Nutritional status of middle-aged and elderly females in Kentucky in two seasons: Part 1. Body weight and related factors

Factors explaining seasonal variation in energy intake: a review

Maintaining a balance between energy intake and expenditure is crucial for overall health. There are seasonal variations in energy intake, with an increase during spring and winter as well as a decrease during summer. These variations are related to a combination of environmental factors, including changes in temperature and daylight hours; social factors, including events and holidays; and physiological factors, including changes in physical activity and emotions. Accordingly, this review aimed to summarize the environmental, social, and physiological factors that contribute to seasonal variations in energy intake. A review of the current literature revealed that changes in temperature and daylight hours may affect eating behavior by altering homeostatic responses and appetite-related hormones. Additionally, increased participation in events and frequency of eating out, especially during winter vacations, may contribute to increased energy intake. Notably, these findings may not be generalisable to all populations since environmental and social factors can vary significantly depending on the local climatic zones and cultural backgrounds. The findings of the present review indicate that seasonal climate, events, and associated hormonal changes should be taken into account in order to maintain adequate energy intake throughout the year.

The Use of Text Messaging to Promote Physical Activity in Working Women: A Randomized Controlled Trial

BACKGROUND

The study evaluated the effects of a text message intervention on physical activity in adult working women.

METHODS

Eighty-seven participants were randomized to an intervention (n = 41) or control group (n = 46). Pedometer step counts and measures of self-efficacy were collected at baseline, 12 weeks, and 24 weeks. Intervention participants received approximately 3 text messages per week that were motivational, informational, and specific to performing physical activity.

RESULTS

ANCOVA results showed a significant difference between groups for mean steps per day at 12 weeks (6540.0 vs. 5685.0, P = .01) and no significant difference at 24 weeks (6867.7 vs. 6189.0, P = .06). There was no change in mean step counts during or after the intervention compared with baseline. There was a significant difference between groups for mean self-efficacy scores at 12 weeks (68.5 vs. 60.3, P = .02) and at 24 weeks (67.3 vs. 59.0, P = .03).

CONCLUSIONS

Intervention participants had higher step counts after 12 and 24 weeks compared with a control group; however, the difference was significant only at the midpoint of the intervention and was attributable to a decrease in steps for the control group. Text messaging did not increase step counts but may be a cost-effective tool for maintenance of physical activity behavior.

Theory of planned behaviour variables and objective walking behaviour do not show seasonal variation in a randomised controlled trial

BACKGROUND

Longitudinal studies have shown that objectively measured walking behaviour is subject to seasonal variation, with people walking more in summer compared to winter. Seasonality therefore may have the potential to bias the results of randomised controlled trials if there are not adequate statistical or design controls. Despite this there are no studies that assess the impact of seasonality on walking behaviour in a randomised controlled trial, to quantify the extent of such bias. Further there have been no studies assessing how season impacts on the psychological predictors of walking behaviour to date. The aim of the present study was to assess seasonal differences in a) objective walking behaviour and b) Theory of Planned Behaviour (TPB) variables during a randomised controlled trial of an intervention to promote walking.

METHODS

315 patients were recruited to a two-arm cluster randomised controlled trial of an intervention to promote walking in primary care. A series of repeated measures ANCOVAs were conducted to examine the effect of season on pedometer measures of walking behaviour and TPB measures, assessed immediately post-intervention and six months later. Hierarchical regression analyses were conducted to assess whether season moderated the prediction of intention and behaviour by TPB measures.

RESULTS

There were no significant differences in time spent walking in spring/summer compared to autumn/winter. There was no significant seasonal variation in most TPB variables, although the belief that there will be good weather was significantly higher in spring/summer (F = 19.46, p < .001). Season did not significantly predict intention or objective walking behaviour, or moderate the effects of TPB variables on intention or behaviour.

CONCLUSION

Seasonality does not influence objectively measured walking behaviour or psychological variables during a randomised controlled trial. Consequently physical activity behaviour outcomes in trials will not be biased by the season in which they are measured. Previous studies may have overestimated the extent of seasonality effects by selecting the most extreme summer and winter months to assess PA. In addition, participants recruited to behaviour change interventions might have higher levels of motivation to change and are less affected by seasonal barriers.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN95932902.

Seasonal changes in amount and patterns of physical activity in women

BACKGROUND

Environmental factors including seasonal changes are important to guide physical activity (PA) programs to achieve or sustain weight loss. The goal was to determine seasonal variability in the amount and patterns of free-living PA in women.

METHODS

PA was measured in 57 healthy women from metropolitan Nashville, TN, and surrounding counties (age: 20 to 54 years, body mass index: 17 to 48 kg/m2) using an accelerometer for 7 consecutive days during 3 seasons within 1 year. PA counts and energy expenditure (EE) were measured in a whole-room indirect calorimeter and used to model accelerometer output and to calculate daily EE and intensity of PA expressed as metabolic equivalents (METs).

RESULTS

PA was lower in winter than in summer (131+/-45 vs. 144+/-54x10(3) counts/d; P=.025) and in spring/fall (143+/-48x10(3) counts/d; P=.027). On weekends, PA was lower in winter than in summer by 22,652 counts/d (P=.008). In winter, women spent more time in sedentary activities than in summer (difference 35 min/d; P=.007) and less time in light activities (difference -29 min/d, P=.018) and moderate or vigorous activities (difference -6 min/d, P=.051).

CONCLUSIONS

Women living in the southeastern United States had lower PA levels in winter compared with summer and spring/fall, and the magnitude of this effect was greater on weekends than weekdays.

Evaluating energy intake measurement in free-living subjects: when to record and for how long?

OBJECTIVE

To nutritionally analyse mean energy intake (EI) from different 3 d intervals within a 7 d recording period and to evaluate the seasonal effect on energy and nutrient intake.

DESIGN

Cross-sectional study of dietary intake collected with 7 d food diaries.

SETTING

Aberdeen, north-east Scotland, UK, between 2002 and 2004.

SUBJECTS

Participants from two long-term trials were pooled. These trials, investigating genetic and environmental influences on body weight, were the Genotyping And Phenotyping (GAP) study and a cohort observational study, Rowett Assessment of Childhood Appetite and metaboLism (RASCAL). There were 260 Caucasian adults, BMI range 16.7-49.3 kg/m2, age range 21-64 years.

RESULTS

Mean EI for Wednesday, Friday and Saturday had the closest approximation to the 7 d mean (0.1 % overestimate). A gender x season interaction (P = 0.019) with a different intake pattern for females and males was observed. For females, lower mean (se) EI was recorded in summer (8117 (610) kJ) and autumn (7941 (699) kJ) compared with spring (8929 (979) kJ) and winter (8132 (1041) kJ). For males, higher mean (se) EI was recorded in summer (10 420 (736) kJ) and autumn (10 490 (1041) kJ) compared with spring (9319 (1441) kJ) and winter (9103 (1505) kJ).

CONCLUSIONS

The study results indicate that 3 d weighed intakes recorded from Wednesday, Friday and Saturday are most representative of 7 d habitual intake in free-living subjects. They also indicate that seasonality has a limited effect on EI and no effect on macronutrient intake.

Seasonal variations in physical activity and implications for human health

This review explores the implications of seasonal changes in physical activity for fitness and human health. Photosensitivity and nutrient shortages mediate animal hibernation via the hypothalamus and changes in leptin and ghrelin concentrations. Opportunities for hunting and crop cultivation determine seasonal activity in under-developed human societies, but in developed societies temperature and rainfall are dominant influences, usually over-riding innate rhythms. Both questionnaire data and objective measurements show that many groups from children to the elderly increase their physical activity from winter to spring or summer. Measurements of maximal oxygen intake and muscle strength commonly show parallel seasonal changes. However, potential effects upon body mass and body fat may be counteracted by changes of food intake; subsistence agriculturists sometimes maintain or increase physical activity at the expense of a decrease in body mass. In developed societies, body fat commonly increases during the winter, with parallel changes in blood lipids, blood pressure and blood coagulability; moreover, these changes are not always fully reversed the following summer. Most developed societies show increased all-cause and cardiac mortalities in the winter. Health consequences of seasonal variations in physical activity including an increased vulnerability to cardiac catastrophe and a year-by-year increase in total body fat seem most likely if the average level of physical activity for the year is low. Public health recommendations should underline the importance of maintaining physical activity during adverse environmental conditions by adapting clothing, modifying behaviour and exploiting any available air-conditioned indoor facilities.

UK adults exhibit higher step counts in summer compared to winter months

BACKGROUND

Seasonal differences in step counts have been observed in a limited number of studies conducted on US adults. Due to the diverse global climate, assessment and interpretation of seasonal patterns in ambulatory activity may vary between countries, and regionally specific studies are necessary to understand global patterns. Currently, no studies have assessed whether a seasonal trend is present when ambulatory activity is measured objectively in adults living in the UK.

AIM

The present study investigated whether pedometer-determined step counts of adults living in the UK vary between summer and winter.

SUBJECTS AND METHODS

Ninety-six adults (52% male, age = 41.0 +/- 12.3 years, BMI = 26.1 +/- 5.1 kg m(-2)) completed a within-subject bi-seasonal pedometer study. All participants completed two 4-week monitoring periods; one during the summer and one the following winter. The same Yamax SW-200 pedometer was worn throughout waking hours during both seasons, and daily step counts were recorded in an activity log. Intra-individual seasonal changes in mean daily steps were analysed using a paired samples t-test.

RESULTS

Summer mean daily step counts (10 417 +/- 3055 steps day(-1)) were significantly higher than those reported during the winter (9132 +/- 2841 steps day(-1)) (p < 0.001). A follow-up study conducted the subsequent summer in a sub-sample (n = 28) reinforced this trend. Summer step counts were significantly higher than winter step counts on all days of the week (p < or = 0.001). A significant day of the week effect was present in both seasons, with step counts reported on a Sunday being on average 1500 steps day(-1) lower than those reported Monday through to Saturday.

CONCLUSION

Step counts in the sample of UK adults surveyed decreased significantly in the winter compared to the summer, suggesting future pedometer surveillance studies should capture step counts throughout the year for a non-biased reflection of habitual ambulatory activity. Public health initiatives should target these seasonal differences, and opportunities should be provided that encourage individuals to increase their activity levels during the colder, darker months of the year.

A preliminary study of one year of pedometer self-monitoring

BACKGROUND

Long-term pedometer monitoring has not been attempted.

PURPOSE

The purpose of this project was to collect 365 days of continuous self-monitored pedometer data to explore the natural variability of physical activity.

METHODS

Twenty-three participants (7 men, 16 women; M age = 38 +- 9.9 years; M body mass index = 27.7 +- 6.2 kg/m2) were recruited by word of mouth at two southern U.S. universities. Participants were asked to wear pedometers at their waist during waking hours and record steps per day and daily behaviors (e.g., sport/exercise, work or not) on a simple calendar. In total, participants wore pedometers and recorded 8,197 person-days of data (of a possible 8,395 person-days, or 98%) for a mean of 10,090 +- 3,389 steps/day. Missing values were estimated using the Missing Values Analysis EM function in SPSS, Version 11.0.1.

RESULTS

A mean of 10,082 +- 3,319 steps/day was computed. Using the corrected data, differences in steps/day were significant for season (summer > winter, F = 7.57, p = .001), day of the week (weekday > weekend, F = 3.97, p = .011), type of day (workday vs. nonworkday, F = 9.467, p = .008), and participation in sport/exercise (day with sport/exercise > day without sport/exercise, F = 102.5, p < .0001).

CONCLUSIONS

These data suggest that surveillance should be conducted in the spring/fall or that an appropriate correction factor should be considered if the intent is to capture values resembling the year-round average.

Seasonal variation in total energy expenditure and physical activity in Dutch young adults

OBJECTIVE

The impact of season on energy expenditure and physical activity is not well quantified. This study focused on summer-winter differences in total energy expenditure (TEE) and physical activity.

RESEARCH METHODS AND PROCEDURES

Twenty-five healthy Dutch young adults, living in an urban environment, were measured in the summer season and the winter season. TEE was measured using doubly labeled water, and sleeping metabolic rate (SMR) was measured during an overnight stay in a respiration chamber. Subsequently, the physical activity level (PAL = TEE/SMR) and activity-related energy expenditure [(0.9 x TEE) - SMR] were calculated. Maximal mechanical power (Wmax) was determined with an incremental test on a cycle ergometer. Body composition was measured with hydrostatic weighing and deuterium dilution using Siri's three-compartment model.

RESULTS

There was no difference in TEE between seasons. PAL was higher in summer than in winter (1.87 +/- 0.22 vs. 1.76 +/- 0.18; p < 0.001), and the difference was higher for men than for women (0.20 +/- 0.14 vs. 0.05 +/- 0.16; p = 0.04). The difference in PAL between seasons was dependent on the initial activity level. There was a strong linear relation (R2 = 0.48) between PAL and physical fitness (Wmax/fat-free mass), but Wmax/fat-free mass did not change between seasons in response to the lower PAL in winter.

DISCUSSION

The extent of the changes in PAL is of physiological significance, and seasonality in physical activity should be taken into account when studying physical activity patterns or relationships between physical activity and health.