Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Manzanares, María Consuelo Sáiz; Arnaiz‐González, Álvar; Pastor, José Francisco Díez; Osorio, César Ignacio García

doi:10.3791/60247

Cited by 4 publications

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“…In this study, differences were specifically found in impairments related to users with rare diseases (in this study we worked with users affected by Coffin-Siris, Prader-Willi, LAMA II, West, Beuren, Mucopolysaccharidosis, Encephalopathy, Schaaf-Yang, and KBG) or communication and language delay. It seems therefore that the personalization of the intervention through the use of web applications that facilitate registration and learning analytics in real time has an important weight for the monitoring that therapists carry out of the development as well as in the personalized proposal of therapeutic intervention programs [8][9][10]. This makes it easier for the intervention to be as personalized as possible, which increases the effectiveness of the treatment, especially at ages 0-60 months.…”

Section: Discussionmentioning

confidence: 99%

Monitoring Educational Intervention Programs for Children and Young People with Disabilities through a Web Application

Sáiz-Manzanares,

Marticorena-Sánchez,

Escolar-Llamazares

et al. 2024

Education Sciences

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Early care professionals have to use instruments for assessing functional skills in children susceptible to early intervention that apply records and produce developmental profiles and personalized intervention proposals. The aims of the study were (1) to analyze the development of functional skills in users with an age range of 48–252 months attending school in a therapeutic intervention center for people with motor impairments; and (2) to analyze the development of functional skills in users with different impairments and ages ranging from 7 to 162 months participating in an early outpatient care program. Study 1 applied a sample of 50 users aged between 48 and 252 months all with motor disabilities and Study 2 included a sample of 71 users aged between 7 and 162 months with different disabilities. Factorial and descriptive–correlational designs were applied in both studies. The Student’s t-test for dependent samples, supervised machine learning techniques (linear regression analysis and logarithmic regression analysis), unsupervised machine learning techniques (k-means), ANOVA, and cross-tabulations were used as contrast tests. In Study 1, no significant changes were found in the development of users’ functional skills, except for a decrease in maladaptive behaviors. Likewise, the chronological age variable did not seem to be a determining factor in the results. In Study 2, significant differences were found in the development of all functional skills between the three measurement time points (initial–intermediate–final). In this group, the type of impairment explained 29% and chronological age 40% of the variance in functional development at the final measurement. This study found that intervention before four years old in outpatient mode produced better results in the acquisition of functional skills, with better results in users affected by rare diseases or communication and language delay at ages 49–60 months.

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Section: Discussionmentioning

confidence: 99%

Monitoring Educational Intervention Programs for Children and Young People with Disabilities through a Web Application

Sáiz-Manzanares,

Marticorena-Sánchez,

Escolar-Llamazares

et al. 2024

Education Sciences

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“…Using technological tools such as computer applications makes it easier to treat and interpret results [ 16 ]. This study began with the creation of the eEarlyCare-T web application, specifically the module for recording and treating data from systematic observations of development of functional abilities in small children with various problems; see the works by Sáiz-Manzanares, Marticorena- Sánchez, Arnaiz-González, Díez-Pastor and García-Osorio [ 13 ] and Sáiz-Manzanares, Marticorena-Sánchez and Arnaiz-González [ 17 ]. This application allows data to be exported to statistical analysis software and allows the application of machine learning techniques [ 8 ].…”

Section: Discussionmentioning

confidence: 99%

“…Studying the scales and protocols measuring development of functional abilities in ages 0–6 years old (“Scale of psychomotor development of early childhood Brunet-Lézine-Revised” [ 25 ], “Battelle Developmental Inventory” [ 26 ], and “The Pediatric Evaluation of Disability Inventory” [ 27 ]). The protocol was validated in two pilot studies [ 13 , 17 ].…”

Section: Methodsmentioning

confidence: 99%

“…Machine learning techniques are being used in interventions for phobias [ 10 ] and in depressive disorders [ 11 ], applying self-instructional therapies and personalized cognitive therapies [ 11 , 12 ]. In addition, these tools are beginning to be implemented in the field of early care for children with a variety of conditions [ 13 ]. This is particularly important for diagnosis and intervention for different developmental pathologies in infancy and childhood, and also leads to more precise interventions [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Improvements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0–6 Years

Manzanares

Arnaiz‐González

2022

IJERPH

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Technological advances together with machine learning techniques give health science disciplines tools that can improve the accuracy of evaluation and diagnosis. The objectives of this study were: (1) to design a web application based on cloud technology (eEarlyCare-T) for creating personalized therapeutic intervention programs for children aged 0–6 years old; (2) to carry out a pilot study to test the usability of the eEarlyCare-T application in therapeutic intervention programs. We performed a pilot study with 23 children aged between 3 and 6 years old who presented a variety of developmental problems. In the data analysis, we used machine learning techniques of supervised learning (prediction) and unsupervised learning (clustering). Three clusters were found in terms of functional development in the 11 areas of development. Based on these groupings, various personalized therapeutic intervention plans were designed. The variable with most predictive value for functional development was the users’ developmental age (predicted 75% of the development in the various areas). The use of web applications together with machine learning techniques facilitates the analysis of functional development in young children and the proposal of personalized intervention programs.

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Using Machine Learning Techniques in eEarlyCare Precision Diagnosis and Intervention in 0–6 years Old

Sáiz-Manzanares

2023

Lecture Notes in Networks and Systems

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Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Cited by 4 publications

References 0 publications

Monitoring Educational Intervention Programs for Children and Young People with Disabilities through a Web Application

Monitoring Educational Intervention Programs for Children and Young People with Disabilities through a Web Application

Improvements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0–6 Years

Using Machine Learning Techniques in eEarlyCare Precision Diagnosis and Intervention in 0–6 years Old

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