Movement Library

A Movement System Built Around How Grip Strength Actually Works

Most fitness apps treat grip training as a footnote. They let you log a dead hang the same way you log a bicep curl — a weight, a rep count, maybe a duration. That works for exercises where those dimensions capture the full picture. It falls apart entirely for grip training, where the difference between a two-hand hang and a one-hand hang, between a bodyweight hold and a weighted hold, between a full gripper close and a partial close, represents completely distinct physiological demands that deserve to be measured and tracked independently.

Gripp's movement system was designed from the ground up to reflect that reality. Every data structure, every tracking schema, every classification choice was made with grip strength physiology in mind — not adapted from a generic model.

Three Grip Types That Reflect the Actual Physiology

Grip strength is not a single ability. Hand and forearm research consistently identifies three independent grip patterns that each engage different muscle groups, recruit different motor units, and respond to different training stimuli. Gripp models all three as first-class categories.

Crush grip is force generated by closing the hand around an object. The fingers flex toward the palm against resistance. This is the pattern trained by hand grippers, towel hangs, and thick-bar holds. Crush grip failure typically presents as the fingers opening rather than the wrist or forearm giving way.

Pinch grip brings the thumb into opposition with the fingers. The thenar muscles — the pad at the base of the thumb — drive this pattern alongside the intrinsic hand muscles. Pinch grip is mechanically distinct from crush grip, which is why a climber with excellent crimp strength can still have a relatively weak pinch, and why exercises like plate pinches, hub lifts, and pinch block hangs exist as their own category.

Support grip is the ability to sustain a hold over time. Dead hangs, bar hangs, and fingerboard work fall here. This is primarily an endurance pattern driven by the flexor digitorum profundus and superficialis, with fatigue accumulating in both the muscles and the connective tissue. Support grip is the foundation of most grip training and the pattern where time under tension is the central variable.

Every movement in Gripp is tagged with the grip types it trains. This classification is not cosmetic — it drives how the exercise is executed, how performance is measured, and how personal bests are tracked.

Six Tracking Schemas, One for Each Way Grip Is Actually Measured

The biggest failure of generic fitness tracking for grip training is applying a single measurement model to exercises that are fundamentally different. Gripp solves this with six distinct personal best schemas, each calibrated to the actual performance variable that matters for that class of exercise.

Timed hang captures your longest continuous hold for support grip exercises. The primary value is duration in seconds. This is the schema used for standard dead hangs, bar hangs, and fingerboard work where the question is simply: how long can you hold on?

Timed side hang recognizes that left-hand and right-hand performance are independent data points. Grip imbalances between sides are common — especially in climbers who favor one hand — and lumping both sides into a single metric obscures the asymmetry. Gripp records separate personal bests for each side so imbalances are visible and trackable over time.

Weighted timed hang adds a second dimension to support grip tracking. Both the duration and the added load are captured as primary and secondary values. This matters because a 45-second hang with 10 kg added and a 20-second hang with 25 kg added are not comparable under a single-value model. Each represents a different point on the strength-endurance curve, and both deserve to be tracked independently.

Weighted timed movement extends this dual-value model to dynamic exercises where load and duration both vary — think farmer carries, loaded pinch holds, or weighted support grip movements with a movement component.

Gripper resistance is the schema for crush grip tracking via hand grippers. The primary value is the resistance rating of the heaviest gripper closed, with metadata capturing whether the close was full or partial. This schema understands that gripper training is progressive in a specific, product-catalogued way — a RB100 and a RB150 are categorically different achievements, not just a numerical difference.

Grip reps tracks repetition-based exercises where the performance variable is count rather than duration. Gripper endurance sets, towel pull-up reps, and similar exercises use this schema. Maximum reps per set is the tracked value.

This schema system means that every personal best in Gripp is a meaningful, apples-to-apples comparison. A timed hang PB is never confused with a weighted hang PB. A one-hand PB is never averaged with a two-hand PB. The data is clean because the model was designed for grip training specifically.

Equipment-Aware Tracking That Preserves Historical Accuracy

Grip training equipment varies enormously. A dead hang on a 25mm bar, a 10mm fingerboard edge, and a 45mm fat grip are three different exercises producing three different adaptations, even though all three could be described as "hanging from something." Gripp's movement system treats these as distinct training inputs.

Every movement is linked to specific equipment through a movement-equipment mapping layer. Equipment in Gripp is classified into four categories — bar, fingerboard, gripper, and other — and individual pieces of equipment carry their own attributes: name, brand, numeric value, and unit. A Beastmaker 2000 fingerboard is not just "fingerboard." A Captains of Crush No. 1 is not just "gripper." The specificity matters for tracking.

When a user completes an attempt, Gripp stores an equipment snapshot with the result. This means that if the athlete later changes equipment — upgrades a fingerboard, acquires a new gripper set, switches bars — their historical data remains accurate to the equipment they were actually using. A personal best from six months ago on a 20mm edge does not get contaminated by a recent session on a 15mm edge.

Equipment-specific personal bests flow from this design. Athletes who train on multiple setups get separate PB records for each piece of equipment, giving them an accurate capability profile across their entire gear inventory rather than a blended average that reflects none of it accurately.

Eight Execution Components That Define How an Exercise Is Performed

Beyond grip type and equipment, Gripp distinguishes how a movement is executed. This is the execution component layer — eight distinct movement patterns that determine which timer interface appears, which tracking schema is active, and how performance data is recorded.

• Timed hang — Continuous bilateral hold, duration tracked
• Timed side hang — Unilateral hold with separate left/right tracking
• Timed movement — Duration-based tracking for non-hang support exercises
• Weighted timed hang — Bilateral hold with added load, dual-value tracking
• Weighted timed movement — Dynamic exercise with load component, dual-value tracking
• Gripper resistance — Crush grip with resistance level as the primary metric
• Reps — Count-based tracking for repetition exercises
• Grip reps — Rep tracking with distinction between full closes and partial closes

Each execution component maps to a specific challenge executor in Gripp's training engine. The movement definition tells the system which executor to use, which schema to apply for personal bests, and what data to capture during the attempt. This is why the app can present the right interface automatically — a fingerboard hang gets a countdown timer, a gripper set gets a rep counter with full/partial close distinction — without requiring the athlete to configure anything manually.

Movement Outcomes as a Unified Attempt Record

Every completed attempt in Gripp — whether from a training program level, a challenge, a daily challenge, or a training routine — is recorded as a movement outcome. This produces a unified activity history indexed by movement rather than by the source that generated it.

The outcome record captures: which movement was performed, which equipment was used (via snapshot), which execution component was applied, and the full set data including per-attempt hang times, whether targets were met, and whether the athlete dropped before completing the set. The source type and source ID are preserved so any outcome can be traced back to the exact challenge or level that produced it.

This design means personal bests can be computed across all training contexts simultaneously. A new personal best timed hang set during a challenge will update the same PB record as one set during a program level, because both are the same movement tracked under the same schema. The source varies; the measurement is consistent.

How It Connects to the Rest of Gripp

The movement system is the data foundation that the rest of Gripp's training engine is built on. Training programs sequence movements into progressive levels with specific execution components and targets. The Challenge Library organizes permanent challenges by movement category. Training routines combine movements into multi-exercise sessions. Equipment management links your gear inventory to movements so challenges can be matched to what you own.

Every layer of the app depends on the movement model being precise. If a movement is misclassified or tracked under the wrong schema, the data that flows into personal bests, program progression, and the Gripp Score loses fidelity. The investment in the movement system's specificity is what makes every other feature reliable.

Why Generic Fitness Tracking Fails Grip Athletes

Generic fitness apps are built around a common denominator: sets, reps, weight, and duration. That model covers the majority of resistance training reasonably well. It does not cover grip training well, because grip training's key variables — grip type, hand laterality, equipment specificity, the difference between a timed hold and a rep-based close — do not map cleanly onto those dimensions.

The result, for athletes who try to track grip training in a general-purpose app, is either lost data (they cannot record the distinctions that matter) or invented workarounds (separate exercises for each hand, manual notes about equipment, custom rep counting schemes). Neither approach produces clean, queryable, historically accurate data.

Gripp's movement system eliminates the workarounds by making the right distinctions native to the data model. The grip type taxonomy, the six tracking schemas, the equipment snapshot system, and the eight execution components are not features on top of a generic model — they are the model, designed specifically for what grip training actually requires.