ArticleFrontiers in physiology2026
Block periodization vs. traditional periodization in high-intensity functional training: a randomized crossover study.
Article in Frontiers in physiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: High-intensity functional training (HIFT) requires the combined development of strength and metabolic conditioning. This study examined whether concentrating high-intensity training loads into specific blocks provides advantages over a traditional evenly distributed approach in experienced athletes. Methods: Twenty experienced HIFT athletes (34.5 ± 9.8 yrs, 80.9 ± 13.7 kg, 1.77 ± 0.11 m, training experience: 5.8 ± 3.2 yrs) completed two six-week, load-matched intervention phases (block periodization (BP) versus traditional periodization (TP)) in a randomized controlled crossover design. Both conditions included five weekly sessions, but differed in the temporal distribution of high-intensity training (HIT) and low-intensity training (LIT) sessions, with BP concentrating HIT sessions into specific loading weeks and TP distributing intensity more evenly across the intervention. Outcome measures included maximal strength, benchmark workouts, physiological stress assessed via countermovement jump (CMJ), and health-related parameters including resting blood pressure, blood glucose concentration, and body composition. Results: BP was associated with greater improvements in maximal strength compared to TP. Specifically, significant group differences favoring BP were observed for the isometric mid-thigh pull (p = 0.038, d = 0.82), deadlift one repetition maximum (p = 0.018, d = 0.85), and CrossFit Conclusion: Concentrated loading through block periodization was associated with favorable changes in selected maximal strength outcomes in trained HIFT athletes without compromising metabolic conditioning or cardiovascular health markers. This approach effectively induces functional overreaching and represents a viable strategy to optimize performance adaptations in HIFT.
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