We tested the validity of the Hexoskin wearable vest to monitor heart rate (HR), breathing rate (BR), tidal volume (V_T), minute ventilation, and hip motion intensity (HMI) in comparison with laboratory standard devices during lying, sitting, standing, and walking. Twenty healthy young volunteers participated in this study. First, participants walked 6 min on a treadmill at speeds of 1, 3, and 4.5 km/h followed by increasing treadmill grades until 80% of their predicted maximal heart rate. Second, lying, sitting, and standing tasks were performed (5 min each) followed by 6 min of treadmill walking at 80% of their ventilatory threshold. Analysis of each individual’s mean values under each resting or exercise condition by the 2 measurement systems revealed low coefficient of variation and high intraclass correlation values for HR, BR, and HMI. The Bland–Altman results from HR, BR, and HMI indicated no deviation of the mean value from zero and relatively small variability about the mean. V_T and minute ventilation were provided in arbitrary units by the Hexoskin device; however, relative magnitude of change from Hexoskin closely tracked the laboratory standard method. Hexoskin presented low variability, good agreement, and consistency. The Hexoskin wearable vest was a valid and consistent tool to monitor activities typical of daily living such as different body positions (lying, sitting, and standing) and various walking speeds.

Background: This study aimed to determine whether morbidly obese women have an alteration of heart rate (HR) kinetics and HR variability (HRV) during the 6-min walk test (6MWT) and if an aerobic exercise training can modify these indexes after gastric bypass surgery (GBS). Design and methods: Nineteen morbidly obese women were randomized to a trained (TG) or control group and 12 women of eutrophic group (EG) were also evaluated. The obese women were tested on two occasions: 1 week before and 4 months after GBS through record of HR and R-R intervals during 6MWT for analysis HR kinetics. The TG underwent an aerobic exercise training program on a treadmill (1-h session, totaling 36 sessions over 12-week). Results: Both obese groups demonstrated a significant reduction of rMSSD and slower HR kinetics during the 6MWT when compared to the EG. In addition, only the TG demonstrated a significant improvement in HRV indexes, walking distance, faster time constant and mean response time of HR during 6MWT after training (p < 0.05). Conclusion: Morbidly obese women have slower HR kinetics and altered cardiac modulation during submaximal exercise. However, aerobic exercise training can produce beneficial adaptations in HRV and faster HR kinetics following GBS.

Currently, oxygen uptake ("Equation missing"<!-- image only, no MathML or LaTex -->) is the most precise means of investigating aerobic fitness and level of physical activity; however, "Equation missing"<!-- image only, no MathML or LaTex --> can only be directly measured in supervised conditions. With the advancement of new wearable sensor technologies and data processing approaches, it is possible to accurately infer work rate and predict "Equation missing"<!-- image only, no MathML or LaTex --> during activities of daily living (ADL). The main objective of this study was to develop and verify the methods required to predict and investigate the "Equation missing"<!-- image only, no MathML or LaTex --> dynamics during ADL. The variables derived from the wearable sensors were used to create a "Equation missing"<!-- image only, no MathML or LaTex --> predictor based on a random forest method. The "Equation missing"<!-- image only, no MathML or LaTex --> temporal dynamics were assessed by the mean normalized gain amplitude (MNG) obtained from frequency domain analysis. The MNG provides a means to assess aerobic fitness. The predicted "Equation missing"<!-- image only, no MathML or LaTex --> during ADL was strongly correlated (r = 0.87, P < 0.001) with the measured "Equation missing"<!-- image only, no MathML or LaTex --> and the prediction bias was 0.2 ml·min⁻¹·kg⁻¹. The MNG calculated based on predicted "Equation missing"<!-- image only, no MathML or LaTex --> was strongly correlated (r = 0.71, P < 0.001) with MNG calculated based on measured "Equation missing"<!-- image only, no MathML or LaTex --> data. This new technology provides an important advance in ambulatory and continuous assessment of aerobic fitness with potential for future applications such as the early detection of deterioration of physical health.

Previous studies in children and older adults demonstrated faster oxygen uptake (V̇O₂) kinetics in males compared with females, but young healthy adults have not been studied. We hypothesized that young men would have faster aerobic system dynamics in response to the onset of exercise than women. Interactions between oxygen supply and utilization were characterized by the dynamics of V̇O₂, deoxyhemoglobin (HHb), tissue saturation index (TSI), cardiac output (Q̇), and calculated arteriovenous O₂ difference (a–vO_2diff) in women and men. Eighteen healthy active young women and men (9 of each sex) with similar aerobic fitness levels volunteered for this study. Participants performed an incremental cardiopulmonary treadmill exercise test and 3 moderate-intensity treadmill exercise tests (at 80% V̇O₂ of gas exchange threshold). Data related to the moderate exercise were submitted to exponential data modelling to obtain parameters related to the aerobic system dynamics. The time constants of V̇O₂, a–vO_2diff, HHb, and TSI (30 ± 6, 29 ± 1, 16 ± 1, and 15 ± 2 s, respectively) in women were statistically (p < 0.05) faster than the time constants in men (42 ± 10, 49 ± 21, 19 ± 3, and 20 ± 4 s, respectively). Although Q̇ dynamics were not statistically different (p = 0.06) between groups, there was a trend to slower Q̇ dynamics in men corresponding with the slower V̇O₂ kinetics. These results indicated that the peripheral and pulmonary oxygen extraction dynamics were remarkably faster in women. Thus, contrary to the hypothesis, V̇O₂ dynamics measured at the mouth at the onset of submaximal treadmill walking were faster in women compared with men.

Physical activity levels are related through algorithms to the energetic demand, with no information regarding the integrity of the multiple physiological systems involved in the energetic supply. Longitudinal analysis of the oxygen uptake (V̇o₂) by wearable sensors in realistic settings might permit development of a practical tool for the study of the longitudinal aerobic system dynamics (i.e., V̇o₂ kinetics). This study evaluated aerobic system dynamics based on predicted V̇o₂ data obtained from wearable sensors during unsupervised activities of daily living (μADL). Thirteen healthy men performed a laboratory-controlled moderate exercise protocol and were monitored for ≈6 h/day for 4 days (μADL data). Variables derived from hip accelerometer (ACC_HIP), heart rate monitor, and respiratory bands during μADL were extracted and processed by a validated random forest regression model to predict V̇o₂. The aerobic system analysis was based on the frequency-domain analysis of ACC_HIP and predicted V̇o₂ data obtained during μADL. Optimal samples for frequency domain analysis (constrained to ≤0.01 Hz) were selected when ACC_HIP was higher than 0.05 g at a given frequency (i.e., participants were active). The temporal characteristics of predicted V̇o₂ data during μADL correlated with the temporal characteristics of measured V̇o₂ data during laboratory-controlled protocol ([ ] = 0.82, P < 0.001, n = 13). In conclusion, aerobic system dynamics can be investigated during unsupervised activities of daily living by wearable sensors. Although speculative, these algorithms have the potential to be incorporated into wearable systems for early detection of changes in health status in realistic environments by detecting changes in aerobic response dynamics. NEW & NOTEWORTHY The early detection of subclinical aerobic system impairments might be indicative of impaired physiological reserves that impact the capacity for physical activity. This study is the first to use wearable sensors in unsupervised activities of daily living in combination with novel machine learning algorithms to investigate the aerobic system dynamics with the potential to contribute to models of functional health status and guide future individualized health care in the normal population.

Recently, low-level laser (light) therapy (LLLT) has been used to improve muscle performance. This study aimed to evaluate the effectiveness of near-infrared light-emitting diode therapy (LEDT) and its mechanisms of action to improve muscle performance in an elite athlete. The kinetics of oxygen uptake (VO₂), blood and urine markers of muscle damage (creatine kinase – CK and alanine), and fatigue (lactate) were analyzed. Additionally, some metabolic parameters were assessed in urine using proton nuclear magnetic resonance spectroscopy (¹H NMR). A LED cluster with 50 LEDs (λ = 850 nm; 50 mW 15 s; 37.5 J) was applied on legs, arms and trunk muscles of a single runner athlete 5 min before a high-intense constant workload running exercise on treadmill. The athlete received either Placebo-1-LEDT; Placebo-2-LEDT; or Effective-LEDT in a randomized double-blind placebo-controlled trial with washout period of 7 d between each test. LEDT improved the speed of the muscular VO₂ adaptation (∼−9 s), decreased O₂ deficit (∼−10 L), increased the VO₂ from the slow component phase (∼+348 ml min⁻¹), and increased the time limit of exercise (∼+589 s). LEDT decreased blood and urine markers of muscle damage and fatigue (CK, alanine and lactate levels). The results suggest that a muscular pre-conditioning regimen using LEDT before intense exercises could modulate metabolic and renal function to achieve better performance.