Comparison of the Sensor-Augmented Pump System with the Advanced Hybrid Closed-Loop Delivery System: Quality of Life, Diabetes Distress, and Glycaemic Outcomes in a Real-Life Context.

BACKGROUND: Type 1 diabetes mellitus (T1D) is a chronic disease that requires exogenous insulin administration and intensive management to prevent any complications. Recent innovations in T1D management technologies include the Advanced Hybrid Closed-Loop delivery system (AHCL). The pioneer AHCL system provides automated basal and automated bolus corrections when needed. OBJECTIVE: This study aimed to compare the Advanced Hybrid Closed-Loop (AHCL) system and the Sensor-Augmented Pump (SAP) with Predictive Low Glucose Management (PLGM) system, in relation to glycaemic outcomes, general and diabetes-related Quality of Life (QoL), and diabetes distress. METHODS: General and diabetes-related QoL were assessed with the Diabetes Quality of Life Brief Clinical Inventory (DQOL-BCI) and the World Health Organization Quality of Life-BREF (WHOQOL-BREF), respectively. Diabetes distress was assessed with the Diabetes Distress Scale for Type 1 diabetes (T1-DDS). RESULTS: Eighty-nine T1D adults participated in the study, mostly females (65.2%), with a mean age of 39.8 (± 11.5 years). They had on average 23 years of diabetes (± 10.7) and they were on continuous subcutaneous insulin infusion therapy. Significant differences favoring the AHCL over the SAP + PLGM system were demonstrated by lower mean glucose levels, less time above range, lower scores on DQOL-BCI, T1-DDS, and higher scores on WHOQOL-BREF. Finally, the linear regression models revealed the association of time in range in most of the above aspects. CONCLUSION: This study highlighted the advantages of the AHCL system over the SAP + PLGM system in the real-world setting in relation to general and diabetes-related QoL, diabetes distress, and glycaemic outcomes.

Diabetes Mellitus Type 1 During COVID-19: Psychological Symptoms and Eating Attitudes.

BACKGROUND: Diabetes Mellitus Type 1 is a chronic metabolic disorder, the treatment of which extremely burdens the patient. The lockdown, as a measure to prevent the spreading of the 2019 coronavirus disease, was a major challenge for the general public. People with chronic diseases, such as T1D patients, have complex emotional and psychological needs and are at increased risk for both depressive symptoms and anxiety. OBJECTIVE: The aim of this study was to evaluate the impact of body mass index of T1D patients on the psychological symptoms and eating habits, as well as the prognostic factors of the psychological symptoms during the lockdown period. METHODS: Diabetes mellitus patients were invited to complete this survey from the outpatient Diabetes clinic of the General Hospital of Chania and the "AHEPA" General University Hospital of Thessaloniki in Greece. Psychological symptoms were assessed using the DASS-42 questionnaire, and eating habits were assessed using the EAT-26 questionnaire. RESULTS: Overall, 112 T1D patients were analyzed (response rate: 28.8%). Obese T1D patients appear to be at greater risk of developing psychological symptoms, such as depression, anxiety, and stress. Furthermore, disordered eating behaviors and bulimia and food preoccupation subscale are associated with psychological symptoms. CONCLUSION: The findings suggest that obese T1D patients are at greater risk of developing psychological symptoms. Moreover, when an eating disorder is triggered, negative psychological symptoms such as depression and anxiety are reduced. The results of this study may guide targeted interventions among T1D patients.

The diabetes quality of life brief clinical inventory in combination with the management strategies in type 1 diabetes mellitus with or without the use of insulin pump.

AIMS: The aims of this study are to evaluate any differences in the Quality of life among Continuous Subcutaneous Insulin Infusion (CSII) and Multiple Dose Injection (MDI) insulin delivery, applying the Diabetes Quality of life Brief Clinical Inventory (DQoL-BCI) questionnaire, and assess the diabetes management strategies between the two groups. METHODS: One hundred and ten adult participants (male/female ratio 1:2.7) with type 1 diabetes were recruited in this online survey. Forty-eight of them were using CSII and the rest 62 (were using) MDI insulin delivery. A 23-item socio-demographic/diabetes management strategies questionnaire and the 15-item DQoL-BCI were administered. RESULTS: CSII users scored statistically, significantly better at the satisfaction treatment subscale (p = 0.032) of the DQoL-BCI and emerged that they were implemented more management strategies such as dietician guidance services (p = 0.002), carbohydrate education seminars (p = 0.03). Predictive factors were also detected regarding the HbA1c < 7% (53 mmol/mol) and ß-coefficients in relation to DQoL-BCI questionnaire with the subscales of a negative impact and satisfaction treatment. CONCLUSION: Diabetes self-management education plays a key role to a better compliance with the treatment. Client-centered multidisciplinary centers in T1DM education are essential so that they be applicable for all T1DM patients irrespective of the type of insulin delivery they used.

A longitudinal study of multiple lifestyle health risk behaviours among nursing students and non-nursing peers.

AIM: The aim of this paper is to compare the evolution of health risk behaviours between undergraduate nursing and social work students. BACKGROUND: Nursing includes the promotion of health and the shaping of healthy behaviours. An important determinant for providing lifestyle advice is the lifestyle of nurses themselves. DESIGN: Longitudinal comparative study. MEASUREMENTS: We compared lifestyle risk behaviours (binge drinking, cannabis/hashish/marijuana use, smoking, oral hygiene/toothbrushing, breakfast/fruit/vegetable consumption, physical activity and screen time/sedentary behaviours) using a self-administered standardized questionnaire in nursing (n = 121) and social work (n = 140) students at the beginning (2012) and the end of their studies (2015). Adjusted multivariable logistic/Poisson regression models were performed. RESULTS: There were no statistically significant differences between the departments in most risk factors in both assessments. However, in relation to their first year, both nursing and social work students displayed higher relative risk of engaging in more behavioural risk factors at the end of their studies (in delivery/junk food consumption, sunburns, hashish/marijuana use and multiple sexual partners). Social work students displayed better behaviours in physical activity and breakfast intake. CONCLUSION: Nursing students share the patterns of their nonnursing peers in behavioural risk factors compromising their future health and health-promoting role. We need strategies to safeguard the professional nursing practice.