Mindfulness, health symptoms and healthcare utilization

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Mindfulness, health symptoms and healthcare utilization: Active facets and possible affective mediators a

Nathan S. Consedine & Haley F. Butler

a

a

Psychological Medicine, University of Auckland , Grafton, Auckland , New Zealand Published online: 05 Aug 2013.

To cite this article: Psychology, Health & Medicine (2013): Mindfulness, health symptoms and healthcare utilization: Active facets and possible affective mediators, Psychology, Health & Medicine To link to this article: http://dx.doi.org/10.1080/13548506.2013.824596

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Psychology, Health & Medicine, 2013 http://dx.doi.org/10.1080/13548506.2013.824596

Mindfulness, health symptoms and healthcare utilization: Active facets and possible affective mediators Nathan S. Consedine* and Haley F. Butler Psychological Medicine, University of Auckland, Grafton, Auckland, New Zealand

Downloaded by [Nathan Consedine] at 01:18 05 August 2013

(Received 24 February 2013; final version received 7 July 2013) Prior work has linked mindfulness with superior physical and psychological health outcomes. However, studies to date have infrequently tested the unique contributions of individual mindfulness facets, inadequately tested links between facets and healthcare utilization, and have not tested whether depression or anxiety may influence these links. In the current report, 40 young, middle aged and older adults (N = 121) completed measures of dispositional mindfulness, health, healthcare utilization and depression/anxiety. As expected, global trait mindfulness did not predict outcomes while individual mindfulness facets predicted both objective and subjective health as well as healthcare utilization. Across models, observe scores – the tendency to attend to thoughts, sensations and feelings – predicted poorer, and non-reactivity scores better, outcomes even when controlling for demographic and health confounds. Depressed and anxious emotion reduced some but not all mindfulness-health links. Results are discussed in terms of the mechanisms by which greater mindfulness may facilitate better health and health behaviour. Keywords: mindfulness; mindfulness facets; health; symptom reporting; healthcare utilization; anxiety; depression

Mindfulness – the ability or tendency towards bringing attention to present experiences in a non-judgmental or accepting way (Baer, Smith, Hopkins, Krietemeyer, & Toney, 2006) – predicts better health. Trait mindfulness predicts better health and health behaviour (Greeson, 2009), fewer symptoms (Brown & Ryan, 2003), better perceived health (Zvolensky et al., 2006) and lower healthcare utilization (Brown & Ryan, 2003). Mindfulness also predicts fewer psychological symptoms, lower anxiety and less depression (McCracken, Gauntlett-Gilbert, & Vowles, 2007). However, aspects of these links remain unclear. First, the most relevant facets of mindfulness are unknown (Schneider, Hough, & Dunnette, 1996). Describing, awareness, non-judging, and non-reactivity facets predict fewer, but observe greater, symptoms (Baer et al., 2008). Specifically relevant to physical health, observe scores index the tendency to attend to internal stimuli such as thoughts, sensations and feelings, while non-reactivity reflects a considered approach to these phenomena and responding with purposeful behaviour (Reynolds, Consedine, & McCambridge, 2013). Second, data have primarily linked mindfulness facets to subjective rather than objective metrics. We contrast the ability of mindfulness facets to predict objective versus subjective outcomes. Third, while total mindfulness predicts health behaviour *Corresponding author. Email: [email protected] Ó 2013 Taylor & Francis


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N.S. Consedine and H.F. Butler

(Brown & Ryan, 2003), how specific facets predict utilization is unclear. This report evaluates which facets predict utilization for chronic and psychological issues. Finally, the mechanisms by which mindfulness predicts health remain unclear (Coffey, Hartman, & Fredrickson, 2010). Although mindfulness may bolster disease resistance (Davidson et al., 2003) or reduce symptom sensitivity (Zeidan et al., 2011), it may also predict better health indirectly by lowering anxiety and depression (Zvolensky et al., 2006). Mindfulness predicts lower anxiety and depression (Williams, Ciarrochi, & Deane, 2010) and negative affect (Giluk, 2009). Commensurately, depressed affects predict greater symptoms (Consedine, 2008) and utilization (Callahan, Kesterson, & Tierney, 1997), while anxiety has weaker links to symptoms but predicts greater utilization (Consedine & Moskowitz, 2007). Effects on subjective outcomes persist even when controlling objective disease metrics (Cohen et al., 1995), implying affect may be more heavily implicated in subjective symptomology. Overall, we expected mindfulness facets would better predict outcomes than a global mindfulness score. We expected the observe facet to predict greater symptomology and other facets to predict less; neither facet nor total scores should predict objective health (number of diagnosed conditions). Finally, depression and anxiety should weaken links between mindfulness and subjective outcomes but leave the objective outcome models essentially unchanged. Methods Participants Forty community-dwelling adults aged 18+ years in each of 18–34, 35–59 and 60+ year brackets (total N = 121) were recruited. Two-thirds were female, 54.5% married/living with partner and 86% of majority ethnicity; 16 years education was average. Procedures Recruitment used advertisements and flyers, with electronic advertisements sent via e-mail and Facebook. Interested participants contacted the coordinator who posted paper questionnaires and return envelopes. No exclusions or incentives were used, and no dropouts were recorded. Measures Demographics and confounds Self-reported gender, age (years), weight (kg), height (cm) smoking history (ever/never) and current exercise frequency (using a 0 “never” to 7 “daily”) scale. Dispositional mindfulness Five Facet Mindfulness Questionnaire (FFMQ) (Baer et al., 2006). The FFMQ assesses five facets and has good psychometric properties (Baer et al., 2008). In this report, reliabilities were .78 (observe), .91 (describe), .88 (awareness), .87 (non-judge) and .83 (non-react). Objective health conditions Participants reported formal diagnoses of physical or mental health conditions. Condition counts were aggregated.

Psychology, Health & Medicine

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Subjective symptomology The 42-item Wahler Physical Symptom Inventory (WPSI) (Wahler, respondents indicate how often they are bothered by symptoms including difficulty breathing, pain and bowel trouble (Frezza, Wachtel, & Gordhamer, WPSI has good internal consistency and is widely used (Consedine et Reliability was .87.

1968) has backaches, 2008). The al., 2006).

Healthcare utilization Participants reported the frequency of doctor/health professional visits for chronic conditions and psychological issues in the past year.


Anxious and depressed affect Hospital Anxiety and Depression Scale (HADS) (Zigmond & Snaith, 1983). The scale excludes somatic symptoms (Bjelland, Dahl, Haug, & Neckelmann, 2002) and has good internal consistency (Bjelland et al., 2002), convergent validity and test–retest reliability. Reliabilities were .77 (anxiety) and .65 (depression). Analytic strategy Data were analysed in two phases. First, we characterized our sample (Table 1). Second, stepwise linear regressions regressed outcomes on demographics/confounds, mindfulness facets and possible affect. Confounds and mindfulness were entered in Step 1 and depression/anxiety at Step 2. Results Characteristics of the sample are displayed in Table 1. Health problems afflicted 57% of the sample and 47% had been diagnosed with a condition (ranging from hay fever to CHD); 10.7% had been diagnosed with a mental condition. Multivariate prediction of objective and subjective health After checking univariate correlations (Table 2), we assessed whether facets versus total mindfulness better predicted objective conditions. Total mindfulness was a poor predictor (Table 3); the initial model was marginal, F(6, 114) = 1.90, p = .09, with no mindfulness effect. Adding depression and anxiety in Step 2 did not improve fit, FΔ(2, 112) = 1.27, n.s. The model using facets was slightly better. Although neither Step 1 nor Step 2 models were significant, non-reactivity predicted fewer conditions (see Table 3). Given this pattern, hypotheses regarding subjective symptoms were tested using facets alone. Symptomology was predicted at Step 1 by being female, greater observe and lower non-reactivity scores. After adding depression and anxiety at Step 2, symptomology remained greater among females but observe and non-reactivity effects were eliminated while greater depression and, marginally, anxiety were now predictors (Table 4). Multivariate prediction of chronic condition and psychological visit frequency The initial model predicting chronic visit frequency was significant (Table 5). Visits were predicted by health conditions and greater observe scores. Adding depression and

Age (years) % Ever smoked Exercise Height (cm) Weight (kg) BMI (kg/m2) Symptoms (WPSI) Past year psych visits Past year chronic visits # Diagnosed conditions Total mindfulness Observe facet Describe facet Aware facet Non-judge facet Non-react facet HADS anxiety HADS depression

Variable

25.13 62.5 3.38 180.75 76.88 23.48 32.38 .00 1.50 .50 122.00 3.02 3.17 2.75 3.48 3.23 7.88 4.75

(1.77) (9.87) (8.32) (1.22) (12.09) (.00) (4.24) (.76) (16.14) (.73) (.58) (.54) (.54) (.60) (2.23) (2.82)

(3.23)

Young 49.78 (8.41) 33.3 4.00 (1.58) 179.21 (8.43) 99.44 (28.59) 31.32 (10.57 22.67 (15.45) .00 (.00) .11 (.33) .22 (.44) 132.44 (12.49) 2.94 (.73) 3.43 (.63) 3.61 (.42) 3.69 (.42) 3.29 (.61) 5.56 (2.51) 2.00 (2.06)

Middle

Male

66.33 50.0 2.67 173.55 79.25 26.41 25.54 .21 1.75 1.00 140.78 3.55 3.66 3.65 3.70 3.48 5.25 3.04 (1.27) (8.21) (10.56) (3.85) (18.60) (.83) (5.30) (.86) (13.90) (.52) (.59) (.61) (.59) (.56) (4.00) (2.29)

(7.00)

Older 25.97 39.4 4.56 165.42 64.10 23.37 36.64 .97 .52 .45 129.67 3.15 3.67 3.25 3.40 3.13 7.24 2.91 (1.50) (6.14) (19.32) (6.73) (17.42) (2.93) (1.03) (.62) (17.43) (.45) (.84) (.61) (.80) (.57) (3.69) (2.71)

(3.44)

Young

Table 1. Means and standard deviations of demographic, health and mindfulness variables across gender and age group.


49.84 54.8 4.90 164.22 65.67 24.28 34.42 .16 .55 .32 135.33 3.45 3.72 3.25 3.65 3.24 6.34 3.00

(1.38) (6.26) (11.42) (3.53) (17.42) (.74) (1.52) (.48) (19.71) (.76) (.82) (.70) (.65) (.61) (2.72) (2.07)

(6.17)

Middle

Female

64.94 62.5 4.44 164.84 70.05 25.70 32.19 .31 1.31 1.00 145.04 3.61 3.94 3.67 3.94 3.39 4.63 2.83

(1.71) (4.23) (14.22) (4.40) (24.06) (1.01) (1.78) (.73) (17.33) (.62) (.57) (.66) (.82) (.86) (2.55) (2.19)

(6.73)

Older

4 N.S. Consedine and H.F. Butler


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Table 2. Zero-order correlations between total mindfulness, mindfulness facets, depression, anxiety, demographics and health outcomes. Mindfulness facet or affect Total


Demographic Age (years) Sex Ever smoke? Exercise BMI (kg/m2) Health # Diagnosed conditions Symptoms (WPSI) Mental condition? Past year psych visits Past year chronic visits HADS anxiety HADS depression Notes: ⁄p < .05,

Observe Describe

Aware

Nonjudge

Nonreact

Anxiety Depress

.34⁄⁄ .00 .03 .23⁄ .06

.29⁄⁄ .02 .12 .20⁄ .01

.08 .17ŧ .06 .19⁄ .07

.36⁄⁄ .09 .08 .08 .17ŧ

.23⁄ .04 .05 .16ŧ .20⁄

.18⁄ .10 .09 .12 .10

.32⁄⁄ .08 .13 .13 .11

.06 .04 .17ŧ .12 .09

.05

.09

.01

.08

.10

.10

.13

.08

.22⁄⁄

.04

.13

.25⁄⁄

.16ŧ

.27⁄⁄

.36⁄⁄

.43⁄⁄

.15 .10

.06 .23⁄

.04 .12

.15 .00

.15 .00

.14 .03

.29⁄⁄ .06

.29⁄⁄ .11

.20⁄

.03

.18⁄

.09

.17ŧ

.29⁄⁄

.23⁄

.28⁄⁄

.51⁄⁄ .44⁄⁄

.12 .09

.23⁄ .42⁄⁄

.46⁄⁄ .36⁄⁄

.57⁄⁄ .31⁄⁄

.34⁄⁄ .26⁄⁄

– .46⁄⁄

.16⁄⁄ –

⁄⁄

p < .01, ŧp < .10.

anxiety changed nothing in this model. The initial model predicting psychological visits explained 43% variance; having a psychological diagnosis, greater BMI, lower age, greater observe, lower non-reactivity and, marginally, lower describe scores predicted more visits. Adding depression and anxiety did not alter these effects; the model remained significant but was not improved. Discussion The current report makes several contributions. First, relative to mindfulness facets, total mindfulness failed to predict outcome; effects held even when controlling for confounds. Second, mindfulness facets were better predicts of subjective versus objective health indices. Finally, depressed or anxious affect may influence some but not all mindfulness-health links. Operationalizing mindfulness in health and health behaviour research Specific mindfulness facets appear more useful than global scores in predicting health. As expected, observe scores predicted greater symptomology but non-reactivity scores predicted less. Similarly, higher observe scores predicted more frequent chronic conditions and psychological visits while non-reactivity scores predicted less frequent psychological visits. High observe individuals may not have objectively worse health but experience health as poorer while non-reactivity may index the ability to manage health issues without responding with anxiety or impulsiveness (Baer, 2009). Overall, notwithstanding whether symptoms, conditions or behaviours were considered, modelling mindfulness facets was consistently more informative. Importantly, it was not

Notes: p < .05,

2

p < .01, p < .10, sr = squared part correlation.

ŧ

FΔ(2, 112) = 1.27, n.s.

.05 .00 .01 .00 .00 .00 – – – – – .01 .02 –

–

⁄

⁄⁄

.25⁄ .06 .08 .05 .03 .00 – – – – – .12 .16 –

sr

Model FΔ

.00 .14 .11 .04 .01 .00 – – – – – .02 .03 .75

β

F(8, 112) = 1.75, n.s.; R2 = .11

.01 .09 .09 .02 .00 .00 – – – – – .03 .05 .46

SE

F(6, 114) = 1.90, n.s.; R2 = .09

.06 .00 .00 .00 .00 .00 – – – – – – – –

.28⁄⁄ .06 .07 .06 .01 .02 – – – – – – – –

B

2

Model F/R2

.00 .14 .10 .04 .01 .00 – – – – – – – .58

sr

β

Step 2

.01 .09 .08 .03 .00 .00 – – – – – – – .43

SE

2

Age Sex Smoke Exercise BMI Global MF Observe Describe Aware Non-judge Non-react Anxiety Depression Constant

B

Step 1

Global mindfulness model

.00 .15 .10 .04 .01 – .12 .10 .12 .11 .13 – – .58

SE .25⁄ .12 .05 .08 .06 – .14 .00 .02 .14 .25⁄ – – –

β .04 .01 .00 .00 .00 – .01 .00 .00 .01 .04 – – –

sr

2

F(10, 110) = 1.76, n.s.; R2 = .14

.01 .18 .06 .03 .01 – .15 .00 .03 .14 .29 – – .61

B

Step 1

.00 .15 .11 .04 .01 – .12 .11 .12 .12 .13 .03 .03 .77

SE

Step 2 .22⁄ .12 .07 .07 .08 – .13 .05 .01 .11 .26⁄ .14 .16 –

β

F(12, 108) = 1.68, p = .08; R2 = .16 FΔ(2, 108) = 1.25, n.s.

.01 .18 .07 .03 .01 – .15 .05 .01 .11 .30 .03 .05 .73

B

Mindfulness facets model

.03 .01 .00 .00 .01 – .01 .00 .00 .01 .04 .01 .02 –

sr2

Table 3. Raw and standardized coefficients from two steps of two linear regressions in which objective health condition frequency was regressed on demographics, controls and either global mindfulness or mindfulness facets (Step 1) before adding affective characteristics (Step 2).




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Table 4. Raw and standardized coefficients from two steps of a linear regression in which subjective health symptomology was regressed on demographics, controls and mindfulness facets (Step 1) and affective characteristics (Step 2). Step 1


Variables

Step 2 β

sr

.08 .21⁄ .14 .05 .15ŧ .14 .20⁄ .08 .17 .02 .22⁄ – – –

.06 .18 .13 .04 .14 .13 .16 .07 .13 .02 .17 – – –

2

B

SE

Age Sex Smoke Exercise BMI # Conditions Observe Describe Aware Non-judge Non-react Anxiety Depression Constant

.08 8.13 3.93 .53 .50 3.56 5.80 1.99 4.62 .64 6.47 – – 43.10

.11 3.69 2.56 1.07 .30 2.34 2.98 2.50 2.91 2.69 3.20 – – 14.35

Model F/R2 Model FΔ

F(11, 109) = 3.06, p < .01; R2 = .24 –

Notes: ⁄p < .05,

B .06 8.25 1.91 .54 .39 3.10 4.49 .51 1.96 2.51 4.69 1.08 2.28 1.59

SE .10 3.46 2.46 1.01 .28 2.22 2.82 2.47 2.81 2.75 3.06 .61 .78 17.79

β .06 .21⁄ .07 .05 .12 .12 .16 .02 .07 .09 .16 .19ŧ .29⁄⁄ –

sr2 .05 .19 .06 .04 .11 .11 .13 .02 .05 .07 .12 .14 .23 –

F(13, 107) = 4.22, p < .01; R2 = .34 FΔ(2, 107) = 8.36, p < .01

⁄⁄

p < .01, ŧp < .10, sr2 = squared part correlation.

just that mindfulness predicted more conditions (Table 3). Even when condition counts (and other confounds) were covaried, persons with greater observe scores consumed more health care while those with greater non-react scores consumed less (see Tables 4 and 5). The mechanism question – depression and anxiety as possible mediators As expected, the link between non-reactivity and objective conditions did not change (Table 4), but effects for observe and non-react facets in predicting subjective symptoms were eliminated after controlling for HADS scores (Table 5). Although low reliability in the HADS-D in this healthy sample is important (see Mykletun, Stordal, & Dahl, 2001), negative affect mediates mindfulness-sleep links (Caldwell, Harrison, Adams, Quin, & Greeson, 2010). Because mindfulness predicts less depression/anxiety and negative affects predict greater symptoms and utilization, adding such variables might weaken mindfulness-health links. In our study, however, observe scores (associated with an internal focus) did not predict depressive symptoms (Table 2), but predicted greater utilization even when controlling for depression. This pattern suggests the link between mindfulness facets and utilization is not merely a function of a depressogenic internal focus or affect more broadly (Table 5). Indeed, the role of negative affect in mindfulness-health links may be most prominent when assessing subjective symptoms. Behaviourally, persons with greater non-reactivity have more flexible cognitive control (Anicha, Ode, Moeller, & Robinson, 2012), and persons who are less reactive may be less prone to reflexively seek care when confronted with symptoms or health anxiety. Obviously, interpretations are constrained by design. Although using distinct metrics to assess mindfulness, symptoms and behaviour should ameliorate method bias (Podsakoff, MacKenzie, & Podsakoff, 2012), only the diagnosis count is objective and

Notes: ⁄p < .05,

Model FΔ

⁄⁄

.01 .02 .00 .00 .00 .07 .05 .01 .00 .00 .01 – – –

sr

2

.02 .58 .41 .17 .05 .37 .47 .42 .47 .46 .51 .10 .13 2.99

SE .15 .15 .01 .06 .01 .25⁄⁄ .27⁄ .16 .04 .06 .15 .17 .18 –

β

F(13, 107) = 2.06, p < .05; R2 = .20 FΔ(2, 107) = 1.73, n.s.

.02 .90 .03 .11 .01 .99 1.17 .63 .16 .26 .70 .15 .21 .85

B

Step 2

.01 .02 .00 .00 .00 .05 .05 .02 .00 .00 .01 .01 .02 –

sr

2

.01 .29 .20 .08 .02 .42 .23 .20 .23 .21 .25 – – 1.12

SE

sr .02 .01 .01 .00 .09 .09 .03 .02 .00 .01 .03 – – –

.18⁄ .10 .11 .05 .34⁄⁄ .31⁄⁄ .23⁄ .17⁄ .08 .10 .22⁄ – – –

2

β

F(11, 109) = 7.45, p < .01; R2 = .43 –

.02 .37 .30 .05 .10 1.69 .59 .39 .21 .23 .59 – – .60

B

Step 1

.01 .29 .21 .08 .02 .44 .24 .21 .24 .23 .25 .05 .07 1.51

SE

Step 2 .18⁄ .10 .11 .05 .34⁄⁄ .31⁄⁄ .22⁄ .17ŧ .09 .10 .22⁄ .00 .02 –

β

F(13, 107) = 6.20, p < .01; R2 = .43 FΔ(2, 107) = .03, n.s.

.02 .37 .29 .05 .10 1.66 .58 .38 .22 .23 .59 .00 .02 .71

B

Psychological visits model

p < .01, ŧp < .10; #Conds are scored 0 (absent)/1 (present) for psychological visit model; sr2 = squared part correlation.

F(11, 109) = 2.09, p < .05; R2 = .17 –

.12 .14 .01 .06 .03 .28⁄⁄ .26⁄ .11 .04 .02 .14 – – –

Model F/R2

.02 .58 .41 .17 .05 .37 .47 .40 .46 .43 .51 – – 2.28

β

.02 .86 .03 .11 .02 1.09 1.16 .43 .19 .08 .62 – – 1.74

SE

Age Sex Smoke Exercise BMI # Conds Observe Describe Aware Non-judge Non-react Anxiety Depression Constant

B

Step 1

Chronic condition visits model

.02 .01 .01 .00 .09 .08 .03 .02 .00 .01 .03 .00 .00 –

sr2

Table 5. Raw and standardized coefficients from two steps of two linear regression in which the frequency of physician visits for chronic conditions and psychological issues were regressed on demographics, controls and mindfulness facets (Step 1) before adding affective characteristics (Step 2).





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our sample were relatively young and healthy. Objective measures of disease are obvious next steps as are designs among older groups or those in poorer health; links between mindfulness and outcome might vary in less healthy groups. We assessed trait mindfulness not meditation experience; measurement may vary with experience (Veehof, ten Klooster, Taal, Westerhof, & Bohlmeijer, 2011). Most interventions include formal training/practice that is supplemented by informal use. In theory, living mindfully rather than training should produce benefits (Dimidjian & Linehan, 2006). Our data do not enable commentary on this issue. Despite such limitations, the present work extends data linking mindful functioning to health. Tendencies to observe and/or react predicted outcomes, for both objective and subjective symptomology, as well as for utilization. While depressed and anxious affect reduced the effects on subjective symptomology, objective health and healthcare utilization models remained unchanged. Providing participants are prepared for initial increases in symptomatic awareness, mindfulness interventions aimed at health might thus profitably target non-reactivity. References Anicha, C.L., Ode, S., Moeller, S.K., & Robinson, M.D. (2012). Toward a cognitive view of trait mindfulness: Distinct cognitive skills predict its observing and nonreactivity facets. Journal of Personality, 80, 255–285. Baer, R.A. (2009). Self-focused attention and mechanisms of change in mindfulness-based treatment. Cognitive Behaviour Therapy, 38, 15–20. Baer, R.A., Smith, G.T., Hopkins, J., Krietemeyer, J., & Toney, L. (2006). Using self-report assessment methods to explore facets of mindfulness. Assessment, 13, 27–45. Baer, R.A., Smith, G.T., Lykins, E., Button, D., Krietemeyer, J., Sauer, S., … Williams, J.M.G. (2008). Construct validity of the Five Facet Mindfulness Questionnaire in meditating and non-meditating samples. Assessment, 15, 329–342. doi: 10.1177/1073191107313003 Bjelland, I., Dahl, A.A., Haug, T.T., & Neckelmann, D. (2002). The validity of the hospital anxiety and depression scale: An updated literature review. Journal of Psychosomatic Research, 52, 69–77. Brown, K.W., & Ryan, R.M. (2003). The benefits of being present: Mindfulness and its role in psychological well-being. Journal of Personality and Social Psychology, 84, 822–848. Caldwell, K., Harrison, M., Adams, M., Quin, R.H., & Greeson, J. (2010). Developing mindfulness in college students through movement-based courses: Effects on self-regulatory, self-efficacy, mood, stress, and sleep quality. Journal of American College Health, 58, 433–442. Callahan, C.M., Kesterson, J.G., & Tierney, W.M. (1997). Association of symptoms of depression with diagnostic test charges among older adults. Annals of Internal Medicine, 126, 426–432. Coffey, K.A., Hartman, M., & Fredrickson, B.L. (2010). Deconstructing mindfulness and constructing mental health: Understanding mindfulness and its mechanisms of action. Mindfulness, 1, 235–253. Cohen, S., Doyle, W.J., Skoner, D.P., Fireman, P., Gwaltney, J.M., & Newsom, J.T. (1995). State and trait negative affect as predictors of objective and subjective symptoms of respiratory viral infections. Journal of Personality and Social Psychology, 68, 159–169. Consedine, N.S. (2008). The health-promoting and health-damaging effects of emotions: The view from developmental functionalism. In M. Lewis, J. Haviland-Jones, & L.F. Barrett (Eds.), Handbook of emotions (3rd ed., pp. 676–690). New York, NY: Guilford. Consedine, N.S., Magai, C., Kudadjie-Gyamfi, E.K., Kaluk Longfellow, J., Ungar, T.M., & King, A.R. (2006). Stress versus discrete negative emotion in the prediction of physical complaints: Does predictive utility vary across groups. Cultural Diversity and Ethnic Minority Psychology, 12, 541–557. Consedine, N.S., & Moskowitz, J.T. (2007). The role of discrete emotions in health outcomes: A critical review. Journal of Applied and Preventive Psychology, 12, 59–75.


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