Small seasonal fluctuations were observed in total calories, carbohydrate, and fat intake, as well as PA and body weight in this cohort. The increase in daily caloric and fat intake during the fall, and decreased PA in the winter coincided with the increase in body weight in the winter. However, these changes were generally small. We did find that seasonal variation was greater in male, middle-aged, non-white, and less-educated subjects, with the most pronounced differences being between white and non-white (mostly Hispanic and Black) subjects. However, the nonwhite group only consisted of 83 subjects, as opposed to 494 white subjects, while race/ethnicity information was not available on 16 participants. The apparent difference may therefore be partially a function of small sample size for nonwhites; this small sample size is a limitation of the study. The results of this study suggest that seasonal variation is not a major factor in the relationship between lifestyle and body weight.
Consistent with results from this study, De Castro (1991)
and Doyle et al. 1999
reported that daily caloric intake varied by season, yet this variation was larger than we noted. De Castro observed a 222 kcal/day difference between the total daily intake in the fall versus the spring. De Castro’s study was conducted in late 1980 and average age of subjects was 32 years old, while our study was conducted between 1994 and 1998 and average age was 48 years. The magnitude of difference found by De Castro was much greater than the 86 kcal/day that we observed in our study (though for nonwhites it was much less). On the other hand, several other studies have suggested that daily caloric intake does not vary by season (Hackett et al., 1985
; Van Staveren et al., 1986
; Subar et al., 1995
; Shahar et al., 1999
), including in less developed countries such as China (Cai et al., 2004
) and India (Hebert et al., 2000
There is evidence suggesting that the intake of total protein does not change by season (Hackett et al., 1985
; Krauchi and Wirz-Justice, 1988
; Hebert et al., 2000
; Cai et al., 2004
), as well as contradictory evidence of increased protein intake during the autumn and winter (Doyle et al., 1999
). A separate study reported that the intake of protein showed suggestive, although not significant, seasonal changes (De Castro, 1991
). However, our study showed no significant seasonal variation in the percentage of calories from protein.
There has been considerably more agreement regarding the significance of seasonal variation in the intake of fat and carbohydrates. Data have suggested that each nutrient exhibits seasonal differences in intake. Previous studies have shown that carbohydrates have a peak intake during the fall (De Castro, 1991
) or winter (Krauchi and Wirz-Justice, 1988
), and a minimum in the summer. One hypothesized reason for this phenomenon is that in some individuals there may be a drop in serotonin levels during the winter (Van Staveren et al., 1986
). Such a drop in serotonin has a tendency to stimulate an urge to eat more carbohydrate-rich foods. The intake of fats follows a similar pattern (De Castro, 1991
), with peaks occurring during the winter (Van Staveren et al., 1986
; Shahar et al., 1999
) and spring (Van Staveren et al., 1986
). However, in our study, we observed the peak percentage of calories from carbohydrates to occur in the spring, which differs from the findings of previous studies. It is important to note that in both China and India variability in micronutrients, such as beta carotene, was larger than macronutrients (Hebert et al., 2000
; Cai et al., 2004
). This may indicate a reasonable shift to more affordable and accessible sources of carbohydrate, fat, and protein to compensate in seasonal variations in cost and availability in what are large sources of macronutrients in virtually all populations.
Reports on seasonal variation of PA and body weight have been relatively consistent. At temperate latitudes, PA has been found to increase in the spring and to decrease in the winter (Van Staveren et al., 1986
; Bergstralh et al., 1990
; Uitenbroek, 1993
; Haggarty et al., 1994
Haggarty et al.,
1996; Matthews et al., 2001a
), while body weight has been found to increase during the winter, and to decrease in the summer (Van Staveren et al., 1986
; Sasaki et al., 1998
; Shahar et al., 1999
A small but statistically significant increase in body weight was shown in the winter. Several factors which are positively related to body weight were increased before the peak of body weight: these include total caloric intake, dietary glycemic index, and percentage of calories from fat and saturated fat (Donato and Hegsted, 1985
; Pi-Sunyer, 1990
; Ludwig, 2000
; Ludwig, 2002
; Ma et al., 2005
). Total PA, which was negatively related to body weight (Ball and Crawford, 2003
; Lazzer et al., 2005
), was lowest in the winter. In nonwhites the increased caloric intake goes along with an increased PA (i.e., both peak in October), but apparently the increased PA was unable to prevent weight gain. In addition, the ratio of caloric intake to expenditure and body weight peaked in the winter. In the SEASONS study, blood lipid levels exhibited seasonal variation, being higher in the winter and lower in the summer (Ockene et al., 2004
). In addition to plasma volume, body weight, physical activity, and saturated fat intake explain some of the variation. Another study found seasonal variation in LDL levels and body mass index, related to variation in dietary fat and saturated fat intake (but not caloric intake, which did not vary significantly); these were all higher in the winter (Shahar et al., 1999
). We suggest that in anticipation of the possible weight gain, individuals need to be conscious of their daily total caloric intake in the winter, their percentage of caloric intake from fat and saturated fat in the fall; and total physical activity before and during the winter.
We observed time lags between the peaks of caloric intake and body weight, the peaks for the percent of calories from fat and body weight, and between the lowest reported total physical activity and the peak of body weight. The delay makes sense given that it would take some time for body weight to change in response to changes in diet and/or physical activity. A cumulative deficit of PA and cumulative increase in calories and fat intake may explain the increase of 0.5 kg in body weight during the winter. Specialized statistical methods will need to be developed in order to understand the nature of the time lag and account for it in future analyses.
A previous study by our group demonstrated that participants with a college degree derived the least benefit from a low-fat, low-calorie dietary intervention, whereas those with no more than a high school education showed a much greater effect (Hebert et al., 1999
). This may be due to more educated patients having received considerable exposure to lifestyle-improvement messages from peers and from the media, and therefore adhering to dietary recommendation, whereas the less-educated have not received these lifestyle improvement messages.
We found that subjects with high school education or less had the least variation in physical activity. It is possible that less-educated subjects are employed in jobs demanding manual labor, so their physical activity remains more constant over the year. In contrast, more educated people tend to have sedentary jobs, so their PA would tend to vary according to the seasonal demands of yard work, availability of outside exercise facilities, and willingness to exercise indoors.
There are several strengths to our investigation. First, both sinusoidal and fixed season approaches were used in the analyses. Sinusoidal method decomposes date into two variables with sine and cosine values to fit into the model, allowing for a continuous function rather than simply four seasons. This retains the maximum amount of information on date when measures were collected. The sinusoidal approach appeared to be more powerful. Secondly, the 24-h dietary recalls were used in the collection of dietary intake, and this provides more accurate data than food frequency questionnaires (FFQs), as recall bias is less of an issue with the 24-h dietary recalls (Hebert et al., 2002
). Thirdly, all participants were not within the same socioeconomic status as indicated by educational levels. Finally, the current study collected information on diet, physical activity, and body weight at the same time over 1-year period, which allows us to examine seasonal variation of these variables simultaneously.
Our study also has several potential limitations. First, information on diet and physical activity was obtained from self-report via 24-h recalls. Although a single 24 h recall is unlikely to capture a person’s true diet and physical activity and could result in misclassification, three recalls (the number per time point) begins to approximate an individual’s usual intake of macronutrients – caloric intake, carbohydrate, protein, and fat (Hebert et al., 1998
; Willett, 1998
) and PA (Matthews et al., 2001a
). Although there is always the potential for misclassification due to error in self-reporting, the error is minimized by using trained dietitians and asking participants to recall the previous day only. It is true that 24HRs are subject to bias in comparison with biochemical markers; however, biochemical marker testing such as the doubly labeled water method is very expensive and therefore impractical for large epidemiological studies. Secondly, prediction of GI and GL values of foods based on existing international tables has some variability between populations, so our GI and GL calculation could be biased. Thirdly, our participants are a generally healthy population in the Central Massachusetts/Worcester area consisted largely of white, middle-class members of an HMO, from a single geographic location. Also, as the study protocol involved a lengthy series of clinic visits, and diet and physical activity assessments, participants needed to have been relatively highly motivated. Selection factors relating to the participants’ interest in their own health and time availability for participation may have contributed to creating a fairly homogeneous study group. For these reasons, our findings may not be fully generalizable to other socio-economic strata, and to other cultures and ethnic groups. Finally, this study was conducted using existing data collected during 1994–1998; it would be useful to do a similar study at this time to examine these factors. However, we believe the data we are presenting are still very useful given the need to understand contributors leading to the current state of obesity, diet, and PA and their variations.
In conclusion, the present study revealed that small seasonal variations of daily caloric intake, diet composition, physical activity, and body weight are in fact present in normal individuals in the United States. These variations, however, do not appear great enough to take into account in the design of longitudinal studies and cross-sectional studies for the collection of dietary, PA, and body weight data. These fluctuations were greater in subjects who were male, middle aged, nonwhite, and less educated. Results of the study lend support to the well-established evidence-based advice to maintain PA and caloric intake at a steady state during the winter in order to avoid weight gain.