exertion–attention–flow linkage under different workloads

Exertion–Attention–Flow Linkage Under Different Workloads

Cathleen T. ConnollyBaptist Behavioral Health

Gershon Tenenbaum1

Florida State University

This study explored a proposed conceptual scheme examining the relationshipbetween perceived exertion, flow, and the attention strategies of association anddissociation. After establishing a maximal baseline, 60 rowers performed at 30%,50%, and 75% of maximal workloads for 10 min on a rowing ergometer. Resultsrevealed that as workload increased, attention shifted from dissociation to associa-tion. Flow also showed a change in endorsement of the 9 dimensions. We found amain effect for gender, but not for experience. Women exhibited higher global flowthan did men during the 75% and maximal sessions. Results lend preliminarysupport for the proposed conceptual scheme in which perceived effort affects atten-tion focus and flow experience. Applied recommendations for coaches and athletesare presented.jasp_613 1123..1145

When asked about their state of mind in the midst of practice or compe-tition, athletes may describe an experience of total absorption, clear goals,enjoyment of the activity, and a balance between their skill levels and thedemands of the task. Csikszentmihalyi (1990) has described this experienceas flow.

The nine dimensions of flow that operationalize the relationship betweenathletes’ perceptions of their performance and flow state include a balancebetween the challenges of the task and the skill level of the performer,merging of action and awareness, clear goals, unambiguous feedback, totalconcentration, sense of control, loss of self-consciousness, time transforma-tion, and autotelic experience. Several studies have explored flow in sport,and the results have demonstrated that the interplay of attention, environ-ment, and task affect both the individual’s perception of performance (orflow state) and the actual performance (Csikszentmihalyi, 1997; Jackson,1992, 1995; Russell, 2001). Csikszentmihalyi (1990, 1997) and Jackson(1992, 1995) proposed that a focused concentration, or complete absorptionof attention, was needed to achieve flow, in addition to directing one’sattention to what is relevant to the performance (e.g., body cues, opposingplayers).

1Correspondence concerning this article should be addressed to Gershon Tenenbaum,Florida State University, College of Education, Department of Educational Psychology andLearning Systems, 307 STB, Tallahassee, FL 32306. E-mail: [email protected]

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Journal of Applied Social Psychology, 2010, 40, 5, pp. 1123–1145.© 2010 Copyright the AuthorsJournal compilation © 2010 Wiley Periodicals, Inc.

The current study aims to investigate the possible link between attentionstrategies and flow, with workload as a mediating variable. To link these twointerrelated concepts, the authors first constructed a conceptual framework,and then designed a study to test several assertions.

A prominent branch of research in attention has been concerned withattention strategies (i.e., association and dissociation). Association is definedas turning focus inward, toward bodily sensations and on task-relevantcues (e.g., pace, breathing), while dissociation is focus away from body sen-sations and on task-irrelevant cues (e.g., daydreaming, the environment;Stevinson & Biddle, 1998). Morgan and Pollock (1977) initiated researchinto this area when they found that elite marathoners reported using anassociative focal strategy during races, and a dissociative strategy duringtraining runs.

Research into the use of association and dissociation has indicated thatattention strategies affect performance outcomes where association results infaster performances (Masters & Lambert, 1989; Tammen, 1996). In addition,attention strategy use has been linked to perceptions of effort, where someparticipants have found association to induce more fatigue and boredom(Pennebaker & Lightner, 1980). Tammen’s work with elite runners indicatedthat a change in physical effort intensity affected the individual’s use ofattention focus. In Tammen’s study, the runners reported focusing internallyand externally, but also felt uncomfortable as a result of the unnatural paceand setting during the submaximal sessions. When running at high intensi-ties, the runners reported feeling pleasant because their attention was focusedinternally, and they were able to center on only one or two features in thisstate.

Tammen’s (1996) research lends support to Tenenbaum’s (2001) effort-related attention model, which builds on Rejeski’s (1985) parallel-processingmodel. Rejeski posited that information of a sensory and emotional naturecan be processed pre-consciously in parallel. However, under cognitively orphysically demanding conditions, only a limited amount of information canbe processed because attention shifts to the main source, which must be takeninto consideration.

Tenenbaum (2001) extended this premise in terms of effort symptomatol-ogy and perceived exertion. During conditions of low physical effort, theindividual can shift attention easily and voluntarily from association to dis-sociation, and from wide to narrow widths. When physical effort increasesand reaches a high or maximal level, however, attention cannot be voluntar-ily shifted or controlled by the individual. Attention is compelled to remainassociative and narrow, as a result of the demands of the environment.Support for Tenenbaum’s effort-related attention model was reported byresearch that has consistently demonstrated a shift from dissociation to

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association as work intensity increased (Hutchinson & Tenenbaum, 2007;Schomer, 1986; Tammen, 1996).

In addition to its relationship with attention, Rejeski (1985) has alsoproposed that perceived exertion should be examined as a social psycho-physiological construct, including physiological, cognitive, and affectivecomponents. For example, it was demonstrated that novices and non-eliteathletes reported vigor and a quick passage of time when dissociating, andfeelings of boredom and fatigue when associating (Pennebaker & Lightner,1980). These results were also supported by Padgett and Hill’s (1989) findingsin which participants reported that time passed more slowly, and they feltmore fatigued when associating than when dissociating during a stationary-bike task.

Thus far, flow and attention focus have fallen into separate areas ofresearch interests. However, flow relies on a participant’s ability to utilizeattention effectively, as evidenced by two of its underlying dimensions (i.e.,total concentration, merging of action-awareness). Both flow and attentionfocus are affected by task characteristics, such as physical load and environ-ment (Csikszentmihalyi, 1997; Jackson, 1992; Russell, 2001; Tammen, 1996).These, in turn, affect performance. Jackson found that ice skaters felt audi-ence response could affect flow and performance, while Russell demonstratedthat “inappropriate focus” (p. 95), “nonoptimal physical state” (p. 98),and “nonoptimal environment/situation” (p. 98) could also disrupt flow andperformance.

Jackson (1995) also supported the result that lack of physical preparation,external stresses, and poor focus could adversely affect flow. In addition, theuse of associative and dissociative attention strategies and perceived exertionhave been found to be mediated by the intensity of the task (Schomer, 1986;Tammen, 1996), while Jackson (1992, 1996) proposed that the type of sport,athletic level, and individual’s experience level may influence the ability toexperience flow.

Figure 1 illustrates a conceptual scheme depicting proposed relationshipsbetween the three concepts of attention focus, flow, and perceived effort.In this scheme, engagement in a physical task—which can range in relativeintensity level—affects both attention focus and flow experience. Attentionfocus is comprised of association (e.g., pace, muscle fatigue, breathing) anddissociation (e.g., daydreaming, environment). Flow is comprised of dimen-sions that may or may not be under the control of the individual or dimen-sions that are affected by the environment or task.

The experience of flow dimensions has been shown to fall on a continuumon which “some dimensions of flow are more extensively experienced thanothers, or even that they occur at different points in time in the flow experi-ence” (Tenenbaum, Fogarty, & Jackson, 1999, p. 286). Tenenbaum, Fogarty,

PERCEIVED EXERTION, ATTENTION, AND FLOW 1125

and Jackson found that autotelic experience was felt most readily, followedby clear goals when they examined how the nine dimensions were endorsedacross a continuum. Similarly, Jackson (1996) demonstrated that somedimensions of flow (e.g., autotelic experience, total concentration, sense ofcontrol, merging of action-awareness) were supported more by elite athletesof seven different sports than were others (e.g., time transformation, loss ofself-consciousness).

Continued exploration of the conceptual scheme demonstrates that theinterplay between the performer’s abilities, task intensity, attention focus,and dimensions of flow ultimately affect the individual’s perception of effortand performance. Thus, perceived exertion and performance (actual andperceived) are determined by the complex relationship between gender, expe-rience, attention focus, flow, and physical effort. As effort increases andattention narrows and becomes associative, several flow dimensions are morepronounced. Similarly, while dissociating, certain flow dimensions are morereadily endorsed than are other dimensions. Thus, the overall flow experienceshifts, as demonstrated by changes in the experience of the nine individualflow dimensions, in accordance with a change in effort and shift in attention.

With regard to gender and flow, Russell (2001) demonstrated a lack of agender effect between collegiate men and women athletes in 28 differentsports, while Shin (2006) and Tenenbaum, Fogarty, and Jackson (1999)showed a significant gender difference in the endorsement of specificflow dimensions. In addition, research in attention strategies has notedgender differences in the use of associative and dissociative strategies

Association Dissociation

Flow: Controlled Uncontrolled dimensions dimensions

Workload/physical effort

Attention focus Performer’s characteristics: * Experience* Gender

Perceived effort and performance

Figure 1. Scheme depicting the linkage between attention focus and flow experiences, as influ-enced by relative physical effort and performer’s personal characteristics.


(Antonini-Philippe, Reynes, & Bruant, 2003; Stevinson & Biddle, 1998;Wrisberg, Franks, Birdwell, & High, 1988). At the same time, gender-relateddifferences in perceived exertion have also been equivocal (O’Connor,Raglin, & Morgan, 1996; Wrisberg et al., 1988). As a result of this discrep-ancy in the literature, a more robust investigation of gender differences, asillustrated in the conceptual scheme, is warranted.

With respect to experience and flow, Jackson (1995) suggested that expe-rienced athletes were more capable of dealing with situations that disruptedflow when compared with less experienced athletes. Tenenbaum, Fogarty,and Jackson’s (1999) results demonstrated that older nonelite athletes(M age = 46 years) more highly endorsed clear goals and loss of self-consciousness, while younger elite athletes (M age = 22 years) endorsedmerging of action-awareness and autotelic experience. However, Martin andCutler (2002) found no difference on the scores of the flow dimensionsbetween undergraduate and graduate theater majors. With regard to experi-ence level and attention, it has been proposed that the use of association ismore a matter of choice among elite athletes (Morgan & Pollock, 1977), whileothers have found that exercise intensity influences attention use (Schomer,1986; Tammen, 1996; Tenenbaum, 2001). Task experience has not beensystematically controlled, despite its appealing relevance to the constructsunder study, which the current study attempts to remedy.

To provide support for this conceptual scheme, a pilot study was con-ducted to examine the relationship between flow and attention, as mediatedby perceived effort. Participants were asked to hold a handgrip device untilthey reported perceived exertion levels (RPE) on the RPE 10-point scale(Borg, 1998) of 3, 5, 7, and maximal. During the task, participants reportedtheir use of attention on a continuum ranging from dissociation (0) to asso-ciation (10). After each perceived exertion level was achieved, participantscompleted the Flow State Scale–2 (FSS-2; Jackson & Eklund, 2002).

The results demonstrated that participants’ attention shifted from disso-ciation to association as perceived effort increased across the four levels ofRPE. In addition, changes in flow dimensions occurred with the RPEincrease. The dimensions of merging of action-awareness, sense of control,and loss of self-consciousness decreased as RPE increased. Total concentra-tion and time transformation increased as RPE level increased. Finally,challenge–skill balance, clear goals, unambiguous feedback, and autotelicexperience remained unchanged as perceived exertion increased. When exam-ining the main effect of flow, the flow dimensions of balance between chal-lenge and skills, clear goals, unambiguous feedback, sense of control, andloss of self-consciousness were felt more intensely than were the dimensionsof time transformation, autotelic experience, total concentration, andmerging of action-awareness.


A gender effect in the use of flow also emerged in which women’s meanflow scores and global flow scores decreased as effort increased, whilemen’s flow scores increased as effort increased. Overall, the results of thepilot study are in line with Tenenbaum’s (2001) model and the proposedconceptual scheme in which attention shifted to association with an RPEincrease, and flow dimensions shifted as task intensity increased. In addi-tion, men and women demonstrated a tendency to experience flow differ-ently, a phenomenon that may have been specific to this sample or task andthat will require further exploration in the current study. The purpose ofthe present study is to explore the conceptual scheme further and toexamine (a) whether flow state will vary as perceived exertion and attentionfocus shift; (b) whether men and women will differ in experiencing flow aseffort and attention shift; and (c) whether experienced and novice rowerswill differ in flow experience as effort increases and attention shifts fromdissociation to association.

Based on the conceptual scheme, Tenenbaum’s (2001) model, and theresults from the pilot study, we derived the following hypotheses:

1. Participants will dissociate during lower levels of effort perceptionand will associate during higher levels of effort perception.

2. During lower levels of effort perception (i.e., dissociation), partici-pants will endorse merging of action-awareness and sense ofcontrol more highly; while during higher levels of effort perception(i.e., association), participants will endorse the dimensions of totalconcentration and time transformation more highly.

3. A gender difference will emerge for global flow scores as perceivedexertion increases. Women will experience higher global flowduring lower levels of effort perception, while men will experiencehigher global flow during higher levels of effort perception.

4. Novice and experienced participants will demonstrate similar flowexperiences during easy and hard physical effort.

Method

Participants

Study participants in the rowing ergometer task were 60 individuals (30women, 30 men) who were recruited from local high school club crew teams(age range = 14–18 years) and a southeastern university Division I rowingteam (age range = 18–25 years). The group of rowers consisted of 15 experi-enced men (M age = 19.7 years, SD = 2.1), 15 experienced women (Mage = 19.1 years, SD = 1.8), 15 novice men (M age = 16.1 years, SD = 2.4),


and 15 novice women (M age = 16.5 years, SD = 2.3) who completed thestudy. Novice rowers were defined as participants who had less than 1 year ofrowing experience, while experienced rowers were defined as participants whohad at least 3 years of rowing experience. Average years of experience formale and female experienced rowers were 5.7 years (SD = 1.9) and 5.1 years(SD = 1.7), respectively, while average years of experience for male andfemale novice rowers were 0.4 years (i.e., 4.7 months; SD = 0.1 months) and0.4 years (i.e., 5.0 months; SD = 0.0 months), respectively.

Instruments

Ratings of perceived exertion (RPE; Borg, 1982). The RPE is an 11-pointcategory-ratio scale ranging from 0 (nothing) to 10 (extremely strong) and wasused to gauge participants’ verbal reports of perceived exertion throughoutthe task. The scale has been shown to have high intra-test (r = .93) andtest–retest (r = .83-.94) reliabilities (Borg, 1998), in addition to being a reli-able measure of physical discomfort and being strongly correlated withseveral other physiological measures of exertion, such as heart rate and VO2

max (Borg, 1982, 1998). Regarding the CR-10 scale, researchers have indi-cated that “the simplicity of the scale should make it applicable in manydifferent kinds of situations where estimates of subjective intensities areneeded” (Noble & Robertson, 1996, p. 73). The scale was explained toparticipants before use, and instructions were given according to guidelinesfrom Borg (1998) in that participants were given simple examples of intensi-ties and were asked to verify their comprehension.

Attention. Participants were asked to report their attention focusthroughout the task on an 11-point scale ranging from 0 (external thoughts,daydreaming, environment) to 10 (internal thoughts, how body feels, breathing,technique). The scale is designed to represent the continuum of attentionfocus from 0 ( pure dissociation) to 10 ( pure association). Tammen (1996)reported similar assessment of association and dissociation. Tammen foundthe one-question scale to be an efficient, valid measure of attention focus usedduring effort engagement of elite runners.

Okwumabua, Meyers, and Santille (1987) also used a one-question scaleasking runners to estimate the percentage of time they spent associating ordissociating during a 10 km run. They found that participants’ use of asso-ciation and dissociation varied along a continuum, and that the one-itemmeasure was an effective way of capturing participants’ attention focus.Masters and Ogles (1998) surveyed attention strategies and found that the useof a one-item measure is an efficient method of capturing participants’ use ofattention in the immediate moment during the session. They also suggested


that the use of multiple methods of measuring association and dissociationwas more effective than was the use of a sole measurement.

Schomer’s classification (Schomer, 1986). Participants were asked towrite down their thoughts after each session. Following testing, participants’thoughts were classified (interrater reliability = .96) according to an estab-lished classification method developed by Schomer. The classificationmethod endorses 10 valid and reliable attention focus subclassifications:feelings and affects (A); body monitoring (B); command and instruction (C);pace monitoring (P); environmental feedback (E); reflective activity thoughts(R); personal problem solving (S); work, career, and management (W);course information (I); and talk and conversational chatter (T).

Categories A, B, C, and P comprise the task-related (i.e., associative)attention focus classification. The task-unrelated (i.e., dissociative) attentionfocus classification embraces the categories of R, S, W, E, I, and T. Schomer’s(1986) classification method has been applied in several studies encompassinga range of different exercise modes, such as laboratory treadmill running(Smith, Gill, Crews, Hopewell, & Morgan, 1995), swimming (Couture,Jerome, & Tihanyi, 1999), and stationary cycling (Breus & O’Connor, 1998).

Flow State Scale–2 (FSS-2; Jackson & Eklund, 2002). The FSS-2 consistsof 36 items that assess nine dimensions of flow. Each dimension is scoredby adding up 4 items, and an overall global score of flow is obtained bysumming all 36 items. The possible range of scores for each dimension is 4 to20, while the possible range for the global score is 36 to 180. Jackson andEklund found that internal consistency in the item identification sample forthe FSS-2 ranged from .80 to .90 (mean a = .85), while reliability for thecross-validation sample ranged from .80 to .92 (mean a = .87).

Commitment check. The commitment check scale measured each partici-pant’s commitment to and effort investment in the task. Participants wereasked to report their commitment, effort investment, and ability to toleratephysical discomfort on a 5-point scale ranging from 1 (completely committed/very little effort/not very well) to 5 (very highly committed/a large amount ofeffort/very well) following each trial. Tenenbaum, Fogarty, Stewart et al.(1999) demonstrated the use of one-item measures and their validity in assess-ments of runners’ perceptions of task demand, ability to withstand sufferingduring a race, and various symptoms of discomfort during a race.

Apparatus

A Concept II ergometer (Concept II, Inc., Morrisville, VT) was used forrowing, which was the physical task employed in the present study. A rowingergometer serves as a land training machine that simulates the rowing motion


for one participant. It possesses a screen that is situated in front of the rowerand displays constant information about the amount of time elapsed, dis-tance rowed, power output values (in wattage), and number of slides of therower per minute. In the present study, the screen was covered so only thepower output was displayed.

Heart rate (HR) data were measured using a Vantage XL Polar HRMonitor (Polar Electro, Finland) that was strapped around each partici-pant’s chest. Data output was viewed via a wristwatch. HR was visible to theparticipants as the monitor was on the participant’s wrist.

Procedure

Participants were asked to complete a university-approved informedconsent form, a health history form, and a demographic profile before takingpart in the study. On the first day, participants performed a maximal baselinemeasure by performing a modified ramped ergometer step test on the rowingergometer (Nolte, 2005). The ergometer screen was set to watts (i.e., power),so that the participant and the experimenter could view the amount of energythe participant was expending.

During the ergometer protocol, participants warmed up on the ergometerfor 5 min. Next, they began to row at a steady-state pace for 3 min. Asteady-state pace was defined as moderate intensity, during which the par-ticipant would be able to converse easily. At 3 min, the participant was askedto increase the wattage by 20 watts, and to hold the new power output. After1 min, the participant was asked to increase the wattage again by 20 watts.This pattern continued until the participant could no longer row and electedto stop. The maximal wattage served as the baseline measure.

At 60-s intervals throughout the step test, participants were asked toexpress verbally their perceived effort and attention focus from scales thatwere presented to them briefly to assist dissociation toward the scale. HR wasalso recorded at 60-s intervals. After participants chose to stop rowing, theycompleted the FSS-2 to measure flow state at that moment, the commitmentcheck to ascertain commitment to the task, and recorded their thoughtsduring the piece.

On a separate day, participants performed three additional ergometerpieces, with a minimum 15-min rest between pieces in a counterbalancedorder to eliminate any order effects. After maximal output had beenachieved, the experimenter randomly assigned the order of the three trials thenight before the test date. The experimenter verified that for each subdivision(e.g., experienced men, novice women), each order of pieces was representedequally.


Based on individuals’ maximal power output, calculations of 30%, 50%,and 75% relative power outputs were performed. The power outputs werechosen to correlate with Borg’s RPE scale on which 3 represents moderateeffort, 5 represents strong effort, and 7 represents very strong effort (Borg, 1998;Noble & Robertson, 1996). Participants were asked to row at the requiredpower output for 10 min. At 60-s intervals, participants were asked to expressverbally their perceived exertion and attention focus. HR data were alsorecorded at 60-s intervals. After participants stopped rowing, they completedthe FSS-2, the commitment checks, and recorded their thoughts. The research-ers maintained the confidentiality of the participants and their results.

Results

Preliminary Analyses

Commitment check. Participants displayed very high ratings for the threemanipulation-check questions for commitment to the task (M = 4.31,SD = 0.10), ability to withstand discomfort (M = 4.34, SD = 0.10), and effortinvestment in the task (M = 3.79, SD = 0.10), indicating that they werededicated to the task. Effort increased as workload level increased from 30%to 75%.

RPE. We conducted a 2 (Gender) ¥ 2 (Experience Level) ¥ 3 (Work-load) ¥ 10 (Minutes) repeated-measures ANOVA on the measure of per-ceived exertion. The results revealed that the main effect of workload wassignificant (Wilks’s L = .03), F(2, 55) = 1060.10, p < .001, h2 = .98. Tukey’shonestly significant difference (HSD) post hoc tests indicate that RPEsignificantly ( p < .05) increased as workload increased.

Main Results

Attention. To test Hypothesis 1 linking an increase in perceived effortto an attention shift from dissociation to association, we conducted a 2(Gender) ¥ 2 (Experience Level) ¥ 3 (Workload) ¥ 10 (Minutes) repeated-measures ANOVA on the measure of attention. The main effect for workloadwas significant (Wilks’s L = .13), F(2, 55) = 183.59, p < .001, h2 = .87, reveal-ing that as relative workload increased, attention shifted from dissociation toassociation.

Tukey’s HSD post hoc tests reveal significant ( p < .05) differences amongthe three workload levels. At the 30% workload, the mean attention scorewas 2.78 (SD = 1.92; i.e., dissociative), while at the 75% workload, the mean


attention score was 7.85 (SD = 1.24; i.e., associative). Comparisons of thethree workloads demonstrate effect sizes of 1.17 between 30% and 50%workloads (M = 4.78, SD = 1.97); 1.91 between 50% and 75% workloads; and1.79 between 30% and 75% workloads.

Schomer’s classification. Two raters (the first author and an individualblind to the nature of the study) categorized the thoughts according toSchomer’s (1986) thought classification scheme and established interraterreliability (r = .96). A Kendall’s W test was conducted to examine the rela-tionship between attention focus and relative workload level. There were 17participants who were excluded from the analysis because they failed to writecomments after all three sessions.

Thought-frequency count differed significantly across the three relativeworkload levels (Kendall’s W = .24), c2(4, N = 43) = 20.67, p = .000. Rowers’use of associative strategies increased from the 30% workload level (30.2%) tothe 75% workload level (88.4%). In addition, as relative workload increased,dissociative thoughts decreased from the 30% workload level (34.9%) to the75% workload level (0.0%).

Flow. Hypothesis 2 posited that during lower levels of effort perception(i.e., dissociation), participants would highly endorse the flow dimensionsof merging of action-awareness and sense of control. In contrast, it waspredicted that during higher levels of effort perception (i.e., association),participants would experience more total concentration and time transfor-mation. We conducted a 2 (Gender) ¥ 2 (Experience Level) ¥ 4 (Workload)repeated-measures MANOVA on measures of the nine dimensions offlow. A significant main effect of workload emerged (Wilks’s L = .86), F(3,54) = 2.93, p = .04, h2 = .14. The results revealed that the mean of the ninedimensions varied as a result of the increase in effort perception. Tukey’sHSD post hoc tests were not significant.

In addition, a significant Workload ¥ Flow dimensions interaction effectwas evident (Wilks’s L = .18), F(24, 33) = 6.17, p < .001, h2 = .82, indicatingthat the individual flow dimensions varied significantly as perceived effortincreased. Tukey’s HSD post hoc analysis reveals that significant ( p < .05)differences were obtained for the dimensions of challenge–skill balance,merging of action-awareness, clear goals, total concentration, sense ofcontrol, and loss of self-consciousness (see Figure 2).

The flow dimension of challenge–skill balance was experienced signifi-cantly more highly ( p < .05) at the workload levels of 50% (M = 15.23,SD = 2.44; effect size = 0.57), 75% (M = 15.71, SD = 2.34; effect size = 0.77),and maximal (M = 15.75, SD = 2.00; effect size = 0.83) than at the 30% work-load level (M = 13.52, SD = 3.47). Rowers did not feel as strong a balancebetween their skill levels and the challenges of the ergometer task at the 30%workload.


However, merging of action and awareness was felt significantly ( p < .05)more at the 30% (M = 16.44, SD = 2.85) workload level than at the 50%(effect size = 0.44), 75% (effect size = 0.95), and maximal (effect size = 0.64)workload levels. Rowers also felt it significantly ( p < .05) more at the 50%workload level (M = 15.23, SD = 2.74) than at the 75% workload level(M = 13.52, SD = 3.46; effect size = 0.55). Thus, rowers demonstrated adecrease in merging of action-awareness as workload increased from 30% to75%, thus supporting Hypothesis 2. Similarly, goals were significantly( p < .05) clearer for rowers during the 30% (M = 16.68, SD = 3.13; effectsize = 0.47) and 50% (M = 16.78, SD = 2.17; effect size = 0.62) workload ses-sions than during the maximal (M = 15.37, SD = 2.41) workload session.

Total concentration significantly ( p < .05) increased as workloadincreased from 30% (M = 10.19, SD = 4.10) to 50% (M = 12.71, SD = 3.80) to75% (M = 14.28, SD = 3.06), thus supporting Hypothesis 2. Comparisons ofthe three workloads demonstrate effect sizes of 0.64 between 30% and 50%workloads; 0.46 between 50% and 75% workloads; and 1.15 between 30%and 75% workloads.

Sense of control was significantly ( p < .05) more pronounced at work-loads of 30% (M = 16.45, SD = 2.47; effect size = 0.60) and 50% (M = 16.17,

Figure 2. Mean flow dimension score (range = 4–20) at each workload for each flow dimensionfor all rowers (N = 60). *p < .05.


SD = 2.24; effect size = 0.52) than at 75% (M = 14.85, SD = 2.81). This sup-ports the hypothesis that the sense of control dimension would be endorsedmore at lower levels of effort perception.

Loss of self-consciousness decreased as workload increased. Significantdifferences ( p < .05) demonstrated that during the maximal workload(M = 13.22, SD = 3.87), rowers felt the lowest loss of self-consciousness thanat the 30% (M = 16.04, SD = 3.47; effect size = 0.76), 50% (M = 15.32,SD = 3.60; effect size = 0.56), and 75% workload levels (M = 14.42,SD = 4.13; effect size = 0.30). Thus, rowers became more self-conscious aseffort perception increased. Finally, the flow dimensions of unambiguousfeedback, time transformation, and autotelic experience remained relativelyunchanged. This result does not support Hypothesis 2 that sense of timewould change with effort increase.

In addition, the 2 (Gender) ¥ 2 (Experience Level) ¥ 4 (Workload)repeated-measures ANOVA for global flow score demonstrates that the maineffect of workload was significant (Wilks’s L = .85), F(3, 54) = 3.30, p = .03,h2 = .16. As workload increased, a change in global flow was evident.However, Tukey’s HSD post hoc tests revealed that significant differencesdid not emerge. Flow was felt slightly stronger at the 50% workload level(M = 134.04, SD = 14.50) than at 30% (M = 131.90, SD = 14.53; effectsize = 0.15), 75% (M = 130.01, SD = 14.34; effect size = 0.28), and maximalworkloads (M = 129.88, SD = 13.88; effect size = 0.29).

Gender. Hypothesis 3 proposed that men and women would differ in flowexperience, as measured by the global flow score. The repeated-measuresANOVA revealed that the main effect of gender was nonsignificant, F(1,56) = 2.13, p = .15, h2 = .04, indicating that male (M = 129.24, SD = 12.92)and female rowers (M = 133.68, SD = 15.46) did not exhibit significant dif-ferences on average global flow score. However, the Workload ¥ Genderinteraction (Wilks’s L = .85), F(3, 54) = 3.10, p = .03, h2 = .15, and post hocanalysis reveal significant ( p < .05) gender differences at the 75% (effectsize = 0.42) and maximal workloads (effect size = 0.42). Women demon-strated significantly ( p < .05) higher global flow scores than did men at theseworkload levels. Figure 3 illustrates the change in global flow as workloadincreased for male and female rowers.

Experience. Hypothesis 4 proposed that novice and experienced rowerswould demonstrate similar flow experiences during easy and hard workloads.Repeated-measures MANOVA demonstrated that the Workload ¥ FlowDimensions ¥ Experience Level interaction was nonsignificant (Wilks’sL = .48), F(24, 33) = 1.47, p = .15, h2 = .52, similar to the Workload ¥Experience Level ¥ Gender interaction (Wilks’s L = 1.00), F(3, 54) = 0.08,p = .97, h2 = .01. These interactions indicate that gender and experience leveldid not result in a difference in flow dimensions as workload increased from


30% to maximal. The main effects of Experience level, F(1, 56) = 0.02,p = .90, h2 = .00; and the Experience ¥ Gender interaction, F(1, 56) = 0.14,p = .72, h2 = .002, were also nonsignificant. These effects indicate that maleand female experienced and novice rowers did not significantly differ in theirmean flow dimension scores. Finally, the Workload ¥ Experience Level inter-action was nonsignificant (Wilks’s L = .90), F(3, 54) = 1.99, p = .13, h2 = .10.Thus, experience in rowing failed to reveal flow changes at 30%, 50%, 75%,and maximal workload levels.

The Flow Dimension ¥ Experience ¥ Gender interaction effect reachedstatistical significance (Wilks’s L = .68), F(8, 49) = 2.95, p = .01, h2 = .33.Tukey’s HSD post hoc tests showed differences only in the dimension of timetransformation. Time transformation was significantly higher ( p < .05) forfemale novices (M = 14.67, SD = 3.82) than for male novices (M = 12.63,SD = 3.82; effect size = 0.53), but it was nonsignificant when compared tofemale experienced (M = 13.00, SD = 3.82; effect size = 0.44) and maleexperienced rowers (M = 13.82, SD = 3.82; effect size = 0.23).

Discussion

The findings reveal that attention shifted from dissociation to associationas workload increased, thus supporting Hypothesis 1. As effort levels sub-stantially elevated to adapt to the change in workload, attention couldno longer voluntarily shift between dissociation and association, but was

Figure 3. Average global flow score (range = 36–180) at each workload for male (n = 30) andfemale (n = 30) rowers. *p < .05.


compelled to remain internal and narrow (i.e., associative). As workloadincreased, individuals reported attention scores in the associative range, and88% of comments were classified as solely associative, while no dissociativecomments were present at the highest workload level. During the 30% work-load level, 35% of comments included looking around at the environment ordaydreaming about school or the weekend, and 35% of comments were acombination of association and dissociation. Thus, attention resources weremore open and flexible to encompass a wider variety of stimuli at the lowereffort levels. These results support Tenenbaum’s (2001) model and otherresearch findings that have demonstrated a shift from dissociation to asso-ciation as work intensity increases (Hutchinson & Tenenbaum, 2007;Schomer, 1986; Tammen, 1996).

It was hypothesized that differences in flow dimension endorsementwould be affected by the increase in workload and shift in attention. Thishypothesis was based on the results of the pilot study and research demon-strating an affective component to perceived exertion and the use ofassociation–dissociation (Padgett & Hill, 1989; Pennebaker & Lightner,1980; Tammen, 1996). Significant changes were noticed for the dimensions ofchallenge–skill balance, merging of action-awareness, clear goals, total con-centration, sense of control, and loss of self-consciousness as effort percep-tion increased. As attention narrowed and became more internal to cope withthe increase in workload, individuals required greater total concentration tomaintain their aerobic output, while also experiencing a greater balancebetween their skill and fitness levels and the increased effort and copingdemands of the task.

Most ergometer practices require the rower to demonstrate power outputsof 50% or greater. Thus, rowers would feel more comfortable at the higherworkloads than at 30%, since they are used to performing at the higherintensities. These results support findings indicating that focused concentra-tion and a balance between abilities and the task demands are needed toachieve flow (Csikszentmihalyi, 1990, 1997; Jackson, 1992, 1995).

Rowers also felt that goals were clearer when working at 30% and 50%workloads than at the maximal workload. This may have been affected by thetask design. During the 30% and 50% workloads, rowers were informed bythe researchers to maintain a certain wattage value for the entire 10 min,whereas during the maximal workload session, rowers were asked to increasetheir wattage by 20 until they could no longer hold the power output. Thus,the rowers may have been unsure of when to stop, or how long they felt theycould maintain the power output in the maximal session.

Finally, a decrease in the dimensions of sense of control, loss of self-consciousness, and merging of action-awareness was demonstrated as work-load intensity increased. As workload increased, rowers were physiologically


working harder, and may have felt greater self-consciousness with regard tothe researchers during these stages because the power outputs were harder tomaintain than during the 30% session. In addition to this increase in self-consciousness, rowers also may have felt less in control as the workloadincreased, and less automaticity (i.e., merging of action-awareness) in theirmovements and technique. The rowers may have felt that they needed toincrease their concentration to maintain the correct technique and musclemovements to maintain their appropriate power output.

In addition to the change in individual flow dimensions as workloadintensity increased, a change in global flow was evident for all participants.Global flow was higher at the 50% workload intensity, but it was almostequivalent at the 30% and 75% workloads. The difference, though small, inglobal flow may indicate that at 50%, rowers may have felt a greater overallflow state than at the other work intensities. During this workload session,rowers were utilizing both attention strategies of association and dissocia-tion, and perceived their exertion as moderate. Thus, when working at 50%effort, rowers were able to alter their attention between association anddissociation to handle the task demands, while also exhibiting a more intenseflow state. When only associating (i.e., 75% workload) or utilizing moredissociation (i.e., 30% workload), the rowers experienced slightly lower flowstate. Thus, when effort was too easy or too intense, the flow state shifted inaccordance with attention.

Since gender is a characteristic that could impact performance and flow,it was hypothesized that a gender difference would emerge in the profile of theglobal flow score. Tenenbaum, Fogarty, and Jackson (1999), Shin (2006),and the pilot study indicated some evidence of a gender effect with regard toflow and the nine dimensions. The pilot study demonstrated that womenreported higher flow as perceived exertion increased when using a handgripdevice, while Tenenbaum, Fogarty, and Jackson’s findings indicated thatwomen found it easier to experience the dimensions of autotelic experienceand merging of action-awareness, while men found it easier to experiencechallenge–skill balance.

In the current study, women reported significantly higher global flowscores at the 75% and maximal workloads, while men demonstrated slightlyhigher global flow scores at the 30% workload level. Thus, women were in ahigher flow state at higher levels of effort perception when attention wasforced to become internal and associative. In contrast, men felt more in flowat lower levels of effort perception when their attention was more flexible anddissociative. This indicates that participants’ gender played a role in theaffective perception of the performance, but not in the use of attention asworkload increased. Men and women demonstrated similar attention strat-egies to cope with the increases in physical demands.


Although a gender difference did not emerge for the individualflow dimensions, the overall flow state was different. Women showedhigher scores in challenge–skill balance, merging of action-awareness, lossof self-consciousness, sense of control, and autotelic experience at the75% and maximal workloads. Men were slightly higher in total concen-tration, sense of control, merging of action-awareness, clear goals, andunambiguous feedback at the 30% workload level. Although men andwomen did not demonstrate significantly different flow dimensions, theydid reveal global flow differences. Thus, the nine dimension scores may failto illustrate the entire flow state, while the global flow score calls attentionto the gestalt or total flow experience (Lindsay, Maynard, & Thomas,2005).

The presence of a gender difference in global flow may be the resultof different affective evaluations of environmental demands by men andwomen. Research in perceived exertion has also demonstrated genderdifferences in perceptions during tasks (Goss et al., 2003; O’Connor et al.,1996; Wrisberg et al., 1988). Wrisberg et al. found that women showedhigher RPE ratings during self-focus, while men showed higher RPEratings during external focus. These differences in affective experiencesmay also be related to how each gender experiences association anddissociation. Although both groups showed similar attention patterns—that is, as workload increased, attention shifted toward associativemode—they may have had different feelings during their performance,which then affected flow state. Research in this area will need to be con-ducted to examine what may account for these gender differences in affec-tive experiences.

Based on the conceptual scheme, it was hypothesized that a differencewould emerge in flow between experienced and novice rowers. The researchhas been equivocal in demonstrating differences in flow between experiencelevels (Jackson, 1995; Martin & Cutler, 2002; Tenenbaum, Fogarty, &Jackson, 1999). The results of the current study reveal that experienced andnovice rowers had similar flow experiences as workload increased from 30%to 75%. The only significant difference with regard to experience level wasthat average time transformation across the workloads was felt more exten-sively by female novice rowers than by male novice rowers, but was notsignificantly different than female experienced or male experienced rowers.Female novices may have felt that they were working very hard, and timemay have seemed to slow down for them as they were associating. The lackof a significant difference between the two groups (i.e., novice and experi-enced) may have been affected by the population chosen. An examination ofelite athletes and novices may have resulted in a significant difference in flowexperience.


Conceptual Scheme

The results of both the pilot study and the main study lend preliminarysupport for the study’s conceptual scheme (see Figure 1). Attentionshifted from dissociation to association as perceived exertion increased.Endorsement of the flow dimensions also shifted as perceived exertionincreased. During dissociation, the dimensions of merging of action-awareness, sense of control, clear goals, and loss of self-consciousnesswere more highly experienced than were other dimensions. As the taskbecame more intense and workload increased, participants experiencedless control and merging of action-awareness or automaticity in their abili-ties. Thus, the increase in exercise intensity affected not only the shiftin attention, but also how effort was perceived and experienced by theindividual.

In the current study, the dimensions of challenge–skill balance and totalconcentration were experienced more intensively when rowers experiencedhigh physical effort, and shifted to an associative mode of attention. As thetask became more intense, participants were forced to work harder, as evi-denced by the increase in workload, while utilizing greater concentration toaccomplish the task. Participants also felt a greater balance between theirskill levels and the demands of the task at the higher workloads than at the30% workload level.

In both the pilot study and the main study, a gender effect was demon-strated in that men and women showed different endorsement of mean andglobal flow across the workloads. In the pilot study, women revealed greatermean flow at each RPE level, but mean flow decreased as work intensityincreased. Men’s mean flow increased as work intensity increased, so thatmen and women felt similar average flow at maximal intensity when utilizinga handgrip device. However, in the current study, women experienced highermean and global flow at the 50%, 75%, and maximal workloads than did themale participants when rowing on the ergometer. Men experienced slightlyhigher global flow at the 30% workload level. Thus, men and women maydiffer in their flow states and affective evaluation of the task when associatingor dissociating.

Finally, experience of the performer failed to affect the flow ofexperienced or novice rowers. The difference between experienced andnovice rowers was about 5 years. Since researchers have proposed that 10years (Bloom, 1985) or 10,000 hours (Ericsson, Krampe, & Tesch-Romer,1993) of deliberate practice are necessary before an athlete attains anelite skill, a stronger manipulation of the variable of experience may haveproduced the hypothesized effect if elite athletes were compared withnovice athletes.


Conclusions, Limitations, and Applied Recommendations

First, attention focus during exercise sessions is largely dependent on theintensity of the task. Specifically, during high-intensity exercise activities,attention is focused on overwhelming physiological sensations, which domi-nate focal awareness. Second, different dimensions of flow are perceiveddistinctly during exercise, and the experience of these dimensions is influ-enced by the demands and nature of the task. Flow experience shifts asintensity changes from easy to moderate to hard, and as attention shifts fromdissociation to association. Third, a change in the global flow experience canbe different for men and women as workload intensity changes, and attentionshifts from dissociation to association mode.

The conceptual scheme that was proposed for the present study wassupported and has suggested a complex relationship between perceivedeffort, attention, and flow, as mediated by task intensity and participants’experience level and gender. Further research with this conceptual scheme isrecommended to verify if the scheme should be modified or if other con-structs should be added (e.g., additional performer’s characteristics).

A few limitations in the present study must be considered when interpret-ing the findings. We examined experienced versus novice differences byemploying collegiate and high school athletes. The factor of age may haveaffected the present findings, as age was confounded with experience level.The use of a retrospective measure of attention can be considered as asomewhat limiting factor, although we employed two different measures ofattention and demonstrated similar findings with both instruments. In addi-tion, the use of single-item measures may have resulted in less reliable mea-sures than multiple-item instruments. However, two measures of attentionwere included to increase validity in the results, and the commitment-checkmeasures have been used in previous research to serve as manipulationchecks.

An additional possible limitation may have been that since 17 of the 60participants failed to write down their thoughts after at least one of theworkloads, their commitment could be questioned. However, manipulationchecks of commitment within each workload level were high, regardless ofreporting having “no thoughts,” which is in line with other studies in whichparticipants have had difficulty remembering details of their thoughts whenexamining associative and dissociative attention strategies (Sacks, Milvy,Perry, & Sherman, 1981). Finally, the sample size may have been too smallfor some of the analyses, possibly resulting in Type I error. Thus, replicationof the study should be performed with a larger sample size.

Research should continue to elucidate the relationship among perceivedexertion, attention strategies, and flow. By altering the task, a change in the


experience of the flow dimensions may result. Follow-up of the gender effectis warranted, since few studies in flow (Shin, 2006; Tenenbaum, Fogarty, &Jackson, 1999) have demonstrated a gender difference, while most research(Jackson & Eklund, 2002; Russell, 2001) has not revealed a gender differencein the use of flow or the FSS-2 (Jackson & Eklund, 2002).

Future research should also look at populations that are considered to beat risk for non-adherence to exercise programs, such as older adults or adultswith a sedentary lifestyle. By examining the affective perceptions of perfor-mance (i.e., flow), researchers may help to design programs of moderateintensity that facilitate a balance between the abilities of the individual andthe demands of the task that will help increase exercise adherence.

The results of the present study may be especially useful for athletes,coaches, or individuals working with these groups. Overall, athletes andcoaches can expect that attention will narrow as workload increases and willshift from dissociation to association. This is important to keep in mind interms of the amount of cues in the environment that athletes can attend to intheir sports. As athletes become fitter and are used to more intense or harderworkloads/practices, they will be able to attend to more cues in the environ-ment, as compared to when they may be less fit, coming off a break, orreturning from an injury.

Coaches and athletes can use the information from the flow results whendesigning practices and viewing how athletes feel about their performance.Athletes demonstrate more flow and enjoyment when they feel their abilitiesmatch the demands of the task and when they feel they are in control of theirperformance. Coaches can help design programs that assist athletes in match-ing their skill–fitness level to the practice demands, while athletes can workon skills that help them feel more in control of the situation. Finally, flowshifts as workload increases and can result in different affective experiencesfor men and women at different workloads. Coaches who work with bothgroups may want to keep this in mind if they notice a difference in how eachgroup feels about the intensity of the workout.

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exertion–attention–flow linkage under different workloads

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