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Ferrer-Mallol E, Matthews C, Aziza R, Mendoza A, Sahota N, Komarzynski S, Lakshminarayana R, Davies EH. Video-based assessments of activities of daily living: generating real-world evidence in pediatric rare diseases. Expert Rev Pharmacoecon Outcomes Res 2024; 24:713-721. [PMID: 38789406 DOI: 10.1080/14737167.2024.2360201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Accepted: 05/22/2024] [Indexed: 05/26/2024]
Abstract
INTRODUCTION Preserving function and independence to perform activities of daily living (ADL) is critical for patients and carers to manage the burden of care and improve quality of life. In children living with rare diseases, video recording ADLs offer the opportunity to collect the patients' experience in a real-life setting and accurately reflect treatment effectiveness on outcomes that matter to patients and families. AREAS COVERED We reviewed the measurement of ADL in pediatric rare diseases and the use of video to develop at-home electronic clinical outcome assessments (eCOA) by leveraging smartphone apps and artificial intelligence-based analysis. We broadly searched PubMed using Boolean combinations of the following MeSH terms 'Rare Diseases,' 'Quality of Life,' 'Activities of Daily Living,' 'Child,' 'Video Recording,' 'Outcome Assessment, Healthcare,' 'Intellectual disability,' and 'Genetic Diseases, Inborn.' Non-controlled vocabulary was used to include human pose estimation in movement analysis. EXPERT OPINION Broad uptake of video eCOA in drug development is linked to the generation of technical and clinical validation evidence to confidently assess a patient's functional abilities. Software platforms handling video data must align with quality regulations to ensure data integrity, security, and privacy. Regulatory flexibility and optimized validation processes should facilitate video eCOA to support benefit/risk drug assessment.
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Haberfehlner H, Roth Z, Vanmechelen I, Buizer AI, Jeroen Vermeulen R, Koy A, Aerts JM, Hallez H, Monbaliu E. A Novel Video-Based Methodology for Automated Classification of Dystonia and Choreoathetosis in Dyskinetic Cerebral Palsy During a Lower Extremity Task. Neurorehabil Neural Repair 2024; 38:479-492. [PMID: 38842031 DOI: 10.1177/15459683241257522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
BACKGROUND Movement disorders in children and adolescents with dyskinetic cerebral palsy (CP) are commonly assessed from video recordings, however scoring is time-consuming and expert knowledge is required for an appropriate assessment. OBJECTIVE To explore a machine learning approach for automated classification of amplitude and duration of distal leg dystonia and choreoathetosis within short video sequences. METHODS Available videos of a heel-toe tapping task were preprocessed to optimize key point extraction using markerless motion analysis. Postprocessed key point data were passed to a time series classification ensemble algorithm to classify dystonia and choreoathetosis duration and amplitude classes (scores 0, 1, 2, 3, and 4), respectively. As ground truth clinical scoring of dystonia and choreoathetosis by the Dyskinesia Impairment Scale was used. Multiclass performance metrics as well as metrics for summarized scores: absence (score 0) and presence (score 1-4) were determined. RESULTS Thirty-three participants were included: 29 with dyskinetic CP and 4 typically developing, age 14 years:6 months ± 5 years:15 months. The multiclass accuracy results for dystonia were 77% for duration and 68% for amplitude; for choreoathetosis 30% for duration and 38% for amplitude. The metrics for score 0 versus score 1 to 4 revealed an accuracy of 81% for dystonia duration, 77% for dystonia amplitude, 53% for choreoathetosis duration and amplitude. CONCLUSIONS This methodology study yielded encouraging results in distinguishing between presence and absence of dystonia, but not for choreoathetosis. A larger dataset is required for models to accurately represent distinct classes/scores. This study presents a novel methodology of automated assessment of movement disorders solely from video data.
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Affiliation(s)
- Helga Haberfehlner
- Department of Rehabilitation Sciences, KU Leuven Bruges, Bruges, Belgium
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Rehabilitation Medicine, Amsterdam, The Netherlands
- Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, The Netherlands
| | - Zachary Roth
- Department of Rehabilitation Sciences, KU Leuven Bruges, Bruges, Belgium
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Inti Vanmechelen
- Department of Rehabilitation Sciences, KU Leuven Bruges, Bruges, Belgium
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Annemieke I Buizer
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Rehabilitation Medicine, Amsterdam, The Netherlands
- Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, The Netherlands
- Amsterdam UMC, Emma Children's Hospital, Amsterdam, The Netherlands
| | | | - Anne Koy
- Department of Pediatrics, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Jean-Marie Aerts
- Department of Computer Science, Mechatronics Research Group (M-Group), KU Leuven Bruges, Distrinet, Bruges, Belgium
| | - Hans Hallez
- Department of Biosystems, Division of Animal and Human Health Engineering, Measure, Model and Manage Bioresponse (M3-BIORES), KU Leuven, Leuven, Belgium
| | - Elegast Monbaliu
- Department of Rehabilitation Sciences, KU Leuven Bruges, Bruges, Belgium
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
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Nedungadi P, Shah SM, Stokes MA, Kumar Nair V, Moorkoth A, Raman R. Mapping autism's research landscape: trends in autism screening and its alignment with sustainable development goals. Front Psychiatry 2024; 14:1294254. [PMID: 38361829 PMCID: PMC10868528 DOI: 10.3389/fpsyt.2023.1294254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 12/29/2023] [Indexed: 02/17/2024] Open
Abstract
Introduction Autism Spectrum Disorder is a complex neurodevelopmental syndrome that profoundly affects social interactions, communication, and sensory perception. The research traced the evolution of autism research from 2011-2022, specifically focusing on the screening and diagnosis of children and students. Methods Through an analysis of 12,262 publications using the PRISMA framework, bibliographic coupling, science mapping, and citation analysis, this study illuminates the growth trajectory of ASD research and significant disparities in diagnosis and services. Results The study indicates an increasing trend in autism research, with a strong representation of female authorship. Open Access journals show a higher average citation impact compared to their closed counterparts. A keyword co-occurrence analysis revealed four central research themes: Child Development and Support Systems, Early Identification and Intervention, Prevalence and Etiology, and Mental Health. The pandemic's onset has prioritized research areas like mental health, telehealth, and service accessibility. Discussion Recommendations on a global level stress the importance of developing timely biological markers for ASD, amplifying Disability Inclusion research, and personalizing mental health services to bridge these critical service gaps. These strategies, underpinned by interdisciplinary collaboration and telehealth innovation, particularly in low-resource settings, can offer a roadmap for inclusive, context-sensitive interventions at local levels that directly support SDG3's aim for health and well-being for all.
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Affiliation(s)
- Prema Nedungadi
- Amrita School of Computing, Amrita Vishwa Vidyapeetham, Kollam, India
| | | | | | | | - Ajit Moorkoth
- Seed Special Education Center, Dubai, United Arab Emirates
| | - Raghu Raman
- Amrita School of Business Amritapuri, Amrita Vishwa Vidyapeetham University, Coimbatore, Tamil Nadu, India
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Gao Q, Yao S, Tian Y, Zhang C, Zhao T, Wu D, Yu G, Lu H. Automating General Movements Assessment with quantitative deep learning to facilitate early screening of cerebral palsy. Nat Commun 2023; 14:8294. [PMID: 38097602 PMCID: PMC10721621 DOI: 10.1038/s41467-023-44141-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
The Prechtl General Movements Assessment (GMA) is increasingly recognized for its role in evaluating the integrity of the developing nervous system and predicting motor dysfunctions, particularly in conditions such as cerebral palsy (CP). However, the necessity for highly trained professionals has hindered the adoption of GMA as an early screening tool in some countries. In this study, we propose a deep learning-based motor assessment model (MAM) that combines infant videos and basic characteristics, with the aim of automating GMA at the fidgety movements (FMs) stage. MAM demonstrates strong performance, achieving an Area Under the Curve (AUC) of 0.967 during external validation. Importantly, it adheres closely to the principles of GMA and exhibits robust interpretability, as it can accurately identify FMs within videos, showing substantial agreement with expert assessments. Leveraging the predicted FMs frequency, a quantitative GMA method is introduced, which achieves an AUC of 0.956 and enhances the diagnostic accuracy of GMA beginners by 11.0%. The development of MAM holds the potential to significantly streamline early CP screening and revolutionize the field of video-based quantitative medical diagnostics.
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Affiliation(s)
- Qiang Gao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Siqiong Yao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Yuan Tian
- Department of Health Management, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chuncao Zhang
- Department of Health Management, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Tingting Zhao
- Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, NHC Key Laboratory of Medical Embryogenesis and Developmental Molecular Biology & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Dan Wu
- Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, NHC Key Laboratory of Medical Embryogenesis and Developmental Molecular Biology & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Guangjun Yu
- Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, NHC Key Laboratory of Medical Embryogenesis and Developmental Molecular Biology & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai Jiao Tong University, Shanghai, China.
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, China.
| | - Hui Lu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China.
- Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, NHC Key Laboratory of Medical Embryogenesis and Developmental Molecular Biology & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai Jiao Tong University, Shanghai, China.
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Wedasingha N, Samarasinghe P, Senevirathna L, Papandrea M, Puiatti A, Rankin D. Automated anomalous child repetitive head movement identification through transformer networks. Phys Eng Sci Med 2023; 46:1427-1445. [PMID: 37814077 DOI: 10.1007/s13246-023-01309-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 07/24/2023] [Indexed: 10/11/2023]
Abstract
The increasing prevalence of behavioral disorders in children is of growing concern within the medical community. Recognising the significance of early identification and intervention for atypical behaviors, there is a consensus on their pivotal role in improving outcomes. Due to inadequate facilities and a shortage of medical professionals with specialized expertise, traditional diagnostic methods have been unable to effectively address the rising incidence of behavioral disorders. Hence, there is a need to develop automated approaches for the diagnosis of behavioral disorders in children, to overcome the challenges with traditional methods. The purpose of this study is to develop an automated model capable of analyzing videos to differentiate between typical and atypical repetitive head movements in. To address problems resulting from the limited availability of child datasets, various learning methods are employed to mitigate these issues. In this work, we present a fusion of transformer networks, and Non-deterministic Finite Automata (NFA) techniques, which classify repetitive head movements of a child as typical or atypical based on an analysis of gender, age, and type of repetitive head movement, along with count, duration, and frequency of each repetitive head movement. Experimentation was carried out with different transfer learning methods to enhance the performance of the model. The experimental results on five datasets: NIR face dataset, Bosphorus 3D face dataset, ASD dataset, SSBD dataset, and the Head Movements in the Wild dataset, indicate that our proposed model has outperformed many state-of-the-art frameworks when distinguishing typical and atypical repetitive head movements in children.
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Affiliation(s)
- Nushara Wedasingha
- Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy Rd, Malabe, 10115, Colombo, Sri Lanka.
| | - Pradeepa Samarasinghe
- Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy Rd, Malabe, 10115, Colombo, Sri Lanka
| | - Lasantha Senevirathna
- Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy Rd, Malabe, 10115, Colombo, Sri Lanka
| | - Michela Papandrea
- Information Systems and Networking Institute (ISIN), University of Applied Sciences and Arts of Southern Switzerland, Via Pobiette, Manno, 6928, Switzerland
| | - Alessandro Puiatti
- Institute of Digital Technologies for Personalized Healthcare (MeDiTech), University of Applied Sciences and Arts of Southern Switzerland, Via Pobiette, Manno, 6928, Switzerland
| | - Debbie Rankin
- School of Computing, Engineering and Intelligent Systems, Ulster University, Northland Road, Derry-Londonderry, BT48 7JL, Northern Ireland, UK
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Chung HW, Chang CK, Huang TH, Chen LC, Chen HL, Yang ST, Chen CC, Wang K. Mobile Device-Based Video Screening for Infant Head Lag: An Exploratory Study. CHILDREN (BASEL, SWITZERLAND) 2023; 10:1239. [PMID: 37508736 PMCID: PMC10378382 DOI: 10.3390/children10071239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023]
Abstract
INTRODUCTION Video-based automatic motion analysis has been employed to identify infant motor development delays. To overcome the limitations of lab-recorded images and training datasets, this study aimed to develop an artificial intelligence (AI) model using videos taken by mobile phone to assess infants' motor skills. METHODS A total of 270 videos of 41 high-risk infants were taken by parents using a mobile device. Based on the Pull to Sit (PTS) levels from the Hammersmith Motor Evaluation, we set motor skills assessments. The videos included 84 level 0, 106 level 1, and 80 level 3 recordings. We used whole-body pose estimation and three-dimensional transformation with a fuzzy-based approach to develop an AI model. The model was trained with two types of vectors: whole-body skeleton and key points with domain knowledge. RESULTS The average accuracies of the whole-body skeleton and key point models for level 0 were 77.667% and 88.062%, respectively. The Area Under the ROC curve (AUC) of the whole-body skeleton and key point models for level 3 were 96.049% and 94.333% respectively. CONCLUSIONS An AI model with minimal environmental restrictions can provide a family-centered developmental delay screen and enable the remote monitoring of infants requiring intervention.
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Affiliation(s)
- Hao-Wei Chung
- Department of Pediatrics, Kaohsiung Medical University Chung Ho Memorial Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Department of Biological Science and Technology, National Yang Ming Chiao-Tung University, Hsinchu 300, Taiwan
- Department of Pediatrics, Kaohsiung Municipal Siaogang Hospital, Kaohsiung, Kaohsiung Medical University, Kaohsiung 812, Taiwan
| | - Che-Kuei Chang
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Tzu-Hsiu Huang
- Department of Rehabilitation Medicine, Kaohsiung Medical University Hospital, Kaohsiung 807, Taiwan
| | - Li-Chiou Chen
- Department of Physical Therapy, Fooyin University, Kaohsiung 831, Taiwan
| | - Hsiu-Lin Chen
- Department of Pediatrics, Kaohsiung Medical University Chung Ho Memorial Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Department of Respiratory Therapy, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Shu-Ting Yang
- Department of Pediatrics, Kaohsiung Medical University Chung Ho Memorial Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Chien-Chih Chen
- Center for Fundamental Science, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Kuochen Wang
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- Center for Fundamental Science, Kaohsiung Medical University, Kaohsiung 807, Taiwan
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Khodadadzadeh M, Sloan AT, Jones NA, Coyle D, Kelso JAS. 2D Capsule Networks Detect Perceived Changes in Infant∼Environment Relationship Reflected in 3D Movement Dynamics. RESEARCH SQUARE 2023:rs.3.rs-3088795. [PMID: 37503229 PMCID: PMC10371075 DOI: 10.21203/rs.3.rs-3088795/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Can infant exploration and causal discovery be detected using Artificial Intelligence (AI)? A recent experiment probed how purposeful action emerges in early life by manipulating infants' functional connection to an object in the environment (i.e., tethering one foot to a colorful mobile). Vicon motion capture data from multiple infant joints were used here to create Histograms of Joint Displacements (HJDs) to generate pose-based descriptors for 3D infant spatial trajectories. Using HJDs as inputs, machine and deep learning systems were tasked with classifying the experimental state from which snippets of movement data were sampled. The architectures tested included k-Nearest Neighbour (kNN), Linear Discriminant Analysis (LDA), Fully connected network (FCNet), 1D-Convolutional Neural Network (1D-Conv), 1D-Capsule Network (1D-CapsNet), 2D-Conv and 2D-CapsNet. Sliding window scenarios were used for temporal analysis to search for topological changes in infant movement related to functional context. kNN and LDA achieved higher classification accuracy with single joint features, while deep learning approaches, particularly 2D-CapsNet, achieved higher accuracy on full-body features. For each AI architecture tested, measures of foot activity displayed the most distinct and coherent pattern alterations across different experimental stages (reflected in the highest classification accuracy rate), indicating that interaction with the world impacts the infant behaviour most at the site of organism∼world connection. Pairing theory-driven experimentation with AI tools thus opens a path to developing functionally-relevant assessments of infant behaviour that are likely to be useful in clinical settings.
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Affiliation(s)
- Massoud Khodadadzadeh
- Intelligent Systems Research Centre, Ulster University, Derry/Londonderry, BT48 7JL, United Kingdom
- The Bath Institute for the Augmented Human, University of Bath, Bath, BA2 7AY, United Kingdom
| | - Aliza T. Sloan
- Human Brain and Behaviour Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, United States
| | - Nancy Aaron Jones
- Human Brain and Behaviour Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, United States
| | - Damien Coyle
- Intelligent Systems Research Centre, Ulster University, Derry/Londonderry, BT48 7JL, United Kingdom
- The Bath Institute for the Augmented Human, University of Bath, Bath, BA2 7AY, United Kingdom
| | - J. A. Scott Kelso
- Intelligent Systems Research Centre, Ulster University, Derry/Londonderry, BT48 7JL, United Kingdom
- Human Brain and Behaviour Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, United States
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Cheng HYK, Shieh WY, Yu YC, Li PW, Ju YY. Video-Based Behaviorally Coded Movement Assessment for Adolescents with Intellectual Disabilities: Application in Leg Dribbling Performance. SENSORS (BASEL, SWITZERLAND) 2022; 23:179. [PMID: 36616777 PMCID: PMC9824743 DOI: 10.3390/s23010179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/21/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
Measuring motor performance in individuals with intellectual disabilities (ID) is quite challenging. The objective of this study was to compare the motor performances of individuals with ID and those with typical development (TD) during soccer dribbling through video-based behavior-coded movement assessment along with a wearable sensor. A cross-sectional research design was adopted. Adolescents with TD (N = 25) and ID (N = 29) participated in the straight-line and zigzag soccer dribbling tests. The dribbling performance was videotaped, and the footage was then analyzed with customized behavior-coding software. The coded parameters were the time for movement completion, the number of kicks, blocks, steps, the number of times the ball went out of bounds, the number of missed cones, and the trunk tilt angle. Participants with ID exhibited significantly poorer performance and demonstrated greater variances in many time and frequency domain parameters. It also revealed that participants with ID kicked with both feet while dribbling, whereas those with TD mainly used the dominant foot. The present findings demonstrated how the ID population differed from their peers in lower-extremity strategic control. The customized video-based behavior-coded approach provides an efficient and effective way to gather behavioral data and calculate performance parameter statistics in populations with intellectual disabilities.
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Affiliation(s)
- Hsin-Yi Kathy Cheng
- Graduate Institute of Early Intervention, College of Medicine, Chang Gung University, No. 259, Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan 333, Taiwan
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, 5 Fu-Hsing Street, Kwei-Shan, Tao-Yuan 333, Taiwan
| | - Wann-Yun Shieh
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, 5 Fu-Hsing Street, Kwei-Shan, Tao-Yuan 333, Taiwan
- Department of Computer Science and Information Engineering, College of Engineering, Chang Gung University, No. 259, Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan 333, Taiwan
| | - Yu-Chun Yu
- Taoyuan Municipal Taoyuan Special School, No. 10, Deshou Street, Taoyuan District, Tao-Yuan 330, Taiwan
| | - Pao-Wen Li
- Graduate Institute of Early Intervention, College of Medicine, Chang Gung University, No. 259, Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan 333, Taiwan
| | - Yan-Ying Ju
- Department of Adapted Physical Education, National Taiwan Sport University, No. 250, Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan 333, Taiwan
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Suir I, Boonzaaijer M, Oudgenoeg-Paz O, Westers P, de Vries LS, van der Net J, Nuysink J, Jongmans MJ. Modeling gross motor developmental curves of extremely and very preterm infants using the AIMS home-video method. Early Hum Dev 2022; 175:105695. [PMID: 36459886 DOI: 10.1016/j.earlhumdev.2022.105695] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/17/2022] [Accepted: 11/17/2022] [Indexed: 11/20/2022]
Abstract
BACKGROUND Motor development is one of the first signals to identify whether an infant is developing well. For very preterm (VPT) infants without severe perinatal complications, little is known about their motor developmental curves. AIMS Explore gross motor developmental curves from 3 until 18 months corrected age (CA) of VPT infants, and related factors. Explore whether separate profiles can be distinguished and compare these to profiles of Dutch term-born infants. STUDY DESIGN Prospective cohort study with parents repeatedly recording their infant, using the Alberta Infant Motor Scale (AIMS) home-video method, from 3 to 18 months CA. SUBJECTS Forty-two Dutch infants born ≤32.0 weeks gestational age and/or with a birthweight (BW) of <1500 g without severe perinatal complications. OUTCOME MEASURES Gross motor development measured with the AIMS. RESULTS In total 208 assessments were analyzed, with 27 infants ≥five assessments, 12 with <four, and three with one assessment. Sigmoid-shaped gross motor curves show unidirectional growth and variability. No infant or parental factors significantly influenced motor development, although a trend was seen for the model where lower BW, five-minute Apgar score <7, and Dutch native-speaking parents were associated with slower motor development. Three motor developmental profiles of VPT infants were identified, early developers, gradual developers, and late bloomers, which until 12 months CA are comparable in shape and speed to profiles of Dutch term-born infants. CONCLUSIONS VPT infants show great intra- and interindividual variability in gross motor development, with three motor profiles being distinguished. From 12 months CA onwards, VPT infants appear to develop at a slower pace. With some caution, classifying infants into motor developmental profiles may assist clinical decision-making.
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Affiliation(s)
- I Suir
- Research Group Lifestyle and Health, Research Centre Healthy and Sustainable Living, HU University of Applied Sciences, Utrecht, the Netherlands; Utrecht University, Faculty of Social and Behavioral Sciences, Department of Pedagogical and Educational Sciences, Utrecht, the Netherlands.
| | - M Boonzaaijer
- Research Group Lifestyle and Health, Research Centre Healthy and Sustainable Living, HU University of Applied Sciences, Utrecht, the Netherlands; University Medical Center Utrecht, Wilhelmina Children's Hospital, Department of Neonatology, Utrecht, the Netherlands
| | - O Oudgenoeg-Paz
- University Medical Center Utrecht, Wilhelmina Children's Hospital, Department of Neonatology, Utrecht, the Netherlands
| | - P Westers
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - L S de Vries
- University Medical Center Utrecht, Wilhelmina Children's Hospital, Department of Neonatology, Utrecht, the Netherlands
| | - J van der Net
- University Medical Centre Utrecht, Wilhelmina Children's Hospital, Department of Child Development, Exercise and Physical Literacy, Utrecht, the Netherlands
| | - J Nuysink
- Research Group Lifestyle and Health, Research Centre Healthy and Sustainable Living, HU University of Applied Sciences, Utrecht, the Netherlands
| | - M J Jongmans
- Utrecht University, Faculty of Social and Behavioral Sciences, Department of Pedagogical and Educational Sciences, Utrecht, the Netherlands; University Medical Center Utrecht, Wilhelmina Children's Hospital, Department of Neonatology, Utrecht, the Netherlands
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