Moderate Exercise and Bright Light Treatment in Overweight and Obese Individuals
Bibliographic record
Abstract
OBJECTIVE: Increased physical activity is important given the concern over the growing rates of obesity. The aim of this study is to conduct a controlled investigation of the effects of bright light therapy and exercise on weight loss and body composition in overweight and obese individuals. RESEARCH METHODS AND PROCEDURES: Twenty-five overweight and obese subjects were assigned to 6 weeks of moderate exercise with or without bright light treatment. Outcome measure included changes in body mass and body composition and ratings of mood, seasonality, and sleep. RESULTS: Body weight decreased significantly with exercise in subjects in the light and non-light treatment groups, but the change was not significantly different between the groups. Similar results were found for BMI. With exercise, body fat decreased significantly only in the light treatment group. There was a significant effect of the interaction of group by time on body fat composition, but the group by time interaction failed to reach statistical significance for body weight and BMI. Mood scores improved significantly with exercise in the light group, but no significant changes were noted regarding sleep. DISCUSSION: This preliminary study is the first to show that addition of bright light treatment to a 6-week moderate exercise program can alter body composition by significantly reducing body fat. The reduction in body fat mass is of particular importance, because visceral fat has been particularly implicated as a major factor in the development of the metabolic syndrome. This study is an important step toward finding ways to maximize the effects of exercise.
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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) | 0.003 | 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".