Background Evaluating exercise fidelity during neuromuscular training (NMT) warm-ups (i.e., completing prescribed repetitions and performing exercises correctly) is important to inform the dose-response relationship of warm-up programs. Wearable technology can be used to measure exercise fidelity.
Objective To determine the accuracy of measuring NMT exercise volume and quality with wearable technology.
Design Cross-sectional study
Setting Youth basketball; Calgary, Canada
Participants Twenty-seven youth basketball players
Assessment of Risk Factors Players wore a triaxial accelerometer on the lower back during an NMT warm-up with concurrent video recording. A trained observer (physiotherapist) used an observation tool to determine whether each athlete performed the prescribed exercise volume and rate posture.
Main Outcome Measurements The number of repetitions during running, skipping and jumping were extracted from the accelerometer signal using a custom peak detection algorithm and compared to the prescribed exercise volume. The algorithm accuracy was calculated as a percentage, with the trained observer evaluation through video-analysis considered the gold standard.
For the plank, participants were evaluated on ‘Good Posture (straight body, head to ankle)’ and received a score of ‘Yes,’ ‘No,’ or ‘Partial.’ The coefficient of variation (CV) of the accelerometer signal in all three axes was compared for the three fidelity assessment outcomes.
Results The algorithm had an accuracy of 100% for the running, skipping and jumping exercise volume.
Participants who scored ‘Yes’ had a lower CV in the medial-lateral (median: 47.2%) and vertical (42.3%) axes, than participants who scored ‘Partial’ (85.4% and 67.6%) and ‘No’ (115.1% and 115.5%). There were no differences in CV in the anterior-posterior axis.
Conclusions A custom algorithm can be used to measure the number of running, skipping and jumping repetitions. The variability of the accelerometer signal can identify postural changes during a plank. Accelerometer signals may be used to evaluate movement quantity and quality during NMT.
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