scholarly journals The role of a school nurse in the care of a child with diabetes mellitus type 1 - the perspectives of patients and their parents: Literature review

2018 ◽  
Vol 57 (3) ◽  
pp. 166-174
Author(s):  
Anna Stefanowicz ◽  
Joanna Stefanowicz
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Literature Review ◽  
Type 1 Diabetes Mellitus ◽  
Metabolic Control ◽  
School Nurse ◽  
Parents And Children

Abstract Introduction The aim of this literature review was to explore the views of parents and children with type 1 diabetes mellitus regarding having a school nurse. Methods Six databases were selected for the analysis. The research strategy was based on the PICO model. The research participants were children with type 1 diabetes mellitus and/or their parents. Results The present review of research papers includes 12 publications. The majority of works deal with the perspectives of children with type 1 diabetes and their parents on various aspects related to the role of a school nurse in the care of a child with type 1 diabetes: the presence of a school nurse; the role of a school nurse in the prevention and treatment of hypoglycaemia, in performing the measurements of blood glucose, and in insulin therapy; the role of a nurse in improving metabolic control of children with type 1 diabetes; a nurse as an educator for children with type 1 diabetes, classmates, teachers, teacher’s assistants, principals, administrators, cafeteria workers, coaches, gym teachers, bus drivers, and school office staff; a nurse as an organiser of the care for children with type 1 diabetes. Conclusions According to parents and children with type 1 diabetes mellitus, various forms of school nurse support (i.e., checking blood glucose, giving insulin, giving glucagon, treating low and high blood glucose levels, carbohydrate counting) are consistently effective and should have an impact on the condition, improvement of metabolic control, school activity and safety at school.

The role of free-play physical activity in healthy blood glucose maintenance in children with type 1 diabetes mellitus

2008 ◽  
Vol 14 (1) ◽  
pp. 48-52
Author(s):  
Sonia Marrone ◽  
Jessica White Plume ◽  
Patrick Kerr ◽  
Anna Pignol ◽  
Nancy Vogeltanz-Holm ◽  
...  
Keyword(s):  
Physical Activity ◽  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Type 1 Diabetes Mellitus ◽  
Free Play

scholarly journals Evaluation of neurophysiological parameters and good metabolic control in patients with type 1 diabetes mellitus

2012 ◽  
Vol 140 (5-6) ◽  
pp. 285-289
Author(s):  
Dragana Matanovic ◽  
Srdjan Popovic ◽  
Biljana Parapid ◽  
Emilija Dubljanin ◽  
Dejana Stanisavljevic ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Insulin Therapy ◽  
Type 1 Diabetes Mellitus ◽  
Metabolic Control ◽  
Intensive Insulin Therapy ◽  
Sural Nerve ◽  
F Wave

Introduction. Numerous authors have indicated the beneficial effect of glycoregulation on micro- and macro-angiopathic complications. Objective. The aim of the study was to examine whether intensive treatment with maintaining blood glucose concentrations close to normal range could improve electrophysiological parameters. Methods. The study involved 81 patients with type 1 diabetes mellitus type 1 randomly assigned to intensive insulin therapy. The patients were followed for a period of 3 months by metabolic and electrophysiological control. The metabolic control included daily measurement of concentration of blood glucose and HbA1c and lipid status, while the neurophysiological control included nerve conduction velocity (NCV) of median, peroneal, tibial and sural nerve and latency of F wave. Results. In the beginning of our study blood glucose was 9.10?3.69 mmol/l and HbA1c 8.12?1.20%. After 3 months of administered intensive insulin therapy, blood glucose was 7.88?2.79 mmol/l and HbA1c 6.63?1.33. After 3 months NCV improved in the tibial, median and sural nerve (p<0.05) and latency of F wave. Conclusion. We found a significant association between the metabolic control and NCV findings which suggests that good metabolic control influences the improvement of neurophysiological parameters in patients with type 1 diabetes mellitus.

scholarly journals A Comparison of Feature Selection and Forecasting Machine Learning Algorithms for Predicting Glycaemia in Type 1 Diabetes Mellitus

2021 ◽  
Vol 11 (4) ◽  
pp. 1742
Author(s):  
Ignacio Rodríguez-Rodríguez ◽  
José-Víctor Rodríguez ◽  
Wai Lok Woo ◽  
Bo Wei ◽  
Domingo-Javier Pardo-Quiles
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 1 Diabetes ◽  
Feature Selection ◽  
Blood Glucose ◽  
Type 1 Diabetes Mellitus ◽  
Support Vector ◽  
Chronic Hyperglycemia ◽  
Predictive Algorithms

Type 1 diabetes mellitus (DM1) is a metabolic disease derived from falls in pancreatic insulin production resulting in chronic hyperglycemia. DM1 subjects usually have to undertake a number of assessments of blood glucose levels every day, employing capillary glucometers for the monitoring of blood glucose dynamics. In recent years, advances in technology have allowed for the creation of revolutionary biosensors and continuous glucose monitoring (CGM) techniques. This has enabled the monitoring of a subject’s blood glucose level in real time. On the other hand, few attempts have been made to apply machine learning techniques to predicting glycaemia levels, but dealing with a database containing such a high level of variables is problematic. In this sense, to the best of the authors’ knowledge, the issues of proper feature selection (FS)—the stage before applying predictive algorithms—have not been subject to in-depth discussion and comparison in past research when it comes to forecasting glycaemia. Therefore, in order to assess how a proper FS stage could improve the accuracy of the glycaemia forecasted, this work has developed six FS techniques alongside four predictive algorithms, applying them to a full dataset of biomedical features related to glycaemia. These were harvested through a wide-ranging passive monitoring process involving 25 patients with DM1 in practical real-life scenarios. From the obtained results, we affirm that Random Forest (RF) as both predictive algorithm and FS strategy offers the best average performance (Root Median Square Error, RMSE = 18.54 mg/dL) throughout the 12 considered predictive horizons (up to 60 min in steps of 5 min), showing Support Vector Machines (SVM) to have the best accuracy as a forecasting algorithm when considering, in turn, the average of the six FS techniques applied (RMSE = 20.58 mg/dL).

scholarly journals MECHANISMS IN ENDOCRINOLOGY: Mechanisms and evaluation of bone fragility in type 1 diabetes mellitus

2016 ◽  
Vol 174 (4) ◽  
pp. R127-R138 ◽  
Author(s):  
F S Hough ◽  
D D Pierroz ◽  
C Cooper ◽  
S L Ferrari ◽  
_ _
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Type 1 Diabetes Mellitus ◽  
Bone Fragility ◽  
Mineral Density ◽  
Good Glycaemic Control ◽  
Marrow Adiposity ◽  
Igf1 Deficiency

Subjects with type 1 diabetes mellitus (T1DM) have decreased bone mineral density and an up to sixfold increase in fracture risk. Yet bone fragility is not commonly regarded as another unique complication of diabetes. Both animals with experimentally induced insulin deficiency syndromes and patients with T1DM have impaired osteoblastic bone formation, with or without increased bone resorption. Insulin/IGF1 deficiency appears to be a major pathogenetic mechanism involved, along with glucose toxicity, marrow adiposity, inflammation, adipokine and other metabolic alterations that may all play a role on altering bone turnover. In turn, increasing physical activity in children with diabetes as well as good glycaemic control appears to provide some improvement of bone parameters, although robust clinical studies are still lacking. In this context, the role of osteoporosis drugs remains unknown.

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