Introduction The Swiss Armed Forces have released a fitness app for personalised physical training that takes into account the current endurance performance. This is done by means of an integrated digital self-test of physical performance. The Cooper test (12-minute run), which is widely used in the military setting, is too long for the digital selftest. This study aims to investigate whether a self-paced 4-minute all out outdoor run (4Minmax run) is a valid method to assess endurance performance for personalised training planning in the app. Methods On the same day, the subjects completed a 4Minmax run on a flat 300 m circular track and a maximal exercise test (VO2max-Test) using a graded protocol (figure 1). Average speed was calculated from the 4Minmax run (v4Min), the relative maximum oxygen uptake (VO2max) and the maximum speed (vend) from the VO2max-Test. Maximum heart rate (HRmax) was measured during the 4Minmax run and the VO2max-Test. Respiratory gases were analysed during the VO2max-Test using a mixing chamber (Cosmed srl, Rome, Italy). Pearson correlations and linear regressions were used to test whether v4Min predicted vend and the relative VO2max. Two regression analyses were performed separately for women and men. Results Table 1 shows the descriptive statistics of the 4Minmax run and the VO2max-Test of the 18 subjects. In both men and women, vend and v4Min were strongly correlated (r = 0.79, p = 0.001, 95% CI: 0.424 - 0.934 and r = 0.974, p = 0.005, 95% CI: 0.647 - 0.998, respectively; figure 2). Also the VO2max and the v4Min were strongly correlated in men and in women (r = 0.742, p = 0.004, 95% CI: 0.324 - 0.918 and r = 0.897, p = 0.039, 95% CI: 0.073 - 0.993, respectively; figure 3). The linear regression model of the relative VO2max explained by the v4Min was statistically significant in women (adjusted R2 = 0.74, F(1,3) = 12.41, p = 0.039) and in men (adjusted R2 = 0.51, F(1,11) = 13.504, p = 0.004). The regression equation for women was: VO2max [ml*min-1*kg-1] = -28.792+5.758 (v4Min [km*h-1]). The regression equation for men was: VO2max [ml*min-1*kg-1] = -21.627+4.709*(v4Min [km*h-1]). Conclusion The performance in the 4Minmax run can accurately estimate both VO2max and vend with a slightly higher adjusted R2 between v4Min and vend than between v4Min and VO2max. As the VO2max is comparable to reference values, it is recommended to calculate the estimated VO2max from the 4Minmax run to assess the endurance performance in the digital self-test. The regression equation for women should be further investigated with a larger number of subjects and should therefore be used with caution. Military Impact The Swiss Armed Forces offer their recruits and soldiers a digital solution for physical training before and between military service. The digital performance test implemented in this application, which can be carried out independently, regardless of time and place, and without any special equipment, enables the generation of a personalised physical training. The 4Minmax run is a promising solution to assess the endurance performance and is an alternative to conventional laboratory and field tests.
In military service, marching is an important, common, and physically demanding task. Minimizing dropouts, maintaining operational readiness during the march, and achieving a fast recovery are desirable because the soldiers have to be ready for duty, sometimes shortly after an exhausting task. The present field study investigated the influence of the soldiers' cardiorespiratory fitness on physiological responses during a long-lasting and challenging 34 km march.; Heart rate (HR), body core temperature (BCT), total energy expenditure (TEE), energy intake, motivation, and pain sensation were investigated in 44 soldiers (20.3 ± 1.3 years, 178.5 ± 7.0 cm, 74.8 ± 9.8 kg, body mass index: 23.4 ± 2.7 kg × m-2, peak oxygen uptake (VO2peak): 54.2 ± 7.9 mL × kg-1 × min-1) during almost 8 hours of marching. All soldiers were equipped with a portable electrocardiogram to record HR and an accelerometer on the hip, all swallowed a telemetry pill to record BCT, and all filled out a pre- and post-march questionnaire. The influence of aerobic capacity on the physiological responses during the march was examined by dividing the soldiers into three fitness groups according to their $\dot{\rm{V}}$O2peak.; The group with the lowest aerobic capacity (VO2peak: 44.9 ± 4.8 mL × kg-1 × min-1) compared to the group with the highest aerobic capacity (VO2peak: 61.7 ± 2.2 mL × kg-1 × min-1) showed a significantly higher (p
The purpose of this study was to evaluate the wrist-worn device Mio FUSE, which estimates heart rate (HR) based on photo-plethysmography, 1) in a large study group during a standardised activity, 2) in a small group during a variety of activities and 3) to investigate factors affecting HR accuracy in a real-world setting. First, 53 male participants (20 ±1 years; 1.79 ±0.07 m; 76.1 ±10.5 kg) completed a 35-km march wearing the Equivital EQ-02 as a criterion measure. Second, 5 participants (whereof 3 female; 29 ±5 years; 1.74 ±0.07 m; 67.8 ±11.1 kg) independently performed 25 activities, categorised as sitting passive, sitting active, standing, cyclic and anti-cyclic activities with the Polar H7 as a criterion device. Equivalence testing and Bland-and-Altman analyses were undertaken to assess the accuracy to the criterion devices. Third, confounders affecting HR accuracy were investigated using multiple backwards regression analyses. The Mio FUSE was equivalent to the respective criterion measures with only small systematic biases of -3.5 bpm (-2.6%) and -1.7 bpm (-1.3%) with limits of agreements of ±10.1 bpm and ±10.8 bpm during the 35-km march and during different activities, respectively. Confounding factors negatively affecting the accuracy of the Mio FUSE were found to include larger wrist size and intensified arm and/or wrist movement. The wrist-worn Mio FUSE can be recommended to estimate overall HR accurately for different types of activities in healthy adults. However, during sporting activities involving intensified arm and/or wrist movement or for detailed continuous analysis, a chest strap is preferred to the Mio FUSE to optimise HR estimation accuracy.
The aim of this study was to develop and cross-validate two models to estimate total energy expenditure (TEE) based on respiration variables in healthy subjects during daily physical activities. Ninety-nine male and female subjects systematically varying in age (18-60 years) and body mass index (BMI; 17-36 kg*m-2) completed eleven aerobic activities with a portable spirometer as the criterion measure. Two models were developed using linear regression analyses with the data from 67 randomly selected subjects (50.0% female, 39.9 ± 11.8 years, 25.1 ± 5.2 kg*m-2). The models were cross-validated with the other 32 subjects (49% female, 40.4 ± 10.7 years, 24.7 ± 4.6 kg*m-2) by applying equivalence testing and Bland-and-Altman analyses. Model 1, estimating TEE based solely on respiratory volume, respiratory rate, and age, was significantly equivalent to the measured TEE with a systematic bias of 0.06 kJ*min-1 (0.22%) and limits of agreement of ±6.83 kJ*min-1. Model 1 was as accurate in estimating TEE as Model 2, which incorporated further information on activity categories, heart rate, sex, and BMI. The results demonstrated that respiration variables and age can be used to accurately determine daily TEE for different types of aerobic activities in healthy adults across a broad range of ages and body sizes.
To quantify gait asymmetry in well-trained runners with and without previous injuries during interval training sessions incorporating different distances.; Twelve well-trained runners participated in 8 high-intensity interval-training sessions on a synthetic track over a 4-wk period. The training consisted of 10 × 400, 8 × 600, 7 × 800, and 6 × 1000-m running. Using an inertial measurement unit, the ground-contact time (GCT) of every step was recorded. To determine gait asymmetry, the GCTs between the left and right foot were compared.; Overall, gait asymmetry was 3.3% ± 1.4%, and over the course of a training session, the gait asymmetry did not change (F1,33 = 1.673, P = .205). The gait asymmetry of the athletes with a previous history of injury was significantly greater than that of the athletes without a previous injury. However, this injury-related enlarged asymmetry was detectable only at short (400 m), but not at longer, distances (600-1000 m).; The gait asymmetry of well-trained athletes differed, depending on their history of injury and the running distance. To detect gait asymmetries, high-intensity runs over relatively short distances are recommended.
Abstract The elevation gain is an important contributor to the total workload in endurance sports. The purpose of this study was to evaluate the influence of the arm swing on elevation gain in three sport watches (Garmin® Forerunner 910XT, Polar® RS800CX and Suunto® Ambit2) on a flat 400 m outdoor track. Altogether, a total of 120 repetitions of 1,200 m were performed at self-selected speeds corresponding to strolling, walking, jogging and running. During the assessment two devices of each sport watch, one secured on the hip and one on the wrist, were worn by the participants. A small but significant (effect size = .39; p < .001) influence of the arm swing on elevation was revealed in all sport watches. Elevation indication errors recorded on the wrist were significantly larger than the ones recorded on the hip (4.0-7.4 vs. 1.2-5.7 m per 1,200 m; p < .05). Furthermore, when wearing the devices on the wrist, errors in elevation indication increased when gait speed increased. Users should be aware that wearing the devices on the hip can significantly decrease measurement errors. This might be especially relevant for activities with high dynamics, such as jogging and running.
Elevation gain (EG) is a significant contributor to the total workload in many endurance sports. Hence, the aim of this study was to evaluate the accuracy of elevation recording as assessed by popular sport watches. Eighteen participants walked and ran at different speeds in various weather conditions in two terrain types: on a hilly 2490 m course with a total EG of 90 m and on a flat 1200 m outdoor track with 0 m EG. In total, 180 recordings from each sport watch were analyzed and compared according to two processing types: filtered and unfiltered EG data. Compared to the reference values, regarding default settings, on hilly terrain, EG was underestimated by −3.3 to −9.8 %, and on flat terrain, EG was overestimated by 0.0–4.8 m per 1200 m. These errors could be reduced to −3.3 to +0.4 %, when filtering conditions were adjusted according to the terrain. Gait speed (ranging from 1.47 to 4.89 m s−1) or fluctuations in weather conditions between- or within-trials did not influence EG accuracy. A straightforward comparison between manufacturers is hampered as the filter conditions set by default differ. In conclusion, all devices measure EG adequately; however, the displayed default feedback on EG data is not always the most accurate measurement. Consequently, accuracy in elevation recordings could be increased if users appropriately post-process EG data.
The aim of this study was to validate the detection of ground contact time (GCT) during running in 2 differently working systems: a small inertial measurement sensor, PARTwear (PW), worn on the shoe laces, and the optical measurement system, Optojump (OJ), placed on the track. Twelve well-trained subjects performed 12 runs each on an indoor track at speeds ranging from 3.0 to 9.0 m·s. GCT of one step per run (total 144) was simultaneously obtained by the PW, the OJ, and a high-speed video camera (HSC), whereby the latter served as reference system. The sampling rate was 1,000 Hz for all methods. Compared with the HSC, the PW and the OJ systems underestimated GCT by -1.3 ± 6.1% and -16.5 ± 6.7% (p-values ≤ 0.05), respectively. The intraclass correlation coefficients between PW and HSC and between OJ and HSC were 0.984 and 0.853 (p-values < 0.001), respectively. Despite the constant systematic underestimation of GCT, analyses indicated that PW successfully recorded GCT over a wide range of speeds. However, results showed only moderate validity for the OJ system, with increasing errors when speed decreased. In conclusion, the PW proved to be a highly useful and valid application, and its use can be recommended not only for laboratory settings but also for field applications. In contrast, data on GCT obtained by OJ during running must be treated with caution, specifically when running speed changes or when comparisons are made with GCT data collected by other measurement systems.
