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Psychology of Sport and Exercise 7 (2006) 477514

The effect of acute aerobic exercise on positive activated affect:

A meta-analysis

Justy Reeda,, Deniz S. Onesb

aDepartment of Health, Physical Education, and Recreation, Chicago State University, 9501 South King Drive,

Chicago, IL 60628, USAbDepartment of Psychology, Elliot Hall, 75 E. River Road, University of Minnesota, Minneapolis, MN 55455, USA

Received 27 November 2004; accepted 23 November 2005

Available online 9 March 2006

Abstract

Objective: The purpose of this meta-analysis was to examine the effect of acute aerobic exercise on self-

reported positive-activated affect (PAA). Samples from 158 studies from 1979 to 2005 were included

yielding 450 independent effect sizes (ESs) and a sample size of 13,101.

Method: Studies were coded for moderators related to assessment time, exercise variables such as intensity,duration, and dose (combination of intensity and duration), and design characteristics. The analysis

considered multiple measures of affect and corrected for statistical artifacts using Hunter and Schmidt

[(1990). Methods of meta-analysis: Correcting error and bias in research findings. Newbury Park: Sage;

(2004). Methods of meta-analysis: Correcting error and bias in research findings (2nd ed.). Thousand Oaks:

Sage] meta-analytic methods.

Results: The overall estimated mean corrected ES( dcorr) and standard deviation (SDcorr) were .47 and .37,

respectively. Effects were consistently positive (a) immediately post-exercise, (b) when pre-exercise PAA

was lower than average, (c) for low intensity exercise o1539% oxygen uptake reserve (%VO2R), (d) for

durations up to 35 min, and (e) for low to moderate exercise doses. The effects of aerobic exercise on PAA

appear to last for at least 30 min after exercise before returning to baseline. Dose results suggest the

presence of distinct zones of affective change that more accurately reflect post-exercise PAA responses thanintensity or duration alone. Control conditions were associated with reductions in PAA ( dcorr :17,SDcorr .25).

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www.elsevier.com/locate/psychsport

1469-0292/$ - see front matter r 2006 Elsevier Ltd. All rights reserved.

doi:10.1016/j.psychsport.2005.11.003

Corresponding author. Fax: +1 773 995 3644.

E-mail address: [email protected] (J. Reed).
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Conclusion: The typical acute bout of aerobic exercise produces increases in self-reported PAA, whereas

the typical control condition produces decreases. However, large SDcorr values suggest that additional

variables, possibly related to individual differences, further moderate the effects of exercise on PAA.

r 2006 Elsevier Ltd. All rights reserved.

Keywords: Exercise; Affect; Well-being; Meta-analysis; Doseresponse

Introduction

The majority of research on the exerciseaffect relationship has examined the impact of exercise

on negative psychological states (e.g., Gauvin & Spence, 1996; McAuley & Rudolph, 1995). Since

1981, more than 70 reviews have been published and the vast majority of them, narrative and

quantitative, have found that exercise reduces self-reported anxiety and depression (e.g., Biddle,2000; Brosse, Sheets, Lett, & Blumenthal, 2002; Landers, & Arent, 2001). However, health is not

merely the absence of disease and negative affect, but a condition of physical and psychological

well-being as well (McAuley, 1994; USDHHS, 1996, p. 141).

There is ample evidence to support the practical importance of well-being, defined as positive

mood, engagement, life satisfaction, and meaning (Seligman, 2002). For example, well-being and

positive mood predict job satisfaction and productivity (George & Brief, 1992; Hersey, 1932;

Miner, 2001), and marital satisfaction (Rogers & May, 2003). Higher well-being and positive

mood also correlate with better physical (Ostir, Markides, Black & Goodwin, 2000) and mental

health (Diener & Seligman, 2004), lower all-cause mortality (Fiscella & Franks, 1997), greater

longevity and lower rates of non-fatal heart attack (Kubzansky, Sparrow, Vokonas, & Kawachi,

2001), lower physiological stress reactivity (Smith, Ruiz, & Uchino, 2001), and improved immunefunction (Cohen, Doyle, Turner, Alper, & Skoner, 2003). In addition, studies show that people

who report higher well-being exercise more compared to those reporting lower well-being (e.g.,

Lox, Burns, Treasure, & Wasley, 1999). In sum, people with higher levels of well-being are

healthier and function more effectively (Diener & Seligman, 2004).

This study addresses the affective component of well-being. For the purpose of this paper,

affect is defined as the quality of a subjective experience relative to the two independent

dimensions of valence and activation or what Russell describes as core affect (Russell, 2003). For

most researchers, affect includes self-reportable states such as happiness, elation, tension, and

relaxation (Russell & Carroll, 1999). Self-reported affect can be described as a circumplex formed

by two dimensions of activation (activateddeactivated) and valence (positivenegative). Thismeta-analysis specifically examines positive activated affective states as defined by the upper right

quadrant of Fig. 1. Subscales from self-report instruments such as the Energy subscale of the

ActivationDeactivation Adjective Checklist (AD-ACL; Thayer, 1996) and the Positive Affect

subscale of the Positive and Negative Affect Schedule (PANAS; Watson, Clark & Tellegen, 1988)

have been shown to occupy this quadrant (see Yik, Russell, & Feldman Barrett, 1999). Although

this area of the circumplex and its associated affective constructs has been named pleasant

activated (Yik et al., 1999), positive activation (Watson, Wiese, Vaidya, & Tellegen, 1999),

activated pleasant (Larsen & Diener, 1992), and energy (Thayer, 1996), we refer to these affective

states as positive activated affect (PAA).

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Why study PAA in the context of exercise? First, theoretically, there is evidence that changes in

self-reported PAA could be a function of an evolutionary adaptive behavioral facilitation system

(BFS; Depue & Iacono, 1989; Depue, Luciana, Arbisi, Collins & Leon, 1994) that mediates goal-

directed approach behaviors (Depue & Collins, 1999; Depue, et al., 1994; Tomarken & Keener,

1998). Dopamine pathways are associated with the BFS (Depue & Collins, 1999) and recent data

suggest that DNA sequence variations in dopamine receptor genes are related to self-reportedphysical activity levels (Simonen et al., 2003).

Second, PAA appears to facilitate and reward behaviors mediated by the BFS (Watson,

2000) whereas low levels of PAA are associated with depressed mood (Mineka, Watson, &

Clark, 1998). From an evolutionary viewpoint, exercise might be considered a behavioral

means to obtain resources such as food and should increase feelings of vigor and energy, thereby

providing both a reward and incentive to repeat the activity. Indeed, narrative reviews con-

clude that exercise improves PAA (e.g., Ekkekakis & Petruzzello, 1999; Gauvin & Spence,

1996) and individual studies report that these improvements are more consistent than changes

in depression and anxiety (e.g., Gauvin, Rejeski, & Norris, 1996; Thayer, 1996; Watson,

2000).Third, several investigators have suggested that affective changes related to exercise are an

important part of exercise adherence (Sallis & Hovell, 1990; Thayer, 1996; Wankel, 1993). The

psychological states associated with exercise adherence may therefore be related to changes in

positive and not just negative affect. Unfortunately, there are very few guidelines available to

assist health professionals in planning and prescribing physical activity to increase the adoption

and maintenance of exercise programs, largely due to the lack of data available to develop them

(Dishman, 2001).

The purpose of this meta-analysis is to provide data on the effect of acute aerobic exercise

on PAA relative to three main questions. First, how does baseline PAA influence the magnitude

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(-) Valence (+)

(+)

Activation

(-)

Positive

Activated

Negative

Deactivated

Positive

Deactivated

Negative

Activated

Fig. 1. A circumplex model of self-reported affect. Adapted from Yik et al. (1999).

J. Reed, D.S. Ones / Psychology of Sport and Exercise 7 (2006) 477514 479

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of change in post-exercise PAA? Second, how do various levels of exercise intensity, dura-

tion, and combinations of intensity and duration (dose) affect the magnitude of change in

post-exercise PAA? Third, what is the magnitude of PAA change across various post-exercise

assessment times? That is, how long do the acute effects of aerobic exercise on PAA last?This study improves and extends current knowledge and provides quantitative answers not

found in prior narrative reviews. To this end, we tested the following potential moderator

variables.

Potential moderators

Baseline PAA

Acute studies (e.g., Focht, 2002; Reed, Berg, Latin, & La Voie, 1998; Rejeski, Gauvin, Hobson,& Norris, 1995), chronic studies (e.g., Blumenthal, Emery, & Rejeski, 1988; Simons &

Birkimer, 1988; Wilfley & Kunce, 1986), and quantitative reviews (e.g., Craft & Landers, 1998;

North, McCullagh, & Tran, 1990) show that participants with less positive or more negative pre-

exercise affect report greater post-exercise improvement compared to those with higher baseline

scores. The relation appears to hold for active and sedentary participants (e.g., Reed et al., 1998),

but has yet to be quantified for PAA across various self-report scales. From a theoretical and

practical standpoint, Thayer (1996) proposed that exercise serves as a self-regulatory strategy

to improve low mood and energy and delay the urge to snack or smoke (Thayer, Peters,

Takahashi, & Birkhead-Flight, 1993). Also, based on the law of initial value (Wilder, 1957) those

with less positive baseline affect and energy might be expected to improve more because they have

more room for improvement. Thus, we hypothesize that there would be larger effects for lowerbaseline scores.

Exercise intensity

Ekkekakis and Petruzzello (1999) reviewed 31 studies and found a general inverse relation

between intensity and post-exercise PAA. Walking increases PAA (e.g., Ekkekakis, Hall, Van

Landuyt, & Petruzzello, 2000; Thayer, 1987a) while maximal exercise produces deceases (e.g.,

Pronk Crouse, & Rohack, 1995). On the other hand, others have suggested that optimal

benefits occur following moderate, but not low or high intensity exercise (Berger & Motl,

2000). These suggestions point to the notion of either a critical intensity, i.e., threshold stimulus(Raglin & Morgan, 1985) or inverted-U (Ojanen, 1994) relation between intensity and affective

benefit. However, high-intensity exercise may result in affective improvement by way of a

rebound effect whereby the post-exercise affective state rebounds from an affective

decline during exercise (see Bixby, Spalding, & Hatfield, 2001). Theoretically, intensity appears

to be a key variable moderating post-exercise affective response (Ekkekakis, 2003) and is of

practical importance as a potential exercise-related barrier to the adoption and maintenance of

exercise (Dishman, 2001). Because the intensity literature appears mixed, it is tentatively

hypothesized that there will be a general inverse relation between intensity and post-exercise PAA

improvement.

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Exercise duration

Exercise of at least 20 min has been proposed for PAA improvement (Berger & Motl, 2000).

However, individual studies (e.g., Blanchard, Rodgers, Courneya, & Spence, 2002; Petruzzello &Landers, 1994; Rudolph & Butki, 1998) and quantitative reviews (e.g., North, et al., 1990;

Petruzzello, Landers, Hatfield, Kubitz, & Salazar, 1991) do not appear to support this threshold

duration. Shorter bouts (10 min) might result in better exercise adherence than longer bouts

(e.g., Jakicic, Wing, Butler, & Robertson, 1995) and lack of time is a barrier to exercise

participation (Trost, Owen, Bauman, Sallis, & Brown, 2002). The importance of shorter bouts

for adherence and psychological benefit should therefore be emphasized, but the effects of

duration on post-exercise PAA have not been quantified across studies. Therefore, we

hypothesized that there will be no differential effect of exercise time on PAA across typical

exercise durations (e.g., 1540 min).

Exercise dose

Dose is the product of exercise frequency, intensity, and duration (Kesaniemi, et al., 2001). For

acute exercise, dose is the product of intensity and duration. Establishing a doseresponse effect is

one form of evidence for evaluating whether the exerciseaffect relationship might be interpreted

as causal (Mondin et al., 1996). Practically, exercising at the dose likely to improve affect may lead

to better exercise adherence (Dishman, 1995). An important point about dose is that the

strenuousness of any exercise bout is a function of intensity and duration (McArdle, Katch, &

Katch, 2001, p. 195) and therefore conclusions about exercise dose and affective change based on

either intensity or duration alone may be misleading (He, 1998). Generally, studies employing lowdoses (e.g., Thayer, 1987a) find short-term increases in PAA and those examining extreme bouts

show decreases (e.g., Hassmen & Blomstrand, 1991). Based on this information, we hypothesize

that there will be a general inverse relationship between dose and post-exercise PAA

improvement.

Post-exercise assessment time

Post-exercise assessment time is an important consideration. For example, when affect is

measured only once following a delay of several minutes, the results are limited because any

immediate effects of the exercise bout may have dissipated (Ekkekakis & Petruzzello, 1999).Increases in PAA often peak within 5 min (e.g., Ekkekakis, et al., 2000; Petruzzello, Hall, &

Ekkekakis, 2001; Petruzzello, Jones, & Tate, 1997; Steptoe & Bolton, 1988; Steptoe, Kearsley &

Walters, 1993a), and remain significantly elevated above baseline for 20 (e.g., Bixby et al., 2001;

Van Landuyt, Ekkekakis, Hall, & Petruzzello, 2000) to 30 min (e.g., Steptoe & Bolton, 1988;

Focht & Hausenblas, 2001), or longer (e.g., Daley & Welch, 2004). With the exception of extreme

exercise, in which PAA is typically reduced after exercise (e.g., Acevedo, Gill, Goldfarb, & Boyer,

1996), the literature supports a pattern of initial improvement followed by gradually decreasing

effects. The consistency of this pattern has been noted for both positive and negative affect

(Ekkekakis & Petruzzello, 1999), but the magnitude of this pattern of change for PAA remains

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unknown. Therefore we expected to find the largest effects within the first 5-min post-exercise and

lower effects thereafter.

Study quality and source

Study quality (internal validity) and source (published vs. unpublished) warrant examination.

While Eysenck (1994) and Slavin (1986) contend studies with methodological faults should not be

meta-analyzed, others disagree (Dickersin & Berlin, 1992; Glass, 1983; Hunter & Schmidt, 2004,

p. 382). Excluding poorer quality and unpublished studies may produce inflated results since

journals tend to accept methodologically superior studies and studies with larger effects (e.g.,

Cook et al., 1992; Greenland, 1998; Hunter & Schmidt 1990, p. 509). Meta-analyses on exercise

and negative affect have found either larger effects for studies with moderate levels of internal

validity (North et al., 1990), or have failed to find differences between validity levels (Long &

Stavel, 1995; Petruzzello, et al., 1991). Findings are also equivocal for source. Differences betweenpublished and unpublished studies have been shown in some meta-analyses (Craft & Landers,

1998; North et al., 1990; Petruzzello et al.), but not in others (Arent, Landers, & Etnier, 2000;

Long & Stavel, 1995). The conflicting results and lack of comparative data for PAA preclude

justification of a formal hypothesis for these two potential moderators.

Method

Description of the database

Searches were performed for studies on mood or affect in relation to aerobic exercise to includeactivities such as aerobic dance, walking, jogging, running, swimming, and cycling. Relevant

English-language studies from 1979 to December 2005 were identified using computer databases

(PsychINFO, ERIC, Medline, SPORTDiscus, World Cat, Pub Med, and Dissertation Abstracts

International), manual searches of narrative reviews published between 1980 and 2003,

quantitative reviews (e.g., McDonald & Hodgdon, 1991; North, et al., 1990; Petruzzello et al.,

1991), and books (e.g., Biddle, 2000; Seraganian, 1993; Thayer, 1996; Watson, 2000). Reference

lists of published articles, theses, and dissertations were examined for additional studies. We

contacted the authors of all studies with missing information for the calculation of effect sizes

(ESs), the standardized unit of analysis in meta-analysis. A total of 47 authors were contacted.

Twenty-six authors responded, the others either did not respond or could not be located. Of thosewho responded, 13 provided the requested data.

We included studies that assessed affect with scales considered representative of PAA. To avoid

confounding theoretically separate constructs, we did not include scales representative of positive

deactivated affect (lower right quadrant in Fig. 1). The right now time set represented

85.35% of the samples. Although time set was not reported in 14.65% of the samples, studies

using extended time sets such as the today or past month were excluded. Table 1 lists the

instruments and subscales contributing to the meta-analysis.

The psychometric work of Yik et al. (1999) serves as a logical anchor for the inclusion of the

affect subscales in Table 1. Briefly, Yik et al., using structural equation modeling, found that

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Table 1

Mood instruments, subscales and effect sizes (ESs) contributing to the meta-analysis

Instrument name (Reference) Subscale ESs

ActivationDeactivation Adjective Checklist Energy 137

(AD-ACL; Thayer, 1986)

Brunel University Mood Scale Vigor 9

(BRUMS; Terry, Lane, & Fogarty, 2003)

Exercise-Induced Feeling Inventory Positive Engagement 72

(EFI; Gauvin & Rejeski, 1993) Revitalization

Eigenzustands-Skala Activation 5

(The Self-State Scale; Nitsch, 1976)

Modified Morris Mood Questionnaire Positive Mood 6

(Williamson et al., 2001)

Mood Adjective Checklist Pleasantness 4

(MACL; Nowlis, 1965) Activation

Positive and Negative Affect Schedule Positive Affect 84

(PANAS; Watson et al., 1988)

Profile of Mood States Bi-Polar Energetic-Tired 6

(POMS-BI; Lorr & McNair, 1988)

Profile of Mood States Long Form Vigor 15

(POMS-LF; McNair et al., 1992)a

Profile of Mood States Short Form Vigor 23

(POMS-SF; McNair et al., 1992)b

Stress Arousal Checklist Arousal 2

(SACL; Makay, Cox, Grenville, & Lazzerini, 1978)

Subjective Exercise Experiences Scale Positive Well-Being 140

(SEES; McAuley & Courneya, 1994)

UWIST Mood Adjective Checklist Energetic Arousal 2

(UMACL; Matthews et al., 1990)

Visual Analogue Mood Scale Joy 6

(VAS; Folstein & Luria, 1973) Euphoria

Note: For instruments with two subscales, ESs were computed for each and the average of the two used in the meta-analysis. The POMS-Bi, SACL, UMACL, AD-ACL (bipolar version) and VAS are scored using bipolar format. We

assumed that bipolar terms acted as reciprocal pairs such that a decrease in unpleasant deactivated states (e.g., tired,

drowsy) resulted in a corresponding increase in pleasant activated states (e.g., active, lively) allowing for comparable

ESs with the other unipolar scales in the database. Bipolar scales comprised 6% of the total number of ESs.aAdditional versions of the POMS included: Profile of Mood States-A (POMS-A; Terry et al., 1999); Profile of Mood

States-C (POMS-C; Terry et al., 1996); Profile of Mood States-Japanese Version (Yokoyama et al., 1990).bThe analysis also included the POMS-SF versions of Grove and Prapavessis (1992) and Shacham (1983).


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FeldmanBarrett and Russells Activated (Feldman Barrett & Russell, 1998), Watson, Clark, and

Tellegens Positive Affect (PA; Watson et al., 1988), Thayers Energy (Thayer, 1986), and Larsen

and Dieners Activated Pleasant (Larsen & Diener, 1992), all fell within the quadrant of the affect

circumplex we call PAA (see Yik et al. (1999, Fig. 6). We determined the number of affect termsfrom these subscales that matched terms in the subscales of the instruments listed in Table 1 and

found the following: ActivationDeactivation Adjective Checklist (AD ACL; Thayer, 1986), 5 of

5, Brunel University Mood Scale (BRUMS; Terry et al., 2003), 4 of 4, Exercise-Induced Feeling

Inventory (EFI; Gauvin & Rejeski, 1993), 3 of 6 (2 terms from Positive Engagement and 1 term

from Revitalization), Eigenzustands-Skala (The Self-State Scale; Nitsch, 1976), 5 of 6, Modified

Morris Questionnaire (Williamson, Dewey, & Steinberg, 2001), 6 of 8, Mood Adjective Checklist

(MACL; Nowlis, 1965), 7 of 8, Positive and Negative Affect Schedule (PANAS; Watson et al.,

1988), 10 of 10, Profile of Mood States Bipolar (POMS-BI; Lorr & McNair, 1988), 5 of 7, Profile

of Mood States Long Form (POMS-LF; McNair, Lorr, & Droppleman, 1992), 6 of 8, Profile of

Mood States Short Form (POMS-SF; McNair et al., 1992), 5 of 5, Stress Arousal Checklist(SACL; Makay, Cox, Burrows, & Lazzerini, 1978), 6 of 8, Subjective Exercise Experiences Scale

(SEES; McAuley & Courneya, 1994), 1 of 4, UWIST Mood Adjective Checklist (UMACL;

Matthews, Jones, & Chamberlain, 1990), 4 of 4, and Visual Analogue Mood Scale (VAS; Folstein

& Luria, 1973), 3 of 3. For other versions of the POMS we found: POMS-A ( Terry, Lane, Lane, &

Keohane, 1999), 4 of 4, POMS-C (Terry, Keohane, & Lane, 1996), 4 of 4, Japanese POMS

(Yokoyama, Araki, Kawakami, & Takeshita, 1990), 6 of 8, POMS-SF (Grove & Prapavessis,

1992), 5 of 6, and POMS-SF (Shacham, 1983), 5 of 6.

Two subscales deserve further justification. First, although only the terms upbeat, refreshed, and

revived of the 6 terms from the EFI Positive Engagement and Revitalization subscales were

matches, Rejeski, Reboussin, Dunn, King, and Sallis (1999) argue for the inclusion of these

subscales in the PAA quadrant (see Rejeski, et al., 1999, p. 98). To strengthen this argument,Positive Engagement and Revitalization correlate with PANAS PA at r :69 (po:001) andr :58 (po:001), respectively (see Gauvin & Rejeski, 1993). Second, for the SEES, while only theterm strong on the Positive Well-Being (PWB) subscale was a match, we believe the other terms,

great, positive, and terrific, qualify as positively activated. In support of our position that PWB

should occupy the PAA quadrant, McAuley and Courneya (1994) found that PWB correlated

with PANAS PA (r :71, po:01). Finally, Lox, Jackson, Tuholski, Wasley, and Treasure (2000)found high correlations between the SEES PWB and EFI Revitalization (r :81, po:01), SEESPWB and EFI Positive Engagement (r :78, po:01), and EFI Revitalization and PositiveEngagement (r :86, po:01) indicating a substantial amount of common variability between

these subscales and suggesting a common underlying construct. Thus, based on logical, semantic,and statistical grounds, we believe the subscales in Table 1 are representative of PAA.

We excluded studies where investigators introduced confounders before the post-exercise

assessment such as mental arithmetic (e.g., Szabo et al., 1993), or manipulation of efficacy

expectations (e.g., McAuley, Talbot, & Martinez, 1999). Table 2 lists studies of potential relevance

that were eventually excluded. Studies with similar authors were evaluated for sample overlap.

When sample overlap occurred, studies with more complete data were included. Duplicate articles

were excluded.

The database included 158 studies and 450 independent ESs with a sample size of 13,103 (total

number of ESs was 711). Study year ranged from 1979 to 2005 (M 1997:10; SD 5.20). Age

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Table 2

Relevant studies not included in the meta-analysis

Study Reason for exclusion

Allen and Desmond (1987) Dependent t test; prepost correlation not reported.

Annesi (2002a) Insufficient data for ES calculation.

Barabasz (1991) POMS Vigor not reported.

Berger et al. (1988) POMS Vigor nor reported.

Berger and Owen (1988) POMS Vigor means and SDs not reported.

Berger and Owen (1992b) POMS Vigor not reported.

Bird (1981) Insufficient data for ES calculation.

Boutcher and Landers (1988) POMS Vigor not reported.

Butki et al. (2003) Affect assessed only post-exercise.

Courneya and McAuley (1993) Affect assessed only post-exercise.

Daniel et al. (1992) Dependent t test; prepost correlation not reported.

Fallon and Hausenblas (2005) Positive affect not assessed.Fillingim et al. (1992) Post-exercise POMS confounded by imagery

manipulation.

Friedman and Berger (1991) POMS Vigor means and SDs not reported.

Gurley, Neuringer, and Massee (1984) Dependent t test; prepost correlation not reported.

Hobson and Rejeski (1993) Post-exercise PANAS confounded by mental stressor.

Hochstetler et al. (1985) Affect assessed only pre-exercise.

Jin (1989) POMS means and SDs not reported.

Jin (1992) POMS means and SDs not reported.

Jerome et al. (2002) Post-exercise affect confounded by efficacy

manipulation.

Johnson (1994) POMS Vigor not reported.

Kell et al. (1993) Insufficient data for ES calculation.

Kerr and Svebak (1994) SACL SDs not reported.

Kerr and Vlaswinkel (1993) SACL SDs not reported.

Kilpatrick, Hebert, Bartholomew, Hollander, and

Stromberg (2003)

Affect assessed only post-exercise.

LaCaille et al. (2004) Affect assessed only post-exercise.

Laguna and Dobbert (2002) Affect assessed only post-exercise.

Lane et al. (2005) Post-exercise BRUMS not reported

Lichtman and Poser (1983) POMS Vigor SDs not reported.

McAuley et al. (1999) Post-exercise affect confounded by efficacy

manipulation.

McAuley et al. (2000) Insufficient data for ES calculation.

McGowan et al. (1985) Affect assessment confounded by mental stressor

McIntyre et al. (1990) PANAS SDs not reported.Moore (1997) Post-exercise affect confounded by distraction and

mastery information.

Morris and Salmon (1994) Positive affect SDs not reported.

Naruse and Hirai (2000) Prepost UWIST MACL scores not reported.

Nelson (1994) Positive affect not reported.

Otto (1990) Post-exercise affect assessment confounded by induced

mood.

Oweis and Spinks (2001) AD-ACL means and SDs not reported.

Pernell (1997) Unable to determine exercise type.

Parente (2000) Insufficient data for ES calculation.


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was reported in 91.80% of the studies with a mean age of 24.47 (SD 11.64). Gender wasreported in 97.00% of the studies. Male participants comprised 22.40%, female participants

34.90%, and mixed gender 39.70% (mixed gender was defined as having less than 75% of either

gender). Participant source was provided in 97.80% of the studies. College students represented

62.40% of samples, community samples 19.00%, athletes 7.80%, clinical participants 3.90%,

and mixed samples of faculty, staff and students, 4.70%. Studies conducted in the US represented

74.68% of the ESs, those from the UK, 10.73%, Canada, 4.29%, Australia and Japan, 3.43%,

Sweden, 1.72, Korea, 1.29, and Estonia, Greece, and Norway .43%.

Coding

Baseline PAA

We examined the influence of pre-exercise affect using the following method. First, we copied

affect scale names, pre-exercise means, and sample size values from the original database to a

separate database then sorted by affect scale name. Because some affect scales had a relatively

small number of pre-exercise means, we increased the number of pre-exercise means for these

scales by adding pre-exercise means from studies not included in the meta-analysis and from

normative data reported in test manuals. Distribution sample sizes ranged from 180 for the

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Table 2 (continued)

Study Reason for exclusion

Prusaczyk et al. (1992) Unable to determine when affect was assessed relative to

exercise bout.

Rehor et al. (2001) Insufficient data for ES calculation.

Rejeski et al. (1991) Post-exercise POMS confounded with introduction of

stress task.

Rejeski et al. (1991) AD-ACL SDs not reported.

Rocheleau et al. (2004) Positive affect not assessed.

Rosenfeld (1998) Prepost SEES means and SDs not reported.

Rudolph and McAuley (1996) Affect assessed only pre-exercise.

Sakolfske et al. (1992) AD-ACL SDs not reported.

Steinberg et al. (1997) Dependent t test; pre-post correlation not reported.

Steptoe and Bolton (1988) POMS Vigor SDs not reported.

Steptoe and Cox (1988) POMS Vigor SDs not reported.Steptoe et al. (1993b) Post-exercise POMS Vigor means and SDs not reported.

Szabo et al. (1993) Affect assessment confounded by mental stressors.

Takenaka (1993) Insufficient data for ES calculation.

Thayer (1987a) AD ACL SDs not reported.

Thayer et al. (1993) Dependent t test; prepost correlation not reported.

Tredway (1978) Affect scale SDs not reported.

Turnbull and Wolfson (2002) Prepost POMS means and SDs not reported.

Tuson et al. (1995) Insufficient data for ES calculation.

Vasilaros (1988) POMS Vigor SDs not reported.

Watson (1988) Temporal sequence of exercise and affect not given.

Note: Contact the first author at [email protected] to obtain a complete list of all studies reviewed.

mailto:[email protected]:[email protected]

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POMS-LF (McNair et al., 1992) to 6 for the MACL (Nowlis, 1965) and the VAS (Folstein &

Luria, 1973). Next, in order to compare ESs associated with pre-exercise means from different

affect scales, we standardized the pre-exercise means for each affect scale by computing separate

sets ofz-scores with the sample-size weighted mean and SD from the respective scale. Scores frominstruments with more than one version or alternative scoring methods were converted to the

same scale before calculating z-scores. Each z score was then placed in the original database in a

new column next to the appropriate pre-exercise mean. Zscores were then sorted from low to high

and divided into three groups: less than .5z, .5z to .5z, and greater than .5z. This procedureallowed for a comparison of ESs between samples of participants having average pre-exercise

affect scores either in the lower third (less than .5 z), middle third (.5 z to .5 z), or upper third(greater than .5 z) of the distribution for the affect scale on which they were assessed.

Exercise intensity and duration

Intensity was coded using the classification system of the American College of Sports Medicine(ACSM). In this system, intensity can be classified as a percentage of oxygen uptake reserve

(%VO2R), a relative measure of intensity, which permits consistent coding of intensity whether

expressed as percent oxygen uptake (%VO2max), heart rate, or perceived exertion (Howley, 2001).

When necessary, we converted %VO2max to %VO2R using the appropriate equations (see Swain

& Leutholtz, 1997; Swain, Leutholtz, King, Haas, & Branch, 1998). The database allowed for the

formation of low (1539% VO2R), moderate (4059% VO2R), and high (6085% VO2R)

categories based on these guidelines (see Howley, 2001). We coded intensity as moderate for the 5

studies in the database where participants self-selected exercise intensity. Duration was coded in

minutes of exercise, excluding the warm-up and cool-down.

Exercise doseWe quantified dose as the product of the relative exercise intensity and duration. The following

four dose categories were formed from natural gaps in dose values: (a) low, 1030 min low

intensity to 720 min moderate intensity, (b) moderate, 3040 min moderate intensity to 2030 min

high intensity, (c) high, 6090 min moderate intensity to 4060 min high intensity, (d) very high,

1801400 min moderate to 300 min of high intensity exercise.


Post-exercise assessment times were coded for all ESs. We sorted post-exercise times and

created intervals based on natural breaks in the coded values: 02, 510, 1530, and 401440 min

post-exercise. Additional ESs were available for this moderator because all individual ESs foreach time interval were entered instead of being averaged as in the overall analysis.


Based on questions concerning the use of a control group, randomization procedures, etc., we

coded the number of threats to internal validity that investigators attempted to control, a higher

number indicating greater internal validity and study quality. The following threats were

considered: history, maturation, testing, statistical regression, selection bias, experimental

mortality, compensatory rivalry, resentful demoralization, Hawthorne effect, demand character-

istics, halo effect, and expectancy effect (see Cook & Campbell, 1979; Thomas & Nelson, 2001).

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Due to limited drop out rates, experimental mortality was controlled in 86% of the studies.

Maturation and selection bias were controlled in 63% of the studies, followed by testing

(62.30%), history (51.30%), statistical regression (50.60%), compensatory rivalry, resentful

demoralization, Hawthorne effects and demand characteristics (13.60%), and halo andexpectancy effects (2.60%). To address publication bias, we coded source as an unpublished

masters thesis or doctoral dissertation, or as a published journal article or abstract. It should be

noted that the database contained only two abstracts both from the same author who we

corresponded with concerning this information. Thus, we feel confident the abstract data does not

bias the results of the source moderator analysis.

Coder reliability

The first author recoded 12 randomly selected studies 2 weeks after the coding phase for data

relevant to the results (moderator variables, reliability coefficients, sample sizes, and ESs). Datathat could be coded without error such as publication dates were not included to avoid

overestimating coder reliability (Kuncel, Hezlett, & Ones, 2001). The agreement rate was 99.10%

(464 agreements out of 468 items). Two discrepancies involved estimating the length of the warm-

up and cool-down resulting in exercise durations that differed by about 2 min between the recoded

and original values. An internal validity disagreement resulted from not recoding a threat

controlled for in one of the studies (the Hawthorne effect). A reliability difference occurred in a

study using the Exercise-Induced Feeling Inventory (EFI; Gauvin & Rejeski, 1993). This

inventory required Mosiers formula (Hunter & Schmidt, 2004, p. 438) to estimate reliability from

two subscales. The recalculated reliability differed trivially from the original (87 vs. .89). Coding

errors were therefore minor and agree with prior research on the reliability of meta-analytic data

(Zakzanis, 1998).

Analysis

The data were analyzed with the Hunter and Schmidt (1990, 2004) meta-analytic method. This

method employs a random effects approach, which, unlike the fixed-effects model, allows for the

possibility that population effects vary from to study to study (Hedges, 1992). Because varying

population effects appear to be the rule rather than the exception for most real-world data, the

random-effects model is preferred to fixed-effects models and procedures (Field, 2003; Hunter &

Schmidt, 2000). The Hunter and Schmidt method corrects ESs for the attenuating effects of errorsuch as measurement unreliability and provides an estimate of the amount of ESvariance due to

sampling error and other artifacts.

Calculation of ESs

Mean values and pooled standard deviations (SDp) were used to calculate ESs. The use of SDpresulted in Cohens d statistic (Cohen, 1977). For within-subjects study designs, the following

within-subjects ESs were calculated: prepost treatment, prepost control, pre-treatment vs. pre-

control and post-treatment vs. post-control. For between-subjects study designs both within and

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between-subjects ESs were calculated. The between-subject ESs included pre-treatment vs. pre-

control and post-treatment vs. post-control and the within-subjects ESs included prepost

treatment and prepost control. For studies having both within and between ESs, the average

was entered to maintain statistical independence (Hunter & Schmidt, 2004, p. 431). Three r-valueswere converted to ds using formulas described by Hunter and Schmidt (2004, Chapter 7).

Within ESs comprised 75.33% of the total number of ESs, between 17.68%, average of

within and between 6.57%, and r-values .42%, respectively. Effect size calculations based on

paired t and F ratios from studies without the necessary means and SDs were not included

because r-values were not reported in these studies. Calculation of ESs for paired-sample data

without appropriate r-values results in an upward bias of the effect (Dunlap, Cortina, Vaslow, &

Burke, 1996).

Each ES was weighted by the study sample size and corrected for measurement error using

the affect scale internal consistency reliability (a) reported in the study. When the reliability was

not reported, the internal consistency reliability either from the validation study for the affectscale or from the appropriate test manual was used. Descriptive statistics for the internal

consistency reliabilities for the main meta-analyses are presented in Table 3. Further correc-

tions were made for small sample size bias, unequal sample size, and treatment by subject

interaction (Hunter & Schmidt, 2004, pp. 266, 279, 282). Sampling error variance was calcula-

ted separately for within and between ESs (Hunter & Schmidt, 2004, p. 305, 370) and the aver-

age sampling error variance of a within and between ES was entered when appropriate for a

particular study.

Most data sets contain errors, which can arise from various sources such as transcriptional or

computational mistakes (Gulliksen, 1986). Some of these data errors are likely to be outliers

(Schmidt, et al., 1993) and any meta-analysis containing a large number of ESs should be

examined for outliers to eliminate cases of bad data (Hunter & Schmidt, 1990, p. 262). Unlikeother approaches, the Hunter and Schmidt method employs a random effects model that focuses

on the accurate estimation of the standard deviation of population ESs (SDcorr) because they play

an important role in the interpretation of the results of the meta-analysis (Hunter & Schmidt,

1990, p. 453). Unfortunately, the presence of a single outlier can bias the estimation of SDcorr by

producing variance above that predicted by sampling error and other artifacts (Schmidt, et al.,

1993). Therefore, we employed Tukeys (1977) method to identify and omit outlier ESs prior to

the meta-analyses. This method is roughly equivalent to removing values greater than 2.5 SDs

from the mean, a common benchmark for outliers (Kirk, 1995, p. 169).

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Table 3Descriptive statistics for internal consistency reliabilities (a) for the main meta-analyses

Artifact distribution N K Mean a SD a Meanffiffiffi

a

pSD

ffiffiffi

a

p

Pre-exercise vs. pre-control 2666 66 .89 .02 .95 .01

Pre-post control 1666 66 .89 .04 .94 .02

Pre-post exercise 8362 230 .89 .03 .94 .01

Note: N, total number of participants; K, number of reliability values in the distribution; Mean a, average alpha

reliability; SD a, standard deviation of alpha reliabilities; Meanffiffiffi

a

p, average square root of alpha reliabilities; SD

ffiffiffi

a

p,

standard deviation of the square root of alpha reliabilities.


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Meta-analyses

We computed the following for all meta-analyses (including moderator analyses): total sample

size (N), number ofESs (K), mean sample-size weighted observed ES( dobs), dobs 95% confidenceinterval (95% CI), mean sample-size weighted corrected ES ( dcorr), corrected standard deviation

(SDcorr), residual standard deviation (SDres), percent of dobs variance due to sampling error

(%Vare), 90% credibility interval (90% CrI), and dcorr fail-safe N ( dfs). The dcorr fail-safe N

estimates the number of unlocated ESs with null results needed to reduce dcorr to the lowest

critical ES considered practically or theoretically important (Hunter & Schmidt, 2004, p. 500;

Orwin, 1983). The critical ESwas set at .20, considered a small effect (Cohen, 1988). The dcorr and

SDcorr were interpreted as best estimates of the population parameters.

We employed a random effects model for the calculation of the 95% CI, which provided an

estimate of the sampling error in dobs. The 90% CrI represents a distribution containing 90% of

the true population values of d. The SDcorr and the 90% CrI were used to identify moderators.The width of the 90% CrI depends on SDcorr. If SDcorr is large relative to dcorr and the 90% CrI

includes zero, dcorr is the mean of several population parameters, indicating the presence of

moderators. If the 90% CrI does not include zero, dcorr estimates a single population parameter

and moderators are not operating (Whitener, 1990). This interval also determines whether dcorr is

a generalizable effect. When the 90% CrI does not include zero, the magnitude of ESs may vary,

but 90% of the true ESs will retain a positive sign (or a negative sign for negative ESs) and

generalize across settings (Ones, Viswesvaran, & Schmidt, 1993).

Overall meta-analyses

The first meta-analysis compared exercise and control groups prior to treatment using between

and within ESs. We tested pre-activity equivalence because large differences may confoundconclusions about moderator variables associated with exercise groups. For this meta-analysis,

positive ESs indicated greater average PAA in exercise samples. The second meta-analysis tested

pre- to post-changes for control samples using within ESs. Attention controls such as lecture

contributed 90.62% and activity controls such as very light exercise 6.25% of the control ESs,

respectively. Control type was not reported for 3.13% of the control ESs. The third meta-analysis

examined prepost changes in PAA across exercise samples using within, between, and average of

within and between ESs, the majority being within ESs. Positive ESs reflected increased PAA

relative to baseline. For all analyses, the average ESwas entered for studies with multiple post- or

post-exercise assessments to ensure independence and we used the study sample sizes as the weight

for the average ES (Hunter & Schmidt, 2004, p. 432).

Moderator analyses

As a first step, a moderator variable correlation matrix was generated to explore the possibility

that moderators were substantially intercorrelated, making the results difficult to interpret. Next,

the appropriate ESsubgroups for each moderator variable were meta-analyzed. Moderators were

explored by examining differences between dcorr values and changes in the SDcorr across

moderator subgroupings (Hunter & Schmidt, 2004, p. 293). The 90% CrI values were used to

determine the generalizability of a subgroup effect, or to suggest the presence of additional

unexamined moderators.

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Results

Overall meta-analyses

Pre-test differences between exercise and control groups were very small, dcorr :05(SDcorr .10). This indicates that on average, there was only .05 of a SD difference in self-reported PAA between exercise and control samples prior to experimental conditions. It appears

safe to assume equivalence between groups on the dependent variable at pre-test. The mean

corrected ES of.17 (SDcorr .25) for the control group meta-analysis indicated that controlconditions are associated with small decreases in PAA. The dobs for exercise groups was .45 (95%

CI: .40 to .50) and the dcorr was .47 (SDcorr .37), which is a robust, moderate ES, nearly fourtimes that associated with control samples. The SDcorr suggests the presence of moderators and

the 90% CrI included zero indicating that the effects of exercise on PAA do not generalize. Fail-

safe Ns of 48297 suggest good tolerance to availability bias. That is, 48 additional ESs with adcorr of .40 for pre-exercise vs. pre-control, and 117 additional ESs with a dcorr of .40 for prepost

control would have to be found and included in the analysis to increase the current dcorr values to

.20, the critical ESwe set based on Cohens (1988) criterion. For prepost exercise, 297 additional

ESs with a dcorr of .00 would have to be found and included to reduce the current dcorr to .20.

Results are presented in Table 4.

Moderator analyses

Results for moderator correlations are presented first, followed by baseline PAA, exercise

characteristics, study quality, and source. Table 5 displays the moderator correlations and

correlations between moderators and corrected ESs (dcorr). Tables 6 and 7 display results forbaseline PAA, exercise characteristics, post-exercise assessments time, study quality, and source.

Moderator correlations

The majority of correlations were small to negligible, suggesting moderator variables were

unrelated. As anticipated, however, duration and dose were moderately correlated indicating the

general influence of duration on dose. The number of threats controlled was inversely related to

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Table 4

Overall meta-analyses of the effects of acute exercise on post-exercise positive activated affect

Analysis N K dobs 95% CI dcorr SDcorr SDres %Vare 90% CrI dfs

Pre-exercise vs. pre-control 2582 64 .05 .02 to .12 .05 .10 .10 88.28 .08 to .18 48Prepost control 1602 63 .17 .23 to .10 .17 .25 .25 18.54 .49 to .14 117Prepost exercise 8094 223 .45 .40 to .50 .47 .37 .35 17.64 .01 to .94 297

Note: N, total sample size; K, number of ESs; dobs, mean sample-size weighted observed ES; 95% CI, dobs 95%

confidence interval; dcorr, mean sample-size weighted corrected ES; SDcorr, sample-sized weighted corrected standard

deviation; SDres, residual standard deviation; %Vare, percent of dobs variance due to sampling and measurement error;

90% CrI, 90% credibility interval; dfs, dcorr fail-safe N with dcritical :20, dunlocated :00 for dcorr values above .20 anddunlocated :40 for dcorr values below .20. Boldface entries are best estimates of the population mean ES.


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duration and dose. This shows that studies with greater internal validity (e.g., Petruzzello & Tate,

1997; Van Landuyt et al., 2000), typically conducted in a lab setting, tended to employ shorter

bouts, while most of the higher dose longer duration studies were conducted outside a laboratory

with less experimental control (e.g., Odagiri, Shimomitsu, Iwane, & Katsumura, 1996). Mostmoderators were inversely related to dcorr with an exception being the number of internal validity

threats controlled suggesting a trend toward higher ESs in studies with greater internal validity.

Baseline PAA

We had hypothesized larger effects for lower baseline scores. Individuals reporting less

favorable pre-exercise affect experienced greater affective improvement than those reporting a

more favorable affect (e.g., Parfitt, Rose, Markland, 2000; Nabetani & Tokunaga, 2001). The dcorrfor the lower third of the distribution was .63 (SDcorr .37), nearly twice the magnitude of theother two categories and the 90% CrI did not include zero, indicating that lower than average

baseline scores produce generalizable increases in PAA. The smaller effects associated with higherbaseline scores were not confounded by studies examining extreme bouts. The 90% CrI for the

middle and upper third of the distribution included zero indicating the presence of additional

moderators. The findings show that when baseline affect is below average, aerobic exercise results

in consistent, generalizable increases in PAA, in line with our hypothesis.

Exercise intensity

We had tentatively hypothesized a general inverse relation between intensity and post-exercise

PAA improvement. The dcorr of .57 (SDcorr .33) for low intensity (e.g, Ekkekakis et al., 2000;Thayer, 1987b) was nearly twice that of moderate (e.g., Parfitt & Gledhill, 2004; Reed et al., 1998)

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Table 5

Intercorrelations between moderators and moderators and corrected ESs

Moderator 1 2 3 4 5 6 7 8

1. Baseline PAA .06 .11 .12 .01 .02 .09 .20(167) (195) (166) (196) (197) (200) (200)

2. Exercise intensity .05 .03 .10 .04 .04 .11(180) (180) (179) (185) (185) (185)

3. Exercise duration .69 .20 .24 .08 .40(180) (205) (216) (216) (216)

4. Exercise dose .21 .26 .08 .44(179) (180) (180) (180)

5. Post-exercise assessment time .01 .09 .14(218) (218) (218)

6. Threats controlled .03 .28

(227) (227)7. Publication status .11

(230)

8. Corrected ES (dcorr)

Note: All moderators were correlated using actual coded values such as time (min) for duration, except for publication

status where published 1 and unpublished 2. Values in parentheses are sample sizes.


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and high (e.g., Springer, Bartholomew, & Loukas, 2003; Steptoe, et al., 1993a) lending support to

the tentative hypothesis of larger effects for lower intensity aerobic exercise. Based on the 90%

credibility intervals, effects for low intensity also generalize across settings while those for

moderate and high intensity do not. Large SDcorr values leave room for additional moderators,

especially for moderate and high intensities.

One possible moderator for high-intensity aerobic exercise is fitness level. Ekkekakis and

Petruzzello (1999) have suggested that affective benefits following high-intensity exercise occur

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Table 6

Moderator analyses for baseline PAA, exercise characteristics, and post-exercise assessment times

Analysis N K dobs

95% CI dcorr

SDcorr

SDres

%Vare

90% CrI dfs

Baseline PAA

o.5 z 2861 75 .60 .51 to .69 .63 .37 .35 18.97 .16 to 1.10 162.5 z to .5 z 2159 66 .33 .25 to .41 .34 .30 .29 24.24 .04 to .72 474.5 z 1686 49 .39 .30 to .48 .39 .32 .32 23.45 .02 to .79 48

Exercise intensity

Low 748 23 .54 .40 to .69 .57 .33 .31 22.71 .16 to .98 42

Moderate 2732 91 .34 .26 to .43 .35 .39 .38 14.70 .14 to .85 70High 1679 60 .31 .20 to .43 .31 .42 .42 12.48 .22 to .84 29Not reported 2578 41 .49 .39 to .59 .53 .31 .29 21.69 .13 to .92 67

Exercise duration (min)

7 to 15 1317 33 .55 .41 to .68 .56 .38 .37 16.51 .07 to 1.05 5620 to 28 2063 69 .44 .36 to .52 .46 .31 .30 25.20 .07 to .86 91

30 to 35 1940 62 .55 .46 to .64 .57 .33 .32 19.94 .15 to .99 113

40 to 60 1323 35 .36 .24 to .48 .37 .31 .30 30.15 .02 to .76 29475 331 12 .72 1.11 to .33 .72 .66 .66 5.28 1.56 to .13 55Not reported 1102 13 .36 .27 to .45 .38 .14 .13 41.72 .21 to .56 12

Exercise dose

Low 2447 73 .44 .36 to .52 .45 .30 .29 26.51 .06 to .84 92

Moderate 2146 82 .46 .37 to .54 .46 .34 .34 21.07 .02 to .91 108

High 279 15 .09 .07 to .26 .09 .27 .27 36.31 .25 to .43 8Very high 216 6 .98 1.31 to .66 .98 .37 .37 15.99 1.45 to .51 35Not reported 2754 41 .48 .38 to .58 .52 .32 .30 20.30 .11 to .92 65

Post-exercise assessment timea

0 to 2 3512 87 .60 .51 to .68 .61 .40 .39 13.89 .10 to 1.12 179

5 to 10 3184 110 .41 .34 to .48 .43 .35 .33 17.10 .03 to .88 12515 to 30 1744 57 .27 .17 to .37 .27 .34 .34 19.28 .16 to .71 2140 to 1440 677 29 .09 .03 to .21 .10 .31 .29 25.01 .28 to .48 15Not reported 1033 14 .52 .40 to .64 .58 .21 .19 34.16 .32 to .85 27



deviation; SDres, residual standard deviation; %Vare, percent of dobs variance due to sampling and measurement error;

90% CrI, 90% credibility interval; dfs, dcorr fail-safe N with dcritical :20, dunlocated :00 for dcorr values above .20 anddunlocated

:40 for dcorr values below .20. Boldface entries are best estimates of the population mean ES.

aPost-exercise assessment time in minutes (min).


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only for fit participants. To test this claim, we recoded high-intensity ESs for fitness level (active

vs. sedentary) and found a difference in the predicted direction: active dcorr :48 (SDcorr .39),sedentary dcorr :14 (SDcorr .30). In sum, low-intensity effects were generalizable and largerthan moderate and high-intensity exercise. Some caution is warranted in interpreting the low-

intensity subsample results because the smaller K can result in greater sampling error and a less

stable ES estimate.

Exercise duration

We had hypothesized that there would be no differential effect of exercise time on PAA across

typical exercise durations (e.g., 1540 min). All dcorr values for bouts from 7 to 60 min were within

.20 of a SD, providing support for the hypothesis of no differential effect of exercise duration on

post-exercise PAA. Additional moderators may be operating due to the relatively large SDcorrvalues. Generalizable effects were found for bouts ranging from 7 to 35 min, although the lower

bound 90% CrI values for bouts less than 30 min were close to zero. Bouts from 40 to 60 min

produce increases, (e.g., Bodin & Hartig, 2003; Janal, Colt, Clark, & Glusman, 1984; McInman &

Berger, 1993), but effects do not generalize. Exercise durations longer than 75 min likely result in

decreased PAA (e.g., Hassmen & Blomstrand, 1991), but the smaller K for this subsamplewarrants some caution in the interpretation of the results.

The 3035 min duration produced the largest effect ( dcorr :57, SDcorr .33). To assess theextent to which higher ESs associated with studies using lower intensity bouts influenced this

generalizable result, we correlated ESs and intensity for this subsample. The correlation was small

and in the opposite direction of the expected confounding influence: r (60) .15, p .25, addingto the strength of the generaliziblity of this finding.

Ekkekakis and Petruzzello (1999) also point out that duration effects may be difficult to

substantiate because many studies assess affect several minutes after exercise, potentially allowing

duration effects to subside. To address this, we also analyzed duration using the same time

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Table 7

Moderator analyses for study quality and source information

Analysis N K dobs

95% CI dcorr

SDcorr

SDres

%Vare

90% CrI dfs

Threats controlled

12 2687 67 .28 .17 to .38 .30 .45 .42 5.13 .28 to .88 3334 1267 25 .47 .35 to .60 .49 .25 .24 43.61 .17 to .81 37

56 3202 107 .49 .43 to .55 .50 .29 .28 31.11 .14 to .87 162

710 489 16 .47 .23 to .71 .49 .48 .46 12.83 .12 to 1.09 23

Source

Unpublished 1173 44 .26 .14 to .38 .26 .37 .37 15.62 .22 to .74 14Published 7082 184 .45 .39 to .51 .47 .39 .37 18.59 .03 to .97 252



deviation; SDres, residual standard deviation; %Vare, percent ofdobs variance due to sampling and measurement error;

90% CrI, 90% credibility interval; dfs, dcorr fail-safe N with dcritical :20, dunlocated :00 for dcorr values above .20 anddunlocated :40 for dcorr values below .20. Boldface entries are best estimates of the population mean ES.


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intervals with only the 87 ESs measuring affect within 2 min post-exercise and found comparable

results: ESs ranged from .60 to .37 for bouts from 7 to 15 and 4060 min, respectively, and all 95%

confidence and 90% credibility intervals were comparable.

Exercise dose

We had hypothesized a general inverse relation between dose and post-exercise PAA

improvement. The dcorr values of .98 (SDcorr .37) for very high, .46 (SDcorr .34) formoderate, and .45 (SDcorr .30) for low doses, supports the hypothesis of an inverse relationbetween exercise dose and ES. The lower 90% credibility values for low and moderate doses are

close to zero, indicating that while not likely to be negative, effects may be small in some

instances. Moderators influence high dose effects due to the large SDcorr and the 90% CrI

straddling zero. Very high doses are associated with reduced post-exercise PAA that generalizes

across settings. That is, in most situations, very high doses of aerobic exercise result in at least

temporary reductions in PAA below baseline scores (e.g., Hassmen & Blomstrand, 1991; Knapiket al., 1991). Note that the high and very high dose subsample analyses are based on small sample

sizes (K) and should be interpreted with some caution.


We had expected to find the largest effects within 5 min post-exercise and lower effects

thereafter. PAA peaked shortly after exercise then declined, a result in line with the hypothesis.

The reduction in dcorr from .61 (02 min) to .10 (401440 min) and the associated change in SDcorracross intervals suggest a moderating effect of post-exercise assessment time. The immediate post-

exercise increase generalized, but the 90% credibility intervals for the other subsamples indicate

that a declining pattern across the time intervals examined might not hold in all settings (e.g., Cox,

Thomas, & Davis, 2001; Tate, 1991) and may be dependent on unexamined moderators. The401440 min subsample results are based a relatively small K and should interpreted with some

caution. In sum, the results show that post-exercise assessment time is an important consideration

in the interpretation and comparison of these studies.


There was no hypothesis for these two potential moderators. A positive relationship emerged

between ESs and the number of threats controlled suggesting a trend toward greater effects for

studies attempting to control more threats to internal validity (e.g., Jarvekulg & Viru, 2002; Van

Landuyt et al., 2000). It should be noted, however, that the potential moderating effect of study

quality is tempered by the almost complete overlap of the 90% credibility intervals. This resultindicates that population ESs across the categories occupy nearly the same range of values. The

results for the 34 and 710 threats controlled subsamples should be interpreted with some

caution because of the relatively small K in these categories.

There were 184 and 44 ESs associated with published and unpublished studies, respectively. Thedcorr for published studies of .47 (SDcorr .39) was nearly double that for unpublished studies at.26 (SDcorr .37). However, the overlapping 90% CrI indicate that the potential moderatingeffect of source is not as apparent at the population level. The fail-safe N ( dfs) for unpublished

studies also points to the possibility of a file drawer effect influencing this moderator analysis.

That is, there is a reasonable possibility of 14 additional unlocated theses or dissertations with

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dcorr of .00 in file drawers that, if added to the analysis, would reduce the dcorr to the critical

value of .20.

Discussion

The results indicate that, in general, exercise is associated with increased PAA. This

improvement is nearly one half of a SD higher after exercise than before ( dcorr :47). Thetypical control condition produces decreases in PAA of about one fifth of a SD ( dcorr :17). Onaverage, exercise and control groups do not report different pre-test levels of PAA ( dcorr :05).These findings provide support for the favorable effects of exercise on positively activated

affective states.

Studies with individuals having pre-exercise scores in the lower third of the distribution were

associated with greater increases in PAA than those in the middle and upper thirds. Thedcorr forlower pre-exercise scores was nearly twice that of the other two levels and generalizes across

participants and settings. This difference held for observed and corrected ESs, supporting the

hypothesis that baseline scores moderate PAA (see Rejeski et al., 1995). An important implication

of this finding is the practical application of exercise as a self-regulatory strategy to improve

feelings of energy and positive affect (Thayer, 1996).

Our results challenge suggestions of a moderate intensity threshold for optimal PAA benefits

because moderate and high-intensity exercise produced similar, non-generalizable post-exercise

improvements that were smaller on average than those for low-intensity exercise. For low-

intensity exercise, reviewers often cite Thayer (1987a) who found that short bouts of brisk walking

significantly increased feelings of energy. Recently, Ekkekakis et al. (2000) replicated these

findings across different measures and exercise settings. Our data agree: low-intensity exerciseproduced generalizable improvements, a finding with implications for exercise adherence. Overall,

the results agree with the narrative literature for low-intensity exercise. We found little evidence to

support a moderate intensity threshold or inverted-U hypothesis.

Exercise of at least 2030 min has been suggested as a duration threshold for improvement of

PAA (Berger & Motl, 2000). This meta-analysis suggests that shorter and longer bouts, even up to

60 min, result in affective improvement while decreases are likely for durations longer than 75 min,

results that remained even when the effects were controlled for post-exercise assessment time. In

agreement with quantitative reviews on negative affect (e.g., Craft & Landers, 1998; Landers,

1997; North et al., 1990; Petruzzello et al., 1991), our results do not provide evidence to support a

threshold hypothesis for typical exercise duration.In line with public health recommendations regarding dose to improve exercise adherence (e.g.,

USDHHS, 1996) low doses were generally associated with improved PAA. Identical results were

found for moderate doses, which may consist of 30 min of higher intensity exercise, as defined in

this meta-analysis. Low and moderate doses represent optimal zones for PAA change because

improvements tend to generalize. High doses represent an unstable zone. This level resulted in

nearly null effects on average, but may produce improvements or decrements depending on

moderators such as fitness level (see Ekkekakis & Petruzzello, 1999) or other individual

differences related to the preference and tolerance of various exercise intensities (see Ekkekakis,

Hall, & Petruzzello, 2005b). Another explanation for the instability at high doses may be related

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to the method of defining exercise intensity. For example, higher exercise intensities (e.g., X70%

VO2max) associated with higher doses may force many less fit participants to rely more heavily on

anaerobic processes, resulting in greater physiological and perceptual discomfort during, and less

positive affect post-exercise, compared to more fit individuals, even though the relative intensityof the exercise stimulus is the same for all participants (see Bixby et al., 2001; Ekkekakis &

Petruzzello, 1999). This potential metabolic response variability within samples may have

produced smaller ESs in samples with a greater percentage of participants exercising above the

aerobicanaerobic transition and larger ESs for samples with a greater percentage of participants

exercising at or below the aerobicanaerobic transition. Individual metabolic response variability

may therefore explain some of the instability around the null effect for high doses. A shift to

defining exercise intensity relative to the aerobicanaerobic transition may help clarify affective

responses to higher doses. Very high doses, bouts outside the norm of a typical exercise session,

represent an aversive zone. These bouts are associated with substantial physiological and

psychological fatigue resulting in short-term affective decrements in nearly all situations.Dose results point to the importance of understanding affective changes as a function of

intensity and duration together (He, 1998). In contrast to the generally positive effects found for

intensity and duration separately, a clear interaction effect occurred with dose whereby corrected

ESs dropped dramatically from low and moderate to high doses. Inspection of the database

revealed that the decline in ESmagnitude started at 40 min, as intensity increased from ESs coded

as moderate intensity at 40 min (upper end of moderate dose) to those coded as high intensity at

40 min (lower end of high dose), a result not apparent from duration or intensity effects alone.

Explanations for this obvious drop in post-exercise PAA may be related to peripheral and central

factors associated with fatigue such as changes in blood glucose (Coyle, Coggan, Hemmert, & Ivy,

1986), or brain serotonin (Davis & Bailey, 1997). The psychological consequences of these

changes may also vary with fitness level, but the significance of this apparent transition requiresfurther investigation.

Post-exercise time points produced a pattern of immediate increase followed by progressively

smaller effects. At the population level, the immediate post-exercise effect generalizes, a result

consistent with narrative reviews (Ekkekakis, 2003; Ekkekakis & Petruzzello, 1999). However,

others have found improvements lasting anywhere from 1 (e.g., Cox et al., 2001; Daley & Welch,

2004) to 4 h post-exercise (e.g., Thayer, 1987a). Our data clarify these conclusions by showing that

sustained increases do not appear to generalize across participants and settings. For example,

Petruzzello et al. (2001) found that treadmill running produced immediate increases in PAA for

high and low to moderately fit individuals, but affect remained elevated above baseline during

30-min of recovery only in the high-fit group. The moderators associated with different patterns ofpost-exercise affective response should be a continued area of examination in future research.

Publication status results agree with some meta-analyses in the exercise psychology literature

(e.g., Craft & Landers, 1998; Petruzzello et al., 1991), but not others (e.g., Arent et al., 2000; Long

& Stavel, 1995). Methodological weaknesses are likely reasons for lack of publication. However,

similar to North et al.s (1990) meta-analysis, we found no relationship between the number of

threats controlled (internal validity) and publication status, indicating that the source bias in our

results is more likely related to the size of the effect than study quality per se. This argues for the

importance of including both published and unpublished studies to avoid positively biased

conclusions in meta-analyses (Begg, 1994).

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The dual-mode model of exercise and affect (Ekkekakis, 2003) offers some theoretical

clarification for the intensity results. The model proposes that intensity is a key variable that

produces changes in the salience of cognitive and physiological factors that influence the pattern

of affective response during and after exercise. Cognitive factors are proposed to dominate at lowand moderate intensities. At higher intensities, hedonic tone (valence) decreases as physiological

cues increase due to rising blood lactate levels. The model also suggests that low, moderate, or

high intensities can result in post-exercise affective improvement. At low intensities due to

cognitive factors and mild increases in activation that may be perceived as pleasant, and at higher

intensities due to an affective opponent process (Solomon, 1980), possibly driven by

endogenous opiates that quickly reverse the negative affective valence reported during exercise.

The results concerning exercise intensity from the present meta-analysis fit the model, as all

levels were associated with positive effects, especially for low intensity, which generalized and was

larger on average than moderate or high intensities. An additional albeit very speculative

explanation for the low-intensity effects involves the notion of an activity-stat, an innatebiological mechanism that promotes and rewards habitual spontaneous activity such as brisk

walking or play behavior (Rowland, 1998). The affective changes related to these activities may

serve as a reward to promote a balance between energy intake and energy expenditure through

daily physical activity. Several lines of genetic research appear to support the concept of biological

control of physical activity (e.g., Perusse, Tremblay, Leblanc, & Bouchard, 1989). If shown to

exist, such a system may help further clarify the generalizable increases in PAA for low-intensity

exercise.

Moderate and high-intensity effects did not generalize, indicating the presence of unexamined

moderators. A possible moderator of moderate intensity exercise is self-efficacy, a construct

shown to correlate with affective valence during moderate exercise (e.g., Ekkekakis, Hall, &

Petruzzello, 1999a). Blood lactate often begins to rise in sedentary individuals during moderateexercise, increasing the metabolic strain and effort perception. The interpretation of this challenge

may depend on self-efficacy with higher efficacy related to affective improvement and lower

efficacy with affective decline during exercise. According to the dual-mode model, either response

can lead to improved post-exercise affect: those who report improved valence during exercise via

cognitive factors and those reporting decreased valence via physiological factors related to an

opponent process (Solomon, 1980). However, because Solomon (1980) hypothesized that the

strength of the opponent process increases with repeated exposure to the stimulus, for sedentary

participants with little prior exercise experience who report negative affect during exercise, the

opponent process may be weak, resulting in decreased post-exercise affect. Thus, some of the

unexplained post-exercise variability associated with moderate intensity effects may be due tothe heterogeneity of affective responses during moderate exercise.

From the conclusions of narrative reviews (Ekkekakis & Petruzzello, 1999) and the results of

this meta-analysis, fitness level appears to moderate post-exercise PAA for high-intensity exercise.

One explanation for the moderating effect of fitness, based on the dual-mode model, may be the

perception of fatigue related to the increased salience of physiological cues such as respiration and

blood lactate. Unfit individuals may be more likely than fit individuals to experience fatigue

associated with these physiological changes. Because fatigue (an unpleasant deactivated affective

state) exhibits bipolarity with PAA (see Yik et al., 1999), higher feelings of fatigue may result in

lower reported PAA. Thus, fitness level, possibly due to differences in self-reported fatigue,

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appears to be a moderator of post-exercise PAA particularly for high-intensity exercise. Taken as

a whole, our intensity results appear consistent with the dual-mode model.

A practical implication of this meta-analysis involves the development of guidelines for

improving exercise adherence and maintenance of health-related quality of life. Dishman (2001)notes the lack of data regarding the dose of physical activity for the development of these

guidelines. Feelings of energy and vigor are important aspects of health-related quality of life

(Wilson & Cleary, 1995) and they tend to be good predictors of health over time ( Dixon, Dixon, &

Hickey, 1993). An important question, however, has been the effect that exercise has on these

positive affective states. This study offers quantitative evidence that low to moderate doses

represent zones that improve energy and vigor across a range of personal and situational

variables. The present results, the consistency with which exercise is associated with increased

positive affect (e.g., Steinberg et al., 1998; Watson, 2000), and the tendency for people to choose

activities associated with positive affective experiences (Emmons & Diener, 1986), all point to the

potentially important role of PAA in exercise adherence.This meta-analysis also adds to the literature in several ways. First, findings are based on 158

studies and 450 independent ESs. A greater number of ESs improves the validity of the meta-

analysis by increasing the accuracy of population estimates and enhancing the statistical power of

the moderator analyses (Hunter & Schmidt, 2004, Chapter 9). Second, we examined only PAA

rather than combine activated and deactivated positive affect, the objective being to eliminate the

confounding relative to the different post-exercise patterns of change in each of these distinct

affective states (Ekkekakis, 2003). Third, prior reviews have not considered the influence of

statistical artifacts related to methodological imperfections in research studies. This is a limitation

as failure to correct for measurement error in the dependent variable results in attenuated mean

ESs, while failure to correct for sampling error results in artificial variation in ESs not due to true

moderator variables (Hunter & Schmidt, 2004, Chapter 3).There are a couple of limitations that deserve mention. First, this meta-analysis did not consider

affective responses during exercise. This poses a conceptual limitation because affective changes

from pre to post-exercise are often not linear and the dynamic changes that take place during

exercise allow for a more complete picture of the overall affective response. Second, in some of the

moderator analyses, the number of ESs was small enough to warrant caution concerning the

stability of the estimates of effect. For example, Field (2001) has shown that both fixed and

random effects methods do not control the Type I error rate in estimates of the mean uncorrected

ES in meta-analyses with 15 or fewer studies. Any meta-analysis is limited by the number of

available studies in a given research domain which has implications for second-order sampling

error in meta-analyses (Hunter & Schmidt, 2004, p. 399400). For this reason, the results of someof the moderator analyses should be considered preliminary. Even with this limitation, however, a

meta-analysis based on a theoretical framework can provide valid conclusions, including accurate

estimates of the magnitude of an effect and the examination of potential moderator variables,

procedures that overcome problems associated with other approaches to understanding the data,

including the traditional narrative review.

Future studies using theory-driven approaches (e.g., Ekkekakis, 2003) that test self-efficacy,

fitness, and other individual difference variables are needed as such variables may account for a

sizeable portion of the true variance in post-exercise affective responses. A very important

unanswered question is how these individual differences interact and change with repeated bouts

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of acute exercise. From a meta-analytic standpoint, because moderators only describe the

conditions under which ESs vary, additional theory-driven research will not only increase the

number of studies for future meta-analyses, but provide additional meaning to the results as well.

Affective responses during exercise also appear to play an important role in the overallrepresentation of the affective dynamics of acute exercise, but relatively few studies are currently

available.

In summary, the results indicate that increased PAA can last up to 30 min post-exercise.

Furthermore, the effects remain positive across personal and situational settings immediately

post-exercise, when pre-exercise PAA is lower than average, for low intensities, for durations up

to 35 min, and for intensity/duration combinations (doses) ranging from low to moderate as

defined in this meta-analysis. We did not find evidence supporting a threshold hypothesis for

intensity or duration. In contrast, dose results suggest the presence of distinct zones of affective

change that more accurately reflect post-exercise responses across the literature than intensity or

duration alone. In this regard, the dose analysis is unique and informative. Beyond thesegeneralizable effects, however, large SDcorr values suggest that additional variables, possibly

related to individual differences, moderate the effects of exercise on PAA.

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