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.

The new additions build upon the success of the V3 algorithm. If you’d like a big-picture overview of how the expenditure algorithm works, and the types of problems we have to solve to optimize the performance of the algorithm, you should give this article a quick read. V3 was a pretty significant overhaul, whereas the expenditure modifiers are more of an expansion and refinement of the system we built for V3. So, I won’t be rehashing everything covered in the previous article; instead, I’m just going to hop right into the changes and updates.

Change #1: Small tweaks within the existing framework

The first change is that we slightly tweaked the weights of several of the variables that were already used to generate expenditure updates in V3 of the expenditure algorithm.

As discussed in the previous article, there’s an inherent tradeoff between stability and responsiveness of the expenditure algorithm. But, these tradeoffs are nonlinear. An extremely responsive algorithm tends to be more accurate than a less responsive algorithm when its outputs are averaged over any reasonable period of time. But, this increase in accuracy comes at the expense of day-to-day unusability, since your Calorie targets would regularly increase or decrease by several hundred calories every week.

Using a shorter lookback window is one way to make an algorithm more responsive. This graph from our prior article visually illustrates the pitfalls of maximizing responsiveness at the expense of stability.

Conversely, a maximally stable algorithm would never update your expenditure at all, leading to very poor accuracy, on average.

Using a longer lookback window is one way to make an algorithm more stable. This graph from our prior article visually illustrates the pitfalls of maximizing stability at the expense of responsiveness.

We can visualize these tradeoffs by plotting an efficiency curve.

For responsiveness, the metric we’ll use is average absolute monthly weight change error: the extent to which monthly weight change differs from what the expenditure algorithm tacitly predicted, based on your Calorie intake. This is technically a metric to assess algorithmic accuracy rather than responsiveness per se, but increased accuracy is the entire point of increasing responsiveness, so it serves as a good proxy (i.e., if an algorithm got more “responsive” without also getting more accurate, it’s not “responding” to whatever it’s supposed to be responding to).

For stability, the metric we’ll use is the average absolute daily expenditure change: the typical amount of day-to-day change in your calculated expenditure.

Here’s the resulting efficiency curve for an algorithm built upon the same principles as MacroFactor’s V2 algorithm:

On this graph, values closest to the bottom left corner are ideal, offering the best blend of accuracy and stability. This coincides with the part of the graph with the prominent bend. So, let’s zoom in on that region to see how the tweaks made in the recent update impact this tradeoff.

First, I’ll start by plotting the stability versus responsiveness relationship using the prior weights of the V3 expenditure algorithm. As you can see below, the upgrades in V3 allowed us to push beyond the efficient frontier of the V2 algorithm, achieving a blend of responsiveness and stability that was previously unachievable.

The precise gains in responsiveness and stability will be contingent on the dataset used for testing purposes (in this case, I’m using the first 100 days of data from new MacroFactor users who participated in our New Year’s Challenge), but within this dataset, the V3 algorithm is about 19% more responsive than a V2-style algorithm with similar stability, and about 20% more stable than a V2-style algorithm with similar responsiveness.

So now, let’s see the impact of the small tweaks we made to the weighting variables that were already present in the V3 algorithm.

Like I said: they were quite small tweaks. The net effect is that they reduce stability by about 3.9%, while increasing responsiveness by around 5.8%. I won’t pretend like this isn’t a fairly small change, but it’s a slightly bigger change than meets the eye.

For starters, through this region of the responsiveness versus stability curve, any gains in responsiveness typically come with a disproportionately larger decrease in stability (i.e., you’d expect a 5.8% increase in responsiveness to reduce stability by around 7%, give or take).

Additionally, this curve depicts average absolute error versus average daily expenditure change over the entire 100-day period for the contest participants, but a pretty large chunk of the total error during that period comes from the first 30 days of using the app (before the expenditure algorithm is fully calibrated for the user). And, during that initial calibration period, you should expect expenditure updates to be larger (larger updates mean that any initial estimation error is being mitigated quicker).

So, for a better idea of the net improvement for most users, it’s helpful to focus on the stability versus responsiveness tradeoff from day 30 onward. And, after the 30-day initial calibration period, these tweaks result in 6.9% better responsiveness, compared to a 2.6% decrease in stability. In other words, these tweaks lead to modest net improvements for brand-new users, and somewhat larger net improvements for people who’ve already been using the app for a while.

Change #2: Incorporating step count data

For the first time, MacroFactor’s expenditure algorithm will directly incorporate activity data if you enable “Step-Informed Updates.” To enable this modifier just go to More > Expenditure (under Feature Settings) > Step-Informed Updates (under Expenditure modifiers) and toggle the modifier on.

For the time being, we’re only using step counts. The reasons for this decision are pretty straightforward:

1. If you have a smartphone, you also have a reasonably accurate pedometer – using step count data doesn’t require people to buy an additional product to reap the benefits.
2. Even if you have a smartwatch, you have a device that’s quite good at measuring step counts, and quite bad at estimating energy expenditure. When given the option, we lean in favor of using more accurate data sources.

Note that step counts won’t be used to additively increase or decrease your calorie targets on individual days. Rather, step data will be incorporated into MacroFactor’s algorithms in a manner similar to the data you’re already logging (weight and nutrition data), meaning it will smoothly and progressively increase or decrease your estimated expenditure and calorie targets over time.

It actually took quite a bit of testing to find a way that step data could be used productively, since the expenditure algorithm already works quite well with just the use of weight and nutrition data. More often than not, activity levels are associated with energy status and weight change status (in other words, if you’re eating less and losing weight, you tend to also move less, and vice versa when you’re eating more and gaining weight). Furthermore, due to the effects of energy compensation, changes in activity levels tend to impact energy expenditure a bit less than you might otherwise expect, especially when you’re losing weight. When we’ve said that MacroFactor’s algorithms didn’t need activity data to function well, we weren’t bullshitting.

However, we did find a way for step data to slightly improve the performance of the expenditure algorithm. Much like the tweaks discussed above, however, the impact was quite small. Here it is, plotted on the same efficiency curve we used previously.

On the surface, we’re looking at a ~3% decrease in stability, compared to a ~2% improvement in responsiveness (on top of the changes resulting from the previously discussed tweaks). Once again, the upside becomes more apparent after the 30-day mark, where the decrease in stability is still only 3%, compared to a 3.3% increase in responsiveness.

Compared to expenditure V3, these two tweaks combined increase responsiveness by nearly 11%, while reducing stability by a bit less than 6%.

Change #3: Accounting for new goals

Last year, we published a series of articles about Basal Metabolic Rate (BMR), culminating in the release of our new BMR equation (and calculator).

In two of those articles, we discussed how weight gain and weight loss impact BMR. But, BMR changes only account for a fraction of the excess changes in total energy expenditure resulting from attempts to gain or lose weight. However, there’s quite a bit of research documenting the effects of weight gain or weight loss on BMR, but there’s considerably less research comprehensively documenting the effects of weight gain or weight loss on excess changes in total energy expenditure.

So, I’ve been pretty sure that MacroFactor’s initial expenditure estimates tended to slightly overestimate energy needs for people aiming to lose weight, and tended to slightly underestimate energy needs for people aiming to gain weight. But, I was hesitant to make any adjustments to MacroFactor’s initial expenditure estimates without first having the necessary data to inform those adjustments.

However, the data from participants in MacroFactor’s New Year’s Challenge provided me with a large dataset to address the problem.

MacroFactor’s expenditure algorithm is, functionally, a prediction engine. If your energy intake matches your estimated expenditure, MacroFactor is tacitly predicting that you’ll maintain your weight. If your energy intake is above or below your estimated expenditure, MacroFactor is tacitly predicting that you’ll gain or lose weight (respectively), and it’s predicting the rate at which you’ll gain or lose weight. The degree to which your estimated expenditure increases or decreases over time is more-or-less determined by prediction errors. If you lose weight faster or gain weight slower than would be predicted based on your current estimated expenditure, that implies you’re burning more energy than was previously predicted, so your estimated expenditure will increase (and vice versa if you gain weight faster or lose weight slower than would be predicted).

So, to determine if MacroFactor’s initial expenditure estimates are too high when people set a weight loss goal, and too low when people set a weight gain goal, all I had to do was compare people’s target rate of weight change to the weight change estimation error during their first month of using the app.

Here were the results.

Now, this is obviously a noisy dataset, but it’s also a very large dataset. Though the correlation was fairly weak (r = 0.27), it was associated with a p-value of p < 0.00001.

To correct for this bias, the initial expenditure estimate needed to be multiplied by four-times the intended rate of weight change (as a percentage of body weight per week). In other words, if someone was aiming to lose 1% of body weight per week, their initial expenditure estimate needed to be 4% lower.

Applying this correction eliminated the bias seen in the graph above:

This correction comports pretty well with the research that does exist on the topic.

In tightly controlled studies, weight loss appears to lead to ~10-15% reductions in total energy expenditure beyond what one would expect based solely on changes in fat and lean mass (i.e., total energy expenditure winds up 10-15% lower than standard TDEE equations and calculators would predict).

Our BMR equation already assumes that BMR will be 5-8% lower when losing weight. This 5-8% reduction in BMR already leads to a 5-8% reduction in total estimated energy expenditure, since we initially estimate expenditure by estimating BMR, and then multiplying that value with an activity correction factor (i.e., 0.92 × 1.5 is still 8% less than 1 × 1.5). Furthermore, the typical intended rate of weight loss tends to be around 1% for most users, which would lead to an additional 4% decrease in our initial expenditure estimate. A 5-8% reduction due to the BMR equation, compounded with an additional 4% reduction, would lead to a total reduction in initial expenditure estimates of around 10% (8.8-11.7%). And, if someone was targeting an aggressive rate of weight loss closer to 2%, the additional 8% reduction would lead to a total reduction in initial expenditure estimates of around 15% (12.6-15.4%). So, this adjustment produces values that are in line with what we’d expect from the literature.

Bringing back the “Efficient Frontier” graph, here’s the impact of also adding in this modifier.

The net effect: a 9.1% increase in responsiveness compared to V3 without modifiers, with no meaningful change in stability. The new version of the algorithm with both modifiers enabled is nearly 30% more responsive than a V2-style algorithm with similar stability, and about 29% more stable than a V2-style algorithm with similar responsiveness.

Past the 30-day mark, the overall change compared to V3 with no modifiers is a 10.7% increase in responsiveness, compared to a scant 3% reduction in stability. And, just to put a 3% stability reduction in perspective, the average daily expenditure change (in this dataset) was 7.25 Calories per day with V3 (and no modifiers), versus 7.49 Calories per day with both modifiers enabled. I tend to be fairly obsessive about trying to improve each version of the expenditure algorithm in all metrics, but users were already very happy with the boost in stability when transitioning from V2 to V3, and I doubt that a grand loss in stability of less than a quarter of a Calorie per day (on average) will even be noticeable.

Also, I think these metrics probably undersell the boost in performance users will experience with this new version of the expenditure algorithm. After all, the title of this section is “Change #3: Accounting for new goals,” not “Change #3: A tweak to our initial expenditure estimates.”

The same correction that improves initial expenditure estimates can also be applied when you set a new goal in the app, which is where the second expenditure modifier comes in: “Predictive Goal Adjustment.” We know that your expenditure is expected to increase when you switch from losing to gaining weight, and decrease when you switch from gaining to losing weight. So, if you’re currently losing 1% of your body weight per week, and you set a new goal to gain 0.5% of your body weight per week, the expenditure algorithm will be able to take this into account, and proactively nudge your expenditure (and your resulting calorie targets) up by an additional ~6% over the course of a couple of weeks. This will reduce the lag time some users experience between the time they set a new goal, and the time their weight starts increasing or decreasing at their desired rate.

To enable this modifier, just go to More > Expenditure (under Feature Settings) > Predictive Goal Adjustment (under Expenditure modifiers) and toggle the modifier on.

Quantifying the impact of the modifiers

In a recent article, we discussed the accuracy of the V3 expenditure algorithm. So, how much do these modifiers actually improve algorithmic performance?

For starters, enabling both modifiers reduces monthly weight change absolute prediction error by around 6% overall, and around 8% after the initial adaptation period.

The cumulative impact of this persistent edge in accuracy compounds over time. Over the full 100 days, cumulative daily weight change prediction error is reduced by nearly 20%.

As a result, even more people wind up with estimation errors below 5%, 10%, and 20% of TDEE.

So, in total, the modifiers make the algorithm about 6-8% more accurate in the short-term, and nearly 20% more accurate in the long-term. Though, it’s worth acknowledging that the V3 algorithm without modifiers is already exceptionally accurate in the long term. In real terms, the median weight change prediction error over 100 days shrinks from a little over 3lb without modifiers to approximately 2.5lb with modifiers (meaning the median expenditure error over 100 days is about 108 Calories without modifiers versus 89 Calories with modifiers). So, not a night-and-day difference, but still a very solid, tangible improvement.

One other small tweak: slightly increased protein recommendations

Bundled with this update, we also adjusted our protein recommendations to better align with evolving evidence on the topic. Namely, protein recommendations increased slightly for lifters (users who indicate that they regularly engage in resistance training) who are either bulking or maintaining, with larger increases for lifters who are cutting and prefer higher protein intakes.

And, that wraps things up! These updates highlights our focus on consistently updating and improving our core systems on the basis of ongoing internal research and emerging external evidence, in order to help MacroFactor users achieve the best possible results.

The post An Examination of MacroFactor’s Expenditure Modifiers appeared first on MacroFactor.

Obviously, 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).

At the same time, MyFitnessPal is the most widely used food logger for a reason. Because it’s free to use and backed by a large database, it remains one of the most downloaded apps. While MacroFactor has clear advantages, we also recognize that MyFitnessPal may be a better fit for some people, particularly those with limited budgets.

This article aims to give you a clear, side-by-side comparison of MacroFactor and MyFitnessPal 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 two bars a good macro tracker should clear:

1. It needs to be well-equipped to help you reach your goals.
2. Logging food should be as quick and painless as possible.

Regarding goals, when most people download a macro tracker, they have a goal related to weight regulation or improving their body composition. Other popular goals tend to revolve around gaining a better understanding of your diet — that is, understanding which nutrients you under- or over-consume. A good macro tracker should provide nutrition recommendations that help you reach your goals and include a food database with accurate nutrition information. It should also offer robust analytics to help you better understand your nutrient intake patterns.

Regarding the process of food logging, we’ll readily acknowledge that most people don’t love logging their food. It can be a bit tedious, and it is a bit of a chore. After all, a food logger is a utility app, not a mobile game. But to reach the goals that inspired you to use a macro tracker in the first place, you’ll need to log your food consistently for several weeks to a few months. So, a good macro tracker will reduce that friction as much as possible, and provide you with a fast, efficient food logging system. If logging your food takes two minutes per day instead of ten, you’ll have a much easier time building the habit. 

With those two broad criteria in mind, there are seven key factors to focus on when evaluating macro trackers. They all directly influence how easy it is to accurately log your food, and how useful the app will be for helping you reach your goals:

1. Food logging speed: How efficiently can you log your meals?
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 size: Can you find all of the foods you’d like to log?
4. Food database quality: Do the foods in the database have complete and accurate nutrition information?
5. Analytics: Does the app make it easy for you to understand your intake and progress?
6. 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?
7. 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?

Keep in mind that this article compares premium to premium. It would be unfair to MyFitnessPal to compare its free version with MacroFactor, since MacroFactor is premium-only. For the sake of fairness, this article will compare MacroFactor against MyFitnessPal’s Premium plan.

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

1. Food logging speed 

When comparing apps, it makes sense to start with food logging speed. Regardless of features, coaching, or integrations, most people spend most of their time on any nutrition app doing one thing: logging their food. Logging food can be a chore, and like any chore, you want to do it well and quickly. The easier it is to log, the more likely you’ll keep logging over time. That’s why logging speed is one of the best predictors of whether someone sticks with tracking long enough to reach their nutrition goals.

To evaluate logging speed fairly, we use the Food Logging Speed Index (FLSI). The system utilizes a series of case tests to measure the number of actions required to complete common 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 MyFitnessPal, MacroFactor required 10 actions for logging foods via food search, whereas MyFitnessPal required 15 actions. On average, MyFitnessPal requires approximately 1.5 times more discrete actions than MacroFactor across all 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 by a wide margin. Across every workflow, MyFitnessPal requires about 1.5 times more taps or swipes, taking extra time at every meal, which adds up quickly.

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. 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, which makes it easier to log meals at restaurants or gatherings without posted nutrition information. 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. 

MyFitnessPal includes some, but not all, of these quality-of-life features. It covers basics like a recipe importer, widgets, and its own AI meal scan, but much of its recent development has focused elsewhere. MyFitnessPal has put a heavy emphasis on social utilities, recipe ideas, celebratory streaks, and weekly habit goals. These additions may appeal to users seeking community features or extra meal inspiration. Still, they don’t meaningfully reduce the friction of food logging itself.

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. While MyFitnessPal still covers the basics, it has placed more focus on social utilities or habit tracking rather than the core logging experience.  For users who prioritize speed and efficiency, MacroFactor is the clear winner.

3. Food database size

It’s no secret that MyFitnessPal has the largest food database of any nutrition app, with more than 20 million foods. That sheer volume means you can almost always find what you’re looking for, even if you live outside Anglosphere markets (the United States, Canada, the United Kingdom, Australia, Ireland, or New Zealand), where most apps have stronger database coverage. In many parts of the world, MyFitnessPal’s user-fed database gives it a notable edge in branded and packaged product coverage.

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 fresh staples like chicken breast, rice, apples, or onions, both apps have similar coverage.

Where MyFitnessPal’s size is 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 exactly what you need are higher in MyFitnessPal.

Winner: MyFitnessPal

If we’re looking at size alone, especially for international branded foods and packaged products, this one goes to MyFitnessPal. Its user-fed database means you’re more likely to find niche or region-specific items outside of the U.S., Canada, U.K., Australia, Ireland, New Zealand, and much of Western Europe.

4. Database quality

Where MacroFactor separates itself is in the reliability of its data. MyFitnessPal’s database is primarily unverified and user-generated, which has left users frustrated by duplicate and inaccurate entries. They’ve recently added a “best match” system, where some entries are reviewed by dietitians, but unverified and inconsistent data are still common. For many users, this means sifting through multiple options to find the right one, or accidentally logging something inaccurate.

MacroFactor exercises more quality control. Entries come from vetted research databases and verified user submissions that are reviewed by humans before being added. This significantly reduces duplicates and errors. While small inaccuracies still exist (usually because manufacturers reformulate products faster than databases can update them), they’re far less common than what you’ll encounter in MyFitnessPal. In practice, this means you can generally log foods in MacroFactor without needing to double-check every line for accuracy. And if you do run into a missing or reformulated item, MacroFactor’s label scanner makes it easy to create custom foods.

Lastly, beyond everyday macros, 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 MyFitnessPal, 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 MyFitnessPal, 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: MacroFactor

MacroFactor’s verified database may be smaller, but it’s cleaner, faster to navigate, more reliable, and it allows you to track far more nutrients. Over time, that accuracy saves you effort and gives you data you can actually trust. You’ll also be able to track more nutrient data overall and add greater depth to your food logging. 

5. Analytics and progress tracking

When you log data in a nutrition app, you’re building a personal dataset that shows whether your choices move you toward your goals. A good analytics system makes 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’re not just taking the app’s word for it, but can see the connection between your intake and your progress as it unfolds.

MyFitnessPal provides access to a solid amount of data, but much of it is hidden in deeper menus rather than being front and center. And while they do provide progress graphs, such as a simple weight line over time, these aren’t connected to a deeper view of how your intake relates to your energy expenditure and progress.

On the main dashboard for MyFitnessPal, the emphasis is on celebrating habits and logging streaks. While this can be motivating for some users, it’s not always easy to see how well your actions contribute to progress toward your goals. Since MyFitnessPal’s analytics are ultimately built on static Calorie estimates that may not be very accurate, the progress reports can feel underwhelming.

MacroFactor takes a more comprehensive approach. On its main dashboard, you can see your estimated expenditure, weight trend, and energy balance, along with your percentage progress toward your current goal. Instead of just showing a weight line, these tools help you connect the dots between what you’ve logged and whether you’re on track.

Additionally, MacroFactor provides a deeper understanding of nutritional intake through micronutrient reporting, advanced body metrics, and trend tools that can help you quickly identify or troubleshoot problems. This is the difference between summarizing and educating, and MacroFactor ticks all the boxes when it comes to informing the user.

Winner: MacroFactor

MacroFactor turns your data into usable insights, and its dashboard immediately shows trends, energy balance, and progress toward your goals. While MyFitnessPal has data, it’s mostly buried in the interface and focuses more on extra features and streak celebrations.

6. Price & consumer friendliness

When it comes to pricing, the comparison looks different  depending on whether you’re considering a free versus premium service. MyFitnessPal is well-known for its free tier, which is a major reason for its popularity. Of course, “free” comes with trade-offs: the app is supported by ads, and those ads generally require sharing user data with third parties. Additionally, over the years, many of the most useful features (like barcode scanning) have been moved behind the paywall. That said, if price is your primary consideration when choosing a nutrition app, it’s hard to beat “free.”

There are valid reasons people want to avoid paying for a food logger. One apt analogy for food logging apps is a gym. You can exercise at home, but training at a gym offers more equipment, options, and a space that can help you stay consistent. A food logger can work similarly. You can jot down meals anywhere (on paper or in a free app), but MacroFactor provides more than just a place to log; it adds analytics, insights, and weekly adjustments that make your data more useful. The question is whether you’re in the market for the best option or just the cheapest.

As discussed, MacroFactor is the fastest food logger on the market, providing far more accurate nutrition targets than the static equation approach. That means you’re not just saving time when logging meals, you’re also saving time and frustration later by getting more consistent results. MyFitnessPal takes a different approach, with a stripped-down free tier and a premium tier that doesn’t deliver the same level of accuracy, speed, or insights you’d expect for the cost. 

This ultimately comes down to comparing free versus premium, or premium versus premium. MacroFactor is a premium-only app, but it offers faster logging, more accurate recommendations, and smarter analytics than MyFitnessPal Premium — and at a lower price. If you subscribe monthly, MacroFactor costs $11.99 compared with $19.99 for MyFitnessPal Premium. Both apps reward longer commitments, but MacroFactor still comes out ahead on yearly plans at $71.99 a year, or about $5.99 a month, versus $79.99 a year ($6.67 a month) for MyFitnessPal. In other words, you get more features and better value at a lower price.

Winner: Tie (half point each)

This category is split since affordability and value can be measured in different ways. MyFitnessPal earns credit for offering a free tier. But when the comparison shifts to premium service, MacroFactor comes out ahead. It’s more affordable, faster, and more accurate than MyFitnessPal Premium. So, half a point to MyFitnessPal for the free option, and half a point to MacroFactor for delivering the better premium product at a lower cost.

7. 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 MyFitnessPal 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.

An alternate approach of estimating energy requirements involves the use of wearable devices or activity formulas to estimate energy expenditure. MyFitnessPal also offers the option of letting the app estimate your energy needs on a sedentary day, and using exercise data to add additional calories to your daily allotment. However, both of these options have serious drawbacks.

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.

With MyFitnessPal’s approach of estimating baseline energy requirements using a population-based TDEE formula, and estimating additional energy requirements based on data from a wearable device or formulas that estimate the energy cost of exercise, you’re essentially getting the worst of both worlds. You still have the error associated with estimating energy requirements using TDEE formulas (discussed above), but it’s now being compounded by the error associated with estimating additional energy expenditure during exercise (which can be off by 50% or more).

Winner: MacroFactor

As a result, MacroFactor is the clear winner in this category. If you’d like your nutrition app to help you determine appropriate Calorie and macronutrient targets to aid you in achieving your goals, MacroFactor’s coaching algorithms are quantifiably more effective, whereas the approach employed by MyFitnessPal has serious shortcomings.

Summary

Category	MacroFactor	MyFitnessPal	Winner
Food logging speed	Fastest food logging workflows on the market; scored best on the Food Logging Speed Index.	Workflows are slower than MacroFactor’s, requiring about 50% more actions on average.
Features	AI food logging with editable ingredients, expandable recipes, copy/paste, favorites, and hourly go-tos.	Basic recipe importer, widgets, streak, social features, and habit features.
Food database size	1.36 million verified search foods, plus another 4 million in its barcode database.	Over 20 million foods in total, with broad international packaged food coverage.
Food database quality	Verified search entries and gold-standard research database with complete micronutrient data.	Large user-submitted database with many duplicate or inaccurate entries; limited micronutrient data.
Analytics	Advanced weight trending, adaptive coaching, and detailed micronutrient reporting.	Covers basic Calorie/weight tracking; advanced analytics are limited.
Price & consumer friendliness	Lower cost than MFP Premium; no ads, no data-sharing, and no hidden paywalls.	Offers a free tier, but the premium plan costs more with fewer efficiency features.
Coaching accuracy	Adaptive coaching adjusts to your data; significantly more accurate than static equations.	Provides simple Calorie targets based on formulas that don’t adapt to metabolic changes.
Total points	5.5 points	1.5 points

Which app is right for you?

MyFitnessPal may be the better option if you only want a free app or if you place a high value on social features. Those strengths are real, and for some people, they may be compelling reasons to use MyFitnessPal.

Otherwise, MacroFactor makes food logging faster and easier, 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. MyFitnessPal: Which Macro Tracking App Wins in 2025? appeared first on MacroFactor.

But, before we get too deep in the weeds, we need to first cover a bit of background information to briefly explain how the algorithm works, and how it differs from the approach taken by most other nutrition apps.

The typical approach of estimating energy needs

Virtually all nutrition apps provide recommendations on the basis of the CICO principle: long-term weight change is principally determined by energy balance. If you consume more energy (“Calories In”: CI) than you expend (“Calories Out”: CO) over time, you’ll be in a state of positive energy balance, and therefore you’ll gain weight. Conversely, if you consume less energy than you expend, you’ll be in a state of negative energy balance, and therefore you’ll lose weight.

If you’re diligent about logging your food, it’s not too hard to quantify “Calories In” with reasonable accuracy (even if your food logging isn’t perfect, or some foods have nutrition labeling errors). The tricky part of the CICO equation is the “Calories Out” term.

To estimate “Calories Out” (i.e., your total daily energy expenditure, or TDEE), the most popular approach is to use a calculator or equation that estimates your energy expenditure on the basis of some combination of your height, weight, age, sex, body composition, and activity levels. This process will typically provide most people with a reasonable ballpark estimate of their energy expenditure, but you also shouldn’t expect this value to be extremely accurate. It’ll be spot-on for some people, but individual errors of >500 Calories are quite common, and errors exceeding 1,000 Calories per day aren’t unheard of.

This graphic, and the simulation used to generate it, were originally published in this article.

An alternate approach is to estimate your energy expenditure using a wearable device (like a smartwatch), but research on wearables suggests that they’re similarly inaccurate, with a systematic review reporting that in free-living settings, wearable devices under- or over-estimate energy expenditure by more than 10% a whopping 82% of the time.

But, as mentioned above, estimating energy expenditure using a TDEE equation is by far the most common approach used by other nutrition apps, so that’s what we’ll use as a point of comparison for the rest of this article.

Once an app estimates your energy expenditure, it will typically recommend an energy intake target to you based on your goals. For example, using the typical energy density assumption of 3,500 Calories per pound (or 7,700 Calories per kilo), if you have a goal of losing a pound per week, your recommended energy intake target would be 500 Calories per day below your estimated TDEE. So, if your estimated energy expenditure is 2,500 Calories per day, the app would recommend an energy intake target of 2,000 Calories per day.

Some apps will explicitly show and tell you about these calculations, and some will keep them obscured, but this is the standard process that’s used almost universally. (And if this isn’t something a nutrition app does for you, these are the basic calculations most dieters will do themselves. This is also the basic process employed by LLMs and most nutrition coaches if you ask them for initial energy intake targets.)

From there, most apps don’t update or adjust your energy intake targets over time, even if your energy intake target turns out to be way too high or too low to help you reach your goals. And, of the handful of apps that do recommend updates, the most common approach is to just increase or decrease calorie targets proportionally with body weight.

The MacroFactor approach

When you first start using MacroFactor, the app will initially estimate your energy expenditure and provide energy intake targets using the same process described above, using a TDEE equation. The major difference between MacroFactor and other apps is that, as you log your food and weight over time, MacroFactor will continually update its estimate of your energy expenditure, and therefore recommend updates to your nutrition targets to reflect expenditure changes.

The expenditure algorithm itself is fairly complex, but here’s a simplified illustration of how it works:

When you start using MacroFactor, the app may initially estimate that your energy expenditure is 3,000 Calories per day. You have a goal to lose a pound per week, so the app recommends that you aim to consume 2,500 Calories per day.

However, as you log your weight and nutrition, you’re consistently eating 2,500 Calories per day, but you’re not losing any weight. This strongly suggests that your initial energy expenditure estimate (and, consequently, your initial caloric intake targets) was too high: if you’re maintaining your weight while eating 2,500 Calories per day, your energy expenditure is probably closer to 2,500 Calories per day than 3,000. So, your estimated expenditure will adjust downward, and your energy intake targets will decrease to reflect this new information, until your energy intake target does actually result in your desired rate of weight loss.

In other words, unlike other apps, MacroFactor is able to identify and correct errors in its nutrition recommendations. If your energy intake targets are too high or too low to help you reach your goals at your desired rate, MacroFactor’s algorithms will make adjustments to get you back on track. And, crucially, these updates are continuous as your goals, lifestyle, and energy expenditure change over time.

How we evaluate our algorithms

At MacroFactor, we’re big fans of quantification. We don’t just want to be able to say our algorithms work well in theory, we want to be able to measure how well they actually work in practice.

Accuracy versus predictive validity

The default method of quantifying the accuracy of something like MacroFactor’s expenditure algorithm is to run a validation study. For example, we could have 20 people live in a metabolic chamber for a few months while researchers fastidiously monitor their weight and provide them with meals prepared in the lab’s kitchen with perfectly accurate nutrition information. Then, we’d log all of the data in MacroFactor and compare how closely MacroFactor’s estimated expenditure values matched the values derived from the metabolic chamber. While this would be the gold standard approach for quantifying the accuracy of MacroFactor’s algorithms, it would be prohibitively expensive, and we actually care way more about a close cousin of accuracy: predictive validity. Predictive validity tells you how well your predictions match subsequent observations.

MacroFactor’s expenditure algorithm is a prediction engine. If MacroFactor estimates that you’re burning 3,000 Calories per day, and you also consume 3,000 Calories per day, the app is tacitly predicting that your weight will remain stable. If you instead gain or lose weight, that suggests the prediction was incorrect to some degree. So, we can calculate the difference between your predicted rate of weight change (based on your logged energy intake and your estimated expenditure) and your actual rate of weight change to quantify the predictive validity of our algorithms.

We care the most about predictive validity with real-world data for two main reasons.

The first reason is that predictive validity tells us how well our algorithms actually work for our users. In the real world, people don’t always log their food with perfect accuracy or weigh themselves every day under perfectly standardized conditions. Any estimation of accuracy under perfectly controlled laboratory conditions would almost necessarily overestimate how well the algorithms actually work for real users — it would just be an unrealistic vanity metric. What actually matters to us is knowing how well the algorithms can deal with the messiness of the real world.¹

The second reason is related to the first: When predictive validity and raw accuracy differ, optimizing for predictive validity means optimizing for usefulness, which is what we ultimately care about. Optimizing for accuracy at the expense of predictive validity would actually lead to worse recommendations for our users.

That may not sound particularly intuitive, but it’s a simple principle to illustrate.

Let’s assume that we can know with perfect certainty that you burn exactly 3,000 Calories per day. So, if you want to maintain your weight, you should aim to consume 3,000 Calories per day. Simple enough.

But, what if you don’t log your energy intake with absolutely perfect accuracy? Maybe you eat a couple of foods every day that under-label their Calorie content. Maybe you eyeball portion sizes for some foods, or forget to log some sauces or condiments. You do a pretty good job of logging your food, but you ultimately end up typically consuming 3,200 Calories when you think you’re eating 3,000. So, you think you’re eating the right amount to maintain your weight, but the number on the scale starts gradually ticking up.

If you just care about optimizing for accuracy, there’s nothing that needs to change about the recommendation: You should aim to eat 3,000 Calories per day. Since we know you’re burning 3,000 calories per day, that is the correct recommendation. But, practically speaking, it’s a recommendation that’s resulting in an undesired outcome (gaining weight instead of maintaining weight). The only way it would lead to your desired outcome is if you became utterly obsessive about logging your food with 100% perfect accuracy, which is almost never feasible (at least long-term) in the real world.¹

Since MacroFactor primarily cares about optimizing for predictive validity, we’d see that you’re slowly gaining weight when you’re logging 3,000 Calories per day, which would suggest that your energy intake target should be less than 3,000 Calories per day to help you achieve your goal of weight maintenance. So, the app would settle on an estimate that you’re burning around 2,800 Calories per day, and therefore, it would recommend that you should aim to consume 2,800 Calories per day to achieve your goal of weight maintenance.

You tend to underestimate your intake by 200 Calories per day (in this illustration), so when you aim to eat 2,800 Calories per day, you’ll actually end up eating around 3,000 Calories per day and maintaining your weight. In other words, the recommendation to consume 2,800 Calories per day is technically a less accurate recommendation, but it’s the recommendation that results in the desired outcome while simultaneously maximizing predictive validity.

In essence, accuracy and predictive validity are identical (in this context) if users always log their energy intake with absolutely zero error. But, if there are any errors in measuring energy intake, more accurate nutrition recommendations would necessarily lead to worse outcomes than recommendations that reflect and account for those errors.

For the sake of simplicity, I’ll be using the term “accuracy” for the rest of this article. But, just know that “accuracy” refers to the accuracy of MacroFactor’s implicit predictions related to prospective rates of weight change (i.e., predictive validity).

Assessing algorithmic performance

There are four key metrics we pay attention to when evaluating how well our algorithms perform:

1. Absolute monthly weight change prediction error: to what extent do users’ actual rates of weight change differ from their predicted rates of weight change?
   • We focus on absolute values (i.e., making all values positive) because the simple average is (essentially) zero
2. The correlation between actual and predicted rates of weight change
3. Changes in cumulative accuracy over time
4. The ability of the algorithms to avoid large errors

As we go along, most of these metrics will be fairly intuitive, but I think it’s worth being explicit about how predicted rates of weight change are calculated.

Over a 30-day period, we calculate MacroFactor’s cumulative estimate of your energy expenditure and then subtract your cumulative energy intake. Finally, we divide that value by 3,500, which is the implied energy content of each pound of weight change.²

Here’s an example: At the start of a 30-day period, MacroFactor estimated that your expenditure was 3,100 Calories per day, and that estimate decreased linearly to 3,000 over those 30 days. So, MacroFactor is tacitly predicting that you burned 91,500 Calories during that 30-day period. Over the same 30 days, you consumed 84,500 Calories. So, MacroFactor is tacitly predicting that you expended 7,000 more Calories than you burned, meaning that you should have lost 7,000/3,500 = 2lb. If you actually lost 2.3lb over that same 30-day period, the weight change prediction error is: -2.3 – (-2) = -0.3lb. In other words, your weight is 0.3lb lower at the end of the month than MacroFactor predicted, given your energy intake. Since we primarily focus on absolute values, this corresponds to an absolute monthly error of 0.3lb.

All of the analyses performed below use data from new users of the app who participated in our 2025 transformation challenge. This is a useful dataset for this article for a few reasons. First, all of the individual datasets cover the same length of time (100 days), so there are no issues related to sample sizes varying over time. Second, long-time users already understand how well the algorithms work. By focusing on new users, we can show how well you can expect the algorithms to work for you if you’re not already using MacroFactor. Third, new user data helps to clearly demonstrate how long it takes for the algorithms to achieve peak performance.

For most of the subsequent analyses, we’ll compare MacroFactor’s approach to the common default approach of estimating energy expenditure using a TDEE equation or calculator. This also happens to be the approach used by most other food logging apps. This should help contextualize the performance metrics.

For these comparisons, expenditure estimates using the “TDEE calculator” approach are re-calculated on a daily basis (rather than using a single static value for the entire 100 days) to scale proportionally with weight gain or loss. Without this daily recalculation, the relative inaccuracy of this approach could just be attributed to one-time TDEE estimates becoming outdated (i.e., if your calculated TDEE was perfectly accurate today, that discrete value would probably be considerably too high two months from now if you lost 20lb), but I want to show the potential for error even if you used a TDEE calculator every single day to estimate your energy needs.

Finally, all of the metrics below use the data from users with scant evidence of partial logging. We emphasize to our users that they don’t need to log all of their food with perfect accuracy, that it’s totally fine to just make a good-faith estimate for meals that are tough to log, and that they don’t even need to log absolutely every morsel of food that they consume. Partial logging is the single cardinal sin we ask users to avoid, there are plenty of easy ways to avoid it, and we even try to flag partially logged days for users when they do their weekly check-in. So, we feel justified excluding this data from the analyses presented below.

To estimate partial logging frequency, we tagged days where logged energy intake was <50% of the values from other logged days within the same week. So, for instance, if a user was eating around 2,000 Calories most days, but they had a day where they only logged 600 Calories, that would be flagged as a day that was probably partially logged. Days where users logged zero calories were not flagged, since these were likely to be days when users were intentionally fasting.

Obviously, this is an imperfect metric. There were almost certainly partially logged days that slipped through (i.e., if someone logged breakfast and lunch but didn’t log dinner, they may have still logged ~60–70% as many Calories as were logged on surrounding days), and some accurately logged days that were incorrectly tagged as being partially logged (i.e., if you have a stomach bug, you may truly have a day or two where you only consume 500 Calories). But, we’re dealing with enough data that a handful of misclassified users will have virtually no effect on the aggregate metrics. For a user’s data to be included in the analyses presented below, they could have at most five days of nutrition data that were flagged for potential partial logging. Ultimately, less than 10% of users were excluded from these analyses due to evidence of consistent partial logging, so the analyses presented below will certainly still provide a fair assessment of algorithmic performance for typical users.

After excluding the handful of users with evidence of consistent partial logging, we were left with data from 748 challenge participants.

Update: November 2025

Following the introduction of additional expenditure modifiers, the expenditure algorithm is now about 7% more accurate in the short term, and approximately 20% more accurate in the long term. You can read more about these improvements here.

Monthly weight change prediction errors

As mentioned above, when you first start using MacroFactor, it will initially estimate your energy expenditure and Caloric needs using a process similar to any other app, by applying a series of predictive equations that account for factors like height, weight, age, sex, body composition, and activity levels. These initial recommendations are not “the algorithm.” Rather, the algorithms kick in once you start logging your weight and nutrition data, and serve to refine MacroFactor’s understanding of your unique energy requirements.

So, as an initial test of MacroFactor’s algorithms, we can quantify how much more accurate MacroFactor’s recommendations become over time as personalized algorithmic recommendations replace recommendations derived from population-based equations.

In the graph above, the x-coordinate is the day at the beginning of each 30-day period. So, day 10 is the median weight change prediction error for days 10-39, day 20 is the median weight change prediction error for days 20-49, etc. The median prediction error decreases from approximately 1.9lb for the first 30-day period, to approximately 1.15lb from day 24 onward. So, it would appear that MacroFactor’s algorithms have nearly twice the accuracy of population-based TDEE prediction equations (i.e., error rates decrease by ~40%) when we compare the typical errors observed during the first 30-day period to the errors observed after people have been using the app for at least 3-4 weeks.

But, appearances can be deceiving. In fact, accuracy more than doubles. MacroFactor’s algorithms begin updating their estimates of your energy expenditure on the third day after you start using the app. So, those estimates are gradually improving for basically the entirety of the initial 30-day period. Without those improvements, prediction errors for the initial 30-day period would be considerably larger. We can see this when we compare MacroFactor’s accuracy to the accuracy of weight change predictions using the typical approach of just plugging your height, weight, age, sex, body composition, and activity levels into a TDEE formula or calculator (I’ll just refer to this as the “formula-based approach” for the rest of this article).

The formula-based approach had a median prediction error of about 2.6lb for the first 30 days. Rather than decreasing, these errors increased to a median prediction error of about 3.1lb for the last 30 days (likely due to metabolic adaptation, which TDEE prediction equations don’t account for).

So, after using MacroFactor for 3–4 weeks, the app’s nutrition recommendations are about 120–170% more accurate than the recommendations provided by a standard TDEE equation (i.e., typical errors are 55–63% smaller with MacroFactor).

Based on an assumed energy density of 3,500kcal per pound gained or lost, we can convert these weight change prediction errors into caloric terms (since, after all, we’re discussing the accuracy of MacroFactor’s algorithms, which estimate energy expenditure in terms of Calories). 

After 3–4 weeks of consistent use, MacroFactor’s energy expenditure estimation errors typically fall within the range of 60–240 Calories (median = 135 Calories), whereas formula-based estimation errors typically fall within the range of 155–590 Calories (median = 335).³ Furthermore, relatively large errors in MacroFactor (errors in the 75th percentile) are considerably smaller than the median errors of formula-based TDEE estimates, and relatively small errors for formula-based TDEE estimates (errors in the 25th percentile) are still larger than the median errors for MacroFactor users.

Strength of associations

As another method of quantifying how well MacroFactor’s algorithms estimate users’ energy needs, we can calculate the strength of the association between predicted and observed rates of weight change over time.

A correlation coefficient (Pearson’s r) tells you the strength and direction of an association. A positive r-value denotes a direct association (meaning that as one value increases, the other value also increases), whereas a negative r-value denotes an inverse association (meaning that as one value increases, the other value decreases). Furthermore, values closer to 0 denote a weak association, whereas values closer to 1 or -1 denote a strong association.

Since MacroFactor’s algorithms aim to predict users’ rates of weight change based on their energy intake, strong performance would coincide with a large, positive r-value. Smaller r-values would suggest that we were doing a poor job of estimating energy needs, and negative r-values would mean we were doing an extremely poor job of estimating energy needs.

Starting with our point of comparison, formula-based TDEE estimations were able to predict monthly rates of weight change with an r-value of 0.595. This is right in between a “moderate” and a “strong” correlation. Not too bad!

However, MacroFactor’s weight change predictions are far more strongly associated with users’ observed rates of weight change, with a r-value of 0.869. This is comfortably a “very strong” correlation. This also means that MacroFactor’s equations explain more than twice as much variance in observed rates of weight change compared to predictions from TDEE formulas (r² = 0.755 for MacroFactor vs. r² = 0.354 for TDEE formulas).

Of course, there’s plenty of noise in monthly data, not to mention that each user has 70 individual data points represented in both of the graphs above (meaning that each data point isn’t independent of all other data points, which is technically not advised for simple correlation analysis). So, let’s take a look at how well MacroFactor and TDEE formulas were able to predict users’ total weight change over the 100-day challenge period, with one data point per user.

The correlation coefficients increase for both (r = 0.94 for MacroFactor vs. r = 0.61 for TDEE formulas), but now MacroFactor’s predictions explain nearly 90% of the variance in observed weight change, while predictions from TDEE formulas still explain less than 40% of the variance.

Cumulative accuracy

Since we primarily focus on predictive validity when evaluating the performance of MacroFactor’s algorithms, there’s something of an upper limit on assessed performance over any finite time scale, because short-term weight changes don’t always reflect changes in energy status.

To illustrate, let’s just assume that MacroFactor’s algorithms are literally perfect (I’m not saying that they are — this is purely for illustrative purposes): they perfectly track with your energy expenditure, so they can always predict the size of your cumulative energy surplus or deficit over any time scale with zero error. Over relatively long time scales, your cumulative energy surplus or deficit is the primary determinant of weight change, but in the short term, your weight can fluctuate for reasons unrelated to energy balance. So, if you assessed how well this perfect algorithm could predict weight changes over any finite period of time, it would never get a “perfect score.”

For example, if you assess the performance of this perfect algorithm over a month, some people are going to start or end that month weighing a couple of pounds more or less than their “true” weight simply due to dehydration, low glycogen stores, bloating, constipation, etc. Observed rates of weight change will differ — at least to some degree — from theoretically perfect predictions pegged to energy balance, just due to the inherent noise in weight data.

However, this apparent error due to the noisiness of weight data should be similar on an absolute basis over basically all finite time scales. In other words, the number on the scale may change by a pound or two more than it theoretically “should” due to things like water weight, bloating, glycogen stores, etc., but these apparent errors should be similar in magnitude (on average) over a week, a month, a year, or a decade. So, when you instead calculate per day error rates, they should be expected to decrease over time. A pound of “error” just resulting from the noisiness of weight data is 0.14lb of prediction error per day over the course of a week, 0.03lb of prediction error per day over the course of a month, and 0.003lb of prediction error per day over the course of a year.

So, by assessing the behavior of cumulative per-day error rates, we can gain confidence that the algorithms are actually doing a good job of helping people achieve their desired rate of weight change over time, and we can cut through some of the inherent noisiness of that weight data.

Overall, this is the type of pattern we like to see: per-day error consistently decreasing over time. By the end of the 100-day challenge period, the median weight change prediction error per day was only 0.031lb, with an interquartile range (IQR) of 0.014–0.054lb per day. In other words, if you set a goal of gaining or losing some amount of weight, and you perfectly followed MacroFactor’s recommendations for 100 days, you should generally expect your final weight to be within about 3.1lb of your goal.

By comparison, if you continuously used a TDEE formula to estimate your energy expenditure (the process used by most other nutrition apps), instead of the per-day error rate gradually decreasing, the median per-day error rate essentially plateaus at about 0.085lb, with an IQR of 0.042–0.151lb per day. 

In caloric terms, this suggests that the typical error over the course of a user’s first 100 days with MacroFactor is about 4.4% of TDEE, or about 110 Calories per day. In other words, if your average expenditure for the first 100 days you use MacroFactor is estimated to be 2,500 Calories, you can be fairly confident that your actual average expenditure over that time span was between 2,390 and 2,610 Calories per day. Furthermore, the IQR for Caloric estimation errors covers the span of 50–185 Calories per day, or 1.9–7.8% of TDEE. So, cumulative errors smaller than 50 Calories per day are more common than cumulative errors exceeding 200 Calories per day.

If you instead continuously used a TDEE formula, you could expect a median error of approximately 300 Calories per day (around 12% of TDEE), with an IQR of 135–525 Calories per day (5.4–21.1% of TDEE). In other words, there’s a less than 1-in-4 chance that your Calorie targets would be as accurate as those of a typical MacroFactor user, and a greater than 1-in-4 chance that your Calorie targets would be more than 500 Calories too high or too low per day.

It’s also worth mentioning that around a quarter of the cumulative error for MacroFactor users during their first 100 days of using the app comes from errors in their initial expenditure estimate (i.e., before the algorithms take over and converge on highly personalized recommendations after 3-4 weeks). Of the median error of 3.1lb over the first 100 days, approximately 0.8lb can be directly attributed to error in the initial estimates. Once the algorithms fully take over, the cumulative error is approximately 0.023lb per day, equivalent to a median Caloric estimation error of approximately 80 Calories per day, or 3.25% of TDEE. That is roughly the degree of ongoing accuracy that users can expect after their first 3-4 weeks of consistently using the app.

Within-individual comparisons of accuracy

All of the comparisons above paint MacroFactor’s algorithms in a very favorable light, but they actually still undersell the accuracy of MacroFactor’s algorithms for each individual user.

When you just compare the medians and interquartile ranges seen in the graphs and text above, it might look like MacroFactor should be more accurate for around 75–80% of people, with TDEE formulas being more accurate for around 20–25%. However, in actuality, MacroFactor is more accurate for essentially everyone.

Instead of just looking at the distribution of prediction errors for the entire cohort of users, we can instead make within-individual comparisons: for each user, how large was the absolute prediction error with MacroFactor compared with the absolute prediction error from a TDEE formula?

Overall, prediction errors were smaller with MacroFactor for 94.1% of users, whereas prediction errors would have been smaller with a TDEE formula for just 5.9% of users. However, even that split still undersells the accuracy of MacroFactor.

The 5.9% of users who would have had nominally lower prediction errors from TDEE formulas were just users that would have gotten very accurate recommendations with both methods. In this small group of people, the average difference in 100-day prediction errors was just 0.66lb in favor of TDEE formulas.

On the flip side, the 94.1% of users who got more accurate recommendations from MacroFactor’s algorithms typically got way more accurate recommendations from MacroFactor’s algorithms, compared with the recommendations that would have come from a TDEE formula. For these users, the average 100-day prediction error with MacroFactor was 7.48lb smaller than the average 100-day prediction error from TDEE formulas.

In other words, there’s a ~94% chance that MacroFactor’s algorithms will predict your energy needs more accurately than a TDEE formula would over a 100-day period. Furthermore, if you happen to be in the ~6% that would get slightly more accurate recommendations from the TDEE formula, the difference between the two would be practically imperceptible (a difference in predicted weight change error of 0.66lb over 100 days is equivalent to a difference in Calorie recommendations of about 23 Calories per day). In essence, the only time that TDEE formulas “win out” is when they just so happen to produce recommendations that are virtually identical to MacroFactor’s recommendations.

But, for the ~94% of people who got more accurate recommendations from MacroFactor, the benefits were typically quite large, with an average difference in weight change prediction errors of 7.48lb over 100 days. That means that, for the vast majority of people, MacroFactor’s nutrition recommendations are more accurate than the recommendations of a TDEE formula by about 262 Calories per day, on average. Furthermore, the larger the difference between MacroFactor’s estimate of your energy needs and a TDEE formula’s estimate of your energy needs, the more likely it is that MacroFactor’s estimate is considerably more accurate.

All in all, MacroFactor’s algorithms estimate your energy needs nearly three times more accurately than estimates derived from the types of TDEE formulas that other apps solely rely on.

Avoiding large errors

The final thing we assess when evaluating the algorithms is their ability to ensure that everyone can be confident in their recommendations. It’s all well and good for most users to receive accurate recommendations from the app, but we’d be extremely disappointed if a sizable minority was still receiving bad recommendations.

Our metric here is simple: How frequently do the observed weight change prediction errors imply that the app’s energy expenditure estimate is off by 10% or more?

Ten percent is our benchmark for one simple reason: MacroFactor users often want to know if they should put more trust in the app’s estimate of energy needs versus their smartwatch (or other wearable device).

As we’ve covered previously, wearables are notoriously bad at estimating energy expenditure. That’s a near-unanimous finding in the scientific literature, with a systematic review finding that wearables under- or over-estimate energy expenditure in free-living humans by at least 10% in 82% of the studies that have tackled the question.

So, as a motivating metric to aim for, we want to be able to flip those numbers: Instead of generating errors larger than 10% at least 82% of the time, we want MacroFactor to generate errors smaller than 10% at least 82% of the time.

We’re doing pretty well on that front.

When we assess errors on a rolling 30-day basis, implied expenditure estimation errors exceeding 20% of TDEE are very rare (occurring in less than 5% of user-months following the initial 3-4 weeks of calibration). A little over three-quarters of implied monthly errors are smaller than 10% (75.9%, to be exact) after day 30, so we do still fall slightly short of our 82% goal. Furthermore, a little over 45% of implied monthly errors are smaller than 5% of TDEE.

However, when we look at periods of time exceeding a month, we do reach our goal of producing errors smaller than 10% more often than wearables produce errors larger than 10%. Over 100 days, errors exceeding 20% occur less than 2% of the time, around 55% of users have cumulative errors below 5%, and nearly 84% of users have errors below 10% of TDEE.

General notes

Although we excluded users with strong evidence of consistent partial logging from the analyses above, I thought it might still be worth showing the impact partial logging can have. As you can see in the graph below, occasional partial logging (for example, slipping up and partially logging one day per month) isn’t that much worse than never partial logging. However, as partial logging rates exceed 5% of days, and certainly as partial logging rates exceed 10% of days, accuracy decreases considerably.

I should also note that all of the analyses above only screened out people with extensive evidence of partial logging. We didn’t exclude people based on their demographic characteristics (age, sex, activity levels, etc.), or for not logging every single day of eating, weighing infrequently, or occasionally having logging data that looked fairly implausible (for example, 300lb men not losing weight while logging 1,500 Calories per day).

The purpose of this analysis wasn’t to show the theoretical upper limits of our algorithms’ performance with pristine, highly curated data. The purpose was to show how well the algorithms work for a broad, representative sample of typical users, some of whom don’t always log with perfect accuracy or consistency. Even with imperfect data, the algorithms are still able to consistently generate excellent recommendations.

But, if you are someone who weighs everything down to the gram and never misses a day of logging, you can reasonably expect that the algorithms will be even more accurate for you than the analyses presented in this article would suggest. However, the algorithms still work very well even if you aren’t overly obsessive with your logging habits, as long as you do avoid partial logging.

Takeaways

Since this is an article on the MacroFactor website, I’m sure you expected us to conclude that our algorithms work very well. If so, your expectations were correct. The only way to measure accuracy is to measure error, so the analyses were all presented in terms of the errors the algorithms produce. The magnitude of those errors tells us two things:

1. The algorithms aren’t perfect. There is still some degree of error that’s not simply attributable to initial estimation errors or the inherent noisiness of weight data.
2. Our algorithms are quantifiably much better than anything else on the consumer market at estimating energy expenditure (for the purpose of recommending nutrition targets to help you reach your goals).

Just to drive the second point home, here’s a final head-to-head comparison of MacroFactor versus the two most popular methods of estimating energy expenditure: TDEE formulas and wearable devices. Since the only metric we have for wearables is the frequency of errors smaller than 10%, that’s the metric we’ll need to use for the comparison.

By this metric, MacroFactor is about twice as accurate as TDEE formulas, and about 4-5 times more accurate than wearable devices. Not too shabby.

Ultimately, we care very deeply about the quality of our algorithms, because they’re at the heart of the MacroFactor experience, and they’re one of the main reasons why people use and trust MacroFactor instead of some other nutrition app. Our main goal is to help our users reach their goals, and being able to consistently provide accurate nutrition recommendations is central to that aim. That’s why we’ve invested so much effort into developing our market-leading algorithms, and it’s why we’re still constantly researching ways to improve them even further.

Footnotes

1. To perfectly account for your energy intake, you’d need to burn some of each individual food you consume in a bomb calorimeter to adjust for nutrition labeling inaccuracies, and you’d also need to collect and burn all of your feces to account for the chemical energy in food that you don’t absorb. Even if you weigh everything you eat to the nearest gram, never eat at restaurants, etc., I can promise you that you still have some non-trivial degree of energy intake estimation error.
   
2. The same basic process works for any discrete period of time. We just tend to focus on monthly data because it’s a convenient and common unit of time, and analyses over shorter or longer time periods don’t lead to materially different conclusions.
   
3. Formula-based errors increased over time, even when accounting for changes in body weight, likely due to the fact that most participants in the MacroFactor challenge were losing weight, and were thus experiencing progressively greater energy compensation and metabolic adaptation over time. Unlike a static formula, MacroFactor can dynamically adapt to these changes. But, for people in a state of energetic maintenance, you shouldn’t necessarily expect formula-based estimation errors to increase over time. I didn’t want to call attention to the increased error magnitudes with formula-based estimations in the text of the article since these increased errors probably have limited generalizability.

The post How Accurate is MacroFactor’s Expenditure Algorithm? appeared first on MacroFactor.

There are plenty of ways to compare food logging apps, but one of the most important is speed and ease of logging. After all, logging is the action you’ll perform most often. Ideally, you want a frictionless experience that makes logging easy and helps you stick with it. But how do you actually measure speed?

That’s where the Food Logging Speed Index (FLSI) comes in. It’s an objective way to measure the number of steps required to complete the most common logging tasks. In 2022, we introduced the FLSI to determine which apps made the process the fastest. This year, we decided to rerun the tests to see how MacroFactor still stacks up against the competition and whether we could improve on our benchmark from before.

Do we still hold the top spot? And how did the rest of the field perform? Let’s dive into the 2025 results.

Why test fast food logging?

There are two big reasons this makes sense for app comparisons. 

First, while there are plenty of valid ways to evaluate a food logging app, the most meaningful rankings are based on objective measures.  For example, design choices such as colors or layout may be important for personal preference, but they’re harder to measure in a way that produces fair and repeatable comparisons.

Second, speed matters. The less friction an app puts between you and logging your food, the more likely you are to keep using it. Shaving seconds off every meal adds up. Logging in 30 seconds is better than one minute, and one minute is better than two.

What is the FLSI?

There wasn’t an established, evidence-based way to measure speed in food logging, even though it’s central to how people use these apps. To address this, Cory Davis from MacroFactor developed the Food Logging Speed Index (FLSI), a scoring framework designed to objectively compare the number of discrete actions (e.g., taps, clicks, selections) required to complete the most common food-logging tasks.

If you’re interested in more background on how Cory developed this scoring system, I recommend checking out the original post here. 

How does the FLSI work?

The FLSI uses four main methods of logging food to measure performance:

Logging through search

Searching a database and selecting a relevant result is the most common way people log food.

Logging with a multi-add function

When you’ve logged a food before, or the search result already matches the serving and quantity you need, multi-add or fast logging shortcuts can save time.

Logging using a scanning feature

Directly scanning the barcode on branded product packaging is often the fastest way to log packaged foods.

Logging using a quick-add of Calories

When you already know the Calorie and/or macronutrient numbers you want to log (and you don’t need to associate them with a specific food), this is the simplest and fastest method.

How we score each app

• We measure the entire process, not just small steps like choosing a serving size or typing a number.
• A test is considered complete when all goals are met, with no more than two simple steps for each goal.
• Scores represent the number of actions it takes from the starting screen to the ending screen.
• Lower scores are better, since fewer steps mean faster logging.
• If an app cannot perform a test, it receives the worst possible score.
• A strong score means performing better than the midpoint between the best and worst scores in that category.

Ground rules for testing

• We use the fastest possible settings for each logging scenario, even if those settings wouldn’t work for every scenario at once.
• We follow the shortest list of actions to finish the task, even if the path isn’t obvious to a new user.
• All actions must be reasonable in real use. Unusual gestures or gimmicks that may look cool but are impractical don’t count.
• If the app has a premium version, we use it to see what the app can do at its best.

Allowing leeway for competitors

Sometimes an app’s setup and design make it tricky to compare action counts exactly. In these cases, the FLSI allows small exceptions, or “freebies”:

• There’s no penalty if the settings for one logging scenario don’t work for another.
• Scrolling to the best starting point isn’t counted as an action.
• Autocomplete quirks or an extra submit tap in search aren’t counted.

These small allowances can make logging feel slower, but they’re difficult to measure consistently across apps. And because none of them affect MacroFactor’s scores, they end up giving other apps a bit of an advantage in the rankings.

For example, some apps hide results unless you’ve logged that food before, or require an extra tap to search online. In practice, that slows you down. But for fairness and consistency, the FLSI ignores those steps. The goal is to compare the fastest realistic path in each app, even if that path takes a little longer in real life.

Competitors 

• 1st Phorm
• Avatar Nutrition
• Cal AI
• Carb Manager
• Carbon Diet Coach
• Cronometer
• FatSecret
• Fitatu
• FitGenie
• Food Noms
• Foodvisor
• LifeSum
• LoseIt
• MacroFactor
• MyDietCoach
• MyFitnessPal
• MyMacros+
• MyNetDiary
• MyPlate
• Noom
• Yazio

How were these apps chosen?

Competitors were selected by reviewing the top charts on the Apple App Store and Google Play store, as well as apps frequently mentioned within the MacroFactor community. We excluded certain apps, such as the RP Diet app, because their workflows aren’t representative of an FLSI comparison. With RP Diet, the combination of forced meal planning and nontraditional macronutrient accounting means its food logging cannot be compared apples to apples with other apps.

Some apps have been added or removed since the original testing. For example, Cal AI was not on the market when we first analyzed apps in 2022, but it qualifies for this analysis in 2025. By contrast, MyPlate is no longer included as it was discontinued.

A quick note on speed versus context

If you look closely at the raw testing data, you’ll see separate results for speed and context modes in MacroFactor. Most users stick with speed mode, since it’s MacroFactor’s default. In speed mode, you’re taken back to the food logging workflow immediately after adding food to your plate. This is the same pattern most other apps use, and it’s what we include in the final results and graphs for direct comparison.

Context mode works a little differently. Instead of logging the food immediately, it closes the search and shows your plate view, where all the foods you’ve logged for that meal are visible at once. This lets you adjust portions and view all the contents of a meal together as you add foods or ingredients before logging anything. It’s a bit like looking at an actual plate and deciding if you want more or less of each item. It’s slightly slower but provides more context (hence the name) as you’re logging.

Again, speed mode is the default mode in MacroFactor, but in the interest of thoroughness, we also wanted to see how the (slightly) slower context mode stacked up against the field. 

Updated cutoffs for strong scores in 2025

When we first established the FLSI in 2022, each strong score was based on the midpoint between the best and worst scores for that use case, rounded up when necessary. That way, “strong” meant you were performing in the top half of the category. In the 2025 update, the raw scores shifted slightly, and therefore the midpoints shifted as well. For example, in Case 1, the fastest app remained quick, but the slowest app in 2025 is actually slightly slower than the slowest app in 2022. As a result, the midpoint nudged up from 16 to 17 actions.

It’s a small change, but worth keeping in mind when comparing 2025 scores with those from 2022.

Putting the FLSI to the test

Now that you know what the FLSI is, here’s how it’s tested. We use four tests that represent the most common ways people log food: logging through search, logging with multi-add, logging by scanning barcodes, and logging with a quick-add of Calories. Each test measures how many actions it takes to complete the task, with fewer actions meaning faster logging.

Case 1: Logging through search

Objective 1: Log Greek yogurt through search using a non-default serving and non-default three-digit quantity.

Objective 2: Log honey through search using a non-default serving and non-default three-digit quantity.

Strong score: Fewer than 17 actions.

What we took into consideration for this test:

• No relying on defaults. Using a non-default serving and quantity avoids situations where an app just happens to match the example (e.g., auto-selecting 170g for a single-serve yogurt).
• Larger quantities. A three-digit amount ensures the app can handle bigger weights, which are common when logging by weight.
• Multiple items. Logging two foods reflects a typical meal without penalizing slower methods too heavily by adding extra, repetitive steps.

Strong

• MacroFactor (speed): 10
• MyNetDiary: 11
• FitGenie: 13
• LoseIt: 13
• MacroFactor (context): 13
• Fitatu: 15
• Nutracheck: 15
• MyFitnessPal: 15
• Yazio: 16
• MyMacros+: 16

Weak

• Avatar Nutrition
• Noom
• Carbon Diet Coach
• Cronometer
• FatSecret
• LifeSum
• Foodvisor
• MyDietCoach
• Food Noms
• Cal AI
• Carb Manager
• 1st Phorm 

Case 2: Logging with multi-add

Objective 1: Log a banana through search using a default serving and quantity.

Objective 2: Log peanut butter through search using a default serving and quantity.

Strong score: Fewer than 9 actions.

What we took into consideration for this test:

• Multi-add conditions. Multi-add is only faster when a food already has the correct serving and quantity saved from the last time you logged it.
• Realistic usage. Most people log a mix of new and repeated foods, so multi-add opportunities increase over time.
• Limit of two foods. Logging more would just repeat the same action and unfairly penalize slower apps.

Strong

• MacroFactor (speed): 6
• MacroFactor (context): 6
• FitGenie: 6
• LoseIt: 7
• MyNetDiary: 7
• Fitatu: 7
• LifeSum: 8
• Nutracheck: 8
• Foodvisor: 8
• FatSecret: 8
• Carbon Diet Coach: 8
• Cal AI: 8

Weak

• Cronometer
• Carb Manager
• MyDietCoach
• MyFitnessPal
• MyMacros+
• Food Noms
• Avatar Nutrition
• Noom
• Yazio
• 1st Phorm

Case 3: Logging by scanning

Objective: Scan a branded item’s barcode and log it using a non-default serving and a non-default three-digit quantity.

Strong score: Fewer than 9 actions.

What we took into consideration for this test:

• Not relying on defaults. Using a non-default serving and quantity ensures we’re testing how quickly the app allows changes, not whether it guesses correctly.
• Common single-food scenario. Scanning is often used for logging one complete item like a snack or packaged meal, so only one food was logged in this test.

Strong

• MacroFactor (speed): 5
• MacroFactor (context): 6
• MyFitnessPal: 7
• Cronometer: 7
• MyNetDiary: 7
• Nutracheck: 7
• LoseIt: 7
• MyMacros+: 8
• FatSecret: 8
• Fitatu: 8
• Avatar Nutrition: 9
• Carbon Diet Coach: 9
• FitGenie: 9
• LifeSum: 9
• MyDietCoach: 9

Weak

• Yazio
• Food Noms
• Cal AI
• Carb Manager
• Noom
• Foodvisor
• 1st Phorm

Case 4: Quick-add of Calories

Objective: Quick-add an arbitrary Calorie value without associating it with a specific food.

Strong score: Fewer than 8 actions.

What we took into consideration for this test:

• Quick-add speed. The focus here is on how quickly you can enter Calories without searching and selecting a food.

Strong

• MacroFactor (speed): 3
• MacroFactor (context): 3
• 1st Phorm: 4
• LoseIt: 5
• Carbon Diet Coach: 5
• MyNetDiary: 5
• Fitatu: 5
• MyFitnessPal: 5
• Nutracheck: 5
• MyMacros+: 7
• Cronometer: 7
• LifeSum: 7
• Food Noms: 7

Weak

• Carb Manager
• Cal AI
• FitGenie
• MyDietCoach
• Noom
• Foodvisor
• Avatar Nutrition
• FatSecret
• Yazio

Final results

Results discussion 

Competitors have made meaningful improvements, but MacroFactor still holds the top spot in the FLSI. Even in the slower “context” mode, MacroFactor outpaces most apps running at their fastest. Compared with MacroFactor’s speed mode, the strongest competitor requires 25% more discrete actions to log foods, and the average entrant requires about 70% more taps or swipes.

New entrant Nutracheck landed in the strong tier for all workflows and achieved a solid overall score. Cal AI also entered the rankings this year, staying mostly in the middle of the pack. While these new names shook up the middle rankings, MacroFactor’s top spot remained unchanged.

In 2022, we stated that a cumulative score of 26 was the benchmark for the FLSI but not the limit. At the time, we recognized that a user could achieve better scores through customized workflows, and that future development could push the benchmark down further. In 2025, we’ve now hit a score of 24 in our default speed mode. As a reminder, lower is better in the FLSI , and MacroFactor continues to set the standard.

You can download the most recent scoring sheet here. 

Closing remarks

Compared with our first 2022 test, MacroFactor not only held the top spot but also improved its FLSI score by two points. We led in every category and continue to set the standard for logging speed. While this year saw new entries and steady improvements from competitors, MacroFactor remains comfortably ahead, setting the standard for food logging speed.

The post Is MacroFactor Still the Fastest Food Logger? (2025 FLSI Update) appeared first on MacroFactor.