Hevy Alternatives (2026): 8 Apps Tested on the Apple Watch

The best Hevy alternative depends on the job: Strong, JEFIT and Boostcamp are logbooks like Hevy; Motra, Gymatic, Rep Up and Fitnexx count reps from your wrist; one app also scores how hard the set was. Hevy is a very good logbook, but a logbook makes you the sensor. Riven is the Apple Watch app that scores muscle failure.

First, in fairness: most "Hevy alternative" posts open by pretending Hevy is bad. It isn't. 98% of roughly 490 recent US App Store reviews are four or five stars, and it holds 4.9 across 87,970 US iOS ratings. Hevy has taken the logbook about as far as a logbook goes, and that is the problem: a logbook still can't answer the only question that decides whether your session mattered.

What every "Hevy alternative" list gets wrong

I fetched the ten pages currently ranking for "Hevy alternative." Every one is a competing logging app's marketing page comparing the same axis: exercise-library size, routine folders, how far back the graphs go. Every app on every one of those lists is the same product as Hevy, a place to type numbers into. Swapping one for another changes the font, not the job.

Not one mentions effort, fatigue, RIR, RPE, velocity loss, or proximity to failure. Not one mentions automatic rep counting either.

That's an entire search results page arguing about storage and admin. Nobody compares what the app actually measures about your set, which is odd, because the measurement is the only part that touches whether you grow.

So here's the reframing: Hevy is an excellent ledger, and a ledger makes YOU the sensor. You supply the reps, the weight, the effort estimate; the app supplies arithmetic and graphs. Everything Hevy shipped in 2026, Hevy Trainer (a generator that progresses weight from the numbers you typed last time) and HevyGPT, makes the typed data smarter. Nothing removes the typing, and nothing measures the lift. The real alternative to a logbook isn't a nicer logbook. It's an app that measures the set instead of asking you to describe it, and the rest of this post is the evidence for why that distinction is the only one worth switching over.

Loggers and measurers: the comparison that matters

Loggers know only what you typed. Measurers read the lift from a sensor. Look at that last column: it is No all the way down until the final row. Four manual loggers can't measure effort because nothing is sensing anything. Four automatic counters are sensing, and still stop at the count, reporting tempo and rep speed without ever converting it into an answer. One row says Yes. That's not a marketing flourish, it's the finding: the entire category ends at "you did 8 reps," and only the last row continues to "and here's how close that got you."

Does Hevy count reps automatically?

No. Hevy's Apple Watch app is 100% manual: it syncs a live workout, runs routines from the wrist, reads heart rate, runs rest timers, and lets you type in weight and reps. Hevy's own documentation describes the flow: "Input weight (in KG/LBS), reps, and optional RPE for each set… tap the set to mark it complete."

There is no automatic rep counting, set detection, velocity measurement or effort estimation anywhere in Hevy, not in the App Store description, not in the feature index, not in the Help Centre. The only sensor Hevy reads on the watch is heart rate, and one independent review notes Hevy simply "doesn't connect to wearables." Which is a strange place for a strength app to be in 2026: the device on your wrist is already reading the motion of the lifting arm, and the app is asking you to describe what just happened to it. If you want the watch to do that job, the automatic apps in the table above do it without a tap; the automatic rep counter roundup and what the Apple Watch can and can't count natively cover the rest of the field.

If you're leaving because the Apple Watch app keeps breaking

This is Hevy's one genuine functional weakness, and no ranking page addresses it. Almost every recent negative review is watch-related:

• "Every time I use my watch to progress the current exercise or change the weight or reps, it jumps to the next exercise. That, in turn, forces me to take my phone out again… Tedious and continuously broken since I started using the app a year ago." (Tayytot, 3-star review)

Another 3-star review asks Hevy to "stop adding new features until you have fixed core functionality — Apple Watch disconnection problems have been unaddressed for more than a year." Hevy maintains a dedicated sync troubleshooting guide, which suggests this is systemic.

Notice the shape of the failure, though. Every one of those reviews describes a data-entry problem: the crown scrolled, the set jumped, the phone had to come back out of the pocket. That whole class of bug only exists because there is something to enter. An app with no weight field, no reps field and no checkmark on the watch has nothing to desync; the watch reads the session on its own and the phone stays in the locker. The fix for a broken input surface isn't a better input surface. It's not having one.

If you're leaving because tapping between sets is slower than lifting

Count the interactions. Per set: tap the weight field, type the number, tap the reps field, type the number, optionally tap RPE, tap the checkmark. About five deliberate touches per set. A 20-set session is 100+ touch interactions, every one squeezed between sets, and barbell logging adds a step, since you total the loaded weight yourself. That per-set friction is a large part of why people abandon tracking within a few months, which is why the paper vs app vs watch comparison ranks methods by touches per set rather than features.

Even a five-star reviewer calls it "tedious just spinning my watch's wheel for a good 30-60 seconds." The demand for a fix is documented: on Bevel's feature board, "Automatic rep and strength training logger" has 119 votes and a "planned" status.

Automatic counting is table stakes, and it is where everyone else stops

There is a second category of Hevy alternative that the ten ranking pages never mention: apps that read the lift off your wrist instead of asking you to type it. Motra (formerly Train Fitness) and Gymatic identify the exercise and count reps. Rep Up and Fitnexx simply buzz you when you reach a rep number you set in advance, which is a timer with extra steps, not a measurement.

But automating the typing solves the smaller half of the problem. You end up with the same log Hevy gave you, filled in by a robot: 8 reps at 60 kg, and still no idea whether those eight reps were worth doing. Motra reports acceleration, velocity and tempo. Gymatic reports velocity, power, tempo, rep speed and range consistency. Both are, right now, holding the raw material of a fatigue signal in their hands, and neither converts it into a verdict. Kinematics, never mapped to failure. The category counts beautifully and concludes nothing, a gap I take apart properly in the Motra alternative post.

What the published validation says about wrist sensing

Wrist sensing is often oversold, so here is what the peer-reviewed work actually shows. The best published validation of an Apple Watch for lifting velocity, Achermann et al. 2023, tested the free-weight back squat against optical motion capture: a barbell-mounted Apple Watch tracked mean velocity at r 0.971–0.979 (SEE 0.049 m/s) and a wrist-worn one at r 0.952–0.965 (SEE 0.064 m/s). Both placements track the bar closely, and placement matters more on some lifts, such as the hip thrust and full squat (Balsalobre-Fernández et al. 2017). What matters for effort isn't an absolute speed to three decimal places; it's how much the reps slow within the set, and that is the part a wrist can see.

For rep counting, the published baseline for a general-purpose wrist counter is Oberhofer et al. 2021: 363 sets on an Apple Watch, exercise recognition 88.4% correct, rep counts accurate for back squat and deadlift but poor for bench press. That's what happens when an app tries to recognise a movement by resemblance to a library. Where physics genuinely runs out, an honest app says so: on an exercise where the wrist rides the torso rather than tracking the load, like a crunch machine, no watch can see the rep, and the right answer is to leave the set uncounted rather than fill it in with a guess.

What a workout log cannot tell you, and why it matters

A log records that a set happened. It cannot tell you whether that set did anything.

"8 reps × 60 kg" is the same row in your history whether it was a comfortable warm-up or a shaking, last-rep-barely-moved grinder. The variable that separates them, proximity to failure, is the one thing your log never stores, because nothing measures it. That is also why logging every rep is not what builds muscle; the log is a memory aid, not a measurement.

The claim is proximity, not failure

This is where fitness content usually overclaims, so: the claim is not "train to failure."

The counter-evidence is real. The ACSM Position Stand 2026, 137 systematic reviews, over 30,000 participants, concluded that "training to momentary muscle fatigue… did not consistently impact training outcomes." Wu et al. 2026, 20 RCTs, found non-failure training slightly better for dynamic strength (SMD 0.24, p=0.01), with no hypertrophy difference.

Both are compatible with the point, because the point isn't that you must hit failure. It's that how close you got is a graded variable deciding how much a set contributes, and no consumer logbook measures it. The 2025 Jyväskylä consensus review (Roberts et al. 2026) lists what moves hypertrophy: "weekly sets, volume-load, rest interval duration, and training proximity to failure." Your log captures the first two and has nothing to say about the fourth.

How much is one rep worth?

Moving one rep closer to failure is worth as much as, plausibly twice, adding an entire extra set to your week. Robinson's own caveat sharpens the point: RIR was estimated by reviewers, not measured. Even meta-analyses have to guess at this variable, because nobody has an instrument for it.

The knockout: the set your log scores as zero

Counts et al. 2016 ran a within-subject study: 13 people, 18 sessions. One arm trained at 70% of 1RM. The other did "NO LOAD" training: maximal voluntary contractions against zero external weight. Muscle thickness increased identically at all three sites (50%: 2.7→2.9 cm; 60%: 2.9→3.1; 70%: 3.2→3.5). Strength favoured the heavy arm; the growth was the same.

Now do the arithmetic your app does. Volume load = sets × reps × load. With load = 0, the log evaluates that entire block as zero volume and graphs a flat line. Effort was the only input, and no logging app records it.

The everyday version is Lasevicius et al. 2022: unilateral knee extension, volume-load equated within the same person. Low load to failure grew quadriceps CSA +7.8%; low load stopping short, +2.8%, not significant. Same log; one leg grew nearly three times as much. Set-counting inherits it too: Baz-Valle et al. 2021 qualifies the metric as "sets to failure, or near to" (junk volume).

Why "just estimate it yourself" doesn't close the gap

Hevy's answer to effort is RPE: a manual, optional field, off by default. RIR isn't a field at all; Hevy's help centre tells you to infer it from the RPE descriptions. And once typed, one independent review notes it goes nowhere: "it doesn't do anything with the data. There's no analytics on perceived exertion trends, no adaptation based on RPE, no coach feedback."

The deeper problem is the estimate itself. Zourdos et al. 2021, 25 trained men squatting to failure: 2.05 ± 1.73 reps of error at 1 RIR, 3.65 ± 2.46 at 3 RIR, 5.15 ± 2.92 at 5 RIR. The gauge only becomes reliable once you no longer need it. Steele et al. 2017 put the standard error at 2.64–3.38 reps depending on the lift; Halperin et al. 2022 found accuracy collapsing on sets longer than 12 reps. To be fair, Refalo et al. 2024 found trained lifters benching close to failure were accurate to −0.17 ± 1.00 reps; the error lives at light loads, high reps and far from failure, which is most of a hypertrophy session.

The consequence shows up in real gyms. Barbosa-Netto et al. 2021 asked 160 trained men what weight they use for 10 reps on bench, then had them do as many reps as possible with it. They averaged 16 ± 5, six reps from failure while believing they were at it. And Glass 2008 taught lifters to pick heavier weights in a dedicated session: "A learning trial of the bench press exercise to increase self-selected workload is not enough to change load self-selection." Telling people to try harder demonstrably doesn't work; measuring it is a different intervention. See how to know when you've hit muscle failure and how to measure reps in reserve.

The channel that separates when perception can't

Gómez-Redondo et al. 2025 is the cleanest demonstration: 25 older adults, chest press at 65% 1RM, three RIR targets. RPE couldn't discriminate them at all: 8.0, 7.6, 7.3, all p>0.05. Velocity loss called every one: 16%, 10%, 0%.

Velocity loss is the validated objective fatigue marker. Sánchez-Medina & González-Badillo 2011, using a linear velocity transducer and blood lactate, 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 you see everywhere is a later construction. See velocity loss thresholds explained.)

Its honest weakness is as an RIR estimator: Mansfield et al. 2023 found set-1 velocities predicted 5-to-0 RIR for only 30.9% of bench reps. But Martínez-Rubio et al. 2025 found errors ≤1 rep at RIR 2 and RIR 0: velocity is the mirror image of human judgement, worst mid-set and best near failure. That's why reps slowing at the end of a set is the signal worth instrumenting, and why an Apple Watch can read proximity to failure from the wrist at all.

And heart rate, the one sensor Hevy does read, can't substitute for it. In volume-matched leg press at 80% versus 40% 1RM, heart rate was 84/84/84 bpm versus 81/83/83 (p=0.353). Doubling the weight on the bar moved it by about one beat per minute.

The alternative that measures the set instead of recording it

Full disclosure: this is our app, so test the claim yourself rather than taking it from a comparison page.

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.

That is the whole pitch, and the limits are worth stating plainly. It isn't EMG and it isn't a lab instrument; the score ranks your sets against each other rather than certifying a percentage. Where the wrist can't see the lift, as on a crunch machine where the wrist rides the torso instead of tracking the load, the set is left uncounted and unscored rather than filled in with a guess. It's free to download, and the cheapest test is the one at the end of this post: wear it for a few sessions alongside Hevy and compare the two records.

How to move your training history out of Hevy

The biggest blocker to switching is your history. Hevy supports CSV export.

1. Open Hevy and go to your profile → settings.
2. Find the export data option and request the CSV.
3. Hevy emails you a file: one row per set, with date, exercise, set number, weight, reps, RPE and notes. Most loggers accept a CSV import; even if yours doesn't, a spreadsheet is a durable archive. Hevy also exports to Apple Health via HealthKit, and the web app at hevy.com is the easier place to do a bulk export. And nothing forces an either/or: running an automatic tracker while keeping Hevy as your ledger for a few sessions is the fastest way to see whether the automatic count matches what you actually did (how to track gym progress).

The bottom line

Be honest about which of these you actually are:

• You want a slightly different logbook. Strong, JEFIT, Boostcamp and Alpha Progression exist and they're fine. You will type the same numbers into a different font, and in six months you will be reading a "best Strong alternatives" list. Nothing about your training changed.
• You're tired of typing. Automatic counting is worth having, but on its own it just fills in the same log for you. You'll have a robot-written record of eight reps and still be guessing about all eight.
• You want to know whether the set did anything. This is the only reason on the list that changes an outcome, and only one app in the table answers it: the set is found, the lift is named, the reps are counted, and the set is scored the moment you rack it, with nothing to tap. See the automatic rep counter roundup and the wider Apple Watch strength training apps review for the full field.

Hevy will faithfully record that you did 8 reps at 60 kg. It cannot tell you whether that set was worth doing, and neither can any app that stops at counting. One rep closer to failure buys roughly twice what an entire extra weekly set does, and your own estimate of that rep is off by two to five. You are wearing a motion sensor on the arm doing the lifting. Guessing is a choice now.

FAQ

Does Hevy count reps automatically?

No. Hevy's Apple Watch app is entirely manual: you type weight and reps for each set and tap to complete it. There is no automatic rep counting, set detection or exercise recognition anywhere in the product, and the only sensor Hevy reads on the watch is heart rate.

What is the best Hevy alternative for Apple Watch users?

If you want the wrist to do the work, Riven is the Apple Watch app that scores muscle failure; it scores each set the moment you rack it. Motra and Gymatic also count reps automatically but stop at the count and never measure effort. Rep Up and Fitnexx buzz at a rep target you set in advance. Strong, JEFIT and Boostcamp are manual logbooks, like Hevy, and the right pick among them is whichever interface you will keep using.

Is Hevy actually bad? Why do so many alternative lists exist?

Hevy isn't bad: 98% of its recent US App Store reviews are four or five stars, across nearly 88,000 iOS ratings. Most alternative lists exist because competing logging apps write them, which is why every one recommends another app you type into. The real question isn't whether Hevy logs well. It's whether a record of numbers you typed is worth switching between at all, when the variable that decides whether the set counted was never in the log.

Does Hevy measure effort, RIR or proximity to failure?

No. RPE is a manual, optional field that's off by default. RIR isn't a field at all; Hevy's help centre suggests inferring it from the RPE descriptions. "Failure" exists in Hevy only as a set-type label you tap yourself. Nothing is measured from motion.

How do I export my workout history out of Hevy?

Hevy offers a CSV export from the settings in your profile; it emails you a file with one row per set, including date, exercise, weight, reps, RPE and notes. Hevy also exports workouts to Apple Health via HealthKit. Do the export from the web app at hevy.com if you want to inspect the file before migrating.

Can an Apple Watch really count reps accurately?

Yes, and the method matters more than the hardware. The published academic baseline for a general-purpose wrist counter (Oberhofer et al. 2021, 363 sets) is 88.4% exercise recognition with rep counts that hold up on squat and deadlift but fall apart on bench press, because that approach recognises movements by resemblance to a library. The apps in the automatic rows of the table above do better than that baseline on the lifts they support, and the way to check any of them is to count a few sets yourself and compare. The genuine limit is physical, not statistical: if your wrist barely moves, as on some machine work, no watch can see the rep, and an honest app flags those lifts rather than guessing.

Do I have to train to failure for this to matter?

No, and the evidence on literal failure is genuinely mixed. The 2026 ACSM position stand found training to momentary fatigue "did not consistently impact training outcomes," and a 2026 meta-analysis found non-failure slightly better for dynamic strength. The claim here is narrower: proximity to failure influences how much a set contributes, and it's the one variable nothing in your logbook measures.

Why can't I just estimate my reps in reserve myself?

You can, but the error is large exactly where it matters. RIR error runs about 2 reps at 1 RIR and 5 reps at 5 RIR; 160 trained men asked to use their "10-rep weight" averaged 16 reps. Trained lifters are accurate on heavy, familiar, bilateral lifts close to failure, and much less accurate at lighter loads, higher reps and unfamiliar exercises.

Is heart rate a useful measure of how hard a set was?

No. In volume-matched leg press at 80% versus 40% of 1RM, heart rate differed by about one beat per minute (interaction p=0.353). Heart rate tracks how long a set took and how much total work was done, not load or proximity to failure.

Can I use an automatic tracker and keep Hevy at the same time?

Yes, and it's a comfortable way to switch. Wear the automatic app for a few sessions while still logging in Hevy and you'll have both records side by side, except that one of them also says how close each set came to failure, which is the column your Hevy history has never had. Once the two agree on the count, the logbook has nothing left to add.

Sources

• Hevy — Track Workouts feature documentation — https://www.hevyapp.com/features/track-workouts/
• Hevy Help Centre — RPE vs RIR — https://help.hevyapp.com/hc/en-us/articles/34490600233111-RPE-vs-RIR-What-They-Mean-and-How-to-Use-Them-in-Hevy
• Hevy Help Centre — Apple Watch Sync Issues Troubleshooting Guide — https://help.hevyapp.com/hc/en-us/articles/33996260919703-Hevy-Apple-Watch-Sync-Issues-Step-by-Step-Troubleshooting-Guide
• Soma — Hevy app review (2026) — https://trysoma.app/blog/hevy-app-review/
• RepReturn — Hevy app review — https://repreturn.com/hevy-app-review/
• Bevel feature board — "Automatic rep and strength training logger" — https://feedback.bevel.health/feature-requests/p/automatic-rep-and-strength-training-logger
• Robinson et al. 2024, Sports Medicine 54(9) — proximity to failure and hypertrophy — https://pubmed.ncbi.nlm.nih.gov/38970765/
• Pelland et al. 2026, Sports Medicine 56(2) — volume dose-response — https://link.springer.com/article/10.1007/s40279-025-02344-w
• Schoenfeld et al. 2017, J Sports Sci 35(11) — https://pubmed.ncbi.nlm.nih.gov/27433992/
• Counts et al. 2016, Physiol Behav 164 — "NO LOAD" resistance training — https://europepmc.org/article/MED/27329807
• Lasevicius et al. 2022, JSCR 36(2) — volume-equated effort — https://pubmed.ncbi.nlm.nih.gov/31895290/
• Hammert et al. 2024, Physiol Meas 45(8) — https://europepmc.org/article/MED/39178897
• Baz-Valle et al. 2021, JSCR 35(3) — quantifying training volume — https://europepmc.org/article/MED/30063555
• Roberts et al. 2026, J Appl Physiol 140 — Jyväskylä consensus review — https://pmc.ncbi.nlm.nih.gov/articles/PMC13322123/
• ACSM Position Stand 2026, Med Sci Sports Exerc 58(4) — https://pmc.ncbi.nlm.nih.gov/articles/PMC12965823/
• Wu et al. 2026, BMC Sports Sci Med Rehabil 18:333 — https://doi.org/10.1186/s13102-026-01861-z
• Zourdos et al. 2021, JSCR 35(2S) — RIR accuracy by proximity — https://pubmed.ncbi.nlm.nih.gov/30747900/
• Steele et al. 2017, PeerJ 5:e4105 — predicting reps to failure — https://pmc.ncbi.nlm.nih.gov/articles/PMC5712461/
• Halperin et al. 2022, Sports Medicine 52(2) — https://link.springer.com/article/10.1007/s40279-021-01559-x
• Refalo et al. 2024, JSCR 38(3) — trained lifters' RIR accuracy — https://europepmc.org/article/MED/37967832
• Barbosa-Netto et al. 2021, JSCR 35(Suppl 1) — self-selected 10-rep loads — https://europepmc.org/article/MED/29112055
• Glass 2008, JSCR 22(4) — self-selected workload learning trial — https://europepmc.org/article/MED/18438209
• Gómez-Redondo et al. 2025, Exp Gerontol 210:112884 — https://europepmc.org/article/MED/40902458
• Sánchez-Medina & González-Badillo 2011, MSSE 43(9) — velocity loss as a fatigue marker — https://pubmed.ncbi.nlm.nih.gov/21311352/
• Jukic et al. 2023, Sports Medicine 53(1) — velocity loss review — https://pmc.ncbi.nlm.nih.gov/articles/PMC9807551/
• Mansfield et al. 2023, Eur J Sport Sci 23(12) — https://pubmed.ncbi.nlm.nih.gov/37552530/
• Martínez-Rubio et al. 2025, J Sports Sci 43(10) — https://pubmed.ncbi.nlm.nih.gov/40125884/
• Kambic et al. 2021, IJERPH 18(8):3905 — heart rate, volume-matched — https://pmc.ncbi.nlm.nih.gov/articles/PMC8068143/
• Achermann et al. 2023, Sports (Basel) 11(7):125 — Apple Watch velocity validation — https://pmc.ncbi.nlm.nih.gov/articles/PMC10383699/
• Balsalobre-Fernández et al. 2017, Front Physiol 8:649 — wrist vs barbell placement — https://pmc.ncbi.nlm.nih.gov/articles/PMC5581394/
• Oberhofer et al. 2021, Sports (Basel) 9(9):118 — Apple Watch exercise recognition and rep counting — https://pmc.ncbi.nlm.nih.gov/articles/PMC8471343/