PAIesque — Open Source Productivity App

PAIesque helps athletes and fitness enthusiasts monitor their training through a simple three-step logic:

1. Measure training impulse (TRIMP) — The faster and longer your heart beats, the higher your daily score.
2. Analyze patterns over time — Track how your TRIMP accumulates and distributes
3. Monitor your body's response — Compare how your body reacts to training load

PAIesque 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.

1. TRIMP:

• Banister TRIMP — The original exponential model with sex-specific coefficients (a=0.64/0.86, b=1.92/1.67) [Banister, 1991; Morton et al., 1990]
• iTRIMP — Individualized TRIMP with customizable b coefficient (1.5-4.0) [Stagno et al., 2007; Akubat et al., 2012]
• LT-TRIMP — 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]
• PAI-esque — PAI-inspired metric using EWMA (not the official commercial algorithm) [Nes et al., 2017; Kieffer et al., 2021]

2. Patterns over time:

• Intensity zones — Time and TRIMP spent in low/moderate/high zones (polarized training model) [Seiler and Tønnessen, 2009; Stöggl and Sperlich, 2014]
• EWMA — Exponentially Weighted Moving Average for rolling loads (more sensitive than simple averages)
• ACWR — Acute:Chronic Workload Ratio for injury risk monitoring (0.8-1.3 = sweet spot) [Murray et al., 2017; Griffin et al., 2021; Gabbett, 2016]
• Polarized Training Score — 0-100 measure of how closely your distribution matches your targets

3. Body's response:

• Resting Heart Rate (RHR) — Calculated from your defined sleep window (adaptive percentile: 5th-15th)
• Heart Rate Variability (HRV) — Daily RMSSD averages during sleep [Task Force, 1996; Plews et al., 2013; Buchheit, 2014]
• EWMA trends — Exponentially weighted moving averages for both RHR and HRV (acute and chronic windows)
• Combined interpretation — RHR ↓ + HRV ↑ = positive adaptation; RHR ↑ + HRV ↓ = possible fatigue

Data Management:

• CSV export/import
• Complete backup/restore (db)
• All data stays on your device — no accounts, no cloud uploads, no tracking

Creative Use Cases:

• Coach analyzing athletes — Import athlete exports, analyze charts, provide feedback
• Research analysis — Export CSV files for custom analysis in R, Python, or spreadsheets
• Switch between athletes — Use "Delete All Data" + CSV import to analyze multiple individuals

Requirements:

• Google Health Connect installed on your device
• Heart rate (and HRV) data in Health Connect from your wearable device (Gadgetbridge, Garmin, Polar, Samsung, etc.)
• Android 8.0 (API 26) or higher

Note on PAI:

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.