Institute of Molecular Systems Biology, ETH Zurich, Zurich, Zurich, Switzerland.
Kieser Training AG, Zürich, Zurich, Switzerland.
PLoS One. 2021 Jul 20;16(7):e0254164. doi: 10.1371/journal.pone.0254164. eCollection 2021.
It was shown that single repetition, contraction-phase specific and total time-under-tension (TUT) can be extracted reliably and validly from smartphone accelerometer-derived data of resistance exercise machines using user-determined resistance exercise velocities at 60% one repetition maximum (1-RM). However, it remained unclear how robust the extraction of these mechano-biological descriptors is over a wide range of movement velocities (slow- versus fast-movement velocity) and intensities (30% 1-RM versus 80% 1-RM) that reflect the interindividual variability during resistance exercise.
In this work, we examined whether the manipulation of velocity or intensity would disrupt an algorithmic extraction of single repetitions, contraction-phase specific and total TUT.
Twenty-seven participants performed four sets of three repetitions of their 30% and 80% 1-RM with velocities of 1 s, 2 s, 6 s and 8 s per repetition, respectively. An algorithm extracted the number of repetitions, single repetition, contraction-phase specific and total TUT. All exercises were video-recorded. The video recordings served as the gold standard to which algorithmically-derived TUT was compared. The agreement between the methods was examined using Limits of Agreement (LoA). The Pearson correlation coefficients were used to calculate the association, and the intraclass correlation coefficient (ICC 2.1) examined the interrater reliability.
The calculated error rate for the algorithmic detection of the number of single repetitions derived from two smartphones accelerometers was 1.9%. The comparison between algorithmically-derived, contraction-phase specific TUT against video, revealed a high degree of correlation (r > 0.94) for both exercise machines. The agreement between the two methods was high on both exercise machines, intensities and velocities and was as follows: LoA ranged from -0.21 to 0.22 seconds for single repetition TUT (2.57% of mean TUT), from -0.24 to 0.22 seconds for concentric contraction TUT (6.25% of mean TUT), from -0.22 to 0.24 seconds for eccentric contraction TUT (5.52% of mean TUT) and from -1.97 to 1.00 seconds for total TUT (5.13% of mean TUT). Interrater reliability for single repetition, contraction-phase specific TUT was high (ICC > 0.99).
Neither intensity nor velocity disrupts the proposed algorithmic data extraction approach. Therefore, smartphone accelerometers can be used to extract scientific mechano-biological descriptors of dynamic resistance exercise with intensities ranging from 30% to 80% of the 1-RM with velocities ranging from 1 s to 8 s per repetition, respectively, thus making this simple method a reliable tool for resistance exercise mechano-biological descriptors extraction.
已经证明，使用智能手机加速度计从阻力运动机器获得的数据，可以可靠且有效地提取单次重复、收缩阶段特异性和总张力时间（TUT），使用用户确定的 60% 最大重复次数（1-RM）的阻力运动速度。然而，在反映阻力运动个体间差异的广泛运动速度（慢运动速度与快运动速度）和强度（30% 1-RM 与 80% 1-RM）范围内，这些力学生物学描述符的提取是否稳健仍然不清楚。
在这项工作中，我们研究了速度或强度的变化是否会破坏算法对单次重复、收缩阶段特异性和总 TUT 的提取。
二十七名参与者分别以 1 秒、2 秒、6 秒和 8 秒的速度进行三次重复的三组，每组重复三次，重复次数为 30%和 80%的 1-RM。算法提取了重复次数、单次重复、收缩阶段特异性和总 TUT。所有运动都进行了视频记录。视频记录被用作与算法衍生的 TUT 进行比较的金标准。使用界限（LoA）来检查方法之间的一致性。使用 Pearson 相关系数来计算关联，使用组内相关系数（ICC 2.1）来检查评分者间的可靠性。
从两部智能手机加速度计算法中计算出的单次重复次数的计算错误率为 1.9%。将算法衍生的收缩阶段特异性 TUT 与视频进行比较，结果表明两种运动器械的相关性均很高（r>0.94）。两种方法在两种运动器械、强度和速度上的一致性都很高，如下所示：单次重复 TUT 的 LoA 范围为 0.21 秒至 0.22 秒（平均 TUT 的 2.57%），向心收缩 TUT 的 LoA 范围为 0.24 秒至 0.22 秒（平均 TUT 的 6.25%），离心收缩 TUT 的 LoA 范围为 0.22 秒至 0.24 秒（平均 TUT 的 5.52%），总 TUT 的 LoA 范围为-1.97 秒至 1.00 秒（平均 TUT 的 5.13%）。单次重复、收缩阶段特异性 TUT 的评分者间可靠性很高（ICC>0.99）。
强度和速度都不会破坏所提出的算法数据提取方法。因此，智能手机加速度计可以用于提取动态阻力运动的科学力学生物学描述符，其强度范围从 30%到 80%的 1-RM，速度范围从 1 秒到 8 秒，因此，这种简单的方法是阻力运动力学生物学描述符提取的可靠工具。
