Secondary Logo

Journal Logo

Original Articles: Nutrition

Physical Activity and Skipping Breakfast Have Independent Effects on Body Fatness Among Adolescents

Cayres, Suziane U.; Urban, Jacqueline B.; Fernandes, Rômulo A.

Author Information
Journal of Pediatric Gastroenterology and Nutrition: November 2018 - Volume 67 - Issue 5 - p 666-670
doi: 10.1097/MPG.0000000000002081
  • Free

Abstract

What Is Known

  • Diet and physical activity have an essential role on adolescent health. Skipping breakfast is associated with obesity during childhood.
  • It is, however, not clear whether active adolescents who have regular breakfast intake have lower risk for childhood obesity.

What Is New

  • Breakfast intake and physical activity appear to have independent effects on body fatness.
  • Adolescents who eat breakfast regularly have lower body fatness.
  • Trunk fatness decreases in adolescents who increased physical activity.

Adolescence is an important phase of human life, in which behaviors learned by adolescents will likely be maintained until adulthood. Compelling evidence now highlight the essential role of diet and physical activity on adolescent health and the possible tracking of these behaviors until adult life (1,2).

The impact of consumption of some nutrients on childhood obesity has been widely investigated, whereas the habit of skipping meals receives less attention. The impact of skipping breakfast on health seems more important than other meals (3,4). Among adolescents, skipping breakfast is a behavior identified in approximately 45% of adolescents (5). Frequency and type of food intake during breakfast is associated with daily energy intake (6), and the habit of skipping breakfast has frequently been linked to a higher body mass index (BMI) and prevalence of obesity in adolescents (7,8). Moreover, adolescents who skip breakfast present higher trunk fatness (TF) than their counterparts who do not (4), whereas obese breakfast skippers demonstrate dyslipidemia and increased fasting glucose levels (3).

In contrast, physical activity is simultaneously related to lower adiposity and a better lipid profile among adolescents (9). According to physical activity guidelines, school-aged adolescents (6–19 years old) should engage in at least 60 minutes per day of moderate-to-vigorous physical activity (MVPA) (10), a level equivalent to 12,000 steps/day (11). Many studies (12–14) are, however, limited by the subjects not achieving the recommended levels for MVPA, and this scenario is generally attributed to the large amount of time spent in sedentary behaviors (15).

In fact, although physical activity protects against many of the unhealthy outcomes related to skipping breakfast, the relationship between skipping breakfast and physical activity is unclear (5). Moreover, it is not clear whether active adolescents who have regular breakfast intakes have higher protection against childhood obesity (6).

We analyzed the longitudinal relationship between breakfast intake and absolute changes in adiposity among adolescents, and the possible role of physical activity to mediate this phenomenon. We hypothesized that regular breakfast intake will reduce adiposity independent of physical activity level.

METHODS

Sampling

The longitudinal study entitled “Effect of habitual physical activity and body fatness (BF) on the cardiovascular system of adolescents: cohort of 12 months” was approved by the Ethics Committee for Research involving human subjects of the Sao Paulo State University (UNESP [process: 322.650/2013]) from Presidente Prudente (200,000 inhabitants and human development index of 0.806), Brazil.

Sampling and data collection were conducted from 2013 to 2014. The researchers responsible for the present study contacted the municipal authorities for authorization. After approval by Municipal Department of Education, adolescents were contacted in 7 school units located in the metropolitan region of the city (overall, 495 adolescents of both sexes were enrolled in the 7 schools contacted). Three school units agreed to participate in the research, and authorized contacting the adolescents. The research project, its aims, and inclusion criteria were explained to all adolescents. The following inclusion criteria were adopted: age between 11 and 14 years; regularly enrolled in the school unit; absence of any known chronic disease; no regular medicine use; and written consent signed by parents or legal guardians.

Initially, measures were obtained at baseline from 120 adolescents, and at 12-month follow-up from 86 adolescents (42 boys and 44 girls). Taking into account the final sample size after 12 months, the sample of 86 adolescents allows identification of significant coefficients of correlation of r = ≥0.29 (statistical power of 80% and Z = 1.96) (16). The cross-sectional relationship between BF and skipping breakfast was identified as r = −0.32 (4), requiring a minimum sample size of 75 participants to identify relationships with statistical power of 80% and Z = 1.96. Therefore, our final sample size after follow-up was adequate to investigate this relationship.

Breakfast Intake

Breakfast intake was reported through face-to-face interviews accounting the number of days with breakfast consumption in a typical week (variable ranging from zero to 7 days) using the following questions: “How many days per week do you usually eat breakfast? The possible answers were as follows: zero (score 0); 1–2 days (score 1); 3–5 days (score 2); every day of the week (score 3). Face-to-face interviews were conducted at the baseline and the follow-up time points. Adolescents who reported consumption of breakfast “every day” in both interviews were treated as nonskipping breakfast (n = 37), whereas adolescents who reported any frequency below “every day” at any moment were treated as skipping breakfast (n = 49). Moreover, for statistical analyzes, the scores of both interviews were summed (score of each interview ranged from zero [0] to six [6]).

Body Fatness

Percent whole BF and TF were estimated using a densitometry scanner (General Electrics; model, Lunar-DPX-NT, General Electric Healthcare, Little Chalfont, Buckinghamshire, UK) equipped with the software GE Medical System Lunar (version 4.7). Before each assessment the accuracy of the device was evaluated following the recommended procedures of the manufacturer. During the test, the adolescents remained immobile for 15 minutes in the supine position, with their arms and legs extended on a stretcher. The subjects removed their shoes and any metal objects before the evaluation. All measurements were taken by the same trained researcher, in a room with a constantly controlled temperature. BF and TF were estimated at baseline and follow-up, and the absolute change (Δ) between the 2 moments was calculated.

Physical Activity

Physical activity was estimated through step counts using pedometers (Digi-walker, model SW200, Yamax, Shropshire, UK), which were worn for 7 consecutive days, fixed on the clothes near the hip. The battery status of each device was tested previously by a voltmeter (brand Minipa, model ET-1002) to avoid bias during measurements. Pedometers are sensitive to movements on the vertical axis. Participants were instructed to remove the device during all periods of sleep and water-based activities. At the end of each day before sleep, adolescents registered the number of steps attained. At both time points of the study, only adolescents with 7 consecutive days of physical activity were analyzed. In the present study, taking into account the amount of physical activity recorded, the adolescents were classified daily as “sufficiently active” if the step count was ≥7500 steps (17). The number of days the adolescent reached the cut-point for steps was calculated at baseline (score ranging from 0 to 7) and follow-up (score ranging from 0 to 7), and the absolute change (Δ) between the 2 moments was calculated (score ranging from −7 to 7). The physical activity cut-point adopted in the present study was developed for adults (≥7500 steps/day is considered “physically active” meeting the recommendations of MVPA) (17). Given the fact that for adolescents more steps/days is required (≥12,000 steps/days) (10) and in our sample few number of adolescents reached this higher physical activity cut-point (11), and thus a lower physical activity cut-point was adopted (17). After the baseline measurements, the adolescents were not encouraged to increase their level of physical activity, given the fact that our study was an observational follow-up of 12 months.

Biological Maturation

Body weight was measured using an electronic scale (Filizzola PL 150, model Filizzola Ltda, Brazil), and height by using a wall-mounted stadiometer (Sanny, model American Medical of the Brazil Ltda, Brazil). BMI was calculated as body weight divided by height squared (kg/m2). All anthropometric measurements were performed applying standardized techniques (18). Using anthropometric data, mathematical equations were used to estimate the maturity level (19), which was expressed in time (years) (negative score [time to reach biological maturation] and positive score [the adolescent had already reached the maturity level]). Maturity level was estimated at baseline and follow-up, and the absolute change (Δ) between the 2 moments was calculated.

Additional Information

Concurrent with the face-to-face interview, sex, and chronological age were established.

Statistical Analyses

Initially, descriptive analyses were composed of mean and 95% confidence intervals (95% CI). Absolute change (Δ) was performed during both moments of the study. The Student t test for independent samples was applied to analyze differences between adolescents stratified into “nonskipping breakfast” and “skipping breakfast.” The Pearson correlation analyzed the relationship between numerical variables. Structural equation models (SEMs) were used to assess the relationship between nonskipping breakfast (summed baseline and follow-up) and absolute changes (Δ) in whole body and TF, analyzing the potential mediation effect of physical activity (Δ) in these relationships. The SEMs were adjusted by the covariates (sex, age [baseline], maturity level [baseline], and maturity level [Δ]) and also, the relative chi-square test (χ2/DF) was used as a fit index to SEM (satisfactory value <0.05). Total, direct, and indirect effects are presented in values of r (95% CI) by means of maximum likelihood. Statistical significance was set at a P value <0.05 and analyzed using the software BioEstat (version 5.0) and Stata (version 15.0).

RESULTS

After 12 months, 86 adolescents (mean age of 11.6 ± 0.7 years) remained in the study and were assessed at the follow-up time. At baseline, adolescents who did and did not skip breakfast were similar in terms of age (P = 0.454), height (P = 0.588), body weight (P = 0.054), maturity level (P = 0.465), and physical activity (P = 0.984). At baseline, adolescents who consumed breakfast regularly presented with lower BMI (P = 0.032), TF (P = 0.019), and BF (P = 0.012) than their counterparts (Table 1). After 12 months of follow-up, TF (−3.5% [95% CI: −6.9 to −0.2]) and BF (−2.3% [95% CI: −3.9 to −0.7]) of adolescents who had consumed breakfast regularly decreased more than adolescents who did not (Table 1).

TABLE 1
TABLE 1:
General characteristics of the adolescents stratified by breakfast intake (n = 86)

Breakfast intake frequency was negatively correlated with BF (r = −0.312 [95% CI: −0.491 to −0.107]). At baseline, maturity level was also significantly related to absolute changes in BF (r = 0.213 [95% CI = 0.001–0.407]). Conversely, a negative relationship was found between changes in physical activity and TF (r = −0.270 [95% CI: −0.457 to −0.060]) (Table 2).

TABLE 2
TABLE 2:
Univariate relationship between changes in adiposity, physical activity, and skipping breakfast in adolescents (Brazil, n = 86)

Figure 1 A shows that adolescents stratified as nonskipping breakfast demonstrated an inverse relationship with absolute changes in BF (r = −0.274 [95% CI: −0.498 to −0.051]); however, this relationship was not mediated by physical activity. Figure 1B shows that nonskipping breakfast was not significantly related to TF; however, physical activity seems to reduce TF in our sample (r = −0.281 [95% CI: −0.479 to −0.083).

FIGURE 1
FIGURE 1:
Structural equation modeling describing the relationship between breakfast intake, physical activity, body fatness (A), trunk fatness (B) in adolescents (n = 86) adjusted by sex, age (B), maturity level (B), and maturity level (Δ). Data are presented in values of Pearson correlation (r) and 95% confidence intervals (95% CI). Total, direct, and indirect effects are presented in values of r (95% CI) by means of maximum likelihood and statistical significance (P < 0.05). In (A), breakfast intake is the independent variable, body fatness is the dependent variable, physical activity is the mediator variable (it explains the relationship between the dependent variable and the independent variable), and all the other variables adjusted the structural equation modeling. In (B), breakfast intake is the independent variable, trunk fatness is the dependent variable, physical activity is the mediator variable (it explains the relationship between the dependent variable and the independent variable), and all the other variables adjusted the structural equation modeling. B, Baseline; Δ, absolute change between both moments of the study.

DISCUSSION

Our results show that regular consumption of breakfast is associated with reduction in BF; however, the relationship is not mediated by physical activity. Moreover, TF decreases in adolescents who increase their physical activity level, a relationship not affected by skipping breakfast.

In the present study, physical activity was not associated with skipping breakfast. Some theories link skipping breakfast and physical activity. One theory hypothesizes that energy and nutrients obtained from breakfast provide more energy for better physical activity performance (5). Another theory is that skipping breakfast may be a habit linked to higher mental and physical fatigue, leading to lower physical activity (5). Moreover, it is possible that physical activity and healthy diet behaviors are related with other behaviors among adolescents (20). In fact, the relationship between physical activity and skipping breakfast is inconsistent in the literature, with significant results being observed mainly in surveys, with larger sample sizes (5). Apparently, the association between these variables has low magnitude, making a higher number of observations necessary to reach sufficient statistical power to identify significant results.

Previous data report that the habit of skipping breakfast in adolescents may be associated with an unhealthy BMI (21). Similarly, in the present study, at baseline, breakfast skippers had higher BMI, BF, and TF than nonskippers. Although the characteristics related to skipping breakfast and its association with the development of overweight and obesity during the first decades of life have been investigated, the mechanisms linking are still unclear (7,8,21).

Our results may be attributable to appetite control and quality of diet developed by regularly eating breakfast (7). Adolescents who skip breakfast remain prolonged (night and morning) in fasting state. In these circumstances, an empty stomach is a physical signal to the human body, mainly the stomach, to release ghrelin (22), leading to increased appetite and thus higher likelihood of hyperphagia episodes. The hypothesis that higher energy intake leads to increased adiposity seems even more probable, particularly since the weight gain linked to skipping breakfast does not appear to be attributable to reduced physical activity (5).

In this group of adolescents, increases in habitual physical activity decreased TF. The relationship between increased physical activity and lower adiposity is not a surprise, because the impact of physical activity on overall energy expenditure is relevant to maintenance of adequate BF levels (23,24). Therefore, the message with the most impact provided by our findings is that meeting 7500 steps per day is relevant to adiposity control, despite being lower than currently recommended for children and adolescents (10).

Our study has some limitations of the study. The high number of drop-outs at follow-up limits the statistical power to detect significant relationships. Our sample size has a power to detect relationships ≥0.29, whereas the relationships observed in our sample were slightly lower than ≥0.29. Cross-sectional data suggest that our sample size is enough to detect significant relationships with adequate statistical power (4), but it is possible that this relationship between skipping breakfast and BF require larger sample sizes in longitudinal designs, due the large variance observed in this type of study design. Furthermore, no additional data were available about the food consumed during breakfast, or where (home or school) and when (weekday and weekend) the meal occurred. , information about breakfast intake was self-reported, rendering our data subject to biased reporting. The absence of data about why the adolescents skipped their breakfast is a relevant limitation, as it is unclear if this was due to personal option or poverty.

The primary strength of the present study was the criteria adopted for the adolescent to be considered “nonskipping breakfast.” Several studies have developed a large number of cut points and the majority considered breakfast intake at least 4 days per week as regular consumption of breakfast (1,7). In our study adolescents were considered as “nonskipping breakfast” when they ate this meal every day of the week. Moreover, most of the reports about the issue are cross-sectional in design and our study overcame this limitation. Finally, adiposity was measured using an accurate method, instead of anthropometric tools.

In conclusion, breakfast intake and physical activity do not appear to be interrelated with each other, whereas adolescents who eat breakfast regularly present lower BF and TF decreases in adolescents who improved physical activity, independent of breakfast intake.

Acknowledgments

The authors thank all volunteers who have agreed to participate in this research, and also all members from the Scientific Research Group Related to Physical Activity (GICRAF)—UNESP/Brazil that have helped with the sampling and logistics of the data collection.

REFERENCES

1. Merten MJ, Williams AL, Shriver LH. Breakfast consumption in adolescence and young adulthood: parental presence, community context, and obesity. J Am Diet Assoc 2009; 109:1384–1391.
2. Lima MCS, Cayres SU, Machado-Rodrigues A, et al. Early sport practice promotes better metabolic profile independently of current physical activity. Med Sport 2014; 18:172–178.
3. Freitas Júnior IF, Christofaro DG, Codogno JS, et al. The association between skipping breakfast and biochemical variables in sedentary obese children and adolescents. J Pediatr 2012; 161:871–874.
4. Cayres SU, Júnior IF, Barbosa MF, et al. Breakfast frequency, adiposity, and cardiovascular risk factors as markers in adolescents. Cardiol Young 2016; 26:244–249.
5. Lyerly JE, Huber LR, Warren-Findlow J, et al. Is breakfast skipping associated with physical activity among U.S. adolescents? A cross-sectional study of adolescents aged 12–19 years, National Health and Nutrition Examination Survey (NHANES). Public Health Nutr 2014; 17:896–905.
6. Zakrzewski-Fruer JK, Plekhanova T, Mandila D, et al. Effect of breakfast omission and consumption on energy intake and physical activity in adolescent girls: a randomised controlled trial. Br J Nutr 2017; 118:392–400.
7. Ahadi Z, Qorbani M, Kelishadi R, et al. Association between breakfast intake with anthropometric measurements, blood pressure and food consumption behaviors among Iranian children and adolescents: the CASPIAN-IV study. Public Health 2015; 129:740–747.
8. Duncan S, Duncan EK, Fernandes RA, et al. Modifiable risk factors for overweight and obesity in children and adolescents from São Paulo, Brazil. BMC Public Health 2011; 11:585.
9. Silva DR, Werneck AO, Collings, et al. Physical activity maintenance and metabolic risk in adolescents. J Public Health (Oxf) 2017. 1–8.
10. Davis MM, Gance-Cleveland B, Hassink S, et al. Recommendations for prevention of childhood obesity. Pediatrics 2007; 120:229–253.
11. Colley RC, Janssen I, Tremblay MS. Daily step target to measure adherence to physical activity guidelines in children. Med Sci Sports Exerc 2012; 44:977–982.
12. Ishii K, Shibata A, Adachi M, et al. Gender and grade differences in objectively measured physical activity and sedentary behavior patterns among Japanese children and adolescents: a cross-sectional study. BMC Public Health 2015; 15:1254.
13. Colley RC, Garriguet D, Janssen I, et al. Physical activity of Canadian children and youth: accelerometer results from the 2007 to 2009 Canadian Health Measures Survey. Health Rep 2011; 22:15–23.
14. Tammelin T, Ekelund U, Remes J, et al. Physical activity and sedentary behaviors among Finnish youth. Med Sci Sports Exerc 2007; 39:1067–1074.
15. Ferreira NL, Claro RM, Mingoti AS, et al. Coexistence of risk behaviors for being overweight among Brazilian adolescents. Prev Med 2017; 100:135–142.
16. Miot HA. Sample size in clinical and experimental trials. J Vasc Bras 2011; 10:275–278.
17. Tudor-Locke C, Craig CL, Thyfault JP, et al. A step-defined sedentary lifestyle index: <5000 steps/day. Appl Physiol Nutr Metab 2013; 38:100–114.
18. Gordon CC, Chumlea WC, Roche AF. Lohman TG, Roche AF, Martorell R. Stature, recumbent length, and weight. Anthropometric Standardization Reference Manual. Champaign, IL: Human Kinetics Books; 1988. 3–8.
19. Mirwald RL, Baxter-Jones AD, Bailey DA, et al. An assessment of maturity from anthropometric measurements. Med Sci Sports Exerc 2002; 34:689–694.
20. Fernandes RA, Christofaro DG, Casonatto J, et al. Cross-sectional association between healthy and unhealthy food habits and leisure physical activity in adolescents. J Pediatr 2011; 87:252–256.
21. Smith KJ, Breslin MC, McNaughton SA, et al. Skipping breakfast among Australian children and adolescents; findings from the 2011–12 National Nutrition and Physical Activity Survey. Aust NZ J Public Health 2017; 41:572–578.
22. Depoortere I. Targeting the ghrelin rector to regulate food intake. Regul Pept 2009; 156:13–23.
23. Dâmaso AR, Da Silveira Campos RM, Caranti DA, et al. Aerobic plus resistance training was more effective in improving the visceral adiposity, metabolic profile and inflammatory markers than aerobic training in obese adolescents. J Sports Sci 2014; 32:1435–1445.
24. Lee S, Deldin AR, White D, et al. Aerobic exercise but not resistance exercise reduces intrahepatic lipid content and visceral fat and improves insulin sensitivity in obese adolescent girls: a randomized controlled trial. Am J Physiol Endocrinol Metab 2013; 305:E1222–E1229.
Keywords:

health; obesity; pedometer

Copyright © 2018 by European Society for Pediatric Gastroenterology, Hepatology, and Nutrition and North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition