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Psychology of Sport and Exercise 7 (2006) 477–514
The effect of acute aerobic exercise on positive activated affect:
A meta-analysis
Justy Reed
a,
, Deniz S. Ones
b
a
Department of Health, Physical Education, and Recreation, Chicago State University, 9501 South King Drive,
Chicago, IL 60628, USA
b
Department 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 (SD
corr
) 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 o15–39% oxygen uptake reserve (%VO
2
R), (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 than
intensity or duration alone. Control conditions were associated with reductions in PAA ( ¯
dcorr ¼:17,
SD
corr
¼.25).
ARTICLE IN PRESS
www.elsevier.com/locate/psychsport
1469-0292/$ - see front matter r2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.psychsport.2005.11.003
Corresponding author. Fax: +1 773 995 3644.
E-mail address: ereed@csu.edu (J. Reed).
Conclusion: The typical acute bout of aerobic exercise produces increases in self-reported PAA, whereas
the typical control condition produces decreases. However, large SD
corr
values suggest that additional
variables, possibly related to individual differences, further moderate the effects of exercise on PAA.
r2006 Elsevier Ltd. All rights reserved.
Keywords: Exercise; Affect; Well-being; Meta-analysis; Dose–response
Introduction
The majority of research on the exercise–affect 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 immune
function (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 (activated–deactivated) and valence (positive–negative). This
meta-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
Activation–Deactivation 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-reported
physical 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) 477–514 479
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 lower
baseline 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., 15–40 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 dose–response effect is
one form of evidence for evaluating whether the exercise–affect 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 low
doses (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 between
published 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 include
activities 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 those
who 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
Activation–Deactivation 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.
a
Additional 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).
b
The analysis also included the POMS-SF versions of Grove and Prapavessis (1992) and Shacham (1983).
J. Reed, D.S. Ones / Psychology of Sport and Exercise 7 (2006) 477–514 483
Feldman–Barrett and Russell’s Activated (Feldman Barrett & Russell, 1998), Watson, Clark, and
Tellegen’s Positive Affect (PA; Watson et al., 1988), Thayer’s Energy (Thayer, 1986), and Larsen
and Diener’s 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 terms
from these subscales that matched terms in the subscales of the instruments listed in Table 1 and
found the following: Activation–Deactivation 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 r¼:69 (po:001) and
r¼:58 (po:001), respectively (see Gauvin & Rejeski, 1993). Second, for the SEES, while only the
term 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 (r¼:71, po:01). Finally, Lox, Jackson, Tuholski, Wasley, and Treasure (2000)
found high correlations between the SEES PWB and EFI Revitalization (r¼:81, po:01), SEES
PWB and EFI Positive Engagement (r¼:78, po:01), and EFI Revitalization and Positive
Engagement (r¼:86, 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 (M¼1997:10; SD ¼5.20). Age
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Table 2
Relevant studies not included in the meta-analysis
Study Reason for exclusion
Allen and Desmond (1987) Dependent ttest; pre–post 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 ttest; pre–post 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 ttest; pre–post 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) Pre–post 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.
J. Reed, D.S. Ones / Psychology of Sport and Exercise 7 (2006) 477–514 485
was reported in 91.80% of the studies with a mean age of 24.47 (SD ¼11.64). Gender was
reported 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) Pre–post 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 ttest; 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 ttest; pre–post correlation not reported.
Tredway (1978) Affect scale SDs not reported.
Turnbull and Wolfson (2002) Pre–post 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 ereed@csu.edu to obtain a complete list of all studies reviewed.
J. Reed, D.S. Ones / Psychology of Sport and Exercise 7 (2006) 477–514486
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 of z-scores with the sample-size weighted mean and SD from the respective scale. Scores from
instruments with more than one version or alternative scoring methods were converted to the
same scale before calculating z-scores. Each zscore 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,.5zto .5z, and greater than .5z. This procedure
allowed 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 zto .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
(%VO
2
R), a relative measure of intensity, which permits consistent coding of intensity whether
expressed as percent oxygen uptake (%VO
2max
), heart rate, or perceived exertion (Howley, 2001).
When necessary, we converted %VO
2max
to %VO
2
R using the appropriate equations (see Swain
& Leutholtz, 1997;Swain, Leutholtz, King, Haas, & Branch, 1998). The database allowed for the
formation of low (15–39% VO
2
R), moderate (40–59% VO
2
R), and high (60–85% VO
2
R)
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 dose
We 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, 10–30 min low
intensity to 7–20 min moderate intensity, (b) moderate, 30–40 min moderate intensity to 20–30 min
high intensity, (c) high, 60–90 min moderate intensity to 40–60 min high intensity, (d) very high,
180–1400 min moderate to 300 min of high intensity exercise.
Post-exercise assessment time
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: 0–2, 5–10, 15–30, and 40–1440 min
post-exercise. Additional ESs were available for this moderator because all individual ESs for
each time interval were entered instead of being averaged as in the overall analysis.
Study quality and source
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).
ARTICLE IN PRESS
J. Reed, D.S. Ones / Psychology of Sport and Exercise 7 (2006) 477–514 487
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 and
expectancy effects (2.60%). To address publication bias, we coded source as an unpublished
master’s 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). Data
that 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 Mosier’s 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 error
such as measurement unreliability and provides an estimate of the amount of ES variance due to
sampling error and other artifacts.
Calculation of ESs
Mean values and pooled standard deviations (SD
p
) were used to calculate ESs. The use of SD
p
resulted in Cohen’s dstatistic (Cohen, 1977). For within-subjects study designs, the following
within-subjects ESs were calculated: pre–post treatment, pre–post control, pre-treatment vs. pre-
control and post-treatment vs. post-control. For between-subjects study designs both within and
ARTICLE IN PRESS
J. Reed, D.S. Ones / Psychology of Sport and Exercise 7 (2006) 477–514488
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 pre–post
treatment and pre–post control. For studies having both within and between ESs, the average
was entered to maintain statistical independence (Hunter & Schmidt, 2004, p. 431). Three r-values
were converted to d’s 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 tand Fratios 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 affect
scale 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). Unlike
other approaches, the Hunter and Schmidt method employs a random effects model that focuses
on the accurate estimation of the standard deviation of population ESs (SD
corr
) 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 SD
corr
by
producing variance above that predicted by sampling error and other artifacts (Schmidt, et al.,
1993). Therefore, we employed Tukey’s (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).
ARTICLE IN PRESS
Table 3
Descriptive statistics for internal consistency reliabilities (a) for the main meta-analyses
Artifact distribution NKMean aSD aMean ffiffiffi
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; Mean ffiffiffi
a
p, average square root of alpha reliabilities; SD ffiffiffi
a
p,
standard deviation of the square root of alpha reliabilities.
J. Reed, D.S. Ones / Psychology of Sport and Exercise 7 (2006) 477–514 489
Meta-analyses
We computed the following for all meta-analyses (including moderator analyses): total sample
size (N), number of ESs(K), mean sample-size weighted observed ES (¯
dobs), ¯
dobs 95% confidence
interval (95% CI), mean sample-size weighted corrected ES (¯
dcorr), corrected standard deviation
(SD
corr
), residual standard deviation (SD
res
), percent of d
obs
variance due to sampling error
(%Var
e
), 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 ES was set at .20, considered a small effect (Cohen, 1988). The ¯
dcorr and
SD
corr
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 SD
corr
and the 90% CrI were used to identify moderators.
The width of the 90% CrI depends on SD
corr
.IfSD
corr
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 confound
conclusions 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 pre–post 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 ES was 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 ES subgroups for each moderator variable were meta-analyzed. Moderators were
explored by examining differences between ¯
dcorr values and changes in the SD
corr
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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J. Reed, D.S. Ones / Psychology of Sport and Exercise 7 (2006) 477–514490
Results
Overall meta-analyses
Pre-test differences between exercise and control groups were very small, ¯
dcorr ¼:05
(SD
corr
¼.10). 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 (SD
corr
¼.25) for the control group meta-analysis indicated that control
conditions are associated with small decreases in PAA. The ¯
dobs for exercise groups was .45 (95%
CI: .40 to .50) and the ¯
dcorr was .47 (SD
corr
¼.37), which is a robust, moderate ES, nearly four
times that associated with control samples. The SD
corr
suggests the presence of moderators and
the 90% CrI included zero indicating that the effects of exercise on PAA do not generalize. Fail-
safe N’s of 48–297 suggest good tolerance to availability bias. That is, 48 additional ESs with a
¯
dcorr of .40 for pre-exercise vs. pre-control, and 117 additional ESs with a ¯
dcorr of .40 for pre–post
control would have to be found and included in the analysis to increase the current ¯
dcorr values to
.20, the critical ES we set based on Cohen’s (1988) criterion. For pre–post 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(d
corr
). Tables 6 and 7 display results for
baseline 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
ARTICLE IN PRESS
Table 4
Overall meta-analyses of the effects of acute exercise on post-exercise positive activated affect
Analysis NK
¯
dobs 95% CI ¯
dcorr SD
corr
SD
res
%Var
e
90% CrI ¯
dfs
Pre-exercise vs. pre-control 2582 64 .05 .02 to .12 .05 .10 .10 88.28 .08 to .18 48
Pre–post control 1602 63 .17 .23 to .10 .17 .25 .25 18.54 .49 to .14 117
Pre–post 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;SD
corr
, sample-sized weighted corrected standard
deviation; SD
res
, residual standard deviation; %Var
e
, percent of d
obs
variance due to sampling and measurement error;
90% CrI, 90% credibility interval; ¯
dfs,¯
dcorr fail-safe Nwith ¯
dcritical ¼:20, ¯
dunlocated ¼:00 for ¯
dcorr values above .20 and
¯
dunlocated ¼:40 for ¯
dcorr values below .20. Boldface entries are best estimates of the population mean ES.
J. Reed, D.S. Ones / Psychology of Sport and Exercise 7 (2006) 477–514 491
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). Most
moderators were inversely related to d
corr
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 ¯
dcorr
for the lower third of the distribution was .63 (SD
corr
¼.37), nearly twice the magnitude of the
other 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 higher
baseline 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 (SD
corr
¼.33) 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)
ARTICLE IN PRESS
Table 5
Intercorrelations between moderators and moderators and corrected ESs
Moderator 1 2 3 45678
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 (d
corr
) —
Note: All moderators were correlated using actual coded values such as time (min) for duration, except for publication
status where published ¼1 and unpublished ¼2. Values in parentheses are sample sizes.
J. Reed, D.S. Ones / Psychology of Sport and Exercise 7 (2006) 477–514492
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 SD
corr
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
ARTICLE IN PRESS
Table 6
Moderator analyses for baseline PAA, exercise characteristics, and post-exercise assessment times
Analysis NK
¯
dobs 95% CI ¯
dcorr SD
corr
SD
res
%Var
e
90% CrI ¯
dfs
Baseline PAA
o.5 z2861 75 .60 .51 to .69 .63 .37 .35 18.97 .16 to 1.10 162
.5 zto .5 z2159 66 .33 .25 to .41 .34 .30 .29 24.24 .04 to .72 47
4.5 z1686 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 70
High 1679 60 .31 .20 to .43 .31 .42 .42 12.48 .22 to .84 29
Not 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 56
20 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 29
475 331 12 .72 1.11 to .33 .72 .66 .66 5.28 1.56 to .13 55
Not 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 8
Very high 216 6 .98 1.31 to .66 .98 .37 .37 15.99 1.45 to .51 35
Not reported 2754 41 .48 .38 to .58 .52 .32 .30 20.30 .11 to .92 65
Post-exercise assessment time
a
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 .88 125
15 to 30 1744 57 .27 .17 to .37 .27 .34 .34 19.28 .16 to .71 21
40 to 1440 677 29 .09 .03 to .21 .10 .31 .29 25.01 .28 to .48 15
Not reported 1033 14 .52 .40 to .64 .58 .21 .19 34.16 .32 to .85 27
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;SD
corr
, sample-sized weighted corrected standard
deviation; SD
res
, residual standard deviation; %Var
e
, percent of d
obs
variance due to sampling and measurement error;
90% CrI, 90% credibility interval; ¯
dfs,¯
dcorr fail-safe Nwith ¯
dcritical ¼:20, ¯
dunlocated ¼:00 for ¯
dcorr values above .20 and
¯
dunlocated ¼:40 for ¯
dcorr values below .20. Boldface entries are best estimates of the population mean ES.
a
Post-exercise assessment time in minutes (min).
J. Reed, D.S. Ones / Psychology of Sport and Exercise 7 (2006) 477–514 493
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 ¼:48 (SD
corr
¼.39),
sedentary ¯
dcorr ¼:14 (SD
corr
¼.30). In sum, low-intensity effects were generalizable and larger
than moderate and high-intensity exercise. Some caution is warranted in interpreting the low-
intensity subsample results because the smaller Kcan 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., 15–40 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 SD
corr
values. 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 Kfor this subsample
warrants some caution in the interpretation of the results.
The 30–35 min duration produced the largest effect ( ¯
dcorr ¼:57, SD
corr
¼.33). To assess the
extent 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) ¼.15, p¼.25, adding
to 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
ARTICLE IN PRESS
Table 7
Moderator analyses for study quality and source information
Analysis N K ¯
dobs 95% CI ¯
dcorr SD
corr
SD
res
%Var
e
90% CrI ¯
dfs
Threats controlled
1–2 2687 67 .28 .17 to .38 .30 .45 .42 5.13 .28 to .88 33
3–4 1267 25 .47 .35 to .60 .49 .25 .24 43.61 .17 to .81 37
5–6 3202 107 .49 .43 to .55 .50 .29 .28 31.11 .14 to .87 162
7–10 489 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 14
Published 7082 184 .45 .39 to .51 .47 .39 .37 18.59 .03 to .97 252
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;SD
corr
, sample-sized weighted corrected standard
deviation; SD
res
, residual standard deviation; %Var
e
, percent of d
obs
variance due to sampling and measurement error;
90% CrI, 90% credibility interval; ¯
dfs,¯
dcorr fail-safe Nwith ¯
dcritical ¼:20, ¯
dunlocated ¼:00 for ¯
dcorr values above .20 and
¯
dunlocated ¼:40 for ¯
dcorr values below .20. Boldface entries are best estimates of the population mean ES.
J. Reed, D.S. Ones / Psychology of Sport and Exercise 7 (2006) 477–514494
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 40–60 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 (SD
corr
¼.37) for very high, .46 (SD
corr
¼.34) for
moderate, and .45 (SD
corr
¼.30) for low doses, supports the hypothesis of an inverse relation
between 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 SD
corr
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;Knapik
et 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.
Post-exercise assessment time
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 (0–2 min) to .10 (40–1440 min) and the associated change in SD
corr
across 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. The
40–1440 min subsample results are based a relatively small Kand 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.
Study quality and source
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 result
indicates that population ESs across the categories occupy nearly the same range of values. The
results for the 3–4 and 7–10 threats controlled subsamples should be interpreted with some
caution because of the relatively small Kin these categories.
There were 184 and 44 ESs associated with published and unpublished studies, respectively. The
¯
dcorr for published studies of .47 (SD
corr
¼.39) was nearly double that for unpublished studies at
.26 (SD
corr
¼.37). However, the overlapping 90% CrI indicate that the potential moderating
effect 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 ¼:47). The
typical control condition produces decreases in PAA of about one fifth of a SD (¯
dcorr ¼:17). On
average, exercise and control groups do not report different pre-test levels of PAA (¯
dcorr ¼:05).
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. The ¯
dcorr for
lower 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 exercise
produced 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 20–30 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%
VO
2max
) 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 intensity
of 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
aerobic–anaerobic transition and larger ESs for samples with a greater percentage of participants
exercising at or below the aerobic–anaerobic 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 aerobic–anaerobic 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 ES magnitude 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 requires
further 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 of
post-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 low
and 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 innate
biological 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 moderate
exercise, 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 to
the 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. 399–400). For this reason, the results of some
of 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 overall
representation 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 these
generalizable effects, however, large SD
corr
values suggest that additional variables, possibly
related to individual differences, moderate the effects of exercise on PAA.
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Further reading
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Psychosomatic Medicine,45(5), 425–433.
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