THE RELATIONSHIP BETWEEN STRESS, FATIGUE, AND COGNITIVE FUNCTIONING

There is a plethora of research suggesting that daily stressors and fatigue can have a significant effect on learning and various cognitive functions in young adults. Little is known, however, about how these effects impact learning and other neurocognitive functions in students with learning challenges when compared to their counterparts without learning difficulties. In the present study, a total of 60 college-aged students completed an extensive battery of neurocognitive functioning and learning including measures of fatigue, stress, executive functioning, working memory, and scholastic ability. The results indicated that fatigue and perceived stress had significant negative effects on participants' learning and cognitive performance and that these two factors (i.e., fatigue and stress) remain underestimated factors in learning. Future directions and implications are discussed in the context of the current findings. Keywords: Stress, fatigue, learning, cognition.






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Publication: College Student Journal
Author: Palmer, Laura K
Date published: June 1, 2013

Dr. Laura K. Palmer

Seton Hall University

Dept of Professional Psychology and Family Therapy

Daily stressors and fatigue can have a significant impact on learning and cognitive functioning in young adults (Beckner, Tucker, Delville, & Möhr, 2006; Beilock & DeCaro, 2007; Biondi & Picardi, 1999; Cohen, 1980; Lupien & Schramek, 2006; van der Linden & Eling, 2006 ). Cognitive resources are unevenly distributed across the population, and each student presents with their own unique set of cognitive abilities that likely here-to-fore served them sufficiently to be successful in the competition for a seat in the college classroom. However, the cognitive system during the secondary school years generally has the benefit from external structure, a better controlled sleep schedule, adequate nutrition, and general explicit structure inherent in high school. These external structures which serve the developing cortical structures across high school fade considerably to completely upon entering college - for most students (Arnsten, 1999). Typically developing students, as well as those with trauma histories or cognitive compromise, fare better with the appropriate degree of structure and support - and many flounder in its absence (Salmon, Pearce, Smith, Heys, Manyande, Peters, et al., 1988). Nutrition, sleep hygiene, structure and task management are often early victims of the new found freedom of college life - resulting in quickly accumulating physiological and psychological stress. The impact of this stress has not heretofore been effectively examined in non-learning disabled or learning-disabled college aged adults. This study evaluated the general impact of cumulative and recent stress on the cognitive functioning of the college age population. The relative contributions can then be extrapolated to explain the struggles of students with known forms of cognitive compromise (e.g., learning disabilities) as well as those experiencing histories of chronic stress, psychological trauma and documented posttraumatic stress disorders (PTSD).

Impact of Stress on Learning

The impact of stress on learning has been widely studied, and research has shown that cognitive abilities are affected by the physical and psychological manifestations of stress. However, there is limited consensus in the literature as to whether stress plays an inhibitory or facilitative role in the learning process (Joels, Pu, Wiegert, Oitzl, & Krugers, 2006).

On the one hand, stress and the exposure to stressful events has been shown to have an inhibitory effect on cognitive functioning across a number of domains. Research has indicated that stressful environments, and associated elevations of stress-related hormones, may result in the impairment of logical reasoning, reaction time, and vigilance (Lieberman, Bathalon, Falco, Morgan, Niro, &Tharion, 2005). As well as limitations in spatial reasoning (Kirschbaum, Wolf, May, Wippich, & Hellhammer, 1996), language deficits, processing speed, hand-eye coordination, executive functioning, and visuoconstruction (Lee et al., 2007).

Research examining the influence of stress on executive functioning has found that stress selectively impairs attentional control and disrupts functional connectivity in an attention shifting frontoparietal network (Liston, McEwen, & Casey, 2009). In addition, studies have shown that latent inhibition is lessened in participants who experience higher levels of stress. This suggests that stress may hinder participants' inhibition of pre-exposed, irrelevant stimuli (Braunstein-Bercovitz, Dimentman-Ashkenazi, & Lubow, 2001).

Studies have also shown that stress may have selective and mixed effects on aspects of memory. Findings suggest that people who are subject to stressful or traumatic experiences may have difficulty recalling details of the event (Christianson, 1992). Specifically, research indicates that stress inhibits declarative memory (Kirschbaum, Kudielka, Gaab, Schommer, & Hellhammer, 1999), verbal, visual memory (Lee et al., 2007), and working memory (Luethi, Meier, & Sandi, 2009). Studies have also shown that extreme stress is associated with "flashbulb memories;" vivid and detailed recollections that have been found to be unexpectedly accurate when compared against objective records (Berntsen & Thomsen, 2005). Conversely, studies have found that explicit verbal and implicit memory for neutral stimuli is not impacted by stress (Luethi, Meier, & Sandi, 2009). In fact, stress appears to facilitate implicit memory for negative emotional material (Luethi, Meier, & Sandi, 2009).

In addition to the role of stress in memory, the literature suggests that stress may play a crucial facilitative role in cognitive performance and learning (Cahill, Gorski, & Le, 2003; Lupien & Schramek, 2006). Studies have shown that in high-stress situations, the release of stress-related hormones serves to enhance cognitive performance (Regehr, LeBlanc, Blake, «fe Barath, 2008), and aid the consolidation of verbal information (Beckner, Tucker, Delville, & Möhr, 2006).

Joels, Pu, Wiegert, Oitzl and Krugers (2006) proposed that the timing and context of stress determines whether it facilitates or inhibits learning. The authors argued that within the context of a learning experience, stress focuses attention and improves memory and retrieval of relevant information. In addition, they asserted that the release of stress-related hormones normalizes the activity of circuits that are essential for adequate learning and memory. However, in individuals who have experienced sustained or traumatic stress, the release of stress-related hormones may be disrupted, preventing the hormones' ability to regulate neural activity and contain the biological stress response (Joels et al., 2006; Ye- huda, 2002). This may explain why traumatic memories are strongly encoded in individuals with PTSD, along with strong and persistent feelings of fear and distress (Yehuda, 2002).

Research has also suggested that the impact of stress on learning may vary depending on the innate cognitive abilities of the individual (Samson, Prior, & Smart, 1996). Studies examining the impact of high and low stress environments on working memory suggest that individuals with higher levels of innate working memory are at greater risk for stress-induced failure (Beilock & DeCaro, 2007). This implies that in a low stress context, individuals with higher levels of working memory are able to employ conceptually based problem solving techniques, whereas individuals with lower levels of working memory may use simpler techniques for solving the same problem. Alternatively, when the demands of the environment are greater, individuals with higher working memory capacities will resort to problem-solving strategies of equal or less desirability than their counterparts with lower working memory capacities.

Impact of Fatigue on Learning

Several researchers have examined fatigue within the context of stress and learning performance (Cohen, 1980; LePine, LePine & Jackson, 2004). Fatigue has been shown to inhibit attention and local processing (Van der Linden & Eling, 2006) as well as the retrieval of relevant information (LePine et al., 2004). In addition, fatigue has been found to disrupt short-term memory and negatively impact recall (Johnson, DeLuca & Diamond, 1998). In individuals with Chronic Fatigue Syndrome (CFS), studies have shown that reports of mental fatigue predict impairment in spatial working memory and sustained attention (Capuron, et. al., 2006).

Self-report studies have shown that high levels of fatigue often coexist with other psychological, physical, and emotional symptoms; such as stress (Montgomery, 1983). In one study, researchers found that fatigue was significantly associated with stress, specifically with a stress-avoidant coping style (Tanaka, Fukuda, Mizuno, Kuratsune, «fe Watanabe, 2009). Studies have also indicated that stress is independently associated with, and predictive of fatigue (Chen, 1986). Sustained job stress has been linked to high levels of fatigue and insomnia (Nakata et al., 2004). Similarly, individuals with PTSD report higher levels of daytime fatigue, impairment in functioning, and insomnia (Inman, Silver, & Doghramji, 1990).

Stress, Fatigue, and Learning Challenges

A body of research has examined the relationships between stress and learning challenges among young adults, with somewhat contradictory results. The literature suggests that college students with learning disabilities are particularly vulnerable to socio-emotional problems. Approximately half of young adults who have difficulty with reading, writing, and math skills have been diagnosed with a co-occuring emotional or behavioral concern (Tsovili, 2004). In addition, a study comparing learning-disabled (LD) and non learning-disabled college students found that LD students reported more stress, nervousness, frustration, and helplessness than non-LD students (Heiman & Precel, 2003). However, other researchers (LePine, J. LePine M. & Jackson, 2004) have found that LD students may actually experience less stress than their non-LD peers. Specifically, a study found that students with LD were less likely to report stress, and obtained significantly higher levels of resiliency and need for achievement than non-LD students (Hall, Spruill, & Webster, 2002). Similarly, researchers have reported that while students with learning challenges have lower grades, test scores, and perceptions of their academic abilities than non-LD students, they do not differ in regard to their global self-esteem, assessment of domain-specific competencies, or the importance they place on academic achievement (Cosden & McNamara, 1997). Likewise, in a study of adolescents with learning challenges, LD students did not differ from non-LD students in their perception of the stressfulness of school-related events (Geisthardt & Munsch, 1996).

The relationship between fatigue and learning challenges in young adults remains an understudied area of research. However, there are numerous studies indicating that learning challenges can increase college students' risk of anxiety and depression (Lackaye, Margalit, Ziv, & Ziman, 2006; McConaughy, Mattison & Peterson, 1994), which are commonly associated with fatigue (Lavidor, Weller & Babkoff, 2002; Montgomery, 1983; Williamson, et al., 2005). Symptoms of fatigue include cognitive impairment (e.g., feeling slowed down or forgetful), behavioral changes (e.g., irritation), and somatic complaints (e.g., sore muscles) (Petrie & Dawson, 1997). In students, unaddressed fatigue can contribute to absenteeism, poor academic performance, and dropout (Law, 2007). More research is necessary to determine whether learning challenges place students at a higher risk of fatigue, as well as to explore the possible links between fatigue, stress, and learning difficulties.

College students with learning challenges are often accustomed to highly structured and supportive educational environments, and many flounder when they face the brave new world of adult autonomy and responsibility at college. The current study explored the relationship between fatigue, stress, and learning challenges in college students, with the goal of providing support for educational and counseling interventions for young adults with learning challenges.

Definition of Stress

According to Lobel and Dunkel-Schetter (1990), stress is defined as an external and internal response to a stimulus. Stress can therefore be viewed as an interaction between environmental stimuli and an individuals' automatic physiological response (Lobel «fe Dunkel-Schetter, 1990). More specifically, and in accordance with the Transactional model (Lazarus & Folkman, 1984), stress is the subjective perception of an individual that creates a discrepancy between the environmental demands or threat, and the individuals' biological, psychological, or social resources (Lazarus & Folkman, 1984). The current study utilized the above mentioned definition of stress, an internal and external response to the perceived discrepancy between individuals' resources and their environment.

Definition of Fatigue

The operational definition of the term fatigue includes an assessment of subjective feelings of tiredness, changes in indices of physiological function, and alterations in working capacity (Zinchenko, Leonova, «fe Strelkov, 1985). Fatigue was viewed within the context of the current study as the failed coordination of mental and physiological functions in the maintenance of concentration, which is expressed as decreased mental efficiency, enthusiasm, and/or attention. The current study utilized a three method approach to measuring fatigue in order to capture the physiological, behavioral and subjective response to fatigue. The physiological response was measured through sampling of salivary Cortisol levels. The behavioral response was measured through the administration of neurocognitive measures that have been proven to be sensitive to fatigue. Finally, the subjective appraisal of fatigue was measured using validated self-report measures.

Hypotheses

Based on previous empirical research, the following hypotheses guided the current investigation:

Hypothesis 1: It was hypothesized that there would be a higher level of basal fatigue, and a difference in post-activity fatigue (as measured by the morning and afternoon salivary Cortisol and alpha amylase levels) in the presence of greater learning challenges.

Hypothesis 2: It was hypothesized that cognitive fatigue as measured by scores

from specific neuropsychological tests and self-report measures would be significantly correlated with learning challenges.

Hypothesis 3: It was hypothesized that there would be a relationship between fatigue, self report of perceived stress and learning challenges.

Hypothesis 4: It was hypothesized that post-activity fatigue levels, as measured by salivary Cortisol and salivary alpha-amylase, would be negatively correlated with reading and numerical discrepancy index scores.

Methods

Participants

The sample consisted of 60 college-aged students from a private Catholic university in the New York Metropolitan area. Participant age ranged from 17.1 to 22.5 years with a mean age of 19.45 (SD = 1.15) and a median age of 19.28 years. Sixty-five percent of the participants were female (n=39) and 35% were male («=21). The mean age for male participants was 19.64 years with a standard deviation of .87, and 19.34 years with a standard deviation of 1.27 for females. Information on participants' background including occupational history, general medical history, psychiatric history, and substance use was collected using a demographic questionnaire developed by the researchers.

Procedure

The study protocol was reviewed and approved by the university's institutional review board. Participants volunteered as a part of their undergraduate psychology course and gave written informed consent prior to their participation in the study. Measures of fatigue, stress, executive functioning, working memory, and scholastic ability were administered to each participant. The duration of each administration was two to three hours.

Instruments

Physiological Measures of Fatigue

Each participant provided a salivary sample before the testing began (i.e., after the informed consent was signed), and again upon completion of the last measure. Each vial was sent for biochemical analysis. Alpha amylase was measured by way of an enzyme kinetic reaction and the salivary Cortisol was measured by way of an enzyme immunoassay technique.

Fatigue and Stress

Each participant completed the Iowa Fatigue Scale, and the Perceived Stress Scale for subjective measures of fatigue and stress. To induce additional fatigue, participants were asked to complete the Serial 7's test. The Iowa Fatigue Scale (Hartz, Bentler, & Watson, 2003) is an 11-item scale measuring cognitive fatigue, energy, and productivity within the past month. The participants rated the questions regarding their experience of these symptoms on a Likert scale ranging from 1 (not at all) to 5 (extremely). Higher scores indicate greater levels of fatigue.

The Perceived Stress Scale (Cohen, Kamarch, & Mermelstein, 1983) is a 14-item self-report questionnaire used to determine the extent to which a person perceives her or his life to be unpredictable, uncontrollable, and overloading. Ratings include a 5-point Likert scale ranging from 0 (never) to 4 (very often). Total scores range from 0 to 40 with higher scores indicating greater overall stress.

The Serial 7s technique prompts the participant to start at the number 100 and count backwards in increments of 7 until they reach 0. The first five subtractions are typically scored; the maximum is 5 correct. This measure was used only to induce additional cognitive fatigue and the scores were not included in the analyses.

Neurocognitive Measures and Executive Functioning

Several neurocognitive measures were used to assess the cognitive abilities of the participants: The Neuropsychological Assessment Battery (NAB), Kaplan Baycrest Neurocognitive Assessment (KBNA), the Behavior Rating Inventory of Executive Functioning (BRIEF; Gioia, Isquith, Guy, & Kenworthy, 2000), Meslum and Weintrub Cancellation task, and the KBNA Cancellation task.

The NAB (Stern & White, 2003) objectively measured executive functioning abilities. The four subtests were individually administered; Mazes, Judgment, Categories, and Word Generation. The Mazes subtest consists of mazes which increase with difficulty as the items progress. The Judgment subtest instructs the participant to provide a response to general questions about community, health, and safety. The Categories subtest instructs the participants to generate as many groups possible based on the physical presentation of the stimulus. The Word Generation subtest provides each participant with a list of letters of which the participant produces as many three letter words as possible.

Various subtests of the KBNA (Leach et al., 2000) were administered including Word List Recall «fe Recognition and Complex Figure Recall & Recognition. Word list includes a list of various objects including drinks and office supplies. After a thirty minute delay, participants are asked to recall the target items testing auditory memory functioning. The Complex figure subtest is an abstract design with various shapes which also includes a thirty minute delay to test visual recall and memory functioning.

The BRIEF (Gioia, Isquith, Guy, & Kenworthy, 2000) is a 75-item self-report measure designed to capture the participants' views of their own executive functioning or self-regulation in their daily environment. Items ask the responder to choose from three choices (i.e., never, sometimes, or often) in response to how often they take part in the behavior asked (e.g., "Gets out of control more than friends"). .

In both the Mesulam and Weintraub Cancellation task (Weintraub & Mesulam, 1985) and KBNA Cancellation task (Leach et al., 2000) the participants were required to scan a large field of either letters or symbols for a target letter, or a specific form, and then cancel the target items as quickly as possible. This task is a measure of visual scanning, perception, attention and accuracy of selective attention.

Working Memory

The Wechsler Memory Scale 3rd Ed. (WMS-III) Digit Span subtest was administered as a measure of working memory and attention. This subtest has two sections, Digits Forward and Digits Backwards. In digits forward the participant listens to and repeats strings of numbers that get progressively longer. In digits backwards, the participant listens to the strings of numbers and repeats each set backwards. This task is a measure of auditory short-term memory, sequencing, and attention skills. The Digits Backward task also requires auditory attention, working memory, and complex cognitive operations.

Standardized Scholastic Measures

To obtain an approximate measure of IQ, the Wechsler Abbreviated Scale of Intelligence (WASI; Wechsler, 1999) was administered; two subtest. The Vocabulary subtest measures the participant's available vocabulary, verbal comprehension and fluency, and conceptual thinking. The Matrix Reasoning measures visual-perceptual organization and processing, attention to detail, reasoning, and concentration.

Additionally, the Numerical Operations and Word Reading subtests of the Wechsler Individual Achievement Test 2nd Ed. (WIAT-II; Wechsler, 2002) were administered to provide information regarding reading and math learning discrepancies. The Numerical Operations subtest is designed to measure a participant's concentration, attention, and numerical reasoning. The Word Reading subtest measures phonological and reading skills. The predicted achievement formula provided math and reading discrepancy scores between the WIAT and WASI. This allowed the researchers to make a meaningful comparison between the scores on both measures, and to subsequently evaluate each participant's learning ability.

Results

Preliminary Analysis

All analyses were performed using SPSS (Version 16 for Windows). An analysis of missing data suggested that missing values were rare (i.e., .03%) and randomly distributed. Standardized residuals were calculated in order to detect discrepant cases. Post salivary alpha-amylase contained two outliers. In all of these cases, the outliers were 3 standard deviations above the mean. After determining accuracy of data entry, we used Tabachnick and Fidell's (2001) recommended procedure to reduce the influence of the outliers. Specifically, the outlying cases were assigned a new raw score that was one unit smaller than the next most extreme score in the distribution. An additional case was identified as a multivariate outlier through inspection of Mahalanobis distance with pc.001. This case was removed and excluded from all further analysis resulting in the total of 60 participants.

Sample Characteristics

Means and standard deviations for study participants are shown in Table 1. A series of t-tests for independent samples were performed in order to compare gender differences on the neurocognitive measures. On the KBNA Symbol Cancellation task, male participants had lower average reaction times (A/=54.16, 50=18.16) compared to females (M=65.84,50=20.45), i(45) = 2.01,/?<.05. On the BRIEF Self-Monitoring, male participants scored significantly higher (M=55, SD= 8.66) compared to woman (M=47.67, SD=9.76), t(58) = -2.89,ρ <.05. Gender differences were also observed on the BRIEF Plan/Organize subtest whereby male students scored higher on average (M=54.71, SD= 10.36) compared to female students (M=49.15, SD=8.44), t(58) = -2.25, ρ <.05. No other statistically significant gender differences emerged on the measured neuropsychological variables.

Learning and Fatigue

We hypothesized that post-activity fatigue levels, as measured by salivary Cortisol and salivary alpha-amylase, would be negatively correlated with reading and numerical discrepancy index scores. Consistent with our predictions, post-activity salivary Cortisol negatively correlated with the reading discrepancy index scores, r(60) = -.28, ρ <.05. No significant linear relationships emerged between post-activity salivary alpha-amylase levels and scores on the numerical operations discrepancy index.

Table 2 shows the results of the bivariate correlation analyses for the assessments of learning and neurocognitive functioning and the measures of cognitive and physiological fatigue. Basal salivary Cortisol significantly correlated with reading discrepancy index scores, r(60)=-.28, ρ <.05. Basal salivary alpha-amylase levels negatively correlated with WASI Matrix Reasoning, r(60) = -.48, ρ <.01, WASI (2 subtest) FSIQ, r(60) = -.34, ρ <.01, WLAT-II Numerical Operations subtest, r(60) = -.36, ρ <.01 and positively correlated with the KBNA symbol cancellation reaction time, r(60)=.41, ρ <.01. Post-activity salivary al- pha-amylase levels negatively correlated with performance on the BRIEF Task Monitoring, r(60) = -.26, ρ <.05 and MWCT Symbol Search Task Accuracy, r(60)=-.38 and positively with performance on the KBNA Word List 1 performance, r(60)=0.29,p < .01.

Stress, Learning and Fatigue

It was hypothesized that there would be a significant relationship between fatigue, self report of perceived stress, and learning challenges. The results of the correlation analysis indicated that as an individual's level of fatigue increased so did his or her reported level of stress, r(60)=0.58, ¿K0.01. In order to assess the relationship between reading and numerical operations discrepancy index scores and cognitive and physiological fatigue and stress measures, two separate multiple regression analyses were performed (See Table 3). The criterion variables were the reading discrepancy index scores for model one and the numerical operations discrepancy index scores for model two. The predictors were perceived stress (i.e., PSQ scores), cognitive fatigue (i.e., IFS scores) and physiological fatigue, as measured by post-activity salivary Cortisol and alpha-amylase. The multiple regression model for the reading discrepancy index scores was significant, F(4, 51) = 2.74, ρ < .05. Post-activity salivary Cortisol emerged as a significant predictor of reading discrepancy index scores (0 = -.34, ρ < .05). The ANOVA testing for significance of the multiple regression model did not reach statistical significance for numerical operations discrepancy index scores, F(4, 50) = 1.11,/? < .05. Regarding the impact of perceived stress on performance for specific neuropsychological tests, negative correlations emerged between perceived stress and KBNA Complex Figure Immediate Recall (r=-.21,p <.05) and WMS Digit Span Forward (r=-.27, ρ < .05). Cognitive fatigue negatively correlated with WMS Digit Span Forward (r=-.26, ρ < .05) and positively correlated with the following BRIEF subtests: Working Memory (r=.64, ρ < .01), Plan/Organize (r=.44, ρ <.01), Task Monitoring (r=.45, ρ <.01), and Organization of Materials (r=A5,p <.01).

Discussion

A multimethod approach was used in the present study in order to examine the impact of stress and fatigue on learning and neuropsychological functions. Significant results found that fatigue had a negative impact on participants' learning and cognitive performance. An important finding of the present study was that physiological fatigue, as measured by post-activity salivary Cortisol levels, was predictive of learning challenges in college-aged adults. These findings are consistent with previous research (i.e., Capuron et al., 2006) and congruent with the aforementioned hypotheses. The negative impact of fatigue on learning was also observed in a recent study by Fukuda et al. (2010) where the authors concluded that increasing levels of fatigue and sleep problems negatively impacted student learners.

Stress and fatigue had differential effects on neuropsychological test performance. Participants' increased perceptions of stress negatively impacted performance on cognitive tasks.

Regarding attention and working memory, stress as well as cognitive and physiological fatigue (post-activity salivary alpha-amylase) was negatively correlated with working memory. Baddeley (1999) conceptualized working memory as a multi-component construct that "refers to a brain system that provides temporary storage and manipulation of the information necessary for such complex cognitive tasks as language comprehension, learning, and reasoning." Research has shown that working memory is vulnerable to the effects of fatigue and stress (i.e., Lieberman et al., 2005). Compromised working memory functions reduces cognitive efficiency, may result in poor encoding, storage and retrieval of information and can manifest through poor problem solving and reasoning skills (Beilock «fe DeCaro, 2007) . This is particularly relevant for college-aged adults who will need to increasingly rely on higher order cognitive skills during their academic development. A recent study by Holtzer, Shuman, Mahoney, Lipton and Verghese (2011) found that fatigue negatively impacted executive attention, but not orienting and alerting suggesting divergent effects on attention and working memory networks. This should be further explored in future research to examine the multiple components of attention and working memory and how they are impacted by cognitive and physiological fatigue and stress.

Fatigue and stress remain underestimated factors in learning. The aforementioned results support the inclusion of consideration of fatigue and stress in educational planning for individuals with and without learning challenges. The results of the current study revealed that stress and fatigue has detrimental effects on learning and neurocognitive functioning that can challenge student learners as they navigate through their college years. Closer attention should be given to highlighting the importance of good sleep hygiene, nutrition, coping and stress management as part of the standard "curriculum" for entering college students. This information can be incorporated into orientation, and monitored across the freshman year to assist students to establish basic behaviors that enhance learning and cognitive health.

Admittedly, this study had a number of limitations that should be kept in consideration when examining the results. The sample was solely comprised of college students, who were predominantly Caucasian. Participants in the study were generally within the ages of 18-22. As such, this may limit the generalizability of the results. Given these considerations, future studies of this nature would benefit by incorporating a more diverse sample of participants. The study did not account for possible moderating or mediating variables such as social/ family support, religio s ity/spirituality, or other factors that may influence stress/fatigue and learning/cognitive performance relationships. While the current study examined the role of stress and fatigue on learning, it did not examine traumatic stress and it's impact on learning processes. The authors are however, concluding additional studies which examine the differential impact of these learning processes when there is a history of trauma.

More research is needed to extend these findings among individuals classified with a learning disability. Functional imaging studies would be helpful to compare brain activity of learning disabled to their non-disabled peers to further delineate differences and evidence of cognitive and physiological fatigue. It is anticipated that this study will contribute to the body of literature regarding the negative impact of fatigue and stress on learning processes for young adults with and without learning difficulties.

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