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Ming W, Guo X, Zhang G, Liu Y, Wang Y, Zhang H, Liang H, Yang Y. Recent advances in the precision control strategy of artificial pancreas. Med Biol Eng Comput 2024; 62:1615-1638. [PMID: 38418768 DOI: 10.1007/s11517-024-03042-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 02/03/2024] [Indexed: 03/02/2024]
Abstract
The scientific diagnosis and treatment of patients with diabetes require frequent blood glucose testing and insulin delivery to normoglycemia. Therefore, an artificial pancreas with a continuous blood glucose (BG) monitoring function is an urgent research target in the medical industry. The problem of closed-loop algorithmic control of the BG with a time delay is a key and difficult issue that needs to be overcome in the development of an artificial pancreas. Firstly, the composition, structure, and control characteristics of the artificial pancreas are introduced. Subsequently, the research progress of artificial pancreas control algorithms is reviewed, and the characteristics, advantages, and disadvantages of proportional-integral-differential control, model predictive control, and artificial intelligence control are compared and analyzed to determine whether they are suitable for the practical application of the artificial pancreas. Additionally, key advancements in areas such as blood glucose data monitoring, adaptive models, wearable devices, and fully automated artificial pancreas systems are also reviewed. Finally, this review highlights that meal prediction, control safety, integration, streamlining the optimization of control algorithms, constant temperature preservation of insulin, and dual-hormone artificial pancreas are issues that require further attention in the future.
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Affiliation(s)
- Wuyi Ming
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, 450002, Zhengzhou, China
| | - Xudong Guo
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, 450002, Zhengzhou, China
| | - Guojun Zhang
- Guangdong HUST Industrial Technology Research Institute, 523808, Dongguan, China
| | - Yinxia Liu
- Prenatal Diagnosis Center of Dongguan Kanghua Hospital, 523808, Dongguan, China
| | - Yongxin Wang
- Zhengzhou Phray Technology Co., Ltd, 450019, Zhengzhou, China
| | - Hongmei Zhang
- Zhengzhou Phray Technology Co., Ltd, 450019, Zhengzhou, China
| | - Haofang Liang
- Zhengzhou Phray Technology Co., Ltd, 450019, Zhengzhou, China
| | - Yuan Yang
- Laboratory of Regenerative Medicine in Sports Science, School of Sports Science, South China Normal University, 510631, Guangzhou, China.
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Smee SN, Johnson R, Rush A, Davey RJ. A very low carbohydrate diet for minimising blood glucose excursions during ultra-endurance open-water swimming in type 1 diabetes: a case report. Appl Physiol Nutr Metab 2024; 49:554-559. [PMID: 38109711 DOI: 10.1139/apnm-2023-0266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Carbohydrate-restricted diets are used by people with type 1 diabetes (T1D) to help manage their condition. However, the impact of this strategy on blood glucose responses to exercise is unknown. This study describes the nutritional strategies of an athlete with T1D, who follows a very low carbohydrate diet to manage her condition during an ultra-endurance open-water swimming event. The athlete completed the 19.7 km distance in 6 h 43 min. She experienced minimal disruptions to glycaemia, reduced need for supplemental carbohydrate, and no episodes of symptomatic hypoglycaemia. This case report will hopefully encourage further experimental studies that inform and expand current clinical practice guidelines.
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Affiliation(s)
- Shania N Smee
- Curtin School of Allied Health, Curtin University, Whadjuk Noongar Country, Perth, Western Australia, Australia
- Rio Tinto Children's Diabetes Centre, Telethon Kids Institute, Whadjuk Noongar Country, Perth, Western Australia, Australia
| | - Rebecca Johnson
- Type 1 Diabetes Family Centre, Whadjuk Noongar Country, Perth, Western Australia, Australia
| | - Amy Rush
- Type 1 Diabetes Family Centre, Whadjuk Noongar Country, Perth, Western Australia, Australia
| | - Raymond J Davey
- Curtin School of Allied Health, Curtin University, Whadjuk Noongar Country, Perth, Western Australia, Australia
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Riddell MC, Gal RL, Bergford S, Patton SR, Clements MA, Calhoun P, Beaulieu LC, Sherr JL. The Acute Effects of Real-World Physical Activity on Glycemia in Adolescents With Type 1 Diabetes: The Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study. Diabetes Care 2024; 47:132-139. [PMID: 37922335 DOI: 10.2337/dc23-1548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/16/2023] [Indexed: 11/05/2023]
Abstract
OBJECTIVE Data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study were evaluated to understand glucose changes during activity and identify factors that may influence changes. RESEARCH DESIGN AND METHODS In this real-world observational study, adolescents with type 1 diabetes self-reported physical activity, food intake, and insulin dosing (multiple-daily injection users) using a smartphone application. Heart rate and continuous glucose monitoring data were collected, as well as pump data downloads. RESULTS Two hundred fifty-one adolescents (age 14 ± 2 years [mean ± SD]; HbA1c 7.1 ± 1.3% [54 ± 14.2 mmol/mol]; 42% female) logged 3,738 activities over ∼10 days of observation. Preactivity glucose was 163 ± 66 mg/dL (9.1 ± 3.7 mmol/L), dropping to 148 ± 66 mg/dL (8.2 ± 3.7 mmol/L) by end of activity; median duration of activity was 40 min (20, 75 [interquartile range]) with a mean and peak heart rate of 109 ± 16 bpm and 130 ± 21 bpm. Drops in glucose were greater in those with lower baseline HbA1c levels (P = 0.002), shorter disease duration (P = 0.02), less hypoglycemia fear (P = 0.04), and a lower BMI (P = 0.05). Event-level predictors of greater drops in glucose included self-classified "noncompetitive" activities, insulin on board >0.05 units/kg body mass, glucose already dropping prior to the activity, preactivity glucose >150 mg/dL (>8.3 mmol/L) and time 70-180 mg/dL >70% in the 24 h before the activity (all P < 0.001). CONCLUSIONS Participant-level and activity event-level factors can help predict the magnitude of drop in glucose during real-world physical activity in youth with type 1 diabetes. A better appreciation of these factors may improve decision support tools and self-management strategies to reduce activity-induced dysglycemia in active adolescents living with the disease.
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Affiliation(s)
- Michael C Riddell
- School of Kinesiology and Health Science, Muscle Health Research Centre, York University, Toronto, Canada
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Katz A, Shulkin A, Talbo MK, Housni A, Yardley J, Brazeau AS, Rabasa-Lhoret R. Hyperglycemia-related anxiety during competition in an elite athlete with type 1 diabetes: A case report. DIABETES & METABOLISM 2023; 49:101476. [PMID: 37689238 DOI: 10.1016/j.diabet.2023.101476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 08/31/2023] [Accepted: 09/04/2023] [Indexed: 09/11/2023]
Abstract
AIM Managing blood glucose (BG) levels during intense physical activity is challenging for elite athletes with type 1 diabetes (T1D), as it can lead to unpredictable hyper- or hypoglycemia, which can affect performance. This case study presents an 18-year-old male hockey goalie with hyperglycemia-related anxiety during competition and its impact on his T1D management. METHODS Mixed-methods approach, incorporating qualitative data from an unstructured interview and responses from the Hyperglycemia Avoidance Scale along with quantitative data retrieved from Diasend and laboratory results. RESULTS The athlete experiences physical and cognitive symptoms during hyperglycemia, affecting his performance. Hyperglycemia-related anxiety influences insulin dosage adjustments and eating habits on game days. Glycemic variability analysis reveals lower BG levels during game time. CONCLUSION Hyperglycemia-related anxiety leads to modified therapeutic and lifestyle regimens on competition day. Tailored treatment programs are needed for elite athletes with T1D and hyperglycemia-related anxiety.
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Affiliation(s)
- Alexandra Katz
- Montreal Clinical Research Institute, Montreal, Quebec, Canada; Faculté de Médecine, Université de Montréal, Montreal, Quebec, Canada; School of Human Nutrition, McGill University, Montreal, Quebec, Canada.
| | - Aidan Shulkin
- Faculté de Médecine, Université de Montréal, Montreal, Quebec, Canada
| | - Meryem K Talbo
- School of Human Nutrition, McGill University, Montreal, Quebec, Canada
| | - Asmaa Housni
- School of Human Nutrition, McGill University, Montreal, Quebec, Canada
| | - Jane Yardley
- Augustana Faculty, University of Alberta, Camrose, Alberta, Canada; Alberta Diabetes Institute, Edmonton, Alberta, Canada; Women and Children's Health Research Institute, Edmonton, Alberta, Canada; Faculty of Kinesiology, Sport and Recreation, University of Alberta, Edmonton, Alberta, Canada
| | | | - Rémi Rabasa-Lhoret
- Montreal Clinical Research Institute, Montreal, Quebec, Canada; Service d'Endocrinologie du Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, Quebec, Canada
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Multi-Task Classification of Physical Activity and Acute Psychological Stress for Advanced Diabetes Treatment. SIGNALS 2023. [DOI: 10.3390/signals4010009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023] Open
Abstract
Wearable sensor data can be integrated and interpreted to improve the treatment of chronic conditions, such as diabetes, by enabling adjustments in treatment decisions based on physical activity and psychological stress assessments. The challenges in using biological analytes to frequently detect physical activity (PA) and acute psychological stress (APS) in daily life necessitate the use of data from noninvasive sensors in wearable devices, such as wristbands. We developed a recurrent multi-task deep neural network (NN) with long-short-term-memory architecture to integrate data from multiple sensors (blood volume pulse, skin temperature, galvanic skin response, three-axis accelerometers) and simultaneously detect and classify the type of PA, namely, sedentary state, treadmill run, stationary bike, and APS, such as non-stress, emotional anxiety stress, mental stress, and estimate the energy expenditure (EE). The objective was to assess the feasibility of using the multi-task recurrent NN (RNN) rather than independent RNNs for detection and classification of AP and APS. The multi-task RNN achieves comparable performance to independent RNNs, with the multi-task RNN having F1 scores of 98.00% for PA and 98.97% for APS, and a root mean square error (RMSE) of 0.728 calhr.kg for EE estimation for testing data. The independent RNNs have F1 scores of 99.64% for PA and 98.83% for APS, and an RMSE of 0.666 calhr.kg for EE estimation. The results indicate that a multi-task RNN can effectively interpret the signals from wearable sensors. Additionally, we developed individual and multi-task extreme gradient boosting (XGBoost) for separate and simultaneous classification of PA types and APS types. Multi-task XGBoost achieved F1 scores of 99.89% and 98.31% for the classification of PA types and APS types, respectively, while the independent XGBoost achieved F1 scores of 99.68% and 96.77%, respectively. The results indicate that both multi-task RNN and XGBoost can be used for the detection and classification of PA and APS without loss of performance with respect to individual separate classification systems. People with diabetes can achieve better outcomes and quality of life by including physical activity and psychological stress assessments in treatment decision-making.
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