Andrea Nicolò

Respiratory frequency is strongly associated with perceived exertion during time trials of different duration

ABSTRACT In order to provide further insight into the link between respiratory frequency (fR) and the rating of perceived exertion (RPE), the present study investigated the effect of exercise duration on perceptual and physiological responses during self-paced exercise. Nine well-trained competitive male cyclists (23 ± 3 years) performed a preliminary incremental ramp test and three randomised self-paced time trials (TTs) differing in exercise duration (10, 20 and 30 min). Both RPE and fR increased almost linearly over time, with a less-pronounced rate of increase when absolute exercise duration increased. However, when values were expressed against relative exercise duration, no between-trial differences were found in either RPE or fR. Conversely, between-trial differences were observed for minute ventilation (E), O2 and heart rate (HR), when values were expressed against relative exercise duration. Unlike the relationship between RPE and both E and HR, the relationship between RPE and fR was not affected by exercise duration. In conclusion, fR, but not E, HR or O2, shows a strong relationship to RPE and a similar time course, irrespective of exercise duration. These findings indicate that fR is the best correlate of RPE during self-paced exercise, at least among the parameters and for the range of durations herein investigated.

Comparing Continuous and Intermittent Exercise: An “Isoeffort” and “Isotime” Approach

The present study proposes an alternative way of comparing performance and acute physiological responses to continuous exercise with those of intermittent exercise, ensuring similar between-protocol overall effort (isoeffort) and the same total duration of exercise (isotime). This approach was expected to overcome some drawbacks of traditional methods of comparison. Fourteen competitive cyclists (20±3 yrs) performed a preliminary incremental test and four experimental 30-min self-paced protocols, i.e. one continuous and three passive-recovery intermittent exercise protocols with different work-to-rest ratios (2 = 40∶20s, 1 = 30∶30s and 0.5 = 20∶40s). A “maximal session effort” prescription was adopted for this experimental design. As expected, a robust perceived exertion template was observed irrespective of exercise protocol. Similar between-protocol pacing strategies further support the use of the proposed approach in competitive cyclists. Total work, oxygen uptake and heart rate mean values were significantly higher (P<0.05) in the continuous compared to intermittent protocols, while lactate values were lower. Manipulating the work-to-rest ratio in intermittent exercise, total work, oxygen uptake and heart rate mean values decreased with the decrease in the work-to-rest ratio, while lactate values increased. Despite this complex physiological picture, all protocols showed similar ventilatory responses and a nearly perfect relationship between respiratory frequency and perceived exertion. In conclusion, our data indicate that overall effort and total duration of exercise are two critical parameters that should both be controlled when comparing continuous with intermittent exercise. On an isoeffort and isotime basis, the work-to-rest ratio manipulation affects physiological responses in a different way from what has been reported in literature with traditional methods of comparison. Finally, our data suggest that during intermittent exercise respiratory frequency reflects physiological strain better than oxygen uptake, heart rate and blood lactate.

Neuromuscular and Metabolic Responses to High-Intensity Intermittent Cycling Protocols With Different Work-to-Rest Ratios

PURPOSE To investigate the effects of work-to-rest-ratio manipulation on neuromuscular and metabolic responses during 2 high-intensity intermittent training (HIT) protocols to exhaustion. Since different exercise durations were expected, the authors hypothesized that the protocol registering a longer duration would have a more pronounced effect on neuromuscular responses, while the other would challenge the cardiopulmonary system more. METHODS Thirteen competitive cyclists (age 19 ± 2 y) performed a preliminary incremental test to identify their maximal power output and 2 intermittent protocols to exhaustion (40:20s and 30:30s) at a fixed work rate of 135%Pmax interspersed by passive recovery. Surface electromyographic (sEMG) parameters (including muscle-fiber conduction velocity), cardiopulmonary parameters, and blood lactate concentration [La-] were recorded. RESULTS Time to exhaustion and total work were significantly higher for the 30:30s (38 ± 13 min, 495 ± 161 kJ) than for the 40:20s (10 ± 3 min, 180 ± 51 kJ). No differences were found in sEMG parameters for the 2 protocols. Mean and peak values of VO2, heart rate, ventilatory parameters (except for the peak value of respiratory frequency), and [La-] were significantly higher in the 40:20s than in the 30:30s. CONCLUSIONS These results do not support the hypothesis that a longer time spent at high intensity has a more pronounced effect on neuromuscular responses, as no differences in EMG parameters were found in the 2 HIT protocols. Regarding metabolic responses, while the 40:20s led to maximal values of VO2, [La-], and ventilatory parameters within a few minutes, the 30:30s allowed maintenance of moderately high values for a considerably longer period, especially for [La-] and ventilatory parameters.

Differential control of respiratory frequency and tidal volume during high-intensity interval training

What is the central question of this study? By manipulating recovery intensity and exercise duration during high‐intensity interval training (HIIT), we tested the hypothesis that fast inputs contribute more than metabolic stimuli to respiratory frequency (fR) regulation. What is the main finding and its importance? Respiratory frequency, but not tidal volume, responded rapidly and in proportion to changes in workload during HIIT, and was dissociated from some markers of metabolic stimuli in response to both experimental manipulations, suggesting that fast inputs contribute more than metabolic stimuli to fR regulation. Differentiating between fR and tidal volume may help to unravel the mechanisms underlying exercise hyperpnoea.

Control of the depth and rate of breathing: metabolic vs. non‐metabolic inputs

We read with great interest the review paper by Tipton et al. (2017). The authors examined the ventilatory response to a wide range of stressors, with a special interest in the differential regulation of respiratory frequency (fR) and tidal volume (VT). Despite a clear differential control of fR and VT emerging from the reviewed studies, the authors highlight how their regulation was typically neglected. An exception to this is our recent work on the differential control of fR and VT during high-intensity exercise (Nicolò et al. 2017), which was inspired by previous observations (Nicolò et al. 2015, 2016). Our findings fit nicely with some of the evidence reviewed by Tipton et al. (2017) despite the very different conditions explored. Therefore, the integration of information from both contributions has allowed us to expand on the breathing control perspective proposed by Tipton et al. (2017). We present here a deliberately simple viewpoint on the differential control of fR and VT which, despite the complexity underlying breathing control, applies in a wide range of conditions. By manipulating recovery intensity and exercise duration during high-intensity interval training, we found that fR, unlike VT, responded rapidly and in proportion to variations in workload, and was dissociated from some markers of metabolic stimuli in response to both experimental manipulations (Nicolò et al. 2017). We concluded that fast inputs (including central command) appear to contribute more than metabolic stimuli to fR regulation. We also supported our conclusions by presenting evidence that VT, unlike fR, is regulated by metabolic stimuli in various non-exercise conditions. This notion is even more convincing in the light of a number of findings reviewed by Tipton et al. (2017). The authors report that the prototypic human response to stress (fight or flight) includes a fast increase in ventilation, mediated preferentially by fR. This response is common to various stressors including cold, panic and pain. For instance, immersion in cold water produces the ‘cold shock response’, which includes a fast increase in minute ventilation (V̇E) primarily determined by an increase in fR, with VT not increasing until fR begins to fall back towards pre-immersion levels (Tipton et al. 1991). This fR response is possibly driven by the dynamic response of the peripheral cold thermoreceptors determining the ‘cold shock response’, which attenuates within the first 2 min of immersion. Thereafter, the respiratory response to cold water immersion is mainly driven by the metabolic demands of shivering, which is an involuntary form of skeletal muscle contraction (Tipton et al. 2017). This alteration in the inputs driving ventilation determines a diminution of the contribution of fR to V̇E and an increasing contribution of VT, which becomes the predominant component of V̇E with shivering (Tipton et al. 2017). Panic is a sudden sensation of fear or anxiety which affects ventilation through central mechanisms rather than metabolic stimuli. The evidence collected by Tipton et al. (2017) suggests that panic increases fR, but not VT, when it is evoked by nonmetabolic interventions. Conversely, panic increases VT more than it does fR when it is induced by hypercapnia, which is also a potent metabolic stimulus (Tipton et al. 2017). Pain is an unpleasant sensory experience which affects ventilation through a mixture of behavioural response and a direct effect on medullary respiratory centres (Tipton et al. 2017). When a behavioural V̇E response is induced by warning the subjects of an incoming painful stimulus, anticipation of pain causes an increase in fR (Willer, 1975). On the other hand, when the hormonal stress response induced by pain is reproduced by intravenous infusion of cortisol, adrenaline and glucagon, an increase in V̇E mediated by VT – but not fR – is observed (Weissman et al. 1986). Collectively, the reported findings point to a non-metabolic regulation of fR, while VT appears to be strongly regulated by metabolic stimuli. This is further supported by remarkable findings on animals (Borison et al. 1977) and humans (Guz et al. 1966) showing that some metabolic stimuli, including hypercapnia, have no direct effect on fR. Instead, the increase in fR observed with severe levels of hypercapnia may be evoked by volume feedback, panic and other behavioural responses induced by hypercapnia (Tipton et al. 2017). Therefore, for those conditions where fR seems to partly respond to metabolic stimuli, potential alternative explanations should be considered. On the other hand, fR seems to be primarily regulated by fast inputs, some of which may not contribute substantially to regulating VT. In this context, fast inputs are central command and other brain inputs (e.g. from brain areas involved in emotional processing), muscle and other non-chemical afferent inputs driving ventilation. Temperature is another non-metabolic stimulus which has a strong effect on fR, but the underlying mechanisms need further clarification (Tipton et al. 2017). For a series of additional aspects that can affect the ventilatory pattern (e.g. mechanical aspects), the reader is referred to Tipton et al. (2017). The fact that fR and VT are regulated by different inputs may also help explain why they differ in the timing of their responses. With the onset of a variety of stressors (Tipton et al. 2017) including exercise (Nicolò et al. 2017), a fast increase in fR and a delayed response of VT is often observed. Among respiratory physiologists, it is commonly proposed that fast inputs drive the rapid increase in ventilation in such conditions, while metabolic stimuli fine-tune the control of ventilation in order to meet the metabolic requirements. We propose that the fast increase in V̇E is guaranteed by a preferential regulation of fR, while the fine-tuning role is played by VT. Therefore, fR and VT can be respectively regarded as the behavioural and metabolic components of V̇E. This differential control of fR and VT is particularly favourable if we consider how they affect alveolar ventilation (Tipton et al. 2017). While we acknowledge that our interpretation of the control of fR and VT is a simplification of the complex regulation of breathing, it has important implications for teaching and future research in different disciplines.

Carbohydrate Mouth Rinsing: Improved Neuromuscular Performance During Isokinetic Fatiguing Exercise

PURPOSE To determine whether repeated carbohydrate (CHO) mouth rinsing would improve neuromuscular performance during high-intensity fatiguing contractions. METHODS Eighteen young men (age 26.1 ± 5.0 y, BMI 22.9 ± 1.9) performed 3 maximal voluntary isometric contractions (MVICPRE). Immediately after, they completed 10-second mouth rinse with 6.4% maltodextrin solution (MAL), 7.1% glucose solution (GLU), water (W), artificially sweetened solution (PLA), or a control trial with no rinse (CON) in a crossover protocol. Subjects performed 5 sets of 30 isokinetic fatiguing contractions at 180°/s, and an MVICPOST with their elbow flexors was performed after each mouth rinse. Mechanical and electromyographic (EMG) signals were recorded from the biceps brachii and parameters of interest analyzed. RESULTS When rinsing the mouth with a solution containing CHO, independently of the sweetness, isokinetic performance was enhanced as shown by the greater total work achieved in comparison with CON. The decay of torque and mean fiber-conduction velocity (MFCV) recorded at the end of the fatiguing task was lower when rinsing the mouth with GLU than with CON. The torque recorded during the MVICPOST was greater with CHO with respect to CON, and this was associated to a lower decay of MFCV. CONCLUSIONS CHO mouth rinse counteracts fatigue-induced decline in neuromuscular performance, supporting the notion that CHO rinse may activate positive afferent signals able to modify motor output. Repeated mouth rinsing with sweet and nonsweet CHO-containing solutions can improve neuromuscular performance during an isokinetic intermittent fatiguing task.

Respiratory Frequency during Exercise: The Neglected Physiological Measure

The use of wearable sensor technology for athlete training monitoring is growing exponentially, but some important measures and related wearable devices have received little attention so far. Respiratory frequency (fR), for example, is emerging as a valuable measurement for training monitoring. Despite the availability of unobtrusive wearable devices measuring fR with relatively good accuracy, fR is not commonly monitored during training. Yet fR is currently measured as a vital sign by multiparameter wearable devices in the military field, clinical settings, and occupational activities. When these devices have been used during exercise, fR was used for limited applications like the estimation of the ventilatory threshold. However, more information can be gained from fR. Unlike heart rate, V˙O2, and blood lactate, fR is strongly associated with perceived exertion during a variety of exercise paradigms, and under several experimental interventions affecting performance like muscle fatigue, glycogen depletion, heat exposure and hypoxia. This suggests that fR is a strong marker of physical effort. Furthermore, unlike other physiological variables, fR responds rapidly to variations in workload during high-intensity interval training (HIIT), with potential important implications for many sporting activities. This Perspective article aims to (i) present scientific evidence supporting the relevance of fR for training monitoring; (ii) critically revise possible methodologies to measure fR and the accuracy of currently available respiratory wearables; (iii) provide preliminary indication on how to analyze fR data. This viewpoint is expected to advance the field of training monitoring and stimulate directions for future development of sports wearables.

Investigating the effect of exercise duration on functional and biochemical perturbations in the human heart: total work or 'isoeffort' matching?

Intensity and duration of exercise are the two fundamental components of an acute bout of exercise. Understanding the effect of intensity and duration of exercise on physiological responses is of paramount importance for exercise prescription. In a recent paper by Stewart et al. (2016) published in The Journal of Physiology the authors investigated the effect of exercise duration on functional and biochemical perturbations in the human heart. Stewart et al. compared a 90-min trial at 110% of the gas exchange threshold (GET) with a 120-min trial at 80% of GET, matching total external mechanical work between trials. Overall cardiac stress was found to be considerably higher during and following the 90-min trial. This led the authors to conclude that the GET demarcates a threshold for exercise-induced cardiac functional stress and for the release of cardiac biomarkers. While we recognise the value of this investigation, we believe that the methodology used by the authors may have biased their results. Specifically, we are concerned that physical stress (and more precisely effort) was not appropriately matched by balancing total mechanical work. This matching method presents a confounding factor that potentially explains per se the between-trial differences in cardiac stress that Stewart et al. (2016) instead attribute to exercising at an intensity higher than GET. The practice of matching total work for different exercise durations has been widely used in exercise physiology, but it is now recognised that this method is not appropriate as it results in between-trial differences in the participants’ overall effort (Seiler et al. 2013; Nicolò et al. 2014). This is important because participants’ effort rather than work done is likely to be more indicative of the stress experienced during exercise. To overcome this limitation, an ‘isoeffort’ approach can be used, which is based on the exercise prescription method commonly used by athletes (Seiler et al. 2013; Nicolò et al. 2014). An ‘isoeffort’ matching approach can be achieved for constant workload trials if exercise is prescribed by means of the highly predictable curvilinear relationship between exercise intensity and duration. This relationship between exercise intensity and duration is described by a power-law curve when effort is constant. This relationship has been found to hold true across different species and exercise modalities (Kennelly, 1906; Morton & Hodgson, 1996). Consequently, the relationship between the total work done and exercise duration should be described by a linear function, i.e. a longer exercise duration is matched to a higher (not the same) total work done. Thus as in the study of Stewart et al. where the increased exercise duration is not matched by an increase in total work, the effort exerted for the whole exercise bout will be decreased. The situation is analogous to asking a runner to cover exactly the same distance (a proxy for total work when running) in 90 min or 120 min. In this example it is clear that the effort and cardiac stress of the 120-min trial would be notably lower even though total work was the same. The fact that one trial was performed above GET and the other below may be seen only as a contributing factor, not necessarily the reason for the lower cardiac stress. The most practical way to measure effort during exercise is by means of the rating of perceived exertion (RPE). Trials with different duration are matched for effort if no between-trial differences in RPE are observed when values are normalised to relative exercise duration (Nicolò et al. 2016). Yet, when RPE is normalised in this way it is seen that the total work increases with exercise duration (Nicolò et al. 2016). A physiological correlate of effort is respiratory frequency (fR) (Nicolò et al. 2014, 2016). Similar to RPE, ‘isoeffort’ trials differing in exercise duration do not show differences in fR when values are normalised to relative exercise duration (Nicolò et al. 2016). Whilst fR appears to be a very robust correlate of RPE (Nicolò et al. 2014, 2016), there is also evidence suggesting that perceived exertion and fR are linked by a common regulation mechanism, i.e. central command (Nicolò et al. 2015, 2016). Therefore, physiologically, effort can be defined as the degree of motor effort (i.e. the magnitude of central command). Given the important role of central command in cardiovascular control during exercise (Williamson et al. 2006; Green & Paterson, 2008), it is plausible that the lower cardiac strain observed by Stewart et al. (2016) in the 120-min trial was influenced by the lower central command. An amendment to the experimental design by Stewart et al. (2016) could experimentally help verify whether the observed responses were determined by a lower effort (our interpretation) or by the fact that the subjects were exercising below the cardiac stress intensity threshold (authors’ interpretation). This would be determined by including an extra 120-min bout of exercise at an exercise intensity identified to require the same effort as the 90-min trial (i.e. somewhere between 110% of GET and 80% of GET). Note too that the exact required power output could be predicted in advance from the power–duration relationship for each individual. Using the power–duration relationship for exercise prescription is straightforward, and can be adopted to prescribe both maximal and submaximal exercise. During the trials valuable insight into effort levels could be obtained by measuring RPE and fR. The proposed addition to the experimental design of Stewart et al. (2016) would further our understanding of the dose–response relationship between endurance exercise and acute cardiac stress/injury. At present, we are concerned that the findings of Stewart et al. (2016) do not necessarily support their conclusion that the GET demarcates a threshold for cardiac stress, because, together with exercise duration, effort was altered too. Further studies are needed to address this issue and inform exercise prescription.

A Wearable System for Real-Time Continuous Monitoring of Physical Activity

Over the last decades, wearable systems have gained interest for monitoring of physiological variables, promoting health, and improving exercise adherence in different populations ranging from elite athletes to patients. In this paper, we present a wearable system for the continuous real-time monitoring of respiratory frequency (fR), heart rate (HR), and movement cadence during physical activity. The system has been experimentally tested in the laboratory (by simulating the breathing pattern with a mechanical ventilator) and by collecting data from one healthy volunteer. Results show the feasibility of the proposed device for real-time continuous monitoring of fR, HR, and movement cadence both in resting condition and during activity. Finally, different synchronization techniques have been investigated to enable simultaneous data collection from different wearable modules.

Parameters of the 3-Minute All-Out Test: Overestimation of Competitive-Cyclist Time-Trial Performance in the Severe-Intensity Domain

PURPOSE To verify the accuracy of predicting performance in the severe-intensity domain by means of end-test power output (EP) and the work performed above EP (WEP) obtained from a 3-min all-out test in competitive cyclists. METHODS Ten welltrained cyclists performed a ramp incremental test and a 3-min all-out familiarization test. Subsequently, they performed a 3-min all-out experimental test to obtain EP and WEP and a 10-min time trial (TT). The actual 10-min-TT mean power output was then compared with the power output predicted as P = WEP/Tlim + EP, where Tlim corresponds to 600 s. The ramp-test peak power output (PPO) was compared with PPO predicted as [Formula: see text], where S represents the ramp slope (0.5 W/s). RESULTS The actual (347 ± 30 W) and predicted (376 ± 48 W) 10-min TT mean power output were correlated (r = .87, P = .001) but significantly different (P < .01). The coefficient of variation (CV) between the predicted and actual performance was 5.6% ± 4.4%. The error of prediction was positively correlated to EP (r = .80, P = .005) and negatively correlated to WEP (r = -.71, P = .021). No significant difference was found between the 10-min-TT mean power output and EP (351 ± 53 W). The actual (438 ± 30 W) and predicted (472 ± 41 W) ramp PPO were correlated (r = .88, P < .001) but significantly different (P < .001). The CV between the predicted and actual PPO was 5.2% ± 3%. The error of prediction was positively correlated to EP (r = .63, P = .051). CONCLUSIONS EP and WEP obtained from a 3-min all-out test overestimate severe-intensity performance in competitive cyclists.