Getting up for brain health: Association of sedentary behavior breaks with cognition and mental health in children
Bibliographic record
Abstract
Children spend most of their waking hours sedentary and reducing this behavior has been challenging. Interrupting prolonged episodes of sedentary behavior with active breaks can provide mental and cognitive health benefits. Considering the multifactorial nature of these health aspects, this study aimed to verify the role of body mass index (BMI), cardiorespiratory fitness (CRF), and moderate to vigorous physical activity (MVPA) in the relationship between the break in sedentary time with cognitive and mental health in children. This is a cross-sectional study with 129 children (62 boys), aged between 6 and 11 years (mean 8.73 ± 1.53) from a public school in southern Brazil. For the assessment of fluid intelligence, psychologists applied Raven’s Colored Progressive Matrices test. Mental health was measured using the Strengths and Difficulties Questionnaire. Sedentary breaks were measured using accelerometers, and CRF was determined using the 6-min walk test. Generalized linear regression analyses were used to verify associations of sedentary breaks with fluid intelligence and mental health, according to children’s BMI, CRF, and MVPA. All models were adjusted for sex, age, somatic maturation, and total time of accelerometer use. Our results indicated that sedentary breaks were associated with fluid intelligence in overweight/obese (β = 0.108; p = 0.021) and physically inactive children (β = 0.083; p = 0.010). Regarding mental health, no association was identified with sedentary breaks. In conclusion, sedentary breaks should be encouraged for the benefits of fluid intelligence, especially in children who do not meet physical activity recommendations and are overweight.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".