NSCA-CPSS cheat sheet

Force-velocity relationship: as load increases, concentric shortening velocity decreases; peak power typically occurs at ~30-45% of 1RM for many multi-joint lifts, though this varies by exercise and athlete.

Length-tension relationship: active force is maximal near mid-range sarcomere length; excessive shortening or lengthening reduces cross-bridge formation.

Rate coding and motor unit recruitment follow the size principle: low-threshold units recruit first, with firing frequency increasing force output before higher-threshold units are added.

Eccentric actions can produce ~1.2-1.6x greater force than concentric actions at the same angular velocity, driving higher mechanical stress and muscle damage.

Acute hormonal responses (testosterone, GH, cortisol) to resistance exercise are transient; chronic hypertrophy is driven primarily by mechanical tension and volume, not acute hormone spikes.

Cardiac output = heart rate x stroke volume; steady-state VO2 relates to cardiac output and arteriovenous oxygen difference (Fick principle).

Anaerobic power is estimated via vertical jump or Wingate testing; the phosphagen system dominates efforts under ~10 seconds.

Muscle fiber typing: Type I fibers are fatigue-resistant and oxidative; Type IIx fibers produce high power but fatigue rapidly and can shift toward IIa with training.

Validity tiers: criterion (concurrent/predictive) > construct > convergent; a device reporting jump height must be compared against force plate or 3D motion capture as criterion standard.

Reliability metrics to require: ICC >= 0.90 for key variables, CV <= 5-10% depending on variable, and typical error reported in absolute units with 95% confidence limits.

Smallest worthwhile change (SWC) is often set at 0.2 x between-athlete SD; a device is only useful if typical error < SWC.

GPS/LPS validity degrades with indoor use, high-speed cutting, and satellite obstruction; report sampling rate (Hz) and number of satellites for context.

Velocity-based training devices differ by method: linear position transducers measure displacement directly, accelerometers require double integration and drift, and camera systems depend on frame rate.

Force plate sampling: >= 1000 Hz for jump/landing kinetics to avoid underestimating peak force; analog-to-digital resolution and filtering cutoffs must be reported.

Wearable sleep/HRV monitors show variable agreement with polysomnography and ECG; use them for within-athlete trends, not clinical diagnosis.

Decision rule: adopt technology only when it improves a decision the practitioner would otherwise make differently, and when measurement error is smaller than the smallest meaningful change.

Hierarchy of evidence: systematic reviews/meta-analyses > randomized controlled trials > cohort > case-control > cross-sectional > expert opinion.

Internal validity threats: selection bias, attrition, maturation, testing effects, and instrumentation drift; randomization and blinding mitigate these.

Statistical significance (p < 0.05) does not equal practical significance; report effect sizes (Cohen's d, eta squared) and confidence intervals.

Magnitude-based inference uses thresholds (e.g., 0.2, 0.6, 1.2 standardized units) to classify effects as trivial, small, moderate, or large.

Power analysis determines sample size; underpowered studies inflate false-negative and false-positive risk and overestimate effect sizes (winner's curse).

A case study or single-subject design (e.g., multiple baseline) is appropriate for elite or rare populations where group RCTs are infeasible.

Preregistration, pre-specified outcomes, and reporting checklists reduce selective reporting and p-hacking.

Correlation does not establish causation; cross-sectional associations between training load and injury cannot determine direction of effect.

Needs analysis sequence: sport analysis (movement, physiology, injury profile) -> athlete assessment -> gap identification -> program priorities.

Physiological profile components: VO2max, lactate threshold, anaerobic capacity, maximal strength, power, rate of force development, and movement economy.

Biomechanical analysis identifies key positions and joint actions (e.g., hip hinge in a deadlift, triple extension in a jump) that drive exercise selection.

Injury epidemiology: use surveillance data to identify most common and most severe injuries (e.g., hamstring strain, ACL rupture in field sports) and target prevention.

Test selection criteria: relevance to sport, reliability, validity, and practicality; avoid redundant tests that measure the same quality.

Normative comparison should use sport-, sex-, age-, and position-specific reference data; generic population norms mislead elite assessment.

Prioritization rule: rank qualities by (a) importance to performance and (b) current deficit; the largest weighted gap receives the most training emphasis.

Context factors to document: competition calendar, travel, training age, facility access, and athlete goals, which constrain program design.

Progressive overload: increase load, volume, density, or complexity over time; avoid simultaneous large increases in multiple variables.

Periodization models: linear (volume decreases, intensity increases), block (sequential concentrated blocks), and undulating (frequent variation within weeks).

Volume-load = sets x reps x load; use it to track and compare training stress across sessions and mesocycles.

Acute:chronic workload ratio (ACWR) is calculated as acute (e.g., 7-day) load divided by chronic (e.g., 28-day rolling average) load; values are commonly flagged outside ~0.8-1.3, though the method has known statistical limitations.

Strength prescription: >= 85% 1RM, 1-6 reps, 2-6 sets for maximal strength; 30-60% 1RM with high velocity for power.

Hypertrophy is driven by moderate loads (60-80% 1RM) taken near failure with sufficient weekly volume; power training requires low fatigue to preserve velocity.

Tapering: reduce volume by ~40-60% while maintaining intensity and frequency for 1-3 weeks to peak performance.

Context-specific adaptation: adjust for travel, heat, altitude, injury status, and competition density; use autoregulation (RPE, velocity loss thresholds) to modify daily loads.

Translational gap: research findings require adaptation to the specific athlete, sport, and training phase before application.

External validity depends on sample similarity (training status, sex, age, sport) to the target athlete; untrained-subject findings often do not transfer to elites.

Monitoring data should be interpreted with within-athlete baselines and smallest detectable difference rather than group means.

Effect sizes from meta-analyses should be weighted by study quality and heterogeneity (I-squared) before adoption.

Practitioner-scientist loop: hypothesis -> intervention -> measure -> adjust, with documented rationale for each program change.

Ethical and consent requirements apply to data collection and sharing, especially with minors and wearable data.

Replication and multi-site studies reduce the risk of adopting spurious findings into practice.

Establish measurement error before using a device to detect change; if typical error exceeds the SWC, the device cannot reliably detect meaningful change.

Standardize testing protocols (warm-up, surface, footwear, time of day) to reduce noise and improve reliability.

Report both absolute and relative reliability; ICC alone can mask poor agreement when between-subject variability is large.

Bland-Altman analysis quantifies bias and limits of agreement between a new device and a criterion method.

Data governance: secure storage, consent, and clear ownership of athlete data are required before deploying monitoring technology.

Cost-benefit: weigh device cost, staff time, and data burden against the decision value it provides.

Pilot testing in the actual environment (e.g., on-field GPS) is required before full implementation.

Profile each athlete against position-specific demands rather than team averages.

Use a battery covering strength, power, speed, endurance, and mobility; keep the battery stable across testing occasions for comparability.

Identify the performance-limiting quality through gap analysis between current and target profiles.

Injury history and movement screening inform exercise selection and risk mitigation.

Document sport demands: work-to-rest ratios, dominant energy systems, and key movement patterns.

Set priorities based on both importance and trainability within the available timeframe.

Reassess periodically to update the profile as the athlete develops or the season progresses.