Related factors associated with fear of hypoglycemia in parents of children and adolescents with type 1 diabetes - A systematic review

Lu, Zengjun; Xu, Huiwen; Liu, Lin; Bi, Yang; Li, Xiangning; Kan, Yinshi; Liu, Hongyuan; Li, Shuang; Zou, Yan; Yuan, Yuan; Gong, Weijuan

doi:10.1016/j.pedn.2022.05.022

Cited by 12 publications

(6 citation statements)

References 69 publications

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“…However, high-intensity exercise increases the risk of rapid deviations of glucose levels [26], including hypoglycemia [27]. Frequent and severe hypoglycemia is associated with early mortality [28], increased cardiovascular risk [29], reduced quality of life and fear of hypoglycemia [30,31], and reduced exercise efficiency, which discourages people with diabetes from being physically active [32,33]. The risk of hypoglycemia during and after exercise can be lowered if specific insulin-dose adjustments are made or by consuming additional carbohydrates [34].…”

Section: Discussionmentioning

confidence: 99%

Comparative Analysis of Predictive Interstitial Glucose Level Classification Models

Kistkins,

Mihailovs,

Lobanovs

et al. 2023

Sensors

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Background: New methods of continuous glucose monitoring (CGM) provide real-time alerts for hypoglycemia, hyperglycemia, and rapid fluctuations of glucose levels, thereby improving glycemic control, which is especially crucial during meals and physical activity. However, complex CGM systems pose challenges for individuals with diabetes and healthcare professionals, particularly when interpreting rapid glucose level changes, dealing with sensor delays (approximately a 10 min difference between interstitial and plasma glucose readings), and addressing potential malfunctions. The development of advanced predictive glucose level classification models becomes imperative for optimizing insulin dosing and managing daily activities. Methods: The aim of this study was to investigate the efficacy of three different predictive models for the glucose level classification: (1) an autoregressive integrated moving average model (ARIMA), (2) logistic regression, and (3) long short-term memory networks (LSTM). The performance of these models was evaluated in predicting hypoglycemia (<70 mg/dL), euglycemia (70–180 mg/dL), and hyperglycemia (>180 mg/dL) classes 15 min and 1 h ahead. More specifically, the confusion matrices were obtained and metrics such as precision, recall, and accuracy were computed for each model at each predictive horizon. Results: As expected, ARIMA underperformed the other models in predicting hyper- and hypoglycemia classes for both the 15 min and 1 h horizons. For the 15 min forecast horizon, the performance of logistic regression was the highest of all the models for all glycemia classes, with recall rates of 96% for hyper, 91% for norm, and 98% for hypoglycemia. For the 1 h forecast horizon, the LSTM model turned out to be the best for hyper- and hypoglycemia classes, achieving recall values of 85% and 87% respectively. Conclusions: Our findings suggest that different models may have varying strengths and weaknesses in predicting glucose level classes, and the choice of model should be carefully considered based on the specific requirements and context of the clinical application. The logistic regression model proved to be more accurate for the next 15 min, particularly in predicting hypoglycemia. However, the LSTM model outperformed logistic regression in predicting glucose level class for the next hour. Future research could explore hybrid models or ensemble approaches that combine the strengths of multiple models to further enhance the accuracy and reliability of glucose predictions.

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Section: Discussionmentioning

confidence: 99%

Comparative Analysis of Predictive Interstitial Glucose Level Classification Models

Kistkins,

Mihailovs,

Lobanovs

et al. 2023

Sensors

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show abstract

“…The studies were classified as high, medium, or low quality. The quality of the literature was divided as follows: high (> 5 times of "yes" score), moderate (3-5 times of "yes" score), or low (0-2 times of "yes" score) methodological quality (Zhang et al, 2022).…”

Section: Quality Assessment Criteriamentioning

confidence: 99%

The efficacy of interventions provided by nurses to improve glycemic control of children with type 1 diabetes: A systematic review

Bakir

Sezer

2022

Specialist Pediatric Nursing

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Purpose Nurses have a very important role in diabetes education of children and adolescents, however, no systematic review has been performed that evaluates the effectiveness of the independent interventions of the nurses. This systematic review aims to identify and assess randomized controlled trials (RCT) that included interventions implemented by nurses to improve glycemic control for children and adolescents with type 1 diabetes. Eligibility Criteria PubMed, the Cochrane Central Register of Controlled Trials, Scopus, Web of Science, Science Direct databases were systematically searched up to 2022. Interventions provided by nurses for any child ≤18 years diagnosed with type 1 diabetes mellitus were included. Sample Seven RCT articles met our review criteria. Results All included studies were published between 2005 and 2021. Studies varied in terms of intervention type; telephone case management, coping skills training, motivational interview as well as home visit education programs. Interventions were found to significantly decrease the HbA1c level in three of seven studies. Conclusions Our systematic review suggests that nurses can improve the glycemic control of children with type 1 diabetes by conducting care interventions. According to current evidence, this study suggests that telephone contact by nurses and motivational interviewing can be beneficial in improving glycemic control in this population. Implications Nurses may encourage children and adolescents with diabetes to engage in self‐management of their glucose levels. There is a need for more randomized controlled studies assessing the efficacy of nursing interventions for children with type 1 diabetes.

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“…The handling of hypoglycemic remains a challenge with adolescents due to their varied physical activities and requirements. Only less than 70% the youth affected with T1DM achieve glycemic targets despite advances in insulin therapies, educational awareness, insulin pumps, and ‘continuous glucose monitoring systems 11 . The daily tasks of dietary intake control, strict medical control and continuous monitoring makes it difficult for young youth to control their T1DM situations.…”

Section: Introductionmentioning

confidence: 99%

Efficacy and safety of closed-loop control system for type one diabetes in adolescents a meta analysis

Jabari

2023

Sci Rep

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This meta-analysis compares the efficacy and safety of Closed-Loop Control (CLC) to Sensor-Augmented Insulin Pump (SAP) for adolescent patients with Type 1 Diabetes Mellitus (T1DM). Eleven randomized-controlled trials were included with a total of 570 patients, from a total of 869 articles found adhering to PRISMA guidelines. The efficacy of the therapies were evaluated from the day, night and during physical activities monitoring of the of the mean blood glucose (BG), Time In Range (TIR), and Standard Deviation (SD) of the glucose variability. The safety measure of the therapies, was assessed from the day and night recording of the hypoglycemic and hyperglycemic events occurred. Pooled results of comparison of mean BG values for day, night and physical activities, − 4.33 [− 6.70, − 1.96] (P = 0.0003), − 16.61 [− 31.68, − 1.54] (P = 0.03) and − 8.27 [− 19.52, 2.99] (P = 0.15). The monitoring for day, night and physical activities for TIR − 13.18 [− 19.18, − 7.17] (P < 0.0001), − 15.36 [− 26.81, − 3.92] (P = 0.009) and − 7.39 [− 17.65, 2.87] (P = 0.16). The day and night results of SD of glucose variability was − 0.40 [− 0.79, − 0.00] (P = 0.05) and − 0.86 [− 2.67, 0.95] (P = 0.35). These values shows the superiority of CLC system in terms of efficacy. The safety evaluation, of the day, night and physical activities observations of average blood glucose goal hypoglycemic events − 0.54 [− 1.86, 0.79] (P = 0.43), 0.04 [− 0.20, 0.27] (P = 0.77) and 0.00 [− 0.25, 0.25] (P = 1.00) and hyperglycemic events − 0.04 [− 0.20, 0.27] (P = 0.77), − 7.11 [− 12.77, − 1.45] (P = 0.01) and − 0.00 [− 0.10, 0.10] (P = 0.97), highlights the commendable safety factor of CLC. The CLC systems can be considered as an ideal preference in the management of adolescents with type 1 diabetes to be used during a 24 h basis.

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Related factors associated with fear of hypoglycemia in parents of children and adolescents with type 1 diabetes - A systematic review

Cited by 12 publications

References 69 publications

Comparative Analysis of Predictive Interstitial Glucose Level Classification Models

Comparative Analysis of Predictive Interstitial Glucose Level Classification Models

The efficacy of interventions provided by nurses to improve glycemic control of children with type 1 diabetes: A systematic review

Efficacy and safety of closed-loop control system for type one diabetes in adolescents a meta analysis

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