3 Strength test pillars: test choice, standardization, technology

Not all strength tests are created equal. In this article, you learn which test to use in different scenarios. We also highlight why accurate data matters, and how it depends on two key factors: standardizing your testing protocols and using valid measurement technology.

By Antonio Squillante, Ph.D CSCS*D RSCC*D

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Real-Time Insights: Data collection made simple

Training session structure also influences data collection. Progression is often organized in ascending order, beginning with lighter, explosive, plyometric drills, followed by heavier, slower lifts. This sequencing minimizes fatigue effects on measurements and improves data quality. For those who are keen to learn more about the difference between ascending and descending order, and learn more about PAP and French contrast method I recommend reading the review published by Cormie et al.  in 2022. 

Understanding the relationship between load, velocity, and fatigue enables two key assessment categories:

  • Force-dependent measures: Metrics derived from direct or approximated peak isometric force are valuable for tracking long-term progress. These are best obtained from strength-oriented lifts that emphasize concentric actions at high loads. To ensure test-retest reliability, consistent range of motion and a brief pause between the eccentric and concentric phase are recommended, minimizing the risk of artificially inflated readings caused by a rapid reversal. Heavy sets at 80–90% 1RM, such as the back squat, are particularly effective for monitoring changes in maximal strength over time. Heavy lifts are less sensitive to fatigue and provide stable, reliable indicators of strength capacity. Bare in mind: fluctuations in strength are normal. Peak isometric force can vary between +3% and -20% on a day to day basis, and that is to be considered normal. It is important to look at trends more than individual data points. 
  • Time-dependent measures: Metrics focused on contraction velocity are useful for monitoring the impact of fatigue on neuromuscular performance. A reduction in rate of force development (RFD) often signals heightened fatigue—primarily of central origin—since RFD reflects the ability to generate force rapidly. Isometric testing is considered the gold standard for RFD measurement in laboratory settings, where standardized protocols minimize variability. However, the ecological validity of isometric RFD testing in applied environments has been questioned. Dynamic assessments, such as countermovement jumps (CMJ), offer a more practical option and are frequently incorporated into warm-up routines. When preceded by a structured RAMP protocol, CMJs provide reliable, repeatable data while ensuring athletes are warm but not potentiated, thus enabling consistent monitoring across sessions. RFD, or any of its proxies such as jump height or RSI, must vary within a narrow range of roughly ±10% compared to baseline.  

Time to peak force (TTPF) is also a very valuable metric to consider. It serves as a validation tool, to track progress over time. An increase in peak isometric force increases over time is good. An increase in peak isometric force with a decrease in TTPF is excellent. An increase in rate of  force development over time is good. An increase in rate of  force development over time with a decrease in TTPF is excellent.

Controlling the Controllables

Collecting objective measurements is vital, but they are only meaningful when paired with robust testing protocols that ensure face validity—meaning the tests genuinely measure what they are intended to. This necessitates a fundamental understanding of muscle physiology, which guides exercise selection for capturing key variables such as peak force, time to peak force, and RFD. Whether using force plates or LPTs, understanding these principles enhances data relevance and interpretability.

Three core variables influence skeletal muscle behavior under load:

  • Muscle length: Tied to the length–tension relationship, this variable describes how force production varies with sarcomere length. In practical terms, the range of motion impacts force output—greater joint excursion stretches muscles more, potentially increasing force, especially near their resting length where fibers operate most effectively.
  • Contraction history: Muscle properties depend on prior activity. The stretch–shortening cycle exemplifies how eccentric and concentric actions combined can amplify contraction velocity and RFD, enhancing performance through a phenomenon known as pre-activation or potentiation.
  • Load: As described by A.V. Hill nearly a century ago, load and contraction velocity share an inverse relationship: increased load generally results in decreased velocity. Optimal RFD occurs within a specific load range, which can shift as an athlete strength trains and adapts.

The multitude of variables can generate background noise in performance data, complicating the quest for valid, reliable metrics. To mitigate this, standardizing testing protocols is essential, ensuring consistency and control over influencing factors. Once these conditions are stabilized, most resistance exercises become opportunities for valuable data collection, integrating athlete monitoring seamlessly into routine training.

Inside the black box: understanding technology

A linear position transducer (LPT) does what the word suggests: it converts tether displacement into coordinates, allowing accurate measurement of movement changes over time. From these data, velocity is derived as the first derivative of position, following fundamental physics principles. The GymAware RS exemplifies this technology, offering high accuracy with an error margin around 0.05 m/s. GymAware has set the industry standard for reliable velocity measurement.

Force plates operate on a similar principle but measure force directly through force transducers or load cells. These deform under load, translating mechanical deformation into force data over time. Both devices rely heavily on precise motion and time measurements, which are crucial for accurate force and rate of force development (RFD) calculations. Since force correlates with acceleration (force = mass × acceleration), we can infer force and RFD from changes in velocity and displacement, provided the data quality is high. The GymAware RS has set industry benchmarks in delivering such accurate data with consistent reliability.

Wrap up 

In conclusion, understanding the underlying mechanics and physiological principles governing muscle performance allows for the effective use of technology like LPTs and force plates. Standardized testing protocols optimize data validity, providing meaningful insights into athlete capacity, fatigue, and adaptation. Importantly, incorporating LPTs into regular training sessions offers a significant advantage: it enables real-time monitoring of performance changes and fatigue levels during actual training. This proactive approach allows coaches and athletes to make immediate adjustments, optimizing training effectiveness and reducing injury risk. Overall, the integration of LPTs facilitates a data-driven training environment where performance can be continuously tracked and managed, leading to more precise training interventions and enhanced athletic development.

References

  • D’Emanuele, S., Maffiuletti, N. A., Tarperi, C., Rainoldi, A., Schena, F., & Boccia, G. (2021). Rate of force development as an indicator of neuromuscular fatigue: a scoping review. Frontiers in human neuroscience, 15, 701916.
  • Norris, D., Joyce, D., Siegler, J., Clock, J., & Lovell, R. (2019). Recovery of force–time characteristics after Australian rules football matches: Examining the utility of the isometric midthigh pull. International journal of sports physiology and performance, 14(6), 765-770.
  • Cormier, P., Freitas, T. T., Loturco, I., Turner, A., Virgile, A., Haff, G. G., … & Bishop, C. (2022). Within session exercise sequencing during programming for complex training: historical perspectives, terminology, and training considerations. Sports Medicine, 52(10), 2371-2389.
Antonio Squillante

Antonio Squillante
Ph.D CSCS*D RSCC*D

Antonio Squillante is an Assistant Professor of Kinesiology at Point Loma Nazarene University in San Diego. He serves as the Head of Sport Performance and Training for the USA Cycling National Track Sprint Program. Since 2023, Antonio has been a member of the NSCA Board of Directors. In addition to his academic and professional roles, Antonio is a published author and a highly sought-after international speaker and lecturer.