Measuring countermovement jump (CMJ) height accurately is essential for evaluating lower-body explosive power in athletes and other active populations. With technological advancements, various portable tools have been developed for this purpose, including force platforms, contact mats, and video-based software. This study aimed to (a) investigate the test-retest reliability of the KINVENT K-Deltas force platform for CMJ height measurement and (b) compare its accuracy with a contact mat (Chronojump, Spain) and a video-based software (My Jump app, version 3). Twenty-two physically active collegiate athletes (mean age of 19.7 ± 1.2 years) from various sports backgrounds completed five CMJ trials with simultaneous height measurements using all three tools. Intra-class correlation coefficients (ICC), Cronbach's alpha, and coefficient of variation (CV) were calculated to assess reliability. In contrast, Pearson correlations and Bland-Altman plots were used to compare device results. The K-Deltas force platform exhibited high test-retest reliability (ICC = 0.981), closely matching the contact mat (ICC = 0.987) and the My Jump app (ICC = 0.986). Correlations between the instruments were strong (force platform vs. contact mat: r = 0.987; force platform vs. My Jump: r = 0.987; contact mat vs. My Jump: r = 0.996), with no between-instrument differences (-test = 0.203-0.935, effect size ≤ 0.01-0.16), demonstrating the interchangeability of these tools for practical purposes. However, Bland-Altman analysis revealed limits of agreement between the devices, indicating small but consistent measurement differences. While all instruments were reliable, discrepancies in the absolute values suggest practitioners should consider device-specific variations when comparing CMJ data. These findings highlight the reliability of the K-Deltas force platform as a viable alternative for measuring CMJ height, though differences between devices should be accounted for in applied settings. Therefore, the portable force plates can monitor training, predict injury risk, assess neuromuscular fatigue, and lead to informed decision-making.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11595741 | PMC |
http://dx.doi.org/10.3390/life14111394 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!