1 Division of Research, Kaiser Permanente, Oakland, CA.
2 Department of Epidemiology and Preventive Medicine, School of Medicine, University of California at Davis, Davis, CA.
3 Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI.
Received for publication November 2, 2001; accepted for publication May 7, 2002.
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
exercise; menstrual cycle; menstruation; prospective studies
Abbreviations: Abbreviations: BMI, body mass index; MET, metabolic equivalent; SE, standard error.
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
The extent to which such menstrual cycle alterations occur with the lower levels of more moderate intensity physical activity typically seen in the population of women at large is not well understood. A few epidemiologic studies suggest that women participating in moderate recreational activity have longer and more variable cycles than do sedentary women (21, 22), and amenorrhea has been induced in previously sedentary women with the initiation of a vigorous training program (23). From a public health perspective, understanding the effects on the menstrual cycle of moderate levels of physical activity is most relevant because few women engage in high-level, vigorous exercise, but as many as 20 percent regularly participate in moderate-intensity activity (24). Therefore, the purpose of this investigation was to determine the influence of physical activity on menstrual cycle characteristics in two epidemiologic cohorts on whom menstrual cycle data were prospectively collected. Specifically, this study examined the relations between physical activity and mean cycle length, variability of cycle length, and mean bleed length in an ethnically diverse cohort of women employed in the semiconductor industry and in a cohort of White women participating in a longitudinal community health study. The extent to which these relations varied by body fat was also addressed.
![]() |
MATERIALS AND METHODS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
The second cohort consisted of women aged 2448 years in 1992, who were living in Tecumseh, Michigan, and who completed both the 19921993 and 19931994 examinations for the Michigan Bone Health Study. Recruitment for the Michigan Bone Health Study has been described previously (26). Originally begun in 1988, the Study recruited 542 nonpregnant women between ages 20 and 40 years who were identified either from family records of participants in the original Tecumseh Community Health Study in 19591960, or from various community outreach efforts. In 1992, an additional 122 community women between ages 35 and 45 years were identified and recruited into the study, resulting in a potential cohort of 664 women. Of these, 64 members of the 1988 cohort refused follow-up in 1992 or had moved, leaving 600 examinees in 19921993. For our analysis, 272 were excluded for the following reasons: 1) oral contraceptive, fertility drug, or other hormone use (n = 113); 2) pregnant or lactating (n = 29); 3) hysterectomy, bilateral oophorectomy, or other gynecologic surgery (n = 100); 4) naturally postmenopausal (n = 4); 5) missing menstrual cycle data (n = 8); or 6) follow-up refused in 19931994 or moved away (n = 18). After exclusions, 328 women remained, referred to here as the Michigan cohort.
Assessment of physical activity
Physical activity was assessed in the Semiconductor cohort during the baseline interview. Women were asked about their participation during the previous month in 62 different recreational physical activities, such as aerobics, running/jogging, bicycling, hiking, and walking. For each activity in which a respondent participated, she was asked how many times a month and for how many minutes, on average, she did that activity. Using the Compendium of Physical Activities (27), we assigned each activity a metabolic equivalent (MET) value that reflected the energy expenditure or intensity of that activity. Intensity was then multiplied by frequency and duration and summed over all activities, resulting in a summary measure of recreational physical activity expressed in MET-minutes per week. A similar measure was constructed for vigorous physical activity by limiting the sum to only those activities with a MET value of six or greater.
In addition, women in the Semiconductor cohort were asked to keep a daily diary of menstrual bleeding in which they noted how many minutes of vigorous exercise they had performed each day. Two variables were constructed from these data: a per-woman average number of minutes of daily vigorous exercise over the length of study participation and a per-cycle average number of minutes of daily vigorous exercise for each cycle.
In the Michigan cohort, the Minnesota Leisure Time Physical Activity survey, a quantitative activity history with established reliability and validity that has been widely used in epidemiologic studies (28, 29), was adapted by adding occupational and household activities and deleting many activities uncommon in women (e.g., hunting). In contrast to the previous-month time frame used in the Semiconductor interview, the time frame for the Michigan survey was July and January prior to the baseline interview, allowing for the appreciable seasonal variation in physical activities that may occur in an environment such as that in Michigan. For each month, participants were asked about the number of times per week they performed each activity and the average duration of each session. Activities were assigned MET values, and intensity, duration, and frequency were used to compute an overall measure of total activity in MET-minutes/week by averaging the two monthly measures of total activity. To create measures directly comparable with those of the Semiconductor study, the MET-minutes per week in recreational physical activity were computed by considering only recreational activities, and the MET-minutes per week in vigorous recreational physical activity were computed from only the recreational activities with a MET value of six or higher.
Assessment of menstrual cycle characteristics
Starting immediately after the baseline interview, the Semiconductor cohort used daily diaries to record the presence or absence of menstrual bleeding on each day. The number of menstrual cycles for which daily diaries were completed ranged from one to eight, with a median of five completed cycles and with 62.9 percent of the cohort providing data for four or more cycles. The total number of cycles used in our analysis was 1,527. Three variables were created based on the daily diary: 1) mean cycle length, defined as the per-woman mean cycle length over all cycles; 2) variability of cycle length, defined as the per-woman standard deviation of cycle length, computed for women with at least two valid cycles; and 3) mean bleed length, defined as the per-woman mean bleed length over all cycles.
In the Michigan cohort, monthly menstrual calendars on which women marked each day of menstrual bleeding provided data for construction of the same three variables. A total of 3,497 cycles of menstrual calendar data were available for computing cycle length and variability, and 3,095 cycles were available for computing bleed length. The per-woman mean was 10.7 cycles of valid cycle data (median = 11) and 9.4 cycles of valid bleed data (median = 10), with a per woman range of 115 and 015, respectively.
Covariates
In both cohorts, age, ethnicity, education, marital status, parity, smoking status, and alcohol consumption were self-reported at baseline in response to either interviewer-administered or self-administered questions. Total caloric intake was assessed with the Block Food Frequency Questionnaire (30) for 285 (86.9 percent) women in the Michigan cohort and 288 (78.5 percent) women in the Semiconductor cohort. Body mass index (BMI) was computed as weight (kg)/height (m2) from self-reported height and weight in the Semiconductor cohort and from measured height and weight in the Michigan cohort.
Data analyses
The distributions of all variables were examined for outliers and violations of assumptions of normality. Because the distribution of the per-woman standard deviation of cycle length, a measure of the within-woman variability of cycles, was highly skewed, it was log-transformed for analysis by using a natural log and adding 0.25 to all standard deviations before transformation to account for women with a cycle variability of zero. Summary variables of physical activity were also highly skewed, but transformation did not substantially change the distribution. Since these were considered as independent variables, no transformation was used. Differences in the characteristics of the two cohorts were compared by using the chi-square test for differences in proportions for categorical variables, the t test for differences in means in continuous variables, and the Wilcoxon rank sum test for differences in medians for the physical activity variables.
Spearman correlation coefficients were used to evaluate the bivariate relations between physical activity measures (recreational physical activity and vigorous recreational activity in both cohorts, per-woman average daily vigorous exercise in the Semiconductor cohort, and total physical activity in the Michigan cohort) and menstrual cycle characteristics. Multivariable linear regression was used to model separately each menstrual cycle characteristic (cycle length, cycle variability, and bleed length) as a function of each physical activity variable, adjusting for potential confounders. Covariates included in the final models were those that showed a bivariate association with both physical activity and cycle characteristics (p < 0.10). To explore whether the relation between a physical activity variable and a menstrual cycle characteristic varied by BMI, age, or ethnicity (Semiconductor cohort only), models were constructed that included the appropriate cross-product terms or that stratified on the variable of interest. To examine relations between cycle-specific menstrual cycle or bleed length and the amount of vigorous exercise either in the same cycle or in the immediately preceding cycle, multivariable linear regression with repeated measures, with correlation structure specified as unstructured, was performed using the Semiconductor data.
All analyses were stratified by cohort.
![]() |
RESULTS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
|
However, the repeated-measures analysis revealed a more complex relation. Table 2, which presents the adjusted association between cycle-specific cycle length and per-cycle mean minutes of daily vigorous exercise in the concurrent cycle, shows a statistically significant positive association. The model suggests that an increase of 10 minutes per day of vigorous exercise during a given menstrual cycle would be associated with an increase in the length of that cycle of approximately two tenths of a day. When this model was stratified by race/ethnicity, the magnitude of the association between vigorous exercise and the length of that cycle was greater in Filipinas (ß = 1.2752, SE = 0.384, p = 0.02) and other Asians (ß = 2.472, SE = 0.952, p = 0.01) than in Whites (ß = 0.1355, SE = 0.036), and the association was not statistically significant in Hispanics (ß = 0.0097, SE = 0.023, p = 0.26). The mean minutes of daily vigorous exercise for the previous cycle were also positively and significantly related to cycle length after adjustment for covariates (ß = 0.035, SE = 0.015); the magnitude of this association did not appear to differ by race/ethnicity.
|
|
![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
The finding of longer cycles related to greater physical activity is generally in agreement with previous work. Numerous studies have reported greater prevalence of amenorrhea and oligomenorrhea among female athletes compared with sedentary women (7, 10, 11), and anovulation and subsequent absence of menstrual bleeding have been induced with the initiation of vigorous exercise training in some studies (23), although not in others (31, 32). In a study of the relation of more moderate amounts of activity and menstrual cycle characteristics in college women, greater activity was associated with slightly increased probability of cycles longer than 43 days and was more strongly associated with long cycles in women who experienced both long and regular-length cycles (21). In a study of women aged 2931 years, daily vigorous activity was associated with increased cycle variability and a nonstatistically significant increase in risk of at least one long cycle but was not associated with mean cycle length as a continuous variable (22).
These findings suggest that moderate activity, like vigorous activity, may have hormonal effects that may lengthen the menstrual cycle, resulting, over a lifetime, in lower levels or less cyclic fluctuations of estrogen and progesterone.
The finding in the Semiconductor cohort of a relation only between cycle-specific exercise and cycle length may suggest that an increase in menstrual cycle length is more of an acute response to physical activity than an adaptive chronic response. This interpretation could be consistent with evidence of athletic amenorrhea, since an acute response would occur repeatedly in women engaged in regular exercise. The finding may also indicate that the intraindividual variability in physical activity in this cohort was great enough that using activity in 1 month (the baseline measure) as indicative of activity level over the course of the study resulted in a nondifferential misclassification and attenuation to the null. Misclassification and attenuation toward the null could also have occurred by averaging daily reports of vigorous exercise over all cycles if activity varied greatly within woman from cycle to cycle. Finally, the finding may have been observed only in the repeated-measures analyses because of the greater statistical power it provided, as well as the greater precision of exposure measurement.
The fact that no association between activity and cycle length was observed in the Michigan cohort before adjustment for BMI may illustrate a type of confounding in which the bivariate relation is attenuated rather than inflated; this could occur because BMI and activity are inversely associated, but BMI and cycle length are directly related. On the other hand, the effect modification by BMI may have a biologic basis in the contribution of energy conservation to exercise-associated menstrual cycle alterations. Although it is now well established that these alterations are not caused by low body fat (33), evidence supports the theory that when energy output far exceeds energy intake, a stable body weight may be maintained by shutting down specific biologic processes, such as reproductive capability, to conserve energy (34, 35). Such a mechanism may be more operative in women with lower stores of body fat.
The direct association between all measures of physical activity and bleed length observed in the Michigan cohort is unexpected and is not in accord with two previous studies that reported inverse associations between activity and days of bleeding (22, 36). The explanation for this discrepancy is not readily apparent; the finding of our study may be due to chance or may point to a true association. Although days of menstrual bleeding were well documented, heaviness of flow was not, so it is unknown whether or not longer bleeding was accompanied by heavier flow. In either case, more research in this area is warranted.
This investigation suffered from one major limitation that deserves attention: The limitation of assessment of physical activity is based on self-report. Although the quantitative physical activity history approach used in both cohorts, in which type of activity, frequency, and duration were reported for a defined time frame, is probably one of the most reliable and valid methods of physical activity measurement for most epidemiologic studies, it still has many sources of measurement error (37). Most important, this approach depends on the ability of the participants to recall and report their activity accurately. It also depends on the comprehensiveness of the questionnaire (38); inclusion or exclusion of specific activities and domains of activity can result in either systematic under- or overreporting (39). The apparent difference in activity level between the Semiconductor and Michigan cohorts, which precluded classifying activity levels in the two cohorts into meaningful and comparable categories, may be due in part to these measurement problems. For instance, overreporting was likely in both cohorts. National surveys estimate that fewer than 15 percent of women of childbearing age participate in regular, vigorous recreational physical activity (24), while more than 50 percent of the women in the Michigan cohort and more than 30 percent in the Semiconductor cohort reported the equivalent of this level of activity. However, within each cohort, it is likely that these sources of error were nondifferential and did not affect the relative ranking of persons. As a result, the measurement errors are unlikely to have systematically biased the results.
This study also had two notable strengths. In both cohorts, menstrual cycle characteristics were collected prospectively, with daily diaries in the Semiconductor cohort and monthly menstrual calendars in the Michigan cohort. This allowed for more accurate ascertainment of the outcomes than could be achieved with retrospective reporting. In addition, the cohorts provided a larger sample size and, in the case of the Semiconductor cohort, a more racially/ethnically diverse sample than most previous studies of this question. In addition, both cohorts were broadly representative of the populations from which they were drawn, with the Michigan cohort constituting a community-based sample that drew from all socioeconomic and demographic strata. As a result, it may be reasonable to generalize these findings to other populations of women in the United States.
The finding that more physical activity was related to longer menstrual cycles is suggestive of a mechanism by which physical activity could reduce risk of breast cancer. From a public health perspective, the finding is supportive of the benefits of promoting regular participation in physical activity in the population as a whole. However, these data do not allow any firm conclusion about whether the observed associations imply a meaningful, clinically relevant difference in reproductive hormone levels. Future studies should be directed toward assessment of the influence of physical activity on the underlying cyclical hormone feedback loops that govern ovulation and the more overt characteristics of the menstrual cycle.
![]() |
ACKNOWLEDGMENTS |
---|
The authors acknowledge the following persons for their contributions to study design, data collection, and data analysis of the original cohort studies: Dr. Marc Schenker, Dr. Bill L. Lasley, Dr. Steven Samuels, Dr. Brenda Eskenazi, Dr. Farla Kaufman, Marianne ONeill Rasor, and Mary Janausch.
![]() |
NOTES |
---|
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|