Patterns of objectively measured motor activity among infants developing ASD and concerns for ADHD
Bibliographic record
Abstract
BACKGROUND: Heightened motor activity is a hallmark of attention-deficit/hyperactivity disorder (ADHD), yet high activity levels are also often reported in young children with autism spectrum disorder (ASD). It is currently unclear whether increased motor activity represents a distinct versus shared early predictor of ASD and ADHD; no prior studies have directly examined this prospectively. We investigated differences in longitudinal patterns of objectively measured motor activity during early development. METHODS: Participants included 113 infants at high and low risk for ASD or ADHD. Continuous motion-based activity was recorded using tri-axial accelerometers at 12, 18, 24, and 36 months of age. At 36 months, participants were categorized into one of three outcome groups: ASD (n = 19), ADHD Concerns (n = 17), and Typically Developing (TD; n = 77). Group differences in trajectories of motor activity were examined in structured and semistructured contexts. Associations with behaviors relevant to ASD, ADHD, and general development were also examined. RESULTS: In both structured and semistructured contexts, both the ASD and ADHD Concerns groups exhibited heightened activity relative to the TD group by 18 months; the ASD group exhibited higher activity than the ADHD Concerns group at 24-36 months in the structured context only. Attention/behavior regulation, nonverbal, and verbal development-but not social engagement-were differentially associated with objectively measured activity by outcome group across contexts. CONCLUSIONS: Overactivity may be a shared, rather than distinct, precursor of atypical development in infants/toddlers developing ASD and concerns for ADHD, emerging as early as 18 months. Group differences in overactivity may be context-specific and associated with different underlying mechanisms.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".