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The Exercise Group
Q: Does caffeine have an effect on recovery from exercise?
- Random assignment to caffeine/no caffeine group (avoid cross-over effect).
- Record baseline measurements for heart rate, systolic and diastolic blood pressure
- Survey (caffeine? how much? when? previous exercise today? when? eat breakfast? usual exercise amount? usual caffeine intake?)
- Exercise (walk/run flights of stairs until heartrate is approx. 150 beats per minute)
- Record same three measurements at 0, 1, 5, and 10 minute marks after end of exercise
- Switch groups, repeat steps 2-5 next day, same time.
- Boxplots and scatterplots.
We used the differences between each timed measurement and the baseline reading for that measurement.
- Systolic rates: The middle 50% rose higher over resting rate with caffeine than without, but then seemed to drop more rapidly, then leveled off with some dropping to below resting rate. (See graph.)
- Diastolic rates: Except at the ten minute mark, the medians at the various times seemed similar, but the variability seemed greater in the no caffeine data than in the caffeine data.
- Heart rate: Both effects rose noticeably above resting rate, dropped quickly, and leveled out, but all were still slightly above resting rate at the ten minute mark. The drop rate with caffeine lagged slightly at the one minute mark.
- Connected line plots of mean readings in each category (immediate with caffeine, immediate without caffeine, one minute with caffeine, one minute without caffeine, etc.). These graphs were easier to read:
- Systolic rates: These means rose to the same level, with caffeine ratings dropping more rapidly, but not dramatically.
- Diastolic rates: We noted a dramatic difference in recovery times of subjects with caffeine versus without caffeine, with readings for caffeine dropping much more quickly.
- Heart rate: These graphs showed the same tendencies as boxplots and scatterplots.
Regression analysis attempts:
The graphs of the means against time, particularly for the diastolic rates, motivated our search for a regression model. The data appeared to be exponential or quadratic, so we experimented with various transformations: log, log(log), reciprocal, or square root in search of a linear model. When we couldnąt find a linear model, we tried multiple regression in search of a quadratic model: time & time2, time & time2 & caffeine/no caffeine, or time & time2 & caffeine/no caffeine(1 + time + time2).
Comparison of Variance:
The variability seemed greater in the no caffeine data than in the caffeine data in the diastolic study. We first observed this phenomenon in the boxplots, then confirmed it by looking at the descriptive statistics.
Increase sample size.
Control caffeine intake and timing of exercise in relation to intake.
Exercise on a bike or treadmill (muscles quickly gave up on stairs).
Continuously monitor heart rate and blood pressure.
Find a regression model.
The data records the differences from the individuals' resting rates at each of times 0,1,5,10 minutes.
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