Light exercise heart rate on-kinetics: a comparison of data fitted with sigmoidal and exponential functions and the impact of fitness and exercise intensity

This study examined the suitability of sigmoidal (SIG) and exponential (EXP) functions for modeling HR kinetics at the onset of a 5‐min low‐intensity cycling ergometer exercise test (5MT). The effects of training status, absolute and relative workloads, and high versus low workloads on the accuracy and reliability of these functions were also examined. Untrained participants (UTabs; n = 13) performed 5MTs at 100W. One group of trained participants (n = 10) also performed 5MTs at 100W (ETabs). Another group of trained participants (n = 9) performed 5MTs at 45% and 60% max (ET45 and ET60, respectively). SIG and EXP functions were fitted to HR data from 5MTs. A 30‐s lead‐in time was included when fitting SIG functions. Functions were compared using the standard error of the regression (SER), and test‐retest reliability of curve parameters. SER for EXP functions was significantly lower than for SIG functions across all groups. When residuals from the 30‐s lead‐in time were omitted, EXP functions only outperformed SIG functions in ET60 (EXP, 2.7 ± 1.2 beats·min−1; SIG, 3.1 ± 1.1 beats·min−1: P \u3c 0.05). Goodness of fit and test–retest reliability of curve parameters were best in ET60 and comparatively poor in UTabs. Overall, goodness of fit and test–retest reliability of curve parameters favored functions fitted to 5MTs performed by trained participants at a high and relative workload, while functions fitted to data from untrained participants exercising at a low and absolute workload were less accurate and reliable

Effects of lower limb light-weight wearable resistance on running biomechanics

Wearable resistance allows individualized loading for sport specific movements and can lead to specific strength adaptations benefiting the athlete. The objective was to determine biomechanical changes during running with lower limb light-weight wearable resistance. Fourteen participants (age: 28 ± 4 years; height: 180 ± 8 cm; body mass: 77 ± 6 kg) wore shorts and calf sleeves of a compression suit allowing attachment of light loads. Participants completed four times two mins 20-m over-ground shuttle running bouts at 3.3 m*

Light exercise heart rate on-kinetics: a comparison of data fitted with sigmoidal and exponential functions and the impact of fitness and exercise intensity

This study examined the suitability of sigmoidal (SIG) and exponential (EXP) functions for modeling HR kinetics at the onset of a 5‐min low‐intensity cycling ergometer exercise test (5MT). The effects of training status, absolute and relative workloads, and high versus low workloads on the accuracy and reliability of these functions were also examined. Untrained participants (UTabs; n = 13) performed 5MTs at 100W. One group of trained participants (n = 10) also performed 5MTs at 100W (ETabs). Another group of trained participants (n = 9) performed 5MTs at 45% and 60% Embedded Image max (ET45 and ET60, respectively). SIG and EXP functions were fitted to HR data from 5MTs. A 30‐s lead‐in time was included when fitting SIG functions. Functions were compared using the standard error of the regression (SER), and test‐retest reliability of curve parameters. SER for EXP functions was significantly lower than for SIG functions across all groups. When residuals from the 30‐s lead‐in time were omitted, EXP functions only outperformed SIG functions in ET60 (EXP, 2.7 ± 1.2 beats·min−1; SIG, 3.1 ± 1.1 beats·min−1: P < 0.05). Goodness of fit and test–retest reliability of curve parameters were best in ET60 and comparatively poor in UTabs. Overall, goodness of fit and test–retest reliability of curve parameters favored functions fitted to 5MTs performed by trained participants at a high and relative workload, while functions fitted to data from untrained participants exercising at a low and absolute workload were less accurate and reliable

Effects of acute wearable resistance loading on overground running lower body kinematics

© 2020 Trounson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Field-based sports require athletes to run sub-maximally over significant distances, often while contending with dynamic perturbations to preferred coordination patterns. The ability to adapt movement to maintain performance under such perturbations appears to be trainable through exposure to task variability, which encourages movement variability. The aim of the present study was to investigate the extent to which various wearable resistance loading magnitudes alter coordination and induce movement variability during running. To investigate this, 14 participants (three female and 11 male) performed 10 sub-maximal velocity shuttle runs with either no weight, 1%, 3%, or 5% of body weight attached to the lower limbs. Sagittal plane lower limb joint kinematics from one complete stride cycle in each run were assessed using functional data analysis techniques, both across the participant group and within-individuals. At the group-level, decreases in ankle plantarflexion following toe-off were evident in the 3% and 5% conditions, while increased knee flexion occurred during weight acceptance in the 5% condition compared with unloaded running. At the individuallevel, between-run joint angle profiles varied, with six participants exhibiting increased joint angle variability in one or more loading conditions compared with unloaded running. Loading of 5% decreased between-run ankle joint variability among two individuals, likely in accordance with the need to manage increased system load or the novelty of the task. In terms of joint coordination, the most considerable alterations to coordination occurred in the 5% loading condition at the hip-knee joint pair, however, only a minority of participants exhibited this tendency. Coaches should prescribe wearable resistance individually to perturb preferred coordination patterns and encourage movement variability without loading to the extent that movement options become limited

Effects of lower limb light-weight wearable resistance on running biomechanics

Wearable resistance allows individualized loading for sport specific movements and can lead to specific strength adaptations benefiting the athlete. The objective was to determine biomechanical changes during running with lower limb light-weight wearable resistance. Fourteen participants (age: 28 ± 4 years; height: 180 ± 8 cm; body mass: 77 ± 6 kg) wore shorts and calf sleeves of a compression suit allowing attachment of light loads. Participants completed four times two mins 20-m over-ground shuttle running bouts at 3.3 m*s−1 alternated by three mins rest. The first running bout was unloaded and the other three bouts were under randomised loaded conditions (1%, 3% and 5% additional loading of the individual body mass). 3D motion cameras and force plates recorded kinematic and kinetic data at the midpoint of each 20-m shuttle. Friedman-test for repeated measures and linear mixed effect model analysis were used to determine differences between the loading conditions (α = 0.05). Increased peak vertical ground reaction force (2.7 N/kg to 2.74 N/kg), ground contact time (0.20 s to 0.21 s) and decreased step length (1.49 m to 1.45 m) were found with additional 5 % body mass loading compared to unloaded running (0.001 > p < 0.007). Marginally more knee flexion and hip extension and less plantarflexion was seen with higher loading. Differences in the assessed parameters were present between each loading condition but accompanied by subject variability. Further studies, also examining long term effects, should be conducted to further inform use of this training tool

Joint angles during early sprint acceleration with wearable resistance among Australian Rules football players

Rapid acceleration is an important quality for field-based sport athletes. Technical factors contribute to greater acceleration and these can be deliberately influenced by coaches through implementation of constraints, which afford particular coordinative states or induce variability generally. Lightweight wearable resistance is an emerging training tool, which can act as a constraint on acceleration. At present, however, the effects on whole-body coordination resulting from different WR configurations and magnitudes are unknown. To better understand these effects, five male Australian Rules football athletes performed a series of 20 m sprints with either relatively light or heavy wearable resistance applied to the anterior or posterior aspects of the thighs or shanks. Whole body coordination during one complete stride in early acceleration was examined across eight wearable resistance conditions and compared with baseline (unresisted) acceleration coordination using group- and individual-level hierarchical cluster analysis. Self-organising maps and a joint-level distance matrix were used to further investigate specific kinematic changes in conditions where coordination differed most from baseline. Across the group, relatively heavy wearable resistance applied to the thighs resulted in the greatest difference to whole-body coordination compared with baseline acceleration. On average, heavy posterior thigh wearable resistance led to altered pelvic position and greater hip extension, while heavy anterior thigh wearable resistance led to accentuated movement at the shoulders in the transverse and sagittal planes. These findings offer a useful starting point for coaches seeking to use wearable resistance to promote the emergence of greater hip extension or upper body contribution during acceleration. Importantly, individuals varied in how they responded to heavy thigh wearable resistance, which coaches should be mindful of. The present findings also offer more focussed future research directions around the question of coordinative changes in response to wearable resistance, which may be investigated among larger sample groups.Study design Testing was undertaken in the Biomechanics Laboratory at Victoria University, Footscray Park, Melbourne, Australia. Five participants attended the laboratory on 10 occasions in total, comprising one familiarisation session, one baseline testing session, and eight WR testing sessions. Each testing session was separated by at least 1 week. During testing sessions, participants performed four maximal 20 m sprints commencing from a stationary position, interspersed with 3 min rest periods. During WR testing sessions, participants were exposed to one of eight unique WR loading configurations and magnitudes when performing sprints 2-4. The order of exposure to each WR loading configuration and magnitude was randomised. In each sprint, 10 m split and 20 m sprint times were recorded, and whole-body spatiotemporal measures and joint kinematics were captured at the 4 m mark to examine coordination during the early acceleration phase of sprinting. Experimental setup A 20 m section of the Biomechanics Laboratory with Mondo track surface defined the sprint area. Infrared timing gates (Smart Speed, Fusion Sport, Brisbane, Australia) were situated at the 0, 10, and 20 m marks along the sprint area. For each sprint, participants adopted a self-selected 2-point upright starting stance with the front foot 0.9 m behind the starting line. Timing began when the timing gates at the 0 m mark were triggered by the participant commencing their sprint. Motion analysis cameras were arranged around the 4 m mark and the approximate capture volume was 5.0 m long, 2.5 m high, and 3.0 m wide. A 10-camera VICON motion analysis system (T-40 series, Vicon Nexus v2, Oxford, UK) sampling at 250 Hz was used for collection of whole-body spatiotemporal and joint kinematic data. A total of 58 reflective markers with 12.7 mm diameter were attached to body landmarks on the upper arms, trunk, pelvis, thighs, shanks, and feet according to the Plug-In-Gait model (Plug-In-Gait Marker Set, Vicon, Oxford, UK). Wearable resistance Throughout testing, participants wore LilaTM ExogenTM (Sportboleh Sdh Bhd, Kuala Lumpur, Malaysia) compression shorts and calf sleeves. During WR exposure trials, a combination of 50, 100, and 200 g fusiform shaped loads (with Velcro backing) totalling the required loading magnitude were attached to the compression garments. Four loading configurations were investigated – anterior thigh, posterior thigh, anterior shank, and posterior shank – with both "light" and "heavy" loading magnitudes in each, totalling eight WR conditions. "Light" and "heavy" loading magnitudes corresponded to an increase of 3% and 6% in the moment of inertia about the hip throughout an acceleration stride, respectively, in accordance with sagittal plane lower limb motion previously observed during early acceleration. Participant height and weight was used to determine the specific loading magnitudes required at each segment to satisfy these conditions based on Plagenhoef's estimations of segment parameters. Table 1 provides an example of the loading magnitudes per leg for a 180 cm, 70 kg male. Fusiform loads were added in an alternating fashion between a proximal-dominant and distal-dominant orientation. The smallest number of possible loads to achieve the required loading magnitude was used. Table 1. Example loading magnitudes for a 180 cm, 70 kg male participant. Magnitude Configuration Light (g per leg) Heavy (g per leg) Anterior thigh 550 1100 Posterior thigh 550 1100 Anterior shank 250 500 Posterior shank 250 500 Data collection Following the application of compression garments and attachment of reflective markers, participants undertook an initial warm-up, which consisted of a series of dynamic mobility drills followed by four sub-maximal 20 m sprints. The 15-grade Borg rating of perceived exertion (RPE) scale was explained to participants, and instruction was given to perform the four warm-up sprints corresponding to "fairly light", "somewhat hard", "hard", and "very hard" levels of exertion, respectively. Participants then performed four maximal 20 m sprints, each separated by 3 minutes rest. The only instruction provided to participants was to sprint as fast as possible. In WR testing sessions, researchers applied the requisite WR loads to the participant during the rest period between the first and second sprint, and the WR was left on for the remaining three sprints. Data processing Raw marker data were labelled in Vicon Nexus with spline filling used in instances of marker drop out (up to a maximum of 10 frames). Whole body spatiotemporal measures and joint kinematics were then obtained from Visual 3D software (C-motion, Rockville, MD, USA). First, a global reference system was defined with the positive Y-axis horizontal in the direction of the sprint, the positive X-axis perpendicular to the Y-axis – horizontal in the right direction, and the positive Z-axis in the vertical direction. Marker trajectories were smoothed via a fourth order low-pass Butterworth filter with 10 Hz cut-off frequency, based on mean results of residual analyses. A 10-segment model (upper arms, trunk, pelvis, thighs, shanks, and feet) was then constructed for each participant. Each sprint was trimmed to one complete stride cycle, which was defined as the period between two consecutive toe-off events on the same limb. Toe-off was defined by the initial rise in vertical displacement of the toe marker proceeding its lowest point at the end of the stance phase. Without explicit instruction, all participants chose to commence sprints with the left foot forward. Analysis was therefore able to be carried out on the stride defined by left foot toe-off to left foot toe-off corresponding to steps 3 and 4 of the sprint effort. This stride was taken as representative of the first phase of acceleration identified by Nagahara et al., (2014), who reported, on average, a definitive breakpoint in acceleration kinematics beyond step 4. Of the 180 captured sprints, only three were unable to be successfully reconstructed according to the above process and these were excluded from analysis. In all instances, sprints 2-4 from each testing session were used when comparing effects between conditions, unless otherwise stated. For whole-body spatiotemporal measures; centre of mass (COM) velocity was taken as the average COM velocity in the Y-axis across the stride. Flight time was defined as the point of take-off from one foot to the point of ground contact on the contralateral foot. Ground contact time was defined as the point of initial ground contact until the point of take-off on the same foot. Step length was defined as the horizontal distance between successive toe-off events of each contralateral foot. Flight time, ground contact time, and step length were all calculated as an average across the two steps composing the stride cycle. Step frequency was defined as the number of steps taken per second and was calculated as the inverse of stride duration multiplied by two. For joint kinematics, sagittal, frontal, and transverse plane angles were computed from the transformation between two adjacent segments' local coordinate systems described by an XYZ Cardan sequence of rotations. The following joints/segments were included: pelvis, thorax, right and left side hips, knees, ankles, and shoulders. In all cases, proximal segments were used as reference segments, except for the pelvis in which angles were defined in relation to the global reference frame. A total of 30 kinematic variables therefore contributed to defining whole body coordination profiles. All angles were normalised to 101 data points (0-100% of the stride cycle) prior to further analysis

Antibodies to a CA 19-9 Related Antigen Complex Identify SOX9 Expressing Progenitor Cells In Human Foetal Pancreas and Pancreatic Adenocarcinoma

The Sialyl Lewis A antigen, or CA 19-9, is the prototype serum biomarker for adenocarcinoma of the pancreas. Despite extensive clinical study of CA 19-9 in gastrointestinal malignancies, surprisingly little is known concerning the specific cell types that express this marker during development, tissue regeneration and neoplasia. SOX9 is a transcription factor that plays a key role in these processes in foregut tissues. We report the biochemistry and tissue expression of the GCTM-5 antigen, a pancreatic cancer marker related to, but distinct from, CA19-9. This antigen, defined by two monoclonal antibodies recognising separate epitopes on a large glycoconjugate protein complex, is co-expressed with SOX9 by foregut ductal progenitors in the developing human liver and pancreas, and in pancreatic adenocarcinoma. These progenitors are distinct from cell populations identified by DCLK1, LGR5, or canonical markers of liver and pancreatic progenitor cells. Co-expression of this antigen complex and SOX9 also characterises the ductal metaplasia of submucosal glands that occurs during the development of Barrett’s oesophagus. The GCTM-5 antigen complex can be detected in the sera of patients with pancreatic adenocarcinoma. The GCTM-5 epitope shows a much more restricted pattern of expression in the normal adult pancreas relative to CA19-9. Our findings will aid in the identification, characterisation, and monitoring of ductal progenitor cells during development and progression of pancreatic adenocarcinoma in man

Blood-Based Protein Biomarkers for Diagnosis of Alzheimer Disease

Objective: To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD). Design: Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data. Setting: General community-based, prospective, longitudinal study of aging. Participants: A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort. Results: A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, β2 microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD. Cross-validated accuracy measures from the AIBL cohort reached a mean (SD) of 85% (3.0%) for sensitivity and specificity and 93% (3.0) for the area under the receiver operating characteristic curve . A second validation using the ADNI cohort attained accuracy measures of 80% (3.0%) for sensitivity and specificity and 85% (3.0) for area under the receiver operating characteristic curve. Conclusions: This study identified a panel of plasma biomarkers that distinguish individuals with AD from cognitively healthy control subjects with high sensitivity and specificity. Cross-validation within the AIBL cohort and further validation within the ADNI cohort provides strong evidence that the identified biomarkers are important for AD diagnosis

Blood-based protein biomarkers for diagnosis of Alzheimer disease

Conclusions: This study identified a panel of plasma biomarkers that distinguish individuals with AD from cognitively healthy control subjects with high sensitivity and specificity. Cross-validation within the AIBL cohort and further validation within the ADNI cohort provides strong evidence that the identified biomarkers are important for AD diagnosis.