The Impact of Sleep on Sensory Processing in Typically Developing Children: Insights from Cross-Sectional and Longitudinal Data
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Previous research suggests that sleep quality and duration may significantly impact sensory experiences, yet the specific relationships in healthy early childhood remain unclear. This study explores the relationship between sleep parameters and sensory processing outcomes in typically developing children followed longitudinally from 3 to 12 months to 4 years of age. METHODS: We assessed sleep problems, sleep duration, sleep onset latency, parasomnias, and sensory processing in a sample of typically developing children (N = 85). Sleep parameters were reported by parents, and sensory processing was evaluated using the Sensory Processing Measure-Parent version (SPM-P). Cross-sectional and longitudinal analyses examined predictors of sensory outcomes based on sleep patterns at 3-12 months, 18 months, 2 years, and 4 years of age. RESULTS: Our findings indicate that greater sleep problems correlate with heightened sensory sensitivity across modalities, including touch and vision, as well as higher-order cognitive functions such as planning and social interactions. Specifically, sleep problems at 18 months were significant predictors of sensory processing at 4 years, while sleep duration at 2 years predicted planning skills. Additionally, longer sleep duration during infancy (3-12 months) positively influenced social participation at 4 years. CONCLUSIONS: This study underscores the critical role of sleep in shaping sensory processing outcomes in early childhood. Promoting healthy sleep habits may mitigate sensory processing difficulties, ultimately supporting emotional and social development.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".