1.
Skipping Breakfast for 6 Days Delayed the Circadian Rhythm of the Body Temperature without Altering Clock Gene Expression in Human Leukocytes.
Ogata, H, Horie, M, Kayaba, M, Tanaka, Y, Ando, A, Park, I, Zhang, S, Yajima, K, Shoda, JI, Omi, N, et al
Nutrients. 2020;(9)
Abstract
Breakfast is often described as "the most important meal of the day" and human studies have revealed that post-prandial responses are dependent on meal timing, but little is known of the effects of meal timing per se on human circadian rhythms. We evaluated the effects of skipping breakfast for 6 days on core body temperature, dim light melatonin onset, heart rate variability, and clock gene expression in 10 healthy young men, with a repeated-measures design. Subjects were provided an isocaloric diet three times daily (3M) or two times daily (2M, i.e., breakfast skipping condition) over 6 days. Compared with the 3M condition, the diurnal rhythm of the core body temperature in the 2M condition was delayed by 42.0 ± 16.2 min (p = 0.038). On the other hand, dim light melatonin onset, heart rate variability, and clock gene expression were not affected in the 2M condition. Skipping breakfast for 6 days caused a phase delay in the core body temperature in healthy young men, even though the sleep-wake cycle remained unchanged. Chronic effects of skipping breakfast on circadian rhythms remain to be studied.
2.
The effect of attentional bias modification on eating behavior among women craving high-calorie food.
Zhang, S, Cui, L, Sun, X, Zhang, Q
Appetite. 2018;:135-142
Abstract
In individuals with healthy weight and overweight, the level of food cravings experienced is closely related to the individual's attentional bias to food cues. Furthermore, an attentional bias toward food cues, especially high-calorie food cues, is often accompanied by poor eating habits, overweight or obesity, eating disorders, and other problems. Therefore, this study aimed to explore the effect of attentional bias modification on the eating behavior of women craving high-calorie food. Sixty-five female college students with a high level of craving for high-calorie foods were randomly assigned to a training group (attended to images of low-calorie food) and a control group (attended equally to images of high- and low-calorie food). An attentional re-training paradigm was used in the training session to modify the participants' attentional bias to these food cues. Compared to the control group, attentional bias to high-calorie food cues in the training group was significantly reduced after training (p < 0.05). The training group consumed less high-calorie food and more low-calorie food than the control group (p < 0.05) in a post-training taste test. However, there was no significant difference between the groups in their level of food cravings (p > 0.05). These findings suggest that attentional bias modification training is a promising brief intervention to improve eating behavior and develop healthy eating habits.
3.
Applications of social network analysis to obesity: a systematic review.
Zhang, S, de la Haye, K, Ji, M, An, R
Obesity reviews : an official journal of the International Association for the Study of Obesity. 2018;(7):976-988
Abstract
People's health behaviours and outcomes can be profoundly shaped by the social networks they are embedded in. Based on graph theory, social network analysis is a research framework for the study of social interactions and the structure of these interactions among social actors. A literature search was conducted in PubMed and Web of Science for articles published until August 2017 that applied social network analysis to examine obesity and social networks. Eight studies (three cross-sectional and five longitudinal) conducted in the US (n = 6) and Australia (n = 2) were identified. Seven focused on adolescents' and one on adults' friendship networks. They examined structural features of these networks that were associated with obesity, including degree distribution, popularity, modularity maximization and K-clique percolation. All three cross-sectional studies that used exponential random graph models found individuals with similar body weight status and/or weight-related behaviour were more likely to share a network tie than individuals with dissimilar traits. Three longitudinal studies using stochastic actor-based models found friendship network characteristics influenced change in individuals' body weight status and/or weight-related behaviour over time. Future research should focus on diverse populations and types of social networks and identifying the mechanisms by which social networks influence obesity to inform network-based interventions.