The studies described in this thesis were undertaken to examine the factors affecting adolescents cognitive function across the school morning. Specifically, the effects of breakfast consumption, breakfast glycaemic index (GI) and a mid-morning bout of exercise were examined, whilst the final experimental chapter examined their combined effects. The battery of cognitive function tests used in the present study was administered via a laptop computer and took approximately 15 min to complete. Across all experimental chapters, the visual search test (assessing visual perception), the Stroop test (assessing attention) and the Sternberg paradigm (assessing working memory) were used. Furthermore, in chapter V the Flanker task (also assessing attention) was added to the testing battery. The first experimental study (chapter IV) examined the effects of consuming a self-selected breakfast on cognitive function, compared to breakfast omission. Ninety-six adolescents (12 to 15 years old) completed two experimental trials (breakfast consumption and breakfast omission), scheduled seven days apart, in a randomised crossover design. Following breakfast consumption, accuracy on the more complex level of the visual search test was higher than following breakfast omission (p = 0.021). Similarly, accuracy on the Stroop test was better maintained across the morning following breakfast consumption when compared with breakfast omission (p = 0.022). Furthermore, responses on the Sternberg paradigm were quicker later in the morning following breakfast consumption, on the more complex levels (p = 0.012). Breakfast consumption also produced higher self-report energy and fullness, lower self-report tiredness and hunger, and higher blood glucose concentrations, compared with breakfast omission (all p < 0.001). Overall, the findings suggested that breakfast consumption enhanced adolescents cognitive function, when compared with breakfast omission. The second experimental study (chapter V) examined the effects of consuming a high GI breakfast, a low GI breakfast and breakfast omission on cognitive function. Forty-one adolescents (12 to 14 years old) completed three experimental trials, each scheduled seven days apart, in a randomised crossover design. There was a greater improvement in response times across the morning following a low GI breakfast, compared to breakfast omission on the complex level of the Stroop test (p = 0.009) and both levels of the Flanker task (p = 0.041), and compared to following a high GI breakfast on the complex level of the visual search test (p = 0.025) and all levels of the Sternberg paradigm (p = 0.013). Furthermore, accuracy was enhanced following a low GI breakfast, compared to breakfast omission on the more complex levels of the visual search test (p = 0.032), Sternberg paradigm (p = 0.051) and Flanker task (p = 0.001), and compared to following a high GI breakfast on both levels of the Stroop test (p = 0.033) and the more complex levels of the Sternberg paradigm (p = 0.002) and Flanker task (p = 0.014). Furthermore, participants exhibited a lower glycaemic response following the low GI breakfast (p < 0.001), though there was no difference in the insulinaemic response (p = 0.063), compared to following the high GI breakfast. Overall, the findings suggest that a low GI breakfast is the most beneficial for adolescents cognitive function, compared with a high GI breakfast and breakfast omission. The third experimental study (chapter VI) examined the effects of a mid-morning bout of exercise, following a self-selected breakfast, on cognitive function. Forty-five adolescents (12 to 13 years old) completed two experimental trials (exercise and resting), scheduled seven days apart, in a randomised crossover design. There was a greater improvement in response times across the morning following the mid-morning bout of exercise on all levels of the Sternberg paradigm (p = 0.010). There was also a greater improvement in response times across the morning on the visual search test following the exercise (p = 0.009), but this improved speed was combined with a greater decrease in accuracy following the exercise (p = 0.044). This suggests that following exercise, the adolescents exhibited a speed-accuracy trade-off, whereby they responded quicker, but this was to the detriment of accuracy. Overall, the findings suggest that whilst the mid-morning bout of exercise improved some components of cognitive function (e.g. response times on the Sternberg paradigm), it did not affect other components (e.g. Stroop test performance). The final experimental study (chapter VII) examined the combined effects of breakfast GI and a mid-morning bout of exercise on adolescents cognitive function. Forty-two adolescents (11 to 13 years old) were allocated to matched high GI (n = 22) and low GI (n = 20) breakfast groups. Within the matched groups, participants completed two experimental trials (exercise and resting) in a randomised, crossover design. The findings indicate that, for the complex level of the Stroop test, following the high GI breakfast there was a greater improvement in response times across the morning on the resting trial, whereas following the low GI breakfast response times improved across the morning on both the exercise and resting trials, though the magnitude of the improvement was greatest on the exercise trial (p = 0.012). On the Sternberg paradigm, response times improved across the morning following the low GI breakfast regardless of exercise, whereas following the high GI breakfast response times improved across the morning on the exercise trial, though remained similar across the morning on the resting trial (p = 0.019). Overall, the findings suggest that the effects of the mid-morning bout of exercise were dependent upon the breakfast GI and the component of cognitive function being examined and that, for the Stroop test, the beneficial effects of the low GI breakfast and mid-morning bout of exercise were additive. Overall, the results from this thesis suggest that breakfast consumption is more beneficial than breakfast omission and more specifically, that a low GI breakfast is more beneficial than both a high GI breakfast and breakfast omission, for adolescents cognitive function across the school morning. However, the effects of exercise appear to be more variable, with the effect of exercise depending upon the component of cognitive function examined and the GI of the breakfast consumed. Overall, the findings presented in this thesis suggest that the nutritional effects on adolescents cognitive function (i.e. the effects of breakfast consumption and GI) were stronger and more consistent than the exercise induced effects.

Background: Obesity continues to be one of the largest public health concerns in our nation. The role of eating patterns as a means for weight management has been studied extensively. However, the role of breakfast in weight management is still poorly understood. The purpose of this study was to understand the role of breakfast in weight management by observing the relationships of energy intake and macronutrient composition, specifically protein and fiber, with weight status during early morning and late morning eating occasions. Methods: Data from two multiple pass 24h dietary recalls from NHANES 2005-2010 were used. N= 4542 non-pregnant, non-lactating participants aged 20-65 y who did not perform shift work and who had a BMI between 18.5 and 60 kg/m2 were included. Individuals with missing data for any of the variables were excluded. Data were analyzed with SPSS software version 21. Each of the 2 days was divided into four time periods: time period 1 defined as the first intake of the day occurring between 12:00 a.m. and 4:59 a.m., time period 2 defined as the first intake occurring between 5:00 a.m. and 8:59 a.m., time period 3 defined as the first intake occurring between 9:00 a.m. and 11:30 a.m., and time period 4 defined as the first intake occurring after 11:30 a.m. Time period 2 was designated as “early morning intake” and time period 3 was designated as “late morning intake”. The other two time periods were designated as energy intake eaten the rest of the day. Energy (kcal), protein (g), and fiber (g) intakes were then calculated for the whole day and for each time period. For early morning and late morning intake, energy, protein and fiber were also divided into 5 categories. Those reporting no intake (0 kcals) made up the first category and quartiles were calculated for those reporting energy intakes of ≥ 0.1 kcal. Modified quartiles for the late morning period using the quartile cutoffs for the early morning time period were also calculated. Similarly, those reporting no intake (0 grams) made up the first category for protein and fiber and quartiles were calculated for those reporting protein or fiber intakes of ≥ 0.01 g. Estimated energy requirements (EER) were determined using the prediction equations developed by the Institute of Medicine (IOM 2005). To determine energy intake reporting plausibility, reported energy intake as a percent of EER was calculated. Standard classifications were used for weight status based on BMI. Descriptive statistics (median and 95% confidence interval) were computed for all variables. Multinomial logistic regression analysis was performed to determine associations between morning energy intake, protein, and fiber categories and risk for overweight (OW) and obesity (OB) for both early morning and late morning time periods. For the energy intake categories, Model 1 was controlled for race/ethnicity, age, gender, poverty-income ratio (PIR), smoking status, alcohol consumption, physical activity, self-reported chronic disease, daily eating frequency, and the two day morning eating pattern. Model 2 was controlled for all of the covariates in Model 1 plus energy intake before and after morning eating. Model 3 was controlled for all of the covariates in Model 2 plus energy intake reporting plausibility. For the protein and fiber categories, Model 1, 2, and 3 controlled for the same covariates as the energy intake categories and also controlled for reported energy intake during the early or late morning eating occasions. A p-value of <0.05 was considered statistically significant. Results: For the energy intake categories during the early morning, compared to no morning intake, Model 1 showed a lower risk for OB in Q2, but no other relationships were seen in any of the other quartiles. Similar results were seen in Model 2 where a lower risk for OB in Q2 was present. In Model 3, however, (controlled for energy intake reporting plausibility) the relationship between energy intake in Q2 and a lower risk for OB disappeared and a higher risk for OW and OB became apparent in Q4. For the late morning analysis, Models 1 and 2 were similar in that there was no association between morning energy intake category and weight status, but for Model 3 there was a higher risk for OW and OB in Q2-Q4. When we used the modified late morning quartile cutoffs in the analysis to eliminate potential bias due to the different quartile cutoffs for the early and late morning eating occasions, the higher risk for OW and OB was still present in Q2-Q4 and the ORs were attenuated compared to when the original late morning cutoffs were used. In terms of composition, compared to no morning intake, there were no significant associations seen between early or late morning protein consumption and weight status in any of the models. Additionally, for the early morning analysis of fiber, Models 1 and 2 did not show an association between morning fiber intake category and weight status, but for Model 3 there was a lower risk for OB in Q4. For the late morning analysis, Model 1 showed a higher risk for OW in Q2, but no other relationships were seen in any of the other quartiles. Similar results were seen in Model 2 where a higher risk for OB in Q2 was present. In Model 3, however, this relationship disappeared and no other associations were seen in any of the other quartiles. Conclusion: In comparison to having no morning intake (i.e., “skipping”) there was an elevated risk for OW and OB when consuming higher amounts of energy during the early morning and moderate to high amounts of energy during the late morning. The risk for OW and OB was higher in the late morning compared to the early morning eating occasions, in part, but not entirely, because of the higher amounts of energy consumed during the later morning in comparison to the early morning. Therefore, higher energy in both early morning and late morning increase the risk for OW and OB. Furthermore, later timing may increase the risk for OW and OB, independent of energy intake the rest of the day, since individuals who ate later also had higher energy intakes in the later morning compared to the early morning. In addition, compared to no morning intake of fiber, having a very high fiber intake in the early morning, but not the late morning, may decrease the risk for OB independent of energy intake and fiber intake the rest of the day. These associations may not be apparent unless energy intake reporting plausibility is taken into account.

Background: Loneliness, a feeling of distress, has aggravated due to the COVID-19 pandemic lockdowns and reduced social interactions. The objective of this study was to explore whether increased loneliness due to the COVID-19 pandemic was associated with various eating and activity behaviours in adolescence, a critical period for the development of lasting lifestyle habits. Methods: In this cross-sectional study, we used self-reported data from 43,588 and 40,521 Canadian adolescents aged 12-19 years (collected between November 2020 and June 2021) for eating habits and the 24-hour movement behaviours, respectively. Binary and multinomial logistic regression were used to predict the odds of various lifestyle behaviours among adolescents with increased loneliness due to the COVID-19 pandemic. Results: We found higher odds of skipping breakfast [boys: OR 1.41 (95% CI: 1.33, 1.50), girls: OR 1.64 (95% CI: 1.56, 1.74)], fast food consumption [1-2 days in the past week: girls - OR: 1.14 (95% CI: 1.08, 1.21); ≥3 days in the past week: boys - 1.12 (95% CI: 1.02, 1.24), girls - OR: 1.42 (95% CI: 1.29, 1.57)], not meeting screen time [boys: OR 1.43 (95% CI: 1.24, 1.66), girls: OR 1.72 (95% CI: 1.54, 1.92)], and sleep duration guidelines [boys: OR 1.38 (95% CI: 1.28, 1.48), girls: OR 1.36 (95% CI: 1.27, 1.45)] among adolescents that reported increased loneliness due to the pandemic (versus those in the decreased/stayed the same loneliness group). Conclusion: Future longitudinal studies in adolescents are needed to confirm the directionality of these associations. It is important to raise awareness of these findings among public health practitioners, policymakers, physicians, schools and parents to promote healthier eating habits and increase adherence to the 24-hour movement behaviours. Recovery efforts post-pandemic are needed to reduce loneliness levels to support adolescent social health and establish healthy behavioural habits across the lifespan.

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