The role of DHEA in relation to problem solving and academic performance

Biological Psychology 85 (2010) 53–61

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Biological Psychology journal homepage: www.elsevier.com/locate/biopsycho

The role of DHEA in relation to problem solving and academic performance Stephanie Wemm a , Tiniza Koone a , Eric R. Blough b , Steven Mewaldt a , Massimo Bardi a,∗ a b

Psychology Department, Marshall University, Huntington, WV, USA Biological Sciences and Cell Differentiation and Development Center, Marshall University, Huntington, WV, USA

a r t i c l e

i n f o

Article history: Received 18 November 2009 Accepted 19 May 2010 Available online 26 May 2010 Keywords: Coping Stress Cortisol DHEA Cardiovascular activity The DASS The COPE questionnaire Academic performance

a b s t r a c t Dehydroepiandrosterone (DHEA) has been correlated with lower susceptibility to anxiety and mood disturbance. Since coping styles have been shown to be a critical component of academic achievement, we aimed to assess the relationship between DHEA and coping mechanisms in college students. Participants were recruited and tested twice, one week apart. Cardiovascular measurements and saliva samples were taken for each participant. The behavioral task consisted of a set of anagrams of increasing difficulty (possible to impossible). American College Testing (ACT) scores, number of college courses failed and dropped along with current grade point average (GPA) were recorded. Results indicated that successfully coping with challenging tasks is a function of behavioral flexibility and physiological neuroprotection. When presented with challenging tasks, individuals who vary their behavioral response to fit the task’s demands have the lowest probability of failing the task. The same individuals also have higher levels of resiliency hormones, demonstrated by a lower ratio of cortisol versus DHEA levels. © 2010 Elsevier B.V. All rights reserved.

1. Introduction Effective coping strategies likely evolved to provide neurobiological adaptations to stressful stimuli in order to maintain allostasis (Hayley et al., 2005; Anisman et al., 2008). Chronic and long-term dysregulation, or allostatic load, can have deleterious effects on an individual’s ability to function, wearing on the body’s neurobiological responses (Le Moal, 2007; McEwen, 2005). Long-term consequences of allostatic load have been shown to be related to later health problems including cardiovascular diseases (Grippo and Johnson, 2008), greater risk of breast cancer (Jacobs and Bovasso, 2000), adverse health outcomes in aging (Gruenewald et al., 2009), depression (Hayley et al., 2005), and immunosuppression (Butts and Sternberg, 2008). Resilient individuals can efficiently adapt and maintain allostasis in a challenging environment, thereby reducing the negative consequences of allostatic load (Feder et al., 2009). Recent evidence has shown that positive psychological states or traits, such as optimism or happiness, characterize resilient individuals and correlate to reduced hypothalamic-pituitary-adrenocortical (HPA) axis activity during stress (Chida and Hamer, 2008).

∗ Corresponding author at: Psychology Department, Marshall University, One John Marshall Drive, Harris Hall 212, Huntington, WV 25575, USA. Tel.: +1 304 696 2775; fax: +1 304 696 2784. E-mail address: [email protected] (M. Bardi). 0301-0511/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.biopsycho.2010.05.003

Traditionally, stress responses have been measured, among others, by the levels of cortisol, corticotrophin-releasing hormone (CRH), neuropeptide-Y, and norepinephrine. There is little research regarding dehydroepiandrosterone (DHEA) and stress, although compelling evidence exists indicating that this hormone plays a critical role in the stress response and coping mechanisms (Charney, 2004). Studies using saliva samples have shown that DHEA is secreted in response to psychosocial stressors such as public speaking (Izawa et al., 2008; Shirtcliff et al., 2007). Also, DHEA has been shown to be released parallel to cortisol during physical stress (Charney, 2004; Izawa et al., 2008) to protect the body against the negative effects of prolonged exposure to glucocorticoids (Morgan et al., 2004). Research has demonstrated that DHEA can act centrally to decrease glucocorticoid-induced neuronal death in the hippocampus (Maninger et al., 2009) and to promote neurogenesis in the dentate gyrus of the hippocampus and in sensory dorsal root ganglion neurons (Pinnock et al., 2009; Ulmann et al., 2009). Higher levels of DHEA have also been correlated with reduced mental illness symptomology and better task performance in patients suffering from Post-Traumatic Stress Disorder (PTSD) (Rasmusson et al., 2004) and more accurate spatial navigation in an underwater scuba diving task (Morgan et al., 2009). Furthermore, the ratio between cortisol and DHEA and has been found to be a reliable index of neuroprotection (see review on the neuroprotective evidence of DHEA by Maninger et al., 2009). A lower cortisol/DHEA ratio is associated with higher protection against stressful events and better performance in a military survival training camp, including interrogation and application problem-solving

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techniques learned previously in a classroom setting (Morgan et al., 2004). Lower cortisol/DHEA ratios have also been associated with lower symptoms of depression (Young et al., 2002), lower rates of treatment resistant depression (Markopoulou et al., 2009), and correlated with lower susceptibility to anxiety and general mood disturbance (van Niekerk et al., 2001; Shirotsuki et al., 2009). Individual variation in resiliency can be measured by monitoring cardiovascular stress responsivity as well (Pico-Alfonso et al., 2007). In a meta-analysis of laboratory mental stressors, individuals who were characterized by anxiety, neuroticism, or negative affect exhibited slower cardiac recovery (Chida and Hamer, 2008). Persistent individuals who already have a chronically high stress level show higher cardiac reactivity to new acute stressors as compared to persistent individuals who were not chronically stressed (Keltikangas-Jarvinen and Heponiemi, 2004). Elevated basal cortisol levels have been correlated to slower cardiac recovery from a psychosocial stressor (Pico-Alfonso et al., 2007). The link between physiological responses and behavioral coping has been extensively investigated in the past in animals, but not in humans (Korte et al., 2005). Given the possible clinical implications of an effective behavioral screening tool helpful in identifying phenotypic traits associated with resiliency (Sgoifo et al., 2003), assessing the interactions of behavioral coping and physiological responses could be extremely useful. A number of tools have been developed to evaluate self-reported coping styles (Vitaliano et al., 1987; Folkman and Lazarus, 1988). To assess the relationship between perceived stress, coping mechanisms and physiological responses, we used the Coping Orientation to Problems Experienced (COPE) Questionnaire (Carver et al., 1989) and the Depression Anxiety Stress Scale (DASS) (Lovibond and Lovibond, 1995). Standardized tests (i.e. the SAT and the ACT) are used extensively by colleges to predict how well applicants will perform in college (Hawkins and Clinedinst, 2006). Academic performance has been measured by the students’ grade point average (GPA), however, the use of GPA as an indicator of academic performance is suspect, due to the variation between instructors’ grading and selection of classes based on ease or difficulty (Berry and Sackett, 2009). Thus, in addition to GPA, we included classes dropped and classes failed. In previous studies of behavioral coping, persistent individuals spend more energy on difficult tasks than avoidance driven individuals (Capa et al., 2008). When confronted with a real-life coping situation, such as an extremely difficult class, persistent individuals should also be more likely to fail a class than to drop it. Using the classes dropped and failed should provide us with a measure of academic achievement that also has more relevance to real-life coping situations. The ability to adapt coping styles to fit the situation, or flexible coping, has been shown in many different studies to be a critical component of academic achievement and preventing burnout (Sheilds, 2001; Wang and Yeh, 2005; Gan and Shang, 2007). While many studies have examined the amount of stress students experiences, these studies utilize methods that rely upon the student’s subjective experience of stress (Robotham and Julian, 2006). Given that resilient individuals are marked by efficiently adapting to a stressful situation, a person who copes with academic challenges effectively should also be marked by these same psychological and biological markers. The aim of the present study was to assess natural variations in coping mechanisms associated with academic challenges. Participants were screened for their basal HPA and autonomic activity, and behavioral coping was observed using a variable task paradigm. Participants self-awareness of their coping mechanisms and perceived stress levels were gauged with the COPE and DASS questionnaires. These variables were then used as predictors of the students’ achievement as measured by their GPA, number of classes

failed, and number of classes dropped. Several different alternative hierarchical models were tested to study the reciprocal interactions among these predictors and to assess which variables contributed the most to student resiliency and achievement. 2. Methods 2.1. Participants and academic performance Sixty-six students (mean age = 22.3 ± 7.8 years) were recruited as a part of a larger pilot study on the psychobiological correlates of resilience. This study was approved by the Office of Research Integrity (Marshall University Institutional Review Board # 2 – Social/Behavioral – 1/9/2009 IRBNet ID# 104702-1; IRB # 00003206), and it has therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki. All participants gave their informed consent prior to their inclusion in the study. Participants were tested on a week-day morning between 10:00 am and 12:00 pm and tested again at the same time one week later. All participants reported awakening time within 2 h of the beginning of the test. Sleeping patterns (regular/irregular) were assessed by asking the participants if they had regular or irregular sleep (how many days) in the week before the test. The second test was done one week later to check participant reliability and select a time when the student would be available again during their academic schedule. Seven of the 66 participants (about 10%) did not return for the second session. American College Testing (ACT) composite scores, GPA at the time of the participation in the study, and the number of college courses failed and dropped in the previous semesters were collected for all the participants to assess their overall academic performance. 2.2. Self-report assessments The amount of perceived stress or anxiety experienced during the week prior to the experiment was assessed using the DASS (Lovibond and Lovibond, 1995). Although anxiety and stress are generally distinct from one another, people who have an elevated stress response or those who have an anxiety response to problems are usually characterized by similar symptoms of elevated negative emotions such as irritability and physiological hyper-arousal (Clark and Watson, 1991). Examples from this scale include: “I was aware of dryness of my mouth”, and “I was aware of the action of my heart in the absence of physical exertion”. Participants responded using a 4-point Likert scale, ranging from 1 (I usually don’t do this at all) to 4 (I usually do this a lot), to rate their agreement with each statement. The COPE scale was employed to assess how participants usually responded when they confronted difficult or stressful events in their lives (Carver et al., 1989). These scales have been found to be consistent and reliable in both clinical and nonclinical samples (Brown et al., 1997; Crawford and Henry, 2003) and they have been used extensively as a self-report inventory, including studies on academic performance (Carver and Scheier, 1994), maternal coping (Eisengart et al., 2006), addictive behavior (Hasking and Oei, 2002), mood and depressive disorders (Stowell et al., 2008; Wei et al., 2008), and coping with health problems (Roussi et al., 2007). Examples of items from this scale include: “I try to grow as a person as a result of the experience” (active coping), “I get upset, and am really aware of it” (cognitive coping), “I try to get emotional support from friends or relatives” (emotional coping), and “I drink alcohol or take drugs, in order to think about it less” (avoidance coping). Participants used a 4-point Likert scale, ranging from 1 (I usually don’t do this at all) to 4 (I usually do this a lot), to rate their agreement with each statement. 2.3. Cardiovascular assessments At the beginning of each experimental session, cardiovascular measurements at rest were taken for each participant. Measurements provided the following cardiovascular parameters: systolic blood pressure (SBP), diastolic blood pressure (DBP), and heart rate (HR). All cardiovascular measurements were collected via an automated sphygmomanometer, model 3MD1-CVS (CVS Pharmacy Inc, Woonsocket, RI). 2.4. Endocrinological assessments Saliva samples were collected for the cortisol and DHEA assessment in both weeks, and the average values were calculated to assess baseline individual levels. Participants were asked to refrain from eating, drinking, or brushing their teeth before experimental sessions, and to rinse their mouth with water just before sample collection. Participants were all non-smokers, and their awakening time was checked to normalize the individual secretion patterns. Participants were then asked to expectorate through a plastic straw into a collection tube. Immediately after the collection, samples were stored at −70 ◦ C until the assays. Prior to extraction, saliva samples were thawed at room temperature and placed in a glass tube with 1 mL of ethyl acetate (for DHEA) and diethyl ether (for cortisol) for every milliliter of sample. The contents of the tube were shaken vigorously in a mixer for approximately 30 s. Next, the tube was centrifuged for 15 min at 3000 rpm. Using a transfer pipette, the supernatant was transferred to a 13 × 100 mm glass test tube. This process was

S. Wemm et al. / Biological Psychology 85 (2010) 53–61 repeated three times. The recovery of the samples was 102% for DHEA and 96.5% for cortisol. The final step of the extraction procedure was to dilute the sample in EIA buffer (concentration 1:20 for DHEA and 1:4 for cortisol) from ImmunoAssay kit (AssayDesigns, Ann Arbor, MI). Assay procedures were carried out using materials and protocols provided by an Enzyme ImmunoAssay (EIA) kit (DHEA Enzyme Immunoassay Kit, Catalog No. 900-093, and Cortisol Enzyme Immunoassay Kit, Catalog No. 900-071). Assays were performed in duplicate. The cross-reactivity of the DHEA kit was 100% with DHEA, 30% with DHEA-sulfate, and considered negligible for other steroids (under 1% of androstenedione, androsterone, and so forth). The cross-reactivity of the cortisol kit was 100% with cortisol and prednisolone (122%), 27.68% with corticosterone, 4% with 11-deoxycortisol, and negligible for other steroids (under 1%). The assayed standards generated a line with slopes ranging from 0.91 to 1.1 and a correlation coefficient of 0.98. Intra-assay precision (%CV) of the DHEA kit was 6.4%, 5.8%, and 4.8% for low (195 pg/mL), medium (780 pg/mL), and high (3125 pg/mL) concentrations of DHEA, respectively. Inter-assay precision (%CV) was 8.4%, 8.8%, and 6.6% for low (195 pg/mL), medium (780 pg/mL), and high (3125 pg/mL) concentrations of DHEA, respectively. Sensitivity of the kit was 2.90 pg/mL. Intra-assay precision (%CV) of the cortisol kit was 10.54%, 6.6%, and 7.3% for low (156 pg/mL), medium (625 pg/mL), and high (2500 pg/mL) concentrations of cortisol, respectively. Interassay precision (%CV) was 13.4%, 7.8%, and 8.6% for low (156 pg/mL), medium (625 pg/mL), and high (2500 pg/mL) concentrations of cortisol, respectively. Sensitivity of the kit was 56.80 pg/mL. Sample readings were completed using an automated micro-plate reader (BIOTEK, Richmond, VA, model # EL x 800) and the KCjunior software (BIO-TEK, Richmond, VA, version 1.3, Part 5270501). Readings were assessed at a wavelength of 405 nm, with corrections at 570 nm. 2.5. Behavioral assessment Behavioral response to the anagram task was videotaped for all participants. Maximum attempt duration was set at 5 min. The average completion time for the possible task was 23 ± 7 s. The average completion time for the impossible task approached 5 min, although in a few instances participants gave up before the time expired. Videotapes of participants’ behavior during the anagram task were scored and analyzed in both duration (s/min) and frequency (events/min) for the following behavioral responses: writing, freezing, movements, displacement activities, satisfaction, rejection, frustration, self-touching (see Table 1 for the complete list of operational definitions of the behavioral categories).

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Table 1 Behavioral response operational definitions. Behavioral measure

Definition

Writing

Movements of the pen in the attempt to resolve the anagram Suddenly becoming motionless Any non-stereotypical movement of the body Stereotypical movements to bring comfort, such as playing with the pen or paper, massaging part of the body, stroking the hair Any contact of the hands with other parts of the body Visible expression of accomplishment: smiling, nodding, positive visual reinforcements Visible expression of anger or disappointment: shaking head, raising eyebrow, addressing the researcher Momentary or definitive rejection of the task

Freezing Moving Displacement

Self-touching Satisfaction Frustration

Rejection

collections, participants were asked to fill out the COPE and DASS questionnaires. Participants were then given a sheet of paper with a set of three anagrams. Some of the anagrams (impossible anagrams) were composed by letter sequences which could not be arranged into a word. The others were composed by words, 4–6 letters in length, drawn from Paivio et al. (1968) norms so that all were highly concrete, imageable, and meaningful (rated above 6 in all dimensions). All the possible anagrams were arranged in a letter order maximally similar to the word, to make them easily solvable to everyone (Foley et al., 1989). Participants were instructed that their goal was to resolve these anagrams within 5 min and that the average individual was able to resolve the provided set of anagrams in less than 3 min. This statement was intended to increase the probability the participants would feel frustration when attempting to solve an impossible anagram. A video camera was used to record participants’ behavior during the task. At the end of the session, they were given a third questionnaire to collect demographic information. One week later, at the same time of day, participants repeated the same sequence of tasks and were then debriefed. Participants were randomly assigned to one of four possible conditions on the basis of the difficulty of the anagrams they received in the two sessions (possible or impossible): possible–possible (Condition 1); possible–impossible (Condition 2); impossible–possible (Condition 3); and impossible–impossible (Condition 4).

2.6. Procedure 2.7. Statistical analysis All experimental sessions started at 10:00 a.m. and all saliva samples were collected between 10:15 and 11:15 a.m. to minimize circadian variations of steroid levels. A diagram of the design and flow of events during the experiment has been provided (Fig. 1). Samples were stored at −70 ◦ C within 30 min of their collection (mean time = 12 min). On arrival, participants initially rested for a few minutes while they were informed of the procedure and of the general goals of the research. Saliva samples and cardiovascular activity were collected at the beginning of the experiment for the purpose of measuring at rest levels. Following the saliva sample

For descriptive purposes, we used univariate analyses. All statistical analyses were conducted using the SPSS computer program (SPSS 16.0, Chicago, IL). All measures are expressed as mean ± SD. Mixed-model ANOVAs (2 × 4) were used to assess the behavioral responses of the participants. Week of testing was the repeated measure and experimental condition the fixed factor. ANOVAs with repeated measures were used to assess the differences for all physiological variables from Week 1 to Week 2. Correlation among variables was assessed using Pearson’s product-moment

Fig. 1. Design and flow of events during the experiment. Participants were tested twice for their physiological (hormonal and cardiovascular), psychological (perceived stress and coping mechanisms), and behavioral response to a challenging event (resolving impossible anagrams). Participants were assigned randomly to one of four possible conditions on the basis of the difficulty of the anagram (possible or impossible). Their academic performance (current ACT and GPA, and number of classes dropped and failed in previous semesters) was correlated to their response during the experiment using hierarchical regression analysis.

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coefficients. Variables that were statistically significant (p < 0.05) in the bivariate analysis were retained for use in multiple hierarchical regression models to examine associations between academic success and coping mechanisms, physiological activation, and behavioral responses to the task. Prior to analysis, normal distribution for all dependent variables (physiological measures, cardiovascular measures, and physiological measures) was established using the Kolmogorov–Smirnov test (p > 0.1). When necessary, corrections for inequality of variance or lack of sphericity were applied.

the difference in perceived stress between males and females approached significance (male mean score = 50.96 ± 13.22; female mean score = 58.68 ± 19.90; t(64) = 1.9, p = 0.06). All the other dimensions of coping did not significantly differ between males and females. Coping styles and perceived stress were not related to age, income, race, sleeping patterns (regular or irregular), or medication as well.

3. Results

3.3. Physiological levels

3.1. Demographic and academic performances

All three cardiovascular measures (SBP, DBP, and HR) were significantly correlated with each other in both weeks (r(SBP vs. DBP) = 0.68, p < 0.001; r(SBP vs. HR) = 0.53, p < 0.001; r(DBP vs. HR) = 0.32, p = 0.01). There were no significant differences between cardiovascular measures in Week 1 and in Week 2 (t(58) < 1 in all cases). In addition, none of the cardiovascular measures were related to gender, age, income, race, sleeping patterns, or medication. Values of cortisol and DHEA did not correlate with each other in either week (r = 0.14, ns). Cortisol and DHEA concentrations did not differ significantly between Week 1 and 2 (cortisol: t(58) = 1.71, p = 0.08; DHEA: t(58) = 1.41, p = 0.16), although cortisol in the second week showed a trend for higher values. While there was no significant difference between genders in baseline DHEA concentrations (t(64) = 0.29, ns), cortisol showed a trend for higher concentrations in Week 2 for females (t(64) = 1.75, p = 0.08). Hormones were significantly related to sleeping patterns over the previous week. Participants with an irregular sleeping pattern secreted higher levels of DHEA in saliva (t(64) = 2.75, p = 0.01). A similar trend was found for cortisol (t(64) = 1.69, p = 0.09). Cortisol and DHEA levels did not differ significantly with age, race, income, or medication. Cortisol/DHEA ratios were calculated as the averages of the measures in Week 1 and Week 2.

Forty participants (61%) were female, 26 (39%) were male. The mean ages of the female and male participants were not significantly different (mean age females = 22.15 years; mean age males = 22.50 years; t(64) = 1.18, p = 0.86). The majority of the participants were of Caucasian ethnicity (n = 57, 86.4%), with the remaining participants being of African-American, Hispanic, or Asian descent and in approximately equal numbers. Twentytwo (33%) participants declared a family income of less than $20,000/year, 21 (32%) declared an income between $20,000 and $58,000/year, and the remaining 23 (35%) declared an income above $58,000/year (data compared with the national averages). The majority of participants (96%) declared that they had not been intoxicated in the 24 h before the experiment, whereas 23% declared that they had been intoxicated at least once during the previous week. Forty-four percent of participants took some form of medication in the 24 h before the experiment, and 53% took medications during the previous week. The most common medications were aspirin or cold medicines. No one declared taking medications or dietary supplements that could significantly affect the HPA axis activity. The majority of participants (74%) declared that they had a regular night’s sleep the night before the experiment, whereas just 44% had regular sleep for the entire week preceding the experiment. GPA scores ranged from a minimum of 1.25 to a maximum of 4.0; the average GPA score for the participants was 2.88 ± 0.11. Participants had dropped an average of 2.15 ± 0.36 classes (maximum number = 12) and had failed an average of 0.68 ± 0.21 classes (maximum number = 8). We computed a composed index (DF index = number of classes dropped + number of classes failed) that returned an average score of 2.83 ± 0.50. ACT composite scores ranged from a minimum of 9 to a maximum of 33, with an average value of 22.42 ± 0.58. 3.2. Self-report scores Scores on the DASS ranged from 28 to 102 (mean score = 55.6 ± 17.9). Higher scores indicate a greater amount of perceived stress or anxiety. Scores for active coping style ranged from 5.12 to 10.83 (mean score = 8.29 ± 1.26); scores for cognitive coping style ranged from 5.33 to 10.00 (mean score = 7.42 ± 1.11); scores for emotional coping style ranged from 4.75 to 10.62 (mean score = 7.91 ± 1.24); and scores for avoidance coping style ranged from 6.75 to 13.50 (mean score = 9.81 ± 1.49). All four dimensions of coping and perceived stress were significantly correlated from Week 1 to Week 2 (active: r = 0.74, p < 0.001; cognitive: r = 0.51, p < 0.001; emotional: r = 0.78, p < 0.001; avoidance: r = 0.80, p < 0.001; stress: r = 0.79, p < 0.001), showing stability of perceived coping strategies and perceived stress across weeks. In addition, the four dimensions of coping were all highly correlated with each other (Pearson’s product-moments ranging from 0.65 to 0.91, all p-values < 0.0001). However, only cognitive coping was significantly correlated with perceived stress (r = 0.39, p = 0.01). Active coping was the only coping style that differed significantly between males and females (male mean score = 8.68 ± 1.29; female mean score = 8.03 ± 1.19; t(64) = 2.09, p = 0.04). In addition,

3.4. Behavioral responses The amount of time participants spent attempting to write solutions for the anagrams was affected by the experimental condition (F(3,55) = 3.68, p < 0.05). Participants spent more seconds per minute writing a solution when the anagram was possible (25.4 ± 6.5 s average writing time in the possible condition versus 14.1 ± 7.8 s in the impossible condition) (Fig. 2A). Duration of self-touching behavior (a nonverbal measure of anxiety) was affected by the experimental condition as well (F(3,55) = 15.8, p < 0.001). Participants in the impossible condition showed eight times more self-touching behavior than in the possible condition (25.7 ± 9.3 s versus 3.9 ± 3.3 s) (Fig. 2B). Participants did not show satisfaction in the impossible condition whereas they showed satisfaction in the possible condition (F(3,55) = 33.1, p < 0.001) (Fig. 2C). In fact, in the impossible condition participants showed increased signs of frustration (F(3,55) = 17.6, p < 0.001) (Fig. 2D). All the other behavioral responses (freezing, moving, displacement activities, and rejection of the task) did not change on the basis of the experimental condition. 3.5. ACT composite scores ACT scores were positively correlated with college GPAs (r = 0.44, p < 0.001) and negatively correlated with both the number of classes dropped and failed (dropped: r = −0.39, p < 0.001; failed: r = −0.66, p < 0.001). ACT scores were also negatively correlated with heart rate (r = −0.28, p = 0.33), but not with blood pressure and baseline hormonal values. ACT scores were negatively correlated with active and emotional coping mechanisms as measured by the COPE (active: r = −0.32, p = 0.016; emotional: r = −0.30,

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Fig. 2. Behavioral responses to the task (resolving a set of anagrams). Participants were tested twice one week apart (dark bars represent Week 1, white bars Week 2). Each individual was randomly assigned to one of four experimental conditions based on the task: impossible–impossible (II), impossible–possible (IP), possible–impossible (PI) and possible–possible (PP). (A) Duration of writing; (B) duration of self-touching; (C) frequency of satisfaction; (D) frequency of frustration. All measure are expressed in seconds or frequency of behavior per unit of time (1 min) ± the standard error of the mean (SEM) (n = 66).

p = 0.025), but not with cognitive and avoidant coping mechanisms and perceived anxiety and stress. ACT scores were also negatively correlated with frustration and with rejection of the task during the experimental procedure (frustration: r = −0.47, p < 0.001; rejection: r = −0.28, p = 0.032) and positively correlated with the satisfaction (r = 0.41, p < 0.001) and time spent writing (r = 0.28, p < 0.032). 3.6. Hierarchical models A four-step hierarchical regression model was performed for each indicator of academic performance, GPA and the DF index (Table 2). Only predictors significantly correlated with the two indices of academic performance were retained in these models. Behavioral responses to the laboratory task were first entered into the model, followed by baseline autonomic and hormonal activation, and by perceived stress/anxiety and coping mechanisms predictors; finally, ACT scores were included as the last step of the models. In step 1 of the GPA model, the rate of satisfaction and the amount of moving displayed during the task provided a significant model accounting for almost 12% of the variance (F(2,63) = 4.15, p = 0.02) (Table 3). Neither predictor was significantly correlated with GPA scores. In step 2 of the GPA model, systolic blood pressure (SBP) was entered in the model, and the percentage of variance explained jumped to almost 19% (F(3,62) = 4.73, p < 0.01) (Table 3). SBP showed a significant partial correlation with GPA (ˇ = 0.266, p = 0.024). Since no predictors in the perceived stress/coping mechanisms category showed a significant correlation with GPA scores,

Table 2 Bivariate correlations of academic performance (GPA and DF index) with perceived stress/coping mechanisms (Section 1), physiological responses (Section 2), and behavioral responses (Section 3). Measures

GPA

1. The COPE/DASS scales Active coping Cognitive coping Emotional coping Avoidance Perceived stress

−0.047 −0.065 0.051 −0.021 −0.053

2. Physiological values DPB SPB HR Cortisol DHEA Cort/DHEA ratio

−0.207 −0.265* −0.075 0.018 0.074 −0.062

0.089 0.071 0.234 0.069 −0.342** 0.449**

3. Behavioral responses Writing Freezing Moving Displacement Self-touching Satisfaction Frustration Rejection

0.120 −0.198 0.262* 0.023 0.019 0.283* −0.221 −0.135

−0.270* 0.317** −0.238 −0.071 0.163 −0.346** 0.520** 0.535**

4. High school standardized test ACT * **

p < 0.05. p < 0.01.

0.447**

DF index 0.305* 0.314* 0.329** 0.298* 0.165

−0.592**

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Table 3 Multiple hierarchical regression models predicting academic performance (college GPA scores). Step 1: Behavioral responses are entered in the model. Step 2 physiological measures are entered in the model. Step 3: ACT scores are entered in the model. ˇ

GPA model

R

Adjusted R2

b

SE

Step 1 Satisfaction Moving

0.341

0.116

13.556 4.567

7.332 2.835

0.228 0.199

1.849 1.611

0.069 0.112

0.432

0.186

11.929 5.192 −0.327

7.127 2.756 0.142

0.201 0.226 −0.266

1.674 1.884 −2.308

0.099 0.064 0.024

0.459

0.211

6.331 3.602 −0.226 0.932

8.099 3.221 0.171 0.507

0.107 0.152 −0.173 0.270

0.782 1.118 −1.326 1.838

0.438 0.269 0.191 0.072

Step 2 Satisfaction Moving SBP Step 3 Satisfaction Moving SBP ACT

t

p-Value

in step 3 we added ACT composite scores. These four predictors were able to explain 21% of the variance (F(4,61) = 3.47, p < 0.01) (Table 3), with ACT scores showing a partial correlation. Thus, the model including just satisfaction, moving, and SBP was the best predictor for GPA scores. In step 1 of the DF index model, the rate of frustration and rejection of the task provided a significant model accounting for more than 37% of the variance (F(2,63) = 20.22, p < 0.01) (Table 4). Both predictors showed significant independent correlations with the DF index (rejection: ˇ = 0.357, p < 0.01; frustration: ˇ = 0.384, p < 0.01). In step 2 of the DF index model, the ratio between cortisol and DHEA levels was included (cortisol/DHEA ratio), and the percentage of variance explained increased to almost 48% (F(3,62) = 18.97, p < 0.01) (Table 4). Cortisol/DHEA ratio showed a significant partial correlation with the DF index (ˇ = 0.327, p < 0.01). In step 3 of the DF index model, emotional coping was entered, but the variation in the percentage of variance explained by the model was negligible (adjusted R2 change = 0.018) (Table 4). Considering that we found a very significant association between emotional coping and the DF index (r = .33, p < .001), this result indicated that this association was mediated by the students’ physiological response. In the final

Table 4 Multiple hierarchical regression models predicting academic performance (DF index). Step 1: Behavioral responses are entered in the model. Step 2 physiological measures are entered in the model. Step 3: Perceived stress and coping mechanisms are entered in the model. Step 4: ACT scores are entered in the model. ˇ

DF model

R

Adjusted R2

b

SE

Step 1 Frustration Rejection

0.625

0.372

11.073 62.716

3.368 17.732

0.357 0.384

3.288 3.537

0.002 0.001

0.692

0.479

9.316 56.326 0.706

3.188 16.657 0.219

0.300 0.345 0.307

2.922 3.381 3.227

0.005 0.001 0.002

0.727

0.497

8.959 47.400 0.752 0.754

3.061 16.357 0.210 0.298

0.289 0.290 0.327 0.231

2.927 2.898 3.571 2.535

0.005 0.005 0.001 0.014

0.793

0.593

4.656 41.939 0.747 0.287 −0.349

3.085 14.827 0.195 0.301 0.091

0.154 0.269 0.340 .087 −0.384

1.509 2.829 3.830 0.953 −3.814

0.137 0.007 0.001 0.345 0.001

Step 2 Frustration Rejection C/D ratio Step 3 Frustration Rejection C/D ratio Emotional Step 4 Frustration Rejection C/D ratio Emotional ACT

t

p-Value

step of the DF index model, ACT composite scores were entered and the percentage of variance explained jumped to almost 60% (adjusted R2 change = 0.10) (Table 4) leading us to conclude that the model derived from measures of rejection, frustration, cortisol/DHEA ratio and ACT scores was the most effective to predict the rate of courses dropped and failed by participants. To disentangle the relationships between the students’ behavioral and physiological characteristics, we compared the changes in frustration and rejection from Week 1 to Week 2 with the average cortisol/DHEA levels, and we found that individuals with lower cortisol/DHEA ratios were also more likely to change their behavior to adapt to the difficulty of the task (r = 0.49, p < 0.001).

4. Discussion The present study indicated that the ability to cope with challenging tasks is a function of behavioral flexibility and physiological neuroprotection. When presented with a difficult task, such as attempting to resolve impossible anagrams, individuals who can vary their behavioral response to fit the task’s demands have the lowest probability of failing at that task. The same individuals also have higher levels of resiliency hormones, as indicated by a higher ratio of DHEA versus cortisol peripheral levels. This is consistent with several previous studies that have found that DHEA is able to counteract some of the negative effects of chronic stress (van Niekerk et al., 2001; Shirotsuki et al., 2009). Perceived coping mechanisms, as measured by the COPE, did not play a significant role. Individuals must adapt or they risk oblivion. The environment in which individuals function is constantly changing, always providing new sets of challenges; this constant exposure to stress is moderated by cognitive processes, which allow us to solve novel problems and adjust our behavior in light of new information (Williams et al., 2009). Aside from early development, the greatest changes that individuals experience are likely those associated with the transition to adulthood (Miczek et al., 2008). For many young people, a significant part of this transition happens during their college years. What does it take to succeed in college? To what extent are the skills and characteristics associated with optimal academic performance related to general adaptability? What biological markers can help us to identify individuals at risk? Our preliminary data showed that a combination of inappropriate behavioral responses (frustration and rejection of the task), together with a propensity for high allostatic loads (as represented by low DHEA/cortisol ratios), can predict as much as 48% of the variance of long-term strategies in dealing with difficult tasks during college, as measured by the number of courses dropped and failed by the participants. In our assays we had a significant crossreactivity with DHEA-sulfate as well, but since DHEA-S cannot be measured in saliva, because the sulfate prevent passive diffusion (Shirtcliff et al., 2007), this high cross-reactivity does not confound our results. Moreover, when blood samples were collected, DHEAS demonstrated similar effects on the individual stress response (Wang et al., 2009). Although it is somewhat remarkable that a simple 5-min long task could reveal so much about the inherent behavioral choices usually applied in more complex activities, such as the decision of whether or not to drop classes; in evolutionary terms, it makes perfect sense. Most of our decisions and behavioral reactions are based on snap judgments (Fazio and Olson, 2003; Bar and Neta, 2006). This is essential for our survival. Assessment of potential threats, as demonstrated by the stress response, must be fast (Sapolsky, 2003). A few seconds could be the difference between a fatal decision that might lead to pain and suffering, and an adaptive choice. Since these mechanisms assure our survival, it is evolutionarily advantageous

S. Wemm et al. / Biological Psychology 85 (2010) 53–61

to base our long-term decisions on mechanisms that work well in the short-term. Several models combining the students’ high school GPAs and scores of standardized tests such as the SAT and ACT can predict as much as 60% of the variance of the college GPA scores (Berry and Sackett, 2009). Our models, however, were not particularly satisfactory in terms of predicting GPA scores (just 18% of the variance was explained), even when ACT scores were included. It is clear that both the behavioral task and physiological measurements taken in this study only marginally correlated with how individuals perform in classes they complete. The use of GPA as a measure of academic achievement may not be an adequate depiction of students’ actual performance in a college setting, considering it is subject to other factors including students’ course choices based on ease or difficulty, professor evaluations, and merit-based scholarship programs (Association for the Study of Higher Education, 2005). Basic autonomic responses may be more attuned to how individuals react and perform in response to a challenging situation, whether it is a 5-min task, a standardized test, or decision to persist in an academic course. In our model using classes dropped and failed, participants’ behavioral response to a 5-min task in addition to indicators of neuroprotection predicted 48% of the variance. While the addition of the ACT increased the predicted variance to 60%, it is important to note that the large portion of the variance was explained by baseline hormone levels and observed behavioral response to a 5-min task. Previous studies on the relationship between stress appraisal and coping responses have revealed conflicting findings. Greater control over the stressor has been generally associated with more cognitive-focused coping often coupled with distraction methods such as seeking social support, and less emotion-focused coping strategies (Park et al., 2004; Rao, 2009). On the other hand, other studies have found that feelings of low-control in combination with high levels of emotion-focused coping are associated with the least amount of perceived stress (Zakowski et al., 2001). In our findings, higher levels of cognitive-focused coping were associated with higher levels of perceived stress. Excessive self-monitoring, defined as the process through which people regulate their own behavior in order to “look good” so that they will be perceived by others in a favorable manner (Gangestad and Snyder, 2000), and continuous coping adjustment with excessive self-monitoring can be detrimental, as the data support (Kilduff and Day, 1994). Indeed, while continuous monitoring activity can be advantageous in the short-term, if prolonged, it can significantly increase the activation of the stress system leading to overexertion (Sapolsky, 2005). The opposing roles of emotion-focused coping and self-monitoring find support from previous studies on depression and counterfactual thinking (Roese et al., 1999). Depressed people tend to ruminate on negative events, increasing their anxiety and the probability of long-term neuroendocrine damages, which can prevent them from effectively counterbalancing stressors and other negative events in their life (McEwen, 2003). When asked why they tend to spend so much time reflecting on negative episodes, depressed people rationalize their behavior claiming that it enables them to get useful insights related to their situation, which is not true in most cases (Gangestad and Snyder, 2000). Counterfactual thinking (if only) research also shows that “what might have been” thoughts are functional for controllable events but not for uncontrollable events (Roese et al., 1999). Chronic stress is probably one of the most pervasive hazards to Americans’ health. Studies have shown that chronic stress is related to cardiovascular disease, immune dysfunction, and mood disorders (van Niekerk et al., 2001; Shirotsuki et al., 2009). Sometimes experts refer to stress as the “silent killer”. Unfortunately, stress is unavoidable: it is our body’s response to being challenged and, in life, challenges are the norm, not the exception. Consequently, identifying effective coping strategies is important for building

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resilience. Our results suggest that effective coping strategies influence not only our ability to stay healthy, but also to perform well. Suzuki et al. (2003) found that problem-focused coping intensified cardiovascular responses, while emotion-focused coping was related to skin conductance level. A number of complex biological events are set in motion in response to stress, some of which are outside the individual’s awareness (Feder et al., 2009). This psychogenic stress is the result of responses that are both within and outside an individual’s awareness, and self-reports cannot accurately reflect the entire process. Neurohormonal regulation of behavioral responses to a stressful situation, which is at the basis of coping mechanisms, has been linked to the activation of neurons in the hypothalamus and brain stem, leading to the increased activation of the sympathetic preganglionic neurons regulating cardiac and adrenal medullary functions (Jansen et al., 1995; Kerman, 2008). At the same time, individuals are not passive victims of stress and adversity. Stressors evoke complex cognitive, behavioral, emotional, and biological processes that serve the purpose of adapting to the stress with the minimum amount of allostatic load for the organisms (Ulrich-Lai and Herman, 2009). Maladaptive stress-induced neuroplastic changes in specific neural circuits can lead to extreme consequences, such as anxiety (Alleva and Francia, 2009), post-traumatic stress disorders (Heim and Nemeroff, 2009), and depression (Krishnan and Nestler, 2008). Our results suggest that, in order to capture the complexity of these psychobiological mechanisms, a combination of measurements, including behavioral, physiological, and self-reported measures can improve our ability to predict which people will decide to quit. A single measure cannot capture the complexity of the behavioral mechanisms which are both within and outside the individual’s awareness. In conclusion, our results clearly point toward the importance of individual variation in the behavioral and physiological responses to challenging situations in terms of evaluating academic success. The transition from high school to college represents a significant challenge for many young people. Failing to adapt can have dramatic consequences for both the individual students and the society at large. Unfortunately, many students are not able to adapt to this transition. Only 40% of entering freshmen at public colleges and universities graduate, and while the numbers are better, at private institutions still only 57% of entering freshmen graduate (Raley, 2007). Whereas socio-demographic, psychological, and developmental studies have indicated several potential avenues to prevent students from dropping out, there are no studies attempting to integrate these data with their neurobiological characteristics. Our preliminary data indicated that investigating the psychobiological bases of flexible coping can help us better understand the mechanisms underpinning the neural processes that resilient people adopt to persist in the face of adversity. Acknowledgments We are grateful to Laura Good and Christina Knopp for their help with data collection, and Satyanarayana Paturi, Kevin Rice, and Miaozong Wu for their assistance during enzyme-immuno-assays for this project. We would also like to recognize and thank the College of Liberal Arts and the College of Science of Marshall University for their continuing support of our research. This research founded in part by the West Virginia NASA-REA Space Consortium. References Alleva, E., Francia, N., 2009. Psychiatric vulnerability: suggestions from animal models and role of neurotrophins. Neuroscience and Biobehavioral Reviews 33, 525–536. Anisman, H., Merali, Z., Hayley, S., 2008. Neurotransmitter, peptide and cytokine processes in relation to depressive disorder: comorbidity between depression and neurodegenerative disorders. Progress in Neurobiology 85, 1–74.

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