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Record W4390079081 · doi:10.1017/s135561772300293x

89 A Pilot Study of a Parent-Delivered Game-Based Cognitive Intervention in Children Born Preterm

2023· article· en· W4390079081 on OpenAlexaffabout
Sunny Guo, Sarah J. Macoun, Ella Ryan, Stella Heo, Amanda Ip, Mehak Stokoe, Daria Merrikh, John C. Sheehan, Signe Bray

Bibliographic record

VenueJournal of the International Neuropsychological Society · 2023
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsUniversity of CalgaryUniversity of Victoria
Fundersnot available
KeywordsIntervention (counseling)NumeracyCognitionExecutive functionsMedicineNeuropsychologyPsychologyClinical psychologyPsychiatryLiteracy

Abstract

fetched live from OpenAlex

Objective: Preterm birth (gestational age < 37 weeks) is associated with delays in the development of executive functions and their precursors, including controlling attention and retaining task-relevant information. In the current study, we aim to examine the potential therapeutic effects of a novel cognitive tablet game, Dino Island (DI), and its implementation through a parent-delivered intervention program on cognitive development in preterm children. Participants and Methods: In total, 34 participants (M = 4.99 years old) were recruited from a perinatal follow-up clinic or through the community in Calgary, AB. Participants were randomly assigned to either the DI intervention (n = 20) or tablet-based educational control games (C; n = 14). Parents completed a 2-hour training program that included information about how to support their child through the intervention using behavioural supports and metacognitive strategies. Neuropsychological assessment was done prior to beginning the intervention and after 12 weeks of intervention. Various tests were used to assess near transfer measures of sustained attention, shifting attention, executive function, verbal working memory and inhibition, and to assess far transfer measures of language skills and early numeracy. Families tracked weekly progress using journals, with the goal of 3-4 30-minute sessions per week. Multiple ANCOVA analyses were run to analyze quantitative data using the pre-test score as a covariate. Results: A total of 21 participants completed the 12 weeks of intervention (DI: n = 11 and C: n = 10). Those who did not complete the intervention withdrew from the study or were unable to make a follow-up assessment due to COVID-19 restrictions. Groups did not significantly differ in age (DI: M = 4.92, C: M = 4.61), sex (DI: Female = 6, C: Female = 6), or in weeks preterm (DI: M = 29.49 weeks, C: M = 32.7 weeks). Multiple ANCOVAs were run to determine the effect of either the DI or Control intervention on the cognitive measures after controlling for the pre-intervention score of participants. As compared to the Control group, the DI intervention group showed near transfer gains in sustained attention (F(1,7) = 5.1, p = 0.043), and executive functions (F(2,18) = 5.41, p = 0.014), as well as far transfer gains in phonetic awareness (F(2,16) = 11.63, p = 0.001), vocabulary and oral language skills (F(2,7) = 5.54, p = 0.014), and number identification fluency (F(2,17) = 11.37, p = 0.001). Detailed analyses will be discussed in the poster. Conclusions: This study provides preliminary support for the potential efficacy of the DI intervention when delivered by parents to children born preterm. Pre-post testing after 12 weeks of intervention indicated both near and far transfer gains. These results highlight the benefits of utilizing a tablet game format to facilitate collaborative parent-child interactions in cognitive intervention. This intervention provides a potential affordable and engaging alternative to existing cognitive interventions. Further investigation with a larger and more diverse sample is required.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.045
GPT teacher head0.331
Teacher spread0.286 · 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 designNon-randomized 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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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