PAIesque
Fitness tracker and analyzer based on published scientific papers
New in version 58
Release Notes - PAIesque v58
What's New in This Update
- Fixed crashes when syncing large amounts of heart rate data from
Health Connect. The app now processes data smoothly regardless of
how many years of history you have.
- Improved memory usage during calculations. If you wear your device
24/7 or have long training sessions with second-by-second recording,
the app will no longer run out of memory.
- Fixed an issue where "No heart rate data available" would sometimes
appear incor
What's New in This Update
- Fixed crashes when syncing large amounts of heart rate data from
Health Connect. The app now processes data smoothly regardless of
how many years of history you have.
- Improved memory usage during calculations. If you wear your device
24/7 or have long training sessions with second-by-second recording,
the app will no longer run out of memory.
- Fixed an issue where "No heart rate data available" would sometimes
appear incor
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.
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.
- Author: OJ Random
- License: MIT License
- Website
- Issue Tracker
- Source Code
- Build Metadata
- Reproducibility Status
Versions
Although APK downloads are available below to give you the choice, you should be aware that by installing that way you will not receive update notifications and it's a less secure way to download. We recommend that you install the F-Droid client and use that.
Download F-Droid-
This version requires Android 14 or newer.
It is built and signed by the original developer, and guaranteed to correspond to this source tarball.
Download APK 4.0 MiB PGP Signature | Build Log
-
This version requires Android 14 or newer.
It is built and signed by the original developer, and guaranteed to correspond to this source tarball.
Download APK 4.0 MiB PGP Signature | Build Log
-
This version requires Android 14 or newer.
It is built and signed by the original developer, and guaranteed to correspond to this source tarball.
Download APK 4.0 MiB PGP Signature | Build Log
New in version 57
Release Notes - PAIesque v57
What's New in This Update
Fixed: Charts Now Show Your Recovery Data Immediately
Previously, if you only had one day of Resting Heart Rate (RHR) or Heart Rate Variability (HRV) data, the charts would appear empty even though the data was there. This is now fixed - your recovery metrics will show up right away, even with just a single day of measurements.
Fixed #29: Historical Sync No Longer Times Out
Some users with large amounts of historical heart rate data exp
What's New in This Update
Fixed: Charts Now Show Your Recovery Data Immediately
Previously, if you only had one day of Resting Heart Rate (RHR) or Heart Rate Variability (HRV) data, the charts would appear empty even though the data was there. This is now fixed - your recovery metrics will show up right away, even with just a single day of measurements.
Fixed #29: Historical Sync No Longer Times Out
Some users with large amounts of historical heart rate data exp
New in version 56
Release Notes - PAIesque v56
Fixed: Stale Recovery Values After Settings Changes
What Was Happening
When you changed certain settings (like your sleep window or fitness level),
the app would correctly mark your data for recalculation. However, old RHR
and HRV values sometimes stuck around for days that no longer had enough
valid heart rate readings.
This meant you might see:
- RHR values on the chart for days when you didn't wear your device
- Resting heart rates that didn't match your curre
Fixed: Stale Recovery Values After Settings Changes
What Was Happening
When you changed certain settings (like your sleep window or fitness level),
the app would correctly mark your data for recalculation. However, old RHR
and HRV values sometimes stuck around for days that no longer had enough
valid heart rate readings.
This meant you might see:
- RHR values on the chart for days when you didn't wear your device
- Resting heart rates that didn't match your curre