An evidence-based workout tracker based training science. Released under the public domain.

• Auto-generates workout plans w/ autoregulation (adjust training based on performance).
• Smart progressive-overload & plateau detection.
• Exercise videos.
• SwiftUI-based core for a lightweight structure.
• Runs 100% offline. No servers, nothing weird going on.
• Accessible & supports VoiceOver!

Showroom

Smart onboarding	Tailor-made workout plans	Adjust workout frequency	Auto-logging	Exercise tutorials

Backend

Backend architected with Realm SwiftUI.

Inner workings

The foundation of the algorithm is determining the volume per muscle group per week, adjusted for the user's:

• Training frequency
• Experience level

The volumePerWeekPerBodypart function dynamically determines the weekly set volume per muscle group, accounting for user experience, frequency, and unique muscle properties.

func volumePerWeekPerBodypart(muscle: MuscleGroupEnum) -> Int {
    var volume = 0
    
    if userModel.last!.workoutFrequency.int <= 2 {
        switch userModel.last!.experience {
        case .beginner: volume = 6
        case .intermediate: volume = 6
        case .advanced: volume = 8
        }
    } 
}

Then, the distributeSets function splits the weekly set volume into smaller, actionable groups for each session. (Example: Distribution: 17 sets → [4, 4, 3]. So, 3 exercises — the first two with 4 sets and the third one with 3.)

Finally, addExercises takes all the inputs and creates a day’s routine while accounting for both primary & secondary muscle activations

func addExercises(muscleGroup: MuscleGroupEnum)  {
    let distribution = distributeSets(volumePerWeekPerBodypart(muscle: muscleGroup))
    let exerciseObjects = getExerciseObject(muscleGroup: muscleGroup, distribution: distribution)
    let exercises = createExercisesFromExerciseObject(exerciseObjects, distribution: distribution)

    for exercise in exercises {
        var processedMuscleGroups: Set<String> = []
        
        for secondaryMuscleGroup in exercise.realmObj.muscleGroups {
            if secondaryMuscleGroup.activation_percent >= 0.6 && 
               secondaryMuscleGroup.group != exercise.realmObj.primaryMuscleGroups.first!.group &&
               !processedMuscleGroups.contains(secondaryMuscleGroup.group) {
                // ... 
                }
                processedMuscleGroups.insert(secondaryMuscleGroup.group)
            }
        }
    }

    allExercises.append(contentsOf: exercises)
}

Note: Local SPW is the weekly volume per muscle. To avoid exhausting weekly volume in just one single session, it is adjusted based on the number of sessions per week targeting that muscle preventing overtraining.

To further avoid overtraining, the removeSynergistics function adjusts exercises by removing excessive secondary activations to balances direct & indirect training volume.

func removeSynergistics(_ exercises: [ExerciseViewDataModel]) {
    muscleGroupData.allCases.forEach {
        if $0.bucket.indirect > 0 {
            let numberOfSets = $0.bucket.direct - $0.bucket.indirect
            removeAndReAddExercise(muscleGroup: $0.enumValue, newNumberOfSets: (numberOfSets >= 3 ? numberOfSets : 2))
        }
    }
}

Resources

Fitwise is fully open-source and I intend to be as transparent as possible. Here are the resources used during development:

• A large part of the training algorithm was based on Dr. Mike Israetel's course on hypertrophy!
• https://pubmed.ncbi.nlm.nih.gov/27433992/
• https://pubmed.ncbi.nlm.nih.gov/35291645/
• https://journals.lww.com/nsca-jscr/Fulltext/2010/07000/The_Effect_of_Autoregulatory_Progressive.3.aspx
• https://journals.lww.com/nsca-jscr/Abstract/2010/01001/The_Effect_Of_Daily_Undulated_Periodization_As.1.aspx
• https://pmc.ncbi.nlm.nih.gov/articles/PMC2956949/

Community

License

MIT License

Copyright (c) 2023 K. Khaneja

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Note
You can technically clone Fitwise and sell it on the App Store, but I'd appreciate it if you didn't do this. Instead, please make any changes you want to the main repo — all pull requests are welcome!