Physical Activity Predicts Emotion-Context-Sensitivity

PHYSICAL ACTIVITY PREDICTS EMOTION-CONTEXT-SENSITIVITY

A thesis submitted to the Kent State University Honors College in partial fulfillment of the requirements for University Honors

Morgan Christina Shields

May, 2014

Thesis written by

Morgan Christina Shields

Approved by

______, Advisor

______, Chair, Department of Psychology

Accepted by

______, Dean, Honors College

TABLE OF CONTENTS

LIST OF TABLES ...... iv

ACKNOWLEDGMENTS ...... v

INTRODUCTION ...... 1 Emotion Regulation ...... 2 Executive Functioning ...... 4 Physical Activity and Cardiovascular Flexibility ...... 5 Current Investigation ...... 6 Hypothesis I ...... 7 Hypothesis II ...... 7

METHODS ...... 8 Participants ...... 8 Procedure ...... 8 Questionnaire Measures ...... 9 Wisconsin Card Sorting Task (WCST)...... 10 Rejection/Acceptance Task ...... 11 Emotion-Context-Sensitivity ...... 13

RESULTS ...... 15 Analytic Plan ...... 15 Preliminary Analysis ...... 15 Analysis for Hypothesis I...... 18 Analysis for Hypothesis II ...... 20 Change in Positive Response from Rejection to Acceptance ...... 21

DISCUSSION ...... 25 Limitations ...... 29 Future Directions and Implications ...... 30 Conclusion ...... 31

REFERENCES ...... 33

iii

LIST OF TABLES

Table

1. Demographics Including Controls ...... 9

2. Descriptives of Key Variables ...... 14

3. Partial Correlation of Key Study Variables. Controlling for: Age, Gender, Handedness, BMI, and Distress...... 16

4. Regression Analysis Examining the Association between Negative Emotion during Rejection and Physical Activity...... 19

5. Regression Analysis Examining the Association between Positive Emotion during Acceptance and Physical Activity ...... 22

6. Regression Analysis Examining the Association between the Change in Negative Emotion from Rejection to Acceptance and Physical Activity ...... 23

7. Regression Analysis Examining the Association between the Change in Positive Emotion from Rejection to Acceptance and Physical Activity...... 24

ACKNOWLEDGMENTS

I would like to extend the warmest gratitude to my advisor, Dr. Karin Coifman, for giving me the freedom to pursue such an ambition project and for being readily available in moments of confusion and frustration. Although challenging, this journey has allowed me to flourish as a young research scientist and has prepared me for transition to graduate study. I would also like to express gratitude towards our Lab Manager, Danielle

Halachoff, and my fellow Research Assistants for tirelessly aiding with data collection and management. Further, I would like to thank Drs. Mary Beth Spitznagel, Ellen

Glickman, and Susan Roxburgh for serving on my thesis committee and for providing such thorough feedback. In particular, I would like to thank Dr. Spitznagel for being available to meet with me on several occasions to discuss this project.

I would like to express my appreciation to Jessica Flynn for encouraging and sympathizing with me during these past two years. In my moments of deepest despair and insecurity, she used her exceptional intellect and warmest compassion to help me process theory and lift both my confidence and spirits.

I would like to thank Kelsey Kennedy, who has been my rock on the outside of these academic walls. She served to remind me of who I am, where I have been, and how amazing of an accomplishment this project is for me.

Lastly, I am forever indebted to my parents for not extinguishing my imagination, for raising me around color and adventure, and for loving me.

INTRODUCTION

It has been well-established that physical activity is beneficial for both psychological and physical well-being (Peluso & Andrade, 2005; Penedo & Dahn, 2005).

The mechanisms by which physical activity improves physical health have been robustly documented (as reviewed in Warburton et al., 2006; Luft et al., 2009). However, the ways in which physical activity enhances psychological health are not completely understood.

While, there has been substantial research linking physical activity to improved mood states (Peluso & Andrade, 2005) as well as decreased symptoms of psychopathology

(Mota-Pereira et al., 2011; Brosse et al., 2002; Wolff et al., 2011), there has been very little research examining the connection between physical activity and adaptive and flexible emotional response. Though moods and emotions are at times referred to synonymously, and while they are indeed related, moods and emotions are actually distinct constructs. Moods are more long-lasting and enduring; whereas emotions are both rapidly produced and quickly dissolved in response to specific environmental and internal demands (Ekman, 1992). Moreover, emotions are more strongly linked to psychopathology and adaptive behavior as opposed to moods (Cole et al., 2004). In this investigation we explored whether physical activity might predict adaptive emotion regulatory responses by way of cardiovascular flexibility and neurocognitive functioning.

1 2

Emotion Regulation

Emotions have evolved to serve specific functions that aid organisms to respond to internal and external demands in ways that are goal-relevant (Cole et al., 2004). The ability to appropriately regulate emotions is vital to well-being and survival (Thompson et al., 1994; Cole et al., 2004; Morris & Reilly, 1987), and maladaptive emotion regulation is thought to underlie many mental disorders, including depression and anxiety

(Gross & Muñoz 1995).

Adaptive emotion regulation is the process of implicitly and explicitly modifying emotional responses to environmental demands in order to promote healthy functioning

(Cole et al., 2004; Gross, 1998). While we at times incorporate effortful-awareness to regulate certain emotions, most of our emotional responses and regulatory effects are too subtle for conscious awareness (Mauss et al., 2007; Thompson, 1994). These non- conscious regulatory responses have been shaped throughout development (Kopp, 1989) and can serve adaptive functions within a matched context. Campos and colleagues

(2004) postulated that emotional states and regulatory processes are intimately dependent and predictive of one another, where one’s emotional state activates a certain emotion regulatory technique. This technique then affects the organism’s interaction with the environment in such ways that influence subsequent emotional responses. Dependent on whether the technique employed is adaptive to environmental demands, the consequences of one’s regulation can promote well-being or produce negative consequences.

Emotion regulation has traditionally been studied at the trait level, where the focus has been on how various emotion regulatory techniques are generally applied, such

as reappraisal, which involves cognitively reinterpreting the meaning of an event or emotional response, or suppression, which is involves diverting one’s attention away from their emotional experience (Gross, 1999). The examination of emotion-context- sensitivity (ECS) explores adaptive emotion regulation in a broader sense, and serves as an index of emotional flexibility. Instead of evaluating specific techniques, ECS looks at the bigger picture of whether adaptive and flexible emotion regulation is occurring by indexing emotional responses in specific contexts, often in relation to adjustment

(Coifman & Bonanno, 2009). In particular, ECS is comprised of two components: emotion generation and emotion control. Emotion generation incorporates one’s ability to respond with appropriate emotion valence given a certain context. For example, in most instances a negative context should elicit a negative emotion and a positive context should elicit a more positive emotion. Emotion control is the ability to be flexible and shift emotions between contrasting contexts. For example, effective emotion control would be indicated by attenuating a negative response when transitioning from a negative context to a more positive one. ECS is typically examined in-vivo, where automatic or spontaneous emotional responses are evaluated through channels of facial behavior and self-report affect (Coifman & Bonanno, 2010). Assessment of ECS in this way is more precise and ecologically valid than self-report measures alone. By evaluating appropriate flexibility in emotional responses to environmental demands, we are able to capture a more complete picture of emotion regulatory responses.

Executive Functioning

Proper emotion generation and control are likely to be dependent on neurocognitive functions, including the executive functions of inhibitory control, attention, and set-shifting (Ochsner & Gross, 2005). These constructs are generally thought to be housed in the prefrontal cortex (PFC; Miyake et al., 2000). Systems of the

PFC have been implicated in both the conscious and non-conscious regulating of emotions (Fox & Calkins, 2003); particularly in the modulating of emotional and psychological states to fit current contextual demands (as reviewed in Davidson et al.,

2003). Specifically, there is evidence to suggest the orbitoprefrontal cortex (OFC) modulates the time-frame of emotional responding through its bidirectional relationship with the amygdala (Salzman & Fusi, 2010). The amygdala is the region of the brain responsible for processing most emotional cues and subsequently activating an organism’s autonomic nervous system to respond (Adolphs, 2003).

For the current investigation, we were particularly interested in the neurocognitive construct of set-shifting, as it is typically employed throughout the regulating of emotions

(Gross & Thompson, 2007). Effective set-shifting is marked by the ability to engage and disengage with contexts in ways that are goal-promoting and incorporates the neurocognitive domains of attention and inhibitory control (Hofmann et al., 2012).

Inability to set-shift is associated with emotional inflexibility and is often marked by rumination (Davis & Nolen-Hoeksema, 2000) and perseveration (Demakis, 2003).

Johnson and colleagues (2009) conducted a study where participants underwent a series of tasks that were either emotionally stressful or neutral. They found that individuals who

were able to effectively switch from an emotionally stressful context to a neutral context were better able to regulate their emotions and persist at the stressful task.

Physical Activity and Cardiovascular Flexibility

Cardiovascular flexibility is similar to emotional flexibility in that it allows an organism to adapt to shifting contexts (Porges, 1995 & 1992). More specifically, cardiovascular flexibility involves the ability to adapt to stress at both the physical and neurocognitive level (Thayer et al., 2009). Capacity for cardiovascular flexibility is typically indexed by vagal tone, which serves as a prominent mediator between the heart and executive control (Hansen et al., 2003). Vagal tone is conventionally assessed through examination of variability within the inter-beat-time interval, referred to as heart- rate-variability (HRV; Malik & Camm, 1990; Porges & Byrne, 1992). High resting HRV is accompanied by a more activated parasympathetic nervous system (Higgins et al.,

1973), which enables allocation of resources to higher-order cognitions and strengthens the ability to adapt to both neurocognitive and physical stress (Thayer & Lane, 2009;

Thayer et al., 2009 & 2012; Porges, 1995 & 1992). Low resting HRV is associated with a more activated sympathetic nervous system, which limits cardiovascular flexibility, and predicts reduced capacity for neurocognitive functioning and flexibility (Hansen et al.,

2003; Luft et al., 2009).

Physical activity has consistently been demonstrated to improve cardiovascular flexibility (Jurca et al., 2004; Melanson et al., 2001). While HRV is typically suppressed directly after engagement in exercise due to activation of the sympathetic nervous system

(Christensen & Galbo, 1983), regular physical activity has been shown to have a long-

term effect on increasing resting HRV (Luft et al., 2009; Hansen et al., 2004; Rennie et al., 2003). Additional research has been able to demonstrate a link between physical activity and neurocognitive functioning, with resting HRV serving as the primary mediator (Albinet et al., 2010; Luft et al., 2009).

It appears likely that the effects of physical activity might extend to emotional flexibility. While studies have documented that physical activity works to improve self- reported mood immediately after exercise (Lane & Lovejoy, 2001) as well as decrease depressive and anxious symptoms (Byrne & Byrne, 1993; Paluska & Schwenk, 2000), there have not been any studies to date to examine the potential path between activity level and the ability to appropriately generate and shift emotional responses to fit contextual demands (ECS).

Current Investigation

The current study primarily sought to evaluate the association between physical activity and emotion-context-sensitivity. Specifically, we examined appropriate emotion generation during a social rejection task and emotional control when shifting from social rejection to acceptance. Appropriate emotional responsivity was examined in-vivo through facial behavior and self-reported affect. We expected to see a positive association between physical activity and negative emotion during the context of rejection and a positive association between activity level and positive emotion during the context of acceptance. In regards to control, we expected to see a negative association between activity level and the change in negative emotional response from rejection to acceptance

(appropriate decrease in negative response). We also expected to see a positive relation

between activity level and the change in positive emotional responses from rejection to acceptance (appropriate increase in positive response).

To more thoroughly examine the association between physical activity and adaptive emotional responsivity, we investigated the potential mediating roles of HRV and executive functioning. HRV was assessed during a neutral state and executive functioning was indexed by perseverative errors on a neurocognitive task. We expected to see evidence for mediation such that activity level would be positively associated with

HRV and negatively associated with perseverative errors, and in turn, HRV would be positively associated with ECS and perseverative errors would be negatively associated with ECS. In general, we expected to see an association between physical activity and

ECS, and evidence for at least partial mediation of this association through HRV and executive functioning.

Hypothesis I

Activity level would predict appropriate emotion generation during both the rejection and acceptance trials, such that activity level would be associated with greater negative generation during rejection and greater positive generation during acceptance.

Hypothesis II

Activity level would predict appropriate emotion-control from rejection to acceptance, such that activity level would be associated with a decrease in negative and an increase in positive response from rejection to acceptance.

METHODS

Participants

Eighty-four college students were recruited from a large Midwestern university.

Participants needed to be at least eighteen years of age, speak English as a first language, and have normal or corrected-to-normal vision and hearing. Of these participants, four were excluded from analyses due to malfunctions with the heart-rate monitors, four were excluded because the video-camera did not work during the session, seven were excluded because the video-camera focused on only part of the participant’s face, and five were dropped because they incorrectly filled out the physical activity questionnaire. Sixty-four participants remained in the final sample (61% female). The age range of this sample was

18 – 32 (M = 19.72, SD = 2.48). The breakdown of class standing was as follows: 56%

Freshmen, 23% Sophomores, 11% Juniors, and 9% Seniors. The ethnic and racial breakdown of the sample was as follows: 86% Caucasian, 11% African American, 2%

Asian, 2% Pacific Islander, and 2% Hispanic or Latino. Participants were compensated with course credit. See Table 1 for complete demographics.

Procedure

Participants were first instructed to write a brief autobiography and to take a portrait for their “player profile” in order to aid the deception during the rejection/acceptance task (i.e., Cyberball). Participants then completed questionnaires

Table 1: Demographics, Including Controls

M SD

Age 19.54 2.25 Sex 61.9% Female

Education 55.6% Freshman 23.8% Sophomore 11.1% Junior 9.5% Senior

Race and Ethnicity 85.7% Caucasian 11.1% African American 1.6% Asian 1.6% Pacific Islander 1.7% Hispanic or Latino

Distress 11.28 6.96

Body-Mass-Index 23.96 5.34

Left-handed 11.1%

related to demographics, symptoms of psychopathology, and activity level. They subsequently engaged in the Wisconsin Card Sort Test (WCST). Upon completion of the

WCST, participants took a brief break, and then were hooked up to physiological monitoring equipment and completed three games of Cyberball. After completion of all tasks, participants were debriefed about study procedures and compensated.

Questionnaire Measures

Physical Activity. The International Physical Activity Questionnaire (IPAQ) is designed to assess engagement in physical activity at the following three differentiating levels: Sedentary, Moderate, and Vigorous. The Metabolic Equivalent Task (MET) score

was calculated using the IPAQ guidelines for data processing and analysis (IPAQ

Research Committee, 2005); the MET reflects an overall index for intensity and duration of weekly physical activity. The IPAQ has been shown to be valid and reliable (Craig et al., 2003; Booth et al., 2003). Mean MET for this sample was: 5,621.59 (SD = 5,523.44).

Body-Mass-Index (BMI). Participants self-reported height and weight. BMI was then determined utilizing the Centers for Disease Control and Prevention’s formula

(2004); participants’ weight in pounds was divided by their height in inches squared, multiplied by 703 Mean BMI for this sample was: 23.96 (SD = 5.34).

Depressive Symptomatology. The Center for Epidemiologic Studies Depression

Scale (CES-D) is a commonly used and validated assessment of depressive symptomatology ( = .80; Radloff, 1977). The CES-D is comprised of 20 questions, with responses indicated on a three-point Likert scale. Mean CES-D for this sample was:

11.28 (SD =6.96).

Wisconsin Card Sorting Task (WCST)

The WCST is a widely used assessment of executive processes, requiring participants to integrate environmental feedback and adapt in goal-relevant ways to shifting contexts (Heaton, 2003; Grant & Berg, 1948). During the WCST, four key-cards are displayed with varying shapes and colors. The objective of the task is to match given cards to one of the four key cards according to an implicit pattern. The matching pattern changes after every ten correct matches without informing the participant of the change.

The number of perseverative errors reflects the number of mistakes made once matching

patterns change. Previous research has demonstrated that perseverative errors is a valid and reliable indicator of executive control and prefrontal functioning (Barcelo´& Knight,

2002).

Rejection/Acceptance Task

Cyberball is a commonly used computer paradigm designed and validated for the simulation of social rejection and acceptance (Williams & Jarvis, 2006). In this adapted version of the paradigm, participants were deceptively told that they would be playing a series of ball-tossing games with three other “students” logged in at the same time.

Participants were also informed that their performance (i.e., number of times they successfully caught the ball) was going to be monitored during both trial one and trial two by the experimenter, who stood behind them keeping a tally. Each participant completed three “games”; the first was a neutral (warm-up) game, then a rejection (trial one) game, followed by an acceptance (trial two) game. Participants’ experience was programmed so that the rate of rejection and acceptance were consistent across individuals. For example, in the neutral trial, participants received the ball 25% of the time. In the rejection trial, participants received the ball 7% of the time. Finally, in the acceptance trial, participants received the ball 50% of the time.

Baseline Heart-Rate-Variability. During the warm-up trial of Cyberball, heart rate was measured for three minutes using the Polar Watch RS800CX sd (sampling rate of

1000), which has been validated as a reliable measure of heart rate (Jonckheer-Sheehy &

Ortolani, 2012). Heart rate was measured during the warm-up trial because it was a fairly

neutral context without induced emotional or mental stress. The 2.0 version of Kubios software was used to derive HRV and to analyze its time and frequency domains

(Tarvainen et al., 2014). Time domains of mean R-R intervals per ms and mean heart rate per minute were analyzed along with high frequency bands (0.15-0.4Hz). The Fast

Fourier Transform (FFT) algorithm was used to compute the Fourier transform, which comprises the variability of heart rate. Artifacts were detected and removed using both visual methods and the Kubios standard medium level of artifact correcting.

Facial Behavior. Facial behavior was recorded using a high definition video camera. Following data collection, Research Assistants - blind to the study’s details- rated facial behavior on 7-point Likert scales for degree ratings of negative emotional and positive emotional behavior for each trial of the Cyberball game. Coders were sufficiently reliable (negative:  = .80; positive:  = .90). However, in order to increase reliability further, these ratings were averaged across coders to comprise four overall scores for each participant: negative facial emotion during rejection, positive facial emotion during rejection, negative facial emotion during acceptance, and positive facial emotion during acceptance.

Self-Report Affect. After each trial of Cyberball, participants rated the following emotion-words on a 7-point Likert scale: Fear, Relief, Sadness, Enjoyment, Distress,

Guilt, Happiness, Anger, Amusement, Disgust, and Affection. The emotion-words of

Distress, Anger, Fear, Guilt, Sadness, and Disgust were aggregated together to derive the mean score for negative affect (internal consistency for trial one  = .83; trial two  =

.58). Mean scores for negative affect for this sample were: Rejection = 1.24 (SD = .56);

Acceptance = 1.05 (SD = .15). The emotion-words of Relief, Happiness, Amusement,

Affection, and Enjoyment were aggregated together to derive the mean score for positive affect (internal consistency for trial one  = .75; trial two  = .78). Mean scores for positive affect for this sample were: Rejection = 2.19 (SD = .99); Acceptance = 2.43 (SD

= 1.12).

Emotion-Context-Sensitivity

Emotion Generation. There were only small to no correlations among indices of emotion responsivity. When measuring these constructs, it was assumed that there would be discrepancies between facial behavior and self-report affect, and that combining the channels into one index would give us the most complete picture of participants’ emotion-response system. Facial behavior and self-report affect ratings were standardized to a z-score and combined into two scores: mean level of negative emotion during rejection and mean level of positive emotion during acceptance.

Emotion-Control. In order to measure emotion-control, we created a change score subtracting the combined mean level of negative emotion during rejection from the combined mean level of negative emotion during acceptance, with lower scores indicating the expected decrease in negative responsivity from rejection to acceptance and thus greater emotion-control. This process was repeated utilizing the positive variables, where combined positive emotion during rejection was subtracted from combined positive emotion during acceptance, with higher scores indicating an increase

in positive emotion responsivity from rejection to acceptance and thus greater emotion- control.

Table 2: Descriptives of Key Variables

M SD

Physical Activity 5621.59 5523.44 kcal Resting Heart-Rate-Variability 53 16

Perseverative Errors 8.80 5.35

Negative Emotion (Rejection) -.02 1.31

Positive Emotion (Acceptance) .001 1.39

Negative Emotion change from Rejection to -.18 .69 Acceptance Positive Emotion change from Rejection to .22 1.05 Acceptance

RESULTS

Analytic Plan

We were interested in answering the following overall questions: Does activity level predict adaptive emotional responding, as indexed by context-appropriate emotion generation and emotion-control? If so, how might resting HRV and executive processes fit-in within this association? To answer these questions we first performed partial correlation analyses among the key variables. We then tested the possible mediators of resting HRV and perseverative errors by examining each path by using OLS regression.

We subsequently examined the linear association between activity level and indices of

ECS using OLS regression.

Preliminary Analysis

In order to determine if Cyberball was effective in inducing emotion responsivity, we evaluated the overall change in means between the rejection and acceptance contexts using a Repeated Measures ANOVA. We plotted patterns of means for facial behavior and affect by valence across trials of Cyberball. Means conformed to the expected patterns of responses (e.g., more negative emotion during rejection and more positive emotion during acceptance). However, changes across the sample were non-significant.

We performed partial correlation analyses between the key variables to see if there were expected associations (see Table 3). We predicted that activity level would be

15 16

Table 3: Partial Correlation Analysis of Key Study Variables. Controlling for: Age, Gender, Handedness, BMI, and Distress.

(1) (2) (3) (4) (5) (6) (7)

1. Activity level -

2. Resting Heart- -.05 - Rate-Variability

3. Perseverative -.15 -.31* - Errors

4. Negative .47*** .07 -.26* - Emotion (Rejection)

5. Positive .11 .09 .17 .09 - Emotion (Acceptance)

6. Negative -.36** -.05 .13 -.62*** -.09 - emotion change from Rejection to Acceptance 7. Positive emotion .09 .18 .18 .21 -.25† -.31* change from Rejection to Acceptance

***p<.001;**p≤.008; *p<.05; †p≤.08

related to indices of emotion-context-sensitivity. Further, we hypothesized that perseverative errors and resting HRV would be closely related to each-other and to activity level, and possibly related to emotion generation and control. We entered age and sex as controls because they have been shown to be predictors of emotion, activity level and HRV (de Paula et al., 2005; Gregoire et al., 1996; Kring & Gordon, 1998). We also controlled for BMI due to likely effects upon HRV and executive functioning (Kim et al., 2005; Gunstad et al., 2007). Finally, the WSCT was performed using a right-handed computer mouse; we controlled for handedness to account for any discrepancies. Expectedly, activity level was correlated with the mean level of negative generation during rejection, r = .47, p <.001, and negatively correlated with the change in negative response from rejection to acceptance, r = -.36, p = .005, meaning that the more physical activity an individual reported that they engaged in, the greater the reduction of negative response during the acceptance trial relative to the rejection. Unexpectedly, activity level was not significantly correlated with any of the positive emotion variables or to resting HRV and perseverative errors. As predicted, perseverative errors and resting HRV were correlated, r = -

.31, p =.02. In addition, perseverative errors was negatively associated with negative generation during rejection, r = -.26, p =.05, though the association was marginally significant. See table 3 for correlations.

To test potential mediating effects of resting HRV and perseverative errors between physical activity and both emotion generation and emotional control, a series of OLS regressions were applied according to Kenny’s four steps to testing a mediation hypothesis (Kenny, 2014).

However, the expectation that resting HRV and perseverative errors would mediate this relation was not substantiated.

Analysis for Hypothesis 1

In order to test hypothesis 1, we performed step-wise OLS regression analyses to evaluate the association between activity level and emotion generation. We entered the controls of age and sex into step 1 of the model as they have been documented to contribute to emotion, activity level and HRV (de Paula et al., 2005; Gregoire et al., 1996; Kring & Gordon, 1998). We also entered handedness into step 1 to control for effects with perseverative errors, as the WCST was conducted using a right-handed mouse. BMI was entered as a control into step 1 because BMI has been shown to influence HRV and executive functioning (Kim et al., 2005; Gunstad et al.,

2007). We decided to also control for distress by entering it into step 1, given its association with emotion and to provide a more rigorous measure of the relation between physical activity and emotion generation. We decided to also control for resting HRV and perseverative errors by entering them into step 2 of the model. Although mediation pathways were not evidenced, there are likely still some associations. Activity level was entered into the third step.

Negative emotion generation during rejection. Our dependent variable in this model was mean negative emotion generated during rejection. This model was significant F(8, 54) = 2.89, p

=.009, with activity level a significant predictor of greater negative emotion (ß = .46, p<.001) above and beyond all controls. See Table 4.

Table 4: Regression Analysis Examining the Association between Negative Emotion during Rejection and Physical Activity.

Dependent Variable: Negative Emotion during Rejection B SE ß sr2 R2 ∆R2

Step 1 Age -.06 .07 -.12 .01 .07 .07 Sex -.29 .35 -.11 .01 Handedness -.62 .56 -.15 .02 Body-Mass-Index .01 .03 .04 .002 Distress .03 .02 .14 .14 F (5, 57) = .87, p = .51 Step 2 Age -.04 .07 -.08 .01 .12 .05 Sex -.4 .36 -.15 .02 Handedness -.53 .6 -.13 .01 Body-Mass-Index .03 .04 .12 .01 Distress .02 .03 .09 .01 Resting Heart-Rate-Variability .00 .01 .004 .00 Perseverative Errors -.06 .04 -.25 .04 F(7, 55) = 1.1, p = .40 Step 3 Age -.02 .06 -.04 .002 .30 .18* Sex .01 .34 .004 .004 Handedness -.54 .51 -.13 .01 Body-Mass-Index .04 .03 .17 .02 Distress .01 .02 .05 .002 Resting Heart-Rate-Variability -.004 .03 .05 .002 Perseverative Errors .04 .01 -.17 .02 Physical Activity .6 .16 .46** .18 F(8, 54) = 2.89 , p =.009 *p<.05; **p<.001

Positive emotion generation during acceptance. Our dependent variable in this model was mean positive emotion generated during acceptance. The overall model fit was marginal, F(8,55)= 1.94, p=.07. Unexpectedly, activity level was not a significant predictor (ß = .18, p = .17), though BMI (ß = -.31, p =.03) and perseverative errors (ß =

.26, p =.07) were predictive of positive emotion. See Table 5.

Analysis for Hypothesis 2

In order to test hypothesis 2, we performed step-wise OLS regression analyses to evaluate the association between activity level and emotion-control .We entered the controls of age and sex into step 1 of the model as they are likely to influence HRV, executive functioning, and emotion (de Paula et al., 2005; Gregoire et al., 1996; Kring &

Gordon, 1998). We also entered handedness into step 1 to control for discrepancies in regards to perseverative errors, as the WCST was conducted using a right-handed mouse.

BMI was entered as a control into step 1 to control for its impact upon HRV and executive functioning (Kim et al., 2005; Gunstad et al., 2007). Distress was also entered into step 1 of the model in order to control for its association with emotion and to provide a more rigorous measure of the relation between physical activity and emotion generation. Resting HRV and perseverative errors were entered into step 2. Activity level was entered into the third step.

Change in negative response from rejection to acceptance. Our dependent variable in this model was the negative emotion change score from rejection to Table 5 acceptance

(with a lower number indicating a decrease in negative emotion suggesting adaptive emotion responsivity). The overall fit of this model was significant, F(8,54) = 2.14, p =

.05, with activity level serving as a significant predictor of adaptive negative emotion control (ß = -.35 , p = .009) above and beyond all controls. See Table 6.

Change in Positive Response from Rejection to Acceptance

Our dependent variable in this model was the positive emotion change score from rejection to acceptance (with a higher number indicating an increase in positive emotion suggesting adaptive emotion responsivity). The overall fit of this model was marginal,

F(8,54)=1.9, p =.08. Surprisingly, activity level was not a significant predictor (ß = .14, p = .27), but both perseverative errors and resting HRV were predictive of an adaptive increase in positive emotion (ß = .31, p = .04; ß = .29, p = .04, respectively). See Table 7.

Table 5: Regression Analysis Examining the Association between Positive Emotion during Acceptance and Physical Activity

Dependent Variable: Positive Emotion during Acceptance B SE ß sr2 R2 ∆R2

Step 1 Age .07 .07 .12 .01 .15 .15 Sex -.72 .36 -.29* .08 Handedness -1.0 .57 -.22 .04 Body-Mass-Index -.08 .04 -.3* .07 Distress -004 .03 -.02 .00 F(5,58) = 1.20, p = .09 Step 2 Age .07 .07 .12 .01 .19 .05† Sex -.68 .36 -.24 .05 Handedness -.88 .58 -.20 .03 Body-Mass-Index -.08 .04 -.32* .08 Distress .01 .03 .05 .002 Resting Heart-Rate-Variability .02 .01 .17 .02 Perseverative Errors .06 .04 .24 .04 F(7,56) = 1.92, p = .08 Step 3 Age .08 .07 .14 .02 .22 .3† Sex -.5 .38 -.18 .02 Handedness -.91 .57 -.21 .04 Body-Mass-Index -.08 .04 -.31* .07 Distress .01 .03 .04 .002 Resting Heart-Rate-Variability .02 .01 .18 .03 Perseverative Errors .07 .04 .26† .05 Physical Activity .00 .00 .18 .03 F(8,55)= 1.94, p=.07 *p<.05; †p<08

Table 6: Regression Analysis Examining the Association between the Change in Negative Emotion from Rejection to Acceptance and Physical Activity

Dependent Variable: Negative emotion change from Rejection to Acceptance B SE ß sr2 R2 ∆R2

Step 1 Age .04 .04 .14 .02 .13 .13 Sex .15 .18 .11 .01 Handedness -.37 .29 -.170 .03 Body-Mass-Index .01 .02 .09 .01 Distress -.02 .01 -.21 .04 F(5,57) = 1.67, p = .16 Step 2 Age .03 .04 .12 .01 .14 .01 Sex .16 .19 .11 .01 Handedness -.41 .30 -.19 .03 Body-Mass-Index .01 .02 .05 .002 Distress -.02 .01 -.20 .04 Resting Heart-Rate-Variability .01 .01 -.06 .002 Perseverative Errors -.002 .02 .07 .003 F(7, 55) = 1.4, p = .30 Step 3 Age .03 .03 .09 .01 .24 .11* Sex .00 .19 .00 .00 Handedness -.41 .28 -.19 .03 Body-Mass-Index .002 .02 .01 .00 Distress -.02 .01 -.18 .03 Resting Heart-Rate-Variability -.004 .01 -.09 .01 Perseverative Errors .001 .02 .01 .00 Physical Activity -.24 .09 -.35* .1 F(8,54) = 2.14, p = .05 *p<.05

Table 7: Regression Analysis Examining the Association between the Change in Positive Emotion from Rejection to Acceptance and Physical Activity

Dependent Variable: Positive emotion change from Rejection to Acceptance B SE ß sr2 R2 ∆R2

Step 1 Age -.08 .05 -.19 .04 .11 .11 Sex -.55 .28 -.26* .06 Handedness -.46 .44 -.14 .02 Body-Mass-Index -.02 .03 -.12 .01 Distress -.002 .02 -.01 00 F(5, 57) = 1.42, p = .23 Step 2 Age -.08 .05 -.19 .03 .20 .09† Sex -.41 .27 -.19 .03 Handedness -.33 .43 -.1 .01 Body-Mass-Index -.03 .03 -.14 .01 Distress .01 .02 .08 .01 Resting Heart-Rate-Variability .02 .01 .29* .01 Perseverative Errors .06 .03 .28* .06 F(7, 55) = 1.95, p = .08 Step95 3 Age -.08 .05 -.18 .03 .22 .02† Sex -.31 .29 -.14 .02 Handedness -.33 .43 -.1 .01 Body-Mass-Index -.03 .03 -.13 .01 Distress .01 .02 .07 .004 Resting Heart-Rate-Variability .02 .01 .29* .07 Perseverative Errors .06 .03 .31* .07 Physical Activity .00 .00 .14 .02 F(8,54)=1.9, p =.08 *p<.05, †p<.09

DISCUSSION

This study investigated the association between physical activity and emotion- context-sensitivity. We found support for our prediction that physical activity would be positively associated with adaptive emotional responding. Specifically, we were able to document a link between physical activity and context-appropriate negative emotional responding as well as increased ability to control negative emotional responsivity during a context shift from negative to positive. We did not see contrasting associations with positive emotional responsivity. This suggests that physical activity may be particularly related to the ability to appropriately generate and control negative emotions. These findings extend the body of literature linking physical activity with improved psychological functioning to include a specific link with contextually appropriate expression and regulation of negative emotions.

Prior research has demonstrated a link between physical activity and improved mood (Peluso & Andrade, 2005). Most studies have examined mood immediately after engagement in exercise and found an increase in self-reported positive affect following exercise (Yeung, 1996; Hansen et al., 2001). There are many mechanisms that might underlie an association between physical activity and mood immediately following exercise, such as a release of endorphins, an increase in core temperature, and an increase in blood and oxygen supplied to the brain (Dubnov & Berry, 2000). Other studies linking physical activity to mood have utilized both exercise interventions and self-report

25 26

measures of mood, where mood was assessed apart from recent engagement in physical activity (as reviewed in Penedo & Dahn, 2005). Moreover, research has suggested regular physical activity might have a long-term effect of attenuating symptoms of psychopathology in individuals who are otherwise treatment resistant (Mota-Pereira et al., 2011). These studies capture more long-lasting effects of physical activity upon mood-states and mental well-being.

The current study expands upon this body of literature by specifically examining the association between physical activity and the ability to be emotionally flexible in response to changing contexts. While emotions are related to mood-states, they are distinct (Ekman, 1992). Emotional flexibility has been shown to be predictive of resilience to stressful events (Bonanno et al., 2004), whereas emotional inflexibility is thought to underlie depression and anxiety (Rottenberg et al., 2005). Therefore, exploring appropriate fluctuations of emotions is important to understanding the exact mechanisms underlying general maladjustment and psychopathology. Where moods are typically assessed using self-report measures, context appropriate emotional responding is examined through channels of both self-report affect and facial behavior (Coifman &

Bonanno, 2010), which provides a more comprehensive assessment of the emotional experience. This is the first study to document a connection between physical activity and adaptive negative emotional responsivity as measured with such rigorous methodology.

It is interesting that we did not see associations between physical activity and positive emotional flexibility. However, it can be argued that control of negative emotions between contrasting contexts, as opposed to positive emotions, might be more

meaningful in regards to adjustment. Negative emotions are more closely linked to cardiovascular reactivity and the body’s stress response; a prolonged state of negative emotionality could therefore be stressful to the body (Kubzansky & Kawachi, 2000;

Brosschat & Thayer, 2003). Moreover, the consequences of a negative emotional response during a positive context, as opposed to the mere absence of a positive emotional response during a positive context, potentially carries more damaging social consequences. For example, displaying an angry emotion during a context of social acceptance could isolate an individual and promote subsequent social rejection, whereas simply not displaying positive emotion during a context of social acceptance would not promote such powerful social consequences (Keltner & Kring, 1998).

With this being a correlational study, there is no way to interpret the direction among the associations between physical activity and adaptive negative emotional responding. It is perhaps more likely that the connection between physical activity and emotion-context-sensitivity is bidirectional, where the ability to adaptively regulate negative emotions influences healthy living, including physical activity, and physical activity modulates the ability to adaptively regulate negative emotions. In this way, the negative effects of physical inactivity would be compounding, while the positive effects of an increase in physical activity would be exponential. Perhaps the only way to parcel out the extent to which increased physical activity might strengthen one’s ability to regulate negative emotions, would be to conduct an exercise intervention. Determining the direction of this association is a rich area for future study.

While examining the direct relation between physical activity and adaptive emotional responding was our primary focus, we were also interested in exploring the potential mechanism of HRV and executive functioning linking physical activity with adaptive emotional responding. Emotion regulation employs certain resources of executive control (Ochsner & Gross, 2005). Because activity level and resting HRV have been shown to increase these resources (Albinet et al., 2010), we were expecting to see evidence for resting HRV and executive functioning serving as mediators between physical activity and emotion-context-sensitivity. While there was surprisingly no evidence linking the expected mechanism of resting HRV and executive functioning to physical activity in particular, there were linear associations between resting HRV, executive functioning, and modulation of positive emotional responsivity. However, executive functioning’s association was in the opposite direction of what was expected; whereas HRV was in the expected direction. We perhaps did not utilize the most ideal measure to capture executive functioning in a college population, as the WCST is typically utilized within clinical populations and is therefore not sensitive to more conservative differences. Regardless, these distinct findings suggest that the role of resting HRV and executive functioning within the interplay of physical activity and adaptive emotional responding may have more complexities than what this investigation was able to elucidate.

It is particularly surprising that resting HRV and executive functioning were related to appropriate modulation of positive emotion, but that physical activity was distinctly related to appropriate modulation of negative emotion. It is possible that

positive and negative emotions are regulated under varied mechanisms, and that this would account for the discrepancies we are seeing. Kim & Hamann (2011) found different activation in both the cortical and subcortical regions of the brain during the regulating of positive compared to negative emotions during context specific tasks.

Future research should explore the mechanisms underlying the regulation of positive and negative emotions.

It is also surprising that we did not see significant associations between physical activity and resting HRV and executive functioning. There may not have been enough variance within our physical activity measure to capture these expected associations, and measurement of physical activity was not entirely accurate as it was self-reported.

Moreover, values of physical activity were not normally distributed; some participants reported a large amount of physical activity where others reported none. It is entirely possible that measurement error of physical activity could account for the lack of connection with HRV and executive functioning. Future research should clarify these associations.

Limitations

An important limitation to this study was the self-report measurement of physical activity. Many factors could have influenced participants’ report of physical activity, such as social desirability and social approval (Adams et al., 2005). However, despite this limitation, we were still able to find strong associations between self-reported physical activity and adaptive negative emotional responses. While more rigorous methodological approaches to measuring physical activity should be taken in future research of this

phenomenon, there is significant meaning in the fact that we were able to find such a strong link between self-reported physical activity and emotional flexibility.

An additional limitation to this study was the way in which we measured executive functioning. While the WCST is a widely used assessment of neurocognitive set-shifting (Heaton, 2003), it is potentially limited in its ability to comprehensively capture other relevant neurocognitive domains such as attention. While we did not see an association between physical activity and perseverative errors, we did see associations in the expected direction between resting HRV and perseverative errors, as well as associations between perseverative errors and appropriate emotional responsivity. These linkages suggest that there was enough variability within our sample to capture meaningful differences as a function of perseverative errors on the WCST. It is likely that measurement of set-shifting during an emotionally neutral computer task does not completely map-on to set-shifting during adaptive emotion regulation, as utilizing cognitive set-shifting during an emotion provocation may employ more complex neurocognitive resources than what the WCST elicits. Future research should more thoroughly explore these complex relations by employing a wider variety of neurocognitive assessments that involve both neutral and emotional paradigms.

Future Directions and Implications

In order to clarify the direction of the association between physical activity and adaptive emotion regulation, future research should examine the benefits of an exercise intervention upon emotional flexibility. Further, while the current study did not evaluate psychopathology in particular, it has provided the foundation to do so in the future. Prior

exercise intervention studies have shown physical activity to be associated with a decrease in symptoms (Wolff et al., 2011). In light of the current study’s findings, it might be the case that ECS underlies the relation between physical activity and symptom alleviation. Future research should more thoroughly evaluate the potential dynamic among physical activity, ECS, and symptom alleviation.

The current study did not differentiate among negative emotions. While a general negative response during social-rejection is considered to be adaptive, it is not necessarily the case that every emotion with a negative valence would be adaptive during such a context. For example, fear and shame would not be adaptive during social rejection, but sadness might be (Buss & Kiel, 2004; Ferguson et al., 2000). Future research should attempt to parcel out the relation between physical activity and various negative emotions during context-specific tasks (e.g., sadness, anger, fear, and shame).

Conclusion

The current study was able to document a link between physical activity and adaptive negative emotion responsivity. While it has been robustly documented that physical activity is beneficial to psychological well-being, this is the first study to show evidence for a specific association with adaptive emotional responding using combined methods of facial behavior and self-report affect. While we did not see evidence for mediation through resting HRV and executive functioning, we did find linear associations between resting HRV, executive functioning, and adaptive positive emotion repsonsivity.

Future research is needed to clarify this complex interplay in order to illuminate the vast benefits of physical activity upon psychological well-being. In better understanding the

associations between physical activity and psychological functioning, we might be able to more precisely monitor clinical applications of physical activity.

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