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SportsFlow
SPORTSFLOW · SPORTSFLOW SCORES

Neuromuscular Readiness Score

Is your neuromuscular system ready to train hard or does it need recovery?

Noah Wickliffe, M.S. · Founder, MyoSport Inc. · 8 min read
100%Wearable-derived,
5Biometric inputs
18%Weight in daily
§ 01 THE STORY

The Story

COMPOSITE PORTRAIT

The Neuromuscular Readiness Score is the body’s daily vote on whether to train hard or recover. It uses five wearable-derived metrics — HR decoupling rate, LF/HF autonomic ratio, slow-wave sleep percentage, acute-to- chronic workload ratio (ACWR), and resting HR deviation from baseline — to calculate whether your neuromuscular system has absorbed yesterday’s training and is ready for today’s. No questionnaire. No subjective input. Pure physiology. The NRS is the most objective score in the system.

This is a composite portrait. The story reflects patterns documented across many athletes. No individual is depicted.

NEUROMUSCULAR READINESS WEARABLE OBJECTIVE

Measure → Understand → Train → Improve

HR
DECOUPLINGCardiovascular

The Neuromuscular Readiness Score is the body’s daily vote on whether to train hard or recover. It uses five wearable-derived metrics — HR decoupling rate, LF/HF autonomic ratio, slow-wave sleep percentage, acute-to- chronic workload ratio (ACWR), and resting HR deviation from baseline — to calculate whether your neuromuscular system has absorbed yesterday’s training and is ready for today’s. No questionnaire. No subjective input. Pure physiology. The NRS is the most objective score in the system. Neuromuscular Readiness Score — NRS NRS 100% wearable-derived readiness score from HR, HRV, sleep, and training load data. Weight: 18% of daily composite. NEUROMUSCULAR READINESS WEARABLE OBJECTIVE Measure → Understand → Train → Improve Continuous measurement → AI interpretation → Personalized prescription → Tracked improvement Cardiovascular drift under load LF/HF ratio from HRV SWS% for neural recovery Acute vs chronic workload balance

§ 02 WHAT THE RESEARCH TELLS US

What the Research Tells Us

100%Wearable-derived,
5Biometric inputs
18%Weight in daily

Plews et al. (2013) showed HRV-guided training produced 5× greater VO₂max improvement than fixed plans. Buchheit (2014) established that the combination of HRV, resting HR, and sleep metrics provides the most sensitive readiness

Neuromuscular Readiness: Is Your Body Ready to Perform? The NRS is 100% wearable-derived — no questionnaire. HR decoupling, autonomic balance, deep sleep percentage, training load ratio, and resting HR deviation tell you whether your neuromuscular system is ready to train hard or needs recovery. Noah Wickliffe, M.S. · Founder, MyoSport Inc. · 8 min read

assessment available from consumer wearables. ACWR monitoring (Gabbett, 2016) identifies the training load balance that maximizes adaptation while minimizing injury risk. “Your NRS is the most honest number in your daily report. It does not care how motivated you feel. It cares whether your body has recovered from what you did yesterday. Listen to it.” — Noah Wickliffe, M.S., Flowbase Score Series 100% Wearable-derived, no questionnaire 5 Biometric inputs combined 18% Weight in daily composite

§ 03 HOW THE AI COACH USES THIS SCORE

How the AI Coach Uses This Score

THE STRATEGIC ADVANTAGE

The NRS is the overtraining circuit breaker. When your body says no, the AI Coach listens — even when your mind says yes.

The AI Coach uses your NRS as the primary gate for high-intensity training. If NRS is below threshold, the AI prescribes recovery or Zone 2 work regardless of what the plan says. If NRS is high, the AI greenights hard sessions. This single mechanism prevents more overtraining than any other feature in the system. The NRS is the overtraining circuit breaker. When your body says no, the AI Coach listens — even when your mind says yes. Know Your Numbers. Own Your Performance. All 18+ scores are built into every Flowbase athlete account.

References

  1. [1] Plews, D. J. et al. (2013). HRV-guided training. Int. J. Sports Physiology & Performance, 8(6), 601–614.
  2. [2] Buchheit, M. (2014). Monitoring training with HR measures. Int. J. Sports Physiology & Performance, 9(5), 883–898.
  3. [3] Gabbett, T. J. (2016). The training-injury paradox. Br. J. Sports Medicine, 50(5), 273–280.
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