This article covers practical information you need to know, including how long you may need to rest between sets, whether different training goals require different rest times, and how to approach rest if you’re struggling to fit it in.

Let’s dig in. 

What do rest times do between sets?

With topics like these, it’s best to zoom out and consider what you’re looking to achieve with rest times, rather than treating them like an abstract rule you should simply follow. The best place to start is to look at what rest is actually doing for you between sets.

When you lift, you’re asking your muscles to produce force, and this comes with an energy cost. That cost will vary depending on your training factors such as volume, exercise selection, or how close you’re training to failure. For instance, a biceps curl (an isolated exercise) is mostly going to create local fatigue in the muscles doing the work. In contrast, a compound exercise like a squat will demand more from multiple muscle groups, increase your heart rate more, and create a higher overall metabolic demand.

So, what happens when we stop a round of lifting?

We start recovering, and that break gives your breathing, heart rate, and muscles time to settle so that you can push toward your next set. 

At its simplest, rest is about giving yourself enough time to recover so you can perform again.

How do rest times work with different set styles?

One quick point that can be confusing for less experienced trainees is the distinction between rest times between sets and set styles that intentionally manipulate rest during the active round.

For example, let’s say instead of doing straight sets, you’re doing a style of programming like myo reps. With myo reps, you take the first set pretty close to failure, rest for 15 seconds, do another set close to failure, rest again for 15 seconds, and then finish your final round of that set. That whole sequence is one round or one full set. After that set is complete, your rest time clock starts. 

There can be some differences in how programs define these setups, but a simple way you can look at it is this: If multiple exercises or mini-sets are part of that set style, your rest time starts once that whole sequence is finished.

With that clear, let’s look at the difference between rest times for muscle growth and strength, and how to think about rest time in relation to your goals and the amount of time you actually have to train.

Rest times for muscle growth

When it comes to adding muscle, there are still details and mechanisms we do not fully understand. People still debate the best way to think about muscle growth, from rep ranges to mechanical tension and everything in between. But for our purposes here, let’s say that muscle growth generally comes down to creating enough mechanical tension, training close enough to failure, and getting in enough high-quality volume over time.

And to be clear, that is a simplification of a very complex topic. But for this discussion, the question comes down to whether rest time helps or limits those training factors. 

Do shorter rest times limit muscle growth?

Let’s start with a study by Schoenfeld et al that helped shift the conversation about rest times, because hormone spikes and shorter rest periods had dominated the narrative up to that point. This study lasted eight weeks and included two groups of resistance-trained men. Both groups performed seven exercises for 3 sets of 8-12 reps to failure. They had one group rest for 1 minute, while the other rested for 3 minutes.

For muscle thickness, the longer-rest group showed a slightly greater increase in some muscles than the shorter-rest group.

While the results weren’t dramatic, they did provide some clarity on short-term hormone spikes and opened the door to other areas of focus. It also helped move the conversation toward volume, varied rest times, and a clearer view of what we might be trying to achieve with rest during a session.

A systematic review from Grgic et al came out shortly after looking at six studies that compared 60 seconds or less of rest against longer rest times. It found that the longer rest times offered a small advantage for muscle growth compared with rest times that were under 60 seconds.

This led to more debate. Did the difference come from rest time itself? Did the rest time provide time to complete more volume within a workout? After all, it’s logical to think that shorter rest times could lead to less recovery, which could reduce total volume or too many variables on proximity to failure.

Does rest time matter, or does training volume matter more?

A study from Longo et al leans into answering some of these questions. It took untrained athletes and had them perform a unilateral leg press two times a week for 10 weeks, using either one-minute or three-minute rest times. Half of the subjects performed three sets with three minutes of rest between sets with one leg. With their other leg, they performed as many sets as were required to match the volume load of their first leg while resting for one minute between sets. The other half performed three sets with one minute of rest between sets with one leg. With their other leg, they performed as many sets as were required to match the volume load of their first leg while resting for three minutes between sets.

The results showed that the higher-volume conditions produced more quad growth, regardless of their rest time. In this study, the growth seemed to track more closely with volume load than rest time itself. 

I wouldn’t treat this single study as a “case closed” answer on rest times or overstate its importance. But it still adds to the broader idea that rest time may matter partly because it affects how much quality work you can complete.

A more recent systematic review and Bayesian meta-analysis from Singer et al looked at nine studies and compared four ranges of rest times and their effects on hypertrophy.

Overall, muscle growth occurred across all the rest time categories. The results slightly favored resting longer than 60 seconds for arm and thigh measurements, but there was some overlap between categories. The intermediate range had the largest effect estimate, but, again, it’s important to note all the variables at play, from measurement techniques to how close sets were taken to failure.

Can we safely say from the Singer et al study that going too short on rest will hinder muscle growth? I’d be careful with the absolutism of the wording, but it leans toward rest times that are a little longer. Some also use this study to say that longer rest times are worse for muscle growth, and I wouldn’t take that away from these results either. Mostly, this gives us a range to consider for more examination.  

One of the last studies I’ll throw into the ring is a recent 2026 paper from Attarieh et al, which had 17 untrained young men train knee extensions twice per week for 10 weeks. One leg trained with 2 minutes of rest between sets, while the other trained with 20 seconds of rest. Importantly, the shorter-rest condition performed additional sets as needed to match the total number of repetitions completed in the longer-rest condition.

The results showed no significant differences in muscle growth or knee extension strength between those two conditions. It’s worth noting that the study was done in untrained young men, used a single-joint exercise, and could have had possible order effects because the exercise order was not alternated.

Key takeaways for rest times and muscle growth

Overall, for muscle growth, context matters here. Lots of variables can affect your rest needs from exercise selection to program design. A short rest after leg extensions is not going to be affected the same as a short rest after back squats. So, the details matter.

That said, I’d argue we can’t really say short rest times are bad for muscle growth. A better way to put it is that short rest times could be a problem if they reduce the amount of productive work and volume you can complete.

If I had to lean in one direction, I’d lean toward slightly longer rest times because they usually give you more opportunity to perform well. That said, it’s still hard to pin down exactly how long rest periods need to be, or where the benefits start to level off.

Rest times for strength 

Are rest times for strength the same as rest times for muscle growth?

Not exactly. Strength is more directly tied to performance, so rest times may matter more when the goal is to lift more weight and maintain a certain level of output across your sets. If your goal is to get stronger, then you need to support a kind of training, including a rest time system, that will allow for your best performance.

Longer rest times and strength performance

Before getting into strength research, I want to address a few questions that may have come up in the muscle growth section. Some of those studies also reported strength outcomes, and at first glance, the results may seem a little mixed. For example, you might have noticed in the Longo et al study that strength results were pretty similar across all conditions.

However, if you read the details of the study, you’ll see that participants trained at 80% of 1RM on a unilateral leg press and were young, untrained men and women. That setup is going to create a different recovery demand than heavy bilateral strength work like squats, deadlifts, or the bench press.

This is part of what makes exercise training research tricky. You might see a headline that may be true within the study design, but that does not necessarily mean it applies equally to every training context. So, I’d be careful using this study to say, “Shorter rest times aren’t even that bad for strength.” That may be true in some setups, especially when the exercise is more isolated. But as you move toward heavier compound lifts, short rest can interfere with your performance.

The Schoenfeld study gives a better look at that contrast because the researchers used resistance-trained men. And unlike Longo et al, this study included multiple exercises, including free-weight back squats and bench press. One group rested 1 minute between sets, while the other rested 3 minutes. In this setup, the longer-rest group showed greater strength gains.

Now, this doesn’t mean three minutes is the perfect rest time for strength. But it does lend support to the idea that maybe we should scale rest with the exercise’s demand and our goals.

Another study from Millender et al looked at acute performance and rest intervals for upper- and lower-body exercises in resistance-trained women. 14 women performed 4 sets to failure on the chest press and leg press using either 1 minute or 3 minutes of rest between sets.

Note: Total volume was not matched across conditions, and again this was an acute performance study, not a long-term strength-gains study. 

The women completed the same number of sets with the same load, but the longer rest allowed them to complete more total volume. The shorter-rest group also produced a higher fatigue index and saw more performance drops set to set. While this study doesn’t show that longer rest leads to greater strength gains, it does show that longer rest creates more opportunity for work within the session.

So that was acute, but what about the longer term for strength gains? Let’s be honest, when it comes to strength gains over time, we need more than a few sessions or a few weeks.

What happens with longer rest times over time?

A really important study for this conversation comes from Salles et al that looked at 36 trained men with at least 4 years of resistance training experience over 16 weeks. They trained 4 days per week and were split into groups using 1-, 3-, or 5-minute rest intervals between sets.

All groups followed the same general program, alternating between heavy and moderate sessions, and strength was tested in the bench press and leg press. By the end of 16 weeks, all groups gained strength, but there was a pattern that reveled itself regarding time.

For the bench press, differences between groups were not significant at 8 weeks. But by 16 weeks, the 5-minute rest group had pulled ahead of the 1-minute group. For the leg press, the 5-minute group had already pulled ahead by 8 weeks, and by 16 weeks, both longer-rest groups had gained more strength than the 1-minute group. Meaning? Over the long-term it’s possible longer rest time may be more important.

Overall, there’s a good body of evidence showing neutral to positive outcomes for strength with longer rest periods (here, here, and here). Some studies show clear benefits, while others show similar strength gains between rest intervals. But I’d say the most important point is that when we move from acute performance to long-term strength gains, we get a better sense of what rest times may be doing in real life programs.

That does not mean everyone needs to rest 5 minutes between every set of every exercise. But if you’re pushing near-rep-max heavy compound lifts, you might want to give yourself more than a few minutes. If you’re going for a hard 3-rep set on deadlifts, 3 minutes might not be enough. You might need 5, 6, or even 8 minutes before your next set is actually productive. 

Practical tips for understanding rest between sets and time saving tips

I hope I’ve driven home the points about why you may or may not need more rest time between lifts, now let’s help you apply this to your actual training sessions. 

What to think about before your next set

Whether you use fixed rest times or self-selected rest times can really come down to preference, your training goals, and your ability to tell when you’re actually ready for your next set.

In practice, many people probably use some form of self-selected rest. You finish a set, wait until you feel ready enough to go again, and then start your next set. That can work perfectly well, especially if you’re honest with yourself and your performance is moving in the right direction. However, fixed rest times can be useful if you tend to rush your sets and/or your progress has stalled.

So, instead of treating rest times rigidly, I think it’s useful to learn some physical cues so you can use a mix of that knowledge with tracking your data over time (and get a real feel for what’s working for you). 

For example, let’s say you reach your rest time of 3 minutes and you’re still feeling shaky. If that’s the case, take that physical cue to not rush the set just because your rest time is up. That might sound obvious, but I’ve worked with enough people to know that some lifters will blindly follow rules without understanding their intent. The reverse is also true. If you feel ready to go sooner, especially on smaller exercises or lighter sets, you may not need to wait around just because a timer says so.

So, before jumping into your next set, you can use this quick checklist to decide whether you’re ready to lift again.

How to organize programming around rest

Rest times are probably not the first thing you think of when you’re designing your program, but they can become an important consideration if you’re short on total training time.

When you plan your training, you obviously want to think about your goals. But you also have to think about your daily logistics, such as driving to the gym, getting dressed, and total training time which should include your rest times. All of that adds up, and it’s not unusual for rest times to get sacrificed in the name of saving time.

With that in mind, let’s look at a few ways to handle this.

1. Consider your goals

Based on what we’ve covered so far, if muscle growth is your goal, you likely have a little more room to play with rest times. If strength is your goal, you’d probably be wise to make more deliberate time for rest between sets.

A good place to start is to ask yourself whether you’re training for strength, muscle growth, or some mix of both. From there, you can think about how much priority your rest times need.

2. Determine your training schedule 

Take an honest look at your schedule and what you can realistically give to your training sessions. Throwing together a packed program isn’t going to help much if you constantly end up cutting exercises or rushing rest times.

For strength, it’s better to give your bigger lifts enough time than to force everything into crowded sessions. That might mean training two really good days per week with longer rest times and a stronger focus on performance. Also, keep in mind that strength sessions may require fewer total exercises, as the priority should go to your main lifts.

For muscle growth, you have a little more room to play with organization, but you may also need more total volume across the week. 

If you’re really struggling with time, keep in mind that you can also play with your training structure and the number of days you train. Maybe it makes more sense for you to train more days per week with fewer exercises at a time. For someone else, it may make more sense to have 2 longer sessions for their main work and 2 shorter sessions for accessory work. 

The point is, don’t feel locked. Rest times are one variable, as is your exercise selection and the number of sessions you have per week. 

3. Consider supersets where it makes sense

Another thing to consider if you’re short on time is supersets. If you find yourself cutting out exercises or cutting down on rest time that you think would be better left in, supersets can be a useful option.

If I were writing an entire article on supersets, I would need to discuss a long list of styles and setups. But for this article, I’d like to focus on pairing exercises that don’t (meaningfully) compete with each other to save time while minimizing fatigue. 

For example, let’s say your training sessions are full body, and within one session you have a squat, a deadlift, a press, a row, a biceps curl, a triceps extension, and a core movement. If you run through all of those exercises with long rests between every set, the session could get pretty long for muscle growth and even longer for strength. But if you cut rest too aggressively on the big lifts, you may reduce the quality of the sets you care about most.

A possible setup and solution to that problem could look something like this:

Example of saving lifting time with supersets

It’s not perfect, but as you can see in the MacroFactor Workouts screenshot above, this kind of setup can save time and allow you to get more work done in a session. I’d still watch your progress over the long term to make sure you’re moving in the direction you want. But if you’re short on time, I’d argue this is usually better than cutting the work altogether.

How to customize rest times in MacroFactor Workouts

MacroFactor Workouts include default rest timers based on the type of movement you’re performing. For example, compound lower exercises default to 3:00, compound upper exercises default to 2:00, and isolation exercises default to 1:30.

You can change those defaults if you need more (or less) time, and you can also set rest times for specific exercises. For example, you might give yourself more time for deadlifts while using shorter rest times for some accessory work.

To change your default rest timers, go to More > Feature Settings > Workouts > Rest Timer > Timer Duration.

You can read more about the rest timer here and here.  

Take home

Rest times do matter, but they do not need to be treated as rigid rules. The right amount of rest depends on factors such as your exercises, training style, and how you handle overall performance during a session. 

For muscle growth, there is usually more room for a range of rest times. Many sets can work well with about 1-2 minutes of rest, especially isolation exercises or smaller muscle groups. Just make sure rest times aren’t limiting volume. 

For strength, longer rest times should be a priority. Again, performance and gains over time are the goal, so you want enough rest to perform each set with the force and technique you need. For heavy compounds, start at 3 minutes but be open to 5 minutes or even longer when needed. 

The main idea is to avoid letting rest times become a limiter for the work you’re trying to do. Be it rushing your sets or setting up unrealistic programming, just make sure your setup works for your training goals. 

The post Do Rest Times Matter for Performance or Muscle Growth? appeared first on MacroFactor.

New in MacroFactor: Food Logging AI Upgrade

MacroFactor’s food logging AI got a major upgrade, with stronger food identification and greatly improved instruction-following abilities.

You can now upload multiple photos, combine photos with text, and give the AI more specific instructions. Add a meal photo, individual meal components, a restaurant menu, or extra context like portions, exclusions, and anything else you want the AI to consider.

The AI tab has also been streamlined into two modes: Snap and Describe. Snap is for quick in-the-moment photo logging, while Describe gives you more flexibility with one or more photos and/or text.

And for something a little different, we added MacroFactor’s first mini-game. Celebrate your goals, head into space, clear the meteor storm, and see how long you can stay in orbit. The game will launch when you reach your goal, but you can also access it anytime by tapping the check-in button 10 times and then going to More >  Mini Games.  🚀

New in Workouts: Live Activity and Workout Change Log 

The “live activities” feature in MacroFactor Workouts is here! Now you can track your lifts, time your rest, and quickly access MacroFactor Workouts from your lock screen. 

You can also inspect changes made to the workout plan during an active session prior to committing those changes to your program or saved workout.

New Article

How to Warm-Up For Resistance Training

Warming up before training should be a pretty simple topic. But, as with a lot of things involving resistance training, it can get complicated quickly. A big reason for that is the amount of pressure placed on the expected benefits of warming up. There is often an assumption that warm-ups will prevent injury or produce exceptional gains in performance. 

But does warming up actually do those things? Where is the line between sensible preparation and overpromising benefits? And how should you approach warm-up sets if you are newer to resistance training or have never really thought about them before?  

The goal of this article is to help you understand what a warm-up is meant to do and how to apply it in a practical way.

How to Warm-Up For Resistance Training

Read the article

New Case Study

Nic’s Long Game With A Little Help From MacroFactor

At MacroFactor, we know we are not always the biggest part of every transformation, but we are proud to play any role. For Nic, we came in during the final stretch of a lot of hard work that was already underway. We are grateful to have played that part, and we hope sharing a bit of Nic’s journey helps someone else take their next step.

At 32, he feels better than he did at 22. Learn how Nic did it, his favorite MacroFactor features, and the advice he’d give to others.

Nic’s Long Game With A Little Help From MacroFactor

Read the article

In Case You Missed It

We share a lot of informative content on our Instagram and in our groups on Facebook and Reddit throughout the month. 

Here are some of our favorites from the past few weeks, in case you missed them. 

What We’re Working On

For MacroFactor, we’re continuing to work on further improvements to the AI food-logging workflows. Now, we’re focused on using MacroFactor’s smart AI capabilities to make it easier to create custom foods and to improve the label scanning feature. 

For MacroFactor Workouts, we’re continuing to make updates to the smart progression logic in the app, working on program updates (such as support for a PPL split option in program generation), and creating a super high-quality Apple Watch experience. 

To learn more about what we’re working on, check our public roadmap​ to see our plans for new features and improvements. You can also submit features for consideration and vote on the upcoming features that are the highest priority to you. 

The post Food Logging Upgrade, Live Activity, and How to Use Warm-Ups in Your Lifting Programs appeared first on MacroFactor.

But does warming up actually do those things? Where is the line between sensible preparation and overpromising benefits? And how should you approach warm-up sets if you are newer to resistance training or have never really thought about them before?

The goal of this article is to help you understand what a warm-up is meant to do and how to apply it in a practical way.

Let’s dive in.

What is warming up?

You’ll note the title of this article is warming up for resistance training, which means this isn’t about aerobic, cardio, endurance, or sport-specific warm-ups that can have different factors and reasoning. For this article, we are going to focus on how these changes relate to performance and training readiness for resistance training, such as strength or hypertrophy.

The biggest factor to keep in mind while absorbing this information is that warming up should be, first and foremost, about getting you warmer. When you go from sitting or very low-intensity activity to an exercising state, a range of physical processes begin to take place that help prepare you for movement, from increases in muscle temperature to improved neuromuscular readiness.  

As for how to warm up, the conversation and research can range from walking on a treadmill to more explosive lifts. I’m not going to get too deep into every aspect of warm-ups (spoiler: I’m not really going to cover foam rolling). But this article should cover the general ideas well enough that you can gauge for yourself what might be most useful, which is always the goal here at MacroFactor.

Let’s look at the different types of warm-ups:

General: Any light aerobic movement that increases heart rate and raises body temperature. The main goal is to increase muscle temperature and prepare the body for general neuromuscular readiness.

Stretching: This includes static or dynamic stretching or movement work and can also include antagonist stretching. 

Tissue prep: This typically includes things like foam rolling or other forms of manual soft tissue work. 

Specific warm-up: This is where you perform the exercise or work muscle groups you’re planning to train, but at a lighter load before your working sets. This category can also include heavier or more explosive preparation strategies, such as post-activation performance enhancement approaches.

Now that we have a general idea of some key terms, let’s start by examining one of the main reasons people think about warm-ups: injury prevention.

Injury and rates of injury for resistance training 

Regarding strength and hypertrophy styles of resistance training, I want to lead with the fact that we do not have a large body of direct evidence of warm-ups in relation to injuries. And it sort of makes sense when you consider the realities of research funding and priorities. It is often more practical to study high-profile injuries in sports, like ACL tears in pro football players, than what is happening to a 30-year-old lifter in the gym doing an upper/lower split.

That said, we do have some research to review.

A helpful starting point is to examine what tends to get hurt during resistance training. A 2025 narrative review by Kawa et al identified the shoulder, lower back, knee, and hand or wrist as the most commonly reported injury locations in resistance training settings.

To be clear, this review does not establish causation. However, it is useful for helping us understand general patterns. Across the literature, most reported injuries we are seeing in resistance training tend to be mild strains, sprains, or tendon-related issues that resolve with rest or training modification.

While the Kawa et al paper focused on injury identification, a systematic review by Tung et al looked at injury incidence across studies. They reported a range of roughly 2.4 to 3.3 injuries per 1,000 hours of training in weightlifting and 1.0 to 4.4 injuries per 1,000 hours of training in powerlifting. It is also worth noting that some of the injury data in weightlifting and powerlifting includes competitions.

Comparatively, in sports like soccer (football), you see a much steeper increase during competition. Injury rates are typically around 3.7 injuries per 1,000 hours during training, but increase to about 36 injuries per 1,000 hours during matches.

In short, resistance training tends to have a relatively low injury risk compared to many other sports and activities. That doesn’t mean injury prevention isn’t important, but when you consider research funding priorities, it’s easier to understand why direct evidence in this area is a little thin.

So, now that you know how often and where injuries tend to occur, the next step is to examine whether warming up helps reduce injury risk.

Do warm-ups help prevent injury?

Again, with a caveat that resistance training injury prevention is not a heavily studied topic in and of itself, let’s dive into what we do have. 

One of the more commonly cited papers on this topic is a 2014 systematic review and meta-analysis that examined exercise interventions as a whole (not warm-ups specifically). They found that strength training programs in general were associated with reduced injury risk, while static stretching alone showed little preventive effect.

If we look at static stretching more specifically for warm-ups, one argument has been that improving range of motion over time could be helpful, especially if limited mobility changes how a movement is performed or increases stress on a joint or muscle. However, it is probably best to separate that discussion into short-term and long-term effects. Even then, you also have to consider alternatives, such as using strength training itself to improve mobility or movement tolerance.

For example, a 2025 systematic review, meta-analysis, and multivariate meta-regression found that both acute and chronic stretching can reduce stiffness, but only chronic stretching produced more meaningful stretch tolerance and sustained range of motion. And to be clear, much of the stretching examined in this review involved repeated sessions performed over weeks to months, not just brief stretching performed as just part of a pre-training warm-up.

With dynamic movements, you are usually working through a range of motion under some level of force. And in general, if there is going to be a small hiccup in coordination or comfort, one could argue it’s better to experience that with a lighter load before progressing to heavier working sets. And while there is some research to consider, there are limitations when it comes to direct application to resistance training. Much of the work in this area focuses on sport performance or requires more consistent, repeated exposure over time.

For more specific warm-ups or adding any resistance training or re-warm ups, the research usually dives into more sports like golf, baseball, or basketball. We can certainly learn from these studies, but in general, it’s worth being cautious about making direct translations to resistance training.

Injury take-home:

Resistance training tends to fall on the lower end of injury occurrence and severity compared to other sport activities, and the benefits of participating in resistance training clearly outweigh avoiding it altogether.

There are not many studies that directly compare different warm-up types or systems specifically for injury prevention in resistance training. From a mechanistic standpoint, you can make logical arguments for why certain warm-up approaches might help place you in a better position to avoid injury, but the current evidence does not allow us to draw clear cause-and-effect conclusions on any specific type of warm-up at this time. 

What about performance or hypertrophy outcome and warm-ups?

It would seem that there isn’t a large body of evidence examining warm-ups and injury risk, at least not research specifically tailored to resistance training. Much of the discussion relies on inference and mechanistic reasoning. So what about performance? Are there clearer lines to draw, or more obvious benefits or drawbacks?

Let’s take a brief look at a few different types of warm-ups and how they may affect performance.

Stretching 

Stretching also comes up frequently in discussions about performance, not just injury risk. Over the last decade, there has been ongoing debate about whether static stretching is detrimental to performance. In general, the duration of the stretch appears to be the biggest factor.

If you’re heading into a situation that requires greater force production, such as an explosive lift, you likely want to avoid longer static stretch holds. There is some flexibility in the exact timing, but the main concern is prolonged stretching that may temporarily reduce force production. Shorter stretches that are part of a general warm-up or combined with dynamic movement do not appear to have the same effect.

Dynamic stretching has received more favorable outcomes in recent years and has shown some potential for improving performance. However, the improvements tend to be small, and there are still relatively few long-term studies that focus directly on resistance training.

One study from Benine et al, examined both dynamic and static stretching performed before resistance training over an 8-week period. In this study, the static stretching condition totaled roughly 80 seconds, while the dynamic stretching condition involved a series of limb movements taken through a full range of motion. And the third group performed no stretching.

All groups completed the same training program, and all groups showed similar improvements in strength and muscle thickness, with no meaningful differences between them.

Antagonist stretching is another consideration. The idea is that stretching the opposing muscle group could potentially improve performance in the working muscles. For example, stretching the chest and anterior shoulder muscles before performing a seated row. The research for this topic is in its early days, but there have been some small positive results (here, here, and here). 

One recent 2025 study examined whether antagonist static stretching for 40, 80, or 120 seconds affected strength and power performance compared with no stretching (the control). They tested this in young, recreationally active men, who performed different durations of stretching before doing their lifts that included isometric contractions and slow and fast isokinetic movements.

Overall, the antagonist stretching did not meaningfully change performance outcomes. So, this isn’t to say there couldn’t be something with antagonist stretching, just that it’s likely pretty small and the circumstance more specific. 

Take-home regarding stretching and performance 

Static stretching could potentially have a negative effect on performance depending on how close it occurs to your training session and how long the stretch is held. 

That said, some dynamic movements can take you through ranges of motion in a more explosive manner. Therefore, it is usually worth starting with a small amount of general movement, even something as simple as a short walk, before moving into more intensive dynamic stretching. I think it’s sensible to warm up by practicing the movement you are about to perform, and gradually expose your body to the load and demands of that task. This keeps the focus on preparation and capacity, which is ultimately what matters.

Lastly, research on antagonist stretching is mixed. Short-duration antagonist stretching is unlikely to harm performance, but it also may not provide a meaningful benefit.

Specific versus general warm-ups

A general warm-up can vary, ranging from walking on a treadmill to cycling or other light aerobic activity. A useful way to think about a general warm-up is that you’re not trying to practice the exact movement or muscle groups before the exercise. Instead, the goal is to raise body temperature, increase readiness, and improve blood flow.

Specific warm-ups can vary in research definitions as well, but they usually involve preparing the same muscle groups that are about to be used in the exercise. In resistance training, this often means performing warm-up sets and gradually increasing the load until you reach your working sets. In some cases, it may also involve a related movement, such as performing push-ups before a bench press.

Starting with the general warm-up side of things, a recent 2025 systematic review and meta-analysis with meta-regression examined 33 studies to determine whether increases in muscle temperature improve maximal force, rate of force development, and power output. The researchers found improvements in power and rate of force development, but little to no meaningful change in maximal force.

In more plain terms, this means that in a warmer state, your muscles might contract a little faster and produce power more quickly, but it is probably not going to make a big difference in your peak strength.

Do we see a difference when we jump into more specific warm-ups versus general?

A 2021 narrative review examined 11 studies on different warm-up strategies and their effects on muscular performance. Overall, the findings were mixed. Some studies showed improvements in strength or performance, while others showed little to no effect. However, most of the studies reported either positive or neutral outcomes, with very few showing negative effects. That said, individual study design and training context still matter when interpreting these results.

Looking at some more recent research on this topic, one 2024 study by Viveiros et al examined 15 men in their twenties who had at least 6 months of resistance training experience. Each participant completed three separate training conditions, with each condition using a single warm-up set at a different load percentage.

They took each set of exercise to failure (3 total) and rested between sets for 2 minutes. The exercises included the bench press, an incline leg press, and a wide-grip lat pulldown. In this study, performance showed a slight advantage for the higher-load warm-up condition. 

A recent 2025 study from Enes et al looked at 29 trained men and women in their twenties using a randomized crossover design (meaning each participant completed every condition). Participants completed three different warm-up strategies: 1 set of 3 to 4 repetitions at 75% of their 10RM load, 2 sets of 3 to 4 repetitions at 55% and 75% of their 10RM load, and a control condition in which no specific warm-up was performed. The performance was tested at several time intervals after the warm-up.

The researchers found that heavier or more explosive warm-ups sometimes improved performance, but not consistently. Fatigue and recovery likely played a role in that variation, which highlights the need to balance readiness with fatigue. In other words, the goal of a warm-up is to help you perform your main lift, not to turn the warm-up into the workout itself.

There are also some practical points to take away from this study when thinking about hypertrophy. In this case, the different warm-up strategies did not meaningfully change how many repetitions participants completed or the total amount of work performed during the working sets. Since hypertrophy is strongly tied to total work and sets performed close to failure, this could suggest that the warm-up itself was not contributing much to muscle growth. Instead, the warm-up is more likely preparing the lifter to perform the work that actually drives hypertrophy.

What about postactivation performance enhancement, or PAPE?

This also gives us a chance to dig a little more into the concept of postactivation performance enhancement, or PAPE. Just as there is growing interest in approaches like antagonist stretching, there is also increasing attention on strategies that use heavier or more explosive movements before a main lift. In simple terms, PAPE refers to performing a heavier or more explosive movement before your working sets of the same muscle group, such as performing a heavy single bench press before your normal bench press sets.

Quite a few papers have examined this idea (here, here, and here), and most of them are really looking at heavier, more specific warm-ups. As we are seeing, this approach can sometimes improve short-term performance. However, the size of the effect raises a fair question about whether it meaningfully stands out from more traditional warm-up sets. We also still need more direct research in resistance training settings.

A new 2026 study used a randomized crossover design to test half-squats performed for 3 sets to failure, comparing a traditional warm-up with a postactivation performance enhancement (PAPE) condition that included a heavy conditioning activity followed by a longer rest period. This was a small study of 9 young, college-aged men who were trained but not advanced lifters. The researchers found that the PAPE condition led to slightly better performance and higher total training volume across the three sets compared with the control condition. And again, rest duration may have played a role here for modest results. 

Another recent study from Souza et al had 14 men in their twenties perform parallel back squats. They had to at least be able to squat their body mass and have an average of about 9 years of resistance training experience. The researchers compared lower- and higher-load conditioning activities across multiple sets of back squats, using a Smith machine to standardize the movement.

In this study, heavier or lighter conditioning activities did not meaningfully improve performance across multiple sets. However, a small benefit was observed in the first set, suggesting that any advantage from the warm-up condition may be short-lived.

This is a good example of the back-and-forth pattern we keep seeing on this topic. It’s not to say that there isn’t something there, just that there is still a lot of clarity to be gained.

Take-home on warm-ups for performance

Overall, it is still difficult to pinpoint exactly where performance improvements from warm-ups are coming from, but there is enough evidence to suggest that some type of specific warm-up can lead to a neutral or modest positive effect on performance. That said, building up with heavier warm-up sets or explosive movements may provide a small edge for some people, but it can also introduce fatigue. While there could be performance benefits, there is also a risk of reducing performance if the warm-up becomes too demanding and starts to resemble a working set.

In the next section, we will move into some practical tips and walk through how to perform more traditional warm-up sets if you are new to them.

Practical framework for implementing warm-up sets

Now that you have a better understanding of different types of warm-ups, let’s shift to practical advice. The goal here is to help you find the sweet spot between getting warm enough to support performance without doing so much that you feel fatigued before your working sets begin.

Decide whether you need general or specific warm-ups

Depending on how your training days are structured, you may not need much more than a small amount of general movement to get warm, such as a few minutes on a treadmill or bike before starting your session. If your training session does not include heavier compound lifts, you can usually keep warm-ups simple and avoid overthinking load progression.

The heavier the load, the more specific warm-ups start to matter. And in general, it usually makes the most sense to focus more on specific warm-ups for your biggest lifts. That said, some people prefer to run through a quick round of each exercise at a reduced load just to get the feel of the movement before training. There is nothing wrong with including a specific warm-up for any exercise, but giving them more attention is usually most useful for the lifts that place the highest demands on your body.

Learn how to progress loads

Let’s assume you’ve decided to include warm-up sets before your working lifts. One of the easiest things to remember is that warm-up sets should gradually increase in weight while the reps decrease. 

For example, if your working set is 275lb for 5 reps, a progression might look like:

That’s it. And you can use this progression as a general model for warm-ups, and make adjustments for different types of lifts. 

For example:

• Compound lifts may use 3 warm-up sets.
• Isolation exercises may use fewer sets.
• Core or lighter movements may skip warm-ups entirely.

This allows you to match the warm-up with the demands of the exercise. 

In the MacroFactor Workouts app, this process can be handled automatically. If you enable warm-up automation or use a preset scheme, the app will calculate warm-up sets based on your target weight using the same progression logic. 

And remember, you only to warm-up enough to feel prepared. If you reach a 60% warm-up set and already feel ready to move into your main lift, there is no requirement to continue to an 80% set. You can simply transition into your working sets.

There should always be a bit of self-regulation involved in warming up. Some days you may need several gradual steps, while on other days you might feel ready after just an empty bar and one heavier percentage jump. These examples are simply meant to serve as examples for people who are not used to structured warm-ups or who are curious about trying them for the first time.

Pay attention to joint comfort and general readiness

If you are returning to training after a period of time off or are in an older age bracket, warm-ups can be helpful for general joint comfort and minor aches. This is not about preventing injury, and the topic can get nuanced, but at a basic level it is about helping your joints feel ready to move.

You can do this with a general warm-up, a few lighter warm-up sets, or by gradually moving a joint through its working ranges before heavier training. Sometimes that alone can make the session feel more comfortable. For example, if you are planning to do overhead pressing, you might start with a set or two of light external rotations with a cable machine. Nothing heavy, just higher-repetition work to move the shoulder through its working ranges and get some blood flowing. After that, you would move into your normal pressing work.

If you are not sure which movements to use, tools like the exercise library in the MacroFactor Workouts app can help identify exercises that target the joint or muscle group you are about to train. That can be especially useful when you are easing back into training or trying to find movements that feel comfortable.

Closing 

There is no right way to warm up, and we are still lacking a lot of specific evidence for warm-ups in resistance training. That might be surprising, given how common warm-ups are in the gym. That said, we do have enough information to form some practical, sensible guidelines.

Here are a few points to keep in mind:

• Keep warm-ups simple. You don’t need complicated or long warm-up sessions. A good warm-up should only take a few minutes early in your workout. Then, prioritize based on whether you are performing compound or accessory lifts.
• Strength performance may benefit more than hypertrophy. We just don’t see strong evidence that warm-ups meaningfully affect muscle growth on their own.
• Warm-up needs may vary from day to day. Adjust the number of sets or intensity based on how prepared you feel.
• Stretching type matters, but the effects are pretty small. Dynamic or antagonist stretching tends to look slightly more favorable than longer-duration static stretching, but the differences are modest and results are mixed at best.
• Specific warm-up sets might help performance, especially for larger compound lifts. The heavier the planned load, the more useful gradual load increases tend to be.
• Progress gradually as you warm up. Loads usually increase while repetitions decrease as you get closer to your working sets.
• Find the balance between readiness and fatigue. Warm-ups should prepare you for the lift, not become a workout in their own right.
• More targeted, joint-specific warm-ups could help improve joint comfort. Moving a joint through its working range with light resistance might help reduce stiffness and make training feel a little better.

The post How to Warm-Up for Resistance Training appeared first on MacroFactor.

For Nic, let’s go back to 2023, when he first started a meaningful transformation.

Where Nic’s transformation began

While working as a Parts Employee at a dealership in Kansas, Nic, then around 30 years old, found himself weighing around 250lb and feeling pretty unhappy about it.

“My diet at the time before I lost weight was horrible,” he said. “I never felt full and always ate way too much of whatever was put in front of me. Cookies, cereal, pizza, all sorts of sweets and ice cream were eaten on a daily basis.”

“I realized I wasn’t getting any younger, and if I didn’t start making changes now, it was only going to get harder later in life,” he continued. “I needed to start prioritizing my health. I have nieces and a nephew who can run circles around me, and I wanted to be able to spend more of my life with them, playing, keeping up with their sports, and maybe being someone they could look up to.”

That kind of pattern can be hard to break. We have written a good deal on this site about satiety and practical ways to feel more satisfied with what you are eating, but sometimes habits take hold, and diving into what tastes good can become routine. And the hardest part can be breaking that habit and shifting into a new direction.

But in late 2023, that is exactly what Nic did. He cut back on most treats, started walking more, and began exercising daily. What started as a small shift quickly gained momentum.

“When I cut most of that out and started walking and exercising daily, my weight came off quickly,” he said. “It was addictive.”

From pen and paper to MacroFactor 

Nic did not start with personal trainers or expensive custom plans. He grabbed a pen and paper and started tracking his Calories the old-fashioned way.

“I counted Calories with pen and paper for a few months before switching to another calorie tracking app,” he said. “But I wasn’t always the best at tracking everything or being honest about what I was eating.”

Even though his tracking wasn’t always perfect, Nic was making progress. Before MacroFactor entered the picture, he had already lost about 50lb through most of 2024. But eventually, progress slowed, and he started looking for a little more outside help and inspiration to reach that next level. 

That push came while watching Jeff Nippard’s videos. Nic was not trying to become a bodybuilder, but the idea of getting stronger and healthier caught his attention.

“I had been watching Jeff’s training videos and seen just how easy the app made tracking,” he said. “At the time I had quit tracking entirely, so I figured this was a great time to restart fresh. I wanted to be strong, look good, and feel better about myself physically and mentally.”

Success and the features that made it work

Once Nic started using MacroFactor, progress began moving again, making it easier to stay consistent.

“I started using the MacroFactor app, and after the initial learning curve, I finally felt like I was seeing numbers that made sense to me,” he said. “By July 2025, I had lost 25lb more, my expenditure had climbed, my lifts had progressed, and what I was seeing in the mirror made all the hard work worth it.”

For Nic, keeping food simple was a big part of making everything work. He found staple meals he could rely on, tracked consistently, and used MacroFactor’s recipe builder to make the process easier.

“I track everything with MacroFactor,” he said. “The recipe builder made it so easy to add the staple foods I eat to the app, and now everything I eat is basically just a few simple clicks and boom, tracked. The barcode scanner makes adding new foods quick and easy, and the AI is very good at getting me close enough to not feel bad about my dinner dates or snacks at work.”

By the end of 2025, Nic had not only lost about 75lb, but he had also moved through a gaining phase and a smaller cut as he continued working on his body composition.

That part is worth highlighting because fat loss is not an uninterrupted straight line. For many people, progress is easier to sustain when it comes in phases. You might lose fat for a while, spend time maintaining, shift into a bulking phase, and then cut again later. The process can take a lot of patience and investment, and Nic saw the bigger picture and learned to tackle it in pieces.

“I give the MacroFactor apps and team full credit for helping me understand my body and pushing myself to new limits,”he said. “The support from the communities they have built and the involvement the team has with its users is incredible. I can’t thank you all enough.”

Parting advice from Nic

We always ask users for advice for others working toward similar goals, and Nic’s advice is simple: keep going.

“I’m 32 years old, and I feel better now than I did at 22, physically and mentally,” he said. “My confidence is at an all-time high. I’m stronger, and I no longer feel embarrassed being outside with my shirt off. I have fully accepted the new lifestyle I built for myself, and I love every moment of it. I couldn’t be happier with how everything is coming together, and the best part is I feel like I’m just getting started.”

“Just give it your all, and don’t leave anything on the table. If you end every day knowing you gave your best, you’ll realize that everything you thought was hard or impossible is just a little effort away from being achieved. And once you start conquering these challenges, you can use that new power you’ve gained in other aspects of your life. Nothing is out of reach once you start. You’re always one step closer every time you try. Everyone should try to be their own superhero.”

If you’d like to follow more of Nic’s journey, you can find him on Instagram here.

Try MacroFactor for yourself with a 7-day free trial

Confidently control your nutrition with MacroFactor, a science-backed diet coach and macro tracker app that empowers you with the tools you need to reach your goals without rigidity.

You can try it free for 7 days on the App Store or Google Play, or learn more here.

Do you have a MacroFactor success story?

We love to learn about, celebrate, and share MacroFactor users’ success. If you’re interested in your story being featured as a MacroFactor case study, you can learn more and submit your story here.

The post Nic’s Long Game, With a Little Help From MacroFactor appeared first on MacroFactor.

• iOS Live Activity: Track your lifts, time your rest, and quickly access MacroFactor Workouts from your lock screen.
• Workout Change Log: You can now inspect changes made to the workout plan during an active session prior to committing those changes to your program or saved workout.

The post Versions 1.1.9 – 1.2.0 Release Notes appeared first on MacroFactor.

• MacroFactor’s food logging AI got a major upgrade! You can expect even stronger food identification, and greatly improved instruction following.
• Upload your meal, individual meal components, or even the restaurant menu. Optionally guide photo analysis—e.g., highlight specifics, alter portions, exclude foods, or define logic.
• The AI tab was streamlined into two modes: Snap and Describe. Snap is for quick in-the-moment photo logging. In Describe, you can use one or more photos and/or text.
• Celebrate your goals with MacroFactor’s first mini-game. Go into space, shoot, score points, and stay in orbit as long as you can.

The post Version 5.5.7 Release Notes appeared first on MacroFactor.

New in MacroFactor

This month, we released updates to our recipe creation workflow. It’s now easier than ever to make a recipe in MacroFactor. 

You can snap a photo of a written recipe in a cookbook, and MacroFactor will scan the ingredients and text and pre-fill the recipe details for you. You can then review the results and make any changes or corrections.

Check out our step-by-step instructions for this new workflow here.

As a reminder, you can also import recipes from links!

Whether you’re following a recipe from your favorite food blog, cooking an old favorite from a family cookbook, or creating your own recipe on the spot, MacroFactor’s recipe creation tools have you covered. 

New in Workouts

We also shipped several new updates in MacroFactor Workouts this month. 

More analytics in Levels

First up, you can now see contributing lifts for Levels. In the app, go to Levels, then click on a muscle group to see what lifts are contributing to the average weekly set count. 

Warm-up improvements

Warm-ups are also easier to manage now, with a new option to add warm-ups from a custom scheme and an option to show or hide completed warm-up sets during your workout.  

Skip exercises

Does your program call for an exercise to be done on one week but not the other? You can now skip an exercise for any day in the cycle. 

Import more Jeff Nippard programs

With this update, you can now upload all of the Jeff Nippard programs that are still offered for sale on his website. 

And more! 

• Program generation improvements

• New program create/edit customization options

• Search improvements

• Exercise database updates

• Added daily and weekly program editor muscle group set counts

• Improved exercise description layout and added linked article content

• New setting that lets you choose reps-first instead of weight-first for set-to-set adjustments

• Various settings UI intuition fixes

• Bug fixes and performance improvements

New App Insight 

MacroFactor vs Cal AI: Which App Wins in 2026?

​We compared MacroFactor to Cal AI​, a newer app focused on AI-driven food logging. While the approach emphasizes simplicity, how well does it hold up for tracking Calories and macros?

This article provides a fair and clear side-by-side comparison of MacroFactor and Cal AI, so you can decide which app makes the most sense for you right now.

MacroFactor vs Cal AI: Which App Wins in 2026?

Read the article

New Article

How to Use Reps in Reserve for Lifting

Effective training programs rely on systems and a shared language of measurement. Without clear standards, you’re essentially guessing at your progress.

Reps in Reserve (RIR) is a vital part of that vocabulary. It’s a system designed to help you better understand the intensity of your training by measuring how close a set was to failure. By mastering this tool, you can move past guesswork and start regulating your training with more precision.

​In this article​, we cover the origin of Reps in Reserve (RIR) and how the system could help you gauge effort, rather than choosing weights at random.

How to Use Reps in Reserve for Lifting

Read the article

In Case You Missed It

We share a lot of informative content on our Instagram and in our groups on Facebook and Reddit throughout the month. 

Here are some of our favorites from the past few weeks, in case you missed them. 

What We’re Working On

The “live activities” feature in MacroFactor Workouts is now in ​beta testing​! Our team is also working on big updates to the strength estimation and fatigue logic in the app, which will smooth out a lot of the irregularities in assigned weight and reps in your workouts.

We’re also making good progress on program updates (i.e., adding a Push Pull Legs split option in program generation), and the Apple Watch experience for MacroFactor Workouts.

To learn more about what we’re working on, check our public roadmap​ to see our plans for new features and improvements. You can also submit features for consideration and vote on the upcoming features that are the highest priority to you. 

The post Recipe creation updates, Workouts improvements, and MacroFactor vs Cal AI appeared first on MacroFactor.

• Program generation improvements

• New program create/edit customization options

• More programs supported in program importer

• Search improvements

• New detail page for Levels

• Exercise database updates

• Various settings UI intuition fixes

• Bug fixes and performance improvements

Release 1.1.7

• New warmup from custom scheme option

• New option to hide/show completed warmups

• Various settings UI intuition fixes

• Bug fixes and performance improvements


Release 1.1.6

• Added daily and weekly program editor muscle group set counts

• Improved exercise description layout and added linked article content


Releases 1.1.4 – 1.1.5

• New setting that lets you choose reps-first instead of weight-first for set-to-set adjustments

• Exercise search improvements

The post Versions 1.1.4 – 1.1.8 Release Notes appeared first on MacroFactor.

Reps in Reserve (RIR) is a vital part of that vocabulary. It’s a system designed to help you better understand the intensity of your training by measuring how close a set was to failure. By mastering this tool, you can move past guesswork and start regulating your training with more precision.

Let’s dive in.

A brief history of Reps in Reserve (RIR)

We’ll begin with a few definitions and explain where these systems came from and what they have in common.

The two most common terms in this conversation are:

Reps in Reserve (RIR): An estimate of how many reps you can perform before reaching failure. A typical example is if you are performing a barbell back squat and stop because you believe you have 2 reps remaining. That set would be rated as 2 RIR.

Rating of Perceived Exertion (RPE): A scale you can use to rate or describe how easy or difficult your effort feels. The starting and end points of these scales can vary, but generally in resistance training they work on a 1-10 scale, with 1 being the easiest and 10 being the hardest in terms of perceived effort.

A brief note on where these scales came from

We will talk in a moment about the importance of measurement in research methods, but when discussing RIR and RPE specifically, we’ll look to classic research from Gunnar Borg. He initially developed an RPE scale to help estimate the relationship between aerobic work and heart rate intensity. The original scale ranged from 6-20, and the numbers were designed to correspond to heart rate during exercise. For example, an RPE of 13 would roughly correspond to a heart rate of about 130 beats per minute, while an RPE of 20 would correlate with a heart rate of about 200 beats per minute, and so on. 

He created variations of the scale over the years, but the core idea remained the same. It was a practical way to estimate how hard someone was working, and whether the effort represented a moderate, heavy, or near maximal level of exertion.

Again, these scales have their basis in aerobic exercise. For resistance training, Thomas DeLorme began refining a system in the 1940s while working with rehabilitation patients. It became a foundation for using repetition maximum and percentage-based training. By the late 1980s, prescribing loads using a percentage of one-repetition maximum (1RM) had become pretty standard in both research and programming as a way to measure and control intensity.

With 1RM, you’re essentially working from a percentage of the maximum amount of weight you can lift for a single repetition, usually within the boundaries of proper form (though this can be debated). Once you know what your maximum is, you would then design your programming around the percentage of those numbers.

RIR is a relatively new system of measurement developed by Mike Tuchscherer in his Reactive Training Systems manual. RIR has been examined in research alongside measures like bar velocity. The current iteration of RIR typically stands on its own and uses a numbering system similar to an RPE-type score. RIR helps you understand reps left in reserve and the intensity of a set. However, accuracy tends to decrease when you’re estimating very high numbers of repetitions.

To be clear, there isn’t a gold standard for estimating training intensity or one’s proximity to failure. Typically, reference methods default to a percentage of 1RM, but that approach also has its limitations. Ideally, what you are looking for in any estimation method is one that allows you to create a consistent testing system and clearly understand your reference points to your performance.

What does the research say about RIR?

If a research subject says they believe they have 2 reps left in reserve, but they continue the set to failure, were they right? If they are wrong, do they tend to be wrong by underestimating or overestimating their abilities? Are they staying within a reasonable range? In this instance, let’s first take a look at accuracy.

To answer those questions, we can look at a scoping review with an exploratory meta-analysis from Halperin et al that included 12 studies and 414 participants. Researchers examined how accurately people could predict the repetitions they had left before reaching failure across multiple resistance training studies. They found that participants tended to underestimate their repetitions to failure by roughly 1 rep on average. But for the most part, people were close enough to their true limit when compared with their estimations.

A few things to note: prediction accuracy improved as individuals got closer to failure, and accuracy also improved slightly as sets progressed compared with the first set.

In a study with male and female resistance-trained individuals, researchers looked at the accuracy of intra-set RIR predictions during the bench press. They essentially wanted to know whether lifters could correctly determine how many repetitions they had left before reaching failure. They found that the lifters did a pretty good job staying within about 1 rep of their failure range. It is also worth noting that the lifters had prior experience using RIR, so there is a strong chance that being familiar with the system helped. 

A more recent 2025 study in older adults (average age about 68 years) with some resistance training experience found they had a tendency to underestimate their ability to perform additional repetitions. However, even with this reduced prediction accuracy, RIR appeared to function in a helpful way for guiding their training effort and volume.

Regarding validity, research suggests that RIR, while subjective, can be evaluated by comparing estimates to performance markers such as velocity. For example, studies have looked at the relationship between bar speed and proximity to failure. As we get closer to failure, bar speed tends to slow down. This relationship gives researchers a reference point for evaluating RIR estimates.

We talked a little about this pattern earlier in work from Zourdos et al. More recently, a study from Kraft et al in 2026 examined hang cleans across multiple sets and intensities, in which velocity at a given RIR remained relatively stable. In other words, when lifters report that they are close to failure, their bar speed usually reflects that change in effort. This can help support the use of RIR for these types of estimates.

Is working within a range instead of training to failure as effective? Are there benefits or downsides?

The balance between managing fatigue and reaching failure is complex. RIR is a strategic tool that can help you trend toward failure, allowing for high intensity even when a set isn’t taken to a total grind. If you are staying within that range close to failure, you are likely to see meaningful progress. This will obviously be affected by age, training status, and even the type of exercise you’re doing.

A 2025 study from Hermann et al examined muscular adaptations in single-set resistance training in both men and women. Participants either performed sets to failure or stopped within roughly 2 reps in reserve. The experiment lasted 8 weeks, with 2 sessions per week and 9 exercises per session. Researchers measured strength, power, muscle size, and endurance.

In the end, both groups improved across all outcomes. There were small trends that favored training to failure for muscle size and power, but the overall differences between groups were modest. This lines up with a meta-regression from Robinson et al that showed a small dose-response relationship with muscle growth, where training closer to failure was associated with slightly greater hypertrophy.

In practical terms, the analysis suggests that if training to failure led to about a 9% increase in muscle size, stopping with 1 to 2 reps in reserve might produce roughly an 8 to 8.5% increase. Strength outcomes were largely similar across a range of RIR values.

Another study from Refalo et al looked at how people felt (specifically, their perceived discomfort and perceived effort) when training to failure versus working at roughly 1 to 2 RIR. The results were fairly similar, and there wasn’t a large difference in outcomes.

However, when analyzing perceived discomfort, participants indicated a slight preference for working within RIR versus working to failure. If you struggle with sticking to your lifting routine, experimenting with more RIR-based training and less failure-based training may be worth considering.

Lastly, a meta-analysis and systematic review from Grgic et al compared training to failure with non-failure training. The authors stated that while training to failure is not necessarily harmful, it may not always be needed for progress, and that training close to failure appears to be sufficient for most strength and muscle outcomes. 

Takeaways from this section: Once you’re accustomed to using RIR, it can serve as a decently accurate method for estimating effort, increasing intensity in your training, and gaining a better understanding of what failure feels like. While you don’t have to train to failure to see good results, there is something to be said for knowing where failure is and understanding your proximity to it. RIR isn’t perfect, but for estimation purposes and for consistently hitting a targeted range, it does a good enough job.

Establishing reference points

Now that we understand what RIR is and have reviewed the research on its use, let’s take a moment to understand why having a consistent reference point for measurement is actually useful in practice.

Imagine you’re trying out a new exercise at the gym. You pick a weight that intuitively seems “heavy enough,” but you aren’t quite sure. You might be able to get a good workout or two this way. But over time, it becomes more difficult to measure your progress and know if you’re truly reaching your potential. That’s where a system like RIR comes in handy.

The importance of anchoring the scale

In research, the term “anchoring” appears in statistical and psychology literature as a method for calibration and improving consistency in measurement. While anchoring does not eliminate bias entirely, the goal is to improve a person’s reference points so their judgments become more reliable.

Let’s start with a simple visualization exercise.

If I asked 100 people, without any basis or training, how many parked cars could fit on a football field, the range of answers would likely be very wide. Someone might know that a football field is 100 yards long (120 if including the end zones), but have no clear sense of the field’s width (53 yards) or the space an average car occupies (roughly 14 feet depending on the vehicle). Not to mention, the question itself contains many variables, such as the spacing between cars or the layout. With so much uncertainty, estimates will have wide ranges.

However, if I provided those same individuals a picture showing one horizontal and one vertical row of cars on the field, there would likely be an immediate and noticeable improvement in their guesses. If I then showed half of the field filled with cars, the estimates would likely improve again. As more of the unknown space is replaced with visible reference points, people will have less to guess about. The results would not be perfectly accurate, but they would likely move closer to the true value.

With RIR, you are dealing with something pretty complex: a person’s rating tied to their beliefs about their physical effort and fatigue. One person’s estimate of 2 RIR might reflect stopping with several repetitions still available, while another person’s estimate might be much closer to the actual limit.

So what’s the solution? In this instance, it’s exposure to true failure, which helps anchor the scale.

We need a safe way to evaluate each individual’s endpoint reference so they can begin estimating effort relative to it. Once a clear reference exists, individuals can work backward from that endpoint when judging how many repetitions remain. 

How to make meaningful starting estimates 

If you aren’t comfortable making starting estimates for testing failure (or you are venturing into a new lift that aren’t comfortable with), I’ve provided step-by-step instructions below.

If you are already comfortable taking a guess or estimate to test true failure, you can skip to the next section.

Step 1: Pick a stable exercise and focus on the movement first

I’m a fan of starting with more stable movements when you are learning how to test your limits. When the movement is well-supported and stable, it can be easier to control the set. So, the goal is to choose an exercise where the risk of losing control is lower.

For example, if you are new to training and decide to test failure with a lateral raise, you could run into problems quickly. Smaller muscle groups and less stable movements can fatigue quickly, and form may break down before you have a clear sense of your limit. Instead, when new to testing RIR, look for movements that are more stable and easy to stop when fatigue builds.

Good exercises for testing RIR when beginning training:

Dumbbell box or bench squats
Seated cable rows
Neutral grip dumbbell bench press
Lat pulldown
Dumbbell bicep curls
Cable triceps pushdowns

Step 2: Warm up with light weight and a repeatable moderate load

Take the time to get comfortable with the movement and feel out how much strength and how many repetitions you have available on that day. Start with a light weight and increase the load over a few sets, and pay attention to how your form holds or changes as fatigue sets in with each rep. Keep in mind that endurance fatigue can become its own limiting factor, so typically you’ll want to keep most warm-up sets under about 12 reps and well short of failure.

During your warm-up, feel free to adjust the weight and increase gradually as you test the movement. Unless you notice you are very far from the target range, resist making big jumps in weight during the warm-up.

Lastly, don’t neglect rest if you feel you got closer to failure than you meant to during a warm-up set. 

The goal of the warm-up sets is to hone in on a solid first working set estimate and gauge how close you are to failure by the time you hit your last set. Based on these warm-up sets, you should have a reasonable idea of a weight that will place you within a useful range for testing true failure.

Testing true failure and RIR estimates

Now we are assuming that you have a decent understanding of the exercise you want to perform and have a meaningful starting estimate. From here, you can start to get an anchor for your scale.

Quick note on what defines failure

For our purposes here, we’ll define “failure” as the following:

Failure – The point at which the lifter can no longer complete a repetition in proper technical form for the exercise.

This can, but does not always, coincide with momentary muscular failure. Technical failure and momentary muscular failure can be the same, but they are not always identical. Safety should be the priority, and there are times when you can feel that the last completed rep started to show failure components, such as losing form, so you do not need to attempt another rep to know it would result in failure. The difference between these two points is usually small, so go with safety and what feels best within this range.

Step 1: Select your exercise and choose a weight with a repeatable load

You should select a weight where you expect to reach failure somewhere within the range of 6-12 repetitions. The most important aspect of this test is to ask yourself: How many reps can I do with this weight before failure?

For example, let’s say you are performing a seated cable row test, and based on warm-ups and estimates, you think you can lift 80lb for about 10 reps before reaching failure. That’s your estimate. 

Step 2: Perform the set and test your RIR estimate

You will now perform the seated cable rows while trying to reach failure, keeping in mind ideal form and technique. This is important because you do not want to achieve reps by, for example, rounding your shoulders or jerking the cable and breaking down in form. All 10 reps should ideally be performed with proper technique and show muscular fatigue taking place, not just mental fatigue or boredom with the movement. 

Note: If the movement produces a sharp pain, you should stop. But if you are simply feeling mentally uncomfortable or tired of the effort, that is not the type of failure we are describing here.

Continue the set until you can no longer complete another repetition with proper technique, despite trying. As a reminder, in this example we estimated that using 80lb would fail rep 10. You should, if your estimate is correct, fail to complete rep 11 or not attempt an 11th rep due to how your 10th rep went. 

Result of your test? You actually reached 12 reps before failure. You now know that, at this stage of your testing, 80lb performed for 10 reps would represent about a 2 RIR, and that your original estimate was slightly off.

Do not worry about being perfect with your estimate. Be more concerned with maintaining good form and noting where you actually are in your RIR range. 

Step 3: Continue testing different exercises and building your RIR judgment

Depending on the exercise selection, training experience, or training session, you may not want to take every exercise to failure in one session. This is largely a matter of judgment and should take into account your current nutrition, recovery, and conditioning.

In short, if you feel well-rested, well-fed, and ready to test, then proceed. If you feel that testing the seated row to failure might negatively affect your lat pulldown later, simply wait until your next workout to test again.

Understand that this is a conservative approach and assumes a little less experience. If you are just getting back into training, it can be wise to lean toward the conservative side to manage fatigue. If you have been training for a while but have never formally tested your limits, you can likely push your limits a little more.

Step 4: Apply RIR to your regular sets 

Now that you understand anchoring and where failure occurs, you can start dialing back and aim to hit the target RIR range within your sets, if your program calls for it.

For example, let’s say you are training seated rows and your target is 2 RIR for each set. From previous testing, you now know that 80lb left you with roughly 2 reps in reserve during testing. That gives you a starting point.

From here, the goal is to build a general sense of where 1 RIR, 2 RIR, or failure occurs across the exercises in your program so that you can make more confident estimates during training.

Step 5: Periodically retest to keep your RIR estimates updated

Over time, you should test those estimates by occasionally performing a set-to-failure test. How often varies for each person and depends largely on your fatigue management and recovery. Some people test their last set at the end of most sessions, while others rotate and test a different exercise each session or only at the beginning of each cycle. 

When deciding whether it is time to test again, ask yourself a few simple questions:

• Are you performing a new exercise or using unfamiliar equipment?
• Have you made noticeable progress in strength or repetitions over time?
• Are recent sets feeling easier or harder than expected?
• Has training volume or intensity changed recently?
• Have you taken time off from training and are now returning?

You do not need to test failure every session, but you should do it often enough to land in a good range of estimates. 

Understanding RIR in relation to MacroFactor Workouts

If you’re using MacroFactor Workouts, the app uses a smart progression algorithm to help make suggestions based on your logged performance.

In MacroFactor Workouts, RIR starts at 0 and goes to 6. A lower RIR, such as 0 to 1, means you were very close to your limit. A higher RIR, such as 4 to 6, means the set felt easier and you had more reps left in reserve.

Let’s say you have a program where you are doing barbell back squats, and you log that you lifted roughly 275lb for 5 reps. The app does not know if that set represents a deload, a moderate working set, or something close to your maximum. However, if you also log that 275lb felt like roughly 2 RIR, the app can estimate that your strength capacity may fall within a higher range – for example, around 305-325lb. It can then adjust your progression accordingly based on the algorithm.

In short, RIR helps the app understand how hard each set actually was, not just how many reps you completed or how much weight you used. This allows recommendations to better reflect your actual effort and fatigue over time.

You can read more about smart progression here and updating RIR targets here. 

Takeaways

For any resistance training program, you want to establish a system for estimating your efforts during lifts. There are many methods you can use, but currently one of the most practical and widely used approaches is reps in reserve (RIR).

While it is a system with limitations, it does a good job of helping you estimate effort and stay within an effective range of intensity for progressing in your lifts. And what’s more, you do not need perfect accuracy for RIR to be useful, you just need to be consistent and work on your own estimates.

Lastly, if you’re using MacroFactor Workouts, RIR becomes even more valuable because it helps the system understand how hard your sets actually were. This context allows the app to make better recommendations and adjust your progression based on reported effort, not just the numbers you logged.

Some final tips

• There is often a tendency to underestimate your RIR slightly, so make sure you are safely pushing your limits and occasionally testing where failure actually occurs.
• Warm up properly and continue to assess effort as the set progresses, not just at the beginning, to improve your accuracy.
• Stay conscious of your RIR during training. Avoid being passive or assuming the effort level; instead, test it.
• Periodically taking your last set of an exercise to failure can be a useful way to recalibrate your estimates.
• Working at lower repetitions or closer to failure can sometimes make effort easier to judge than very high-repetition sets, especially when learning the system.

The post How to Use Reps in Reserve for Lifting appeared first on MacroFactor.

Now, this is an article on the MacroFactor website, so we acknowledge that we’re not a neutral third party. We think MacroFactor is generally the best option for most people most of the time. And we’re not the only ones who feel that way. MacroFactor won the Google Play award for “Best Everyday Essential.” It also comes highly recommended by tech publications like Lifehacker, and it’s consistently the most-recommended nutrition app in neutral online fitness communities (one, two, three, four, five, six, seven, eight).

That said, Cal AI is becoming one of the top downloaded apps in the nutrition app space, and it’s worth having a conversation about what it brings to the table. While MacroFactor has clear advantages, we also recognize that Cal AI may be a better fit for people seeking a simpler app. 

This article aims to give you a clear, side-by-side comparison of MacroFactor and Cal AI so you can decide which app makes the most sense for you right now. 

Criteria for our head-to-head comparison

When selecting a macro tracking app, the most important thing to keep in mind is that you’re looking for a utility app. As such, there are three bars a good macro tracker should clear:

1. It needs to be well-equipped to help you reach your nutrition-related goals (such as weight loss, muscle gain, or general health). 
2. Logging food should be as quick and painless as possible. Meaning, the workflows are quick, and the food data is accurate. 
3. The tool should offer robust analytics to help you better understand your nutrient intake patterns.

With that criteria in mind, there are seven factors to focus on when evaluating macro trackers:

1. Food logging efficiency: How quickly can you log your meals and is the workflow efficient?
2. Efficiency-focused quality of life features: Does the app make nutrition tracking quick and easy beyond the core food logging workflows?
3. Food database Can you find all of the foods you’d like to log? Do the foods in the database have complete and accurate nutrition information?
4. Analytics: Does the app make it easy for you to understand your intake and progress?
5. Price and consumer friendliness: Is the app affordable enough to help you achieve and maintain your progress long-term? And, if it’s a premium app, does it actually provide a premium experience?
6. Accuracy and flexibility of the nutrition recommendations: Does the app provide nutrition recommendations that will actually help you achieve your goals, while also accommodating your lifestyle?

With that laid out, let’s see how the two apps compare.

1. Food logging efficiency

Regardless of features, coaching, or integrations, most people spend most of their time on any nutrition app doing one thing: logging their food. The easier it is to log, the more likely you’ll keep logging over time. 

AI photo logging

The main feature Cal AI focuses on is AI photo logging. Users can snap a photo of their plate, and the app will estimate the Calories and macros of the food shown. It’s a fast, beginner-friendly way to start tracking your meals. However, it can lack the depth and functionality necessary to be a complete solution for the dedicated user.

MacroFactor offers a more serious alternative with intentional application of AI. MacroFactor’s AI features provide a dual benefit: maximizing efficiency for power users and lowering the barrier to entry for beginners. 

Let’s break down the difference between AI photo logging features in MacroFactor vs Cal AI. 

Food results

MacroFactor AI does not rely on LLMs to generate all food entries. Instead, we prioritize real, lab-analyzed results with complete nutrition data. MacroFactor AI will only generate a food entry when it is necessary or otherwise optimal, and its generation will be grounded in real life foods.

Cal AI generates food results by analyzing the image and estimating the overall contents of the meal. Rather than relying on a database-first approach, the system produces an estimate based on visual recognition of the foods shown. In most cases, users can adjust the identified items or portion sizes after the result is generated. However, the level of detail returned was limited, and the output focused mostly on Calories and macros rather than a full nutrient profile.

Result transparency

MacroFactor AI will break down your meal into individual ingredients instead of giving you a single opaque result. Because we build each result from individual food entries, our output is fully inspectable.

During testing, Cal AI provided a mix of summarized and itemized estimates. While users can review and edit the foods, some components were occasionally hidden when the system reported low confidence in its identification, making it harder to verify or fine-tune the exact composition of a meal.

Integrated control

MacroFactor’s plate is a unified interface for interacting with the AI result and human-added foods. AI is not perfect and can make mistakes, and we give users the control to edit and modify its results.

Cal AI does not utilize a plate or unified meal timeline system. When the AI generates a result, it typically appears as a single outcome, which can create additional workflow for users who want to manage the full structure of their meals. However, for users who prefer very simple, quick logging, this approach can work well.

Streamlined experience

MacroFactor AI will do its best to group foods logically, using recipes and single foods, so it is easy for you to reason about your nutrition. Our AI interface has no fluff, no performative chat interface – just streamlined results.

Cal AI is designed around simplicity and minimal input. The system focuses on generating a quick estimate from a photo or description, which can make logging feel accessible for those just starting out. However, during testing, this approach came with some tradeoffs in flexibility and depth of detail, particularly for users who want more control over their food logging.

Speed of other logging workflows

Beyond AI food logging, we use the Food Logging Speed Index (FLSI) to evaluate logging speed. The system utilizes a series of case tests to measure the number of actions required to complete common food-logging workflows. We specifically test searching for foods, using multi-add functions, scanning barcodes, and quick-adding Calories. The best score is the lowest, since fewer steps mean faster, easier logging.

We recently updated the FLSI rankings with all the large, comparable apps on the market, and MacroFactor emerged as the leader of the pack, with the lowest action scores overall. For example, when compared to Cal AI, MacroFactor required 10 actions for logging foods via food search, whereas Cal AI required 19 actions. Overall, Cal AI requires roughly 1.9 times as many discrete actions as MacroFactor across the most common logging methods.

These differences might sound small, or like we’re debating minor details, but they add up quickly. Saving 15-30 seconds per meal means saving several minutes a day, and hours over a year. MacroFactor’s logging system minimizes the friction associated with food logging so you spend less time tapping.

Winner: MacroFactor

Logging food is the one thing you’ll do every day in a nutrition app, and MacroFactor makes it faster and more efficient by a wide margin. Across every workflow, Cal AI requires roughly 1.9 times as many taps or swipes, taking extra time at every meal, which adds up quickly. Additionally, differences in AI workflow, speed, and editability can create extra steps for users who want detailed logs or need to make adjustments, making MacroFactor the clear winner in this category.

2. Efficiency and quality-of-life features

An ideal food logger is shaped by the features that make tracking smoother. We consider these “efficiency and quality-of-life” features because they reduce logging friction, enhance daily workflows, and make the difference between an app you tolerate and one you actually enjoy using.

Food logging speed in common workflows (discussed in the previous section) is the easiest factor to directly measure and compare across apps, because most apps rely on similar food logging methods. However, other features like copy and paste or recipe sharing aren’t as universal, but they can have a similarly large impact on daily ease of use. So, instead of just assessing the efficiency of each of these workflows and features (which are entirely absent in many apps), we primarily evaluate total feature coverage when comparing apps.

MacroFactor devotes most of its development effort to features that reduce logging friction. Favorites, smart history, and flexible copy-and-paste features save time on frequently logged foods. And perhaps more directly comparable, the AI logging system doesn’t just spit out a static estimate from a photo or description; it breaks meals into editable ingredients you can adjust until the log reflects your plate. This makes it easier to log meals at restaurants or gatherings without posted nutrition information and it also produces these results relatively quickly. 

In MacroFactor, you can also customize your dashboard and food logger so that the nutrients or shortcuts you care most about are at your fingertips. These small additions may seem minor, but they add up to a smoother experience that saves time. 

Cal AI includes some, but not all, of these quality-of-life features. It covers the basic logging methods, from manual entry to AI photo scanning. Beyond that, interface customization and detailed food journaling options are pretty limited, with no specific food timelines, copy-and-paste, or quick actions to make logging more defined or efficient. Their models for logging lean heavily on the assumption that most users will use AI photo logging as their main source. 

Cal AI does put a focus on gamification with badges and streaks. They also recently introduced in-app communities organized around different goals, such as New Year’s resolutions, weight loss, or muscle gain. However, during our testing, we were unable to access this part of the app despite ensuring the phone and app were up to date and had been reinstalled and restarted. We also saw that the total number of members in those communities appeared to be pretty low, which may indicate potential access issues that could be resolved at another time or may not be an issue for some users. Overall, these are features that may appeal to users who value gamification and in-app community support, though functionality may differ from app to app.

Winner: MacroFactor

MacroFactor has a pretty big edge here because its quality-of-life features are built around efficiency and reducing friction in everyday logging. Cal AI places a strong emphasis on its AI photo logging feature, along with social elements such as gamification and in-app groups.

However, compared with MacroFactor, Cal AI has a less refined food logging process overall and tends to focus its development efforts more on gamification and social features than on improving the logging experience. Therefore, if a user needs speed and features that support efficiency, MacroFactor is the clear winner.

3. Food database 

At the time of writing this article, it was confirmed that Cal AI has been using MyFitnessPal’s nutrition database since December 2025. This is a big development, as MyFitnessPal maintains one of the largest food databases in the industry. By leveraging this database for search, Cal AI now offers a level of coverage that most independent apps do not have, especially for users outside major English-speaking markets.

With that being said, when comparing the searches of Cal AI to those of MyFitnessPal, there were differences in the items that were returned during our testing. For example, certain types of apples or European and Asian branded food items, when searched in MyFitnessPal, did not appear in Cal AI, and attempts were made to match term for term. However, there may be a different search function or ranking system used in Cal AI than in MyFitnessPal, leading to fewer search results and making the comparisons seem different.  

In comparison, MacroFactor’s database is still substantial, with about 1.36 million verified foods accessible via search, and an additional 4 million foods in our barcode database. For most people in the Anglosphere and large parts of Western Europe, this covers daily needs because MacroFactor already offers robust branded and barcode support. And when it comes to those fresh common food items like chicken breast, rice, or apples, both apps have similar coverage.

Where the size differences are most noticeable is in countries with limited coverage in other apps or for people who rely on niche, region-specific packaged foods. In those cases, the odds of finding what you need should, in theory, be higher in Cal AI, as it is now using MyFitnessPal’s database.

MacroFactor also integrates roughly 26,500 foods from the NCC Food and Nutrient Database, a gold-standard food composition resource frequently used in nutrition research. That means you can track a wider range of micronutrients than in Cal AI, which largely limits detailed nutrient reporting. MacroFactor lets you track 54 items, from macro- and micronutrients to alcohol, caffeine, and water, compared with just 14 in Cal AI, which excludes most vitamins and minerals. For anyone whose goals go beyond just calories and macros, MacroFactor’s higher-quality database makes a real difference. Below, you can see the list of nutrients that can be tracked in each app.

Winner: Tie

If we were looking at database size alone, especially for international branded foods and packaged products, this category would likely go to Cal AI. However, during actual use of the app, search results still returned a relatively limited number of items compared with both MyFitnessPal and MacroFactor. It is possible that Cal AI users have access to the MyFitnessPal database, but the search system may still be evolving, or the database integration may still be developing within Cal AI.

Additionally, MacroFactor provides far more micronutrient detail and clearer categorization, showing whether foods are common, branded, or sourced from Open Food Facts. Because of these differences, there is not a clear winner in this category at this time.

4. Analytics and progress tracking

When you log data in a nutrition app, you’re building your own dataset that shows whether your dietary choices move you toward your goals. Any good system should make that information easy to see and interpret so you don’t have to guess if your plan is working.

For example, MacroFactor’s coaching and expenditure estimation algorithms can generate accurate recommendations, but the analytics let you verify and interpret those recommendations. The app also displays a weight trend that filters out day-to-day fluctuations, allowing you to see whether you’re actually moving toward your goal. That way, you can really see the connection between your intake and your progress.

Cal AI provides access to data, but it is spread across multiple tabs and sections. On the main dashboard, users are shown Calories and macros consumed for the day, along with a snapshot of recently uploaded or logged foods.

In the progress tab, Cal AI users can view additional details such as current weight, weight changes, progress over time, and average calorie intake. Deeper within the profile settings, there is also an option to request a PDF export of meal, exercise, weight, and calorie and macro history via email. However, during testing, repeated report errors occurred when attempting to generate these exports despite having a fully updated phone and app version, so we were unable to evaluate the content or usefulness of those reports.

MacroFactor takes an easier access approach to data reporting. On its main dashboard, you can see your estimated expenditure, weight trend, and energy balance, along with your percentage progress toward your current goal. Additionally, MacroFactor provides more analytics regarding your nutritional intake through micronutrient reporting, advanced body metrics, and trend tools that can help you quickly troubleshoot problems.

Winner: MacroFactor

MacroFactor works hard to turn your data into usable insights and in one section alone shows trends, energy balance, and progress toward your goals. While Cal AI includes basic progress tracking, it is still difficult to access or interpret much of the underlying data during testing, and the analytics provide less detail and context around trends or how your intake relates to progress over time.

5. Price & consumer friendliness

MacroFactor and Cal AI are both premium apps and offer free trials to test the app, with Cal AI providing 3 days and MacroFactor providing 7 days. If you subscribe monthly, MacroFactor costs $11.99 compared with about $9.99 per month for Cal AI. MacroFactor rewards longer commitments with a lower effective monthly cost on its annual plan, which is $71.99 per year, or about $5.99 per month.

Cal AI also offers annual options, but its pricing appears to vary between users and promotional offers. Annual pricing was not listed on the website or within the app. However, when we contacted customer support, we were told, “Our annual subscription costs $30 per year (displayed as $3 per month) and includes a 3-day free trial.” There was also an option to upgrade to a family plan covering up to six individuals for $59.99 per year.

This is a premium versus premium evaluation across the criteria we have discussed, and it mostly comes down to value for your dollar and what you want from a paid nutrition app.

From a consumer friendliness standpoint, we also encountered several issues during testing. Data exports repeatedly failed to generate, and the groups section remained inaccessible even after multiple attempts, reinstalls, and confirming that both the phone and app were fully updated. The app also struggled with some core functions, including barcode scanning (scanning was often met with a blank white screen instead of a result), and nutrition label and meal analysis often had noticeably long load times. By comparison, MacroFactor was consistently efficient with load times, exports, and overall functionality.

Winner: Tie

While Cal AI is technically the cheaper option monthly or annually, the overall value per dollar depends on what a user prioritizes. MacroFactor offers a more stable app environment, more quality-of-life features, and a dynamic adaptive algorithm. However, users who prioritize a lower subscription price may still prefer Cal AI. For that reason, this category is best considered a tie.

6. Accuracy of nutrition recommendations

When most people download a nutrition app, they usually want to lose weight in a way that preserves muscle, or gain weight in a way that maximizes muscle growth without excessive fat gain. To this end, nutrition apps typically aim to provide energy intake recommendations that will help users achieve their goals.

The process of generating these nutrition recommendations is usually quite simple. When a user signs up, they enter relevant information like their height, weight, age, sex, and activity levels. With this information, the app estimates their total daily energy expenditure (TDEE) using formulas that were developed using population-based data. Once the app estimates your energy expenditure, it can recommend Calorie targets based on your goals, which means setting an intake target above your TDEE if you want to gain weight, or below your TDEE if you want to lose weight. When users first sign up, this is the basic process employed by both MacroFactor and Cal AIl to generate initial energy intake recommendations.

This process does typically generate a reasonable ballpark estimate of your TDEE. However, it has the possibility for considerable estimation error. It’s not too uncommon for this initial calculation to over- or under-estimate your energy expenditure by 500 Calories or more. As a result, your recommended Calorie intake could be considerably too high or too low.

With most other nutrition apps, this is where the process of generating nutrition recommendations ends: they give you a rough estimate of your energy intake requirements, but if those recommendations are too high or too low, the user is left on their own to figure it out. This can pose a problem, since nutritional requirements often shift over time due to changes in lifestyle, body weight, and activity levels. As a result, the user is either left with the tedious and frustrating ongoing task of micromanaging their diet, or they need to hire a nutrition coach, which can cost hundreds of dollars per month.

Unlike other apps, MacroFactor handles this ongoing process of data analysis using the weight and nutrition data you log in the app.  Advanced algorithms then provide updated nutrition targets on a weekly basis to reflect changes in energy intake requirements over time. As a result, MacroFactor’s nutrition recommendations are quantifiably more accurate than recommendations provided by static TDEE formulas. Based on real user data, we see that MacroFactor’s nutrition recommendations are nearly three times as accurate as nutrition recommendations coming from static TDEE formulas.

There are a few alternative approaches to estimating energy requirements, including using wearable devices, activity-based formulas to estimate energy expenditure, or AI-driven estimates based on user data.

If you only rely on a wearable device to estimate your energy requirements, research has found that those estimates are off by at least 10% in 82% of the studies on the topic (and obviously, if average errors regularly exceed 10%, individual errors can be much larger). Furthermore, when people are aiming to lose weight, overestimating the impact of exercise on total energy expenditure is especially common due to the process of metabolic adaptation. When you exercise more, you tend to reduce your energy expenditure throughout the rest of the day, which can offset much of the energy burn during your exercise session.

From what we could determine, Cal AI appears to use a static formula to generate its initial estimates and will utilize exercise activity logging to increase your Calorie intake allowance for the day. Increasing the number of calories burned via exercise may not always be the best approach, since exercise may not increase your total daily energy expenditure in a predictable fashion. Everyone’s bodies are different, and your body may compensate for the calories burned during exercise in a unique way. (We talked about this extensively in our article “The Drawbacks of Using Wearable Devices to Inform Nutrition Targets.”) 

Cal AI does provide Calorie targets based on the pace of weight change you select and an estimated total daily energy expenditure. However, the app does not provide a specific expenditure number or display one in the reporting we were able to access. The progress section includes a graphic showing expenditure changes over time (7-day, 14-day, 30-day, and 90-day), but it does not display a baseline number.

According to the Cal AI support team, the expenditure graph reflects activity-related Calories from Apple Health or logged exercise. These values are not influenced by user intake or body weight logged over time.

During our testing, the baseline Calorie recommendation did not change despite the expenditure graphic showing updates. This occurred while logging a wide range of intake levels, tracking weight changes, and maintaining entries over a reasonable period to allow the app time to update. Lastly, when we generated a new baseline Calorie recommendation with the updated expenditure information in the system, the app still returned the same base Calorie target, which appeared to be based on a BMR formula plus an activity estimate.

Therefore, Cal AI’s Calorie targets appear to be determined primarily from the questionnaire completed when setting goals. While the app does allow users to add Calories from logged exercise on days when activity is recorded, it does not appear to dynamically adjust intake recommendations over time, only to day-to-day manually added exercise.

Winner: MacroFactor

MacroFactor is the clear winner in this category. If you want a nutrition app that helps determine appropriate Calorie and macronutrient targets to support your goals, MacroFactor’s coaching algorithms provide a more responsive and data-driven approach, making this a clear win for MacroFactor.

Summary

MacroFactor vs. Cal AI Summary
Category	MacroFactor	Cal AI	Winner
Food logging efficiency	Fastest food logging workflows on the market; scored best on the Food Logging Speed Index.	Requires substantially more actions across common logging workflows. On average, logging tasks required ~1.9x more discrete actions.
Features	AI food logging with editable ingredients, expandable recipes, favorites, and hourly go-tos.	AI photo logging, gamification features, in-app communities, and a family plan option.
Food database size	1.36 million verified search foods, plus 4 million barcode entries. Includes gold-standard research database.	Access to MyFitnessPal database; broad international coverage but could not be confirmed for “Best Match” verified accuracy.
Analytics	Advanced weight trending, adaptive coaching, and detailed micronutrient reporting.	Covers basic Calories/macros and weight. Micronutrient reporting and advanced analytics were limited.
Price & consumer friendliness	Responsive, data-driven coaching algorithms and stable, user-friendly app experience.	Slightly lower monthly price and family plan, but inconsistent feature access during testing.
Coaching accuracy	Adaptive coaching adjusts to your data and is significantly more accurate than static equations over time.	Relies primarily on static formula-based Calorie targets generated during initial setup.
Total points	5 points	1 point

Which app is right for you?

Both apps take very different approaches to tracking nutrition. Cal AI focuses on simplicity, with AI photo logging as the primary way to estimate your intake. It offers fewer traditional journaling and food logging tools, but that simplicity may appeal to users who want the easiest possible way to track what they eat.

Otherwise, MacroFactor offers smart AI photo logging, while also making food logging faster and easier. MacroFactor offers more customization, provides stronger analytics, and delivers adaptive coaching to help you reach your goals. So, for premium users who prioritize speed, accuracy, and long-term value, MacroFactor is the clear choice.

The post MacroFactor vs Cal AI: Which App Wins in 2026? appeared first on MacroFactor.