anxious moods as a risk factor for depressed moods: …

The Pennsylvania State University

The Graduate School

Department of Psychology

ANXIOUS MOODS AS A RISK FACTOR FOR DEPRESSED MOODS: AN

ECOLOGICAL MOMENTARY ASSESSMENT OF THOSE WITH CLINICAL

ANXIETY AND DEPRESSION

A Thesis in

Psychology

by

Nicholas C. Jacobson

2015 Nicholas C. Jacobson

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Master of Science

August 2015

The thesis of Nicholas C. Jacobson was reviewed and approved* by the following:

Michelle G. Newman

Professor of Psychology

Thesis Advisor

Louis C. Castonguay


Karen Gasper


Peter Molenaar

Professor of Human Development and Family Studies

Sy-Miin Chow

Associate Professor of Human Development and Family Studies

Melvin M. Mark


Head of the Department of Department or Graduate Program

*Signatures are on file in the Graduate School

iii

ABSTRACT

Anxious and depressed moods are highly concurrently related. Despite their strong

concurrent relationship, little research has examined the longitudinal relationship between them.

Using ecological momentary assessment, the current study examined when anxious moods

predicted later depressed moods and whether arousal and fatigue mediated this relationship in

those with pure clinical anxiety, pure clinical depression, mixed clinical anxiety-depression, and

non-anxious/non-depressed controls. Participants (N = 159) completed momentary measures of

anxious and depressed moods, arousal, heart rate, and fatigue every hour while they were awake

for seven days. The results were analyzed using differential time-varying effect models and state

space models to investigate when constructs were associated with one another. The results

suggested that anxious moods positively predicted later depressed moods, and depressed moods

positively predicted later anxious moods. Further, low perceived arousal and high fatigue jointly

mediated the relationship between anxious and later depressed moods for all groups. High heart

rate mediated the relationship between anxious and depressed moods for those with both pure

clinical anxiety and pure clinical depression. For those with pure clinical depression, high heart

rate variability and high fatigue mediated a positive relationship between anxious and later

depressed moods, and, for those with pure clinical anxiety, low heart rate variability and low

fatigue mediated a negative relationship between anxious and depressed moods. Taken together,

these findings indicate that anxious and depressed moods are bi-directional short-term risk factors

for one another, and arousal and fatigue mediate this relationship.

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TABLE OF CONTENTS

List of Figures .......................................................................................................................... vi

List of Tables ........................................................................................................................... vii

Acknowledgements .................................................................................................................. viii

Chapter 1 Introduction ............................................................................................................. 1

The Current Study ............................................................................................................ 7 Hypotheses ....................................................................................................................... 7

Chapter 2 Method .................................................................................................................... 9

Trait Measures of Anxiety and Depression ...................................................................... 9 Depression Anxiety and Stress Scale (DASS) Depression Scale. ............................ 9 Generalized Anxiety Disorder Questionnaire - IV (GAD-Q-IV). ............................ 10 Social Phobia Diagnostic Questionnaire (SPDQ). ................................................... 10

Trait Measures of Anxiety and Depression ...................................................................... 11 Profile of Mood States – Brief (POMS-B) Anxiety and Depression Scales ............ 11 POMS-B Fatigue Scale ............................................................................................ 12 Arousal ..................................................................................................................... 12 Heart rate and heart rate variability .......................................................................... 13

Phone apparatus ............................................................................................................... 13 Participants ....................................................................................................................... 14 Procedure ......................................................................................................................... 14 Planned Analyses ............................................................................................................. 15

Compliance Rates. .................................................................................................... 15 Concurrent Between and Within-Person Associations. ........................................... 16 Differential Time-Varying Effect Model (DTVEM). .............................................. 16

Chapter 3 Results ..................................................................................................................... 20

Compliance ...................................................................................................................... 20 Cross-Sectional and Within-Person Concurrent Associations ......................................... 20 Hypothesis 1: Anxious Moods Predicting Depressed Moods .......................................... 24 Hypothesis 2: Self-Reported Arousal, Heart Rate, Heart Rate Variability and Fatigue

will Mediate the Relationship between Anxious and Depressed Moods ................. 30 Pure clinical anxiety group. ...................................................................................... 30 Pure clinical depression group ................................................................................. 33 Mixed clinical anxiety-depression group. ................................................................ 36 Non-anxious/non-depressed control group .............................................................. 39

Hypothesis 3: Group Differences with respect to Mediational Pathways ........................ 41

Chapter 4 Discussion ............................................................................................................... 46

v

Appendix A DASS .......................................................................................................... 66 Appendix B GAD-Q-IV .................................................................................................. 68 Appendix C SPDQ .......................................................................................................... 70 Appendix D Anxious and Depressed Mood Questions ................................................... 72 Appendix E Arousal and Fatigue .................................................................................... 73

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LIST OF FIGURES

Figure 3-1. Anxious moods predicting depressed moods for the pure clinical anxiety

group. ............................................................................................................................... 25

Figure 3-2. Anxious moods predicting depressed moods for the pure clinical depression

group. ............................................................................................................................... 26

Figure 3-3. Anxious moods predicting depressed moods for the mixed clinical anxiety-

depression group. ............................................................................................................. 27

Figure 3-4. Anxious moods predicting depressed moods for the non-anxious/non-

depressed control group. .................................................................................................. 28

Figure 3-5. Anxious moods, arousal, heart rate, and heart rate variability on depressed

moods for the pure clinical anxiety group. ....................................................................... 32

Figure 3-6. Anxious moods, arousal, heart rate, heart rate variability, and fatigue on

depressed moods for the pure clinical anxiety group. ...................................................... 33


moods for the pure clinical depression group. ................................................................. 35


depressed moods for the pure clinical depression group. ................................................. 36


moods for the mixed clinical anxiety-depression group. ................................................. 38


depressed moods for the mixed clinical anxiety-depression group. ................................. 39


moods for the non-anxious/non-depressed control group. ............................................... 40


depressed moods for the non-anxious/non-depressed control group. .............................. 41

vii

LIST OF TABLES

Table 3-1. Cross-Sectional Between-Person Correlations Associations between Measures ... 21

Table 3-2. Cross-Sectional Between-Person Correlations Associations between Measures ... 23

viii

ACKNOWLEDGEMENTS

I would like to express my gratitude for the guidance from my committee members, and

the support of my family and my wife.

I would like to thank Drs. Michelle Newman, Louis Castonguay, Karen Gasper, Peter

Molenaar, and Sy-Miin Chow for their advice and counsel. This research required expertise in a

large number of domains including psychopathology, psychophysiology, and longitudinal data

analysis. Without the guidance, support, and expertise of my committee, I would not have been

able to carry out this project. My committee members provided essential feedback in the design,

analysis, and interpretation of this project. In particular, I would like to thank my adviser,

Michelle Newman, for her guidance from the inception to the completion of this project. Your

input was invaluable in enhancing this project and helping my ideas come to fruition.

I would next like to thank my parents for their support. You have worked very hard to

give me the best education possible, and I appreciate your reassurance and advice.

Finally, I would like to thank my wife, Amanda. Your patience, support, encouragement,

and love have made everything that I have accomplished possible. For the past decade, you have

been by my side encouraging me to peruse my passions and supporting me every step of the way.

1

Chapter 1

Introduction

Anxiety and depressive disorders are regularly comorbid with each other with lifetime

prevalence estimates of 16-50% (Angold, Costello, & Erkanli, 1999; Seligman & Ollendick,

1998). Whereas a principal diagnosis of major depression is associated with a lifetime diagnosis

of any anxiety disorder in 73% of individuals, a principal diagnosis of any anxiety disorder is

associated with a lifetime diagnosis of depression in 27-77% of people (Brown, Campbell,

Lehman, Grisham, & Mancill, 2001). In addition, when compared to those with pure diagnoses,

individuals with comorbid anxiety and depressive disorders suffer from greater chronicity and

severity of each diagnosis; poorer work and psychosocial functioning; lower perceived quality of

life; and a heightened risk of suicide (Brown, Schulberg, Madonia, Shear, & Houck, 1996;

Kessler et al., 1998; Olfson et al., 1997; Pfeiffer, Ganoczy, Ilgen, Zivin, & Valenstein, 2009;

Sherbourne, Wells, Meredith, Jackson, & Camp, 1996). Thus, a greater understanding of risk and

maintaining factors for anxiety and depression comorbidity is important.

To explain the origin of this striking comorbidity, anxiety has been offered as a greater

risk factor for later depression than is depression for later anxiety. However, to evaluate this

putative risk factor, the following would need to be established: (1) anxiety covaries with

concurrent depression, (2) anxiety is distinct from depression along some important dimensions;

(3) anxiety at time 1 predicts depression at a later time point; (4) depression is a weaker predictor

of later anxiety than is anxiety of later depression; and (5) other risk factors do not better explain

the relationship between anxiety and depression (Kazdin, Kraemer, Kessler, Kupfer, & Offord,

1997).

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In fact, there is some evidence to support the above criteria. For example, when measured

concurrently, trait anxiety has been found to covary with depression with correlations ranging

from .45 to .73 (Bjelland, Dahl, Haug, & Neckelmann, 2002; Cannon & Weems, 2006; Morgan,

Wiederman, & Magnus, 1998; Norton, Cosco, Doyle, Done, & Sacker, 2013; Watson et al.,

1995). In addition, anxiety has been distinguished from concurrent depression along a number of

dimensions. For example, arousal and fear are more consistent with anxiety than depression, and

anhedonia (lack of positive affect) is consistent with depression but not anxiety (Barlow,

Chorpita, & Turovsky, 1996; Bjelland et al., 2002; Brown et al., 2001; Cannon & Weems, 2006;

Chorpita, Albano, & Barlow, 1998; Clark & Watson, 1991; Morgan et al., 1998; Tellegen, 1985;

Watson et al., 1995). Anxiety and depression can also be distinguished in their etiologies as they

are associated with distinct environmental factors (Eley & Stevenson, 1999; Eley, 1997; Kendler,

Heath, G., & J., 1987; Kendler, Neale, Kessler, Heath, & Eaves, 1992; Thapar & McGuffin,

1997). Loss, school stress, family relationship problems, and friendships are uniquely related to

the onset of depression, whereas anxiety is uniquely related to threats of future danger (e.g.

anxiety induced from a father’s recent diagnosis of cancer and the concern that he may die in the

near future) (Eley & Stevenson, 2000; Finlay-Jones & Brown, 1981; Goodyer & Altham, 1991).

Taken together, these studies provide some support for the first and second criteria for

establishing a risk factor (Kazdin et al., 1997). Likewise, trait anxiety tends to predict heightened

trait depression over prospective time periods with estimates ranging from .16 to .45, but trait

depression does not significantly predict later heightened trait anxiety. Similarly, the hazards ratio

of persons with an anxiety disorder developing a later depressive disorder ranged from 1.49 to

7.1, and the relationship was not significant in depressive disorders predicting the later onset of

an anxiety disorder (Gallerani, Garber, & Martin, 2010; Goodwin & Surgeons, 2002; see

Jacobson & Newman, 2012 for a meta-analysis; Viana & Rabian, 2009). These findings satisfy

the third and fourth criteria for establishing a risk factor.

3

The above data provide support for trait anxiety as a prospective risk factor for trait

depression, but may ignore important fluctuations between anxiety and depression that occur less

than one month apart. Both anxiety and depression are conceptualized as mood states (varying

based on an individual’s environment) and traits (stable across different environments) (Krohne,

Spaderna, Spielberger, & Schmukle, 2002; Spielberger, Gorsuch, Lushene, Vagg, & Jacobs,

1983). Nonetheless, there is very little data on the impact of fluctuating mood states of anxiety

and depression on anxious and depressive traits. Given that the trait-based scales often summarize

the past weeks, months, or years, fluctuations in the mood state relationship between anxiety and

depression may explain the trait-based co-occurrence findings (as the co-occurrence rates could

be overemphasized because the time trajectory of individual symptoms are not taken into

account).

In addition to focusing on traits as opposed to mood states, research on the prospective

relationship between anxiety and depression has relied mostly on self-reported recall of symptom

chronicity (Adams & Boscarino, 2011; Aderka, Weisman, Shahar, & Gilboa-Schechtman, 2004;

Alipour, Lamyian, & Hajizadeh, 2012; Alpert et al., 1999; Alpert, Maddocks, Rosenbaum, &

Fava, 1994; Andrade, Eaton, & Chilcoat, 1996; Aronson & Logue, 1987; Austin, Tully, & Parker,

2007; Van Ameringen, Mancini, Styan, & Donison, 1991). For example, these studies often ask

persons to recall their symptoms from the past few months to years (Adams & Boscarino, 2011;

Alpert et al., 1999; Alpert et al., 1994; Andrade et al., 1996; Aronson & Logue, 1987; Van

Ameringen et al., 1991), weeks (Aderka et al., 2004; Austin et al., 2007), or “in general” (i.e., no

specified time period) (Aderka et al., 2004; Alipour et al., 2012; Austin et al., 2007). Thus, the

methodology of these studies may be flawed due to substantial recall biases (Shiffman et al.,

1997; Solhan, Trull, Jahng, & Wood, 2009; Stone, Broderick, Shiffman, & Schwartz, 2004; van

den Brink, Bandell-Hoekstra, & Abu-Saad, 2001). The study of mood state processes rectifies

recall biases through the collection of real-time assessments or the use of small reflection periods.

4

Momentary assessments have additional advantages. Such advantages include targeting

intensive repeated measures and leading to the collection of a substantial sampling of everyday

life (Bolger & Laurenceau, 2013). In addition, this methodology is implemented outside of the

laboratory and in participants’ natural settings. As mood state processes occupy much of each

person’s time, memory, and attention (Wheeler & Reis, 1991), this research is capable of

addressing much of “life as it is lived” (Allport, 1942). It is therefore more ecologically valid than

reliance on recall across time.

In implementing these novel methodologies to assess the relationship between anxious

and depressed moods, only one study has examined individual differences in concurrent

covariation using intensive repeated measures. This study of inter-individual variability, using

mood questionnaires completed once per day, showed that in persons whose mood was accounted

for by valence or pleasantness of an affective experience, concurrent anxious and depressed

moods covaried highly (r = .90; mean r = .68). However, this relationship was weaker in those

whose mood was focused mostly on arousal (r = .16) (Feldman, 1995). Thus, there may be

individual differences in the degree to which anxious and depressed moods covary when

measured concurrently.

In addition to the little research on concurrent covariation between measured anxious and

depressed moods, only a few studies have examined the temporal relationship between these

mood states using intensive repeated measures, and hence tested the third and fourth criteria for a

risk factor (Starr & Davila, 2011; Swendsen, 1998, 1997). Using ecological momentary

assessments with paper and pencil measures, Swendsen (1997) and Swendsen (1998) measured

anxious and depressed moods at random interval prompts (in separate three hour blocks) five

times per day for seven days in non-clinical undergraduate samples. Analyzing only the data

collected one day after each participant’s most stressful event of the week (averaging the data

from the entire day following the stressful event), Swendsen (1997) found that baseline anxious

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moods predicted depressed moods one day after the occurrence of a stressful event, and baseline

depressed moods did not predict anxious moods. Similarly, in the hours following each

participant’s most stressful event, Swendsen (1998) found that anxious moods predicted later

depressed moods sometime between one to nine hours later (Swendsen reported that more

analyses were conducted but did not specify which time periods were used), and not vice versa.

Starr and Davila (2011) employed an end of the day diary assessment for 21 days among those

with generalized anxiety disorder who had a history of depressive symptoms. These authors

found that anxious moods significantly predicted later depressed moods, and not vice versa (when

controlling for concurrent depressed moods). Starr and Davila also found that the strongest lagged

predictor within the 21-day period was a two-day period. Thus across three studies, anxious

moods significantly predicted depressed moods across hours and days, whereas depressed moods

did not predict later anxious moods.

Although these studies were among the first to examine the longitudinal relationship

between anxious and depressed moods, each of them has notable limitations, which should be

addressed. For example, despite having data to assess the temporal relationship between anxious

and depressed moods at other time points, Swendsen (1997) and Swendsen (1998) only examined

times following the most stressful event. Swendsen (1998) also did not control for prior auto-

correlations of anxious and depressed moods. Thus, the author’s findings could be due to a high

stable concurrent correlation between anxious and depressed moods, rather than being due to their

temporal relationship. Moreover, the generalizability of Starr and Davila’s (2011) findings may

be limited as generalized anxiety disorder was participants’ primary diagnosis. Hence participants

may have considered anxious thoughts to be more concerning than depressive ones in the moment

and may have pushed aside depressive thoughts until they were no longer in an anxious mood.

Most importantly, each of these studies only chose to examine lagged time points that were

convenient and ignored more than 80% of their available data regarding the time relationship

6

between anxious and depressed moods. Thus, more research is needed to address the

generalizability of these findings and to assess this relationship across additional time periods.

Moreover, Swendsen (1997; 1998) and Starr and Davila (2011) did not examine

mediators between anxious and depressed moods, which might contribute to a greater

understanding of why and how anxious moods predict depressed moods. If there is variation in

the relationship between anxious and depressed moods across shorter time periods, then such

variability can only be explained by a mechanism that also fluctuates.

Proposing a mechanism that may fluctuate across short-time periods, Akiskal (1985)

hypothesized that anxious moods produce depressed moods through hyperarousal and fatigue.

Based on this theory, anxious moods create excessive hyperarousal, and the heightened degree of

energy expended during anxious moods produces fatigue over time. This fatigue is theorized to

lead to depression based on an inability to function at a normal capacity (e.g. low energy, reduced

activity levels). Thus, Akiskal theorized the existence of two mediators of anxious and depressed

moods: hyperarousal and fatigue.

There is some support for Akiskal’s theory in studies focused on trait anxiety and

depression. For example, trait anxiety predicts later fatigue, (Fleer, Sleijfer, Hoekstra, Tuinman,

& Hoekstra-Weebers, 2005), and fatigue predicts later trait depression (Corwin, Brownstead,

Barton, Heckard, & Morin, 2005). However, no studies have examined hyperarousal and fatigue

as mediators of anxious and depressed moods. Both perceived arousal and physiological

assessments may be means to test this theory. Hyperarousal is evidenced by high heart rate and

low heart rate variability (Friedman & Thayer, 1998). High heart rate and low heart rate

variability have been associated with both trait anxiety and trait depression (Gorman & Sloan,

2000). Additionally, one ecological momentary assessment study found that state worry

significantly predicted concurrent and later low-heart rate variability (Brosschot, Van Dijk, &

Thayer, 2007). Concurrent hyperarousal has also been shown to be negatively concurrently

7

associated with fatigue (Crofford, 1998). However, no research has been published regarding

anxious moods predicting later hyperarousal, hyperarousal predicting later fatigue, or

hyperarousal or fatigue mediating the relationship between anxious moods and later depressed

moods. We examined hyperarousal (through self-reported arousal, heart rate, and heart rate

variability) and fatigue (through self-reported fatigue) as potential mechanisms of the relationship

between anxiety and depression at each assessment period.

The Current Study

The proposed study examined the short-term temporal relationship between anxious and

depressed moods among participants with pure clinical anxiety (generalized anxiety disorder

and/or social phobia), pure clinical depression (major depression), mixed clinical anxiety-

depression (comorbid generalized anxiety disorder/social phobia with major depressive disorder),

and non-anxious/non-depressed controls. It utilized an ecological momentary assessment design

wherein participants periodically recorded their moods. Smartphones were used to randomly

prompt participants once per hour for seven days during the times in which the participants were

awake to assess momentary anxious moods, depressed moods, arousal, and fatigue. Group

differences were examined to determine if the relationship between anxious and depressed moods

was different across diagnostic groups (e.g. pure clinical anxiety versus pure clinical depression,

mixed clinical anxiety-depression versus non-anxious/non-depressed controls).

Hypotheses

We hypothesized that: (1) anxious moods would positively predict later depressed moods

within a one-day interval, (2) anxious moods and later depressed moods would be mediated by

8

arousal and fatigue such that anxiety would positively predict arousal, arousal would positively

predict fatigue, and fatigue would positively predict depression; (3) the relationship between

anxiety and later depression, as well as each of the mediational relationships between anxiety and

later depression would be stable across the pure clinical anxiety group, pure clinical depression

group, mixed clinical anxiety-depression group, and non-anxious/non-depressed control group.

9

Chapter 2

Method

Trait Measures of Anxiety and Depression

Depression Anxiety and Stress Scale (DASS) Depression Scale.

The DASS Depression scale was used to assess DSM-IV major depressive disorder.

When compared to a structured interview, this scale has good sensitivity (80-86%) and specificity

(64-80%) in assessing DSM-IV major depressive disorder using a cutoff score of 12 or higher as

indicative of depression (Dahm, Ponsford, Wong, & Schönberger, 2009; Mitchell, Burns, &

Dorstyn, 2008). Accordingly, the cut-point of 12 was used to select individuals with major

depressive disorder. The Depression scale has excellent convergent validity (r = .78 with the

Personal Disturbance Depression scale, r = .66 with the Hamilton Depression Scale) (Crawford &

Henry, 2003) and adequate discriminant validity (r = .62 for Personal Disturbance Anxiety scale,

r = .59 for the Hamilton Anxiety Scale)(Crawford & Henry, 2003). The Depression scale also has

excellent internal consistency (α = .95) (Crawford & Henry, 2003). In regard to the Diagnostic

and Statistical Manual 5th Edition (DSM-5), the only change between DSM-IV major depressive

disorder and DSM-5 major depressive disorder is DSM-5’s removal of bereavement as an

exception to the diagnosis. As the Depression Scale does not assess bereavement, it is likely that

the DASS would have high convergent validity with DSM-5 major depressive disorder.

10

Generalized Anxiety Disorder Questionnaire - IV (GAD-Q-IV).

The GAD-Q-IV was used to assess DSM-IV generalized anxiety disorder (Newman et

al., 2002). The GAD-Q-IV demonstrated good sensitivity (76-90%) and specificity (83-100%)

(Moore, Anderson, Barnes, Haigh, & Fresco, 2013; Newman et al., 2002); in addition, the GAD-

Q-IV has adequate kappa agreement (kappa =.67) (Newman et al., 2002). We used DSM-IV

criteria to determine the diagnosis of generalized anxiety disorder based on the participants’ self-

report. The GAD-Q-IV has also showed good discriminant validity among other measures of

anxiety (r = .45 with the PTSD Checklist, r = .34 with the Social Interaction Anxiety Scale) and

depression (r = .26 in correlation with the Zung Self-Rating Depression Scale); in addition to

adequate convergent validity (r range .55-.66 with the Penn State Worry Questionnaire)

(Newman et al., 2002). Although originally designed to measure the DSM-IV criteria, the DSM-5

criteria for generalized anxiety disorder has remained the same; thus, this measure assesses both

DSM-IV and DSM-5 generalized anxiety disorder.

Social Phobia Diagnostic Questionnaire (SPDQ).

The SPDQ is a 25-item self-report measure to diagnose DSM-IV social phobia (Newman,

Kachin, Zuellig, Constantino, & Cashman-McGrath, 2003). The SPDQ showed good sensitivity

(82%) and specificity (85%), and adequate kappa agreement (.66) with a structured interview

(Newman et al., 2003). The SPDQ showed good discriminant validity with other anxiety and

depression measures (r = .31 for the Panic Disorder Severity Scale, r = .38 for the Penn State

Worry Questionnaire, r = .28 for the GAD-Q-IV, r = .32 for the Beck Depression Inventory), and

adequate convergent validity (r = .61 with the Social Avoidance and Distress Scale) (Newman et

al., 2003). In regard to the DSM-5 diagnoses, the content of the DSM-IV criteria for social phobia

11

(called social anxiety disorder in the DSM-5) has widely remained the same (American

Psychiatric Association, 2013). Although there are many semantic changes to the diagnoses (and

changes to the specifiers), the only substantive difference is that the DSM-IV requires the client

to believe that the phobia is excessive and unreasonable (American Psychiatric Association,

2000); however in the DSM-5, that determination is left to the clinician (American Psychiatric

Association, 2013). Given these minimal changes, it is likely that the SPDQ will assess DSM-5

social anxiety disorder with high convergent validity.

Trait Measures of Anxiety and Depression

Profile of Mood States – Brief (POMS-B) Anxiety and Depression Scales

The POMS-B tension/anxiety (5-items) and depression/dejection scales (5-items) were

used to measure momentary levels of anxiety and depression. The POMS-B items were

constructed by using the five largest factor loadings from each of the POMS scales (McNair &

Heuchert, 2005). For the purpose of this manuscript, the tension/anxiety scale will hereinafter be

referred to as the POMS anxiety scale, whereas the depression/dejection scale will be hereinafter

referred to as the POMS depression scale. Both scales ask persons how they are feeling right now.

Example items include “nervous” and “anxious” for the anxiety scale, and “sad” and “gloomy”

for the depression scale. The POMS-B showed high internal consistencies for both the anxiety

and depression scales in both males and females in college and clinical samples (α range .86-.89)

(McNair & Heuchert, 2005). Moreover, the POMS-B is highly representative of the full POMS,

which demonstrates validity based on external validation using emotion manipulations (McNair

& Heuchert, 2005; Pillard, Atkinson, & Fisher, 1967; Pillard & Fisher, 1967). As the scale was

collected via smartphone, the scale was modified from a categorical number system to a

12

continuous slider scale to assess anxiety and depression to ensure continuity between different

scales and to measure the scales continuously. The left and right anchors of the scale were

identical to the original measures.

POMS-B Fatigue Scale

The POMS-B fatigue/inertia scale was used to measure fatigue, and it will be hereafter

referred to as the POMS fatigue scale. Example items include “exhausted” and “fatigued”. The

POMS fatigue scale has demonstrated validity based on external validation using experimental

manipulation of physical exertion (Steptoe & Cox, 1988). The POMS-B fatigue scale has

demonstrated high internal consistency in psychiatric samples for males and females (α range .92-

.93). As with the POMS anxiety and depression scales, the POMS fatigue scale was modified to

work as a continuous slider.

Arousal

A face-valid self-reported measure of arousal (Russell, 2003) measured persons’ level of

arousal at the current time prompt (asking: “Which of these things best describes how you feel

right now?,” scale: “Sleepy to Activated” continuous slider). This item combines both poles of an

arousal scale used in a previous study, and this item is similar to the items used in the previous

study (Feldman, 1995). An item with the same pole has been validated using experimental

manipulations and shown significant changes within-persons (Cohen & Andrade, 2004), and

items using nearly identical poles have also been found to change based on experimental

manipulations of arousal (e.g. “sleepy/alert”, Nilsson, Johansson, & Frantzich, 2009) (e.g.

"sleepy/active", Septianto, 2014).

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Heart rate and heart rate variability

Heart rate was assessed using an open-source application utilizing the camera on

smartphones (Wetherell, 2013). The application uses photoplethysmographic signals that are

obtained by taking pictures of the color changes in the index finger when the finger is pressed

against the phone’s camera. This method of obtaining heart rate and heart rate variability through

smartphone applications has recently been validated (Scully et al., 2012) and has been shown to

have high convergence with traditional measures (r =.98 – 1.00 with heart rate, and r = .90-.97 in

RMSSD) (Bolkhovsky, Scully, & Chon, 2012). The application collects the amount of time

between each heart rate. When running feasibility tests for this study, the application had high

agreement with the gold standard of measurement using the ECG150 in the Biopac MP150 with

calculations of BPM (r = .79). The amount of time between successive heartbeats enables the

calculation of the rMSSD, which is the square root of the mean of the sum of square differences

between adjacent RR intervals over the length of the analyses (Massin, Derkenne, & von Bernuth,

1999). The rMSSD is highly correlated with other measures of heart rate variability including the

ultralow frequency index (r = .76), very low frequency index (r = .78), low frequency index (r =

.80), high frequency index (r = .91), low to high frequency index ratio (r = .60), and combined

low and high frequency indices (r = .80) (Massin et al., 1999).

Phone apparatus

Participants used their own smartphones or were given LG LS670 Android 2.2 phones for

data collection (specifications: 320 x 480 pixels, 3.2 inch screen, multitouch, Qualcomm

Snapdragon S1 MSM7627 600 MHz processor) if they did not have an Android phone. A

14

customized application was used for all of the questionnaires and to launch the heart rate

application at each prompt.

Participants

The pure clinical anxiety group (N = 56, 38% Male, M = 19.05 years old, 67.8%

Caucasian, 7.1% African American, 8.9% Hispanic/Latino, 12.5% Asian American, 1.8%

Multiracial/multiethnic, 1.8% other), pure clinical depression group (N = 7, 29% Male, M = 18.42

years old, 57.1% Caucasian, 28.6% Asian American, 14.3% other), mixed clinical anxiety-

depression group (N = 43, 42% Male, M = 19.21 years old, 62.8% Caucasian, 9.3% African

American, 4.7% Hispanic/Latino, 18.6% Asian American, 2.3% Multiracial/multiethnic, 2.3%

other), and the non-anxious/non-depressed control group (N = 53, 62% Male, M = 19.36 years

old, 77.4% Caucasian, 9.4% African American, 1.9% Hispanic/Latino, 1.9% Asian American,

1.8% Multiracial/multiethnic, 7.5% other) were selected from an undergraduate subject pool.

Based on the cut-off procedures described above, participants diagnosed with major depressive

disorder, generalized anxiety disorder, and social phobia were sampled.

Procedure

After being selected as possible participants for the pure clinical anxiety (generalized

anxiety disorder or social phobia), pure clinical depression (major depressive disorder), mixed

clinical anxiety-depression group (generalized anxiety disorder/major depressive disorder and

social phobia/major depressive disorder), and non-anxious/non-depressed control (no diagnoses)

groups, potential participants were contacted via email inviting them to participate in a study

predicting moods. Those who expressed interest were asked to attend a short information session.

15

During the information session, participants gave consent to participate in the study.

Next, they installed applications on their smartphone or were provided an Android smartphone if

they did not own a compatible device, and received instructions on how to complete the

questionnaires. During the introductory session, participants were shown a PowerPoint

demonstration regarding the use of the questionnaires and heart rate application. In an attempt to

control for ceiling and floor effects, participants were asked to try not to use the ends of the

scales, and to only use the ends of the scales in extreme circumstances for each of the

questionnaires. Additionally, each question in the survey was explained so that participants had a

clear understanding of what they were being asked to report.

For the next seven days following the information session, participants were

prompted once per hour during the times in which they were awake. At each prompt, participants

were asked to complete the Heart Rate/Heart Rate Variability assessment (30 seconds long),

POMS-B anxiety (5-items), POMS-B depression (5-items), self-reported arousal item (1 item),

and POMS-B fatigue (5-items). As such, participants completed 16 questions at each prompt.

Following the assessment, participants came to a debriefing session to return the phones.

Planned Analyses

Compliance Rates.

Compliance rates were calculated by dividing the number of completed prompts by the

number of total prompts (note that this would be different for every subject based on the number

of hours that each subject indicated he/she would be awake).

16

Concurrent Between and Within-Person Associations.

Prior to the primary analyses, between and within-person associations were examined. To

examine between-person associations, averaging the raw data for each person within each group

created summary statistics. The correlation, mean, and standard deviation of the mean scores of

each person’s average scores were then assessed. Next, the percentage of variation accounted for

by between-person variation compared to within-person variation was estimated using multilevel

models of each item with only the group-level intercept and the intercept within persons. The

percentage of between-person variation was estimated based on the percentage of variation

explained by the random intercepts divided by the total variation (i.e. this is an intraclass

correlation coefficient). The rest of the variation not accounted for based on the residual variation

explained the percentage of within-person variation. Within-person concurrent associations were

examined by using state-space models modeling only autoregressive lags of each item and

examining the residual covariation of each item for each group.

Differential Time-Varying Effect Model (DTVEM).

The primary analyses were conducted using DTVEMs, which allow one to investigate

when processes optimally predict one another at an individual or group level (Jacobson, Chow, &

Newman, 2015). DTVEM includes two primary stages: (1) an exploratory stage used to identify

optimal time lags and (2) a confirmatory stage to model the optimal time lags in the time series.

The exploratory stage of DTVEM operates within the domain of generalized additive mixed

models (GAMMs), using a varying coefficient framework. Specifically, DTVEM allows for the

estimation of the linear relationship between predictors and the outcome as a nonlinear function

of time differences. Following the initial exploratory stage, the confirmatory stage is used to

17

estimate the lags identified in the first stage to test if these individual points are significant in a

more traditional fashion. In this case, the confirmatory phase was implemented through state-

space models with each person’s lagged estimates constrained to be equal for all persons within

the group. All analyses were carried out through the DTVEM function, utilizing both mgcv (for

stage 1) (Wood, 2014) and OpenMx (for stage 2) (Boker et al., 2010). Note that once significant

lags have been identified, the exploratory phase is conducted again to ensure that any significant

lags are not missed (see Jacobson et al., 2015 for more details). Note that the state-space approach

represents the final stage for each DTVEM.

As an example of the steps involved in DTVEM, the first stage of DTVEM was

conducted using the following equations for anxious moods predicting depressed moods: mean of

depression, 𝜇𝑖 ≡ 𝐸(Depression𝑖,𝑗), is linked to a semiparametric predictor, 𝜂𝑖, expressed as

𝜂𝑖 = 𝑓𝑎(𝛥𝑡𝑎𝑙𝑙 𝑙𝑎𝑔𝑠)𝐴𝑛𝑥𝑖𝑒𝑡𝑦𝑎𝑙𝑙 𝑙𝑎𝑔𝑠 + 𝑓𝑑(𝛥𝑡𝑎𝑙𝑙 𝑙𝑎𝑔𝑠)𝐷𝑒𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛𝑎𝑙𝑙 𝑙𝑎𝑔𝑠 + 𝑓𝑡(𝑡)+𝑏𝑖

The term i represents each participant, j denotes the measurement occasion, t denotes the

time, and 𝛥𝑡 denotes the time differences between the predictor and the outcome. 𝑓𝜃𝑖 denotes an

exponential family distribution, with canonical parameter 𝜃𝑓𝑖. The terms Anxietyall lags and

Depressionall lags are the data for anxious and depressed moods, but lagged in a stacked format,

such that all potential lag times are stacked onto a single variable with each associated lagged

anxious and depressed mood term paired with the outcome Depressioni,j. The terms fa and fd are

smoothed functions based on spline basis, representing the varying-coefficient’s estimates,

meaning that anxious and depressed moods are represented as a linear function of non-linear time

differences. The term ft are smoothed functions based on spline basis, and represents the non-

linear effect of time. The term bi represents the random intercept of depression levels for each

person, and the residuals are assumed to be normally distributed (bi~N(0,ψb)). Following the

18

exploratory phase, the confirmatory phase was implemented by a state-space model, modeling

only the lags identified in the first stage.

Although DTVEM can utilize all potential time lags in the time series, a max lag of 24

hours was put into place as there is an exponential increase in the amount of time it takes to run

models as the number of lags increases (this is due to the implementation of state space models at

the confirmatory stage, e.g. DTVEM with only two predictors and one outcome took two weeks

to converge with a max lag of 24 hours). Additionally, although DTVEM allows for the modeling

of both univariate and multivariate outcomes, which facilitates modeling all mediating

relationships in a single model, the models take extremely long to converge (again due to the state

space models taking a long time to run with a lagged structure). As such, mediation was tested in

a piecewise fashion, using the joint significance test. Note that mediation will be considered to be

established upon the joint significance of a predictor on a mediator and a mediator on an outcome

alone (i.e. no significant direct effect required) (Hayes, 2009).

Group differences were tested using the log-likelihood ratio test, Akaike information

criterion (AIC), and Bayesian information criterion (BIC). Specifically, the fit of the model with

unconstrained parameters was compared to the fit of the model after parameters have been

constrained to be equal across groups. When there is disagreement among the fit indices, the

model fit was determined based on the majority of the fit indices (note that, in practice, the log

likelihood ratio test always supported the overall decision). Model comparison proceeded in three

primary steps for each potential pairwise relationship: (1) all cross-lags between the predictor and

the outcome were constrained to be equal simultaneously, (2) each cross lag was constrained to

be equal separately, and (3) each combination of the individual groups was constrained to be

equal for each lag. Proceeding in this fashion allows one to test if there are any group differences

for any of the cross-lags, determine whether any lags have significant differences between

groups, and determine whether any groups are significantly different from one another. All

19

hypotheses were tested using group-level confirmatory state-space models. The presented results

are based on the final models after applying group differences.

20

Chapter 3

Results

Compliance

Compliance rates were examined based on the number of prompts completed per person.

Note that because participants completed prompts once per hour for every hour in which they

were awake, participants had different numbers of prompts to complete in total. The median

compliance rates were 70.4% for the anxiety group (median 57 prompts completed), 73.5% for

the pure clinical depression group (median 46 prompts completed), 64.3% for the mixed clinical

anxiety-depression group (median 56 prompts completed), and 69.4% for the non-anxious/non-

depressed control group (median 57 prompts completed). This led to a total of 3,305 observations

for the pure clinical anxiety group, 365 observations for the pure clinical depression group, 2,420

observations for the mixed clinical anxiety-depression group, and 2,965 observations for the non-

anxious/non-depressed control group.

Cross-Sectional and Within-Person Concurrent Associations

Prior to the primary analyses, the between-person relationship between constructs was

examined within each group by averaging the items across each person and correlating measures

across persons. See Table 1 for a summary of the findings. The strongest associations across

groups were: (1) anxious mood levels positively correlated with depressed mood levels, (2)

anxious mood levels positively correlated with fatigue levels, (3) depressed mood levels

positively correlated with fatigue levels, (4) arousal levels negatively correlated with fatigue

21

levels, (5) heart rate levels positively correlated with heart rate variability levels (note that

because N = 8 for the pure clinical depression group, the power was quite low for the cross-

sectional associations, and, as such, even strong associations were not statistically significant).

Additionally, note that the correlation between self-reported arousal level and both heart rate

levels and heart rate variability levels was not significant for any group.

Next, the between- and within-person variation was estimated through intraclass

correlation coefficients (ICCs) (see Table 1). All groups demonstrated substantial within-person

variation of anxious moods (48-58% variance is within-persons), depressed moods (27-54%

variance is within-persons), arousal (72-83% variance is within-persons), heart rate (53-68%

variance is within-persons), heart rate variability (70-80% variance is within-persons), and fatigue

(55-70% variance is within-persons). Thus, all measures demonstrated substantial within-person

variation across all groups.

Table 3-1. Cross-Sectional Between-Person Correlations Associations between Measures

Anxiety Group Anxiety Depression Arousal Heart

Rate

Heart Rate

Variability Fatigue

Anxiety .

Depression 0.75* .

Arousal -0.22 -0.36* .

Heart Rate -0.07 -0.06 -0.06 .

Heart Rate Variability 0.14 0.10 -0.06 0.58* .

Fatigue 0.38* 0.52* -0.70* -0.02 0.09 .

M 15.78 15.31 49.19 87.46 65.45 35.08

SD 9.99 12.30 12.63 17.07 20.12 17.09

ICC 0.42 0.53 0.19 0.32 0.12 0.45

Depression Group Anxiety Depression Arousal Heart

Rate

Heart Rate

Variability Fatigue

Anxiety .

Depression 0.75* .

Arousal -0.22 -0.36* .

Heart Rate -0.07 -0.06 -0.06 .


Fatigue 0.38* 0.52* -0.70* -0.02 0.09 .

M 15.78 15.31 49.19 87.46 65.45 35.08

SD 9.99 12.30 12.63 17.07 20.12 17.09

22

ICC 0.42 0.53 0.19 0.32 0.12 0.45

Comorbid Group Anxiety Depression Arousal Heart

Rate

Heart Rate

Variability Fatigue

Anxiety .

Depression 0.89* .

Arousal -0.23 -0.24 .

Heart Rate 0.12 0.16 -0.25 .


Fatigue 0.71* 0.70* -0.50* 0.02 0.09 .

M 24.82 25.31 50.16 88.37 68.71 39.46

SD 14.70 14.58 11.33 25.88 29.31 14.53

ICC 0.52 0.50 0.17 0.47 0.30 0.37

Control Group Anxiety Depression Arousal Heart

Rate

Heart Rate

Variability Fatigue

Anxiety .

Depression 0.89* .

Arousal -0.29* -0.25 .

Heart Rate 0.06 0.02 -0.01 .

Heart Rate Variability -0.02 -0.03 0.06 0.83* .

Fatigue 0.56* 0.51* -0.54* -0.16 -0.11 .

M 11.05 10.09 56.49 92.04 76.18 28.63

SD 9.61 8.98 16.37 32.48 36.68 16.77

ICC 0.46 0.46 0.27 0.48 0.16 0.45 Note. * p < .05. These between-person associations were obtained by creating finding the average

score for each person, and calculating the metrics between persons. The ICC reflects the

intraclass correlation coefficient and is a measure of between-person variation based on

individual intercepts of the time series.

Next, the concurrent within-person associations were examined. See Table 2 for a

summary of the findings. The within-person associations suggested that, for all groups, anxious

moods were positively concurrently related to depressed moods; anxious moods were positively

concurrently related to fatigue; depressed moods were positively concurrently related to fatigue;

arousal was negatively concurrently related to fatigue; and heart rate was positively concurrently

related to heart rate variability. Most groups also had a significantly positive concurrent

association between arousal and heart rate, and a negative concurrent association between arousal

and depressed mood (for all but the pure clinical depression group). For the pure clinical anxiety

and the mixed clinical anxiety-depression group, there was also a significantly negative

concurrent relationship between anxious moods and arousal.

23

Table 3-2. Cross-Sectional Between-Person Correlations Associations between Measures

Anxiety Group Anxiety Depression Arousal Heart

Rate

Heart Rate

Variability Fatigue

Anxiety .

Depression 0.57* .

Arousal -0.05* -0.14* .

Heart Rate 0.00 -0.01 0.07* .

Heart Rate Variability 0.03 0.01 0.09* 0.50* .

Fatigue 0.17* 0.25* -0.49* -0.03 -0.03 .

Depressed Group Anxiety Depression Arousal Heart

Rate

Heart Rate

Variability Fatigue

Anxiety .

Depression 0.58* .

Arousal -0.04 -0.12 .

Heart Rate 0.01 0.08 -0.07 .

Heart Rate Variability -0.06 0.00 -0.03 0.68* .

Fatigue 0.15* 0.19* -0.49* -0.01 -0.06 .

Comorbid Group Anxiety Depression Arousal Heart

Rate

Heart Rate

Variability Fatigue

Anxiety .

Depression 0.71* .

Arousal -0.09* -0.16* .

Heart Rate -0.02 -0.04 0.08* .

Heart Rate Variability -0.04 -0.06* 0.03 0.52* .

Fatigue 0.26* 0.32* -0.52* -0.04 -0.03 .

Control Group Anxiety Depression Arousal Heart

Rate

Heart Rate

Variability Fatigue

Anxiety .

Depression 0.59* .

Arousal -0.02 -0.09* .

Heart Rate 0.00 -0.01 0.07* .

Heart Rate Variability -0.03 -0.02 0.03 0.54* .

Fatigue 0.14* 0.23* -0.47* -0.05* -0.04 . Note. * p < .05. These associations were obtained based on estimates from a state-space model

including only autoregressive lag 1 components and the residual concurrent associations.

24

Hypothesis 1: Anxious Moods Predicting Depressed Moods

The first hypothesis, that anxious moods would significantly predict later depressed

moods, was tested with a multi-group confirmatory state-space model. The results presented here

will include both group estimates that have been constrained to be equal if there were no

significant differences across groups and groups’ specific estimates.

For the pure clinical anxiety group, anxious moods significantly positively predicted later

depressed moods 1 hour later (𝜷 = 0.23, SE = 0.02, Z = 10.151, p < .001), 7 hours later, (𝜷 =

0.04, SE = 0.02, Z = 2.208, p = .027), 8 hours later (𝜷 = 0.06, SE = 0.02, Z = 3.028, p = .002), 10

hours later (𝜷 = 0.12, SE = 0.03, Z = 3.705, p < .001), 13 hours later (𝜷 = 0.14, SE = 0.03, Z =

3.961, p < .001), and 14 hours later (𝜷 = 0.16, SE = 0.02, Z = 7.173, p < .001). For the pure

clinical anxiety group, the autoregressive relationship of depressed moods on themselves was

significant 1 hour later (𝜷 = 0.23, SE = 0.02, Z = 10.151, p < .001), 2 hours later (𝜷 = 0.33, SE =

0.02, Z = 16.996, p < .001), and 12 hours later (𝜷 = 0.22, SE = 0.02, Z = 10.559, p < .001).

25

Figure 3-1. Anxious moods predicting depressed moods for the pure clinical anxiety group.

This figure depicts the effects of anxious moods on depressed moods for the pure clinical anxiety

group. Note that in this figure “anxiety” reflects anxious moods and “depression” reflects

depressed moods. Note that the term lag here represents an hour later in time (i.e. lag 1 is 1 hour

later). Crossregressive lags are symbolized of the arrows between anxious and depressed moods,

and autoregressive lags are symbolized by arrows originating and pointing towards the same

variable.

For the pure clinical depression group, anxious moods positively predicted depressed

moods 7 hours later (𝜷 = 0.04, SE = 0.02, Z = 2.208, p = .027), 8 hours later (𝜷 = 0.06, SE = 0.02,

Z = 3.028, p = .002), 13 hours later (𝜷 = 0.29, SE = 0.13, Z = 2.274, p = .023), and 14 hours later

(𝜷 = 0.16, SE = 0.02, Z = 7.173, p < .001). For the pure clinical depression group, the

autoregressive relationship of depressed moods on themselves occurred 1 hour later (𝜷 = 0.38, SE

26

= 0.05, Z = 6.977, p < .001), 2 hours later (𝜷 = 0.13, SE = 0.06, Z = 2.064, p = .020), and 12 hours

later (𝜷 = 0.22, SE = 0.05, Z = 4.207, p < .001).

Figure 3-2. Anxious moods predicting depressed moods for the pure clinical depression group.

This figure depicts the effects of anxious moods on depressed moods for the pure clinical

depression group. Note that in this figure “anxiety” reflects anxious moods and “depression”

reflects depressed moods. Note that the term lag here represents an hour later in time (i.e. lag 1 is

1 hour later). No cross-regressive lags were significant. Autoregressive lags are symbolized by

arrows originating and pointing towards the same variable.

For the mixed clinical anxiety-depression group, anxious moods positively predicted later

depressed moods 1 hour later(𝜷 = 0.31, SE = 0.02, Z = 12.494, p < .001), 7 hours later (𝜷 = 0.04,

SE = 0.02, Z = 2.208, p = .027), 8 hours later (𝜷 = 0.06, SE = 0.02, Z = 3.028, p = .002), 14 hours

(𝜷 = 0.16, SE = 0.02, Z = 7.173, p < .001), and 16 hours later (𝜷 = 0.10, SE = 0.04, Z = 2.183, p =

27

.029). For the mixed clinical anxiety-depression group, the autoregressive relationship of

depressed moods on themselves was significant 1 hour later (𝜷 = 0.41, SE = 0.02, Z = 17.206, p <

.001), 2 hours later (𝜷 = 0.18, SE = 0.02, Z = 7.228, p < .001), and 12 hours later (𝜷 = 0.14, SE =

0.02, Z = 6.429, p < .001).

Figure 3-3. Anxious moods predicting depressed moods for the mixed clinical anxiety-depression

group.

This figure depicts the effects of anxious moods on depressed moods for the mixed clinical

anxiety-depression group. Note that in this figure “anxiety” reflects anxious moods and

“depression” reflects depressed moods. Note that the term lag here represents an hour later in

time (i.e. lag 1 is 1 hour later). Crossregressive lags are symbolized of the arrows between

anxious and depressed moods, and autoregressive lags are symbolized by arrows originating and

pointing towards the same variable.

28

For the non-anxious/non-depressed control group, anxious moods positively predicted

depressed moods 1 hour later (𝜷 = 0.26, SE = 0.02, Z = 11.241, p < .001), 7 hours later (𝜷 = 0.04,

SE = 0.02, Z = 2.208, p = .027), 8 hours later (𝜷 = 0.06, SE = 0.02, Z = 3.028, p = .002), and 14

hours later (𝜷 = 0.16, SE = 0.02, Z = 7.173, p < .001). Likewise for the non-anxious/non-

depressed control group, depressed moods predicted themselves 1 hour later (𝜷 = 0.31, SE = 0.02,

Z = 15.422, p < .001), 2 hours later (𝜷 = 0.16, SE = 0.02, Z = 7.407, p < .001), and 12 hours later

(𝜷 = 0.15, SE = 0.02, Z = 5.947, p < .001). See Figures 1-4 for a graphical summary of these

findings. These results suggest that anxious moods significantly predicted later depressed moods

for all groups.

Figure 3-4. Anxious moods predicting depressed moods for the non-anxious/non-depressed

control group.

29

This figure depicts the effects of anxious moods on depressed moods for the mixed non-

anxious/non-depressed control group. Note that in this figure “anxiety” reflects anxious moods

and “depression” reflects depressed moods. Note that the term lag here represents an hour later in

time (i.e. lag 1 is 1 hour later). Crossregressive lags are symbolized of the arrows between

anxious and depressed moods, and autoregressive lags are symbolized by arrows originating and

pointing towards the same variable.

The bi-directional relationship of depressed moods on anxious moods was also examined.

For the pure clinical anxiety group, depressed moods positively predicted anxious moods 1 hour

later (𝜷 = 0.26, SE = 0.01, Z = 21.482, p < .001), 2 hours later (𝜷 = 0.14, SE = 0.02, Z = 6.361, p

< .001), and 7 hours later (𝜷 = 0.21, SE = 0.02, Z = 8.354, p < .001). Also, anxious moods

predicted themselves 1 hour (𝜷 = 0.42, SE = 0.02, Z = 23.136, p < .001), 3 hours (𝜷 = 0.10, SE =

0.02, Z = 5.460, p < .001), 6 hours (𝜷 = -0.05, SE = 0.03, Z = -1.979, p = .024), and 7 hours later

(𝜷 = 0.18, SE = 0.03, Z = 5.369, p < .001).

For the pure clinical depression group, depressed moods positively predicted positively

anxious moods 1 hour later (𝜷 = 0.26, SE = 0.01, Z = 21.482, p < .001) and 7 hours later (𝜷 =

0.42, SE = 0.06, Z = 7.451, p < .001). Also, anxious moods predicted themselves 1 hour (𝜷 =

0.38, SE = 0.02, Z = 17.268, p < .001), 3 hours (𝜷 = 0.09, SE = 0.02, Z = 4.591, p < .001) and 7

hours later (𝜷 = 0.29, SE = 0.03, Z = 8.398, p < .001).

For the mixed clinical anxiety-depression group, depressed moods positively predicted

anxious moods 1 hour (𝜷 = 0.26, SE = 0.01, Z = 21.482, p < .001), 2 hours (𝜷 = 0.10, SE = 0.02,

Z = 4.344, p < .001), and 7 hours later (𝜷 = 0.26, SE = 0.03, Z = 9.969, p < .001). In addition,

anxious moods predicted themselves 1 hour (𝜷 = 0.31, SE = 0.02, Z = 15.081, p < .001), 3 hours

(𝜷 = 0.08, SE = 0.02, Z = 4.433, p < .001), and 7 hours later (𝜷 = 0.25, SE = 0.03, Z = 8.022, p <

.001).

With respect to the non-anxious/non-depressed control group, depressed moods

positively predicted anxious moods 1 hour (𝜷 = 0.26, SE = 0.01, Z = 21.482, p < .001), 2 hours

30

(𝜷 = 0.05, SE = 0.02, Z = 2.580, p = .010) and 7 hours later (𝜷 = 0.29, SE = 0.03, Z = 11.100, p <

.001). Further, anxious moods predicted themselves 1 hour later (𝜷 = 0.42, SE = 0.07, Z = 6.132,

p < .001).

Hypothesis 2: Self-Reported Arousal, Heart Rate, Heart Rate Variability and Fatigue will

Mediate the Relationship between Anxious and Depressed Moods

As mediational relationships were analyzed using pairwise models, separate models were

implemented for each potential mediator within each group.

Pure clinical anxiety group.

High anxious moods predicted low arousal 1 hour later (𝜷 = -0.08, SE = 0.01, Z = -5.674,

p < .001). Low arousal predicted high fatigue 1 (𝜷 = -0.26, SE = 0.02, Z = -10.700, p < .001) and

2 hours later (𝜷 = -0.13, SE = 0.02, Z = -7.667, p < .001). However, low arousal predicted low

fatigue 7 (𝜷 = 0.10, SE = 0.04, Z = 2.429, p = .015), 8 hours later (𝜷 = 0.05, SE = 0.02, Z = 2.106,

p = .035), and 13 hours later (𝜷 = 0.11, SE = 0.02, Z = 4.804, p < .001). High fatigue significantly

predicted depressed moods 1 hour later (𝜷 = 0.24, SE = 0.02, Z = 12.665, p < .001). Thus, arousal

and fatigue formed a mediational chain between anxious and depressed moods, and these

pathways explained a positive relationship between anxious and depressed moods 3 hours later

and 4 hours later. Additionally, arousal and fatigue mediated a significantly negative pathway

betweeen anxious and depressed moods 9, 10, and 15 hours later. Thus, the results showed two

types of pathways that operated at different times: (1) high anxious moods predicted low arousal,

low arousal predicted high fatigue immediately, and high fatigue predicted high depressed moods,

31

and (2) high anxious moods predicted low arousal, low arousal predicted delayed low fatigue, and

low fatigue predicted low depressed moods.

High anxious moods also predicted high heart rate 12 hours later (𝜷 = 0.25, SE = 0.02, Z

= 14.622, p < .001). Heart rate did not significantly predict fatigue. However, high heart rate

directly predicted higher depressed moods 6 hours later (𝜷 = 0.06, SE = 0.03, Z = 2.371, p =

.018). Thus, heart rate significantly mediated the relationship between anxious and depressed

moods, explaining a significantly positive relationship between anxious and depressed moods 18

hours later.

High anxious moods predicted lower heart rate variability 10 (𝜷 = -0.24, SE = 0.04, Z = -

6.757, p < .001) and 12 hours later (𝜷 = -0.24, SE = 0.04, Z = -6.922, p < .001). lower heart rate

variability predicted lower fatigue 11 hours later (𝜷 = 0.08, SE = 0.03, Z = 2.620, p = .009). low

fatigue significantly predicted low depressed moods 1 hour later (𝜷 = 0.24, SE = 0.02, Z =

12.665, p < .001). Thus, heart rate variability and fatigue formed a joint mediational pathway

between anxious and depressed moods such that high anxious moods predicted low depressed

moods 22 and 24 hours later. See Figures 5-6 for a visual depiction of these findings.

32

Figure 3-5. Anxious moods, arousal, heart rate, and heart rate variability on depressed moods for

the pure clinical anxiety group.

This figure depicts the effects of anxious moods, arousal, heart rate, and heart rate variability on

depressed moods for the pure clinical anxiety group. Note that in this figure “anxiety” reflects

anxious moods and “depression” reflects depressed moods. Note that the term lag here represents

an hour later in time (i.e. lag 1 is 1 hour later). Crossregressive lags are symbolized of the arrows

between anxious and depressed moods, and autoregressive lags are symbolized by arrows

originating and pointing towards the same variable.

33

Figure 3-6. Anxious moods, arousal, heart rate, heart rate variability, and fatigue on depressed

moods for the pure clinical anxiety group.

This figure depicts the effects of anxious moods, arousal, heart rate, heart rate variability, and

fatigue on depressed moods for the pure clinical anxiety group. Note that in this figure “anxiety”

reflects anxious moods and “depression” reflects depressed moods. Note that the term lag here

represents an hour later in time (i.e. lag 1 is 1 hour later). Crossregressive lags are symbolized of

the arrows between anxious and depressed moods, and autoregressive lags are symbolized by

arrows originating and pointing towards the same variable.

Pure clinical depression group

With respect to the pure clinical depression group, high anxious moods predicted low

arousal 1 hour later (𝜷 = -0.08, SE = 0.01, Z = -5.674, p < .001). Low arousal predicted high

fatigue 1 (𝜷 = -0.35, SE = 0.08, Z = -4.394, p < .001) and 2 hours later (𝜷 = -0.13, SE = 0.02, Z =

-7.667, p < .001). Low arousal predicted low fatigue 8 (𝜷 = 0.05, SE = 0.02, Z = 2.106, p = .035)

and 13 hours later (𝜷 = 0.11, SE = 0.02, Z = 4.804, p < .001). High fatigue predicted high

depressed moods 1 hour later (𝜷 = 0.28, SE = 0.06, Z = 4.724, p < .001). Thus arousal and fatigue

34

formed a mediational chain to explain a positive relationship between anxious and depressed

moods 3 and 4 hours later. Additionally, the results suggested that arousal and fatigue also

mediated a negative relationship between anxious and depressed moods 10 and 15 hours later, as

high anxious moods predicted low arousal, low arousal predicted low fatigue, and low fatigue

predicted low depressed moods.

High anxious moods also predicted high heart rate 4 (𝜷 = 0.18, SE = 0.07, Z = 2.518, p =

.012) and 12 hours later (𝜷 = 0.25, SE = 0.02, Z = 14.622, p < .001). High heart rate predicted

high fatigue 18 hours later (𝜷 = 0.37, SE = 0.09, Z = 4.170, p < .001). High fatigue predicted high

depressed moods 1 hour later (𝜷 = 0.28, SE = 0.06, Z = 4.724, p < .001). Therefore, heart rate and

fatigue mediated a significantly positive relationship between anxious and depressed moods 23

and 31 hours later.

High anxious moods predicted high heart rate variability 10 (𝜷 = 0.18, SE = 0.09, Z =

2.066, p = .039) and 12 hours later (𝜷 = 0.54, SE = 0.10, Z = 5.329, p < .001). High heart rate

variability predicted high fatigue 11 hours later (𝜷 = 0.24, SE = 0.11, Z = 2.272, p = .023). High

fatigue predicted high depressed moods 1 hour later (𝜷 = 0.28, SE = 0.06, Z = 4.724, p < .001).

Thus, heart rate variability and fatigue form a mediational chain to explain a positive relationship

between anxious and depressed moods 22 and 24 hours later. See Figures 7-8 for a visual

depiction of these findings.

35


the pure clinical depression group.


depressed moods for the pure clinical depression group. Note that in this figure “anxiety” reflects

anxious moods and “depression” reflects depressed moods. Note that the term lag here represents

an hour later in time (i.e. lag 1 is 1 hour later). No cross-regressive lags were significant.

Autoregressive lags are symbolized by arrows originating and pointing towards the same

variable.

36


moods for the pure clinical depression group.


fatigue on depressed moods for the pure clinical depression group. Note that in this figure

“anxiety” reflects anxious moods and “depression” reflects depressed moods. Note that the term

lag here represents an hour later in time (i.e. lag 1 is 1 hour later). No cross-regressive lags were

significant. Autoregressive lags are symbolized by arrows originating and pointing towards the

same variable.

Mixed clinical anxiety-depression group.

For this group, high anxious moods predicted low arousal 1 hour later (𝜷 = -0.08, SE =

0.01, Z = -5.674, p < .001). Low arousal predicted high fatigue 1 (𝜷 = -0.34, SE = 0.03, Z = -

11.933, p < .001) and 2 hours later (𝜷 = -0.13, SE = 0.02, Z = -7.667, p < .001). Low arousal also

predicted low fatigue 8 (𝜷 = 0.05, SE = 0.02, Z = 2.106, p = .035) and 13 hours later (𝜷 = 0.11,

SE = 0.02, Z = 4.804, p < .001). High fatigue predicted high depressed moods 1 hour later (𝜷 =

0.30, SE = 0.02, Z = 13.398, p < .001). Therefore, arousal and fatigue jointly mediated a positive

relationship between anxious and depressed moods 3 and 4 hours later. Arousal and fatigue also

37

jointly mediated a negative relationship between anxious and depressed moods 10 and 15 hours

later, as high anxious moods predicted low arousal, low arousal predicted delayed low fatigue,

and low fatigue predictd low depressed moods.

High anxious moods predicted low heart rate 4 hours later (𝜷 = -0.06, SE = 0.03, Z = -

2.202, p = .028) and predicted high heart rate 12 hours later (𝜷 = 0.25, SE = 0.02, Z = 14.622, p <

.001). Heart rate did not significantly predict fatigue or depressed moods. Thus for the mixed

clinical anxiety-depression group, heart rate did not mediate the relationship between anxious and

depressed moods.

High anxious moods predicted low heart rate variability 10 (𝜷 = -0.16, SE = 0.05, Z = -

3.513, p < .001) and 12 hours later (𝜷 = -0.20, SE = 0.05, Z = -3.789, p < .001). However, heart

rate variability did not significantly predict fatigue or depressed moods. As such, heart rate

variability did not mediate the relationship between anxious and depressed moods for the mixed

clinical anxiety-depression group. See Figures 9-10 for a visual depiction of these findings.

38


the mixed clinical anxiety-depression group.


depressed moods for the mixed clinical anxiety-depression group. Note that in this figure


lag here represents an hour later in time (i.e. lag 1 is 1 hour later). No cross-regressive lags were

significant. Autoregressive lags are symbolized by arrows originating and pointing towards the

same variable.

39


moods for the mixed clinical anxiety-depression group.


fatigue on depressed moods for the mixed clinical anxiety-depression group. Note that in this

figure “anxiety” reflects anxious moods and “depression” reflects depressed moods. Note that the

term lag here represents an hour later in time (i.e. lag 1 is 1 hour later). No cross-regressive lags

were significant. Autoregressive lags are symbolized by arrows originating and pointing towards

the same variable.

Non-anxious/non-depressed control group

For the control group, high anxious moods predicted low arousal 1 (𝜷 = -0.08, SE =

0.01, Z = -5.674, p < .001) and 23 hours later (𝜷 = -0.07, SE = 0.03, Z = -2.205, p = .027). Low

arousal predicted high fatigue 1 and 2 hours later (𝜷 = -0.13, SE = 0.02, Z = -7.667, p < .001).

Low arousal also predicted low fatigue 7 (𝜷 = 0.10, SE = 0.04, Z = 2.429, p = .015), 8 (𝜷 = 0.05,

SE = 0.02, Z = 2.106, p = .035) and 13 hours later (𝜷 = 0.11, SE = 0.02, Z = 4.804, p < .001). high

fatigue predicted high depressed moods 1 hour later (𝜷 = 0.17, SE = 0.02, Z = 8.246, p < .001).

Thus, for the non-anxious/non-depressed control group arousal and fatigue mediated a

significantly positive relationship between anxious and depressed moods 3, 4, 25, and 26 hours

40

later. Additionally, arousal and fatigue mediated a significant negative relationship between

anxious and depressed moods 9, 10, 15, 31, 32, and 37 hours later, as high anxious moods

predicted low arousal, low arousal predicted delayed low fatigue, and low fatigue predicteed low

depressed moods.

Anxious moods positively predicted heart rate 12 hours later (𝜷 = 0.25, SE = 0.02, Z =

14.622, p < .001). However, heart rate did not predict fatigue or depressed moods. Therefore,

heart rate did not significantly mediate the relationship between anxious and depressed moods for

the non-anxious/non-depressed control group .

Anxious moods also did not predict heart rate variability and heart rate variability did not

predict fatigue or depressed moods for this group . Thus, heart rate variability did not

significantly mediate the relationship between anxious and depressed moods . See Figures 11-12

for a visual depiction of these findings.


the non-anxious/non-depressed control group.

41


depressed moods for the mixed clinical anxiety-depression group. Note that in this figure


lag here represents an hour later in time (i.e. lag 1 is 1 hour later). Crossregressive lags are

symbolized of the arrows between anxious and depressed moods, and autoregressive lags are

symbolized by arrows originating and pointing towards the same variable.


moods for the non-anxious/non-depressed control group.


fatigue on depressed moods for the mixed clinical anxiety-depression group. Note that in this

figure “anxiety” reflects anxious moods and “depression” reflects depressed moods. Note that the

term lag here represents an hour later in time (i.e. lag 1 is 1 hour later). Crossregressive lags are

symbolized of the arrows between anxious and depressed moods, and autoregressive lags are

symbolized by arrows originating and pointing towards the same variable.

Hypothesis 3: Group Differences with respect to Mediational Pathways

Lastly, we examined whether arousal, heart rate, heart rate variability, and fatigue

mediated the relationship between anxious and depressed moods differently across the diagnostic

groups. For arousal and fatigue, the results suggested that there were no group differences, such

42

that for all groups high anxious moods predicted low arousal, low arousal predicted immediate

high fatigue and delayed low fatigue, and high fatigue predicted high depressed moods.

However, there were group differences in the mediational pathways for heart rate and

fatigue. Specifically, both heart rate and fatigue only significantly mediated the relationship

between anxious and depressed mood for the pure clinical depression group. Additionally, heart

rate directly mediated the relationship between anxious and depressed mood only for the pure

clinical anxious group.

Lastly, there were significant group differences in the mediational pathway for heart rate

variability and fatigue. For the pure clinical depression group, high anxious moods predicted high

heart rate variability, high heart rate variability predicted high fatigue, and high fatigue predicted

high depressed moods. In contrast, for the pure clinical anxiety group, high anxious moods

predicted low heart rate variability, low heart rate variability predicted low fatigue, and low

fatigue predicted low depressed moods. Thus, heart rate variability mediated a positive

relationship between anxious and depressed moods for the pure clinical depression group,

whereas it mediated a negative relationship between anxious and depressed moods for the pure

clinical anxiety group. See Table 3 for a comprehensive list of all the findings between groups.

43

Table 3

All Cross-Regressive Relationships with Between Group Differences

Relationship Lag LR Anx GP DepGP MixGP ControlGP AllGroups

Anxiety -> Depression All 100.10 (33)*

Anxiety -> Depression 1 16.82 (3)* 0.23 (0.02)*dc

0.15 (0.08)a 0.31 (0.02)* 0.26 (0.02)*

a

Anxiety -> Depression 7 3.18 (3)

0.04 (0.02)*


0.06 (0.02)*

Anxiety -> Depression 10 14.52 (3)* 0.12 (0.03)* -0.03 (0.09)mc

0.00 (0.04)dc

0.02 (0.03)dm

Anxiety -> Depression 13 11.24 (3)* 0.14 (0.03)*dm

0.29 (0.13)*a 0.07 (0.04)

ac 0.04 (0.04)

m


0.16 (0.02)*

Anxiety -> Depression 16 18.62 (3)* -0.06 (0.04)dc

0.08 (0.11)amc

0.10 (0.04)*d -0.04 (0.04)

ad

Anxiety -> Arousal All 25.78 (6)*

Anxiety -> Arousal 1 6.82 (3)

-0.08 (0.01)*

Anxiety -> Arousal 23 20.06 (3)* -0.04 (0.03)dc

-0.11 (0.08)amc

0.06 (0.03)d -0.07 (0.03)*

ad

Anxiety -> Heart Rate All 109.02 (12)*

Anxiety -> Heart Rate 4 27.94 (3)* 0.04 (0.03)

c 0.18 (0.07)* -0.06 (0.03)*

c 0.04 (0.03)

am

Anxiety -> Heart Rate 12 -55.72 (3)

0.25 (0.02)*

Anxiety -> Heart Rate

Variability All 88.04 (6)*


Variability 10 56.42 (3)* -0.24 (0.04)*

m 0.18 (0.09)* -0.16 (0.05)*

a 0.03 (0.06)


Variability 12 69.66 (3)* -0.24 (0.04)*

m 0.54 (0.10)* -0.20 (0.05)*

a 0.05 (0.07)

Anxiety -> Fatigue All 121.88 (21)*

Anxiety -> Fatigue 1 11.78 (3)* 0.15 (0.02)*m 0.04 (0.07)

c 0.15 (0.03)*

a 0.08 (0.03)*

d

Anxiety -> Fatigue 2 4.68 (3)

0.07 (0.02)*


0.06 (0.02)*


0.15 (0.02)*

Anxiety -> Fatigue 22 50.80 (3)* 0.08 (0.03)*c 0.23 (0.07)*

c -0.08 (0.04)* 0.13 (0.03)*

ad

44

Anxiety -> Fatigue 23 9.88 (3)* 0.02 (0.03)dm

0.08 (0.07)amc

-0.01 (0.03)ad

0.08 (0.03)*d

Arousal -> Depression All 142.08 (24)*

Arousal -> Depression 1 23.78 (3)* -0.01 (0.02)

dc -0.02 (0.08)

amc -0.10 (0.03)*

d 0.03 (0.02)

ad

Arousal -> Depression 2 54,025.68

(3)* -0.12 (0.02)* 0.20 (0.07)* 0.02 (0.03)

c 0.00 (0.03)

m

Arousal -> Depression 9 9.38 (3)* 0.06 (0.04)mc

-0.12 (0.10)c 0.09 (0.05)*

ac 0.01 (0.04)

adm

Arousal -> Depression 10 11.98 (3)* 0.01 (0.04)

dc -0.12 (0.13)

ac 0.12 (0.04)* 0.00 (0.05)

ad

Arousal -> Depression 11 10.34 (3)* -0.02 (0.04)

dc 0.13 (0.13)

amc 0.11 (0.04)*

dc 0.03 (0.05)

adm

Arousal -> Depression 12 11.72 (3)* 0.10 (0.04)*

d 0.14 (0.13)

amc -0.02 (0.04)

dc -0.01 (0.04)

dm

Arousal -> Depression 13 16.06 (3)* 0.09 (0.04)*

c -0.29 (0.13)* -0.04 (0.05)

c 0.01 (0.05)

am

Arousal -> Depression 14 9.78 (3)* 0.08 (0.04)

mc -0.26 (0.12)* 0.02 (0.05)

ac 0.07 (0.05)

am

Arousal -> Fatigue All 79.46 (18)*

Arousal -> Fatigue 1 10.44 (3)* -0.26 (0.02)*dc

-0.35

(0.08)*amc

-0.34 (0.03)*

d -0.27 (0.03)*

ad

Arousal -> Fatigue 2 2.28 (3)

-0.13 (0.02)*

Arousal -> Fatigue 7 15.82 (3)* 0.08 (0.03)*c -0.14 (0.13)

m -0.03 (0.04)

d 0.10 (0.04)*

a


0.05 (0.02)*


0.11 (0.02)*

Heart Rate -> Fatigue 18 25.62 (3)* 0.03 (0.03)mc

0.37 (0.09)* 0.04 (0.04)ac

0.00 (0.03)am

Heart Rate Variability -> Fatigue 11 13.46 (3)* 0.08 (0.03)*dm

0.24 (0.11)*a 0.06 (0.04)

a -0.02 (0.04)

Fatigue -> Depression 1 32.24 (3)* 0.24 (0.02)*

d 0.28 (0.06)*

am 0.30 (0.02)*

d 0.17 (0.02)*

Depression -> Anxiety All 48.76 (9)*

Depression -> Anxiety 1 5.12 (3)

0.26 (0.01)*

Depression -> Anxiety 2 14.94 (3)* 0.14 (0.02)*dm

0.09 (0.06)amc

0.10 (0.02)*ad

0.05 (0.02)*d

Depression -> Anxiety 7 25.60 (3)* 0.21 (0.02)* 0.42 (0.06)* 0.26 (0.03)*

c 0.29 (0.03)*

m

Note. * p < .05. This table depicts the summary of all cross-regressive relationships in the analyses. Relationship depicts the cross-regressive

construct of interest, lag represents the lag of interest (“All” reflect the group difference test when all lags were constrained to be equal across

groups), LR reflects the log likelihood ratio test, AnxGP represents the pure clinical anxiety group, DepGP represents the pure clinical

depression group, MixGP represents the mixed clinical anxiety-depression group, ControlGP represents the non-anxious/non-depressed

control group, All represents the coefficients when the coefficients were not significantly different from one another across groups. When

45

there were significant differences, individual coefficients from the unconstrained model, and individual differences between groups were

tested by constraining two relationships to be different. The superscripts: a (for pure clinical anxiety), d (for the pure clinical depression), m

(for the mixed clinical anxiety group), and c (for the non-anxious non-depressed control group) represent that the reference group was not

significantly different from the other group.

46

Chapter 4

Discussion

Supporting my first hypothesis and consistent with previous research (Starr & Davila,

2011; Swendsen, 1998), these results suggested that anxious moods positively predicted

depressed moods multiple times within a day. Given that the relationships controlled for the

effect of depressed moods on themselves, anxious moods appear to have an important and

powerful role in predicting depressed moods for all groups. Thus, anxious moods are a short-term

risk factor for depressed moods for those with pure clinical anxiety, pure clinical depression,

mixed clinical anxiety-depression, and non-anxious/non-depressed controls.

Contrary to research showing that anxious moods were unidirectional predictors of later

depressed moods (Starr & Davila, 2011; Swendsen, 1998), we found a bidirectional relationship

for all four diagnostic groups. Thus, any increase in one mood was followed by a subsequent

increase in the other. Notably, the strength of anxious moods on depressed moods was greater

than the strength of depressed moods on anxious moods. Consequently, a variation in anxious

moods is a more powerful influence than a variation in depressed moods in this bi-directional

relationship.

In addition to demonstrating direct pathways between anxious and depressed moods, I

also identified common mechanisms for this relationship across diagnostic groups. Supporting my

second hypothesis and consistent with the theory of Akiskal (1985), perceived arousal and fatigue

mediated the prospective positive relationship between anxious and depressed moods for all

groups. However, our results were slightly divergent from what I originally hypothesized and

what would be predicted by Akiskal as high anxious moods were associated with later low

47

perceived arousal, low perceived arousal was shortly followed by high fatigue, and high fatigue

was associated with later high depressed moods for all groups. Also, in contrast to my second

hypothesis and with a slight variation to Akiskal’s (1985) theory, perceived arousal and fatigue

mediated a significantly negative relationship between anxious and depressed moods that

occurred later in time, showing that low perceived arousal first predicted more fatigue and more

depression but later predicted less fatigue and less depression.

Although the negative relationship beween anxious moods and perceived arousal was

inconsistent with Akiskal’s theory, an association between anxious moods and subsequent low

perceived arousal has been theorized by Thayer (1989). However, Thayer suggests that whereas

high anxious moods are associated with low perceived arousal, high anxious moods predict

subsequent increases in objectively measured physiological arousal. Thayer theorizes that, upon

experiencing anxious moods, persons immediately feel as if they have low energy levels. He

suggests that this feeling occurs due to individuals shifting their attention to perceived dangers

after the onset of anxious moods. Specifically, Thayer reasons that this attentional shift coincides

with not only individuals’ thoughts that the perceived danger will require a large amount of

energy to solve the problem, but also individuals feeling unable to summon enough energy to

solve the instant problem. Consequently, although the results partially contrast Akiskal’s

hypothesis, Thayer has provided theoretical backing for these findings.

As predicted by Thayer, the heart rate findings diverged from the findings for perceived

arousal. Specifically, high anxious moods were associated with later high heart rate across all of

the groups. Also consistent with previous research (Cacioppo, Tassinary, Stonebraker, & Petty,

1987), there was a small positive concurrent relationship between perceived arousal and heart rate

across all groups (with the exception of the pure clinical depression group), suggesting that the

systems of perceived arousal and physiological arousal are largely distinct as there is only 1%

shared variance between perceived arousal and physiological arousal across the groups. Taken

48

together, these results suggest that perceived arousal and physiological arousal are concurrently

independent and that anxious moods have unique relationships with perceived arousal and

physiological arousal over time.

Thayer offers numerous reasons to explain the divergence in how anxious moods predict

perceived arousal and physiological arousal. First, Thayer theorizes that perceived arousal is

defined by one’s potential to become aroused rather than be aroused, and, as such, perceived

arousal may conflict with physiological arousal because perceived arousal does not often

accompany energetic behavior (i.e. thinking one has enough arousal to perform well on a 100

meter dash, but not having the accompanying heart rate that would be associated with this

increase). Thayer secondly explains that perceived arousal is often dictated by perceived societal

norms (i.e. during work you should be active) and reasons that these patterns would not hold for

individuals at young ages before they are socialized to adopt societal norms. Thus, Thayer

theorizes that social conventions negatively impact an individual’s ability to accurately predict

his/her arousal. Thayer’s third reason as to why perceived arousal and heart rate diverge is that

perceived arousal may combine multiple separate systems of arousal, including muscle tension,

heart rate, breathe rate, and perspiration. Lastly, Thayer states that one may not perceive changes

in arousal because an individual’s attentions shift as a result of distractions in his/her

environment, and, consequently, perceived arousal would not correspond to changes in

physiological arousal due to the decrease in accuracy of perceived arousal. Consequently, Thayer

theorizes that perceived arousal would diverge from heart rate as perceived arousal is affected by

societal norms, expectations, distractions, and focus on other independent facets of physiological

activity.

Despite all groups having a positive relationship between anxious moods and heart rate,

there were large differences between the groups as to whether heart rate and fatigue mediated the

relationship between anxious and depressed moods. Namely, heart rate and fatigue jointly

49

mediated the relationship between anxious and depressed moods only for the pure clinical

depression group. Specific to this group, the pattern was as theorized by Akiskal such that high

anxious moods predicted high heart rate, high heart rate predicted high fatigue, and high fatigue

predicted high depressed moods. Paired with the evidence that fatigue levels were higher for the

pure clinical depression group compared to any other group, these findings may suggest that

those with pure clinical depression are more chronically fatigued, and, as such, an increase in

energy expenditure from physiological activity may be more draining for these persons because

they have less energy to begin with (i.e. they run out of fuel because they are almost always

“running on empty”). However, perhaps due to the lower levels of stable fatigue in the other

groups, the results may suggest that the other groups have enough of an energy reserve that

physiological activity does not affect them (i.e. they still have “gas in the tank”).

Partially in line with Akiskal’s (1985) theory, the results also indicated that heart rate

directly mediated the relationship between anxious and depressed moods for the pure clinical

anxiety group, such that high anxious moods were associated with high heart rate, and high heart

rate was associated with high depressed moods. As heart rate did not predict fatigue for the pure

clinical anxiety group, this suggests that for those with pure clinical anxiety, physiological

arousal is directly related to the onset of depressed moods. It is possible that these findings are

due to increases and subsequent decreases in cortisol as cortisol degrades over approximately the

same time period as the relationship between heart rate and depressed moods (Iranmanesh,

Lizarralde, Johnson, & Veldhuis, 1989). Consequently future research should assess if the direct

relationship between heart rate and depressed moods is due to the effects of decreases in

neurotransmitter levels. Paired with the findings for the pure clinical depression group, these

findings suggest that heart rate only mediates the relationship between anxious and depressed

moods for those with pure clinical anxiety and pure clinical depression.

50

Similar to the results for heart rate, there were large between-group differences in heart

rate variability and fatigue mediating the relationship between anxious and depressed moods. For

the pure clinical depression group, both heart rate variability and fatigue mediated a positive

relationship between anxious and depressed moods, supporting my second hypothesis. However,

contrary to this hypothesis, the manner in which heart rate variability and fatigue mediated this

relationship occurred in the opposite direction for the pure clinical depression group. Specifically

for this group, high anxious moods actually predicted high heart rate variability, high heart rate

variability predicted high fatigue, and high fatigue predicted high depressed moods. Thus, the

cross-sectional research that generally shows that low heart rate variability is related to anxiety

(Gorman & Sloan, 2000) does not inform the short-term longitudinal relationship of anxious

moods predicting later heart rate variability. For the pure clinical depression group, these findings

suggest that high heart rate variability aids in the maintenance of the relationship between anxious

and depressed moods.

The findings for heart rate variability and fatigue mediating the relationship between

anxious and depressed moods for the clinical anxiety group were different than what was found

for the pure clinical depression group. For this group, and consistent with the research (e.g.,

Gorman & Sloan) high anxious moods predicted low heart rate variability, thereby mediating a

negative relationship between anxious and depressed moods. Given that heart rate variability was

associated with itself over time for both the pure clinical anxiety group and the pure clinical

depression group, these findings may suggest that anxious moods can predict stable low or high

heart rate variability depending on whether one has pure clinical anxiety or pure clinical

depression. Further, lower heart rate variability predicts low fatigue and depressed moods.

Nonetheless, since this study did not examine the relationship between heart rate variability and

later anxious moods, it is possible that low heart rate variability is a risk factor for later anxious

moods. Consequently, those with pure clinical anxiety experience less fatigue and depressed

51

moods by maintaining a stable heart rate, and those with pure clinical depression put themselves

at a greater risk for later fatigue and depressed moods by having an unstable heart rate.

Differences in rigidity may explain the differential relationship between anxious moods

and heart rate variability for the pure clinical anxiety group compared to the pure clinical

depression group. Namely, those with anxiety disorders have been hypothesized to maintain a

state of rigidity, wherein persons with anxiety disorders do not react to environmental situations

because they adhere to the rigid patterns within their daily lives (Newman, Llera, Erickson,

Przeworski, & Castonguay, 2013). Others have also found that people with anxiety disorders tend

to have a high stable heart rate (Newman et al., 2013). In contrast to anxiety disorders, individuals

with depressive disorders have been theorized to possess overflexibility in mood (i.e. “lability”)

(Roberts & Kassel, 1997). As heart rate variability has been theorized to be a measure of

autonomic rigidity (Appelhans & Luecken, 2006), these findings may support the notion that

when persons with pure anxiety disorders experience high anxious moods, they react with

increased rigidity, whereas those with pure depressive disorders react with increased flexibility.

Regarding group differences in how anxious moods predicted depressed moods, the

pattern of the results may also suggest there are both common and specific mediators for the

groups due to the pattern of when anxious moods predict depressed moods (and vice versa).

Group similarities and differences in when anxious moods predict depressed moods are important

as they may imply that some mechanisms are overlapping and some are specific to each group. It

is possible that common times of anxious moods predicting depressed moods across groups

reflect common mechanisms between anxious and depressed moods that unfold over the same

course of time for all groups. However, times that are specific to certain groups may also suggest

that there are unique mechanisms of how anxious moods predict depressed moods for different

diagnostic groups. Given these findings, future research should evaluate other mechanisms

between anxious and depressed moods that may include behavior (i.e. avoidance), emotions (i.e.

52

valence versus arousal focus), cognitions (i.e. helplessness and hopelessness, perseverative

negative thinking), and other potential mediators of this relationship as each of these potential

mediators may influence this relationship over different time courses.

Despite the strengths of the current study, there are also limitations. Firstly, I did not

measure anxiety and depressive disorders using clinical interviews. Secondly, the participants

may not be representative of the general population given that they were selected from an

undergraduate sample, and it is possible that students may have unique relationships between

anxious and depressed moods due to college lifestyle characteristics. Further research should

examine these relationships with a clinical interview and include individuals from the general

population. Due to the emphasis on ecological validity, the results sacrificed experimental

control. Consequently, future research should examine whether anxious moods are actually a

cause of depressed moods by applying an experimental manipulation to examine whether

perturbations in anxious moods are associated with increases in later depressed moods. Future

studies should also examine other potential mediators to explain the relationship between anxious

and depressed moods.

53

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Appendix A

DASS

Please read each statement and circle a number 0, 1, 2 or 3 that indicates how much the statement applied to you over the past week. There are no right or wrong answers. Do not spend too much time on any statement.

The rating scale is as follows:

0 Did not apply to me at all 1 Applied to me to some degree, or some of the time

2 Applied to me to a considerable degree, or a good part of time 3 Applied to me very much, or most of the time

1 I found myself getting upset by quite trivial things 0 1 2 3

2 I was aware of dryness of my mouth 0 1 2 3

3 I couldn't seem to experience any positive feeling at all 0 1 2 3

4 I experienced breathing difficulty (eg, excessively rapid breathing, breathlessness in the absence of physical exertion)

0 1 2 3

5 I just couldn't seem to get going 0 1 2 3

6 I tended to over-react to situations 0 1 2 3

7 I had a feeling of shakiness (eg, legs going to give way) 0 1 2 3

8 I found it difficult to relax 0 1 2 3

9 I found myself in situations that made me so anxious I was most relieved when they ended

0 1 2 3

10 I felt that I had nothing to look forward to 0 1 2 3

11 I found myself getting upset rather easily 0 1 2 3

12 I felt that I was using a lot of nervous energy 0 1 2 3

13 I felt sad and depressed 0 1 2 3

14 I found myself getting impatient when I was delayed in any way (eg, elevators, traffic lights, being kept waiting)

0 1 2 3

15 I had a feeling of faintness 0 1 2 3

16 I felt that I had lost interest in just about everything 0 1 2 3

17 I felt I wasn't worth much as a person 0 1 2 3

18 I felt that I was rather touchy 0 1 2 3

67

19 I perspired noticeably (eg, hands sweaty) in the absence of high temperatures or physical exertion

0 1 2 3

20 I felt scared without any good reason 0 1 2 3

21 I felt that life wasn't worthwhile 0 1 2 3

Reminder of rating scale:

0 Did not apply to me at all 1 Applied to me to some degree, or some of the time

2 Applied to me to a considerable degree, or a good part of time 3 Applied to me very much, or most of the time

22 I found it hard to wind down 0 1 2 3

23 I had difficulty in swallowing 0 1 2 3

24 I couldn't seem to get any enjoyment out of the things I did 0 1 2 3

25 I was aware of the action of my heart in the absence of physical exertion (eg, sense of heart rate increase, heart missing a beat)

0 1 2 3

26 I felt down-hearted and blue 0 1 2 3

27 I found that I was very irritable 0 1 2 3

28 I felt I was close to panic 0 1 2 3

29 I found it hard to calm down after something upset me 0 1 2 3

30 I feared that I would be "thrown" by some trivial but unfamiliar task

0 1 2 3

31 I was unable to become enthusiastic about anything 0 1 2 3

32 I found it difficult to tolerate interruptions to what I was doing 0 1 2 3

33 I was in a state of nervous tension 0 1 2 3

34 I felt I was pretty worthless 0 1 2 3

35 I was intolerant of anything that kept me from getting on with what I was doing

0 1 2 3

36 I felt terrified 0 1 2 3

37 I could see nothing in the future to be hopeful about 0 1 2 3

38 I felt that life was meaningless 0 1 2 3

39 I found myself getting agitated 0 1 2 3

40 I was worried about situations in which I might panic and make a fool of myself

0 1 2 3

41 I experienced trembling (eg, in the hands) 0 1 2 3

42 I found it difficult to work up the initiative to do things 0 1 2 3

68

Appendix B

GAD-Q-IV

1. Do you experience excessive worry?…………………………………………………………. NO YES

2. Is your worry excessive in intensity, frequency, or amount of distress it causes?…………….

NO

YES

3. Do you find it difficult to control your worry (or stop worrying) once it starts?……………...

NO

YES

4. Do you worry excessively and uncontrollably about minor things such as being late for

an appointment, minor repairs, homework,

etc.?………………………………………………...

NO

YES

5. Please list the most frequent topics about which you worry excessively and uncontrollably:

a.

d.

b.

e.

c.

f.

6. During the last six months, have you often been bothered by excessive and

uncontrollable worries more days than not?

…………………………………………………………………

NO

YES

IF YES, CONTINUE. IF NO, DISCONTINUE.

7. During the last six months, have you often been bothered by restlessness or feeling keyed

up or on edge more days than

not?………………………………………………………………

NO

YES

8. During the last six months, have you often been bothered by difficulty falling/staying

asleep or restless/unsatisfying sleep more days than not?

………………………………………….

NO

YES

9. During the last six months, have you often been bothered by difficulty concentrating or

your mind going blank more days than

not?……………………………………………….……..

NO

YES

10. During the last six months, have you often been bothered by irritability more days than

not?......................................................................................................................... ......................

NO

YES

11. During the last six months, have you often been bothered by being easily fatigued more

days than not? …………………………………………………………………………………..

NO

YES

12. During the last six months, have you often been bothered by muscle tension more days

than not? ………………………………………………………………………………………..

NO

YES

13. How much do worry and physical symptoms interfere with your life, work, social activities, family,

etc.? Circle one number:

0 1 2 3 4 5 6 7 8 | | | | | | | | |

Not at All Mildly Moderately Severely Very

69

Severely

14. How much are you bothered by worry and physical symptoms (how much distress does it cause

you)? Circle one number:

0 1 2 3 4 5 6 7 8 | | | | | | | | |

No Distress Mild Distress Moderate

Distress

Severe

Distress

Very

Severe

Distress

70

Appendix C

SPDQ

1. In social situations where it is possible that you will be noticed or evaluated

by other people, do you feel excessively nervous, fearful or uncomfortable?…………………… NO YES

2. Do you tend to be overly worried that you may act in a way that might embarrass or humiliate

yourself in front of other people, or that others may not think well of you?……….…

NO

YES

3. Do you try to avoid social situations?…………………………………………………………

NO

YES

Below is a list of some situations that are fear provoking for some people. Rate the severity of your anxiety

and avoidance on the following scales:

0 = No fear 0 = Never avoid

1 = mild fear 1 = Rarely avoid

2 = Moderate fear 2 = Sometimes avoid

3 = Severe fear 3 = Often avoid

4 = Very severe fear 4 = Always avoid

a) Fear

b) Avoidance

4. Parties

0 1 2 3 4 0 1 2 3 4

5. Meetings

0 1 2 3 4 0 1 2 3 4

6. Eating in a public location

0 1 2 3 4 0 1 2 3 4

7. Using public bathrooms when others are

present

0 1 2 3 4 0 1 2 3 4

8. Becoming the focus of attention

0 1 2 3 4 0 1 2 3 4

9. Writing in front of other people (signing

checks, filling out forms)

0 1 2 3 4 0 1 2 3 4

10. Dating circumstances

0 1 2 3 4 0 1 2 3 4

11. A first date

0 1 2 3 4 0 1 2 3 4

12. Meeting people in authority

0 1 2 3 4 0 1 2 3 4

13. Speaking with people in authority

0 1 2 3 4 0 1 2 3 4

14. Saying “no” to unreasonable request

0 1 2 3 4 0 1 2 3 4

15 Asking others to do something differently

0 1 2 3 4 0 1 2 3 4

16. Being introduced

0 1 2 3 4 0 1 2 3 4

17. Initiating a conversation

0 1 2 3 4 0 1 2 3 4

71

18. Keeping a conversation going

0 1 2 3 4 0 1 2 3 4

19. Giving a speech

0 1 2 3 4 0 1 2 3 4

20. Using the telephone

0 1 2 3 4 0 1 2 3 4

21. Others judging you

0 1 2 3 4 0 1 2 3 4

22. Being under observation by others

0 1 2 3 4 0 1 2 3 4

23. Being teased

0 1 2 3 4 0 1 2 3 4

24. Do you tend to experience fear each time you are in feared social

situations………………… NO YES

25. Does the fear come on as soon as you encounter feared social

situations?……….………….

NO

YES

26. Would you say that your social fear is excessive or

unreasonable?…………………………..

NO

YES

27. Circle the degree to which your social fear interferes with your life, work, social activities, family, etc?

0 1 2 3 4 5 6 7 8 | | | | | | | | |

Not at All

Mildly Moderate

ly

Severely Very

Severely/

Disabling

28. How distressing do you find your social fear? (Circle one)

0 1 2 3 4 5 6 7 8 | | | | | | | | |

No

distress

Mild

distress

Moderate

distress

Severe

distress

Very

Severe

29. Has what you have been able to achieve in your job or in school been negatively effected

by your social fear?

NO

YES

Start here

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Appendix D

Anxious and Depressed Mood Questions

Anxious Mood Questions:

1. How ANXIOUS do you feel right now?

a. Not at all - Extremely

2. How TENSE do you feel right now?


3. How NERVOUS do you feel right now?


4. How SHAKY do you feel right now?


5. How UNEASY do you feel right now?


Depressed Mood Questions:

6. How SAD do you feel right now?


7. How DISCOURAGED do you feel right now?


8. How LONELY do you feel right now?


9. How UNWORTHY do you feel right now?


10. How GLOOMY do you feel right now?


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Appendix E

Arousal and Fatigue

Self-reported arousal:

1. Which of these things best describes how you feel right now?

a. Sleepy – Activated

Physiological arousal:

1. Heart rate and heart rate variability

2.

Fatigue:

2. How WORNOUT do you feel right now?


3. How FATIGUED do you feel right now?


4. How EXHAUSTED do you feel right now?


5. How SLUGGISH do you feel right now?


6. How WEARY do you feel right now?


anxious moods as a risk factor for depressed moods: …

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