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Impact of free-living pattern of sedentary behaviour on intra-day glucose regulation in type 2 diabetes.

ABSTRACT: PURPOSE:To investigate how the pattern of sedentary behaviour affects intra-day glucose regulation in type 2 diabetes. METHODS:This intensive longitudinal study was conducted in 37 participants with type 2 diabetes (age, 62.8?±?10.5 years). Glucose and sedentary behaviour/physical activity were assessed with a continuous glucose monitoring (Abbott FreeStyle Libre) and an activity monitor (activPAL3) for 14 days. Multiple regression models with generalised estimating equations (GEEs) approach were used to assess the associations of sedentary time and breaks in sedentary time with pre-breakfast glucose, pre-lunch glucose, pre-dinner glucose, post-breakfast glucose, post-lunch glucose, post-dinner glucose, bedtime glucose, the dawn phenomenon, time in target glucose range (TIR, glucose 3.9-10 mmol/L) and time above target glucose range (TAR, glucose >?10 mmol/L). RESULTS:Sedentary time was associated with higher pre-breakfast glucose (p?=?0.001), pre-dinner glucose (p?

SUBMITTER: Paing AC

PROVIDER: S-EPMC6969863 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Impact of free-living pattern of sedentary behaviour on intra-day glucose regulation in type 2 diabetes.

Paing Aye C AC McMillan Kathryn A KA Kirk Alison F AF Collier Andrew A Hewitt Allan A Chastin Sebastien F M SFM

European journal of applied physiology 20191108 1

<h4>Purpose</h4>To investigate how the pattern of sedentary behaviour affects intra-day glucose regulation in type 2 diabetes.<h4>Methods</h4>This intensive longitudinal study was conducted in 37 participants with type 2 diabetes (age, 62.8 ± 10.5 years). Glucose and sedentary behaviour/physical activity were assessed with a continuous glucose monitoring (Abbott FreeStyle Libre) and an activity monitor (activPAL3) for 14 days. Multiple regression models with generalised estimating equations (GEE ...[more]

PMID: 31705275

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Project description:ObjectiveWith sensors, we are increasingly able to assess sitting behaviour during the day. However, there is no consensus among researchers on the best outcome measures for representing the accumulation of sedentary time during the day.MethodsWe analysed the pattern measures of sedentary behaviour. Articles reporting patterns measures in adults, in which behaviour data was collected with a sensor were included. We discuss the strengths and weaknesses of the pattern measures of sedentary behaviour and provide recommendations for choosing objective measures of sedentary behaviour.ResultsMost studies report the number of sitting bouts during the day. Others focus on the number of breaks and/or periods of physical activity. Simple measures of sedentary behaviour were most popular. More complex pattern measures, such as the Gini index or the half-life bout duration, that capture the distribution of lengths of sitting periods in a single number, were reported sparsely. The sedentary patterns that were reported in the various studies were difficult to compare, due to the differences among measurement devices, data analysis protocols and a lack of basic outcome parameters such as total wear-time and total sedentary time.ConclusionsObjective sedentary measures can be grouped into simple and complex measures of sedentary time accumulation during the day. These measures serve different goals. The answer to the question as to which measures are most suitable to report, is strongly dependent on the research question. We have shown that the reported measures were dependent on (a) the sensing method, (b) the classification method, (c) the experimental and data cleaning protocol and (d) the applied definitions of bouts and breaks. We recommend that studies should always report total wear-time, total sedentary time, number of bouts and at least one measure describing the diversity of bout lengths in the sedentary behaviour such as the half-life bout duration. Additionally, we recommend reporting the measurement conditions and data processing steps.

Project description:BACKGROUND AND OBJECTIVE:Current prototypes of closed-loop systems for glucose control in type 1 diabetes mellitus, also referred to as artificial pancreas systems, require a pre-meal insulin bolus to compensate for delays in subcutaneous insulin absorption in order to avoid initial post-prandial hyperglycemia. Computing such a meal bolus is a challenging task due to the high intra-subject variability of insulin requirements. Most closed-loop systems compute this pre-meal insulin dose by a standard bolus calculation, as is commonly found in insulin pumps. However, the performance of these calculators is limited due to a lack of adaptiveness in front of dynamic changes in insulin requirements. Despite some initial attempts to include adaptation within these calculators, challenges remain. METHODS:In this paper we present a new technique to automatically adapt the meal-priming bolus within an artificial pancreas. The technique consists of using a novel adaptive bolus calculator based on Case-Based Reasoning and Run-To-Run control, within a closed-loop controller. Coordination between the adaptive bolus calculator and the controller was required to achieve the desired performance. For testing purposes, the clinically validated Imperial College Artificial Pancreas controller was employed. The proposed system was evaluated against itself but without bolus adaptation. The UVa-Padova T1DM v3.2 system was used to carry out a three-month in silico study on 11 adult and 11 adolescent virtual subjects taking into account inter-and intra-subject variability of insulin requirements and uncertainty on carbohydrate intake. RESULTS:Overall, the closed-loop controller enhanced by an adaptive bolus calculator improves glycemic control when compared to its non-adaptive counterpart. In particular, the following statistically significant improvements were found (non-adaptive vs. adaptive). Adults: mean glucose 142.2?±?9.4vs. 131.8?±?4.2mg/dl; percentage time in target [70, 180]mg/dl, 82.0?±?7.0vs. 89.5?±?4.2; percentage time above target 17.7?±?7.0vs. 10.2?±?4.1. Adolescents: mean glucose 158.2?±?21.4vs. 140.5?±?13.0mg/dl; percentage time in target, 65.9?±?12.9vs. 77.5?±?12.2; percentage time above target, 31.7?±?13.1vs. 19.8?±?10.2. Note that no increase in percentage time in hypoglycemia was observed. CONCLUSION:Using an adaptive meal bolus calculator within a closed-loop control system has the potential to improve glycemic control in type 1 diabetes when compared to its non-adaptive counterpart.

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Impact of free-living pattern of sedentary behaviour on intra-day glucose regulation in type 2 diabetes.

Publications

Impact of free-living pattern of sedentary behaviour on intra-day glucose regulation in type 2 diabetes.

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