Racial Sentencing Disparities and the Damage to African American Communities

Outline
Thesis Statement: Due to racism, African Americans are more
likely to face higher sentencing than the average American.
Argument #1- Mass Incarceration
Argument #2- Effects from Racial Sentencing
Argument #3- Community Damage
Opposing View Point
Body Paragraph #1
Argument#1- Mass Incarceration
Example #1- Overcrowded Jails/ Prisons
Example #2- Physical/ Mental Health Issues
Example #3- History
Body Paragraph #2
Argument #2- Effects of Racial Sentencing
Example #1- Broken Families
Example #2- Suicide / Death
Body Paragraph #3
Argument #3- Community Damage
Example #1- Employment
Example #2- Homelessness
Body Paragraph #4
Opposing View Point- How African Americans are sentenced
fairly
Conclusion
Sum up Thesis Statement/ Body
Am J Health Behav.™ 2018;42(3):47-55 47
The obesity epidemic has a dominant glob-al and national

presence. Research shows that 35% of American men and 40.4%
of
women over the age of 19 years are obese.1 These
statistics demonstrate that a high proportion of the
population in the United States (US) is impacted
directly by the obesity epidemic, which has been
proven to be both economically and physiologi-
cally taxing. Obesity is defined as the excess accu-
mulation of body fat to the point that it can have a
negative impact on health. Numerous factors have
been identified as obesogenic (those contributing
to the development of obesity), including decreased
energy expenditure, increased energy intake, and
decreased levels of physical activity.2 Concerted ef-
forts are being made to understand this epidemic
from all possible viewpoints.
Insufficient and poor sleep have emerged as obe-
sogenic risk factors. Sleep pattern disturbances are
associated with impaired cognitive abilities, poor
memory, confusion, reduced intellectual capacity,
and altered motor function.3 Impaired sleep also
can decrease academic performance,4 and increase
the incidence of vehicular accidents.5 Furthermore,
poor sleep quality and reduced sleep duration may
be associated with weight gain.6 College students
often report chronic reduced sleep quality and
sleep duration.7
The specific causes of poor sleep quality and du-
ration are diverse, but the presence of media de-
vices within the bedroom, such as smart phones
and tablets, is a novel point of discussion in terms
of their effect on sleep quality and duration. The
effect of cell phone presence in the bedroom on

sleep has been described in adolescents and adults
and implicated as a potential obesogenic factor,8,9
Jonathon Whipps, Doctoral Student, Translational Biomedical
Sciences, Ohio University, Athens, OH. Mark Byra, Professor,
Division of Kinesiology
and Health, University of Wyoming, Laramie, WY. Kenneth G
Gerow, Professor, Department of Statistics, University of
Wyoming, Laramie, WY.
Emily Hill Guseman, Assistant Professor, Diabetes Institute and
Department of Family Medicine, Ohio University Heritage
College of Osteopathic
Medicine, Athens, OH.
Correspondence Dr Guseman; [email protected]
Evaluation of Nighttime Media Use and Sleep
Patterns in First-semester College Students
Jonathon Whipps, MS
Mark Byra, PhD
Kenneth G. Gerow, PhD
Emily Hill Guseman, PhD
Objective: We evaluated how nighttime media use is associated
with sleep behaviors in first-
semester college students, and variation by weight status.
Methods: In September 2016, first-se-
mester college students (N = 114) completed surveys evaluating
nighttime media usage (NMU)
and sleep behaviors. Height, weight, and waist circumference
were measured, and weight status
was determined by body mass index. Results: Students reported
a mean sleep duration of 7.26 ±
0.93 hours. Only 33% (N = 38) reported sleeping at least 8
hours/night on average. Higher scores
on the Pittsburgh Sleep Quality Index were correlated with
reports of texting after bed (r = .199,

p = .04). Total time in bed was correlated with texting in bed (r
= .217, p = .026) and device-
related sleep interruptions (r = .215, p = .028). Social media
usage (r = 0.270, p = .005), mobile
gaming (r = .208, p = .033), and texting (r = .293, p = .002)
were correlated with sleep interrup-
tions. NMU was positively correlated with weight and weight
status. Conclusions: These results
suggest NMU is associated with reduced sleep quality.
Key words: media use; first-semester college students; sleep
quality; sleep patterns; weight status; college student health
Am J Health Behav.™ 2018;42(3):47-55
DOI: https://doi.org/10.5993/AJHB.42.3.5
Evaluation of Nighttime Media Use and Sleep Patterns in First-
semester College Students
48
but has not been well described in young adults
(18-24 years old) or college students specifically.
Also, research has yet to explore whether a direct
relationship exists between nighttime media habits
and sleep hygiene.
College freshmen prove to be an important group
for studying this phenomenon. Young adults mov-
ing from the high school to college setting undergo
a transition in behaviors as described by Chicker-
ing’s Theory of Identity Development.10 Because
these individuals are undergoing a change in be-
havior and have been raised in a technological envi-
ronment, they may provide valuable insight about

this area.
Better understanding of the associations among
sleep patterns, nighttime media usage, and weight
status can provide novel insight as to how these fac-
tors may impact weight. Although some research
has explored the relationship that nighttime media
use has with sleep habits, most have not described
a direct relationship between NMU and sleep, and
most have not looked at this relationship in young
adults or colleges students. Therefore, the purpose
of this study was to determine the association be-
tween presence and use of media devices at night-
time, such as tablets and smart phones, and sleep
patterns of first-semester college students (young
adults), and whether these behaviors are associated
with weight gain. We hypothesized that first-semes-
ter college students who exhibited suboptimal sleep
patterns would show increased incidence of sleep
disturbance via nighttime media usage and would
be associated with weight gain over the course of
one semester.
METHODS
Participants
Participants were 18-24-year-old first-semester
students at the University of Wyoming. Instructors
of 34 first-year seminar (FYS) courses were contact-
ed in September of 2016 and 6 instructors invited
the researchers to recruit participants from their
classrooms. The main investigator (JW) attended
each FYS course in September 2016 to explain the
study, invite students to participate, and explain
the consent form; freedom of consent was stressed
during this process. Of 142 potentially-eligible

students, 128 (90.1%) chose to participate. Sur-
veys were completed during the class period with
assistance from the main investigator (JW) when
necessary (ie, clarifying survey items). For anthro-
pometric measures, participants were taken to an
adjacent room or area divided by a privacy screen.
All members of the study team were trained in hu-
man subject research protections, anthropometric
measures, and the survey instruments. The main
investigator (JW) and/or faculty supervisor (EHG)
were present at all data collection sessions.
Sleep Quality and Duration
We assessed sleep quality and duration using
the Pittsburgh Sleep Quality Index (PSQI).11 The
PSQI measures 7 variables associated with sleep:
sleep quality, sleep duration, habitual sleep effi-
ciency, sleep disturbances, use of medications to
assist with sleep, and daytime dysfunction.7,11 The
combination of scores from the measured sleep vari-
ables determines an individual’s global sleep quality
score (GSQ). The PSQI scores can range from 0 to
21, with higher scores indicative of lower quality
of sleep. Individuals with a GSQ greater than 5 are
classified as poor sleepers, whereas those with scores
less than 5 are considered to be good sleepers. The
survey evaluated the prevalence of the above vari-
ables from the last month.7 The PSQI has been vali-
dated to assess sleep quality and duration.11 Scoring
procedures specific to the PSQI were used to quan-
tify survey responses for statistical analysis.
Nighttime Media Usage
We assessed nighttime media usage (NMU) us-

ing 7 questions adapted from those used by Ada-
chi-Mejia et al.8 Table 1 shows the survey items
and coding. In this survey, items 1-6 were scored
using 5-point Likert scale responses used to quan-
tify responses (1 meaning never to 5 meaning all
the time) and were collapsed as necessary. Item 7
was entered as nights per week (up to 7). We used
this information to determine the frequency with
which nighttime media disturbs sleep, and how fre-
quently students access media devices prior to bed.
We compared these activities to other variables.
Anthropometric Measures
We measured height, weight, body mass index
(BMI), and waist circumference at baseline and at
a follow-up date approximately 10.5 ± 1.02 weeks
Whipps et al
Am J Health Behav.™ 2018;42(3):47-55 49 DOI:
https://doi.org/10.5993/AJHB.42.3.5
later (range = 9.3 to 12.3 weeks). Prior to taking
height and weight measurements, we instructed
participants to remove their shoes and any bulky
articles of clothing. Height was determined using
a portable stadiometer and reported in meters. We
measured weight using a portable digital scale and
we reported it in kilograms (kg). Using the mea-
sured heights and weights of the participants, we
calculated BMI using the standard BMI equation
(BMI = Weight (kg) / Height2 (m2)). We measured

waist circumference (WC) using a flexible measur-
ing tape. We instructed participants to cross their
arms across their chest and to place their hands
upon their shoulders. Using the right side of the
body, a trained research team member located the
ileum and marked the site using a non-permanent
marker. A cross was made across this mark in line
with the mid-axillary line. Once marked, the par-
ticipants were instructed to stand up straight with
the arms at the side and relaxed, head facing for-
ward, and with the feet shoulders distance apart.
The measuring tape was wrapped around the par-
ticipant’s waist, ensuring the tape was parallel to
the floor and was crossing the iliac mark. Once the
tape was correctly positioned, a measurement was
taken and recorded in centimeters (cm) at the end
of a normal exhalation.
Data Analysis and Statistics
We computed descriptive statistics for physical
characteristics and used independent sample t-tests
to identify any potential differences between men
and women. No statistically significant differences
between men and women existed, so analyses of
the main outcome variables were not completed
separately for each sex. Frequency distributions
were calculated for categorical scores from the
PSQI and responses to the NMU questionnaire.
Pearson correlations were used to determine the ex-
tent to which the presence of electronic devices in
the bedroom and NMU correlate with categories
of sleep latency, sleep duration, sleep quality, and
sleep efficiency. A type-I error rate of .05 was used
to determine statistical significance. We used the

IBM Statistical Package for Social Science (SPSS)
version 23.0 to conduct statistical analyses.
RESULTS
Participant Characteristics
Table 2 displays participant characteristics. Of
128 students initially surveyed, 114 completed the
entire study (retention rate = 89.1%). The partici-
pants that did not complete the study (N = 14)
were either not present for the follow-up assess-
ment period or had incomplete survey responses.
Participants who were not present for the follow-
up assessment were not contacted to complete the
study. Of participants who completed the study,
just over half (55%) identified as female (N = 52).
The mean age of the students at the initial visit was
18.7 ± 0.4 years. Ninety-three percent (N = 106)
of students identified as Caucasian/white, and the
other 7% of students (N = 8) reported another
ethnicity, not unlike the university overall (10%
minority, 53% female). There were no statistically
significant differences between sexes in anthropo-
metric measures except for height. Table 2 reports
differences for participant characteristics. Of the
Table 1
Nighttime Media Usage (NMU) Questionnaire Items and
Abbreviations
NMU1 Do you take your cell phone and/or tablet to bed with
you?
NMU2 Is your cell phone and/or tablet turned off when you
sleep?

NMU3 Do you use your cell phone and/or tablet as your alarm?
NMU4 Do you text or use a messaging app after you go to bed?
NMU5 Do you play games on your cell phone and/or tablet after
you have gone to bed?
NMU6 Do you use social media on your device(s) after you
have gone to bed?
NMU7 How many nights of the week do you get awakened by a
text or other notification from a friend or social media after you
go to bed to sleep
50
population measured, 2 males (3.8%) and 10 fe-
males (16.1%) had elevated waist circumferences
after initial measurements. These values did not
change significantly between pre- and post-mea-
surements. Average student BMI at the initial visit
was 24.1 kg/m2, which is slightly below the cutoff
for an overweight classification. Of participants,
25.4% (N = 29) were overweight (OW; BMI >
25.0 and < 30.0) and 8.8% were obese (OB; BMI
> 30.0). Between initial and final measurements,
participants’ mean weight increased 0.6 kg ± 1.92
and BMI increased 0.1 kg/m2 ± 5.74. Mean waist
circumference decreased by 1.1 cm ± 5.74, which
is approximately equal to the tolerable error limit

identified in the study protocol (1 cm).
Sleep Quantity and Quality
Table 3 displays sleep measurements. The most
common time (mode) students of both sexes re-
ported going to bed was 11:00 PM. The most
common rise time was 8:00 AM and the average
self-reported sleep duration was 7.26 ± 0.93 hours.
The mean time spent in bed was 8.12 ± 0.93 hours,
with an average sleep latency (time spent in bed
prior to sleep) of 19.6 ± 16.9 minutes. There were
Table 3
Average Sleep Characteristics from Pittsburgh Sleep Quality
Index (PSQI)
Male (N = 52) Female (N = 62) Total (N = 114)
Mean Mean Mean
Average Bed Time (Mode) 11:30 PM 11:00 PM 11:00 PM
Average Rise Time (Mode) 7:15 AM 8:00 AM 8:00 AM
Average Sleep Latency (min) 18.3 (15.8) 20.8 (17.8) 19.6 (16.9)
Reported Sleep Duration (h) 7.21 (0.81) 7.30 (1.02) 7.26 (0.93)
Calculated Time in Bed (h) 8.05 (0.87) 8.18 (0.99) 8.12 (0.93)
Sleep Efficiency (%) 90.0 (8.8) 89.4 (9.3) 89.7 (9.0)
Average Global PSQI 4.94 (2.15) 5.51 (2.71) 5.25 (2.48)
Table 2

Participant Characteristics
Male (N = 52) Female (N = 62)
Pre Post Pre Post
Age 18.7 (0.5) -- 18.6 (0.3) --
Height (m) 1.80 (0.1) 1.81 (0.1) 1.66 (0.1) 1.66 (0.1)
Weight (kg) 79.9 (13.0) 80.7 (13.2) 65.1 (13.0) 65.6 (13.2)
WC (cm) 82.8 (11.6) 82.7 (9.1) 78.6 (9.8) 76.6 (9.6)
BMI (kg/m2) 24.6 (3.9) 24.8 (4.0) 23.6 (4.5) 23.8 (4.6)
OW (%) 30.8 36.5 21.0 24.2
OB (%) 9.6 7.7 8.1 6.5
Note.
Values are mean (SD) unless otherwise indicated.
WC = waist circumference; BMI = body mass index; OW =
overweight; OB = obesity
Whipps et al
no statistically significant gender differences. Only
one-third of participants (N = 38, 33.33%) re-
ported meeting sleep recommendations (sleeping
at least 8 hours per night). Of those who slept less

than 8 hours per night, 25.4% (N = 29) slept 6.5
hours per night or less.
Global PSQI
Overall sleep quality was assessed using global
scores calculated from the PSQI responses. Table
3 reports the average Global PSQI, and Table 4
displays the Global PSQI categories and frequen-
cies. Respondents who had a Global PSQI score
between 1 and 5 were categorized as optimal sleep-
ers (N = 68); scores between 6 and 7 were classi-
fied as borderline (N = 26); and poor sleepers were
those with scores greater than or equal to 8 (N =
20) based on categories defined within the PSQI
scoring guidelines as well as those presented by
Lund et al.7 Only 59.6% of respondents were con-
sidered optimal sleepers, whereas the other 40.4%
were either borderline to poor sleepers.
Nighttime Media Usage Questionnaire
Figure 1 displays the NMU response frequencies.
In some cases, responses were collapsed due to low
Table 4
Global PSQI Category Frequencies
Male (N = 52) Female (N = 62) Total (N = 114)
Frequency Percent Frequency Percent Frequency Percent
Optimal (1-5) 34 65.4 34 54.8 68 59.6
Borderline (6-7) 11 21.2 15 24.2 26 22.8
Poor (>8) 7 13.5 13 21.0 20 17.5

NMU1 NMU2 NMU3 NMU4 NMU5 NMU6 NMU7
0
20
40
60
80
100
Questionnaire Item
Pe
rc
en
t o
f T
ot
al
S
am
pl
e
Figure 1
NMU Questionnaire Response Frequencies
Note.

Responses to individual NMU question items (Table 1) as a
percent. NMU 1-6 are scored using 5-point Likert scale
responses used to quantify responses (1 meaning never to 5
meaning all the time) and were collapsed as necessary. NMU
7 is nights per week (up to 7).
52
response frequency; this is noted in the figure cap-
tion where appropriate. Almost all respondents (N
= 105, 92.1%) have their smartphones or tablets in
their rooms as they sleep frequently or all the time,
and most (N = 108, 94.7%) use their cell phone
or tablet as their alarm. Those who did not have
their cell phone on and in the bedroom (N = 9)
were removed from analyses for NMU questions 4
through 7.
Table 5 shows the correlations between anthro-
pometric variables and NMU responses. Those
who reported playing games in bed frequently
were significantly more likely to have a higher
initial weight (p = .006), post-weight (p = .006),
initial waist circumference (p = .006), post-waist
circumference (p = .037), initial BMI (p = .029),
and post-BMI (p = .035). None of the other cor-
relations were significant. Unsurprisingly, there was
Table 5
Correlations between NMU Variables and Anthropometric
Characteristics

NMU1 NMU2 NMU4 NMU5 NMU6 NMU7
r r r r r r
Pre Weight -0.083 -0.019 -0.039 0.263 * -0.032 -0.172
Post Weight -0.081 -0.029 -0.043 0.263 * -0.028 -0.174
WC -0.068 0.040 -0.029 0.263 * -0.026 -0.151
Post WC -0.047 -0.012 -0.039 0.201 * -0.014 -0.097
BMI 0.034 0.075 -0.023 0.210 * 0.001 -0.139
Post BMI 0.035 0.062 -0.036 0.203 * -0.004 -0.132
Note.
**Correlation is significant at the .01 level (2-tailed)
* Correlation is significant at the .05 level (2-tailed)
Table 6
Correlations between NMU Variables and Sleep Behaviors
Sleep
Duration
Bed
Hours
Global
PSQI
Texting
(NMU4)

Nighttime
Gaming
(NMU5)
Social
Media
(NMU6)
Nighttime
Interruptions
(NMU7)
r r r r r r r
Sleep Duration 1 - - - - - -
Bed Hours .684** 1 - - - - -
Global PSQI -.355** .053 1 - - - -
Texting (NMU4) .199* .217* -.032 1 - - -
Nighttime
Gaming (NMU5) .147 .103 .053 .379** 1 - -
Social Media
(NMU6) .102 .058 .074 .652** .328** 1 -
Nighttime
Interruptions
(NMU7)
.122 .215* .128 .293** .208* .270** 1

Note.
**Correlation is significant at the .01 level (2-tailed)
* Correlation is significant at the .05 level (2-tailed)
Whipps et al
no relationship between NMU and change in any
anthropometric variable over the 8-week period, as
there were no statistically significant changes in any
of these variables.
After selecting for students who frequently or al-
ways have their cell phones and/or tablets on while
sleeping (N = 105), bivariate Pearson correlation
models were run to determine how NMU respons-
es were correlated with sleep behaviors and PSQI
scores (Table 6). Texting after bed was moderately
correlated with participants’ Global PSQI score (r
= .199, p = .04). Bed hours (time spent in bed) was
moderately correlated with both texting in bed (r =
.199, p = .04) and device interruptions (r = .270, p
= .005). Social media usage and playing games in
bed were weakly to moderately correlated with in-
terruptions (r = .270, p = .005; r = .293, p = .002)
and playing mobile games in bed was moderately
correlated with interruptions (r = .208, p = .033).
DISCUSSION
In 2015, the American Academy of Sleep Medi-
cine and Sleep Research Society released a consen-

sus statement stating adults need 7 or more hours
of sleep per night and, for young adults, sleeping
more than 9 hours may be appropriate.12 Overall,
first year students in our sample reported chroni-
cally low levels of sleep compared to current recom-
mendations. Total reported sleep time and chronic
low sleep in this population were found to be simi-
lar to other American college students.7,13 These re-
sults emphasize the need to address sleep problems
that persist in college-age populations, especially
because sleep deprivation has been associated with
decreased academic performance,4 increased nega-
tive moods,14 increased anxiety and depression,15,16
drowsy driving,17 and reduced productivity.18 Inad-
equate time spent sleeping has been demonstrat-
ed across both US-born and foreign-born college
students as well. Eliasson and Lettieri conducted a
study comparing the sleep habits of US-born col-
lege students to those of foreign-born students.
Although both groups generally reported restrict-
ed sleep, foreign-born students were more likely
to stay awake longer studying, whereas US-born
students delay sleep for more social activities. This
suggests that strategies aimed at improving sleep
hygiene may need to be tailored based on cultural
backgrounds.19
Sleep deprivation is pervasive among young
adults in general in the US. Data from the 2014 Be-
havioral Risk Factor Surveillance System (BRFSS)
found that 32.2% of young adults (age 18-24 years)
surveyed reported short sleep duration (<7 hours
per night) on average.20 Chronic short sleep may
not be unique to the US; the prevalence of short
sleep or borderline short sleep has been document-
ed in young adult populations in other countries.

There is evidence showing reduced sleep duration
in countries such as Japan,21 and this trend may ex-
tend to other countries as well.22 Furthermore, the
role that nighttime media usage plays in short sleep
duration may extend outside of young adult col-
lege students to young adults in general. Overall,
sleep problems may not be unique to the US and
potential interventions may need to be explored in-
ternationally in the future.
Our results support the hypothesis that night-
time media usage is related to sleep quality. The
directionality of the relationship cannot be deter-
mined from the current study. Participants who
more frequently reported playing games, using so-
cial media, or texting after bed were more likely
to report sleep interruptions by their devices. This
suggests that the presence of a smartphone or tablet
in the bedroom does increase the likelihood that
sleep will be disturbed and can have an overall im-
pact on sleep quality, however prospective studies
are necessary to confirm this.
Participants who reported playing games, tex-
ting, or using social media were also more likely to
report engaging in more than one type of activity.
Weak to moderately strong correlations were found
between those reporting to play games, use social
media, and/or text after they are in bed. This sug-
gests that first-semester college students are engag-
ing with their smartphone devices in multiple ways
at night prior to going to sleep; the increased use
of multiple types of media may exaggerate the po-
tential for sleep disturbances. Future studies should
focus on further describing these relationships.

Other studies have suggested that nighttime me-
dia use has no negative effect on sleep among young
adults, but instead, suggest that these individuals
use their media devices to cope with their existing
sleep problems. Taverniew and Willoughby sought
to determine the directionality of the relationship
between media use and sleep deprivation in first-
54
year Canadian college students. They found that,
although sleep problems acted as strong predictors
of increased time spent watching TV and online,
the relationship was not true when using media
time to predict sleep problems.18 Further studies
may be necessary to determine whether emerging
adults are looking to their media devices to cope
with their sleep problems or to prove further that
the relationship may be unidirectional.
Although this study demonstrated that night-
time media usage in the bedroom is associated with
sleep behaviors, we did not find that it was associat-
ed with change in weight or BMI. On average, our
participants (both male and female) gained 0.55
kg over the duration of the study. Whereas still
an overall increase in weight, the increase is small
enough not to be statistically significant. Other
studies have shown that first-year students do tend
to gain weight, but generally allow for more time
between measurements. Increases in weight for

first-year college students in other studies range
from 1.1 kg to 1.3 kg over about 3 months.23,24
However, none of the anthropometric variables
changed significantly over the course of the study
period. Given that the time between initial and fi-
nal measurements was only 8 weeks, the period of
time may not have been long enough to show any
significant change in weight or other markers. Fu-
ture studies should consider a longer period of time
between measurements.
Limitations
Self-reported behaviors can be inaccurate de-
pending on participants’ perception, potentially
confounding results. Self-reported sleep and NMU
may be associated with either recall or response bias.
Further, we did not assess whether participants had
any clinical conditions, such as obstructive sleep
apnea, that could interfere with sleep independent-
ly of sleep hygiene. Unfortunately, we were not able
to assess physical activity and dietary habits, limit-
ing our ability to discuss whether NMU is associat-
ed with weight change. However, this is unlikely to
be an issue in our study because weight remained
stable over the study period. Our results did show
that NMU was positively associated with weight
and weight status; however, the directionality of
this relationship could not be determined due to
the cross-sectional study design and the relatively
small convenience sample. Due to the small sam-
ple size, some of the statistically significant results
may have been due to chance alone; future studies
should be conducted using a larger sample size to
validate these findings.

Conclusions
Our data support the current literature in dem-
onstrating that college students, specifically first-
semester students, are likely to experience chronic
sleep deprivation and is novel in describing the sleep
patterns and NMU habits of first-semester college
students. This study also adds to the literature by
suggesting a relationship exists between sleep be-
haviors and nighttime media usage – as nighttime
usage (texting, social media, gaming, etc.) increas-
es, overall incidence of sleep interruptions increases
and sleep quality tends to decrease. Future studies
should focus on determining whether nighttime
media usage and poor sleep hygiene do have a re-
lationship with weight status and other markers of
health, as well as determine the true directionality
of the relationship between nighttime media usage
and sleep quality.
Human Subjects Statement
The University of Wyoming Institutional Review
Board (protocol #20160728JW01264) approved
this study. and all students provided informed con-
sent prior to participation.
Conflict of Interest Statement
The authors have no conflicts of interest to
disclose.
Acknowledgements
The authors acknowledge members of the Uni-

versity of Wyoming Pediatric Physical Activity
Lab for their assistance in data collection and data
management.
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