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Giving myself the gift of data

It can be easy to get discouraged when working on a personal health project during the time period that stretches from late fall to early winter. The “food holiday” season that stretches from October to January is filled with celebrations and their leftovers. Colder temperatures and shorter days also reduce the daily time window for comfortable outdoor exercise.

The combination of social eating events and reduced exercise time has always been a double whammy for me in managing my weight. I used to think that my holiday eating patterns led me to New Year’s resolutions to lose weight, increase my daily activity, and address risk biomarkers like blood pressure and cholesterol levels.

But is my theory correct?

Looking back at my historical weight data since graduate school, I can see I didn't always collect the data to support my holiday weight gain theory. And when I did collect the data, it didn't necessarily support it. Although my weight trended up from October to January in 2002-3 and 2007-2008, I’ve actually maintained my weight fairly well during many past holiday seasons.

Holiday weight fluctations 2

When needed, I’ve even made weight loss progress during this time of the year. In graduate school, I lost 15 pounds during this period, and repeated this success twice post-pregnancy (shown with green reference ranges and annotations above). I even reached my leanest point ever in maintenance during the 2013-2014 holiday season!

This fall, I’ve been working on correcting a small upward swing in my weight that happened over the summer. I hope to avoid major changes in my eating and workout habits at the end of this year so that I can avoid needing to make any major weight-related resolutions come January.

Recent Holiday weight fluctations 2

Biggest lessons learned

So what have I learned about the key ingredients to making and keeping diet and exercise-related meeting goals this late in the year?

Setting a concrete and measureable goal is critically important. In the case of holiday weight gain, it’s easy to identify weight as the variable to measure and maintenance through the eating season as my goal. To monitor how I am doing, I commit to monitoring my weight each morning by stepping on my wireless scale. If I see my weight trend going in the wrong direction, I adjust my eating and exercise plans to compensate. Similarly, if I set a goal to work out three times a week, I track each workout.

Making data collection a habit is essential to assessing my progress. Examining my eating and exercise data from past years helped me verify that I have been most successful during the holiday seasons when tracking my eating and exercise habits. Exploring data collected during other times of the year also provides critical baseline information that helps me plan ahead for holiday eating events where I know I will eat special (and calorically dense) foods. If I exceed my typical limits, I have collected enough data to avoid overreacting to the inevitable weight spike caused by the water retention from the extra food, carbs and sodium in my system. I know that this effect is temporary, similar to weekend weight spikes I’ve observed.

Weekly weight fluctuations

A gift from me, to me

This holiday season, I will continue to give myself the gift of data. If you too are interested in health and fitness data collection during the holidays and into the New Year, take a look at my list of ideas based on my own ongoing projects. I promise, this list has nothing to do with whether you’ve been naughty or nice! Some ideas involve collating historical data, and others make use of measurements from various sensors. (By the way, sensors are simply devices that detect and measure changes in a system. Many of our JMP customers use sensors in the development, manufacturing or operation of a product, and some even produce sensors as products.)

Sensors can greatly simplify your self-tracking efforts, and you may be wearing one on your wrist or carrying one in your pocket right now. In addition to monitoring steps and physical activity, many activity monitors, smartphones and smartwatches can help you track your sleep. If you own analog sensors that are not connected to the Internet of Things (IoT), or want to collect data on your eating habits or workouts, free or inexpensive apps can help you collate your data, and many even let you export CSV files that JMP can read. You'll see some ideas below for exploring metrics and test results from your personal health history, some of which may have been generated using sensors in a medical setting.

Start with one or two new data types

Like foods on a restaurant menu, it would be overwhelming to consider all the items on this list at once. Instead, I recommend prioritizing by selecting one or two data types to begin collecting and exploring. Once you have had the chance to assess and learn from your data, then consider adopting additional tracking projects.

I’ve organized this list loosely into inputs and outputs, with the idea that changing the inputs to your system affects various outputs. I’ve also included one item that I call supplemental, and that is location data. I find this is useful for helping generate or recall the details of other tracked variables like locations for a specific meal or the path taken during a workout.

Without further ado, here are a few ideas for data types you might consider measuring and visualizing with JMP:


1. Weight (medical records, home testing)

2. Blood cholesterol/lipid profiles (lab testing)

3. Blood pressure (lab testing, home testing)

4. Blood glucose profiles (lab testing, home testing)

5. Body fat percentage (lab testing, gym testing, home testing)

6. Resting heart rate (medical records, home testing)

7. Heart rate variability (home testing)


8 Food log items, macros, calories consumed

9 Daily steps/baseline activity

10 Base calories burned (home testing)

11 Specific workout data (home testing)


12. Location data

I hope you maintain your goals over the holidays and start the new year off by tracking or exploring some new data! I'll share more about what I've learned from exploring my own data starting next month.

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