『Fit For Science』のカバーアート

Fit For Science

Fit For Science

著者： Stephan Reichl and Rob ter Horst
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概要

 Two scientists discuss how they live their best life, using science, data, tech, wearables, and systems. Fit For Science is a deep-dive podcast hosted by two biological data scientists, Rob and Stephan, exploring the intersection of research, health tech, and data-driven lifestyle design. The hosts provide evidence-based systems, layered with practical "N=2" personal experimentation, to cut through the noise and enable everyone to become their best N-of-1. The Quantified Scientist (Rob): youtube.com/TheQuantifiedScientist Stephan's Website: http://polytechnist.meStephan Reichl and Rob ter Horst 衛生・健康的な生活
エピソード
  • How to Actually Read Your Sleep Data (Beyond Accuracy) + 7 Scientific "Cumulative Biomarkers" for Longevity (Fit For Science Episode 10)

    How to Actually Read Your Sleep Data (Beyond Accuracy) + 7 Scientific "Cumulative Biomarkers" for Longevity (Fit For Science Episode 10)
    2026/02/09
    Rob and Stephan discuss why sleep stage trends matter more than absolute accuracy, review Oura's latest metrics, and define seven essential cumulative biomarkers for long-term health.📝SummaryIn this episode, biological data scientists Rob and Stephan challenge the standard approach to sleep tracking validation, proposing that detecting deviations from an individual's baseline is often more valuable for the user than absolute agreement with polysomnography. The hosts shortly brainstorm the creation of an independent, crowd-funded wearable testing institute to provide unbiased data for the quantified self community and research. Then they analyze the utility of Oura’s new Sleep Debt and Cumulative Stress features, discussing how these metrics align with subjective experiences of recovery after social events like the Viennese ball season. The conversation expands into a deep dive on "cumulative biomarkers," where Stephan outlines a suite of stable, long-term health indicators, including HbA1c, VO2 max, Grip Strength, and the Omega-3 Index, that serve as superior proxies for longevity compared to transient measurements.⏳Chapters00:00:00 Sleep Study Analysis: User centric comparisons00:10:39 Testing Philosophy: Why "more or less than usual" matters most00:16:13 The Vision: A crowd-funded independent wearable testing lab00:24:37 Oura's Trend Features: Analyzing Sleep Debt and recovery timelines00:34:43 Cumulative Stress: Physiological stress vs “Distress” vs "Eustress"00:41:51 Hardware Woes: The decline of Fitbit and device longevity00:45:15 Feature Disparity: Oura Health Panels and US vs. EU regulations00:51:22 Cumulative Biomarkers: Stable markers vs. transient snapshots00:52:23 Metabolic Health: Why HbA1c trumps fasting glucose00:57:55 Fitness Markers: VO2 Max and the utility of Grip Strength01:01:31 Nutritional Status: The Omega-3 Index and cell membrane saturation01:05:22 Organ Health: Cystatin C for kidney function and DXA for body composition01:09:47 Cardiovascular Risk: The Coronary Artery Calcium (CAC) score01:12:25 Smart Scales: Bio-impedance limitations and the need for handles📚ResourcesIn the episode we call the discussed biomarkers “integrative”, but “cumulative” better captures the intended meaning.Rob's sleep studyPolysomnography Cohen's Kappa (Statistic)Sensitivity and specificity Oura's Sleep Debt FeatureOura's Cumulative Stress FeatureOura's Resilience FeatureOura's Daytime (Physiological) Stress featureDistress vs EustressElectrodermal activity as proxy for stressFitBit Sense 2 (with cEDA sensor) Oura's Health Panel featureRed blood cellGlycated hemoglobin (HbA1c) HbA1c > 6.5% is used for diabetes diagnosisVO2 max Grip strength as a mortality predictorOmega-3 Index (Dr. Rhonda Patrick)Cystatin C (Kidney Function)DXA Scan Radiation comparison (DXA ~0.001mSv, US coast-to-coast round-trip flight ~0.03mSv)Coronary Artery Calcium (CAC) ScoreThe limits of coronary calcium Visceral FatPreprint introducing "Peakspan"Nature Medicine paper "Shared and specific blood biomarkers for multimorbidity"🎙️AboutFit For Science is a deep-dive podcast hosted by two biological data scientists, Rob and Stephan, exploring the intersection of research, health tech, and data-driven lifestyle design. The hosts provide evidence-based systems, layered with practical "N=2" personal experimentation, to cut through the noise in the health and fitness industry.Learn more and subscribe on your favorite platforms:YouTubeSpotifyApple PodcastsAmazon MusicCollection of all show notes⚠️Disclaimer: This podcast represents our own opinions and is for informational purposes only. It does not constitute medical or financial advice or a professional relationship.
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    1 時間 18 分
  • The “Dark Side” of Tracking & VO2 Max as Longevity Predictor: Testing, Training & Our Results (Fit For Science Episode 9)

    The “Dark Side” of Tracking & VO2 Max as Longevity Predictor: Testing, Training & Our Results (Fit For Science Episode 9)
    2026/02/02
    In this episode, Rob and Stephan explore the psychological risks of self-quantification, the science of aerobic capacity, and the physiological nuances of lactate thresholds.📝SummaryBiological data scientists Rob and Stephan discuss the "dark side" of the quantified self, specifically focusing on orthosomnia, a condition where sleep tracking leads to increased anxiety and worsened sleep quality. They reflect on the importance of using technology as a tool for a specific purpose rather than making the tracking itself the goal. The conversation transitions into a deep dive on VO2 max, explaining its critical role as a longevity predictor and the varying results obtained from different exercise modalities like cycling and running. Finally, the hosts break down the science of lactate thresholds, explaining how the body's metabolic shift from aerobic to anaerobic states serves as a vital biomarker for training optimization.⏳Chapters00:00:00 Introduction: The dark side of tracking and VO2 max00:00:55 Orthosomnia: When sleep tracking causes insomnia00:05:09 The psychological impact of metrics and obsession00:13:13 Tracking with purpose: Avoiding the identity trap00:25:59 Oura Ring experiences: “Injuries” and data accuracy00:30:50 Strength training and basal metabolic rate00:36:47 VO2 Max: The ultimate longevity marker?00:38:26 Hazard Ratios: Comparing fitness to smoking00:44:39 The U-shaped curve of exercise volume00:49:37 Gold Standard: VO2 max lab testing protocols01:04:25 Training for capacity: The Norwegian 4x4 protocol01:07:51 Lactate thresholds and metabolic switching01:16:09 Wearable estimations: Garmin vs. Apple vs. Oura01:21:47 VO2 Max Records: Oskar Svendsen (97.5) and Tadej Pogačar (96)01:23:42 Teaser: Biological age and integrative biomarkers📚ResourcesOrthosomniaThe Molecular Precision Medicine Master’s Programme at Medical University of Vienna (where Rob and Stephan teach)Quote for purposeful tracking: "I shall not waste my days in trying to prolong them" - Jack LondonNatural language processing (NLP)Semantic analysisDevelopment of a scale for measuring orthosomnia: the Bergen Orthosomnia Scale (BOS)Sleep tracker use nears 50%, AASM survey findsPrevalence of Orthosomnia in a General Population Sample Dark triad (Personality Traits)Basal metabolic rate (BMR)BMR Calculator Lean body mass was found to be the single predictor of BMRPhelps supposedly consumed 8,000-10,000 kcal per training day before the Olympic GamesVO2 maxHazard ratioHow does VO2 max correlate with longevity? - Peter Attia Physical activity types, variety, and mortality: results from two prospective cohort studies Peak oxygen uptake was strongly correlated to total heart volumeRob's VO2 max results: 58 for cycling, 54 for runningStephan's VO2 max results: 42 for cycling, 49 for runningVO2 max percentile calculatorVO2 Max ChartAerobic high-intensity intervals improve VO2max more than moderate training (Norwegian 4x4) How to Improve Your Cardio Capacity (VO2 Max)Lactate threshold for aerobic to anaerobic switch at 2mmol/litreLactate shuttle hypothesis Maximum heart rate formula: 220 - age in yearsCooper test for VO2max estimationWalking test for VO2max estimation🎙️AboutFit For Science is a deep-dive podcast hosted by two biological data scientists, Rob and Stephan, exploring the intersection of research, health tech, and data-driven lifestyle design. The hosts provide evidence-based systems, layered with practical "N=2" personal experimentation, to cut through the noise in the health and fitness industry.Learn more and subscribe on your favorite platforms:YouTubeSpotifyApple PodcastsAmazon Music⚠️Disclaimer: This podcast represents our own opinions and is for informational purposes only. It does not constitute medical or financial advice or a professional relationship.
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    1 時間 25 分
  • AI is Changing Wearables in 2026(?) and Predicts 130 Diseases from Sleep! (Fit For Science Episode 8)

    AI is Changing Wearables in 2026(?) and Predicts 130 Diseases from Sleep! (Fit For Science Episode 8)
    2026/01/26
    Rob and Stephan evaluate current AI features in wearables, break down a revolutionary paper predicting diseases from a single night of sleep, and discuss the future of medical integration into wearables.📝SummaryIn this episode, biological data scientists Rob and Stephan critically assess the current use of AI in the wearable market, ranging from the practical limitations of Oura and Whoop coaches to the potential of Google’s Gemini and Withings’ biomarker-tracking devices. The central scientific discussion focuses on "SleepFM," a groundbreaking foundation model published in Nature Medicine that utilizes self-supervised learning on polysomnography data to predict over 130 diseases, biological age, and mortality risk from a single night of sleep with unprecedented accuracy. The hosts speculate on how this technology could bridge the gap between clinical sleep labs and consumer wearables, potentially transforming preventive medicine through longitudinal tracking and non-invasive sensors.⏳Chapters00:00:00 AI in wearables and their current capabilities00:01:21 AI Coaches: Testing the limits of Oura, Whoop, and Garmin 00:12:24 The Smart Toilet: Withings U-Scan and the value of waste biomarkers 00:23:00 Environmental Health: PVC off-gassing and vinyl records 00:28:15 Generative AI: ChatGPT Health and Claude for Life Sciences 00:37:17 SleepFM: A multimodal sleep foundation model for disease prediction 00:43:00 Self-Supervised Learning: How foundation models learn from sleep data 00:51:00 Disease Prediction: Predicting 130 conditions with unseen accuracy00:59:46 The Future: Translating clinical models to consumer wearables 01:19:25 Community Feedback📚ResourcesIntroducing Oura Advisor (not Coach)WHOOP Coach Powered by OpenAIActive Intelligence With Garmin Connect+U-Scan NutrioNews: Withings latest smart scale (‘longevity station’)Withings IntelligenceBody ScanKetone bodiesKetosis: Definition, Benefits & Side EffectsKeto Breath (“dragon breath”)Air Quality Measurement DeviceVINYL: Maybe it's time we had an intervention.Introducing ChatGPT HealthSegment about AI in health(care)Claude in healthcare and the life sciencesClarification: Anthropic's product is called Claude with three differently sized models named Haiku, Sonnet, and Opus.ICD-10 and ICD-11 Codes: International Classification of Diseases (ICD)Understanding ICD-10 | Johns Hopkins MedicineHealthcare Spending - Our World in DataFederated learningSwarm LearningSleepFM - Nature Medicine paperCodeStanford Sleep Bench v1.0Foundation modelAttention Is All You Need (Transformers)Self-supervised learningImageNetFine-tuningReinforcement learning from human feedback (RLHF)PolysomnographyRecurrent neural network (LSTM)Long short-term memory (RNN)C-index: Evaluating Survival ModelsBest Wearables for Sleep: Scientific Rankings (2024-05)Best Wearables for Sleep: Scientific Rankings (2025-10)Philips Somnolyzer 24x7 for automated sleep stagingWhoop listened(?) and is looking for a VP for Foundation AIAUROC of blood pressure to predict ASCVD ~0.80Podcast Recommendation: Drug Story Atorvastatin (Lipitor)Life expectancy: Netherlands (82.2) vs Austria (82.0)Diagnostic and Statistical Manual of Mental Illnesses (DSM-5)Mechanism does not imply outcome. Outcome implies mechanism. - Layne NortonNo Biological Free Lunches🎙️AboutFit For Science is a deep-dive podcast hosted by two biological data scientists, Rob and Stephan, exploring the intersection of research, health tech, and data-driven lifestyle design. The hosts provide evidence-based systems, layered with practical "N=2" personal experimentation, to cut through the noise in the health and fitness industry.Learn more and subscribe on your favorite platforms:YouTubeSpotifyApple PodcastsAmazon Music⚠️Disclaimer: This podcast represents our own opinions and is for informational purposes only. It does not constitute medical or financial advice or a professional relationship.
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    1 時間 25 分
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