Prolonged exercise shifts ventilatory parameters at the moderate-to-heavy intensity transition

PURPOSE

To quantify the effects of prolonged cycling on the rate of ventilation ([Formula: see text]), frequency of respiration (FR), and tidal volume (VT) associated with the moderate-to-heavy intensity transition.

METHODS

Fourteen endurance-trained cyclists and triathletes (one female) completed an assessment of the moderate-to-heavy intensity transition, determined as the first ventilatory threshold (VT1), before (PRE) and after (POST) two hours of moderate-intensity cycling. The power output, [Formula: see text], FR, and VT associated with VT1 were determined PRE and POST.

RESULTS

As previously reported, power output at VT1 significantly decreased by ~ 10% from PRE to POST. The [Formula: see text] associated with VT1 was unchanged from PRE to POST (72 ± 12 vs. 69 ± 13 L.min-1, ∆ - 3 ± 5 L.min-1, ∆ - 4 ± 8%, P = 0.075), and relatively consistent (within-subject coefficient of variation, 5.4% [3.7, 8.0%]). The [Formula: see text] associated with VT1 was produced with increased FR (27.6 ± 5.8 vs. 31.9 ± 6.5 breaths.min-1, ∆ 4.3 ± 3.1 breaths.min-1, ∆ 16 ± 11%, P = 0.0002) and decreased VT (2.62 ± 0.43 vs. 2.19 ± 0.36 L.breath-1, ∆ - 0.44 ± 0.22 L.breath-1, ∆ - 16 ± 7%, P = 0.0002) in POST.

CONCLUSION

These data suggest prolonged exercise shifts ventilatory parameters at the moderate-to-heavy intensity transition, but [Formula: see text] remains stable. Real-time monitoring of [Formula: see text] may be a useful means of assessing proximity to the moderate-to-heavy intensity transition during prolonged exercise and is worthy of further research.

Can Respiration Complexity Help the Diagnosis of Disorders of Consciousness in Rehabilitation?

BACKGROUND

Autonomic Nervous System (ANS) activity, as cardiac, respiratory and electrodermal activity, has been shown to provide specific information on different consciousness states. Respiration rates (RRs) are considered indicators of ANS activity and breathing patterns are currently already included in the evaluation of patients in critical care.

OBJECTIVE

The aim of this work was to derive a proxy of autonomic functions via the RR variability and compare its diagnostic capability with known neurophysiological biomarkers of consciousness.

METHODS

In a cohort of sub-acute patients with brain injury during post-acute rehabilitation, polygraphy (ECG, EEG) recordings were collected. The EEG was labeled via descriptors based on American Clinical Neurophysiology Society terminology and the respiration variability was extracted by computing the Approximate Entropy (ApEN) of the ECG-derived respiration signal. Competing logistic regressions were applied to evaluate the improvement in model performances introduced by the RR ApEN.

RESULTS

Higher RR complexity was significantly associated with higher consciousness levels and improved diagnostic models' performances in contrast to the ones built with only electroencephalographic descriptors.

CONCLUSIONS

Adding a quantitative, instrumentally based complexity measure of RR variability to multimodal consciousness assessment protocols may improve diagnostic accuracy based only on electroencephalographic descriptors. Overall, this study promotes the integration of biomarkers derived from the central and the autonomous nervous system for the most comprehensive diagnosis of consciousness in a rehabilitation setting.

Validity of a novel respiratory rate monitor comprising stretchable strain sensors during a 6-min walking test in patients with chronic pulmonary obstructive disease

BACKGROUND

Breathing frequency is rarely measured during a field walking test since the current monitoring system using a face mask is cumbersome for older adults. For effective clinical application, we aimed to validate the new respiratory monitor using wearable strain sensors during a 6-min walk test (6MWT) in young adults and patients with chronic obstructive pulmonary disease (COPD).

METHODS

The study included young adults and patients with stable COPD voluntarily recruited from three hospitals. Breathing frequency during 6MWT were measured by the strain sensor and a nasal capnometer. Total breathing frequencies were measured by the capnometer. The Bland-Altman method was used to estimate the mean limit of agreement for breathing frequency.

RESULTS

A total of 23 young adults (age = 23.1 ± 3.7, mean ± SD) and 50 patients with COPD (age = 75.2 ± 7.2, %FEV1 = 59.1 ± 19.7) were analyzed. During the entire test period, the total breathing frequencies were measured based on an average of 252 ± 46 breaths, and the total breathing frequency was higher in patients with COPD than in young adults (mean difference = -3.349, p < 0.0013). The mean difference in breathing frequency between the strain sensors and capnometer was -0.28 (95%CI: 0.75 to 0.20), and the limit of agreement ranged from -4.1 to 3.6. The CI of the limit of agreement included the limit of equivalence (4 counts/min).

CONCLUSIONS

The novel respiratory monitor with wearable sensors achieved the target accuracy in both young adults and patients with COPD in the 6MWT.

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 (f_R), for example, is emerging as a valuable measurement for training monitoring. Despite the availability of unobtrusive wearable devices measuring f_R with relatively good accuracy, f_R is not commonly monitored during training. Yet f_R 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, f_R was used for limited applications like the estimation of the ventilatory threshold. However, more information can be gained from f_R. Unlike heart rate, V˙O₂, and blood lactate, f_R 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 f_R is a strong marker of physical effort. Furthermore, unlike other physiological variables, f_R 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 f_R for training monitoring; (ii) critically revise possible methodologies to measure f_R and the accuracy of currently available respiratory wearables; (iii) provide preliminary indication on how to analyze f_R data. This viewpoint is expected to advance the field of training monitoring and stimulate directions for future development of sports wearables.