News | The Science of Strength Institute

What We’re Reading

Evidence, translated into context.

This page highlights primary research and credible industry signals that inform The Science of Strength Institute’s developing approach. We link to the original source, summarize it in our own words, and explain why it matters for training. This is educational information—not medical advice or a claim about the Institute’s outcomes.

Athlete Monitoring2026 Review

From dashboards to decisions: monitoring enters its next phase

What they found: A recent review argues that athlete monitoring is most useful when practitioners interpret multiple dimensions together, account for individual baselines, and distinguish short-term readiness signals from longer-term adaptation.

Why it matters for training: More data is not automatically better. The practical value comes from turning reliable measures into context, applying professional judgment, and reassessing whether the chosen action produced a meaningful change.

Rebelo A. et al. “Monitoring Training Effects in Athletes: A Multidimensional Framework for Decision-Making.” Sports Medicine, 2026.

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Strength Data2026

Is the 1RM still the best anchor?

What they found: The authors reassess traditional one-repetition maximum testing and describe how movement velocity against submaximal loads can provide useful information about changes in strength.

Why it matters: Efficient reassessment can help a coach compare current performance with an individual baseline without treating a single test as the whole story.

Loturco I. et al. “Reassessing the Role of 1-Repetition Maximum Testing for Resistance-Training Prescription and Monitoring.” International Journal of Sports Physiology and Performance, 2026.

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AI + Biomechanics2026

Biomechanics, wearables, and AI converge

What they found: A Frontiers research collection highlights the growing overlap between biomechanical models, wearable systems, and explainable AI across sport and human-movement research.

Why it matters: The Institute’s data-to-outcome direction depends on organizing different inputs into information that practitioners can interpret—not on replacing their judgment.

Zhang G. et al. “Editorial: Revolutionizing Sports Science: Biomechanical Models, Wearable Tech, and AI.” Frontiers in Sports and Active Living, 2026.

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Editorial Standard

Source first. Claims last.

The Institute’s News page links to primary research or recognized professional sources. Summaries do not reproduce full articles, figures, or tables. Inclusion reflects relevance to the Institute’s evolving platform; it does not imply endorsement, medical guidance, or guaranteed performance outcomes.

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