Search

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.

OBJECTIVE To estimate time with diabetes distress using ecological momentary assessment (EMA) in people with type 1 diabetes and analyze its associations with glycemic management based on continuous glucose monitoring (CGM). RESEARCH DESIGN AND METHODS We used EMA to assess diabetes distress in a sample of recently hospitalized adults with type 1 diabetes once a day for 17 consecutive days in an ambulatory setting. Additionally, participants were asked daily about hypoglycemia distress (180 mg/dL [10 mmol/L]), and variability distress (glucose fluctuations). Per person, the percentage of days with elevated distress was calculated (time with distress). Multilevel regression was used to analyze daily associations of distress ratings with CGM-derived parameters. EMA-derived associations between diabetes distress and glycemic outcomes were compared with questionnaire-derived associations. RESULTS Data of 178 participants were analyzed. Participants spent a mean (SD) of days in a state of diabetes distress, 54.6 ± 26.0% in hyperglycemia distress, 45.2 ± 27.5% in variability distress, and 23.0 ± 19.3% in hypoglycemia distress. In multilevel analyses, higher daily ratings of diabetes distress were significantly associated with hyperglycemia (β = 0.41). Results showed high between-person variability as explanation of variance of the models ranged between 22.2 and 98.8%. EMA-derived diabetes distress showed a significant association with mean glucose (r = 0.25), while questionnaire-based diabetes distress did not (r = 0.10). Prospectively, time with diabetes distress was associated with HbA1c at the 3-month follow-up (r = 0.27), while questionnaire-based distress showed no association (r = 0.11). CONCLUSIONS Time with distress as assessed with EMA showed a comparative advantage over distress as determined by questionnaire-based assessment of diabetes distress regarding associations with glycemic management.

People with diabetes often have a depressive disorder in addition to diabetes, which makes therapy more difficult and worsens the prognosis of the disease. Depression is an often underestimated risk factor in the context of diabetes. The aim of this article is to illustrate the significance of depression in diabetes for the course of the disease and to provide an overview of the most important diagnostic and therapeutic recommendations for practice.

ZUSAMMENFASSUNGMenschen mit Diabetes müssen die wesentlichen Therapiemaßnahmen im Rahmen des Selbstmanagements eigenverantwortlich umsetzen. Der Erfolg der Selbstbehandlung ist nicht nur für die Prognose des Diabetes mellitus entscheidend, sondern auch für Wohlbefinden und Lebensqualität. Smartphone-Apps bieten zahlreiche Möglichkeiten, das Selbstmanagement zu unterstützen und können zur Verbesserung der glykämischen Kontrolle beitragen. Obwohl Effekte auf Wohlbefinden und Lebensqualität nur selten berücksichtigt werden, demonstrieren einzelne Befunde positive Auswirkungen auf diabetesbezogenen Distress, Hypoglykämieangst und gesundheitsbezogene Lebensqualität. Um künftig als Smartphone-App im Rahmen des Digitale-Versorgung-Gesetzes erstattungsfähig zu werden, müssen Hersteller nachweisen, dass die Anwendung patientenrelevante Effekte hinsichtlich einer Verbesserung der Lebensqualität erzielt.

ZUSAMMENFASSUNGDas Essverhalten spielt beim Diabetes mellitus eine wesentliche Rolle – sowohl für die Entstehung als auch für den Verlauf und die Prognose der Erkrankung. Das Spektrum eines gestörten Essverhaltens reicht dabei von Essproblemen über auffälliges Essverhalten (subklinische Essstörungen) bis hin zu klinischen Essstörungen. Auch wenn es keine belastbaren Hinweise darauf gibt, dass Essstörungen bei Menschen mit Diabetes im Vergleich zu stoffwechselgesunden Menschen gehäuft auftreten, stellen ein gestörtes Essverhalten und manifeste Essstörungen bei Diabetes einen Risikofaktor für eine erhöhte Morbidität und Mortalität dar. In den vergangenen Jahren lag der Fokus der Forschung auf dem Binge Eating und den sog. „nicht näher bezeichneten Essstörungen“, wie dem nächtlichen Essen (Night-Eating-Syndrom).

ObjectiveTo examine SARS-CoV-2 vaccine confidence, attitudes and intentions in Australian adults as part of the iCARE Study.Design and settingCross-sectional online survey conducted when free COVID-19 vaccinations first became available in Australia in February 2021.ParticipantsTotal of 1166 Australians from general population aged 18–90 years (mean 52, SD of 19).Main outcome measuresPrimary outcome: responses to question ‘If a vaccine for COVID-19 were available today, what is the likelihood that you would get vaccinated?’.Secondary outcome: analyses of putative drivers of uptake, including vaccine confidence, socioeconomic status and sources of trust, derived from multiple survey questions.ResultsSeventy-eight per cent reported being likely to receive a SARS-CoV-2 vaccine. Higher SARS-CoV-2 vaccine intentions were associated with: increasing age (OR: 2.01 (95% CI 1.77 to 2.77)), being male (1.37 (95% CI 1.08 to 1.72)), residing in least disadvantaged area quintile (2.27 (95% CI 1.53 to 3.37)) and a self-perceived high risk of getting COVID-19 (1.52 (95% CI 1.08 to 2.14)). However, 72% did not believe they were at a high risk of getting COVID-19. Findings regarding vaccines in general were similar except there were no sex differences. For both the SARS-CoV-2 vaccine and vaccines in general, there were no differences in intentions to vaccinate as a function of education level, perceived income level and rurality. Knowing that the vaccine is safe and effective and that getting vaccinated will protect others, trusting the company that made it and vaccination recommended by a doctor were reported to influence a large proportion of the study cohort to uptake the SARS-CoV-2 vaccine. Seventy-eight per cent reported the intent to continue engaging in virus-protecting behaviours (mask wearing, social distancing, etc) postvaccine.ConclusionsMost Australians are likely to receive a SARS-CoV-2 vaccine. Key influencing factors identified (eg, knowing vaccine is safe and effective, and doctor’s recommendation to get vaccinated) can inform public health messaging to enhance vaccination rates.

IntroductionDisordered eating behaviours (DEB) and eating disorders (ED) are among the most common mental health comorbidities of type 1 diabetes. However, research on diabetes-specific risk and protective factors is limited. To this end, comprehensive characterisations of DEB and ED in type 1 diabetes, as well as longitudinal research on the course of DEB and ED, are needed to gain more insight. The ‘Disordered eating behaviours and eating disorders in diabetes type I’ (DEBBI) study aims to describe DEB/ED and their correlates in people with type 1 diabetes, to identify key diabetes-specific, psychosocial risk and protective factors, and to describe the course of DEB over time.Methods and analysisThe DEBBI study is a longitudinal online survey with follow-up assessments after 6, 12 and 18 months, targeted at adults who have been diagnosed with type 1 diabetes for at least 12 months. The survey covers data on diabetes diagnosis and self-management (eg, diabetes treatment and complications), lifestyle (eg, eating habits, physical activity), psychosocial well-being (eg, anxiety, depressive symptoms) and demographic and medical information. It includes validated instruments and self-generated items. One key aspect of the data analysis will be latent profile analyses to determine latent subtypes of DEB manifestation in people with type 1 diabetes and their courses over time, including data on the clinical picture and symptoms, behaviours and diabetes-specific complications.Ethics and disseminationThe study protocol was approved by the Ethics Committee of the State Medical Chamber of Rhineland-Palatine, Germany (ID 2021-16040). Participants give informed written consent before starting the survey. The DEBBI study will provide more clarity in the so far inconsistent empirical evidence base and will help to inform research on prevention and intervention strategies that are tailored to diabetes-specific needs.Trial registration numberThe study is registered with DRKS German Clinical Trials Register (DRKS00028833).

CGM offers insights into daily glucose fluctuations. However, little is known how these insights impact feelings of diabetes distress in everyday life. We analyzed associations between subjective experiences of distress due to glucose fluctuations and CGM-derived parameters. In the DIA-LINK study, 203 people with type 1 diabetes completed ecological momentary assessment while using unblinded CGM over 17 days. Every evening, participants rated diabetes distress they experienced over a day using 5 adapted questions from the PAID questionnaire (feeling overwhelmed, alone, guilty, deprivation, diabetes takes up too much mental/physical energy). Also, they rated their daily distress specifically due to glucose fluctuations from 0 (not at all) to 10 (very much). Mean daily scores of these distress ratings were calculated per participants. Glucose parameters were calculated for the study period (mean glucose, coefficient of variation (CV) of glucose, % 180 mg/dl). Correlations were analyzed using Pearson’s r (Table 1). Ratings of distress were highly associated. CGM-derived glucose CV was neither associated with overall distress nor with specific distress due to glucose variability. This may indicate that glucose fluctuations can be a source of diabetes distress, but subjective experiences seem to be more important than objectively measured glucose fluctuations. Disclosure L. Priesterroth: None. D. Ehrmann: Speaker’s Bureau; Self; Berlin-Chemie AG. N. Hermanns: Advisory Panel; Self; Abbott, Research Support; Self; Berlin-Chemie AG, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH. A. J. Schmitt: None. B. Kulzer: Advisory Panel; Self; Berlin-Chemie AG, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Research Support; Self; Berlin-Chemie AG, Novo Nordisk, Speaker’s Bureau; Self; Berlin-Chemie AG, Dexcom, Inc., Novo Nordisk. Funding German Center for Diabetes Research (DZD) (82DZD01102)

Low and high glucose levels can cause diabetes-specific aversive symptoms. These symptoms can be a direct marker of the impact of diabetes on living with the disease. We used Ecological Momentary Analysis (EMA) in combination with a flash glucose meter (Freestyle Libre, Abbott) to assess the impact of glucose levels on symptoms. We selected symptoms typically associated with high and low glucose levels, as well as control symptoms. In the DIA-LINK study, 203 people with type 1 diabetes were asked to rate the severity of diabetes symptoms on a “0” to “10” scale four times a day over a two-day period. Two measurement points were fixed (morning and evening) and two were randomly assigned. Glucose data from the 2-hour period before each symptom assessment were extracted. Multilevel regression analysis was used to determine the difference between 2-hour periods with low or high glucose values (glucose value 250 mg/dl, respectively) and without such hypoglycemic or hyperglycemic periods. Figure shows T values (values > 1.96 = p Disclosure N. Hermanns: Advisory Panel; Self; Abbott, Research Support; Self; Berlin-Chemie AG, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH. A. J. Schmitt: None. B. Kulzer: Advisory Panel; Self; Berlin-Chemie AG, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Research Support; Self; Berlin-Chemie AG, Novo Nordisk, Speaker’s Bureau; Self; Berlin-Chemie AG, Dexcom, Inc., Novo Nordisk. L. Priesterroth: None. D. Ehrmann: Speaker’s Bureau; Self; Berlin-Chemie AG. Funding German Center for Diabetes Research (DZD)

Aim of this analysis was to analyse daily factors that lead to diabetes distress using data from an ecological momentary assessment study in combination with CGM. In the DIA-LINK study, 203 people with type 1 diabetes were asked four times a day over 17 days (EMA) while using unblinded CGM. They rated their sleep quality and the anticipated stress in the morning and their current mood, energy, and stress level four times a day. Daily diabetes distress was assessed in the evening using five adapted items from the Problem Areas in Diabetes (PAID) scale (feeling overwhelmed, alone, guilty, deprivation, diabetes takes up too much mental/physical energy). Glucose data from the 12-hour period prior to each distress rating were extracted. Using multilevel regression analysis, the sum score of daily distress was entered as dependent variable (person as nested factor). The following independent variables were used: sleep quality, anticipated stress level, mean level and coefficient of variation (CV) of mood, energy, and stress, CV of glucose, time in range, and % Disclosure D. Ehrmann: Speaker’s Bureau; Self; Berlin-Chemie AG. A. J. Schmitt: None. L. Priesterroth: None. B. Kulzer: Advisory Panel; Self; Berlin-Chemie AG, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Research Support; Self; Berlin-Chemie AG, Novo Nordisk, Speaker’s Bureau; Self; Berlin-Chemie AG, Dexcom, Inc., Novo Nordisk. N. Hermanns: Advisory Panel; Self; Abbott, Research Support; Self; Berlin-Chemie AG, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH. Funding German Center for Diabetes Research (DZD) (82DZD01102)

Aims In this systematic review, we aimed (1) to identify and describe research investigating the use of advanced diabetes technologies (continuous subcutaneous insulin infusion, CSII; continuous glucose monitoring, CGM; automated insulin delivery, AID) in people with type 1 diabetes (T1DM) and disordered eating and (2) to discuss potential (dis)advantages of diabetes technology use in this population, derived from previous research. Methods We conducted a systematic literature search in two electronic databases for English language articles published between 2000 and 2020 addressing eating disorders and/or dysfunctional eating behaviours and diabetes technology use in children, adolescents and adults with T1DM (PROSPERO ID CRD42020160244). Results Of 70 publications initially identified, 17 were included. Overall, evidence on the use of diabetes technologies in people with T1DM and disordered eating is scarce. The majority of the studies reports findings on CSII in people with T1DM and dysfunctional eating behaviours or eating disorders. Findings predominantly stem from observational data and are, in most cases, secondary findings of the respective studies. Providing the greatest flexibility in diabetes management, CSII may have benefits in disordered eating. CGM data may complement the diagnostic process of disordered eating with a physiological indicator of insulin restriction (i.e., time spent in hyperglycaemia). Conclusions Results on possible (dis)advantages of diabetes technology use in people with T1DM and disordered eating are based on observational data, small pilot trials and anecdotical evidence from case reports. Prospective data from larger samples are needed to reliably determine potential effects of diabetes technology on disordered eating in T1DM.

Background: Diabetes management apps may have positive effects on diabetes self-management. It remains unclear, however, which app features are particularly effective and encourage sustained app usage. Behavior change techniques (BCTs) and gamification are promising approaches to improve user engagement. However, little is known about the frequency BCTs and gamification techniques (GTs) are actually used. This app review aims to provide an overview of BCTs and GTs in current diabetes management apps. Methods: Google’s Play Store was searched for applications using a broad search strategy (keyword: “diabetes”). We limited our research to freely available apps. A total of 56 apps matched the inclusion criteria and were reviewed in terms of the features they offer to support self-management. We used a taxonomy comprising 29 BCTs and 17 GTs to evaluate the applications. Two independent raters tested and evaluated each app. Results: Interrater agreement was high (ICC = .75 for BCTs; ICC = .90 for GTs). An average of 7.4 BCTs (SD = 3.1) and an average of 1.4 out of 17 GTs (SD = 1.6) were implemented in each app. Five out of 29 BCTs accounted for 55.8% of the BCTs identified in total. The GT most often identified was “feedback” and accounted for 50% of the GTs. Conclusions: The potential of BCTs and GTs in diabetes management apps has not been fully exploited yet. Only very restricted sets of BCTs and gamification features were implemented. Systematic research on the efficacy of specific BCTs and GTs is needed to provide further guidance for app design.

People with diabetes often have a depressive disorder in addition to diabetes, which makes therapy more difficult and worsens the prognosis of the disease. Depression is an often underestimated risk factor in the context of diabetes. The aim of this article is to illustrate the significance of depression in diabetes for the course of the disease and to provide an overview of the most important diagnostic and therapeutic recommendations for practice.

BACKGROUND A large variety of smartphone-based interventions (SBIs) have emerged recently that aim to contribute to mental healthcare. SBIs that integrate a training of cognitive reappraisal (CR) may be particularly effective. Little is known whether CR as an evidence-based mechanism for mental health promotion is included in SBIs. OBJECTIVE The aim of this study was to systematically evaluate the integration of CR training in recent SBIs for clinical and non-clinical adult populations and meta-analyze their effect on prevalent mental health outcomes (depression, anxiety, perceived stress, and well-being). METHODS A comprehensive literature search in four databases revealed 1,836 unique records, which were screened for eligibility by two independent reviewers. 15 randomized controlled or controlled trials (N = 1406) including an adult sample that received an SBI with CR training and a comparison condition were identified. We performed a random-effects meta-analysis to calculate the standardized mean difference (SMD) between SBIs and control conditions. Subgroup analyses were conducted for sample population (clinical vs. non-clinical) and degree of human support (SBIs with human support vs. SBIs without human support). RESULTS Most of the studies assessed a clinical population, had depression as primary outcome, and were performed without human face-to-face support for the participants. The CR training of all SBIs involved active engagement of the users that was realized with a wide range of multimedia material. We found evidence for a small effect (SMD = –0.21, 95% CI –0.40 to –0.02; P = .04) on mental health (i.e., the reduction of symptoms of mental disorders) in favor of the SBIs including CR, with moderate to substantial heterogeneity (I2 = 60%; Chi2(df) = 35.32 (14); P = .001). The overall quality of the studies was relatively low. Subgroup analyses revealed evidence for larger effect sizes of studies with clinical compared to non-clinical populations. There was no evidence for a subgroup difference between interventions with vs. without human support. CONCLUSIONS CR can successfully be implemented in SBIs and reduce mental health symptoms.

Aim To identify key psychosocial research in the domain of diabetes technology. Results Four trajectories of psychosocial diabetes technology research are identified that characterize research over the past 25 years. Key evidence is reviewed on psychosocial outcomes of technology use as well as psychosocial barriers and facilitating conditions of diabetes technology uptake. Psychosocial interventions that address modifiable barriers and psychosocial factors have proven to be effective in improving glycaemic and self-reported outcomes in diabetes technology users. Conclusions Psychosocial diabetes technology research is essential for designing interventions and education programmes targeting the person with diabetes to facilitate optimized outcomes associated with technology uptake. Psychosocial aspects of diabetes technology use and related research will be even more important in the future given the advent of systems for automated insulin delivery and the increasingly widespread digitalization of diabetes care.

Zusammenfassung Hintergrund Mehr als die Hälfte aller Menschen mit Diabetes in Deutschland ist älter als 65 Jahre. Für eine erfolgreiche Insulinpumpentherapie (Continuous Subcutaneous Insulin Infusion; CSII) im hohen Erwachsenenalter sind Wünsche, Sorgen und Erfahrungen älterer Menschen mit Typ-1-Diabetes, erlebte Barrieren und förderliche Bedingungen von zentraler Bedeutung. Methodik Ältere Menschen mit Typ-1-Diabetes, die sich mit einer CSII behandeln (n = 136), sowie Fachkräfte aus dem Diabetesbereich (Diabetes Health Care Professionals; HCP; n = 96) nahmen an einem Fragebogensurvey teil, in dem anhand zweier offener Fragen Wünsche, Sorgen und Erfahrungen älterer Menschen mit Typ-1-Diabetes erfasst wurden. Durch systematische, induktive Datenreduktion wurden aus den Antworten zentrale Kategorien und Subkategorien identifiziert. Ergebnisse Es konnten vier Inhaltskategorien abgeleitet werden: (1) Fremdhilfe, (2) Technische Innovation, (3) Gesundheitspolitik und (4) Umstellung auf ICT. Am häufigsten wurde das Vorhandensein von kompetenter Hilfe von außen durch den HCP aus dem Diabetesbereich sowie durch Angehörige und andere Betroffene gewünscht. Die Befragten formulierten klare Wünsche an ein altersgerechtes Design von Insulinpumpen (z. B. großes Display, Sprachsteuerung). Sorgen der Pumpenträger/-innen bezogen sich u. a. auf Hilflosigkeit, Akutkomplikationen und verschlechterte Stoffwechsellage. Schlussfolgerung Die Befragten äußerten große Sorgen und Unsicherheiten bezüglich CSII im Alter, auf die seitens der Diabetologie, Industrie und Gesundheitspolitik eingegangen werden sollte, um Insulinpumpentherapie altersgerecht zu gestalten.

Flash sensor-based glucose monitoring (FSGM) provides people with diabetes with an exhaustive overview about their glycemic control. However, the plethora of data can also be challenging and overwhelming. We developed a treatment and education program for FSGM-use (named FLASH) and evaluated its efficacy in a randomized controlled trial. We included people with diabetes on an intensified insulin therapy who either already used FSGM before the study (FSGM-experienced) or who newly used FSGM with the beginning of the study (FSGM-naïve). All participants were either randomized to receiving the FLASH education or to a treatment-as-usual control group. We report on results of ANCOVA with baseline-adjusted HbA1c and diabetes distress values at follow-up, respectively. A total of 199 participants were analyzed. FSGM-experienced participants were using FSGM for a mean duration of 7.9 ± 9.5 months before study start. At the six-month follow-up, FLASh led to a significant reduction in HbA1c (8.06% vs. 8.22%; p=0.033) and diabetes distress (1.16 vs. 1.00; p = 0.027). However, independent from the group allocation, FSGM-experienced participants (n=121) had significantly higher distress levels at follow-up than FSGM-naïve (n=78) participants (1.16 vs. 1.01; p = 0.032). FSGM-experienced users that never received education had a significantly higher distress level (1.26) than FSGM-experienced users that received education (1.06; p = 0.045), FSGM-naïve users with education (0.95; p = 0.003) and without education (1.06; p = 0.021). The FLASH education led to a reduction in distress. Previous FSGM-use without education was associated with the highest distress, whereas FSGM-naïve participants who received the FLASH education had the lowest distress levels. Thus, results indicate that FSGM-use accompanied with FLASH education is more beneficial than FSGM-use alone. In addition, FSGM-specific education should be employed better sooner than later. Disclosure B. Kulzer: Advisory Panel; Self; Ascensia Diabetes Care, Berlin-Chemie AG, Medtronic, Novo Nordisk Inc., Roche Diabetes Care. Research Support; Self; Abbott, Berlin-Chemie AG, Roche Diabetes Care. Speaker's Bureau; Self; Abbott, Ascensia Diabetes Care, Berlin-Chemie AG, Eli Lilly and Company, Novo Nordisk Inc., Roche Diabetes Care. L. Priesterroth: None. N. Hermanns: Advisory Panel; Self; Abbott. Board Member; Self; Eli Lilly and Company. Research Support; Self; Abbott, Berlin-Chemie AG, Dexcom, Inc., Roche Diabetes Care. Speaker's Bureau; Self; Berlin-Chemie AG, Dexcom, Inc., Novo Nordisk A/S. T. Haak: Advisory Panel; Self; Abbott, AstraZeneca, Merck Sharp & Dohme Corp., Roche Diabetes Care. Research Support; Self; Abbott, AstraZeneca, Boehringer Ingelheim International GmbH. Speaker's Bureau; Self; Abbott, AstraZeneca, Berlin-Chemie AG, Eli Lilly and Company, Novo Nordisk Inc. D. Ehrmann: Advisory Panel; Self; Medtronic. Speaker's Bureau; Self; Abbott, Berlin-Chemie AG, Dexcom, Inc. Funding Abbott Diabetes Care Germany