Correspond with all authors via:
Dr. Peter B. Anderson
Department of Human Performance and Health Promotion
University of New Orleans - Lakefront
New Orleans, LA 70148
(504) 280-7061, FAX 280-6018, e-mail: PBAHP@uno.edu

This study examines the relationship between marathon runners' performance and sexual activity plus four other health-related behaviors in the 48 hours prior to a marathon race. Results showed that marathon runners sexual activity was not related to their relative running performance. Those who slept more and took in more calories, compared to the amount they slept and the calories they consumed in previous events, performed better in this marathon, compared to their performance in previous events. Alcoholic drinking and cigarette smoking were not related to self-reported running success.

Running is a popular exercise among people of all ages. Research studies have linked success in competitive running to many factors such as physical fitness, psychological readiness, and health-related behaviors. Prior research has indicated a significant relationship between running performance and behavior variables (Applegate, 1989; Chidley, 1996; Clark, 1997; Reiken, 1991; Ward, 1992).

Perhaps the most challenging running event is the marathon, which is considered an aerobic sport requiring a high level of ATP(adenosine triphosphate) production, combined with strong muscular and cardio-respiratory endurance, high cardiac output and strong will power. Also, compared with running events of shorter distance, health-related variables play a greater role in marathon running success. Research has linked sexual activity (Chidley, 1996), sleeping (Ward, 1992), nutrition (Applegate, 1989), alcohol intake (Reiken, 1991), and smoking (Chidley, 1996) with marathon performance.

The fundamental components of physical fitness (cardiovascular endurance, flexibility, muscular strength, and muscle endurance), as well as the skill-related components ( reaction time, agility, speed, coordination, balance, and power) relevant to successful physical performance, are of concern to athletes who want to perform at optimum levels (Corbin & Lindesy, 1988). The following is a discussion of five major health-related behaviors measured in this study and their impact on athletic performance.

Sexual Activity

Perhaps the most powerful distraction at the Atlanta Olympic Games was the allure of romance and its corollary, sex (Chidley,1996). Tales of the heart from the Olympics and other international competitions have been circulating among the amateur sporting community for years. Indeed, given the dynamics of the Games and the pressure competitors face, Olympic romance is inevitable. Although the commonly believed benefits of sexual abstinence have a long tradition, the majority opinion among athletes, coaches, and physicians now is that sex itself has little or no effect on sporting performance (Chidley, 1996).

James (1990) indicated that sexual abstinence before an athletic event has been advocated by crusty football coaches, Olympic athletes, and even Muhammad Ali in his prime. The truth is no one really knows how the practice got started or whether it is in the athlete's best interest, but everyone has an opinion about it. Depending on the athlete's frame of mind, it couldn't hurt--and it might help (James). In view of the lack of scientific evidence on the subject, athletes these days seem confused about having sex the night before an athletic contest (Fisher, 1997).

Sleeping

Sleep time has been found to be related to athletic performance. Ward (1992) stated that the stability of the sleep pattern continues to be the most consistent indicator of health. Savis (1994) summarized research about sleep and noted that sleep and performance are highly individual specific, with interdependent factors (e.g., time of day, anxiety) influencing the expression of both variables. For the competitive athlete factors such as alterations in diet, age, gender, fitness status, travel across time zones, and anxiety may negatively impact sleep and thus indirectly affect athletic performance.

Alcohol

Alcohol consumption, especially long-term (chronic) alcohol abuse and short-term (acute) alcohol consumption, has been reported to negatively affect physical fitness and athletic performance (Reiken, 1991; New Scientist, 1993). The consumption of alcohol affects virtually every organ and system of the body: cardiovascular, digestive, nervous, endocrine, reproductive, muscular-skeletal, immune, and respiratory (Reiken).

Some investigators have suggested that anaerobic strength is diminished after alcohol consumption (Blomquist, Saltin, & Mitchell, 1970). A reduction in strength likely reduces the skill-related fitness components of speed and power. Alcohol adversely affects the other skill-related components of physical fitness. Balance and steadiness are impaired (Belgrave et al., 1979), as are reaction time, fine and complex motor coordination (Breckenridge & Berger, 1990), visual functioning (Hill & Toffolon, 1990), and information processing (Michel & Batting, 1989).

Nutrition

Many active athletes do not consume enough zinc or iron, which are important for oxygen-activation, electron transport, and injury healing. Sub-clinical deficiencies may impair performance and healing. People who exercise regularly hold many myths about nutrition (Walsh & Lee, 1995) and may need counseling about the importance of adequate dietary intake of iron and zinc (Loosli,1993).

Studies of the health-related effects of coffee in the past few years have produced conflicting results based on the specific sport analyzed (Applegate, 1989). Research indicates that caffeine can boost both mental and physical performance in some cases and impair it in others. Runners can, for the most part, feel secure that drinking moderate amounts of coffee--no more than two cups a day-- will not harm them and may provide some benefits. But they should be on the alert for "coffee nerves" or other side effects of caffeine consumption (Applegate).

Smoking

Despite the diversity and scope of research conducted on the effects of cigarette smoking over the past decades, an exhaustive search, both traditional and electronic, of medical and health journals yielded no specific studies of the direct effect of smoking on sports or athletic performance.

In summary, sleeping, alcoholic drinking, smoking, nutrition and sexual activities all are reported to be or are hypothesized to be directly or indirectly related to athletic performance. Few studies have explored these variables related to marathon running. This study will examine the relationship between marathon running performance and these health-related behaviors in the 48 hours before a running event.Hypotheses

This study examines the relationship between marathon runners' performance and five specific health-related behaviors in the 48 hours prior to the event.

The hypotheses are as follows:

• Relative amount of sexual activity is positively related to reported running performance.
• Relative amount of sleep is positively related to reported running performance (ie., the more sleep a runner has in the 48 hours prior to a marathon, the more likely they are to report their finish as above average or best).
• Relative caloric intake is positively related to reported running performance.
• Relative number of cigarettes is negatively related to reported running performance (ie., the more cigarettes a runner smokes in the 48 hours prior to a marathon, the more likely they are to report their finish as below average or worst).
• Relative number of drinks of alcohol is negatively related to reported running performance.

Students from Taiwan who were completing an executive Master's Degree program in Human Performance and Health Promotion at the University of New Orleans (UNO) approached finishers of the Mardi Gras Marathon held in New Orleans, Louisiana on Saturday, January 17^th, 1998. Each runner was asked to complete a one-page questionnaire that included questions about their pre-race activities and their race performance.

Subjects

The respondent sample included 61 men and 14 women, whose ages ranged from 17 to 65 years old. On average, the subjects were 41.44 years old (S.D. = 10.09), had been running regularly for 12.55 years (S.D. = 5.97), had run an average of 15 previous events (S.D. =16.1).

Procedures

First, the researchers gained the approval of the Human and Animal Subjects Committee at UNO and the New Orleans Track Club, who organized and supervised the race. The day of the race, researchers asked the marathon finishers to volunteer to complete an anonymous questionnaire (see Appendix A) regarding their running performance and health related behaviors in the previous 48 hours.

Instrument

The single page questionnaire was based on a review of relevant literature and questions generated by the research team. Researchers conducted a pretest to support the face and content validity of this questionnaire, which included the following sections:

Part 1: General information (six items) included age, height, weight and gender. Also included was running performance, which consisted of a self-report of running time, place in event, and comparison with previous finishes.

Part 2. Health-related behaviors (eleven items) contained the following behaviors: sexual activity, exercise, sleeping, cigarette smoking, food intake, alcohol intake, non-alcohol intake, watching TV and other non-active recreation, walking, housework and other active recreation in the 48 hours prior to the event. Subjects were asked to provide estimates for each behavior and make comparisons to other events on a scale of one to five, with one standing for least ever, two for below average, three for average, four above average, and five for most ever.

Statistical Analysis

A multiple regression was performed using comparative running performance as the dependent variable and comparative sexual activity, sleeping, smoking, eating (calorie intake), and alcohol intake as independent variables. Also Pearson correlation coefficients were calculated for the variables mentioned above and frequency data were computed for each variable.

Table 1 compares how the subjects performed relative to previous events. Thirty four and seven tenths percent reported below average, 24% average, and 20% above average performance.

Table 2 describes the subjects' sexual activity in the 48 hours prior to the race and relative to previous events. Twenty two and seven tenths percent reported below average, and 57.3% an average number of sexual intercourse experiences.

Table 3 reports the subjects alcoholic drinking behavior in the 48 hours prior to the event and relative to previous events. Twenty percent reported below average, and 58.7% average alcohol intake.

Table 4 reflects the subjects' cigarette smoking behavior in the 48 hours prior to the race and relative to previous events. Sixteen percent reported below average, and 61.3% average number of cigarettes smoked.

Table 5 shows how much the subjects slept in the 48 hours prior to the race and relative to previous events. Twenty two and seven tenths percent reported average, 48% above average and 24% the most sleep ever.

Table 6 presents the subjects' eating behavior 48 hours prior to the race and relative to previous events. Fourteen and seven tenths percent reported below average, 69.3% average, and 14.7% above average caloric intake.

It was hypothesized that increases in the variables of sexual activity, sleeping, and calorie intake, compared with previous events, would predict improved finishes. Results of multiple regression analysis supported two of these hypothesis. Marathon runners who slept more in the past 48 hours, compared to the amount they slept in previous events, performed better in this marathon, compared to their performance in previous events (F(1,73) = 12.71, p = 0.0006). The sleep variable accounted for 13.67% of the variance in performance. Marathon runners who took in more calories in the past 48 hours, compared to the amount they ate in previous events, performed better in this marathon, compared to their performance in previous events (F(1,73) = 4.68, p = 0.0338). Caloric intake accounted for 4.73% of the variance in performance. Comparative sexual activity was not related to comparative performance in this analysis.

It was also hypothesized that for smoking and drinking, decreases in these variables, compared to previous events, would predict improved finishes. These hypotheses were not supported by the multiple regression analysis.

Table 7 provides the Pearson correlation coefficients between comparative running time and comparative sleeping, drinking, smoking, caloric intake and sexual activity. The only significant correlation between comparative performance and health-related behaviors is between comparative running time and comparative caloric intake at p<0.05.

The results of this study confirmed findings of previous studies (Applegate, 1989; Savis, 1994) that sleeping and nutrition play an important role in determining athletic performance. The hypothesized positive relationship between sexual activity and running performance and the negative predicted relationships between cigarette smoking, alcohol intake and running performance did not yield significant results, perhaps because the surveyed marathon runners demonstrated little involvement in these behaviors, therefore leaving little room for a measurable impact on their performance. The low R squares of the multiple regression analysis suggests that there are other variables that can be used to explain the variation in performance that were not included in the model.

The current study yielded very low reliability coefficient (alpha = 0.0352), indicating that there is room for the questionnaire to be further modified. To minimize the impact of inaccurate estimates of the key hypothesized variables, only comparative values were used in the present study. Higher explanatory powers might be obtained if the actual values of each variable were used. Future studies should provide better directions in the questionnaire about how the important variables are defined both qualitatively and quantitatively. For example, choices may be given for various level of calorie intake and each level may be related to typical meal plans so as to get a more accurate estimate the subjects' nutritional status before competition. The same thing should be done for the number of alcoholic drinks, given the fact that the containers are of different sizes and thus liquid ounces are probably better estimates of the amount of alcoholic drinking. Sexual behavior is always hard to quantify. Nevertheless, the number of sexual intercourse events may not be enough to stand out as a predictor for athletic performance. A rating scale of the intensity of sexual intercourse may produce higher explanatory power. Also, the hypothesized negative impact of smoking might only create a measurable effect over a longer period of time. Instead of asking how many cigarettes the subjects smoked 48 hours prior to the event, perhaps they should be asked about this behavior over a longer time period. Other limitations of this study included using a convenience sample of volunteers, reliance on self-reported data, memory and recall difficulties for participants who have completed multiple past events over several years, potential interpretation and communication difficulties, and the emotional or mental state of runners who have just finished running a marathon.

As more recreational athletes choose to participate in competitive events, it becomes more important for educators, trainers, and coaches to be aware of the impact of health-related behaviors on performance so that we can provide the best training, coaching, and education possible for these athletes. Our physical health is only one of the seven dimensions of our health (Anderson & Morgan, 1994). Dispelling myths about abstaining from sexual activity prior to competition or helping adult recreational athletes understand the connections between their spiritual, sexual, and physical aspects of health are important contributions that health and physical educators can make to the lives of adult recreational athletes, especially those who take up the challenge of an event like the marathon.

Table 1. Number and % of runner's rating of running performance in this event.

	Frequency	Percent	Cum Percent
WORST	9	12	12
BELOW AVERAGE	26	34.7	46.7
AVERAGE	18	24	70.7
ABOVE AVERAGE	15	20	90.7
BEST	7	9.3	100
TOTAL	75	100

Table 2. Number and % of runner's reports of sexual activity in the 48 hours prior to this event.

	Frequency	Percent	Cum Percent
LEAST	5	6.7	6.7
BELOW AVERAGE	17	22.7	29.3
AVERAGE	43	57.3	86.7
ABOVE AVERAGE	10	13.3	100
MOST	0	0
TOTAL	75	100

Table 3. Number and % of runner's reports of alcoholic drinking behavior in the 48 hours prior to this event.

	Frequency	Percent	Cum Percent
LEAST	4	5.3	5.3
BELOW AVERAGE	15	20	25.3
AVERAGE	44	58.7	84
ABOVE AVERAGE	7	9.3	93.3
MOST	5	6.7	100
TOTAL	75	100

Table 4. Number and % of runner's reports of cigarette smoking behavior in the 48 hours prior to this event.

	Frequency	Percent	Cum Percent
LEAST	1	1.3	1.3
BELOW AVERAGE	12	16	17.3
AVERAGE	46	61.3	78.7
ABOVE AVERAGE	7	9.3	88
MOST	9	12	100
TOTAL	7	100

 

Table 5. Number and % of runner's reports of sleeping behavior in the 48 hours before this event.

	Frequency	Percent	Cum Percent
LEAST	0	0	0
BELOW AVERAGE	4	5.3	5.3
AVERAGE	17	22.7	28
ABOVE AVERAGE	36	48	76
MOST	18	24	100
TOTAL	75	100

 

Table 6. Number and % of runner's reports of calorie intake in the 48 hours prior to this event.

	Frequency	Percent	Cum Percent
LEAST	1	1.3	1.3
BELOW AVERAGE	11	14.7	16
AVERAGE	52	69.3	85.3
ABOVE AVERAGE	11	14.7	100
MOST	0	0
TOTAL	75	100

Table 7. Pearson correlation coefficients

	Comp. Running Perf.	Comp. # of Cigarette	Comp. # of Drinks	Comp. Calorie Intake	Comp. Amount of Sex	Comp. Amount of Sleep
C. Run. Perf.	1	0.0177	0.1693	.2454*	-0.0155	0.115
C. # Cigs.	0.0177	1	0.0985	- .2974**	0.0816	-0.0974
C. # Drinks	0.1693	0.0985	1	.3403**	0.1647	0.0091
C. Calories	.2454*	-.2974**	.3403**	1	.4350**	.4898**
C. Amt. Sex	-0.0155	0.0816	0.1647	.4350**	1	0.1744
C. Amt. Sl.	0.115	-0.0974	0.0091	.4898**	0.1744	1

* significant at 0.05; ** significant 0.01

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