycemia Fear Survey II (HFS-II) and the T1 Diabetes Distress Scale (T1-DDS) by rating each item on a 5-point scale (-2: strong deterioration to +2: strong improvement) according to the potential change due to using glucose predictions. RESULTS: For all glucose prediction periods, 30 minutes, up to 2 hours, and at nighttime, the surveyed participants expected moderate improvements in both fear of hypoglycemia (HFS-II: 0.57 ± 0.49) and overall diabetes distress (T1-DDS = 0.44 ± 0.49). The T1-DDS did not differ for type of therapy or diabetes. CONCLUSIONS: People with T1D and T2D would see glucose predictions as a potential improvement regarding reduced fear of hypoglycemia and diabetes distress. Therefore, glucose predictions represent a value for them in lowering the burden of diabetes and its management.

Counting the Minutes: Perceived Diabetes Mental Load and its Associations With Technology Use and Mental Disorders.

Little is known about mental load in people with diabetes and associations with demographic, clinical, and treatment characteristics, such as the use of diabetes technologies. To explore perceived mental load, 503 adults with diabetes answered the one-item survey "How much time (in minutes) would you spontaneously estimate that you spend each day thinking about your diabetes?" Mental load estimations varied widely within the sample and between subgroups. Perceived mental load was higher in type 1 diabetes than in type 2 diabetes, higher in women than in men and increased with treatment intensity (ie, insulin therapy, technology use) and the number of mental disorders. Further research may explore associations with diabetes-related distress and determine whether (perceived) mental load has relevance in technology use.

Cognitive reappraisal in mHealth interventions to foster mental health in adults: a systematic review and meta-analysis.

Background: An increasing number of mHealth interventions aim to contribute to mental healthcare of which interventions that foster cognitive reappraisal may be particularly effective. Objectives: To evaluate the efficacy of mHealth interventions enhancing cognitive reappraisal to improve mental health in adult populations. Methods: The literature search (four databases) yielded 30 eligible randomized controlled trials (comprising 3,904 participants). We performed a multi-level meta-analysis to examine differences between intervention and comparator conditions at post-intervention assessment. Moderator analyses were conducted for potential moderator variables (e.g., type of comparators). Results: Most interventions were CBT-based with other training components in addition to cognitive reappraisal. We found preliminary evidence for a small to medium effect favouring mHealth interventions to enhance cognitive reappraisal over comparators, M(SMD) = 0.34, p = .002. When analysing single symptoms, there was evidence for a small to medium effect of mHealth interventions on anxiety and depressive symptoms, but not for psychological distress and well-being. All analyses showed substantial heterogeneity. Moderator analyses revealed evidence for more favourable effects in studies with passive comparators. There was an overall high risk of bias in most of the studies. Conclusions: We found preliminary evidence for a small to medium effect of mHealth interventions including a cognitive reappraisal component to improve mental health. However, most of the interventions were complex (i.e., reappraisal was provided alongside other components), which prevents us from examining reappraisal-specific effects beyond general mental health promotion in mHealth. Dismantling studies examining the effects of single intervention components are warranted to corroborate these promising results. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=142149, identifier [CRD42019142149].