مبارزه، پرواز و یا سقوط: اتونومیک واکنش پذیری سیستم عصبی در طول چتربازی

Abstract Sensation seeking (SS) traits may drive individuals toward high-intensity environments because of a desire for novelty and risk; however, research has not identified the psychophysiological mechanisms underlying SS behavioral expression or disentangled the relative contribution of novelty-seeking for physiological arousal. We used an ambulatory device to measure autonomic nervous system (ANS) activity before, during, and after skydiving in 44 jumpers. To identify the contribution of novelty, we compared novice jumpers to experienced jumpers. Hierarchical linear modeling revealed (1) whether there was physiological activation for each individual and (2) whether there were differences in responsivity between novice and experienced jumpers. All jumpers displayed increases in HR during the jump, indicating that repeated exposure to an unwavering risk did not habituate the response. Group differences in baseline functioning suggest that novelty may initially motivate propensities toward SS behaviors. Interestingly, coactivation of the SNS and PNS emerged during the jump and fall, suggesting that both components of the ANS are necessary to facilitate coping with intense challenges.

1. Introduction The psychophysiological mechanisms driving individuals to partake in high-risk activities is understudied and not well understood. What is known is that novelty and risk may motivate the decision to participate in high-risk activities in individuals with a broad range of sensation seeking (SS) trait expression (Zuckerman, 1994). This investigation intended to identify the pattern of autonomic nervous system (ANS) activity that instantiates novelty and risk in the progression of SS into behavioral expression. In this study, skydiving was used as a window into the SS trait as it is a socially-sanctioned, intense, yet risky behavior. ANS activation was quantified while participants were skydiving, which permitted us to directly assess the proposed physiological mechanisms underlying SS propensities in an ecologically-valid setting. 1.1. The sensation seeking trait In 1969, Marvin Zuckerman introduced sensation seeking as a measurable construct with wide individual differences in trait expression. As research involving SS expanded over the next decade, an emphasis was placed on the SS-related behavioral outcomes. Individuals with the SS trait are more likely to participate in high-risk sports, jobs, and sexual behaviors (Freixant, 1991, Musolino and Hershenson, 1977 and Thornquist et al., 1991), yet it is still not entirely clear what biological processes motivate or sustain these behaviors. SS is defined as “the tendency to seek novel, varied, complex, and intense sensations and experiences and the willingness to take risks for the sake of such experience” ( Zuckerman, 1994). Based on this definition, novelty and risk emerge as important factors influencing the process by which individuals are motivated to engage in SS behaviors. Research involving animal ( Piazza et al., 1993) and human ( Zuckerman, 1996) models of sensation seeking implicate the stress response systems and the reward pathway as central to novelty- and risk-seeking, respectively. One theory postulates that high sensation seekers have increased stress resistance and threshold for aversive situations ( Netter, Hennig, & Roed, 1996); sensation seeking scores have been associated with gonadal and endocrine hormone levels ( Gerra et al., 1999). However, the direct influence of sensation seeking activities on acute physiological functioning remains understudied. Disentangling the role of novelty and risk may be most feasible during a high-risk activity, because physiological processes are most likely to be activated during an SS activity. 1.2. Physiological arousal The present investigation focuses on the ANS because, in addition to being the forerunner of the stress response, the neural circuitry implicated in SS behaviors communicates directly with the ANS (Joseph, Liu, Jiang, Lynam, & Kelly, 2008). High levels of parasympathetic (PNS) control suggest a more controlled and less aroused state, as well as adaptive orientation and self-regulation in response to attention demanding situations (Porges, 2003). Conversely, decreased PNS control suggests diminished socioemotional flexibility (Kennedy, Rubin, Hastings, & Maisel, 2004), and increased risk for arrhythmic death in coronary heart disease (Kleiger et al., 1991). We evaluated the root mean square successive difference (RMSSD) as an index of HRV and, thus, indirectly captured PNS influence (Berntson, Lozano, & Chen, 2005) during tonic functioning and in response to the extreme challenge of skydiving. HR has also been used extensively in previous research quantifying the sympathetic nervous system (SNS; Kudielka, Schommer, Hellhammer, & Kirschbaum, 2004). Increases in HR facilitate acute psychological and physical changes that provide the individual with the ability to fight or flee. Increased HR in response to a challenge is considered an adaptive coping strategy; in fact, decreases in HR in response to a challenge can indicate behavioral dysregulation, including aggression (Gottman et al., 1995) and antisocial behavior (Ortiz & Raine, 2004). The classical conception of the ANS involves an increase in SNS control and decrease in PNS control in response to demanding situations (Cacioppo, 1994), but the autonomic space theory offers that stimuli can evoke a range of autonomic patterns of input to bodily systems (Berntson, Cacioppo, & Quigley, 1991). The theory suggests that the SNS and PNS have a dynamic relationship in which coactivation and coinhibition are possible, complimenting research suggesting that a variety of symmetrical and asymmetrical patterns of activation between and across physiological systems is necessary for coping with an environment that simultaneously requires active engagement and relaxation (Hastings et al., 2011). 1.3. Behavioral expression of the SS trait The context of skydiving is an ecologically valid setting in which evaluating the real-world physiological valence of risk-taking behaviors is possible. The context of skydiving is anticipated to elicit ANS activation, as the body attempts to adaptively regulate functioning in the face of environmental change; we expected that skydiving would elicit ANS reactivity. At a behavioral level, the activation of these systems may be perceived as rewarding in individuals with higher SS tendencies, motivating them toward engaging further in risky behaviors. The particular pattern of psychophysiological activation during a specific high-risk challenge may provide insight into how the brain and body communicate levels of risk, reward, and novelty during that preferred high-risk activity. Including individuals with differences in previous exposure to the activity (novice versus experienced jumpers) may identify the contributions of novelty and risk in motivating behavioral expression. We expected group differences in ANS reactivity as a function of previous exposure, such that experienced jumpers would exhibit blunted reactivity in comparison to first time jumpers. Novelty is expected to wear off over time and this phenomenon may motivate the SS individual to engage in varied SS-behaviors, though the risk is unwavering. The process whereby novelty wears off is of utmost importance in regard to possible wear-and-tear on the physiological systems because different physiological systems are anticipated to reduce activity to novel stimuli at varying rates. Last, we expected that levels of SS would influence patterns of ANS reactivity, as previous research suggests an association between SS scores and other physiological systems (Gerra et al., 1999).

. Results 3.1. Sympathetic nervous system activity 3.1.1. Did individual differences contribute to HR responsivity? First, we investigated what portion of the variance seen in HR was due to differences between individuals, and, conversely, what portion of the variance was due to differences within individuals. The null model showed that there was significant between individual variability in HR, χ2(35) = 9254.44, p < .0001, such that 42.5% of the variability in HR was a stable, trait-like component and attributable to differences between individuals. Conversely, 57.5% of the variability was attributable to differences or changes in HR within individuals during the experiment. 3.1.2. Did participants experience HR responsivity? Importantly, RMSSD did not significantly predict HR as a level-1 variable (β = .09, t = .19, p = .85). This finding is essential in our argument that HR was indexing SNS activity. Since we centered the data on the time of jump, the level-1 intercept represents the HR levels at the time of jump. The model revealed that the preliminary interval did not significantly predict HR levels, (β = 38.70, t = 1.16, p = .256), such that HR remained lower than the peak at jump throughout this interval. There was a trend toward the quadratic preliminary interval predicting HR (β = 39.03, t = 1.75, p = .08), such that HR more than an hour before the jump was non-linear. During the anticipation interval HR rose significantly, β = 12.86, t = 3.03, p = .005, and followed a linear trend. The jump interval captured the peak in HR levels, β = 288.72, t = 8.19, p < .0001, and the quadratic transformation of this variable was also significant, β = −1489.87, t = −13.45, p < .0001, indicating peak levels were achieved during the relatively brief interval immediately before and after the jump. The recovery interval showed further decline in HR, β = −67.47, t = −2.71, p < .011. The time varying fluctuations in HR accounted for 55% of the total variance in HR levels. There was significant between-individual variability during the preliminary interval (χ2(12) = 535.59, p < .0001), anticipation (χ2(12) = 488.76, p < .0001), jump (χ2(12) = 1646.23, p < .0001) and recovery (χ2(12) = 842.21, p < .0001). These findings suggest that the pattern of HR activity was different for each individual. 3.1.3. Were there group differences in HR responsivity? We entered a dummy-coded novice/experienced variable in order to ascertain any group differences in HR activity. After controlling for the effects of time, this variable accounted for 3% of the total variance in HR levels. During the preliminary interval, experienced jumpers exhibited more decrease in HR (i.e., steeper slopes) but maintained higher HR levels, β = 121.44, t = 3.08, p < .005. While this difference might reflect a consequence of repeated skydiving on baseline physiological functioning, we were cautious to overly interpret this group difference. It is possible that, for experienced jumpers, the preliminary interval reflected a recovery from a previous jump that same day, or the physical activity associated with preparing jump formations with other skydivers. The intercept revealed that, at the time of jump, experienced jumpers attained higher HR levels than novice jumpers, β = 77.84, t = 3.21, p < .003. The other time intervals did not exhibit significant group differences between novice and experienced jumpers. 3.1.4. Did sensation seeking scores predict HR responsivity? After controlling for time and novice/experienced, sensation seeking score explained 21% of the total variance in HR levels. Higher SS scores significantly predicted lower HR in the preliminary interval, β = −3.48, t = −2.26, p = .031, but not during any of the reactivity intervals. The final HR model is shown in Fig. 1. Heart rate in novice and experienced jumpers before, during and after skydiving. Fig. 1. Heart rate in novice and experienced jumpers before, during and after skydiving. Figure options 3.2. Parasympathetic nervous system activity 3.2.1. Did individual differences contribute to RMSSD responsivity? As in the HR analysis, a null model demonstrated the contributions of differences between individuals and differences within individuals in order to test for dependency in the data. The null model showed that there was significant between-individual variability in RMSSD, χ2(35) = 5055.73, p < .0001 and that 32.6% of the variability in RMSSD was attributable to stable, trait-like differences between individuals, while 67.4% was attributable to differences within individuals. 3.2.2. Did participants experience RMSSD responsivity? The level-1 model revealed that the interval representing the preliminary interval did not significantly predict RMSSD levels, β = .083, t = .85, p < .339. During the anticipation interval, RMSSD declined significantly for most individuals, β = −1.21, t = −4.44, p < .0001, and the change during anticipation appeared quadratic, β = −.853, t = −3.33, p < .0001. Interestingly, RMSSD reached its peak during the jump, β = 5.22, t = 3.87, p < .0001, and the quadratic transformation of this variable was also significant, β = −24.40, t = −3.51, p < .0001, indicating a sharp increase immediately before the jump and a sharp decrease immediately after the jump. The recovery interval showed a trend toward decreasing RMSSD, β = 1.14, t = −1.85, p < .072. The time intervals accounted for 10% of the total variance in RMSSD. There was significant between-individual variability during the preliminary interval (χ2(12) = 22.61, p < .046), anticipation (χ2(12) = 75.56, p < .0001), jump (χ2(12) = 219.60, p < .0001) and recovery (χ2(12) = 154.59, p < .0001). These findings suggest that the pattern of RMSSD activity was different for each individual. 3.2.3. Were there group differences in RMSSD? There were no group differences in RMSSD during the preliminary interval. Rather, during the anticipation interval, experienced jumpers exhibited increasing RMSSD, β = .558, t = 1.87, p < .0001, but novices maintain higher RMSSD levels, until the actual jump. 3.2.4. Did sensation seeking scores predict RMSSD responsivity? SS scores did not significantly predict RMSSD functioning during any of the time intervals. The final RMSSD model is shown in Fig. 2. RMSSD in novice and experienced jumpers before, during and after skydiving. Fig. 2. RMSSD in novice and experienced jumpers before, during and after skydiving.