Technological innovation of Continuous Glucose Monitoring (CGM) as a tool for commercial aviation pilots with insulin-treated diabetes and stakeholders/regulators: A new chance to improve the directives?

Strollo, Felice; Furia, Antonello; Verde, Paola Azzurra La; Bellia, Alfonso; Grussu, M.; Mambro, Andrea; Petrelli, M.D.; Gentile, Sandro

doi:10.1016/j.diabres.2020.108638

Cited by 9 publications

(3 citation statements)

References 46 publications

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“…From this perspective, attention would be focused on recognizing the recurrence of glycemic fluctuations that differ in value and duration, and not only the consideration of glycemia in an isolated manner. Currently, simple technologies such as the glucometer and more advanced ones, such as an insulin pump and continuous blood glucose monitoring devices (Strollo et al, 2021), can assist in this assessment, in addition to the 1,5 anhydroglucitol marker (1,5-AG) (Kim & Park, 2013).…”

Section: Discussionmentioning

confidence: 99%

Risk for Imbalanced Blood Glucose Pattern: Construct Analysis and Nursing Diagnosis Proposal

et al. 2022

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To identify a clinical judgment of susceptibility referring to the development of glycemic fluctuations in adults with Diabetes Mellitus undergoing treatment. Theoretical study with construct analysis. The exploration of the phenomena of glycemic variations provided clues for the description of the blood glucose pattern construct and the proposal of a new risk Nursing diagnosis as a judgment of susceptibility to the balance of this pattern. The risk factors for “Risk for Imbalanced Blood Glucose Pattern” are cognitive dysfunction; excessive alcohol consumption; excessive daily exercise; inadequate follow through with treatment regimen; increased frequency of self-monitoring of blood glucose; inadequate knowledge of disease process; inadequate management of amount of food; inadequate regularity of meal consumption; obesity; overweight; smoking; underweight. The elaboration of “Risk for Imbalanced Blood Glucose Pattern” Nursing diagnosis contributes to the advancement in the Nursing classifications and to the elaboration of planning actions and specific interventions.

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Section: Discussionmentioning

confidence: 99%

Risk for Imbalanced Blood Glucose Pattern: Construct Analysis and Nursing Diagnosis Proposal

et al. 2022

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“…Therefore, we prioritized the participants’ stress from blood sampling with the understanding that considerable errors would be observed between glucose levels measured using LibrePro and plasma glucose levels measured through blood sampling. In addition, to substitute intermittent blood sampling at regular time intervals in OGTT, we employed FGM in this study rather than continuous glucose monitoring (CGM), which is more accurate than FGM 19 , 41 and has been internationally standardized for evaluating glucose tolerance 42 . The increased error associated with FGM would have an equal effect on all sound conditions, leading to difficulties in the detection of any differences between sound conditions harder to detect than when plasma glucose or CGM is used.…”

Section: Discussionmentioning

confidence: 99%

Positive effect of inaudible high-frequency components of sounds on glucose tolerance: a quasi-experimental crossover study

Kawai

Honda

Nishina

et al. 2022

Sci Rep

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Although stress significantly impacts on various metabolic syndromes, including diabetes mellitus, most stress management techniques are based on psychological and subjective approaches. This study examined how the presence or absence of the inaudible high-frequency component (HFC) of sounds, which activates deep-brain structures, affects glucose tolerance in healthy participants using the oral glucose tolerance test (OGTT). Sounds containing HFC suppressed the increase in glucose levels measured by incremental area under the curve in the OGTT compared with the otherwise same sounds without HFC. The suppression effect of HFC was more prominent in the older age group and the group with high HbA1c. This suggests that sounds with HFC are more effective in improving glucose tolerance in individuals at a higher risk of glucose intolerance.

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“…Many tools have been developed to monitor the regularity of the glucose level, such as continuous glucose monitoring (CGM) devices to minimize the problems experienced by diabetes patients and to improve their life quality (Doğan & Kılıç, 2021;Kap, Kilic, Hardy, & Horzum, 2021;Mercan, Kılıç, & Şen, 2021). CGM devices measure blood glucose periodically, like every 5 minutes, which allows controlling the glucose irregularity (Strollo et al, 2021). However, knowing the current glucose value is not enough to maintain a life quality.…”

Section: Introductionmentioning

confidence: 99%

Smartphone-based Multi-parametric Glucose Prediction using Recurrent Neural Networks

PALAZ¹,

Doğan²,

Kılıç³

2022

European Journal of Science and Technology

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Diabetes Mellitus causes many deadly diseases, including pancreatic cancer as irregularity of glucose level triggers dysfunctions like unchecked cell growth. The critical stages in glucose irregularity are categorized as hyperglycemia (high blood glucose) and hypoglycemia (low blood glucose) which needs to be detected in advance for the quality of human life. In that sense, many tools have been developed based on artificial intelligence (AI) systems which mostly consider the glucose measurement as a prediction parameter. However, in this study, we propose to employ multi-parameter in glucose prediction based on a Recurrent Neural Network (RNN), a subset of AI, to enhance predictability. The proposed system utilizes a Long-Short Term Memory (LSTM) based RNN to handle complex memory operations caused by multi-parametric prediction. Training and validation scores on the OhioT1DM dataset show the advantage of our proposed system over the baseline systems for predicting glucose levels with a significantly reduced error. The system is later integrated with our custom-designed Android application, BffDiabetes PRO, capable of reading the glucose levels from the sensors via Bluetooth. The BffDiabetes PRO transfers the current glucose level, acceleration, and baseline skin temperature to the server via a cloud system to predict the next level. It receives the prediction result to evaluate whether the glucose level tends to reach the critical stages. In case of this tendency is detected, the BffDiabetes PRO alerts the user for necessary precautions.

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Technological innovation of Continuous Glucose Monitoring (CGM) as a tool for commercial aviation pilots with insulin-treated diabetes and stakeholders/regulators: A new chance to improve the directives?

Cited by 9 publications

References 46 publications

Risk for Imbalanced Blood Glucose Pattern: Construct Analysis and Nursing Diagnosis Proposal

Risk for Imbalanced Blood Glucose Pattern: Construct Analysis and Nursing Diagnosis Proposal

Positive effect of inaudible high-frequency components of sounds on glucose tolerance: a quasi-experimental crossover study

Smartphone-based Multi-parametric Glucose Prediction using Recurrent Neural Networks

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