ArticleBMC sports science, medicine & rehabilitation2026
Micro-pacing and performance determinants in skiathlon: linking speed profiles, sub-technique selection, and cycle characteristics.
Article in BMC sports science, medicine & rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
purposePerformance analyses in cross-country skiing often focus on lap or terrain-level splits. However, few studies have explored micro-pacing strategies-particularly in Skiathlon, an Olympic event requiring athletes to complete both classical and freestyle techniques on the same course.
methodsThirteen national-level male skiers were tracked during an International Ski Federation-certified Skiathlon using GNSS and trunk-mounted sensors. Instantaneous speed profiles were analysed using one-dimensional statistical parametric mapping (SPM) to identify "race-critical clusters": contiguous intervals where speed significantly predicted section time (α = 0.05) across all eight laps (four classical, four freestyle).
resultsFreestyle laps were 4% faster than classical, with greater terrain-specific speed differences and pacing variability in classical, especially downhills. Seven race-critical clusters were identified: two uphill, four downhill, and one flat. These accounted for 11.3 s (classic) and 10.9 s (freestyle) of the time gap between the fast and slow group. In these segments, faster skiers used higher-gear sub-techniques and exhibited longer cycle lengths and/or higher frequencies (p < 0.05).
conclusionsWithin race-critical clusters, the faster skiers gained substantial time advantages. Secondary analyses showed clear differences in sub-technique selection and kinematic profiles, suggesting that technical execution plays a critical role in these performance gains. Athletes and coaches may consider integrating GNSS-based tracking, SPM, and wearable-derived technique analysis into race evaluation to move beyond traditional split times and focus training on the most decisive segments of the course.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.