Effects of Psychophysiological Reactivity to a School-Related Stressor and Temperament on Early Adolescents’ Academic Performance

Abstract

This study investigated early adolescents’ psychophysiological response to a school-related stressor (SRS) as indexed by heart rate variability and examined the unique and interactive effects of heart rate variability and temperament on academic achievement. A total of 91 seventh graders watched an SRS video-clip while their heart rate variability was registered. Temperament and grades were also assessed. Heart rate variability decreased from baseline while watching the video-clip, indicating the activation of a stress response, and returned to basal level when recovering. Regression analyses revealed that among youth with an inhibited temperament, higher heart rate variability in the poststressor phase was related to better performance, whereas socially outgoing students performed well regardless of their recovery rate. These are among the first data to report on the link between early adolescents’ parasympathetic self-regulatory activity in response to an SRS, temperamental profiles, and academic functioning. Implications for theory and educational practice are discussed.

Keywords

Recently, the American Psychological Association (APA) has reported that during the school year, teens say their stress levels are higher than those reported by adults (APA, 2014). The same report states that in teens and adults alike, stress has an adverse impact on healthy behaviors such as exercising, sleeping well, and eating healthy foods.

More specifically, school-related stress (SRS) is a widespread phenomenon that has been shown to negatively affect youth’s emotional well-being (e.g., Bimmel, Van IJzendoorn, Bakermans-Kranenburg, Juffer, & De Geus, 2008; Galaif, Sussman, Chou, & Wills, 2003) and academic performance (Kaplan, Liu, & Kaplan, 2005; Scrimin, Mason, & Moscardino, 2014), as well as to contribute to school failure or even dropout (de Anda et al., 2000). SRS is often caused by repetitive judgments and fear to be assessed and fail when facing academic demands that can be particularly distressing during early adolescence. Since the school environment is valued as highly significant in this developmental period, sources of stress within this context may be especially evocative and disruptive. Moreover, during early adolescence, important changes occur not only within the individual, such as hormonal variations, but also in the environment, especially in terms of academic demands at school. These modifications are perceived as challenging and sometimes stressful by youth, who need good self-regulatory abilities to adapt adequately. Research has widely investigated the effects of SRS on youth’s emotional (e.g., anxiety problems; Galaif et al., 2003) and health-related problems (e.g., somatic complaints; Reynolds, O’koon, Papademetriou, Szczygiel, & Grant, 2001). Yet, data concerning the effects of these experiences on academic functioning are still scarce.

Academic functioning is thought to be a reliable indicator of students’ adjustment in response to challenging events experienced in everyday life (e.g., Holen, Lervåg, Waaktaar, & Ystgaard, 2012). A good performance at school is linked to individual well-being in the short and long term (e.g., Suldo, Riley, & Shaffer, 2006). In particular, grades are a robust indicator of achievement and have been found to predict future school success in terms of a promising academic trajectory into late adolescence and college attendance in early adulthood (e.g., Shim, Ryan, & Anderson, 2008). Yet, given the strong emphasis on academic accomplishments and grades, students often do not perceive exams and tests as a positive challenge, but rather as a negative stressor (Cassady & Johnson, 2002; Kouzma & Kennedy, 2004). Hence, it is of paramount importance for students’ health and life success to understand the link between SRS and academic functioning. Specifically, the role of individual differences in self-regulation as reflected in psychophysiological response patterns and temperamental characteristics should be addressed to plan interventions specifically targeting those students who are at increased risk of failure and less capable of adjusting to school-related challenges and demands.

Indeed, previous research suggests that self-regulatory skills in response to stressors are largely shaped by intrinsic factors, such as cardiovascular activity and temperament, which are capable of affecting cognitive performance (Thayer, Hansen, Saus-Rose, & Johnsen, 2009). However, data on how individual differences in students’ physiological response to an SRS and temperamental characteristics, as well as their interaction, may affect academic achievement are still lacking. Developing a better understanding of this relation will allow the implementation of interventions aimed at promoting effective self-regulatory skills, which could boost adaptation to school challenges and increase academic performance.

In this article, we focus on two facets of self-regulation that have been shown to affect individuals’ cognitive functioning, namely, temperament and heart rate variability (HRV). Based on extant theory suggesting that temperament reflects individual differences in neurological and physiological functioning (Porges, 2001; Rothbart & Rueda, 2005), we aim to examine whether early adolescents’ temperament profiles and HRV in response to a stress-inducing film-clip independently and/or jointly contribute to academic achievement in terms of grades.

Temperament and Academic Functioning

Temperament has been defined as a set of biologically based individual differences in emotional and behavioral response styles which are observable early in life, relatively stable over time, and subject to environmental influences (Bates, Schermerhorn, & Goodnight, 2010). Although theories of temperament vary widely in their conceptualizations, most scholars agree that activity level, emotionality, and attention are key components of temperament (Gartstein, Putnam, Aron, & Rothbart, 2016; Valiente et al., 2013). Research has shown that temperamental traits are linked to different developmental outcomes in a variety of domains, including cognitive abilities, social competence, and psychological adjustment (Pine & Fox, 2015). Specifically, studies conducted in the school context indicate that children exhibiting high levels of negative emotionality, distractibility, and inhibition tend to have both short- and long-term academic problems (Hirvonen, Aunola, Alatupa, Viljaranta, & Nurmi, 2013; Keogh, 2003). On the opposite side, studies have shown that the abilities to focus attention, persist at tasks, and regulate emotions are paramount for healthy development and academic success (Kim, Nordling, Yoon, Boldt, & Kochanska, 2013).

Despite the importance of single temperamental traits for children’s school adjustment and academic functioning, mounting evidence suggests that a person-centered approach may be more useful to study individual differences in temperamental characteristics and their relations with other constructs of interest (Moran et al., 2017; Stifter, Putnam, & Jahromi, 2008). In this approach, multiple dimensions of temperament are taken into account to describe specific constellations of temperamental dimensions using cluster analytic techniques (e.g., McClowry et al., 2013), thereby allowing to identify typologies based on these combinations. Such typologies may increase our understanding of children’s behavior by providing a more nuanced picture of temperament (Prokasky et al., 2017). Across a number of studies, the most frequently identified clusters refer to children with inhibited/reactive, impulsive/uncontrolled, resilient/adaptable, or well-adjusted temperaments (e.g., Sanson et al., 2009). Although these studies used a variety of measures reflecting different conceptualizations of temperament, most of these profiles included approach vs. inhibition tendencies, activity level (high vs. low), and positive vs. negative affectivity as central aspects. Yet, extant research has heavily focused on preschoolers, leaving open the question of whether such typologies are applicable to early adolescents, who are the focus of this study.

Empirical evidence suggests that in early adolescence, the magnitude of associations between temperament and academic achievement tends to decrease (Al-Hendawi, 2013). This finding has been attributed to the numerous changes occurring at the physical, cognitive, and socioemotional levels during the transition from childhood to adolescence. Preadolescents tend to increasingly exhibit independent behaviors and response to peers while seeking support from parents and teachers. In addition, the rapid hormone changes significantly affect their everyday emotional life, leading to abrupt mood fluctuations and irritability (Hollenstein & Lougheed, 2013).

Here, we propose that the possible modest association between temperament and academic functioning in early adolescence may be due to the influence of other factors, such as cardiac vagal activity. Previous research reports that children with diverse temperamental characteristics exhibit different patterns of physiological reactivity, as indexed by respiratory sinus arrhythmia (RSA), in response to emotional stimuli (Blandon, Calkins, Keane, & O’Brien, 2010), and that a decrease in RSA during challenging situations is related to better emotion regulation abilities and fewer problem behaviors (Calkins & Keane, 2004). In line with a biopsychosocial framework (Calkins et al., 2013) and with multifactorial models of psychopathology (Cicchetti, 2014), these biological characteristics may serve to magnify or dampen the potential associations between maladaptive temperament traits and adjustment problems. However, to our knowledge, the extent to which temperament profiles and cardiac vagal activity represent independent or interactive risks for low academic performance among middle school children remains largely underexplored.

Psychophysiological Regulation in Response to Stress

Self-regulation refers to the ability to control one’s thoughts, emotions, and behavior, thereby enabling an individual to select optimal responses to meet situational demands (Thayer & Lane, 2000). This ability is critical for adaptive functioning across various domains of human life (Graziano, Reavis, Keane, & Calkins, 2007). A key physiological correlate of children’s self-regulatory skills (Porges, 2007) is HRV (Li et al., 2009).

HRV measures are derived by estimating the variation among a set of temporally ordered interbeat intervals (IBIs). The set of IBIs is used as input data for computing two main HRV classes of analyses. The first is the statistical class of analyses, consisting of variance-based calculations performed on the set of IBIs and yielding numerical estimates for HRV in either temporal units (e.g., milliseconds) or number of beats. The second, the frequency class of analyses, is used to partition the variance of the IBI through a technique known as power spectral analysis, in which the amount of variance within a given frequency range, represented by the area under the curve, is referred to as power. In the present study, we used a statistical analysis to compute HRV. Specifically, both SDNN, which refers to the standard deviation of NN intervals computed across all 5-minute recording segments, and the number of pairs of adjacent NN intervals differing by more than 50 ms (NN50) have been computed. Such variance-based indices are thought to represent parasympathetically mediated HRV (Task Force, 1996) and are usually strictly correlated with the high-frequency (HF-HRV) component of the power spectrum as computed through a frequency class analysis. This index also primarily reflects cardiac parasympathetic influence due to RSA (Berntson et al., 1997; Ernst, 2016). In this article, we use the terms HRV and cardiac vagal tone interchangabely, but we use the terms RSA (i.e., the rhythmic fluctuation of heart rate during spontaneous breathing which is governed by the myelinated vagus nerve; Porges, 1995) and HF-HRV when reporting study findings.

Resting HRV is thought to index the amount of regulatory resources to draw upon during times of challenge that are critical for the child’s interaction with the environment. Specifically, greater HRV, which results from higher parasympathetic activity, implies that the cardiac system is slowed down and prevented from becoming overexcited, an important function for effective self-regulation (Porges, 2007). Greater HRV has also been typically been linked to better outcomes in terms of emotional and cognitive resources, whereas low-HF HRV has been considered a biomarker of psychopathology (Beauchaine & Thayer, 2015). Furthermore, parasympathetic input to the heart is related to the ability to recover after a negative event, and people with high HRV at rest cope better with stress (Brosschot, Van Dijk, & Thayer, 2007).

Individual differences in biobehavioral stress response are reflected not only in cardiac activity at rest (also denominated tonic HRV) but also in changes in cardiac vagal tone in response to emotional stimuli (also denominated phasic HRV) and their importance for self-regulation (Appelhans & Luecken, 2006; Butler, Wilhelm, & Gross, 2006). Reduced phasic cardiac vagal tone has long been construed as an autonomic response to stress (Beauchaine, Gatzke-Kopp, & Mead, 2007; El-Sheikh, Hinnant, & Erath, 2011). When people are directly exposed to a stressor, such as a video-clip depicting a negative emotional event (El-Sheikh et al., 2011), a reactive emotion decrease in RSA is registered while cardiac vagal tone suppression occurs. This trend has been conceptualized as a physiological response that represents the withdrawal of cardiac vagal control (which is typical of a variation in heart rate) and the activation of the defensive system (Butler et al., 2006). The withdrawal of cardiac vagal tone in response to an emotional stressor has been found to be positively associated with higher HRV at rest, suggesting that phasic cardiac vagal tone may reflect emotion regulation and serves a protective function against environmental challenges (El-Sheikh et al., 2011). Moreover, a recent meta-analysis confirmed the role of cardiac vagal control, as measured via RSA withdrawal, in contributing to children’s adaptive functioning across externalizing, internalizing, and cognitive/academic domains (Graziano & Derefinko, 2011). However, the same study reported opposite findings in clinical/at-risk populations, suggesting that vagal withdrawal does not uniformly enhance social functioning and adaptation. Furthermore, other studies have found that both internalizing and externalizing symptoms are associated with more blunted reductions in cardiac vagal tone during emotion evocation (Calkins et al., 2007; Pang & Beauchaine, 2013). These data suggest that excessive HF-HRV reactivity to emotional challenge, expecially when associated with low resting HF-HRV, might mark one or more core self-regulatory functions that are disrupted across diverse forms of psychopathology (see Beauchaine & Thayer, 2015), whereas moderate vagal withdrawal associated with greater cardiac vagal tone at rest is linked to better self-regulation and overall functioning.

Importantly, when the stressor is over, people with higher HRV at rest also show an increased phasic HRV during the recovery phase (Weber et al., 2010). Such increase is linked to better adaptation to both cognitive and emotional environmental challenges (Appelhans & Luecken, 2006; Beauchaine et al., 2007; El-Sheikh et al., 2011). That is, the way in which HRV changes during and after a stressor affects how an individual adapts to the environment (Porges, 1992) and is related to better performance in cognitive tasks (Marcovitch et al., 2010).

Recent studies have shown that beyond triggering emotions and subjective stress, school settings can also elicit physiological stress responses such as changes in HRV measurements (e.g., Minkley & Kirchner, 2012). Hence, high HRV at rest and vagal suppression in response to a stressor are important for positive adjustment to environmental demands also within the school context. These psychophysiological correlates of self-regulation might be of great importance to study students’ adjustment to school challenges. In addition, the recovery phase once the stressor is over might play an important role in terms of coping resources available to adapt to the environment (El-Sheikh et al., 2011), especially in terms of the time and effort children spend in dealing with the situation (Appelhans & Luecken, 2006).

HRV and Cognitive Functioning

Greater reduction in cardiac parasympathetic activity in response to stress (decrease in HRV) works as a protective factor against environmental changes and is linked with better cognitive processing. For the same reason, higher basal cardiac vagal tone indexes a better self-regulation and has been found to be associated with fluid intelligence in school-age children (Staton, El-Sheikh, & Buckhalt, 2009). Higher levels of this biomarker are predictive of better performance on standardized scales assessing working memory and executive functioning (Marcovitch et al., 2010). The adult literature reports that greater cardiac parasympathetic withdrawal in response to stress is associated with better memory, advantaged problem solving, higher general intelligence (Melis & van Boxtel, 2001), as well as greater attention (Hansen, Johnsen, & Thayer, 2003).

Although research has shown that a decline in HRV during cognitive stress is linked to better cognitive processing, data are lacking on the real-world implications of this knowledge. For example, despite the importance of individual reactivity to unpleasant school-related emotional stressors, to our knowledge, there are no published studies on the role of HRV, as an index of self-regulatory abilities, in explaining youth’s academic performance. Every day at school, students experience a number of events that may be perceived as threatening. Whether it is the difficulty implied in the achievement process, the stress related to the mastery of academic subject matters, or the fear of examinations and insecurity derived from continuous assessment of performance (Phillips, 1993), school provides a context characterized by many academic stressors. Conley and Lehman (2011) showed that the exposure to these negative emotional events may result in elevations in systolic blood pressure, particularly when students are going through periods of acute academic stress. In addition, in the same study, participants with higher test anxiety exhibited particularly pronounced elevations in systolic blood pressure during times of acute academic stress. This research, conducted among college students, suggests that everyday academic stressors are linked to temporary increases in blood pressure, and that test anxiety may contribute to these elevations. Yet, more data are needed to understand whether such physiological response to SRSs also affects early adolescents’ academic achievement both independently and in interaction with temperamental characteristics while accounting for test anxiety, consistent with a dual risk perspective.

The Present Study

To extend current research, the aim of the present study was to investigate early adolescents’ HRV before (basal), during, and after (post) exposure to an SRS video-clip. More importantly, we aimed to assess the direct and interactive effects of HRV at rest, changes in parasympathetic activity (i.e., during and after the stressor), and temperamental profiles on youth’s academic performance in terms of mean grades in core subjects.

Three research questions guided the study:

Research Question 1: How does students’ HRV vary in response to an SRS, that is, does parasympathetic cardiac activity differ before, during, and after exposure to a video-clip depicting an SRS?

Research Question 2: Can different temperamental profiles be reliably identified in this sample?

Research Question 3: Do the three measures of HRV (namely, at rest, while responding to an SRS, and after the stressor) and students’ temperamental profiles influence students’ academic performance? And is there an interactive effect of the two on youth’s academic performance?

In relation to the first research question, based on the literature reviewed above, we expected HRV to decrease while watching the SRS video-clip, and to newly increase (i.e., return to basal values) during recovery once the distressing video was over. In relation to the second research question, we expected to identify at least two temperamental profiles characterized by varying degrees of approach/inhibition, activity level, and positive/negative emotionality. As regards the third research question, consistent with a dual risk perspective, we anticipated that adaptive temperamental profiles and good self-regulatory skills (i.e., higher HRV at rest and greater decrease in HRV during the stressor) would each be positively related to better school performance. Specifically, we expected youth with a greater basal HRV to report higher mean grades. In contrast, we expected youth with high reactivity in response to SRS (i.e., little or no HRV decline during the stressor) and scarce recovery after the video-clip to report lower school performance. Furthermore, given the documented association between temperament and basal cardiac activity (Blandon et al., 2010), we expected that students with adaptive temperamental profiles and greater HRV at baseline would report better academic outcomes, whereas those with maladaptive profiles and lower HRV at rest were expected to show worse academic performance. We also speculated that youth with maladaptive temperament profiles and high levels of reactivity and scarce recovery after exposure to the SRS video-clip would report lower academic grades. However, given the paucity of data on the interaction between cardiac activity in response to an SRS and temperament, this analysis will be mainly exploratory.

Because previous research suggests that the relation between self-regulation in response to academic stressors and academic functioning may be influenced by youth’s anxiety levels (Conley & Lehman, 2011), we included test anxiety in our analyses. Indeed, this variable has been shown to be related to academic performance (Spielberger, Anton, & Bedell, 2015) as well as to biological (e.g., physiological arousal) and emotional factors. Yet, no specific hypothesis was made given the exploratory nature of the role of test anxiety in the relation between HRV, temperament, and academic performance. Moreover, given the well-known gender and age differences in emotional self-regulation (Weis, Heikamp, & Trommsdorff, 2013) and temperament (Else-Quest, 2012), gender was also included as a control variable in our analyses.

Method

Participants

A total of 91 seventh graders aged between 12 and 14 years ( $\bar{X} = 12.71$ , SD = 0.56) who were attending lower secondary schools in the northeastern region of Italy took part in the study. Of these, 49 (53.8%) were boys. Most students were from middle- and upper-middle-class families and reported a moderate (30%) or high (65%) score on the Family Affluence Scale (FAS; Boyce, Torsheim, Currie, & Zambon, 2006), with an overall mean of 7.58 (SD = 1.50, range 3-10). Parental written permission and youth’s verbal assent were required for participation after approval of the study by the University Ethics Committee.

Procedure

Participants were tested in two sessions on different days. In the first session, they were asked to complete a number of questionnaires and to report on their grades in the main subjects. In the second session, approximately 1 week after the first, students’ HRV was registered individually in a quiet room of the school while they were comfortably seated in front of a computer screen. Heart rate was recorded continuously for 24 minutes. First, to collect baseline cardiac activity, HRV was registered at rest (8 minutes), then while watching a video-clip which showed a student who was highly distressed because he or she was unable to complete a test (8 minutes), and finally during an 8-minute recovery phase. Before and after viewing the SRS video-clip, students completed a measure assessing their current affective state (Positive and Negative Affect Scale [PANAS], see below). Finally, before they left the room, students were debriefed to make sure they were in a positive affective state.

Measures

Academic performance

Youth’s academic performance in the core subjects of Italian, Mathematics, Science, and English was measured using students’ grade in those subjects at the time of assessment. Grades in Italy are thought to be a good indicator of academic achievement and school success. In the Italian school system, grades range from 1 (lowest grade) to 10 (highest grade). We computed a mean grade as a measure of overall achievement.

Heart rate variability

To obtain HRV, photoplethysmography was recorded in a standardized fashion using a multimodality physiological monitoring device that encodes biological signals in real-time (ProComp Infiniti; Thought Technology, Montreal, Canada). Photoplethysmography is a simple, noninvasive technique used to detect blood volume pulse (BVP). It allows to collect reliable HRV data (Giardino, Lehrer, & Edelberg, 2002) without being disturbing for students. The photoplethysmograph sensor bounces infrared light against the skin surface and records the amount of reflected light that reflects the amount of blood in the capillaries, which in turn varies with the cardiac period. Since cardiac electrical and mechanical activities are coupled, a greater amount of blood corresponds to systole, whereas a lower amount of blood corresponds to diastole. BVP can be used to determine the IBIs and derive heart rate, which corresponds to the reciprocal of IBI.

The photoplethysmograph sensor was attached to the index fingertip of the participant’s nondominant hand and BVP was recorded continuously. BVP signal was processed via a 12-bit analog-to-digital converter with a sampling rate of 256 times per second and stored sequentially for analysis. The BVP signal was recorded during all the three phases (baseline, SRS video-clip, and recovery). Each phase lasted for 8 minutes; IBI data recorded during the entire duration of each phase were computed, and artifacts were controlled for. Then, the time distances between each beat were exported in Kubios-HRV 2.2 software (Kuopio, Finland) to correct artifacts with a piecewise cubic spline interpolation method that generates missing or corrupted values into the IBI series. Next, we calculated the NN50. This is an index of the variations in IBIs between heartbeats. Specifically, NN50 corresponds to the number of consecutive RR intervals differing more than 50 ms, where the prefix NN stands for normal-to-normal intervals (i.e., intervals between consecutive QRS complexes, that is, the combination of three of the graphical deflections seen on a typical electrocardiogram, resulting from sinus node depolarizations). This HRV measure is sensitive to short-term fast heart period fluctuations specifically reflecting parasympathetic activity through vagal nerve on the heart (Berntson et al., 1997; Porges & Byrne, 1992).

Video material

The SRS video-clip was emotionally negative and showed a student who was highly distressed because he was unable to complete a surprise test. Specifically, the video was created by same-age students and featured a 13-year-old boy who was unable to concentrate and showed a number of behavioral and physiological signals of distress (such as sweating, muscle tension, and difficulty in sitting still) while he was supposed to take a test. The kid saw his classmates writing and concentrating on the questions, whereas he was panicked. In the end, he received a bad grade, since he did not manage to complete the test.

Affective state

Participants completed the PANAS (Watson, Clark, & Tellegen, 1988), a questionnaire assessing participants’ current emotional state. This scale requires participants to rate a series of adjectives describing how they feel at the moment on a 5-point scale ranging from 1 (not at all) to 5 (extremely). The list of emotional terms can be grouped into subsets for assessing positive (e.g., happy and cheerful) and negative affect (e.g., anger, fear, and sadness). In the present sample, the coefficient alpha was .87 for the negative affect scale and .84 for the positive affect scale.

Temperament

Participants were asked to complete the Emotionality Activity Sociability Temperament Survey—Child Form (EAS-CF; Buss & Plomin, 1984). This self-report questionnaire consists of 20 items rated on a 5-point Likert-type scale ranging from 1 (not typical of me) to 5 (very typical of me). Items are used to generate four subscales: Negative Emotionality, Activity Level, Sociability, and Shyness. The questionnaire was translated using standard translation–back translation procedures. Previous research has demonstrated that the self-report version of the EAS can be reliably used with children over the age of 9 (Holder & Klassen, 2010). In our sample, alpha internal consistencies were .75, .77, .79, and .74 for Negative Emotionality, Activity Level, Sociability, and Shyness, respectively.

Control Variables

Demographics

Students reported on their age and gender. In addition, family socioeconomic status (SES) was assessed via the FAS, a 4-item measure developed and validated in the Health Behaviour in School-aged Children study (Boyce et al., 2006). It includes four indicators of family affluence: family car ownership, unshared rooms, number of computers at home, and time spent on holiday in the last 12 months. Responses were summed and the total score (ranging from 0 to 9) was divided into three groups following recommended cut points (Boyce et al., 2006).

Test anxiety

This variable was measured with the test- and school-related anxiety subscale of the AMOS 8-15 (Cornoldi, De Beni, Zamperlin, & Meneghetti, 2005), a comprehensive battery designed to assess environmental, motivational, and strategic variables in learning. The subscale is composed of seven items assessing children’s degree of distress at school on a 3-point scale ranging from 1 (little) to 3 (a lot). Examples of items are “When teachers test me in front of my classmates I feel upset” or “During exams I feel overwhelmed by anxiety and confused.” In the present study, Cronbach’s α for this scale was .83.

Results

Descriptive Statistics and Preliminary Analyses

In preliminary analyses, we computed group means and bivariate correlations (see Table 1). Because gender and age were not related to the dependent variable (i.e., students’ grades), they were not included in subsequent analyses. We also ran a series of t tests to examine whether there were any gender differences in academic achievement, HRV at baseline, HRV during the stressor, and HRV poststressor, and temperament dimensions. All tests were nonsignificant.

Table 1.

Descriptive Statistics and Correlations Between the Examined Variables.

	2	3	4	5	6	7	8	9	10	11	$\bar{X}$ (SD)	Range
1. Age	.15	−.23*	.02	−.11	−.17	.09	−.27**	−.23*	.21*	.01	12.71 (0.56)	12-15
2. Gender		−.14	−.06	−.03	.07	−.06	.02	.09	−.06	−.06	1.54 (0.50)	1-2
3. Grades			.08	−.06	.01	−.01	.25*	.20	−.19	−.15	7.35 (0.88)	4-10
4. Basal NN50				.65**	.69**	−.31*	−.23*	−.13	.27*	.11	216.04 (41.55)	110-339
5. NN50 during the stressor					.56**	.18	.17	.16	.27*	.12	131.09 (24.46)	69-182
6. NN50 poststressor						.18	.09	.08	.32*	.13	227.13 (43.17)	144-334
7. Emotionality							.12	.24*	.28*	.39*	0.95 (0.62)	0-2.75
8. Activity								.82**	−.34*	.05	2.70 (0.82)	1.25-4
9. Sociability									−.37**	.05	2.39 (0.89)	0.50-4
10. Shyness										.28**	1.15 (0.66)	0-3
11. School anxiety											1.38 (0.73)	0-3.8

Note. NN = normal-to-normal intervals.

p < .05. **p < .01.

HRV in Response to the School-Related Stressor

Before addressing the first research question, we assessed whether watching the SRS video-clip was able to change the valence of participants’ affective state. A repeated measures analysis of variance (ANOVA) was performed on mean scores of positive affect before and immediately after exposure to the SRS video. This analysis revealed a significant difference between the two testing times, Wilks’ lambda = .61, F(1, 90) = 56.63, p < .001, $η_{p}^{2} = 0.39$ , with a decrease in positive affect from before ( $\bar{X} = 34.24$ , SD = 9.39) to after the SRS video ( $\bar{X} = 28.85$ , SD = 11.34). The same procedure was applied to the mean scores of negative affect. Results revealed the opposite pattern, Wilks’ lambda = .79, F(1, 90) = 23.32, p < .001, $η_{p}^{2} = 0.21$ , that is, students’ reported negative affect increased from before ( $\bar{X} = 4.32$ , SD = 3.62) to after the SRS video ( $\bar{X} = 7.67$ , SD = 6.35). Thus, watching the SRS video-clip was able to evoke a state of negative emotionality in students.

Subsequently, to address the first research question, we examined how HRV—as indexed by NN50¹—varied in response to the SRS. A repeated measures ANOVA was performed comparing mean NN50 at baseline, during the viewing of the SRS video-clip, and in the poststressor phase. As shown in Figure 1, results indicated a significant effect of phase, Wilks’ lambda = .09, F(2,90) = 430.37, p < .001, $η_{p}^{2} = 0.91$ . As expected, HRV significantly decreased from baseline ( $\bar{X} = 216.04$ , SD = 41.55) to viewing the SRS video ( $\bar{X} = 131.09$ , SD = 24.46), and newly increased during recovery, that is after the stressor ( $\bar{X} = 227.13$ , SD = 43.17).

Figure 1.

Changes in HRV (i.e., NN50) during the three phases (i.e., baseline, while watching the SRS video, and during recovery).

Identification of Temperament Profiles

To address the second research question, we performed hierarchical cluster analysis using Ward’s method to examine whether there were subgroups of students who reported reliably distinct patterns of temperamental characteristics in terms of emotionality, activity, sociability, and shyness.

This analysis was deemed particularly appropriate as it allows to identify specific temperament profiles based on a constellation of multiple variables (i.e., temperament subscales), thus providing useful information on the interactions among behavioral components (Magnusson, 1990; Stifter et al., 2008). From a methodological perspective, the exploratory nature of this analytic approach fits well with the observation that the number of temperament profiles identified across extant studies varies greatly, thereby not allowing to make specific hypotheses. After performing this analysis, we inspected the resulting dendrogram to identify the largest gaps or distances between cluster groups and to determine the appropriate number of meaningful clusters that agglomerated the data (Olson & Biolsi, 1991). One of such gaps emerged in the dendrogram. To further corroborate this finding, an additional method (i.e., centroid) was used. This analysis yielded exactly the same results as Ward’s method.

Overall, the findings revealed the presence of two temperamental profiles. Independent samples t tests indicated that early adolescents in the first cluster (n = 48) scored significantly higher on activity, t(1, 59) = 13.03, p < .001, d = 2.73 and sociability, t(1, 59) = 15.31, p < .001, d = 3.27, and lower on negative emotionality, t(1, 59) = −3.58, p = .021, d = 1.44 and shyness, t(1, 59) = −8.29, p = .002, d = 1.09, compared to their peers in the second cluster (n = 43; see Figure 2). Hence, the two groups were labeled outgoing and inhibited, respectively.

Figure 2.

Mean scores on the measures indexing youth’s temperament as a function of cluster group.

Association Between HRV, Temperament, and Academic Achievement

To address the last research question, a linear regression analysis (see Table 2) was performed. In this model, we included all main effects of our study variables as well as their two-way interactions. We relied on an exploratory rather than a confirmatory model selection approach, based on the assumption that academic outcomes are a complex phenomenon that can hardly be captured in a single confirmatory model. For this reason, the best model was selected based on the Akaike information criterion (AIC; Akaike, 1973; McElreath, 2016; Wagenmakers & Farrell, 2004). As indicated by McElreath (2016), an Akaike model weight is an estimate of the probability that the model is the best, given the data and the set of models considered. The supplemental use of Akaike weights in addition to standard AIC provides greater insight into the merits of the competing models by specifying the plausibility of models on a continuum, thus facilitating the interpretation of results (Wagenmakers & Farrell, 2004). The most plausible model, with a probability 164 times larger of being better than the full model and superior to all other models, is presented in Table 2. This model explained 21.92% of the variance (adjusted R²: 14.31%). Results indicated that NN50 in response to the SRS and temperamental profiles were each significantly and positively associated with academic performance. In addition, a significant interaction between NN50 poststressor and temperamental profiles emerged. To investigate the interaction effect, we performed tests of the simple slopes (Aiken & West, 1991). Results showed that NN50 poststressor had a significant effect on academic achievement only among students with an inhibited temperament (B = 0.05, SE = 0.03, t = 1.60, p = .02), whereas no significant effect was found for students with an outgoing temperament (B = 0.01, SE = 0.02, t = 0.05, p = .09).

Table 2.

Summary of Regression Analysis for Variables Predicting Academic Achievement.

Predictor	B (SE)^a	p	$η_{p}^{2}$
Basal NN50	0.002 (0.004)	.522	0.004
NN50 during the stressor	0.021 (0.008)	.019	0.051
NN50 poststressor	−0.006 (0.004)	.542	0.003
Temperament cluster^b	−1.803(1.032)	.008	0.067
Basal NN50 × Temperament cluster	−0.008 (0.006)	.162	0.018
NN50 during the stressor × School anxiety	−0.007 (0.004)	.120	0.022
NN50 poststressor × Temperament cluster	0.014 (0.006)	.020	0.051
Total R²	0.219
N	91

Note. NN = normal-to-normal intervals.

Unstandardized coefficient.

Cluster temperament coded 1 = outgoing temperament, 2 = inhibited temperament.

As can be seen in Figure 3, among youth with an inhibited temperament, those with the ability to recover well (i.e., showing a higher increase in HRV during the recovery phase) reported a good academic performance, whereas among youth with an outgoing temperament, academic achievement was fairly good regardless of the degree of HRV poststressor.

Figure 3.

Effects of the interaction between NN50 poststressor and temperament profiles on youth’s academic achievement (grades).

Discussion

The present study aimed to investigate youth’s psychophysiological response to an SRS as indexed by changes in HRV. Furthermore, we examined the direct and interactive effects of HRV (i.e., basal, during and poststressor) and temperament on youth’s academic functioning.

As expected, students’ HRV varied in response to the SRS video-clip. Specifically, mean HRV decreased from baseline to the SRS phase. Furthermore, parasympathetic activity newly increased between the stressor and the recovery phase, that is, once the distressing video was over. The autonomic response to sources of emotional stress has been linked to a reduced phasic HRV or vagal suppression (e.g., El-Sheikh et al., 2011). This physiological response is thought to represent the withdrawal of cardiac vagal control and the activation of the defensive system, which is associated with an increased arousal (Thayer et al., 1996). In line with this literature, our participants experienced an emotional arousal during exposure to the SRS video, which resulted in a reduction of HRV.

This finding underlines the importance of specific emotional situations in the life of a student. In the present study, early adolescents were not directly experiencing an academic stressor, but they were simply watching a video in which a student is under stress because he is unable to complete a test. Nevertheless, such media exposure was able to trigger the withdrawal of cardiac vagal control (i.e., a decrease in HRV) together with a self-reported significant increase in negative affect and a decrease in positive affect. Similar to what happens to children who watch a video-clip depicting two people fighting (El-Sheikh et al., 2011), seventh graders’ observation of a student who fails to perform a test causes the activation of the defensive system. A short time period is then sufficient for the majority of youth to return to a state of balance between the two branches of the autonomic nervous system, which is conceptualized as a mean HRV during the poststressor phase similar to the one recorded during the initial tonic phase. Yet, the same positive outcome may not be found after real and direct exposure to an SRS. Although more research is needed, physiological data from our study add important information to the previously reported link between SRS and psychological distress (Scrimin et al., 2014), as well as to the existence of an association between school anxiety and biological factors (Lowe & Lee, 2008; Lowe et al., 2008).

The next analytic step aimed at exploring whether distinct temperamental typologies could be reliably identified in our sample. Cluster analysis using four temperamental characteristics as grouping variables (i.e., negative emotionality, activity, sociability, and shyness) yielded a two-group solution: Early adolescents in the “outgoing” group were characterized by high activity and extroversion and low negative affectivity and shyness/inhibition, while youth in the “inhibited” group were characterized by the opposite pattern. These profiles bear some similarities with those found in other studies with younger children. For example, the first cluster is similar to the bold group found by Prokasky et al. (2017) and the outgoing group identified by Sanson et al. (2009). Children in such groups are active and excitable, easily engage with proposed school activities, and demonstrate average levels of attention span. The inhibited cluster resembles the high reactive cluster identified by Prokasky et al. (2017) and the inhibited group found by Sanson et al. (2009), as these children exhibit high levels of negative affectivity (e.g., anger and fear) and shyness, and average-to-low levels of activity.

Finally, but most importantly, we examined the direct and interactive effects of HRV during the three phases (i.e., basal, during, and poststressor) and temperament clusters (i.e., outgoing and inhibited) on youth’s academic functioning while controlling for test anxiety. Regression analysis revealed significant main effects of HRV under stress and temperament profiles. Moreover, the interaction effect of parasympathetic activity in the poststressor phase and temperament cluster on youth’s academic functioning was statistically significant.

HRV while watching the SRS video-clip was positively related to students’ grades. As discussed previously, vagal withdrawal when experiencing a stressful event is thought to be an index of self-regulation. This is conceptualized as the ability to put on a “brake” when reacting to the events in the environment (Porges, 1992). Such ability, which reflects the overall decrease in HRV when shifting from the basal to the SRS phase, has been typically associated with positive outcomes. However, extremely low levels of vagal withdrawal have been associated with negative outcomes (e.g., Beauchaine et al., 2007), suggesting that these individuals tend to be easily distressed and overaroused. In line with these studies, we found a positive link between HRV while watching the SRS video and academic achievement. Apparently, students who were less aroused in response to the SRS also had higher academic performance. Thus, students who felt less threatened by the school-related stressor were the ones performing better academically.

Furthermore, as expected, students with an inhibited temperament were more likely to have lower academic grades. This finding supports previous research highlighting the importance of temperament for academic performance (Al-Hendawi, 2013) and suggests that in the school context, students who are prone to experience intense negative emotions and are more fearful and shy than other peers may have difficulties in approaching new school activities, asking school mates or teachers for help when needed, or regulating distress after failure.

More interestingly, however, an interaction was found between HRV during the poststressor phase and temperament profiles. The relation between mean grades and HRV after the stressor differed in the two groups of students with outgoing and inhibited temperaments. Specifically, from a descriptive point of view, it could be noted that among youth with an inhibited temperament, higher HRV during the poststressor phase (i.e., better self-regulation) was related to better academic performance, whereas among students with an outgoing temperament, academic grades were overall fairly good regardless of the HRV after the stressor. Thus, the ability to effectively regulate psychophysiological arousal in response to emotion-laden situations may serve as a protective factor for students with a temperamental profile that potentially puts them at risk for low academic functioning.

Overall, these findings are consistent with prior studies showing that temperamental reactivity interacts with emotion regulation (as indexed by HRV) to predict socioemotional outcomes (e.g., Eisenberg, Smith, & Spinrad, 2011) and indicate that the buffering effect of HRV extends to early adolescence in links with academic functioning. Furthermore, they underscore the usefulness of considering multiple (as opposed to single) components of the parasympathetic response, namely, baseline, during, and after the stressor to shed light on the dynamics underpinning youth’s reactions to an SRS. In the present study, no interactions were found between temperament and HRV at rest and during the SRS phase. This result underlines the importance of the recovery phase, as a correlate of the ability to self-regulate and re-adjust to the situation after a stressor, as a protective factor in students with inhibited temperament.

Previous studies have shown that exposure to repeated academic stress results in elevations of systolic blood pressure, particularly among students with high test anxiety (Conley & Lehman, 2011). Here, we report that, after controlling for test anxiety, students with an inhibited temperament are at risk for low academic performance; however, those who are able to adjust well after exposure to an SRS can perform better. The good news is that, even if HRV is a fairly stable characteristic (Li et al., 2009), a number of well-documented techniques exist that teach individuals to effectively modify it (e.g., Whited, Larkin, & Whited, 2014). Teaching youth to self-regulate and improve their ability to restore homeostasis after a stressor, especially when experienced within the school context, may help them to better cope with environmental challenges and consequently enhance their academic performance. This seems to be particularly true for those students who are more vulnerable due to an inhibited temperament.

This work is one of the first in school psychology to record psychophysiological responses before, during, and after exposure to an SRS video to study how such responses are related to seventh graders academic performance. Although findings are interesting and promising from both a theoretical and an applied perspective, we acknowledge some limitations of this study. The first concerns the limited number of participants, which did not allow us to control for a number of other environmental and emotional variables that may be involved in the direct and interactive associations between cardiac vagal activity, temperament, and academic functioning. Further research on larger samples of students is needed to draw a more complete picture taking into account motivational factors, students’ approaches to learning, self-regulatory learning strategies, and psychosocial context influences (see Richardson, Abraham, & Bond, 2012, for a review). In addition, a larger sample size would allow to include children living in more heterogeneous socioeconomic environments and hence both increase the generalizability of findings and address the interaction between HRV and temperament in prediction academic functioning in students with different SES.

Furthermore, the present study cannot demonstrate any causal relationships among the variables under consideration; a longitudinal data collection would be desirable in future investigations. A third limitation concerns the use of a single physiological measure of parasympathetic activity. To assess the degree of arousal in response to a stressor, it would also be interesting to record sympathetic activity of the autonomic nervous system (ANS), for example, through electrodermal activity or pupil dilation.

Despite these limitations, our study carries several implications for future research. It is among the first to show an association between youth’s cardiac parasympathetic inputs in relation to an SRS and their academic performance, also taking into account students’ temperament profiles. Understanding the psychophysiological processes underlying students’ responses to an SRS has important implications for academic success and merits further attention. By using the exploratory results from this study, future research may benefit from adding the objectivity of physiological measures when addressing student’s adaptation to the school environment and its effect on emotional, behavioral, and academic functioning.

Results of this study also provide new implications for practice in educational settings with students. Our findings emphasize the protective role of self-regulation (as indexed by a greater HRV after the SRS) among students with an inhibited temperament. Hence, secondary school students—particularly when their individual characteristics place them at risk for academic difficulties—may benefit from interventions supporting self-regulatory strategies (Zorza, Marino, & Acosta Mesas, 2017). A number of programs exist that promote self-regulation in school-age children (e.g., Fishbein et al., 2016) that may be effective with students with inhibited temperaments. Moreover, the use of specific interventions based on HRV biofeedback may be particularly recommended with small, targeted groups of at-risk students (McCraty & Zayas, 2014).

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was supported by a grant to the last author under the project ‘Cognition and Emotion in Processing, Evaluation, and Comprehension of Online Conflicting Information: A Multi-Method Approach’ (CPDA158418) from the University of Padova. The authors are very grateful to all the students, their parents and teachers, and the school principals, who made this study possible.

Notes

Author Biographies

Sara Scrimin is an assistant professor in the Department of Developmental Psychology and Socialization at the University of Padova, Italy. Her research interests focus on the effects of stress on cognitive and socioemotional well-being as well as psychophysiological measures and emotional self-regulation.

Ughetta Moscardino is an associate professor in the Department of Developmental Psychology and Socialization at the University of Padova, Italy. Her research interests focus on temperament and socioemotional well-being as well as psychological adaptation in adolescents exposed to stressful events.

Livio Finos is an associate professor in the Department of Developmental Psychology and Socialization at the University of Padova, Italy. His research interests focus on statistical methods for nonparametric multivariate inference as well as statistical methods for psychological sciences (i.e., mixed-effects and structural equation models, and Bayesian inference).

Lucia Mason is a full professor in the Department of Developmental Psychology and Socialization at the University of Padova, Italy. Her research interests focus on educational psychology as well as emotion and learning in primary and secondary school students.

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