PAIesque

Record body

Abstract

<strong>PAIesque</strong> helps athletes and fitness enthusiasts monitor their training through a simple three-step logic:<br> <br> <strong>1. Measure training impulse (TRIMP)</strong> — The faster and longer your heart beats, the higher your daily score.<br> <strong>2. Analyze patterns over time</strong> — Track how your TRIMP accumulates and distributes<br> <strong>3. Monitor your body's response</strong> — Compare how your body reacts to training load<br> <br><strong>PAIesque</strong> is different from commercial fitness apps (e.g. Garmin, Whoop, Polar). Every metric comes from published, peer-reviewed research with transparent methods that can be calculated from heart rate data alone. The app only includes metrics we can verify and reproduce from first principles — no proprietary black boxes, no undisclosed algorithms. And, all your data stays on your device.<br> <br> <strong>1. TRIMP</strong>:<br><br> • <strong>Banister TRIMP</strong> — The original exponential model with sex-specific coefficients (a=0.64/0.86, b=1.92/1.67) [Banister, 1991; Morton et al., 1990]<br> • <strong>iTRIMP</strong> — Individualized TRIMP with customizable b coefficient (1.5-4.0) [Stagno et al., 2007; Akubat et al., 2012]<br> • <strong>LT-TRIMP</strong> — Lactate Threshold-based model with β coefficient (0.04-0.11) and smooth transition at LT [Cheng et al., 1992; Mader et al., 1976; Gaesser and Poole, 1986]<br> • <strong>PAI-esque</strong> — PAI-inspired metric using EWMA (not the official commercial algorithm) [Nes et al., 2017; Kieffer et al., 2021]<br> <br> <strong>2. Patterns over time</strong>:<br> <br> • <strong>Intensity zones</strong> — Time and TRIMP spent in low/moderate/high zones (polarized training model) [Seiler and Tønnessen, 2009; Stöggl and Sperlich, 2014]<br> • <strong>EWMA</strong> — Exponentially Weighted Moving Average for rolling loads (more sensitive than simple averages)<br> • <strong>ACWR</strong> — Acute:Chronic Workload Ratio for injury risk monitoring (0.8-1.3 = sweet spot) [Murray et al., 2017; Griffin et al., 2021; Gabbett, 2016]<br> • <strong>Polarized Training Score</strong> — 0-100 measure of how closely your distribution matches your targets<br> <br> <strong>3. Body's response</strong>:<br> <br> • <strong>Resting Heart Rate (RHR)</strong> — Calculated from your defined sleep window (adaptive percentile: 5th-15th)<br> • <strong>Heart Rate Variability (HRV)</strong> — Daily RMSSD averages during sleep [Task Force, 1996; Plews et al., 2013; Buchheit, 2014]<br> • <strong>EWMA trends</strong> — Exponentially weighted moving averages for both RHR and HRV (acute and chronic windows)<br> • <strong>Combined interpretation</strong> — RHR ↓ + HRV ↑ = positive adaptation; RHR ↑ + HRV ↓ = possible fatigue<br> <br> <strong>Data Management:</strong><br> <br> • CSV export/import<br> • Complete backup/restore (db)<br> • All data stays on your device — no accounts, no cloud uploads, no tracking<br> <br> <strong>Creative Use Cases:</strong><br> <br> • <strong>Coach analyzing athletes</strong> — Import athlete exports, analyze charts, provide feedback<br> • <strong>Research analysis</strong> — Export CSV files for custom analysis in R, Python, or spreadsheets<br> • <strong>Switch between athletes</strong> — Use "Delete All Data" + CSV import to analyze multiple individuals<br> <br> <strong>Requirements:</strong><br> <br> • Google Health Connect installed on your device<br> • Heart rate (and HRV) data in Health Connect from your wearable device (Gadgetbridge, Garmin, Polar, Samsung, etc.)<br> • Android 8.0 (API 26) or higher<br> <br> <strong>Note on PAI:</strong><br> <br> Our PAI-esque implementation is NOT the official commercial PAI® algorithm (which is proprietary). It uses EWMA and scaled TRIMP values to provide a similar intensity-weighted weekly score. The 100 PAI target remains the evidence-based health outcome from the HUNT Study research.