Ironman Training: A 20-Week Beginner’s Guide to Success

December 1, 2025

TitaniumJohnny: The Crash and Journey Back to Running – PT 7

December 5, 2025

Blood Testing vs AI Metrics: Elite Runners’ Choice 2025

Published by Johnny Shelby LMT at December 1, 2025

How Blood Lactate Testing Works for Runners

Blood lactate testing has established itself as the benchmark for endurance athletes seeking to optimize training zones. This physiological assessment measures the concentration of lactate – a byproduct produced during exercise and normal metabolism – which accumulates when production exceeds removal rates [14]. Lactate threshold training techniques for triathletes leverage this data to enhance endurance performance and delay fatigue. By strategically training at or near the lactate threshold, athletes can improve their ability to sustain higher intensities over longer durations. Incorporating these techniques into a triathlon training regimen can lead to significant improvements in race times and overall fitness.

Invasiveness and Accuracy of Blood Sampling

Remember the last time you had blood drawn at the doctor’s office? Blood lactate testing feels similar, though thankfully much quicker. The testing procedure involves collecting small blood samples, typically from the fingertip or earlobe, during incremental exercise [14]. Lactate testing for athletic performance provides valuable insights into an athlete’s endurance and recovery. By analyzing lactate levels, coaches can tailor training regimens to optimize performance and minimize the risk of injury. This personalized approach not only enhances physical output but also supports long-term athletic development.

The process requires puncturing the skin to access blood droplets for analysis. Most athletes handle this procedure well, having experienced it during regular health checks. Some may experience anxiety or discomfort, particularly if they’re needle-sensitive [3].

Accuracy depends on proper technique. Blood samples must be taken carefully to avoid contamination from sweat or other substances [14]. Modern microsampling technologies have made collection more user-friendly, with devices like hemaPEN allowing precise collection of small blood volumes (10.96 µl) that can be easily stored and processed [4].

Blood-based methods show less bias and smaller variance than ventilation-based methods when predicting performance [3]. Among blood-based approaches, the inflection point between steady-state lactate and rising lactate (INFL) demonstrates superior predictive power for time-trial outcomes [3].

Lactate Threshold and Performance Prediction

The lactate threshold (LT) represents the work rate or oxygen uptake at which blood lactate begins accumulating more rapidly [15]. Think of it as your body’s tipping point – the moment when lactate production outpaces removal. This critical physiological marker is considered perhaps the best single predictor of endurance performance [15].

Here’s how the test unfolds. Athletes exercise at progressively increasing intensities until near exhaustion, with blood samples collected at regular intervals [16]. Normal resting lactate levels range between 1-2 mmol/L but can exceed 20 mmol/L during intense exertion [14].

The test identifies key indicators:

LT1 (typically at 2.0mmol/L) – marks the initial lactate increase
LT2 (measured at 4.0mmol/L) – indicates significant lactate accumulation
OBLA (Onset of Blood Lactate Accumulation) – the point where athletes can sustain effort for 20-60 minutes [14]

These thresholds correlate directly with sustainable performance capacity. Athletes with better training demonstrate higher lactate thresholds and lower blood lactate concentrations at any absolute intensity above resting [15]. By tracking changes in these values over time, runners can measure training progress objectively [14]. Understanding elite runners’ lactate threshold benefits can provide valuable insights into optimizing training routines. By focusing on increasing these thresholds, athletes may enhance their endurance capabilities and perform better in competitive situations. Consistent evaluation of lactate response during workouts can be instrumental in tailoring training to individual needs for maximum efficiency.

Lab Requirements and Cost Implications

Professional lactate testing demands specialized equipment including a lactate analyzer, testing strips, lancets for blood sampling, and either a treadmill or cycling ergometer [17]. The test should be conducted in a controlled environment by trained personnel who can administer the progressive exercise protocol correctly [16]. lactate threshold testing benefits for runners as it helps to determine the optimal exercise intensity, enabling them to improve their performance effectively. By identifying the point at which lactate begins to accumulate in the bloodstream, athletes can tailor their training regimens to enhance endurance and increase speed. This targeted approach not only aids in better race preparation but also minimizes the risk of overtraining and injury.

The financial reality hits hard for many runners. At university sports medicine centers, initial evaluations typically cost around $150, with subsequent tests priced at approximately $125 [18]. More affordable options at some clinics start at $75 [18]. Comprehensive tests combining lactate measurements with oxygen consumption analysis command premium prices due to their value for serious endurance athletes [18].

Equipment and facility costs represent just part of the equation. Proper interpretation of results requires expertise. The data obtained – particularly heart rate, speed, and/or power at lactate threshold – must be translated into practical training parameters [14]. Many elite runners undergo testing 2-4 times annually to track progression and adjust training zones accordingly [17]. Triathlon performance testing techniques are essential for athletes looking to enhance their training effectiveness. These methods provide critical insights that guide adjustments in workouts and recovery strategies. By frequently assessing their performance metrics, triathletes can fine-tune their preparation for competition and maximize their potential on race day.

AI Running Metrics Explained: A 2025 Overview

Wearable technology currently ranks as the #1 fitness trend according to the American College of Sports Medicine (ACSM) Fitness Trends Report, with mobile apps and data-driven training closely following [9]. The shift feels dramatic when you consider that just a few years ago, most runners relied solely on stopwatches and gut instinct.

What Are AI Running Metrics?

AI running metrics measure runner performance through advanced algorithms instead of invasive blood tests. These systems analyze multiple data points simultaneously, creating a picture of running efficiency that traditional methods simply can’t capture. understanding heart rate variability can offer insights into how well a runner is recovering and managing stress. This information helps athletes tailor their training plans and avoid overtraining. By incorporating heart rate variability into their routines, runners can optimize their performance and enhance their overall well-being.

Modern AI systems track several key metrics:

Biomechanical factors: Joint symmetry, stride length, cadence, and foot strike patterns
Physiological indicators: Heart rate variability, estimated VO₂ max, and recovery readiness
External variables: Distance, speed, terrain, elevation, and environmental conditions

The technology combines smart sensors that monitor movements with sophisticated algorithms processing information in the cloud, enabling real-time insights about your running technique [10]. Platforms like Stryd provide power-based insights invisible to traditional methods, including metrics like Running Stress Score (RSS) that monitor lower body stress to optimize recovery [11].

ChatGPT Running Plan Capabilities

ChatGPT-generated six-week training plans have transformed how runners approach program design. When you provide detailed information about current fitness levels, goals, and training history, the AI generates structured programs tailored to individual needs.

A recent study evaluated these AI-generated plans based on input information quality. Coaching experts rated plans using 22 quality criteria on a 1-5 Likert Scale, with the most detailed input receiving the highest median rating of 4 [1]. The reality? ChatGPT lacks individualization beyond user-provided information and fails to incorporate health screening protocols [1].

The plans typically feature progressive structure – base building, speed development, and race preparation phases similar to professionally designed programs [12]. ChatGPT can quickly generate suitable training frameworks, but effectiveness depends heavily on the quality of information you provide.

Smart Device Integration and Real-Time Coaching

Samsung’s Galaxy Watch8 exemplifies the most significant advancement in AI running metrics. Its AI-powered Running Coach feature delivers personalized training plans through Google Gemini integration – marking the first appearance of this AI assistant on a smartwatch [2].

This system places users into one of 10 training levels based on assessment, then provides 15-19 customized sessions across 3-5 week programs [2]. The system combines multiple health metrics to prevent overtraining, using a 0-100 Energy Score that incorporates sleep quality, activity levels, and heart rate variability [2].

The Galaxy Watch8’s sensors monitor split pace, heart rate, and left-right balance, offering real-time coaching that helps build proper form [2]. Stryd’s system adapts training to your schedule and fitness level, with workouts evolving weekly based on progress, upcoming events, and life commitments [11].

The integration between AI metrics and wearable technology continues narrowing the gap between data collection and actionable insights, making sophisticated training methodology accessible to runners of all levels.

Accuracy and Reliability: Blood Tests vs AI Models

Elite athletes face a fundamental question: which method actually delivers the most reliable lactate threshold estimates? The battle between traditional blood testing and artificial intelligence has reached a fascinating tipping point in 2025.

Lactate Threshold Prediction: Blood vs RNN Models

Recurrent Neural Network (RNN) models represent the cutting edge of AI-based lactate threshold prediction. These sophisticated algorithms can estimate lactate threshold heart rate (LTHR) with a mean absolute error (MAE) of just 4.37 beats per minute, alongside lactate threshold pace (LTP) predictions within 0.36 km/h of measured values [13]. Even for outdoor running applications, where variables multiply exponentially, the accuracy remains impressive with MAEs of 9.18 bpm and 1.23 km/h respectively [13].

Blood testing takes a different approach entirely. Rather than predicting, it measures what’s actually happening inside your body right now. Racewalking velocity at blood lactate threshold exhibits a remarkable correlation (r = 0.94) with actual performance pace [14]. The oxygen uptake at lactate threshold shows equally strong correlation (r = 0.89) to performance outcomes [14]. The impact of cardiac drift on performance can also be significant, particularly in endurance athletes during prolonged activities. As heart rate increases while maintaining a steady pace, the imbalance can lead to premature fatigue and affect overall race outcomes. Understanding these physiological changes allows athletes to tailor their training and racing strategies for optimal performance.

Here’s the fundamental difference: blood testing measures your current physiological state, while AI models predict what should be happening based on patterns from thousands of other runners.

Validation Metrics: R = 0.89 vs Expert Ratings

Machine learning systems can accurately estimate the lactate threshold for 89.52% of tested athletes, with correlation to experimental measurements reaching R = 0.89 [3]. That places AI predictions remarkably close to direct physiological measurements. Fat oxidation strategies for triathletes are essential for optimizing performance over longer distances. By enhancing the body’s ability to utilize fat as a fuel source, athletes can improve endurance and delay fatigue. This approach aligns well with current research emphasizing the role of nutrition and metabolic efficiency in athletic training. Understanding the power to weight ratio explained is crucial for athletes looking to maximize their performance. By optimizing this metric, competitors can enhance their speed without compromising strength, leading to better race outcomes. Additionally, attention to this balance supports the development of training regimens that specifically target performance improvements in endurance sports.

The numbers get even more compelling when you examine anaerobic threshold predictions:

Threshold	Algorithm	R²	MAE	RMSE	MAPE%
AnT	LightGBM	0.803	3.439	4.577	1.978
AeT	Random Forest	0.645	4.630	6.667	3.238

AI models now explain approximately 80.3% of the variance in anaerobic threshold values [4]. Mean absolute error rates have dropped below 3.5 beats per minute for heart rate predictions.

ChatGPT running plans follow a different validation path. When provided with detailed user information, these AI-generated training plans achieve median expert ratings of 4 out of 5 on a Likert scale across 22 quality criteria – approaching professional coach standards.

Generalization Across Runner Profiles

The true test? How well does each method work across different types of runners – from weekend warriors to elite marathoners.

Blood testing maintains consistent reliability across all populations. The reason is straightforward: it directly measures physiological responses rather than making educated guesses.

AI models face bigger challenges here. Early lactate threshold modeling struggled with what researchers call the “generalization-accuracy tradeoff” – systems highly accurate for specific individuals often performed poorly with new runners [3]. Modern models using transfer learning techniques have dramatically improved cross-profile reliability.

For Random Forest algorithms predicting aerobic threshold, training variables contribute 26.5% of predictive power, while anthropometric variables and body composition account for 20.6% [15]. This explains why AI models sometimes struggle with athletes whose body composition or training patterns differ significantly from the training dataset.

Most elite runners have figured out the solution: combine both approaches. Periodic blood testing calibrates and validates AI predictions. This hybrid methodology provides the scientific rigor of direct measurement with the convenience of daily AI monitoring, creating a running metrics vertical that tracks performance changes over time.

Don’t worry – you’re not alone if this seems complicated. The smartest approach might be simpler than you think.

Personalization and Adaptability in Training Plans

Static training plans feel outdated in 2025. Elite athletes have discovered something better – plans that actually respond to how their body feels and performs each day.

Static Zones vs Dynamic AI Adjustments

Traditional training zones trap you in predetermined boundaries. You’re stuck running tempo at 75-85% of 5k pace whether you feel fantastic or completely drained. AI-powered coaching takes a different approach – it responds to your progress, fatigue levels, and subjective feedback [16].

AI Endurance calculates individualized zones based on your actual training data instead of generic percentages [17]. Your zones evolve as you do. The system analyzes your heart rate variability data to establish boundaries that match your current physiological state.

The adaptation speed is remarkable. After each session, algorithms like the “Performance Tracker” crunch multiple factors – pace, heart rate, elevation gain, recovery – to calculate your rolling performance value [18]. Performance capacity drops by 0.2 km/h after a tough week? The plan adjusts immediately and notifies you [18].

Running Metrics Regularity and Vertical Analysis

Smart systems create what coaches call a “running metrics vertical” – your complete performance story over time. This detailed tracking reveals patterns invisible to traditional methods.

These systems monitor crucial elements:

Biomechanical factors: Cadence (180 steps per minute optimal), stride length, ground contact time (200-300ms range) [19]
Symmetry indicators: Left-right balance (49-51% acceptable range) [5]
Efficiency markers: Vertical oscillation (5-10cm ideal range) [5]

Consistent analysis of these values catches subtle form changes before they become injuries or performance declines. Advanced algorithms identify when metrics drift outside optimal ranges, triggering adjustments to maintain efficient running economy [20].

Real-Time Feedback and Plan Modifications

Picture this: you’re mid-run when your AI coach speaks directly into your earbuds. SHFT’s AI coach talks to you during workouts, offering immediate feedback on your technique [20]. Cadence dropping? Ground contact too long? You get instant corrections.

The intelligence extends beyond single sessions. Systems like Garmin Coach reshape entire training blocks based on your performance and recovery [21]. Crush your workout targets? Your plan pushes harder next week. Miss a session due to life demands? The algorithm recalibrates to keep you on track. Poor sleep last night? Training intensity automatically adjusts [21].

This adaptability considers contextual factors that static plans completely ignore. Some platforms even let you chat directly with AI coaches that remember every detail of your training history [22]. Your plan becomes truly personal – responding to your body, your schedule, and your goals in real time.

Monitoring Load and Contextual Factors

Have you ever wondered why some training days feel effortless while others drain every ounce of energy? The answer lies in understanding both what stresses your body experiences and how your body responds to those stresses. This dual perspective forms the foundation of modern load monitoring for elite runners in 2025.

Internal Load: Heart Rate and Perceived Exertion

Your body tells a story through internal load measures. Heart rate monitoring serves as your primary objective metric, though daily variations can reach 6.5% for submaximal heart rate, making it critical to control for hydration, environment, and medications [23].

Don’t underestimate the power of how you feel. Rate of Perceived Exertion (RPE) offers a surprisingly accurate subjective measure that many runners overlook. Swedish psychologist Gunnar Borg developed this approach in the 1960s, and modern RPE uses a simplified 0-10 scale that correlates with physiological markers [24]. A 2024 study involving over 6,000 participants confirmed RPE as a reliable indicator of physical signs like blood lactate, VO2 max, and heart rate [25].

Here’s something fascinating: mental perception of effort may actually determine performance limitations more than physical fatigue. As Matt Fitzgerald explains, “Exhaustion may actually be more of a psychological barrier than physical fatigue” [25]. This makes RPE training valuable both physiologically and psychologically.

External Load: Distance, Speed, Terrain

External load represents objective work performed independently of individual characteristics [6]. Think of it as what you actually did, regardless of how it felt. Beyond simple distance tracking, sophisticated external metrics now include:

Biomechanical factors like stride length and cadence
Running power (measured in watts)
Accumulated elevation gain
Total ground reaction forces

The relationship between external and internal loads creates what researchers call the “efficiency index” (Effindex), calculated through ratios like meters per minute divided by %HRmax [6]. This index helps identify fatigue, as it typically decreases throughout matches and correlates with anaerobic threshold [6].

Environmental Inputs: Temperature, Altitude

Environmental conditions can make or break your training session. Temperature emerges as the most significant factor – studies reveal optimal running temperatures range between 3.8°C and 9.9°C for maximal performance [26]. When temperatures exceed this range, running performance declines predictably – approximately 7.73-7.78 minutes slower race times in high temperature conditions [27].

Altitude throws another curveball. Above 5,000 feet, performance typically declines, with VO2max dropping 12-16% upon initial exposure to altitude [28]. Even after acclimation, endurance performance at altitude never fully matches sea level capability [28].

The good news? Monitoring systems now integrate these contextual factors, with platforms adjusting training prescriptions based on temperature, humidity, and altitude data. The most sophisticated systems calculate an internal-to-external load ratio, creating a personalized efficiency benchmark that evolves with training adaptations [6].

Limitations and Risks of Each Approach

Both approaches come with serious drawbacks that can derail your training progress. Don’t worry – you’re not alone if you’ve experienced some of these pitfalls.

Overtraining and Injury Risk with AI Plans

Have you ever followed an AI training plan that ramped up too quickly? Many runners report that AI systems push them harder than appropriate, transitioning beginners from walking to 20 minutes of continuous running within just a week . This rapid progression typically ignores the strength, mobility, and stretching work that prevents injury .

The problem runs deeper. AI-generated plans can’t account for external stressors like work pressure, poor sleep, or family demands that affect your recovery capacity . These systems might schedule intense workouts when you’re mentally or physically unprepared, creating a recipe for overtraining .

Human coaches recognize subtle cues that algorithms miss entirely. They notice when you’re dragging during warm-ups or when your usual enthusiasm seems forced. AI systems can’t perform this kind of nuanced assessment.

Blood Testing’s Clinical Demands vs AI’s Supervision Gap

Blood testing brings its own complications. Frequent testing can lead to anemia from repeated blood draws or disrupted sleep patterns from early morning lab visits . Many tests get scheduled too close together, making it impossible to detect meaningful physiological changes between measurements .

Meanwhile, AI approaches often lack necessary clinical oversight. Research shows exercise-based interventions only reduce injury risk when supervised (p < 0.001) . This supervision gap creates a blind spot – AI can’t perform hands-on movement assessments or understand your complete injury history the way a sports medicine professional can.

The Human Elements Both Methods Miss

Neither approach captures the psychological factors that often determine your performance limits. As Fitzgerald notes, “Exhaustion may actually be more of a psychological barrier than physical fatigue” – something blood tests can’t measure and AI can’t understand . AI platforms fail to provide the genuine encouragement and accountability that human coaching offers .

Nutritional aspects get equally overlooked. Ultra-runners typically consume only about 50% of their energy expenditure during events , creating significant nutritional deficits. Issues like gastrointestinal distress during races require human expertise that no algorithm can replace .

Time matters when these limitations surface during important races or training blocks. Recognition of these gaps helps you make better decisions about which approach fits your specific needs.

Blood Testing vs AI Running Metrics: The Complete Breakdown

Your training method choice comes down to these fundamental differences. Here’s everything you need to know about both approaches:

Feature	Blood Testing	AI Running Metrics
Accuracy	Direct physiological measurements; r = 0.94 correlation with race performance	89.52% accuracy in lactate threshold estimation; R = 0.89 correlation with experimental measurements
Data Collection Method	Invasive; requires blood samples from fingertip or earlobe	Non-invasive; uses wearable sensors and smart devices
Cost	$75-150 per test; requires specialized equipment and trained personnel	Cost not specifically mentioned; requires wearable devices
Testing Environment	Controlled laboratory setting required	Can be used anywhere during regular training
Measurement Parameters	– Lactate levels (1-2 mmol/L at rest) – LT1 (2.0mmol/L) – LT2 (4.0mmol/L) – OBLA	– Biomechanical factors – Heart rate variability – VO₂ max estimates – Environmental conditions
Real-time Feedback	No immediate feedback; results require analysis	Immediate feedback during runs; real-time coaching
Adaptability	Static measurements; requires new tests for updates (2-4 times annually)	Dynamic adjustments based on daily performance and recovery
Main Limitations	– Risk of anemia from frequent testing – Requires clinical supervision – Cannot track daily changes	– Can’t fully account for external stressors – Risk of overtraining – Limited psychological assessment – Lacks clinical supervision

The choice isn’t always black and white. Many elite runners combine periodic blood testing with daily AI monitoring to get the best of both worlds—scientific precision when they need it, convenient tracking when they don’t.

What This Means for Your Training

Blood lactate testing and AI running metrics aren’t enemies competing for your attention. They’re powerful allies that work best when combined intelligently.

Think about your current training approach. Blood testing delivers the scientific foundation—those direct physiological measurements that reveal exactly what’s happening inside your body during exercise. Meanwhile, AI metrics provide the daily guidance that keeps you on track between those lab visits.

Elite runners have figured this out. They use periodic blood testing to establish physiological baselines and validate their AI predictions, while relying on daily AI metrics to track subtle performance changes. This creates what we call a running metrics vertical—a complete performance history that captures both immediate feedback and long-term trends.

Your choice depends on your goals and commitment level. Serious competitors benefit from blood testing 2-4 times annually, supplemented with AI monitoring between sessions. Recreational runners often find AI systems sufficient, especially when budget constraints make regular lab testing impractical.

The technology gap continues to narrow. Machine learning models now predict lactate threshold with remarkable accuracy, suggesting future developments might eventually match laboratory precision while maintaining superior convenience.

Don’t ignore the limitations. AI systems struggle with psychological factors and external stressors that human coaches recognize instinctively. Blood testing provides only periodic snapshots of your physiology. The most effective training approach combines technological insights with self-awareness and human judgment.

The future holds exciting possibilities. Non-invasive continuous monitoring of blood biomarkers represents the next frontier—potentially offering laboratory accuracy with wearable convenience. Until then, the smartest runners use both methods while remembering that no technology replaces the human element needed for truly optimal training.

Remember: your body holds the answers. These tools simply help you listen more carefully.

Key Takeaways

Elite runners in 2025 are navigating between traditional blood testing and cutting-edge AI metrics to optimize their training. Here’s what the data reveals about these competing approaches:

• AI accuracy rivals lab testing: Machine learning models now predict lactate threshold with 89.52% accuracy (R = 0.89 correlation), nearly matching direct blood measurements for performance prediction.

• Hybrid approach dominates elite training: Top runners use periodic blood testing (2-4 times annually) to establish baselines while relying on AI metrics for daily monitoring and real-time adjustments.

• Real-time adaptability gives AI the edge: Unlike static blood test results, AI systems provide immediate feedback during runs and automatically adjust training plans based on recovery, sleep, and performance data.

• Cost and convenience favor AI long-term: Blood testing requires $75-150 per session plus lab visits, while AI metrics offer continuous monitoring through wearable devices without invasive procedures.

• Neither method is complete alone: Blood testing misses daily variations and psychological factors, while AI systems can’t fully account for external stressors or provide clinical supervision for injury prevention.

The future belongs to integrated approaches that combine the scientific rigor of physiological testing with the convenience and adaptability of artificial intelligence, creating comprehensive performance tracking systems that evolve with each runner’s unique needs.

FAQs

Q1. How accurate are AI running metrics compared to blood lactate testing? AI running metrics have shown impressive accuracy, with some studies reporting 89.52% accuracy in predicting lactate threshold. While blood testing remains the gold standard for direct physiological measurements, AI metrics are increasingly reliable for daily training guidance.

Q2. What are the main advantages of using AI running metrics over traditional blood testing? AI running metrics offer real-time feedback, daily adaptability, and non-invasive monitoring through wearable devices. They can provide immediate coaching during runs and automatically adjust training plans based on performance and recovery data, which blood testing cannot do.

Q3. How often should elite runners undergo blood lactate testing? Most elite runners undergo blood lactate testing 2-4 times per year. This frequency allows them to establish physiological baselines and validate AI predictions while avoiding the risks associated with too frequent blood sampling.

Q4. Can AI running metrics completely replace blood testing for athletes? While AI metrics are highly advanced, they cannot completely replace blood testing. A hybrid approach is often recommended, where periodic blood tests validate and calibrate AI predictions, providing a comprehensive view of an athlete’s performance and physiology.

Q5. What are the limitations of relying solely on AI-generated training plans? AI-generated plans may not fully account for external stressors, psychological factors, or injury history. They also lack the clinical supervision that can be crucial for injury prevention. There’s a risk of overtraining if runners follow AI plans without considering their overall well-being and recovery needs.

References

[1] – https://www.sciencedirect.com/science/article/abs/pii/S1568494617306993
[2] – https://www.nsca.com/education/articles/kinetic-select/using-lactate-threshold-data/?srsltid=AfmBOopA2i2fwjUxdfP2cIvF3vrUyo1Hr5oKjDRf4-qHwPZqch4xaHWa
[3] – https://pmc.ncbi.nlm.nih.gov/articles/PMC10915606/
[4] – https://mjssm.me/clanci/MJSSM_March_2024_Vijay.pdf
[5] – https://health.ucdavis.edu/sports-medicine/resources/lactate
[6] – https://upsidestrength.com/lactate-testing-for-athletes/
[7] – https://worldathletics.org/download/downloadnsa?filename=cf27ed38-00dd-4fad-ba13-fb8e07f10c6c.pdf&urlslug=practical-consequences-of-blood-sampling
[8] – https://pmc.ncbi.nlm.nih.gov/articles/PMC8191458/
[9] – https://pmc.ncbi.nlm.nih.gov/articles/PMC3137529/
[10] – https://pmc.ncbi.nlm.nih.gov/articles/PMC2769631/
[11] – https://www.nsca.com/education/articles/kinetic-select/performing-a-lactate-threshold-test/?srsltid=AfmBOopzaNQ48cUq_nuciHRAmWqLHVEtiAQXelHhkVbP-nbNz-3FuMUC
[12] – https://trainright.com/the-performance-benefits-of-lactate-threshold-testing-and-training/
[13] – https://health.ucdavis.edu/sports-medicine/sports-performance/performance-tests
[14] – https://www.mayoclinichealthsystem.org/locations/onalaska/services-and-treatments/orthopedic-surgery/sports-medicine/performance
[15] – https://bluebellfunrun.com/smart-running-tech-wearables-that-will-change-your-training/
[16] – https://runsmarterdaily.com/performance-analytics-tracking/ai-running-coach-performance-tracking-metrics/
[17] – https://www.stryd.com/
[18] – https://marathonhandbook.com/expert-coaches-examine-chatgpts-ability-to-create-running-training-plans/
[19] – https://www.polar.com/blog/i-asked-chatgpt-to-be-my-running-coach/?srsltid=AfmBOoprkD6QBvpM3NqZ8TQPQBzHNS7PsbJyy_X-KSegAltPTY4_1xge
[20] – https://www.techbuzz.ai/articles/samsung-galaxy-watch8-debuts-ai-powered-running-coach-feature
[21] – https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5417016
[22] – https://pubmed.ncbi.nlm.nih.gov/6621317/
[23] – https://ar5iv.labs.arxiv.org/html/1803.06030
[24] – https://pmc.ncbi.nlm.nih.gov/articles/PMC11361594/
[25] – https://www.mdpi.com/1660-4601/17/21/8289
[26] – https://rundown.runtheday.com/p/can-ai-replace-your-running-coach
[27] – https://aiendurance.com/blog/the-ai-endurance-training-zones
[28] – https://blog.runningcoach.me/en/2024/06/21/dynamic-training-plan-heres-how-running-coach-adapts-your-training/
[29] – https://medium.com/runners-life/a-beginners-guide-to-running-metrics-a54ced5e19b7
[30] – https://www.d3multisport.com/d3-university/running-performance-metrics
[31] – https://shft.run/
[32] – https://www.garmin.com/en-US/garmin-coach/overview/
[33] – https://www.reddit.com/r/Strava/comments/1lwwm59/i_built_an_aipowered_adaptive_running_coach_that/
[34] – https://pmc.ncbi.nlm.nih.gov/articles/PMC4213373/
[35] – https://trainright.com/perceived-exertion-run-and-train-by-feel-rpe/
[36] – https://www.runnersworld.com/training/a29706103/what-is-rpe/
[37] – https://pmc.ncbi.nlm.nih.gov/articles/PMC9331329/
[38] – https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0037407
[39] – https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2021.665761/full
[40] – https://us.humankinetics.com/blogs/excerpt/the-effects-of-altitude-on-performance?srsltid=AfmBOorDT8D-DSWuInpIRXbMR9yHAGr6SkejGgNeBEzdrTgyFhwQRxAb

Johnny Shelby LMT

Wishing you the best in training - #TitaniumJohnny