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Ahola AJ, Parente EB, Harjutsalo V, Groop PH. Modifiable self-management practices impact nocturnal and morning glycaemia in type 1 diabetes. Prim Care Diabetes 2024; 18:435-440. [PMID: 38852028 DOI: 10.1016/j.pcd.2024.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 04/03/2024] [Accepted: 06/02/2024] [Indexed: 06/10/2024]
Abstract
AIMS To identify risk factors for nocturnal/morning hypo- and hyperglycaemia in type 1 diabetes. METHODS Data on self-management practices were obtained from 3-day records. We studied the associations between self-management practices on the first recording day and the self-reported blood glucose (BG) concentrations on the subsequent night/morning. RESULTS Of the 1025 participants (39 % men, median age 45 years), 4.4 % reported nocturnal hypoglycaemia (<3.9 mmol/l), 9.8 % morning hypoglycaemia, 51.5 % morning euglycaemia, and 34.3 % morning hyperglycaemia (≥8.9 mmol/l). Within hypoglycaemic range, insulin pump use was associated with higher nocturnal BG concentration (B=0.486 [95 % Confidence Interval=0.121-0.852], p=0.009). HbA1c was positively (0.046 [0.028-0.065], p<0.001), while antecedent fibre intake (-0.327 [-0.543 - -0.111], p=0.003) and physical activity (PA) (-0.042 [-0.075 - -0.010], p=0.010) were inversely associated with morning BG concentration. The odds of morning hypoglycaemia were increased by previous day hypoglycaemia (OR=2.058, p=0.002) and alcohol intake (1.031, p=0.001). Previous day PA (0.977, p=0.031) and fibre intake (0.848, p=0.017) were inversely, while HbA1c (1.027, p<0.001) was positively associated with the risk of morning hyperglycaemia. CONCLUSIONS Alcohol avoidance may prevent nocturnal hypoglycaemia, while PA and fibre intake may reduce hyperglycaemia risk. Avoidance of daytime hypoglycaemia and keeping HbA1c in control may help maintain normoglycaemia also at night-time.
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Affiliation(s)
- Aila J Ahola
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland; Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Finland
| | - Erika B Parente
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland; Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Finland
| | - Valma Harjutsalo
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland; Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Finland; National Institute for Health and Welfare, Helsinki, Finland
| | - Per-Henrik Groop
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland; Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Finland; Department of Diabetes, Central Clinical School, Monash University, Melbourne, Victoria, Australia.
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Drenthen LCA, Ajie M, Bakker EA, Abbink EJ, Thijssen DHJ, Tack CJ, de Galan BE. Daily unstructured physical activity affects mean glucose, occurrence of hypoglycaemia and glucose variability in people with type 1 diabetes. Diabetes Obes Metab 2023; 25:3837-3840. [PMID: 37722974 DOI: 10.1111/dom.15277] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/21/2023] [Accepted: 08/28/2023] [Indexed: 09/20/2023]
Affiliation(s)
| | - Mandala Ajie
- Department of Internal Medicine, Radboudumc, Nijmegen, The Netherlands
| | - Esmée A Bakker
- Department of Physiology, Radboudumc, Nijmegen, The Netherlands
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
| | - Evertine J Abbink
- Department of Internal Medicine, Radboudumc, Nijmegen, The Netherlands
| | - Dick H J Thijssen
- Department of Physiology, Radboudumc, Nijmegen, The Netherlands
- Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, UK
| | - Cees J Tack
- Department of Internal Medicine, Radboudumc, Nijmegen, The Netherlands
| | - Bastiaan E de Galan
- Department of Internal Medicine, Radboudumc, Nijmegen, The Netherlands
- Department of Internal Medicine, Maastricht UMC+, Maastricht, The Netherlands
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Gardner D, Tan HC, Lim GH, Zin Oo M, Xin X, Kingsworth A, Choudhary P, Rama Chandran S. Association of Smartphone-Based Activity Tracking and Nocturnal Hypoglycemia in People With Type 1 Diabetes. J Diabetes Sci Technol 2023:19322968231186401. [PMID: 37439017 DOI: 10.1177/19322968231186401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/14/2023]
Abstract
BACKGROUND Nocturnal hypoglycemia (NH) remains a major burden for people with type 1 diabetes (T1D). Daytime physical activity (PA) increases the risk of NH. This pilot study tested whether cumulative daytime PA measured using a smartphone-based step tracker was associated with NH. METHODS Adults with T1D for ≥ 5 years (y) on multiple daily insulin or continuous insulin infusion, not using continuous glucose monitoring and HbA1c 6 to 10% wore blinded Freestyle Libre Pro sensors and recorded total daily carbohydrate (TDC) and total daily dose (TDD) of insulin. During this time, daily step count (DSC) was tracked using the smartphone-based Fitbit MobileTrack application. Mixed effects logistic regression was used to estimate the effect of DSC on NH (sensor glucose <70, <54 mg/dl for ≥15 minutes), while adjusting for TDC and TDD of insulin, and treating participants as a random effect. RESULTS Twenty-six adults, with 65.4% females, median age 27 years (interquartile range: 26-32) mean body mass index 23.9 kg/m2, median HbA1c 7.6% (7.1-8.1) and mean Gold Score 2.1 (standard deviation 1.0) formed the study population. The median DSC for the whole group was 2867 (1820-4807). There was a significant effect of DSC on NH episodes <70 mg/dl. (odds ratio 1.11 [95% CI: 1.01-1.23, P = .04]. There was no significant effect on NH <54 mg/dl. CONCLUSION Daily PA measured by a smartphone-based step tracker was associated with the risk of NH in people with type 1 diabetes.
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Affiliation(s)
- Daphne Gardner
- Department of Endocrinology, Academia, Singapore General Hospital, Singapore
| | - Hong Chang Tan
- Department of Endocrinology, Academia, Singapore General Hospital, Singapore
| | - Gek Hsiang Lim
- Health Sciences Research Unit, Singapore General Hospital, Singapore
| | - May Zin Oo
- Medicine Academic Clinical Program, Singapore General Hospital, Singapore
| | - Xiaohui Xin
- Health Sciences Research Unit, Singapore General Hospital, Singapore
| | - Andrew Kingsworth
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Pratik Choudhary
- Department of Population Health Sciences, University of Leicester, Leicester, UK
- Leicester Diabetes Centre, Leicester General Hospital, Leicester, UK
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Alhaddad AY, Aly H, Gad H, Elgassim E, Mohammed I, Baagar K, Al-Ali A, Sadasivuni KK, Cabibihan JJ, Malik RA. Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115003. [PMID: 37299733 DOI: 10.3390/s23115003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 05/09/2023] [Accepted: 05/16/2023] [Indexed: 06/12/2023]
Abstract
Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes.
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Affiliation(s)
- Ahmad Yaser Alhaddad
- Department of Mechanical and Industrial Engineering, Qatar University, Doha 2713, Qatar
| | - Hussein Aly
- KINDI Center for Computing Research, Qatar University, Doha 2713, Qatar
| | - Hoda Gad
- Weill Cornell Medicine-Qatar, Doha 24144, Qatar
| | | | - Ibrahim Mohammed
- Weill Cornell Medicine-Qatar, Doha 24144, Qatar
- Department of Internal Medicine, Albany Medical Center Hospital, Albany, NY 12208, USA
| | | | - Abdulaziz Al-Ali
- KINDI Center for Computing Research, Qatar University, Doha 2713, Qatar
| | | | - John-John Cabibihan
- Department of Mechanical and Industrial Engineering, Qatar University, Doha 2713, Qatar
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Deichmann J, Bachmann S, Burckhardt MA, Pfister M, Szinnai G, Kaltenbach HM. New model of glucose-insulin regulation characterizes effects of physical activity and facilitates personalized treatment evaluation in children and adults with type 1 diabetes. PLoS Comput Biol 2023; 19:e1010289. [PMID: 36791144 PMCID: PMC9974135 DOI: 10.1371/journal.pcbi.1010289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 02/28/2023] [Accepted: 01/16/2023] [Indexed: 02/16/2023] Open
Abstract
Accurate treatment adjustment to physical activity (PA) remains a challenging problem in type 1 diabetes (T1D) management. Exercise-driven effects on glucose metabolism depend strongly on duration and intensity of the activity, and are highly variable between patients. In-silico evaluation can support the development of improved treatment strategies, and can facilitate personalized treatment optimization. This requires models of the glucose-insulin system that capture relevant exercise-related processes. We developed a model of glucose-insulin regulation that describes changes in glucose metabolism for aerobic moderate- to high-intensity PA of short and prolonged duration. In particular, we incorporated the insulin-independent increase in glucose uptake and production, including glycogen depletion, and the prolonged rise in insulin sensitivity. The model further includes meal absorption and insulin kinetics, allowing simulation of everyday scenarios. The model accurately predicts glucose dynamics for varying PA scenarios in a range of independent validation data sets, and full-day simulations with PA of different timing, duration and intensity agree with clinical observations. We personalized the model on data from a multi-day free-living study of children with T1D by adjusting a small number of model parameters to each child. To assess the use of the personalized models for individual treatment evaluation, we compared subject-specific treatment options for PA management in replay simulations of the recorded data with altered meal, insulin and PA inputs.
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Affiliation(s)
- Julia Deichmann
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Switzerland
- Life Science Zurich Graduate School, Zurich, Switzerland
| | - Sara Bachmann
- Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, Basel, Switzerland
- Department of Clinical Research, University Hospital Basel, Basel, Switzerland
| | - Marie-Anne Burckhardt
- Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, Basel, Switzerland
- Department of Clinical Research, University Hospital Basel, Basel, Switzerland
| | - Marc Pfister
- Department of Clinical Research, University Hospital Basel, Basel, Switzerland
- Pediatric Pharmacology and Pharmacometrics, University Children’s Hospital Basel, Basel, Switzerland
| | - Gabor Szinnai
- Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, Basel, Switzerland
- Department of Clinical Research, University Hospital Basel, Basel, Switzerland
| | - Hans-Michael Kaltenbach
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Switzerland
- * E-mail:
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Abstract
Regular physical activity improves cardiometabolic and musculoskeletal health, helps with weight management, improves cognitive and psychosocial functioning, and is associated with reduced mortality related to cancer and diabetes mellitus. However, turnover rates of glucose in the blood increase dramatically during exercise, which often results in either hypoglycaemia or hyperglycaemia as well as increased glycaemic variability in individuals with type 1 diabetes mellitus (T1DM). A complex neuroendocrine response to an acute exercise session helps to maintain circulating levels of glucose in a fairly tight range in healthy individuals, while several abnormal physiological processes and limitations of insulin therapy limit the capacity of people with T1DM to exercise in a normoglycaemic state. Knowledge of the acute and chronic effects of exercise and regular physical activity is critical for the formulation of clinical strategies for the management of insulin and nutrition for active patients with T1DM. Emerging diabetes-related technologies, such as continuous glucose monitors, automated insulin delivery systems and the administration of solubilized glucagon, are demonstrating efficacy for preserving glucose homeostasis during and after exercise in this population of patients. This Review highlights the beneficial effects of regular exercise and details the complex endocrine and metabolic responses to different types of exercise for adults with T1DM. An overview of basic clinical strategies for the preservation of glucose homeostasis using emerging technologies is also provided.
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Affiliation(s)
- Michael C Riddell
- Muscle Health Research Centre, School of Kinesiology and Health Science, York University, Toronto, Ontario, Canada.
- LMC Diabetes and Endocrinology, Toronto, Ontario, Canada.
| | - Anne L Peters
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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7
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Zhang L, Yang L, Zhou Z. Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice. Front Public Health 2023; 11:1044059. [PMID: 36778566 PMCID: PMC9910805 DOI: 10.3389/fpubh.2023.1044059] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 01/10/2023] [Indexed: 01/27/2023] Open
Abstract
Background and objective Hypoglycemia is a key barrier to achieving optimal glycemic control in people with diabetes, which has been proven to cause a set of deleterious outcomes, such as impaired cognition, increased cardiovascular disease, and mortality. Hypoglycemia prediction has come to play a role in diabetes management as big data analysis and machine learning (ML) approaches have become increasingly prevalent in recent years. As a result, a review is needed to summarize the existing prediction algorithms and models to guide better clinical practice in hypoglycemia prevention. Materials and methods PubMed, EMBASE, and the Cochrane Library were searched for relevant studies published between 1 January 2015 and 8 December 2022. Five hypoglycemia prediction aspects were covered: real-time hypoglycemia, mild and severe hypoglycemia, nocturnal hypoglycemia, inpatient hypoglycemia, and other hypoglycemia (postprandial, exercise-related). Results From the 5,042 records retrieved, we included 79 studies in our analysis. Two major categories of prediction models are identified by an overview of the chosen studies: simple or logistic regression models based on clinical data and data-based ML models (continuous glucose monitoring data is most commonly used). Models utilizing clinical data have identified a variety of risk factors that can lead to hypoglycemic events. Data-driven models based on various techniques such as neural networks, autoregressive, ensemble learning, supervised learning, and mathematical formulas have also revealed suggestive features in cases of hypoglycemia prediction. Conclusion In this study, we looked deep into the currently established hypoglycemia prediction models and identified hypoglycemia risk factors from various perspectives, which may provide readers with a better understanding of future trends in this topic.
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Jaggers JR, King KM, McKay T, Dyess RJ, Thrasher BJ, Wintergerst KA. Association between Intensity Levels of Physical Activity and Glucose Variability among Children and Adolescents with Type 1 Diabetes. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1623. [PMID: 36674378 PMCID: PMC9865470 DOI: 10.3390/ijerph20021623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 06/17/2023]
Abstract
Studies would indicate a reduction in hemoglobin A1c levels following moderate and/or vigorous physical activity (PA) for people managing diabetes. However, prior investigations rarely looked at glucose variability in an adolescent population. PURPOSE The purpose of this investigation was to test the relationship between physical activity intensity levels and glucose variability in a sample of adolescents with type 1 diabetes mellitus, and if the amount of time accumulated for each intensity level is predictive of changes in glucose variability. METHODS Glucose variability was determined using continuous glucose monitor data and physical activity intensity time was retrieved through Fitabase®. Both glucose and physical activity data were collected over a two-week timeframe. Data analysis was completed using Pearson's correlation and a simple linear regression with a p-value of 0.05 to determine significance. RESULTS A significant inverse relationship was observed (p = 0.04) between glucose variability and average minutes of daily moderate-intensity activity (r = -0.59), as well as moderate and vigorous physical activity (MVPA) combined (r = -0.86; p = 0.03). A simple linear regression indicated that only MVPA was a significant predictor of glucose variability (β = -0.12; 95% CI: -0.23--0.01, p = 0.03). CONCLUSION These data demonstrated that the total amount of daily physical activity is important when properly managing type 1 diabetes mellitus, but time spent in MVPA over two weeks may have an inverse relationship with glucose variability in children and adolescents over a span of two weeks.
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Affiliation(s)
- Jason R. Jaggers
- Wendy Novak Diabetes Center, Division of Pediatric Endocrinology, School of Medicine, University of Louisville, Louisville, KY 40202, USA
- Department of Health and Sport Sciences, University of Louisville, Louisville, KY 40292, USA
| | - Kristi M. King
- Wendy Novak Diabetes Center, Division of Pediatric Endocrinology, School of Medicine, University of Louisville, Louisville, KY 40202, USA
- Department of Health and Sport Sciences, University of Louisville, Louisville, KY 40292, USA
| | - Timothy McKay
- Department of Kinesiology and Health Sciences, Georgetown College, Georgetown, KY 40324, USA
| | - Ryan J. Dyess
- Wendy Novak Diabetes Center, Division of Pediatric Endocrinology, School of Medicine, University of Louisville, Louisville, KY 40202, USA
- Norton Children’s Medical Group, University of Louisville School of Medicine, Louisville, KY 40202, USA
| | - Bradly J. Thrasher
- Wendy Novak Diabetes Center, Division of Pediatric Endocrinology, School of Medicine, University of Louisville, Louisville, KY 40202, USA
- Norton Children’s Medical Group, University of Louisville School of Medicine, Louisville, KY 40202, USA
| | - Kupper A. Wintergerst
- Wendy Novak Diabetes Center, Division of Pediatric Endocrinology, School of Medicine, University of Louisville, Louisville, KY 40202, USA
- Norton Children’s Medical Group, University of Louisville School of Medicine, Louisville, KY 40202, USA
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Adolfsson P, Taplin CE, Zaharieva DP, Pemberton J, Davis EA, Riddell MC, McGavock J, Moser O, Szadkowska A, Lopez P, Santiprabhob J, Frattolin E, Griffiths G, DiMeglio LA. ISPAD Clinical Practice Consensus Guidelines 2022: Exercise in children and adolescents with diabetes. Pediatr Diabetes 2022; 23:1341-1372. [PMID: 36537529 PMCID: PMC10107219 DOI: 10.1111/pedi.13452] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/07/2022] [Indexed: 12/24/2022] Open
Affiliation(s)
- Peter Adolfsson
- Department of PediatricsKungsbacka HospitalKungsbackaSweden
- Institute of Clinical Sciences, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
| | - Craig E. Taplin
- Department of Endocrinology and DiabetesPerth Children's HospitalNedlandsWestern AustraliaAustralia
- Telethon Kids InstituteUniversity of Western AustraliaPerthWestern AustraliaAustralia
- Centre for Child Health ResearchUniversity of Western AustraliaPerthWestern AustraliaAustralia
| | - Dessi P. Zaharieva
- Division of Endocrinology, Department of PediatricsSchool of Medicine, Stanford UniversityStanfordCaliforniaUSA
| | - John Pemberton
- Department of Endocrinology and DiabetesBirmingham Women's and Children's HospitalBirminghamUK
| | - Elizabeth A. Davis
- Department of Endocrinology and DiabetesPerth Children's HospitalNedlandsWestern AustraliaAustralia
- Telethon Kids InstituteUniversity of Western AustraliaPerthWestern AustraliaAustralia
- Centre for Child Health ResearchUniversity of Western AustraliaPerthWestern AustraliaAustralia
| | | | - Jonathan McGavock
- Faculty of Kinesiology and Recreation ManagementUniversity of ManitobaWinnipegManitobaCanada
- Diabetes Research Envisioned and Accomplished in Manitoba (DREAM) ThemeChildren's Hospital Research Institute of ManitobaWinnipegManitobaCanada
- Department of Pediatrics and Child HealthUniversity of ManitobaWinnipegManitobaCanada
- Diabetes Action Canada SPOR NetworkTorontoOntarioCanada
| | - Othmar Moser
- Division Exercise Physiology and Metabolism, Department of Sport ScienceUniversity of BayreuthBayreuthGermany
- Interdisciplinary Metabolic Medicine Trials Unit, Division of Endocrinology and Diabetology, Department of Internal MedicineMedical University of GrazGrazAustria
| | - Agnieszka Szadkowska
- Department of Pediatrics, Diabetology, Endocrinology & NephrologyMedical University of LodzLodzPoland
| | - Prudence Lopez
- Department of PaediatricsJohn Hunter Children's HospitalNewcastleNew South WalesAustralia
- University of NewcastleNewcastleNew South WalesAustralia
| | - Jeerunda Santiprabhob
- Siriraj Diabetes CenterFaculty of Medicine Siriraj Hospital, Mahidol UniversityBangkokThailand
- Division of Endocrinology and Metabolism, Department of PediatricsFaculty of Medicine Siriraj Hospital, Mahidol UniversityBangkokThailand
| | | | | | - Linda A. DiMeglio
- Department of Pediatrics, Division of Pediatric Endocrinology and DiabetologyIndiana University School of Medicine, Riley Hospital for ChildrenIndianapolisIndianaUSA
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Gal JJ, Li Z, Willi SM, Riddell MC. Association between high levels of physical activity and improved glucose control on active days in youth with type 1 diabetes. Pediatr Diabetes 2022; 23:1057-1063. [PMID: 35822348 DOI: 10.1111/pedi.13391] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 05/19/2022] [Accepted: 07/10/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Sixty minutes per day of at least moderate to vigorous physical activity (MVPA) is recommended for children for a variety of physical and psychological reasons. Adherence to these guidelines is confounded by challenges with glucose control during exercise in type 1 diabetes (T1D). OBJECTIVES This study examined the potential association between physical activity level on active days and glucose control in youth with T1D. METHODS Blinded continuous glucose monitors (CGM: Abbott Libre Pro) and physical activity data as measured from a body monitor patch (Metria IH1) were collected for up to 3 weeks in youth aged 9-17 years with T1D. The association between physical activity levels, expressed as mean active metabolic equivalent minutes (MET-minutes) per day, with CGM-based mean glucose, percent time in range (TIR: 70-180 mg/dl), % time above range (TAR) and % time below range (TBR) were assessed using a linear regression model adjusted for age, gender, and baseline HbA1c. RESULTS Study participants were deemed physically active, as defined by at least 10 min of continuous moderate-to-vigorous activity, on 5.2 ± 1.9 days per week, with a median accumulated physical activity time of 61 [IQR: 37-145] minutes per day. Higher physical activity levels were associated with lower mean glucose levels (r = -0.36; p = 0.02) and lower TAR (r = -0.45; p = 0.002) on active days. Higher activity levels were also associated with greater TIR (r = 0.54; p < 0.001) without being associated with more, or less, TBR. CONCLUSIONS Higher amounts of physical activity are associated with improvements in TIR without significantly increasing TBR. These data suggest that youth ages 9-17 years with T1D can benefit from a high level of physical activity without undue fear of hypoglycemia.
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Affiliation(s)
- Jordan J Gal
- Department of Biological Sciences, Clemson University, Clemson, South Carolina, USA
| | - Zoey Li
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Steven M Willi
- Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Michael C Riddell
- School of Kinesiology and Health Science, Muscle Health Research Center, York University, Toronto, Canada
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Molveau J, Rabasa-Lhoret R, Myette-Côté É, Messier V, Suppère C, J. Potter K, Heyman E, Tagougui S. Prevalence of nocturnal hypoglycemia in free-living conditions in adults with type 1 diabetes: What is the impact of daily physical activity? Front Endocrinol (Lausanne) 2022; 13:953879. [PMID: 36237197 PMCID: PMC9551602 DOI: 10.3389/fendo.2022.953879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/18/2022] [Indexed: 12/02/2022] Open
Abstract
Objective Studies investigating strategies to limit the risk of nocturnal hypoglycemia associated with physical activity (PA) are scarce and have been conducted in standardized, controlled conditions in people with type 1 diabetes (T1D). This study sought to investigate the effect of daily PA level on nocturnal glucose management in free-living conditions while taking into consideration reported mitigation strategies to limit the risk of nocturnal hyoglycemia in people with T1D. Methods Data from 25 adults (10 males, 15 females, HbA1c: 7.6 ± 0.8%), 20-60 years old, living with T1D, were collected. One week of continuous glucose monitoring and PA (assessed using an accelerometer) were collected in free-living conditions. Nocturnal glucose values (midnight-6:00 am) following an active day "ACT" and a less active day "L-ACT" were analyzed to assess the time spent within the different glycemic target zones (<3.9 mmol/L; 3.9 - 10.0 mmol/L and >10.0 mmol/L) between conditions. Self-reported data about mitigation strategies applied to reduce the risk of nocturnal hypoglycemia was also analyzed. Results Only 44% of participants reported applying a carbohydrate- or insulin-based strategy to limit the risk of nocturnal hypoglycemia on ACT day. Nocturnal hypoglycemia occurrences were comparable on ACT night versus on L-ACT night. Additional post-meal carbohydrate intake was higher on evenings following ACT (27.7 ± 15.6 g, ACT vs. 19.5 ± 11.0 g, L-ACT; P=0.045), but was frequently associated with an insulin bolus (70% of participants). Nocturnal hypoglycemia the night following ACT occurred mostly in people who administrated an additional insulin bolus before midnight (3 out of 5 participants with nocturnal hypoglycemia). Conclusions Although people with T1D seem to be aware of the increased risk of nocturnal hypoglycemia associated with PA, the risk associated with additional insulin boluses may not be as clear. Most participants did not report using compensation strategies to reduce the risk of PA related late-onset hypoglycemia which may be because they did not consider habitual PA as something requiring treatment adjustments.
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Affiliation(s)
- Joséphine Molveau
- Institut de recherches cliniques de Montréal, Montréal, QC, Canada
- Département de Nutrition, Faculté de Médecine, Université de Montréal, Montréal, QC, Canada
- Univ. Lille, Univ. Artois, Univ. Littoral Côte d’Opale, ULR 7369 - URePSSS - Unité de Recherche Pluridisciplinaire Sport Santé Société, Lille, France
| | - Rémi Rabasa-Lhoret
- Institut de recherches cliniques de Montréal, Montréal, QC, Canada
- Département de Nutrition, Faculté de Médecine, Université de Montréal, Montréal, QC, Canada
- Département des Sciences Biomédicales, Faculté de Médecine, Université de Montréal, Montreal, QC, Canada
- Endocrinology Division, Montreal Diabetes Research Center, Montréal, QC, Canada
| | - Étienne Myette-Côté
- Institut de recherches cliniques de Montréal, Montréal, QC, Canada
- Department of Applied Human Sciences, Faculty of Science, University of Prince Edward Island, Charlottetown, PE, Canada
| | - Virginie Messier
- Institut de recherches cliniques de Montréal, Montréal, QC, Canada
| | - Corinne Suppère
- Institut de recherches cliniques de Montréal, Montréal, QC, Canada
| | | | - Elsa Heyman
- Univ. Lille, Univ. Artois, Univ. Littoral Côte d’Opale, ULR 7369 - URePSSS - Unité de Recherche Pluridisciplinaire Sport Santé Société, Lille, France
- Institut Universitaire de France (IUF), Paris, France
| | - Sémah Tagougui
- Institut de recherches cliniques de Montréal, Montréal, QC, Canada
- Département de Nutrition, Faculté de Médecine, Université de Montréal, Montréal, QC, Canada
- Univ. Lille, Univ. Artois, Univ. Littoral Côte d’Opale, ULR 7369 - URePSSS - Unité de Recherche Pluridisciplinaire Sport Santé Société, Lille, France
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12
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Alhaddad AY, Aly H, Gad H, Al-Ali A, Sadasivuni KK, Cabibihan JJ, Malik RA. Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection. Front Bioeng Biotechnol 2022; 10:876672. [PMID: 35646863 PMCID: PMC9135106 DOI: 10.3389/fbioe.2022.876672] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/12/2022] [Indexed: 12/12/2022] Open
Abstract
Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes and healthy individuals, especially athletes. Wearable devices and non-invasive sensors for blood glucose monitoring have witnessed considerable advances. This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. Convolutional and recurrent neural networks, support vector machines, and decision trees are examples of such machine learning algorithms. Finally, we address the key limitations and challenges of these studies and provide recommendations for future work.
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Affiliation(s)
- Ahmad Yaser Alhaddad
- Department of Mechanical and Industrial Engineering, Qatar University, Doha, Qatar
| | - Hussein Aly
- KINDI Center for Computing Research, Qatar University, Doha, Qatar
| | - Hoda Gad
- Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Abdulaziz Al-Ali
- KINDI Center for Computing Research, Qatar University, Doha, Qatar
| | | | - John-John Cabibihan
- Department of Mechanical and Industrial Engineering, Qatar University, Doha, Qatar
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13
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Maximal Oxygen Uptake, VO 2 Max, Testing Effect on Blood Glucose Level in Adolescents with Type 1 Diabetes Mellitus. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19095543. [PMID: 35564936 PMCID: PMC9102981 DOI: 10.3390/ijerph19095543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/22/2022] [Accepted: 04/30/2022] [Indexed: 02/05/2023]
Abstract
Assessing maximal oxygen uptake (VO2 max) is generally considered safe when performed properly for most adolescents; however, for adolescents with type 1 diabetes mellitus (T1DM), monitoring glucose levels before and after exercise is critical to maintaining euglycemic ranges. Limited guidance exists for glucose level recommendations for the pediatric population; therefore, the purpose of this retrospective clinical chart review study was to determine the effects of VO2 max testing on blood glucose levels for adolescents with T1DM. A total of 22 adolescents (mean age = 15.6 ± 1.8 years; male = 13, 59.1%) with a diagnosis of T1DM participated in a Bruce protocol for VO2 max from January 2019 through February 2020. A statistically significant reduction in glucose levels between pretest (<30 min, mean = 191.1 mg/dL ± 61.2) and post-test VO2 max (<5 min, mean = 166.7 mg/dL ± 57.9); t(21) = 2.3, p < 0.05) was detected. The results from this current study can help guide health and fitness professionals in formulating glycemic management strategies in preparatory activities prior to exercise testing and during exercise testing.
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Riddell MC, Shakeri D, Scott SN. A Brief Review on the Evolution of Technology in Exercise and Sport in Type 1 Diabetes: Past, Present, and Future. Diabetes Technol Ther 2022; 24:289-298. [PMID: 34809493 DOI: 10.1089/dia.2021.0427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
One hundred years ago, insulin was first used to successfully lower blood glucose levels in young people living with what was then called juvenile diabetes. While insulin was not a cure for diabetes, it allowed individuals to resume a near normal life and have some freedom to eat more liberally and gain the strength they needed to live a more active lifestyle. Since then, a number of therapeutic and technical advances have arisen to further improve the health and wellbeing of individuals living with type 1 diabetes, allowing many to participate in sport at the local, regional, national or international level of competition. This review and commentary highlights some of the key advances in diabetes management in sport over the last 100 years since the discovery of insulin.
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Affiliation(s)
- Michael C Riddell
- School of Kinesiology and Health Science, Muscle Health Research Centre, York University, Toronto, Canada
| | - Dorsa Shakeri
- School of Kinesiology and Health Science, Muscle Health Research Centre, York University, Toronto, Canada
| | - Sam N Scott
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Bern University Hospital, University of Bern, Bern, Switzerland
- Team Novo Nordisk Professional Cycling Team, Atlanta, Georgia, USA
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15
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Integration of Consumer-Based Activity Monitors into Clinical Practice for Children with Type 1 Diabetes: A Feasibility Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182010611. [PMID: 34682355 PMCID: PMC8535604 DOI: 10.3390/ijerph182010611] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 10/05/2021] [Accepted: 10/07/2021] [Indexed: 12/11/2022]
Abstract
Current technology commonly utilized in diabetes care includes continuous glucose monitors (CGMs) and insulin pumps. One often overlooked critical component to the human glucose response is daily physical activity habits. Consumer-based activity monitors may be a valid way for clinics to collect physical activity data, but whether or not children with type 1 diabetes (T1D) would wear them or use the associated mobile application is unknown. Therefore, the purpose of this study was to test the feasibility of implementing a consumer-based accelerometer directly into ongoing care for adolescents managing T1D. Methods: Adolescents with T1D were invited to participate in this study and instructed to wear a mobile physical activity monitor while also completing a diet log for a minimum of 3 days. Clinical compliance was defined as the number of participants who were compliant with all measures while also having adequate glucose recordings using either a CGM, insulin pump, or on the diet log. Feasibility was defined as >50% of the total sample reaching clinical compliance. Results: A total of 57 children and teenagers between the ages of 7 and 19 agreed to participate in this study and were included in the final analysis. Chi-square results indicated significant compliance for activity tracking (p < 0.001), diet logs (p = 0.04), and overall clinical compliance (p = 0.04). Conclusion: More than half the children in this study were compliant for both activity monitoring and diet logs. This indicates that it is feasible for children with T1D to wear a consumer-based activity monitor while also recording their diet for a minimum of three days.
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Lysy PA, Absil H, Gasser E, Boughaleb H, Barrea T, Moniotte S. Combined Algorithm-Based Adaptations of Insulin Dose and Carbohydrate Intake During Exercise in Children With Type 1 Diabetes: Results From the CAR2DIAB Study. Front Endocrinol (Lausanne) 2021; 12:658311. [PMID: 34512541 PMCID: PMC8427034 DOI: 10.3389/fendo.2021.658311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 07/30/2021] [Indexed: 11/13/2022] Open
Abstract
Objectives To evaluate the evolution of subcutaneous glucose during two sessions of monitored aerobic exercise in children or adolescents with type 1 diabetes after adaptation of insulin doses and carbohydrate intake according to a combined algorithm. Methods Twelve patients with type 1 diabetes (15.1 ± 2 years; diabetes duration: 9.5 ± 3.1 years) performed two series of exercise sessions after cardiac evaluation. The first series (TE#1) consisted in a monitored exercise of moderate to vigorous intensity coupled with a bout of maximum effort. The second series of exercises (TE#2) was carried out in real life during exercises categorized and monitored by connected watches. TE#2 sessions were performed after adaptation of insulin doses and fast-acting carbohydrates according to decision algorithms. Results Patients did not experience episodes of severe hypoglycemia, symptomatic hyperglycemia, or hyperglycemia associated with ketosis. Analysis of CGM data (15 h) during TE#2 sessions revealed an overall improvement in glycemic average [± standard deviation] (104 ± 14 mg/dl vs. 122 ± 17 mg/dl during TE#1; p < 0.001), associated with a decrease in proportion of hyperglycemia in periods ranging from 4 h to 15 h after performing the exercises. The proportion of hypoglycemia was not changed, except during the TE#2 +4-8 h period, where a significant increase in hypoglycemia <60 mg/dl was observed (25% vs. 6.2%; p = 0.04), yet without concurrent complications. Conclusion In our pediatric series, the application of algorithmic adaptations of insulin doses and carbohydrate intake has globally improved glycemic control during 15 h after real-time exercises performed by children and adolescents with type 1 diabetes.
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Affiliation(s)
- Philippe Antoine Lysy
- Pediatric Endocrinology, Cliniques universitaires Saint-Luc, Brussels, Belgium
- Pôle PEDI, Institut de Recherche Expérimentale et Clinique, UCLouvain, Brussels, Belgium
| | - Hélène Absil
- Pediatric Endocrinology, Cliniques universitaires Saint-Luc, Brussels, Belgium
| | - Emy Gasser
- Pediatric Endocrinology, Cliniques universitaires Saint-Luc, Brussels, Belgium
| | - Hasnae Boughaleb
- Pediatric Endocrinology, Cliniques universitaires Saint-Luc, Brussels, Belgium
| | - Thierry Barrea
- Pediatric Endocrinology, Cliniques universitaires Saint-Luc, Brussels, Belgium
| | - Stéphane Moniotte
- Pôle PEDI, Institut de Recherche Expérimentale et Clinique, UCLouvain, Brussels, Belgium
- Pediatric Cardiology, Cliniques universitaires Saint-Luc, Brussels, Belgium
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King KM, Jaggers JR, Della LJ, McKay T, Watson S, Kozerski AE, Hartson KR, Wintergerst KA. Association between Physical Activity and Sport Participation on Hemoglobin A1c Among Children and Adolescents with Type 1 Diabetes. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18147490. [PMID: 34299946 PMCID: PMC8306132 DOI: 10.3390/ijerph18147490] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 07/07/2021] [Accepted: 07/09/2021] [Indexed: 12/17/2022]
Abstract
Purpose: To determine associations between physical activity (PA) and sport participation on HbA1c levels in children with type 1 diabetes (T1D). Method: Pediatric patients with T1D were invited to complete a PA and sport participation survey. Data were linked to their medical records for demographic characteristics, diabetes treatment and monitoring plans, and HbA1c levels. Results: Participants consisted of 71 females and 81 males, were 13 ± 3 years old with an average HbA1c level of 8.75 ± 1.81. Children accumulating 60 min of activity 3 days or more a week had significantly lower HbA1c compared to those who accumulated less than 3 days (p < 0.01) of 60 min of activity. However, there was no significant difference in HbA1c values based on sport participation groups. A multiple linear regression model indicated that PA, race, age, duration of diagnosis, and CGM use all significantly predicted HbA1c (p < 0.05). Conclusion: This study demonstrated the significant relationship between daily PA and HbA1c. Those in this sample presented with lower HbA1c values even if accumulating less than the recommended number of days of activity. Further, it was shown that sport participation alone may not be adequate enough to impact HbA1c in a similar manner.
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Affiliation(s)
- Kristi M. King
- Wendy Novak Diabetes Center, Division of Pediatric Endocrinology, School of Medicine, University of Louisville, Louisville, KY 40202, USA; (J.R.J.); (T.M.); (S.W.); (K.A.W.)
- Department of Health and Sport Sciences, University of Louisville, Louisville, KY 40292, USA
- Correspondence: ; Tel.: +1-502-852-8843
| | - Jason R. Jaggers
- Wendy Novak Diabetes Center, Division of Pediatric Endocrinology, School of Medicine, University of Louisville, Louisville, KY 40202, USA; (J.R.J.); (T.M.); (S.W.); (K.A.W.)
- Department of Health and Sport Sciences, University of Louisville, Louisville, KY 40292, USA
| | - Lindsay J. Della
- Department of Communication, University of Louisville, Louisville, KY 40292, USA;
| | - Timothy McKay
- Wendy Novak Diabetes Center, Division of Pediatric Endocrinology, School of Medicine, University of Louisville, Louisville, KY 40202, USA; (J.R.J.); (T.M.); (S.W.); (K.A.W.)
| | - Sara Watson
- Wendy Novak Diabetes Center, Division of Pediatric Endocrinology, School of Medicine, University of Louisville, Louisville, KY 40202, USA; (J.R.J.); (T.M.); (S.W.); (K.A.W.)
| | - Amy E. Kozerski
- Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
| | | | - Kupper A. Wintergerst
- Wendy Novak Diabetes Center, Division of Pediatric Endocrinology, School of Medicine, University of Louisville, Louisville, KY 40202, USA; (J.R.J.); (T.M.); (S.W.); (K.A.W.)
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Ray MK, McMichael A, Rivera-Santana M, Noel J, Hershey T. Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies. JMIR Diabetes 2021; 6:e27027. [PMID: 34081017 PMCID: PMC8212634 DOI: 10.2196/27027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 03/26/2021] [Accepted: 04/03/2021] [Indexed: 11/13/2022] Open
Abstract
Type 1 diabetes (T1D) is one of the most common chronic childhood diseases, and its prevalence is rapidly increasing. The management of glucose in T1D is challenging, as youth must consider a myriad of factors when making diabetes care decisions. This task often leads to significant hyperglycemia, hypoglycemia, and glucose variability throughout the day, which have been associated with short- and long-term medical complications. At present, most of what is known about each of these complications and the health behaviors that may lead to them have been uncovered in the clinical setting or in laboratory-based research. However, the tools often used in these settings are limited in their ability to capture the dynamic behaviors, feelings, and physiological changes associated with T1D that fluctuate from moment to moment throughout the day. A better understanding of T1D in daily life could potentially aid in the development of interventions to improve diabetes care and mitigate the negative medical consequences associated with it. Therefore, there is a need to measure repeated, real-time, and real-world features of this disease in youth. This approach is known as ecological momentary assessment (EMA), and it has considerable advantages to in-lab research. Thus, this viewpoint aims to describe EMA tools that have been used to collect data in the daily lives of youth with T1D and discuss studies that explored the nuances of T1D in daily life using these methods. This viewpoint focuses on the following EMA methods: continuous glucose monitoring, actigraphy, ambulatory blood pressure monitoring, personal digital assistants, smartphones, and phone-based systems. The viewpoint also discusses the benefits of using EMA methods to collect important data that might not otherwise be collected in the laboratory and the limitations of each tool, future directions of the field, and possible clinical implications for their use.
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Affiliation(s)
- Mary Katherine Ray
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, United States
| | - Alana McMichael
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, United States
| | - Maria Rivera-Santana
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, United States
| | - Jacob Noel
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, United States
| | - Tamara Hershey
- Department of Psychiatry, Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States
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Bertachi A, Viñals C, Biagi L, Contreras I, Vehí J, Conget I, Giménez M. Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1705. [PMID: 32204318 PMCID: PMC7147466 DOI: 10.3390/s20061705] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 03/10/2020] [Accepted: 03/17/2020] [Indexed: 12/16/2022]
Abstract
(1) Background: nocturnal hypoglycemia (NH) is one of the most challenging side effects of multiple doses of insulin (MDI) therapy in type 1 diabetes (T1D). This work aimed to investigate the feasibility of a machine-learning-based prediction model to anticipate NH in T1D patients on MDI. (2) Methods: ten T1D adults were studied during 12 weeks. Information regarding T1D management, continuous glucose monitoring (CGM), and from a physical activity tracker were obtained under free-living conditions at home. Supervised machine-learning algorithms were applied to the data, and prediction models were created to forecast the occurrence of NH. Individualized prediction models were generated using multilayer perceptron (MLP) and a support vector machine (SVM). (3) Results: population outcomes indicated that more than 70% of the NH may be avoided with the proposed methodology. The predictions performed by the SVM achieved the best population outcomes, with a sensitivity and specificity of 78.75% and 82.15%, respectively. (4) Conclusions: our study supports the feasibility of using ML techniques to address the prediction of nocturnal hypoglycemia in the daily life of patients with T1D on MDI, using CGM and a physical activity tracker.
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Affiliation(s)
- Arthur Bertachi
- Institute of Informatics and Applications, University of Girona, 17003 Girona, Spain; (A.B.); (L.B.); (I.C.)
- Federal University of Technology—Paraná (UTFPR), Guarapuava 85053-525, Brazil
| | - Clara Viñals
- Diabetes Unit, Endocrinology and Nutrition Dpt. Hospital Clínic de Barcelona, 08036 Barcelona, Spain; (C.V.); (I.C.); (M.G.)
| | - Lyvia Biagi
- Institute of Informatics and Applications, University of Girona, 17003 Girona, Spain; (A.B.); (L.B.); (I.C.)
- Federal University of Technology—Paraná (UTFPR), Guarapuava 85053-525, Brazil
| | - Ivan Contreras
- Institute of Informatics and Applications, University of Girona, 17003 Girona, Spain; (A.B.); (L.B.); (I.C.)
| | - Josep Vehí
- Institute of Informatics and Applications, University of Girona, 17003 Girona, Spain; (A.B.); (L.B.); (I.C.)
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), 08036 Barcelona, Spain
| | - Ignacio Conget
- Diabetes Unit, Endocrinology and Nutrition Dpt. Hospital Clínic de Barcelona, 08036 Barcelona, Spain; (C.V.); (I.C.); (M.G.)
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), 08036 Barcelona, Spain
| | - Marga Giménez
- Diabetes Unit, Endocrinology and Nutrition Dpt. Hospital Clínic de Barcelona, 08036 Barcelona, Spain; (C.V.); (I.C.); (M.G.)
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), 08036 Barcelona, Spain
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