Impact of a Public Health Emergency on Behavior, Stress, Anxiety and Glycemic Control in Patients With Pancreas or Islet Transplantation for Type 1 Diabetes.

A public health emergency such as the COVID-19 pandemic has behavioral, mental and physical implications in patients with type 1 diabetes (T1D). To what extent the presence of a transplant further increases this burden is not known. Therefore, we compared T1D patients with an islet or pancreas transplant (ß-cell Tx; n = 51) to control T1D patients (n = 272). Fear of coronavirus infection was higher in those with ß-cell Tx than without (Visual Analogue Scale 5.0 (3.0-7.0) vs. 3.0 (2.0-5.0), p = 0.004) and social isolation behavior was more stringent (45.8% vs. 14.0% reported not leaving the house, p < 0.001). A previous ß-cell Tx was the most important predictor of at-home isolation. Glycemic control worsened in patients with ß-cell Tx, but improved in control patients (ΔHbA1c +1.67 ± 8.74 vs. -1.72 ± 6.15 mmol/mol, p = 0.006; ΔTime-In-Range during continuous glucose monitoring -4.5% (-6.0%-1.5%) vs. +3.0% (-2.0%-6.0%), p = 0.038). Fewer patients with ß-cell Tx reported easier glycemic control during lockdown (10.4% vs. 22.6%, p = 0.015). All T1D patients, regardless of transplantation status, experienced stress (33.4%), anxiety (27.9%), decreased physical activity (42.0%), weight gain (40.5%), and increased insulin requirements (29.7%). In conclusion, T1D patients with ß-cell Tx are increasingly affected by a viral pandemic lockdown with higher fear of infection, more stringent social isolation behavior and deterioration of glycemic control. This trial has been registered in the clinicaltrials.gov registry under identifying number NCT05977205 (URL: https://clinicaltrials.gov/study/NCT05977205).

Clinical impact of an integrated e-health system for diabetes self-management support and shared decision making (POWER2DM): a randomised controlled trial.

AIMS/HYPOTHESIS: There is a lack of e-health systems that integrate the complex variety of aspects relevant for diabetes self-management. We developed and field-tested an e-health system (POWER2DM) that integrates medical, psychological and behavioural aspects and connected wearables to support patients and healthcare professionals in shared decision making and diabetes self-management. METHODS: Participants with type 1 or type 2 diabetes (aged >18 years) from hospital outpatient diabetes clinics in the Netherlands and Spain were randomised using randomisation software to POWER2DM or usual care for 37 weeks. This RCT assessed the change in HbA1c between the POWER2DM and usual care groups at the end of the study (37 weeks) as a primary outcome measure. Participants and clinicians were not blinded to the intervention. Changes in quality of life (QoL) (WHO-5 Well-Being Index [WHO-5]), diabetes self-management (Diabetes Self-Management Questionnaire - Revised [DSMQ-R]), glycaemic profiles from continuous glucose monitoring devices, awareness of hypoglycaemia (Clarke hypoglycaemia unawareness instrument), incidence of hypoglycaemic episodes and technology acceptance were secondary outcome measures. Additionally, sub-analyses were performed for participants with type 1 and type 2 diabetes separately. RESULTS: A total of 226 participants participated in the trial (108 with type 1 diabetes; 118 with type 2 diabetes). In the POWER2DM group (n=111), HbA1c decreased from 60.6±14.7 mmol/mol (7.7±1.3%) to 56.7±12.1 mmol/mol (7.3±1.1%) (means ± SD, p<0.001), compared with no change in the usual care group (n=115) (baseline: 61.7±13.7 mmol/mol, 7.8±1.3%; end of study: 61.0±12.4 mmol/mol, 7.7±1.1%; p=0.19) (between-group difference 0.24%, p=0.008). In the sub-analyses in the POWER2DM group, HbA1c in participants with type 2 diabetes decreased from 62.3±17.3 mmol/mol (7.9±1.6%) to 54.3±11.1 mmol/mol (7.1±1.0%) (p<0.001) compared with no change in HbA1c in participants with type 1 diabetes (baseline: 58.8±11.2 mmol/mol [7.5±1.0%]; end of study: 59.2±12.7 mmol/mol [7.6±1.2%]; p=0.84). There was an increase in the time during which interstitial glucose levels were between 3.0 and 3.9 mmol/l in the POWER2DM group, but no increase in clinically relevant hypoglycaemia (interstitial glucose level below 3.0 mmol/l). QoL improved in participants with type 1 diabetes in the POWER2DM group compared with the usual care group (baseline: 15.7±3.8; end of study: 16.3±3.5; p=0.047 for between-group difference). Diabetes self-management improved in both participants with type 1 diabetes (from 7.3±1.2 to 7.7±1.2; p=0.002) and those with type 2 diabetes (from 6.5±1.3 to 6.7±1.3; p=0.003) within the POWER2DM group. The POWER2DM integrated e-health support was well accepted in daily life and no important adverse (or unexpected) effects or side effects were observed. CONCLUSIONS/INTERPRETATION: POWER2DM improves HbA1c levels compared with usual care in those with type 2 diabetes, improves QoL in those with type 1 diabetes, improves diabetes self-management in those with type 1 and type 2 diabetes, and is well accepted in daily life. TRIAL REGISTRATION: ClinicalTrials.gov NCT03588104. FUNDING: This study was funded by the European Union's Horizon 2020 Research and Innovation Programme (grant agreement number 689444).

Problem-based shared decision making: The role of canonical SDM steps.

OBJECTIVE: To evaluate the extent to which the canonical steps of shared decision making (SDM) take place in clinical encounters in practice and across SDM forms. METHODS: We assessed 100 randomly selected video-recorded primary care encounters, obtained as part of a randomized trial of an SDM intervention in patients with type 2 diabetes. Two coders, working independently, noted each instance of SDM, classified it as one of four problem-based forms to SDM (weighing alternatives, negotiating conflicting issues, solving problems, or developing existential insight), and noted the occurrence and timing of each of the four canonical SDM steps: fostering choice awareness, providing information, stating preferences, and deciding. Descriptive analyses sought to determine the relative frequency of these steps across each of the four SDM forms within each encounter. RESULTS: There were 485 SDM steps noted (mean 4.85 steps per encounter), of which providing information and stating preferences were the most common. There were 2.7 (38 steps in 14 encounters) steps per encounter observed in encounters with no discernible SDM form, 3.4 (105 steps in 31 encounters) with one SDM form, 5.2 (129 steps in 25 encounters) with two SDM forms, and 7.1 (213 steps in 30 encounters) when ≥3 SDM forms were observed within the encounter. The prescribed order of the four SDM steps was observed in, at best, 16 of the 100 encounters. Stating preferences was a common step when weighing alternatives (38%) or negotiating conflicts (59.3%) but less common when solving problems (29.2%). The distribution of SDM steps was similar to usual care with or without the SDM intervention. CONCLUSION: The normative steps of SDM are infrequently observed in their prescribed order regardless of whether an SDM intervention was used. Some steps are more likely in some SDM forms but no pattern of steps appears to distinguish among SDM forms. CLINICAL TRIAL REGISTRATION: ClinicalTrial.gov: NCT01293578.

Problem-based shared decision-making in diabetes care: a secondary analysis of video-recorded encounters.

OBJECTIVES: To describe the range of collaborative approaches to shared decision-making (SDM) observed in clinical encounters of patients with diabetes and their clinicians. DESIGN: A secondary analysis of videorecordings obtained in a randomised trial comparing usual diabetes primary care with or without using a within-encounter conversation SDM tool. SETTING: Using the purposeful SDM framework, we classified the forms of SDM observed in a random sample of 100 video-recorded clinical encounters of patients with type 2 diabetes in primary care. MAIN OUTCOME MEASURES: We assessed the correlation between the extent to which each form of SDM was used and patient involvement (OPTION12-scale). RESULTS: We observed at least one instance of SDM in 86 of 100 encounters. In 31 (36%) of these 86 encounters, we found only one form of SDM, in 25 (29%) two forms, and in 30 (35%), we found ≥3 forms of SDM. In these encounters, 196 instances of SDM were identified, with weighing alternatives (n=64 of 196, 33%), negotiating conflicting desires (n=59, 30%) and problemsolving (n=70, 36%) being similarly prevalent and developing existential insight accounting for only 1% (n=3) of instances. Only the form of SDM focused on weighing alternatives was correlated with a higher OPTION12-score. More forms of SDM were used when medications were changed (2.4 SDM forms (SD 1.48) vs 1.8 (SD 1.46); p=0.050). CONCLUSIONS: After considering forms of SDM beyond weighing alternatives, SDM was present in most encounters. Clinicians and patients often used different forms of SDM within the same encounter. Recognising a range of SDM forms that clinicians and patients use to respond to problematic situations, as demonstrated in this study, opens new lines of research, education and practice that may advance patient-centred, evidence-based care.

Key Factors Relevant for Healthcare Decisions of Patients with Type 1 and Type 2 Diabetes in Secondary Care According to Healthcare Professionals.

Purpose: Understanding which factors are important for healthcare decisions of patients with diabetes in clinical practice is important to personalise diabetes care strategies and tailor care plans to the individual. The main drivers for these healthcare decisions remain unclear. This study assessed which key factors are relevant for healthcare decisions during clinical consultations for patients with type 1 diabetes (T1DM) and type 2 diabetes (T2DM), according to healthcare professionals. Materials and Methods: Annual diabetes reviews were performed as part of a trial assessing the impact of a consultation model facilitating person-centred diabetes care in six hospital outpatient clinics. After each consultation, we asked healthcare professionals to choose a maximum of three out of 20 factors that were most relevant for healthcare decisions about treatment goals and the professional support needed during the upcoming year. Factors were characterised as either person or disease-related. Percentages reflect the number of annual diabetes reviews in which the key factor was reported. Results: Seventeen physicians and eight diabetes specialist nurses reported the key factors relevant for healthcare decisions in 285 annual diabetes reviews (T1DM n = 119, T2DM n = 166). Healthcare professionals most often reported quality of life (31.9%), motivation (27.0%) and diabetes self-management (25.6%), and to a lesser extent glycaemic control (24.2%), to be important for decisions about treatment goals. For decisions about the professional support needed during the upcoming year patient's preferences (33.7%), diabetes self-management (33.3%), quality of life (27.0%) and motivation (25.6%) were most often considered relevant by healthcare professionals. Conclusion: According to healthcare professionals, person-related factors such as quality of life, diabetes self-management and motivation are predominantly relevant for healthcare decisions about treatment goals and the professional support needed during the upcoming year.

Sleep duration and lipid metabolism in patients with diabetes mellitus: from the POWER2DM study.

This study assesses the association between sleep duration and plasma lipid profiles in people with diabetes mellitus (DM). Sleep duration data were obtained in 91 patients from the POWER2DM study (NCT03588104). The patients were divided in tertiles, based on their sleep duration, and blood samples were obtained at the beginning and after 9 months. Significant differences were found, specifically, patients in Tertile 3 (≥ 7.51 h) showed lower plasma levels of high-density lipoprotein cholesterol HDL-c (p < 0.05), apolipoprotein A1 (apo-A1; p < 0.05) and low HDL-c/apo-A1 ratio (p < 0.05). This study shows that sleep duration is associated with plasma lipid profiles in people with DM.

Making Diabetes Care Fit-Are We Making Progress?

The care of patients with diabetes requires plans of care that make intellectual, practical, and emotional sense to patients. For these plans to fit well, patients and clinicians must work together to develop a common understanding of the patient's problematic human situation and co-create a plan of care that responds well to it. This process, which starts at the point of care, needs to continue at the point of life. There, patients work to fit the demands of their care plan along with the demands placed by their lives and loves. Thought in this way, diabetes care goes beyond the control of metabolic parameters and the achievement of glycemic control targets. Instead, it is a highly individualized endeavor that must arrive at a care plan that reflects the biology and biography of the patient, the best available research evidence, and the priorities and values of the patient and her community. It must also be feasible within the life of the patient, minimally disrupting those aspects of the patient life that are treasured and justify the pursuit of care in the first place. Patient-centered methods such as shared decision making and minimally disruptive medicine have joined technological advances, patient empowerment, self-management support, and expert patient communities to advance the fit of diabetes care both at the point of care and at the point of life.

Increased stress, weight gain and less exercise in relation to glycemic control in people with type 1 and type 2 diabetes during the COVID-19 pandemic.

INTRODUCTION: Lockdown measures have a profound effect on many aspects of daily life relevant for diabetes self-management. We assessed whether lockdown measures, in the context of the COVID-19 pandemic, differentially affect perceived stress, body weight, exercise and related this to glycemic control in people with type 1 and type 2 diabetes. RESEARCH DESIGN AND METHODS: We performed a short-term observational cohort study at the Leiden University Medical Center. People with type 1 and type 2 diabetes ≥18 years were eligible to participate. Participants filled out online questionnaires, sent in blood for hemoglobin A1c (HbA1c) analysis and shared data of their flash or continuous glucose sensors. HbA1c during the lockdown was compared with the last known HbA1c before the lockdown. RESULTS: In total, 435 people were included (type 1 diabetes n=280, type 2 diabetes n=155). An increase in perceived stress and anxiety, weight gain and less exercise was observed in both groups. There was improvement in glycemic control in the group with the highest HbA1c tertile (type 1 diabetes: -0.39% (-4.3 mmol/mol) (p<0.0001 and type 2 diabetes: -0.62% (-6.8 mmol/mol) (p=0.0036). Perceived stress was associated with difficulty with glycemic control (p<0.0001). CONCLUSIONS: An increase in perceived stress and anxiety, weight gain and less exercise but no deterioration of glycemic control occurs in both people with relatively well-controlled type 1 and type 2 diabetes during short-term lockdown measures. As perceived stress showed to be associated with glycemic control, this provides opportunities for healthcare professionals to put more emphasis on psychological aspects during diabetes care consultations.

Non-alcoholic fatty liver disease: a multidisciplinary approach towards a cardiometabolic liver disease.

Non-alcoholic fatty liver disease (NAFLD) is a growing health problem with a global prevalence of over 25% and prevalence rates of over 60% in high-risk populations. It is considered the hepatic component of the metabolic syndrome and is associated with an increased risk of the development of various liver-associated and cardiometabolic complications. Given the complexity of NAFLD and associated comorbidities and complications, treatment requires interventions from a variety of different healthcare specialties. However, many clinicians are currently insufficiently aware of the potential harm and severity of NAFLD and associated comorbidities, complications and the steps that should be taken when NAFLD is suspected. Recognizing which patients suffer from non-progressive simple steatosis, metabolically active NASH with high risk of developing cardiovascular disease and which patients have a high risk of developing cirrhosis and hepatocellular carcinoma is important. Unfortunately, this can be difficult and guidelines towards the optimal diagnostic and therapeutic approach are ambivalent. Here we review the pathogenesis, diagnostics and treatment of NAFLD and discuss how multidisciplinary care path development could move forward.