Exercise is increasingly recognized as an effective, nonpharmacological approach for managing both acute and chronic pain. However, accurate pain assessment methods tailored to different exercise settings and populations are essential. This review evaluates existing pain assessment techniques, highlighting self-report measures such as the Visual Analog Scale and Numerical Rating Scale, as well as quantitative measures, such as Pressure Pain Threshold and Conditioned Pain Modulation. These techniques provide valuable insights into the neurophysiological mechanisms behind pain reduction. Although exercise commonly produces a temporary reduction in pain sensitivity known as exercise-induced hypoalgesia (EIH), the effectiveness of this response depends on various factors, such as exercise intensity, psychological state, and pre-existing pain conditions. In addition, exercise itself can sometimes lead to short-term discomfort due to muscle fatigue or delayed-onset muscle soreness. This review offers strength and conditioning providers, as well as researchers, a clear framework for (a) selecting appropriate pain assessment tools, (b) designing research protocols, and (c) customizing exercise interventions for specific populations and pain conditions. By examining self-report and quantitative assessment techniques, along with considerations for data analysis and practical application, this review helps strength and conditioning professionals interpret the literature surrounding EIH and apply practical pain monitoring strategies to guide training and recovery in various populations.

This article is part of a continuing series of tactical strength and conditioning (TSAC) research reviews. It is designed to bring awareness to new research findings of relevance to tactical strength and conditioning communities.

This article is part of a continuing series of tactical strength and conditioning (TSAC) research reviews. It is designed to bring awareness to new research findings of relevance to tactical strength and conditioning communities.

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