Food intake response to exercise and active video gaming in adolescents: effect of weight status
Bibliographic record
Abstract
Although a few data are available regarding the impact of video games on energy intake (EI) in lean adolescents, there is no evidence on the effect of passive and active video gaming on food intake in both lean and obese youth. It is also unknown whether isoenergetic active video games and exercise differently affect food consumption in youth. In all, twelve lean and twelve obese adolescent boys (12-15 years old) had to complete four 1-h sessions in a cross-over design study: control (CON; sitting), passive video game (PVG; boxing game on Xbox 360), active video game (AVG; boxing game on Xbox Kinect 360) and exercise (EX; cycling). The exercise and active video game activities were designed to generate the same energy expenditure (EE). EE was measured using a K4b2 portable indirect calorimeter. Ad libitum food intake and appetite sensations were assessed following the sessions. AVG and EX-EE were significantly higher in obese participants and significantly higher compared with PVG and CON in both groups. Obese participants significantly ate more than lean ones in all four conditions (P<0·001). EI did not differ between conditions in obese participants (CON: 4935 (SD 1490) kJ; PVG: 4902 (SD 1307) kJ; AVG: 4728 (SD 1358) kJ; EX: 4643 (SD 1335) kJ), and was significantly lower in lean participants after EX (2847 (SD 577) kJ) compared with PVG (3580 (SD 863) kJ) and AVG (3485 (SD 643) kJ) (P<0·05). Macronutrient intake was not significantly different between the groups or conditions. Hunger was significantly higher and satiety was lower in obese participants but no condition effect was observed. Overall, moderate-intensity exercise provides better effect on energy balance than an isoenergetic hour of active video gaming in lean adolescent boys by dually affecting EE and EI.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".