AppID:	org.ojrandom.paiesque
Author:	OJ Random
License:	MIT
Donation:
InRepoSince:	2025-11-12
LastRepoUpdate:	2026-04-25
LastAppUpdate:	Unknown
LastVersion:	60
Categories:	Connectivity, Sports & Health
APK source:
Google Play:	Check if it's there

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.