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Estimating Calories: Activity Databases?

An important part of tracking energy expenditure is knowing how many calories you are burning during exercise and non-exercise activity. There are a number of tools available for this purpose: Activity Database (e.g. MFP), Machine readouts, Heart Rate Monitors, Free-living monitors such as Body Bugg. Since I have discussed HRMs in great detail in other blogs, I am going to concentrate on Activity Databases. 

A brief review: a calorie is a measure of heat. The reason we can use it to estimate energy expenditure is that the biochemical processes that occur to provide our bodies with the energy they need to function give off fixed amounts of heat. 

How do we actually measure calories? There are two most common methods used in research are: measurement of oxygen uptake through analysis of expired air, and metabolic chambers. 

Movements/exercises that are simple, rhythmic, and consistent are easier to measure, and the equations used to predict energy expenditure are more accurate. They are also easier to generalize to the population as a whole. 

Movements/exercises that are complex, intermittent, that derive energy from different metabolic pathways (i.e. anaerobic vs aerobic), and can occur under varied conditions are very difficult to measure and very diffcult to generalize. 

In addition, energy expediture is very specific to the exact level of workoad intensity--so the more general the description of the activity, the less accurate the energy estimate will be. 

Reliable energy prediction equations have been available for years for activities such as walking, running, stair climbing, and cycle ergometer (stationary bike that accurately measures workload). 

Energy estimates for other exercise activities have been derived by studying a group of subjects, measuring their oxygen uptake while performing the activity at various tempos, and then deriving a "best fit" equation that is used for everyone. The accuracy of the equation/table is dependent on the quality of the study, the size and makeup of the sample group, etc. 

There are fairly extensive lists predicting the energy cost of various recreational and occupational activties. They were developed primarily to evaluate when someone who had suffered a heart attack or job injury could return to work. These were developed using the same methods described in the previous paragraph and are subject to the same limitations. A well known example is the Taylor Codes Compendium of Physcial Activities.

(This was during those quaint, older times when A) there were still jobs available that required physical labor; B) there were still jobs available for older American workers who had suffered an injury or a heart attack.) 

Activity databases include all of the above. Therefore, some listed activities will be very accurate and some won't be accurate at all. Let's look at some typical groups of activities in a little more detail:

Walking: if you are on a treadmill at 4.2 mph or less, and you are not holding on to the handrails, then the database numbers and the machine numbers will be very accurate (assuming you can enter your body weight), both for level walking and incline walking. They should be more accurate than any other method, including at HRM or Body Bugg. (The exception would be for someone who is extremely overweight/morbidly obese). 

This holds true only if you can enter the actual speed and elevation numbers. A database entry such as "brisk walking with hills" will not be that accurate. If that is your only choice, or if you are walking outside over varied terrain, an HRM will usually be more accurate than the database (only if it is set up properly). 

Running: The equations for running are also well-established, but I have seen research that suggests that, on treadmills, and as speeds increase above 6.5 mph, they start to overestimate energy expenditure by up to 15%. So database and machine numbers should be OK with that adjustment. For outdoor running on a flat surface or a track, the database numbers should be even a little more accurate--otherwise, with varied terrain, it's the same as walking. 

Computerized (Commercial) Stationary Bike/Stepmill: The machine readouts should be pretty accurate, althougth you have to minimize handrail support on the Stepmill. Most commercial exercise bikes have calibrated resistance levels that, while not research level accurate, are accurate enough for our purposes. The problem with the databases is that they don't allow you to enter precise workloads (e.g. avg Watts)--they just use descriptions such as "light", "moderate" and "hard". In this case, I would go: machine, HRM, and use database only as a last resort. 

Cross Trainers: When it comes to calorie estimates, cross trainers have the worst reputation for accuracy--and deservedly so. It's because of the non-standard nature of the movement--every manufacturer has a different movement design, so each machine needs to have its own specific energy-prediction algorithm. Some do it better than others, but the list of cross trainers with accurate calorie readouts is very small. Database numbers are probably going to be significantly off as well, and need to be approached with caution. 

Group exercise: Energy expenditure is so dependent on coordination, skill level, willingness/ability to push oneself, choreography of the class, etc, that any database entry will be mostly a random guess. Flawed as they can be, HRMs are the best choice for these activities (keep in mind that at best an HRM will only be about 80% accurate). 

Circuit Training/CrossFit/P90X, etc: These types of activities are very difficult to predict--because of the varied movements, different metabolic pathways, etc, there is no method that will be consistently accurate--database, HRM, nothing. If this is your main form of exercise, you can use the HRM numbers as a starting point, but you will likely also have to use the "trial and error" method. 

Recreational/Occupational Activities: See "Circuit Training" ---only worse. The extra problem with using tables for these types of activities is that they often assume you are doing the activity continuously at a consistent pace. If you put down "gardening, 2 hours" you will get a huge number. If we did a time-study of you doing the activity, we would likely find that in 2 hours, you did only 30 minutes of the sustained "gardening" activity on which the database numbers were based. I would be extremely cautious and conservative about entering ANY recreational/occupational activities into your daily energy estimates. 

Hopefully, I didn't wander too far off the path here (what's the calorie burn for "rambling on"?), but gave you some perspective on how you can rely on different sources for calorie numbers. 

If you see big discrepancies--between MFP and the machine, between MFP and your HRM, the machine and your HRM, etc--consider the nature of the activity and whether it falls into one of the "more accurate" or "less accurate" categories described above. That will help you make a better "educated guess" on how to include those numbers. 

Consider the nature of the activity (simple & consistent or varied & intermittent?)

Can you enter in precise workloads? (speed/elevation, watts, distance/time)? 

Is the activity mainly aerobic, anaerobic, or a combination? 

Is your effort dependent on mastering specific sport skills? 

How continuous is the activity movement? Is it a sustained effort or intermittent? 

This will help you determine the most accurate source of calorie information. 
 
A general rule is that the more "general" the estimate, the more caution you should exercise when including those numbers in your eating plan.  



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