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Article
Evaluation of the FitBark Activity Monitor for Measuring
Physical Activity in Dogs
Jessica Colpoys 1, * and Dean DeCock 2
Citation: Colpoys, J.; DeCock, D.
Evaluation of the FitBark Activity
Monitor for Measuring Physical
Activity in Dogs. Animals 2021,11,
781. https://doi.org/10.3390/
ani11030781
Academic Editor: Andrew
Nicholas Rowan
Received: 29 December 2020
Accepted: 8 March 2021
Published: 11 March 2021
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Attribution (CC BY) license (https://
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4.0/).
1Department of Agricultural Science, Truman State University, Kirksville, MO 63501, USA
2Department of Statistics, Truman State University, Kirksville, MO 63501, USA; decock@truman.edu
*Correspondence: jcolpoys@truman.edu
Simple Summary:
Altered activity in a dog can be an early indicator of health and welfare concerns.
Accelerometers, a type of activity monitor, are being used more frequently in dogs and may be a
simple way for owners and veterinarians to monitor a dog’s changing health and welfare needs.
However, there are few peer-reviewed studies evaluating the accuracy of these devices. Therefore,
this study evaluated the accuracy of the FitBark 2 accelerometer (FitBark) by comparing activity
data recorded using the FitBark to dog physical activity recorded using video analysis of dog step
count. Dog step count and FitBark activity were highly correlated when the dogs were exploring
a room off-leash and when they were interacting with their owner. However, when the dogs were
being walked on a leash, low correlations between step count and FitBark activity were observed.
In conclusion, the FitBark is a valid tool for tracking off-leash activity in dogs; however, more work
should be done to identify the best method of tracking activity in on-leash situations.
Abstract:
Accelerometers track changes in physical activity which can indicate health and welfare
concerns in dogs. The FitBark 2 (FitBark) is an accelerometer for use with dogs; however, no studies
have externally validated this tool. The objective of this study was to evaluate FitBark criterion
validity by correlating FitBark activity data to dog step count. Dogs (n= 26) were fitted with a
collar-mounted FitBark and individually recorded for 30 min using a three-phase approach: (1)
off-leash room explore; (2) human–dog interaction; and (3) on-leash walk. Video analysis was used to
count the number of times the front right paw touched the ground (step count). Dog step count and
FitBark activity were moderately correlated across all phases (r = 0.65, p< 0.001). High correlations
between step count and FitBark activity were observed during phases 1 (r = 0.795, p< 0.001) and
2 (r = 0.758, p< 0.001), and a low correlation was observed during phase 3 (r = 0.498, p< 0.001). In
conclusion, the FitBark is a valid tool for tracking physical activity in off-leash dogs; however, more
work should be done to identify the best method of tracking on-leash activity.
Keywords:
animal behavior; animal welfare; dog; canine; pet; accelerometer; activity monitor;
FitBark; wearable technology; validation
1. Introduction
Changes in physical activity level can be indicative of health and welfare concerns in
dogs. Below-normal activity levels can be an indicator of animal sickness [
1
,
2
], impaired
dog mobility [
3
], and pain [
4
]. Above-normal activity levels can also signal health concerns,
such as canine pruritus [
5
]. Additionally, physical activity levels can be an indicator of
the amount of rest dogs are getting, as poor sleep quality can relate to welfare concerns
in dogs [
6
]. In order to provide individualized animal treatment, it is important for dog
owners, veterinarians, researchers, and other animal professionals to know the normal
activity level for an individual dog, as deviations in either direction can serve as early
indicators of health concerns. Thus, an accurate and accessible activity monitor for dogs
can help differentiate an individual dog’s normal activity level and activity abnormalities.
Animals 2021,11, 781. https://doi.org/10.3390/ani11030781 https://www.mdpi.com/journal/animals
Animals 2021,11, 781 2 of 8
Accelerometers, a type of activity monitor, are non-invasive tools used to track changes
in acceleration [
7
,
8
]. As technology has advanced, activity monitors have become smaller
and can provide automated feedback through integration with mobile devices [
9
]. Modern
activity monitors serve as a more feasible option for research monitoring dog activity
than live or video-recorded behavioral observations, which can be time-consuming and
often require expensive software. While wearable technology is increasingly popular for
humans [
10
], it is also becoming popular for pets, and multiple devices marketed toward
pets are available [
11
]. A study evaluating an owner’s use of activity monitors for dogs
determined that these devices are typically used to increase dog activity and improve the
dog’s health and care [
12
]. Thus, these devices have the potential to impact human–dog
interactions and ultimately dog welfare.
Actical monitors (Respironics Inc, Murrysville, PA, USA) are externally validated for
use with dogs [
13
] and are currently the most widespread monitors used for dog activity
research [
3
,
8
,
11
,
14
–
19
]. However, Actical monitors currently do not integrate with mobile
devices [
20
]. Two other monitors, the PetPace [
11
] and Whistle [
17
], have been evaluated
by correlating activity data to the Actical monitors. These studies showed significant
correlations of the activity data to Actical monitors; however, these data are likely not as
robust as validating the activity monitors directly to the behavioral observation of activity.
For an activity monitor to have a widespread impact on dog health and welfare, it
needs to be accurate, affordable, user-friendly, and easily accessible to potential users.
The FitBark 2 dog activity monitor (referred to herein as Fitbark; FitBark Inc., Kansas
City, MO, USA) which is a 3-axis accelerometer, is affordable, user-friendly, and easily
accessible. However, to our knowledge, although they have been used to monitor dog
activity in published studies [
21
–
24
], no peer-reviewed studies have externally evaluated
FitBark criterion validity. Therefore, the objective of this study was to evaluate FitBark
criterion validity by correlating FitBark activity data to dog physical activity, measured via
step count. It was hypothesized that FitBark activity monitors would be a valid tool for
measuring physical activity in dogs.
2. Materials and Methods
2.1. Ethical Statement
All experimental procedures were approved by the Truman State University Animal
Care and Use Committee. This experiment was conducted in November and December
2019 at Truman State University.
2.2. Animals
Pet dogs (n= 26; female: n= 14; male: n= 12) were recruited for this study by advertis-
ing to students, staff, and faculty at Truman State University. Participating dogs were given
a free item (toy or bone of the dog’s choosing) and entered into a drawing to win a free
FitBark activity monitor. Dogs from a variety of breeds, sizes (mean
±
SD:
21.8 ±9.90 kg
;
minimum: 5.0 kg; maximum: 38.6 kg), and ages (mean
±
SD: 3.7
±
4.25 years; minimum:
8 months; maximum: 15 years) were recruited for the study to increase sample variation
(Supplementary Table S1). Upon scheduling, owner consent, dog information includ-
ing name, sex, birth date, primary breed, spay/neuter status, and any known medical
conditions were obtained.
2.3. Testing
Dogs were brought to the Truman State University Farm for testing. Upon arrival
at the testing facility, each dog was weighed on a non-slip, digital platform scale (W C
Redmon Co., Peru, IN, USA). Gait analysis was visually evaluated using the numerical
rating scale for visual assessment of gait on a 0–5 scale (0 = clinically sound and 5 = non-
weight bearing on a limb while standing or moving) [
25
]. All dogs included in this study
had a gait score of 1 or below.
Animals 2021,11, 781 3 of 8
Each dog’s typical collar was removed and replaced with a flat nylon collar (Vibrant
Life, Walmart Inc, Bentonville, AR, USA) with a FitBark monitor attached via zip ties
following FitBark collar attachment instructions [
26
]. Each FitBark monitor was connected
via Bluetooth to an iPad (Apple Inc, Cupertino, CA, USA) and had the following informa-
tion for each dog entered into the FitBark app for the corresponding monitor: name, sex,
spay/neuter status, birth date, weight, primary breed, and any known medical conditions.
Each collar was adjusted to ensure a snug fit with a two-finger gap between the collar and
neck [
17
,
19
] and to ensure the FitBark monitor was located ventrally and the D-ring was
located dorsally on each dog’s neck.
All testing occurred in an open 12 m ×12 m room with concrete flooring. Four color
cameras recording on a DVR system (Amcrest Technologies LLC, Houston, TX, USA)
were mounted 1 m from the floor to record dog activity. A fifth camera recorded the
time displayed on the iPad that was synced to the FitBark monitor to ensure consistency
in recording time between the DVR and the FitBark. Each dog was video recorded and
individually tested for 30 min within the facility using a three-phase approach intended to
elicit different dog behaviors.
The test began by letting the dog off-leash in the enclosed room. Phase 1 consisted of
10 min for the dog to explore the room off-leash. A dog bed, toys, and a water bowl were
present in the room for the dog to interact with. To avoid interaction, the dog owner was
not present in the room. A research assistant monitored the dog on a screen connected to
the camera system outside of the room. As a result of concerns about separation anxiety, the
owner of one dog (Dog 9) was present in the room during phase 1 but had minimal contact
with the dog during this phase. Phase 2 consisted of 10 min of human–dog interaction.
Phase 2 began when the owner entered the room. The owner was asked to interact with the
dog off-leash as he/she typically would, including activities such as petting and playing
with the dog. The owner was provided with a chair, dog toys, and treats to use while
interacting with the dog. Phase 3 consisted of a 10-min on-leash walk. A nylon leash
(1.83 m long, Vibrant Life, Walmart Inc) was provided, and the owner was asked to walk
the dog for 10 min. Owners were allowed to choose how they walked the dog, so walking
patterns and speed varied. As a result of owner concerns, the owners of two dogs (Dogs
8 and 17) chose to hook the leash to a harness instead of the provided flat collar. Thus,
phase 3 data were not analyzed for these dogs. All phases were completed in the same,
consecutive order for all dogs.
Following testing of each dog, FitBark data were downloaded at a 1-min epoch length.
The video of dog testing was continuously analyzed for step count by one trained observer
with an intra-observer reliability of 0.994 (p< 0.001; Intraclass Correlation Coefficient,
calculated using “irr” R package, RStudio 1.2.5019, Boston, MA, USA). A step was counted
every time the front right paw touched the ground. Observations were manually collected
using a clicker, and total steps per minute were recorded. If there were any occurrences
where the dog was not visible on the cameras (e.g., exited the room), then the data within
that minute were excluded from the analysis.
2.4. Statistical Analysis
Statistical analyses were performed utilizing Minitab software (Minitab 17.3.1, Minitab
LLC, State College, PA, USA). Phase differences in both step count and FitBark activity were
determined using Welch’s ANOVA with Games–Howell adjustments (95% confidence).
Pearson’s correlation coefficients were calculated to assess the correlation between activity
level indicators for the entire dataset as well as at the phase and individual dog level. A
correlation coefficient of 0.9–1.0 was considered very strong, 0.7–0.9 was considered high,
0.5–0.7 was considered moderate, 0.3–0.5 was considered low, and 0.0–0.3 was considered
negligible [27]. The significance level was fixed at p< 0.05.
Animals 2021,11, 781 4 of 8
3. Results
A total of 745 min of video step count and FitBark activity data (referred to as Bark-
Points by FitBark Inc.) were analyzed across all 26 dogs. Evaluation of the step count
data showed differences between all three phases (p< 0.001: Figure 1a) with the step
count increasing during each phase of the experiment. Alternatively, evaluation of the
FitBark activity data also indicated differences in activity level between all stages (p< 0.001;
Figure 1b) but with the greatest level of activity during phase 2.
Animals 2021, 11 4 of 8
3. Results
A total of 745 min of video step count and FitBark activity data (referred to as Bark-
Points by FitBark Inc.) were analyzed across all 26 dogs. Evaluation of the step count data
showed differences between all three phases (p < 0.001: Figure 1a) with the step count
increasing during each phase of the experiment. Alternatively, evaluation of the FitBark
activity data also indicated differences in activity level between all stages (p < 0.001;
Figure 1b) but with the greatest level of activity during phase 2.
Figure 1. Interval plot showing the 95% confidence interval for the mean of dog step count (a) and activity recorded by a
FitBark 2 monitor (b) across phase 1 (off-leash room explore), 2 (human-dog interaction), and 3 (on-leash walk). Different
superscripts indicate significance at p < 0.05.
Total dog step count and FitBark activity showed a moderate correlation across all
phases (r = 0.65, p < 0.001; Figure 2a). When evaluating data across each phase individu-
ally, a high correlation between step count and FitBark activity was observed during
phases 1 (r = 0.795, p < 0.001; Figure 2b) and 2 (r = 0.758, p < 0.001; Figure 2c) and a low
correlation was observed during phase 3 (r = 0.498, p < 0.001; Figure 2d).
Figure 2. Plot showing the relationship between step count and activity recorded by a FitBark 2 monitor for all dogs across
all phases (a), phase 1 (off-leash room explore; (b)), 2 (human-dog interaction; (c)), and 3 (on-leash walk; (d)).
Figure 1.
Interval plot showing the 95% confidence interval for the mean of dog step count (
a
) and activity recorded by a
FitBark 2 monitor (
b
) across phase 1 (off-leash room explore), 2 (human-dog interaction), and 3 (on-leash walk). Different
superscripts indicate significance at p< 0.05.
Total dog step count and FitBark activity showed a moderate correlation across all
phases (r = 0.65, p< 0.001; Figure 2a). When evaluating data across each phase individually,
a high correlation between step count and FitBark activity was observed during phases 1
(r = 0.795, p< 0.001; Figure 2b) and 2 (r = 0.758, p< 0.001; Figure 2c) and a low correlation
was observed during phase 3 (r = 0.498, p< 0.001; Figure 2d).
Animals 2021, 11 4 of 8
3. Results
A total of 745 min of video step count and FitBark activity data (referred to as Bark-
Points by FitBark Inc.) were analyzed across all 26 dogs. Evaluation of the step count data
showed differences between all three phases (p < 0.001: Figure 1a) with the step count
increasing during each phase of the experiment. Alternatively, evaluation of the FitBark
activity data also indicated differences in activity level between all stages (p < 0.001;
Figure 1b) but with the greatest level of activity during phase 2.
Figure 1. Interval plot showing the 95% confidence interval for the mean of dog step count (a) and activity recorded by a
FitBark 2 monitor (b) across phase 1 (off-leash room explore), 2 (human-dog interaction), and 3 (on-leash walk). Different
superscripts indicate significance at p < 0.05.
Total dog step count and FitBark activity showed a moderate correlation across all
phases (r = 0.65, p < 0.001; Figure 2a). When evaluating data across each phase individu-
ally, a high correlation between step count and FitBark activity was observed during
phases 1 (r = 0.795, p < 0.001; Figure 2b) and 2 (r = 0.758, p < 0.001; Figure 2c) and a low
correlation was observed during phase 3 (r = 0.498, p < 0.001; Figure 2d).
Figure 2. Plot showing the relationship between step count and activity recorded by a FitBark 2 monitor for all dogs across
all phases (a), phase 1 (off-leash room explore; (b)), 2 (human-dog interaction; (c)), and 3 (on-leash walk; (d)).
Figure 2.
Plot showing the relationship between step count and activity recorded by a FitBark 2 monitor for all dogs across
all phases (a), phase 1 (off-leash room explore; (b)), 2 (human-dog interaction; (c)), and 3 (on-leash walk; (d)).
Animals 2021,11, 781 5 of 8
When evaluating data for each dog across all phases, the strongest correlations ob-
served between step count and FitBark activity were r = 0.969 (p< 0.001; Dog 16) and
r = 0.935 (p< 0.001; Dog 25; Figure 3a). The weakest correlations observed between step
count and FitBark activity across all phases were r = 0.213 (p= 0.257; Dog 19; Figure 3b)
and r = 0.275 (p= 0.156; Dog 10). Examination of the data for individual dogs with poor
correlations often found anomalies with FitBark activity during phase 3. While on-leash,
many of the dogs showed a relatively low FitBark to step count ratio (Figure 3b) indicating
that the FitBark was under-sensing activity. When phase 3 data were removed from these
dogs and only phases 1 and 2 were analyzed, the step count and FitBark activity correlation
typically improved (e.g., Dog 19: r = 0.796, p< 0.001; Dog 10: r = 0.779, p< 0.001).
Animals 2021, 11 5 of 8
When evaluating data for each dog across all phases, the strongest correlations ob-
served between step count and FitBark activity were r = 0.969 (p < 0.001; Dog 16) and r =
0.935 (p < 0.001; Dog 25; Figure 3a). The weakest correlations observed between step count
and FitBark activity across all phases were r = 0.213 (p = 0.257; Dog 19; Figure 3b) and r =
0.275 (p = 0.156; Dog 10). Examination of the data for individual dogs with poor correla-
tions often found anomalies with FitBark activity during phase 3. While on-leash, many
of the dogs showed a relatively low FitBark to step count ratio (Figure 3b) indicating that
the FitBark was under-sensing activity. When phase 3 data were removed from these dogs
and only phases 1 and 2 were analyzed, the step count and FitBark activity correlation
typically improved (e.g., Dog 19: r = 0.796, p < 0.001; Dog 10: r = 0.779, p < 0.001).
Figure 3. Plot showing the relationship between step count and activity recorded by a FitBark 2 monitor for Dog 25 (an
example of a dog showing a very strong correlation; (a) and 19 (an example of a dog showing a negligible correlation; (b).
4. Discussion
4.1. Step Count and FitBark Activity Correlations
Evaluating criterion validity of dog activity monitors is important for increasing ac-
cess to accurate data for dog owners, veterinarians, researchers, and other animal profes-
sionals. Results of the current study were comparable to prior studies validating the Acti-
cal accelerometer [13], which has since been widely used to measure physical activity in
dogs [3,8,11,14–19]. In the current study, high correlations were observed between dog
step count and FitBark activity during phase 1 (off-leash room explore; r = 0.795, p < 0.001)
and phase 2 (human–dog interaction; r = 0.758, p < 0.001). This is comparable to the corre-
lations observed between the collar-mounted Actical accelerometer and the distance trav-
eled and time spent moving in dogs (r = 0.89) [13]. While this study did not correlate ac-
celerometer data to distance traveled or time spent moving as done by Hansen and col-
leagues [13], this study correlated FitBark activity to step count, similar to the methodol-
ogy used in studies with other animals [28–30].
Other studies have correlated Actical accelerometer data to alternative activity mon-
itors including the Actigraph (r = 0.72), PetPace (r = 0.59) [11], and Whistle monitors (r =
0.925) [17]. While the current study did not correlate FitBark and Actical data, the meth-
odology of the current study utilized behavioral observation of step count. Although sub-
ject to human error, behavioral observation is a common method used to validate human
[31–33] and other animal [28–30,34,35] activity monitors and should produce more accu-
rate data compared to correlating the device to a previously validated tool.
4.2. Three-Phase Approach
This study was unique in that it used a three-phase approach to elicit a range of be-
haviors in participating dogs. Although step count was the only behavior formally rec-
orded during this study, general dog behaviors were noted during step count analysis.
During phase 1, dogs were primarily observed engaging in light-intensity activities such
Figure 3.
Plot showing the relationship between step count and activity recorded by a FitBark 2 monitor for Dog 25 (an
example of a dog showing a very strong correlation; (a) and 19 (an example of a dog showing a negligible correlation; (b).
4. Discussion
4.1. Step Count and FitBark Activity Correlations
Evaluating criterion validity of dog activity monitors is important for increasing access
to accurate data for dog owners, veterinarians, researchers, and other animal profession-
als. Results of the current study were comparable to prior studies validating the Actical
accelerometer [
13
], which has since been widely used to measure physical activity in
dogs [
3
,
8
,
11
,
14
–
19
]. In the current study, high correlations were observed between dog step
count and FitBark activity during phase 1 (off-leash room explore; r = 0.795, p< 0.001) and
phase 2 (human–dog interaction; r = 0.758, p< 0.001). This is comparable to the correlations
observed between the collar-mounted Actical accelerometer and the distance traveled and
time spent moving in dogs (r = 0.89) [
13
]. While this study did not correlate accelerometer
data to distance traveled or time spent moving as done by Hansen and colleagues [
13
], this
study correlated FitBark activity to step count, similar to the methodology used in studies
with other animals [28–30].
Other studies have correlated Actical accelerometer data to alternative activity mon-
itors including the Actigraph (r = 0.72), PetPace (r = 0.59) [
11
], and Whistle monitors
(r = 0.925) [
17
]. While the current study did not correlate FitBark and Actical data, the
methodology of the current study utilized behavioral observation of step count. Although
subject to human error, behavioral observation is a common method used to validate
human [
31
–
33
] and other animal [
28
–
30
,
34
,
35
] activity monitors and should produce more
accurate data compared to correlating the device to a previously validated tool.
4.2. Three-Phase Approach
This study was unique in that it used a three-phase approach to elicit a range of
behaviors in participating dogs. Although step count was the only behavior formally
recorded during this study, general dog behaviors were noted during step count analysis.
During phase 1, dogs were primarily observed engaging in light-intensity activities such as
walking, sitting, and interacting with toys. During phase 2, dogs engaged in light-intensity
Animals 2021,11, 781 6 of 8
activities such as walking, sitting, and interacting with toys and vigorous-intensity activities
such as running, jumping, and playing with toys. Human–animal interaction was also
observed during phase 2, including the owner petting and playing with the dog. During
phase 3, dogs were primarily observed engaging in moderate-intensity activity while
walking on-leash. The step count analysis showed that dogs took the fewest steps during
phase 1, an increased number of steps in phase 2, and the greatest number of steps during
phase 3. However, the FitBark data indicated that dogs were more active during phase 2
compared to phase 3.
High correlations were observed between dog step count and FitBark data during
phases 1 and 2; however, a low correlation was observed during phase 3. We hypothesize
that various on-leash factors impacted the accuracy of phase 3 FitBark activity data such as
leash attachment to the collar and modifications to the dog’s gait and stride length. Martin
and colleagues [
19
] reported stronger correlations when a leash was attached to a harness
instead of a collar; thus, more research evaluating collar style and attachment location
impacts on FitBark activity data is warranted.
4.3. Benefits and Drawbacks of the FitBark Activity Monitor
FitBark monitors have multiple benefits that make them a feasible option as a dog
behavior and health indicator, intervention, and research tool. FitBark monitors record data
through a 3-axis accelerometer, are fairly low cost, and have a battery life of ~6 months.
They come in a relatively small size (41
×
28
×
13.5 mm, 10 g), affix to a dog’s collar with
zip ties, and are waterproof. Currently, FitBark monitors integrate into an app that syncs
the FitBark data for mobile device integration, which is free to users. Data can be viewed
online or activity can be downloaded in 1-min epoch lengths. Veterinarian and researcher
access to data can also be permitted by the dog owner [
36
]. Thus, FitBark monitors are
affordable, user-friendly, and accessible tools for monitoring dog physical activity.
While FitBark currently has many benefits for users of this tool, we understand that
companies can change rapidly. Thus, it is important that this tool continues to be made
accessible and user-friendly for a broad population of users. Additionally, as a result of
the wide variation in correlations between step count and FitBark data between dogs and
activity type, it may be inaccurate to draw wide conclusions between diverse populations
of dogs. Instead, understanding what is normal for an individual dog by collecting baseline
data before applying a treatment would be a more accurate approach. Additionally, because
the step count during the on-leash walk (phase 3) showed a low correlation to the FitBark
data, more work evaluating collar style and the leash attachment method should be done
to evaluate the best method of collecting activity data for on-leash situations. Prior studies
have also identified potential privacy concerns with pet wearable technology [
37
]. While
companion animal wearable technology has the potential to improve the human–animal
bond, there are concerns that this may negatively affect it as well [
38
]. Thus, further
evaluation of how these tools can be best used to improve the human–animal bond and
dog welfare will be critical.
5. Conclusions
Wearable technology is becoming increasingly popular for dogs; thus, it is critical
that the information provided to users is accurate. This study showed high correlations
between step count and FitBark activity during off-leash situations, indicating that the
FitBark 2 monitor is a valid tool for tracking physical activity in off-leash dogs. However,
low correlations were observed during on-leash activity; thus, more work should be done
to identify the best method of tracking activity in on-leash situations. Because the FitBark 2
is an affordable monitor primarily marketed toward dog owners, this tool, if used correctly,
has the potential to make an impact on the health and welfare of dogs.
Supplementary Materials:
The following are available online at https://www.mdpi.com/2076-261
5/11/3/781/s1, Table S1. Description of dogs participating in the study.
Animals 2021,11, 781 7 of 8
Author Contributions:
Conceptualization, J.C.; methodology, J.C. and D.D.; software, D.D.; valida-
tion, J.C. and D.D.; formal analysis, D.D.; investigation, J.C.; resources, J.C.; data curation, J.C. and
D.D.; writing—original draft preparation, J.C. and D.D.; writing—review and editing, J.C. and D.D.;
visualization, D.D.; supervision, J.C.; project administration, J.C.; funding acquisition, J.C. and D.D.
Both authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by Morris Animal Foundation, grant number D20CA-843. The
APC was funded by Morris Animal Foundation and the Truman State University Department of
Agricultural Science.
Institutional Review Board Statement:
This study was conducted according to the guidelines of the
Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of
Truman State University (protocol code IACUC 19-5 approved August 2019).
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author.
Acknowledgments:
We would like to thank undergraduate research assistants in the Colpoys Lab
and Bill Kuntz for assistance in data collection. We would also like to thank study participants for
volunteering their time for this study.
Conflicts of Interest:
The authors declare no conflict of interest. None of the authors have any
commercial or financial relationships that could inappropriately influence or bias the content of
the paper.
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