Affect responses to acute bouts of aerobic exercise in fit and unfit participants: an examination of opponent-process theory.

Bixby, Walter R. ; Lochbaum, Marc R.

For more than 30 years researchers have been touting the feel-better effects of aerobic exercise (e.g., Morgan, Roberts, & Feinerman, 1971; Thayer, Newman, & McClain, 1994; Yeung, 1996). To date, the mechanisms which underlie these positive changes in affective state are not understood (Landers & Arent, 2001). Unfortunately though numerous hypotheses have been forwarded, most research examining the "feel-good" phenomenon to date has been descriptive in nature as opposed to theoretical (Ekkekakis, Hall, & Petruzzello, 1999). For instance, researchers have examined the distraction hypothesis (Bahrke & Morgan, 1978; Morgan, 1985), the monoamine hypothesis (Chaouloff, 1989; Morgan, 1985), the cerebral lateralization hypothesis (Hatfield & Landers, 1987), and the endogenous opioid hypothesis (Janal, Colt, Clark, & Glusman, 1984). In addition to these hypotheses, one theoretical model that has generated interest is Solomon's opponent-process model of acquired motivation (Solomon, 1980; Solomon & Corbit, 1974).

Solomon's theory (1980) proposes that the brain is organized to maintain homeostasis and oppose extreme emotional processes (e.g., pleasure or pain). This is accomplished through two 'opponent' processes that occur as a result homeostasis disruption. For instance when a stimulus is encountered, the organism responds to minimize the impact of the stimulus through the elicitation of two processes, the a and b. The a process responds immediately, in proportion to the stimulus, and returns to resting levels when the stimulus is no longer present. The opponent or b process is slower acting, responds in proportion to the a process, and gradually returns to resting levels when the stimulus is no longer present. The summation of the a and b process leads to the emergent response of the overall system. A key component of Solomon's theory (1980) relates to repeated exposure to a stimulus. With repeated exposure, the a process remains relatively constant in its reaction while the b process becomes stronger in its reaction. Thus, over time the stimulus will have less of an effect on the emergent state of the system during engagement and a larger effect on the system when the stimulus is disengaged.

In the context of exercise, the opponent-process theory would predict that individuals feel worse during and better following the exercise session. This prediction has been supported in parts given the vast amount of research that has demonstrated an improvement of mood following exercise (e.g., Bahrke & Morgan, 1978; Bixby et al., 2001; Lochbaum, Karoly, & Landers, 2004) and worsening of mood during exercise (Bixby, Spalding, & Hatfield, 2001, Hall, Ekkekakis, & Petruzzello, 2002; Lochbaum et al., 2004). Even with all of this supportive evidence, few researchers have mentioned Solomon's theory as a potential explanation for their results (Blanchard, Rodgers, Spence, & Courneya, 2001; Boutcher & Landers, 1988) and only a few researchers have specifically investigated the opponent-process theory (Bixby et al., 2001; Lochbaum et al., 2004; Petruzzello, Jones, & Tate, 1997).

The support for Solomon's theory has been mixed (Lochbaum et al., 2004). Lochbaum and colleagues (2004) noted several serious methodological weaknesses in past research that has limited conclusions concerning the viability of Solomon's theory as an explanation for the affective reactions of participants to exercise. These weaknesses included the failure for adequate measuring of affect across the entire exercise experience (Blanchard et al., 2001; Boutcher & Landers, 1988) and the lack of distinct aerobic fitness or exercise history differences between participant groups (Bixby et al., 2001; Petruzzello et al., 1997). In their attempt to replicate and extend past research examining Solomon's theory (1980), Lochbaum and colleagues (2004) reported that their results generally failed to support Solomon's notion of an opponent reaction. The authors themselves noted that their post exercise time point measurement was somewhat limited and that the exercise intensity calculation from a fixed percent of maximal oxygen consumption might have lead to differing metabolic requirements across all of the participants. More recent research has demonstrated that exercise prescription should be based on percent of ventilatory threshold (Bixby et al., 2001; Hall et al., 2002). In addition, Lochbaum and colleagues (2004) failed to measure ratings of perceived exertion to verify whether or not differences existed within or between the groups and two conditions. These potential perceived effort differences may well have assisted Lochbaum et al. in their data interpretation. Hence, the purpose of the present investigation was to further the study of Solomon's theory by extending past research with improvements in exercise intensity prescription, participants' perceptions of the intensities, and post exercise affect measurement.

To achieve our desired end, we recruited participants of both a history of high physical fitness activity and those of low activity. This anticipated difference was verified with a maximal oxygen consumption test. Target heart rates for both the low and high exercise intensity conditions were based on ventilatory threshold. Last, we measured affect repeatedly after the cessation of both exercise conditions. By designing our research as specified, we generated and tested several hypotheses based on Solomon's theory (1980). We hypothesized (1) that regardless of intensity and fitness level affect balance would follow a rebound model; (2) that regardless of intensity high fit participants would report more positive affect overall compared to low fit participants; (3) that regardless of intensity fitness would interact with time in that high fit participants would report less negative affect during and more positive affect after the exercise conditions compared to the low fit participants; (4) that regardless of fitness group a greater rebound effect will be apparent in the high intensity compared to low intensity condition; and (5) fitness group, intensity, and time will interact so that all of the previously mentioned hypotheses will be supported.

Methods

Participants

Participants were 32 volunteer (12 male; 20 female), healthy, right-handed, and nonsmoking university students. The high fit (7 male; 8 female) participants were recruited via advertisements and personal communication from the cycling club and triathlon club at a large east coast university. The low fit participants (5 male; 12 female) were also recruited via advertisements and personal communication from kinesiology classes at the same university. These participants had little to no experience with cycling as an activity. To confirm fitness classification, all participants completed a maximal oxygen consumption test.

Measures

Self-reported affect. The Activation Deactivation Adjective Checklist (AD ACL; see Thayer, 1989, Appendix A, pp. 178-180) was used to assess affect. The AD ACL is a 20-item self-report inventory that assesses energetic arousal (EA) and tense arousal (TA). EA and TA are consistent with dimensions of positive affect and negative affect. The AD ACL was utilized to derive a measure of positive affect balance (EA-TA). Positive affect balance yields an index of the weight of positive over negative affect (Watson et al., 1988) and is consistent with Solomon's opponent-process theory of acquired motivation (i.e., State A and B). Positive affect balance has been used in past research (Lochbaum et al., 2004; Petruzzello et al., 1997) where it has been labeled "affective valence."

The AD ACL was chosen over other measures of affect for several important reasons. First the AD ACL is a theoretically-based model of activation that is relevant in an exercise setting. Recently, the problems of measures that do not incorporate activation in an exercise context have been reviewed by Ekkekakis, Hall, and Petruzzello (1999). Second, the AD ACL provides a representation of the global affective space. Finally, the AD ACL's reliability and construct validity are well established (Thayer, 1986).

Maximal Oxygen Consumption (V[O.sub.2max]). V[O.sub.2]max (ml/kg/min) was assessed on a cycle ergometer (Monark Exercise AB, model 818, Sweden). The graded exercise test (cr., Astrand & Rodahl, 1977) began with a warmup period during which the participant cycled for 3 min with no load. Thereafter, the load (determined by output wattage) was increased every min until voluntary exhaustion was reached. The increase in load was based on the participant's exercise history and weight (Wasserman, Hansen, Sue, Whipp, & Casaburi, 1994). ECG was monitored throughout via a Sensormedics VMAX 229 metabolic cart (Sensormedics, Inc., Yorba Linda, CA) from pregelled, disposable Ag/AgCL electrodes (Marquette Medical Systems, #900703-230, Jupiter, FL) attached to the participant in a V5 configuration. Ratings of perceived exertion (RPE; Borg, 1985) were obtained every minute during the test. Expired gases were analyzed with a calibrated Sensormedics VMAX 229 metabolic cart (Sensormedics, Inc., Yorba Linda, CA) to obtain breath-by-breath averages of minute volume and fractional gas concentrations of oxygen and carbon dioxide. An estimate of V[O.sub.2]max was deemed valid if two of the following three criteria were met: 1) heart rate equaled the agepredicted maximum, 2) the increase in oxygen consumption was less than 150 ml with an increase in workload, and 3) the respiratory quotient exceeded 1.10 (Taylor, Buskirk, & Henschel, 1955). Ali participants met these criteria. The calculation of V[O.sub.2]max was based on the highest oxygenconsumption value obtained.

Ventilatory Breakpoint (VB). VB was defined as the percentage of aerobic capacity associated with an upward deflection in VE/V[O.sub.2] without a change in VE/VC[O.sub.2] (Wilmore & Costill, 1994) and was chosen with the use of VMAX229 software (Sensormedics, Inc., Yorba Linda, CA). The calculated values were also compared to values derived from visual inspection of the data for accuracy.

Procedures

The experiment involved testing on three separate days over a 10-day period. Prior to each visit to the laboratory participants were instructed to refrain from eating within two hours of testing; to abstain from exercising, alcohol, and caffeine on the day of testing; and to be wellrested (i.e., to obtain 8 hr of sleep) the night before testing. On day one, participants were given a brief description of the study and provided informed consent. After consent was obtained, demographic data (i.e., age, gender, exercise habits) as well as height and weight were obtained (see table 1) and participants completed the V[O.sub.2]max test.

On the second and third days of testing, participants completed 30 minutes of steady state exercise at either a low or high intensity. The order of exercise intensities was randomly assigned and counterbalanced across participants. To control for diurnal variations in affective state, participants completed both exercise sessions at the same time of day. Except for the exercise intensity, the following procedures on the second and third days were identical. Each participant was seated on a recumbent cycle (Lifecycle, Inc., model 9500R, Franklin Park, IL) that was equipped with toe straps to secure the participant's feet on the pedals and the seat position was adjusted to maximize the efficiency and comfort of pedaling. Throughout the experiment the participant was seated on the cycle with the feet secured on the pedals. The exercise session testing protocol involved three contiguous periods: a 15min baseline, a 30min bout of exercise, and 30-min of recovery. Participants were instructed to sit quietly during the baseline. After the baseline ended the participant began pedaling at between 80-90 rpm and 67 watts. The load (i.e., wattage) was progressively increased during the first 5 minutes of the exercise period to bring the participant's heart rate to the appropriate level. The AD ACL was recorded 5 min into the baseline period, at the start of the exercise period, at 10, 20, and at the end of the exercise session, and 10, 20, and 30 min into the recovery period.

In the high intensity condition participants maintained a heart rate at or just below (-3 bpm) ventilatory threshold (low-fit: HR mean = 150.7 [+ or -] 8.2; high-fit: HR mean = 152.4 [+ or -] 8.9). In the lowintensity condition participants maintained heart rate at a level corresponding to 75% of that observed at ventilatory breakpoint (low-fit: HR mean = 112.6 [+ or -] 5.4; high-fit: HR mean = 114.2 [+ or -] 6.8). Heart rate was derived from continuous ECG recordings (Hewlett Packard, model 78352C) using a V5 configuration to ensure that the participant worked at the targeted intensity.

Upon completion of both exercise sessions, each participant was allowed 2 5 min of active recovery (i.e., pedaling < 60 rpm with no load), after which he or she rested until the end of recovery. Self-report affect data were analyzed using repeated measures ANOVA. In addition, effect sizes were calculated to demonstrate meaningfulness using Hedges (1981) formulas.

Results

Group Differences

As can be seen in Table 1, age and height did not differ as a function of group. As a confirmation of the activity classification, a significant main effect emerged for V[O.sub.2]max such that active participants had a greater V[O.sub.2]max than inactive participants. There was also a significant main effect for weight such that inactive participants weighed more than active participants.

Exercise Manipulation Checks

Paired t-tests were conducted to determine whether intensity, as measured by level of cycling, average HR and average RPE differed between the high and low exercise conditions. The level, HR, and RPE for subjects in the high condition (Ms = 3.75 mph [+ or -] 1.54; 149.45 [+ or -] 9.50 bpm; & 14.55 [+ or -] 6.21) showed (p < .05) that they were cycling faster (ES = 1.67) and expending more energy based on HR (ES = 4.72) and RPE (ES = 1.11) than subjects in the low intensity condition (Ms = 1.81 [+ or -] .78; 111.89 [+ or -] 6.43 bpm; 10.10 [+ or -] 1.81).

Tests of Main Hypotheses

Mean, standard deviations, and within group ESs for the affective balance data are found in Table 2. To examine our fifth hypothesis concerning exercise intensity interactions with group (fit or unfit) and time, a 2 (Group) x 2 (Intensity) x 8 (Time) repeated measures ANOVA for affective balance yielded a nonsignificant 3-way interaction, F(7, 24) = .58,p > .05, Wilks' [lambda] = .85, suggesting that affective balance as reported by the two groups did not significantly interact with the intensity of exercise over time. However, we did obtain a significant Intensity by Time interaction (hypothesis 3, F(7, 24) = 3.04,p < .05, Wilks' [lambda] = .53. The Intensity by Time interaction (see figure 1) partially supported our fourth hypothesis after inspection of the collapsed group data over time indicated that participants reported greater positive affect during the low exercise condition when compared to the high exercise condition. No apparent differences emerged during recovery from both exercise conditions. Unfortunately, the Group by Time (hypothesis 3) interaction was not significant.

[FIGURE 1 OMITTED]

Finally, in support of Solomon's basic premises of group and temporal affective patterns (hypotheses 1 and 2), the main effect for Group, F(1, 30) = 7.58, p < .01, Wilks' [lambda] = .79, and Time, F(7, 24) = 5.71, p < .01, Wilks' [lambda] = .37, were significant. Inspection of the data verified that the fit participants, on average, reported greater positive affect balance than did the unfit participants across all exercise time points. As for the temporal pattern, the effect size values (during exercise and post exercise affect subtracted from baseline affect) demonstrated a rebound model as predicted by Solomon.

Discussion

The purpose of the present investigation was to extend past research that has examined Solomon's opponent-process theory of acquired motivation as viable explanation for the temporal patterns of affective response to acute aerobic exercise. The most comprehensive past investigation (Lochbaum et al., 2004) demonstrated partial support. Lochbaum and colleagues' investigation had several methodological flaws such as the temporal measurement of affect during recovery and potential variability within and between groups concerning exercise intensity. This potential variability was due in part to exercise being prescribed based on V[O.sub.2]max and a failure to ask participants to rate their perceived exercise exertion. The present investigation addressed these methodological flaws by measuring affect during recovery for 30 minutes, prescribing exercise intensity based on ventalitory threshold, and by having participants report RPE. It is also important to note that the present investigation's participant groups (fit and unfit) was nearly identical with respect to V[O.sub.2]max as in Lochbaum and colleagues (2004) participant groups (active and inactive); hence, any differences in results would be attributable to methodological improvements and not sample differences.

By designing our study with these improvements, we specifically examined four hypotheses based on Solomon's theory (1980) that stemmed from the interaction of participant group, time, and the two exercise intensities. Though this 3-way interaction was not statistically significant, analyses for three of our four main hypotheses were statistically significant. First, Solomon's two basic tenets that the temporal pattern of affective responding (see figure 2) would follow a rebound model (more negative during exercise, more positive after exercise) and that more fit or more experienced aerobic exercisers would report overall greater positive affect during exercise compared to less fit or inexperienced exercisers were supported. Lochbaum et al. (2004) also reported these findings. Second, the significant intensity by time interaction partially supported our hypothesis based on Solomon's theory (1980) that high intensity exercise would elicit less positive affect during and more positive affect after exercise when compared to a low intensity condition. Follow-up analyses (see figure 1) to this significant interaction suggested that Solomon's predictions were correct during exercise but not after exercise. Again, these results were reported by Lochbaum and colleagues. Last, the group by time interaction was not significant and was contrary to our hypothesis and Lochbaum et al.'s (2004) findings.

[FIGURE 2 OMITTED]

In light of our findings and those of Lochbaum and colleagues, Solomon's theory appears to be partially supported; yet, one important aspect of his theory applied to an exercise setting has not been supported. It appears that regardless of exercise intensity participants of high and low fitness levels report similar positive affective experiences during recovery from exercise. Our results are consistent with several investigations (Ekkekakis, Hall, VanLanduyt, & Petruzzello, 2000; Felts & Vaccaro, 1988; Lochbaum et al., 2004; Porcair, Ebbeling, Ward, Freedson, & Rippe, 1989). These results are encouraging in that it would appear that all participants are able to receive mental health benefits from exercise participation of varying intensities.

Though the present investigation does not speak to exercise adherence, the results suggest that affective experiences during exercise may be the most critical difference between fit or active and unfit or inactive aerobic exercisers. Lochbaum and colleagues (2004) reported similar data suggesting that affect measured during recovery from exercise is similar regardless of exercise intensity and participant fitness or activity level. In contrast, within the exercise sessions, unfit or inactive participants report less positive affect compared to fit or active participants as would be predicted by Solomon's theory (1980). Future research is needed that specifically examines the relationship between affect during exercise and future intentions to engage in structure exercise.

Lochbaum, Bixby, and Lutz (2005) have demonstrated via path analysis that affective responses concerning one's ability to adhere to exercise accounts for roughly 8-9% of typical 7-day strenuous exercise participation. Hence, a more focused examination of the role of affect on exercise intentions is warranted. In addition, it is interesting to speculate that Solomon's theory (1980) may be only most viable under high intensity exercise conditions. In our country, only 23% of the adult population reports engaging in vigorous (strenuous) physical activities 3 or more days a week for at least 20 minutes a session (U.S. Department of Health and Human Services, 2000). Examination of our data and effect sizes (see table 2) suggests that the high fit group reported a substantial improvement in positive mood during exercise recovery (ES range .60-.82) compared to their baseline affect value, whereas the low fit group demonstrated an initial improvement or potentially a relief effect ("Thank goodness this is over!") in positive affect (ES = .62) then only a small enhancement in positive affect was reported (ES range .27-.28).

This investigation was specifically designed to improve upon the methodological flaws of a past investigation (Lochbaum et al., 2004) in order to determine the viability of Solomon's opponent-process theory of acquired motivation as an explanation for affect responses to acute bouts of aerobic exercise. The results supported several hypotheses based on Solomon's theory and in general supported a basic rebound model of affect reporting. Yet, one of the most important tenets that would assist in explaining an individual's acquired motivation for exercise was not supported. This finding casts doubts as to the importance of Solomon's theorywithin the broader contexts of understanding exercise participation patterns unless future research specifically examines the theory with strenuous physical activity participation and adherence.

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Walter R. Bixby

Elon University

Marc R. Lochbaum

Texas Tech University

Address Correspondence To: Walter R. Bixby, Ph.D., Health & Human Performance, 2700 Campus Box, Elon, North Carolina 27244-2010, Email: wbixby@elon.edu

Table 1.
Demographic and exercise parameter means and standard deviations
by participant group

 Fit (n =15) Unfit (n = 17)

 M SD M SD

Demographic Variables
 Age (yrs.) 23.53 3.44 23.52 2.98
 Weight (kg) 63.12 11.14 71.62 13.67
 Height (in) 67.86 4.38 67.82 4.88
 V[O.sub.2max] (ml/kg/min) (a) 48.99 7.02 34.74 5.43

Exercise Parameters

Heart Rate (bpm)
Low Intensity Condition
 Baseline 65.60 13.58 72.67 6.71
 During 111.86 7.92 111.90 5.02
 Recovery 66.95 13.28 71.43 5.49
High intensity condition
 Baseline 67.46 12.08 72.47 6.84
 During 149.84 11.52 149.09 7.64
 Recovery 77.68 13.42 79.53 8.84

Ratings of Perceived Exertion
Low intensity condition
 During 10.11 1.91 10.09 1.77
 Recovery 6.11 .34 625.00 .67
High intensity condition
 During 13.57 1.59 15.41 8.41
 Recovery 6.08 26.00 627.00 .65

Note: (a) F (1, 30) = 41.70, p <.00 1, ES = 2.29

Table 2.
Positive affect balance means, standard deviation, and effect sizes
by the exercise conditions and participant groups

 Baseline During Recovery

Intensity Condition 0 10 20 30 10 20 30

Low

Fit
 (n = 15)
M 8.00 6.80 7.00 7.06 9.60 6.86 9.60 8.46
SD 5.51 5.12 3.54 4.41 4.28 5.23 4.22 4.98
FS -.22 -.18 -.17 .29 -.21 .29 .08

Unfit
 (n = 17)
M 5.05 4.00 4.70 4.17 6.70 4.00 6.52 6.41
SD 5.78 4.25 3.42 3.16 6.40 4.18 3.77 3.31
FS -.18 -.06 -.15 .29 -.18 .25 .24

High

Fit
 (n = 15)
M 5.06 7.20 4.66 5.33 3.73 10.40 8.53 9.13
SD 6.50 4.98 4.87 4.92 4.52 4.18 3.77 3.31
FS .33 -.06 .04 -.20 .82 .60 .70

Unfit
 (n = 17)
M 3.58 2.23 2.29 2.88 1.88 7.53 5.17 5.29
SD 6.40 4.65 3.78 4.82 5.46 5.21 4.65 3.82
FS -.21 -.20 -.10 -.26 .62 .28 .27