Strength

1. Introduction

FitHalo suggests a weight and rep count for every working set in a Traditional Strength routine. The suggestions come from the sets you've logged, so you don't have to decide when to add weight or reps yourself. The Automatic Progression guide in the app explains what FitHalo does. This article explains why: where each number came from, what the research and public training data say, and which choices are my own judgment.

FitHalo does not follow a branded program (e.g., Starting Strength, StrongLifts 5×5, or 5/3/1). Published research comparing branded programs against each other is rare or nonexistent, so there is little evidence that favors one program over another. FitHalo, instead, applies progressive overload, which is the one principle the research agrees on: do slightly more than last time.

Disclaimer: FitHalo's strength programming is general fitness guidance. It is not physical therapy, injury rehabilitation, or medical advice, and FitHalo knows nothing about your joints. It is always advisable to consult a healthcare professional before starting a new type of workout.

2. How It Works

Every suggestion starts from your one-rep max (1RM): the most weight you could lift for a single rep. You rarely test that directly, and FitHalo never asks you to. Instead, FitHalo relies on the Epley formula, one of the standard formulas that relate a one-rep max to the weight you can do for any other number of reps, and uses it in both directions. After each workout, FitHalo estimates your one-rep max for each exercise from the weight you used, the reps you did, and your reps in reserve (RIR), which is how many more reps you could have done when you stopped the set. Before your next workout, FitHalo expects that estimate to have grown a little, then uses the formula in reverse to find the weight you should be able to do for the target reps at the target RIR.

Building that took three kinds of numbers, and the next three sections explain each in turn:

1. How to turn a logged set into an estimate of your strength.
2. How hard each set should be.
3. How fast to expect improvement.

The sections after that cover choosing the weight, time away, and deloads and injuries.

3. Estimating Your Strength

Everything FitHalo suggests rests on how well it knows your current strength. FitHalo uses your heaviest working set during a workout session to estimate your strength, in four steps:

1. After a workout, FitHalo finds your heaviest working set for each exercise. Say it was 185 lb for 8 reps, and you reported 2 reps in reserve.
2. FitHalo adds the reps in reserve to the reps you did. If you stopped at 8 with 2 left, you could have done 10, so FitHalo treats the set as 185 lb for 10 reps.
3. FitHalo converts that into a one-rep max with the Epley formula (Epley, 1985): the weight multiplied by (1 + reps / 30). Here, that is 185 × (1 + 10 / 30), or 247 lb.
4. That one-rep max is what FitHalo remembers for the exercise. Because reps in reserve are counted, a heavy set of 5 and a lighter set of 15 both produce a one-rep max, so sets from any rep range contribute.

Warm-up sets, sets rated 6+ RIR, sets with only partial reps, deload workouts, and exercises that trained a muscle flagged as injured are not used. Partial reps logged after the full reps of a set taken to failure count as half a rep, however many there were; partial reps on any other set are not counted.

Strength Estimation Formula

Several formulas convert a set into a one-rep max, and they agree closely on short sets but drift apart on long ones. That matters because accessory exercises are often done for 15 to 20 reps, and a formula that only works for heavy sets would progress those exercises wrongly. The two best known are Epley (1985), from Boyd Epley's 1985 training manual, and Brzycki (1993), from a 1993 article in the Journal of Physical Education, Recreation & Dance. Epley multiplies the weight by (1 + reps / 30), and Brzycki multiplies it by 36 / (37 − reps). Figure 1 shows the percentage of your one-rep max that each formula assigns to a set, by the number of reps in it.

A set of 8 with 1 rep in reserve counts as 9 reps, which Epley puts at 77% of your one-rep max and Brzycki at 76%. At 20 reps they are 60% and 46%, and Brzycki reaches zero at 37 reps. A study that tested seven formulas against real sets of up to 40 reps found Epley among the three most accurate, while Brzycki overestimated badly on long sets (Wood et al., 2002). FitHalo therefore uses Epley for every suggestion.

Table 1 shows the same comparison in pounds, for typical sets, with reps in reserve already counted. The two formulas are within 10 lb of each other up to 8 reps and identical at 10, then diverge quickly.

Reported RIR

Less-experienced lifters often misjudge how many reps they have in reserve, and if FitHalo took those reports at face value, it would underestimate their strength and keep suggesting weights that are too light. This is one of the reasons FitHalo asks for your training experience.

A 2022 review gathered the studies in which people said how many reps they had left and then continued to failure (Halperin et al., 2022). On average, they had about one more rep left than they predicted, and the error grew in sets of more than about 12 reps. A study of 141 gym members found that those with less than six months of training had 3 to 5 more reps left than they predicted, while members with more than three years had about 1 more (Steele et al., 2017).

If your experience is set to Beginner, FitHalo therefore adds 1 to any RIR of 1 or more. The evidence on experience is mixed, so a single rep is a cautious adjustment. A set you report at 0 RIR is always treated as a set taken to failure, and FitHalo never adds a rep to it. If you truly had nothing left, an added rep would raise your estimate, and your next workout would ask for a weight you can't complete. For intermediate or advanced lifters, FitHalo uses the RIR you reported.

Considering Your Workout History

If FitHalo based your next suggestion on your last workout alone, one poorly performed workout would lower the weight on every set the following week, and one unusually good workout would set you up to fail. A single set is simply too rough a measurement to build on.

An analysis of 269 studies found that the reps people can do at a given fraction of their one-rep max differ a great deal between people and between exercises, so a fixed formula can be off by 5% to 10% for one person (Nuzzo et al., 2024). A review of 32 studies found that even a tested one-rep max varies by about 4% from one day to the next (Grgic et al., 2020). Add an RIR that is off by a rep and a set logged in whole reps, and I estimate that a single set carries an error of about 4% to 5%. The formula's own error is about the same each time for the same person and exercise, and FitHalo only ever compares an exercise with itself, so that part cancels out.

FitHalo therefore combines your estimates from the last 12 weeks, with each workout counting half as much as the workout four workouts after it. Older estimates are first adjusted to today using your expected rate of improvement. A single unusually good or bad workout then has only a small effect on your next suggestion. Figure 2 shows the difference over six months of an example history: each dot is the one-rep max recorded from one workout's heaviest set, and the line is the effective one-rep max, the combined estimate FitHalo uses. The standout days and the off days have little effect on the combined estimate, which follows the slow, steady growth underneath them, and that estimate is what your next suggestion is built on.

Starting Fresh

The first time you do an exercise, there is nothing to estimate from, so FitHalo has to measure your strength directly. It leaves the weight empty for you to choose, sets the RIR target of each working set to 0, and marks the sets as calibration sets. Truly taking these calibration sets all the way to failure ensures that FitHalo gives as accurate a prediction as possible the next time you perform that same exercise. Choose a weight you can lift safely for about the number of reps the routine asks for, and use safety bars or a spotter on exercises such as the squat and bench press. FitHalo doesn't calibrate during a deload, or on an exercise that uses a muscle flagged as injured.

4. Designing Your Routine

You decide how hard each set will be by giving it a rep range and an RIR target, and FitHalo chooses the weight to match. FitHalo aims for the middle of the rep range at the RIR target, so with a range of 8 to 12 and a target of 1 RIR, it suggests the weight you should be able to do for 10 reps with 1 rep in reserve. For a rep range without an exact middle, such as 8 to 15, FitHalo aims for the lower number, 11, and if an exercise's working sets have different RIR targets, it uses the first working set's. Only Traditional Strength routines are progressed this way; other types of routine, such as HIIT or Functional Strength, always start with the sets exactly as you planned them.

Your routine's own numbers are always used first. FitHalo needed to answer three questions to choose sensible defaults for routines:

1. How close to failure should a set go?
2. How many reps should a set have?
3. How long should the rest between sets be?

Reps in Reserve

Training to failure feels productive, but if the last rep costs more recovery than it returns, a routine built on it would leave you weaker for the next workout. The first question was therefore how close to failure a set should go.

A 2024 meta-analysis of 55 trials found that muscle growth increased as sets were stopped closer to failure, while strength gains were about the same across a wide range of RIR (Robinson et al., 2024). A trial the same year tested the last rep directly: 18 experienced people trained one leg to failure and the other leg one to two reps short of failure, both legs grew by a similar amount, and the leg trained to failure lost more reps from the first set to the last (Refalo et al., 2024).

The last rep before failure adds little and costs the most. Sets for muscle size therefore stop one rep short, where that trial found no measurable loss. Sets for strength stop two reps short, because strength gains do not depend on going closer, and because heavy compound sets taken to failure are the hardest to recover from. In one study, lifters who took their squat and bench press sets to failure showed a larger immediate drop in performance and were still recovering 24 to 48 hours later, while lifters who had done the same total work in sets stopped short had already recovered (Morán-Navarro et al., 2017). The hypertrophy guidelines of the International Universities Strength and Conditioning Association (IUSCA) make the same point: free-weight compound movements are more technically demanding and use more muscle, so taking them to failure may hurt recovery and raise the risk of overtraining, while isolation and machine exercises can tolerate a higher effort (Schoenfeld, Fisher, et al., 2021).

Rep Ranges

How many reps a set should have is really a question of how heavy the weight should be, because more reps means a lighter weight. If heavy weights built more muscle, or light weights built more strength, the defaults would need to reflect it.

A 2021 meta-analysis compared 28 trials using heavy, moderate, and light weights (Lopez et al., 2021). Muscle growth was about the same at every weight, while strength gains were larger with heavy and moderate weights than with light ones. A review by Schoenfeld, Grgic, et al. (2021) reached the same conclusion and describes moderate weights as the most efficient choice for muscle size, because sets with light weights take much longer.

Strength sets on compound exercises therefore use 3 to 6 reps, because the weight is the one thing that clearly matters for strength. Sets for muscle size use 6 to 12 reps: the research does not favor that range over 15 to 20, but it takes less time and most people can do it without a spotter.

Isolation exercises are a different case, because strength is specific to the exercise being trained, and a heavier leg extension does not produce a heavier squat. An isolation exercise in a strength routine is accessory work, and a moderate 8 to 12 reps at 2 RIR keeps it from using recovery the compound exercises need. The IUSCA guidelines describe the same pattern, pairing technically demanding free-weight compound exercises with low to moderate reps and following them with moderate to high reps on isolation or machine exercises "to balance recovery, stress, and performance" (Schoenfeld, Fisher, et al., 2021). The guidelines offer that as a sensible way to structure a session rather than as a tested finding; their own conclusion on load is that similar growth is available across a wide range of loads. I have not yet found a trial that compares rep ranges between compound and isolation exercises.

Rest

Rest between sets has to strike a balance: enough recovery for the next set to be productive, without stretching a workout so long that a lifter gets less done in the time they have. The question was how much rest is enough.

An eight-week trial found that three minutes of rest produced more strength and growth than one minute (Schoenfeld et al., 2016), and a sixteen-week trial found the same for three and five minutes against one (de Salles et al., 2010). Compound strength sets therefore rest three minutes. Neither study tested shorter rests or isolation exercises, so the two-minute and ninety-second defaults follow the IUSCA guidelines, which recommend at least two minutes of rest for multi-joint exercises and 60 to 90 seconds for single-joint and some machine exercises (Schoenfeld, Fisher, et al., 2021). The reasoning is that recovery between sets suffers more in multi-joint exercises. In the study the guidelines cite, resting one minute instead of three cost about three reps across three sets of bench press, but less than one rep on the chest fly (Senna et al., 2011).

The Defaults

Taken together, these are the targets FitHalo uses when a routine doesn't set its own.

These values simply serve as defaults, and FitHalo users can change all of them to match their preferred workout style. A lifter who prefers fewer or more reps, lower or higher RIR targets, or shorter or longer rest times can easily change these defaults in FitHalo's options.

5. Expected Improvement

FitHalo expects you to improve a little every time you do an exercise, but choosing just how much improvement to expect requires adaptability and nuance. If FitHalo predicts too much, its suggestions could take a set to failure even when that was unintended. If FitHalo predicts too little, you may end up doing more reps than your rep range calls for. No published model predicts an individual's next workout from their training log, so FitHalo had to build its own, starting from what the research does show: the shape of the curve.

The Shape of Progress

If FitHalo expected the same improvement every week indefinitely, its suggestions would soon exceed the strength of anyone who had been training for more than a few months. Luckily, multiple large-scale studies show how improvement slows over time, and FitHalo was able to model progress on the records of thousands of lifters.

Steele et al. (2023) analyzed the records of a chain of personal training studios, where 14,690 members did one set to failure on each of six machines about once a week, with a trainer recording the weight, for up to seven years. Strength rose quickly at first and more slowly every month after, following a logarithmic curve: members gained 30% to 50% in the first year and only 50% to 60% in total by the sixth. Latella et al. (2024) applied the same method to powerlifting competition results and found the same shape with smaller gains, about 7.5% to 12.5% in the first competitive year.

FitHalo therefore expects your rate of improvement in an exercise to slow the longer you have been doing it. Spoiler alert: it's just a logarithmic function of time, exactly like you'd expect.

The Data

The research describes how strength improves over years of training. FitHalo needs that in numbers: how much improvement to expect per day at each stage of training. Those were measured from three public datasets, all in the open data repository for the Steele study:

• The fit20 training records behind the Steele study, published in full: 3.68 million rows, one per member, machine, and session. After scrubbing the data, we are left with 13,508 members and 1.88 million sessions. Every set is taken to failure and recorded by a trainer, and many members stay for years, which makes the records unusually consistent. However, the members average 48 years old and train once a week on machines.
• OpenPowerlifting, a public archive of competition results, in its snapshot from September 2020. After scrubbing the data, we are left with 69,621 competitors and 184,764 pairs of consecutive competitions.
• A 2021 survey of r/weightroom, an online strength training community. After scrubbing the data, we are left with 1,049 usable responses, each giving a best squat, bench press, and deadlift and years of training. This is the only source of people training with free weights, the way most FitHalo users do, but the numbers are self-reported and each person appears once.

Figure 3 plots how fast strength improved per day against how long a person had been training, for each of the three sources. (OpenPowerlifting counts time from a lifter's first competition, so its points are placed 2.3 years later, the offset that best lines the sources up.) All three show the same thing: improvement is fastest at the start, falls by about a factor of ten over the first year, and keeps falling after that. Past the first two months, no source averages even 0.5% a day.

Fitting Improvement Rates

FitHalo reads your rate of improvement in an exercise from a curve fitted through your one-rep max estimates: the rate is the slope of that curve today. The choice of curve matters. A straight line fitted through the last few weeks is sensitive to any single workout: with only about six points, each carrying 4% to 5% of error, one outlier changes the fitted slope substantially. A straight line through a longer period assumes the fast improvement of the first months is still happening. Figure 4 shows the result in the fit20 records: a straight line through the first three months passes 100% before the end of the first year, while the members took almost four years to gain that much. A logarithmic curve flattens as the data does.

To compare curve shapes, I split the fit20 members into two halves at random, made every decision on the first half, and had each candidate curve predict the second half's weights four and twelve weeks ahead from each member's earlier sessions alone. Figure 5 shows the error of each candidate across 673,217 predictions at four weeks and 377,813 at twelve.

The logarithmic curve had the lowest error at both horizons. Quadratic and cubic curves, which follow past workouts most closely, had the highest. The limits on the rate affected the error more than the curve shape did: a ceiling of 0.5% a day was more accurate than 1%, and the least accurate candidates were the ones that allowed a rate far above anything in Figure 3. FitHalo therefore fits a logarithmic curve through the last year of your workouts and holds the rate it reads between a floor and a ceiling. The next two sections give the numbers.

Starting Rates

Until an exercise has enough workouts to fit a curve, FitHalo uses a starting rate based on your training experience (Table 3). Each starting rate is the curve in Figure 3 averaged over a whole stage of training rather than its fastest weeks: about 0.1% a day over a beginner's first year, 0.05% a day over the second year, and 0.02% a day beyond three years. A starting rate is used for as long as an exercise is done less than once every three weeks, so it has to be safe indefinitely, and because FitHalo users usually aren't absolute beginners, the Beginner rate is well below the 0.5% a day that the first weeks of the fit20 data would suggest.

Table 3 also holds the floor and ceiling for each tier. These serve as boundaries on the starting rates, and as guard rails on the rate FitHalo later fits from your own workouts, which is never allowed outside them. The slowest rate for each tier is the starting rate of the next tier, down to 0.005% a day, which is what powerlifters gained in their third to fifth year of competition; the floor keeps a run of poor workouts from reducing the expected rate to zero. The fastest rate is the 0.5% a day that was most accurate in the backtest, and 1% a day for a Beginner, because the fastest-improving quarter of fit20 beginners gained 0.94% a day in their first four weeks.

Learning Your Own Rate

Nobody improves at exactly the average rate, and each exercise can improve at a unique rate, so a starting rate is only a placeholder. Once you've done an exercise at least four times over at least three weeks, FitHalo fits a logarithmic curve, the shape chosen in Figure 5, through your last year of estimates for that exercise and replaces the starting rate with the slope of that curve today. Each exercise has its own rate, refitted after every workout and kept between the slowest and fastest rates in Table 3.

6. Choosing the Weight

FitHalo turns your estimated one-rep max back into a weight with the Epley formula run in reverse: the weight for a set is the one-rep max divided by (1 + reps / 30), where the reps are the target reps plus the RIR target. With the estimate of 247 lb from the earlier example, a rep range of 8 to 12, and a target of 1 RIR, the target is 10 reps with 1 in reserve, or 11 reps in total, and the weight is 247 / (1 + 11 / 30), about 181 lb. Figure 6 shows the same conversion for every rep count from 1 to 30. One quirk of the formula is visible at the left edge: at a single rep it returns slightly less than the one-rep max itself (239 lb rather than 247), because Epley's chart was built from sets of several reps, which is where FitHalo uses it.

The calculation gives an exact number, but gym equipment comes in fixed increments. The example above calls for 180.7 lb at 11 reps, and no combination of standard plates adds up to 180.7 lb: a barbell with standard plates goes up in 5 lb steps, and many machines in 10 or 15. So FitHalo takes the two weights your equipment can make that are nearest to the calculated one, one below and one above, and suggests whichever puts your reps closest to the middle of the rep range. The suggested reps are then recalculated for that weight and always stay within your rep range. Adding smaller plates or more weights to your gym gives FitHalo smaller steps to work with.

Reps usually drop from one set to the next, and a suggestion that ignored that would ask for the same reps on set four as on set one. After you've done an exercise twice, FitHalo suggests reps for the later sets based on how much your reps dropped in past workouts, with recent workouts counting the most, but never below the bottom of your rep range.

FitHalo learns from the weight, reps, and RIR you actually log, not from the suggestion, so you can change any suggested weight or rep count. If you do more than FitHalo suggested, your estimate goes up and your next suggestion is heavier. If you do less, your next suggestion is lighter.

Equipment, Gym, and Modifier Fingerprinting

FitHalo identifies each exercise by a fingerprint made up of three parts: the equipment the exercise is done on, the gym that equipment belongs to, and any modifiers or angle applied to it. Two exercises with the same name but different fingerprints are treated as different movements, each with its own history, strength estimate, rate of improvement, and calibration. Your strength estimate is only as good as its match to the equipment, so FitHalo keeps a separate history wherever the same weight could feel different. Machines, cables, bands, and suspension trainers are tracked separately at each gym, because the same weight on two machines can feel very different. Barbells, dumbbells, kettlebells, and bodyweight exercises are tracked together across all of your gyms, and a workout that wasn't assigned to a gym counts toward every gym.

An exercise with a modifier or a different angle has its own history as well, so a bench press with chains calibrates and progresses apart from a bench press without them. Likewise, an incline bench press at 30 degrees will feel very different than one done at 45 degrees. Whenever an exercise has no history in its current form, such as after adding a modifier, changing the angle, or using a machine at a different gym, FitHalo calibrates it again.

Equipment Limits

If the weight you need is heavier than the heaviest weight your equipment can make, FitHalo suggests the heaviest weight with more reps, up to twice the top of your rep range. If it is lighter than the lightest weight, FitHalo suggests the lightest weight with fewer reps. If the next heavier weight is too big a jump, FitHalo stays at the lighter weight and suggests more reps. In each case the set is marked so you can see the reason.

Bodyweight and Assisted Exercises

For bodyweight exercises with no added weight, such as push-ups, FitHalo suggests one more rep than you did in your last workout, up to the top of the rep range. If the routine asks for an exact number of reps, such as 15, FitHalo always suggests that number. Added weight on a bodyweight exercise, such as a weighted pull-up, is progressed like any other weight. For assisted exercises, such as a pull-up with a band, the weight is the amount of assistance and is shown as a negative number, so a higher number always means you are stronger. FitHalo reduces the assistance over time, and once you no longer need it, adds reps instead.

Fixed Reps and Turning Progression Off

To keep an exercise at a fixed number of reps, give each working set the same minimum and maximum in its rep range. FitHalo then always suggests that number of reps and only changes the weight. To stop FitHalo from suggesting a weight for an exercise, leave the RIR target empty on every working set. The exercise then starts each workout with the sets exactly as the routine planned them, and you enter the weight yourself.

7. Time Away

Life interrupts training, and a system that kept expecting improvement through a three-week vacation would greet you back with a weight you could no longer lift. FitHalo counts improvement for up to 10 days after your last workout with an exercise, or up to three weeks if other exercises trained the same muscles in the meantime. After three weeks away from an exercise, FitHalo stops counting improvement. If other exercises kept training the same muscles, your estimate stays where it was. If the muscles went untrained, FitHalo lowers your estimate by 1% for each week after the second, up to 20%. When you return, FitHalo counts only the workouts since, and uses the starting rate until you have again done the exercise four times over three weeks.

The Research Behind Time Away

The first question was how long you can be away before it matters. A meta-analysis of 103 studies of people who stopped training found that the loss of maximal strength became significant from the third week (Bosquet et al., 2013), which is where FitHalo's three weeks comes from. The rate of loss afterwards varied a great deal with the group studied, from 4% to 6% over 24 weeks in active middle-aged adults to 6% to 9% in four weeks in strength-trained men. Lost strength returns quickly: beginners given a ten-week break lost 3.6% to 5.4% and regained it within five weeks, finishing twenty weeks of training as strong as a group that never stopped (Halonen et al., 2024).

The second question was what happens to an exercise you skip while other exercises keep training the same muscles, and I have not yet found a study that measures how much strength in the skipped exercise is kept or lost. The closest research measures how much one exercise improves another. In two trials that trained one exercise and then tested another (Rossi et al., 2018; Schwanbeck et al., 2020), the exercise that was not trained always improved, by anywhere from a quarter as much to just as much as the one that was. The carryover is real but unpredictable, so FitHalo leaves the estimate unchanged. That estimate is probably a little low when you return, and a suggestion that is too light corrects itself in your first workout back.

Checking the Rule

The studies above each cover a few dozen people. The fit20 records include thousands of members who stopped and came back, so I compared the rule with the weight they returned at.

Across 30,100 returns, the rule was within about two and a half percentage points of the typical member at every length of time away, so FitHalo uses it as written. Only 188 of those returns came after six months or more, so the rule is least certain there. The typical change also hides a wide range: after three to six months away, a quarter of members returned more than 28% lower and a quarter less than 5% lower. That is why FitHalo counts only the workouts since you returned. Your first workout back measures your strength better than any rule can predict it, and fit20 members regained strength at 0.13% to 0.18% a day afterwards, far faster than they had been improving before the break.

8. Deloads, Injuries, and Fatigue

Sometimes doing less is more. A deload is a planned easy workout, and an injury is a reason to work around a muscle while it heals. FitHalo treats the two differently, because the evidence behind them is different.

Deloads

During a deload, FitHalo keeps half of each exercise's working sets, rounded up, raises the RIR target of every set by 4, and lowers the weight and the warm-ups to match. Deload workouts are not used to estimate your strength.

Those numbers come from coaching practice rather than from experiments, because almost no experiments exist. An international panel of strength and physique coaches, assembled to reach a formal consensus on deloading, agreed unanimously that there are "limited scientific studies and understanding about deloading" and that current practice rests on coaches' experience (Bell et al., 2023). The experiments that do exist test taking a week off, not training lighter. In the only one with trained lifters, 39 people trained for nine weeks and half of them rested completely during week five; the group that rested gained the same muscle as the group that didn't, and somewhat less strength (Coleman et al., 2024). Two earlier experiments that inserted three-week breaks into untrained people's programs found no difference in strength or muscle over 15 to 24 weeks, as Coleman et al. note. In short, a short break costs little, and whether a lighter week helps has not been tested.

FitHalo's deload therefore follows what the coaching evidence agrees on. The Delphi panel rejected the idea that a deload could keep the same training demand (90.5% disagreed) and agreed universally that volume should come down (Bell et al., 2023). A survey of 246 competitive strength and physique athletes who use deloads found that 78.9% reduce their weekly sets, 83.7% lower the weight on their main exercises, and 84.9% stop further from failure, while most keep the same number of sessions (63.0%) and the same exercises (70.3%); their typical deload lasts about six days and comes around every five to six weeks (Rogerson et al., 2024). The practical guidance written from those two studies recommends cutting volume by 25 to 45% when little recovery is needed, 40 to 60% for moderate needs, and 60 to 90% when fatigue is high, with intensity eased either by about 10% less weight at the same reps or by adding reps in reserve (Bell et al., 2026).

FitHalo's deload halves the sets, which is the middle band, and raising the RIR target by 4 at the same reps works out to about 8 to 10% less weight, which is the guidance's weight option. It keeps your sessions and your exercises as they are, as most athletes do. One difference is worth knowing: the published deload is usually a five- to seven-day block, while FitHalo's applies to whichever workouts you run it on. The guidance allows for that too, noting that a reactive deload "may be a single training session" (Bell et al., 2026), and you can leave FitHalo's deload on for a week if that is what you need.

Injuries

When you report an injured muscle, FitHalo lowers the weight of every exercise that uses it, for as long as the injury is open, and keeps the RIR target the same. The weight drops by 40% when the exercise trains the injured muscle directly, by 25% when the muscle assists, and by 10% when the muscle only stabilizes the movement, and the warm-ups follow the lower weight. Workouts done during the injury stay in your history but are not used to estimate your strength, and exercises that don't use the injured muscle are not affected.

These percentages are defaults, not a prescription, and here the evidence is thin: no study tells a lifter how much weight to take off when a muscle hurts, let alone grades it by how directly an exercise uses the muscle. The closest evidence comes from rehabilitation, where the standard is not a percentage but a pain rule. In a trial of 38 people with Achilles tendinopathy, those allowed to keep running and jumping under a pain-monitoring model (pain up to 5 out of 10 during and after activity, settled by the next morning, and not worsening week to week) recovered as well over a year as those who rested from sport for six weeks, with no negative effects found (Silbernagel et al., 2007). A meta-analysis of seven trials in 385 patients with chronic pain found that exercise programs allowing pain, usually capped around 5 out of 10 and required to settle by the next session or morning, relieved pain slightly better than pain-free programs in the short term and no differently after three months, although the painful programs also used heavier loads, which may explain part of the difference (Smith et al., 2017). Both studies concern long-standing tendon and joint pain in patients, not a strained muscle in a lifter, and that is the point: nothing in the research supports one fixed reduction for every injury.

So treat FitHalo's reduction as a starting point. A sore muscle may have lost little of its strength, and a fresh strain far more, and only you can feel which it is. Choose the weight that suits your injury or soreness on the day, lower or higher, and because these sets never count toward your progress, changing the weight costs you nothing. Even the full 40% cut leaves most working sets at roughly 40 to 50% of a one-rep max, and sets that light still build muscle when they are taken close to failure.

If you are ever in doubt about whether to exercise an area that is painful or injured, consult a qualified healthcare professional, such as a physician or a licensed physical therapist.

Fatigue

Fatigue is harder to see than an injury, because it looks like an ordinary bad week. If you turn on fatigue detection, FitHalo compares each exercise's most recent estimate with its average since your last time away. A drop of 5% or more in at least two exercises that train different muscle groups makes FitHalo show a fatigue symbol and suggest a deload. FitHalo never starts a deload on its own, because poor sleep, illness, and stress cause the same drop as fatigue from training, and FitHalo cannot tell them apart. If you tell FitHalo you're not fatigued, the symbol only returns if the drop gets worse.

References

• Bell, L., Strafford, B. W., Coleman, M., Androulakis Korakakis, P., & Nolan, D. (2023). Integrating deloading into strength and physique sports training programmes: An international Delphi consensus approach. Sports Medicine - Open, 9(1), Article 87. https://doi.org/10.1186/s40798-023-00633-0
• Bell, L., Darragh, I. A. J., Travis, S. K., Rogerson, D., & Nolan, D. (2026). A practical approach to deloading: Recommendations and considerations for strength and physique sports. Strength & Conditioning Journal, 48(4), 413–423. https://doi.org/10.1519/ssc.0000000000000910
• Bosquet, L., Berryman, N., Dupuy, O., Mekary, S., Arvisais, D., Bherer, L., & Mujika, I. (2013). Effect of training cessation on muscular performance: A meta-analysis. Scandinavian Journal of Medicine & Science in Sports, 23(3). https://doi.org/10.1111/sms.12047
• Brzycki, M. (1993). Strength testing: Predicting a one-rep max from reps-to-fatigue. Journal of Physical Education, Recreation & Dance, 64(1), 88–90. https://doi.org/10.1080/07303084.1993.10606684
• Coleman, M., Burke, R., Augustin, F., Piñero, A., Maldonado, J., Fisher, J. P., Israetel, M., Androulakis Korakakis, P., Swinton, P., Oberlin, D., & Schoenfeld, B. J. (2024). Gaining more from doing less? The effects of a one-week deload period during supervised resistance training on muscular adaptations. PeerJ, 12, Article e16777. https://doi.org/10.7717/peerj.16777
• de Salles, B. F., Simão, R., Miranda, H., Bottaro, M., Fontana, F., & Willardson, J. M. (2010). Strength increases in upper and lower body are larger with longer inter-set rest intervals in trained men. Journal of Science and Medicine in Sport, 13(4), 429–433. https://doi.org/10.1016/j.jsams.2009.08.002
• Epley, B. (1985). Poundage chart. In Boyd Epley workout. Body Enterprises.
• Grgic, J., Lazinica, B., Schoenfeld, B. J., & Pedisic, Z. (2020). Test–retest reliability of the one-repetition maximum (1RM) strength assessment: A systematic review. Sports Medicine - Open, 6(1), Article 31. https://doi.org/10.1186/s40798-020-00260-z
• Halonen, E. J., Gabriel, I., Kelahaara, M. M., Ahtiainen, J. P., & Hulmi, J. J. (2024). Does taking a break matter: Adaptations in muscle strength and size between continuous and periodic resistance training. Scandinavian Journal of Medicine & Science in Sports, 34(10), e14739. https://doi.org/10.1111/sms.14739
• Halperin, I., Malleron, T., Har-Nir, I., Androulakis-Korakakis, P., Wolf, M., Fisher, J., & Steele, J. (2022). Accuracy in predicting repetitions to task failure in resistance exercise: A scoping review and exploratory meta-analysis. Sports Medicine, 52(2), 377–390. https://doi.org/10.1007/s40279-021-01559-x
• Latella, C., van den Hoek, D., Wolf, M., Androulakis-Korakakis, P., Fisher, J. P., & Steele, J. (2024). Using powerlifting athletes to determine strength adaptations across ages in males and females: A longitudinal growth modelling approach. Sports Medicine, 54(3), 753–774. https://doi.org/10.1007/s40279-023-01962-6
• Lopez, P., Radaelli, R., Taaffe, D. R., Newton, R. U., Galvão, D. A., Trajano, G. S., Teodoro, J. L., Kraemer, W. J., Häkkinen, K., & Pinto, R. S. (2021). Resistance training load effects on muscle hypertrophy and strength gain: Systematic review and network meta-analysis. Medicine & Science in Sports & Exercise, 53(6), 1206–1216. https://doi.org/10.1249/MSS.0000000000002585
• Morán-Navarro, R., Pérez, C. E., Mora-Rodríguez, R., de la Cruz-Sánchez, E., González-Badillo, J. J., Sánchez-Medina, L., & Pallarés, J. G. (2017). Time course of recovery following resistance training leading or not to failure. European Journal of Applied Physiology, 117(12), 2387–2399. https://doi.org/10.1007/s00421-017-3725-7
• Nuzzo, J. L., Pinto, M. D., Nosaka, K., & Steele, J. (2024). Maximal number of repetitions at percentages of the one repetition maximum: A meta-regression and moderator analysis of sex, age, training status, and exercise. Sports Medicine, 54(2), 303–321. https://doi.org/10.1007/s40279-023-01937-7
• Refalo, M. C., Helms, E. R., Robinson, Z. P., Hamilton, D. L., & Fyfe, J. J. (2024). Similar muscle hypertrophy following eight weeks of resistance training to momentary muscular failure or with repetitions-in-reserve in resistance-trained individuals. Journal of Sports Sciences, 42(1), 85–101. https://doi.org/10.1080/02640414.2024.2321021
• Robinson, Z. P., Pelland, J. C., Remmert, J. F., Refalo, M. C., Jukic, I., Steele, J., & Zourdos, M. C. (2024). Exploring the dose–response relationship between estimated resistance training proximity to failure, strength gain, and muscle hypertrophy: A series of meta-regressions. Sports Medicine, 54(9), 2209–2231. https://doi.org/10.1007/s40279-024-02069-2
• Rogerson, D., Nolan, D., Korakakis, P. A., Immonen, V., Wolf, M., & Bell, L. (2024). Deloading practices in strength and physique sports: A cross-sectional survey. Sports Medicine - Open, 10(1), Article 26. https://doi.org/10.1186/s40798-024-00691-y
• Rossi, F. E., Schoenfeld, B. J., Ocetnik, S., Young, J., Vigotsky, A., Contreras, B., Krieger, J. W., Miller, M. G., & Cholewa, J. (2018). Strength, body composition, and functional outcomes in the squat versus leg press exercises. The Journal of Sports Medicine and Physical Fitness, 58(3). https://doi.org/10.23736/S0022-4707.16.06698-6
• Schoenfeld, B., Fisher, J., Grgic, J., Haun, C., Helms, E., Phillips, S., Steele, J., & Vigotsky, A. (2021). Resistance training recommendations to maximize muscle hypertrophy in an athletic population: Position stand of the IUSCA. International Journal of Strength and Conditioning, 1(1). https://doi.org/10.47206/ijsc.v1i1.81
• Schoenfeld, B. J., Grgic, J., Van Every, D. W., & Plotkin, D. L. (2021). Loading recommendations for muscle strength, hypertrophy, and local endurance: A re-examination of the repetition continuum. Sports, 9(2), Article 32. https://doi.org/10.3390/sports9020032
• Schoenfeld, B. J., Pope, Z. K., Benik, F. M., Hester, G. M., Sellers, J., Nooner, J. L., Schnaiter, J. A., Bond-Williams, K. E., Carter, A. S., Ross, C. L., Just, B. L., Henselmans, M., & Krieger, J. W. (2016). Longer interset rest periods enhance muscle strength and hypertrophy in resistance-trained men. Journal of Strength and Conditioning Research, 30(7), 1805–1812. https://doi.org/10.1519/JSC.0000000000001272
• Schwanbeck, S. R., Cornish, S. M., Barss, T., & Chilibeck, P. D. (2020). Effects of training with free weights versus machines on muscle mass, strength, free testosterone, and free cortisol levels. Journal of Strength and Conditioning Research, 34(7), 1851–1859. https://doi.org/10.1519/JSC.0000000000003349
• Senna, G., Willardson, J. M., de Salles, B. F., Scudese, E., Carneiro, F., Palma, A., & Simão, R. (2011). The effect of rest interval length on multi and single-joint exercise performance and perceived exertion. Journal of Strength and Conditioning Research, 25(11), 3157–3162. https://doi.org/10.1519/jsc.0b013e318212e23b
• Silbernagel, K. G., Thomeé, R., Eriksson, B. I., & Karlsson, J. (2007). Continued sports activity, using a pain-monitoring model, during rehabilitation in patients with Achilles tendinopathy: A randomized controlled study. The American Journal of Sports Medicine, 35(6), 897–906. https://doi.org/10.1177/0363546506298279
• Smith, B. E., Hendrick, P., Smith, T. O., Bateman, M., Moffatt, F., Rathleff, M. S., Selfe, J., & Logan, P. (2017). Should exercises be painful in the management of chronic musculoskeletal pain? A systematic review and meta-analysis. British Journal of Sports Medicine, 51(23), 1679–1687. https://doi.org/10.1136/bjsports-2016-097383
• Steele, J., Endres, A., Fisher, J., Gentil, P., & Giessing, J. (2017). Ability to predict repetitions to momentary failure is not perfectly accurate, though improves with resistance training experience. PeerJ, 5, Article e4105. https://doi.org/10.7717/peerj.4105
• Steele, J., Fisher, J. P., Giessing, J., Androulakis-Korakakis, P., Wolf, M., Kroeske, B., & Reuters, R. (2023). Long-term time-course of strength adaptation to minimal dose resistance training through retrospective longitudinal growth modeling. Research Quarterly for Exercise and Sport, 94(4), 913–930. https://doi.org/10.1080/02701367.2022.2070592
• Wood, T. M., Maddalozzo, G. F., & Harter, R. A. (2002). Accuracy of seven equations for predicting 1-RM performance of apparently healthy, sedentary older adults. Measurement in Physical Education and Exercise Science, 6(2), 67–94. https://doi.org/10.1207/S15327841MPEE0602_1