How do coaches monitor, record and evaluate training to know whether the program is working?
Examine the tools and methods used to monitor, record and evaluate training load and performance, and explain how the resulting data informs program decisions
A focused HSC Health and Movement Science answer on monitoring training load and performance. Covers training logbooks, GPS units, heart-rate monitors, RPE, wellness questionnaires, performance testing batteries, and how to read trends rather than single sessions.
Reviewed by: AI editorial process; not yet individually human-reviewed
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What this sub-topic is asking
NESA wants you to identify the subjective and objective tools used to monitor training load and athlete response, describe how training is recorded over time, and explain how coaches evaluate that data (trends, periodised testing, comparison against pre-set targets) to make informed decisions about progression, deload or program change.
The answer
Monitoring is the ongoing collection of training and athlete data. Recording is the systematic storage of that data. Evaluation is the interpretation that drives the next coaching decision. Done well, the three steps close the loop on the principles of training.
Subjective measures (athlete-reported)
- Rating of Perceived Exertion (RPE)
- Athletes report effort on a 1-10 (Borg CR-10) or 6-20 (Borg) scale. Session RPE (sRPE) multiplied by session duration gives an arbitrary-unit training load (e.g. RPE 7 x 60 min = 420 AU). Cheap, simple, well-validated against heart-rate-based load measures across many sports.
- Wellness questionnaires
- Daily 1-5 ratings on sleep quality, fatigue, muscle soreness, stress and mood. A sustained drop across multiple items can signal accumulating fatigue or under-recovery before performance declines.
- Training logbooks / diaries
- The athlete records what was actually completed (sets, reps, distances, times), how it felt, and contextual factors (sleep, illness, life stress). Indispensable for retrospective analysis when results change.
Objective measures (instrument-based)
- Heart-rate monitors
- Track average and peak HR per session, time in zones, and resting / morning HR. A rising resting HR or suppressed HR response to a standard workload can indicate fatigue or illness.
- GPS units
- Wearable GPS (worn between the shoulder blades in a vest) records distance, speed, accelerations, decelerations and high-intensity efforts. Standard in field-sport (rugby, AFL, football, hockey) and increasingly in court sports via local positioning systems.
- Force plates and jump mats
- Measure vertical jump height, peak force, rate of force development. A drop in countermovement jump performance is a sensitive early indicator of neuromuscular fatigue.
- Wearable sleep / HR-variability devices
- Estimate sleep duration and quality, and HR variability as a proxy for autonomic recovery state. Useful as a trend tool; single-day readings vary too much to act on alone.
- Performance testing batteries
- Periodised tests run pre-block, mid-block and post-block. Common items:
- Yo-Yo Intermittent Recovery test (intermittent aerobic capacity for team sports).
- Multi-stage fitness test (beep / shuttle run) as a general aerobic capacity field test.
- 20m sprint with split times at 5m and 10m (acceleration vs top speed).
- Vertical jump (countermovement or squat jump) for lower-body power.
- 1RM (one-repetition maximum) or predicted 1RM for strength.
- Sit-and-reach and goniometric measures for flexibility.
Reading the data: trends, not single sessions
A single day's RPE, HR or jump score is noisy. Coaches look for trends across 7-28 days. Two widely used constructs:
- Acute:Chronic Workload Ratio (ACWR). The 7-day rolling load divided by the 28-day rolling load. Ratios persistently above ~1.5 are associated with elevated injury risk in some sports; ratios well below 1.0 reflect detraining. The exact thresholds are debated in the sports-science literature, but the concept of comparing acute spike against chronic build is widely used.
- Monotony and strain. Weekly load divided by weekly standard deviation (monotony); monotony multiplied by weekly load (strain). High monotony with high load is a risk pattern.
A wellness drop of one or two points on its own may be noise. A sustained drop across several items over a week, combined with rising sRPE and falling jump height, is a coherent signal.
The chart below shows the construct in action: weekly training load climbing while the acute:chronic workload ratio (ACWR) tracks the spike. Reading the trend - not week 5 in isolation - is what flags the danger zone.
Periodised testing
Tests are not run every week (too disruptive, too fatiguing). A typical periodised approach:
- Pre-block baseline testing in the first week of a new macrocycle.
- Mid-block retest of one or two key markers after 4-6 weeks.
- Post-block full retest after 8-12 weeks, used to evaluate the block and plan the next.
The choice of test must match the sport (specificity). A marathon runner is tested with a treadmill VO2max or field time trial, not with a vertical jump.
Subjective vs objective: use both
Subjective measures are sensitive (athletes often "know" before the numbers move) but vulnerable to bias and motivation effects. Objective measures are reproducible but expensive and sometimes lag behind subjective change. Best practice is to triangulate: a coherent signal from both is the cue to adjust.
Athlete-management systems
Many programs use software platforms to aggregate sRPE, wellness, HR, GPS and test data into per-athlete dashboards. The platform is a tool, not a decision. The coach still interprets, and the program still has to apply the principles of training.
Examples in context
Example 1. AFL pre-season GPS load monitoring. AFL clubs use wearable GPS in every training session and match. Weekly total distance, high-speed running distance and number of accelerations are tracked per player. The strength and conditioning staff use rolling 7-day and 28-day loads to plan the next week; a player returning from injury is progressively reintroduced to high-speed running over several weeks rather than being thrown straight back into full training. This is the monitoring framework operationalised at professional level.
Example 2. A school 1500m runner using a free training-log app and a heart-rate monitor. The student records every session (distance, time, RPE 1-10) and HR data. Over a 12-week build the coach reviews weekly: total mileage, time at threshold pace, and any sessions with abnormally high HR for a given pace (a possible early sign of illness or under-recovery). A pre-block, mid-block and post-block 3km time trial measures aerobic fitness change. The setup is low-cost, but applies the same logic as elite monitoring: track trends, test periodically, act on coherent signals.
Try this
Q1. Identify three subjective and three objective measures used to monitor training load and athlete response. [3 marks]
- Cue. Subjective: RPE / session RPE, wellness questionnaire, training logbook. Objective: heart-rate monitor, GPS, vertical jump (countermovement jump), 1RM or 20m sprint test.
Q2. Explain why coaches interpret training-monitoring data as trends rather than single data points. [4 marks]
- Cue. Day-to-day variability (sleep, stress, hydration) creates noise. A 7-28 day rolling view (e.g. acute:chronic workload ratio) gives a more reliable signal of accumulating fatigue or training adaptation; convergence of multiple subjective and objective markers is the cue to act.
Q3. Justify a monitoring and testing plan for a named athlete over a 12-week training block. [8 marks]
- Cue. Pick a specific athlete (a 400m runner, an AFL midfielder, a school 1500m runner). Specify daily monitoring (RPE, wellness), session monitoring (HR or GPS), and periodised testing (pre / mid / post). Match each test to the sport demand. State what change in the data triggers a deload or program change. Show the link between monitoring data and the principles of training (progressive overload, recovery, specificity).
Practice questions
Original practice questions graded from foundation to exam level, each with a full worked solution. Try them before revealing the solution.
core4 marksExplain why coaches interpret training-monitoring data as trends rather than single data points.Show worked solution →
A 4-mark explain needs the noise problem and the trend solution.
The problem. Day-to-day variation in sleep, stress, hydration and motivation makes any single RPE, heart-rate or jump reading noisy.
The solution. A rolling view over to days (e.g. the acute-to-chronic workload ratio) gives a more reliable signal of accumulating fatigue or adaptation, and convergence of several subjective and objective markers is the cue to act.
Markers reward (1) the variability/noise point, (2) the rolling-trend solution, (3) the idea that converging markers, not one reading, trigger a decision.
exam8 marksJustify a monitoring and testing plan for a named athlete over a 12-week training block.Show worked solution →
An 8-mark justify needs a named athlete, the monitoring and testing cadence, and reasoning tied to the principles of training.
- Choose an athlete
- E.g. an AFL midfielder.
- Daily and session monitoring
- RPE and wellness ratings daily; GPS load and heart rate each session, reviewed as rolling 7-day and 28-day loads.
- Periodised testing
- Sport-specific tests pre-block, mid-block and post-block (Yo-Yo intermittent recovery, 20m sprint, countermovement jump), matched to the sport by specificity.
- Decision rules
- State what change (e.g. a sustained wellness drop plus falling jump height plus a load spike) triggers a deload.
Markers reward (1) a named athlete with sport-matched tests, (2) monitoring versus testing cadence, (3) the link to progressive overload and recovery.
foundation3 marksIdentify three subjective and three objective measures a coach can use to monitor a team-sport athlete's training load and response.Show worked solution →
Subjective (athlete-reported):
- Rating of perceived exertion (RPE) / session RPE - effort rated on a 1 to 10 scale.
- Wellness questionnaire - daily 1 to 5 ratings of sleep, fatigue, soreness, stress, mood.
- Training logbook / diary - what was actually completed and how it felt.
Objective (instrument-based):
- Heart-rate monitor - average/peak HR, time in zones, resting morning HR.
- GPS unit - distance, speed, accelerations, high-intensity efforts.
- Countermovement jump (or 1RM / 20m sprint test) - neuromuscular/power output.
Marking criteria: 1 mark for three correctly classified SUBJECTIVE measures, capped if an objective measure (e.g. GPS) is listed as subjective; up to 2 further marks for three correctly classified OBJECTIVE measures. A measure on the wrong side of the subjective/objective line is not credited.
foundation4 marksDistinguish between validity and reliability of a monitoring measure, and give one training-monitoring example of each.Show worked solution →
Validity = whether a measure assesses what it claims to. Reliability = whether it gives consistent, repeatable results under the same conditions.
- Validity example: a Yo-Yo intermittent recovery test is a valid measure of intermittent aerobic capacity for a team-sport athlete because it reflects the sport's repeated-effort demand; a vertical jump would not validly index that aerobic quality.
- Reliability example: a GPS unit recording the same distance for the same drill on repeated days is reliable; a hand-timed sprint that varies with the timer's reaction is less reliable.
A measure can be reliable but not valid (consistent readings of the wrong thing). Both are needed before data should drive a program decision.
Marking criteria: 1 mark for the validity definition, 1 mark for the reliability definition, 1 mark for a correct validity example, 1 mark for a correct reliability example. Stating "they are the same thing" or swapping the two definitions scores nothing for that part.
core4 marksA coach records weekly training load (arbitrary units, AU) for an athlete across four weeks (illustrative ExamExplained data):
- Week 1: 1800 AU
- Week 2: 2000 AU
- Week 3: 2700 AU
- Week 4: 2900 AU
The athlete's 4-week (chronic) average load is 2350 AU. (a) Calculate the acute:chronic workload ratio for Week 4, using Week 4 as the acute load. (b) Interpret what this ratio suggests and one action the coach should consider.
Show worked solution →
(a) Calculation. ACWR = acute load / chronic load = 2900 / 2350 = 1.23 (to 2 decimal places).
(b) Interpretation. A ratio of about 1.23 sits in the commonly cited "moderate" band (roughly 0.8 to 1.3), so Week 4's load is moderately above the chronic base - a controlled progression rather than a dangerous spike. The week-on-week rise (1800 to 2900 AU) shows progressive overload is being applied. Action: hold or only modestly increase next week, and watch wellness and jump data; if the ratio were pushed above about 1.5 the coach should consider a deload to limit injury risk.
Marking criteria: (a) 2 marks - 1 for the correct method (acute / chronic), 1 for the correct value 1.23. (b) 1 mark for interpreting 1.23 as a moderate/controlled load (not a spike), 1 mark for a sensible linked action (monitor / cap next week / deload if it climbs). A bare number with no interpretation caps at 2.
core5 marksA netball coach reviews a fortnight of data for one player (illustrative ExamExplained dataset). Wellness (sleep + fatigue, out of 5) fell from a baseline of 4 to an average of 2.5; weekly sRPE load rose about 30 percent; countermovement jump fell 6 percent; resting morning heart rate rose 6 bpm; the logbook confirms every session was completed. Explain how a coach should interpret this combination of measures and justify the decision they should make.
Show worked solution →
Read the convergence, not one reading. Five markers move together in the same direction: subjective wellness down (sleep and fatigue), subjective load up (sRPE +30 percent), and two objective markers of fatigue (jump -6 percent, resting HR +6 bpm), while the logbook rules out missed sessions as a cause. Because subjective and objective measures triangulate on the same signal, this is a coherent short-term overreaching pattern, not noise.
Justify the decision. The coach should reduce next week's load by roughly 30 to 40 percent (a deload), prioritise sleep, and retest the jump and sprint after the deload to confirm recovery. Justification: a load spike with falling neuromuscular output and rising resting HR is an early under-recovery signature; continuing to push raises injury and illness risk and risks non-functional overreaching, whereas a brief deload protects the adaptation already banked and times freshness for competition.
Marking criteria: 1 mark for identifying that multiple markers converge (not acting on one), 1 mark for naming the subjective + objective triangulation, 1 mark for classifying it as overreaching/under-recovery, up to 2 marks for a justified decision (deload + monitor/retest) linked to injury risk or recovery. Acting on a single measure, or recommending a further load increase, is not credited.
core5 marksCompare subjective and objective monitoring measures, and explain why best practice uses both.Show worked solution →
Subjective measures (RPE, wellness, logbook) are cheap, quick and sensitive - athletes often perceive fatigue before instruments register it - but they are vulnerable to bias, mood and motivation (an athlete keen to play may under-report soreness).
Objective measures (HR, GPS, jump, performance tests) are reproducible and harder to fake, but can be expensive, require equipment/expertise, and sometimes lag behind subjective change (a jump score may only drop after fatigue is well established).
Why use both. Each covers the other's weakness: subjective data flags an early, sensitive warning; objective data confirms it reliably. Best practice is to triangulate - treat a coherent signal across both as the cue to act, which guards against over-reacting to one noisy reading and against missing a real problem an athlete is masking.
Marking criteria: 1 mark for a correct strength/limitation of subjective measures, 1 mark for a correct strength/limitation of objective measures, 1 mark for an explicit comparison (sensitive-but-biased vs reproducible-but-lagging/costly), up to 2 marks for explaining triangulation and why convergence drives the decision. A two-list answer with no comparison or "use both" reasoning caps at 3.
exam9 marksEvaluate the use of training-monitoring data to guide program decisions for a named athlete over a training block. In your answer, refer to the validity and reliability of the measures and the principles of training.Show worked solution →
This is a 9-mark extended response. Markers reward a judgement (how WELL monitoring guides decisions, and its limits), supported by named measures, the validity/reliability lens, and the principles of training - not a description of tools.
Band 6 PLAN.
- Thesis: monitoring data substantially improves program decisions for a named athlete (e.g. an AFL midfielder) because it lets the coach apply progressive overload and recovery on evidence rather than guesswork - but only when the measures are valid, reliable and read as trends; raw or single-point data can mislead.
- Argument line 1 - it works: daily RPE/wellness and per-session GPS/HR, viewed as 7-day vs 28-day load (ACWR), let the coach time progression and deloads (recovery + progressive overload), and periodised testing (Yo-Yo, 20m sprint, countermovement jump pre/mid/post) evaluates whether the block produced the targeted adaptation (specificity - tests matched to the sport).
- Argument line 2 - validity/reliability caveat: a decision is only as good as the measure. sRPE is well validated against HR-based load and is cheap, but is subjective and biasable; GPS is reliable for distance but units/software differ, so trends within one system matter more than absolute numbers; a single jump or wellness score is noisy (low day-to-day reliability), so acting on one reading is unjustified.
- Argument line 3 - judgement: monitoring is most valuable when several valid measures converge as a trend and the coach triangulates subjective + objective data; it is least valuable when data is collected but unused, or one noisy point drives an over-reaction. The system informs, the coach decides.
- Synthesis: weigh the benefit (evidence-based overload and recovery, earlier fatigue detection) against the limits (cost, bias, measurement error) and conclude monitoring is highly effective WHEN measures are valid and reliable and read as trends tied to the principles of training.
Model paragraph (validity/reliability line). The worth of a monitoring decision is bounded by the quality of its measures. Session RPE, for instance, is a valid index of internal load - it correlates well with heart-rate-based load across many sports - and is cheap enough to collect every session, which is why it anchors most school and professional programs; yet because it is athlete-reported it is open to motivation bias, so a midfielder chasing selection may under-report effort. A countermovement jump is a reliable, objective marker of neuromuscular fatigue, but a single reading has meaningful day-to-day variation, so a 6 percent drop only becomes a decision signal when it persists and is corroborated by rising resting heart rate and falling wellness. The coaching implication is that no one number should trigger a deload; rather, the convergence of several valid measures, each read as a 7 to 28 day trend, justifies adjusting load - which is exactly how monitoring operationalises the principles of progressive overload and recovery.
Marker's note: top-band answers (1) make an explicit JUDGEMENT about how well monitoring guides decisions and under what conditions, (2) name a specific athlete with sport-matched (valid) tests, (3) use validity AND reliability correctly (a measure can be reliable but not valid; single readings are unreliable), (4) tie decisions to named principles of training (progressive overload, recovery, specificity), and (5) sustain evaluation rather than listing tools. Anchoring with a worked construct such as the ACWR (7-day vs 28-day load) signals precision.