Reflections on my ongoing diet and fitness project
Mar 19, 2015 12:46 PM
I've blogged quite a lot recently about using Graph Builder to visualize my diet and fitness data, and you can see all posts in this series. While creating my Discovery Summit 2014 e-poster about this project, I significantly broadened my skills as a JMP user. This was the first time I wrote a custom JSL to automate the import, combination, and formatting of data from a large set of files (~50 Excel and ~50 text files containing activity and food log data, respectively). This time investment really paid off, since I have been able to apply the skills I learned to other work-related data import and visualization tasks.
In addition to gaining greater experience with JMP and JSL, I learned a lot about myself and my habits through this project. One of my most important revelations was that even my carefully collected self-report data contained flaws and biases related to my device usage and food logging patterns. This discovery has influenced my thoughts about large-scale efforts to aggregate diet and fitness data across individuals to understand weight loss patterns and also affected how I continue to collect my own data.
I have blogged before on how I wore my armband activity monitor less during the summer months in an effort to avoid strap tan lines, and how this usage pattern influenced the completeness of my activity data. It seems reasonable that every person using an activity monitor could have a unique use pattern that would influence the completeness and accuracy of their own data. My device gives me a daily "Percent Onbody" metric, which helps me compare across data with similar hours of wear. Although this might still not be perfect, it is a step in the right direction. Below is the graph I included in my poster to illustrate my own wear patterns.
Similarly, food logging patterns are likely to vary by individual. Previous research has confirmed that people have trouble recalling the details of foods they ate days or even hours ago, much less recalling diet patterns from the day, week or year before. Diet study participants tend to under-report food consumption. Portion size estimation is another source of error in food logging, and one that may differ person to person. Whether intentional or unintentional, food logging frequency, timeliness and accuracy is sure to vary across a group of people. (You can read a lot more about these and other issues in a freely available chapter published in 2013 titled Dietary Assessment Methodology.)
I have noted that logging my foods before or just after a meal helps minimize my recall bias, so I do that whenever possible. By exploring my own food log data in Graph Builder, I was able to identify several different groups of outlier days where my own logging data was incomplete for various reasons. Even a large-scale study whose participants used a food logging app like I do would be faced with a choice: Take data from participants at face value knowing logging compliance and data quality might vary widely, or undertake assessment of logging compliance patterns by individual. Without knowing the underlying truth about each individual's eating patterns, it would be extremely difficult to assess whether data was incomplete or incorrect, and how that might affect overall study conclusions.
I have seen many examples of mismatches between quoted serving sizes and actual food weights on food labels, and I have read many articles indicating that calorie counts on menus are often unreliable. Similar-looking food items can have widely varying calorie counts due to differences in largely hidden ingredients like sugar, butter or oil. When possible, I weigh or measure portions to improve the accuracy of my food log data. To compensate for underestimation of calories in packaged or restaurant-prepared foods in my own data collection, I often add 10% to the serving size I log for an item, although I know errors can sometimes be much greater than that!
Errors in estimating consumption become compounded with errors in calculating exercise burn when it comes to calculation of the deficit and surplus numbers that ultimately govern weight loss. Most apps that log food also allow you to log exercise, thus "earning" more calories to eat. Unfortunately, standard estimates of calorie burn for activities often do not match real-life circumstances. They may overestimate true burn for some individuals or include baseline calories that would have been burned even if no exercise was done. The bottom line is that people usually think exercise burns a lot more calories than it does. Especially for short women whose exercise calorie burn is the least, relying exclusively on exercise for weight loss is a very flawed strategy, which unfortunately I have proved to myself several times over a lifetime of weight struggles.
During my recent and successful weight loss phases, I have focused on maintaining a deficit between my intake and burn. I achieve the deficit through eating less than I burn, and adjusting that balance as required to achieve my desired outcome. I shared weight gain and weight loss data from my last pregnancy and post-baby weight loss in an earlier post; a Graph Builder graph clearly showed that when I was in a surplus, I gained weight, and when in a deficit, I lost weight. My gain during pregnancy was well above and beyond the amount attributable to baby/water weight, accounting for about 20 pounds of excess body fat that took me months to lose. I have seen the same connection between calorie balance during my last few years of weight maintenance. Although my weight fluctuations are much smaller, the same basic concepts still hold.
All that I have learned up to this point about self-reported food log data and activity measurements has opened my eyes about the limitations of my own data and the challenges faced by researchers conducting large-scale weight loss studies based on self-reported data. Large-scale studies aim to draw broad conclusions by averaging across a heterogeneous set of individuals with varied genetics, lifestyles and reporting patterns.
I have now collected enough free-living data in my own n=1 study to quantify what works for me to lose weight and maintain in a healthy range for me -- an understanding that largely eluded me up to this point in my life. Not surprisingly, I have converged on the same deficit strategy commonly employed in weight loss studies that treat people like caged rats, closely quantifying their intake and activity to prove that negative calorie balance is the critical factor that causes weight loss. I'm truly grateful that I didn't need to live in a cage to learn what I have over the past few years. In many ways, learning what I have from my data has helped set me free.