Improved motor skills in autistic children after three weeks of neurologic music therapy via telehealth: a pilot study
Bibliographic record
Abstract
Background Many autistic children experience motor skill deficits which can impact other areas of functioning, and research on therapeutic interventions for motor skills in autism is in a preliminary stage. Music-based therapies have been used extensively to address motor skills in non-autistic populations. Though a handful of studies exist on the effects of music-based therapies for movement in autistic children, none have investigated the possibility of administering sessions via telehealth. This mixed-methods pilot study investigated whether nine Neurologic Music Therapy (NMT)® sessions via telehealth would improve motor and attention skills in autistic children. Methods Five autistic children between five and 10 years of age participated in the study, with support from their caregivers. Motor skills were assessed using the Bruininks-Oseretsky Test of Motor Proficiency second edition, short form (BOT-2 SF), and a selective attention and sustained attention task were taken from the Test of Everyday Attention for Children, Second Edition (TEA-Ch2). Caregivers and the two neurologic music therapists involved in the study provided qualitative input about the perceived effectiveness of telehealth NMT for the children involved. Their responses were analyzed using qualitative content analysis. Caregivers also filled out a Sensory Profile 2 assessment prior to the onset of sessions so that each child’s sensory profile could be compared to their motor and attention results. Results Statistically significant improvements in motor skills were observed between pre-test assessment and a two-week follow-up assessment. Results from attention test scores were not significant. Caregivers and neurologic music therapists generally perceived sessions positively and noted the importance of having caregivers actively involved. When compared with individual progress on the BOT-2 SF assessment, sensory profile results revealed that children with fewer sensory sensitivities tended to improve the most on motor skills. The improvements in motor skills and positive caregiver and therapist views of telehealth indicate that NMT motor interventions administered via telehealth are a promising avenue of therapeutic support for movement skill development in autistic children.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".