Differential control of respiratory frequency and tidal volume during exercise

Nicolò, Andrea; Sacchetti, Massimo

doi:10.1007/s00421-022-05077-0

Cited by 15 publications

(49 citation statements)

References 223 publications

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“…There are inherent limitations in this approach as it depends on a precise RR interval measurement along with the issues involving HRV artifact correction [ 20 ]. With respect to the RF control, fundamental mechanisms are also partly under “central command”(periaqueductal gray area), with other factors also included, most notably muscle afferent input [ 27 ]. Additionally, the exact CNS centers responsible for RR interval timing (HRV) [ 38 , 39 , 40 ] and RF may or may not overlap.…”

Section: Discussionmentioning

confidence: 99%

“…Between-method agreement may have been different across more homogenous populations. Finally, both RF and DFA a1 can be affected by prior exercise, fatigue, as well as other factors which should be considered before testing [ 20 , 26 , 27 ].…”

Section: Limitationsmentioning

confidence: 99%

“…A calculation yielding the two instances of the second derivative maxima (curve acceleration points) of this sixth-order trendline is then obtained that corresponds to the first and second thresholds. Differing from the ANS-related DFA a1 metric, exercise-induced RF change is felt to be due to a combination of afferent muscle signaling and a concept termed “central command” [ 26 , 27 ]. Central command refers to RF control via inputs from the periaqueductal gray area, supplementary motor area, the premotor area, and the prefrontal cortex.…”

Section: Introductionmentioning

confidence: 99%

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Improved Estimation of Exercise Intensity Thresholds by Combining Dual Non-Invasive Biomarker Concepts: Correlation Properties of Heart Rate Variability and Respiratory Frequency

Rogers

Schaffarczyk

Gronwald

2023

Sensors

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Identifying exercise intensity boundaries has been shown to be important during endurance training for performance enhancement and rehabilitation. Unfortunately, even though surrogate markers show promise when assessed on a group level, substantial deviation from gold standards can be present in each individual. The aim of this study was to evaluate whether combining two surrogate intensity markers improved this agreement. Electrocardiogram (ECG) and gas exchange data were obtained from 21 participants who performed an incremental cycling ramp to exhaustion and evaluated for first (VT1) and second (VT2) ventilatory thresholds, heart rate (HR) variability (HRV), and ECG derived respiratory frequency (EDR). HRV thresholds (HRVT) were based on the non-linear index a1 of a Detrended Fluctuation Analysis (DFA a1) and EDR thresholds (EDRT) upon the second derivative of the sixth-order polynomial of EDR over time. The average of HRVT and EDRT HR was set as the combined threshold (Combo). Mean VT1 was reached at a HR of 141 ± 15, HRVT1 at 152 ± 14 (p < 0.001), EDRT1 at 133 ± 12 (p < 0.001), and Combo1 at 140 ± 13 (p = 0.36) bpm with Pearson’s r of 0.83, 0.78, and 0.84, respectively, for comparisons to VT1. A Bland–Altman analysis showed mean biases of 8.3 ± 7.9, −8.3 ± 9.5, and −1.7 ± 8.3 bpm, respectively. A mean VT2 was reached at a HR of 165 ± 13, HRVT2 at 167 ± 10 (p = 0.89), EDRT2 at 164 ± 14 (p = 0.36), and Combo2 at 164 ± 13 (p = 0.59) bpm with Pearson’s r of 0.58, 0.95, and 0.94, respectively, for comparisons to VT2. A Bland–Altman analysis showed mean biases of −0.3 ± 8.9, −1.0 ± 4.6, and −0.6 ± 4.6 bpm, respectively. Both the DFA a1 and EDR intensity thresholds based on HR taken individually had moderate agreement to targets derived through gas exchange measurements. By combining both non-invasive approaches, there was improved correlation, reduced bias, and limits of agreement to the respective corresponding HRs at VT1 and VT2.

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Section: Discussionmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Estimation of Exercise Intensity Thresholds by Combining Dual Non-Invasive Biomarker Concepts: Correlation Properties of Heart Rate Variability and Respiratory Frequency

Rogers

Schaffarczyk

Gronwald

2023

Sensors

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“…The control of respiratory frequency is a complex mechanism primarily modulated by type III and IV muscle afferent feedback and central command (i.e. : periaqueductal grey and other subcortical areas) [59]. In absence of studies dedicated to the control of breathing in obese patients, it can be hypothesized that lower muscles mass and weakness, thoracic load carriage and restriction might be slightly aggravated in class3 who were forced to sustain their ventilation by increasing their respiratory rate.…”

Section: Obesity Class 2 Vs Classmentioning

confidence: 99%

Does the level of obesity impact on the respiratory function in adults?

LoMauro

Tringali

Codecasa

et al. 2022

Preprint

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Obesity is frequently associated to breathing disorders. In order to investigate if and how the level of obesity impact on the respiratory function, 10 obese class 2 (median age: 51 years; BMI: 38.7 kg/m2, 5 females), 7 obese class 3 patients (41 years; 45.7 kg/m2, 3 females) and 10 non-obese subjects (49 years; 23.9 kg/m2, 5 females) were studied. Patients were characterized by abdominal obesity, with abdominal volume occupying the 40% and 42% in class 2 and 3, being higher (p<0.001) than non-obese group (31%). Spirometry and lung volumes did not differ between the two classes, while the supine position induced an important reduction of functional residual capacity. At rest, breathing frequency was higher in class 3 (19 breaths/min, p=0.025). In supine position obese patients breathed with higher minute ventilation (class 12.1: L/min, class 2: 11.4 L/min) and lower ribcage contribution (class 3: 4.9%, class 2: 6.1%) than non-obese subjects (7.5 L/min, p= 0.001 and 31.1%, p=0.003, respectively), indicating thoracic restriction. Otherwise healthy obesity might not be characterized by restrictive lung pattern. Other sign of restriction could be poor thoracic expansion at rest in supine position, resulting in increased ventilation. Class 3 obesity made respiratory rate further increase.

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“…One of the physiological variables that is gaining particular interest in the exercise community is respiratory frequency ( f R ) [ 1 , 2 ], also considering the numerous techniques suitable for its measure [ 3 , 4 ]. Unlike other physiological variables (including tidal volume, V T ), f R is closely associated with perceived exertion [ 5 , 6 , 7 , 8 , 9 ], and its time course reflects changes in exercise tolerance [ 10 ]. The fact that the depth (i.e., V T ) and rate (i.e., f R ) of breathing provide different information is supported by their differential control, as V T and f R are mainly modulated by metabolic and non-metabolic inputs, respectively [ 8 , 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Testing of a Smart Facemask for Respiratory Monitoring during Cycling Exercise

et al. 2023

Self Cite

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Given the importance of respiratory frequency (fR) as a valid marker of physical effort, there is a growing interest in developing wearable devices measuring fR in applied exercise settings. Biosensors measuring chest wall movements are attracting attention as they can be integrated into textiles, but their susceptibility to motion artefacts may limit their use in some sporting activities. Hence, there is a need to exploit sensors with signals minimally affected by motion artefacts. We present the design and testing of a smart facemask embedding a temperature biosensor for fR monitoring during cycling exercise. After laboratory bench tests, the proposed solution was tested on cyclists during a ramp incremental frequency test (RIFT) and high-intensity interval training (HIIT), both indoors and outdoors. A reference flowmeter was used to validate the fR extracted from the temperature respiratory signal. The smart facemask showed good performance, both at a breath-by-breath level (MAPE = 2.56% and 1.64% during RIFT and HIIT, respectively) and on 30 s average fR values (MAPE = 0.37% and 0.23% during RIFT and HIIT, respectively). Both accuracy and precision (MOD ± LOAs) were generally superior to those of other devices validated during exercise. These findings have important implications for exercise testing and management in different populations.

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Differential control of respiratory frequency and tidal volume during exercise

Cited by 15 publications

References 223 publications

Improved Estimation of Exercise Intensity Thresholds by Combining Dual Non-Invasive Biomarker Concepts: Correlation Properties of Heart Rate Variability and Respiratory Frequency

Improved Estimation of Exercise Intensity Thresholds by Combining Dual Non-Invasive Biomarker Concepts: Correlation Properties of Heart Rate Variability and Respiratory Frequency

Does the level of obesity impact on the respiratory function in adults?

Design and Testing of a Smart Facemask for Respiratory Monitoring during Cycling Exercise

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