Parent‐recorded videos of infant spontaneous movement: Comparisons at 3–4 months and relationships with 2‐year developmental outcomes in extremely preterm, extremely low birthweight and term‐born infants

Kwong, Amanda K L; Doyle, Lex W.; Olsen, Joy E.; Eeles, Abbey L; Zannino, Diana; Mainzer, Rheanna; Cheong, Jeanie L.Y.; Spittle, Alicia J

doi:10.1111/ppe.12867

Cited by 17 publications

(24 citation statements)

References 52 publications

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“…As the Rasch model assume, Infant with lower developmental level tend to achieve less growth milestones. This can be explained that infant with developmental delay caused by various factors (Extremely preterm, extremely low birthweight) present much lower developmental level than those born at term in gross motor, ne motor, cognition, and language domains(Kwong et al, 2022).The person and item separation index were within the recommended range (> 2), hence we suggest that the CDSC items were enough to differentiate person into at least 12 distinct development level. Further, the person and item reliability were within satisfactory range as well(> 0.8), and the result supports that the CDSC is reliable enough if the same testing items were applied on different sample with the same participants characteristic or our study sample received another item set that belongs to the CDSC test structure.In brief, the CDSC has demonstrated good psychometric properties in our Rasch Model analysis.…”

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confidence: 69%

Psychometric Properties of the China Developmental Scale of Children in Infant Aged 0-1 Years old with developmental delay: A Rasch Analysis

Peng

Wang

Liu

et al. 2022

Preprint

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Background: Children development is a multidimensional construct encompassing gross motor, fine motor, adaptive behavior, speech and language, and social behavior. The China Developmental Scale of Children (CDSC) has been tested using traditional methods. This study aimed to make amendment to previous evidence by using Rasch Model and elaborate the psychometric properties of the CDSC in a new perspective. Methods: 200 children and infants were recruited and finished the CDSC. Data were collected and used for the analysis. Rasch Model was adopted to test the psychometric properties of the CDSC regard the fitness to the prediction of the Model. Unidimensionality was tested using principal component analysis. Differential item functioning (DIF) analysis was performed across gender and age group (older or younger than 6.5 months old). Result: Data from 200 children with developmental delays were obtained. The result showed that the items from the CDSC presented acceptable fitness to the Rasch Model(Person Infit MNSQ:0.83~0.9;Item Infit MNSQ:0.9~0.93). Besides, 11 unfitting(misfitting or overfitting) items were found. The impact of the unfitting items on the average fit statistics was evaluate by reanalysis, and the result showed that the person reliability and separation were lower while the infit MNSQ got higher. The unidimensionality was supported by the result of the principal component analysis(Measures explained variance:79.6%~87.8%). Further, the reliability and the separation index of the item and person indicated that the assumption of the internal consistency was confirmed (Person.Reliability:0.91~0.96; Person.Separation:4.27~3.5; Item.Reliablity:0.99~1; Item.Separation:13.39~19.28). Conclusion: Our study found that the CDSC had shown reasonable fitness to the Rasch Model. The result support that the CDSC was established based on a unidimensional structure with good internal consistency. The misfitting items still required reformulation to demonstrate corresponding content more clearly.

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confidence: 69%

Psychometric Properties of the China Developmental Scale of Children in Infant Aged 0-1 Years old with developmental delay: A Rasch Analysis

Peng

Wang

Liu

et al. 2022

Preprint

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“…We found that this effect was largely dependent on preterm birth, although infants with abnormal GMA predictions scored lower on average regardless of birth cohort. The association between preterm birth and poor neurodevelopmental outcomes is well established 8,13 and this finding reflects the relatively lower predictive validity of GMA ratings, and therefore model predictions, for motor and cognitive outcomes at 2 years.…”

Section: Discussionmentioning

confidence: 97%

“…These 'fidgety movements' are typically recognised using a trained assessor's gestalt perception, while the infant is lying awake on their back with no direct handling or interaction 11 . This assessment is best completed from video recordings of the infant and has high predictive validity for neurodevelopmental outcomes and excellent inter-rater reliability 9,12,13 . The GMA when used during the 'fidgety period' has the potential to be an important screening tool in the diagnosis of CP.…”

Section: Introductionmentioning

confidence: 99%

“…The ability to perform the GMA using video recordings has raised the potential to improve equitable healthcare access for those living in remote regions or in low-resource settings. Recently, the development of smartphone apps that allow the standardised recording of video by an infant's primary carers using a hand-held device, have been shown to improve access to the GMA and allow identification of high-risk infants outside of clinical settings [12][13][14][15] . Automated scoring of the GMA from video can provide a mechanism to overcome these bottlenecks and enable highthroughput assessment for screening programs.…”

Section: Introductionmentioning

confidence: 99%

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Automated identification of abnormal infant movements from smart phone videos

Passmore

Kwong

Greenstein

et al. 2023

Preprint

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Cerebral palsy (CP) is the most common cause of physical disability during childhood. Early diagnosis is essential to improve functional outcomes of children with CP. The General Movements Assessment (GMA) is a strong predictor of CP, but access is limited by the need for trained GMA assessors. Using 503 infant movement videos acquired at 12-18 weeks' term-corrected age, we developed a framework to automate the GMA using smartphone videos acquired at home. We trained a deep learning model to label and track 18 key body points, implemented a custom pipeline to adjust for camera movement and infant size and trained a convolutional neural network to predict GMA. Our model achieved an area under the curve (mean ± S.D.) of 0.80 ± 0.08 in unseen test data for predicting expert GMA classification. This work highlights the potential for automated GMA screening programs for infants.

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“…The GM assessment involves the observation of an infant's movement while lying on their back for 3–5 min from birth to 20 weeks of age (corrected for preterm delivery). Kwong et al 7 found that the Motor Optimality Score from the GM assessment administered at 3–4 months corrected age significantly predicted cognitive, language and motor development at 2 years. Infant movements were recorded by parents at home using an app on their smartphones.…”

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confidence: 99%