Fitness apps have moved beyond simple libraries of workouts. Some guide users through a structured training plan; others adjust the workload after a session or bring in data from connected devices. To see what sets these services apart, it helps to look at the data they use for personalisation and when that data becomes available.
The differences increasingly lie in the information each app collects Freeletics and what it does with it. JustFit builds a plan from an onboarding questionnaire, adjusts training load in response to user feedback, MyFitnessPal brings in data from external services, and Mimika uses the camera and augmented reality to analyse the user’s movements during the session itself.
JustFit: A Plan Built Around an Initial Questionnaire
JustFit organises workouts around a personal plan. During setup, users enter their goal and fitness level, and the app lays out a sequence of sessions they can follow from day to day.
The plan sets the order of workouts, allowing users to move from one session to the next and track their progress. They can change their fitness level or add physical limitations in the settings, so the program can be adjusted if their original choices no longer fit.
Personalisation here is about choosing exercises and arranging them into a sequence. The app builds a training path from the information users provide, then shows how far they have progressed through it.
The point of this model is to remove the need to choose a workout every day. The service sets the order of sessions in advance and walks the user through it.
Sweat: Personalisation Built Around Life Context
Sweat is aimed primarily at women, with strength and high-intensity workouts, Pilates, and programs designed for training at home or in the gym. Within those categories, users can choose sessions that suit different goals and fitness levels.
Programs vary in length and format. Sweat also offers workouts for pregnancy and programs for returning to exercise after childbirth.
Sweat builds personalisation around a person’s circumstances. Rather than offering one general program, it provides training paths for different goals, fitness levels, and stages of life.
This context shapes the choice of program from the start: a beginner, a pregnant woman and an experienced gym-goer follow different training paths instead of variations on the same plan.
Verv: Multiple Tracks Within a Single Profile
Verv takes an all-in-one approach, bringing home workouts, running, walking, meditation, sleep programs, relaxation audio, recipes, and meal plans into a single app. The breadth of this approach is Verv’s defining feature. Users can engage with physical activity, recovery, sleep, and nutrition without switching between apps.
The depth of personalisation depends on how closely those parts of the app work together. Having workouts, meditation and sleep content in one place does not necessarily mean that information from one area shapes the recommendations in another.
Verv is therefore best understood as a wellness app that brings several aspects of health and well-being together in one product.
FitOn: Distributing Functions Across Devices
FitOn spreads the training experience across several devices. Alongside workouts and personalised programs, it offers meditation, nutrition guidance and social features, with each device playing a different role.
Users can cast workouts from a phone to a TV or streaming device, including Apple TV and Roku. Apple Watch, Fitbit, and Garmin can supply heart-rate data during a session. FitOn uses those readings to display heart-rate zones and summarise the workout afterward.
Each device has a role: the phone is used to choose a program and manage the profile, the TV becomes the main screen during a workout and wearables record the user’s readings. The result is a single training experience spread across several devices, with workout data brought together in one user profile.
Freeletics: A Program That Adapts to Workout Results
Freeletics uses each completed workout to inform the next one. Its AI Coach builds a program around the user’s goal, fitness level, available equipment, and time to train.
After a workout, users can rate how they handled the exercises. AI Coach considers that feedback alongside previous sessions when choosing the next workload. As users progress, it can change exercises and the number of repetitions to better match their abilities.
The program follows a recurring cycle:
workout → result and rating → new training load.
The plan is not set once and then left unchanged. It changes as information from completed sessions accumulates.
The Adapt Session feature also lets users adjust a particular workout to their circumstances, whether they lack equipment, have limited space, need to avoid noisy exercises, or want to take the strain off sore muscles.
The main inputs for this kind of personalisation are profile details, workout history and user feedback.
MyFitnessPal: Consolidating Data from External Sources
MyFitnessPal brings data from several external sources into one place, alongside the user’s records of food, physical activity, weight, and energy expenditure.
Users can connect fitness apps, wearables, and compatible smart scales. Supported sources include Apple Watch, Fitbit, Garmin, Samsung Health, Google Fit, and Health Connect. The information MyFitnessPal receives depends on the connection: it may include activity, workout, or weight data.
Integrations differ in the kinds of data they provide. Some send daily activity figures to MyFitnessPal, while others supply individual workout results or weight readings. The combined picture therefore depends on which services and devices a person connects.
MyFitnessPal’s task is to bring these different readings into a usable record. This matters when several connected devices capture the same activity or calculate energy expenditure in different ways.
In this way, MyFitnessPal lets users work with data from several external services in one tracking system, without tying them to a single device or ecosystem.
Flo: Forecasting Based on Long-Term History
Flo focuses on one area of health: reproductive health. The app lets users track their menstrual cycle and symptoms, estimate their fertile window, and follow the progress of a pregnancy.
Flo builds personalisation on a long, continuously updated history: cycle dates, symptoms, and other information the user logs regularly. As new data comes in, the app can account for individual patterns and refine its forecasts.
This differs from services that rely mainly on information provided during setup. Flo draws on observations gathered over a longer period. Flo shows how a long record of user data can support personalisation in an app focused on reproductive health.
Mimika: A Personal Plan for Facial Exercises
Released in 2022, Mimika was the first app in the face yoga and facial fitness space to offer a fundamentally new format for digital facial training: personalised sessions built on AR facial recognition and real-time analysis. Its one-of-a-kind model turns the smartphone camera into an active data source during the workout.
Mimika tracks 1,220 facial vertices and 52 real-time expression signals. As the user follows a session, the system monitors the face and facial-muscle movements in real time and provides immediate feedback. The user can see whether an exercise is being performed correctly and adjust their technique before moving on.
The training library maps individual movements to specific facial regions and aesthetic goals. This creates a continuous loop: the camera captures movement, the system analyses it, and the session responds with guidance. Personalisation therefore happens during the exercise itself, rather than only when a plan is set up or after results have been logged.
Different Apps, Different Sources of Personalisation
Health and fitness apps differ in the data they use, when they receive it, and how they use it to shape the user’s experience.
Some build a program from information supplied during setup. Others take workout history and post-session feedback into account. Apps can also connect to external devices, combining activity readings with information users enter themselves.
Timing matters, too. JustFit creates a plan after an initial questionnaire; Freeletics uses feedback from completed workouts to choose the next training load; and wearables connected to FitOn display readings during a session. Each approach plays a different role in the workout experience.
Receiving live data does not, by itself, mean that an app adjusts exercises or gives immediate feedback on technique. Across the services covered here, personalisation takes the form of program selection, changes between workouts, long-term tracking, and the consolidation of data from different sources.
