Functionality, muscular strength and cardiorespiratory capacity in the elderly: relationships between functional and physical tests according to sex and age.

Introduction: There are several tests that provide information about physical fitness and functionality in older adults. The aims of this study were: (i) to analyze the differences between sex and age in functional, strength and cardiorespiratory tests; and (ii) to study the correlations between functional, strength and cardiorespiratory tests according to sex and age. Methods: A total of 171 older adults (72.09 ± 13.27 kg; 1.59 ± 0.09 m; 72.72 ± 6.05 years) were divided according to sex (men: n = 63; women: n = 108) and age (≥60 <70: n = 65; ≥70 <80: n = 89; ≥80: n = 18). Anthropometry, body composition, upper limb strength (hand grip; HG), lower limb strength (countermovement jump; CMJ), cardiorespiratory capacity (6 min walking test; 6MWT), timed up and go test (TUG) and Short Physical Performance Battery (SPPB) were assessed. Results: Men showed higher values in CMJ height, HG and expired volume (VE) (p < 0.05). There were no significant differences between sexes in TUG and SPPB. Regarding age, there were significant differences in CMJ, VE and peak oxygen uptake (VO2peak), TUG, gait speed, chair and stand test and SPPB total (p < 0.05). The test times were higher in older people. Regarding correlations, the TUG showed significant correlations in all strength and cardiorespiratory tests, regardless of sex and age. The CMJ correlated more significantly with functional tests compared to HG. Discussion: There were sex and age differences in functional, strength, and cardiorespiratory tests. The execution of quick and low-cost tests such as the CMJ and TUG could provide information on overall physical fitness in older adults.

Reliability and validity of a low-cost, wireless sensor and smartphone app for measuring force during isometric and dynamic resistance exercises.

The aim of this study was to investigate the reliability and validity of an affordable wireless force sensor in measuring mean and peak forces during resistance training.A Suiff Pro wireless force sensor (Suiff, Spain) and a MuscleLab force platform (Ergotest, Norway) were used concurrently to assess tensile load and the ground reaction force resulting from an upright row exercise. Thirteen participants (28.2 ± 5.7 years, 76.2 ± 9.6 kg, 178.2 ± 9.2 cm) performed the exercise under three velocity conditions and isometrically. Each condition involved three sets of exercise. Mean (Fmean) and peak (Fpeak) force values from both sensors were collected and compared.Suiff Pro exhibited excellent reliability for Fmean and Fpeak (ICCs = 0.99). When compared to the criterion measures, Suiff Pro showed trivial standardized bias for Fmean (Mean = 0.00 [CI 95% = 0.00 to 0.01]) and Fpeak (-0.02 [-0.04 to 0.00]). The standardized typical error was also trivial for Fmean (0.03 [0.02 to 0.03]) and Fpeak (0.07 [0.05 to 0.09]). Correlations with the MuscleLab force platform were nearly perfect: Fmean (0.97 [0.94 to 0.98]; p<0.001); Fpeak (0.96 [0.92 to 0.97]; p<0.001).The findings demonstrate that the Suiff Pro sensor is reliable and valid device for measuring force during isometric and dynamic resistance training exercises. Therefore, practitioners can confidently use this device to monitor kinematic variables of resistance training exercises and to obtain real-time augmented feedback during a training session.

Validity of a low-cost friction encoder for measuring velocity, force and power in flywheel exercise devices.

The purpose of this study was to investigate the validity of a low-cost friction encoder against a criterion measure (strain gauge combined with a linear encoder) for assessing velocity, force and power in flywheel exercise devices. Ten young and physically active volunteers performed two sets of 14 maximal squats on a flywheel inertial device (YoYo Technology, Stockholm, Sweden) with five minutes rest between each set. Two different resistances were used (0.075 kg · m2 for the first set; 0.025 kg · m2 for the second). Mean velocity (Vrep), force (Frep) and power (Prep) for each repetition were assessed simultaneously via a friction encoder (Chronojump, Barcelona, Spain), and with a strain gauge combined with a linear encoder (MuscleLab 6000, Ergotest Technology, Porsgrunn, Norway). Results are displayed as (Mean [CI 90%]). Compared to criterion measures, mean bias for the practical measures of Vrep, Frep and Prep were moderate (-0.95 [-0.99 to -0.92]), small (0.53 [0.50 to 0.56]) and moderate (-0.68 [-0.71 to -0.65]) respectively. The typical error of estimate (TEE) was small for all three parameters; Vrep (0.23 [0.20 to 0.25]), Frep (0.20 [0.18 to 0.22]) and Prep (0.18 [0.16 to 0.20]). Correlations with MuscleLab were nearly perfect for all measures in all load configurations. Based on these findings, the friction encoder provides valid measures of velocity, force and power in flywheel exercise devices. However, as error did exist between measures, the same testing protocol should be used when assessing changes in these parameters over time, or when aiming to perform inter-individual comparisons.

A novel application of entropy analysis for assessing changes in movement variability during cumulative tackles in young elite rugby league players.

The aim of this study was to identify between-position (forwards vs. backs) differences in movement variability in cumulative tackle events training during both attacking and defensive roles. Eleven elite adolescent male rugby league players volunteered to participate in this study (mean ± SD, age; 18.5 ± 0.5 years, height; 179.5 ± 5.0 cm, body mass; 88.3 ± 13.0 kg). Participants performed a drill encompassing four blocks of six tackling (i.e. tackling an opponent) and six tackled (i.e. being tackled by an opponent while carrying a ball) events (i.e. 48 total tackles) while wearing a micro-technological inertial measurement unit (WIMU, Realtrack Systems, Spain). The acceleration data were used to calculate sample entropy (SampEn) to analyse the movement variability during tackles performance. In tackling actions SampEn showed significant between-position differences in block 1 (p = 0.0001) and block 2 (p = 0.0003). Significant between-block differences were observed in backs (block 1 vs 3, p = 0,0021; and block 1 vs 4, p = 0,0001) but not in forwards. When being tackled, SampEn showed significant between-position differences in block 1 (p = 0.0007) and block 3 (p = 0.0118). Significant between-block differences were only observed for backs in block 1 vs 4 (p = 0,0025). Movement variability shows a progressive reduction with cumulative tackle events, especially in backs and when in the defensive role (tackling). Forwards present lower movement variability values in all blocks, particularly in the first block, both in the attacking and defensive role. Entropy measures can be used by practitioners as an alternative tool to analyse the temporal structure of variability of tackle actions and quantify the load of these actions according to playing position.

Influence of Turn Cycle Structure on Performance of Elite Alpine Skiers Assessed through an IMU in Different Slalom Course Settings.

Small differences in turn cycle structure, invisible to the naked eye, could be decisive in improving descent performance. The aim of this study was to assess the influence of turn cycle structure on the performance of elite alpine skiers using an inertial measurement unit (IMU) in different slalom (SL) course settings. Four SL courses were set: a flat-turned (FT), a steep-turned (ST), a flat-straighter (FS) and a steep-straighter (SS). Five elite alpine skiers (21.2 ± 3.3 years, 180.2 ± 5.6 cm, 72.8 ± 6.6 kg) completed several runs at maximum speed for each SL course. A total of 77 runs were obtained. Fast total times correlate with a longer initiation (INI) time in FT, a shorter steering time out of the turn (STEOUT) in the FT and FS and a shorter total steering time (STEIN+OUT) in the FT and SS courses. The linear mixed model used for the analysis revealed that in the FT-course for each second increase in the INI time, the total time is reduced by 0.45 s, and for every one-second increase in the STEOUT and STEIN+OUT times, the total time increases by 0.48 s and 0.31 s, respectively. Thus, to enhance descent performance, the skier should lengthen the INI time and shorten the STEOUT and STEIN+OUT time. Future studies could use an IMU to detect turn phases and analyze them using the other built-in sensors.

Validity of a Magnet-Based Timing System Using the Magnetometer Built into an IMU.

Inertial measurement units (IMUs) represent a technology that is booming in sports right now. The aim of this study was to evaluate the validity of a new application on the use of these wearable sensors, specifically to evaluate a magnet-based timing system (M-BTS) for timing short-duration sports actions using the magnetometer built into an IMU in different sporting contexts. Forty-eight athletes (22.7 ± 3.3 years, 72.2 ± 10.3 kg, 176.9 ± 8.5 cm) and eight skiers (17.4 ± 0.8 years, 176.4 ± 4.9 cm, 67.7 ± 2.0 kg) performed a 60-m linear sprint running test and a ski slalom, respectively. The M-BTS consisted of placing several magnets along the course in both contexts. The magnetometer built into the IMU detected the peak-shaped magnetic field when passing near the magnets at a certain speed. The time between peaks was calculated. The system was validated with photocells. The 95% error intervals for the total times were less than 0.077 s for the running test and 0.050 s for the ski slalom. With the M-BTS, future studies could select and cut the signals belonging to the other sensors that are integrated in the IMU, such as the accelerometer and the gyroscope.

The Influence of Functional Flywheel Resistance Training on Movement Variability and Movement Velocity in Elite Rugby Players.

The aim of this study was to identify the changes in movement variability and movement velocity during a six-week training period using a resistance horizontal forward-backward task without (NOBALL) or with (BALL) the constraint of catching and throwing a rugby ball in the forward phase. Eleven elite male rugby union players (mean ± SD: age 25.5 ± 2.0 years, height 1.83 ± 0.06 m, body mass 95 ± 18 kg, rugby practice 14 ± 3 years) performed eight repetitions of NOBALL and BALL conditions once a week in a rotational flywheel device. Velocity was recorded by an attached rotary encoder while acceleration data were used to calculate sample entropy (SampEn), multiscale entropy, and the complexity index. SampEn showed no significant decrease for NOBALL (ES = -0.64 ± 1.02) and significant decrease for BALL (ES = -1.71 ± 1.16; p < 0.007) conditions. Additionally, movement velocity showed a significant increase for NOBALL (ES = 1.02 ± 1.05; p < 0.047) and significant increase for BALL (ES = 1.25 ± 1.08; p < 0.025) between weeks 1 and 6. The complexity index showed higher levels of complexity in the BALL condition, specifically in the first three weeks. Movement velocity and complex dynamics were adapted to the constraints of the task after a four-week training period. Entropy measures seem a promising processing signal technique to identify when these exercise tasks should be changed.

Changes in Locomotor Ratio During Basketball Game Quarters From Elite Under-18 Teams.

Quantifying game and training demands in basketball allows to determine player's readiness and optimizes preparation to perform and reduce injury risks. Available research is using tracking technology to perform general descriptions of the game activities at professional levels, but somehow, is not exploring the possibilities of gathering data from new variables that can contribute with complementary information for the coaching staffs. The aim of this study was to identify changes in locomotor ratio, at higher and lower speeds, during the game quarters from elite under-18 basketball teams. Ninety-four male players participated in the study (age: 17.4 ± 0.74 years; height: 199.0 ± 0.1 cm; body mass: 87.1 ± 13.1 kg) from different playing positions, Guards (n = 35), Forwards (n = 42), and Centers (n = 17). Data were gathered from an international tournament and players' movements were measured using a portable ultra-wide band position-tracking system (WIMU PRO®, Realtrack Systems, Almeria, Spain). The following variables were measured: (1) relative distance in different speed zones: walking (<6.0 km·h-1), jogging (6.0-12.0 km·h-1), running (12.1-18.0 km·h-1), high-intensity running (18.1-24.0 km·h-1), and sprinting (>24.1 km·h-1); and (2) player load, vector magnitude expressed as the square root of the sum of the squared instantaneous rates of change in acceleration in each of the three planes divided by 100. Afterward, these variables were used to calculate players' locomotor ratio (player load per meter covered) at higher (running, high-intensity running, and sprinting) and lower speeds (walking and jogging). Results from the locomotor ratio at both lower and higher speeds presented a significant effect for the quarter (F = 7.3, p < 0.001 and F = 7.1, p < 0.001, respectively) and player position (F = 3.1, p = 0.04, F = 9.2, p < 0.001, respectively). There was an increase in the locomotor ratio from game quarter (Q) Q1 to Q4 at lower speeds, but contrary trends at higher speeds, i.e., the values have decreased from Q1 to Q4. Also, forwards and centers of the best teams presented lower values at higher speeds. Altogether, the findings may be used by coaching staffs as a first baseline to elaborate normative behavior models from the players' performance and also to induce variability and adaptation in specific practice planning.

Changes in physical demands between game quarters of U18 elite official basketball games.

PURPOSE: The aim of this study was to describe the physical demands during U18 elite basketball games according to the game quarter and to identify a smaller subset of variables and threshold scores that distinguish players' physical performance in each quarter. METHODS: Data was collected from ninety-four players who participated in the study (age: 17.4 ± 0.74 years; height: 199.0 ± 0.1 cm; body mass: 87.1 ± 13.1 kg) competing in the Euroleague Basketball Next Generation Tournament. Players' movements during the games were measured using a portable local positioning system (LPS) (WIMU PRO®, Realtrack Systems SL, Almería, Spain) and included relative distance (total distance / playing duration), relative distance in established speed zones, high-intensity running (18.1-24.0 km·h-1) and sprinting (> 24.1 km·h-1). player load, peak speed (km·h-1) and peak acceleration (m·s-2) number of total accelerations and total decelerations, high intensity accelerations (> 2 m·s-2) and decelerations (< -2 m·s-2). RESULTS: There was an overall decrease in distance covered, player load, number of high intensity accelerations and decelerations between the first and last quarter of the games in all playing positions. A classification tree analysis showed that the first quarter had much influence of distance covered (above 69.0 meters), distance covered <6.0 km·h-1 and accelerations (> 2 m·s-2), whereas the fourth quarter performance had much influence of distance covered (below 69.0) and distance covered 12.1-18.0 km·h-1. CONCLUSIONS: A significant reduction in physical demands occurs during basketball, especially between first and last quarter for players in all playing positions during basketball games of under 18 elite players.

Physical demands of elite basketball during an official U18 international tournament.

The aims of this study were (a) to compare players' physical demands between different playing positions in elite U18 basketball games and (b) to identify different clusters of performance. Data were collected from 94 male subjects (age: 17.4 ± 0.7 years), competing in a Euroleague Basketball Tournament. Guards covered a greater relative distance than centres and forwards (small to moderate effect). Forwards and guards had more peak accelerations, high accelerations and high decelerations than centres (moderate to large effects). A cluster analysis allowed to classify all cases into three different groups (Lower, Medium and Higher activity demands), containing 37.4%, 52.8% and 9.8% of the cases, respectively. The high accelerations, high decelerations, peak accelerations and total distance covered were the variables that most contributed to classify the players into the new groups. The percentage of cases distributed in the clusters according to playing position, game type (worst vs worst, mixed opposition, best vs best) and team were different. Centres have lower physical demands specially related with the number of accelerations and decelerations at high intensity and the peak acceleration when compared with guards. Each team has a different activity profile, that does not seem to influence the tournament outcome.

Structure of force variability during squats performed with an inertial flywheel device under stable versus unstable surfaces.

The use of unstable surfaces during resistance training has demonstrated a maintenance or reduction on force production. However, the use of unstable surface on force variability has not been assessed using non-linear methods that may be better suited to detect changes in movement variability throughout a given movement. Consequently, this study compared the use of stable vs unstable surfaces on force variability during bilateral squats performed with an inertial flywheel device (Eccoteck, Byomedic System SCP, Spain). Twenty healthy men (mean ±â€¯SD: age 22.9 ±â€¯2.9 years, height 1.81 ±â€¯0.7 m, body mass 76.4 ±â€¯7.6 kg and 1RM back squat 110.9 ±â€¯19.7 kg) with a minimum of four years in resistance training performed six sets of six repetitions of squats at maximal concentric effort with one minute rest between sets. Force output on the vertical axes was measured using a strain gauge and the results were processed using non-linear sample entropy (SampEn). Results showed no differences for any of the dependent variables between stable and unstable conditions. SampEn showed no differences between conditions (chi-squared = 0.048 P = 0.827), while Forcemean and SampEn presented a small correlation (r = 0.184; p < 0.01). No changes in entropy were found over the course of the series. Together, these results suggest that the structure of force variability between stable and unstable surfaces are similar. This lack of difference between surfaces may be due to postural and anticipatory adjustments. Consequently, by introducing unstable surfaces to the flywheel bilateral squat exercise, practitioners may not observe changes in Forcemean and force variability when compared to stable surface training suggesting that increased training volumes or intensity may be required during unstable environments to cause a desired training stimulus.

Criterion Validity of Force and Power Outputs for a Commonly Used Flywheel Resistance Training Device and Bluetooth App.

Weakley, J, Fernández-Valdés, B, Thomas, L, Ramirez-Lopez, C, and Jones, B. Criterion validity of force and power outputs for a commonly used flywheel resistance training device and bluetooth app. J Strength Cond Res 33(5): 1180-1184, 2019-Flywheels are a resistance training device that can increase lean body mass, strength, and power. However, because of their unique design and the inertia from the concentric portion directly relating to the force that is applied during the eccentric portion, monitoring the training stimulus can be difficult. Consequently, the aim of this study was to assess the validity of the kMeter app for quantifying force and power at a range of different isoinertial loads from a flywheel training device when compared against a criterion measure. Eleven subjects volunteered to take part in this study, with subjects completing between 5 and 35 repetitions of the harness squat with 0.05, 0.10, 0.15 kg·m isoinertial load. A synchronized dual force plate and tricamera optoelectronic setup was used as the criterion measure to calculate force and power output, while the kMeter app was used as the practical measure. Very large to nearly perfect relationships were observed between the 2 measures, with trivial to moderate bias reported. In addition, typical error of the estimate (TEE) was found to be <10% at all isoinertial loads. These findings suggest that the kMeter app, when used in conjunction with the kBox flywheel device, demonstrates acceptable levels of validity. However, because of the TEE, the kMeter app may not be able to accurately detect small differences and therefore be suitable for research purposes. These findings suggest that the kMeter app is an acceptable method of monitoring flywheel resistance training. Furthermore, it is advised that practitioners use mean power rather than mean force.

Entropy measures detect increased movement variability in resistance training when elite rugby players use the ball.

OBJECTIVES: This study described the variability in acceleration during a resistance training task, performed in horizontal inertial flywheels without (NOBALL) or with the constraint of catching and throwing a rugby ball (BALL). DESIGN AND METHODS: Twelve elite rugby players (mean±SD: age 25.6±3.0years, height 1.82±0.07m, weight 94.0±9.9kg) performed a resistance training task in both conditions (NOBALL AND BALL). Players had five minutes of a standardized warm-up, followed by two series of six repetitions of both conditions: at the first three repetitions the intensity was progressively increased while the last three were performed at maximal voluntary effort. Thereafter, the participants performed two series of eight repetitions from each condition for two days and in a random order, with a minimum of 10min between series. The structure of variability was analysed using non-linear measures of entropy. RESULTS: Mean changes (%; ±90% CL) of 4.64; ±3.1g for mean acceleration and 39.48; ±36.63a.u. for sample entropy indicated likely and very likely increase when in BALL condition. Multiscale entropy also showed higher unpredictability of acceleration under the BALL condition, especially at higher time scales. CONCLUSIONS: The application of match specific constraints in resistance training for rugby players elicit different amount of variability of body acceleration across multiple physiological time scales. Understanding the non-linear process inherent to the manipulation of resistance training variables with constraints and its motor adaptations may help coaches and trainers to enhance the effectiveness of physical training and, ultimately, better understand and maximize sports performance.