Key result
Commercial velocity sensors show good-to-excellent validity and agreement despite heterogeneity across technologies and exercise types.
Why the study?
Valid 1RM prediction via load-velocity relationships requires high reliability and accuracy, but individual study results show ambivalent prediction.
Are commercially available velocity sensors and velocity-based 1RM prediction models valid and reliable?
Meta-Analysis (n=101)
Are commercially available velocity sensors and velocity-based 1RM prediction models valid and reliable?
Commercial velocity sensors and velocity-based 1RM prediction models demonstrate high average validity and reliability, but substantial heterogeneity and exercise-specific variability require cautious interpretation.
Answers are grounded in this paper and cite their sources.
Supports velocity-based training protocols; confirms pooled sensor validity while leaving technology- and exercise-specific heterogeneity unresolved.
Background For a valid one-repetition maximum (1RM) prediction via load–velocity (LV) relationships, high reliability and accuracy must be assumed. Objective Since individual study results indicate ambivalent prediction, this systematic review and meta-analysis was designed to provide a updated and comprehensive overview, extending knowledge about the validity and reliability of commercially available velocity sensors in Part I and the validity and reliability of velocity-based 1RM prediction models in Part II. Methods A systematic literature search was conducted in PubMed/MEDLINE, Web of Science, and Scopus. Validity and/or reliability studies or velocity-based 1RM prediction evaluations were included. Methodological quality was assessed using adapted COSMIN. The analysis was performed for intraclass correlation coefficient (ICC), Lin’s concordance correlation coefficient (CCC), and Pearson’s correlation coefficient ( r ). The review was preregistered in PROSPERO (CRD42025634595). Results Sixty-three studies were included for sensor validity and reliability and 38 for 1RM prediction models. Part I: Velocity sensors demonstrated good-to-excellent pooled validity and device agreement (ICC = 0.91–0.92 [0.83–0.97]; k = 55 and 439, respectively); intra- and inter-day reliability were classified as good to excellent with ICC = 0.90–0.91 [0.85–0.95] ( k = 228 and 608, respectively), with sensor technology moderating the results. However, substantial heterogeneity and wide ranges of study-level estimates indicated considerable variability across moderators, linear position transducer (LPT) generally showing more consistent performance than inertial measurement units (IMU). Part II: Velocity-based 1RM prediction showed ICCs = 0.90 [0.83–0.94] ( k = 124) and ICC = 0.91 [0.72–0.98] ( k = 9); for reliability and validity, respectively. Discussion Commercial velocity sensors generally provide high relative validity and reliability. Results varied depending on exercise complexity, intensity, sensor technology, and modeling approach. While velocity-based 1RM prediction demonstrated high average validity, large heterogeneity in lower body exercises significantly biased the results. Furthermore, the dearth of measurement error and agreement analyses prohibits final conclusions. Conclusion Therefore, velocity-based monitoring and 1RM prediction require cautious interpretation, as sensor- and exercise-specific evidence remains limited.
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Claassen et al. (2026) conducted a meta-analysis in Healthy participants undergoing resistance training (n=101). Commercially available velocity sensors and velocity-based 1RM prediction models vs. Gold standard systems (e.g., 3D motion capture) or reference sensors was evaluated on Validity and device agreement of velocity sensors (ICC 0.91-0.92, 95% CI 0.83-0.97). Commercially available velocity sensors demonstrated good-to-excellent pooled validity and device agreement, though substantial heterogeneity exists depending on sensor technology and exercise type.
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