Monitoring Training Load: Research on Best Practices

A 2016 systematic review by Malone et al. found that athletes with an Acute:Chronic Workload Ratio (ACWR) above 1.5 had a 2–4 times greater injury risk than those in the 0.8–1.3 range — a finding replicated across rugby, soccer, cricket, and swimming. Yet the same review noted that ACWR alone accounts for only 10–15% of injury variance. Effective training load monitoring is therefore not about any single metric but about constructing a multi-signal system where each metric captures a different aspect of the adaptation-overreach continuum. This review examines the evidence for the leading monitoring tools and how they should be combined in practice.

External load describes the physical work performed — distance run, kilograms lifted, jumps completed. Internal load describes the physiological response to that work — heart rate, blood lactate, session RPE. The relationship between the two is not fixed: the same external load produces a substantially higher internal load when an athlete is sleep-deprived, under-recovered, or in a period of high cumulative stress.

This dissociation is the primary reason monitoring external load alone is insufficient. An athlete completing a prescribed 5×5 squat session generates a fundamentally different internal training stress depending on their readiness state. A coach monitoring only external load — sets, reps, kilograms — sees an identical session; one monitoring internal load sees the actual physiological stimulus delivered. Both are necessary because external load determines what was prescribed and internal load determines what was received.

The ACWR divides the acute (7-day) workload by the chronic (28-day) workload. An ACWR of 1.0 means current load matches the established base. Values above 1.5 represent acute overload; values below 0.8 represent underloading relative to the chronic base — a condition Blanch and Gabbett (2016) called the 'danger zone' for detraining and acute re-injury.

The evidence for ACWR is strongest in collision sports (rugby, AFL) and weakest in technical sports where skill errors, not physical overload, drive injury. Its limitations are well-documented: it does not account for the nature of the load (high-intensity vs. low-intensity accumulation), it assumes a 28-day adaptation window that may be shorter for highly trained athletes, and it performs poorly when athletes have irregular training histories that produce artificially low chronic workloads.

The exponentially weighted moving average (EWMA) version of ACWR — which weights recent sessions more heavily — shows better injury prediction in prospective studies (Murray et al., 2017) and is the recommended calculation method for teams with access to computing resources.

Foster et al.'s session RPE method (2001) — asking athletes to rate overall session difficulty on the modified Borg CR-10 scale 30 minutes post-session, then multiplying by session duration in minutes — produces an internal training load unit that correlates with heart rate-derived training impulse (TRIMP) at r = 0.78–0.85 in team sport athletes. This makes it the most practical internal load metric for field coaches without physiological monitoring equipment.

Its validity weakens in resistance training contexts. Session RPE captures the cardiovascular and metabolic demands of conditioning sessions accurately but underestimates the neuromuscular stress of heavy strength work — particularly eccentric-dominant exercises like Nordic hamstring curls or heavy Romanian deadlifts, which produce significant delayed-onset muscle damage that is not reflected in session RPE collected at 30 minutes post-training.

In resistance training contexts, per-rep velocity data provides a more sensitive and real-time load metric than session RPE. Mean concentric velocity (MCV) on a given exercise declines predictably as neuromuscular fatigue accumulates within and across sessions. Pareja-Blanco et al. (2017) demonstrated that intra-session velocity loss thresholds of 20% MCV correspond to specific fatigue states that predict next-session performance better than any subjective marker.

Cross-session velocity monitoring — comparing the opening-set MCV at the same load across successive training days — functions as a daily readiness test specific to the exercise being trained. An athlete who squats 100kg at 0.80 m/s on Monday and opens Tuesday's session at 0.68 m/s is objectively showing 15% neuromuscular residual fatigue — a signal to reduce volume or load before the session, not after completing it at a compromised stimulus quality.

The countermovement jump (CMJ) is the most extensively validated single-test readiness biomarker in the sports science literature. A 5% or greater decline in CMJ height from a 7-day rolling average has been repeatedly shown to predict elevated session RPE, reduced peak power output, and impaired technical execution across a broad range of sports (Gathercole et al., 2015; Claudino et al., 2017).

The CMJ's utility as a training load indicator derives from its sensitivity to the neuromuscular fatigue component that session RPE and ACWR miss. Heavy resistance training, plyometric volume, and match play all suppress CMJ through mechanisms that resolve on different timelines — making daily CMJ trends a more informative multi-session monitoring signal than any single-session metric.

PoinT GO's 800Hz IMU measures CMJ height, flight time, peak velocity, and reactive strength index in under 90 seconds per test, removing the logistical barrier that previously limited daily CMJ monitoring to high-budget programs with force plates. Track training load effects on neuromuscular readiness with PoinT GO at poin-t-go.com.

An effective monitoring system answers three questions daily: What load was prescribed? What load was delivered? Is the athlete ready for tomorrow's load? Three tools cover these questions without requiring elite-level infrastructure:

1. External load log: Sets, reps, kilograms, and session duration. Spreadsheet-level tracking. This answers 'what was prescribed' and enables ACWR calculation.
2. Session RPE (30 min post-training): CR-10 scale × session minutes. Answers 'what was the internal dose received' and catches days where the external load produced an unexpectedly high physiological response.
3. Daily CMJ (pre-training): 3 maximal jumps averaged. Answers 'is the athlete ready for today's load' and flags residual fatigue before it compounds into overreaching.

The three metrics are complementary, not redundant. A day showing normal external load, normal session RPE, but suppressed morning CMJ identifies an athlete who completed the session but is inadequately recovered — the exact scenario that ACWR and session RPE alone would miss.

Training load monitoring programs fail for predictable reasons that have nothing to do with the validity of the metrics themselves:

• Acting on single data points rather than trends. One suppressed CMJ reading or one high ACWR value does not require an intervention. The 7-day rolling trend is the actionable signal. A single low value may reflect testing error, morning hydration status, or time-of-day variation rather than genuine fatigue accumulation.
• Using monitoring data for compliance enforcement rather than load adjustment. When athletes learn that high RPE triggers a reduced session, they systematically underreport RPE. Monitoring works when data is used to adjust programming, not to reward or penalize. Anonymizing data at the team level — or making individual data visible only to the athlete and their coach — preserves reporting accuracy.
• Monitoring external load without controlling for session type. A week with the same total session RPE load can contain very different injury risk depending on whether that load was distributed as 5 moderate sessions or 2 high-intensity sessions plus 3 low sessions. The distribution pattern, not only the total, predicts overreach risk — a subtlety that simple load totals do not capture.