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Record W2803491033 · doi:10.2196/mental.9564

Computerized Cognitive Training in Children With Autism and Intellectual Disabilities: Feasibility and Satisfaction Study

2018· article· en· W2803491033 on OpenAlexvenueno aff
Songpoom Benyakorn, Catrina A. Calub, S. J. Riley, Andrea Schneider, Ana‐Maria Iosif, Marjorie Solomon, David Hessl, Julie B. Schweitzer

Bibliographic record

VenueJMIR Mental Health · 2018
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Center for Advancing Translational SciencesIntellectual and Developmental Disabilities Research CenterNational Institutes of HealthU.S. Department of Defense
KeywordsIntellectual disabilityAutism spectrum disorderPsychological interventionPsychologyAttritionCognitionAutismClinical psychologyCognitive trainingPopulationPsychiatryMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Researchers are increasingly interested in testing and developing computerized cognitive training interventions for individuals with autism spectrum disorder due to the limited accessibility of treatments for this disorder. Understanding the feasibility of testing cognitive interventions for this population is critical, especially for individuals with ASD who have low to moderate intellectual ability. OBJECTIVE: The aim of the study was to evaluate the feasibility of computerized cognitive training as measured by attrition rate and a parent satisfaction survey. METHODS: A total of 26 participants aged 8-17 years with an autism spectrum disorder diagnosis and significant intellectual impairment were enrolled (mean age 11.1 years). They were instructed to complete 25 sessions of Cogmed Working Memory Training in 5 to 6 weeks with coach assistance. Attrition rate and parent satisfaction surveys were measured after the completion of training. RESULTS: Most participants (96%, 25/26) completed the training and indicated high satisfaction (>88%). However, among the participants who completed the training, 5 participants (19%) were unable to finish in 6 weeks, the recommended training period by Cogmed. Parents noted various positive (eg, voice-overs) and negative (eg, particular graphic and sounds associated with a stimulus) features of the game that they thought affected their child's response. CONCLUSIONS: Children with autism spectrum disorder and intellectual impairments can successfully participate in computerized cognitive training interventions but may require additional weeks to complete the training beyond the time needed for children without intellectual impairments. The overall completion rate, with extended time to complete the training, was high. Developers of cognitive training programs for this population should take into account potential issues regarding the noise level of stimuli and characteristics of the visual graphics.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.063
GPT teacher head0.378
Teacher spread0.315 · 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 designObservational
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".

Quick stats

Citations28
Published2018
Admission routes1
Has abstractyes

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