Older vs. Younger Adult Male Marathon Runners: Participative Motives and Training Habits.

Ogles, Benjamin M. ; Masters, Kevin S.

Motivations for and training habits used to prepare for running a marathon were compared between older adults, age [geq]50 (N = 104), and younger adults, age 20 - 28 (N = 110). Motivations were assessed using the Motivations of Marathoners Scales (MOMS) that includes nine internally consistent scales measuring motives for running a marathon. Older runners reported being more motivated by a general health orientation, weight concern, life meaning, and affiliation with other runners. Younger runners were more motivated by personal goal achievement. Older and younger runners train approximately equal miles and hours per week. However older runners train more months prior to the race, and had completed more previous marathons than younger runners. Younger runners who had completed previous marathons had significantly lower best times than older runners.

Prior to the 1970s, long distance running was an activity primarily reserved for elite athletes who trained for serious competition. With the advent of "Jogging", however, long distance running became a bona tide leisure activity. Today, rank and file runners can be seen shuffling along the roads, in the parks, and in the numerous road races (Yair, 1990). With this change in emphasis from elite to leisure status, leisure researchers have become increasingly interested in investigating various aspects of recreational running (e.g., Clough, Sheperd, & Maughan, 1989; Yair, 1990). One sub-group of long distance runners, marathoners, is particularly interesting to study because of the broad impact training has on their lives. Marathon runners voluntarily expose themselves to physical distress while following rigorous time-consuming schedules and careful diets that are well beyond the levels necessary for general health and fitness. They may schedule vacations around potential races and prioritize the daily run at the top of their to-do lists. Some develop a running identity that includes association with runners' clubs and frequent racing. Some researchers have even gone so far as to use the terms obligatory running or running addiction to describe the dedicated activity of the long distance runner (Blumenthal, O'Toole, & Chang, 1984; Dishman, 1985). Regardless of the terms used, however, marathon runners certainly represent an unusual group of motivated individuals involved in a unique leisure activity.

A number of investigators have explored the participative motives of long distance runners. Carmack and Martens (1979) identified seven categories runners gave for running, including: physical health, psychological health, self-image, affiliation, achievement, rewards, social influence, and availability. Johnsgard (1985a, 1985b), using an open-ended questionnaire, reported similar clustering of subjects' motivations for running. Summers and colleagues (Summers, Machin, & Sargent, 1983; Summers, Sargent, Levey, & Murray, 1982), found goal achievement, a test of personal worth, physical health, and the influence of others to be the most frequently reported reasons for running a marathon while increasing the level of fitness was reported most frequently as the reason for initially beginning to run as a form of exercise. Curtis and McTeer (1981) in their study of 587 marathoners summarized that most runners began running as a way of improving their physical or emotional health (e.g. lose weight, increase cardio respiratory fitness, relieve stress, maintain fitness for other sports), but then move on to marathon running as part of a need for additional challenges or personal achievement. Finally, Clough, Sheperd, and Maughan (1989) investigated the self-report participative motives of over 500 marathon and half-marathon runners. They constructed a comprehensive list of items based on the motivational categories developed at a round table conference of leisure researchers (Crandall, 1980). A factor analysis of runners responses resulted in six basic categories of participative motives: challenge, health/fitness, well-being, addiction, status, and social.

While these studies provide initial formulations concerning the motives of long distance runners, Pargman's suggestion that the motivations of long distance runners are only superficially understood and that no comprehensive theory of running motivation is generally accepted may well be equally applicable now as when first published in 1980. Certainly, many questions regarding the participative motives of runners remain unanswered and some methodologies lack rigor. Few studies have attempted to begin investigating differences in participative motives among various sub-groups of runners. For example, age as a possible confounding variable has frequently been ignored and most studies include runners who train and compete in races of varying distances. Although Johnsgard (1985a, 1985b) asked older runners to retrospectively report their motives for running at a younger age, no study has attempted to investigate differences between varying age groups of runners. Similarly, the evolution of motivations has never been studied using a longitudinal methodology. In addition, the data concerning participative motives has typically been collected through surveys using open-ended questions. The answers are then divided according to content category with totals and percentages reported for each. Recently, a standardized self-report instrument that measures motives for running has been developed (Masters, Ogles, & Jolton, 1993) and includes nine internally consistent scales: health orientation, weight concern, personal goal achievement, competition, life meaning, psychological coping, self-esteem, affiliation, and recognition. Increased measurement precision, examination of running sub-groups, and consideration of the multi-dimensional nature of motivations for running will certainly improve upon previous methodologies for studying the motivations of runners.

This study begins the process of further investigating within-group differences among marathoners by comparing the self-report participative motives and training habits of older and younger male marathon runners. Female marathon runners were not considered in this particular study, because there were only a few women race participants over age 50. It was hypothesized that older and younger marathon runners would report differing motives for training and running a marathon, and that these differences may lead to theoretical formulations regarding the evolution of running motives over the life-span. More specifically, it was hypothesized that older runners would report being more motivated by health and weight concerns while younger runners would be more motivated by competition and personal goal achievement.

Older and younger runners were also hypothesized to have different training habits. The expectation was that older runners would train equal miles per week, but at a slower pace, thus resulting in more training hours per week. It was also expected that older runners would have completed more previous marathons.

Finally, it was hypothesized that difference in motivation either alone or interesting with age would be related to training and performance variables. In particular, runners who reported social motives (affiliation & recognition) as reasons for running were expected to be less likely to train alone when controlling for age. Similarly, age and social motives were hypothesized to interact in their influence on time spent training alone. Runners who reported being more motivated by achievement motives (competition & personal goal achievement) were expected to report training more miles per week, participating in more marathons, and having faster previous finishing times when controlling for their age. Again, age was hypothesized to interact with achievement motives with regards to these variables. Finally, runners who reported general health as a primary motive for running were expected to train fewer miles per week when controlling for age.

Method

Subjects

Runners pre-registering for one of four midwestern marathons were asked to complete a packet of questionnaires and return them by mail. The initial response rates ranged from 38 to 45%. From the final subject pool of 1075 runners, all older male runners (age [geq] 50) were selected (N = 104). In addition, all male runners between the ages of 20 and 28 were selected as a comparison group (N = 110). The sample of 214 runners were predominantly white (94%), married (60%), men who earned an average income of $36,000 per year. The self-reported average height was 70 inches and the average weight was 157 pounds.

Instruments

The packet of questionnaires included the Motivations of Marathoners Scales (MOMS; Masters, Ogles, & Jolton, 1993) and a form to collect demographic and training information as well as several other instruments not relevant to this study.

Demographic and Training Questionnaire. Participants answered questions regarding several variables of interest, including: age; gender; race; yearly income; marital status; miles, hours and days of training per week; years running; percent of time training alone; number of previously completed marathons; best and average finish times for previous marathons; and the number of runners typically associated with during a week.

Motivations of Marathoners Scales (MOMS). The MOMS is a 56-item instrument that has nine scales. The contents of the items and scales were developed based on previous studies investigating the motives of long distance runners (Carmack & Martens, 1979; Curtis & McTeer, 1981; Johnsgard, 1985, 1989; Masters & Lambert, 1989; Summers, et al., 1983; Summers, et al., 1982). Through a review of previous studies, four broad categories of motives for running were identified: psychological, physical, social, and achievement. Within these broad categories nine specific motives for running a marathon were also gleaned from previous research. Psychological motives are comprised of maintaining or enhancing self-esteem, providing a sense of life meaning or aesthetics, and problem solving or coping with negative emotions. Physical motives for marathon running include general health benefits, and weight concern. Social motives include affiliation with other runners, and recognition or approval from family or friends. Finally, achievement motives for marathon running include competition with other runners, and personal goal achievement. The nine scales of the MOMS are adequately reliable both in terms of internal consistency (range .80 to .92) and test-retest estimates (intraclass correlations range from .71 to .90). in addition, evidence for the validity of the instrument has been presented in several studies (Masters & Ogles, 1995; Masters & Ogles, 1998; Masters, Ogles, & Jolton, 1993; Ogles, Masters, & Richardson, 1995).

Procedure

Runners were recruited to participate in the study during pre-race registration at four midwestern marathons. As the marathon entrants registered for the race, they were asked to take home and complete a packet of questionnaires and return them via the mail. A total of 2528 packets were distributed with 1075 (43%) usable tests returned. From this pool of participants our two groups were selected.

Results

Three major analyses were conducted: a comparison of motivations for the two age groups, a comparison of training and performance statistics for the two age groups, and an analysis of the relationship between motives and training behaviors.

Differences in Motives

In order to evaluate the differences in self-report motivations for training for and running a marathon, a one-way (older vs. younger) multivariate analysis of variance (MANOVA) was calculated using the MOMS scales as the dependent variables. The overall MANOVA was significant, F(9,200) = 8.28,p [less than].00l, indicating a significant difference between the two age groups. Univariate analyses revealed that older runners were more strongly motivated to run as part of a broad health orientation, F(1,208) = l8.63, p[less than].001, weight concern, F(1,208) = 6.25, p[less than].01, life meaning, F(1,208)= 6.19,p[less than].01, and affiliation with other runners, F(1,208) = 6.50,p [less than].01. Younger runners were more motivated by personal goal achievement, F(l,08)= l5.05,p[less than].001 (Table 1). Older and younger runners did not differ in their level of competitiveness, p [greater than] .05.

Differences in Training Variables

Differences between the age groups on training variables were evaluated using independent t-tests (Table 2). Older and younger runners train approximately equal miles and hours per week and do not differ in the amount of time they train alone. Older runners, however, did train more months prior to the race, t (211) = -3.O6,p [less than].01, and completed significantly more marathons than the comparison group, t (208) = -4.50,p [less than].001. Finally, younger runners who had completed previous marathons had significantly lower best times than older runners, t (208) = -4.50, p [less than].001.

Predicting Training Differences

In order to examine the interaction of age and motivations on training variables, four stepwise regressions were conducted. First, the influence of age group, competitiveness, and personal goal achievement were examined as to their relationship with the number of attempted marathons. It was hypothesized that runners who were more motivated by competition and personal goal achievement would also participate in more marathons. Because significant differences existed in the number of marathons completed between older and younger runners, a dichotomous variable representing the age group was entered into the equation first followed by a stepwise analysis of the remaining variables. This was done in order to assess the contribution of motivational variables towards the number of completed marathons after accounting for age group differences. As can be seen in Table 3, runners self-report endorsement of competitiveness and an age group by competitiveness interaction term were significantly related to the number of marathons completed after accounting for differences attributed to age group membership. Older runners who reported competition as a more important reason for running were more likely to have participated in more marathons. However, younger runners who endorsed competition as a reason for running were likely to have participated in fewer marathons. Personal goal achievement and the age group by personal goal achievement interaction term did not enter the equation.

Second, the influence of age group, competitiveness, and personal goal achievement were examined in relationship to personal best marathon finish times. Again, because significant differences existed in the best finish times between older and younger runners, a dichotomous variable representing the age group was entered into the equation first followed by a stepwise analysis of the remaining variables. It was hypothesized that runners who reported being more motivated by competitiveness and personal goal achievement would have lower personal best marathon finish times regardless of age. In fact, runners' scores on the competitiveness scale did correlate with best finish times (Table 3). Marathoners who more heavily endorsed competitiveness as a motive for running had lower personal best finish times regardless of the age group to which they belonged.

Third, the influence of age group, competitiveness, personal goal achievement, and health orientation on the number of training miles per week was examined. It was hypothesized that runners more heavily endorsing competitiveness or personal goal achievement as motives for training and running a marathon would train more miles per week. On the other hand, runners who were more motivated by a general health orientation would be more representative of recreational runners and would therefore run fewer training miles. It was also suspected there may be an interaction between age group and competitive, personal goal achievement, or general health orientation motives. Three interaction terms age group X competitiveness, age group X health orientation, and age group X personal goal achievement were therefore included in the analysis.

In fact, competitiveness and general health orientation did enter the equation and predicted training miles per week in the hypothesized direction. Runners endorsing competitive motives trained more miles per week while runners emphasizing general fitness as a motive for running tended to train fewer miles per week. Although personal goal achievement had a significant simple correlation with training miles per week, it did not account for a sufficient amount of unique variance to enter the equation after the competition and general health orientation scales (Table 3). None of the interaction terms were significantly related to training miles.

The final regression analysis examined the influence of age group, affiliation, and recognition on the percent of time runners train alone. Runners more heavily endorsing social motives for running were hypothesized to train less time alone than their counterparts. It also was suspected there may be an interaction between age group and affiliative or approval motives and therefore two interaction terms were included in the analysis: age group X affiliation, and age group X recognition.

Both affiliation and recognition entered the equation and predicted percent of time training alone in the hypothesized direction. Although recognition and percent of time training alone had an insignificant simple correlation, recognition was significantly related to amount of time training alone after accounting for affiliative motives. Runners endorsing affiliative motives were more likely to report training with other runners. Similarly, runners who reported running as a way of gaining approval or recognition were more likely to run with other runners during training. The interaction terms did not enter the equation.

Discussion

Older runners do in fact endorse different reasons for training and running a marathon when compared to younger runners. As a group, older runners more heavily endorsed developing and maintaining a level of fitness and health, including weight benefits, as reasons for running. On the other hand, younger runners more heavily endorsed personal goal achievement. The Personal Goal Achievement scale is based on items, such as: running to beat my best time, trying to push myself beyond my limits, to improve my running speed, etc. As would be assumed, older runners are not as influenced by the notion of personal best, but participate more for general health benefits while younger runners endorse the achievement of personal running goals as the most pressing reason for running a marathon. While they differed in terms of personal goal achievement, older and younger runners did not differ in terms of their endorsement of competition as a motive for running a marathon. In fact, neither group heavily endorsed competitio n as an important reason for training and running a marathon,

Older runners reported life meaning and affiliation with other runners as more influential motives for training and running a marathon. The life meaning scale includes items that center around the idea of running to add a sense of meaning, completeness, peace, and purpose to life or having time to be alone with the world. The affiliation scale measures the degree to which runners report participating as a way of meeting and enjoying other runners' association. We expected that career runners would develop and report more secondary reasons (second to physical fitness) for running since they would be more likely to discover additional positive benefits while participating in an extended training program.

Although the sample was cross-sectional, these differences may be indicative of the evolution of running motives over the life-span. Perhaps younger runners begin running as part of an effort to achieve personal running goals. With added training time they meet other runners, spend many hours on isolated roads with little traffic, and notice the positive psychological and physical benefits of long distance running. As a result, their attributions of motives evolve such that they report additional reasons for running with greater emphasis. In addition, with age they may lose some quickness, endurance, or competitive drive and thus report less achievement oriented motives and more life meaning related motives. On the other hand, perhaps our sample of older runners have always maintained the same set of motives since beginning to run and the more competitive, goal-oriented peer runners have discontinued running. In this case, certain motives may lead to enhanced chances of continued participation in this leisure activity over a longer period of time. Certainly, the most parsimonious explanation is a cohort effect. Nevertheless, more research regarding the changing motives of the distance runner is needed.

Differences also emerged on several training variables. Older runners have been participating in marathons for a longer period of time and as a result have put in more months of training. Yet, younger runners have faster personal best marathon finishing times. This is remarkable considering that older and younger runners train at approximately the same running speed - equal hours and miles per week. Perhaps younger runners perform better in competition than older runners even though they have equivalent amounts of training. Since, younger runners more heavily endorse personal goal achievement as a reason for running, they may be more driven to perform well in the race while older runners train for health reasons and are content to complete the race.

Alternatively, one might suspect that older runners who have participated in an average of 15 marathons each would have a personal best time early in their careers that would be comparable to the younger sample. Maybe the sample of older runners began running later in life. Or, perhaps, the most hard-driving, competitive runners dropped-out through attrition or injury and were therefore not a part of the older sample. Similarly, if a subgroup of runners is systematically eliminated from one cohort through injury or attrition, differences in motivations for these runners may be very useful for developing training strategies. Further examination of participants who run as a result of competitiveness versus those who view the activity as a recreational or leisure activity is warranted. Another conclusion might be that the sample of younger runners included a higher proportion of seriously competitive runners. At the same time, the standard deviations for both groups are quite large and indicate the wide variati on that exists within groups. Clearly, longitudinal research may lead to more conclusive findings.

The final purpose for this study was to investigate the relationship between self-report motivations and training variables while considering both the main effects of age and the possible interaction of motives with age. Runners who reported being motivated more by competition with other runners and less by a general health orientation have different training habits and performance results. Runners more heavily endorsing competitive reasons for running tended to train more miles per week, participate in more marathons, and have faster personal best finishing times. This relationship between competitiveness and training habits is maintained when controlling for age.

One interesting exception to the general relationship between competitiveness and training variables involves the interaction of age and competition with regards to the total number of completed marathons. Younger runners who had participated in fewer marathons were more likely to report competition as a motive for running. Older runners who reported competition as a motive for running were more likely to have completed more marathons. Perhaps this relationship is indicative of runners' perceived chance of success. Older runners are more likely to place because the number of competitors diminishes in the higher age groups. At the same time, younger runners who are participating in a marathon may be former high school or college track athletes who are now increasing their running distance.

Runners who report running as a way of meeting other runners and gaining recognition from friends and family tend to spend less time training alone. This finding generalizes previous research concerning affiliative motives in other content areas where people with a high need for affiliation have been found to learn social roles more quickly, identify faces more quickly, perform better with affiliative incentives, be involved in more social activities, etc. (McClelland, 1985). The importance of affiliative motives for participation in team sports has been duly noted (Butt, 1987), yet affiliation as an important determinant of a typically isolating activity such as running is often neglected. Even though nearly every study of runners' self-report participative motives finds social motives to be important, this is the first study to indicate that runners from different age groups report varying levels of affiliative motives as a reason for their continued involvement in the activity. Not only does this finding have consequences for exercise compliance and fitness, but research regarding the participative motives of people involved in other apparently isolating leisure activities should consider the potential implications of affiliation.

While some clear differences in older and younger runners' participative motives and training habits have been identified, the application of these findings to a developmental theory of motivations for running must remain tentative. Self-report methods for measuring human motives and cognitions have been questioned (Nisbett & Wilson, 1977) and certainly additional and ongoing validation of the MOMS is required. Obvious limitations are also involved when making inferences regarding the development of motives over time while using a cross-sectional design. Similarly, other variables (e.g., personality type) not included in the current study could account for differences in motivations both between and within groups (i.e., Hinkle, Lyons, & Burke, 1989). In addition, our sample included a wide variety of runners in terms of level of competitiveness. Some differences in self-report motives may exist between elite and recreational runners that confuse the data reported here. In the "long run", however, additional research may benefit the marathon community as well as all those interested in leisure activities by delineating the motives for training and running a marathon and their evolution over time.

References

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 Means and Standard Deviations on the
 Masters-Ogles Marathon Scale (MOMS)
 for Older and Younger Runners
 Older Runners Younger Runners
MOMS Scale M SD M SD
Life Meaning [*] 3.59 1.62 3.05 1.56
Self-Esteem 4.82 1.33 4.55 1.45
Psychological Coping 3.23 l.55 3.13 1.52
Weight Concern [*] 3.80 1.71 3.22 1.70
Health Orientation [**] 5.22 1.38 4.32 1.63
Recognition 3.32 1.53 3.32 1.62
Affiliation [*] 3.33 1.39 2.83 1.42
Competition 3.03 1.66 3.37 l.6l
Personal Goal Achievement 4.53 1.38 5.23 1.25
(1.)M per item
(*.)p [less than].01
(**.)p [less than].001
 Means and Standard Deviations on Training
 Variables for Older and Younger Runners
 Older Runners Younger Runners
Training Variable M SD M SD
Miles training per week 46.50 14.17 49.19 19.87
Hours training per week 8.91 7.87 8.79 4.99
Months of training [*] 69.66 77.08 42.19 50.85
% time training alone 75.22 28.68 80.32 23.07
# marathons finished [**] 15.08 29.17 2.34 3.82
Best finishing time [**] 225.36 42.01 196.01 33.38
(*.)p[less than].0l
(**.)p[less than].001
 Step-Wise Regression Analyses Predicting Number of Marathons
 Completed, Best Finishing Time, Miles Training per Week, and
 Percent of Time Training Alone
Variable [B.sup.a] r [R.sup.2]
Predicting Number of Marathons Completed
Age Group -.33 .44 .19
Competition 2.08 .26 .29
Age Group X Competition 1.70 .51 .36
Personal Goal Achievement .08
Age Group X Personal Goal .48
Predicting Best Finishing Time
Age Group 12.51 .33 .11
Competition -8.41 -.36 .21
Personal Goal Achievement -.34
Age Group X Competition .30
Age Group X Personal Goal .27
Predicting Miles Training per Week
Competition 3.77 .34 .12
Health Orientation -2.00 -.15 .15
Personal Goal Achievement .27
Age Group X Competition -.10
Age Group X Health Orient -.01
Age Group X Personal Goal -.06
Predicting Percent of Time Training Alone
Affiliation -6.61 -.23 .06
Recognition 3.77 .03 .10
Age Group -.10
Age Group X Affiliation -.03
Age Group X Recognition -.02
Variable [F.sup.b]
Predicting Number of Marathons Completed
Age Group 35.83 [***]
Competition 30.58 [***]
Age Group X Competition 27.48 [***]
Personal Goal Achievement
Age Group X Personal Goal
Predicting Best Finishing Time
Age Group 14.60 [***]
Competition 15.11 [***]
Personal Goal Achievement
Age Group X Competition
Age Group X Personal Goal
Predicting Miles Training per Week
Competition 27.38 [***]
Health Orientation 18.22 [***]
Personal Goal Achievement
Age Group X Competition
Age Group X Health Orient
Age Group X Personal Goal
Predicting Percent of Time Training Alone
Affiliation 12.25 [***]
Recognition 10.45 [***]
Age Group
Age Group X Affiliation
Age Group X Recognition
(a.)B = weights for final equation with all entered variables
(b.)F = F for full equation after each step.
(***.)p[less than].001