Impact of COVID-19 on Physical Activity in Families Managing ADHD and the Cyclical Effect on Worsening Mental Health
Bibliographic record
Abstract
Physical activity supports symptom management in children with ADHD and reduces the mental health burden associated with caregiving for children with ADHD. Survey-based research shows that COVID-19 reduced physical activity among diverse populations. This study used a qualitative approach situated within a socioecological framework to (1) understand how COVID-19 impacted physical activity of children with ADHD and their caregivers, to (2) identify barriers to their physical activity, and to (3) identify potential areas of support. Thirty-three participants were interviewed between October 2020 and January 2021. Content analysis revealed that physical activity declined for children and caregivers; significant barriers were social isolation and rising intrapersonal difficulties such as diminishing self-efficacy and energy levels and increased mental health difficulties. Worsening mental health further alienated caregivers and children from physical activity, undermining its protective effects on ADHD symptom management and mental wellbeing. Participants identified needing community support programs that offer virtual, live physical activity classes as well as psycho-emotional support groups. There is vital need to support physical activity opportunities during high-stress situations in families managing ADHD to buffer against diminishing mental wellbeing. This will promote further physical activity engagement and allow families to reap the cognitive, psychological, and emotional benefits.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".