ArticleScientific reports2026
Pacing profiles and slowdown patterns in ultra-triathlon performance.
Article in Scientific reports, 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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10 authors.
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Abstract
Pacing in long-distance triathlon has been studied primarily for cycling and running in IRONMAN triathlon and daily-format ultra-triathlons, as managing fatigue is critical for success. However, no study has analysed pacing across all three disciplines in an ultra-triathlon such as the second-longest non-stop triathlon format, the Double Deca Iron ultra-triathlon covering 76 km swimming, 3600 km cycling, and 844 km running. This study examined pacing during a Double Deca Iron ultra-triathlon by analysing split and lap times for all official male and female finishers. We assessed pacing patterns, the influence of pacing variability on performance, and whether faster performance was associated with more frequent moderate slowdowns or with fewer but more pronounced slowdowns. Official race data from the 2023 Swissultra Double Deca Iron ultra-triathlon held in Buchs, Switzerland were analysed for 9 men and 4 women. Swimming splits were recorded manually, and both cycling and running splits were recorded via RFID timing. Variables included mean speed, checkpoint speed variability (ACCS), proportions of slow-down checkpoints (25-50%, 50-75%, > 75% slower than mean speed), and magnitude of slowdown. Athletes showed negative pacing in swimming (decreasing time) but an even or slightly positive pacing in both cycling and running. Running variability did not correlate with running speed. In swimming, faster athletes tended to show fewer relative slowdowns. In cycling, faster cyclists had fewer moderate but more pronounced slowdowns, while in running, faster runners showed more ≥ 75% slowdowns. In summary, higher performers maintained a relatively steady and fast baseline pace interspersed with occasional substantial slowdowns, rather than moving continuously at a slower and more variable pace. Because the timing data do not directly identify intentional rest, these slowdown patterns should not be interpreted as confirmed rest breaks.
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