Is the Relationship Between Trait-Positive Affect and Global Physical Health Mediated by Nature Relatedness and Nature Exposure?

Is the Relationship Between Trait-Positive Affect and Global Physical Health Mediated by Nature Relatedness and Nature Exposure?

Luke Fullagar
Research Project at Monash University
Faculty of Medicine, Nursing and Health Sciences
School of Psychology and Psychiatry


Abstract

Emotions are thought to act as a principal pathway connecting psychological stress to physical disease (Cohen & Pressman, 2006). Much research in health psychology has focussed on associations between negative emotional states and/or affective styles (traits) with negative morbidity and mortality outcomes (see meta-analysis in Pressman & Cohen, 2005). More recently, evidence from a range of experimental methodologies (Diener & Chan, 2011) has provided compelling evidence of associations between trait-positive affect (PA) and increases in a number of physical health indicators including: mortality, morbidity, disease survival, recovery, as well as for specific biological systems (e.g. cardiovascular, endocrine) (Pressman & Cohen, 2005; Cohen & Pressman, 2006).
For brevity, this study is limited to self-reported health measures in healthy populations, and therefore, a review of previous research is limited to morbidity and self-reported symptom findings. Similarly, the current study limits the research question to hedonic trait-PA defined as the enduring disposition to experience feelings that “reflect a level of pleasurable engagement with the environment” (Clark, Watson, & Leeka, 1989; Cohen & Pressman, 1995). This definition distinguishes the current study from research on state-PA, being those that measure or manipulate short-term experiences of PA (Cohen & Pressman, 1995). Similarly, generalisability of subjective wellbeing research (SWB) is limited, and therefore excluded from review, given the inclusion of both eudonic measures (e.g. optimism (Diener & Chan, 2011)) alongside hedonic PA.
Both cross-sectional and prospective studies support an association between trait-PA (herein referred to as “PA”) and lowered morbidity (Pressman & Cohen, 2005). Unsurprisingly, many cross-sectional designs on populations with serious disease demonstrate a link between lowered PA and increased disease severity, for example: fibromyalgia and arthritis (Celiker & Borman, 2001) and hypertension (Knox, Svensson, Waller & Theorell, 1988). However, these cross-sectional research designs preclude causal inferences, rendering it possible that the observed effects of PA are attributable to the chronicity of disease symptoms themselves rather than PA’s unique contribution. Prospective morbidity studies, however, do provide encouraging evidence for a causal and unique contribution of PA to physical health despite strong variation in PA measures and health constructs studied: e.g. in contracting the common cold (Cohen, Doyle, Turner, Alper & Skoner, 2003); the future occurrence of stroke (Ostir, Markides, Peek, & Goodwin, 2001), lowered risk of injury for hockey players (Smith, Stuart, Wiese-Bjornstal, & Gunnon, 1997); and incidence of general injuries (Koivumaa-Honkanen, Viinamaeki, Heikkila, Kaprio & Koskenvuo, 2000).
While exposed to affect-elicited self-report biases (Cohen & Williamson, 1991) considerable research also connects higher PA to lower self-reported pain (for example, in cancer patients (Guadagnoli & Mor, 1989) and hospital inpatients (Kvaal & Patodia, 2000)), and better perceived health and fewer self-reported symptoms in healthy populations (e.g. Takkouche, Regueira, & Gestal-Otero, 2001; Roysamb, Tambs, Reichborn-Kjennerud, Neale, & Harris, 2003). Prospective studies by Hirdes & Forbes (1993) also demonstrate that PA prospectively predicts less symptom reporting and better self-reported health. By contrast, small, or in some cases negative, associations between PA and symptom reporting were observed by Watson and Pennebaker (1989). However, it is important to note that in this study, trait NA was also associated with greater self-reported symptoms and poorer self-reported health, potentially confounding conclusions on the role of trait PA.
Indeed, this potential for confounding is highlighted by ambiguous evidence that NA and PA are both bipolar extremes and orthogonal factors (Pressman & Cohen, 2005; Deiner, Smith & Fujita, 1995). Three of abovementioned studies on morbidity either controlled for NA or assessed NA variables and found no influence on outcomes (Cohen et al., 2003; Ostir et al., 2001; Smith et al.,1997; Pressman & Cohen, 2005), suggesting a unique contribution of PA in limiting morbidity. However, the literature on NA and disease demonstrates convincing associations (e.g. meta-analyses by Krantz & McCeney (2002) and Pressman & Cohen (2005)), and it is therefore essential for PA research to control for NA to assess whether the relationship between PA and self-reported health measures is merely due to the absence of NA, or a unique contribution of an orthogonal PA factor.
Additional variables have been shown to affect both health and PA and are controlled for in this study. SES is a potential spurious factor as it has been strongly linked to health (Adler et. al., 1994), and while no study appears to explore the connection between positive affect and SES, SES has been identified as a common risk factor for emotional health (Hudson, 2005).
Existence of religious affiliation has also been shown to affect health (Ferraro & Albrecht-Jensen, 1991) and positive affect (Fredrickson, 2002; Levin & Chatters, 1998). Employment status also has been shown to affect health (Bartley & Owen, 1996; Bambra, 2011) and positive affect (Puglesi, 1995). Similarly, relationship status has also been shown to affect both health (Verbrugge, 1979; Wyke & Ford, 1992) and positive affect (Wood, Rhodes, & Whelan, 1989)
While many of these studies are atheoretical in nature, two theoretical frameworks have been proposed for the influence of PA on health outcomes. The Main Effect Model proposes that PA affects physical systems such as cardiovascular, nervous system and endocrine pathways, as well as social and behavioural influences, whereas the Stress Buffering Model suggests that PA influences health through its ability to mitigate pathogenic outcomes of environmental stressors (Pressman & Cohen, 2005).
One pathway proposed by Smith and Baum (2003) suggests that PA’s stress buffering role may be achieved through inducing a proclivity toward self-caring restorative activities such as sleep, exercise, relaxation and spending time in natural environments, which in turn reduce the physical and negative affective stress responses which lead to disease. This stress-buffering perspective echoes theoretical and empirical research in the nature exposure, affect and health literature spearheaded by Roger Ulrich (Ulrich, Dimberg & Driver, 1991, Ulrich, Simons, Losito, Fiorito, Zelson. 1991, Ulrich & Parsons, 1992, Ulrich, 1993), which suggests that exposure to natural settings (real and, to a lesser extent, virtual) potentates an affective response which replaces negative affects with positive ones, and resultantly reduces stress activation and susceptibility to stress induction via restoration of cognitive, emotional, social and, importantly, physical resources (Hartig, Book, Garvill, Olsson & Garling, 1996). Together, these lines of research may suggest a causative pathway from trait-PA to nature exposure through to state-PA and stress-reduction leading to better health outcomes.
However, while these associations are well supported, diversity in research findings renders the direction of these relations uncertain (Hartig et al., 1996; Mayer et al., 2008). That is, it remains uncertain the extent to which, nature exposure, connection or relatedness cultivates trait-PA and state-PA versus the extent to which it may assist in sustaining PA which motivates nature-focussed activity in first instance.
The relationship between nature exposure and health is supported by findings which demonstrate that those exposed to nature have reduced heart rate, muscle tension, blood pressure, and improved skin conductance (Ulrich, Simons, Losito, Fiorito & Zelson, 1991); lower frequency of stress symptoms (Moore, 1981; Kaplan and Kaplan, 1989; Leather, Pyrgas, Beale & Lawrence, 1998) lower digestive illnesses and headaches (Moore, 1981) and also recover from illness faster (Lewis, 1996) and with more resilience to subsequent stress (Parsons, Tassinary, Ulrich, Hebl, Grossman-Alexander, 1998).
Strong associations between PA and nature exposure have been evidenced in cross-sectional correlation designs (Mayer, Frantz, Bruehlman-Senecalm & Dolliver, 2009). Mediation of this relationship by one’s sense of nature connectedness, or affective sense of community with nature, as measured by Mayer and Frantz’s (2004) Connectedness to Nature Scale (CNS) has been observed by Mayer, Frantz, Bruehlman-Senecal & Dolliver (1999).
Nisbet, Zelenski and Murphy’s (2009) nature relatedness (NR) construct transcends but includes the domains covered by the CNS by adding physical relation with nature to the construct. Nisbet & Zelenski (2011) demonstrated strong associations between NR and PA, and this was repeated in three cross-sectional correlation designs (Nisbet, Zelenski & Murphy, 2011) where the NR construct strongly correlated with trait-PA measured on the Positive and Negative Affect Scale (Watson, Clark, & Tellegen, 1988). Importantly, NR was also found in these studies to be unrelated to NA prior to the control of environmental measures, suggesting a unique contribution of PA to this relationship. NR has also been shown to mediate the relationship between PA and nature exposure (Nisbet, Nealis, & Zelenski, 2011).
Accordingly, this study aims to assess whether trait-PA and self-reported global physical health are associated when controlling for NA, and demographic variables age, socio-economic status, religious affiliation, relationship status and employment status. In addition, this study aims to assess whether any relationship between trait-PA and global physical health is mediated by nature relatedness and/or exposure.
It is hypothesised that PA will be positively associated with PH, operationalised as a significant correlation between trait-PA scale scores on the PANAS and self-reported global physical health scores on the WHO-BREF (Hypothesis 1). It is further hypothesised that PA will continue to predict PH when controlling for: self-reported trait-NA (reported as a score on the PANAS NA sub-scale), age (reported in years); dichotomous SES by postcode (reported as either below or equal/above average), existence of religious affiliation, relationship status and employment status (Hypothesis 2). Thirdly, it is also hypothesised that NE will mediate the relationship between PA and PH (Hypothesis 3). Due to size limitations of this study, this mediation will employ an augmented version of the methodology for mediation analyses set out by Baron and Kenny (1986), and will be operationalised by: establishing Hypothesis 1, establishing a significant positive correlation between NE scores on the NE Questionnaire and self-reported trait-PA on the PANAS (Path 3a), establishing a significant positive correlation between NE scores on the NE Questionnaire and self-reported global health scores on the WHO-BREF (Path 3b), and then, by controlling for paths in hypotheses 3a and 3b in a hierarchical regression model, evidencing a significant diminution in the strength of the correlation between trait-PA scores on the PANAS and self-reported global health scores on the WHO-BREF. Finally, it is also hypothesised that NR will mediate the relationship between self-reported trait-PA and global physical health (Hypothesis 4), similarly operationalised by establishing Hypothesis 1, establishing a significant positive correlation between NR scores on the NR Scale and self-reported trait-PA on the PANAS (Path 4a), establishing a significant positive correlation between NR scores on the NR Scale and self-reported global health scores on the WHO-BREF (Path 4b), and then, by controlling for paths in hypotheses 4a and 4b in a hierarchical regression model, evidencing a significant diminution in the strength of the correlation between trait-PA scores on the PANAS and self-reported global health scores on the WHO-BREF.

Method
Participants
318 participants were recruited, and 297 completed the study. All were healthy adults aged between 18-79 sampled at convenience through invitation from student experimenters. 68.80% were female (n=192) and 31.20% were male (n=87). Mean age was 38.28 years (SD=11.78). Mean annual household income was between $70,001 - $105,000. 78.5% were employed. 19.3% had completed standard education and 80.7% had completed advanced education. 78.2% had a current partner, and 21.5% were currently single. 34.9% reported having a religious affiliation.

Materials
A copy of the survey used is included in Appendix 1.
Demographic Information. A demographic survey was administered using an online university website.
PANAS. The trait-scale of the Positive and Negative Affect Scale (PANAS; Watson, Clark, & Tellegen, 1988) was used to assess participants’ trait positive and negative affect. The PANAS is a 20-item scale, divided into 2 separable 10-item positive and negative trait affect scales. Positive affect is characterized by feelings of enthusiasm, engagement, and alertness, whereas negative affect is characterized by various types of distress, including anger, contempt, disgust, guilt, fear, and nervousness. Participants rated the extent to which they had felt these mood states during the past few weeks using a modified Likert-type scale (1 = very slightly or not at all, 5 = extremely). The positive and negative affect scales both demonstrate moderately good internal consistency (NA: α=0.84 to 0.87, PA: α=0.86 to 0.90), and 8-week test-retest correlations were also good (PA: 0.47-0.68; NA 0.39-0.71) (Watson, 1988)
WHOQOL-BREF. The WHOQOL-BREF comprises of 26 items, which measure an individual’s self-reported perceptions of themselves across four broad health domains (subscales): physical health (“the PH subscale”), psychological health, social relationships, and environment. Participants rate questions (e.g. How satisfied are you with your health?) on a 5-point Likert-type scale (1 = lowest to 5 = highest). The full WHOQOL-BREF measure was administered, but only the PH subscale was used in the analysis of self-reported global physical health (PH). The test demonstrates moderately good internal consistency (α=.60-.90; PH subscale in healthy populations α=.87), and 8-week test-retest correlations were also above r=.80, PH subscale r=.86 (Murphy, Herrman, Hawthorne, Pinzone & Evert, 2000).
Nature Exposure Scale. The Nature Exposure Scale (Francis, 2011) is a four question measure testing the extent of one’s exposure to, and noticing of, natural and virtual nature settings in everyday life and on specific excursions to nature (e.g. In your everyday home, travel and work environments and activities, please rate your level of exposure to ‘natural environments’). Questions are answered on a 5-point Likert-type scale (1 = lowest to 5 = highest). The test demonstrates moderately good internal consistency (α=.707).
Nature Relatedness Scale. Nature relatedness was measured using the NR Scale (Nisbet, Zelenski & Murphy, 2009). The scale requires participants to rate 21 items on a 5-point Likert scale (1=disagree strongly to 5=agree strongly). Each item asked participants to measure the extent to which they agree with statements evoking cognitive, affective, and physical connection with nature, e.g. “My relationship to nature is an important part of who I am”, “I enjoy digging in the earth and getting dirt on my hands”, “I don’t often go out in nature” (negatively keyed item). The NR Scale has demonstrated good test-retest reliability (r=.85), internal consistency (α=.87), convergent and discriminant validity and construct validity (Nisbet, Zelenski & Murphy, 2009).

Procedure
Participants who agreed to participate were advised by letter from senior Monash University staff that approval for the study was granted by the Monash University Human Research Ethics Committee. Inclusion criteria were that paricipants were over 18 years and had computer access, and exclusion criteria were that participants must not be experiencing a severe or debilitating illness. All materials were administered online and completed in one sitting at participant’s convenience. All scored surveys were collated by university staff and uploaded as a data file on SPSS Version 18 software.

Results
SPSS Results are presented in Appendix 2. Raw data was cleansed and 23 cases removed because of missing information. Three outliers with Mahalanobis distances over 25 were identified and excluded from the analysis. One additional case was eliminated due to an absent employment score. Preliminary analyses were conducted to ensure no violations in the assumptions of linearity, normality, multicollinearity, and homoscedasticity. P-P plots were inspected and confirmed normal distribution, and scatterplots confirmed a linear and homoscedastic distribution. Belsley’s (1991) limits for multicollinearity problems (Variance Inflation Factors (VIFs) above 10 and Tolerance values below 0.10) were not breached in the current regression models, with VIFs below 1.88 and Tolerance values above 0.53.
Descriptive statistics for measures used in the study are summarised in Table 1.
Table 1
Descriptive Statistics Mean (M) and Standard Deviations (SD) for Study Measures
Variable Mean SD
Physical Health 73.98 14.20
PA 34.84 6.69
NR 3.75 .63
NE 9.38 2.98
NA 18.50 6.39
Dichotomous SES from Postcode .91 .29
Existence of Religious Affiliation .35 .48
Relationship Status .78 .41
Employment Status .78 .41

PH means and standard deviations were similar to Australian normative data (M=80, SD=17.1) (Murphy, Herrman, Hawthorne, Pinzone & Evert, 2000). PA means and standard deviations were marginally higher than normative samples of Australian men (M=33.5, SD=5.9) and women (M=33.9, SD=5.1) (Watson & Clark, 1994). Similarly, NA means and standard deviations were higher than normative samples of Australian men (M=14.2, SD=4.1) and women (M=15.5, SD=5.3) (Watson & Clark, 1994). Mean NR scores were similar to the average reported by Nisbet, Zelenski and Murphy (2008) (M=3.71). NE score range was from 0-20. Dichotomous SES from postcode was coded 0 = below average, 1 = average to high. The dichotomous variables existence of religious affiliation, employment status and relationship status dichotomous were coded as (0 = No, 1= Yes).
Pearson product-moment correlations were computed and are presented in Table 2. Due to an error in scoring, the sign of the NE scale in correlation results were manually reversed.

Table 2
Correlations Between Study Variables

Measure 2 3 4 5 6 7 8 9 10
1. Physical Health .33** .11* .15** -.40** .07 .11* -.18** .11* .20**
2. Positive Affect .18** .17** -.13** -.02 .11* -.03 .05 .10
3. Nature Relatedness .66** -.14* .06 .22** -.26** .16** -.01
4. Nature Exposure -.12** .40 .15** -.17** .12* -.03
5. Negative Affect -.02 -.21** .13* -.09 -.02
6. SES .08 -.21** .10* .10**
7. Age -.18** .27** -.05
8. Religion -.10* -.05
9. Relationship Status -.02
10. Employment Status
*p<0.05 (two-tailed), **p<0.01 (two-tailed)
SES = Dichotomous Socioeconomic Status From Postcode.

Pearson product-moment correlations were computed for all study variables and are presented in Table 2. PH was significantly positively correlated with PA (r2=.11), NR (r2=.01), NE (r2=.02), as well as age (r2=.01), relationship (r2=.01) and employment status (r2=.04). PH was also significantly negatively correlated with NA (r2=.16), and existence of religious affiliation (r2=.03). PA was also significantly positively correlated with NR (r2=.03) and NE (r2=.03), and significantly negatively correlated with NA (r2=.02). NR and NE were significantly positively correlated (r2=.44), and both were significantly negatively correlated with NA: NR (r2=.02), NE (r2=.01).
Additionally, a hierarchical multiple regression analysis was performed to examine whether PA could be used to predict PH after controlling for potentially confounding and/or mediating variables: NR, NE, NA, Dichotomous SES, age, existence of religious affiliation, and relationship and employment status.
Results are presented in Table 3.


Table 3
Hierarchical Multiple Regression Predicting Global Physical Health
Criterion Variable: Global Physical Health Β SEB β
Step 1 Constant 82.77 4.91
Negative Affect -.83 .12 -.38**
Dichotomous SES from Postcode .90 2.70 .02
Age -.00 .07 -.00
Religion -3.26 1.68 -.11*
Relationship Status 2.40 1.95 .07
Employment Status 6.63 1.88 .19**
Step 2 Constant 92.95 9.73
Negative Affect -.82 .12 -.37**
Dichotomous SES from Postcode .91 2.70 .02
Age -.01 .07 -.01
Religion -3.10 1.72 -.10
Relationship Status 2.24 1.95 .07
Employment Status 6.75 1.88 .20**
Nature Relatedness -1.26 1.67 -.06
Nature Exposure -.60 .34 -.13
Step 3 Constant 75.00 10.04
Negative Affect -.77 .12 -.35**
Dichotomous SES from Postcode 1.48 2.59 .03
Age -.03 .07 -.02
Religion -3.37 1.65 -.11*
Relationship Status 2.15 1.87 .06
Employment Status 5.75 1.81 .17**
Nature Relatedness -1.86 1.61 -.08
Nature Exposure -.48 .33 -.10
Positive Affect .56 .11 .26**
** p<.01 * p<.05

Durbin-Watson tests revealed no violation of the independence of errors assumption.
The analysis revealed that the variables in Step 1: NA, dichotomous SES from postcode, age, existence of religious affiliation, relationship and employment status (“Step 1 Variables”) significantly predicted PH, F (6, 267) = 12.31, p < .01, accounting for 21.7% (adjusted R2 = .20) of the variability.
Adding NR and NE in Step 2 did not significantly improve the prediction of PH, ∆F (2, 265) = 1.63, p >.05, accounting for only 1% of the variability in PH. Thus, together, the Step 1 variables, NE and NR, significantly predicted PH, F (8, 265) = 9.69, p < .01, accounting for 22.6% (adjusted R2 = .20) of its variability.
Adding PA in Step 3 significantly improved the prediction of PH, ∆F (1, 264) = 23.83, p < .01, accounting for an additional 6.4% of the variability in PH. Thus, together, the Step 1 variables, NE, NR, and PA significantly predicted PH, F (9, 264) = 12.00, p < .01, accounting for 29% (adjusted R2 = .27) of its variability.
Table 3 shows the regression coefficients for this analysis. As can be seen in the table, when only the Step 1 variables were included in the regression model, NA, existence of religious affiliation and employment status were significant predictors of PH. When NR and NE were added to the regression model, they were not significant predictors of PH, and NA and employment status remained significant predictors of PH. When PA was additionally included in the regression model, it, together with NA, employment status, and again, existence of religious affiliation, were each significant and unique predictors of PH.
A meditational analysis was performed to test the hypothesis that NR and NE would mediate the relationship between PA and PH. An additional hierarchical multiple regression was performed to assess the whether there was a diminution in the extent to which PA predicted PH when NR and NE were removed from the regression model.
Step 1 in the analysis above is identical the first regression model. Adding PA in Step 2 of this regression did significantly improve the prediction of PH, ∆F (1, 266) = 25.12, p < .01, accounting for 6.8% of the variability in PH. When contrasted with the first regression model, NA and NE accounted for a very marginal .4% of the variability in PH, consistent with a weak mediation effect.
Discussion
The aim of this study was to assess whether trait-PA and self-reported global physical health are associated when controlling for NA, and demographic variables age, socio-economic status, religious affiliation, relationship status and employment status. In addition, this study also aimed to assess whether any relationship between trait-PA and global physical health is mediated by nature relatedness and/or exposure.
The first hypothesis that PA would be significantly and positively associated with PH was supported.
The second hypothesis that PA would continue to predict PH after controlling for NA, SES, existence of religious affiliation, relationship and employment status, was also supported. PA was a significant independent predictor of PH after controlling for each of these predictor variables in the regression model.
Taken together, these results confirm previous findings of the unique role of trait-PA in predicting self-reported health (e.g. Takkouche, 2001; Roysamb, 2003), and provide contrary evidence to equivocal findings of Watson and Pennebaker (1989) who observed small, or in some cases negative, associations between PA and self-reported health (symptom reporting). The significant and unique contribution of PA to PH after controlling for NA lends further cross-sectional support to prospective studies that suggest an independent role of PA to PH when controlling for NA (Cohen, et al., 2003; Ostir, et al., 2001; Smith, et al., 1997). However, NA was significantly negatively correlated with PA in this study, and remained the strongest unique predictor of PH in the regression model, demonstrating that previously observed PANAS sub-scale independence (e.g. Egloff, 1999; Deiner, Smith & Fujita, 1995) was not present and suggesting that PA and NA were not orthogonal factors in this study. This potentially confounds the independence of these results. Given the strong psychometric properties of the PANAS, further research should seek to replicate the current findings in further populations to assess whether the independence between PA and NA observed in other studies (Cohen, et al., 2003; Ostir, et al., 2001; Smith, et al., 1997) can be repeated.
The third and fourth hypotheses that NR and NE would each mediate the relationship between PA and PH were not strongly supported. While NR and NE both significantly correlated with PA and PH, when added to the regression model, they were not significant predictors of PH at any step. In the comparison between the first regression model and the second that excluded NR and NE, NR and NE accounted for a negligible .4% of the variability in PH. This unexpected result is in contrast to Smith and Baum (2003) and Ulrich’s (1992) suggested pathway for the stress-buffering contribution of PA to PH. Given the strong and repeated correlations between PA, trait-NR and NE (Nisbet, Nealis, & Zelenski, 2011; Nisbet & Zelenski, 2011), myriad health benefits (Nisbet, Zelenski & Murphy, 2011), and also the meditational observations of Mayer, Frantz, Bruehlman-Senecal & Dolliver (1999) and Nisbet, Nealis, & Zelenski, (2011) demonstrating a mediating relationship of NR between NE and PA, these results suggest that a different causative process may account for the relationship of nature exposure and relatedness to PA and PH. Given the strong associations between PA and PH in this study, and previous meditational results, further research should test models that posit PA as a potential mediating factor in the relationship between nature and health.
The current study only tested trait-PA, and not state-PA. Given the theoretical bases for suggesting a restorative role for nature in stimulating stress-buffering health benefits, differential testing of state and trait affect in future studies would be prudent for studies attempting to understand whether trait and state PA affect the meditational pathways between the varieties of PA, nature and health outcomes in different ways (e.g. as a motivator for vs an outcome of nature exposure and relatedness). Similarly, subscales of nature experience were bundled in this study, and future discrimination of types of nature experience and relatedness (e.g. NR-scale subscales, virtual v natural nature view, exposure vs view) should be the subject of additional research.
Because self-report measures are subject to affect-elicited self-report biases (Cohen & Williamson, 1991), future research should consider replicating these results alongside other direct observation measures of health.
Issues of affect valence may affect the current results. The PANAS scale has been repeatedly shown to privilege activated positive affect (happy, cheerful), as opposed to non-activated PA (e.g. contentment, calm) (Egloff, 1999). Ulrich (1992) and Smith and Baum (2003) both focused their research and sound theory on the basis that nature induces non-activated PA, and accordingly the observations which predicate the hypotheses of this study may be affected by issues of valence. To test potential for a differential role of valence, future research should provide for either a measure of low-arousal PA or introduce a range of affect and mood measures sensitive to differences in valence (e.g. Egloff, 1999).
These findings further implicate hedonic trait-PA in the establishment and maintenance of positive health, but do not explain the role of nature relatedness or exposure in that relationship. The unique contribution of PA to health is an important issue for public health research, and especially in the production of preventative health and disease management protocols and campaigns. Importantly, demonstrating the effect of hedonic PA on health adds another feather in the cap of the broader subjective wellbeing research canon which has demonstrated strong associations with eudaimonic measures and health. Ultimately, these results demonstrate that emotions do provide a significant pathway in the connection between psychological stress and physical disease. Future research will hopefully further elucidate the role of nature in this equation.



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