{"id":2182,"date":"2022-07-11T08:00:00","date_gmt":"2022-07-11T08:00:00","guid":{"rendered":"https:\/\/macrofactor.com\/?p=2182"},"modified":"2024-10-17T18:05:35","modified_gmt":"2024-10-17T18:05:35","slug":"wearables","status":"publish","type":"post","link":"https:\/\/macrofactor.com\/wearables\/","title":{"rendered":"The Drawbacks of Using Wearable Devices to Inform Nutrition Targets"},"content":{"rendered":"\n

Many nutrition apps integrate with fitness wearables, or they provide you with the option of telling the app how many calories you burned each day via exercise, for the purpose of adjusting your daily calorie intake target. Many people who are interested in using MacroFactor<\/a> ask if the app has this functionality, and some new users are surprised to learn that it doesn\u2019t. I can certainly understand why people may expect MacroFactor to have this feature, and why they\u2019d view its omission as an oversight. However, this article will explain why we intentionally decided to not<\/em> use energy expenditure data from fitness wearables or manually-entered estimates of exercise energy expenditure to inform MacroFactor\u2019s recommendations. Furthermore, this article will explore why omitting this feature is actually a strength of MacroFactor\u2019s approach. <\/p>\n\n\n\n

Finally, even if you\u2019re not interested in using MacroFactor, I think you\u2019ll still glean plenty of interesting and useful information from this article about the accuracy (or lack thereof) of wearable devices for estimating energy expenditure, and the complications that arise when trying to predict how exercise will impact your total daily energy expenditure.<\/p>\n\n\n\n

To start with, let\u2019s first explore how fitness wearables estimate energy expenditure.<\/p>\n\n\n\n

Each company that produces wearable devices has its own proprietary algorithm, but the basic process they use for estimating energy expenditure is pretty straightforward. First, they estimate how many calories you burn at rest based on basic anthropometric and demographic characteristics: age, height, weight, sex, and perhaps an estimate of body composition. Then, they estimate how many additional calories<\/a> you burn during day-to-day activities (walking around, doing chores, etc.) and exercise using: a) technologies that estimate the amount you\u2019re moving (accelerometry and\/or GPS), b) heart rate data, or c) both heart rate data and estimates of movement. Some wearable devices also estimate skin temperature to further inform their energy expenditure estimates.<\/p>\n\n\n\n

\"How<\/figure>\n\n\n\n

Overall, that\u2019s a perfectly logical process to estimate energy expenditure, since all of those variables do<\/em> correlate with energy expenditure: older people tend to burn fewer calories than younger people, taller and heavier people tend to burn more calories than shorter and lighter people, males tend to burn more calories than females, people who move more tend to burn more calories than people who move less, people who experience larger or longer elevations in heart rate when exercising tend to burn more calories than people who experience smaller or shorter elevations in heart rate when exercising, and an elevation in skin temperature is generally a good indicator that you\u2019re expending more energy. However, estimates always come with some degree of estimation error, which invites us to ask: how accurately do wearables estimate energy expenditure?<\/p>\n\n\n\n

Unfortunately, wearable devices do a notoriously poor job of estimating energy expenditure. The inaccuracy of wearable devices for estimating energy expenditure is thoroughly documented in the research literature, but many consumers aren\u2019t aware of this track record.<\/p>\n\n\n\n

For example, a 2020 systematic review<\/a> found that group-level<\/em> estimates of energy expenditure may be off by more than 50% in controlled settings. And, of course, individual-level errors span an even wider range than group-level errors. If a study finds that a particular device overestimates energy expenditure by an average of 15% for a group of people, that means some people will have errors closer to 0%, but other people will have errors closer to 30%. Furthermore, the same systematic review found that, in free-living settings, wearable devices result in average under- or over-estimates of energy expenditure by more than 10% a whopping 82% of the time.<\/p>\n\n\n\n

\"relative
From Fuller et al. (2020)<\/figcaption><\/figure>\n\n\n\n

To be fair, some of the devices covered in that 2020 systematic review have been discontinued, so it would be fair to ask if newer devices are more accurate. To that end, a 2022 study<\/a> on a more recent crop of wearables investigated their accuracy during sitting, walking, running, cycling, and resistance training. It found that \u201cfor energy expenditure, all 3 devices displayed poor accuracy for all 5 physical activities (CVs between 14.68-24.85% for Apple Watch 6, 16.54-25.78% for Polar Vantage V and 13.44-29.66% for Fitbit Sense).<\/em>\u201d Furthermore, the mean absolute percentage errors ranged from 14.9%-47.8%.<\/p>\n\n\n\n

Let\u2019s drill down into two discrete examples:<\/p>\n\n\n\n

The best performance of any device during any exercise was observed when the Apple Watch 6 was used to estimate energy expenditure during running. Its mean absolute percentage error was 14.9 \u00b1 9.8%. In other words, about two-thirds of individuals can expect errors ranging from 5.1-24.7%, and errors up to 34.5% are perfectly normal \u2013 within 2 standard deviations from the mean (assuming that errors are normally distributed). In other words, if you think you burned a total of 1000 calories jogging last week, it\u2019s entirely possible that you actually burned 1345 calories, but it\u2019s also possible that you only burned 655 calories. Keep in mind, that\u2019s the best-case scenario observed in this study. <\/p>\n\n\n\n

For a more middle-of-the-road example, the Polar Vantage V estimated energy expenditure during resistance exercise with a mean absolute percentage error of 34.6 \u00b1 32.6%. In other words, about two-thirds of individuals can expect errors ranging from 2.0-67.2%, and errors approaching 100% (99.8%, to be exact) are perfectly normal \u2013 within 2 standard deviations from the mean (again, assuming a normal distribution of errors). I wouldn\u2019t be shocked if simply guessing<\/em> your energy expenditure provided you with a more accurate estimate.<\/p>\n\n\n\n

Update<\/strong>: Another systematic review<\/a> on the accuracy of wearable devices was published after this article was initially posted. It included 24 studies on 42 different wearables, concluding that: “For energy expenditure, the [mean absolute percentage error] was >30% for all the brands, showing poor accuracy across devices,” and, “None of the tested devices proved to be accurate in measuring energy expenditure.”<\/em><\/p>\n\n\n\n

Now, I don\u2019t want to make it sound like fitness wearables have no utility whatsoever. The same 2020 systematic review<\/a> that found that wearables do a poor job of estimating energy expenditure also found that wearables actually do a pretty good job of measuring heart rate and estimating step counts. There\u2019s also evidence<\/a> that using devices to track step counts can be used to improve weight loss success. However, it should be clear that wearable devices have a long way to go before they can be used to estimate energy expenditure accurately and reliably for most people. <\/p>\n\n\n\n

With that in mind, the primary reason why we don\u2019t use energy expenditure estimates from wearable devices to inform MacroFactor\u2019s recommendations should be obvious: they don\u2019t provide us with accurate data. Incorporating energy expenditure data from wearable devices would necessarily entail incorporating their estimation error, resulting in worse calorie recommendations overall.<\/p>\n\n\n\n

Scientifically minded readers may raise an understandable objection to the prior paragraph: \u201cmaybe wearable devices don\u2019t accurately<\/em> estimate energy expenditure, but they\u2019re reliable<\/em> enough to provide useful data, right?\u201d<\/p>\n\n\n\n

That\u2019s a perfectly reasonable question to ask. In many cases, inaccurate, reliable data is almost as useful as accurate, reliable data. Reliability tells you how repeatable a measurement is: if you measure the same thing multiple times, do you get the same result each time?<\/p>\n\n\n\n

\"accuracy<\/figure>\n\n\n\n

For example, maybe you weigh exactly 200 pounds, but your scale says you weigh 185 pounds. In that case, your scale would be very inaccurate. However, if you step on the scale five times, and it records your weight as 185 pounds each time, it would be a reliable scale. Furthermore, if you gain two pounds, and your scale tells you that your weight increased by two pounds, that means your scale can be used to measure changes in weight with both accuracy and<\/em> reliability. So, your scale would have poor accuracy, but it would still give you plenty of useful information about weight changes over time.<\/p>\n\n\n\n

Applying this concept to wearable devices, their energy expenditure data would still be quite useful if they were known to have high reliability. If a Garmin watch underestimates your energy expenditure by 600 calories per day, it wouldn\u2019t be ideal for directly determining your calorie targets; however, if the error was consistent in both direction and magnitude, it would be very useful for knowing the degree to which your energy expenditure changed over time.<\/p>\n\n\n\n

So, is that the case? Is the poor accuracy of wearable devices saved by high reliability?<\/p>\n\n\n\n

Surprisingly\u2026we don\u2019t know.<\/p>\n\n\n\n

In their 2020 systematic review<\/a>, Fuller and colleagues were unable to find any<\/em> studies reporting the intradevice reliability of any wearables for estimating energy expenditure. To quote the authors: \u201cNo studies reported intradevice reliability for heart rate or energy expenditure.\u201d<\/em><\/p>\n\n\n\n

It\u2019s possible that a couple of intradevice reliability studies have been published in the intervening years, but we\u2019re still a long way from establishing the generalized reliability of wearables for estimating energy expenditure and, more importantly, changes in energy expenditure.<\/p>\n\n\n\n

So, the basic reason we don\u2019t incorporate data from wearable devices into MacroFactor\u2019s algorithms and recommendations should be obvious at this point: that data is known to be inaccurate, and its reliability is unknown. Furthermore, the magnitude and direction of the errors differ from device to device, and from user to user. We aim to generate nutrition recommendations and adjustments that will help users meet their weight gain, loss, or maintenance goals; based on the current evidence, incorporating low-quality data from wearable devices into our algorithms would run counter to that goal.<\/p>\n\n\n\n

Beyond concerns regarding the accuracy and reliability of the data from wearable devices, there are a few other reasons why we don\u2019t use estimates of energy burned during exercise to inform daily calorie targets in MacroFactor. In some other apps, you can enter the number of calories burned via exercise each day (based on either manual estimates, or estimates from a wearable device) to adjust your daily calorie target. For example, your calorie target may be 2000 calories per day, but if you tell the app you burned 500 calories via exercise, your daily calorie target will increase to 2500 calories. There are a few drawbacks to this approach.<\/p>\n\n\n\n

The Drawbacks of “Eating Back” Calories Burned During Exercise<\/h2>\n\n\n\n

First, this sort of system has the potential to encourage people to adopt an unhelpful approach to exercise.<\/p>\n\n\n\n

Exercise has a whole host of benefits that extend far beyond mere energy expenditure: building and preserving strong muscles and bones, improving mood and mental health, increasing energy levels, reducing stress, improving flexibility, etc. Furthermore, plenty of beneficial forms of exercise don\u2019t<\/em> burn that many calories \u2013 some forms of yoga, tai chi, and most approaches to resistance training immediately come to mind. Putting energy expenditure front and center has the potential to make people focus on one acute outcome of exercise (increasing energy expenditure for the day so you can eat more), which is best accomplished by one particular form of exercise (cardio, primarily), while potentially neglecting the other acute and long-term benefits that other forms of exercise (and varied forms of exercise) can give you.<\/p>\n\n\n\n

More importantly, a system that allows you to \u201ceat back\u201d calories expended via exercise has the potential to encourage an adversarial relationship with exercise, or potentially even encourage disordered eating patterns.<\/p>\n\n\n\n

For example, if you\u2019re attempting to lose weight and you have a relatively low calorie target, it may be tempting to go for a jog simply as a means to an end: you want to be able to tell your app that you burned 500 calories exercising so that you can eat 500 more calories at dinner. Now, there\u2019s nothing wrong with the general mindset of, \u201chey, if I move around more I\u2019ll probably be able to eat more,\u201d but this approach to exercise has the potential to turn quite toxic. You can start to view exercise, not as fun and productive way to challenge yourself, not as a way to improve and expand your physical capabilities over time, and not as a way to relieve stress and improve your mood, but as an unpleasant chore that is simply a means to an end \u2013 being able to eat a few more calories at dinner. This transactional view of trading exercise for food is commonly observed<\/a> in individuals with disordered eating patterns.<\/p>\n\n\n\n

In addition to the host of benefits associated with regular exercise, exercise is a major predictor of successful weight loss maintenance<\/a>. If you develop an adversarial relationship with exercise when attempting to lose weight \u2013 you only do it to burn more calories or to increase your daily calorie targets, but you come to view it as an unpleasant obligation \u2013 you\u2019ll probably be less likely to stick with it long-term, which can make weight loss maintenance considerably more challenging.<\/p>\n\n\n\n

More odiously, however, framing exercise primarily as a method of burning calories might encourage binge-purge behaviors for some people. If you feel out of control over how much you\u2019re eating, it might be tempting to try to burn off the extra calories later. At the end of the day, your nutrition tracking app may show you that your energy intake and energy expenditure were balanced \u2013 high intake offset by a ton of exercise \u2013 but these sorts of behaviors can lead down a bad road if they persist.<\/p>\n\n\n\n

So, even if wearables did<\/em> accurately and reliably estimate energy expenditure during exercise, we\u2019d still need to think long and hard about whether or not the potential benefits of this feature \u2013 letting users log exercise calories, for the purpose of informing daily calorie targets \u2013 outweighed the risks. I\u2019m not sure they do. Thus, this feature would potentially conflict with our core goal of reducing the stress<\/a> associated with dieting and nutrition tracking.  <\/p>\n\n\n\n

However, I doubt we\u2019ll ever need to have that discussion for one simple reason: even if<\/em> wearables accurately measured how many calories you burn during exercise, MacroFactor doesn\u2019t need that data to make appropriate calorie adjustments and recommendations for people trying to gain, lose, or maintain weight.<\/p>\n\n\n\n

That may seem counterintuitive: \u201cif MacroFactor is making nutrition recommendations, don\u2019t I need to tell it how many calories I\u2019m burning via exercise?\u201d<\/p>\n\n\n\n

In actuality, our system works the other way around: based on your nutrition and weight data, we figure out how many calories you\u2019re burning. That may seem like a semantic difference, but it comes with two major advantages and only one disadvantage (which can be easily circumvented).<\/p>\n\n\n\n

Advantage 1: Better accounting for energy compensation<\/h2>\n\n\n\n

First, logging energy expenditure from exercise for the purpose of adjusting daily calorie targets is predicated on a pretty major assumption: burning more calories via exercise increases your total energy expenditure for the day in an additive fashion. On its face, that seems like a reasonable assumption: if you normally burn 2000 calories per day, and you burn an extra 500 calories via exercise, it makes sense that your total energy expenditure for the day would be 2500 calories. However, the actual relationship between activity levels and total daily energy expenditure is quite a bit more complex than that.<\/p>\n\n\n\n

Welcome to the wonderful world of energy compensation<\/a>.<\/p>\n\n\n\n

Exercise doesn\u2019t occur in a vacuum. It displaces other activities, and it has effects on your behaviors throughout the rest of the day. If you go for a morning jog, it might put some pep in your step throughout the rest of the day, leading you to move around a bit more than you otherwise would have. Conversely, if I<\/em> went for a morning jog, I\u2019d feel tired and sluggish for the rest of the day, and therefore move around quite a bit less than I otherwise would have. So, if you burn 500 calories on your morning jog, your total energy expenditure for the day might actually increase by a total of 600 calories \u2013 more than you burned on your jog. If I went for the same jog, my total energy expenditure for the day may only increase by a total of 300 calories \u2013 less than I burned on my jog.<\/p>\n\n\n\n

Research has shown that the second scenario is quite a bit more common: total daily energy expenditure only increases by about 72 calories<\/a>, on average, for every 100 additional calories you expend via physical activity. Furthermore, behind that simple average, there\u2019s quite a bit of nuance.<\/p>\n\n\n\n

\"additive
The additive model of energy expenditure \u2013 if you burn 100 calories on a run, your total energy expenditure for the day goes up by 100 calories \u2013 conflicts with human data. The constrained model \u2013 if you exercise more, other components of energy expenditure decrease to some extent, leading to a smaller overall increase in daily energy expenditure \u2013 is better supported by the research. From Pontzer et al. (2016)<\/figcaption><\/figure>\n\n\n\n

For example, if you\u2019re relatively sedentary<\/a>, burning 100 additional calories via physical activity will increase your total daily energy expenditure by some figure closer to 100 calories (in other words, it may increase by 90 calories, instead of 72); if you\u2019re already very active, burning 100 additional calories via physical activity will increase your total daily energy expenditure by some amount that\u2019s further from 100 calories (in other words, it may only increase by 50 calories). Additionally, if you\u2019re attempting to gain weight<\/a>, additional exercise has a larger impact on total daily energy expenditure; if you\u2019re attempting to lose weight, additional exercise has a smaller impact on total daily energy expenditure. Finally, it also appears that people with higher levels of body fatness experience more energy compensation<\/a> than their leaner counterparts.<\/p>\n\n\n\n

So, if you burn 500 calories via exercise, how much does your total daily energy expenditure increase (and, by extension, how much should you increase your daily calorie targets to account for the energy you burned via exercise)?<\/p>\n\n\n\n

Well, if you\u2019re relatively sedentary and attempting to gain weight, a 500-calorie increase in exercise energy expenditure may actually result in a 500-calorie increase in total daily energy expenditure. If you\u2019re already quite active and attempting to lose weight, burning an additional 500 calories via exercise may result in a mere 200 calorie increase in total daily energy expenditure. However, those would still<\/em> just be rough approximations, because there\u2019s still considerable inter-individual variability lurking beneath these trends.<\/p>\n\n\n\n

In other words, if you burn 500 calories via exercise, your total energy expenditure for the day may only increase by 100 calories, or it might increase by 600 calories. An increase of ~350 calories would be my best guess based on the research, but there\u2019s a very wide range of plausible values. So, even if you knew exactly how many calories you burned during an exercise session, that information would only be minimally informative for adjusting your daily calorie targets<\/em>. In essence, we know that your daily calorie target probably shouldn\u2019t increase by exactly 500 calories (in other words, the way that logging exercise works in other apps runs counter to the research on energy compensation). However, we don\u2019t know what the appropriate increase should actually be for the individual.<\/p>\n\n\n\n

\"energy<\/figure>\n\n\n\n

With MacroFactor, we remove the guesswork. If you start exercising more and you start losing weight considerably faster or gaining weight considerably slower, that tells us that the additional exercise has increased your total daily energy expenditure by quite a lot. Conversely, if you start exercising more, but the change in exercise has minimal impact on your rate of weight gain or weight loss, that tells us that you\u2019re experiencing a larger amount of energy compensation \u2013 additional exercise didn\u2019t<\/em> have a particularly large effect on your total daily energy expenditure. In short, MacroFactor can calculate the impact of exercise on total energy expenditure, and make appropriate dietary adjustments. Conversely, simply logging exercise calories to additively increase your daily calorie targets necessarily involves making an assumption (an assumption that\u2019s typically incorrect) about the relationship between exercise energy expenditure and total energy expenditure, with no mechanism to course-correct if the baked-in assumption happens to be wrong.<\/p>\n\n\n\n

Advantage 2: Accounting for logging and digestive idiosyncrasies<\/h2>\n\n\n\n

There\u2019s another sneaky benefit of determining energy expenditure based on dietary data \u2013 instead of data from wearables \u2013 for the purpose of determining dietary targets: using nutrition data accounts for logging and digestive idiosyncrasies that a wearable device wouldn\u2019t be privy to.<\/p>\n\n\n\n

Let\u2019s talk about poop.<\/p>\n\n\n\n

The vast majority of the energy you consume from food is absorbed in your digestive tract\u2026but not all of it. In people without malabsorptive conditions, about 2-10% (or even up to 20%)<\/a> of the energy you consume winds up being excreted in your urine and feces. The relative proportion of energy excreted likely depends on whether you\u2019re in an energy deficit or surplus, the amount of fiber you consume, and probably a host of other yet-to-be-identified factors.<\/p>\n\n\n\n

\"effects
The impact of fecal energy loss on energy balance. From Lund et al. (2020)<\/figcaption><\/figure>\n\n\n\n

So, how many calories survive your<\/em> digestive tract, only to be excreted in your feces? Unless you make a habit of burning all of your poop in a bomb calorimeter, I suspect you can\u2019t answer that question.<\/p>\n\n\n\n

However, energy excretion is a non-trivial factor when discussing energy regulation and the calories in\/calories out model. If you eat 2500 calories per day, and you excrete 10% of those calories, you\u2019re excreting 250 calories per day. Conversely, if you only excrete 2% of the calories you consume, you\u2019re only excreting 50 calories per day. That 200-calorie difference is roughly equivalent to the energy cost of an average-sized person running two miles.<\/p>\n\n\n\n

People are free to debate how to best account for fecal energy excretion in the calories in\/calories out model of body weight regulation<\/a>. You may prefer to lump it into the \u201ccalories out\u201d side of the equation (it is<\/em> energy leaving your body after all, albeit not in the form of heat or mechanical work). I think it\u2019s actually preferable to simply deduct it from the \u201ccalories in\u201d side of the equation (scroll down to footnote 1 if you care to read my reasoning).1<\/a><\/sup>Footnote 1: If you want to be as rigorous as possible, the energy in the food you consume doesn\u2019t really count as \u201ccalories in\u201d until the energetic nutrients from those foods make it across the intestinal lining. Your entire alimentary canal (your mouth, esophagus, stomach, intestines, and anus) is continuous with the outside world. In other words, you could pass a string down your throat and out of your anus without ever puncturing another tissue \u2013\u2013 in a way, humans are like donuts. As a simple analogy, if you had a hole in your arm, and you dropped a piece of food through that hole, you wouldn\u2019t count that piece of food as \u201ccalories in\u201d when it entered the hole and \u201ccalories out\u201d when it exited the hole. The same concept applies to your alimentary canal. If you treated \u201ccalories in\u201d as all of the energy in the food you consumed, rather than the energy absorbed by your intestines, that would imply that the energy in your feces is energy you expended<\/em>. However, you\u2019re quite clearly not<\/em> expending the energy in your feces \u2013 you simply didn\u2019t absorb or utilize it in the first place. Of course, this is primarily a semantic difference most of the time. If you treat the energy in your feces as a component of \u201ccalories out,\u201d or if you treat the energy in your feces as a deduction from \u201ccalories in,\u201d you ultimately wind up in the same place \u2013 a wearable device used to estimate energy expenditure can\u2019t know how much energy you\u2019re excreting in your feces, and it can\u2019t know the extent to which your energy intake targets should increase to account for fecal energy excretion.<\/span> However, regardless of how you account for fecal energy excretion, it can have a non-trivial impact on total energy balance.<\/p>\n\n\n\n

Similarly, it\u2019s virtually impossible to perfectly<\/em> track energy intake. For example, cooking meat and vegetables can impact their net metabolizable energy<\/a>. Cooling cooked starches can impact their net metabolizable energy (via retrogradation of \u201cnormal\u201d starch into resistant starch<\/a>). You probably don\u2019t weigh out every gram of grease in a pan after cooking meat or saut\u00e9ing vegetables. You probably can\u2019t perfectly estimate how much fat drips off when grilling foods. You may even prefer to just eyeball measurements for some foods you eat (you will never<\/em> catch me precisely measuring spinach), or skip logging some things altogether (there\u2019s some maltodextrin in most granulated non-nutritive sweeteners, and I doubt most people log that). Your kitchen scale may not be perfectly calibrated. Some of the foods you eat may have labeling errors (calorie and macronutrient labeling errors of up to 20%<\/a> are allowed by law). You even extract more energy from foods if you blend them up first (for example, you derive more metabolizable energy<\/a> from almond butter than from an equivalent amount of whole almonds).<\/p>\n\n\n\n

In short, it\u2019s impossible to perfectly<\/em> track food intake. No matter how hard you try, you will always have some amount of food logging error. And, if you eat similar foods and log foods in similar ways most of the time, the food logging errors you have will tend to have a similar directionality.<\/p>\n\n\n\n

So, why does all of this matter?<\/p>\n\n\n\n

Well, if you\u2019re using an estimate of energy expenditure from a wearable device to impact your daily calorie targets, there\u2019s no way for the wearable device to \u201cknow\u201d anything about the idiosyncrasies of your food logging habits, and there\u2019s no way for it to \u201cknow\u201d anything about fecal energy excretion. So, if you use data from wearables to inform your energy intake<\/em> targets, there will be plenty of important information that those devices simply can\u2019t account for.<\/p>\n\n\n\n

To illustrate, let\u2019s assume that we live in a world where wearable devices can perfectly<\/em> calculate your daily energy expenditure. You\u2019re aiming to maintain weight, and your wearable device tells you that you burned 2500 calories today.<\/p>\n\n\n\n

Simple enough, right? You can aim to consume 2500 calories, and maintain energy balance.<\/p>\n\n\n\n

Not so fast.<\/p>\n\n\n\n

Maybe you made some fried eggs for breakfast, and you put a tablespoon of butter in the pan. You logged a tablespoon of butter for the meal, but you didn\u2019t mop every last drop of butter from the pan and from your plate. Maybe you had some leftover rice for lunch; during the cooling process, some of the starch in the rice became resistant starch, reducing its net caloric content. At dinner, maybe you ate two bananas, and you didn\u2019t feel like weighing them, so you just logged them as two medium bananas \u2013 however, they were both a bit smaller than a \u201cstandard\u201d medium banana.<\/p>\n\n\n\n

None of these little food logging errors are all that important, but they might mean you wound up consuming 2400 calories instead of 2500.<\/p>\n\n\n\n

Now, let\u2019s assume that 6% of the energy you consumed survived your digestive tract, and was excreted in your feces. That\u2019s another 150 calories.<\/p>\n\n\n\n

In total, you\u2019d still wind up in a non-trivial calorie deficit: you\u2019re burning 2500 calories per day, while deriving 2250 calories of energy from the foods you\u2019re eating (consistent with losing about half a pound per week), in spite of doing a very good job of logging your food and in spite of knowing your true daily energy expenditure with perfect accuracy.<\/p>\n\n\n\n

So, how would MacroFactor handle this exact scenario?<\/p>\n\n\n\n

Based on your weight and nutrition data, MacroFactor would see that you\u2019re logging 2500 calories per day and losing about half a pound per week. As a result, it would estimate that you\u2019re burning 2750 calories per day, and recommend that you consume about 2750 calories per day to maintain weight.<\/p>\n\n\n\n

At first blush, that may seem inappropriate. Remember, this scenario assumes that your daily energy expenditure is known<\/em> to be 2500 calories per day. However, based on the idiosyncrasies of your food logging and the energy you excrete in your feces, 2750 calories per day is actually the perfect<\/em> intake target for you \u2013 when you log 2750 calories, you end up consuming 2500 calories of net metabolizable energy.<\/p>\n\n\n\n

When you estimate energy expenditure from weight and nutrition data, errors of any reasonable magnitude simply wash out when making nutrition recommendations.<\/p>\n\n\n\n

In fact, this principle even applies if you have considerably larger (but relatively consistent) tracking errors. For example, let\u2019s assume that you don\u2019t care that much about perfectly<\/em> logging your food.<\/p>\n\n\n\n

Harkening back to the precision vs. accuracy discussion, maybe you always have a splash of cream in your morning coffee, and since you have the same splash of cream every day, you don\u2019t bother logging it. Maybe you\u2019re happy to simply not track foods with sufficiently low energy density (fibrous vegetables, for example). Maybe you eyeball serving sizes for cooking oils, spreads, sauces, and condiments.<\/p>\n\n\n\n

We know that most people underestimate energy consumption<\/a>, so let\u2019s assume that all of these little inaccuracies add up to a 30% tracking error: you think you\u2019re consuming 2500 calories per day, but you\u2019re actually consuming 3250 calories per day. <\/p>\n\n\n\n

That may seem like an insurmountable tracking error\u2026but it\u2019s actually fine, since MacroFactor\u2019s intake targets have your tracking error \u201cbaked in,\u201d as long as it\u2019s reasonably consistent. Let\u2019s once again assume that you burn 2500 calories per day, and you\u2019re aiming to maintain your weight.<\/p>\n\n\n\n

Here\u2019s a step-by-step illustration of how this scenario would play out in MacroFactor:<\/p>\n\n\n\n

    \n
  1. You\u2019d aim to consume 2500 calories per day. However, due to your 30% tracking error, you\u2019d actually be consuming 3250 calories per day.<\/li>\n\n\n\n
  2. Since an actual intake of 3250 calories per day would result in a calorie surplus, you\u2019d start gaining a bit of weight.<\/li>\n\n\n\n
  3. MacroFactor would see that when you log<\/em> 2500 calories per day, you gain weight. So, it would start decreasing your calorie intake targets.<\/li>\n\n\n\n
  4. After a couple of weeks, it would settle on a calorie intake target of about 1925 calories per day. Given your 30% tracking error, aiming for 1925 calories per day would result in an actual intake of 2500 calories per day.<\/li>\n<\/ol>\n\n\n\n

    So, even with some pretty significant tracking errors, MacroFactor would settle on appropriate intake targets, such that your actual<\/em> energy intake matches your actual<\/em> energy needs. This self-correcting mechanism is pretty powerful \u2013 it allows MacroFactor to make appropriate nutrition recommendations, even if user inputs aren\u2019t perfect.<\/p>\n\n\n\n

    This may seem counterintuitive at first, but if you want to know your daily energy expenditure for the purpose of generating calorie intake targets to gain, lose, or maintain weight<\/em>, MacroFactor\u2019s algorithm is actually better<\/em> than a theoretical system that would be able to quantify your total daily energy expenditure with perfect accuracy. Such a system could tell you that you\u2019re burning 2500 calories per day with perfect accuracy, but you can\u2019t<\/em> guarantee that you\u2019re consuming 2500 calories per day with perfect accuracy, due to variance in the net metabolizable energy of foods (due to labeling errors or differences in preparation methods), digestive quirks (do you excrete 2% of your daily calorie intake, or 12%?), and good old-fashioned tracking errors.<\/p>\n\n\n\n

    In other words, if your actual calorie intake is 20% higher than what you log, your calorie target should be 20% lower than what it theoretically \u201cshould\u201d be in a world where you could perfectly quantify calorie intake. If your actual calorie intake is 20% lower than what you log, your calorie target should be 20% higher than what it theoretically \u201cshould\u201d be in a world where you could perfectly quantify calorie intake. Since MacroFactor\u2019s inputs (the food intake that you log) contain the error inherent in how you log food and how your body processes food, its outputs (your calorie recommendations for gaining, losing, or maintaining weight) will reflect both the direction and magnitude of those errors, resulting in ideal calorie targets that incorporate and account for the quirks and idiosyncrasies of your digestive system, food logging habits, and food choices.<\/p>\n\n\n\n

    The disadvantage of MacroFactor\u2019s system<\/h2>\n\n\n\n

    There\u2019s one disadvantage to MacroFactor\u2019s system for generating nutrition recommendations, when compared to either a) simply using a wearable device to estimate your total energy expenditure each day or b) using a wearable device to estimate caloric expenditure during exercise in order to change your daily calorie targets.<\/p>\n\n\n\n

    By design, MacroFactor is back-looking and follows trends<\/a>, whereas wearable devices attempt to provide moment-in-time estimates of energy expenditure.

    So, if you\u2019re typically pretty sedentary, but you run a marathon tomorrow, MacroFactor won\u2019t (by default) know to recommend a higher calorie intake to help you fuel up for your marathon, and it won\u2019t \u201cknow\u201d that you burned a ton of extra energy when running a marathon until after the fact. Conversely, despite all of the drawbacks of wearable devices, any wearable will<\/em> be able to tell that you expended more energy on the day you ran a marathon.<\/p>\n\n\n\n

    The same concept applies when discussing significant lifestyle changes. If you go from being very sedentary to very active (or vice versa), a wearable device will probably pick up on that change right away \u2013 its actual estimate of your energy expenditure may not be particularly accurate or reliable, but it will be able to tell right away that your overall energy expenditure has increased or decreased considerably. MacroFactor, on the other hand, will generally take about two weeks to fully \u201cprice in\u201d those changes.<\/p>\n\n\n\n

    \"How<\/figure>\n\n\n\n

    In other words, MacroFactor is far more useful for generating nutrition targets over the medium-to-long term, but wearables are far better at picking up on fluctuations in energy expenditure over the short term.<\/p>\n\n\n\n

    Thankfully, there are two simple workarounds that largely negate this drawback.<\/p>\n\n\n\n

    First, we give you the flexibility to account for different energy needs day-to-day. If you set up a collaborative plan in MacroFactor, you can shift your weekly calories around, in order to better align your daily calorie intake targets with your anticipated energy needs.<\/p>\n\n\n\n

    For example, if you have a grueling workout or go for a long run three days per week, you can budget more calories for those days, and fewer calories for rest days. Instead of aiming for 2500 calories each day (17,500 for the week), you could aim for 2800 calories on your training days, and 2275 calories on your rest days. Ultimately, your weekly calorie budget is the same, but you can align your daily targets with your daily energy needs.<\/p>\n\n\n\n

    \"Collaborative<\/figure>\n\n\n\n

    Second, MacroFactor\u2019s algorithms and adjustments don\u2019t rely on the user to perfectly adhere to our recommendations. So, if your habitual activity levels significantly increase (maybe you\u2019re getting back into serious exercise after an injury, or you\u2019re starting a more physically demanding job), you could simply eat a bit more than your recommended calorie targets for a couple of weeks until the change in activity levels has been fully \u201cpriced in.\u201d<\/p>\n\n\n\n

    Now, I\u2019ll certainly admit that these aren\u2019t perfect<\/em> workarounds. In an ideal world, we\u2019d be able to know exactly how many more calories you burn on workout days than rest days, thus allowing us to generate precise day-by-day recommendations. In that same ideal world, we\u2019d be able to immediately know precisely how much your typical energy needs increase or decrease due to a significant change in activity levels. As it is, MacroFactor can\u2019t do that \u2026 but neither can wearable devices (remember, wearable devices have poor accuracy and unknown precision for estimating energy expenditure).<\/p>\n\n\n\n

    In short, MacroFactor does a very good job of providing appropriate calorie intake targets over the medium-to-long term, and there is no perfect system for tweaking those targets in the short term. As such, we think it makes the most sense to leave short-term fine-tuning up to user discretion.<\/p>\n\n\n\n

    A brief recap<\/h2>\n\n\n\n

    I realize this is a pretty long, pretty dense article, so I figure it\u2019s worth recapping the key points to wrap things up.<\/p>\n\n\n\n

      \n
    1. Wearable devices are known to do a poor job of estimating energy expenditure. We don\u2019t<\/em> know if those inaccurate estimates are even precise and reliable.<\/li>\n\n\n\n
    2. Even if you could perfectly calculate energy expenditure during exercise, that would still be of limited utility due to energy compensation \u2013 increasing energy expenditure via exercise doesn\u2019t increase total daily energy expenditure in a particularly predictable fashion.<\/li>\n\n\n\n
    3. In addition to issues associated with energy compensation, \u201ceating back\u201d calories expended via exercise may also lead some people to develop an undesirable relationship with exercise.<\/li>\n\n\n\n
    4. Even if wearable devices could<\/em> accurately measure energy expenditure<\/em>, they\u2019d still be of limited utility for generating energy intake<\/em> targets, since a perfect measure of energy expenditure would be unable to account for idiosyncrasies related to individualized differences in digestion, food logging habits, food preparation methods, etc.<\/li>\n\n\n\n
    5. Since those idiosyncrasies are already factored into MacroFactor\u2019s inputs <\/em>(the food you log), they\u2019re also accounted for in MacroFactor\u2019s outputs <\/em>(our nutrition recommendations and adjustments).<\/li>\n\n\n\n
    6. Since no system can perfectly account for very short-term fluctuations in energy needs, we leave those adjustments up to user discretion.<\/li>\n<\/ol>\n\n\n\n
      \n\n\n\n

      Footnote 1: <\/h4>\n\n\n\n

      If you want to be as rigorous as possible, the energy in the food you consume doesn\u2019t really count as \u201ccalories in\u201d until the energetic nutrients from those foods make it across the intestinal lining. Your entire alimentary canal (your mouth, esophagus, stomach, intestines, and anus) is continuous with the outside world. In other words, you could pass a string down your throat and out of your anus without ever puncturing another tissue \u2013\u2013 in a way, humans are like donuts.<\/em><\/p>\n\n\n\n

      As a simple analogy, if you had a hole in your arm, and you dropped a piece of food through that hole, you wouldn\u2019t count that piece of food as \u201ccalories in\u201d when it entered the hole and \u201ccalories out\u201d when it exited the hole. The same concept applies to your alimentary canal. If you treated \u201ccalories in\u201d as all of the energy in the food you consumed, rather than the energy absorbed by your intestines, that would imply that the energy in your feces is energy you <\/em>expended. However, you\u2019re quite clearly not expending the energy in your feces \u2013 you simply didn\u2019t absorb or utilize it in the first place.<\/em><\/p>\n\n\n\n

      Of course, this is primarily a semantic difference most of the time. If you treat the energy in your feces as a component of \u201ccalories out,\u201d or if you treat the energy in your feces as a deduction from \u201ccalories in,\u201d you ultimately wind up in the same place \u2013 a wearable device used to estimate energy expenditure can\u2019t know how much energy you\u2019re excreting in your feces, and it can\u2019t know the extent to which your energy intake targets should increase to account for fecal energy excretion.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

      This article breaks down the accuracy (or lack thereof) of wearable devices for estimating energy expenditure, and the complications that arise when trying to predict how exercise will impact your total daily energy expenditure.<\/p>\n","protected":false},"author":2,"featured_media":2201,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[8],"tags":[],"class_list":["post-2182","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles"],"yoast_head":"\nThe Drawbacks of Using Wearable Devices to Inform Nutrition Targets - MacroFactor<\/title>\n<meta name=\"description\" content=\"This article breaks down the accuracy of wearable devices for estimating energy expenditure, and the complications that arise when trying to predict how exercise will impact your TDEE.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/macrofactor.com\/wearables\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Drawbacks of Using Wearable Devices to Inform Nutrition Targets\" \/>\n<meta property=\"og:description\" content=\"This article breaks down the accuracy of wearable devices for estimating energy expenditure, and the complications that arise when trying to predict how exercise will impact your TDEE.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/macrofactor.com\/wearables\/\" \/>\n<meta property=\"og:site_name\" content=\"MacroFactor\" \/>\n<meta property=\"article:published_time\" content=\"2022-07-11T08:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-10-17T18:05:35+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/macrofactor.com\/wp-content\/uploads\/2022\/07\/wearables-featured-images.png\" \/>\n\t<meta property=\"og:image:width\" content=\"900\" \/>\n\t<meta property=\"og:image:height\" content=\"600\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Greg Nuckols\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Greg Nuckols\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"30 minutes\" \/>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"The Drawbacks of Using Wearable Devices to Inform Nutrition Targets - 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