Impact of COVID-19 on lifestyle habits and mental health symptoms in children with attention-deficit/hyperactivity disorder in Canada
Bibliographic record
Abstract
Abstract Objectives The COVID-19 pandemic created an environment of restricted access to health and recreation services. Lifestyle habits including sleep, eating, exercise, and screen use were modified, potentially exacerbating adverse mental health outcomes. This study investigates the impact of COVID-19 on lifestyle habits and mental health symptoms in paediatric attention-deficit/hyperactivity disorder (ADHD) in Canada. Methods An online survey was distributed across Canada to caregivers of children with ADHD (children aged 5 to 18 years) assessing depression (PHQ-9), anxiety (GAD-7), ADHD (SNAP-IV), and lifestyle behaviours. Data were analyzed by gender (male/female) and age category (5 to 8, 9 to 12, and 13 to 18 years). Spearman’s correlations between lifestyle habits and mental health outcomes were conducted. Results A total of 587 surveys were completed. Mean child age was 10.14 years (SD 3.06), including 166 females (28.3%). The PHQ-9 and GAD-7 indicated that 17.4% and 14.1% of children met criteria for moderately severe to severe depression and anxiety symptoms respectively. Children met SNAP-IV cut-off scores for inattention (73.7%), hyperactivity/impulsivity (66.8%), and oppositional defiant disorder (38.6%) behaviours. Caregivers reported changes in sleep (77.5%), eating (58.9%), exercise (83.7%), and screen use (92.9%) in their ADHD child, greatly impacting youth. Sleeping fewer hours/night, eating more processed foods, and watching TV/playing videogames >3.5 hours/day correlated with greater depression, anxiety and ADHD symptoms, and exercising <1 hour/day further correlated with depression symptoms (P<0.01). Conclusions The COVID-19 pandemic has resulted in less healthy lifestyle habits and increased mental health symptoms in Canadian children with ADHD. Longitudinal studies to better understand the relationship between these factors are recommended.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".