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Record W4285089124 · doi:10.2196/39426

Can Gross Motor Skills Be Trained Through Telehealth-Based Training? Insights From a Randomized Controlled Trial of Creative Movement and Play-Based Interventions in Children With Autism Spectrum Disorder

2022· article· en· W4285089124 on OpenAlexvenueno aff
Catherine E. Myers, Sudha Srinivasan, Bansri Patel, Nidhi Amonkar, Wan-Chun Su, Corina Cleffi, Anjana Bhat, Sarah Williams

Bibliographic record

VenueIproceedings · 2022
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
Fundersnot available
KeywordsGross motor skillAutismAutism spectrum disorderRandomized controlled trialMotor skillPsychological interventionPsychologyMovement assessmentPhysical therapyPhysical medicine and rehabilitationMedicineDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Background The motor trajectories of children with autism spectrum disorder (ASD) are often compromised, leading to difficulties in gross motor performance and locomotor skills by late childhood. Our past work has suggested that whole-body movement interventions using rhythmic contexts can facilitate gross motor skills in children with ASD. Objective This study compares the effects of whole-body movement interventions delivered face-to-face (F2F) versus via telehealth (TH) and compares these effects to those of a standard-of-care, seated play (SP) intervention on the gross motor performance of children with ASD. Methods A total of 45 children with ASD aged between 5 and 14 years were seen for 10 weeks, with pretests and posttests conducted during the first and 10th weeks. Children were matched based on age, gender, and level of functioning and then randomly assigned to the general movement (GM), creative movement (CM), or SP groups. An equal number of children in each group received the training via F2F and TH modes. Training was provided in 2 sessions per week for 8 weeks, with sessions lasting from 1 to 1.15 hours. The CM and GM groups received whole-body movement training, whereas the SP group engaged in tabletop activities. The gross and fine motor subtests (fine motor precision, fine motor integration, balance, bilateral coordination, running speed and agility, and strength) of the Bruininks-Oseretsky Test of Motor Proficiency were administered at pre- and posttest. We reported standard scores on body coordination, strength and agility, and fine manual control composites. Results The CM and GM groups showed significant improvements on the body coordination composite (CM group’s scores—pretest: mean 35.3, SE 2.1; posttest: mean 40.5, SE 2.7; P<.005; GM group’s scores—pretest: mean 38.1, SE 3.2; posttest: mean 44.7, SE 3.3; P<.005). The GM group’s scores (pretest: mean 36.1, SE 2.6; posttest: mean 40.9, SE 3.1; P<.001) also improved on the strength and agility composite, and a trend for similar improvements was observed in the CM group’s scores (pretest: mean 32.5, SE 2.4; posttest: mean 34.5, SE 2.7; P=.09). The SP group did not show any improvements on the gross motor composites. The SP group’s scores instead showed significant improvements on the fine manual control composite (pretest: mean 36.4, SE 2.3; posttest: mean 41.6, SE 2.5; P<.005). Fine motor skills remained unchanged in the CM and GM groups. In each of the 3 groups, there were no significant differences in the amount of improvements (posttest − pretest) observed for children seen F2F versus via TH for the body coordination (CM group—F2F: mean 4.1, SE 2.6; TH: mean 5.4, SE 1.6; P=.70; GM group—F2F: mean 6.5, SE 2.7; TH: mean 5.6, SE 2.5; P=.80), strength and agility (CM group—F2F: mean 1.3, SE 1.4; TH: mean 2, SE 1.1; P=.70; GM group—F2F: mean 3.7, SE 1.3; TH: mean 5.6, SE 1.5; P=.40), and fine manual control composite standard scores (SP group—F2F: mean 2.2, SE 2.8; TH: mean 1.9, SE 1.2; P=.80). Conclusions The findings of our pilot study emphasize the importance of training gross and fine motor skills in children with ASD and suggest that virtual modes of intervention delivery can be used by movement clinicians to train these skills. Conflicts of Interest None declared.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.012
GPT teacher head0.268
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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