## Monday, June 29, 2015

### Fitness Week Summary

One last post to wrap up Fitness Week.  I realized I let myself get distracted by tax policy and whether I could save money by driving to Missouri (read: me being cheap) that I forgot my last post for Fitness week.  That post was supposed to be on modeling fitness data, but first, how did I actually do during fitness week?

## FITNESS WEEK STATS

Fitness week ended up being a pretty average week for me, in aggregate above average, as shown below:

A few notes:
• Each weekday was close to its average over time, each day slightly exceeding the Garmin set goal, per my earlier analysis on "targeting."
• Monday and Thursday were my two worst days.
•  Saturday and Sunday were both high days, over 30,000 steps.  Which, is a record, as I've never had two consecutive days over 30K steps

## MODELING THE DATA

Now an update on modeling fitness data to predict activity levels.   I've posted on this a few times before, here's my most recent post for review.

For you nerds here's my model.  I don't have a ton of data (about 100 days) so I have to use a fairly limited methodology.  I'm using a log-log regression model to predict daily steps using six variables (3 logged "priors" and 3 fixed effects).  R-squared is .77, which, is ok, but accounting for variance is fairly easy, because a good amount of the variance is just weekends and travel days being vastly deviant from mean.  Here's what the regression looks like, variable definitions in next section.

For you non-nerds here are the simple results:

Most Important Factors
•  Weekend: On weekends, all else equal, I take approximately 30% more steps than on weekdays.
• Steps_Prior: Each 1% increase in steps yesterday leads to a 0.4% decrease in steps today.  A priori, the more active I am yesterday, the more tired I am today.
• Steps_Three_Prior: Each 1% increase in the steps over the past three days leads to a 0.97% increase in steps today.  Essentially: one of the best predictors of steps today, is how I've done recently.
Less Important Factors
• Hours_Three_Prior: Each 1% increase in sleep in the last three days leads to a 0.37% decrease in steps today.  Likely related to cumulative fatigue theory.
• Alone: Days when I'm home alone I tend to take 13% more steps.
• Travel: Days when I have to travel for work or family, I take 11% fewer steps.

## CONCLUSION

A couple of things here:
1. Fitness week ended up being a fitness tracker success.  I exceeded my weekly average, and set a record for consecutive days over 30K steps.
2. My fitness model is coming together well.  It's fairly predictive, and all the input variables make a lot of sense.  It's nice to make ceteris paribus approximations of the impact of certain factors.  (e.g. How much more will I move on  the weekends?  How much does having a big day today impact tomorrow? etc.)  I will continue working these models, adding additional factors and potentially acquire other people's data (shameless solicitation?)