Research-backed training programs with a gym-ready tracking app — periodized programming, local gym analysis, and persistent workout logging in a single integrated service.
A personal training client transitioning from home-based corrective exercise to structured gym training needed three things working together: a periodized program tailored to their specific compensatory patterns, research on which local gym environment best supported their goals, and a way to log workouts with quality indicators that captured more than just reps and weight.
Existing fitness apps track numbers. They do not capture whether a set was clean, whether form degraded under load, or whether a compensation pattern reappeared. For a trainer focused on corrective exercise, that qualitative signal is the most important data.
A mobile-first Progressive Web App rendering a full periodized training program with expand/collapse exercise cards, three-tier coaching cues (How To, Technical, Feel), and per-set quality tracking (Clean / Grind / Miss). Persistent logging via IndexedDB with cloud backup to Cloudflare D1.
View Sample →Comprehensive analysis of 6 local gym facilities scored across corrective value, schedule compatibility, equipment depth, and proximity. Evidence-tiered with source verification indicators. Includes class-by-class effectiveness scoring for compensatory exercise goals.
View Research →The program viewer is a single-file PWA (220KB assembled) built in vanilla JavaScript. Program data is embedded as JSON following a custom v2.0 schema with warehouse-linked exercise definitions. Each exercise carries structured coaching cues, equipment tags, focus categories (strength, corrective, mobility), and type badges (standard, isometric, AMRAP, conditioning).
Session tracking captures three dimensions per set: what happened (reps and load), perceived exertion, and movement quality. The three-state quality indicator — Clean, Grind, Miss — was designed for minimal cognitive load between sets while producing the signal a corrective-focused trainer actually needs for mesocycle planning.
The backend uses Cloudflare Workers with a D1 database for persistent storage. The app works fully offline via service worker caching and syncs to the server when connectivity is available.
The gym research used a four-tier evidence grading system (GROUND, BRIDGE, EXTENSION, GAP) with each claim tagged to its verification level. 16 primary sources were harvested and processed. Where web-sourced information could not be verified, explicit gap markers identify what still requires phone or in-person confirmation — because acknowledging uncertainty is more useful than hiding it.