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Fresh studies, training methods and fitness tech — distilled for athletes who want to know what actually works.
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AI Training Plans Under the Microscope: When Experts Disagree
You've asked AI for a training plan — or at least thought about it. The real question isn't whether it works, but how you can tell if the result is any good. A new study delivers an uncomfortable finding: even experts barely agree on that.
April 21, 2026 · by Christopher Klenk
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This system learns from your training data — and doesn't need you as a test subject
You look at your watch in the morning, see a low HRV and ask yourself: easy day or push through anyway? A new study shows how a system that learns by trial and error — no rulebook, no chatbot — takes exactly this decision. It gets your training data, simulates how you as a person respond to different loads, and […]
April 21, 2026 · by Christopher Klenk
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Study Check: No AI Chatbot Needed – Tests & Machine Learning Build Your Training Plan
Runners run. Gym-goers bench. Almost nobody trains their actual weak point – because almost nobody knows where it is. Chinese researchers have now published a machine-learning model in Scientific Reports that detects exactly that and builds a training plan around it. With real data, a randomised trial – and a few gaps you should know about.
April 21, 2026 · by Christopher Klenk
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Coaches detect velocity loss without a device – what a new Bar Strategy study shows
Coaches who watch the barbell instead of the athlete detect velocity loss in strength training far more accurately – and it can be trained. A new study shows: after a short intervention, the error drops to an average of 1.5 reps. That's good enough to apply VBT principles meaningfully even without a sensor.
April 21, 2026 · by Christopher Klenk
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AI measures heart rate from face video – researchers solve the motion problem
Researchers have developed an AI model that measures your pulse via camera – and for the first time it works reliably when you are moving. No chest strap, no sports watch, no skin contact. Just a face video, an algorithm, a heart rate. Sounds like science fiction – but it is the state of research as of February 2026. The model is called HBP-Net, published in the journal iScience. What it can do, [...]
April 21, 2026 · by Christopher Klenk
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More training, worse sleep, even more training – what 224 Garmin runners reveal
High training volume worsens sleep quality – and too little sleep slows down the next session. That is what a year-long study of 224 Chinese Garmin runners shows. The finding is plausible and well supported. The caveat: Garmin is not the most accurate wearable on the market when it comes to sleep stage detection – and that hits the core of the study.
April 21, 2026 · by Christopher Klenk
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Study: fatigue markers in football — and what to do with them
Trying to predict fatigue from a single metric misses more often than you would think. Researchers followed 48 college football players for twelve weeks with GPS trackers, heart rate monitors and daily wellness questionnaires — and trained a machine learning model on the combined data that detects fatigue with an AUC of 0.895. Individual indicators come nowhere close. This is a […]
April 20, 2026 · by Christopher Klenk
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Smartphone spots form errors – and beginners stick with it
Training on your own comes with a familiar problem: you can't see yourself. Is your back rounding on the deadlift? Are your knees caving in on the squat? Without a mirror, a training partner or a coach, you're flying blind – and in the worst case you grind in mistakes you only notice once they start to hurt. A South Korean research team has […]
April 21, 2026 · by Christopher Klenk
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188 studies, one verdict: Good data – but no AI coach
A Spanish research team has reviewed the current state of wearable biosensors and machine learning systems in sports training in a comprehensive overview – 188 studies from 16 years of research. The conclusion is sober: the technology delivers usable data and ML models can support training adjustments – but autonomous coaching? We are a long way from that. TL;DR A new review study (188 studies, […]
April 21, 2026 · by Christopher Klenk
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Fort Wearable: Automatic Strength Training Tracking Incl. VBT Data – What's Behind It?
A new wearable aims to track strength training as precisely as Garmin tracks running. Fort, developed by three former Tesla engineers and part of the Y Combinator W26 batch, automatically recognises more than 50 exercises, counts reps, tracks rest times and delivers muscle-specific breakdowns – without manual logging. Sounds like the wearable lifters have been waiting for. But between marketing promises and reality there's often a whole set of reps.
April 21, 2026 · by Christopher Klenk
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Study tests AI marathon training plans — but how good was the test itself?
A new study in the British Medical Bulletin looked at whether current AI models can generate usable marathon training plans. The researchers had Claude, ChatGPT, Gemini and DeepSeek each build a six-month plan for three performance levels. The result: broadly solid plans with familiar weaknesses. Things get interesting when you look at what the abstract reveals about the methodology — and […]
July 1, 2026 · by Christopher Klenk
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Steering deadlift intensity without a 1RM test – new study shows how accurate it gets
A load-velocity profile built from a few submaximal sets can estimate high training intensities in the deadlift with an error of roughly 4 %. That is what a recent study in the Journal of Sports Science and Medicine (Li et al., 2026) shows. For intensities around 80–90 % of 1RM the accuracy is acceptable – without you ever having to push a maximum rep.
April 21, 2026 · by Christopher Klenk