Getting up for brain health: Association of sedentary behavior breaks with cognition and mental health in children
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it