Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
Institute of Applied Mathematics and Physics, Zurich University of Applied Sciences Zurich, Winterthur, Switzerland.
PLoS One. 2020 Jul 15;15(7):e0235156. doi: 10.1371/journal.pone.0235156. eCollection 2020.
Single repetition, contraction-phase specific and total time-under-tension (TUT) are crucial mechano-biological descriptors associated with distinct morphological, molecular and metabolic muscular adaptations in response to exercise, rehabilitation and/or fighting sarcopenia. However, to date, no simple, reliable and valid method has been developed to measure these descriptors.
In this study we aimed to test whether accelerometer data obtained from a standard smartphone placed on the weight stack can be used to extract single repetition, contraction-phase specific and total TUT.
Twenty-two participants performed two sets of ten repetitions of their 60% one repetition maximum with a self-paced velocity on nine commonly used resistance exercise machines. Two identical smartphones were attached on the resistance exercise weight stacks and recorded all user-exerted accelerations. An algorithm extracted the number of repetitions, single repetition, contraction-phase specific and total TUT. All exercises were video-recorded. The TUT determined from the algorithmically-derived mechano-biological descriptors was compared with the video recordings that served as the gold standard. The agreement between the methods was examined using Limits of Agreement (LoA). The association was calculated using the Pearson correlation coefficients and interrater reliability was determined using the intraclass correlation coefficient (ICC 2.1).
The error rate of the algorithmic detection of single repetitions derived from two smartphones accelerometers was 0.16%. Comparing algorithmically-derived, contraction-phase specific TUT against video, showed a high degree of correlation (r>0.93) for all exercise machines. Agreement between the two methods was high on all exercise machines as follows: LoA ranged from -0.3 to 0.3 seconds for single repetition TUT (0.1% of mean TUT), from -0.6 to 0.3 seconds for concentric contraction TUT (7.1% of mean TUT), from -0.3 to 0.5 seconds for eccentric contraction TUT (4.1% of mean TUT) and from -1.9 to 1.1 seconds for total TUT (0.5% of mean TUT). Interrater reliability for single repetition, contraction-phase specific TUT was high (ICC > 0.99).
Data from smartphone accelerometer derived resistance exercise can be used to validly and reliably extract crucial mechano-biological descriptors. Moreover, the presented multi-analytical algorithmic approach enables researchers and clinicians to reliably and validly report missing mechano-biological descriptors.
单次重复、收缩相特定和总张力时间（TUT）是与运动、康复和/或对抗肌肉减少症相关的独特形态、分子和代谢肌肉适应性相关的关键机械生物学描述符。然而，迄今为止，尚未开发出简单、可靠和有效的方法来测量这些描述符。
本研究旨在测试将智能手机放置在举重台上获得的加速度计数据是否可用于提取单次重复、收缩相特定和总 TUT。
22 名参与者在九种常用的阻力运动机器上以自我设定的速度进行了两组 10 次重复，重复次数为其 60%的 1 次重复最大重复次数。将两个相同的智能手机固定在阻力运动杠铃上，并记录所有用户施加的加速度。算法提取了重复次数、单次重复、收缩相特定和总 TUT。所有练习均进行视频记录。算法衍生的机械生物学描述符确定的 TUT 与作为金标准的视频记录进行比较。使用界限（LoA）检查方法之间的一致性。使用 Pearson 相关系数计算关联，使用组内相关系数（ICC 2.1）确定组内可靠性。
从两个智能手机加速度计算法检测单次重复的错误率为 0.16%。与视频相比，算法衍生的收缩相特定 TUT 具有高度相关性（r>0.93），适用于所有运动机器。两种方法之间的一致性在所有运动机器上均很高，如下所示：单次重复 TUT 的 LoA 范围为-0.3 至 0.3 秒（平均 TUT 的 0.1%），向心收缩 TUT 的 LoA 范围为-0.6 至 0.3 秒（平均 TUT 的 7.1%），离心收缩 TUT 的 LoA 范围为-0.3 至 0.5 秒（平均 TUT 的 4.1%），总 TUT 的 LoA 范围为-1.9 至 1.1 秒（平均 TUT 的 0.5%）。单次重复、收缩相特定 TUT 的组内可靠性很高（ICC>0.99）。
智能手机加速度计衍生的阻力运动数据可用于有效可靠地提取关键机械生物学描述符。此外，所提出的多分析算法方法使研究人员和临床医生能够可靠和有效地报告缺失的机械生物学描述符。
