personal analytics personal informatics tools bodymedia review body media review by measured me blogMy second week of using Body Media tracker is about to end, and by now I am very used to wearing the tracker. I feel comfortable wearing it even with the short sleeves and tank-tops. Who knew that a little gadget on your arm could be such a great conversation starter, at the gym and and on the street! I also had enough time to play with the dashboard and reporting tools, and in today’s post would like to discuss the metrics that I have been collecting on a daily basis, and how I am going to use them.

Total daily caloric expenditure: number of calories burned during the given day. I prefer to underestimate it rather than overestimate, and usually round down the reported number by 10% (reported BodyMedia accuracy is 90%). For instance, if BodyMedia reported that I burned 3,720 calories, I would log it as 3,300. I then use this number to compute the calorie balance:

Caloric Balance = Caloric Intake – Caloric Expenditure.

I estimated the caloric intake (number of calories I consumed on that day) using special calculator that I developed in Google Spreadsheet, and which I will share in one of my following posts. The ultimate goal is to accumulate enough data points to see how the caloric balance and total daily amounts of protein, carbs and fat correlate with the body fat loss and muscle mass gain.

Workout efficiency: by selecting specific time period of the day on dashboard, I can isolate the number of calories burned during my workout at the gym. For now, I am just logging the number, but in the future I hope to compare different sets of exercises to see which ones result in greater caloric and body fat loss. This metric, of course, does not take into account the possible “afterburn effect” (it’s when after the workout your body continues to burn calories at the higher than usual rate). I am actually thinking about running a couple of experiments to see if BodyMedia can capture this effect.

Metabolic rate: this may be not a very scientific method, but I am considering to use average number of METS (metabolic equivalent, calculated as a number of calories burned per body mass and per hour) reported by BodyMedia for a time period between 12 a.m. and 6 a.m. on weekdays as an estimate of my metabolism on that week. For instance, if on Monday, Wednesday, and Friday my reported rate was 1.3 mets, and on Tuesday and Thursday, the number was 1.2, then the average sleeping metabolic rate for that particular week was (1.3+1.1 +1.3 + 1.1 + 1.3)/5 = 1.22. My theory is that as my metabolism increases due to the diet and exercises, this number will also be going up, but I may be wrong, and it may be limited by some natural upper boundary (e.g. won’t never exceed 1.4).

Physical activity: every time I move, Body Media records it as an activity. It then further classifies my activity levels as either moderate or vigorous, and records total time for each level. I am not certain yet, how I could use these numbers, but I would try correlating these numbers with my mood and stress levels. My theory is that as a person who likes to be constantly on the move, my higher activity levels may be associated positively with less stress and better mood.

Walking: not sure yet, how can I possibly use this metric, but for now I will also be logging the number of steps taken on a given day. I actually love to walk, so, perhaps, number of steps will be correlated with my stress levels and mood.

Sleep Quality: the two summary numbers, reported by BodyMedia are time spent lying in bed and time spent actually sleeping. These numbers are then used to compute the “sleep efficiency”, which is simply time slept divided by time lying in bed:

Sleep Efficiency = (Time Slept / Time in Bed)* 100%

However, if you look closer the graph on the dashboard, you can also see how often I wake up during the night (show as gray bands), and by hovering the mouse over those breaks, you can see how long they lasted.


I consider these metrics also to be important indicators of sleep quality. For instance, the graph above from July 11 shows that on that night, I woke 12 (!) times, and the total time of those “breaks” was 46 minutes. In a couple of weeks, I will start experimenting with some herbal teas and meditation to improve the quality of my sleep, and these metrics will be very useful.

I am also concurrently collecting comparable data from similar devices (cardio machines at the gym, pedometer and Azumio’s Sleep Time app) to validate some of these BodyMedia numbers, and will be posting results shortly.

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