Running head: MINDFUL ASSESSMENT COMPARING EXERCISE AND BMI

Using Mindful Assessment when Comparing
Exercise Levels Among Public-School Teachers’ BMI

A DISSERTATION
Summited to the Faculty of the School of Graduate Studies and Research of California
University of Pennsylvania in partial fulfillment of the requirements for the degree of Doctor of
Health Science (DHSc) in Health Science and Exercise Leadership

By Clint F. Cepeda

Research Adviser, Dr. Cheryl Rogow
2019
CALIFORNIA UNIVERSITY of PENNSYLVANIA
CALIFORNIA, PA

USING MINDFUL ASSESSMENT

DISSERTATION APPROVAL

Health Science and Exercise Leadership

We hereby approve the Dissertation of

Clint F. Cepeda
Candidate for the degree of Doctor of Health Science (DHSc)

Date

Faculty

11/22/19
___________

Cheryl Rogow
__________________________________________

11/22/2019
___________

Digitally signed by Jennifer Bittner
Jennifer
Bittner
Date: 2020.01.07 10:36:58 -05'00'
__________________________________________

11/22/2019
___________

Date: 2019.12.27 10:28:04 -05'00'
__________________________________________
Signature of Dr. Marc Federico, Committee Member

11/22/2019
___________

Digitally signed by Cheryl Rogow
DN: cn=Cheryl Rogow, o, ou,
email=chesporty@gmail.com, c=US
Date: 2020.01.08 00:11:41 -08'00'

Signature of Dr. Cheryl Rogow, Research Chair

Signature of Dr. Jennifer Bittner, Committee Member

Marc Federico Digitally signed by Marc Federico

Joni Roh
2020.01.08 10:09:25 -05'00'
__________________________________________

Signature of Dr. Joni Roh, Committee Member

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Acknowledgements
I am grateful and humbled to complete this dissertation, but it was not done alone. The
first person that I would like to thank is my research chair, Dr. Cheryl Rogow. Dr. Rogow is my
biggest academic cheerleader with a constructive, yet optimistic, voice at the end of a sometimes
very dark tunnel. Her guidance and academic gift for sentence structure is priceless and I do not
know how I can ever repay her for helping me to achieve this milestone. She is who I strive to
be. To my esteemed committee members whom I am just as eternally grateful for their patience,
professionalism, and unwavering support. Dr. Joni Roh is the definition of what higher learning
instructors should be. May her ‘old school’ ways never go out of style. To Dr. Marc Federico,
you were the first person to greet me on this journey and I am more than happy to greet you at
the end. Thank you for the opportunity of a lifetime. To Dr. Jennifer Bittner, you represent both
Texas charm with a small dose of ‘Git er done’, which is precisely what I needed to finish. You
have no idea what it means to me to have you on my committee. Thank you. Although not a part
of my committee, Dr. Melissa Sovak in my opinion is an honorary member of this elite group of
human beings. Thank you for explaining statistics to a student like me. I am eternally grateful.
Finally, to my wonderful, beautiful wife Amber and our children. There are times when life’s
challenges seem so daunting there is no end in sight. We choose our path to either shy away
from those challenges or run and take them head on. Thank you for running with me, we did it!

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Table of Contents
List of Figures ..............................................................................................................................i
List of Tables ............................................................................................................................. ii
Acknowledgements ................................................................................................................... iii
Abstract ...................................................................................................................................... 8
Methods .................................................................................................................................... 13
a. Research Design ........................................................................................................ 13
b. Subjects ..................................................................................................................... 15
c. Instruments ................................................................................................................ 15
d. Procedures ................................................................................................................. 20
e. Data Analysis............................................................................................................. 22
Results ...................................................................................................................................... 26
Discussion................................................................................................................................. 38
Exploratory Outcomes............................................................................................................... 47
Conclusion ................................................................................................................................ 51
References ................................................................................................................................ 53
Appendix A

Review of Literature........................................................................................... 67
1. The Role of BM in Health .............................................................................. 69
2. Social Cognitive Learning Theory .................................................................. 72
3. Exercise Adherence ........................................................................................ 82
4. Current Cohesive BM Treatment .................................................................... 98
5. Conclusion ................................................................................................... 102

Appendix B

Problem Statement ........................................................................................... 104

Appendix C

Additional Methodology ................................................................................... 107

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C1 Request Letter to Killeen Independent School District (KISD).................... 108
C2 Participation Recruitment ........................................................................... 110
C3 Demographic Information Sheet (DIS) ....................................................... 113
C4 Mindfulness Attention Awareness Scale (MAAS) ...................................... 115
C5 Godin Leisure-Time Exercise (GLTE) ........................................................ 120
C6 IRB Materials ............................................................................................. 123
C7 CITI Training Certificates........................................................................... 141
References .............................................................................................................................. 147

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List of Figures
Figure 1: Level of Mindfulness within Exercise Time Groups .................................................. 30
Figure 2: GLTE Units of Exercise within Exercise Time Groups .............................................. 31
Figure 3: BMI Values within Exercise Time Groups ................................................................ 31
Figure 4: Gym Membership and BMI ....................................................................................... 33

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List of Tables
Table 1:

Correlation of Variables ..................................................................................... 26

Table 2:

Pearson Correlation of Mindfulness and Years of Experience............................. 27

Table 3:

Overall Variable Mean ....................................................................................... 28

Table 4:

Pearson Correlation of Mindfulness, Exercise Levels, BMI Scores and Level of
Activity .............................................................................................................. 29

Table 5:

Summary Statistics of Physical Activity Per Week and BMI .............................. 32

Table 6:

Pearson Correlation of Gym Membership........................................................... 34

Table 7:

BMI Scores Distributed Across the Race Categories .......................................... 35

Table 8:

Likert Scale of Physical Tension and BMI ......................................................... 36

Table 9:

Pearson Correlation of Mindfulness and Physical Tension.................................. 36

Table 10:

Age and Mindfulness ......................................................................................... 37

Table 11:

Pearson Correlation of Mindfulness/EL/BMI and Relationship Status ................ 38

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Abstract
The aim of this research is to measure the current mindfulness levels public-school teachers as it
correlates to individual’s BMI and intensity of exercise activity. The purpose of this study is to
associate mindfulness, BMI, and exercise level (EL) in teachers from a public school system.
The specific goal is to determine the type and strength of correlation between three variables;
mindfulness, mindfulness with BMI and mindfulness with EL. The following questions will be
investigated: What is the degree of association between mindfulness, mindfulness and BMI, and
mindfulness and EL?; Is a public-school teacher’s level of mindfulness associated with their
current BMI?; What is the degree of association measuring mindfulness in the relationship
between BMI and EL; Is there a positive or negative correlation between mindfulness and EL
and mindfulness and BMI; Does the level of mindfulness influence the intensity level of EL?
The sample was recruited from total 2,783 email invitations where n =183 participated. A total
of 29 questions divided through 3 testing instruments the Mindful Attention Awareness Scale
(MAAS), Godin leisure-Time Exercise Questionnaire (GLTE), and the Demographic
Information Sheet (DIS). Current mindfulness levels of public-school teacher’s mindfulness
values had a slight positive correlation with exercise levels (r = 0.34), however, a negative
correlation association with their BMI (r = -0.09). Although, all variables have some correlation,
the addition of influential health factors (IHF) may be used to increase mindfulness levels in this
population.
Keywords: mindfulness, public school teachers, exercise intensity, BMI, physical
activity, Mindfulness Attention Awareness Scale, obesity, influential health factors

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Using Mindful Assessment when Comparing Exercise
Levels Among Public-School Teachers’ BMI
Exercise has reported benefits for various fitness levels in all types of populations
(Grabara, Nawrocka, & Powerska-Didkowska, 2018). For example, the health benefits of
continuous and frequent exercise has been reported in obese/overweight persons (Foright et al.,
2018), senior citizens (Beauchamp et al., 2018), adolescents (Danielsson, Bohlin, Bendito,
Svensson, & Klaesson, 2016; Liu, Alderman, Song, Chen, Hung, & Chang, 2018), post-operative
cancer patients (Huang et al., 2015), and post-operative cardiovascular patients (Babbitt et al.,
2017). Furthermore, in conjunction with proper meal planning and exercise may help combat the
current problem of increasing obesity in the United States.
However, research indicates that those individuals diagnosed as overweight or medically
obese may have accompanying ailments that prohibit them from various physical activities,
enjoying, or reaping the benefits of a healthy lifestyle (Bordignon, Aparício, Bertoletti, &
Trentini, 2017; Kulendran, Borovoi, Purkayastha, Darzi, & Vlaev, 2017). Excessive weight gain
is a physical condition that leads to morbid obesity and higher risks of Type 2 diabetes,
metabolic syndromes, poor social integration (Danielsson, Bohlin, Bendito, Svensson, &
Klaesson, 2016), cardiopulmonary disease, (Abdi et al., 2015), cognitive deficits, self-regulation
failure (Hawkins et al., 2018), chronic diseases such as arthritis, cardiovascular disease,
(Bordignon et al., 2017), hypertension (Marszał-Wiśniewska & Jarczewska-Gerc, 2016), and low
levels of physical energy with poor dietary behaviors (Lloyd, Lubans, Plotnikoff, & Morgan,
2015). Being physically inactive or infrequently participating in exercise can also lead to
chronic diseases associated with being severely overweight (Bordignon et al., 2017). Some
organizations, specifically the World Health Organization (WHO), have estimated that the

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prevalence of obesity has doubled in the past 20 years with half a billion people considered obese
and approximately 2.8 million who die each year from obesity-related diseases (Rocha et al.,
2015). The level and amount of exercise and its many benefits cannot be dismissed as a
component in the overall health and well-being of an individual. While exercise is usually the
initial focus of treating populations that need to create a wellness lifestyle, adherence to longterm structured physical activity is needed to create an actual healthy lifestyle change.
Although exercise is a known deterrent to weight gain (Foright et al., 2018) and used to
control several chronic diseases such as arthritis (Bordignon et al., 2017) and obesity
(Baumgartner et al., 2018); exercise levels is a separate issue regarding individuals participating
in physical activity (Amireault, Godin, Lacombe, & Sabiston, 2015). Moreover, some
individuals who exercise do so at their leisure, and as such, the relationship between motivation
and adherence to exercise consistently for an extended period of time is challenging. Generally,
it is not participation in exercise where individuals are challenged; it is the consistency to
perform physical activity on a regular basis.
Exercise adherence (EA), is defined as an overall performance or an average amount of
exercise participation in a specific exercise program (Huang et al., 2015) (Appendix A).
Dougherty, Luttrell, Burr, Kim, and Haskell, (2016) defined EA when studying cardiovascular
patients as, “performing 80% or more of the intervention as it was prescribed by phase/week:
frequency of days/week (per exercise), and intensity of exercise or percent time in the THR
zone” (p. 130). Kampshoff et al. (2014) viewed EA as the amount and level of effort in
accomplishing regularly prescribed exercises frequently. Babbitt et al. (2017) defined EA as the
level individuals who attempt an exercise program, comply with all intentions of completing the
prescribed exercises consistently, and correct sub-standard attitudes towards physical activity.

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EA can create an impact of positive outcomes on populations trying to lose weight
(Aparecida Rodrigues de Oliveira et al., 2015), and reportedly in physical therapy patients
completing post-operative treatment (Eckard, Lopez, Kaus, & Aden, 2015). Although generally
participating in some exercise is a start, it is not enough without consistent application over time
to truly be considered adherent (Cadmus-Bertram et al., 2014; Faries & Lutz, 2016). EA is
considered successful when measured as either completed or not (Babbitt et al., 2017) (Appendix
A). Encouraging examples of EA studied in various groups such as patients with coronary heart
disease (Slovinec D'Angelo, Pelletier, Reid, & Huta, 2014), post-cardiac rehabilitation patients
(Janssen, De Gucht, van Exel, & Maes, 2014), vascular health support for African Americans
(Babbitt et al., 2017), and physical activity for older adults (Beauchamp et al., 2018) have shown
positive results.
Although viable evidence has reported collective support in EA intervention in different
groups, public school teachers are a group that may be underserved in identifying the benefits of
EA during an academic school year (Appendix B). Female public school teachers, unlike their
male counterparts, reportedly perform below physical standards (Grabara et al., 2018).
Specifically, 58% of teachers were overweight; and among them, 20% were considered obese.
Additionally, public school teachers have also been studied in conjunction with the performance
of their job versus their physical capacity while performing teaching duties (Aparecida
Rodrigues de Oliveira et al., 2015). Furthermore, male teachers were reported to have higher EA
than female teachers through self-reported questionnaires measuring the level of intensity of
moderate to vigorous exercise and were more positive in their outcomes of perceived health
benefits, especially in the categories of vigorous and moderate-intensity physical activity than
women (Grabara et al., 2018). Specifically addressing the impact overweight/obese teachers have

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on students and their profession, Rocha et al. (2015) examined the prevalence of obese teachers
and their impact on their profession and their possible influence on their students. A positive
correlation is suggested that teachers who were unhealthy risk poor role modeling for the
students they instruct especially if their health becomes problematic within their occupation.
Knowing that this positive correlation exists, a teacher may be more willing to exercise daily and
put forth more effort. One method would be to introduce a behavior modification method
(mindfulness) to educators who have an interest. A positive outcome of high levels of adherence
for individuals who want to exercise may be increased by applying a behavior modification
(BM), specifically, mindfulness (Appendix B).
Mindfulness is a conscientious effort to be aware of the present circumstances with
heightened self-interest and has been shown to be used to modify eating, exercise, stress,
happiness, and emotional well-being (Brown & Ryan, 2003; Osman, Lamis, Bagge, Freedenthal,
& Barnes, 2016). Incorporating individuals to be more mindful may relate that more positive
behavior will be present.
Therefore, mindfulness may increase the consistent behavior of EA when applied in a
group like female teachers (Lillis, Thomas, Niemeier, & Wing, 2017). Mindful exercise
treatment for exercise frequency success could also apply to those public school teachers who
experience high stress, high blood pressure, and mental uneasiness. There is some evidence
reporting that when dispositional mindfulness is high, behavior change is also high (Loucks et
al., 2016). Thus, suggesting that the more one is mindful, the more positive, the behavior will be
present. A possible combination of online mindfulness instruction to increase compliance of EA
was demonstrated by Gotink et al. (2017) to have short-term positive effects (Appendix A).
While only short-term effects it must be noted that online mindfulness instruction may still

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benefit female teachers as they see it as a comfortable way to incorporate more EA at their
convenience. There is a need to identify and fit the best treatment plan to increase EA in such an
influential group. Some treatment options include Mindfulness-Based Stress Reduction
(MBSR), Dialectical Behavioral Therapy (DBT), Acceptance and Commitment Therapy (ACT),
and Mindfulness-Based Cognitive Therapy (MBCT) (Ruffault et al., 2017).
The purpose of this study is to associate mindfulness, exercise level (EL), and body mass
index (BMI) in teachers from a public school system. The specific goal is to determine the type
and strength of correlation between two variables: (1) mindfulness and BMI, and (2) mindfulness
and EL. The following questions will be investigated:
1.

What is the degree of association between mindfulness, EL, and BMI?

2.

Is a public school teacher’s level of mindfulness associated with their current BMI?

3. What is the degree of association measuring mindfulness in the relationship between
BMI and EL?
4. Is there a positive or negative correlation between a) mindfulness and EL and b)
mindfulness and BMI?
5. Does the level of mindfulness influence the intensity level of EL?

Methods
The following methods will be reviewed in this section; Research Design, Subjects,
Instruments, Data Analysis, and Procedures.
Research Design
The proposed research is a correlational design which will be used to measure the current
level of mindfulness public school teachers have in association with their current body mass
index (BMI) and exercise levels (EL). Participants will be administered all testing instruments as

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one group, at one time, from an online survey distributed through their public school email. To
control for any extraneous variables the measurement tools, demographic information sheet
(DIS, Beauchamp et al., 2018), the Mindfulness Attention Awareness Scale (MAAS, Brown &
Ryan, 2003), and the Godin Leisure-Time Exercise Questionnaire (GLTE, Amireault, Godin,
Lacombe, & Sabiston, 2015) will be available for download and delivered online. This research
design will be explanatory and used to identify the degree of association and relationship
between three variables: (1) mindfulness, (2) EL, and (3) BMI (Appendix C).
Further, two correlations between two scores of (1) mindfulness and BMI and (2)
mindfulness and EL will be compared. The subjects, before completing the online
questionnaires will be required to complete the DIS (Beauchamp et al., 2018), which will act as
both raw demographic data used to calculate the subjects’ BMI, and implied informed consent
when returned online (Appendix C3). The level of mindfulness will be measured using the
MAAS which is a 15-item mindfulness instrument. The MAAS will identify the current level of
mindfulness public school teachers have as it may correlate to their current BMI and current EL
(Appendix C4). The GLTE instrument is designed to identify the intensity of physical activities
and exercises participated in the last seven days and will be used to measure EL (Appendix C5).
The GLTE is a self-reported instrument with three categories to describe an individual’s intensity
level classifying it as strenuous, moderate, or light (Sari & Erogan, 2016).
This study assumes that those who are participating will answer all questionnaire and
demographic information to the best of their ability and will be the same individuals that
complete all assessment forms. Expected limitations include self-reported data and timing of the
study (during the school year) that may limit participation. Therefore, a correlational study is
best to gather the required data of existing behaviors at one time.

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Subjects
The aim was to recruit 120 public school teachers from the local public school district.
This target sample size will account for possible incomplete data, the one-time administration of
testing instruments, and fulfill the needs of the correlational design analysis. In the interest of
time and potential bias, online surveys will be delivered through the subjects’ own public school
emails. Permission from the Killeen Independent School District (KISD) central office was
needed to deliver a mass email to inform teachers who are interested in a KISD exercise
participation survey for an academic research study (Appendix C1). The ideal participant should
be a public school teacher currently employed in the school district, a current state-certified
public school teacher, and with no previous bariatric surgery. The sample size objective was 120
responses from fully completed and required DIS (implied consent) forms. Exclusions are
previous diagnosis and treatment of eating disorders and psychiatric care and will be addressed
in the email used to recruit subjects and complete the instruments (Appendix C2).
Instruments
The following instruments: (1) DIS, (2) the MAAS, (3) and the GLTE will be used to
assess the variables of the current level of BMI, mindfulness, and EL, respectively of public
school teachers in a single session. Further, the MAAS instrument will be used to assess multiple
correlational relationships between (1) mindfulness and BMI, and (2) mindfulness and EL.
Administration for all instruments is for the proposed sample group of 120 public school
teachers. The DIS will provide the self-reported data (i.e., age, height, weight) used to calculate
the BMI variable.
A BMI is calculated by dividing individual weight (lbs) by height (inches)2 multiplied by
703. For example, an individual who is 220lbs and is 5’7” would be calculated as: 220 (weight
lbs. ) ÷ 4489 (height inches)2 multiplied by 703 would result in a 34.453 value (obese). BMI

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values are categorized as: (1) 18.5 (underweight), (2) 18.5-24.9 (normal weight), (3) 25-29.9
(overweight), and (4) 30 or greater (obese) (Aparecida Rodrigues de Oliveira et al., 2015).
The MAAS will measure the level of current mindfulness while correlating values of
BMI and EL as covariates (Francisco José, Juan Carlos, & Diana, 2016; Loucks et al., 2016;
Mantzios & Wilson, 2014).
The GTLE will measure the exercise level intensity while correlating the values of
mindfulness and EL. Multiple correlations will be evaluated through associations of the three
variables; mindfulness, mindfulness and EL, and mindfulness and BMI.
DIS. A demographic information sheet (DIS) will be administered to the 120 selected
participants (Appendix C3) at the time of recruitment to participate in the study. The following
information is to be collected: (1) age, (2) height, (3) weight, (4) marital status, (5) smoking
status, (6) ethnicity, (7) years of employment, (8) current teaching grade, (9) amount of
exercise/week, and (10) gym membership (Appendix C3). A completed receipt of the DIS will
act as implied consent for the subsequent hyperlink distribution of the MAAS and GLTE
instruments, along with other needed and required data for the proposed correlational study. DIS
questions for age, height, and weight will be used for BMI calculations (Appendix C3). BMI has
been reported as a viable indicator of current health levels and serves as a measure of body
composition (Aparecida Rodrigues de Oliveira et al., 2015; Frazier-Wood et al., 2014; Kulendran
et al., 2017).
MAAS. The MAAS is a 15-item single factor instrument measuring an individual’s
capacity to be aware of current and present behavior during varying levels of consciousness
(Brown & Ryan, 2003; Goh, Marais, & Ireland 2017; Mantzios & Wilson, 2014) (Appendix C4).
Cronbach alpha values of reliability for the MAAS reported ranges of .82-.90 with reputable

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ranges of internal consistency throughout different studies. One such study assessed weight
management while incorporating mindfulness intervention among both male and female
undergraduate students reported an alpha of .85 with an r2 value of .309 (Mantzios & Wilson,
2014). The MAAS reliability from a sample of Columbian undergraduate students with
psychopathological problems reported having an alpha of .82 and a high r2 of .31 among
correlating MAAS items 7 and 8 demonstrating internal consistency measuring psychometric
properties of mindful meditation in meditators and non-mediators (Goh et al., 2017).
A Danish citizen study was conducted during a health and wellness month-long survey
with test-retest reliability scores showing sufficient consistency with both absolute and
individual scores of a Cronbach alpha of .90 and intraclass correlations of .74 (Jensen, Niclasen,
Vangkilde, Petersen, & Hasselbalch, 2016). Similarly, a sample of Columbian undergraduate
students with psychopathological problems reported a Cronbach’s alpha presented .92 reliability
and corrected item-total correlations ranging from .46 to .74 when utilizing the MAAS
(Francisco José et al., 2016).
A wide array of validity for MAAS across different ages and participants has been
reported (Brown, West, Loverich, & Biegel, 2011). For example, MASS scores confirmed
consistent convergent validity in a 6-month intervention of a Danish study (Jensen et al., 2016),
psychometric properties for Argentinean adults (Montes, Ledesma, García, & Poó, 2014), and
confirmation of uniformity among Columbian (Francisco José, et al., 2016) and American
undergraduate students (Osman, Lamis, Bagge, Freedenthal, & Barnes, 2016). The MAAS
instrument has been validated to assess data where a mindfulness intervention was used to
change behavior, evaluating psychometric properties and weight management in different
cultural groups. Some of the same possible anticipated themes (i.e., emotional symptoms,

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automatic negative thoughts, physical stress, psychological inflexibility, and life satisfaction)
with the proposed study emerged through the validated research (Francisco José, et al., 2016;
Goh et al., 2017; Makki, Ajmal, & Bajwa, 2018; Montes et al., 2014; Osman et al., 2016).
The MAAS uses reversed scoring assessing the absence of mindful attention instead of an
individual’s mindful moments (Goh et al., 2017). MAAS single item scores as 1 (almost
always), 2 (very frequently), 3 (somewhat frequently), 4 (somewhat infrequently), 5 (very
infrequently), 6 (almost never) (Brown & Ryan, 2003), on a 6-point Likert scale scores can range
from 15-90 (Francisco José, et al., 2016; Loucks et al., 2016, Mantzios & Wilson, 2014). The
single-item scores from the MAAS questionnaire are used to assess individual mindfulness on a
day-to-day experience (Appendix C4). Higher scores of mindfulness reflect an individual is selfaware of this state (Brown et al., 2011).
GLTE. The Godin Leisure-Time Exercise Questionnaire (GLTE) is a 4-item instrument
used to measure leisure activity with specifically labeled categories for three types of intensities:
(1) strenuous, (2) moderate, and (3) light exercise (Amireault, Godin, Lacombe, & Sabiston,
2015; Kruzliakova et al., 2018; McDaniel, Melton, & Langdon, 2014; Pauline, 2013; Zelener &
Schneider, 2016), and will be used to measure EL (Appendix C5). The categories are labeled to
help participants self-report their level of physical activity in the past seven days from
administering the GTLE. The teachers will be asked to self-report all physical activity
accumulating 15 minutes or more of frequent exercise.
The GLTE questionnaire will be used to collect the physical activity intensity data of the
120 selected participants. The questionnaire data encompasses the last seven days of physical
activity and their self-reported intensity levels. Focusing on energy expenditure as a basis for the
GLTE questionnaire values and scoring for individual’s metabolic equivalent (MET) a formula

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was created for use in the GLTE. Using the MET principle of energy cost and the amount of
oxygen consumed at rest, scores are indicative of resting metabolic rate, resulting in the intensity
of individual activity. Under GLTE, METs values are multiplied by the number of minutes
(exceeding 15 minutes) per activity (Pauline, 2013). Therefore, the calculations of the values are:
total score = strenuous/exhausting (9 METs x times/week) + moderate (5 METs x times/week) +
light (3 METs x times/week). GLTE scoring system of final calculation scores of 24 units or
above is classified as active, scores of 14-23 units are classified as moderately active, and scores
with 13 units and below were inactive (Pauline, 2013; Sari & Erdogan, 2016).
Moderately high test-retest reliability was reported from an overall perspective of .74
when the GLTE was used to measure increases of physical activity in collaboration with student
life and academics (McDaniel et al. 2014). Further, the individual’s intensity categories of light
exercise (.48), moderate exercise (.46), and strenuous exercise (.94) were reported respectively.
A Turkish study with the adapted translated version of the GLTE for diabetic patients reported a
Cronbach alpha (.64), content validity context (.82), test-retest analysis (r = .97), and a
correlation of independent observers of .98 (Sari & Erdogan, 2016). For research regarding a
wide range of demographic ages, the following values were reported; adult reliability
coefficients for GLTE validity ranged from .24 (low) -.84 (high), in school-aged children alpha
values, were .81, and an alpha of .56 was reported in physical activity for adolescents 13-18
years of age (Fischetti, 2015).
Utilizing the GLTE, a study using university students produced an r = .82 reliability
coefficient. A correlational analysis to investigate the relationship between physical activity, the
proximity of exercise facilities, and the amount of home exercise equipment in college
undergraduates (Reed & Phillips, 2005). For those individuals who have pediatric-onset multiple

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sclerosis (POMS), the need for the GLTE instrument was used to measure physical activity in
this special population. Positive correlations scores were reported in both vigorous activities and
moderate to vigorous activity with POMS. Reported positive correlation scores were r =. 736, p
= .0001 (vigorous activity) with significant scores of r = .319 and p = .053 (moderate-vigorous
physical activity) (Kinnett-Hopkins, Grover, Yeh, & Motl, 2016).
The GLTE has been validated through accelerometers, VO2max, and body fat percentage
(Kruzliakova et al., 2018), university adults’ physical activity (Reed & Phillips, 2005), pediatric
multiple sclerosis (Kinnett-Hopkins et al., 2016), and healthy adults (Amireault et al., 2015). A
validation study specifically designed to test the GLTE with a mixed-gender population (men =
27) and (women = 93) reported reliability of .76 (DuBose, Robinson, Rowe, & Mahar, 2006).
GLTE was tested for its reliability in the classification of the GLTE questionnaire categories of
active (highly sufficient) and inactive (insufficiently). A multivariate analysis of covariance
(MANOVA) resulted in analysis of (number of covariates = 5; power = .80; alpha = .05) and an
analysis of covariance (F = 6.15; p =.0001) (Amireault et al., 2015).
Procedures
After the California University of Pennsylvania Institutional Review Board has approved
the proposed correlational design study, (Appendix C6) the following steps will be performed to
complete the research. This study aims to measure the current level of mindfulness in
association with current BMI and exercise levels (EL) in public school teachers. Participants will
be administered all three instruments as one group, a one-time survey distributed through email
requesting their volunteer, anonymous participation.
Consent. A formal letter of permission will be given to the Killeen Independent School
District (KISD) Central Office, 200 N. WS Young Dr., Killeen, TX. 76543. This letter will

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reference the importance of the approved study and its significance to the community and
employees of the school district (Appendix C1). The formal letter of permission will include the
general structure of the research study (correlational design), the sample size needed (120),
nature of the email message (sample recruitment), data collection (online), and delivery (public
school email addresses). During this recruitment process, a completed DIS is requested for
implied consent before full participation in completing the online version instruments of the
MAAS and GTLE.
Recruitment. The recruitment for the aimed sample size of 120 participants, will be
delivered through the KISD public school websites. A mass email announcement of the
proposed research will call for those interested in a KISD exercise participation research survey
(Appendix C2). The mass blast email will contain information that subsequent surveys (MAAS,
GLTE, DIS) will be delivered through their public school email for those interested. A followup mass email will be sent out to encourage those that are still interested in participating in the
KISD exercise participation research survey to respond by April 1, 2019. The information
provided in the recruitment mass email will include; (1) purpose of the study, (2) inclusion
requirements, (3) researcher contact information (Appendix C2). All potential candidates
interested in the research study can contact the lead researcher through the email provided in the
recruitment email (Appendix C2).
The 120 responses will then be sent the required DIS (implied consent) for completion
first. The DIS will contain the following participant information; (1) age, (2) height, (3) weight,
(4) marital status, (5) smoking status, (6) ethnicity, (7) years of employment, (8) current grade
teaching, (9) amount of exercise per week, (10) current gym membership. The sheet will help
the researchers in preparing the needed data for the BMI variable. During this recruitment

USING MINDFUL ASSESSMENT

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process, a completed DIS (Appendix C3) is requested for implied consent before full
participation. A total of 120 fully completed DIS forms was the research aim.
Testing. Once the proposed amount of 120 public school teachers was achieved, a weeklong availability time began. This time allowed collection of data specifically for seven days as
the GLTE (Appendix C5) is founded on the recollection of the last seven days of self-reported
physical activity. The following instruments will be made available through the hyperlink
delivered in the KISD recruitment email (Appendix C2) during the research period and
administered: DIS (BMI), MAAS (mindfulness), and GLTE (EL). Once surveys are completed,
they will be uploaded through the return email hyperlink provided. The researcher will hand
score all instruments (DIS, MAAS, and GLTE) once all surveys have been fully completed and
returned. All online surveys are only to be taken once, and all collected data will then be put into
spreadsheets for further analysis.
Data Analysis
This study aimed to associate level of mindfulness with BMI and EL in public school
teachers. The specific goals are to find a correlation and degrees of association between three
variables; current mindfulness, BMI, and EL. In addition, to identifying correlations between the
three variables (mindfulness, EL, BMI), degree and direction of association for mindfulness to
BMI and mindfulness to EL will also be calculate. The following questions will be investigated:
1.

What is the degree of association between mindfulness, mindfulness and BMI, and
mindfulness and EL?

2.

Is a public school teacher’s level of mindfulness associated with their current BMI?

3. What is the degree of association measuring mindfulness in the relationship between
BMI and EL?

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4. Is there a positive or negative correlation between mindfulness and EL and mindfulness
and BMI?
5. Does the level of mindfulness influence the intensity level of EL?
A high current level of mindfulness is associated with higher intensity EL; therefore,
correlating with lower BMI values in public school teachers is hypothesized. The null hypothesis
posits there will be no difference in EL or value of BMI scores regardless of the level of
mindfulness in public school teachers. We want to indicate an association between the variables
of EL and BMI in relation to public school teacher variable of mindfulness in a correlational
study. The correlation data will be used to identify the direction and degree of association
between three sets of scores: (1) mindfulness, (2) mindfulness/ EL and (3) mindfulness/BMI.
The degree of association is defined as the relationship between variables or sets of scores. In
this case mindfulness, EL, and BMI would be used to identify the correlation coefficient of -1.00
to +1.00 with the value of 0.00 indicating no linear relationship. Regarding the relationship
between all variables, the values of 1.00 or -1.00 can indicate consistency/inconsistency or
predictability.
For this research design, the MAAS 15-item instrument for the levels of mindfulness
public school teachers may have will be administered followed by the GLTE a 4-item survey as
an indicator of EL intensity data (Appendix C4 & C5). An initial BMI will be calculated from
the DIS self-report. All three instruments MAAS, GLTE, and DIS, will help define measures of
associations between the sample size and the three variables. Consequently, the data analysis
looked to describe the degree of association between the level of public school teacher’s current
mindfulness on EL and BMI.

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The data does not look for an r coefficient instead it applies a Spearman rho (rs)
correlation coefficient. The rationale for this type of coefficient is based on the design itself
(correlational), by identifying the degree of association and direction not categorically but
looking at scores in a strictly numerical way. This is known as point-biserial correlation.
Public school teachers’ BMI would help to create an association with either positive or
negative linear plotted scores in the correlation matrix. The data analysis would also consist of
creating a correlation matrix table to show the overall amount of variance between one variable
(mindfulness) and all other variables (mindfulness and EL, mindfulness and BMI).
Data will be collected as one score for teachers and their level of mindfulness, one score
for BMI at the start of the study, and finally, one score will be recorded for completed uploaded
GLTE. Data from the MASS questionnaire is used to assess the presence of mind and individual
awareness as a pre-test procedure. Based on the Likert Scale data will be ordinal, single item
scores between a range of 1 (almost always), 2 (very frequently), 3 (somewhat frequently), 4
(somewhat infrequently), 5 (very infrequently) and 6 (almost never) and analyzed by the
Statistical Analysis System (SAS) software (Appendix C4).
Because this is a correlational design, then an association between all three scores will be
the focus. The scores tend to look for a statistical relationship for mindfulness, EL, and BMI.
This association in correlation statistics is known as a linear relationship. Since this is also an
explanatory correlation, there is interest in explaining the linear relationship of the two variables
(EL, BMI) to covary. Statistically speaking, a change in one variable BMI and EL, could be
reflected in the level of mindfulness for the teachers or vice versa. The collection of data is
limited to a one-time collection with no division of scores. In the case of our proposed research,

USING MINDFUL ASSESSMENT

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multiple correlations may be present; we will collect one score for mindfulness (public school
teachers), one score for mindfulness and BMI, and one score for mindfulness and EL.
Under this premise, the direction of strength or specifically if the three variable statistical
relationships are linear was examined. Therefore, the magnitude and direction of the correlations
between the three variables in the correlational matrix was examined. Because this is a
correlational design, the relationship between the variables would be diagrammed on a scatter
plot. The scatter plot will reveal if the data is linear or U-shaped. If the data is either linear or
U-shaped, then an association between scores as it pertains to the direction of the association, the
form of distribution, and the degree of association/strength can be determined. Dependent on the
plotting of scores in the correlation matrix the linear or U-shaped design will reflect the data in
direction or association (positive/negative). The correlation matrix is a visual representation of
the correlation coefficients of mindfulness, EL, and BMI.
For the numerical representation of both the degree and direction of association a
correlational value of 1.0 is desired, whereas, if a negative relationship is present the correlation
is defined as -1.0. Correlation values to look for are .66-.85 between variables for a strong form
of linear correlation. The coefficients in this value range are considered good. If we take it a
step further and look at the correlation value of .86 and above, then this would indicate high
reliability and validity. Correlation values of the .66-.85 range in the proposed study are
anticipated.
This is a correlational design and further testing is not required because the measurement
protocols are to collect data all at one time. Therefore, there is no statistical need for past or
future assessment of all the data collected (MAAS, GLTE, and DIS). The statistical software
chosen will be the SAS University Edition to be diagrammed into the correlational matrix,

USING MINDFUL ASSESSMENT

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creating a scatter plot, while identifying linear or curvilinear distribution as data permits. All
statistics will be analyzed assuming the 0.05 level of significance.
Results
Correlation of Mindfulness, Exercise Level and BMI
While grouping the hypothesis variables overall results into the correlational matrix
analyzed relationships between MAAS scores, GLTE scores, and BMI. No significant
correlations were found between mindfulness, exercise levels, or BMI scores (Table 1).

Table 1
Correlation of Variables
Variables
1
2
3
1. Mindfulness
.34
-.09
2. Exercise Levels
.34
0.12
3. BMI
-.09
-0.12
M
3.58
28.33
28.87
SD
1.59
29.02
9.77
Note. MAAS=Mindfulness Attention Awareness Scale (n =183);
GLTE=Godin Leisure-Time Exercise (n = 183); BMI= Body Mass Index
(n = 178). *p ≤ .05

Influential Healthy Factors (IHF)
Influential healthy factors (IHF) are identifiers reported on the DIS form which included:
(1) employment, (2) exercise, (3) physical activity, (4) gym membership, (5) race, (6) physical
tension, (7) age, (8) and relationship status. Additional findings were used to help clarify, and
correlate potential negative or positive associations with these variables. Further, the IHF were
combined with distribution analysis and other data tools (e.g., scatter plots, summary statistics)
to add emphasis and detail to the correlational analysis results. The following will illustrate
specific findings in the following paragraphs.

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Employment. The recorded data support that length of time of employment in the
school district could possibly have an impact on a teacher’s mindfulness over time. Mindfulness
scores were used to correlate categorical years of employment in the school district: 1-5-years, 610-years, 10-15-years, and 15+ years. For those teachers (n =74) that fell into the length of
employment time between 1-5-years their mindfulness levels of r = -0.08. Teachers (n = 30)
with employment length 6-10-years had a positive MAAS score of r = 0.10. The third
employment category group of 10-15-years was negative of r = -0.45. Finally, the longesttenured group (n = 38) of employment of 15+ years had a significant negative MAAS score of r
= -0.01 (Table 2).
Table 2
Pearson Correlation of Mindfulness and Years of Experience
1
2
3
4
5
1. Mindfulness
-0.08 0.10
-0.45 -0.01
2. 1-5 yrs
-0.08
3. 6-10 yrs
0.10
4. 10-15 yrs
-0.45
5. 15 yrs +
-0.01
M
3.49
3.46
3.83 3.96
SD
1.61
1.67
1.41 1.38
Note. No significant correlation found; *p ≤ 0.05
Variable Averages. The overall (n =183) mean mindfulness score was 3.59 out of a
maximum score of 6. GLTE scores (n = 183) had an overall average of 28.33 units with a
maximum score of 190 units. Consequently, BMI values (n = 178) had an overall mean of
28.87/overweight with a maximum of 52.93/extreme obesity (Table 3).

USING MINDFUL ASSESSMENT

Table 3
Overall Variable Mean
n
Variable
Mindfulness*
183
EL**
183
BMI
178

28

Mean

SD

Min

Max

3.58
28.33
28.87

1.59
29.20
9.77

0
0
0

6.0
190.0
52.9

Note. *MAAS score based on 1-6; **GLTE scores of ≤ 24 units = active; ≥ 24 units moderately active/sedentary. BMI
values are categorized as: (1) 18.5 (underweight), (2) 18.5-24.9 (normal weight), (3) 25-29.9 (overweight), and (4) 30 or
greater (obese)

Level of Activity. The Influential Health Factors (IHF) ‘level of activity’ is divided into
three measurable subsets (mild, moderate, strenuous). The three subsets were used to help find
the strength of association between the other variables of mindfulness and BMI. The Pearson
coefficient was positive with mindfulness and the IHF subset ‘mild activity’ level of exercise of r
= 0.08 (Table 4); moreover, the GLTE association was reported to be, r = 0.74 (Table 4).
Further, a negative relationship is reported with BMI, r = -0.01 (Table 4). Next, the IHF subset
of ‘moderate activity’ had positive correlations with mindfulness and the GLTE. Notably, the
significant relationship with GLTE scores r = 0.83 (Table 4), whereas mindfulness not as
significant but still positive, at r = 0.07 (Table 4). However, a negative relationship did exist
between ‘moderate activity’ and BMI, r = -0.20 (Table 4). Next, the IHF subset ‘strenuous
activity’ found the strength of association between GLTE and MAAS to be generally positive,
with the MAAS coefficient r = 0.07 (Table 4) and significantly positive with the GLTE variable
r = 0.72 (Table 4). Though ‘strenuous activity’ and BMI have a negative correlation with a r = 0.20 (Table 4).

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Table 4
Pearson Correlations of Mindfulness, Exercise Level, BMI Scores and Level of
Activity
Mild Activity
1
2
3
1. Mindfulness
0.08
0.74
-0.03
2. Exercise Levels
0.74
0.74
3. BMI
-0.03
-0.03
M
4.14
57.68
29
SD
1.53
76.65
15.28
Moderate Activity
1. Mindfulness
2. Exercise Levels
3. BMI
M
SD
Strenuous Activity
1. Mindfulness
2. Exercise Levels
3. BMI
M
SD
Note. *p ≤ 0.05

0.07
0.83
-0.20
4.14
1.41

0.83
0.83
58.05
60.94

0.07
0.72
-0.20
4.17
1.16

0.72
58.43
26.89

-0.20
27.32
14.11
-0.18
0.72
27.14
9.57

Exercise. There were four exercise length time categories: ‘less than 30 minutes’, ‘30-90
minutes’, ‘90-120 minutes’ and ‘120+ minutes’ of exercise activity. The GLTE scores are based
on the Godin Scale Score: 24 units or more (active), 14-23 units (moderately active), less than 14
units (insufficiently active/sedentary). When applying the ‘exercise’ IHF, mindfulness averages
are statistically better when activity is ‘120 minutes or higher’ as reported within the last seven
days. The ‘120 minutes or higher’ exercise group produced the highest mindfulness scores of
4.5 (Figure 1) but with a moderately active GLTE score of 40 (Figure 2). Conversely, the ‘less
than 30 minutes of exercise’ group had a high mindfulness score of 4.5 (Figure 2) but a sedentary
exercise intensity score of 6 (Figure 2) among all four exercise time categories. For individuals
performing exercises between ‘30-90 minutes’ 38% of this group had a mindfulness score of 4

USING MINDFUL ASSESSMENT

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(Figure 1) and 28% had GLTE scores of 12 (Figure 2). The time category of ‘90-120 minutes’ of
exercise activity yielded a slightly higher level of mindfulness of 4.4 (Figure 1) and a GLTE unit
score of 60 (Figure 2).

Figure 1. Level of Mindfulness within Exercise Time Groups

While the highest mindfulness score of 6 was achieved by subjects in the 30-90 minute
exercise group, there were only 5% of sampled group that recorded this level. The next highest
mindfulness score was 5.5 which included those in the < 30 minutes and 120+ minutes exercise
group, represented by 12% for each group of the sample. The greatest representation of the
sample group received a mindfulness score of 4.4 comprised of 53% of the 90-120 minute
exercise group. The next highest representation of the sample group received a mindfulness
score of 4.5 comprised of 49% of the 120+ minute exercise group.

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Figure 2. GLTE Units of Exercise within Exercise Time Groups
Exercise level score greater than 24 is active. In the sample size 22% of those individuals
that are in the < 30 minutes group, 51% of the 30-90 minute group, 69% of the 90-120 minutes
group, and 68% of the 120+ minutes group were represented as being active (24+). The greatest
group represented were those in the 90-120 minutes group.

Figure 3. BMI Values within Exercise Time Groups

The lower the scores for BMI, the more fit the individual is. Looking at Figure 3 one can
see that the most common BMI score was 24 (normal). More than 1/2 (58%) of the individuals

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in the 90-120 minute group, 50% of the individuals in the 120+ minute group, 36% of the
individuals in the 30-90 minute group, and 32% of the individuals in the < 30-minute group all
had a score of 24 for the BMI. The best BMI score of 16 (underweight) is represented by both
the 30-90 exercise group and the 120+ exercise group, each represented by 5%.
Physical Activity. Physical activity per week were categorized as: (1) Never/Rarely, (2)
Sometimes, (3) Often, and (4) zero. A total of 21 subjects reported “zero” activity per week. The
individuals average BMI score is 28.23 (sd = 13.1). More than twice the number (n = 55) of
individuals reported “never/rarely” activity per week. The average BMI score is 31.0 (sd =
10.6). Sixty-one individuals reported a ‘sometimes” activity per week and average BMI score of
26.7 (sd = 8.3). Finally, those who reported ‘often” activity level (n = 42) averaged 28.5 (sd =
8.2). No group fell into the underweight (18.5) or normal weight (18.5-24.9) BMI categories.
However, the “often”, “sometimes”, and “zero” activity per week all fell into the overweight
BMI category (Table 6). In addition, the “never/rarely” group fell into the obese BMI category
(Table 6).

Table 5
Summary Statistics of Physical Activity Per Week and BMI
BMI
Physical Activity/week
Zero
Never/Rarely
Often
Sometimes

n

Mean

SD

Min

Max

21
55
42
60

28.23
31.0
26.7
28.5

13.1
10.6
8.3
8.2

0
0
0
0

52.9
48.0
48.1
49.5

Gym Membership. A scatter plot illustrated if BMI has a visual association for
individuals who have a current gym membership and those who do not (Figure 4). The plot

USING MINDFUL ASSESSMENT

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shows the “Yes” or “No” on the x-axis and BMI values on the y-axis. For those that have a gym
membership, the lowest BMI value was 19 (normal weight) while the highest BMI value was 46
(extreme obesity). Conversely, those who had ‘No’ current gym membership the lowest BMI
reported was 19 (normal weight), and the highest 54 (extreme obesity). The findings of the ‘No’
membership group (n =92) had a negative correlation to mindfulness levels in the ‘strenuous’
activity (r = -0.03) group but positive scores for the mild and moderate groups (Figure 4).

Figure 4. Gym Membership and BMI

Further, data outcomes were mixed for both the EL variable and BMI. Specifically, the
EL ‘strenuous activity’ was positive at r = 0.77, but a negative relationship exists with BMI r = 0.16 (Table 6). ‘Moderate activity’ had a positive association, albeit slightly with r = 0.07
mindfulness and strongly for the EL r = 0.80 (Table 6). However, a negative association for
BMI with r = -0.19 exists. ‘Mild activity’ reported positive associations with r = 0.08
mindfulness and r = 0.63 EL. However, a negative association of r = -0.04 for BMI (Table 6).

USING MINDFUL ASSESSMENT

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The findings of the ‘Yes” membership group reported ‘strenuous activity’ to be positive
with MAAS r = 0.20 and EL r = 0.60 (Table 6). ‘Moderate activity’ coefficients with MAAS r =
0.06 and EL r = 0.86 were positive (Table 6). The ‘mild activity’ levels describe a positive
association with MAAS r = 0.08 and EL r = 0.79 (Table 6). However, BMI data regardless of the
intensity level of activity, reported negative coefficient values in all three activity subsets; r = 0.25 (strenuous), r = -0.22 (moderate), and r -0.03 (mild) (Table 6). Moreover, the highest
although not significant mindfulness levels came from the ‘strenuous exercise’ group with a r =
0.20.
Table 6
Pearson Correlation of Gym Membership
Yes
Mild Activity
(n = 53)
Mindfulness
0.08
Exercise Levels
0.79
BMI
-0.03
M
3.92
SD
4.56

No
(n = 90)
0.08
0.63
-0.04*
2.57
2.30

Moderate Activity
Mindfulness
Exercise Levels
BMI
M
SD

(n = 91)
0.07
0.80
-0.19**
2.97
2.08

(n = 56)
0.06
0.86
-0.22
2.99
2.45

Strenuous Activity
(n = 56)
Mindfulness
0.20
Exercise Levels
0.60
BMI
-0.25
M
2.45
SD
1.91
Note: *(n = 89), **(n = 90), ***(n = 91)

(n = 92)
-0.02
0.77
-0.16***
0.98
1.4

M

SD

4.14
57.68
29

1.53
76.65
15.28

4.14
58.50
27.32

1.41
60.94
14.11

4.17
58.43
27.14

1.16
26.89
9.57

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Race. In terms of which ethnicity represents the highest BMI the American Indian or
Alaskan Native group reported a value of 37(obese); however, this group had the smallest
sample size (n = 2). The second-largest group Black or African American (n = 26) had an
average mean BMI of 32 (obese) along with the highest BMI among all groups 52 (extreme
obesity). Next the White or Caucasian group had an average mean BMI of 28, along with the
second-highest maximum BMI of 51, (extreme obesity) (Table 7). The Hispanic or Latino (n =
23) and Other (n = 7) respondents reported identical BMI mean values with an average mean
BMI of 28 (overweight). Among other data results, the lowest mean of BMI belonged to the
Asian or Asian American (n = 7) group with a 25 (overweight) value (See Table 7).
Table 7
BMI Scores Distributed Across the Race Categories
Race
n
Mean
Am. Indian/Alaskan Native
2
37.3
Black/AA
26
31.7
White/Cauc.
113
28.6
Hispanic
23
27.9
Other
7
27.8
Asian/Asian American
7
24.8

SD
1.71
9.61
9.86
11.3
19.7
4.37

Min
36
0
0
0
19
19

Max
38
52
51
48
37
33

Physical Tension. Physical tension was measured on a 6 point Likert scale with the
anchors 1 = almost always, 2 = very frequently, 3 = somewhat frequently, 4 = somewhat
infrequently, 5 = very infrequently, and 6 = almost never (Table 8). Individuals that had BMI
scores within the healthy weight range reported physical tension as “very frequently” (21) and
“somewhat infrequently” (24). Those who reported physical tension as “very infrequently” had
an overweight BMI score (27). Furthermore, those who reported physical tension as “somewhat
frequently” (30) and “almost never” (32) had BMI scores in the obese category. Finally, those
who reported physical tension as “almost always” had extreme obesity BMI scores (45). Please

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refer to Table 8. A positive linear association with the overall sample was reported with IHF
physical tension and mindfulness r = .80 (Table 9).
Table 8
Likert Scale of Physical Tension and BMI
Physical Tension on Likert Scale*

BMI**

almost
always

very
frequently

somewhat
frequently

somewhat
infrequently

very
infrequently

almost
never

45

21

30

24

27

32

Note. *Likert scale single item scores between a range of 1 (almost always), 2 (very frequently), 3 (somewhat frequently), 4
(somewhat infrequently), 5 (very infrequently) and 6 (almost never). **BMI values are categorized as: (1) 18.5 (underweight),
(2) 18.5-24.9 (normal weight), (3) 25-29.9 (overweight), and (4) 30 or greater (obese). (n = 178)

Table 9
Pearson Correlation of Mindfulness and Physical Tension
1
2
Mindfulness
0.80
Physical Tension
0.80
M
4.29
4.23
SD
1.61
2.75
Note. *p ≤ 0.05, n = 183

Age. The youngest age that participated in the survey was 22, and the oldest participant
was 69. In terms of the greatest deviation between BMI values those participants reported to be
age 31 (n = 3) had a standard deviation of 21.4 (Table 10), with the average BMI value of 20.4
(normal) and a maximum of 42.8 (extreme obesity). The smallest standard deviation of 0.66
(Table 10) was with the 61-year-old age group (n = 2). The largest sample group n = 10 to report
similar BMI values tied between two age groups 32-years of age and 44-years of age. The 32-

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year-old age group had an average BMI of 29.8 (overweight) with a standard deviation of 6.7.
The minimum value for this group had a BMI of 19.7 (normal), with a maximum value of 40.3
(extreme obesity). Conversely, the 44-year-old age group had an average BMI of 29.9
(overweight), with standard deviation of 4.4 (Table 10). Moreover, this group had a minimum
value BMI of 22.5 (normal) and the maximum value of 38.7 (obese). The highest mean (4.83)
mindfulness levels came from the 41-year old group; although, the 31-year-old group had the
lowest mean (2.5), but the highest single (5.8) value belonged to one 68-year-old participant.
Table 10
Age and Mindfulness
Age
n
M
22
1
3.0
24
2
4.4
25
4
2.9
27
4
3.9
28
7
3.7
29
5
3.3
30
3
2.6
31
3
2.6
32
10
3.2
33
3
3.1
34
3
1.4
35
4
3.6
36
7
3.7
37
3
4.4
38
4
2.8
39
4
2.9
40
8
3.7
41
7
4.84
42
8
3.76
43
4
3.60
44
10
4.03

SD
.85
2.02
1.03
1.66
2.15
2.29
2.23
1.41
3.11
1.39
0.98
1.77
0.75
1.91
2.07
0.95
0.69
1.87
1.62
1.53

Maximum
3.0
5.0
4.53
5.13
4.93
5.47
4.2
4.1
4.73
5.6
4.5
4.47
4.87
5.2
4.4
4.8
5.13
5.53
6.0
5.6
5.4

Age
45
46
47
48
49
50
51
52
53
54
55
56
58
59
60
61
63
64
67
68
69
70

N
7
3
5
4
8
7
3
1
5
6
4
4
3
1
4
2
3
2
1
1
1
1

M
2.70
1.22
3.24
3.27
3.63
4.30
4.07
5.47
4.28
4.18
3.48
3.9
3.4
5.0
4.10
4.1
3.8
4.23
0
5.87
4.73
4.13

SD
1.96
2.12
1.94
2.26
1.91
0.93
0.55
0.99
0.39
1.31
0.65
2.95
0.25
0.05
0.96
0.05
-

Maximum
4.87
3.67
5.07
5.13
5.8
5.27
4.53
5.47
5.13
4.8
5.07
4.8
5.3
5.0
4.4
4.13
4.6
4.27
0
5.87
4.73
4.13

Relationships Status. The IHF ‘relationship status’ and the partial variable ‘weight’
found that divorced individuals (n =24) has a positive mindfulness score of r = 0.19. The biggest

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sample size, the ‘married’ group (n =120) report a negative MAAS relationship of r = -0.15
(Table 11). Additionally, other negative mindfulness relationships are found with the ‘single,
never married’ (n = 14) r = -0.30, ‘single but cohabiting’ (n = 5) r = -0.25, and the ‘widow’
group (n = 3) reported a negative correlation with r = -0.50 (Table 11). Additionally, all EL
scores (Table 11) reported negative relationships. ‘Divorced’ r = -0.21, ‘married’ r = -0.16
‘single, never married’ r = -0.17, ‘single, but cohabitating’ r = -0.84, and ‘widowed’ r = -0.50
respectively. Investigating the association of mindfulness, relationship status, and BMI, the
majority of sample size has a negative correlation with the exception of outlier relationships
(domesticated partnership or civil union). The married (r = -0.07), divorced (r = -0.05), single
but cohabitating (r = -0.74), single/never married (r = -0.39), widowed (r = -0.84) groups all
reported negative values (Table 11).
Table 11
Pearson Correlation of Mindfulness/EL/BMI and Relationship Status
Variables
Relationship Status
n
Divorced Married Single/Never
Single/Cohabitating
Married
Mindfulness 166
0.19
-0.15
-0.30
-0.25
EL
166
-0.21
-0.16
-0.17
-.084
BMI
175
-0.05
-0.07
-0.39
-0.74
M
31.43
27.82
30.73
36.77
SD
11.02
9.28
12.10
7.77
Note. *p ≤ 0.05

Widow
-0.86
-0.50
-0.84
32.20
6.68

Discussion
With the rise in childhood obesity, public school teachers are one of the first and most
frequent role models many students encounter. However, public school teachers face a workday
filled with extensive commitments to their students and administration before, during, and after
school. The daily routine of work stress, parent-teacher conferences, and teaching protocols can

USING MINDFUL ASSESSMENT

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have a deleterious toll on the physical body. These extended hours and fatigue may hinder
teachers’ participation in exercise activity after the workday. The motivation to exercise and
maintain long-term physical fitness is encouraged in a variety of inactive populations including
post-operative cardiovascular patients (Babbitt et al., 2017), sedentary adults (Heisz, Tejada,
Paolucci, & Muir, 2016) and public school teachers (Aparecida Rodrigues de Oliveira et al.,
2015). Furthermore, a decline in physical activity through different life stages from young adults
to older adults could be associated with progressive advancements in career, transitions of young
adult responsibilities to adult parenthood, and other challenges to exercise daily (Mailey, Gasper,
& Dlugonski, 2019). This decline in physical activity entrenched with the demands of working
adults and their daily routine may be correlated. Moreover, the level of exercise and regular
exercise adherence (EA) is shown to have a positive impact in a variety of populations with
health concerns; recovering cancer patients (Kampshoff et al., 2014), coronary heart disease
patients (Janssen, Gucht, van Exel, & Maes, 2014; Slovinec D'Angelo, et al., 2014), individuals
with emotional instability (Jihoon et al., 2016), persons seeking weight loss (Aparecida
Rodrigues de Oliveira, et al., 2015), post-recovery physical therapy patients (Eckard et al., 2015),
and obesity (Bordignon et al., 2017). Thus, the more adherent one has with exercise the better
the health outcomes. Mindfulness has the potential to aid in the promotion of EA and helps
increase levels of exercise (Aamot, Dalen, & Støylen, 2016; Barnes, Yong-Chae, & Tallent,
2016; Beauchamp et al., 2018; Cadmus-Bertram et al., 2014; Newman-Beinart et al., 2017). As
current literature reports, mindfulness interventions are inconsistent. Evidence shows that there
needs to be a matching process between individual mindfulness practices and the specific
activity (Champion, Economides, & Chandler, 2018; Chin et al., 2019; Cotter & Kelly, 2018;

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Leyland, Rowse, & Emerson, 2019; Kim & Kwon, 2018; Schneider, Malinowski, Watson, &
Lattimore, 2019; Thorndike, Monteiro, & McGarry, 2019).
For the current study, the sample size that was chosen is a representation of a group of
individuals that have an essential impact on today’s youth. Therefore, the aim was to study and
measure the mindfulness in public school teachers. This led to a better understanding of their
health and fitness challenges. Current studies report a variety of sample sizes for measuring
levels of mindfulness and exercise motivation (Adler et al., 2017; Chin et al., 2019; Cox,
Roberts, Cates & McMahon, 2018; Geisler, Bechtoldt, Oberländer, & Schacht-Jablonowsky
2018; Li et al., 2019; Loucks et al., 2016; Salmoirago-Blotcher et al., 2018; Worthen & Luiselli,
2016). Currently there are no studies focusing on public school teachers and their levels of
mindfulness in association with exercise intensity levels and BMI. Therefore, the goal of this
study was to investigate and identify potential relationships between the level of mindfulness
public school teachers have and the level of exercise resulting in a low BMI.
The study’s current data created a correlation matrix based on three testing instruments:
The Mindfulness Attention Awareness Scale (MAAS), Godin Leisure-Time Exercise
Questionnaire (GLTE), and the Demographic Information Sheet (DIS). The MAAS instrument
is a 15-item questionnaire meant to measure individual mindfulness, the GLTE is a 4-item
instrument intended to quantify the level of reported exercise intensity within the last seven days,
and the DIS is meant to collect demographic data, most notably individual BMI values. To
understand the extracted data from all three instruments, and separate possible correlational data
into hypothesis answers, a preliminary analysis was conducted. Moreover, these preliminary
findings coupled with an application of several health and lifestyle association identifiers,
physical tension, exercise, level of activity, race, relationship status, employment, physical

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activity, and gym membership, were used and created cohesive relationships. This study labeled
identifiers as “influential healthy factors” (IHF). IHF helped to find and correlate potential
negative or positive associations with the variables of the present study for clarification. The
results of several combinations of IHF created directional strength of relationship indicators
through correlation analysis of (1) the research variables (mindfulness scores, exercise levels,
and BMI) and (2) the research variables combined with IHF (race, employment, relationship
status, physical tension, activity, exercise, gym membership, physical activity, and age). Further,
the IHF were used to understand data in relation to everyday health and wellness factors.
Participants
The study’s sample size comprised of public school teachers with a demographic age
group between 22-70-years of age. The sample is made up of males (n =32) and females (n =
151) with an employment history of 1-5 years (n =74), 6-10 years (n=30), 10-15-years (n =27),
and 15+ (n = 38) years of public school service. Additionally, this age group was chosen as a
generalization of the natural progressions of adult life. This sample size of public school
teachers (n = 183) was chosen to the proximity of a diverse and heavily populated public school
district. Further, the district was chosen due to the large population surrounding the area and for
the high student enrollment.
The first question this study attempted to answer was, ‘What is the degree of association
between mindfulness, mindfulness and BMI, and mindfulness and exercise levels (EL)?’ To
answer this, an examination of the correlation matrix (Table 1) of positive or negative outcomes
and to what degree of association (strong/weak) is required. According to the data analysis,

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there is no significant degree of association (Table 1) with the average level of mindfulness,
mindfulness and BMI, and mindfulness and EL of public school teachers.
For example, the average sample mindfulness score had the strongest relationship with
the EL variable. While not a significantly strong relationship, there is some awareness of
exercise intensity. Also, not significant the mindfulness levels in relation to BMI values were
negative. Thus, suggesting that perhaps as mindfulness and EL levels decrease, BMI increases.
Conceivably, an emphasis on the aesthetic benefit of exercise rather than specifically lowering
BMI may be the focus (Brinthaupt & Anshel, 2018). This perception of unrelated cause and
effects of exercise could be a reason as to why overall mindfulness scores were negative
concerning BMI. The degree of strength may be negative. However, BMI may not be seen as a
benefit of high mindfulness or exercise intensity within this sample set.
The second question in this study, ‘Is a public school teacher’s level of mindfulness
associated with their current BMI?’ The data does not support that a high level of mindfulness is
an associative factor for low BMI; however, that is not to say that the level of low mindfulness is
indicative of an individual’s high BMI. The application of IHF ‘employment’ is used to help
investigate why mindfulness scores were varied and mixed with reported BMI values. Over time
increased BMI during a public school teacher’s employment is evidenced to affect occupational
performance. Additionally, it has also been noted to affect students perceptions of their teacher
while in the classroom (Aparecida Rodrigues de Oliveira et al., 2015; Grabara et al., 2018; Hunt
et al., 2017; Jiang et al., 2019; Lambert, Chang, Varner, & Monroe, 2016; Rocha et al., 2015;
Rômulo Mota et al., 2017). When examining the IHF ‘employment’ the longer the individual
was employed, the higher the BMI values. The only positive relationship between length of
employment and mindfulness scores is during the 6-10-year employment time period (Table 2).

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It could be theorized that the 6-10-year work history is the only positive correlation because
experience as a public school teacher is now a manageable routine leaving more opportunities for
healthy habits. Conversely, the least amount of time, 1-5-year employment had a negative
correlation. The low mindfulness scores for this length of employment could be due to the
inexperience of balancing a new professional life, maintaining personal relationships, and
finding a cohesive individual daily routine. However, this time period does not have the extended
stress and weariness of public school teaching during extended employment categories of 10-15years and 15+ years which also reported negative associations (Table 2).
The third question is ‘What is the degree of association measuring mindfulness in the
relationship between BMI and EL?’ The overall mindfulness scores were average representing
an even distribution across the sample size (Table 3). Overall BMI averages reported overweight
with a maximum value of extreme obesity. However, when compared to both EL scores and
BMI values the standard deviations are significantly greater especially in the EL scores (Table
3). This noteworthy deviation could be a product of the mindfulness instrument, limited exercise
recall time, or both. Possible factors could be to increase mindfulness intervention length and
self-reported exercise activity recall. Research supporting mindfulness training being longer than
30 days (Slutsky, Chin, Raye, & Creswell, 2019), and a more prolonged exercise recall time
regardless of what intensity levels may have a stronger relationship in future research.
Salmoirago-Blotcher et al. (2018) suggest mindfulness training could have substantial health
behavior improvements beyond their adolescent sample. A focus on an extended recall time,
moderate to vigorous activity/7-day recall, and a larger sample is recommended. There is
evidence supporting extending the timeframe of mindfulness training programs that resulted in
improved psychosocial wellbeing and job satisfaction (Slutsky et al., 2019; Thomas, 2017;

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Champion et al., 2018). With this in mind, the high values of the collected BMI could be a result
of this samples’ job satisfaction or poor psychosocial circumstances that contributed to the low
levels of mindfulness.
The next question to investigate, ‘Is there a positive or negative correlation between
mindfulness/EL and mindfulness/BMI? To help answer this, a detailed analysis required the use
of IHF to separate the two related variables. The IHF ‘level activity’ was chosen and divided
into the subsets of mild, moderate, and strenuous levels. All activity levels were found to have a
positive relationship with the EL variable, most notably the ‘moderate’ level had the strongest
association. However, the mindfulness with EL association not as statistically strong but still
positive with all activity subsets; mild, moderate, strenuous respectively. Further, if moderate
levels were beneficial as the data suggests, then how long should this level of exercise be
performed? The additional IHF ‘exercise’ was then used to specify what length of exercise time
is substantial in association with mindfulness and the reported strongest level of activity;
‘moderate’ (Figure 1). Exercise length times of ‘less than 30 minutes’ and ‘120 minutes or
more’ resulted in an identical mindfulness level score of 4.5 for both time categories (Figure 1).
Yet, the different BMI values of normal for the ‘120 minutes or higher’ and extreme obesity for
the ‘less than 30 minutes’ group needed to be clarified. Although, mindfulness is high in both
the longest and shortest exercise times, low GLTE scores in the ’less than 30 minutes’ group
could be due to exercise time. Therefore, requiring less intensity/energy expenditure resulting in
higher BMI. Because of their structured day of classes and administration interaction, public
school teachers may have small windows of opportunity to exercise. To help encourage healthy
habits exercise frequency if identified, could be fit into their after-school schedule. Thus, the
encouragement of moderate exercise may be performed if we can identify the amount of exercise

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frequency public school teachers need to adhere to for the correlation benefits. To answer this,
the association of another IHF was needed to help build upon the positive relationship with the
‘120 minutes or more’ and ‘moderate’ activity data. Identifying this information is advantageous
for public school teachers with a demanding work schedule and after school duties, to fit in
exercise in advance and how often.
The IHF ‘physical activity’ was used to answer the frequency and scheduling issues a
public school teacher would need to adapt within their daily or weekly schedule. To establish
the strength of association the IHF ‘physical activity’ data emphasized the ‘Often” per week
group (Table 5) with having the lowest average BMI value of overweight. Exercise frequency of
‘Often’ can now be added to a recommended ‘moderate’ level of exercise totaling ‘120 minutes
or more’ to help public school teachers plan their personal workout schedule for the day or week.
Furthermore, making the transition from formulating the plan to exercise, to action one would
generally need access to a fitness facility. The IHF used ‘gym membership’ to narrow the type
of physical activity required. Commonly, the purchase of a gym membership is generally
synonymous with a healthy lifestyle. This IHF addition is valuable because of the access to a
variety of exercises, weightlifting equipment, cardiovascular machines, several group classes,
aquatic aerobics, and high-intense training with personal trainers. By combining all three IHF
‘level of activity’, ‘exercise’, ‘physical activity’ with ‘gym membership’ the correlation became
clearer. The data showed that a current membership has no influence when added to the
previous IHF.
Moreover, a current membership has no effect on BMI as values are almost equal (Figure
3) in the ‘Yes’ and ‘No’ group. Although access to a gym is generally beneficial because of the
equipment available and the daily opportunity to use the facility, the data is clear. The purchase

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of a current gym membership has no effect on this samples’ BMI (Figure 3). It could be the idea
that a membership is required to motivate and begin healthy habits like exercise, however this is
just not the case. A current gym membership may mask the real challenge of individuals
perception and benefits of health. The thought of paying for a gym membership may satisfy a
mental checkbox that a healthy lifestyle is at their convenience. Nevertheless, this convenience
may blind those who do not understand that it requires more than a gym membership to
permanently change one’s fitness and health. It is unknown if our data would change BMI
values for those who have a gym membership if we addressed the previous IHF as a sample size
inclusion only. Unfortunately, this is not the aim of the current study. Therefore, the use of a
gym is not required when utilizing the IHF ‘level of activity’, ‘exercise’, ‘physical activity’, to
form a strong positive correlation of mindfulness, EL, and BMI.
Next the research investigated, ‘Does the level of mindfulness influence the intensity of
EL?’ The results found that the level of mindfulness does not influence the intensity of exercise
levels in public school teachers. In a study conducted with African American college students,
their level of mindfulness was negatively correlated to their stress (Wright et al., 2018). Wright
et al. (2018) explained that their low percentage of mindfulness variance was due to perceived
stress in their college-aged sample. This sample had “feelings of being overwhelmed by the
responsibilities of college life.” Applying the same reasoning for perceived stress as defined by
Wright et al. (2018), public school teachers may have a higher perceived stress level due to
feelings of being overwhelmed by the responsibilities of public school teacher/adult life. It
would be reasonable to think that the public school teachers participating in this study would
have just as equal to or higher demands of their time, career, relationships in their personal and
professional lives as the college sample. In addition, applying the IHF ‘physical tension’ could

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explain the compounded anxiety anyone would experience shifting from unhealthy to healthy
lifestyles. According to the data, there is a strong linear association of mindfulness concerning
the individual perceptions of ‘physical tension’ (Table 9). The stressors of balancing
professional and personal commitments may have an effect on teacher’s mindfulness if they are
pre-occupied with completing their daily schedule as opposed to exercising. This would explain
why the low levels of mindfulness to exercise correlates with our data. This mindset of
perceived stress may influence their ability to schedule exercise as a priority. Likewise, the
transition from professional workday to personal time would reasonably take precedence over
the perceived stress of a daily exercise.

Exploratory Outcomes
Some of the outcomes from this study may have stronger correlations if a larger sample is
made available in the future. With the unexpected exploratory correlations from the data
describing what this population at large can report, these outcomes should be investigated
further. Potentially strong associations of IHF and specific mindfulness interventions could
proactively help us understand the underlying challenges of public school teachers and their state
of physical health. Thus, separate IHF (employment, level of activity, exercise, physical activity,
gym membership) were used to correlate negative or positive relationships between variables.
Now other IHF were chosen to identify the strongest correlations with individual cultural norms
(race), mental fitness acumen (physical tension), different life stages (age), and supportive
environments (relationship status). For example, evidence reported with the IHF ‘race’ should
explore what type of cultural factors may be identified as healthy or unhealthy indicators for
future BMI challenges. The question then should be asked, ‘If young individuals from a

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particular race become future teachers, while taking cultural health norms with them as adults,
will their BMI match the data presented in this study?’ We found that race can be a starting
point in understanding if healthy habits are products of cultural environment or habits that were
acquired later in life. Out of the six ethnicities surveyed all races were either overweight or
obese (Table 8). Studies successfully using mindfulness intervention identified a foundational
personal interest important when the activity of mindful mediation is needed (Thorndike et al.,
2019). The question then needs to be ‘If a foundational personal interest can be explored in
cultural health norms, are they related to BMI increases?’ This is why the researcher utilized
IHF in combination with the hypothesis variables to replicate the need for different groupings of
a foundational personal interest regarding public school teachers.
IHF such as ‘physical tension’ can create a cognitive picture of how exercise habits relate
to the individual presence of mind. The MAAS statement of physical tension, “I tend not to
notice feelings of physical tension or discomfort until they really grab my attention” were
compared with the level of exercise intensity and BMI values. Consequently, the use of the IHF
‘physical tension’ statement was an indicator of what the participants perceived their acceptable
levels of physical tension are. As with previous studies physical tension or discomfort is
evidenced to create barriers to consistent physical activity (Brinthaupt & Anshel, 2018; Cox et
al., 2018; Schneider et al., 2019). Levels of discomfort and perceived pain at the onset of
exercise were different for everyone. The anticipation of exercise after a long layoff or after
inconsistent activity may add even more physical tension to the MAAS statement than usual.
Moreover, anxieties and past wellness failures may also hasten the start of an exercise program.
This anxiety coupled with personal responsibilities at the end of a workday can compound
elevated levels of their personal ‘physical tension.’ Although the MAAS statement of ‘physical

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tension’ is detailed enough, there is no statement that was specific as to the level of physical
tension that was acceptable to them. Individual definitions of ‘physical tension’ may vary from
person to person and may influence their participation in exercise. Analyzing the MAAS
‘physical tension’ statement symbolizes their perception of physical discomfort when presented
with exercise activity or the thought thereof (Table 8).
Next, the IHF ‘age’ was used to identify if mindfulness scores increase with
chronological age. Based on the data, five-years (41-46 years-old), separate the highest and
lowest mindfulness scores (Table 10). A generalization of this age group may have higher levels
of perceived stress because of their age-appropriate responsibilities. Some of these
responsibilities may include; work versus personal schedules, preparation for potential
retirement, increased demands of growing families, the advancement of professional career, and
maintaining personal daily routines (Brinthaupt, & Anshel, 2018; Cadmus-Bertram et al., 2014;
Grabara, Nawrocka, & Powerska-Didkowska, 2018). The ‘relationship status’ IHF helped to
establish which group resulted in the highest mindfulness levels. The married group displayed
the lowest level of mindfulness in all relationship categories. One reason may be that weight
gain in mid-life is due to the shared responsibilities of the social, familial, and demands needed
to raise children by age 40 (Brown, Abrams, Cohen, and Rehkopf, (2017). These exploratory
IHF (physical tension, age, relationship status) invite a closer investigation possibly adding
another dimension to strengthen the other IHF findings.
One of the limitations of this study is the length of time for exercise recall. The seven-day
recall which was dictated by the GLTE, may have been completed during times of high stress
(i.e., conducting standardized testing, scheduling of parent-teacher meetings, or administrative
staff meetings) in this population. This may not be an adequate length of time for self-reporting

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accuracy. Further, alterations in the type of exercises within the last seven-days could also
impact the accuracy of self-reporting if one activity is more dominant than the other. If
distinctions between cardiovascular exercises and weight training were depicted the effect on
mindfulness levels would be more greatly influenced. There may have been inherent bias to
under or over report in all three self-reported instruments, which may have resulted in skewed
data. Even though the sample size (n = 183) exceeded the initial proposal number (n =120), an
even larger sample in several different school districts would create a deeper cross-section of
data. Given our exercise recall data was recorded in a short window, a future study may want to
increase the level of detail that goes beyond EL or a longer recall instrument. Previous research
with mindfulness-based interventions/recall purports that the greater length of intervention/recall
the higher the mindfulness levels reported. For this reason, the recommendations of designing
specific mindfulness interventions to match a particular physical activity (Schneider et al., 2019).
Based on the current study future researchers may want to ask the following questions. 1) Does
public school teachers, BMI decrease or increase as the school year progresses? 2) What is the
significance of current relationship status on exercise motivation? 3) Do we need to look at the
current state of mindfulness and adaptive motivational experience at the time of intervention as
stated by Cox, Ullrich-French, & French, (2016)? 4) Does measuring mindfulness levels as they
relate to intrinsic motivation, positively impact exercise adherence? The answers to the
aforementioned questions may lie in the increased sensitivity of negative or positive emotions on
current levels of mindfulness (Geisler et al., 2018).
Based on the positive initiation of cognitive executive control the sample of public school
teachers may be able to create more physical activity opportunities. A highly mindful teacher
can act as an effective resource for all students. The mindful public school teacher is a strong

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adult role model. For example, if female high school and collegiate athletes become more
mindful, they improve their athletic ability and decrease the chance of injury (Petterson & Olson,
2017). This practice of mindfulness training emphasized emotional awareness and attention
focusing, the contribution of mindfulness toward team play, the benefit of having coaches learn
mindfulness skills, and the application of mindfulness to other sports (Worthen & Luiselli,
2016). The adaption of this mindfulness training with our sample of public school teachers could
have implications far beyond the classroom. To add mindfulness intervention in public schools
could benefit not only employees but students as well.
Conclusion
The data collected provides insight as to what pressures and stressors involved in the
teaching profession face during the academic school year. While analyzing the data, the life of a
public school teacher is much more intricate than what is commonly known. Utilizing three
instruments to highlight a cognitive perspective (MAAS), exercise levels (GLTE), and current
health status (BMI). At face value all three instruments reported low mindfulness scores.
However, the IHF (physical tension, exercise, level of activity, race, relationship status,
employment, physical activity per week, gym membership) were used to understand the
correlations more clearly. For future research IHF may be used as predictors as to why BMI are
relatively high, and mindfulness levels are low in this group. The results of this study
recommend a combination of IHF level of activity (moderate), exercise (120 minutes or more),
physical activity (often), to increase mindfulness levels and promote a healthy BMI in public
school teachers. Moreover, the highest level of mindfulness came from those teachers who were
employed between 6-10-years in the sampled school district. However, it is not clear as to what
types of mindfulness intervention is needed, and if the overall short-term effects can be

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replicated over long-term intervention with more significant results. The public school teacher is
one of the sturdiest pillars of the community. Knowing the importance of this role, it is noted
that students spend a majority of their time with their teachers. Although the teaching
occupation is vital to the young minds they influence, the constant demands of their profession
can be detrimental to the teacher’s physical well-being in and out of class. Inclusion of
mindfulness intervention training for these everyday heroes can help them cope with influential
factors that limit their exercise participation.

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Appendix A
Review of the Literature

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Currently to be overweight or medically obese is wrought with various physical ailments
that hinder individuals from creating or enjoying a healthy lifestyle (Bordignon, Aparício,
Bertoletti, & Trentini, 2017; Kulendran et al., 2017). Excessive weight gain is a physical
condition that leads to morbid obesity and higher risks of Type 2 Diabetes, metabolic syndromes,
poor social integration (Danielsson et al., 2016), cardiopulmonary disease, (Abdi et al., 2015),
and cognitive deficits or self-regulation failure (Hawkins et al., 2018). In addition, chronic
diseases such as arthritis, cardiovascular disease, (Bordignon et al., 2017), hypertension
(Marszał-Wiśniewska & Jarczewska-Gerc, 2016), and low levels of physical energy with poor
dietary behaviors (Lloyd, Lubans, Plotnikoff, & Morgan, 2015) are common as well. Being
physically inactive or infrequent in the participation of exercise activity can also lead to life or
death even if not severely overweight (Huang et al., 2015).
The literature researched used the foundations of Social Cognitive Theory (self-efficacy,
self-determination, self-regulation, and environment) as behavior modification (BM) to increase
EA. Applying facets of SCT will help in understanding influencing behaviors for potentially
increased exercise frequency and identify behavioral adjustments needed for successful
adherence as it applies to exercise. A positive outcome of high levels of adherence for
individuals who want to exercise may be increased by applying a BM, specifically mindful
exercise treatment for exercise frequency success.
The purpose of this literature review is to understand the challenges of non-compliance
of exercise adherence (EA) in several population groups. One such group where EA can have a
positive impact is female public school teachers. However to understand the measurements,
outcomes, and levels of compliance of EA a background in SCT must be established. The
literature connects both EA and SCT in direct and indirect ways. This review will look at Social

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Cognitive Theory (SCT) as a basis for behavior modification; the role exercise adherence has on
various populations and concludes with mindfulness as possible behavioral modification
treatment. SCT analysis will be applied to exercise adherence in four aspects: self-regulation
(Annesi & Tennant, 2013; Kliemann et al., 2017; Morgan et al., 2014), self-efficacy (Abdi et al.,
2012; Kosteli et al., 2018; Singer et al., 2017), self-determination (Gourlan et al., 2013; Soni,
Mustajoki, & Eriksson, 2018), and environmental/social support (Best et al., 2012; Dewar et al.,
2013; Gorin et al., 2014; Joseph et al., 2017). EA compliance or non-compliance has adopted
the aspects of self-efficacy, self-determination, self-regulation, and environment and applied it
towards the individual/population, prescribed exercise, or both. It cannot be understated that no
one treatment can be accredited to increase all exercise populations in their pursuit of EA for all
fitness levels in all health situations. Current research has examined a need for a cohesive
treatment plan for exercise adherence concerning inactive individuals.

The Role of Behavior Modification in Health Practices
Behavior modification (BM) has been used in studies associated with the obese
population to predict (Rodríguez-Hurtado, 2017), prevent (Peacock, Perry, & Morien, 2018), and
maintain weight loss (Soini, Mustajoki, & Eriksson, 2018). BM can involve cognitive
restructuring where intervention treatment is applied to change a person’s unhealthy lifestyle
through solutions involving individual and family-based interventions (Danielsson et al., 2016),
or adopting positive dietary behaviors creating weight loss maintenance and success (Ahlgren et
al., 2016). Teixeira et al. (2015) viewed BM as the initial step in completing weight loss success
and obesity supervision through an all-inclusive treatment program. BM tools can come in
various treatment forms such as health education (Vinkers, Adriaanse, Kroese, & de Ridder

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2014), SCT based homework assignments and questionnaires (Munsch et al., 2012),
government-sponsored programs (Young et al., 2015), educational parental programs
(Danielsson et al., 2016), and technology (Abdi et al., 2015; Singer et al., 2017).
Adapting BM is neurologically based because of its use of cognitive restraint (Konttinen
et al., 2015; Singer et al., 2017), and it’s relationship between the brain’s executive function
(Kulendran et al., 2017; Peacock et al., 2018; Yang et al., 2018), on various triggers like food
cues (Carnell et al., 2013; Grimm et al., 2012), or emotional eating (Braden et al., 2016).
Varied Interventions and Application of Behavioral Modification
Most BM research suggests shorter time intervals for interventions generally because the
results for the first six months are moderately positive (Armitage et al., 2014; Braden et al.,
2016; Munsch, Meyer, & Biedert, 2012). However, the findings have drawn inconsistencies of
applying treatment beyond the six-month trial period (Algren et al., 2016). For bariatric surgery
patients, BM has been reported to be successful if post operative success expectations are broad
and not specific (Vinkers et al., 2014). The following studies suggest that the initial weight loss
and weight maintenance phase would require more research regarding BM as a long-term
solution that can be replicated with the short-term interval data.
Algren et al. (2016) focused on behavior adoption of healthy emotional eating and
presented BM in phases through their thematic interviews of woman ranging from 54-71 years of
age. The interviews were designed to understand the cognitive process of the participants when
trying to lose weight. The answers were categorized into phases that identified common themes
such as desire, struggle, self-image, priority, emotions, implementation and adjustment, and
medical well-being.

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BM was used to treat external psychological variables that may prevent weight loss
success or maintenance in bariatric surgery patients (Rodriguez-Hurtado et al., 2017). External
variables ranged from self-esteem, social support, coping strategies, and personality (RodriguezHurtado et al., 2017), or psychological characteristics that can affect future predictors of weight
loss treatment success (Lundin et al. 2016). Both Rodriguez-Hurtado et al. (2017) and Lundin et
al. (2016) considered individual personality pre-disposition and attitudes toward weight loss
success before and after bariatric surgery. Rodriguez-Hurtado et al. (2017) understood the
psychology of BM relevant to bariatric patients and their coping skills for post-surgical success.
Their use of BM is assessed through four self-administered scales; (1) Rosenberg’s Self-esteem
scale, (2) MOS Social Support Survey, (3) coping strategies inventory, and (4) The Minnesota
Multiphasic Personality Inventory-2 Restructured Form. The modifications were used to
measure the emotional, cognitive, and social factors that may influence the post-surgery
environment.
Comparatively, Rodriguez-Hurtado et al. (2017) reported that of the 64 patients sampled,
57% were weight loss successes post-surgery through BM. However, the authors acknowledged
the physiological reasons that obesity is prevalent in post-operative maintenance. Generally,
they included the patient’s poor coping skills, psychological inability to absorb healthy habits,
personality traits, and lack of follow up care. The authors maintain that overall findings are
inconsistent, but BM specifically preoperative cognitive function can predict the influence of
postoperative behavior, whereas Lundin et al. (2016) reported, their conservative treatment group
had stronger beliefs in their ability to change their physical activity levels.
Lundin et al. (2016) reported that BM might both apply to their studied sampled groups
with some degree of success if adjustments are made. Lundin et al. (2016) conducted a 10-year

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study with 562 patients divided into two groups one with the conservative treatment group
receiving BM and the other without treatment. They determined that post operative physical
activity success may be linked to expected outcomes in healthy behavior changes if pretreatment
beliefs of SE, goals, and the expectation of future well-being were present. Both studies
acknowledged that in addition to their findings and available current literature it is unclear as to
the consistent effect of BM in both groups that consider pre and post-treatment for bariatric
surgery patients for their weight loss solution (Lundin et al., 2016; Rodriguez-Hurtado et al.,
2017).
Vinkers et al. (2014) also encouraged the need for pretreatment as a necessity for
successful weight management. They applied BM treatment of self-regulatory skills to
overweight patients to influence post operative success. However, their adjustments in
pretreatments focused more on the overall expected outcomes with an emphasis on the negative
rather than positive. Proactive BM must make patients aware of weight loss difficulties
adjusting beliefs to support more weight loss (Vinkers et al., 2014), and address external
struggles with triggers may be the first step to understand the brains function and its relationship
to certain behaviors.
Social Cognitive Learning Theory
As researchers examine various methods to help with EA, some exploration has shifted to
cognitive theories that may present a connection between successful EA and the benefits of
maintaining the activity long-term. While some researchers argue that exercise and diet alone is
the best way to aid a healthy life it is the type of EA that must be identified for long-term health
success. BM treatment can be applied using various theoretical models including The
Transtheoretical Model, (Marszał-Wiśniewska & Jarczewska-Gerc, 2016; Peacock, Perry, &

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Morien, 2018), Counteractive Control Theory (Mantzios & Wilson, 2014), or Cognitive
Restraint (Konttinen et al., 2015; Racine, 2018). These are a few of the available models that
have been useful to change the cognitive process and perspective of exercise application.
However, as more research becomes available to examine EA compliance in a variety of
populations, there are those who believe that Bandura’s SCT, (Bandura, 1986) may provide a
template to analyze and treat groups such as overweight individuals (Young et al., 2015; Joseph
et al., 2017), group exercise (Blackstone, Reeves, Lizzo, & Graber, 2017), or sedentary adults
(Sylvester et al., 2016). SCT, as defined by Bandura (1986), is a
“model of reciprocal causation, action, cognitive, affective, and other personal factors,
and environmental events all operate as interacting determinants. Any account of the
determinants of human action must, therefore, include self-generated influences as a
contributing factor” (p. 1175).
Abdi et al. (2015) agree that SCT is prevalent in the success of weight loss because of its
defining attribute of behavior change. This change is through reciprocal self-determination of
the behavior itself, personal, and environmental factors. Annesi and Tennant (2013) describe a
causal link between SCT to both successful exercise and healthy eating. SCT has been found to
aid behavior change in a variety health challenges such as obesity in gender-specific studies
(Morgan et al., 2014; Young et al., 2015), cultural demographics (Joseph et al., 2017), sedentary
groups (Arigo et al., 2017), excessive weight gain (Braden et al., 2016), self-regulatory
mechanisms (Hawkins et al., 2018), and mindfulness (Ruffault et al., 2017).
Under SCT, a behavior can be transformed through three types of factors: (1) personal,
(2) environmental, and (3) the behavior itself (Joseph et al., 2017). To elicit a behavioral

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change, SCT incorporates the personal aspect of belief and attitudes with a dynamic response to
environmental factors that are both social and physical to collectively modify behavior (Joseph et
al., 2017). Self-regulation, self-efficacy, self-determination, and environmental support are
foundational SCT constructs evaluating individual motivations towards obesity and weight loss
(Dewar et al., 2013; Kliemann et al., 2017; Singer, Swencionis, & Cimino, 2017; Vieria et al.,
2013).
Self-Determination
There are several BM applications to consider for individuals to be more health selfsufficient. One such application is Self-determination theory (SDT) which can cover a broad
range application of psychological treatments that target such behavior (Gourlan et al., 2013).
SDT is based in motivation as it applies to autonomous or controlled behavior in a variety of
habits (Gourlan et al., 2013). SDT also explains the start and ending of behavior patterns and the
reason for an individual’s particular action within the framework of motivation (Blackstone,
Reeves, Lizzo, & Graber, 2017).
Soni, Mustajoki, and Eriksson (2018) used a retrospective study aimed at evaluating
motivational causes for weight loss support and challenges while undertaking weight loss
completion and maintenance using SDT as a basis for intervention. Data complied with the
Finnish Weight Control Registry (FWCR); of 158 formerly obese patients reported two factors
for motivational causes to continuously keep the weight off were health-related concerns and the
physical exterior appearances of the sample group.
These factors were especially true of the women who had more appearance-based goals
than men who valued more health-related factors for current behaviors towards weight

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maintenance. Half (53.2%) of the participants have maintained weight loss for 2 to 3 years,
20.3% for 4 to 5 years, 15.8% 6 to 8 years, and 10.7% for at least nine years. The sample group
also reported other motivational factors such as lack of energy, difficulty in buying clothes, as
reasons to continue to maintain weight loss. Factors for success from high to low were increases
in physical activity (48%), reduction in fast carbohydrates (24%), increasing vegetable intake
(22%), meal consistency (19%), and reducing portion size (17%) (Soni, Mustajoki, and Eriksson,
2018).
Gourlan et al. (2013) noted a possible connection between increased SDT (motivation)
and behavioral attitudes towards physical activity thereby changing established behavior.
However, Gourlan et al. (2013) found that the most critical component of SDT was perceived
competence along with autonomy support provided by healthcare professionals might also
improve SD. In short, it stayed within the framework of SDT to incorporate changes in
autonomous support, change perceptions in amotivation, perceived confidence in health-related
behaviors increases explicitly in physical activity (Gourlan et al., 2013).

Self-Regulation
One cognitive feature to identify and assess is self-regulation (SR) as it pertains to how
individuals apply themselves when presented with changing routine habits. SR is a broad term
associating goal-directed techniques fostering the ability to change behaviors, thought patterns,
emotions, environment, and attention for personal gain (Kliemann et al., 2017). They report that
a total cognitive restructuring needs to take place for successful weight loss intervention.
Kliemann et al. (2017) suggest incorporating habit interventions, whereas Vinkers et al. (2014)
has promoted proactive coping skills rather than Young et al.’s (2015) attempt to improve

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individual behavioral strategies or short-term goal setting (Annesi and Tennant, 2013). Under
this type of cognitive restructuring, behavioral treatment could be custom made to the immediate
needs of a patient or an overweight individual.
One such study encapsulates previous interventions into the premise of cognitive
restructuring for weight loss goals. Annesi and Tennant (2013) reported cognitive-behavioral
nutrition programs were significantly higher in weight loss achievement than standard nutrition
education for SR and self-efficacy in obese individuals. The authors found that applying
cognitive restructuring within the course of the 26-week intervention addressed patients’
personal weight loss goals. Their focus of the study was to start increasing intrinsic SR as a
primary construct thereby leading to subsequent effects of mood enhancement and self-efficacy.
Adding to the complexity of SR, even minute factors like mood enhancement can make a
difference in encouraging active lifestyle changes. Changing the outlook and confidence was
viewed as a pre-emptive strike for future weight loss success in promoting useful coping skills
(Viera et al., 2012). Listed instructions (goal setting) and education is another way of increasing
the strength of SR (Kliemann et al., 2017). SCT based tasks of goal setting and self-monitoring
of weight were introduced to a gender-specific sample of obese men during a three-month
randomized control trial to aid weight loss maintenance (Young et al. 2015).
Young et al. (2015) examined the influence of outcome expectations, goals, and sociostructural barriers and facilitators during the maintenance phase of weight loss. Of the 209
overweight men sampled, reports were positive and encouraging as data resulted in improved
behavioral strategies and perception of social barriers. However, in the end, both the control and
sample group reported no significant improvements in moderately vigorous physical activity

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(MVPA) through cognitive behavior adjustment, goal setting, planning, and social support
(Young et al. 2015). Annesi and Tennant (2013) also reported changes in accomplishment and
competence within the participant’s’ personal goal setting and manageable exercise program
design. The authors support this cognitive-behavioral approach as it improves on obese adults
and their willingness to initiate cognitive restructuring exercises notably SR over the treatment
period. Their findings support increasing SR for as a crucial tool to improve eating behaviors
and include exercise in their daily lives.
Morgan et al. (2014) outlined their SHED-IT (Self-Help, Exercise, and Diet using
Information Technology) program to emphasize SCT’s behavior modification arbitrator of
changing cognition in obese men. Unlike Annesi and Tennant (2013), who reported utilizing SR
as a primary assessment in changing obese behavior, Morgan et al. (2014) wanted to see changes
in cognition as the priority of behavioral weight loss treatment. This priority change could
hypothetically lead to personal confidence in pursuing a healthier weight status over the long
term. The SHED-IT program promoted the belief of implementation of change through assigned
tasks of goal setting, reward setting, the creation of social support, and self-monitoring. As
Annesi and Tennant (2013) pointed out, customizing treatment can have subsequent positive
effects, coupled with Morgan et al.’s (2014) focus on confidence; this combination may provide
a basis for initial BM intervention.
Coping skills applied to body image adds another dimension of cognitive support to SR
and goal setting. Pre- and post-treatment of weight loss can come with challenges that are
unique to pre-and post-treatment phases of weight maintenance. Vinkers et al. (2014)
implemented a case-control study to observe the relationship between pre-treatment proactive

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coping skills and perceptions of likely challenges for weight loss and continued weight loss
management. The sample size was 119 obese individuals who were engaged in a weight
management intervention program. Coping skills with reality-based future weight maintenance
challenges were defined as being proactive in its approach to positively influence weight loss
behavior. The eight-week intervention encompassed three sessions of teaching coping skills;
realistic diet goal setting, recognizing goal attainment barriers, levels of barriers, the design of
successful goal attainment, and evaluation of current progress. The study reported on learned
behavior for pre- and post-goal attainment for impending weight loss as a factor in the success of
weight maintenance. SR skills would be beneficial to help combat the expected difficulties of
weight loss process and its subsequent maintenance (Vinkers et al., 2014). Although the data is
inconclusive as to what skill set is helpful, enrichment of coping skills can help to prepare those
expectations not considered before a weight loss decision.
Viera et al. (2012) conducted a cohort study to understand successful weight loss in
individuals who incorporated health-related lifestyles to cope with eating, self-regulation, and
body image. The participants were 107 women enrolled in the Portuguese Weight Control
Registry, with data taken from the program, collected over a 2- year period. A 31-item
questionnaire was used to assess the quality of life, consisting of five sub-scales: physical
function, self-esteem, sexual life, public distress, and work. Based on the aforementioned
information, women who were successful in weight loss have the quality of life improvements
along with eating self-regulation compared to similarly weighted women. Weight loss
maintenance may contribute to higher levels of motivation towards exercise and self-efficacy in
health-related activities (Viera et al., 2012). In general, the achievement for this group of women

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consisted of various factors for psychological and quality of life influences that created weight
loss maintenance.
If consistent treatment cannot be repeated, it could be possible that SR may be found in
pre- and post BM by understanding gaps in the cognitive process. Hawkins et al. (2018)
conducted a randomized controlled trial created to measure poor SR through cognitive deficits.
Testing was done for acceptance based treatment (ABT) as a viable means of treatment as
opposed to standard behavioral treatment (SBT). Information from biomarkers, cognition, and
SR would then indicate if weight loss or treatment of weight loss differed due to intervention,
then potential long-term success in weight loss can be rooted in the individual’s lifestyle of
persistent engagement of crucial health-related factors (Hawkins et al., 2018).

Self-Efficacy
Another facet of SCT as it applies to promotion of individual behavioral change in the
overweight population is self-efficacy (SE). Singer et al. (2017) defines SE as an individual
choice created in the individual’s ability to be successful within their effort, belief, and
assuredness to elicit change in achieving the end goal (p. 2). Singer et al. (2017) conducted a
randomized controlled trial focusing on the relationship between weight loss attempts via
motivation factors, dieting, increased activity levels, and exercise SE. A sample size of 429
participants were randomly divided into three groups, following treatment with increasing
intensity of workbook only, computer-intervention, and computer intervention with staff. The
most applicable data were significant changes in BMI within the sub-group utilizing the
computer intervention with staff support (CGI+). After 12 months, CGI+ reported the highest

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(6.97) change in BMI versus 6.57 (CGI) and 6.3 (workbook only), respectively. All groups
participated in cognitive-behavioral theory through their respective group interventions.
Research time was assessed at baseline, 6-months, and 12-months, while recording
possible outcomes of body mass index (BMI) changes based on motivation and SE regardless of
group assignment (CGI, CGI+, or workbook) assignment was theorized. Initial findings reported
intrinsic self-motivation was less of a determining factor for weight loss than the engagement in
the intervention with staff assistance. Setting intermittent goals, promotion of educational
health-based content, coupled with behavior management strategies and encouragement,
supported SE in 405 participants during a three-month study (Abdi et al., 2015). The author’s
suggested that a lifestyle intervention through two instruments, telephone or website interaction,
was beneficial for weight loss. Similar to Singer, Abdi et al. (2015) found technology,
specifically website participation, had a positive influence on weight loss belief and practices.
For older adults, SE was equated with promoting increased physical activity and the
perceived confidence to overcome social or environmental barriers (Kosteli, Cumming, &
Williams, 2018). Although Singer et al. (2017) and Abdi et al. (2015) used different applications
of technology to aid in their study, Kosteli et al.’s (2018) study sampled 50-80-year-old obese
men by using mental imagery as a guided tool to promote SE. Through planning and goal
setting, they identified imagery as visualizing oneself exercising and the benefits and sensations
thereof. New research results seem promising for this specific age group as their self-imagery
positively predicted the outcome of increases SE of physical activity.
Environment/Social Support
As Bandura defines it, “…personal factors and environmental events all operate as
interacting determinants” (p. 1175). The personal factor and environmental events in SCT were

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highlighted in Gorin et al.’s (2014) study using short-term partner support as a possible
intervention. Along with an environmental intervention setting, the partner assistance dynamic
was personal as both participants resided in the same household. Gorin et al. (2014) viewed the
personal factor not as a detriment, but the opposite. In their analysis, they theorized that a
partner with the same weight loss goal would encourage and facilitate weight loss in the other.
The specific environmental setting variable proposed in their research is heavily dependent on:
(1) a willing partner, (2) a partner willing to lose weight congruently, and (3) occupying the same
residence. If one were not to have access to an individual partner with these specifications, the
odds of weight loss maintenance and social support would decrease (Gorin et al., 2014).
Environmental setting and social support were also tested under specific ethnic groups.
Among 25 obese African American women, SCT was used as an intervention to increase
physical activity through five SCT constructs behavioral capability, outcome expectations, selfefficacy, self-regulation, and social support (Joseph et al., 2017). The women in the study
advocated the requirement for a strong social need in their personal physical activity choices.
Joseph et al.’s (2017) study were predicated outside of Gorin et al. (2014) partner assisted
success, and Dewar et al. (2013) parental support social network. The authors wanted to
culturally tailor physical activity to relatable behavioral modifications where the sample group
could succeed. The 25 women identified vital individuals (family, friends, and other program
participants) to encourage them for practical guidance for increased physical activity (Joseph et
al., 2017).

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Exercise Adherence
The motivation to exercise and to stay in long-term physical fitness was reviewed in a
variety of settings (Aamot, Karlsen, Dalen, & Støylen, 2016; Adler et al., 2017; Beauchamp et
al., 2018; Cadmus-Bertram et al., 2014; Heisz, Tejada, Paolucci, & Muir, 2016; Slovinec
D'Angelo, Pelletier, Reid, & Huta, 2014). Exercise adherence literature has extended traits that
SCT research has firmly established. EA has also defined gender lines as men prefer to exercise
alone, whereas women prefer group classes and interaction (Blackstone, Reeves, Lizzo, &
Graber, 2017). Regular EA can impact the success of all types of populations and health
concerns; recovering cancer patients (Kampshoff et al., 2014), coronary heart disease patients
(Janssen, Gucht, van Exel, & Maes, 2014; Slovinec D'Angelo, et al., 2014), emotional stability
(Jihoon et al., 2016), weight loss (Aparecida Rodrigues de Oliveira, et al., 2015), and postrecovery physical therapy patients (Eckard, Lopez, Kaus, & Aden, 2015).
Overall Theory of EA/Definition
EA is defined as an overall performance or an average amount of exercise participation in
a specific exercise program (Huang et al., 2014). Dougherty et al. (2016) defines exercise
adherence when studying cardiovascular patients as,
“performing 80% or more of the intervention as it was prescribed by phase/week:
frequency of days/week (per exercise), and intensity of exercise or percent time in the
THR zone” (p. 130).

Kampshoff et al. (2014) views EA as the amount and level of effort in accomplishing regularly
prescribed exercises frequently. Babbitt et al. (2014) defined EA as the levels that all individuals

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are attempting an exercise program comply with all intentions of completing with consistency
the prescribed exercises that correct sub-standard attitudes. The application of EA has currently
combined with other behavioral treatments such as SCT, and the Disconnected Values Model
(Brinthaupt and Anshel, 2018) to help ensure and increase adherence. Kampshoff et al. (2014)
identified that exercise history was a reliable indicator of EA. To understand EA one has to
look at what defines successful EA, the parameters of successful EA, and deterrents of
unsuccessful EA.
EA Prediction
Dougherty et al. (2016) explained four factors that predict exercise adherence from their
cardiovascular patient's study; (1) age, (2) higher peak of VO2, (3) lower ejection fraction, and
(4) not living alone. In the same study, they also report reasons for non-compliance; (1)
scheduling and physical complaints, (2) viral illness, (3) fatigue, (4) travel, and (5) physical pain.
Although Dougherty et al. (2016) used cardiovascular predictors for EA; other sample groups
created prediction factors from their respective studies. Identification of more predictors for EA
came from research that focused on the following factors of physiological, psychological and
demographics of an individual (Cadmus-Bertram et al., 2014). The EA measures used were;
minutes per week, MET-hours per week, and changes in VO2 max over a 12-month intervention.
The exercise prescription was six times a week of sixty minutes of moderate to vigorous
exercise. Adherence assessments conducted with a combination of at-home and facility logs
were collected for weekly review, quarterly interviews, and the use of tracking equipment
(pedometers and treadmills). The authors reported that their peak time for adherence was
between the 4 to 6-month intervals.

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The theory of EA is to look at individuals and their experiences of autonomy,
competence, and relatedness was linked to various efforts of self-determination for individuals to
engage in physical activities (Slovinec D'Angelo et al., 2014). This engagement is predicated on
the level of motivation that is applied based on such experiences. Depending on the degree of
experience it may foster deterrents to self-determination in applying oneself to participate in the
exercise. The author’s note to bring about behavioral changes in individuals the value of the
behavior along with a connection and experience to the desired behavior may bring about change
(Slovinec D'Angelo et al., 2014).
A study of heart disease participants looked at EA regarding the model of exercise
behavior while incorporating motivational orientations and self-efficacy towards positive
behavior outcomes (Cadmus-Bertram et al., 2014). Further, out of the 100 participants (51 men
and 49 women), the women’s most reliable indicator of the adherence assessments was their
BMI measurement. For those women categorized as obese, EA was lower versus those women
who were of normal size. The intervention included increasing the intensity of their prescribed
exercise as the study progressed. EA became peak exercise adherence the more prolonged the
study continued especially during the 4-6 month period (Cadmus-Bertram et al., 2014).
With Doughtery et al. (2016) and (Slovinec D'Angelo et al., 2014) providing EA theory
and predictors, Babbitt et al. (2017) identified EA parameters within the female African
American community specifically those that needed cardiovascular treatment. The author
understood the supportive effects of aerobic exercise were associated with positive vascular
health markers. However, they wanted to understand the importance of EA in the clinical
outcomes of vascular health in normal female African-American adults. EA is measured in 3
ways; (1) exercise percentage, (2) exercise volume, and (3) exercise core. Babbitt et al. (2017)

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further identified influences that increased exercise adherence ranging from demographic,
psychological, (individual), social support and accessibility (environment), or the behavior
(intensity, duration, perceived effort) itself. The authors’ believe that EA is measured in a direct
fashion of performing the exercise or not, adherence should be based on a simple yes or no
answer of completion.
Relationship to Self-Efficacy/Self-Determination
With the parameters of what constitute EA measurements in relationship to SCT as a
behavior modification, EA joined with self-efficacy, self-regulation, and environment to increase
adherence. Slovinec D'Angelo et al. (2014) looked at the model of exercise behavior while
incorporating motivational orientations and self-efficacy in exercise behavior. They aimed to
measure the level of success for both short-term and long-term exercise adherence. The author’s
found that previous confirmation of autonomous motivation enhances exercise maintenance by
changing past maladaptive behaviors. However, only autonomous motivation was substantial in
predicting long-term EA success (12-months) (Slovinec D'Angelo et al., 2014). Because of the
type of basic needs experienced autonomy, competence, and relatedness was linked to different
efforts of self-determination for individuals to engage in activities. This engagement is
predicated on the level of motivation that is applied based on such experiences. Dependent on
the degree of experience it may foster deterrents to self-determination in applying oneself to
participate in the exercise. The author’s note that to bring about behavioral changes the value of
the behavior along with a connection and experience to the desired behavior may bring about
change (Slovinec D'Angelo et al., 2014).

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By using the self-regulation foundation of the SCT model to understand and promote
exercise, Jihoon et al. (2016) provided existing research using both intrinsic motivation and
positive emotion as an influence in regular exercise participation. The authors believe within
418 student’s control of both intrinsic motivation, and positive emotion can elicit a response of
adherence and exercise participation increases their self-regulation ability. They believe an
essential connection between intrinsic motivations and positive emotion can create long-term
EA. Measuring intrinsic motivation, was the 34-item Korean Sport Participation Motivation
Scale was used. The groups that self-reported their exercise participation displayed a higher
ability to self-regulate their activities, intrinsic motivation, with positive emotion (Jihoon et al.,
2016).
Adding to positive emotions to increase EA, positive feedback was utilized for Janssen et
al. (2014) for research involving long-term monitoring of health. This is associated with positive
feedback of the performance of the goal related achievements in the prescribed exercise program.
A self-monitoring program was recommended by Janssen et al. (2014) that engaged individuals
to exercise with integrated reminders such as pedometers. The sample group identified with
powerful reminders like a pedometer to help track and log exercises and adjust exercise
behaviors to assist in their cardiac rehabilitation program (Janssen et al., 2014).
EA Team Effort Social Help
Among the several BM treatments used to increase positive outcomes of EA, there is
evidence of further success with a positive environmental, social setting (Beauchamp et al.,
2018; Blackstone, Reeves, Lizzo, & Graber, 2017; Hinman, Delany, Campbell, Gale, and
Bennell, 2015). In a professional team setting EA may be increased by employing several

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members with different skill sets to create the desired outcome (Hinman, Delany, Campbell,
Gale, and Bennell, 2015). The primary concern that physical therapists have is patient’s noncompliance with prescribed exercise that will aid in their post-operation recovery. EA can be
trying in a physical therapy setting; there is some evidence to suggest that exercise adherence is
more than just a list of exercise to do and left to the individual to perform. Hinman et al. (2015)
looked at integrating semi-structured interviews to help increase EA in patients suffering from
knee pain.
They aimed to incorporate telephone coaching in conjunction with the physical therapy
they received. The telephone coaching was to be divided between four coaches responsible for
six patients with severe knee pain. The literature provided emergent themes were prevalent in a
physical therapy setting to increase EA. Hinman et al. (2015) reported four emergent themes
with this sample group. There was (1) a general interest and collaboration in the integrated
therapy, (2) information and accountability, (3) program structure, and (4) roles and
communication in teamwork. It is during this time that the patients experienced teamwork as the
dominant theme for EA in treating knee pain symptoms. Integrating with physical therapists and
the telephone health coaches were concluded to be helpful and positive in reaching out to this
sample group. The authors promote a collaborative effort from an outside source independent of
just physical therapists alone. Eckard, Lopez, Kaus, and Aden, (2015) also looked at EA and
physical therapy adherence by complying with a home exercise program. Although the
participants were physical therapy patients, exercise adherence can still be measured on a 12point scale of compliance. The authors found the fewer the exercises performed, in this case,
two prescribed exercises per week, the patients were more compliant. When two or more
exercises were performed, the likelihood of compliance was less and the recommendation that

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short supervised bursts were more successful at complying than the 4 or more exercises per
week.
Blackstone, Reeves, Lizzo, & Graber (2017) wanted to understand the motivation for
engaging in group fitness. The authors found four emerging themes among the motivation of 21
females; (1) external motivation, (2) introjected motivations, (3) identified motivations, (4)
integrated motivations. Social support was found to be positively associated with increased
participation in the group activities. The authors also framed their study around selfdetermination to address the motivations to participate in group fitness. Under SDT behaviors
can be accounted for in the beginning, during, and after the action. Blackstone et al. (2017)
identified that a more common reason for the dissolution of exercise adherence and participation
is boredom. However, positive team support from those within the social group in exercise
activities has shown to be encouraging predictors of long-term adherence.
The themes that were reinforced were social support/peers and physical impairments
(external motivation), need of exercise participation (introjected motivation), improvement in
overall physical and mental well-being (integrated motivation), and visual improvements through
goal achievement and self-efficacy (identified motivations). Finally, Blackstone et al. (2017)
understand the importance of exercise adherence, especially when targeting the desires of the
individuals seeking more than their intrinsic motivation. They found those individuals seeking a
link between the motivation of sedentary people and a commitment to EA themes in attitudes,
motivation, and social media support emerged.
Capitalizing on the motivational themes identified from Blackstone et al. (2017), Barnes,
Yong-Chae, and Tallent (2016) studied a three-pronged relationship between exercise adherence,
motivations of physical activity, and social media support. The authors found five emerging

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themes that support an individual’s positive attitudes towards exercise. The identified themes
were connected to both intrinsic and extrinsic motivations. The themes discovered were: (1)
accountability matters, (2) social support for beginning individuals, (3) recognition of efforts, (4)
intergroup social fitness competition, and (5) the importance of creating a full fitness lifestyle.
To motivate individuals to exercise is more than merely referring to an exercise book or video
(Barnes, Rhee, and Tallent, 2016). The prominent theme of one social media community was
evidenced through connected social devices among the participants. This created digital
accountability to show up and perform the chosen exercises. This accountability holds
individuals to what the group wants to accomplish with all members invested in the exercise
success.
Coupled with the emerging themes of group motivation and EA (Blackstone, Reeves,
Lizzo, & Graber (2017) and motivation and social support (Barnes, Rhee, and Tallent, 2016),
Beauchamp et al. (2018) looked at social media traits for increases in EA. With older adults as an
understudied group, the authors wanted to investigate the efficacy of two group-based exercise
programs one group was responsible was based in self-categorization and the other as a standard
group-based exercise program. The authors found that in a group setting that promoted a shared
social atmosphere exercise adherence was more prevalent in those participants that were closer
in age and adherence. These social settings such as coffee after groups exercise sessions, group
t-shirts, influenced the attendance of the group exercise sessions for the self-categorization
group. Identification of traits that are familiar to each member of the group created the social
setting needed to adhere to the group in and out of exercise participation.
EA has evolved from a right social setting to direct messaging to produce more adherence
compliance. Bruijn, Out, and Rhodes (2014) wanted to test the effects of positive message

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framing via a kernel state. The study aimed to measure the effect of framed messages on
exercise intention and resolve. There was a random allocation of one of four messages of either
or adverse outcomes from this type of frame. The type of kernel is attained or avoided outcomes
based on such messaging. This type of message was given to the participants, and their response
was based on their intention to participate in exercise activity and their resolve to engage in
proper exercise. The measured variable was the intention to exercise and resolve to exercise.
For intention, the effects were not supported by the in a positive outcome. Resolved seemed to
promote better exercise adherence regarding the interaction between framing, kernel state, and
exercise adherence.
The relationship between message framing and exercise intentions can increase the
intention to be physically active. Message framing under this setting has been used to predict
behavioral outcomes by using particular messages that; (1) creating an attained outcome message
(2) creating an avoided outcome, (3) creating a loss-framed message with an attained outcome,
and (4) creating a loss-framed message with an avoided outcome. However, the key to
encouraging exercise participation in turn increased exercise adherence may be in the
“experience of variety” as reported in their study of 121 inactive university students (Sylvester et
al., 2015). They aimed to focus on identifying the effect of both variety support and experience
in the context of exercise. To influence exercise behavior, the authors introduced the concept of
“provision of variety.” The provision of variety intervention strategy focused on behavior
relating to specific experiences through an assortment of minimal or known tasks, actions, and
opportunities (Sylvester et al., 2015).
The provision of variety support is defined as,
“the manner in which activities, behaviors, and opportunities are structured to facilitate
(or thwart) the experience of variety, whereas the experience of variety refers to the extent

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to which a person feels as though they experience an assortment of tasks, actions, and
opportunities” (p.214).
The authors concluded that between a high and low variety of prescribed exercises high variety
played a role in higher exercise adherence than the low variety group. The measurement of
exercise behavior was measure through percentages of the recorded participation over a 6-week
period.
Deterrents of EA
There is difficulty starting an exercise regimen and even more so in maintaining one.
Ammot et al., (2015) note this difficulty in EA after the initial 12-week period for most of the
current literature available. What makes the Ammot et al., (2015) study unique is it takes a look
at incorporating a specific type of cardio exercise as opposed to a random set of prescribed
exercises. It may be inferred that a specific type of exercise that is related to an identifiable trait
such as cardiovascular patients and high impact cardiovascular training creates a foundation of
longer-term exercise adherence. In this particular study, the aim was to assess long-term
adherence following a high-intensity cardiac rehabilitation program. Of the two groups tested,
one home-based the other hospital-based both used the high intense exercise; the home-based
group did show an increase in of more physical activity compared to the hospital-based group.
However, both groups showed improved performance for long-term EA as baseline values of the
peak oxygen uptake increased substantially from baseline values after the one-year follow-up
assessment.
The authors note that rehabilitation after cardiac trauma is challenging and not as
successful as medical administrators would like it to be. Further, reports have indicated that
increased vigorous exercise hurt EA. According to Ammot et al., (2015) EA is optimal when in a

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hospital or controlled setting like a physical therapy clinic. Coupled with high-intensity exercise
specifically related to the rehabilitation trauma will increase adherence because of the high-level
exercise output.
Improvement in exercise adherence must first investigate detriments why adherence is a
struggle to maintain. Although the evidence or critical social network and group support
adherence to exercise, other than self-efficacy and self-determination, some challenges lie ahead.
Kampshoff et al. (2014) utilize the term “modifiable detriments” in identifying the noncompliance in exercise adherence within cancer patient survivors. Through their study, the
authors recommend a socio-ecological approach to improve exercise adherence. Improvement of
EA was used as a tool in creating long-lasting health effects in the cancer survivors.
Further, the authors examined the existing literature and came up with five categories in
accordance with the ecological model of health behavior. They are (1) demographical and
clinical, (2) psychological, (3) physical, (4) social, and (5) environmental. Although the target
group was surviving cancer patients the effectiveness of exercise interventions to promote
exercise adherence was based on the timing of the interventions before, during, and after cancer
treatment. Whereas timing may deter or increase ones EA there are other causes for noncompliance.
Foright et al. (2018) found that non-compliance may have roots in the environmental,
psychological, and biological facets of an individual’s life. A critical question that this paper is
also trying to answer the author’s recognize by asking if there is a technique or program that can
increase the reward perception of the total benefits of exercise and its overall adherence in one’s
healthy lifestyle? When investigating beyond Foright et al.’s (2018) external/internal causes for
non-compliance, Faries and Lutz (2016) chose to test self-selected intensity and adherence in

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campus-wide recreation weight lifting setting with novice female lifters. The authors found that
self-selected intensity is not related to exercise adherence. In fact, in over 18 scheduled weight
training sessions, the average attendance of fifty-three participants during the intervention was
seven. What the authors found that even weightlifters who have a minimal motivation to
exercise did not increase adherence when left to choose their intensity levels and amount of
participation. Intensity can be investigated oppositely by using the prescribed exercise itself as
the intensity variable instead of the individual.
Women who face diseases such as chronic illness like cardiorespiratory or cardiovascular
have to incorporate exercise to improve their current health conditions. However, there are those
individuals that need exercise to keep their health between life and death. Huang et al., (2014)
investigated female patients that were undergoing cancer treatment. The authors implemented a
12-week home-based cardiovascular program with progressive states of intensity and fitness
levels the three stages were initial phase, improvement phase, and maintenance phase. The
exercise frequency was three times a week, and the participants were informed to record their
exercise values in an exercise diary. The authors looked at the exercise adherence in two formats
time and intensity. They found that the highest adherence was in week 3 of the program and the
lowest in week 11. Aerobic exercise had a mean weekly total of 185.91 minutes with the best
times in week five and nine at 206.58 and 207.12 minutes. The authors also acknowledge that
the participants exercise adherence was predicated on their attitudes towards exercise. Although
in this study exercised adherence was applied to cancer patients, and the decline of participation
reported it might be attributed to the fatigue effects of cancer itself as opposed to the
participation of the exercise intervention.

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Brinthaupt and Anshel (2018) utilized the Disconnected Values Model (DVM) which is a
values-based cognitive-behavioral intervention exchanging healthy and sustainable routines for
the current unhealthy ones. The model is designed to help individuals identify their unhealthy
behaviors that go against their perceptions and values of what is healthy and identify the
discrepancy in creating new health choices with a program coach and viable action plan. The
level of and intensity of unhealthy habits played a role in how adherence was accepted as a parttime activity or full-time commitment. The authors report that the more unhealthy participants
may need more structuring that the study allowed in turn gave data that represented lower
adherence acceptance. Although Brinthaupt and Anshel (2018) studied EA based on the
Disconnected Values Model (DVM), the views on exercise adherence were collectively positive.
The full-time university employees showed greater fitness scores encouraging weight loss for the
majority of exercise program one behavior at a time. The suggestion is to add multiple
modifications of behavior change urging individuals to participate more, rather than be overcome
by past failures to commit. Another reason failure to adhere to exercise is prevalent is the lack of
individual control for other factors that were designed within the exercise regimen. Factors
include access to personal training, the dynamics involved between fitness coaches and their
clients, current fitness level, development of behaviors that support a new exercise lifestyle
routine. Other possible factors include the very nature of the exercise and its varied comfort
level from individual to individual; simply put it may not be physically enjoyable for some
people. Finally, there may be lifestyle factors and perspectives that hinder exercise adherence.
An individual’s physical comfort, negative attitudes towards exercise, past inexperience and
failure of adherence, and daily schedules have all been mentioned as factors of the lack of

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adherence to regular exercise (Brinthaupt and Anshel 2018). This development of selfregulatory skills has been reported to increase the chances of adherence.
Could exercise adherence be more prevalent if linked with other tools than the adherence
alone? Williams et al. (2016) looked at low activity in this case physical activity that was less
than 60 minutes a week resulted in modest outcomes in linking the three pathways investigated.
The three pathways of self-paced exercise, effective response, and exercise adherence were
tested to see if the relationships of adherence were independent of one another or work as a
collective group. Williams et al. (2016) do contend that the literature has mixed results
regarding exercise adherence if individuals were told to self-pace their workouts versus a
prescribed moderate intensity. Further, the author’s understands that the previous literature
concerning exercise adherence divided the evidence into two distinct lines, self-determination
theory encouraging the individuals to take exercising into their responsibility as opposed to an
outside authority pushing the moderate exercise is what needs to be incorporated for adherence
success. Second, exercise adherence can be successful if the individuals feel good and the
behavior can be repeated through associated past “good” feelings.
Effective Teacher Modeling
An under-reported group within the current literature is that of EA within female publicschool teachers. It has been regarded that a teacher is a role model that not only affects the
classroom but outside as well. Regarding EA, there is minimal research on its efficacy on female
public-school teachers. There is research that understands the benefits of healthy teachers in
performing their jobs while assessing their physical attributes. Hunt et al., (2017) understands
that today’s educators especially physical education teachers influence those children they come

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in contact with and their effect on their students can go beyond the academics of the subject
matter. To assess the physical capacity of teachers Healthy Fitness Zones were created from a
Fitness Gram 9 program from the state. The authors reported the current physical education
teacher role models for students to draw from did not demonstrate effective modeling especially
in a profession that is rooted in prime physical fitness. Accountability measures to keep public
school students graded and assessed and the current state of both male and female teachers in the
physical education department did not match the subject matter with everyday practice.
Not only are teachers known to be models in educating public school students in their
formative years, but they are one of the first role models in a young student’s life. As the
teachers see children physically grow throughout the year students are also seeing teachers grow
regarding increased obesity and unhealthy lifestyle choices. Rocha et al. (2015) examined the
prevalence of obese teachers and their impact on their profession and their possible influence on
their students. Teachers are just at unhealthy risk as the students they instruct especially if their
health becomes problematic within their occupation. A teacher’s BMI is one of the first
indications of wellness, health, and amount of physical activity (Aparecida Rodrigues de Oliveira
et al., 2015). The author’s looked at the typical sedentary behavior of public school teachers
within the nature of performing their professional daily duties. With more excess weight
individual physical activity will decrease the chances of risk factors associated with such weight
gain (Arigo et al., 2017). Anthropometric measures (BMI and waist circumference) were
utilized to gather data from the sample group of female public school teachers. The author’s note
their inability to move no more than the area of their respective classrooms during their work day
as a possible factor of the high percentage of teachers that were either “high” or “very high” in
waist circumference measurement.

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There is some evidence that work ability can be improved when involved in the highintensity physical activity. Although men are reported to be higher in exercise adherence than
female teachers the gap of fitness between the two genders is increasing (Grabara, Nawrocka, &
Powerska-Didkowska, 2018). The authors are focused on the work ability and its relationship
with physical activity among 171 teachers 129 of which are female teachers where consequently
women once again fell behind men in exercise adherence. However, the female teachers with
high work ability index had significantly higher levels of vigorous physical activity. The authors
reported that men were more positive in their outcomes of perceived health benefits, especially
in the categories of vigorous and moderate intensity physical activity than the women. For the
women, their work ability score and age was necessary with the female teachers. What makes
this important is that women fall behind in participation in physical activity not only in
comparison to men but their gender when comparing normal sized and obese women (Grabara,
Nawrocka, & Powerska-Didkowska, 2018). A possible combination of BM and increased
compliance of EA for future research as demonstrated by Gotink et al. (2017) in applying
mindfulness as behavioral intervention. Online mindfulness can be another avenue that female
teachers may see as a comfortable way to incorporate more exercise adherence. For those female
teachers that are experiencing high stress, high blood pressure, and mental uneasiness, on-line
mindfulness in the short-term has positive effects. This study focuses their attention on
Mindfulness-Based Stress Reduction (MBSR) which has been shown to have psychological
enhancements regarding depressive states, stress, and current life choices and practices. MBSR
was credited with increasing cardiovascular improvement, acceptance of negative thoughts, and
emotions. Accessibility of the MBSR online program could have yielded better exercise
adherence for the participants.

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Current Cohesive BM Treatment
While there are many BM treatment plans available, a case could be made to investigate
the causes of maladaptive behavior while promoting exercise participation and exercise
adherence. A possible BM treatment strategy that may have a combined effect to not only create
long-lasting effects of continued exercise adherence is mindfulness. This BM treatment has
origins in SCT more explicitly in the cognitive realm. Mindfulness has shown some success in
treating obese populations (Marszał-Wiśniewska and Jarczewska-Gerc, 2016) or adopting a selfaware eating approach (Kesten & Scherwitz, 2015). However, to have possible successful
compliance in all populations future research may need to address EA in female public school
teachers with mindfulness as an intervention.
Cohesive Treatment Strategies
Mindfulness. A recent trend to address the mindset of the obese person is to implement
mindfulness as a BM. Mindfulness is the focus and fostering of thought and its effects on the
body through awareness in the present action in a non-judgmental way (Loucks et al., 2016;
Mantzios and Wilson 2014; Ruffault et al., 2017). The obesity epidemic has gone outside of
standard western care as other treatments outside the realm of cognitive and physical may be
needed, possibly total immersion of internal and external well-being. As demonstrated in Kesten
& Scherwitz (2015) they adopted a whole-person integrative eating (WPIE) approach which
underlies four facets and six principles of self-care. The authors implement a mixture of
nutrition through biological, psychological, spiritual and social concepts. Under the four facets,
they emphasize eating fresh whole foods (biological), awareness of feelings before, during and

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after eating (psychological), awareness and appreciation connecting each meal (spiritual), and
uniting others through food (social).
They emphasize how individuals eating habits are just as important as what they eat.
Kesten & Scherwitz (2015) aim was to measure the degree of practice applying the WPIE. 5,256
participants completed an 80-item questionnaire ranging from frequency of overeating to
correlations of eating. The data presented found that excessive eating and weight gain are
congruent with a complex arrangement between eating dynamic and the external and internal
nutrition (biological, psychological, spiritual, and social). The whole person integrative is
unique as it goes beyond previous research of cognitive or biomechanical focused treatments.
Although not the norm, a different perspective to add to the treatment may provide insight into a
truly multi-disciplinary approach.
One mindfulness strategy that attempts to progress successful long-term weight loss is
Acceptance and Commitment Therapy (ACT). The characterization of ACT opposes the strict
nature of RI or the guilt of BED. ACT is quite the opposite; it embraces the understanding and
predicament of excessive food consumption associated with emotions thereby giving less value
over the impact of such behavior (Lillis, Thomas, Niemeier, and Wing 2017).
ACT was compared to the standard behavioral treatment (SBT) group with 162
overweight adults assigned randomly to either group. Although both groups produced changes
in weight, they were not significant enough to warrant a full treatment over one or the other. In
fact, during the treatment phase, the ACT applied group lost an average of 8.5kg (18.73 lb.)
against the SBT group of 9.3kg (20.5lb) with only a difference of .8kg (1.77 lb.) over the
treatment phase time (12 months). Lillis et al. (2017) further explain that there could be a
correlation to both changes in internal disinhibition and weight. Even though statistical

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measurements were not significant those participants that displayed a higher change in
disinhibition on average lost more weight.
Marszał-Wiśniewska and Jarczewska-Gerc (2016) also conducted an RCT using two
experiments to test the efficacy of several mental stimulations corresponding to effective weight
loss and the persistence of the maintenance process. The first study used 40 female participants,
who were then randomly assigned to one of four groups: positive outcome simulation, process
simulations, mixed simulations (w/negative outcome), and control (no simulations). The second
group consisted of 106 females in five simulations, this included the previous study along with
processes followed by a positive outcome, and adverse outcome simulations were constructed.
Mental simulations included: self-regulation of the goal striving process, application of the
Transtheoretical Model of Change, and Imagery in Goal Pursuit. In experiment 1 the hypothesis
was realized, and it supported that imagining the construct of activity (physical) intensifies the
efficacy of goal completion. Experiment 2 also confirmed the possible outcome of the study that
imagining the construct of weight loss activity (healthy choices/lifestyles) enhances effectiveness
and persistence of engagement of physical activity. SR techniques have been found to be
accessible and applicable for those individuals that want to lose weight. These mental
simulations can be used to prepare the individual for a weight loss lifestyle and in the future be
used with other methods to enhance weight loss success (Marszał-Wiśniewska and JarczewskaGerc, 2016).
Mantzios and Wilson (2014) also used mindful constructs but incorporated selfcompassion as a part of meditation practice. They took the mental stimulations of MarszałWiśniewska and Jarczewska-Gerc (2016) one step further to create concrete construals. A
construal is an individual’s perception of their environment regarding their behavior or action

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and how it affects them internally. These construals divided the inquiries of how they are eating
or why they are eating in the manner of excessiveness. The authors analyze mindfulness
meditation attached to a loving-kindness meditation to assist in weight loss. Further
investigating self-compassion may directly influence the efficiency of mindfulness. They argue
that self-compassion can regulate the degree of self-judgment thereby alleviating feelings of past
personal failures. Consequently, this sense of mindfulness is meant to foster a self-compassion
about their actions. Three areas of study were presented; 1) success of mindfulness in weight
loss using cognitive tools 2) effectiveness of mindful concrete construals in mindfulness and
self-compassion 3) effectiveness of mindful self-compassionate to assist with weight loss
(Mantzios and Wilson, 2014).
Ruffault et al. (2017) explain that mindfulness, when assessed in a physical capacity, is
more effective and the impact on the cognitive domain is not as significant. This meta-analytic
synthesis study wanted to advance the knowledge base by using the current literature of testing
mindfulness and its connection of cognitive and behavioral cues that influence health-related
activities and attitudes the data was measured by using meta-analytic techniques. RCT’s was
included if they examined any form of mindfulness training on weight loss, impulsive eating,
binge eating, or levels of physical activity on overweight individuals.
Collective descriptions of all studies included 626 participants within 12 trials. From the
initial sample group of 315 were randomized intervention, 311 were the control group. Mean
age was a range of 20-54-years of age and average baseline measurements of 26.1 to 40.3 kg
BMI. Intervention ranged from manual based mindfulness, adapted mindfulness-based stress
reduction (MBSR), dietary counseling, standard behavioral treatment, workshop, weekly group
sessions, yoga, homework, and phone support. Ruffault et al. (2017) found that mindfulness is

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useful if only for the short term and further study would be recommended to focus on the most
successful correlation of mindfulness and increases in physical activity for the obese population.
Raja‐Khan et al., (2017) also looked at MBSR in eighty-six women over an eight-week period
where the author’s found that MBSR does have a meaningful effect to reduce stress. They
concluded that the benefits of an MBSR treatment plan is positive and may play a role in longterm cardiometabolic benefits.
Conclusion
Various factors such as current physical ability (Heisz, Tejada, Paolucci, & Muir, 2016)
or post-surgical procedures (Dougherty et al., 2016) can increase or decrease EA in all
populations if not adequately motivated. It can be fair to say the challenges of maintaining EA
is common to all populations and various levels of wellness and fitness (Barnes et al., 2016). To
incorporate EA long-term the benefits outweigh non-compliance whether an individual suffers
from a debilitating disease (Janssen, De Gucht, van Exel, & Maes, 2014) or recovering cancer
patients (Kampshoff et al., 2014) the reported benefits of EA are vast and overtly positive (Adler
et al., 2017; Babbitt et al., 2017; Barnes, Yong-Chae, & Tallent, 2016; Eckard, Lopez, Kaus, &
Aden, 2015; Jihoon, Hyunsoo, & Sungho, 2016). The most reliable indicator that EA can have
for physical therapy out-patients, obese individuals, cardiac rehabilitation exercise programs, or
post-operation therapy is a social connection with the proper messaging format (Bruijn, Out, &
Rhodes, 2014).
However, for any changes to occur to the physical body, a thorough examination must be
made in understanding the role SCT will have in creating EA in all individuals (Faries & Lutz,
2016; Jihoon, Hyunsoo, & Sungho, 2016). This cognitive process through its various stages is
not easy to define (Frazier-Wood et al., 2014) nor is it easy to interpret data as to what is the best

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treatment approach (Lundin et al., 2013) for increase EA. While BM treatments are varied with
a wide range of results there could be based on self-acceptance (Lillis et al., 2017) or varied
types of mindfulness (Kesten & Scherwitz, 2015) is not reportedly different than other
behavioral treatments that focused on how excessive eating affected the visual stimulation of
food cues (Van der Lann and Smeets, 2014). Research on BM has touched upon possible
combination treatments that can be applied to all demographics, gender, or age groups. The
current gap in the literature is to test the efficacy of EA in female public-school teachers while
applying a BM, namely mindfulness.
What kind of treatment would be best to combat both the cognitive challenges of resisting
food poor food choices and minimize reoccurrence of those habits once weight loss has been
achieved? The answer may lie in a possible cohesive treatment of social networking of EA
(Barnes, Yong-Chae, & Tallent, 2016; Blackstone, Reeves, Lizzo, & Graber, 2017) and
mindfulness (Raja‐Khan et al., 2017; Ruffault et al., 2017). However, this combination that has
yet to be thoroughly researched and tested. Although, not easy to apply the best BM treatment to
encourage EA compliance behaviors if we cannot provide the proper identification of the causes
and effect of individual motivations (Barnes, Yong-Chae, & Tallent, 2016; Blackstone, Reeves,
Lizzo, & Graber, 2017).

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Appendix B
Problem Statement

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With the rise in childhood obesity, public-school teachers are one of the first and
potentially most frequent role models that students encounter. However, public-school teachers
face a workday filled with extensive commitments to their students, school, and administration
before, during, and after school. The daily routines of work stress, parent-teacher conferences,
and teaching protocols can have a deleterious toll on the physical body. These long hours and
fatigue may hinder teachers’ participation in exercise activity. The motivation to exercise and
maintain long-term physical fitness has been encouraging in a variety of populations including
public school teachers (Aparecida Rodrigues de Oliveira et al., 2015), post-operative
cardiovascular patients (Babbitt et al., 2017), and sedentary adults (Heisz, Tejada, Paolucci, &
Muir, 2016) in creating a positive outcome of exercise adherence (EA).
Being physically inactive or infrequently participating in exercise activity can lead to a
number of chronic diseases such as obesity (Bordignon, Aparício, Bertoletti, & Trentini, 2017).
The level of exercise and regular EA has shown to have a positive impact on all types of
populations with health concerns; recovering cancer patients (Kampshoff et al., 2014), coronary
heart disease patients (Janssen, Gucht, van Exel, & Maes, 2014; Slovinec D'Angelo, et al., 2014),
those with emotional instability (Jihoon et al., 2016), those seeking weight loss (Aparecida
Rodrigues de Oliveira, et al., 2015), and post-recovery physical therapy patients (Eckard, Lopez,
Kaus, & Aden, 2015). Thus, the more adherence one has with exercise the better the health
outcomes.
One method to increase EA is mindfulness training. Mindfulness is a behavior
modification that may increase consistent behavior of EA (Lillis, Thomas, Niemeier, & Wing,
2017) when applied in a group like female teachers who have daily responsibilities such as
family, work, and every day stresses. Mindful exercise treatment for exercise frequency success

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could also apply to those female teachers who experience high stress, high blood pressure, and
mental uneasiness. There is a need to identify and fit the best treatment plan to increase EA in
such an influential group. Some treatment options have included Mindfulness-Based Stress
Reduction (MBSR), Dialectical Behavioral Therapy (DBT), Acceptance and Commitment
Therapy (ACT), and Mindfulness-Based Cognitive Therapy (MBCT) (Ruffault et al., 2017).
However, there is no clear treatment using mindfulness for non-compliance of EA and no
research on mindfulness with EA on female public-school teachers. The proposed research
adopts a correlational design which will be used to measure the current level of mindfulness
female public-school teachers have in association with their current BMI and exercise levels
(EL).

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Appendix C
Additional Methodology

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Appendix C1
Request Letter for Solicitation for
Public-School Teachers’
Killeen Independent School District

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Dr. John M. Craft
Superintendent, Killeen Independent School District
Killeen Public School District (KISD) Central Office
200 N. WS Young Dr.
Killeen, TX. 76543

Dr. Craft,
I write this letter to seek your permission to recruit from Killeen Independent School
District (KISD), participants for a correlational research study being conducted for my doctoral
dissertation; Using Mindful Assessment when Comparing Exercise Levels Among Public-School
Teachers’ BMI. This research is conducted in partial fulfillment for the School of Graduate
Studies and Research of California University of Pennsylvania for the requirements for the
degree of Doctor of Health Science (DHSc) in Health Science and Exercise Leadership. My role
is the lead researcher in the project, and all data collection will be done by the research team, and
me only. Your participation and your employees will provide valuable data in the area of
community health, female wellness, and exercise behaviors. The research study and
questionnaire testing instruments are all administered online. All research information and
collected data is strictly confidential. We seek a sample size of 120 teachers to be recruited
through the KISD public-school website emails for mass delivery. Attached for your
convenience is the actual recruitment email that will be sent out via mass email delivery.
Finally, if you have any questions on the nature of the research design or specific concerns,
please do not hesitate to contact myself as the researcher, or my research dissertation chair.
I look forward to your support,

Clint F. Cepeda MS
Doctoral Candidate
cep9869@calu.edu
808-218-3485
Dr. Cheryl Rogow, Dissertation Research Chairperson
rogow@calu.edu

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Appendix C2
Cover Letter for Participation Recruitment

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Killeen Independent School District (KISD) Teachers,
We are looking for individuals who want to participate in a women’s exercise
participation research survey. This is a dissertation research project in partial fulfillment of the
California University of Pennsylvania (CALU), Doctorate of Health Sciences (DHSc), Doctoral
Program. The purpose of this research study is to understand the current level of mindfulness in
relationship to exercise levels and body mass index (BMI) in public-school teachers. This study
is being conducted by Clint F. Cepeda, Doctoral Candidate under the supervision of Dr. Cheryl
Rogow. We are particularly interested in your experience because of the nature of your
profession and its effect on your personal exercise levels. The expected start of the research
project will be 3rd week of April 2019. Please keep in mind your participation in this research
project is strictly voluntary, and there is no pressure or coercion on the part of the research team
for you to participate or complete the study.
Your inclusion in this research process is beneficial in collecting data that represents the
local area public-school teacher and their health and wellness. Data from the online
questionnaires and instruments are anonymous. The data collected for this research study will
help to associate health indicators of exercise levels and BMI. Please read this letter carefully,
and if you are interested in participating, please click the link below to participate. Your
participation is strictly voluntary, and you may withdraw at any time without penalty or change
of status in your employment.
Completion of participation is strictly an online survey only with no foreseeable risks to
participate in the demographic information sheet (DIS), Mindfulness Attention Awareness Scale
(MAAS), or the Godin Leisure-Time Exercise Questionnaire (GLTE) instruments. To be able to
participate in this research study you must be (1) current KISD public-school teacher, and (2) no
previous bariatric surgery. If at any time you feel the need to quit any online questionnaire
activity that is related to any or in part of the research project there is no obligation on your part
to continue in the project.
The research team reserves the right to terminate this study without prior notice, and all
collected data will be destroyed. There is no compensation for your participation in this study.
The information you provide will be kept strictly confidential. This form and other personal
information will be kept separate from the recorded data. Accessibility of the data is privy to
lead researcher (Clint F. Cepeda) and research chair (Dr. Cheryl Rogow) only.
You are to complete all three items online within the first week of the designated
timeframe; completion of all forms is estimated to take 20 minutes. Return receipt of this e-mail
notification will provide your understanding and consent to the research project. Implied consent
will be attributed to those who have completed the DIS online through Survey Monkey. For
those that are interested, please respond to the lead researcher study email provided below.
If you have any questions about this study in part or whole, please contact Clint F.
Cepeda (e) cep9869@calu.edu (p) 808-218-3485; Dr. Cheryl Rogow (e) rogow@calu.edu;
CALU IRB instreviewboard@calu.edu. The Institutional Review Board of California University
of Pennsylvania retains access to all signed informed consent forms.

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Approved by the California University of Pennsylvania Institutional Review Board. This
approval is effective nn/nn/nn and expires mm/mm/mm.

(Hyper Link to Survey Monkey)

Thank you,

Clint F. Cepeda MS
Doctoral Candidate, CALU
cep9869@calu.edu
808-218-3485

112

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Appendix C3
Demographic Information Sheet

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Demographic Information Sheet*
Please write in each blank below

Age___________

Height__________

Weight_________

Gender __________

Please circle one item for each question below
Marital Status

Married

Smoking Status

Ethnicity+

Years of employment

Divorced

Yes

W

No

Chi

A

2-5 years

Gym membership

AA

PI

Ko

Y

LA

J

O

10-15 years

K-5

<30min

Single/other

Sometimes

6-10 years

Current grade teaching

Amount of exercise/week

Common-law

6-8

30-90min

15 years+

9-12

90-120min

120min+

N

*Adapted from Beauchamp, M. R., Ruissen, G. R., Dunlop, W. L., Estabrooks, P. A., Harden, S. M., Wolf, S. A., ... Rhodes, R. E. (2018). Groupbased physical activity for older adults (GOAL) randomized controlled trial: Exercise adherence outcomes. Health Psychology, 37(5), 451-461.
http://doi:10.1037/hea0000615
+Ethnicity; (W) White, (Chi) Chinese, (A) Asian, (AA) African-American, (PI) Pacific Islander, (Ko) Korean, (LA) Latin American, (J) Japanese,
(O) Other

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Appendix C4
Mindfulness Awareness Attention Scale (MAAS)

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Monroe Campus
Department of
Psychology

White House
806 West Franklin Street
P.O. Box 842018
Richmond, Virginia 23284-2018
804 828-6754
Fax: 804 828-2237
TDD: 1-800-828-1120

Dear Colleague,
The trait Mindful Attention Awareness Scale (MAAS) is in the public domain and special
permission is not required to use it for research or clinical purposes. The trait MAAS has been
validated for use with college student and community adults (Brown & Ryan, 2003), and for
individuals with cancer (Carlson & Brown, 2005). A detailed description of the trait MAAS,
along with normative score information, is found below, as is the scale and its scoring. A
validated state version of the MAAS is also available in Brown and Ryan (2003) or upon request.
Feel free to e-mail me with any questions about the use or interpretation of the MAAS. I would
appreciate hearing about any clinical or research results you obtain using the scale.

Yours,

Kirk Warren Brown, PhD
Department of Psychology
Virginia Commonwealth University
806 West Franklin St.
Richmond, VA 23284-2018
e-mail kwbrown@vcu.edu

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Mindful Attention Awareness Scale (MAAS), trait version
Characteristics of the scale:
The trait MAAS is a 15-item scale designed to assess a core characteristic of mindfulness,
namely, a receptive state of mind in which attention, informed by a sensitive awareness of what
is occurring in the present, simply observes what is taking place. This is in contrast to the
conceptually driven mode of processing, in which events and experiences are filtered through
cognitive appraisals, evaluations, memories, beliefs, and other forms of cognitive manipulation.
Across many studies conducted since 2003, the trait MAAS has shown excellent psychometric
properties. Factor analyses with undergraduate, community and nationally sampled adult, and
adult cancer populations have confirmed a single factor scale structure (Brown & Ryan, 2003;
Carlson & Brown, 2005). Internal consistency levels (Cronbach’s alphas) generally range from
.80 to .90. The MAAS has demonstrated high test-retest reliability, discriminant and convergent
validity, known-groups validity, and criterion validity. Correlational, quasi-experimental, and
experimental studies have shown that the trait MAAS taps a unique quality of consciousness that
is related to, and predictive of, a variety of emotion regulation, behavior regulation,
interpersonal, and well-being phenomena. The measure takes 5 minutes or less to complete. A
validated, 5-item state version of the MAAS is also available in Brown and Ryan (2003) or upon
request.
MAAS norms to date:
Normative information on the trait MAAS is available for both community adults and college
students, as follows:
Community adults (4 independent samples): N = 436; MAAS M = 4.20, SD = .69.
College students (14 independent samples): N = 2277; MAAS M = 3.83, SD =.70.
Appropriate validity references for the trait MAAS:
Brown, K.W. & Ryan, R.M. (2003). The benefits of being present: Mindfulness and its
role in psychological well-being. Journal of Personality and Social Psychology,
84, 822-848.
Carlson, L.E. & Brown, K.W. (2005). Validation of the Mindful Attention Awareness
Scale in a cancer population. Journal of Psychosomatic Research, 58, 29-33.

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Day-to-Day Experiences

Instructions: Below is a collection of statements about your everyday experience. Using the
1-6 scale below, please indicate how frequently or infrequently you currently have each
experience. Please answer according to what really reflects your rather than what you
think your experience should be. Please treat each item separately from every other item.
1
Almost
Always

2
Very
Frequently

3
Somewhat
Frequently

4
Somewhat
Infrequently

5
Very
Infrequently

6
Almost
Never

I could be experiencing some emotion and not be conscious of
it until some time later.

1

2

3 4

5

6

I break or spill things because of carelessness, not paying
attention, or thinking of something else.

1

2

3 4

5

6

I find it difficult to stay focused on what’s happening in the
present.

1

2

3 4

5

6

I tend to walk quickly to get where I’m going without paying
attention to what I experience along the way.

1

2

3

5

6

4

I tend not to notice feelings of physical tension or discomfort
until they really grab my attention.

1

2

3 4

5

6

I forget a person’s name almost as soon as I’ve been told it
for the first time.

1

2

3 4

5

6

It seems I am “running on automatic,” without much awareness
of what I’m doing.

1

2

3 4

5

6

I rush through activities without being really attentive to them.

1

2

3

4

5

6

I get so focused on the goal I want to achieve that I lose touch
with what I’m doing right now to get there.

1

2

3 4

5

6

I do jobs or tasks automatically, without being aware of what
I'm doing.

1

2

3

4

5

6

I find myself listening to someone with one ear, doing
something else at the same time.

1

2

3

4

5

6

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1
Almost
Always

2
Very
Frequently

119

3
Somewhat
Frequently

4
Somewhat
Infrequently

5
Very
Infrequently

6
Almost
Never

I drive places on ‘automatic pilot’ and then wonder
why I went there.

1

2

3

4

5

6

I find myself preoccupied with the future or the past.

1

2

3 4

5

6

I find myself doing things without paying attention.

1

2

3

4

5

6

I snack without being aware that I’m eating.

1

2

3 4

5

6

MAAS Scoring
To score the scale, simply compute a mean (average) of the 15 items. Higher scores reflect
higher levels of dispositional mindfulness.

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Appendix C5
Godin Leisure-Time Exercise Questionnaire (GLTE)

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Godin Leisure-Time Exercise Questionnaire
INSTRUCTIONS
In this excerpt from the Godin Leisure-Time Exercise Questionnaire, the individual is asked to
complete a self-explanatory, brief four-item query of usual leisure-time exercise habits.
CALCULATIONS
For the first question, weekly frequencies of strenuous, moderate, and light activities are
multiplied by nine, five, and three, respectively. Total weekly leisure activity is calculated in
arbitrary units by summing the products of the separate components, as shown in the following
formula:
Weekly leisure activity score = (9 × Strenuous) + (5 × Moderate) + (3 × Light)
The second question is used to calculate the frequency of weekly leisure-time activities pursued
“long enough to work up a sweat“ (see questionnaire).
EXAMPLE
Strenuous = 3 times/wk
Moderate = 6 times/wk
Light = 14 times/wk

Total leisure activity score = (9 × 3) + (5 × 6) + (3 × 14) = 27 + 30 + 42 = 99

Godin, G., Shephard, R. J.. (1997) Godin Leisure-Time Exercise Questionnaire. Medicine
and Science in Sports and Exercise. 29 June Supplement:, S36-S38.

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Godin Leisure-Time Exercise Questionnaire

1. During a typical 7-Day period (a week), how many times on the average do you do the
following kinds of exercise for more than 15 minutes during your free time (write on each line
the appropriate number).
Times Per
Week
a) STRENUOUS EXERCISE
(HEART BEATS RAPIDLY)

__________

(e.g., running, jogging, hockey, football, soccer,
squash, basketball, cross country skiing, judo,
roller skating, vigorous swimming,
vigorous long distance bicycling)
b) MODERATE EXERCISE
(NOT EXHAUSTING)

__________

(e.g., fast walking, baseball, tennis, easy bicycling,
volleyball, badminton, easy swimming, alpine skiing,
popular and folk dancing)
c) MILD EXERCISE
(MINIMAL EFFORT)

__________

(e.g., yoga, archery, fishing from river bank, bowling,
horseshoes, golf, snow-mobiling, easy walking)
2. During a typical 7-Day period (a week), in your leisure time, how often do you engage in any
regular activity long enough to work up a sweat (heart beats rapidly)?
OFTEN
1. �

SOMETIMES
2. �

NEVER/RARELY
3. �

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Appendix C6
IRB Materials

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Proposal Number
Date Received

IRB Review Request
Institutional Review Board (IRB) approval is required before beginning any research and/or data
collection involving human subjects
Submit this form to instreviewboard@calu.edu or Campus Box #109

Project Title: Using Mindful Assessment when Comparing Exercise Levels Among Public-School Teachers’ BMI
Researcher/Project Director Clint F. Cepeda
Phone #

808-218-3485

E-mail Address cep9869@calu.edu

Faculty Sponsor (if researcher is a student)
Department

Dr. Cheryl Rogow

Exercise and Health Science

Anticipated Project Dates

January 2019

to

December 2019

Sponsoring Agent (if applicable)
Project to be Conducted at
Project Purpose:

Online

Thesis

Research

Class Project

Other

Keep a copy of this form for your records.
Required IRB Training
All researchers must complete an approved Human Participants Protection training course. The training requirement can
be satisfied by completing the CITI (Collaborative Institutional Training Initiative) online course at
http://www.citiprogram.org New users should affiliate with “California University of Pennsylvania” and select the “All
Researchers Applying for IRB Approval”course option. A copy of your certification of training must be attached to this IRB
Protocol. If you have completed the training within the past 3 years and have already provided documentation to the IRB,
please provide the following:

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Previous Project Title
Date of Previous Project IRB Approval

125

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Please attach a typed, detailed summary of your project AND complete items 2 through 6.
1. Provide an overview of your project-proposal describing what you plan to do and how you will go
about doing it. Include any hypothesis(ses)or research questions that might be involved and explain
how the information you gather will be analyzed. All items in the Review Request Checklist, (see
below) must be addressed.
2. Section 46.11 of the Federal Regulations state that research proposals involving human subjects must
satisfy certain requirements before the IRB can grant approval. You should describe in detail how
the following requirements will be satisfied. Be sure to address each area separately.
(text boxes will expand to fit responses)
a.
How will you insure that any risks to subjects are minimized? If there are potential risks,
describe what will be done to minimize these risks. If there are risks, describe why the risks to
participants are reasonable in relation to the anticipated benefits.
The inherent risk of online survey or questionnaire is minimal at best. There are no known
risks for participation in an online survey utilizing the proposed online instruments
demographic information sheet (DIS), Mindfulness Awareness Attention Scale (MAAS), Godin
Leisure-Time Exercise Questionnaire (GLTE).
b.
How will you insure that the selection of subjects is equitable? Take into account your
purpose(s). Be sure you address research problems involving vulnerable populations such as
children, prisoners, pregnant women, mentally disabled persons, and economically or
educationally disadvantaged persons. If this is an in-class project describe how you will
minimize the possibility that students will feel coerced.
There will be no exclusion for gender, age, current fitness levels, or fitness backgrounds. The
sample population proposed is 120 public-school teachers; with various educational
specialties and experiences. The proposed sample will be adults with various age ranges and
participation is a volunteer basis only is stated so in the informed consent and initial
recruitment email. There are no strenuous or physical requirements to participate in an online
survey to our knowledge.
c.
How will you obtain informed consent from each participant or the subject’s legally
authorized representative and ensure that all consent forms are appropriately documented? Be
sure to attach a copy of your consent form to the project summary.
The cover letter for participation recruitment will be delivered through the public-teachers’
emails emphasizing a KISD teachers’ exercise participation survey. See Appendix C2. The
letter informs interested teachers about the impact of their exercise information and
completion of surveys are beneficial to the research study. This is just a recruitment email
with a hyperlink to the data collection website Survey Monkey to be made available in the
letter if interested. The demographic information sheet (DIS) is a 10-item sheet collecting
basic data. The participant will be notified prior to accessing the DIS that completion and
return of the DIS will be a form of implied consent on the part of the individual. See Appendix
C3.
d.
Show that the research plan makes provisions to monitor the data collected to insure the
safety of all subjects. This includes the privacy of subjects’ responses and provisions for
maintaining the security and confidentiality of the data.

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All data collected will be online and returned to the lead researcher’s university email. This
email will collect the raw data to be stored and kept on the campus of California University of
Pennsylvania.

3. Check the appropriate box(es) that describe the subjects you plan to target.

Adult volunteers

Mentally Disabled People

CAL University Students

Economically Disadvantaged People

Other Students

Educationally Disadvantaged People

Prisoners

Fetuses or fetal material

Pregnant Women

Children Under 18

Physically Handicapped People

Neonates

4. Is remuneration involved in your project?

5. Is this project part of a grant?

Yes or

Yes or

No

No. If yes, Explain here.

If yes, provide the following information:

Title of the Grant Proposal
Name of the Funding Agency
Dates of the Project Period
6.

Does your project involve the debriefing of those who participated?

Yes or

No

If Yes, explain the debriefing process here.

7. If your project involves a questionnaire or interview, ensure that it meets the requirements indicated
in the Survey/Interview/Questionnaire checklist.

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California University of Pennsylvania Institutional Review Board
Survey/Interview/Questionnaire Consent Checklist (v021209)
This form MUST accompany all IRB review requests
Does your research involve ONLY a survey, interview or questionnaire?
YES—Complete this form
NO—You MUST complete the “Informed Consent Checklist”—skip the remainder of this form

Does your survey/interview/questionnaire cover letter or explanatory statement include:
[x] (1) Statement about the general nature of the survey and how the data will be used?
[x] (2) Statement as to who the primary researcher is, including name, phone, and email
address?
[x] (3) FOR ALL STUDENTS: Is the faculty advisor’s name and contact information
provided?
[x] (4) Statement that participation is voluntary?
[x] (5) Statement that participation may be discontinued at any time without penalty and all
data discarded?
[x] (6) Statement that the results are confidential?
[x] (7) Statement that results are anonymous?
[x] (8) Statement as to level of risk anticipated or that minimal risk is anticipated? (NOTE: If
more than minimal risk is anticipated, a full consent form is required—and the Informed
Consent Checklist must be completed)
[x] (9) Statement that returning the survey is an indication of consent to use the data?
[x] (10) Who to contact regarding the project and how to contact this person?
[x] (11) Statement as to where the results will be housed and how maintained? (unless
otherwise approved by the IRB, must be a secure location on University premises)
[x] (12) Is there text equivalent to: “Approved by the California University of Pennsylvania

Institutional Review Board. This approval is effective nn/nn/nn and expires mm/mm/mm”?
(the actual dates will be specified in the approval notice from the IRB)?
[x] (13) FOR ELECTRONIC/WEBSITE SURVEYS: Does the text of the cover letter or
explanatory statement appear before any data is requested from the participant?

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[x] (14) FOR ELECTONIC/WEBSITE SURVEYS: Can the participant discontinue
participation at any point in the process and all data is immediately discarded?

129

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130

California University of Pennsylvania Institutional Review Board
Informed Consent Checklist (v021209)
This form MUST accompany all IRB review requests
Does your research involve ONLY a survey, interview, or questionnaire?
YES—DO NOT complete this form. You MUST complete the “Survey/Interview/Questionnaire
Consent Checklist” instead.
NO—Complete the remainder of this form.
1. Introduction (check each)
[_] (1.1) Is there a statement that the study involves research?
[_] (1.2) Is there an explanation of the purpose of the research?
2. Is the participant. (check each)
[_] (2.1) Given an invitation to participate?
[_] (2.2) Told why he/she was selected.
[_] (2.3) Told the expected duration of the participation.
[_] (2.4) Informed that participation is voluntary?
[_] (2.5) Informed that all records are confidential?
[_] (2.6) Told that he/she may withdraw from the research at any time without penalty or loss
of benefits?
[_] (2.7) 18 years of age or older? (if not, see Section #9, Special Considerations below)
3. Procedures (check each).
[_] (3.1) Are the procedures identified and explained?
[_] (3.2) Are the procedures that are being investigated clearly identified?
[_] (3.3) Are treatment conditions identified?
4. Risks and discomforts. (check each)
[_] (4.1) Are foreseeable risks or discomforts identified?
[_] (4.2) Is the likelihood of any risks or discomforts identified?
[_] (4.3) Is there a description of the steps that will be taken to minimize any risks or
discomforts?
[_] (4.4) Is there an acknowledgement of potentially unforeseeable risks?
[_] (4.5) Is the participant informed about what treatment or follow up courses of action are
available should there be some physical, emotional, or psychological harm?
[_] (4.6) Is there a description of the benefits, if any, to the participant or to others that may
be reasonably expected from the research and an estimate of the likelihood of these benefits?
[_] (4.7) Is there a disclosure of any appropriate alternative procedures or courses of
treatment that might be advantageous to the participant?
5. Records and documentation. (check each)
[_] (5.1) Is there a statement describing how records will be kept confidential?
[_] (5.2) Is there a statement as to where the records will be kept and that this is a secure
location?

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131

[_] (5.3) Is there a statement as to who will have access to the records?
6. For research involving more than minimal risk (check each),
[_] (6.1) Is there an explanation and description of any compensation and other medical or
counseling treatments that are available if the participants are injured through participation?
[_] (6.2) Is there a statement where further information can be obtained regarding the
treatments?
[_] (6.3) Is there information regarding who to contact in the event of research-related injury?
7. Contacts.(check each)
[_] (7.1) Is the participant given a list of contacts for answers to questions about the research
and the participant’s rights?
[_] (7.2) Is the principal researcher identified with name and phone number and email
address?
[_] (7.3) FOR ALL STUDENTS: Is the faculty advisor’s name and contact information
provided?
8. General Considerations (check each)
[_] (8.1) Is there a statement indicating that the participant is making a decision whether or
not to participate, and that his/her signature indicates that he/she has decided to participate
having read and discussed the information in the informed consent?
[_] (8.2) Are all technical terms fully explained to the participant?
[_] (8.3) Is the informed consent written at a level that the participant can understand?
[_] (8.4) Is there text equivalent to: “Approved by the California University of Pennsylvania
Institutional Review Board. This approval is effective nn/nn/nn and expires mm/mm/mm”?
(the actual dates will be specified in the approval notice from the IRB)
9. Specific Considerations (check as appropriate)
[_] (9.1) If the participant is or may become pregnant is there a statement that the particular
treatment or procedure may involve risks, foreseeable or currently unforeseeable, to the
participant or to the embryo or fetus?
[_] (9.2) Is there a statement specifying the circumstances in which the participation may be
terminated by the investigator without the participant’s consent?
[_] (9.3) Are any costs to the participant clearly spelled out?
[_] (9.4) If the participant desires to withdraw from the research, are procedures for orderly
termination spelled out?
[_] (9.5) Is there a statement that the Principal Investigator will inform the participant or any
significant new findings developed during the research that may affect them and influence
their willingness to continue participation?
[_] (9.6) Is the participant is less than 18 years of age? If so, a parent or guardian must sign
the consent form and assent must be obtained from the child
[_] Is the consent form written in such a manner that it is clear that the parent/guardian is
giving permission for their child to participate?
[_] Is a child assent form being used?
[_] Does the assent form (if used) clearly indicate that the child can freely refuse to
participate or discontinue participation at any time without penalty or coercion?

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[_] (9.7) Are all consent and assent forms written at a level that the intended participant can
understand? (generally, 8th grade level for adults, age-appropriate for children)

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California University of Pennsylvania Institutional Review Board
Review Request Checklist

(v021209)

This form MUST accompany all IRB review requests.
Unless otherwise specified, ALL items must be present in your review request.
Have you:

[x] (1.0) FOR ALL STUDIES: Completed ALL items on the Review Request Form?
Pay particular attention to:
[x] (1.1) Names and email addresses of all investigators
[x] (1.1.1) FOR ALL STUDENTS: use only your CalU email address)
[x] (1.1.2) FOR ALL STUDENTS: Name and email address of your faculty
research advisor
[x] (1.2) Project dates (must be in the future—no studies will be approved which have
already begun or scheduled to begin before final IRB approval—NO EXCEPTIONS)
[x] (1.3) Answered completely and in detail, the questions in items 2a through 2d?
[x] 2a: NOTE: No studies can have zero risk, the lowest risk is “minimal
risk”. If more than minimal risk is involved you MUST:
[x] i. Delineate all anticipated risks in detail;
[x] ii. Explain in detail how these risks will be minimized;
[x] iii. Detail the procedures for dealing with adverse outcomes due to
these risks.
[x] iv. Cite peer reviewed references in support of your explanation.
[x] 2b. Complete all items.
[x] 2c. Describe informed consent procedures in detail.
[x] 2d. NOTE: to maintain security and confidentiality of data, all study
records must be housed in a secure (locked) location ON UNIVERSITY
PREMISES. The actual location (department, office, etc.) must be specified
in your explanation and be listed on any consent forms or cover letters.
[x] (1.4) Checked all appropriate boxes in Section 3? If participants under the age of
18 years are to be included (regardless of what the study involves) you MUST:
[_] (1.4.1) Obtain informed consent from the parent or guardian—consent
forms must be written so that it is clear that the parent/guardian is giving
permission for their child to participate.
[_] (1.4.2) Document how you will obtain assent from the child—This must
be done in an age-appropriate manner. Regardless of whether the
parent/guardian has given permission, a child is completely free to refuse to
participate, so the investigator must document how the child indicated
agreement to participate (“assent”).
[x] (1.5) Included all grant information in section 5?
[x] (1.6) Included ALL signatures?
[_] (2.0) FOR STUDIES INVOLVING MORE THAN JUST SURVEYS, INTERVIEWS,
OR QUESTIONNAIRES:
[_] (2.1) Attached a copy of all consent form(s)?
[_] (2.2) FOR STUDIES INVOLVING INDIVIDUALS LESS THAN 18 YEARS OF
AGE: attached a copy of all assent forms (if such a form is used)?

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134

[_] (2.3) Completed and attached a copy of the Consent Form Checklist? (as
appropriate—see that checklist for instructions)
[x] (3.0) FOR STUDIES INVOLVING ONLY SURVEYS, INTERVIEWS, OR
QUESTIONNAIRES:
[x] (3.1) Attached a copy of the cover letter/information sheet?
[x] (3.2) Completed and attached a copy of the Survey/Interview/Questionnaire
Consent Checklist? (see that checklist for instructions)
[x] (3.3) Attached a copy of the actual survey, interview, or questionnaire questions in
their final form?
[ ] (4.0) FOR ALL STUDENTS: Has your faculty research advisor:
[_] (4.1) Thoroughly reviewed and approved your study?
[ ] (4.2) Thoroughly reviewed and approved your IRB paperwork? including:
[ ] (4.2.1) Review request form,
[ ] (4.2.2) All consent forms, (if used)
[_] (4.2.3) All assent forms (if used)
[ ] (4.2.4) All Survey/Interview/Questionnaire cover letters (if used)
[x] (4.2.5) All checklists
[_] (4.3) IMPORTANT NOTE: Your advisor’s signature on the review request form
indicates that they have thoroughly reviewed your proposal and verified that it meets
all IRB and University requirements.
[x] (5.0) Have you retained a copy of all submitted documentation for your records?

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Project Director’s Certification

Program Involving HUMAN SUBJECTS
The proposed investigation involves the use of human subjects, and I am submitting the complete
application form and project description to the Institutional Review Board for Research Involving Human
Subjects.
I understand that Institutional Review Board (IRB) approval is required before beginning any research
and/or data collection involving human subjects. If the Board grants approval of this application, I agree
to:
1. Abide by any conditions or changes in the project required by the Board.
2. Report to the Board any change in the research plan that affects the method of using human
subjects before such change is instituted.
3. Report to the Board any problems that arise in connection with the use of human subjects.
4. Seek advice of the Board whenever I believe such advice is necessary or would be helpful.
5. Secure the informed, written consent of all human subjects participating in the project.
6. Cooperate with the Board in its effort to provide a continuing review after investigations have
been initiated.
I have reviewed the Federal and State regulations concerning the use of human subjects in research and
training programs and the guidelines. I agree to abide by the regulations and guidelines aforementioned
and will adhere to policies and procedures described in my application. I understand that changes to the
research must be approved by the IRB before they are implemented.

Professional (Faculty/Staff) Research
Project Director’s Signature

Student or Class Research
Clint F. Cepeda

Student Researcher’s Signature

Dr. Cheryl Rogow

Supervising Faculty Member’s Signature

ACTION OF REVIEW BOARD (IRB use only)
The Institutional Review Board for Research Involving Human Subjects has reviewed this application to ascertain
whether or not the proposed project:
1.
2.
3.
4.
5.

provides adequate safeguards of the rights and welfare of human subjects involved in the investigations;
uses appropriate methods to obtain informed, written consent;
indicates that the potential benefits of the investigation substantially outweigh the risk involved.
provides adequate debriefing of human participants.
provides adequate follow-up services to participants who may have incurred physical, mental, or emotional
harm.

Approved[_________________________________]

Disapproved

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___________________________________________
Chairperson, Institutional Review Board

136

_________________________
Date

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137

Section 1 Detailed Summary Dissertation
Using Mindful Assessment when Comparing
Exercise Levels Among Female Public-School Teachers’ BMI
Research Design
This study aims to associate level of mindfulness with body mass index (BMI)
and exercise levels (EL) in public-school teachers. The specific goals are to find a
correlation and degrees of association between three variables; current mindfulness, BMI,
and EL. In addition, to identifying correlations between the three variables (mindfulness,
EL, BMI), multiple degree and direction of association for correlations of mindfulness to
BMI and mindfulness to EL will look for degrees of association will also be calculated.
The following questions will be investigated:
1.
2.
3.
4.
5.

What is the degree of association between mindfulness, mindfulness and BMI,
and mindfulness and EL?
Is a public-school teachers’ level of mindfulness associated with their current
BMI?
What is the degree of association measuring mindfulness in the relationship
between BMI and EL?
Is there a positive or negative correlation between mindfulness and EL and
mindfulness and BMI?
Does the level of mindfulness predict the intensity level of EL?

A high current level of mindfulness is associated with higher intensity EL;
therefore, correlating with lower BMI values in public-school teachers is hypothesized.
The null hypothesis posits there will be no difference in EL or value of BMI scores
regardless of the level of mindfulness in public-school teachers. We want to indicate an
association between the variables of EL and BMI in relation to public-school teachers’
variable of mindfulness in a correlational study. The correlational design study is looking
for 120 public-school teachers. This voluntary study involves the administration and
completion of the following instruments; (1) demographic information sheet (DIS), (2)
the Mindfulness Attentional Awareness Scale (MAAS), and the (3) Godin-Leisure Time
Exercise Questionnaire (GLTE). Participants will complete all three instruments online
within the first week of the designated timeframe; completion of all forms is estimated to
take 20 minutes.
Subjects
This study aims for a sample size of 120 public-school teachers. The ideal
participant should be currently employed in the district, and a current Texas state certified
public-school teacher. Participants will be administered all three instruments as one
group, at one time from an online survey distributed through an email hyperlink
requesting their volunteer and anonymous participation.
Consent. A formal letter of permission will be given to the Killeen Independent
School District (KISD). This letter will reference the importance of the approved study

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and its significance to the community and employees of the school district (Appendix
C1). The formal letter of permission will include the general structure of the research
study (correlational design), nature of the email message (sample recruitment), data
collection (online), delivery (public-school email addresses) and a voluntary participation
statement (Appendix C1). During this recruitment process, a completed DIS will indicate
implied consent before full participation in completing the online versions of the MAAS
and GTLE instruments.
Recruitment. The recruitment for the aimed sample size of 120 participants, will
be delivered through the KISD public-school websites emails. A mass email
announcement of the proposed research will call for those interested in a KISD teachers’
exercise participation research survey (Appendix C2). The mass blast email will contain
information that subsequent surveys (DIS, MAAS, GLTE) will be made available
through the hyperlink to Survey Monkey in the email. During this recruitment process, a
completed DIS is requested for implied consent before full participation (Appendix C3).
The DIS will contain the following participant information; (1) age, (2) height, (3)
weight, (4) marital status, (5) smoking status, (6) ethnicity, (7) years of employment, (8)
current grade teaching, (9) amount of exercise per week, and (10) current gym
membership. The sheet will help the researchers in preparing needed data for the BMI
variable. A total of 120 fully completed DIS forms is the research aim.
Instruments
For this research design the following instruments will be used: (1) DIS a 10-item
instrument to collect the self-reported BMI values (Appendix C3), (2) the MAAS 15-item
instrument to measure current levels of mindfulness teachers may have (Appendix C4),
(3) The Godin Leisure-Time Exercise Questionnaire (GLTE) is a 4-item survey
indicating EL intensity categories (Appendix C5). All three instruments MAAS, GLTE,
and DIS, will help define measures of associations between the sample size and the three
variables. Consequently, we are looking to describe the degree of association between
the level of public-school teacher’s current mindfulness on EL and BMI.
Procedures
After the California University of Pennsylvania Institutional Review Board has
approved the proposed correlational design study, (Appendix C6) the following steps will
be performed to complete the research. Participants will be administered all three
instruments as one group, at one time from an online surveys distributed through an email
hyperlink requesting their volunteer, anonymous participation.
Consent. A formal letter of permission will be given to the Killeen Independent
School District (KISD) Central Office, 200 N. WS Young Dr., Killeen, TX. 76543. This
letter will reference the importance of the approved study and its significance to the
community and employees of the school district (Appendix C1). This letter of
permission will include the general structure of the research study (correlational design),
the sample size needed (120), nature of the email message (sample recruitment), data
collection (online), and delivery (public-school email addresses).
Recruitment. The recruitment for the aimed sample size of 120 participants, will
be delivered through the KISD public-school websites. A mass email announcement of
the proposed research will call for those interested in a KISD teachers’ exercise

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participation research survey (Appendix C2). The mass blast email will contain
information that subsequent surveys (DIS, MAAS, GLTE) will be delivered through their
public-school email for those interested. A follow-up mass email will be sent out to
encourage those that are still interested in participating in the KISD teachers’ exercise
participation research survey to respond by April 1, 2019. The information provided in
the recruitment mass email will include; (1) purpose of the study, (2) inclusion
requirements, (3) researcher contact information (Appendix C2). All potential candidates
interested in the research study can contact the lead researcher through the hyperlink
provided in the recruitment email (Appendix C2). The 120 responses will complete the
DIS (implied consent) for completion first. During this recruitment process, a completed
DIS will indicate implied consent before access to the subsequent instruments (MAAS,
GLTE) for full participation (Appendix C4/C5).
Testing. Once the proposed amount of 120 public-school teachers is achieved, a
week-long availability testing time will start. This time will allow collection of data
specifically for seven days as the GLTE is founded on the recollection of the last seven
days of self-reported physical activity (Appendix C5). Surveys will be completed
through the data collection website Survey Monkey. The researcher will hand score all
instruments (DIS, MAAS, and GLTE) once all surveys have been fully completed online.
All online surveys are only to be taken once, and all collected data will then be put into
spreadsheets for further analysis.
Data Analysis
The correlation data will be used to identify the direction and degree of
association between three sets of scores; mindfulness, mindfulness/ EL and
mindfulness/BMI. The degree of association is defined as the relationship between
variables or sets of scores. In this case mindfulness, EL, and BMI would be used to
identify the correlation coefficient of -1.00 to +1.00 with the value of 0.00 indicating no
linear relationship. Regarding the relationship between all variables, the values of 1.00 or
-1.00 can indicate consistency/inconsistency or predictability.
Data will be collected as one score for teachers and their level of mindfulness, one
score for BMI, and finally, one score for the GLTE. Data from the MASS questionnaire
is used to assess the presence of mind and individual awareness as a pre-test procedure.
Based on the Likert Scale data will be ordinal, single item scores between a range of 1
(almost always), 2 (very frequently), 3 (somewhat frequently), 4 (somewhat
infrequently), 5 (very infrequently) and 6 (almost never) and analyzed by the Statistical
Analysis System (SAS) software (Appendix C4).
There are three variables to be applied in this proposed study; they are
mindfulness, EL and BMI. The data does not look for an r coefficient instead it applies a
Spearman rho (rs) correlation coefficient. The variable is the level of mindfulness;
dependent variables are the MASS questionnaire (mindfulness), and GLTE (exercise
intensity level). The variables to be scored are independent of both the MAAS and
GLTE instruments.
For the purposes of the current research and based on the proposed hypothesis a
regression line would be best to fit all the points of scores on the graph. The regression
line would be the best multiple variable analysis (MVA) to apply because there is some
knowledge of a predictor (BMI). Public-school teachers BMI would help to create an

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association with either positive or negative linear plotted scores in the correlation matrix.
The data analysis would also consist of creating a regression table to show the overall
amount of variance between one variable (mindfulness) and all other variables
(mindfulness/EL; mindfulness/BMI).
For the numerical representation of both the degree and direction of association a
correlational value of 1.0 is desired, whereas, if a diminished correlation is plotted a -1.0
is calculated. Correlation values to look for are .66-.85 between variables for some form
of linear correlation. The coefficients in this value range are considered good. The
statistical software chosen will be the SAS University Edition to be diagrammed into the
correlational matrix, creating a scatter plot, while identifying linear or curvilinear
distribution as data permits. All statistics will be analyzed assuming the 0.05 level of
significance.

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Appendix C7
CITI Training Certificates

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