los angeles or eugene, kay porter and judy foster ...€¦ · some 27 % of the respondents trained...

The Sport Psychologtst, 1989, 3, 245-253

An Exploratory Examination of Cognitive Strategies

Used by Masters Track and Field Athletes (Research Note)

Steven Ungerleider Jacqueline M. Golding Integrated Research Services University of California

Eugene, OR Los Angeles

Kay Porter and Judy Foster PorterlFoster Sports and Organizational Counseling

Although there is evidence that mental practice and associative strategies are associated with successful athletic performance (Feltz & Landers, 1983; Porter & Foster, 1986; Suinn, 1984, 1985; Ungerleider, 1985), little is known about the extent to which athletes actually use these techniques (Suinn, 1985). Hence, practicing sport psychology consultants have little information available as to the frequency that client athletes actually use such techniques. Similarly, little is known about the correlates of these techniques among athletes.

The present study was designed to help rectify this situation by examining the use of mental practice and associative strategies by a large sample of Masters track and field athletes who participated in a national championship event. Also examined is the association of using these strategies with demographic characteristics, athletic background, other mental training strategies, and motivations for athletic participation.

Method Athletes who qualified for the National Masters Championships held in Eugene, Oregon, in August 1987 were mailed a survey instrument and asked to volun- tarily complete it. They were also informed that all data would be kept confiden- tial and that they would remain anonymous. After 3 weeks a reminder letter was sent to all athletes asking them to complete the instrument if they had not already done so. Of 1,014 instruments mailed, 587 were returned, constituting a response rate of 58 %.

Steven Ungerleider is with Integrated Research Services, 66 Club Rd., Suite 370, Eugene, OR 97401. Jacqaeline M. Golding is with the School of Public Health at the Univer- sity of California, Los Angeles. Kay Porter and Judy Foster are with PorterIFoster Sports and Organizational Counseling, Eugene, OR.

246 Ungerleider, Golding, Porter, and Foster

The instrument included items concerning demographic characteristics, physical and mental training strategies, injury experience, mood, attitudes, motivations, and social support. The present study was restricted to measures of cognitive strategies, demographic and athletic background, attitudes, motivations, and performance. Mental practice was assessed with the question, "Do you practice imagery, visualization, or mental rehearsal?" Possible responses were "yes" and "no." Athletes were also asked, "Do you create images or visualization before, during, or after your event? (check as many as apply)." To operationalize associative as opposed to dissociative strategies for coping with the stress of competition, athletes were asked, "Do you monitor your 'body signals' and 'pain zones' when competing?" Response categories were "yes" and "no."

Results Demographic Characteristics

The majority of participants were male (79.8 %) and married (73.3 %). Ages ranged from 30 to 88, with a mean of 50.1. Median education was at the bachelor's level, with 28% having a master's and 13% having a doctorate. Forty-five states were represented in the sample, with the majority of athletes living in California (30 %), Oregon (20 %), and Washington (8 %) .

About half of the athletes had competed in their respective events in high school (52 %) and college (5 1 %), and 41 % had competed at the postcollege level. Some 27 % of the respondents trained at least six times a week. In addition, 24 % reported having a coach; about half of them had had a coach for 2 years or less. Average training included 120 miles a month, and the average longest continu- ous training run was 11.3 miles. The average personal best time for the mile was 5.56 minutes, and the mean for 10 kilometers was 40.4 minutes. Some 49 % of the sample ran at least one marathon, with the average time being 3: 16. Most respondents reported being injured at some point in their career (86 %), 37 % had seen a sports medicine physician at least once, and 5 % had worked with a sport psychologist. Most of these demographic and athletic characteristics were roughly comparable to those reported for other samples of Masters runners (Okwurnabua, Meyers, & Santille, 1987).

Use of Cognitive Strategies

Among the respondents, 83.6% had heard of imagery, visualization, or mental practice, and 90.9% of them reported understanding these processes. Seventy percent of the athletes used mental practice techniques. The modal frequency of mental practice use was seven times a week (37.4%). In this sample, 69.9% reported creating images or visualizations before their event, 25.2% reported visualizing during the event, and 22.3% reported visualizing after their event. A similar percentage of athletes (76%) reported monitoring body signals and pain zones during competition.

Some 35.3 % used physical relaxation methods, 13.5 % practiced meditation, 4.6 % practiced yoga, and 1.8 % practiced a martial art. In this group, 45.5 % report dreaming about their performance and 86.2% of them report success in their dream competitions.

Examination of Cognitive Strategies 247

Predictors of Mental Practice

First, chi-square analyses were used to examine the bivariate associations of mental practice with demographic characteristics, athletic background, other mental training strategies, and motivations for athletic participation. Then a multiple logistic regression model was used to estimate the independent associations of these variables with mental practice.

The first column of Table 1 shows the results of the bivariate analysis using responses to the question of whether they used any form of visualization.

Table 1

Percent of Athletes who Practice Visualization and Associative Cognitive Strategies by Demographic and Athletic Characteristics

Associative Demographic and Any Visualization cognitive athletic characteristics visualization before event strategies

Gender Male (n = 466) Female (n = 1 18)

Education High school or less (n = 31) Some college (n = 69) Bachelor's degree (n = 191) Some graduate school (n = 43) Master's degree (n = 159) Doctoral degree (n = 73)

Marital status Not married (n = 156) Married (n = 428)

Athletic background Competed in college

No (n = 271) Yes (n = 284)

Has a coach No (n = 442) Yes (n = 136)

Ever injured No (n = 81) Yes (n = 503)

248 . Ungerleider, Golding, Porter, and Foster

Table 1 (cont.)


Other mental training Physical relaxation

No (n = 366) 60.8 61.8 72.5 Yes (n = 200) 87.2'"' 86.0* * * 82.1 *

Dreaming about competition No (n = 31 2) 63.9 60.8 73.4 Yes (n = 259) 77.7"' 81.7" 79.4

Success in dreams No (n = 29) 59.3 55.2 75.9 Yes (n = 181) 81.3* 86.1 ** ' 80.6

Motivations and attitudes Ways in which training has helped athlete cope with Stress

No (n = 96) Yes (n = 482)

Frustration No (n = 293) Yes (n = 287)

Tension No (n=197) Yes (n = 384)

Anger No (n = 379) Yes (n = 203)

Problem solving No (n = 376) Yes (n = 206)

Ways in which training has increased self-confidence lncreased self-confidence

No (n = 88) 64.6 62.1 63.8 Yes (n = 492) 71.3 71.4 78.5**

lncreased positive self-image NO (n = 66) 61.3 58.5 58.3 Yes (n = 514) 71.3 71.4 78.4" * *

lncreased positive body image No (n = 46) 60.5 70.8 53.7 Yes (n = 534) 71.5 58.7 77.9***


Demographic Characteristics. Age was significantly associated with the use of mental practice techniques, with 85.6% of those in the 30-44-year age bracket reporting visualization compared to 60% of those in the 45-88-year age bracket, x2(1)=65.76, p<.0001. Men and women were equally likely to report mental practice. Educational attainment was significantly associated with mental practice, x2(7)=4.56, 6 . 0 5 . The highest rates of using mental practice were reported by athletes with some graduate school education (85.7 %) and those with some college education (77.4%), and the lowest rates were reported by those with a high school diploma or less (26.2%). Those who were not married were more likely to report using mental practice strategies than those athletes who were married (72.8 vs. 67.9%), x2(1)=13.51, 6 . 0 5 .

Athletic Background. Athletes who had competed at the college level were more likely to practice mental rehearsal strategies than those who had not competed in college (79.8 vs. 63.0%), x2(1) = 17.42, p<.0001. Similarly, those who had a coach were more likely to practice mental rehearsal than those who trained without one (78.1 vs. 67.3%), x2(1)=4.96, 6 . 0 5 . Athletes who had been injured at some time during their career were more likely than others to use mental practice techniques (71.9 vs. 59.2 %), x2(1) =4.44, H.05.

Other Mental Training Techniques. Athletes who employed physical relaxation were more likely than others to use mental rehearsal techniques (87.2 vs. 60.8 %), x2(1) =40.32, p<.0001. Additionally, both dreaming about competition (77.7 vs. 63.9%), x2(1) = 11.78, ~K.001, and successful performance dreams (81.3 vs. 59.3 %), y(1) =5.44,6.05, were related to mental practice.

Motivations for Training. Those athletes who felt that training and competing had helped them cope better with stress were more likely than other athletes to report using mental practice techniques (72.5 vs. 58.0%), y(1) =6.73, p<.01. Training to cope with daily frustration (75.6 vs. 63.8 %), xZ(l) = 8.63,6.01, tension (73.9 vs. 61.7 %), ?(I) = 8.17, 6 . 0 1 , and anger (75.6 vs. 66.7 %), $(1)=4.39,6.05, were also associated with mental rehearsal. Believing that training and competing had increased self-confidence, positive self-image, and body image were not associated with the use of mental practice strategies.

Bivariate and Multivariate Models of Mental Practice

To assess the stability of these results, the data were reanalyzed using responses to the question about visualization before the event as the measure of mental practice. The second column in Table 2 shows the results of this analysis. These results are similar to those for the more general question on visualization.

Logistic regression models were constructed to estimate the independent association of each variable with visualization (measued dichotomously), control- ling for all the other predictors (Cleary & Angel, 1984). The first two columns in Table 2 show the results of this analysis.

Age, marital status, and practicing relaxation were associated with mental rehearsal (column 1). Younger respondents were significantly more likely to practice visualization, /3 = - .48, y(1) = 12.79, 6 .001 , as were the unmarried, /3=-.30, ?(1)=4.31, 6 . 0 5 , and those who practiced relaxation, /3=.62, $(I) =20.74,p<.001.

250 e UngerleidPr, Golding, Porter, and Foster

Table 2

Logistic Regression of Cognitive Strategies on Demographic and Athletic Characteristics


Intercept Age (45-88) Gender (female) Education9

High school or less Some college Some graduate school Master's degree Doctoral degree

Married

Athletic background Competed in high school Competed in college Has a coach Ever injured

Other mental training Relaxation Dreaming about competition

Attitudes and motivation Ways in which training has helped athlete cope with

Stress Frustration Tension Anger Problem solving

Ways in which training has Increased positive self-image lncreased positive body image

acornpared to athletes with a bachelor's degree. 'W.05; "p<.O1; " *p<.OOl. Note: Logistic regression coefficients are shown.

When visualization before the event is considered (column 2), younger respondents, @= - .49, ?(I)= 13.95, p<.001, those who competed in college, P = .34,2(1)=5.01,6.05, those with a history of injury, @= .30, x2(1)=3.89, 6 . 0 5 , those who practiced relaxation, @ = .56, ?(I) = 17.96, p<.001, and those who dreamed about their performance, @ = .42, g(1) = 12.45, fi.001, were more likely to practice mental rehearsal.


Predictors of Associative Thinking

The third column in Table 1 shows the results of bivariate analyses of predictors of associative thinking.

Demographic Characteristics. Respondents 30 to 44 years of age were more likely than older athletes to indicate that they monitored body signals and pain zones while competing (82.1 vs. 72.3 %), y(4) = 10.78,6.05. Gender, education, and marital status were unrelated to associative thinking.

Athletic Background. Neither competing in college nor having a coach was related to associative thinking. Athletes who had been injured were more likely than others to report using an associative strategy (77.6 vs. 65.3%), x2(1)=4.57, 6 . 0 5 .

Other Mental Training Techniques. Those who practiced physical relaxation were more likely than others to use associative strategies (82.1 vs. 72.5%), x2(1) = 5.68,6.05. Meditation, dreaming, and success in dream competitions were unrelated to the use of associative strategies.

Motivations for Training. Monitoring of body signals was associated with the feeling that training and competing had improved one's self-confidence (78.5 vs. 63.8%), ?(I) =7.48, 6 . 0 1 , self-image (78.4 vs. 58.3 %), x2(1)= 10.77, 6 .001 , body image (77.9 vs. 53.7%), 2(1)=10.91,6.001, and ability to cope with stress (79.3 vs. 61.4%), x2(1) = 12.2, p<.001. Training to cope with frustration (81.5 vs. 70.8%), 2(1)=7.8, 6 . 0 1 , and anger (81.9 vs. 73.2%), $(1)=4.67,p<.05, and to solve problems (82.1 vs. 73.0%), 2(1)=5.30,6.05, were associated with monitoring.

Multivariate Models of Associative Thinking

Logistic regression of the use of associative strategies on demographic and athletic predictors revealed that respondents who did not compete in college, P= - .31,2(1) =3.92, p<.05, those with a history of injury, P= .36,2(1)=5.28, 6 . 0 5 , and those who practiced relaxation, P=.28, x2(1)=4.88, 6 . 0 5 , were more likely to use associative strategies in competition.

Discussion About 70% of the elite Masters track and field athletes in the sample reported using mental rehearsal techniques, and 76% used associative strategies during competition. Younger Masters runners, those with a history of injury, and those who practiced relaxation were more likely to report using both cognitive strategies.

Use of associative strategies in the present sample was not directly comparable to use of these strategies in the Okwurnabua et al. (1987) sample because respondents in the latter were asked about their relative use of associative and dissociative strategies, whereas the present study assessed only presence or ab- sence of associative cognition. Future research using more sophisticated instnunen- tation should enhance comparability across studies.

Who Uses Cognitive Strategies?

Younger athletes were more likely to report using both cognitive strategies in bivariate analyses. The correlation of youth with associative strategies disappeared when other predictors of associative strategies were controlled. One possible

252 Ungerleider, Golding, Porter, and Foster

explanation of the greater likelihood of visualization among younger Masters runners is a historical change, in which younger athletes are trained-or train themselves-to use techniques more popular in a later historical period. To the extent that the popularity of visualization as an athletic training strategy is rela- tively recent, such a change might account for the age difference in its use. Another possibility is that younger athletes are more competitive and therefore use a greater variety of training procedures, including nontraditional ones, to increase their competitive edge. It is also possible that older athletes are less likely to use these strategies because they are more confident and do not feel they need them, or because they had tried these strategies earlier in their career and found them ineffective.

Athletes who had competed in college were more likely to practice mental rehearsal and less likely to use ass6ciative strategies than those who did not compete in college. It is possible that this pattern reflects teaching methods by college track and field coaches when these athletes were attending college. It may also indicate a focus on positive aspects of competition by these athletes, since mental rehearsal involves visualizing successful performance whereas dissociative strategies (the opposite of the associative pattern) involve tuning out pain and fatigue (Okwumabua et al., 1987).

The association of relaxation training with visualization may be due to the relaxation component in mental rehearsal procedures such as VMBR (Suinn, 1984). This explanation would lend confidence in the validity of the measure of mental rehearsal. Another possibility, which is also consistent with the association of self-monitoring with relaxation, is that a more general orientation toward the psychological aspects of athletic performance accounts for the use of both cognitive strategies. If this were the case, we might also expect yoga and meditation to be associated with mental practice, neither of which occurred in the present study. Yoga and meditation were much less common than visualization and associative thinking; 4.6 and 13.5 % of the sample, respectively, reported using these techniques. Yoga and meditation may be viewed as more passive types of mindlbody preparation, whereas running calls for a more active approach to psychophysiological training (Green & Green, 1977). Similarly, the present study found that of those who reported using other types of mental preparation, 48.1 % reported goal setting and only 3.6% reported meditation. Clearly, goal setting involves a more active approach than techniques such as yoga and meditation (Botterill, 1977; Burton, 198311989; Locke, Shaw, Saari, & Latham, 1981).

Similarly, employing imagery about competition was associated with the use of mental rehearsal, particularly before competition. Athletes who use mental preparation have an&dotally discussed dream sequences of winning, losing, and not even finishing (Porter & Foster, 1987). The present results suggest that , those athletes who explore holistic training strategies may be more oriented to imaging, actualization of an imagined performance, and other rnindtbody inter- actions (Ungerleider, 1985; Wenz & Strong, 1979). Further, it is possible that these athletes have greater adaptability to competitive environments, less stress, and enhanced coping mechanisms to deal with the pressures of competition (Mahoney & Avener, 1977; May & Sieb, 1987; Williams, 1986). It is not clear from the present study whether imaging precipitates mental practice or vice versa, or whether a third variable accounts for both.

In summary, mental rehearsal before competition and associative cognitive strategies during competition appear to be used by a majority of Masters


track and field athletes participating in a national championship event. Future research should further analyze the content of cognitive strategies used both before and during competition as well as the process by which athletes alternate among various strategies. By collecting such descriptive data, sport psychologists would be better able to understand and fine-tune mental practice techniques for athletes.

References Botterill, C. (1977, September). Goal setting andperformance on an endurance task. Paper

presented at the Canadian Psychomotor Learning and Sport Psychology Confer- ence, Banff, Alberta.

Burton, D. (1989). Winning isn't everything: Examining the impact of performance goals on collegiate swimmers' cognitions and performance. The Sport Psychologist, 3, 105-132. (Doctoral dissertation, 1983, University of Illinois)

Cleary, P.D., & Angel, R. (1984). The analysis of relationships involving dichotomous dependent variables. Journal of Health and Social Behavior, 25, 334-348.

Feltz, D.L., & Landers, D.M. (1983). The effects of mental practice on motor skill learning and performance: A meta-analysis. Journal of Sport Psychology, 5 , 25-57.

Green, E., & Green, A. (1977). Beyond biofeedback. New York: Dell. Locke, E.A., Shaw, K.N., Saari, L.M., & Latham, G.P. (1981). Goal setting and task

performance. Psychological Bulletin, 90, 125-152. Mahoney, M.J., & Avener, M. (1977). Psychology of the elite athlete: An exploratory

study. Cognitive Therapy and Research, 1, 135-141. May, J.R., & Sieb, G.E. (1987). Athletic injuries: Psychosocial factors in the onset,

sequelae, rehabilitation, and prevention. In J.R. May & M.J. Asken (Eds.), Sport psychology. New York: PMA Publ.

Okwumabua, T.M., Meyers, A.W., & Santille, L. (1987). A demographic and cognitive profile of Master runners. Jouml of Sport Behavior, 10, 212-220.

Porter, K., & Foster, J. (1986). The mental athlete: Inner training forpeakperformance. Dubuque, 1.4: W.C. Brown.

Porter, K., & Foster, J. (1987). In your mind's eye: The process of visualization can improve your game. World Tennis, 8, 35.

Suinn, R. (1984). Visual motor behavior rehearsal: The basic technique. Scandinavian Journal of Behavior Therapy, 13, 131-142.

Suinn, R. (1985). Imagery rehearsal applications to performance enhancement. R e Behavior Therapist, 8, 155-159.

Ungerleider, S. (1985). Training for the Olympic games with mind and body: Two cases. Perceptual and Motor Skills, 61, 129 1- 1294.

Wenz, B.J., & Strong, D.J. (1979). Application of stress management and biofeedback procedures to athletic performance. Paper presented to Biofeedback Society of America.

Williams, J.M. (Ed.) (1986). Applied sport psychology: Personal growth to peak pe$or- mance. Palo Alto, CA: Mayfield.

The authors wish to acknowledge the support of Barbara Kousky of the Oregon Track Club. Appreciation is extended to Norma Jeanne Riggs and Stefan Kramer of Integrated Research Services for technical and statistical contributions to this research project. The authors would also like to acknowledge the anonymous reviewers for their useful suggestions on the revision of this manuscript. Readers may receive a copy of the survey questionnaire by writing the first author.

los angeles or eugene, kay porter and judy foster ...€¦ · some 27 % of the respondents trained...

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