1. Student Researcher: Jason Shaw Jr.
Mentors: Dr. Diane Filion, Professor, Mr. William Murphy, Doctoral Student
There is empirical evidence that daily physical activity
improves a person’s overall health. Research supports the
idea that increased physically activity increases stamina,
improves overall satisfaction about oneself, and decreases
risk of heart complications—among other benefits not listed.
Along with the many benefits aforementioned, studies have
been conducted showing a positive correlation between a
healthy daily activity regiment and sleep. Previous studies
have been conducted that demonstrates that increased
physical activities leads to a favorable sleep pattern (Brand,
et al., 2010). Studies also reveal that participants who favored
moderate-intensity physical activity saw a decrease in wake-
time and showed more continuous sleep than participants
who favored low-intensity activity. (Edinger, et al., 1993).
On the other hand, it is possible that the relationship between
physical activity and sleep is bidirectional; the amount and
quality of a person's sleep may have an affect on the level of
physical activity the person chooses to engage in. There are
few studies that have tested the effect of sleep on exercise;
also, the studies have used methods to measure sleep and
exercise (self report, self questionnaires) that have limited
reliability or have measured sleep using polysomnography
methods that are not feasible for measuring sleep over a long
period of time.
PARTICIPANTS
Participants were UMKC students 18 years and older,
with normal or corrected hearing and vision. There
were eleven participants, 8 female and 3 male.
The focus of our study is to examine the relationship
between daily physical activity and sleep, both sleep
quantity and sleep quality, in college students using
wearable technology (FitBit® band).
Results from data we collected from our first round of
participants are shown below. Because of the small number
of participants, we computed descriptive statistics only.
PROCEDURES
Participants were given a FitBit band for a two-week
period. They were instructed to wear the band at all
times and activate sleep mode at times of rest (if given
a “flex” model. The “charge” model automatically
detects sleep).
The FitBits were set up with accounts that are
controlled by the research team so that data aren’t lost
due to account access. However, participants were also
given access to accounts to change sleep schedule
and track workouts.
At the end of the two week period, participants
returned the FitBit bands and participated in a
laboratory test session for a separate project that is
focused on measuring responses to stress.
Figure 1.
Figure 2.
Measures (as defined by FitBit®)
Active Minutes:
Average Active Minutes Per Day: “You earn ‘Very
Active Minutes" when you wear your FitBit® while doing
cardio workouts and high intensity activities like jogging,
running, aerobics, biking, rowing, or anything where you
are working up a sweat.”
Sleep Quantity:
Average Hours of Sleep per night: “During sleep
mode, when your body is completely at rest and
unmoving, your Fitbit tracker records that you are
asleep.”
Sleep Quality:
Average Number of Awakenings per night: “When
your tracker indicates that you are moving so much that
restful sleep would not be possible, your sleep graph
will indicate that you were awake.”
Age Sex Ethnicity
Mean (SD): 26.4 (6.9)
Range: 20-42
8 M
3 F
6 Caucasian
1 Asian
2 African America
1 American Indian
1 Other
In figure 1, our data show a strong relationship between the
average active minutes in the day and the average hours slept
per night, consistent with our predictions.
In figure 2, our data show a weak relationship between the
average active minutes per day and the average number of
awakenings per night, although it is in the expected direction.
With only 11 participants, we were not able to run statistical
analyses on our data. However, when we get to our desired
sample size of 40 participants, we will compute correlations
both within and between participants to test our hypothesis
about the potential bidirectional relationship between these
variables
In the future, we hope that our research will encourage others
to test the relationship of sleep and daily activity in different
populations, such as comparing athletes to non athletes.
0
20
40
60
80
100
120
140
AverageActiveMinutesPerDay
Average Active Minutes Per Day as a function of
Average Hours of Sleep Per Night
<6.0 (n=3) 6.0-7.0 (n=4) >7.0 (n=4)
0
20
40
60
80
100
120
140
AverageActiveMinutesPerDay
Average Active Minutes Per Day as a function of
Average Number of Awakenings Per Night
<10.0 (n=4) 10.0-13.0 (n=3) >13.0 (n=4)