Discussion of “Influence of netball-based exercise on energy intake, subjective appetite and plasma acylated ghrelin in adolescent girls”
Bibliographic record
Abstract
Rumbold and colleagues recently published an innovative and interesting paper addressing the energy intake, subjective appetite, and acylated ghrelin response to a netball-based exercise session in lean adolescent girls (Rumbold et al. 2013). This paper deals with one of their previous publications (Rumbold et al. 2011) and adds some interesting results to the current literature in the field. However, some issues warrant discussion. Rumbold et al. (2013) showed that energy intake of the adolescents at the test meal following the exercise session (lunch time) was not affected compared with the control, sedentary session (Rumbold et al. 2013). First, it needs to be noticed that the authors offered a test meal that was composed only of white pasta in a tomato andherb saucewith gratedmild cheddar cheese. Although this is in line with their previous work, the adolescents were not offered to choose among several items (e.g., buffet-style meal), which limits the ability of the authors to quantitatively analyze energy intake in this sample (Thivel et al. 2012a). Moreover, the authors indicated that the adolescents participated in sedentary activities during the control session and were free to engage in sedentary activities such as reading, watching TV, or completing school work for an hour between the netball-based exercise session and the test meal. Regarding the current literature on the impact of daily activities on subsequent energy intake, it can be argued that the assessed energy intake at lunch time was more affected by those sedentary activities than by the netball session itself. Previous studies have effectively emphasized the stimulating influence of screen-based sedentary activities such as watching television or playing video games on subsequent food intake in children and adolescents (Thivel et al. 2012c). For instance, Nemet and collaborators have observed a higher food consumption after 45 min of TV viewing compared with 45 min of physical activity (swimming or resistance training) in prepubertal kids (Nemet et al. 2010). Similarly, a 1-h passive video game session has been shown to favor increased energy intake in lean adolescent boys without altering appetite sensations comparedwith 1 h of rest (Chaput et al. 2011). Along the same lines, cognitiveworking such as reading andwritinghas been shown to increase foodconsumption in students (Chaput et al. 2008;Chaput and Tremblay 2007). Finally, recent data point out that imposed sedentary behaviors (e.g., prolonged sitting or bed rest) have the opposite effect on energy intake compared with exercise in adolescents (Thivel et al. 2012b); bed rest has been shown to have an orexigenic effect,whereas acute exercise favors reduced energy consumption in adolescents (Thivel et al. 2012b). Collectively, the paper by Rumbold et al. provides interesting new results to the field but also highlights the need for rigorous methodologies (controlling for the influence of screen-based sedentary behaviors on food intake) to ensure valid conclusions. The use of sedentary or imposed sedentary activities as control sessions is an important issue and (or) problem to consider in future studies if we want to avoid the confounding effect of these activities on eating behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".