IntroductionLockdown 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 methodsWe 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.ResultsIn 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).ConclusionsAn 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.

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BackgroundDrop-out is a major problem in weight loss studies. Although previous attrition research has examined some predictors of drop-out, theoretically grounded research on psychological predictors of drop-out from weight interventions has been lacking.PurposeTo examine psychological predictors of drop-out from a weight reduction study in diabetes type 2 patients.MethodA clinical trial was conducted with 101 overweight/obese (body mass index >27) diabetes type 2 patients. Patients were randomly assigned to a self-regulation intervention, an active control group, and a passive control group. Psychological, somatic, socio-demographic, and lifestyle variables were examined as predictors of drop-out from baseline to 6 months follow-up.ResultsMultiple logistic regression analysis indicated that low autonomous regulation or low ‘goal ownership’ was the best predictor of drop-out.ConclusionIt is suggested that the assessment of ‘goal ownership’ prior to a weight reduction intervention could identify patients who are sufficiently motivated to participate. Patients who score low on ‘goal ownership’ may be offered pretreatment interventions to increase their motivation.

Design and evaluation of a personal robot playing a self-management education game with children with diabetes type 1

Multidisciplinary treatment is effective in reducing BMI-SDS and improving HRQOL after 12 months follow-up.

ObjectiveWe aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people’s individual needs, momentary contexts, and psychosocial variables.Materials and MethodsWe propose a template-based digital intervention design mechanism enabling the configuration of evidence-based, just-in-time, adaptive intervention components. The design mechanism incorporates a rule definition language enabling experts to specify triggering conditions for interventions based on momentary and historical contextual/personal data. The framework continuously monitors and processes personal data space and evaluates intervention-triggering conditions. We benefit from reinforcement learning methods to develop personalized intervention delivery strategies with respect to timing, frequency, and type (content) of interventions. To validate the personalization algorithm, we lay out a simulation testbed with 2 personas, differing in their various simulated real-life conditions.ResultsWe evaluate the design mechanism by presenting example intervention definitions based on behavior change taxonomies and clinical guidelines. Furthermore, we provide intervention definitions for a real-world care program targeting diabetes patients. Finally, we validate the personalized delivery mechanism through a set of hypotheses, asserting certain ways of adaptation in the delivery strategy, according to the differences in simulation related to personal preferences, traits, and lifestyle patterns.ConclusionWhile the design mechanism is sufficiently expandable to meet the theoretical and clinical intervention design requirements, the personalization algorithm is capable of adapting intervention delivery strategies for simulated real-life conditions.

Self-regulation and weight reduction in patients with type 2 diabetes Huisman, S.; de Gucht, V.; Maes, S.; Schroevers, M.; Chatrou, M.; Haak, H.

This meta-analysis underlines the importance of a self-regulation approach for weight reduction interventions in diabetes patients, in particular, for A1C outcomes. However, more research is needed to fully understand the relationship among self-regulation, weight, and A1C.