Nextcloud Cookbook vs PAIesque
Side-by-side comparison of two open source alternatives
Nextcloud Cookbook
This app is a viewer for recipes in Nextcloud App. The synchronisation can be done direct (with nextloud) or with the local storage. Synchronize directly works with Single Sign On (SSO) per nextcloud client. The local storage can synchronized in other ways, there is no dependency to a client. First steps After first installation (or with the current update from 2.x.x to 3.0.0) it shows the login screen. If the nextcloud client is installed and you want to synchronize direct with the nextcloud server, you can use the login button to show a list of accounts and you choose the account to login. Then the app will download the recipes with the REST api. If you want to synchronize with the local storage, you can skip the login ("Skip for local storage"). After that, you have to choose the recipe directory in the settings and permit access. In the app "All categories" is the main view, there the app is loading the recipes from the choosen directory. When using the Nextcloud Android client, you find the directory on your storage under Android/media/com.nextcloud .client/nextcloud/<your account>/<folder>. You also can choose the theme in the settings. After that, the start view has a list of recipes and you select a recipe to view the details. IMPORTANT: With direct pick of the nextcloud folder there will no images shown and the uri can't be stored right. Perhaps at next app start no recipes can be loaded. Therefore it is better to use the above mentioned path on the SD card (see First steps).
PAIesque
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.
| Feature | Nextcloud Cookbook | PAIesque |
|---|---|---|
| License | GPL-3.0-or-later | MIT |
| Install sources | F-DroidGitHub | F-Droid |
| Categories | NotesProductivityFitnessMessagingBrowser | ProductivityFitnessMessaging |
| Features | Ad-FreeOpen SourceNo Tracking | Ad-FreeOpen SourceNo Tracking |
| Platforms | Android | Android |
| Website | ||
| Source code |