RESUMEN
Challenges and fears related to managing glucose levels around planned and spontaneous exercise affect outcomes and quality of life in people living with type 1 diabetes. Advances in technology, including continuous glucose monitoring, open-loop insulin pump therapy and hybrid closed-loop (HCL) systems for exercise management in type 1 diabetes, address some of these challenges. In this review, three research or clinical experts, each living with type 1 diabetes, leverage published literature and clinical and personal experiences to translate research findings into simplified, patient-centred strategies. With an understanding of limitations in insulin pharmacokinetics, variable intra-individual responses to aerobic and anaerobic exercise, and the features of the technologies, six steps are proposed to guide clinicians in efficiently communicating simplified actions more effectively to individuals with type 1 diabetes. Fundamentally, the six steps centre on two aspects. First, regardless of insulin therapy type, and especially needed for spontaneous exercise, we provide an estimate of glucose disposal into active muscle meant to be consumed as extra carbohydrates for exercise ('ExCarbs'; a common example is 0.5 g/kg body mass per hour for adults and 1.0 g/kg body mass per hour for youth). Second, for planned exercise using open-loop pump therapy or HCL systems, we additionally recommend pre-emptive basal insulin reduction or using HCL exercise modes initiated 90 min (1-2 h) before the start of exercise until the end of exercise. Modifications for aerobic- and anaerobic-type exercise are discussed. The burden of pre-emptive basal insulin reductions and consumption of ExCarbs are the limitations of HCL systems, which may be overcome by future innovations but are unquestionably required for currently available systems.
RESUMEN
AIMS/HYPOTHESIS: Adults with type 1 diabetes should perform daily physical activity to help maintain health and fitness, but the influence of daily step counts on continuous glucose monitoring (CGM) metrics are unclear. This analysis used the Type 1 Diabetes Exercise Initiative (T1DEXI) dataset to investigate the effect of daily step count on CGM-based metrics. METHODS: In a 4 week free-living observational study of adults with type 1 diabetes, with available CGM and step count data, we categorised participants into three groups-below (<7000), meeting (7000-10,000) or exceeding (>10,000) the daily step count goal-to determine if step count category influenced CGM metrics, including per cent time in range (TIR: 3.9-10.0 mmol/l), time below range (TBR: <3.9 mmol/l) and time above range (TAR: >10.0 mmol/l). RESULTS: A total of 464 adults with type 1 diabetes (mean±SD age 37±14 years; HbA1c 48.8±8.1 mmol/mol [6.6±0.7%]; 73% female; 45% hybrid closed-loop system, 38% standard insulin pump, 17% multiple daily insulin injections) were included in the study. Between-participant analyses showed that individuals who exceeded the mean daily step count goal over the 4 week period had a similar TIR (75±14%) to those meeting (74±14%) or below (75±16%) the step count goal (p>0.05). In the within-participant comparisons, TIR was higher on days when the step count goal was exceeded or met (both 75±15%) than on days below the step count goal (73±16%; both p<0.001). The TBR was also higher when individuals exceeded the step count goals (3.1%±3.2%) than on days when they met or were below step count goals (difference in means -0.3% [p=0.006] and -0.4% [p=0.001], respectively). The total daily insulin dose was lower on days when step count goals were exceeded (0.52±0.18 U/kg; p<0.001) or were met (0.53±0.18 U/kg; p<0.001) than on days when step counts were below the current recommendation (0.55±0.18 U/kg). Step count had a larger effect on CGM-based metrics in participants with a baseline HbA1c ≥53 mmol/mol (≥7.0%). CONCLUSIONS/INTERPRETATION: Our results suggest that, compared with days with low step counts, days with higher step counts are associated with slight increases in both TIR and TBR, along with small reductions in total daily insulin requirements, in adults living with type 1 diabetes. DATA AVAILABILITY: The data that support the findings reported here are available on the Vivli Platform (ID: T1-DEXI; https://doi.org/10.25934/PR00008428 ).
Asunto(s)
Automonitorización de la Glucosa Sanguínea , Glucemia , Diabetes Mellitus Tipo 1 , Ejercicio Físico , Humanos , Diabetes Mellitus Tipo 1/sangre , Diabetes Mellitus Tipo 1/terapia , Diabetes Mellitus Tipo 1/tratamiento farmacológico , Adulto , Femenino , Masculino , Automonitorización de la Glucosa Sanguínea/métodos , Glucemia/metabolismo , Glucemia/análisis , Persona de Mediana Edad , Ejercicio Físico/fisiología , Hemoglobina Glucada/metabolismo , Hemoglobina Glucada/análisis , Insulina/uso terapéutico , Insulina/administración & dosificación , Estudios de Cohortes , Monitoreo Continuo de GlucosaAsunto(s)
Glucemia , Diabetes Mellitus Tipo 1 , Hipoglucemiantes , Sistemas de Infusión de Insulina , Insulina , Humanos , Diabetes Mellitus Tipo 1/tratamiento farmacológico , Diabetes Mellitus Tipo 1/sangre , Insulina/administración & dosificación , Insulina/uso terapéutico , Glucemia/análisis , Glucemia/efectos de los fármacos , Glucemia/metabolismo , Adulto , Hipoglucemiantes/administración & dosificación , Hipoglucemiantes/uso terapéutico , Femenino , Masculino , Comidas , Automonitorización de la Glucosa Sanguínea/instrumentación , Persona de Mediana EdadRESUMEN
Automated insulin delivery (AID) systems enhance glucose management by lowering mean glucose level, reducing hyperglycemia, and minimizing hypoglycemia. One feature of most AID systems is that they allow the user to view "insulin on board" (IOB) to help confirm a recent bolus and limit insulin stacking. This metric, along with viewing glucose concentrations from a continuous glucose monitoring system, helps the user understand bolus insulin action and the future "threat" of hypoglycemia. However, the current presentation of IOB in AID systems can be misleading, as it does not reflect true insulin action or automatic, dynamic insulin adjustments. This commentary examines the evolution of IOB from a bolus-specific metric to its contemporary use in AID systems, highlighting its limitations in capturing real-time insulin modulation during varying physiological states.
RESUMEN
INTRODUCTION: We aimed to investigate the neuromuscular contributions to enhanced fatigue resistance with carbohydrate (CHO) ingestion and to identify whether fatigue is associated with changes in interstitial glucose levels assessed using a continuous glucose monitor (CGM). METHODS: Twelve healthy participants (six males, six females) performed isokinetic single-leg knee extensions (90°·s -1 ) at 20% of the maximal voluntary contraction (MVC) torque until MVC torque reached 60% of its initial value (i.e., task failure). Central and peripheral fatigue were evaluated every 15 min during the fatigue task using the interpolated twitch technique and electrically evoked torque. Using a single-blinded crossover design, participants ingested CHO (85 g sucrose per hour), or a placebo (PLA), at regular intervals during the fatigue task. Minute-by-minute interstitial glucose levels measured via CGM and whole blood glucose readings were obtained intermittently during the fatiguing task. RESULTS: CHO ingestion increased time to task failure over PLA (113 ± 69 vs 81 ± 49 min, mean ± SD; P < 0.001) and was associated with higher glycemia as measured by CGM (106 ± 18 vs 88 ± 10 mg·dL -1 , P < 0.001) and whole blood glucose sampling (104 ± 17 vs 89 ± 10 mg·dL -1 , P < 0.001). When assessing the values in the CHO condition at a similar time point to those at task failure in the PLA condition (i.e., ~81 min), MVC torque, percentage voluntary activation, and 10 Hz torque were all better preserved in the CHO versus PLA condition ( P < 0.05). CONCLUSIONS: Exogenous CHO intake mitigates neuromuscular fatigue at both the central and peripheral levels by raising glucose concentrations rather than by preventing hypoglycemia.