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Does Wearing an Activity Monitor Affect Activity Levels? College Students in Small Study
Use pedometers to Track Steps Over Fifteen Weeks
David A. Queen
March 23, 2015

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Abstract
Physical activity and human health are directly associated, yielding positive and negative
consequences dependent on the amount and type of physical activity. One of the main ways by
which humans are physically active is walking. This study used a Fitbit pedometer to measure
the amount of steps taken in a group of (n=21) students at the University of North Carolina at
Asheville over a period of 15 weeks. The goal of the study was two fold: 1) to assess the
patterns of physical activity levels while wearing the pedometer and 2) to determine if wearing
the pedometer had an effect on activity levels. Overall, pedometer use did not significantly
impact the physical activity levels of the students. Days per week wearing the Fitbit and steps per
day did not change significantly throughout the study. Over the course of the experiment, no
student increased in steps per day wearing the Fitbit, while four participants declined in activity.
Overall, female participants had a significantly higher frequency of using the Fitbit (days/week)
P=0.001. It is impossible to determine if wearing a pedometer changed activity levels from prior
to wearing it. There did not appear to be any significant effect on activity levels during the
course of the experiment.
Introduction
Human physical activity has drastically changed over time with the transition from the hunter-
gatherer lifestyle to the more sedentary lifestyle of modern day living. The activities required in
everyday life of our ancestors varied from hunting and capturing game animals, building shelter,
caring for children, gathering edible vegetation, and making tools. The broad range of activities
speaks to describe the Paleolithic way of life; individuals had to work to eat and live. While this
is still true of the modern day livelihood, the physical demands for type of work have changed.
The environmental pressures and demands of our ancestors have been considered comparable to
the desiderata of modern day Olympic athletes (Ruff 2000). Introduction of more industrialized
and mechanized systems has decreased the need to spend the vast majority of each day
expending energy towards acquiring basic survival needs of food and shelter. These advanced
system developments allowed humans the ability to become less physically active with many
humans being at opposite ends of the spectrum, either exercise enthusiasts or almost completely
sedentary.
Physical activity is defined as any skeletal muscle movement that causes energy expenditure
(Tremblay et al. 2007). The energy expenditure is the amount of calories used in internal
(digestion and basal energy expenditure) and external (all bodily movement) activity completed

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by the body (Abadi et al. 2010). Physical activity can be thought of as light, moderate, or
vigorous, dependent on the heart rate of the individual performing the activity. The maximum
heart rate for an individual can be estimated as 220 beats per minute minus the individual’s age.
Light and moderate physical activity (such as walking and jogging) does not elevate the heart
rate above 60 percent of the maximum heart rate, whereas vigorous physical activity (which
utilizes large muscle groups) will push the heart rate above 60 percent of the maximum heart rate
(Heath 1993).
Physical activity and human health are directly associated, yielding positive and negative
consequences dependent on the amount and type of physical activity. A lack of physical activity
and an increase in inactive time are both risk factors for humans. These risk factors are
implicated in negative health outcomes such as obesity, type II diabetes, atherosclerosis,
coronary hearth disease, cardiovascular disease, colon cancer and hypertension (Archer, 2011).
Conversely, an increase in physical activity has shown to decrease levels of depression (McPhie
2012, 2015), improve cardiorespiratory fitness, increase elasticity of arteries and veins (Myers er
al. 2015) and increase task-related activity in the prefrontal and parietal cortices in the brain
(Colcombe et al. 2004). Physical activity at a young age promotes physical density,
mineralization, and geometry of bone structure; and as individuals age, physical activity and
exercise decrease the potential for bone degradation (Eaton, 2003).
One of the main ways by which all humans are physically active is walking. In the movement of
walking, skeletal muscles use force on the internal (body) and external (ground) environments to
maintain stabilization and move the body’s center of gravity (weight) forward (Kaneko, 1990).
There are numerous variables used in gait analysis such as stride length (the front to back
distance between the feet during one step in the forward motion) and stride width (the side to
side distance between the feet during one step in the forward motion). Cadence (the natural
rhythm of walking) is the preferred step frequency, stride length and width of an individual,
whereas walking speed is the rate of walking motion. Average walking speeds for males are 1.5
m/s and for females are 1.2 m/s (Abadi et al. 2010).
The movement of walking demands multiple muscles to work in cooperation in order to propel
the body forward. Physiologically, hip and knee extensor forces work with the ankle plantar
flexor to give support and forward motion. The soleus and gastrocnemus focus on supporting the

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movement; these muscles contribute predominantly to forward propulsion, leg swing, and weight
stabilization. At higher speeds the iliacus functions to increase leg speed at the start of a step,
while the medial hamstrings function to decrease leg speed at the end of a step (Neptune 2008).
When transitioning from walking to running, muscles will use similar biomechanical
mechanisms to focus on support and forward propulsion, however there is a significant decrease
in soleus contribution in running compared to its prominent role in walking, which is attributed
to the change in kinetics (Sasaki 2006).
Drastic change and increase in technology have helped shape a lifestyle in which humans are
more sedentary than ever before. However, instead of focusing on technology as a problem, it
can be viewed as a solution. Innovations in technology to promote physical activity and healthy
lifestyles range from video games and electronic food and travel diaries, to accelerometers and
geospatial technologies (Hillier, 2008). With the expansion of technology to promote physical
activity and healthy lifestyles has also come a development for use of such technologies to be
used to promote physical activity. These formats of applying technology to promote healthy
behaviors have been termed “interventions” and have increasingly been used in clinical practices
(Sallis et al. 2015), schools, families, and workplaces (Lubans, 2009). Physical activity
assessment technologies vary from heart rate monitors, stabilometers, horizontal time monitors,
pedometers, gait assessment monitors, electronic motion sensors and accelerometers. The study
detailed in this paper has chosen one of these technologies, a pedometer, to measure the amount
of steps taken in a study group (n=21) of students at the University of North Carolina at
Asheville. It is hypothesized that wearing the pedometer will cause an increase in physical
activity levels. The goal of this study is to assess the patterns of physical activity levels and
determine if wearing the pedometer had an effect on the individual’s activity.
Methods and Materials
The pedometer used in this study is termed a Fitbit* (Fitbit inc., United States) which was chosen
for its ease and comfort of use, accessibility, reliability, and inexpensive nature. A comparison to
other activity monitors found the Fitbit to yield reliable and valid measurements of physical
activity (Voojis et al. 2014). The Fitbit uses a 3-axis accelerometer to measure body acceleration
data such as frequency, duration, intensity, and patterns of movement that are then run through
an algorithm to translate these measurements into user friendly data such as steps taken and

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estimated calories burned. It should be noted that the algorithm is designed to pick up motion
patterns that are closely likened to walking. Dependent on if the motion produced is reaching the
designed threshold it will be counted or not counted as a step. This systematic error could
possibly lead to the Fitbit over or undercounting steps, i.e. riding in a vehicle on a bumpy road or
walking on a very soft surface such as a plush carpet (fitbit.com).
Dr. Aubrianne Rote collected and supplied the data used in this study, which was obtained by
incorporating the use of the Fitbit into a 15-week long class in the Health and Wellness
Department at the University of North Carolina at Asheville. The class consisted of 26 women
and 11 men ages 18-38. Students purchased a Fitbit and were instructed to wear it at their own
discretion. At the end of the semester, Dr. Rote had the students respond to a variety of questions
about their experience with the Fitbit and report their daily and weekly step results for the weeks
the Fitbit was worn. Any student who did not wear or have reported steps for more than two
weeks of the 15 week study time were excluded from use in the data analysis. Consequently, 12
of the 33 students had incomplete data and were excluded from the study. A total of 21 students,
15 women and 6 men, comprised the study population of this experiment.
The measurements used in analysis of the data were the total weekly steps taken, days per week
the Fitbit was worn, and the weekly average steps per day. Class data were compiled into three
categories of time period: early (weeks 2,3,4), middle (weeks 7,8,9), and late (weeks 12,13,14).
An ANOVA statistical analysis test was run to test if there were significant trends across the
early, middle, and late time periods for the group as a whole. The class was divided into the male
and female data for all measurements, and the averages of each measurement for the sexes was
calculated. Data for each individual were separated by the measurements of total weekly steps
taken, days per week the Fitbit was worn, and the weekly average steps per day. Regression tests
were used on each week (1-15) for the different measurements in order to identify any significant
trends for the individual student throughout the study. Any individuals who showed significant
(p0.05) increase or decrease in totals steps, days worn, or steps per day were then grouped
together to focus on any demographic differences or similarities within the individuals (such as
students living on or off campus).

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Results
Steps
Groups Average Std Dev SE Mean CV P-value
Steps (early) 194323 82652.4 18036.2 0.425 0.534
Steps (middle) 169468.7 88832.1 19384.8 0.524
Steps (late) 170488.3 70440.3 15371.3 0.413
Days
Groups Average Std Dev SE Mean CV P-value
Days (early) 18.1 2.9 0.6 0.163 0.586
Days (middle) 17.1 3.1 0.7 0.181
Days (late) 18 4 0.9 0.222
Steps per
Day
Groups Average Std Dev SE Mean CV P-value
s/d (early) 10591.3 3476.1 758.5 0.328 0.540
s/d (middle) 9538.9 4049.5 883.7 0.425
s/d (late) 9531.6 3020.4 659.1 0.317
Table 1: Class data averages, P-value, and coefficient of variance (Std/ Mean) of early, middle, and late time periods.
Figure 1: Male andFemale Averages of total weekly steps, days worn, andsteps perday.
50,000
60,000
70,000
Weekly steps
P=0.638
steps
Female
steps
Male
0
2
4
6
8
Days worn
P=0.001
days
Female
days
Male
0
5000
10000
15000
Steps per day
p=0.193
s/d
Female
s/d
Male

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Student ID # Weekly steps Days worn Steps per day
105 F 0.819143021 0.14027 0.42883
106 F 0.90212855 0.28404 0.88457
107 M 0.050721991 0.00419 0.81544
108 F 0.4861727 0.25602 0.89879
110 F 0.81533 0.17506 0.37711
111 F 0.64536 0.09137 0.27294
112 M 0.05273 0.68042 0.04045
113 F 0.97869 0.56815 0.31115
114 F 0.11253 0.68336 0.16457
116 F 0.0074 0.10691 0.00163
201 F 0.10208 0.06622 0.0423
203 M 0.41211 0.08381 0.24964
204 F 0.40542 0.20021 0.0371
209 F 0.83848 0.06358 0.93394
210 F 0.90241 0.01644 0.12976
211 M 0.51232 0.06959 0.27388
213 M 0.76014 0.27052 0.20172
215 F 0.33771 0.07855 0.54041
216 F 0.69046 0.62187 0.16337
217 F 0.26859 0.61528 0.14245
218 M 0.11901 0.64679 0.09714
* Legend F (female) / M (male) Negative trend Positive trend
Table 2: P-values of male and female individuals of each measurement, values of significance (P0.05) highlighted.
ANOVAs run on the early, middle, and late time periods showed no significant differences in
group data as all P-values were greater than 0.05 (table 1). The Coefficient of Variance (standard
deviation / mean) for the early, middle, and late time periods were greater than .20, indicating
high variability of the group data. Regression analysis and averages of male and female data
show a 5.56% difference in total weekly steps and 14.40% difference in steps per day. Males
showed higher averages in total weekly steps and steps per day (Figure 1). Regression tests
showed females had a significantly higher frequency of use (P=0.001) in days worn.

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Regression analysis of the individual data resulted in 28% (n=6) of the students showing
significant changes (p0.005) in the different measurement categories. The positive P-values
indicate an increasing trend and the negative P-values indicate a decreasing trend in the
respective measurement category. Total weekly steps had one positive and two negative trends,
Days worn had two positive trends, and steps per week had four negative trends (table 2).
Overall six of the nine individual significant changes were negative trends, with females
accounting for 80% of those negative trends. Individuals who exhibited significance in more than
one category tended to continue trending the same way for both, being either all positive or all
negative.
Discussion and Conclusion
Among the class data in the early, middle, and late time periods there were no significant
changes in total weekly steps, days worn, or steps per day (table 1). Averages, P-values,
coefficients of variance (CV) show that the overall class data is highly variable, and is likely that
no direct correlation can be drawn. The high degree of variation in the class reported data is
possibly due to the array of demographic variables within the subject group, i.e. age, work, living
and travel arrangements. The students’ knowledge of the required assignment related to use of
the pedometer at the end of the semester might have affected use of the Fitbit, causing further
variation. Assuming the average stride length of 2.5 ft, the averages for steps per day of the
group data can be converted to approximately 5 miles/day. The students in lower end of the
spectrum were traveling 5,000 steps/day or less (~2.5 miles/day); whereas students in the higher
end were traveling 13,000 to 15,000+ steps/day (~6.6miles/day).
Analysis of data between males and females show males having higher averages in the weekly
steps and steps per day, while females had an ~19% higher average in the days worn category
(figure 1). The higher use of the fitbit by females is applicable to existing literature findings
stating that physical activity monitors may be sex specific and requires further inquiry (Ho et al.
2013). The analysis of individual’s levels did reveal over a quarter of the class (28%) exhibiting
significant changes in the measurement categories. Within the individuals who did exhibit
significant changes, over half were negative, showing a decrease occurred in the total weekly
steps and steps per day (table 3). Eighty percent of the decreases which were attributed to
females who exhibited significant changes do not match results of previous research which

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shows that females using activity monitors have higher levels of physical activity than males
(Lubans 2009).
Overall, female participants had a significantly higher frequency of using the Fitbit (days worn)
P=0.001. It is impossible to determine if wearing a pedometer changed activity levels from prior
to wearing it; however there did not appear to be any quantifiable effect on activity levels
throughout the experiment.
Future research and implications
This type of study would benefit greatly from research that is conducted for a longer duration, as
most are 6 months or less. Other research invested in specific age groups for different sexes
could potentially allow for determination of age and sex specific guidance to incorporate and
affect physical activity levels for the various stages of life.
Acknowledgements
Dr. Aubrianne Rote Ph.D., associate professor Health and Wellness University of North Carolina
at Asheville- data collection and provision
Dr. Christopher Nicolay Ph.D., associate professor Biological Sciences University of North
Carolina at Asheville- contribution to data analysis and design

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Literature Cited
Abadi, F., Muhamad, T., & Salamuddin, N. (2010). Energy Expenditure through Walking: Meta Analysis
on Gender and Age. Procedia - Social and Behavioral Sciences, 7, 512-521.
doi:10.1016/j.sbspro.2010.10.069
Archer,E., & Blair, S. (2011). Physical Activity and the Prevention of Cardiovascular Disease:From
Evolution to Epidemiology. Progress in Cardiovascular Diseases, 53(6), 387-396.
doi:10.1016/j.pcad.2011.02.006
Colcombe, S., Kramer,A., Erickson, K., Scalf, P.,McAuley, E., Cohen, N.,... Elavsky, S. (2004).
Cardiovascular Fitness, Cortical Plasticity, And Aging. Proceedings of the National Academy of
Sciences, 101(9), 3316-3321. www.pnas.org/cgi/doi/10.1073/pnas.0400266101
Eaton, S. (2003). An Evolutionary Perspective On Human Physical Activity: Implications For Health.
Comparative Biochemistry and Physiology - Part A: Molecular & Integrative Physiology, 136(1), 153-
159. doi:10.1016/S1095-6433(03)00208-3
Heath,G., Pate,R.,& Pratt, M. (1993). Measuring Physical Activity Among Adolescents. Public Health
Reports, 108(1), 42-46.
Hillier, A. (2008). Childhood Overweight And The Built Environment: Making Technology Part Of The
Solution Rather Than Part Of The Problem. The Annals of the American Academy of Political and Social
Science, 615, 56-82.
Ho, V.,Simmons, R., Ridgway, C., Sluijs, E., Bamber,D., Goodyer, I., ... Corder,K. (2013). Is wearing a
pedometer associated with higher physical activity among adolescents? Preventive Medicine, 56(3), 273-
277. doi:10.1016/j.ypmed.2013.01.015
Kaneko, M. (1990). Mechanics And Energetics In Running With Special Reference To Efficiency.
Journal of Biomechanics, 23(1), 57-63.
Lubans, D.,Morgan, P.,& Tudor-Locke, C. (2009). A Systematic Review Of Studies Using Pedometers
To Promote Physical Activity Among Youth. Preventive Medicine, 48(4), 307-315.
doi:10.1016/j.ypmed.2009.02.014
McPhie, M., & Rawana,J. (2015). The effect of physical activity on depression in adolescence and
emerging adulthood: A growth-curve analysis. Journal of Adolescence, 40, 83-92.
doi:10.1016/j.adolescence.2015.01.008
Mcphie, M., & Rawana,J. (2012). Unravelling the relation between physical activity, self-esteem and
depressive symptoms among early and late adolescents: A mediation analysis. Mental Health and
Physical Activity, 5(1), 43-49. doi:10.1016/j.mhpa.2012.03.003
Myers, J., McAuley, P.,Lavie, C., Despres,J.,Arena, R.,& Kokkinos, P. (2015). Physical Activity and
Cardiorespiratory Fitness as Major Markers of Cardiovascular Risk: Their Independent and Interwoven
Importance to Health Status. Progress in Cardiovascular Dieseases, 57, 306-314.
http://dx.doi.org/10.1016/j.pcad.2014.09.011
Neptune, R., Sasaki, K., & Kautz, S. (2008). The effect of walking speed on muscle function and
mechanical energetics. Gait & Posture, 28(1), 135-143. doi:10.1016/j.gaitpost.2007.11.004
Ruff, C. (2000). Body mass prediction from skeletal frame size in elite athletes. American Journal of
Physical Anthropology, 113, 507-517.

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Sallis, R., Franklin, B., Joy, L., Ross, R., Sabgir, D.,& Stone, J. (2015). Strategies for Promoting Physical
Activity in Clinical Practice. Progress in Cardiovascular Diseases, 57, 375-386.
http://dx.doi.org/10.1016/j.pcad.2014.10.003
Sasaki, K.,& Neptune, R. (2006). Differences In Muscle Function During Walking And Running At The
Same Speed. Journal of Biomechanics, 39(11), 2005-2013. doi:10.1016/j.jbiomech.2005.06.019
Tremblay, M., Esliger, D., Tremblay, A., & Colley, R. (2007). Incidental movement, lifestyle-embedded
activity and sleep: New frontiers in physical activity assessment. Canadian Journal of Public Health,
98(2), S208-S217. Doi:10.1138/H07-130
Voojis, M., Alpay, L., Snoeck-Stroband, J., Beerthuizen, T., Siemonsma, P.,Abbink, J., ... Rovekamp, T.
(2014). Validity and Usability of Low-Cost Accelerometers for Internet-Based Self-Monitoring of
Physical Activity in Patients With Chronic Obstructive Pulmonary Disease. Interactive Journal of
Medical Research, 3(4), 1-9. doi:10.2196/ijmr.3056