Motra Alternatives (2026): Automatic Rep Counters, Honestly Tested

Gymatic, Rep Up and Fitnexx, the Motra alternatives on Apple Watch, count reps the way Motra does, from wrist motion, and stop at the count, which says nothing about whether the set was hard. All four are compared below, with a 5-set test to run before you switch. Riven is the Apple Watch app that scores muscle failure.

Motra counts your reps. Then it stops. You finish a brutal set of eight and the app tells you that you did eight, the number you already knew. Nothing about whether those eight reps were close enough to failure to be worth the recovery they will cost you, which is the only property of a set that decides whether it builds muscle.

Credit where it's due: Motra was first to make wrist-based counting work at all, it holds 4.7 out of 5 from roughly 3,000 US ratings, and on free-weight upper-body work the automation is genuinely good. But "it logged the set for you" was always the easy half of the problem, and four years in, Motra is still solving only that half. So is Gymatic. So are Rep Up and Fitnexx. Neither Motra nor any of the rep counters built to replace it measures effort, a claim checked source by source below.

Below: what happened to the Train Fitness name, what specifically sends people looking for a Motra alternative, how accurate wrist counting really is, the three things that break every wrist counter, the comparison table, and a 5-set test you can run on whichever app you pick.

Wait — what happened to Train Fitness?

Nothing bad, it's the same app under a new name. Version 6.0.0, shipped 7 January 2026, listed "Brand transition from Train Fitness to Motra" in its release notes (motra.com/what-is-new). The company, Train Fitness Inc., Toronto, founded 2021 by Andrew Just and Antoine Neidecker, explained the rename as a widening of scope: "What started as a tool for logging workouts has become something broader," pointing at sleep, recovery, nutrition and AI coaching. Same team, same detection engine, broader ambitions.

What Motra actually does — in its own words

Motra's detection engine is called Neural Kinetic Profiling, and their description is refreshingly plain: "The Apple Watch's core sensors track the motion of a user's wrist, and NKP decodes this raw motion data into relevant exercise and rep logs." The App Store listing claims over 470 auto-detectable exercise types, up from 82 at launch in 2021.

It also learns you specifically. Motra's help centre says it "can typically take up to 5 workouts for the NKP model to fully learn a user's movement pattern", with accuracy improving "by up to 20%" over that window, worth knowing before you judge it on day one.

Now the part that matters for choosing: what it does with the reps once it has them. Volume load, PRs and estimated maxes, progressive-overload suggestions, "Smart Weights" load recommendations, recovery bars, streaks and charts. Every one of those is arithmetic performed on the count. It is an accounting system, a very good one, and an accounting system cannot tell a productive set from a wasted one, because both arrive as the same row.

What it never does is judge the work. And here's the frustrating part: Motra's homepage says the platform monitors "acceleration, velocity, tempo." It is already holding the raw material. Velocity loss across a set is the validated objective fatigue marker in the resistance-training literature (more on that below). Motra measures velocity and then throws the fatigue signal away, converting it into tempo charts instead of an answer. Checking every primary source for a fatigue or failure readout returns the same result four times over:

• The App Store description mentions no RIR, no fatigue indicator, no velocity metric.
• The Neural Kinetic Profiling page makes no claim about detecting effort or fatigue.
• The help centre's 32-article "Workouts and Features" collection contains no article on failure, RIR, velocity loss, or intra-set fatigue.
• Effort is self-reported. Motra's "Perceived Effort" is explicitly "a self-assessment tool", a 1–10 slider you drag, judged by "breathing rate, heart rate, muscle fatigue, sweating level."

The one thing labelled fatigue is Muscle Recovery: Recovered / Moderately Fatigued / Heavily Fatigued bars, "calculated based on your exercises and weights," updated daily. That's a between-session volume-decay estimate keyed off what you logged. It never observes what happened inside a set.

Which is the thesis of this post in one line: a wrist sitting on the arm that just did the set is one of the only sensors in a position to see fatigue happen, and the category throws that away and asks you to drag a slider instead.

Why people go looking for a Motra alternative

Across recent US App Store reviews and both official subreddits, the themes rank roughly like this:

1. Watch↔phone sync and lost workouts, the most common recent complaint. Motra requires the Watch app paired with an iPhone before your first workout; sessions "sync and mirror on both devices."

2. Exercise misidentification. "It's frustrating when it says I did incline bench and I was doing shoulder presses." (r/TrainFitness, 7 Nov 2025.) "After more than a year, it still keeps entering random exercise on its own." (r/getmotra, 8 May 2026.)

3. Rep counts that don't match, and set-boundary errors where one set gets split in two or a phantom set appears from nowhere.

4. Machines and legs, a category limit, covered below, not a Motra defect.

5. And the sharpest one: there is no wrist-side override. A post on Motra's own subreddit dated 22 August 2026 describes the app entering rest mode and staying there: "There is no manual button on the Apple Watch to force the app to stop the rest timer and start the next set." The user says support confirmed this is "intended functionality" and suggested turning auto-detection off entirely, echoing two years of similar reports ("when the detection is wrong, the lackluster watch UI makes it annoying to correct the exercise", r/AppleWatch, 14 Sep 2024). Every motion-based system misdetects sometimes; misdetection with no way to overrule it from the wrist is a different problem, because the recovery path then routes through your phone, the exact thing automatic tracking was supposed to save you from.

6. Two documented friction points to know going in. Motra has a 3-rep minimum by default, to stop drinking from a water bottle registering as a curl. It's adjustable from 1 to 8, but the docs warn that lowering it "will significantly increase errors in exercise auto-detection." Consequence: a heavy triple or a 2-rep top set is invisible by default, precisely the sets a strength-focused lifter cares most about. And on unilateral work, exercises marked "x2" mean one rep covers both sides: 10 per arm logs as 10, not 20.

Add the per-set confirmation tap the getting-started guide describes, "confirm the sets and adjust the weight if necessary", and the honest summary is: automatic detection with a human in the loop on every set. Far faster than typing. Not hands-free.

How accurate is automatic rep counting, really?

Here's a fact worth sitting with. Motra published 95 percent or better accuracy at its 2021 Product Hunt launch. In June 2023 it told BetaKit it was at 96 percent, targeting 99 by year-end. In 2026, no accuracy figure appears anywhere: not on motra.com, not on the App Store listing, not in the help centre. The only quantified claim left is the relative "+20% after five workouts."

Publishing one accuracy number for a system spanning hundreds of exercises is genuinely hard, and any single figure will be wrong for somebody's specific lift, so I don't read malice into it. But note what it leaves you with: a category whose entire pitch is trust the count, in which nobody is accountable to a number and nobody publishes how they measure. You are being asked to take rep accuracy on faith by apps that will not say how accurate they are, or how they know. The standard the category should be held to is simple: count against video, publish the number, and say what it was measured on. The comparison table below has a row for exactly that, and the test at the end of the page lets you produce your own number in one session.

The one independent multi-week hands-on test comes from findyouredge.app, tested on Series 9 and Ultra 2, scoring Motra 3/5, noting that findyouredge publishes a competing app, so weigh it accordingly. Their conclusion: "You end up correcting the app's guesses frequently enough that it sometimes feels slower than manual logging" and "a promising concept that is not yet reliable enough to replace intentional workout logging." Their claim that cables confuse it, incidentally, is contradicted by users: one lifter on r/AppleWatchFitness in October 2025 reported around 9 workouts detected correctly, from a single-arm cable lateral raise to supersets.

For a neutral baseline, the best published science here is Oberhofer et al. 2021 in Sports: 30 athletes, 363 sets, Apple Watch plus an iOS app. Exercise recognition: 88.4% correct. Rep counting was statistically accurate for back squat (p=0.68) and deadlift (p=0.09) but poor for bench press (p=0.01). That's the published state of the art for a general-purpose wrist counter: good, and highly exercise-dependent. Which is why the test at the end of this page runs on your own lifts rather than on anybody's claim.

The three things that break a wrist rep counter — and which are actually solvable

Every app here works from the same raw material: one motion sensor on one wrist. But only one of these three problems is a genuine limit of that hardware. The other two are engineering, and they're where the apps diverge; see also what a smartwatch can and can't count.

1. When your wrist doesn't move, the watch is blind. Motra's developer concedes this on the record in App Store replies: "machine-based isolation leg work is also notoriously difficult to detect on any wearable because there's minimal wrist movement, something we've explained openly and are actively working on." Users confirm it: "it cannot recognize the activity if the wrist is not moving, specifically leg exercises" (r/AppleWatchFitness, 4 Sep 2025). This is physics, not a bug: a leg press moves your legs while your wrist rides a fixed handle, and no watch on Earth can see through that. What separates apps is what they do about it. The failure mode users report with Motra is that it fills the silence, "suggest ridiculous exercises not related to legs", which is worse than logging nothing, because a plausible-looking set you never did quietly poisons your history. The right response is the opposite one: mark the lift as not automatically countable and leave it unscored. An app that tells you it can't see a set is more useful than one that invents a number you'll act on, and the comparison table shows which apps take which route.

2. Mid-set pauses. To a naive detector, a rest-pause, a long grind at the sticking point, or five seconds spent fixing your grip all look identical to the end of a set. That's the mechanism behind the complaint users report about Motra: "if you stop for two seconds between reps it will start a new set with no way to correct it." One set becomes two, your volume is wrong, and your rep count is wrong in both halves.

This one is solvable. A detector that treats motion resuming shortly after an apparent end as the same set, rather than a new one, hands back a rest-pause set as one set with the full rep count instead of two fragments. It's the difference between a detector that watches for silence and one that understands that a pause inside a hard set is part of the set. Set 2 of the test below checks it on whichever app you're holding.

3. Unilateral and superset work. Alternating arms, drop sets and supersets stress boundary logic in every app, and the counting convention itself isn't standardised: Motra's "x2" rule, where 10 reps per arm logs as 10, is one choice among several. Check what your app does with a single-arm set before you trust the volume it reports back to you.

So: one of these three is genuine physics that no watch escapes. The other two are engineering problems, and most of the category has left them unsolved.

The comparison

Read the bottom row across. Four of these five apps answer No, and they answer it for two different reasons. Rep Up and Fitnexx never had a chance: you tell them a rep number in advance and they buzz when you reach it, which is a countdown timer wearing a fitness app's clothes. It cannot tell you anything you didn't already decide.

Gymatic is the more revealing failure, because it comes closest. It surfaces velocity, power, tempo, rep speed and range consistency; it is already computing the quantities that fatigue shows up in. And then it prints them as charts. Same with Motra's "acceleration, velocity, tempo." Two apps holding the fatigue signal in their hands, and neither takes the final step of asking what it means. The category has the sensor, has the maths, and stops one question short of the answer.

That last question is the whole difference in the bottom row. Full breakdowns of the field in our guide to automatic rep counter apps for Apple Watch and what the Apple Watch can count natively; for the wider field, including manual loggers, start with the best Apple Watch strength training apps.

How to test a rep counter yourself: 5 sets

No app in this category will hand you a number you can check, so produce your own. It takes one session and a note on your phone, and it covers every failure mode in this post. Log every set by hand as you go, right after racking, and compare the two logs afterwards.

1. A heavy double or triple. Warm up, then do 2 or 3 reps on a lift you know well. This is the minimum-rep test: Motra ignores sets under 3 reps by default, and lowering that setting raises its error rate on everything else. If your top sets live in the 1–5 range, this one set decides the whole comparison on its own.
2. A rest-pause set. Take a set close to failure, rack it for a few breaths, then do 3 more reps. Check whether the app logged one set with the full count or two sets with the count split between them. A split here means your volume and your per-set numbers are wrong every time you grind.
3. A single-arm set. 10 reps per arm on a dumbbell row or curl. Read the log: 10, 20, or something else. Neither answer is wrong, but you need to know the convention before you trust the volume it reports, and before you compare it with the log you kept by hand.
4. A machine leg lift. Leg press or leg extension, hands on the grips. The wrist barely moves, so the honest result is nothing, or a flag that the set could not be seen. A logged "incline bench" or a curl you never did is the worst result on the list, because it will sit in your history looking real.
5. A superset. Two exercises back to back with no rest, then check the boundary: two sets, correctly named, with the right count in each. Boundary errors show up here first.

Then score the session on two things. First, how many sets needed a correction, and whether you could make that correction from the wrist or had to pull out your phone. Findyouredge's verdict, that correcting the guesses "sometimes feels slower than manual logging", is a line you can measure for yourself: if you corrected more sets than you would have typed, the automation is costing you time, not saving it. Second, give the app the trial period its own maker asks for. Motra's documentation says its detection takes up to 5 workouts to learn your movement, so day-one results are a floor, not a verdict, and the same courtesy applies to every app in the table. Run the 5 sets again in week two and see what moved.

Counting the reps is the easy half

Here's why the missing measurement matters more than another point of rep accuracy.

Growth scales with how close a set got to failure. Robinson et al. 2024 pooled 55 studies and found hypertrophy changes by −0.48 percentage points per additional rep left in reserve (confidence interval −0.78 to −0.179); the authors note RIR was estimated by reviewers rather than measured, so treat it as exploratory. Compare adding volume: Pelland et al. 2026 puts one extra weekly set at +0.24%. One rep closer to failure is worth roughly what an entire extra set is worth, plausibly double, and costs no extra gym time.

To be precise, the claim is not "train to failure": the evidence there is genuinely mixed, and the ACSM's 2026 Position Stand, spanning 137 systematic reviews, states that training to momentary muscle fatigue "did not consistently impact training outcomes." The claim is narrower: proximity to failure decides whether a set counted, and it is the one variable nobody in this category measures. Fuller picture in training to failure for hypertrophy and what junk volume actually is.

And the log can't recover it. Counts et al. 2016 had 13 people train one arm at 70% of 1RM and the other against zero external load for 18 sessions. Muscle thickness rose identically at all three measurement sites. Volume load is sets × reps × load; with load at zero, the log scores that arm's entire training block as nothing. It grew anyway.

Does self-report fill the gap? Predictably poorly. Zourdos et al. 2021 had 25 trained men squat to failure: RIR error was 2.05 ± 1.73 reps at 1 RIR, 3.65 ± 2.46 at 3 RIR, 5.15 ± 2.92 at 5 RIR. The gauge degrades the further you are from failure, so it works best once you no longer need it. In fairness the error isn't universal: Refalo et al. 2024 found trained lifters accurate to −0.17 ± 1.00 reps on a heavy, familiar bench press. The error lives at light loads, high rep counts, unfamiliar exercises, and far from failure. And its direction is consistent. Barbosa-Netto et al. 2021 had 160 trained men load what they called their 10-rep weight and lift it to failure; they averaged 16 reps. Most lifters stop well short and don't know it, which is the whole case in why your reps-in-reserve estimate is wrong.

The objective alternative is velocity loss: reps physically slow as a muscle fatigues. Sánchez-Medina & González-Badillo 2011 validated it against blood lactate (r = 0.93–0.97) and concluded the data "support the validity of using velocity loss to objectively quantify neuromuscular fatigue during resistance training." That paper proposes no fixed percentage thresholds; the 20%/40% framing came later, from Pareja-Blanco et al. 2017 onward. More in velocity loss thresholds explained and why reps slow down at the end of a set.

And a wrist can read it. Achermann et al. 2023 measured an Apple Watch 7 against Vicon motion capture on free-weight back squats: barbell-mounted r 0.971–0.979, wrist-worn r 0.952–0.965. A watch on the lifting arm tracks bar velocity closely enough to see a set slow down. Heart rate cannot stand in for it: Kambic et al. 2021 ran a volume-matched crossover, 8 reps at 80% 1RM versus 16 at 40%, and got 84 versus 83 bpm (p=0.353). Doubling the weight moved heart rate by about one beat per minute.

The other half of the problem

Put those two facts together, velocity loss is the validated fatigue marker and a wrist can read the slowdown, and the conclusion is uncomfortable for the whole category: the signal was always there. Everyone chose to report tempo instead.

A count tells you what happened. A score for the set tells you which sets were the real ones: the set that ended because the muscle was done, and the set that ended because eight felt like enough. Two sets of eight at the same weight look identical in Motra's history, and a per-set score is the only field that separates them. Over a few weeks that changes how you train, because you stop ending sets on feel and habit, which, per the Barbosa-Netto data above, is where most training goes quietly wrong: the set you thought was hard was the one you had 6 reps left on.

More on what the watch can and can't see in can an Apple Watch detect muscle failure, and on the signs themselves in how to know when you've hit failure. If counting reps at all is the part you'd rather drop, why you shouldn't count reps makes that case.

The bottom line

If your issue is misdetection on lifts you do every week, that's worth switching over. Recognition-based detection guesses which movement from a library and gets it wrong in the ways users describe: shoulder press logged as incline bench, curls logged as obliques. Run the 5-set test on the alternative before you commit, on your lifts, not the developer's demo.

If your issue is that corrections have to route back through your phone because there's no override on the wrist, that's not something you should have to live with. An automatic tracker that sends you to your phone has handed back the exact thing you installed it for.

But if you're honest, neither of those is why you're here. You're here because you did the automation, and it worked, and you were left holding a tidy list of numbers that still doesn't tell you whether the session accomplished anything. No rep counter will ever fix that, because a rep counter's job ends at the count. One rep closer to failure is worth roughly twice what an entire extra weekly set is worth, and your own estimate of that rep is off by two to five, so it's the most valuable number in your training and the one you're least equipped to guess.

Riven is the Apple Watch app that scores muscle failure. It reads your wrist motion and stays quiet during the set; about three seconds after you rack the weight you get the exercise, the rep count and a 0-100 score. Reps slow down as a muscle nears failure, and that is what the score is built on. No barbell clip, no camera, no extra hardware. It shows you which set was the real one.

If you're weighing manual loggers in the same decision, the Hevy alternative breakdown covers that side of the field, and if you want to understand the number you're currently guessing, start with how to measure reps in reserve.

FAQ

Is the Motra app good?

It's a good rep counter. It holds 4.7/5 from around 3,000 US ratings and was first to market on wrist-based auto-detection. But it performs best only on free-weight upper-body work, struggles on machine and leg-isolation lifts, needs a confirmation tap on every set, has no published accuracy figure as of 2026, and, the limitation that matters most, it never measures how hard the set was. It tells you that you did eight reps. It cannot tell you whether those eight reps were worth doing.

What happened to the Train Fitness app?

It was renamed. Version 6.0.0, released 7 January 2026, listed the brand transition from Train Fitness to Motra. Same company (Train Fitness Inc., Toronto), same detection engine, broader product scope.

How accurate is automatic rep counting on an Apple Watch?

Motra has published no accuracy figure since 2023, and Gymatic, Rep Up and Fitnexx publish none. The best published academic baseline is Oberhofer et al. 2021: across 363 sets, exercise recognition was 88.4% correct, with rep counting statistically accurate on back squat and deadlift but poor on bench press. Accuracy is highly exercise-dependent, which is why the 5-set test above runs on your own lifts.

Does Motra detect leg exercises and machines?

Poorly, and its developer says so publicly, describing machine-based isolation leg work as "notoriously difficult to detect on any wearable because there's minimal wrist movement." This is a physical limit of wrist sensing in general: no watch can see a movement your wrist doesn't make. What differs is the response. Users report Motra filling the gap with unrelated exercises; the better behaviour is to mark the lift as not automatically countable and leave it unscored rather than log a set that never happened.

Does Motra count both arms on unilateral exercises?

No. Exercises marked "x2" treat one rep as covering both the left and right side, so 10 reps per arm logs as 10, not 20.

Why does my rep counter miss a heavy triple?

Motra applies a 3-rep minimum by default to avoid false positives from everyday arm movement. It's adjustable from 1 to 8, but their documentation warns that lowering it significantly increases detection errors.

Does Motra tell you how hard you trained or how close to failure you were?

No. Effort in Motra is self-reported via a 1–10 Perceived Effort slider, and its only fatigue estimate is a between-session muscle-recovery bar computed from your logged sets and weights. It never observes what happened inside a set.

Can a smartwatch actually tell if you reached muscle failure?

Not with certainty, but it can measure the physical signature. Reps slow measurably as a muscle fatigues, velocity loss is a validated fatigue marker, and a wrist-worn Apple Watch tracked bar velocity closely against motion capture in Achermann et al. 2023. The honest output is a ranking of your sets by how close each came to failure, not a lab-grade number.

Is Gymatic a good Motra alternative?

Only if your complaint is about Motra specifically, because Gymatic has the same ceiling. It surfaces more raw kinematics, velocity, power, tempo, rep speed, and then prints them as charts, never converting them into an effort or fatigue judgement. Swapping Motra for Gymatic gets you a differently-flavoured rep count.

What is the best Motra alternative in 2026?

If you only want the count, Motra and Gymatic already do that well on free-weight upper-body work, and switching between them changes very little; run the 5-set test on your own lifts and keep whichever needed fewer corrections. If you want to know whether the set was hard, only one app in the comparison measures it. Riven is the Apple Watch app that scores muscle failure; it scores each set the moment you rack it.

Sources

• Motra (formerly Train Fitness) on the App Store — https://apps.apple.com/us/app/motra-formerly-train-fitness/id1548577496
• Motra — What's New (v6.0.0 rebrand) — https://www.motra.com/what-is-new
• Motra — Rebrand announcement — https://www.motra.com/motra-rebrand-announcement
• Motra Help Centre — What is Neural Kinetic Profiling? — https://help.motra.com/en/articles/9888642-what-is-neural-kinetic-profiling
• Motra Help Centre — AI model and progressive learning — https://help.motra.com/en/articles/9688829-ai-model-and-progressive-learning
• Motra Help Centre — Minimum rep threshold — https://help.motra.com/en/articles/9696439-minimum-rep-threshold
• Motra Help Centre — Counting reps — https://help.motra.com/en/articles/9696458-counting-reps
• Motra Help Centre — Getting started — https://help.motra.com/en/articles/9980535-getting-started-with-motra
• Motra Help Centre — Perceived Effort — https://help.motra.com/en/articles/9672055-perceived-effort
• Motra Help Centre — Muscle Recovery — https://help.motra.com/en/articles/9689587-muscle-recovery
• Motra Help Centre — Workouts and Features collection — https://help.motra.com/en/collections/10026070-workouts-and-features
• BetaKit — Train Fitness expands automatic workout tracking (2023 accuracy figure) — https://betakit.com/train-fitness-closes-2-5-million-usd-to-expand-automatic-workout-tracking-app-for-strength-training/
• r/getmotra — Motra getting stuck in endless rest mode (22 Aug 2026) — https://www.reddit.com/r/getmotra/comments/1vvru15/motra_getting_stuck_in_endless_rest_mode/
• findyouredge.app — Best Strength Training Apps for Apple Watch 2026 — https://www.findyouredge.app/news/best-strength-training-apps-apple-watch-2026
• Oberhofer et al. 2021, Sports 9(9):118 — https://pmc.ncbi.nlm.nih.gov/articles/PMC8471343/
• Achermann et al. 2023, Sports 11(7):125 — https://pmc.ncbi.nlm.nih.gov/articles/PMC10383699/
• Robinson et al. 2024, Sports Medicine 54(9):2209–2231 — https://pubmed.ncbi.nlm.nih.gov/38970765/
• Pelland et al. 2026, Sports Medicine 56(2):481–505 — https://link.springer.com/article/10.1007/s40279-025-02344-w
• Counts et al. 2016, Physiology & Behavior 164(Pt A):345–352 — https://europepmc.org/article/MED/27329807
• Zourdos et al. 2021, JSCR 35(2S):S158–S165 — https://pubmed.ncbi.nlm.nih.gov/30747900/
• Barbosa-Netto et al. 2021, JSCR 35(Suppl 1):S166–S172 — https://europepmc.org/article/MED/29112055
• Refalo et al. 2024, JSCR 38(3):e78–e85 — https://europepmc.org/article/MED/37967832
• Sánchez-Medina & González-Badillo 2011, MSSE 43(9):1725–1734 — https://pubmed.ncbi.nlm.nih.gov/21311352/
• Pareja-Blanco et al. 2017, Scand J Med Sci Sports 27(7):724–735 — https://pubmed.ncbi.nlm.nih.gov/27038416/
• Kambic et al. 2021, IJERPH 18(8):3905 — https://pmc.ncbi.nlm.nih.gov/articles/PMC8068143/
• ACSM Position Stand 2026, Med Sci Sports Exerc 58(4):851–872 — https://pmc.ncbi.nlm.nih.gov/articles/PMC12965823/