level of agreement between subjective and objective

Level of Agreement Between Subjective and Objective Measures of Youth Physical

Activity

by

Babetta B. Mathai, M.A.

A Dissertation

in

Clinical Psychology

Submitted to the Graduate Faculty

Of Texas Tech University in

Partial Fulfillment of

the Requirements for

the Degree of

DOCTOR OF PHILOSOPHY

Approved

Jason Van Allen, Ph.D.

Chair of Committee

Gregory Mumma, Ph.D.

Shin Ye Kim, Ph.D.

Steven Richards, Ph.D.

Mark Sheridan

Dean of the Graduate School

August 2021

Texas Tech University, Babetta B. Mathai, August 2021

ii

AKNOWLEDGEMENTS

It is hard to condense the immense gratitude I have for individuals who have

molded my career into a short acknowledgment, but I will do my best. Although many

people have contributed to my growth and have made this dissertation possible, there are

a few individuals and groups of people that I would especially like to acknowledge. First,

I would like to thank my dissertation committee. Each of you contributed great amounts

of time and effort to help me get through this milestone amid a global pandemic and other

extraordinary circumstances. Your personal and professional support and feedback were

very much appreciated.

I would also like to sincerely thank my graduate advisor, Dr. Jason Van Allen,

who has inspired me both personally and professionally. It is impossible for me to

articulate my appreciation for everything you have done to improve my future, but I can

truly say that without your encouragement and guidance, I would not have even half the

confidence I currently possess in my abilities as a researcher, clinician, advocate, and

professional in the field of pediatric psychology. Although I may not be able to live up to

your lyricism or culinary artistry, I hope to one day be able to inspire, comfort, and

mentor individuals in the field the same way you have done for me.

Next, I want to acknowledge my cohort. I could not have dreamed of a better

cohort with whom to endure graduate school. Thank you for keeping me grounded, for all

the laughs, and for always re-orienting me towards my values. All of you were

instrumental in making my graduate school experience what it was, and all our memories

together have a (warm) place in my heart.


iii

To all my other friends, loved ones (both alive and passed), and family, it is

because of your faith in me that I was able to persevere and get to this point in my career.

Thank you for always being understanding of my personal and professional goals and for

encouraging me to relentlessly pursue them. Also, thank you to those of you who sang

“good morning” songs first thing in the morning to get me energized for the day, told me

enjoyable animal facts or good corny jokes, played along with silly prompts, or anything

else you did whenever I needed to be uplifted. Lastly, I especially want to acknowledge

my main role models: my parents, Drs. Bhasilal and Valsa Mathai, and my sister

(“Chechy”), Dr. Benita Mathai. The way you modeled how to take things in stride, be

hardworking, and be humble is something I am incredibly grateful for and aim to

emulate. Thank you for all the sacrifices you made for me and Chechy, including moving

to the United States and trying to figure out how to start over, raise us, and appreciate our

biculturalism. I hope we have made the sacrifices worth it, and I hope to continue

showing you how much your sacrifices have meant to me through my work.


iv

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ............................................................................................. ii

ABSTRACT ........................................................................................................................v

LIST OF TABLES ........................................................................................................... vi

LIST OF FIGURES ........................................................................................................ vii

I. INTRODUCTION..................................................................................................1

Measuring Physical Activity in Youth .....................................................................2

Youth Symptoms, Physical Activity, and Discrepancies in

Reports of Physical Activity ........................................................................6

Comorbid Youth Symptoms ....................................................................................8

Purpose of Study ......................................................................................................9

Hypotheses .............................................................................................................10

II. METHOD .............................................................................................................12

Power Analysis ......................................................................................................12

Participants .............................................................................................................12

Procedure ...............................................................................................................14

Measures ................................................................................................................15

Data Analytic Plan .................................................................................................17

III. RESULTS .............................................................................................................20

Initial Analyses ......................................................................................................20

Preliminary Analyses .............................................................................................22

Main Analyses .......................................................................................................23

IV. DISCUSSION .......................................................................................................40

Main Findings ........................................................................................................40

Strengths and Limitations ......................................................................................44

Implications of the Current Study and Future Directions ......................................46

REFERENCES .................................................................................................................49

APPENDICES

A. Extended Literature Review .....................................................................................58


v

ABSTRACT

Although it is recommended that youth engage in daily physical activity

(consisting of at least 60 minutes of moderate to vigorous intensity) to help decrease the

likelihood of negative health outcomes, a majority of adolescents reportedly engage in

insufficient activity. However, there are some limitations related to measuring youth

physical activity, which could lead to inaccurate data in this area. Utilizing subjective

measures in tandem with objective measures has been recommended; therefore, it is

important to examine the level of agreement between subjective and objective measures

of youth physical activity. The current study aimed to examine the level of agreement

between these reporting methods and examine how youth depressive and anxiety

symptoms can impact agreement levels. A sample of 67 youths (8-12 years-old)

completed self-report questionnaires to assess depressive symptoms (i.e., CDI 2: SR[S]),

anxiety symptoms (i.e., SCAS), and physical activity (i.e., PAQ-C). They were also

instructed to wear an ActiGraph wGT3X-BT for at least 7 days. Bland-Altman analyses

were conducted, and hierarchical regression models were built. Results revealed weak

agreement between the PAQ-C and accelerometry data. Depressive and anxiety

symptoms were not found to be independent, significant predictors of the difference

between subjective and objective youth physical activity. However, interaction effects

between depressive symptoms and subtypes of youth anxiety symptoms were found to be

significant predictors of the difference scores between the methods of reporting.

Strengths, limitations, and implications of the study are discussed.


vi

LIST OF TABLES

1 Additional Information on Psychometric Properties of Measures .........................28

2 Information on Variables and Transformations .....................................................29

3 Bivariate Correlations ............................................................................................31

4 Hierarchical Regression Analyses Predicting PA Difference

While Controlling for Youth Anxiety Symptoms ..................................................32

5 Hierarchical Regression Analyses Predicting PA Difference

with Interaction Terms ...........................................................................................33


vii

LIST OF FIGURES

1 Bland-Altman Plot for Hypothesis 1......................................................................36

2 Interaction Plots between Total Anxiety Symptoms and Depressive

Symptoms on PA Difference .................................................................................37

3 Interaction Plots between Separation Anxiety Symptoms and Depressive


4 Interaction Plots between Physical Injury Fears Symptoms and Depressive



1

CHAPTER I

INTRODUCTION

The World Health Organization (WHO, 2018) defined physical activity as “any

bodily movement produced by skeletal muscles that requires energy expenditure” (What

is Physical Activity? section, para. 1). Further, it is recommended that children and

adolescents, aged 5-17 years old, engage in at least 60 minutes of moderate to vigorous-

intensity physical activity daily (Ekelund et al., 2011; WHO, 2018). Engaging in physical

activity has been associated with many positive health outcomes such as reduced risk of

hypertension and other chronic diseases (e.g., Strong et al., 2005, WHO, 2018). However,

according to the WHO, approximately 81% of adolescents reportedly engaged in

insufficient physical activity in 2010 (WHO, 2018), making it a public health concern.

Despite the reported percentage of insufficient activity in adolescents, the literature has

also revealed some limitations related to measuring physical activity in this population,

which could lead to inaccurate reporting of overall activity levels (e.g., Adamo et al.,

2009; Sallis & Saelens, 2000; Trost et al., 2002). For example, being able to recall

physical activity within the context of a self-report questionnaire may be difficult for

adolescents, as it is a task that involves complex cognitive abilities, which may lead to

inaccurate reporting (Chinapaw et al., 2010; Ekelund et al., 2011; Janz et al., 2008; Sallis

& Saelens, 2000). Therefore, in order to obtain more accurate and reliable information on

adolescent activity levels and decrease reporting bias, more research needs to be

conducted to identify appropriate ways of measuring youth physical activity as well as

determine whether or not there is agreement between current methods of measuring

physical activity.


2

Measuring Physical Activity in Youth

Physical activity can be measured subjectively (i.e., indirectly) and objectively

(i.e., directly). Although there are some strengths to each type of measurement, there are

also some limitations. These strengths and limitations to the measurement of physical

activity will be briefly summarized. Further, general discrepancies found between

subjective and objective measures of physical activity will be described.

Subjective Measures

Multiple subjective measures of physical activity exist for youth (e.g., Sallis &

Saelens, 2000). Some examples of subjective measures of youth physical activity include

questionnaires, interviews, or physical activity diaries. Information obtained through self-

report are most commonly used to assess physical activity levels in youth, as they are

easier to obtain and administer, are cost-effective, and involve low participant burden

(Crocker et al., 1997; Ekelund et al., 2011; Janz et al., 2008). On the other hand, there are

some limitations associated with utilizing subjective measures. In addition to the

aforementioned limitation of inaccurate recall of physical activity, another limitation is

that some items on self-report questionnaires may be inaccurately interpreted by children

and adolescents (Janz et al., 2008). For example, if an item on a questionnaire were to ask

how often the individual was “very active” during a certain period of the day, individuals

may have different opinions and interpretations of what “very active” may mean.

Therefore, the individual may underestimate his/her activity, whereas another individual

may overestimate his/her physical activity. Further, factors such as social desirability, the

potential inability of an individual to closely follow instructions for a physical activity

diary, and the inability of an individual to provide a detailed recall of their physical


3

activity (e.g., time spent, intensity) are limitations of subjective measures of physical

activity (Chinapaw et al., 2010; Crocker et al., 1997; Janz et al., 2008; Sallis & Saelens,

2000). Finally, as there are multiple subjective measures of physical activity in youth,

selecting an appropriate measure is difficult and leads to great variability in measurement

methodology, making comparisons across studies difficult (Adamo et al., 2009;

Chinapaw et al., 2010; Crocker et al., 1997).

Objective Measures

Examples of objective measurements of physical activity include accelerometers,

heart rate monitors, and pedometers. Generally speaking, some benefits of utilizing

objective measures include that they are able to provide more detailed information

regarding physical activity in youth and are less biased (Adamo et al., 2009; Reilly et al.,

2008). Additionally, studies that have utilized objective measures of physical activity

have found them to be accurately and consistently associated with health outcomes

(Adamo et al., 2009; Ekelund et al., 2011; Reilly et al., 2008). Though objective

measures have these strengths, limitations of objective measures include that they can be

time-consuming, expensive, and can increase the likelihood of participant burden

(Adamo et al., 2009). Further, objective measures such as accelerometers require some

interpretation of variables within the context of the activity (e.g., whether values would

fall into the moderate intensity or the vigorous intensity ranges). Therefore, results may

be dependent on the cut-off points that are applied by the researcher, and as there are no

well-established cut-off points for some of these variables, there is the potential for some

interpretation bias (Dishman et al., 2001; Ekelund et al., 2011; Welk et al., 2000). Finally,

objective measures such as accelerometers can underestimate some activities (e.g.,


4

swimming, cycling), and there is no “gold standard” among objective measures, as there

is no available objective measure that is able to capture all aspects of the

multidimensional nature of physical activity (e.g., intensity, mode of activity, frequency;

Adamo et al., 2009; Dishman et al., 2001; Hidding et al., 2018).

Comparing Subjective and Objective Physical Activity Measures in Youth

Although subjective and objective measures of physical activity exist and have

their own strengths and limitations, studies and reviews have found that subjective

measures typically result in an overestimation of physical activity levels when compared

to objective measures (Adamo et al., 2009; Hagströmer et al., 2010; Sallis & Saelens,

2000). However, some studies have found that whether or not a person overestimates or

underestimates their physical activity in a subjective measure may depend on their overall

physical activity (e.g., active, inactive; Ekelund et al., 1999). Specifically, the subjective

measure would underestimate individuals who were low in activity but overestimate

individuals who were high in activity (Ekelund et al., 1999). Despite this, subjective

measures remain a relevant and helpful tool to assess physical activity levels; therefore, it

is recommended that subjective measures be used as a complementary tool in conjunction

with objective measures (Helmerhorst et al., 2012).

Based on previous studies and systematic reviews, weak to moderate correlations

exist between subjective and objective measures of youth physical activity (Adamo et al.,

2009; Colley et al., 2012; Nascimento-Ferreira et al., 2018). Some exceptions to this

include when self-reported physical activity intensity (e.g., light, moderate, vigorous) was

taken into consideration. Specifically, Nascimento-Ferreira et al. (2018) found that

physical activity intensity had greater agreement with objective measures (i.e.,


5

accelerometers) when the individual reported engaging in moderate-to-vigorous physical

activity compared to other activity intensity levels. Additionally, one study found

differences in subjective versus objective reports of physical activity when variables such

as age, gender, education level, and weight status were taken into consideration

(Slootmaker et al., 2009). More specifically, Slootmaker et al. (2009) found greater

differences in subjective and objective reports among adolescent girls than boys,

adolescents with higher educational levels than lower education levels, and overweight

adults with regard to vigorous physical activity. Gender was identified as a significant

predictor of the differences in reporting for vigorous physical activity, and educational

level was a significant predictor of discrepancies in moderate physical activity

(Slootmaker et al., 2009).

As it is recommended that subjective and objective measures be used in tandem,

and as there have been some individual differences that have been identified as predictors

of discrepancies between measures, it is important to expand the literature on other

individual characteristics (e.g., symptom presentation) that may be predictors of

discrepancies between subjective and objective measures. Moreover, as correlational

analyses focus on the relationship between variables, more studies should utilize analyses

that evaluate the limits to agreement between measures instead of simply analyzing the

correlation between them (e.g., using Bland-Altman analyses; see Appendix A for more

details; Adamo et al., 2009; Giavarina, 2015). Considering this, one focus of the present

study was to contribute to the literature on the level of agreement between subjective and

objective measures of physical activity in youth.


6

Youth Symptoms, Physical Activity, and Discrepancies in Reports of Physical

Activity

Psychosocial factors, such as youth internalizing and externalizing symptoms, that

may be predictive of the discrepancy between subjective and objective youth physical

activity is one area of the youth physical activity literature that has yet to be examined.

Many youths experience internalizing (e.g., depressive and anxiety symptoms) and

externalizing (e.g., aggressive behaviors) symptoms; however, the literature has revealed

different trends in the presence of internalizing and externalizing symptoms in youth over

time (e.g., Costello et al., 2011). Costello et al. (2011) found that rates of some

internalizing symptoms and problems increased overtime, whereas rates of externalizing

symptoms and problems decreased over time. Therefore, the focus of this study was on

internalizing symptoms.

Previous research has revealed a relation between internalizing symptoms,

including depressive and anxiety symptoms, and health outcomes such that increased

internalizing symptoms were related to negative health outcomes (Jamnik & DiLalla,

2019). Further, Jamnik and DiLalla (2019) found that increased internalizing symptoms

were negatively associated with engagement in physical activity. Generally, depressive

and anxiety symptoms and disorders can lead to some changes in physical activity

(Gunnell et al., 2016; Helgadótti et al., 2015). Various studies and reviews have

highlighted how these symptoms and disorders can impact psychomotor functioning

(e.g., gross and fine motor activity) in adult populations (Burton et al., 2013; Helgadóttir

et al., 2015). In fact, studies have revealed that individuals with increased depressive

symptoms engaged in significantly less light and moderate physical activity compared to


7

those without depressive symptoms (Helgadóttir et al., 2015; Song et al., 2012).

Additionally, Helgadóttir et al. (2015) posited that despite there being limited research on

anxiety symptoms and disorders and physical activity, the symptom presentation of

anxiety (e.g., restlessness, psychomotor agitation) could point towards increased physical

activity in comparison to those without anxiety symptoms.

Within the context of youth populations, there is also some research regarding the

impact of physical activity on youth symptoms of depression and anxiety. Studies have

supported an inverse and bidirectional relationship between depressive symptoms and

physical activity in youth even after controlling for anxiety symptoms (Gunnell et al.,

2016; Jerstad et al., 2010). Further, increased anxiety symptoms in youth have been

found to be associated with lower physical activity even after controlling for depressive

symptoms; however, there was no support for a bidirectional relationship with physical

activity (i.e., lower physical activity leading to increased anxiety symptoms not

supported; Gunnell et al., 2016). Although some studies have examined the impact of

physical activity on youth symptoms, there is limited research on how youth symptoms of

depression and anxiety can impact youth physical activity levels and/or reporting of

youth physical activity.

As aforementioned, the literature has revealed some general differences between

subjective and objective reports of physical activity. Though these general discrepancies

exist, the literature has been limited in how symptoms and problems (e.g., depressive and

anxious symptoms) can influence subjective and objective reports of physical activity;

however, there has been some literature related to measurement discrepancy focusing on

associated health-related variables such as sleep (Forbes et al., 2008; Palermo et al.,


8

2007). For example, Forbes et al. (2008) found that youth with anxiety symptoms and

disorders underreported their sleep problems. Conversely, youth with depressive

symptoms and disorders did not have significant objective sleep disturbances despite

reporting experiencing sleep problems. In another study conducted by Palermo et al.

(2007), youth with depressive symptoms also reported experiencing sleep problems

despite objective measures noting little to no sleep disturbances. Further, Palermo et al.

(2007) found that symptoms of depression and worry were significant predictors of

subjective sleep quality such that as depressive symptoms and/or worries increased,

subjective sleep quality decreased, but objective sleep showed minimal sleep

disturbances. In general, Palermo et al. (2007) found that subjective sleep measures were

significantly associated with objective sleep measures; however, this relationship was not

significant when depressive symptoms and worries were taken into account. In other

words, the presence of depressive symptoms decreased the strength of the relationship

between subjective and objective measures of sleep, indicating more discrepancies

between subjective and objective measures. As reporting discrepancies have been found

in other health-related variables, and as a large percentage of youth report engaging in

insufficient physical activity (WHO, 2018), the literature would likely benefit from

understanding reporting discrepancies in youth physical activity while taking youth

symptoms into account.

Comorbid Youth Symptoms

Generally speaking, symptoms of depression and anxiety are highly prevalent and

comorbid in children and adolescents (Cummings et al., 2014; Merikangas et al., 2009).

Further, the presence of either depression or anxiety increases the likelihood of the


9

presence of the other (Garber & Weersing, 2010) and of additional anxiety subtypes (e.g.,

social phobia; Ohayon & Schatzberg, 2010). More specifically, studies have found that

up to 15% of anxious youth have comorbid depression, and up to 75% of depressed youth

have comorbid anxiety (Axelson & Birmaher, 2001; Cummings et al., 2014).

Additionally, anxiety and depression have many shared features, making them important

to consider independently and interactively in the literature (Cummings et al., 2014). As

the presence of youth depressive and anxiety symptoms increase the likelihood of health

risks and problematic health-related behaviors (Jamnik & DiLalla, 2019), it is important

to take comorbid depressive, anxiety, and subtypes of anxiety symptoms into account in

research studies in order to examine additive effects and allow for more specificity.

Purpose of the Current Study

Taken together, there is an apparent paucity in the literature with regard to youth

physical activity. More specifically, although there has been research conducted on

subjective and objective reports of other health-related outcomes such as sleep, there is

limited research on the level of agreement between subjective and objective reports of

youth physical activity. Even though some studies have attempted to compare subjective

and objective measures of sleep, the studies have focused more on correlations instead of

the limits to agreement between the different measures. Furthermore, little to no research

has examined the impact of youth symptoms of depression and anxiety on reports of

youth physical activity while controlling for comorbid symptoms. To address this gap in

the literature, the current study aimed to: 1) compare subjective reports of youth physical

activity to objective reports of youth physical activity, 2) determine whether or not youth

depressive and anxiety symptoms are uniquely and significantly related to the level of


10

agreement between subjective and objective measures of youth physical activity, and 3)

examine how the aforementioned youth symptoms impact reporting (i.e., underestimate,

overestimate) of youth physical activity. Demographic variables (e.g., child age) were

also taken into consideration when making comparisons between subjective and objective

reports, as some studies have found differences when these variables are taken into

account (e.g., Slootmaker et al., 2009; Trost et al., 2002).

Hypotheses

Hypothesis 1

Consistent with previous findings, subjective and objective reports of physical

activity were expected to have a weak to moderate level of agreement with each other.

Furthermore, it was specifically hypothesized that there would be biases between

subjective and objective reports such that subjective reports of physical activity would

overestimate youth physical activity.

Hypothesis 2a

Youth symptoms of depression were anticipated to be significantly related to the

difference values between subjective and objective measures of physical activity such

that individuals who reported higher levels of depressive symptoms would have larger

differences between their subjective and objective measures of physical activity.

Hypothesis 2b

Similar to results found regarding other health-related outcomes, higher levels of

depressive symptoms were expected to be associated with an overestimation of physical

activity on subjective measures.


11

Hypothesis 3a

Youth symptoms of anxiety were hypothesized to be significantly related to the

difference values between subjective and objective measures of physical activity such

that individuals who reported higher levels of anxiety symptoms would have larger

differences between their subjective and objective measures of physical activity. Further,

it was anticipated that significant interactions would exist between youth symptoms of

anxiety and youth depressive symptoms on the difference values.

Hypothesis 3b

Similar to results found regarding other health-related outcomes, higher levels of

anxiety symptoms were hypothesized to be associated with an underestimation of

physical activity on subjective measures.


12

CHAPTER II

METHOD

Power Analysis

A statistical power analysis was conducted to determine the sample size needed

for this study using G*Power software (Faul et al., 2007). For Hypothesis 1, Bland-

Altman analyses would be conducted, and for Hypotheses 2a-b and 3a-b, hierarchical

regression analyses would be utilized. As Bland-Altman analyses include a linear

multiple regression model to determine whether or not the level of agreement between

measures is consistent across means, similar power analyses were conducted for all

hypotheses. A linear multiple regression: fixed model power analysis, R2 deviation from

zero (Faul et al., 2009) was conducted for Hypothesis 1 and a linear multiple regression:

fixed model power analysis, R2 increase was conducted for Hypotheses 2a-b and 3a-b

with four total predictors (medium effect size, f 2= .15; alpha level = .05; power = .80; as

supported by Cohen, 1992 and Cohen et al., 2003). This indicated that a minimum sample

of 55 participants was needed for the analyses in this study. Further, as other studies

utilizing Bland-Altman analyses have revealed stable agreement between subjective and

objective measures of physical activity in sample sizes ranging from 50 to 99

participants, the sample size of 55 participants was deemed sufficient for this study

(Benítez-Porres et al., 2016; Nascimento-Ferreira et al., 2018).

Participants

A community sample of 74 children and adolescents between 8-12 years of age

was obtained. Individuals were recruited from numerous community events, such as

summer camps, library events, and some seasonal events. Inclusion criteria for this


13

sample consisted of the following: a) children must be between 8-12 years-old; b) the

participants must be able to understand, read, and write in English; c) participants must

be willing to meet with a research assistant at two timepoints (i.e., initial appointment and

pick-up appointment); d) participants must be willing to wear an ActiGraph (i.e.,

objective measurement of physical activity) for at least 7 days, and e) participants must

be willing to complete all the measures with the researcher either at our Texas Tech

University (TTU) laboratory or at their homes. Of the 74 children and adolescents who

participated, four participants did not return the subjective measure of physical activity,

and ActiGraph data could not be analyzed for three participants due to various reasons

(e.g., initialization error, faulty device). Therefore, the final analyses were conducted

with data from 67 participants.

Participant Demographics

Data were collected from 67 children and adolescents (41.8% male, 58.2%

female), ages 8-12 years-old (Mage = 9.4, SDage = 1.2). The sample consisted of 50.7%

Caucasian, 31.3% Hispanic, 4.5% Black/African American, 6.0% Asian/Asian-American,

and 7.5% Other/Mixed ethnicities. Caregivers were asked to indicate their average yearly

income using the following categories: 1) $0-25,000, 2) $25,000-50,000, 3) $50,000-

100,000, 4) $100,000-150,000, 5) 150,000-175,000, 6) $175,000-200,000, and 7)

$200,000 or more. For the current sample, 37.3% had an average yearly income that fell

into category 3, $50,000-100,000 (Mincome = 3.1, SDincome = 1.6). Approximately 16.4% of

families fell into category 1, 17.9% into category 2, 14.9% into category 4, 4.5% into

category 5, 1.5% into category 6, and 7.5% into category 7. Finally, approximately 73.1%

(n = 49) of the sample engaged in at least 60 minutes of moderate-to-vigorous physical


14

activity (MVPA) on average in a 7-day period (MMVPA = 84.34 minutes, SDMVPA = 33.26

minutes).

Procedure

The current study was part of a larger data collection that had been approved by

the Institutional Review Board of TTU. As aforementioned, participants were recruited

from various community events. At these community events, general contact information

was gathered, and participants were informed that they would be contacted later to be

provided more information on the study. If the participant was still interested in

participating after receiving more information about the study, the researcher scheduled a

time to meet with the family either at their home or in the Department of Psychological

Sciences at TTU.

At the time of the initial appointment, informed consent and assent was obtained

from the child’s legal guardian and the child, respectively. Upon agreement to participate,

the caregiver and child were given a general demographic questionnaire, and the child

completed measures to assess for depressive and anxiety symptoms. Then, the research

assistant provided verbal and written instructions on how to wear and care for the

ActiGraph. The child was required to wear the ActiGraph for at least 7 days. The child

was given a self-report questionnaire on physical activity to keep and complete the day

before the pick-up appointment. At the time of the pick-up appointment, a research

assistant collected the ActiGraph and the self-report questionnaire on physical activity.

As per the larger data collection, participants will receive $20 at the initial appointment

and $20 at the pick-up appointment.


15

Measures

Demographic Questionnaire

Caregivers and children were administered a brief demographic questionnaire,

intended to gather general background information on the participants such as their age,

gender, ethnicity/race, and other variables.

Children’s Depression Inventory 2: Self-Report (Short Version; CDI 2: SR[S]; Kovacs,

2011)

The CDI 2:SR(S) is a 12-item, self-report measure for children, ages 7-17 years-

old, that assesses for depressive symptoms. The CDI 2:SR(S) total score can range from

0-24. Higher scores indicate higher depressive symptom severity. Psychometric

properties of the CDI 2:SR(S) have supported high internal consistency for the total score

(α = .82) and good convergent and discriminant validity (see Table 1 for more details;

Kovacs, 2011). Further, factor analyses have revealed strong support for the four-factor

structure of the questionnaire (Kovacs, 2011). For this study, a Cronbach’s alpha of .59

was obtained, indicating poor internal consistency.

Spence Children’s Anxiety Scale (SCAS; Spence, 1997)

The SCAS is a 45-item, self-report measure that has been normed for children,

ages 7-19 (Muris et al., 2000). The SCAS consists of six subscales, which can be

summed to create a total score. The subscales include separation anxiety, social phobia,

obsessive-compulsive, panic/agoraphobia, physical injury fears, and generalized anxiety.

Scores on the SCAS can range from 0-114. Higher scores indicate higher anxiety

symptom severity. Psychometric properties of the SCAS have revealed high internal

consistency for the total score (α = .87-.94) and have ranged from poor to adequate for


16

the subscales: separation anxiety, α = .62-.75; social phobia, α = .56-.78; obsessive-

compulsive, α = .65-.82; panic/agoraphobia, α = .75-.86; physical injury fears, α = .47-

76; generalized anxiety, α = .66-84. The SCAS has also revealed acceptable convergent

and discriminant validity (see Table 1 for more details; Ramme, n.d.; Spence et al.,

2003). Further, the six-factor structure of the SCAS has also been supported by

exploratory and confirmatory factor analyses (Ramme, n.d.; Spence, 1997; Spence, 1998;

Spence et al., 2003). For this study, a Cronbach’s alpha of .90 was obtained for the SCAS

total score, indicating high internal consistency. The Cronbach’s alphas for the subscales

were as follows: separation anxiety α = .62 (questionable), social phobia α = .73

(adequate), obsessive-compulsive α = .69 (questionable), panic/agoraphobia α = .75

(adequate), physical injury fears α = .40 (unacceptable), and generalized anxiety α = .73

(adequate).

Physical Activity Questionnaire for Older Children (PAQ-C; Crocker et al., 1997;

Kowalski, Crocker, & Faulkner, 1997)

The PAQ-C is a 10-item, self-report measure for children, ages 8-14 years-old,

that assesses general levels of physical activity (Kowalski et al., 2004). Specifically, the

PAQ-C requires a 7-day recall of physical activity. Nine items are used to create a total

activity score, and the tenth item is meant to highlight any reason for irregular physical

activity (e.g., illness). The total activity score is then averaged to create a composite

score, rated from 1-5, where higher scores indicate higher levels of physical activity.

Studies have supported the psychometric properties of the PAQ-C such that it has

adequate internal consistency (α = .72-.76) and good convergent and discriminant validity

(see Table 1 for more details; Janz et al., 2008; Kowalski et al., 1997). Further,


17

exploratory and confirmatory factor analyses have supported the two-factor structure of

the PAQ-C (Thomas & Upton, 2014). For this study, a Cronbach’s alpha of .86 was

obtained, indicating high internal consistency.

Accelerometry

The ActiGraph wGT3X-BT is a tri-axial accelerometer that is worn on the wrist

and measures various components related to physical activity (e.g., activity counts,

energy expenditure, steps, activity intensity). Additionally, the ActiGraph wGT3X-BT

has an inclinometer, which can differentiate when an individual is sitting down, lying

down, or standing up (McMinn et al., 2013). Further, it can help to identify periods of

non-wear time (McMinn et al., 2013). All of these features allow the ActiGraph wGT3X-

BT to better discriminate between inactivity and physical activity intensities. Overall, the

ActiGraph wGT3X-BT has revealed good validity such that the data was highly

correlated with energy expenditure levels found by other measures (e.g., indirect

calorimetry; Jimmy et al., 2013a, 2013b; McMinn et al., 2013). For the purposes of this

study, MVPA was analyzed based on the total average time in minutes spent in MVPA

over 7 days, the average time in minutes spent in MVPA over the weekend, and the

average time in minutes spent in MVPA on the weekdays.

Data Analytic Plan

Considering the aims of the current study, two types of analyses were completed

for each of the hypotheses. Specifically, Bland-Altman analyses were utilized for

Hypothesis 1, and hierarchical regression models were utilized for Hypotheses 2a and 3a.

For Hypotheses 2b and 3b, the directionality of the standardized regression coefficients

(i.e., positive or negative values), taken from analyses used in Hypotheses 2a and 3a,


18

were used to determine whether there was an overestimation or underestimation of

physical activity. Procedures for each are described separately.

Analyses for Hypothesis 1

To compare subjective measures (i.e., PAQ-C) of physical activity to objective

measures (i.e., ActiGraph data), Z-score transformations for the total score on the PAQ-C

and the moderate-to-vigorous (MVPA) minutes/day obtained from ActiGraph data were

conducted (as supported by Benítez-Porres et al., 2016). Considering the Z-score

transformations made, the results were taken within the context of a standard distribution,

where a cutoff of ±1.96 was used to indicate the maximum acceptable limit of agreement.

Correlations were analyzed to provide general information regarding the relationship

between the PAQ-C and ActiGraph data. Then, Bland-Altman analyses were completed

using IBM SPSS Statistics to provide more detailed information on the level of

agreement between the measures. Bland-Altman plots were created to provide a graphical

representation of the differences between Z-score transformed data of subjective and

objective reports of youth physical activity. Intraclass correlation (ICC) analyses were

also utilized to provide additional quantitative information on the levels of agreement.

Analyses for Hypotheses 2a-b and 3a-b

Hierarchical regression models were built to examine any unique and interactive

effects of depressive and anxiety symptoms on the level of agreement between subjective

and objective measures of youth physical activity. Initial and preliminary analyses were

conducted to examine relations between the variables of interest (i.e., youth depressive

and anxiety symptoms, subjective and objective youth physical activity) and identify any

variables that would need to be included as covariates in the hierarchical regression


19

models. Additionally, objective youth physical activity was further examined to

determine if separate analyses would need to be conducted for associated variables (e.g.,

weekend MVPA, weekday MVPA). For Hypotheses 2a and 3a, the difference between

the Z-score transformed data for the PAQ-C total score and MVPA minutes/day obtained

via the ActiGraph were entered as the outcome variable (i.e., Z-scores of the objective

data were subtracted from the Z-scores of the subjective data).

For Hypotheses 2b and 3b, the standardized regression coefficients from

Hypotheses 2a and 3a were examined. As the difference values for the outcome variable

in the hierarchical regression models were calculated by having the subjective report

values as the reference group, a standardized coefficient that was negative represented an

underestimation of the subjective measure of physical activity. On the other hand, a

positive standardized coefficient represented an overestimation of the subjective measure

of physical activity. Additional information on the analyses and their results for

Hypotheses 2a-b and 3a-b as well as on how missing data was addressed will be

described in the next chapter.


20

CHAPTER III

RESULTS

Initial Analyses

Based on a Missing Values Analysis completed through IBM SPSS, out of 6,499

data points, 45 were missing (.69%) from 23 individual cases in the overall sample.

Further, the data were found to be missing completely at random (MCAR; p = 1.00)

according to Little’s MCAR test (Little, 1988). As there was <1% of missing data that

was also MCAR, item-level mean imputation was utilized to address missing data over

expectation maximation in order to allow for a more conservative estimate of missing

values (Cheema, 2014; Tabachnick & Fidell, 2013).

After addressing missing data, the data were analyzed to address any potential

outliers using the method suggested by Tabachnick and Fidell (2013). Variables were

first evaluated for skewness and kurtosis. The PAQ-C, generalized anxiety subscale of

the SCAS, and total average time spent in MVPA from ActiGraph data did not display

significant skewness or kurtosis, and z-transformations did not reveal univariate outliers

(i.e., no z-scores were ±3.29; Tabachnick & Fidell, 2013). Therefore, no transformations

were needed for these variables. However, the SCAS total score, subscales of the SCAS

(i.e., separation anxiety, social phobia, obsessive-compulsive, panic, physical injury

fears), and weekend/weekday MVPA data were positively skewed. Therefore,

transformations were completed for the variables, skewness and kurtosis analyses were

re-evaluated, and univariate outliers were deferred. Square root, log, and reciprocal

transformations were applied to all variables until the absolute z-skew and/or z-kurtosis

value was no longer greater than 1.96 (Field, 2013; Tabachnick & Fidell, 2013).


21

Multivariate outliers were assessed using Mahalanobis Distance. Based on the results of

this test, there were no significant multivariate outliers contributing to the skewness of

the data, and no other adjustments were needed.

Curve estimation analyses were examined between the CDI 2: SR(S) and SCAS

total score to determine if a curvilinear pattern existed. According to the analyses, a linear

pattern (R2 = .085, F(1, 65) = 6.03, p = .017) was a better estimate of the relationship

between the variables over a curvilinear pattern (R2 = .088, F(2, 64) = 3.10, p = .052).

Therefore, quadratic variables for the CDI 2:SR(S) and SCAS were not needed for the

analyses.

Initial analyses were also conducted on objective physical activity variables to

determine whether or not separate analyses would be necessary for each. Specifically, the

difference scores between the weekend and weekday MVPA variables were computed.

Then, a one-sample t-test was conducted to determine if significant differences existed

between the variables. There were no significant differences between weekend and

weekday MVPA (t(66) = -.72, p = .476), indicating that youth were as active on the

weekends as on the weekdays. Therefore, the outcome variable was computed only using

the difference between the z-PAQ-C and the z-total average time spent in MVPA. For

conciseness, the outcome variable will be referred to as “PA Difference” for the

remainder of the document. Further, to allow for ease of interpretation and to decrease

multicollinearity in models required for the main analyses, all the independent variables

were centered after transformations were completed (Tabachnick & Fidell, 2013). Taken

together, to test the assumptions of the analyses in this study, the following were used:

PA Difference; centered data for the generalized anxiety subscale of the SCAS; square


22

root transformed and centered data for the CDI 2: SR(S), the obsessive-compulsive

subscale of the SCAS, and the physical injury fears subscale of the SCAS; log

transformed and centered data for the SCAS total score, the separation anxiety subscale

of the SCAS, and social phobia subscale of the SCAS; and reciprocal transformed and

centered data for the panic/agoraphobia subscale of the SCAS. Information on the means,

standard deviations, z-skew, and z-kurtosis for these variables can be found in Table 2.

Testing Assumptions for Analyses

Hierarchical regression models work under the assumptions of 1) linearity, 2)

normality, 3) homogeneity of variances, and 4) multicollinearity. P-P plots, histograms

with normality curves, and the residual plots for the independent variables against the

outcome variable was examined to assess the first three assumptions. There was no

evidence of violation to these assumptions. To assess for multicollinearity, tolerance and

variance inflation factor (VIF) values were analyzed. According to Field (2013),

multicollinearity problems would exist when VIFs > 10 and tolerance statistics < .1.

Based on these guidelines, there was no evidence of multicollinearity. Overall, as the

assumptions of the analyses were met, no other adjustments were needed.

Preliminary Analyses

Analyses were conducted to examine the relationship between sociodemographic

variables and the study variables. Based on the bivariate correlations (Table 3), there

were no significant relationships between PA Difference and child age, the CDI 2: SR(S),

subscales of the SCAS, or the total score for the SCAS. However, child age was

significantly correlated with the separation anxiety subscale of the SCAS (r = -.25, p =

.044). Child sex, child race/ethnicity, and average yearly income were analyzed using


23

one-way ANOVAs. According to the results, there were no significant differences

between these variables and the independent variables (i.e., depressive and anxiety

symptoms) or the outcome variable (i.e., PA Difference). Therefore, child age was only

included in the analysis involving the separation anxiety subscale of the SCAS, and no

other demographic variables were included in the main analyses.

Main Analyses

As aforementioned, Bland-Altman analyses were utilized for Hypothesis 1, and

hierarchical regression models were utilized for Hypotheses 2a-b and Hypotheses 3a-b.

For the hierarchical models, any necessary demographic variables and other variables to

control will be entered in Step 1 depending on the hypothesis (i.e., child age was entered

when the separation anxiety subscale of the SCAS was the predictor variable; youth

anxiety symptoms for Hypotheses 2a-b; youth depressive symptoms for Hypotheses 3a-

b). The predictor variable was entered in Step 2 (i.e., youth depressive symptoms for

Hypotheses 2a-b, youth anxiety symptoms for Hypotheses 3a-b). For Hypothesis 3a, an

interaction term between youth depressive symptoms and the youth anxiety symptom of

interest (i.e., total anxiety symptoms, subscales of anxiety) was entered in Step 3. PA

Difference was the outcome variable for Hypotheses 2a-b and Hypotheses 3a-b. For

Hypotheses 2b and 3b, the directionality of the standardized regression coefficients (i.e.,

positive or negative values), taken from analyses used in Hypotheses 2a and 3a, were

used to determine whether there was an overestimation or underestimation of physical

activity.


24

Hypothesis 1

Correlation analyses were initially conducted in order to provide general

information regarding the relationship between the subjective and objective measures of

physical activity. A Pearson’s correlation analysis revealed a weak and non-significant

correlation between the two measures (r = .09, p = .480). Bland-Altman plots were

created to depict the difference values of physical activity against the mean of the two

methods (Figure 1). The 95% limits of agreement were between -2.65 and 2.65, with

4.0% of individuals displaying low agreement such that the PA Difference fell outside of

the limits of agreement. Due to the standardization of subjective and objective methods,

the average discrepancy between the methods (i.e., bias) was zero. In other words, as

average discrepancy was calculated by subtracting the standardized scores of subjective

and objective youth physical activity, this brought the average discrepancy even closer to

zero. Therefore, the average discrepancy of zero was not simply interpreted as there

being no bias, or no differences between the measures, and other aspects of the Bland-

Altman plots were examined to explicate the level of agreement. According to a linear

regression, predicting PA Difference based on the average discrepancy, there was no

proportional bias (F(1, 65) = 0.00, p = 1.00), suggesting that there was no evidence of the

difference values between subjective and objective methods increasing or decreasing in

proportion to the average of the methods. The range of the limits of agreement were also

considered, along with the overall distribution of the scores. Considering that the PA

Difference was computed based on standardized values, the values obtained for the limits

of agreement were taken within the context of a standard distribution such that values

falling 1.96 standard units away indicated wide limits of agreement. The limits of


25

agreement for the current study revealed lower and upper limits of -2.65 and 2.65

standard units away from the average discrepancy, respectively, suggesting that the

methods have weak agreement, as supported by the wide limits of agreement. Although

this alone also does not conclude weak agreement, based on the Bland-Altman plots,

there also appeared to be slight evidence of overestimation by subjective reports, as there

were more points above the line representing additional differences between the methods.

To further explore this finding, ICC analyses and 95% confidence intervals (CI) were

calculated based on a mean-range, absolute-agreement, 2-way mixed model. ICC values

were interpreted such that values greater than .90 indicated excellent agreement, .75 to

.90 indicated good agreement, .50 to .75 indicated moderate agreement, and less than .50

indicated poor agreement (Koo & Li, 2016). This analysis yielded ICC estimates that

indicated poor agreement (ICC [3,2] = .16), with a 95% CI from -.37 to .49 (p = .238).

Overall, Hypothesis 1 was supported.

Hypothesis 2a

Contrary to expectations, youth depressive symptoms were not significantly

related to PA Difference (ΔR2 = .02, ΔF(1, 65) = .94, p = .335; Table 4). Furthermore, no

variables were found to be significant or unique predictors of PA Difference. Therefore,

Hypothesis 2a was not supported.

Hypothesis 2b

Based on the standardized regression coefficient for depressive symptoms from

the analyses conducted for Hypothesis 2a, there was a negative relationship between

depressive symptoms and PA Difference (e.g., higher levels of depressive symptoms

were associated with lower difference scores). This indicates an underestimation of


26

physical activity on subjective measures by individuals with higher endorsement of

depressive symptoms. Therefore, Hypothesis 2b was not supported.

Hypothesis 3a

Contrary to expectations, youth total anxiety symptoms were not significantly

related to PA Difference (ΔR2 = .03, ΔF(1, 65) = 1.70, p = .197). Furthermore, none of

the subscales of anxiety were significantly or uniquely related to PA Difference (Table

5). However, main effects and interactions for youth depressive and anxiety symptoms

were also analyzed. According to the results, the overall models were not significant, and

there were no main effects seen for total anxiety symptoms, subscales of anxiety

symptoms, or depressive symptoms on PA Difference. However, there were significant

crossover interactions between youth total anxiety symptoms and youth depressive

symptoms (ΔR2 = .08, ΔF(1, 63) = 5.37, p = .024; Table 4), between youth separation

anxiety symptoms and youth depressive symptoms (ΔR2 = .14, ΔF(1, 62) = 7.54, p =

.008; Table 5), and between youth physical injury fears and youth depressive symptoms

(ΔR2 = .07, ΔF(1, 63) = 4.62, p = .036; Table 5). These interactions suggested that PA

Difference was higher when youths reported experiencing higher depressive symptoms

and higher youth total anxiety symptoms, youth separation anxiety symptoms, and youth

physical injury fears, respectively. Additionally, PA Difference was higher when youths

reported experiencing lower depressive symptoms and lower youth total anxiety

symptoms, youth separation anxiety symptoms, and youth physical injury fears,

respectively. Interaction plots were created for the significant interactions at levels of

depressive symptoms 2SD and 1SD below the mean, at the mean, and 1SD and 2SD

above the mean (Figures 2-4; as suggested by McCabe et al., 2018).


27

Hypothesis 3b

Although Hypothesis 3a revealed that anxiety symptoms were not significantly or

uniquely related to PA Difference, the relations between types of anxiety symptoms and

PA Difference were still examined as there were some notable interaction effects between

specific types of anxiety symptoms and depressive symptoms, which may have been

partially influenced by the direction (i.e., positive or negative standardized regression

coefficient) of each symptom presentation. Based on the standardized regression

coefficients for total anxiety symptoms, generalized anxiety symptoms, obsessive-

compulsive symptoms, social phobia symptoms, and separation anxiety symptoms from

the analyses conducted for Hypothesis 3a, there was a positive – though not statistically

significant – relationship between these variables and PA Difference (e.g., higher levels

of these symptoms were associated with higher difference scores). This indicates an

overestimation of physical activity on subjective measures by individuals with higher

levels of total anxiety, generalized anxiety, obsessive-compulsive, social phobia, and

separation anxiety symptoms.

Conversely, according to the standardized regression coefficients for the subscales

of physical injury fears and panic/agoraphobia from the analyses conducted for

Hypothesis 3a, there was a negative, but not statistically significant, relationship between

these variables and PA difference (e.g., higher levels of these symptoms were associated

with lower difference scores). This indicates an underestimation of physical activity on

subjective measures by individuals with higher endorsement of physical injury fears and

panic/agoraphobia symptoms. Overall, Hypothesis 3b was partially supported.


28

Table 1

Additional Information on Psychometric Properties of Measures

Measure Test-Retest

Reliability

Convergent

Validity

Divergent Validity Information on

Normed Sample

Reference(s)

Children’s

Depression

Inventory 2: Self-

Report (Short

Version)

Total Score r =

.92

r = .37-.58 Lower correlations

with measures of

other constructs;

supports good

divergent validity

1, 597 children

and adolescents

(7-17 years-old)

Kovacs, 2011

Spence Children’s

Anxiety Scale

Total Score r =

.60

r = .71-.89 with

various anxiety

measures

Lower correlations

with measures of

other constructs;

supports good

divergent validity

2,052 children

(8-12 years-old)

Olofsdotter et

al., 2016;

Spence, 1998

Physical Activity

Questionnaire for

Older Children

Total Score r =

.75 and .82 for

males and

females,

respectively

r = .45-.63 with

various

measures of

physical activity

No relationship with

behavioral conduct

scale; supports good

divergent validity

215 children

and adolescents

(8-16 years-old)

Crocker et al.,

1997; Kowalski

et al., 1997

Note. This table includes information on and references to studies that have assessed the psychometric properties of the

measures used in the current study.


29

Table 2

Information on Variables and Transformations

Measure Mean SD Z-Skewness Z-Kurtosis

CDI 2: SR(S) 3.7 2.7 2.63 0.37

Transformed b 0.0 0.8 -1.73 0.07

SCAS a

Total 65.9 15.0 3.75 2.45

Transformed c 0.0 0.1 1.48 0.61

Separation Anxiety 10.8

3.1 3.71 3.08

Transformed c 0.0 0.1 0.83 -0.04

Social Phobia 11.0 3.4 3.56 2.48

Transformed c 0.0 0.1 0.72 -0.34

Obsessive-Compulsive 11.6 3.5 2.24 -0.23

Transformed b 0.0 0.5 1.19 -0.78

Panic/Agoraphobia 12.8 3.8 4.90 3.92

Transformed d 0.0 0.0 -0.88 -1.52

Physical Injury Fears 8.7 2.5 2.69 1.49

Transformed b 0.0 0.4 1.36 0.09

Generalized Anxiety 11.1 3.1 1.92 0.26

Transformed e 0.0 3.1 1.92 0.25

PAQ-C 2.8 0.6 -0.23 -0.83

z-transformed f 0.0 1.0 -0.23 -0.83

ActiGraph Total MVPA 84.2 33.3 1.89 -0.30

z-transformed f 0.0 1.0 1.89 -0.30

PA Difference g 0.0 1.3 -1.13 0.23


30

Note. This table consists of the original and transformed means, standard deviations, z-

skew, and z-kurtosis values for the variables. Centering and/or standardization of the

variables occurred after completing square-root, log, or reciprocal transformations. CDI

2: SR(S) = Children’s Depression Inventory 2: Self-Report (Short Version); SCAS =

Spence Children’s Anxiety Scale; PAQ = Physical Activity Questionnaire for Older

Children; Actigraph Total MVPA = total average of moderate-to-vigorous physical

activity as measured by the accelerometer; PA Difference = difference score between the

z-transformed subjective and objective physical activity measures a Information on the SCAS is provided for the total score and each of the subscales. b

Denotes variables that were square-root transformed prior to centering the variable. c

Denotes variables that were log transformed prior to centering the variable. d Denotes

variables where reciprocal transformations were conducted prior to centering the

variable. e Denotes a variable where no transformations were needed prior to centering

the variable. f Denotes variables that were only z-transformed. g For PA Difference,

standardization occurred for the subjective and objective measure prior to computing the

difference score.


31

Table 3

Bivariate Correlations

1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

1. Age -

2. CDI 2: SR(S) -.13 -

3. SCAS Total -.13 .29* -

4. SepAnx -.25* .14 .78** -

5. SocPho .11 .26* .81** .53** -

6. OCD -.20 .31* .75** .48** .48** -

7. Panic/Ag .03 -.19 -.75** -.53** -.60** -.46** -

8. Phys -.13 .07 .65** .42** .49** .41** -.30* -

9. GenAnx -.06 .30* .74** .54** .53** .42** -.44** .43** -

10. PA Difference .08 -.08 .13 .13 .11 .13 -.11 -.06 .18 -

Note. This table displays the bivariate correlations between demographic variables and the study variables. CDI 2: SR(S) =

Children’s Depression Inventory 2: Self-Report (Short Version); SCAS = Spence Children’s Anxiety Scale; SCAS Total =

total score for SCAS; SepAnx = separation anxiety subscale of SCAS; SocPho = social phobia subscale of SCAS; OCD =

obsessive-compulsive subscale of SCAS; Panic/Ag = panic/agoraphobia subscale of SCAS; Phys = physical injury fears

subscale of SCAS; GenAnx = generalized anxiety subscale of SCAS; PA Difference = difference score between the z-

transformed subjective and objective physical activity measures.

* p ≤ .05; ** p ≤ .01; *** p ≤ .001.


32

Table 4

Hierarchical Regression Analyses Predicting PA Difference While Controlling for Youth

Anxiety Symptoms

Predictor R2 ΔR2 ΔF β sr2

Step 1:

SCAS Total

.02 .02 1.14

.17

Step 2:

CDI 2: SR(S)

.03 .01 .94

-.13

Note. This table provides information on the hierarchical regression models that were

built for Hypotheses 2a-b. The control variable is entered in Step 1 and the predictor

variable is entered in Step 2. Betas reported were taken from the final model. SCAS Total

= total score for Spence Children’s Anxiety Scale; CDI 2: SR(S) = Children’s Depression

Inventory 2: Self-Report (Short Version).

* p ≤ .05; ** p ≤ .01; *** p ≤ .001


33

Table 5

Hierarchical Regression Analyses Predicting PA Difference with Interaction Terms

Total Anxiety Separation Anxiety

Predictor R2 ΔR2 ΔF β sr2 R2 ΔR2 ΔF β sr2

Step 1:

CDI 2: SR(S)

.01 .01 .38

-.08

Age a

.01 .01 .34

-.02

.15

Step 2:

SCAS Total

.03 .03 1.70

.09

SepAnx

.04 .03 1.80

.05

Step 3:

Interaction

.11 .08 5.37

.29

.08*

.14 .10 7.54

.35

.10**

Social Phobia Symptoms OCD Symptoms

Predictor R2 ΔR2 ΔF β sr2 R2 ΔR2 ΔF β sr2

Step 1:

CDI 2: SR(S)

.01 .01 .38

-.09

.01 .01 .38

-.08

Step 2:

SocPho

.02 .02 1.20

.13

OCD

.03 .03 1.70

.14

Step 3:

Interaction

.04 .01 .74

.11

.05 .02 1.33

.15


34

Table 5 (continued)

Physical Injury Fears


Step 1:

CDI 2: SR(S)

.01 .01 .38

-.14

Step 2:

Phys

.01 .00 .19

-.05

Step 3:

Interaction

.08 .07 4.62

.27

.07*

Panic/Agoraphobia Symptoms

R2 ΔR2 ΔF β sr2

Step 1:

CDI 2: SR(S)

.01 .01 .38

-.12

Step 2:

Panic/Ag

.02 .02 1.00

-.09

Step 3:

Interaction

.05 .03 2.15

-.18

Generalized Anxiety Symptoms


Step 1:

CDI 2: SR(S)

.01 .01 .38

-.10

Step 2:

GenAnx

.05 .04 2.95

.15

Step 3:

Interaction

.10 .05 3.22

.23

Note. This table provides information on the hierarchical regression models that were built for Hypotheses 3a-b. Betas

reported were taken from the final model. The interaction variable entered in Step 3 is meant to denote the interaction between

the CDI 2: SR(S) (entered in Step 1) and the anxiety subscale of interest (entered in Step 2). SCAS Total = total score for

Spence Children’s Anxiety Scale (SCAS); CDI 2: SR(S) = Children’s Depression Inventory 2: Self-Report (Short Version);


35

SepAnx = separation anxiety subscale of SCAS; SocPho = social phobia subscale of SCAS; OCD = obsessive-compulsive

subscale of SCAS; Panic/Ag = panic/agoraphobia subscale of SCAS; Phys = physical injury fears subscale of SCAS; GenAnx

= generalized anxiety subscale of SCAS. a Age was included in the model with separation anxiety as a predictor, as it was identified as a covariate in the preliminary

analyses.

* p ≤ .05; ** p ≤ .01; *** p ≤ .001


36

Figure 1

Bland-Altman Plot for Hypothesis 1

Note. Bland-Altman plots of the difference score between standardized subjective and

objective physical activity against the mean of the standardized methods. The solid line

represents the mean difference (i.e., bias) between subjective and objective physical

activity. Dashed lines represent the upper and lower 95% limits of agreement (LoA;

computed using: mean difference ± 1.96*SD). Difference = z-PAQ-C – z-total time spent

in MVPA based on ActiGraph data; Average = (z-PAQ-C – z-total time spent in MVPA

based on ActiGraph data)/2.


37

Figure 2

Interaction Plots between Total Anxiety Symptoms and Depressive Symptoms on PA

Difference

Note. This figure displays the simple slopes for levels of depressive symptoms 2SD and

1SD below the mean, at the mean, and 1SD and 2SD above the mean. 95% confidence

intervals are represented by the shaded area. Dashed lines indicate the maximum and

minimum values of the outcome variable. The diamond within each plot represents the

crossover point (i.e., where the change in the independent variable occurs). SCAS Total =

total score for Spence Children’s Anxiety Scale; CDI 2: SR(S) = Children’s Depression

Inventory 2: Self-Report (Short Version); PCTL = percentile; PA Difference = difference

score between the z-transformed subjective and objective physical activity measures.


38

Figure 3

Interaction Plots between Separation Anxiety Symptoms and Depressive Symptoms on

PA Difference





crossover point (i.e., where the change in the independent variable occurs). SCAS

Separation Anxiety = separation anxiety subscale for Spence Children’s Anxiety Scale;

CDI 2: SR(S) = Children’s Depression Inventory 2: Self-Report (Short Version); PCTL =

percentile; PA Difference = difference score between the z-transformed subjective and

objective physical activity measures.


39

Figure 4

Interaction Plots between Physical Injury Fears Symptoms and Depressive Symptoms on

PA Difference





crossover point (i.e., where the change in the independent variable occurs). SCAS

Physical Injury Fears = physical injury fears subscale for Spence Children’s Anxiety

Scale; CDI 2: SR(S) = Children’s Depression Inventory 2: Self-Report (Short Version);

PCTL = percentile; PA Difference = difference score between the z-transformed

subjective and objective physical activity measures.


40

CHAPTER IV

DISCUSSION

Main Findings

This study was one of the first studies to examine the level of agreement between

subjective and objective youth physical activity, both independently and in relation to

youth depressive and anxiety symptoms. As aforementioned, the main aims of this study

were to

1) compare subjective reports and objective reports of youth physical activity, 2)

determine whether or not youth symptoms were uniquely and significantly related to the

level of agreement between subjective and objective measures of youth physical activity,

and 3) examine how youth symptoms impact reporting (i.e., underestimate, overestimate)

of youth physical activity. Results will be discussed in the order of the aims of the study.

To determine the level of agreement between subjective and objective youth

physical activity, a few questions were considered: 1) How big was the average

discrepancy?, 2) How wide were the limits of agreement?, 3) Was there evidence of a

trend between the methods and the average discrepancy?, and 4) Did the points vary

consistently across the graph? Due to standardization of subjective and objective

measures of youth physical activity prior to computing the difference score, the bias was

zero, and the first question was deferred. The limits of agreement were then examined

and displayed wide agreement, which indicated the presence of large variability in the

difference between subjective and objective youth physical activity. In other words, there

was evidence of significant discrepancies between these two methods. Finally, there

appeared to be slight overestimation of subjective reports on youth physical activity (i.e.,


41

more youths tended to report being more physically active than what was suggested by

the accelerometry data); however, based on the linear regression analysis, there was no

evidence of proportional bias (i.e., no increases in variability as average discrepancy

increases). Moreover, the observations made using the Bland-Altman analysis were

further supported by the ICC analysis that was conducted. Despite some of the findings

being impacted by z-transformations made to some study measures, there was ample

support for a weak agreement between subjective and objective youth physical activity,

with some evidence of overestimation on subjective measures. These results were

consistent with the hypothesis and previous literature (e.g., Adamo et al., 2009).

For the second aim of the study, the hypotheses were not supported when

examining the unique relation of youth depressive symptoms or youth anxiety symptoms

to the difference between subjective and objective youth physical activity. These results

may be partially explained by the weak bivariate correlations that were found between

youth symptoms and the difference score between subjective and objective measures.

Furthermore, there were notable interaction effects between youth total anxiety

symptoms and youth depressive symptoms, between youth separation anxiety symptoms

and youth depressive symptoms, and between youth physical injury fears and youth

depressive symptoms, which can help to explain the contradictory results. All interactions

displayed a crossover interaction effect such that for lower levels of total anxiety

symptoms, separation anxiety symptoms, and physical injury fears, low levels of

depressive symptoms predicted more positive PA Difference scores compared to high

levels of depressive symptoms. This pattern points towards overestimation of youth

physical activity on subjective measures when considering these interactions, as


42

evidenced by the positive PA Difference values. Whereas, at higher levels of these

anxiety-related symptoms, high levels of depressive symptoms predicted more negative

PA Difference scores compared to low levels of depressive symptoms. This pattern

points towards underestimation of physical activity on subjective measures when

considering these interactions, as evidenced by the negative PA Difference values.

Finally, based on the analyses to address the third aim of the study, there was

evidence of overestimation of physical activity on subjective measures when individuals

had higher levels of total anxiety, generalized anxiety, obsessive-compulsive, social

phobia, and separation anxiety symptoms. On the other hand, there was evidence of

underestimation of physical activity on subjective measures when individuals had higher

levels of depressive symptoms as well as higher levels of physical injury fears and

panic/agoraphobia symptoms. Given that these variables alone were not significant

predictors of the PA Difference, these findings of over- and under-estimation should be

interpreted with caution. However, as there were notable interaction effects between

youth depressive symptoms and some types of anxiety symptoms on PA Difference, it is

possible that over- or under-estimation on these symptom measures may impact youth

reporting of physical activity and subsequent differences in subjective versus objective

measures. For example, youth depressive and total anxiety symptoms had a significant

interaction, and based on bivariate correlations, youth depressive and total anxiety

symptoms were positively and significantly related. Further, the results of the current

study revealed underestimation from youth with higher levels of depressive symptoms

and overestimation from youth with higher levels of total anxiety. The difference in

estimation of physical activity was reflected in the PA Difference, which was higher at


43

higher levels of youth depressive and total anxiety symptoms. Taken together, being able

to identify patterns of underestimation and overestimation in the presence of various

symptom presentations would be helpful in clarifying any potential interactive effects.

Some of the contradictory findings may be explained by the phenomenological

characteristics of youth depressive symptoms and youth anxiety symptoms and its

subtypes. Forbes et al. (2008) highlighted how youth with anxiety symptoms and

disorders underreported their sleep problems, whereas youth with depressive symptoms

and disorders overreported their sleep problems. Essentially, youth with anxiety

symptoms were more inclined to deny a problem, and youth with depressive symptoms

were more inclined to indicate the presence of a problem. Considering that, within the

context of physical activity, it is possible that youths with anxiety or depressive

symptoms would fall into the same pattern of denying a problem (i.e., saying they are

engaging in more physical activity in subjective reports than what is observed by

objective data) and indicating the presence of a problem (i.e., saying they are not

engaging in enough physical activity on subjective reports than what is observed by

objective data), respectively.

Additionally, it is possible that some of the unshared and shared characteristics

between depressive and anxiety symptoms in youth can influence self-perceptions of

physical activity. Specifically, depression has been found to be more associated with low

positive affect and loss of interest, and anxiety has been found to be more associated with

somatic tension and hyperarousal (Clark & Watson, 1991; Garber & Weersing, 2010).

Therefore, as depressive symptoms increase, there may be a loss of interest, leading to

decreased engagement in physical activity. However, this decreased physical activity may


44

be perceived more harshly (i.e., youth perceives him/herself as engaging in less physical

activity than in reality) due to other factors such as increased negative automatic thoughts

and feelings of helplessness that have also been found to be associated with depressive

symptoms in youth (e.g., Garber et al., 1993). This could lead to an underestimation of

subjective, in comparison to objective, physical activity. On the other hand, as anxiety

symptoms increase, the hyperarousal symptoms may lead to increased engagement in

physical activity. However, as distorted cognitions such as overgeneralization and

catastrophizing have been associated with youth anxiety symptoms (e.g., Weems et al.,

2001), it is possible that youths with anxiety symptoms may perceive themselves as

engaging in more physical activity than their actual levels of physical activity. This could

lead to overestimation of subjective, in comparison to objective, physical activity.

Alternatively, when examining the interactive effect of depressive symptoms with

subtypes of anxiety symptoms, it is possible that the shared characteristics (e.g., negative

cognitions, information processing errors) between the symptom presentations have an

additive effect that leads to over- or under-estimation based on the severity of each.

Strengths and Limitations

As with any study, there were some notable strengths and limitations. This study

utilized a multi-method format to assess for agreement between subjective and objective

reports of youth physical activity. Further, instead of solely relying on correlational

analyses to draw conclusions about measures, this study utilized agreement statistics. As

analyses of agreement can help to quantify limits to agreement for different methods of

measurement, this study has added to the literature by highlighting the weak agreement

between subjective and objective reports of youth physical activity in addition to


45

providing information on the overall relationship between the measures. Further, this

study focused on individual differences, such as the presence of youth depressive and

anxiety symptoms, which has not previously been examined together within the context

of limits to agreement for youth physical activity. Moreover, this study included analyses

for different subtypes of anxiety, allowing for differential relations between subscales of

anxiety and method agreement statistics to be identified. Finally, this study not only

tested for effects of depressive and anxiety symptoms separately but also took into

account the interaction between the variables, which has not previously been done in the

context of determining the agreement level between subjective and objective reports of

youth physical activity. In other words, this study allowed for more specificity with

regard to how individual differences relate to subjective and objective reports of youth

physical activity.

On the other hand, there are some limitations to this study. One limitation of the

study was that the subjective questionnaire used to assess youth physical activity (i.e.,

PAQ-C) did not provide information on estimated caloric expenditure, time spent in each

activity, or specific intensity information. Therefore, more specific comparisons between

those facets and information gathered from objective reports could not be made.

However, as it is difficult to measure these variables, especially using self-reported

assessments (e.g., Kowalski et al., 1997; Kowalski et al., 2004), utilizing a more general

physical activity questionnaire such as the PAQ-C was still deemed to be an appropriate

option. Another limitation was that the subjective and objective measures of youth

physical activity had to be standardized in order to be accurately compared.

Standardizing the variables can cause some loss of information, as the bias between the


46

measures could not accurately be identified. However, as there is not a standard and

agreed upon way of combining the methods on the same metric, standardization allows

for the data to stay as true to the raw data as possible without increasing the risk of error

by using other techniques (e.g., converting PAQ-C scores based on estimates gathered

from a compendium of physical activities). Finally, the relatively low internal

consistency of the CDI 2: SR(S) and the physical injury fears subscale of the SCAS,

which may partially be due to the low number of items for each, possibly impacted some

of the results. Therefore, some results of the study had to be interpreted with caution.

Despite this, the results of the study still highlighted the importance of accounting for

youth symptom presentation in the context of reporting methods of youth physical

activity.

Implications of the Current Study and Future Directions

There are many research implications for this study. First, the results of the study

highlight some important relations, or lack thereof, between subjective and objective

youth physical activity. Additionally, the study did so within the context of individual

differences. The study enhanced the current literature on agreement levels between

subjective and objective physical activity and emphasized how individual differences can

be associated with underestimation and/or overestimation on youth physical activity

reports. Therefore, it is imperative that future studies examining youth physical activity

consider sample characteristics that may influence reporting in order to obtain an accurate

overview of physical activity if objective measures are unavailable. Future studies that

focus on the level of agreement between methods of reporting youth physical activity

should consider youth symptoms of depression and anxiety. Moreover, future studies


47

should examine subtypes of anxiety in order to further elucidate the literature on how

different symptom presentations can impact reporting of youth physical activity.

Importantly, future studies should also consider the independent and additive impact of

comorbid symptoms. As the interaction between youth depressive symptoms and certain

anxiety symptom presentations were identified as significant predictors of the difference

between subjective and objective youth physical activity, it is important for future studies

to conduct appropriate statistical analyses that would continue to expand the literature on

youth symptoms and reporting discrepancies.

Although some study findings were not statistically significant, the findings from

the study still have some clinical implications. Specifically, clinicians working with

youth who exhibit depressive and anxiety symptoms may benefit from understanding

how those symptoms can impact a youth’s ability to accurately report on behavioral

activation levels, especially regarding physical activity. Considering the youth’s

symptom presentation may provide some insight into the way a youth may report on their

activation levels and may highlight points of intervention. For example, a youth with high

depressive and total anxiety symptoms may tend to underestimate their physical activity

when asked within the context of behavioral activation strategies, indicating that they

identify themselves as being less active than they truly are. Therefore, clinicians can

intervene to address discrepancies between real versus perceived engagement in physical

activity, which can potentially improve behavioral activation. Moreover, clinicians may

be able to obtain more accurate information and make a more informed decision on

which reporting method to use for individuals by utilizing knowledge on the individual’s

symptom presentation (e.g., requesting the individual to complete a physical activity


48

log/diary versus relying on objective data from an accelerometry tool depending on levels

of depressive and anxiety symptoms).


49

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Appendix A

Extended Literature Review

The purpose of this extended literature review will be to provide more

information on general components related to the proposed study. First, a broad

background regarding youth physical activity will be discussed. Then, further

justification for the utilization of techniques described in the data analytic plan of the

main document will be described. Finally, common and distinguishing features of youth

depressive and anxious symptoms will be highlighted to provide additional rationale for

the hypotheses of the proposed study.

Overview of Physical Activity

Over the years, there have been various conceptual frameworks proposed to help

explain and better understand physical activity behaviors (e.g., Pettee Gabriel et al., 2012;

Welk, 1999). Generally, the foundation of Welk’s (1999) framework was based on

theories of social-cognitive approaches, expectancy-value based approaches, and mixed

social-learning approaches. Welk (1999) specifically highlighted various determinants of

physical activity such as demographic characteristics (e.g., gender, age), biological

characteristics (e.g., body mass index), psychological characteristics (e.g., self-efficacy,

enjoyment levels, beliefs about activities), sociocultural characteristics (e.g., parental

modeling), and environmental characteristics (e.g., access to facilities). These

determinants were evaluated and were found to be factors that could predispose, enable,

or reinforce physical activity and/or sedentary behaviors in children and adolescents

(Welk, 1999). As the literature on physical activity has increased over time, the

importance of highlighting differences in sedentary behaviors and physical activity (i.e.,


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light, moderate, vigorous) and other health-related outcomes has been emphasized.

General definitions and brief overviews will be provided for sedentary behaviors and

physical activity.

Sedentary behaviors entail minimal to low intensity activities (i.e., metabolic

equivalent [MET] values ranging from 1.0-1.5 METs; Pettee Gabriel et al., 2012), which

can include watching television, reading, and other activities that would not require much

movement or contraction of skeletal muscles (Must & Tybor, 2005). Studies have

emphasized that sedentariness is not necessarily simply the opposite of physical activity;

instead, sedentary behaviors and physical activity can have independent effects on health-

related outcomes (e.g., insufficient activity can increase body fat, whereas physical

activity can improve cardiovascular health and can have independent effects on body fat;

Dietz, 1996; Must & Tybor, 2005; Pettee Gabriel et al., 2012). Further, although some

individuals may engage in more sedentary behavior throughout the day due to their career

choice, they may still be able to maintain an active lifestyle by engaging in the

recommended amount of exercise each day (i.e., active couch potato phenomenon; Owen

et al., 2010; Pettee Gabriel et al., 2012). Regarding measurement of sedentary behaviors,

subjective and objective measures have been used to assess the time and type of

sedentary, or inactive, behaviors in youth and have consistently found a relationship

between sedentary behaviors and the development of obesity, specifically up to

adolescence (Must & Tybor, 2005). In later adolescence, there seems to be inconsistent

findings between sedentary behaviors and the development of obesity, which has been

hypothesized to be due to the increase of technology and screens (Must & Tybor, 2005).

For example, increased cell phone use could lead to both more and less physical activity,


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as some individuals walk around while talking on the phone and others sit. Overall, more

research will be needed on this in the future in order to have a better idea of the effects of

sedentary behaviors on older youth populations.

Physical activity, on the other hand, has evolved from being described as

“contraction of skeletal muscle…that substantially increases energy expenditure” to

“contraction of skeletal muscle that increases energy expenditure above the basal level”

(Pettee Gabriel et al., 2012, p. S12). This differentiation helps to decrease room for

interpretation on what would constitute as physical activity while also encompassing light

physical activity. Although consistent and clear cut-off points for physical activity

intensity levels may be difficult to determine in youth (e.g., Butte et al., 2012; Dishman

et al., 2001), there have been studies that have provided suggested cut-off points for

physical activity in youth (e.g., Joschtel & Trost, 2014; Trost et al., 2011). Moreover,

subjective and objective reports of physical activity have provided support for the

benefits of physical activity on health-related outcomes (e.g., Must & Tybor, 2005). In

general, the literature has supported an inverse relationship between the likelihood of

overweight status and higher blood pressure with time and intensity of physical activity

levels such that the negative health outcomes (i.e., overweight status, high blood

pressure) would decrease as time and intensity of physical activity increased (e.g., Hay et

al., 2012). Additionally, other benefits of physical activity have been identified in the

literature (e.g., decreased long-term health problems, physical growth, biological

maturation; Strong et al., 2005; World Health Organization [WHO], 2018). Taken

together, it is important to consider sedentary behaviors and physical activity in youth, as

it can influence various aspects of youth development.


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Expansion of Data Analytic Plan Rationale

Bland-Altman Analysis. Despite the apparent importance of examining physical

activity, there does not seem to be a “gold standard” way of measuring physical activity

in youth (e.g., Adamo et al., 2009; Dishman et al., 2001). As outlined in the main

document, there have been strengths and limitations to various forms of measuring

physical activity. While efforts have been made to determine the best method of

comparing subjective and objective reports of physical activity to capture a more accurate

representation of youth physical activity, some methods utilized (e.g., correlational

analyses) may not be the most appropriate method (e.g., Adamo et al., 2009; Giavarina,

2015). Further, as a more appropriate method for assessing level of agreement between

subjective and objective reports exists (i.e., Bland-Altman analyses; Bland & Altman,

1986, 2010), this analysis should be utilized more frequently to make these comparisons.

Generally, the purpose of utilizing Bland-Altman analyses over correlational

analyses is to obtain more accurate information regarding the level at which measures of

differing methods agree, instead of obtaining the strength of a relation between these

methods (Bland & Altman, 2010). According to the literature, interpretations of

correlation coefficients have been used to draw conclusions about the agreement between

measures; however, doing so is misleading, as correlation coefficients are a better

indicator of association rather than a good representation of agreement (Bland & Altman,

2003, 2010). For example, although two measures may have a high correlation, this does

not automatically mean that the measures are high in agreement; measures that have

lower levels of agreement can still have high correlation coefficients (Bland & Altman,

2010). On the other hand, Bland-Altman analyses aim to assess levels of agreement


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between measures by first examining the difference between the methods of

measurement against the mean of the measurements (Bland & Altman, 2010; Giavarina,

2015). A priori limits of maximum expected differences are set, and confidence intervals

are created for the Bland-Altman plot to gather more information regarding the

magnitude of any systematic differences between methods (Giavarina, 2015). Regression

lines can also be created based on the two methods to provide more evidence of

agreement between measures (e.g., consistency across means can support level of

agreement; Bland & Altman, 2010; Giavarina, 2015).

Upon review of the literature, there seems to be support for the use of Bland-

Altman analyses to assess agreement between health-related variables in youth.

Specifically, studies have utilized Bland-Altman analyses to determine the level of

agreement between various health-related variables such as with measurements of pain

(e.g., Lal et al., 2011) and quality of life (e.g., Taylor et al., 2011) in youth populations

and have been able to provide more reliable information regarding agreement and

discrepancy levels between the different measures. Additionally, levels of agreement

between subjective and objective reports of physical activity in adults have been

evaluated using Bland-Altman analyses (e.g., Hagströmer et al., 2006; Loney et al.,

2011), providing further support for its general use and promise in evaluations of physical

activity measurement methods in youth populations.

Metric for Subjective and Objective Youth Physical Activity Measurements.

To accurately analyze agreement between subjective and objective measures, the metric

for these methods of measurement should be the same (supported by Bland & Altman,

1986, 2003, 2010). Studies that have attempted to utilize Bland-Altman analyses to assess


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the level of agreement between youth physical activity measurements have used different

ways of adjusting subjective and objective reports of physical activity to be on the same

metric (e.g., Benítez-Porres et al., 2016; Gurd & Klentrou, 2003; Hagströmer et al., 2008;

Taren et al., 1993). For the current study, the method utilized by Benítez-Porres et al.

(2016), in which z-score transformed values for subjective and objective reports, will be

utilized. Other methods that have been used will be briefly outlined, and the benefit of

analyzing the methods on the same metric used by Benítez-Porres et al. (2016) will be

discussed.

Some studies have utilized the rate of energy expenditure for a specific activity, as

outlined in a compendium of physical activities (Ainsworth et al., 2000), in calculations

to transform responses on subjective measures of physical activity into their metabolic

equivalents (e.g., Gurd & Klentrou, 2003; Hagströmer et al., 2008). Although the use of

the compendium can be useful in some respects, the compendium is based on information

gathered from adult populations; therefore, these estimates of energy expenditure may not

be as accurate when utilized in youth populations. Further, Hagströmer et al. (2008) even

briefly discussed how the use of a rate of expenditure for different activities does not

allow for individual differences in youth (e.g., activity levels, metabolic efficiency) to be

taken into account. Therefore, using energy expenditure values gathered from a

compendium to help calculate METs from a subjective measure may not allow for a true

representation of youth physical activity to be obtained.

On the other hand, z-score transformed data (as supported by Benítez-Porres et

al., 2016) allows for the data to be analyzed in the same metric without having to

complete extraneous calculations. In other words, it eliminates the need for the use of


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adult population estimates of energy expenditure to analyze information for youth

populations. Using z-score transformed data could also decrease the likelihood of

calculation and/or activity energy expenditure interpretation errors as well (i.e., being

unsure of which activity would best fit with one listed in the compendium). Taken

together, using the more recent method of z-score transformations seems to be an

appropriate way of examining agreement levels between subjective and objective youth

physical activity measures without the additional limitations of utilizing the compendium

of physical activities to calculate metabolic equivalents.

Shared and Unshared Factors Between Youth Depressive and Anxiety Symptoms

As previously mentioned, youth depressive and anxiety symptoms are highly

comorbid (Cummings et al., 2014; Merikangas et al., 2009). Because of this, taking both

into account by controlling for each other in analyses is important. Further, the shared

and differentiating features of depression and anxiety have been identified in the

literature (Clark and Watson, 1991). These features can help to support the expected

ways in which the symptom presentation of depression and anxiety can relate to ways in

which an individual may report on certain measures such as physical activity. The shared

and unshared factors between depression and anxiety will be explained, and this

information will be synthesized to provide a foundation for the hypothesized way in

which symptoms of depression and anxiety can relate to physical activity reports in

youth.

Some shared and unshared factors of depression and anxiety can be explained

using the Tripartite Model of Anxiety and Depression that was proposed by Clark and

Watson (1991). The tripartite model aims to highlight the ways in which symptoms of


65

anxiety and depression could overlap with each other. Further, the tripartite model of

anxiety and depression emphasizes how mood and anxiety disorders can be differentiated

based on the presence of general distress, physiological hyperarousal, and/or anhedonia

(Clark & Watson, 1991; Garber & Weersing, 2010). The model helps to account for the

symptom overlap by highlighting how depression and anxiety have a shared component

of negative affect but can be differentiated by levels of positive affect and physiological

arousal (Clark & Watson, 1991). Specifically, depression is more associated with low

positive affect and loss of interest, and anxiety is more associated with somatic tension

and hyperarousal, allowing them to be distinguished despite the shared negative affect

(Clark & Watson, 1991; Garber & Weersing, 2010). Finally, studies have also

emphasized how negative cognitions and information processing errors can also be

associated with symptoms of depression and anxiety (e.g., Garber & Weersing, 2010).

Considering the aforementioned information, it is possible that some of the factors

(e.g., information processing errors) can influence self-perceptions of physical activity in

youth with depressive and anxiety symptoms. For example, as depressive symptoms

increase, there may be a loss of interest, leading to decreased engagement in physical

activity. However, any engagement in physical activity may be perceived as more

cumbersome (i.e., youth perceives him/herself as engaging in more physical activity than

in reality) due to other factors such as increased fatigue, negative automatic thoughts, and

feelings of helplessness that have also been found to be associated with depressive

symptoms in youth (e.g., Garber et al., 1993). This could lead to an overestimation of

subjective, in comparison to objective, physical activity. On the other hand, as anxiety

symptoms increase, the hyperarousal symptoms may lead to increased engagement in


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physical activity. However, as distorted cognitions such as overgeneralization,

catastrophizing, and personalizing have been associated with youth anxiety symptoms

(e.g., Weems et al., 2001), it is possible that youths with anxiety symptoms may perceive

themselves as engaging in less physical activity than their actual levels of physical

activity. This could lead to underestimation of subjective, in comparison to objective,

physical activity.

Conclusion of Extended Literature Review

Overall, this literature review has highlighted some gaps in the literature that the

current study aims to address. Although there have been some studies that have examined

subjective and objective measures of youth physical activity; there are limited to no

studies that have done so in the proposed manner. Specifically, there have been limited

studies that have utilized Bland-Altman analyses to measure agreement between methods

of youth physical activity in addition to taking youth symptoms into account. By

examining the level of agreement between subjective and objective reports of youth

physical activity along with the influence of youth symptoms on the level of agreement

between methods, a more accurate interpretation of youth physical activity can be

obtained to help inform future studies that incorporate a multi-method format of

measurement.

level of agreement between subjective and objective

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