XC Athletes

What the log records, and why

The log shape follows published endurance-training research. Each choice below, how a session is broken into blocks, how the five zones work, why RPE is asked for, and why heart rate is not required, is grounded in it. This page names that research and says where it does and does not settle the question.

Sessions and blocks

A session in the log is a set of blocks. Each block is a number of repetitions at a duration, with one intensity zone assigned to that block, not to the whole session. Blocks carry durations, not distances. "8 × 1000m" is recorded as 8 repetitions of 3:25 each, the time it took, not the distance covered.

This matches how the sport is actually coached. A 2024 interview study of twelve Norwegian elite endurance coaches whose athletes have combined for more than 370 international championship medals (Tønnessen et al., 2024) describes interval sessions built around "a progressive increase in intensity throughout the session" and "a clear trend towards shorter intervals and lower work:rest ratio with increasing intensity." A single session-average number cannot carry either of those details.

Averaging is a different method from the block view, and the two disagree. The same 570 elite cross-country-ski sessions, counted by time spent in each heart-rate zone versus counted by the goal set for each session, disagree by roughly a factor of three about how much of the training was hard (Sylta et al., 2014). Recording sessions as blocks avoids picking a side of that disagreement. Seiler (2010) describes the roughly one-fifth of sessions that are not easy as dominated by periods of high-intensity work, such as interval training at about 90% of VO2max, a description of blocks inside a session, not of the session as a whole.

The five zones

Each block carries one of five intensity zones, L1 through L5, chosen by intent rather than computed from a device. L1 through L5 is this platform's own label for a wider convention. The published research more often says "zone 1 through 5," splits training into low, moderate, and high-intensity bands, or works with three zones instead of five. The five-zone label is a naming choice on top of well-studied ground, not a distinct finding of its own.

The convention traces to Norwegian endurance training and has since spread well beyond it. An international survey of 778 endurance practitioners (Seiler-Viken et al., 2025) found a five-zone scale was the single most common framework, used by 47% of respondents, and that Norwegian respondents were 2.7 times more likely to use one than everyone else combined.

The zones describe where a block sits relative to two physiological turn points, the two ventilatory thresholds, rather than a fixed heart-rate number. In eleven junior cross-country skiers, a three-zone version of this scheme, anchored to those two thresholds measured on a treadmill, was cross-checked against blood lactate on 60 sessions (Seiler & Kjerland, 2006). The platform's five-zone label extends the same turn-point idea.

Assigning a block's zone by intent, the way a coach plans a session, rather than deriving it from a heart-rate trace afterward, matches what one line of research calls a "session-goal" method. The literature treats that as a legitimate way to classify training in its own right, not as a rough stand-in for a heart-rate-based one (Sylta et al., 2014). Recording training this way and looking at the resulting distribution across zones is a meaningful way to describe endurance training: descriptive studies of competitive endurance athletes converge on characteristic intensity distributions (Seiler, 2010).

RPE

After a session, the athlete rates how hard the whole thing felt, on a 0 to 10 scale. That kind of perceived-effort rating is a measurable psychophysical quantity, not a rough guess (Borg, 1982). Multiplying that single number by the session's duration produces what the training-science literature calls "session RPE," a method introduced and tested against an objective heart-rate-based method in cyclists and basketball players (Foster et al., 2001). The relationship between the two was highly consistent, though session RPE gave a significantly greater absolute score; the two are correlated, not interchangeable in magnitude. That relationship held across both modes of exercise, which is part of why one rating scale can work for a run, an interval session, and a lift alike.

It holds up specifically in athletes the age this platform serves. A meta-analysis of 16 studies and 278 adolescent athletes, ages predominantly 11 to 18 (Liu et al., 2023), found session RPE correlated with heart-rate-based load at r = 0.74, and its authors concluded the method can be used on its own to monitor internal training load in that age group. A separate study of 19 players with a mean age of 17.6, across 479 sessions (Impellizzeri et al., 2004), found every individual correlation between session RPE and heart-rate-based load significant. A review of the 36 validation studies published through 2016 (Haddad et al., 2017) found the method holds across children, adolescents, and adults.

It also extends past running and skiing. In resistance training, session RPE correctly separated high-, moderate-, and low-load protocols in the expected order (Day et al., 2004).

Why heart rate is not required

The log has no heart-rate field. It does not ask for one, and nothing a device reports overwrites the zone or the effort rating the athlete asserted.

Heart-rate estimates hold up better as a group average than for any one person. Predicting VO2max from heart rate "relies upon several assumptions" and can deviate from the true value by up to 20%. Heart-rate-based energy expenditure "provides a satisfactory estimate ... on a group level, but is not very accurate for individual estimations" (Achten & Jeukendrup, 2003). Heart rate also rises through a session independent of effort. During heat stress it shows "a rise ... that is disproportionate to absolute intensity" (Wingo, 2015), and it drifts upward over a sustained effort even when the pace does not change, a pattern driven by rising heart rate itself rather than a shift in blood flow (Coyle & González-Alonso, 2001).

More directly, converting the same heart-rate recordings into a time-in-zone breakdown versus a session-goal breakdown, on the same 570 elite cross-country-ski sessions cited above, gave answers that disagreed by roughly a factor of three in the hard-training range (Sylta et al., 2014). A zone is not reliably recoverable from a heart-rate trace after the fact. A smaller controlled study found the same story from the other direction: two sessions built from identical work and rest, arranged differently within the session, were scored as meaningfully different by a heart-rate-zone method, no different by a heart-rate-based training-impulse method, and most clearly different by session RPE (Hourcade et al., 2018). A meta-analysis of 122 estimates across 295 athletes and more than 10,000 sessions found perceived-effort load tracked external load, such as distance covered, more consistently than heart-rate-based training impulse did, and every relationship depended heavily on the kind of training involved (McLaren et al., 2018). Interpreting a heart-rate reading well takes context about training phase, load, and intensity distribution that the number alone does not carry (Schneider et al., 2018).

What is deliberately absent

The log does not require a connected heart-rate monitor, a GPS watch, or any other wearable data feed. Every zone and every effort rating in the log is what the athlete or coach entered, never something computed from a device.

One RPE number covers every kind of training the log holds: an interval session, a long run, or an hour in the weight room, rather than a separate scale per activity. Session RPE was built to be mode-independent (Foster et al., 2001), and it has been shown to correctly separate resistance-training loads on its own (Day et al., 2004).

The log does not ask an athlete to explain why a session felt the way it did, or to account for caffeine or anything else that might have shaped that number. Those factors are real. The log leaves the number as the athlete gave it rather than adjusting it after the fact. The next section gives the reasoning.

Honest limits

Zone boundaries are a convention. They vary by region and by sport. In the same international survey cited above, only 47% of respondents used a five-zone scale at all. The boundary between zones 2 and 3 differed significantly by region, and the boundaries between zones 1 through 3 differed by sport, most sharply between running and cycling. Norwegian respondents drew the highest zone-2 and zone-3 boundaries; cyclists drew the lowest across zones 1 through 3 (Seiler-Viken et al., 2025). A coach's own numbers, learned somewhere else, may not match this platform's zones. That is a difference in convention, not an error in either one.

Perceived effort is subjective. A meta-analysis of 21 studies found caffeine alone lowers perceived exertion during exercise by an average of 5.6% (Doherty & Smith, 2005), and a review of the session-RPE method lists several other factors that can move the number (Haddad et al., 2017). The platform's position is that a subjective number the athlete actually records is worth more than an objective number nobody records. That is a design choice, not a research finding.

How a session is counted changes what the count says, and the underlying research is not settled. The same 570 sessions, counted two different ways, disagreed by a factor of three in the hard-training range (Sylta et al., 2014). A meta-analysis of 17 studies and 437 subjects found a polarized intensity distribution outperformed other distributions on one outcome, aerobic capacity, and only in interventions shorter than 12 weeks and only in already highly trained athletes; on time-trial performance, time to exhaustion, and threshold power, the same comparison found no difference (Silva Oliveira et al., 2024). Seiler (2010), the paper most associated with the case for a polarized distribution, states in its own conclusion that intensification studies in already well-trained athletes provide "no convincing evidence" that emphasizing high-intensity work produces long-term performance gains.

Most of this research was done on elite adult endurance athletes. The strongest evidence that any of it applies to high-school-aged athletes is the session-RPE validation work cited above (Liu et al., 2023; Impellizzeri et al., 2004). The block-and-zone model describes how the sport is coached at the level where the research was done. It has not itself been tested on a roster like the ones this platform serves.

Sources

Every citation above, in full.

  • Tønnessen E, Sandbakk Ø, Bucher Sandbakk S, Seiler S, Haugen T (2024). Training Session Models in Endurance Sports: A Norwegian Perspective on Best Practice Recommendations. Sports Medicine 54(11):2935–2953.
  • Sylta Ø, Tønnessen E, Seiler S (2014). From heart-rate data to training quantification: a comparison of 3 methods of training-intensity analysis. International Journal of Sports Physiology and Performance 9(1):100–107.
  • Seiler S (2010). What is best practice for training intensity and duration distribution in endurance athletes? International Journal of Sports Physiology and Performance 5(3):276–291.
  • Seiler-Viken SA, Mentzoni F, Seiler S, Skarli S, Losnegard T (2025). Contextualizing the Norwegian standardized intensity zone framework in an international sample of endurance practitioners. Scientific Reports 15(1):34367.
  • Seiler KS, Kjerland GØ (2006). Quantifying training intensity distribution in elite endurance athletes: is there evidence for an "optimal" distribution? Scandinavian Journal of Medicine & Science in Sports 16(1):49–56.
  • Borg GA (1982). Psychophysical bases of perceived exertion. Medicine & Science in Sports & Exercise 14(5):377–381.
  • Foster C, Florhaug JA, Franklin J, Gottschall L, Hrovatin LA, Parker S, Doleshal P, Dodge C (2001). A new approach to monitoring exercise training. Journal of Strength and Conditioning Research 15(1):109–115.
  • Liu H, Yang W, Liu H, Bao D, Cui Y, Ho IMK, Li Q (2023). A meta-analysis of the criterion-related validity of Session-RPE scales in adolescent athletes. BMC Sports Science, Medicine and Rehabilitation 15(1):101.
  • Impellizzeri FM, Rampinini E, Coutts AJ, Sassi A, Marcora SM (2004). Use of RPE-based training load in soccer. Medicine & Science in Sports & Exercise 36(6):1042–1047.
  • Haddad M, Stylianides G, Djaoui L, Dellal A, Chamari K (2017). Session-RPE Method for Training Load Monitoring: Validity, Ecological Usefulness, and Influencing Factors. Frontiers in Neuroscience 11:612.
  • Day ML, McGuigan MR, Brice G, Foster C (2004). Monitoring exercise intensity during resistance training using the session RPE scale. Journal of Strength and Conditioning Research 18(2):353–358.
  • Achten J, Jeukendrup AE (2003). Heart rate monitoring: applications and limitations. Sports Medicine 33(7):517–538.
  • Wingo JE (2015). Exercise intensity prescription during heat stress: A brief review. Scandinavian Journal of Medicine & Science in Sports 25(Suppl 1):90–95.
  • Coyle EF, González-Alonso J (2001). Cardiovascular drift during prolonged exercise: new perspectives. Exercise and Sport Sciences Reviews 29(2):88–92.
  • Hourcade JC, Noirez P, Sidney M, Toussaint JF, Desgorces F (2018). Effects of intensity distribution changes on performance and on training loads quantification. Biology of Sport 35(1):67–74.
  • McLaren SJ, Macpherson TW, Coutts AJ, Hurst C, Spears IR, Weston M (2018). The Relationships Between Internal and External Measures of Training Load and Intensity in Team Sports: A Meta-Analysis. Sports Medicine 48(3):641–658.
  • Schneider C, Hanakam F, Wiewelhove T, Döweling A, Kellmann M, Meyer T, Pfeiffer M, Ferrauti A (2018). Heart Rate Monitoring in Team Sports: A Conceptual Framework for Contextualizing Heart Rate Measures for Training and Recovery Prescription. Frontiers in Physiology 9:639.
  • Doherty M, Smith PM (2005). Effects of caffeine ingestion on rating of perceived exertion during and after exercise: a meta-analysis. Scandinavian Journal of Medicine & Science in Sports 15(2):69–78.
  • Silva Oliveira P, Boppre G, Fonseca H (2024). Comparison of Polarized Versus Other Types of Endurance Training Intensity Distribution on Athletes' Endurance Performance: A Systematic Review with Meta-analysis. Sports Medicine 54(8):2071–2095.

Feedback

This model changes as evidence and coaching feedback change it. When it does, the change ships as a versioned XC Log revision, never silently. A coach who thinks a zone boundary, a block shape, or anything else on this page is wrong for their sport or their athletes can raise it through the contact form.