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Record W2267178151

The Effects of Exercise During Pregnancy on Infant Neuromotor Skills

2015· dissertation· en· W2267178151 on OpenAlexaboutno aff
Georganna Gower

Bibliographic record

VenueThe Scholarship East Carolina University's Institutional Repository (East Carolina University) · 2015
Typedissertation
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePregnancyAerobic exercisePsychological interventionOffspringObesityPhysical therapyMotor skillGross motor skillChildhood obesityIntervention (counseling)PediatricsOverweightInternal medicinePsychiatry
DOInot available

Abstract

fetched live from OpenAlex

In 2011-2012, the prevalence of obesity in children 2-19 years was 17% in the United States, and North Carolina was ranked fifth in the nation for childhood obesity. Researchers have attempted to prevent obesity by intervening at various times in a child’s life, with limited success. Perhaps the earliest interventions to diminish the prevalence of childhood obesity would be those occurring before birth. Moderate to vigorous aerobic exercise during pregnancy has been shown to contribute to improved cardiovascular health in the offspring. To date research has not investigated the effects of maternal exercise on infants’ neurobehavioral status. The purpose of this study wa¬¬s to determine the effects of maternal exercise during pregnancy on the neuromotor development of offspring. We hypothesized that exercise during pregnancy would be associated with improved neuromotor scores in infants at one and six months of age, based on standard pediatric assessment of motor skills and reflexes.
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\nEighty healthy, pregnant women between 18-35 years were recruited for this study from the Greenville, NC area. Eligible women were assigned to one of three exercise groups or to the control, non-exercise (CTRL) group. Exercise groups performed aerobic exercise, strengthening exercise, or circuit training 3 times per week under supervision, while those in CTRL group maintained usual activity and did not receive an exercise intervention.
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\nPost-delivery, neurodevelopmental exams were performed on the infants at one and six months using the Peabody Developmental Motor Scales, 2nd Edition (PDMS-2) and the Alberta Infant Motor Scales (AIMS). Variables analyzed to determine differences between groups included: AIMS raw score: age ratio, and PDMS-2 subtest percentiles, subtest standard scores, and overall Gross Motor Quotient (GMQ) percentile.
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\nSignificant between-group differences were found as infants in exercise groups had higher GMQ than that of infants in CTRL group (Exercise mean 104.7 ± 4.31 vs. CTRL mean 100 ± 4; p=0.03). The GMQ percentile scores were also significantly greater in infants in exercise groups compared to those in CTRL group (Exercise mean=62.1 ± 10.9 vs. CTRL mean=50 ± 10; p=0.03). Stationary percentile scores were significantly greater in infants in exercise groups compared to those in CTRL group (Exercise mean= 49.5 ± 16.0 vs. CTRL mean=31.3 ± 12.5; p=0.02), as were the Stationary standard scores (Exercise mean= 9.9 ± 1.3 vs. CTRL mean= 8.5 ± 1.0; p=0.03). Locomotion percentile scores were significantly greater in infants in exercise groups compared to those in CTRL group (Exercise mean= 59.1 ± 7.5 vs. CTRL mean= 50 ± 10.6; p=0.03), as were Locomotion standard scores (Exercise mean= 10.6± 1.3 vs. CTRL mean= 10 ± 0.8; p=0.04).
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\nThe infants in this study appeared to benefit from maternal exercise during pregnancy. Infants with higher neurodevelopmental scores in infancy might be expected to have higher scores at later months as well, which may set them up to be “good movers� and more physically active in childhood and beyond. Furthermore, children who are physically active at an early age will likely continue to lead a physically active lifestyle into adulthood and reduce the risk of becoming overweight or obese.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.220
Teacher spread0.211 · 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 teacher head, not a consensus.

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

Citations0
Published2015
Admission routes1
Has abstractyes

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