Seasonal Variation in Household, Occupational, and Leisure Time Physical Activity: Longitudinal Analyses from the Seasonal Variation of Blood Cholesterol Study

Charles E. Matthews1, Patty S. Freedson2, James R. Hebert1, Edward J. Stanek, III2, Philip A. Merriam3, Milagros C. Rosal3, Cara B. Ebbeling4 and Ira S. Ockene3

1 Department of Epidemiology and Biostatistics, School of Public Health, University of South Carolina, Columbia, SC.
2 Departments of Exercise Science and Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA.
3 Department of Medicine, Divisions of Cardiovascular and Preventive and Behavioral Medicine, University of Massachusetts Medical School, Worcester, MA.
4 Department of Endocrinology, Children's Hospital, Boston, MA.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
The authors examined seasonal variation in physical activity in longitudinal analyses of 580 healthy adults from Worcester, Massachusetts (the Seasonal Variation of Blood Cholesterol Study, 1994–1998). Three 24-hour physical activity recalls administered five times during 12 months of follow-up were used to estimate household, occupational, leisure time, and total physical activity levels in metabolic equivalent (MET)-hours/day. Trigonometric models were used to estimate the peak-to-trough amplitude and phase of the peaks in activity during the year. Total activity increased by 1.4 MET-hours/day (121 kcal/day) in men and 1.0 MET-hours/day (70 kcal/day) in women during the summer in comparison with winter. Moderate intensity nonoccupational activity increased by 2.0–2.4 MET-hours/day in the summer. During the summer, objectively measured mean physical activity increased by 51 minutes/day (95% confidence interval: 20, 82) in men and by 16 minutes/day (95% confidence interval: -12, 45) in women. The authors observed complex patterns of seasonal change that varied in amplitude and phase by type and intensity of activity and by subject characteristics (i.e., age, obesity, and exercise). These findings have important implications for clinical research studies examining the health effects of physical activity and for health promotion efforts designed to increase population levels of physical activity.

exercise; monitoring, physiologic; physical fitness; seasons; work

Abbreviations: CI, confidence interval; MET(s), metabolic equivalent(s); SEASON, Seasonal Variation of Blood Cholesterol Study


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
Participation in high levels of physical activity throughout one's lifetime (1Go, 2Go) and consistent participation in physical activity during most months of the year (3Go) have been reported to reduce risk of adverse cardiovascular events. Seasonal variation in physical activity has been reported to coincide with seasonal changes in blood lipid levels (4Go, 5Go), blood pressure (6Go), body mass (7Go), bone density (8Go), and affective disorders (9Go). Environmental changes in ambient temperature, daylight, and monthly precipitation are thought to induce seasonal changes in physical activity (10Go), and recent public health recommendations have noted the importance of environmental factors as potential barriers to regular participation in healthful levels of such activity (11Go). Consistent participation in high levels of physical activity appears to be required for optimal health; thus, environmental factors related to seasonal variation in physical activity behaviors must be considered in intervention and health promotion efforts designed to increase activity levels in the general population.

Seasonal variation in leisure time physical activity has been described in cross-sectional surveys (11Go, 12Go) and in small longitudinal studies in homogenous groups (7Go, 8Go, 13Go). However, there are few extant data describing seasonal variation in all types and intensities of physical activity encountered in daily life in longitudinal analyses of large cohorts of men and women with a wide distribution of demographic characteristics. The Seasonal Variation of Blood Cholesterol Study (SEASON) was a longitudinal study of 641 healthy adults designed to quantify the magnitude and timing of seasonal changes in blood lipid levels and to identify the major factors contributing to this variation (14Go). These factors included dietary fat intake, physical activity, exposure to light, psychological variables, weather patterns, and changes in body mass.

In the present investigation, we examined seasonal variation in reported household, occupational, and leisure time physical activity in the SEASON cohort using three 24-hour activity recalls per season, as well as in a subsample of the group, using an accelerometer as an objective measure of overall activity. We characterized differences in seasonal activity patterns by activity type and intensity, demographic factors, and body mass index classification. Additionally, in order to gain insight into the specific environmental elements influencing physical activity behavior, we compared seasonal variation in activity with concurrent monthly variation in selected environmental factors (i.e., weather and daylight).


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
Participant recruitment and study design
Individuals were recruited from the Fallon Healthcare System, a health maintenance organization located in Worcester, Massachusetts, that serves the central Massachusetts region. Additional minority participants were recruited from the Greater Worcester area. The Institutional Review Boards of the Fallon Healthcare System and the University of Massachusetts Medical School approved all study procedures, and each participant provided informed consent. Individuals were eligible if they were residents of Worcester County, were aged 20–70 years, had telephone service, were free from extreme hypercholesterolemia, and were not taking cholesterol-lowering medication. Recruitment was completed between December 1994 and February 1997, and follow-up ended in March 1998. Of the roughly 5,300 Fallon members and minority individuals contacted by telephone to determine their interest, 1,254 met verbal eligibility criteria and made baseline appointments. Of these, 426 (34 percent) did not keep their appointments, 140 (11 percent) did not meet formal study eligibility requirements, and 47 (4 percent) did not complete baseline questionnaires. Thus, 641 (51 percent) participants were considered to have formally entered the study.

At baseline and in each of four subsequent quarters of follow-up, individuals came to the clinic for blood lipid measurements and to return self-administered questionnaires. Data on physical activity, diet, and light exposure were collected using three 24-hour recall interviews during each of the five quarters of data collection. The 24-hour interviews were conducted within a 42-day call window (-28 days to +14 days) surrounding each clinic visit. Additionally, a subsample of the SEASON population (n = 62; 45 percent male) wore an Actillume physical activity and light exposure monitor (15Go, 16Go) in four of the five quarters of the study.

Demographic data (e.g., age, gender, marital status, education, employment) and information on habitual physical activity behaviors (17GoGo–19Go) was collected by questionnaire at baseline. Anthropometric data (measurements of body mass (kg), height (m), and waist and hip circumferences (cm)) were gathered during clinic visits. Classifications of weight status (i.e., normal weight, overweight, obese) were made according to body mass index (weight (kg)/height (m)2) using recently published guidelines (20Go), with a minor variation. Classifications were as follows: normal weight, body mass index of 18.5–24.9; overweight, body mass index of 25.0–29.9; obese, body mass index of >=30.0. Four women with body mass indices less than 18.5 were classified as being of normal weight (see table 1), although the guidelines technically would have classified them as underweight.


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TABLE 1. Descriptive characteristics of the cohort, Seasonal Variation of Blood Cholesterol Study, Worcester, Massachusetts, 1994–1998

 
Physical activity assessments
Twenty-four-hour physical activity recalls.
The 24-hour physical activity assessment, as well as relative validity studies of the method used, have been described in detail elsewhere (15Go). Briefly, trained registered dietitians conducted unannounced telephone-administered 24-hour interviews on two randomly selected weekdays and one randomly selected weekend day during the call window surrounding each clinic visit. In the interview, participants recalled the amount of time they spent in activity of four intensities, in each of three activity domains (household, occupational, leisure time). A standardized interview script was used, and data were entered directly into an Epi Info database (21Go). Methods described by Ainsworth et al. (22Go) were employed to calculate estimates of physical activity energy expenditure using standard metabolic equivalent (MET) values. A weighted sum of daily physical activity energy expenditure (MET-hours/day) was calculated using the reported amount of time (hours/day) spent in activity of each intensity and the following MET weights: light, 1.5 METs; moderate, 4.0 METs; vigorous, 6.0 METs; very vigorous, 8.0 METs. One MET-hour/day is approximately equivalent to 1 kcal/kg body mass/hour, or to the resting metabolic rate of a 60- to 70-kg person (22Go, 23Go). Summary scores for each quarter of follow-up were calculated for weekdays and weekend days, and an overall average (per day) was calculated by weighting weekday and weekend day data by five sevenths and two sevenths, respectively.

We previously evaluated the relative validity of three 24-hour recalls in estimating short-term physical activity (15Go). At study baseline, correlations between the 24-hour recall and the modified Baecke questionnaire (17Go, 18Go) for household, occupational, and leisure time activity, deattenuated for within-subject variation, were 0.45 and 0.54, 0.63 and 0.74, and 0.68 and 0.68 (all p's < 0.01) for men and women, respectively (15Go). Correlations between the 24-hour recall and the Actillume monitor were 0.74 (n = 16, p < 0.01) and 0.32 (n = 33, p = 0.07) in men and women, respectively, on matched days of observation (15Go).

Physical activity monitor.
The Actillume monitor (Ambulatory Monitoring, Inc., Ardsley, New York) was used as an objective measure of physical activity in a subsample of study participants. Participants in this subgroup were a convenience sample from the SEASON cohort, and recruitment was balanced by gender and age. The Actillume monitor is small (7 x 3.8 x 2.2 cm) and light (100 g). It contains a uniaxial piezoresistive accelerometer and microprocessor that samples accelerations 20 times per second (20 Hz) with an 8-bit analog/digital converter. These data are amplified and sent through a low-pass filter and stored as a byte of data for user-defined epoch lengths (e.g., activity counts/minute). In laboratory testing, the monitor discriminated between sedentary and moderate intensity activities (i.e., >=3 METs) and changes in walking speed, and it accounted for 79 percent of the variance in measured oxygen consumption across a range of sedentary and walking activities (15Go).

Participants were instructed to wear the monitor for 4–7 days (depending on monitor availability), including at least one weekend day. During waking hours, the monitor was worn in a close-fitting neoprene pouch on the waist. The monitor was initialized to collect activity data at 2-minute intervals. We visually inspected and coded each day of data to identify periods in which the monitor was worn. We summarized coded data files using a Statistical Analysis System program (SAS Institute, Cary, North Carolina) to average daily values, for days on which the monitor was worn for at least 12 hours. We calculated average counts per minute per day and the number of minutes recorded in Actillume count ranges consistent with participation in physical activity of moderate or greater intensity (i.e., >20 counts/minute) (15Go).

Exercise and sports participation.
To examine differences in activity patterns among individuals reporting that they "exercised regularly" or "played a sport," two additional physical activity assessments were utilized. Physical activity questions (19Go) from a 7-day dietary recall (24Go) that asked about the frequency (per week) and duration (per session) of 12 conditioning and sports activities, including "brisk walking," were employed to determine exercise participation (minutes/week) and the length of exercise participation (1–12 months or >=12 months). Individuals reporting 0 minutes/week of exercise activities were classified as sedentary, and those reporting 1 or more minutes/week were classified as exercisers. Individuals were classified by sports participation (yes or no) using the sports data from the Baecke physical activity questionnaire (17Go).

Environmental data
Daily weather data, average daily temperature (degrees Fahrenheit), barometric pressure (inches of mercury), humidity (percent), total precipitation (inches), and average daylight cloud cover (percent) were compiled using data from the regional weather station maintained at the Worcester municipal airport by the National Oceanographic and Atmospheric Administration's National Climatic Data Center (station 97446, latitude 42 degrees 16'N, longitude 71 degrees 52'W, elevation 986 feet (303 m)). Data on sunrise and sunset times were obtained from the United States Naval Observatory (Eastern Standard Time), and hours of daylight were calculated, adjusting for Daylight Savings Time. To evaluate changes in daylight available for physical activity on workdays (assuming a 9:00–5:00 schedule), the variables "a.m. hours" and "p.m. hours" were calculated as the numbers of hours of available daylight before 8:00 a.m. and after 5:00 p.m., respectively.

Statistical methods
We modeled longitudinal changes in physical activity using the assumption that the seasonal pattern over 1 year had a 12-month periodicity and that high and low levels of activity occurred 6 months apart. This approach describes seasonal variation as the amplitude and phase of a cosine curve. Amplitude was calculated as peak-to-trough distance, or the maximal difference between the highest and lowest physical activity levels during the year. Timing of the peak activity level during the year, or the phase, was also identified and was reported as the calendar date. Sine and cosine functions were calculated on the basis of the clinic visit dates at each activity assessment point, after conversion of the dates to radians. Mixed models that controlled for the sequence of measures were fitted to the physical activity data (dependent variable), with subjects treated as random effects. Variance components were estimated using restricted maximum likelihood and SAS PROC MIXED (25Go). Simple functions of the parameters were used to estimate the amplitude and phase of seasonal effects (see Appendix for details). Taylor series expansion was evaluated for estimation of the variance, and interval estimates were calculated. Seasonal variation in physical activity was described separately for men and women, by activity type and intensity. Additionally, estimates of physical activity patterns by month were plotted for selected types of physical activity and for selected participant subgroups. Of the 641 individuals entering the SEASON study, five provided no 24-hour data and 56 had fewer than two quarters of study participation. These individuals were excluded from the present analyses.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
Descriptive characteristics of the SEASON cohort are presented in table 1. Men and women had seasonal changes in total activity of 1.4 MET-hours/day and 1.0 MET-hours/day, respectively, with peak amplitudes in July (table 2). However, when different types and intensities of activity were examined separately, complex seasonal patterns emerged. We found a striking summertime increase in combined moderate intensity leisure and household activity of approximately 2.0–2.4 MET-hours/day in women and men, respectively (table 2). No seasonal variation in vigorous leisure time activity was observed. In contrast, total light activity peaked in early January, coinciding with the end-of-the-year holiday season. Among men, total vigorous activity peaked in mid-January, and this was driven by vigorous household activity. Among women, occupational activity peaked in late January. These countervailing seasonal activity changes resulted in an attenuation of the overall summertime increase in total activity in comparison with the larger increase in summertime moderate intensity activities.


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TABLE 2. Seasonal variation{dagger} in physical activity measured through 24-hour recalls (MET*-hours/day) and Actillume monitoring in men and women, by activity type and intensity, Seasonal Variation of Blood Cholesterol Study, Worcester, Massachusetts, 1994–1998

 
Results from the objective measure of activity derived from the Actillume subsample revealed similar seasonal activity patterns evident in the self-reported data (table 2). Because of a relatively high level of activity recorded by the monitor in January, the summer increase in recorded activity was obscured in the statistical models. Exclusion of the month of January from the analyses resulted in an observation of seasonal peaks of physical activity in the late spring and early summer in both the counts/minute/day and the minutes/day Actillume summary measures (table 2).

Tables 3 and 4 present seasonal variation results for household and leisure time activity, according to selected participant characteristics. In general, household activity peaked during the warmer months, and men aged 60–70 years appeared to report a larger seasonal change than men aged <60 years, although confidence intervals were wide (table 3). Otherwise, obese, college-educated, white collar, and sedentary men appeared to have less seasonal variation in household activity. In terms of leisure time activity, blue collar, sedentary, non-sports-playing men appeared to have a smaller increase in activity during the summer months (table 3). Among women, those who were obese or sedentary reported a smaller summer-related increase in leisure time activity in comparison with their leaner and more active counterparts. Sedentary men and women reported less of a seasonal increase in leisure time activity than exercisers or players of sports (tables 3 and 4 and figure 1). Individuals who had begun exercising only recently (1–12 months) appeared to have a reduction in leisure activity in the late summer, while long-term exercisers (i.e., >=12 months) and players of sports maintained high levels of leisure activity into October (figure 1).


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TABLE 3. Seasonal variation in household and leisure time physical activity (MET*-hours/day) in men, by demographic and physical activity characteristics, Seasonal Variation of Blood Cholesterol Study, Worcester, Massachusetts, 1994–1998

 

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TABLE 4. Seasonal variation in household and leisure time physical activity (MET*-hours/day) in women, by demographic and physical activity characteristics, Seasonal Variation of Blood Cholesterol Study, Worcester, Massachusetts, 1994–1998

 


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FIGURE 1. Seasonal variation* in leisure time physical activity (MET-hours/day) according to exercise and sports participation, Seasonal Variation of Blood Cholesterol Study, Worcester, Massachusetts, 1994–1998. *Least-squares mean values adjusted for study quarter, age, gender, and education. MET, metabolic equivalent. Bars, standard error.

 
Household and leisure time activity was generally higher on weekend days, and the amplitude of the seasonal variation in these behaviors appeared to be greater on these days (figure 2). For example, the amplitude of moderate intensity household activity on weekend days (1.74 MET-hours/day; 95 percent confidence interval (CI): 1.0, 2.4) was 20 percent greater than on weekdays (1.33 MET-hours/day; 95 percent CI: 1.0, 1.7). The amplitude of leisure time activity was 35 percent greater on weekend days (1.17 MET-hours/day; 95 percent CI: 0.8, 1.5) than on weekdays (0.76 MET-hours/day; 95 percent CI: 0.6, 1.0). In terms of the timing of seasonal changes in physical activity relative to changes in environmental factors, weekend household activity began to increase in April, coinciding with the change to Daylight Savings Time, peaked in mid-June, and then diminished as temperatures increased in mid- to late summer. Leisure time activity was increased between May and October, coinciding with peaks in available daylight, reduced cloud cover, and average temperatures of 50°F or greater (figure 2). The previously described peak in vigorous household activity among men, presumably derived from snow removal activities, coincided with the January peak in solid precipitation (figure 2, upper panel).



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FIGURE 2. Seasonal variation* in selected environmental factors and weekday and weekend day moderate intensity household and overall leisure time activity (MET-hours/day), Seasonal Variation of Blood Cholesterol Study, Worcester, Massachusetts, 1994–1998. *Least-squares mean values adjusted for study quarter, age, gender, and education (n = 577). MET, metabolic equivalent; ppt, precipitation; HH mod-WE, household moderate intensity activity on weekends; HH mod-WD, household moderate intensity activity on weekdays; LTPA-WE, overall leisure time activity on weekends; LTPA-WD, overall leisure time activity on weekdays. Bars, standard error.

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
This investigation extended our understanding of seasonal variation in physical activity by providing quantitative estimates of seasonal changes in activity energy expenditure from longitudinal analyses of a large cohort of healthy adults in the northeastern United States. We observed complex patterns of seasonal change in activity that varied, in peak-to-trough amplitude and phase (i.e., month of the year), by the type and intensity of activity. Patterns of seasonal changes in nonoccupational activity (i.e., household and leisure) also varied according to participant characteristics. Physical activity energy expenditure increased by 1.4 MET-hours/day (121 kcal/day) in men and 1.0 MET-hours/day (70 kcal/day) in women, while combined moderate intensity household and leisure time activity increased by more than twice as much (i.e., 2.0–2.4 MET-hours/day) during the summer in comparison with winter. The amplitude of the summer peak in total activity energy expenditure was attenuated by countervailing increases in wintertime vigorous household activity in men, occupational activity in women, and total light intensity activity in both men and women. In a subsample of the cohort, objectively measured physical activity also increased during the warmer months, after we accounted for a high level of activity in January. These findings have important implications for epidemiologic and clinical investigation of the health effects of physical activity, for health promotion efforts designed to increase physical activity levels in the general population, and potentially, for risk reduction regarding several health outcomes with seasonality in their incidence.

Our longitudinal finding of a significant increase in nonoccupational activity during the warmer months was consistent with prior cross-sectional studies that reported (12Go, 26GoGo–28Go) increased summertime physical activity that was due to yard work and exercise or recreational activities (26Go, 28Go). Estimates of the amplitude of seasonal variation in activity energy expenditure in this report were consistent with those in the Framingham Offspring Study (26Go) (i.e., summertime increases of 114 kcal/day and 54 kcal/day (approximately 1.4 and 0.8 MET-hours/day) among men and women, respectively); in postmenopausal women from Boston, Massachusetts (27Go) (a 35 percent reduction in winter activity (i.e., -80 kcal/day, or about -1.3 MET-hours/day)); and in young Dutch women (7Go) (a nonsignificant 17 minutes/day summer increase in walking and sports activity (about 69 kcal/day, or 1.1 MET-hours/day)).

Seasonal variation in physical activity may be an important consideration for health outcomes with a seasonal variation in incidence and physical inactivity as a risk factor. Seasonal variation in the incidence of coronary heart disease has been attributed to cold exposure and seasonal changes in blood pressure and hemostatic factors (29GoGo–31Go). Physical activity has been shown to favorably influence blood pressure and hemostatic factors in studies of acute exercise (32Go, 33Go) and chronic exercise (32Go, 34Go, 35Go). Recently, van den Burg et al. (34Go) reported that 12 weeks of vigorous exercise (approximately 2.8 MET-hours/day) attenuated unfavorable seasonal changes in fibrinolytic parameters compared with a control group. Magnus et al. (3Go) reported that habitual participation (>=8 months/year) in walking, gardening, and cycling conferred greater protection against acute coronary events (odds ratio = 0.5; 95 percent CI: 0.3, 0.6) than did seasonal participation (4–8 months/year) (odds ratio = 0.7; 95 percent CI: 0.5, 1.0), in comparison with <4 months/year. Taken together, these clinical and epidemiologic findings suggest that seasonal variation in physical activity may be associated with the seasonal variation in cardiovascular events through effects on risk factors for the disease.

Multiple cross-sectional associations between physical activity and measures of depression, anxiety, positive affect, and general well-being in both clinical and nonclinical populations have been reported (11Go). More recently, a meta-analytic review of the effect of exercise on clinical depression reported an inverse association between exercise and depression (36Go). Low levels of physical activity coincide with seasonal elevation in the incidence of depression (9Go, 37Go, 38Go), which suggests a potential association. Future studies should examine the ability of regular participation in activity throughout the year to attenuate the effects of seasonal affective disorder.

Our findings have clear implications for epidemiologic and clinical investigations of the health effects of physical activity, as well as physical activity intervention studies. The seasonal changes in activity that we observed were relatively large and complex, varying in magnitude and timing by the specific domains and intensities of the activity. The magnitude of seasonal variation in physical activity that we observed was roughly the same as changes in activity reported at 6 months (1.4 MET-hours/day) and 24 months (0.8 MET-hours/day) in response to a physical activity intervention (39Go, 40Go). These changes in activity were also associated with changes in the cardiovascular risk factors, cardiorespiratory fitness, blood pressure, and total cholesterol (39Go, 40Go). The present findings underscore the importance of balanced recruitment of intervention and control groups in future studies and consideration of seasonal effects in interval outcome measures. Alternatively, study designs could take advantage of seasonality in physical activity behaviors or the physiologic outcomes of interest and could have as a study objective the examination of the attenuation of these seasonal effects. Finally, the present findings clearly support the practice of obtaining estimates of "habitual" physical activity levels (i.e., past 12 months), rather than short-term measures (i.e., the past 7 or 30 days), in traditional epidemiologic studies examining the health effects of physical activity.

To interpret our findings appropriately, the methodological limitations of the study and the potential generalizability of the findings should be considered. This cohort was a convenience sample of primarily healthy Caucasians enrolled in a health maintenance organization who consented to completing five clinic visits for blood draws, a series of diet and psychological questionnaires, and a total of 15 24-hour physical activity, diet, and light exposure interviews over a period of 1 year. Clearly, selection factors relating to the participants' interest in their own health and their time availability for participation was operating in this cohort. In terms of physical activity, we compared our 24-hour recall-derived leisure time physical activity values from SEASON with 1994 Massachusetts data from the Behavioral Risk Factor Surveillance System (11Go), using a similar scoring algorithm. Men and women in the entire state of Massachusetts reported a mean of 2.7 (standard deviation 4.0) and 1.9 (standard deviation 3.0) MET-hours/day of leisure time activity in 1994, respectively (unpublished observations). This compares favorably with the 1-year average values of 2.1 (standard deviation 2.1) and 1.7 (standard deviation 1.8) MET-hours/day in the SEASON cohort, and suggests that SEASON participants were similar to other adults in the state in this regard.

The northeastern United States is characterized by cold, often snowy winters but relatively mild summers. In the present study, the average temperature between December and February was 28°F, and the average between July and September was 66°F. Solid precipitation in winter was temporally associated with increased vigorous household activity, particularly among men. Increased participation in wintertime snow and ice removal and in the chopping and/or carrying of firewood has been reported by other investigators in this region (26Go). In contrast, the opposite seasonal variation in climatic conditions in the southern United States (heat and humidity) has been hypothesized to attenu-ate summer reductions in sedentary behavior in southern states as compared with northern states (10Go). Clearly, extreme environmental factors (e.g., hot or cold temperatures) modify physical activity behaviors. Therefore, the present findings may be most applicable to regions with environmental conditions similar to those of the northeastern United States.

The validity of self-reported measures of physical activity is difficult to assess directly, since there is no "gold standard" with which to compare actual free-living activity energy expenditure, particularly for the individual activity domains (41Go). However, the concurrent seasonal variation in our objective measure of activity in the activity monitor substudy and our previous validation work with the 24-hour recall methods (15Go) support the validity of the present results. It could be argued that 3 days of 24-hour activity recalls were insufficient to reliably characterize short-term activity patterns within a season (42Go). However, for groups of individuals, less reliable estimates of activity in a season would only serve to reduce the precision of the seasonal estimates while still providing a reasonable estimate of the average activity level for the cohort in a particular season (43Go, 44Go).

In conclusion, we observed complex seasonal patterns in physical activity that varied, in peak-to-trough amplitude and time of year, according to the type and intensity of activity and participant characteristics. The present investigation has important implications for research studies examining the health effects of physical activity and for health promotion efforts designed to increase levels of physical activity in the general population. Given that several important health outcomes have physical inactivity as a risk factor and also demonstrate seasonal variation in their incidence, the pres-ent findings support the concept that health promotion campaigns need to encourage year-long participation in healthful levels of physical activity, and that preplanned strategies for overcoming apparent environmental barriers to physical activity under unfavorable environmental conditions will be needed to attain this objective.


    APPENDIX
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
Estimating Seasonal Variation in Physical Activity as Amplitude and Phase using Sine and Cosine Functions
Several methods have been proposed for estimating the amplitude (peak-to-trough distance) of seasonal effects (45Go). Perhaps the simplest strategy is to assume that the measure of physical activity is made within a certain window (e.g., ±21 days) of the common zenith in physical activity (e.g., July 1) for all subjects, and to similarly assume that physical activity levels are made at the common azimuth (e.g., January 1 ± 21 days) for all subjects. Paired comparison of the two measures will result in estimates of the amplitude of the seasonal effects for the subjects. This method has the appeal of simplicity. Limitations of the method include the practical difficulty of obtaining physical activity measures within the specified windows for all patients; the approximation of "peak" and "trough" estimates of amplitude, even though the timing of the measures may not coincide with the actual subject-specific peaks and troughs; the arbitrariness of the "window" width; and the lack of utilization of other physical activity measures in estimating seasonal effects at the fall and spring times.

A second strategy for estimating amplitude is to use the trigonometric functions to decompose the five longitudinal measures of physical activity in the SEASON Study into three parameters, with combinations of these parameters representing the amplitude (peak-to-trough distance) and phase (timing) of peak physical activity levels. Such a decomposition is a simple example of a time series approach based on spectral analysis (46GoGo–48Go).

This method defines time in radians, such that the Julian day (1 to 365) is converted to "t," where

then physical activity for the ith subject at time t may be represented as

where Ai is the amplitude and Fi is the phase. Thus, with Fi = 0, the model predicts a maximum physical activity value on December 31 of Bi0 + Ai/2. Using a standard trigonometric identity, we can reexpress physical activity at time t as

which, after setting Bi1 = (-Ai/2) Sin(Fi) and Bi2 = (Ai/2) Cos(Fi), can be expressed as a standard regression equation,

Values of the phase and amplitude derived from this equation can be recovered from the regression coefficients using the following expressions:


With up to five measures of physical activity per subject, estimates of the amplitude are constructed for each subject, with averages of the amplitude used as the measure of the seasonal effect. This strategy is conceptually appealing, since it makes use of physical activity information collected in all seasons. It was the approach adopted in this paper. Interval estimates for the amplitude and phase were calculated using first-order Taylor series approximations to the estimated variances from the mixed models. (Additional details are given at http://www-unix.oit.umass.edu/~seasons/pdffiles/se35.pdf, or they may be obtained from the corresponding author.)


    ACKNOWLEDGMENTS
 
This work was supported by National Heart, Lung, and Blood Institute grant HL52745. The work was partially supported by the American College of Sports Medicine's Fellowship Fund for Epidemiological Research on Physical Activity and Health, through a 1998 award to Dr. Charles E. Matthews.

The authors thank Laura Robidoux and Priscilla Cirillo for their assistance with study recruitment and the clinic-based data collection; Donna Gallagher for her assistance with study mailings; Kelly Scribner for coordination of the 24-hour recalls; and the SEASON dietitians who conducted the recalls: Susan Nelson, Christine Singelton, Pat Jeans, Karen Lafayette, Deborah Lamb, Stephanie Olson, and Eileen Capstraw. The authors also thank Yunsheng Ma and Thomas Hurley for their organizational and data management expertise.


    NOTES
 
Correspondence to Dr. Charles E. Matthews, Department of Epidemiology and Biostatistics, University of South Carolina School of Public Health, Columbia, SC 29208 (e-mail: cematthe{at}sph.sc.edu).


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 

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Received for publication August 11, 1999. Accepted for publication February 22, 2000.