Strong and Hevy are already very good at what they do. They help you log workouts quickly, keep your history organized, and show progress over time.
Strong is especially polished and feature-rich, with things like CSV export, Apple Health integration, Apple Watch support, and a big set of training utilities built in. Hevy nails the “log fast, stay consistent” flow too, and it stands out if you like sharing workouts, following friends, and using a social feed for motivation.
We still decided to build Treined because we kept running into the same friction: as you get more serious, a workout log is not enough. You start wanting better answers, better structure, and better feedback from your data. Most people end up juggling multiple apps to get there.
Treined is our attempt to put the whole loop in one place: training plan, execution, context, and analysis.
The difference is not “more features,” it’s what we prioritize
AI that is actually tied to training decisions
Instead of guessing what is best for you, you ask a question and get an answer that is meant to be grounded in evidence, because the AI is built to reference 50+ scientific studies.
What makes that useful in practice is that it lives inside the same training log. So the conversation is not generic. It can stay anchored to how you actually train, including what you logged for each set, like RPE and RIR, and what you have been doing over time. That is how it helps you adjust progression, volume, and exercise choices without turning every change into a research project.
It also matters that the AI “knows” your metrics. If you are tracking measurements and body analytics in the app, the AI can use that information as part of the conversation too. So you can ask questions like why a cut is stalling, whether your training needs a small shift, or what trend in your measurements is actually meaningful, and you are not doing the mental work of pulling numbers from one app and search endlessly on the internet for advice . The goal is to keep things smooth and reduce the friction that usually pushes people into spreadsheets.
You might be wondering, “Why can’t I just use ChatGPT or Gemini for this?” You can ask them workout questions, but they are general-purpose assistants, so they usually do not know your training history, your measurements, or what your last few weeks actually looked like unless you manually paste that context in every time, which is why the answers often come out generic.
They also tend to respond from what they already “know” unless you explicitly push them to cite primary research, and they do not automatically pull up and verify the latest studies in the background the way a dedicated in-app system can. On top of that, these systems have knowledge cutoffs, so even when the guidance is reasonable, it can miss newer findings unless web browsing and source checking are used.
Intensity tracking that goes beyond “I did the set”
This is the big one for us. Treined is built to track intensity at the set level, using RPE and RIR in a way that feels natural during the workout. We built it because intensity is one of the most useful signals for progress, fatigue, and load selection, but it is usually treated as optional metadata.
To be clear, Strong includes RPE support, and Hevy also talks about RPE as part of its tracking. The difference we are aiming for is that Treined treats intensity as a first-class training variable, not just a field you sometimes fill in. That design choice affects everything, from how you review sessions to how you adjust progression.
Perceived intensity, mood, and “how it felt”
We also wanted space for the human side of training. Treined tracks mood and perceived energy alongside your lifts, especially at the end of a session.
This is not about turning training into journaling. It is about helping you notice patterns like, “My performance is fine but everything feels harder,” or, “I feel great but my top sets are stalling.” Those patterns matter when you are trying to train consistently for months and years.
Measurements and body composition, without spreadsheet work
Treined tracks a wide set of measurements and includes full body analytics, including automatic body fat calculation. We also built it so you can track basically any measurement you care about, and calculate body fat immediately using 7 or more formulas. The point of these features is to make the experience as smooth as possible. You should not need a second app, a notes file, and a spreadsheet just to keep body data consistent.
One app means you should not need a separate timer app
A lot of lifters end up using a separate WOD timer for conditioning finishers, interval work, or simple formats like EMOM and Tabata. SmartWOD Timer is a good example of how clean this experience can be, with timers for AMRAP, EMOM, For Time, Tabata, and custom intervals.
We decided to include that inside Treined so your lifting session and your conditioning work can live together. No switching apps mid-workout, no losing focus, no “where did I log that” later.
Where Strong and Hevy might be better for some people
Strong has years of polish, a huge set of features, and deep platform support like Apple Watch and integrations. If you want a mature, stable logger with exports, integrations, and lots of utilities, Strong is a very strong choice.
Hevy is hard to beat if the social layer is what keeps you consistent. It is built around community and sharing, and it also supports a wide exercise library, stats, routines, and multi-device access. If motivation comes from training with friends, Hevy can be the better fit.
Who Treined is for
Treined is meant for lifters who do not want to bounce between multiple pieces of software, who want to train better over time, and who want an app that supports good decisions instead of just recording history.
It is for people who want to ask questions during the process, tailor their training to their needs, track intensity in a meaningful way, and keep everything in one place, including timers, body metrics, and the context of how training actually felt.
That is the reason we built it. Not because logging is broken, but because serious training usually becomes a system. We wanted that system to fit into one app.