Melitta A. McNarry

Beetroot juice supplementation speeds O2 uptake kinetics and improves exercise tolerance during severe-intensity exercise initiated from an elevated metabolic rate

Recent research has suggested that dietary nitrate (NO3(-)) supplementation might alter the physiological responses to exercise via specific effects on type II muscle. Severe-intensity exercise initiated from an elevated metabolic rate would be expected to enhance the proportional activation of higher-order (type II) muscle fibers. The purpose of this study was, therefore, to test the hypothesis that, compared with placebo (PL), NO3(-)-rich beetroot juice (BR) supplementation would speed the phase II VO2 kinetics (τ(p)) and enhance exercise tolerance during severe-intensity exercise initiated from a baseline of moderate-intensity exercise. Nine healthy, physically active subjects were assigned in a randomized, double-blind, crossover design to receive BR (140 ml/day, containing ~8 mmol of NO3(-)) and PL (140 ml/day, containing ~0.003 mmol of NO3(-)) for 6 days. On days 4, 5, and 6 of the supplementation periods, subjects completed a double-step exercise protocol that included transitions from unloaded to moderate-intensity exercise (U→M) followed immediately by moderate to severe-intensity exercise (M→S). Compared with PL, BR elevated resting plasma nitrite concentration (PL: 65 ± 32 vs. BR: 348 ± 170 nM, P < 0.01) and reduced the VO2 τ(p) in M→S (PL: 46 ± 13 vs. BR: 36 ± 10 s, P < 0.05) but not U→M (PL: 25 ± 4 vs. BR: 27 ± 6 s, P > 0.05). During M→S exercise, the faster VO2 kinetics coincided with faster near-infrared spectroscopy-derived muscle [deoxyhemoglobin] kinetics (τ; PL: 20 ± 9 vs. BR: 10 ± 3 s, P < 0.05) and a 22% greater time-to-task failure (PL: 521 ± 158 vs. BR: 635 ± 258 s, P < 0.05). Dietary supplementation with NO3(-)-rich BR juice speeds VO2 kinetics and enhances exercise tolerance during severe-intensity exercise when initiated from an elevated metabolic rate.

The influence of training status on the aerobic and anaerobic responses to exercise in children: a review.

Abstract Exercise training represents a potent stimulus to the development of aerobic and anaerobic fitness in adults; whether the same is true in young children is unclear. With the possible exception of peak , many parameters of aerobic and anaerobic fitness remain scarcely investigated in children, especially pubertal children. Despite this lack of empirical evidence, it has been suggested that children may lack trainability and that this may be related to the presence of a maturational threshold below which significant adaptations to training cannot occur. This suggestion requires investigation, not least because the findings of some studies which appear to support this contention may in reality be a reflection of the use of an inappropriate test modality or training programme for the investigation of training status influences. The purpose of this review is therefore to provide insight into the current consensuses and controversies regarding the influence of training in young people.

A Review of Emerging Analytical Techniques for Objective Physical Activity Measurement in Humans

Physical inactivity is one of the most prevalent risk factors for non-communicable diseases in the world. A fundamental barrier to enhancing physical activity levels and decreasing sedentary behavior is limited by our understanding of associated measurement and analytical techniques. The number of analytical techniques for physical activity measurement has grown significantly, and although emerging techniques may advance analyses, little consensus is presently available and further synthesis is therefore required. The objective of this review was to identify the accuracy of emerging analytical techniques used for physical activity measurement in humans. We conducted a search of electronic databases using Web of Science, PubMed, and Google Scholar. This review included studies written in English and published between January 2010 and December 2014 that assessed physical activity using emerging analytical techniques and reported technique accuracy. A total of 2064 papers were initially retrieved from three databases. After duplicates were removed and remaining articles screened, 50 full-text articles were reviewed, resulting in the inclusion of 11 articles that met the eligibility criteria. Despite the diverse nature and the range in accuracy associated with some of the analytic techniques, the rapid development of analytics has demonstrated that more sensitive information about physical activity may be attained. However, further refinement of these techniques is needed.

Heart rate variability reproducibility during exercise

The use of heart rate variability (HRV) parameters during exercise is not supported by appropriate reliability studies. In 80 healthy adults, ECG was recorded during three 6 min bouts of exercise, separated by 6 min of unloaded cycling. Two bouts were at a moderate intensity while the final bout was at a heavy exercise intensity. This protocol was repeated under the same conditions on three occasions, with a controlled start time (pre-determined at the first visit). Standard time and frequency domain indices of HRV were derived. Reliability was assessed by Bland–Altman plots, 95% limits of agreement and intraclass correlation coefficients (ICC). The sample size required to detect a mean difference ≥30% of the between-subject standard deviation was also estimated. There was no systematic change between days. All HRV parameters demonstrated a high degree of reproducibility during baseline (ICC range: 0.58–0.75), moderate (ICC: 0.58–0.85) and heavy intensity exercise (ICC range: 0.40–0.76). The reproducibility was slightly diminished during heavy intensity exercise relative to both unloaded baseline cycling and moderate exercise. This study indicates that HRV parameters can be reliably determined during exercise, and it underlines the importance of standardizing exercise intensity with regard to fitness levels if HRV is to be reliably determined.

Aerobic Function and Muscle Deoxygenation Dynamics during Ramp Exercise in Children

PURPOSE This study aimed to characterize changes in deoxyhemoglobin ([HHb]) response dynamics in boys and girls during ramp incremental exercise to investigate whether the reduced peak oxygen uptake (peak V˙O2) in girls is associated with poorer matching of muscle O2 delivery to muscle O2 utilization, as evidenced by a more rapid increase in [HHb]. METHODS Fifty-two children (31 boys, 9.9 ± 0.6 yr, 1.38 ± 0.07 m, 31.70 ± 5.78 kg) completed ramp incremental exercise on a cycle ergometer during which pulmonary gas exchange and muscle oxygenation parameters were measured. RESULTS When muscle [HHb] was expressed against absolute work rate and V˙O2, girls had an earlier change in [HHb], as evidenced by the lower c/d parameter (girls, 54 ± 20 W, vs boys, 67 ± 19 W, P = 0.023; girls, 0.82 ± 0.28 L·min(-1), vs boys, 0.95 ± 0.19 L·min(-1), P = 0.055) and plateau (girls, 85 ± 12 W, vs boys, 99 ± 18 W, P = 0.031; girls, 1.02 ± 0.25 L·min(-1), vs boys, 1.22 ± 0.28 L·min(-1), P = 0.014). However, when expressed against relative work rate or V˙O2, there were no sex differences in ([HHb]) response dynamics (all P > 0.20). Significant correlations were observed between absolute and fat-free mass normalized peak V˙O2 and the HHb c/d and plateau parameters when expressed against absolute work rate or V˙O2. Furthermore, when entered into a multiple regression model, the [HHb] plateau against absolute V˙O2 contributed 12% of the variance in peak V˙O2 after adjusting for fat-free mass, gas exchange threshold, and body fatness (model R2 = 0.81, P < 0.001). CONCLUSIONS The sex difference in peak V˙O2 in 9- to 10-yr-old children is, in part, related to sex-specific changes in muscle O2 extraction dynamics during incremental exercise.

Influence of exercise intensity on pulmonary oxygen uptake kinetics in young and late middle-aged adults.

It is unclear whether pulmonary oxygen uptake (Vo2) kinetics demonstrate linear, first-order behavior during supra gas exchange threshold exercise. Resolution of this issue is pertinent to the elucidation of the factors regulating oxygen uptake (Vo2) kinetics, with oxygen availability and utilization proposed as putative mediators. To reexamine this issue with the advantage of a relatively large sample size, 50 young (24 ± 4 yr) and 15 late middle-aged (54 ± 3 yr) participants completed repeated bouts of moderate and heavy exercise. Pulmonary gas exchange, heart rate (HR), and cardiac output (Q) variables were measured throughout. The phase II τ was slower during heavy exercise in both young (moderate: 22 ± 9; heavy: 29 ± 9 s; P ≤ 0.001) and middle-aged (moderate: 22 ± 9; heavy: 30 ± 8 s; P ≤ 0.001) individuals. The HR τ was slower during heavy exercise in young (moderate: 33 ± 10; heavy: 44 ± 15 s; P ≤ 0.05) and middle-aged (moderate: 30 ± 12; heavy: 50 ± 20 s; P ≤ 0.05) participants, and the Q τ showed a similar trend (young moderate: 21 ± 13; heavy: 28 ± 16 s; middle-aged moderate: 32 ± 13; heavy: 40 ± 15 s; P ≥ 0.05). There were no differences in primary component Vo2 kinetics between age groups, but the middle-aged group had a significantly reduced Vo2 slow component amplitude in both absolute (young: 0.25 ± 0.09; middle-aged: 0.11 ± 0.06 l/min; P ≤ 0.05) and relative terms (young: 15 ± 10; middle-aged: 9 ± 4%; P ≤ 0.05). Thus Vo2 kinetics do not demonstrate dynamic linearity during heavy intensity exercise. Speculatively, the slower phase II τ during heavy exercise might be attributable to reduced oxygen availability. Finally, the primary and slow components of Vo2 kinetics appear to be differentially influenced by middle age.

Parental influences on children's physical self-perceptions, body composition, and physical activity levels

Abstract Background In the UK, 28% of children are overweight or obese, the deleterious effects of which are well documented. Promotion of physical activity is one solution to preventing obesity. Previous studies have identified parental influence as a factor that can shape a childs physical self-perceptions, and act as a stimulus for physical activity. Therefore, we aimed to assess parental influence and physical self-perceptions on childrens physical activity, and to examine whether these factors affect body composition. Methods We recruited a convenience sample of 13 children from a local primary school in Wales. Testing was done at two timepoints with a 1 week interval. At baseline, anthropometric data of the children were collected, and ActiSleep+ accelerometers (ActiGraph, Pensacola, FL, USA) distributed. Participants were directed to go about their normal activities for 7 consecutive days while wearing the monitor. At the second timepoint, parental influence and the childrens physical self-perception were measured with questionnaires based on the Youth Physical Activity Model and the Childrens Physical Self-Perception Profile, respectively. Spearmans correlation coefficient was used to measure associations between variables of parental influence, physical self-perception, and physical activity. Additionally, multiple regressions were used to measure pathway coefficients. Findings Mean age of the children was 10·46 years (SD 0·52), with mean weight 45·18 kg (11·51) and mean height 1·44 m (0·07). Most of the variables were poorly correlated (p>0·05), with certain exceptions. The strongest correlation was between moderate-to-vigorous physical activity (MVPA) levels and physical condition, a subcategory of physical self-perception ( r =0·752, p=0·002). The weakest correlation was between MVPA and parental involvement ( r =0·644, p=0·009). Analysis of correlations between subcategories of parental influence and childs physical self-perception showed that physical condition was strongly correlated with parental involvement ( r =0·729, p=0·002). Physical condition was also indirectly associated with physical activity levels (path coefficient association with parental involvement r =0·213, p=0·05). Interpretation The present study supports the notion that parental influence, in the form of parental involvement, has a direct, statistically significant, and positive effect on a childs levels of physical activity; it also has an indirect positive effect through a childs perception of their own physical condition, which can subsequently increase physical activity levels. Practitioners should encourage parents to become more involved in their childs choice of physical activity; and parents could provide positive appraisal of their childs perceived physical condition. However, further research with a larger sample size is needed. Funding Applied Sports Science Technology and Medicine Research, Swansea University.

Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers

To enable inter- and intrastudy comparisons it is important to ascertain comparability among accelerometer models. Purpose The purpose of this study was to compare raw and count data between hip-worn ActiGraph GT3X+ and GT9X Link accelerometers. Methods Adults (n = 26 (n = 15 women); age, 49.1 ± 20.0 yr) wore GT3X+ and Link accelerometers over the right hip for an 80-min protocol involving 12–21 sedentary, household, and ambulatory/exercise activities lasting 2–15 min each. For each accelerometer, mean and variance of the raw (60 Hz) data for each axis and vector magnitude (VM) were extracted in 30-s epochs. A machine learning model (Montoye 2015) was used to predict energy expenditure in METs from the raw data. Raw data were also processed into activity counts in 30-s epochs for each axis and VM, with Freedson 1998 and 2011 count-based regression models used to predict METs. Time spent in sedentary, light, moderate, and vigorous intensities was derived from predicted METs from each model. Correlations were calculated to compare raw and count data between accelerometers, and percent agreement was used to compare epoch-by-epoch activity intensity. Results For raw data, correlations for mean acceleration were 0.96 ± 0.05, 0.89 ± 0.16, 0.71 ± 0.33, and 0.80 ± 0.28, and those for variance were 0.98 ± 0.02, 0.98 ± 0.03, 0.91 ± 0.06, and 1.00 ± 0.00 in the X, Y, and Z axes and VM, respectively. For count data, corresponding correlations were 1.00 ± 0.01, 0.98 ± 0.02, 0.96 ± 0.04, and 1.00 ± 0.00, respectively. Freedson 1998 and 2011 count-based models had significantly higher percent agreement for activity intensity (95.1% ± 5.6% and 95.5% ± 4.0%) compared with the Montoye 2015 raw data model (61.5% ± 27.6%; P < 0.001). Conclusions Count data were more highly comparable than raw data between accelerometers. Data filtering and/or more robust raw data models are needed to improve raw data comparability between ActiGraph GT3X+ and Link accelerometers.

Investigating optimal accelerometer placement for energy expenditure prediction in children using a machine learning approach

Accurate measurement of energy expenditure (EE) is imperative for identifying and targeting health-associated implications. Whilst numerous accelerometer-based regression equations to predict EE have been developed, there remains little consensus regarding optimal accelerometer placement. Therefore, the purpose of the present study was to validate and compare artificial neural networks (ANNs) developed from accelerometers worn on various anatomical positions, and combinations thereof, to predict EE. Twenty-seven children (15 boys; 10.8  ±  1.1 years) participated in an incremental treadmill test and 30 min exergaming session wearing a portable gas analyser and nine ActiGraph GT3X+  accelerometers (chest and left and right wrists, hips, knees, and ankles). Age and sex-specific resting EE equations (Schofield) were used to estimate METs from the oxygen uptake measures. Using all the data from both exergames, incremental treadmill test and the transition period in between, ANNs were created and tested separately for each accelerometer and for combinations of two or more using a leave-one-out approach to predict EE compared to measured EE. Six features (mean and variance of the three accelerometer axes) were extracted within each 15 s window as inputs in the ANN. Correlations and root mean square error (RMSE) were calculated to evaluate prediction accuracy of each ANN, and repeated measures ANOVA was used to statistically compare accuracy of the ANNs. All single-accelerometer ANNs and combinations of two-, three-, and four-accelerometers performed equally (r  =  0.77-0.82), demonstrating higher correlations than the 9-accelerometer ANN (r  =  0.69) or the Freedson linear regression equation (r  =  0.75). RMSE did not differ between single-accelerometer ANNs or combinations of two, three, or four accelerometers (1.21-1.31 METs), demonstrating lower RMSEs than the 9-accelerometer ANN (1.46 METs) or Freedson equation (1.74 METs). These findings provide preliminary evidence that ANNs developed from single accelerometers mounted on various anatomical positions demonstrate equivalency in the accuracy to predict EE in a semi-structured setting, supporting the use of ANNs in improving EE prediction accuracy compared with linear regression.

A spherical-plot solution to linking acceleration metrics with animal performance, state, behaviour and lifestyle

BackgroundWe are increasingly using recording devices with multiple sensors operating at high frequencies to produce large volumes of data which are problematic to interpret. A particularly challenging example comes from studies on animals and humans where researchers use animal-attached accelerometers on moving subjects to attempt to quantify behaviour, energy expenditure and condition.ResultsThe approach taken effectively concatinated three complex lines of acceleration into one visualization that highlighted patterns that were otherwise not obvious. The summation of data points within sphere facets and presentation into histograms on the sphere surface effectively dealt with data occlusion. Further frequency binning of data within facets and representation of these bins as discs on spines radiating from the sphere allowed patterns in dynamic body accelerations (DBA) associated with different postures to become obvious.MethodWe examine the extent to which novel, gravity-based spherical plots can produce revealing visualizations to incorporate the complexity of such multidimensional acceleration data using a suite of different acceleration-derived metrics with a view to highlighting patterns that are not obvious using current approaches. The basis for the visualisation involved three-dimensional plots of the smoothed acceleration values, which then occupied points on the surface of a sphere. This sphere was divided into facets and point density within each facet expressed as a histogram. Within each facet-dependent histogram, data were also grouped into frequency bins of any desirable parameters, most particularly dynamic body acceleration (DBA), which were then presented as discs on a central spine radiating from the facet. Greater radial distances from the sphere surface indicated greater DBA values while greater disc diameter indicated larger numbers of data points with that particular value.ConclusionsWe indicate how this approach links behaviour and proxies for energetics and can inform our identification and understanding of movement-related processes, highlighting subtle differences in movement and its associated energetics. This approach has ramifications that should expand to areas as disparate as disease identification, lifestyle, sports practice and wild animal ecology.UCT Science Faculty Animal Ethics 2014/V10/PR (valid until 2017).