Cardiovascular Health Benefits of Exercise Training in Persons Living with Type 1 Diabetes: A Systematic Review and Meta-Analysis

Wu, Nana; Bredin, Shannon S. D.; Guan, Yanfei; Dickinson, Kyra; Kim, David D.; Chua, Zongyu; Kaufman, Kai L.; Warburton, Darren E. R.

doi:10.3390/jcm8020253

Cited by 78 publications

(61 citation statements)

References 74 publications

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“…control group) should be taken into consideration when designing studies, as they can greatly increase model accuracy. While it is true that even an improved set of covariates could lead to more accurate results, we showcase here that the framework we have now is able to demonstrate results that converge with current literature showing favorable impact of exercise upon blood glucose control metrics (38). We also note that one assumption of our model is that the intervention does not affect any of the covariates in the post-intervention period.…”

Section: Discussionsupporting

confidence: 78%

Bayesian Structural Time Series for Biomedical Sensor Data: A Flexible Modeling Framework for Evaluating Interventions

Liu

Spakowicz

Ash

et al. 2020

Preprint

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The development of mobile health technology has the potential to contribute greatly to personalized medicine. Wearable sensors can assist with determining the proper treatment plans for individuals, provide quantitative information to physicians, or give individuals an objective measurement of their health. However, though treatments and interventions have become more targeted and specific, measuring the causal impact of these actions require more careful considerations of complex covariate structures as well as temporal and spatial properties of the data. Thus, emerging data from sensors and wearables in the near future will make use of and require complex models. Here, we describe a general statistical framework for sensor and wearable data that applies a Bayesian structural time series model to analyze and understand various behavior and health data collected in different environments. We show the wide applicability of this modelling framework, and how it corrects for covariates and biases to provide accurate assessments of intervention. Furthermore, it allows for a time dependent confidence interval of impact through its use of Bayesian estimation. We give three main examples, physical sensor data, environmental air sensors and longitudinal behavioral data to show the effect of various interventions through parameter estimation and comparison in pre-and post-intervention periods. The Bayesian structural time series model shows robust performance in a wide variety of tasks, further supporting its applicability to current and future mobile health and sensor data types.

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Section: Discussionsupporting

confidence: 78%

Bayesian Structural Time Series for Biomedical Sensor Data: A Flexible Modeling Framework for Evaluating Interventions

Liu

Spakowicz

Ash

et al. 2020

Preprint

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“…Wu et al conducted a meta-analysis of 24 articles on the impact of aerobic physical activity on patients with type 1 diabetes. The results show that physical exercise improves physical fitness, muscle strength and lipid profile and reduces the risk of cardiovascular diseases [27]. Similar conclusions were reached by Chimen et al, who analysed 48 studies on the influence of aerobic physical activity on patients with type 1 diabetes.…”

Section: Physical Exercise Influence On the Organism Of Patients Withsupporting

confidence: 77%

“…Importantly for type 1 diabetics, regular training reduces the need for insulin after physical exercise and increases the sensitivity of cells to insulin, so that the daily dose of insulin may be reduced. This is an important factor in reducing the risk of cardiovascular diseases, cancer, and mortality [25][26][27][28][29][30][31].…”

Section: Physical Exercise Influence On the Organism Of Patients Withmentioning

confidence: 99%

Physioprophylaxis of type 1 diabetes in children using physical exercise

Zaworski

Ławnik

Kubińska

et al. 2019

hpc

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Authors' contribution Wkład autorów: A. Study design/planning zaplanowanie badań B. Data collection/entry zebranie danych C. Data analysis/statistics dane-analiza i statystyki D. Data interpretation interpretacja danych E. Preparation of manuscript przygotowanie artykułu F. Literature analysis/search wyszukiwanie i analiza literatury G. Funds collection zebranie funduszy Summary According to the Physiotherapist Act, physioprophylaxis is an important component of physiotherapy and of the physiotherapist's professional activities. Physioprophylaxis can be oriented towards practicing healthy lifestyle behaviours, minimizing disease risk factors, or attenuating the consequences of the disease, surgery, and/or disability. Type 1 diabetes is a chronic metabolic disease characterized by hyperglycaemia, i.e. high blood glucose levels. It is caused by the dysfunction or destruction of β cells of pancreatic islets of Langerhans, which are responsible for the production and secretion of insulin. Type 1 diabetes is most common in children and adolescents. According to the 2018 report, 6,400 children aged 0-14 years and about 180,000 people over 14 years old suffer from type 1 diabetes in Poland alone. Physical activity is an important stimulus for optimal physiological development of children and adolescents, and is an important factor in reducing the risk of cardiovascular diseases, cancer, and mortality. Physical exercise reduces the need for insulin and increases the sensitivity of cells to insulin, so that the daily dose of insulin can be reduced. The Polish Diabetes Association recommends that children with type 1 diabetes should exercise for more than one hour a day in order to reduce the risk of vascular complications associated with the disease. The aim of this paper is to examine the effects of type 1 diabetes physioprophylaxis in children in the form of physical exercise, based on previous literature. The majority of research indicates physical activity has a positive effect on physiological function in children with type 1 diabetes, specifically reducing the risk of hypoglycaemia, high blood glucose level, insulin demand, and premature death.

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“…Both of these aspects of diabetes management are still very challenging for individuals with type 1 diabetes, even with access to specialised diabetes care [2]. Being regularly active with the disease improves cardiometabolic health [3] and is associated with increased longevity [4].…”

Section: Introductionmentioning

confidence: 99%

The competitive athlete with type 1 diabetes

et al. 2020

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Regular exercise is important for health, fitness and longevity in people living with type 1 diabetes, and many individuals seek to train and compete while living with the condition. Muscle, liver and glycogen metabolism can be normal in athletes with diabetes with good overall glucose management, and exercise performance can be facilitated by modifications to insulin dose and nutrition. However, maintaining normal glucose levels during training, travel and competition can be a major challenge for athletes living with type 1 diabetes. Some athletes have low-to-moderate levels of carbohydrate intake during training and rest days but tend to benefit, from both a glucose and performance perspective, from high rates of carbohydrate feeding during longdistance events. This review highlights the unique metabolic responses to various types of exercise in athletes living with type 1 diabetes.

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Cardiovascular Health Benefits of Exercise Training in Persons Living with Type 1 Diabetes: A Systematic Review and Meta-Analysis

Cited by 78 publications

References 74 publications

Bayesian Structural Time Series for Biomedical Sensor Data: A Flexible Modeling Framework for Evaluating Interventions

Bayesian Structural Time Series for Biomedical Sensor Data: A Flexible Modeling Framework for Evaluating Interventions

Physioprophylaxis of type 1 diabetes in children using physical exercise

The competitive athlete with type 1 diabetes

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