We wish to thank the Vice-rectorate for Research and Knowledge Transfer of the University of Burgos for making the development of the software possible through the &#8220;VI Edici&#243;n Convocatoria Prueba Concepto: Impulso a la valorizaci&#243;n y comercializaci&#243;n de resultados de investigaci&#243;n&#8221; [VI Edition of the Call for Proof of Concept: Impulse to the valorization and marketing of research results]. We likewise thank the Educational Programs Area of the Provincial Directorate of Education and to the Special Education Center &#34;Fray Pedro Ponce de Le&#243;n&#34;, both in Burgos (Spain), and to the families of the children who agreed to participate in this pilot study with prior informed consent, without whose collaboration the software (eEarlyCare) would not have been possible. The video was edited by Alberto Calvo Rodr&#237;guez TVUBU technician and the voice over was done by Caroline Martin, from the Department of English Philology of the University of Burgos.

This protocol was performed in compliance with the procedural regulations of the Bioethical Committee of the University of Burgos (Spain). Prior to their participation, both the students and their parents and in some cases legal tutors had all provided their informed consent and had been fully made aware of the objectives of study. No financial compensation was offered for their participation.
1. Participant recruitment
<ol>
	<li>Recruit children between 0-6 years of age with a history of special educational needs related to alteration in the areas of motor development, cognitive, personal autonomy, and socialization and with moderate to severe intellectual disabilities. A total of 11 participants (7 boys and 4 girls) were recruited for the present study.</li>
	<li>Include children who have been diagnosed with an intellectual disability (moderate or severe) in accordance with the Diagnostic and Statistical Manual of Mental Disorders (DSM-5)16 and attend a Special Education Center for their schooling (Table 2). 
	NOTE: In each case (depending on the detection of the problem and the place where the intervention is performed), the diagnosis should have been confirmed by a pediatric neurologist at a hospital or a psychologist within a multidisciplinary team, in accordance with DSM-5. Ideally, there should be no fewer than 15 participants, since the insertion of the observation results is laborious.</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="3">Sex</td>
			<td rowspan="3">n</td>
			<td rowspan="3">Mage</td>
			<td rowspan="3">SDage</td>
			<td rowspan="3">Rank age (months)</td>
			<td colspan="2">Disability&#8226;</td>
			<td colspan="2" rowspan="2">Schooling</td>
		</tr>
		<tr>
			<td colspan="2">degree</td>
		</tr>
		<tr>
			<td>a</td>
			<td>b</td>
			<td>c</td>
			<td>d</td>
		</tr>
		<tr>
			<td>Boys</td>
			<td>7</td>
			<td>95.2</td>
			<td>11.77</td>
			<td>86-114</td>
			<td>4</td>
			<td>3</td>
			<td>2</td>
			<td>5</td>
		</tr>
		<tr>
			<td>Girls</td>
			<td>4</td>
			<td>83.5</td>
			<td>23.56</td>
			<td>45-112</td>
			<td>1</td>
			<td>3</td>
			<td>2</td>
			<td>2</td>
		</tr>
		<tr>
			<td colspan="9">NOTE:* Degree of disability: a = moderate intellectual disability; b = severe intellectual disability; Type of Schooling: c = Combined Schooling; d = Schooling in a Specific Special Education Center; M = Mean age in months; SD = Standard Deviation. * The children were diagnosed using the criteria of DSM516</td>
		</tr>
	</tbody>
</table>
Table 2: Characteristics of the sample.
<ol start="3">
	<li>Exclude children with normal development in functional areas (motor, cognitive, personal autonomy and socialization development) and children with an Intellectual functioning limitation.</li>
</ol>
2. Data collection
<ol>
	<li>Collect data on the development of the children in different functional areas (Food Autonomy, Personal Care and Hygiene, Dressing and Undressing Independently, Sphincter Control, Functional Mobility, Communication and Language, Daily Life Routines, Adaptive Behavior and Attention).</li>
	<li>Perform the observation using the Scale for the measurement of functional abilities (SFA)5 in children between 0-6 years old (SFA) (Table of Materials) and conduct the observation in a natural context (e.g., school settings).</li>
	<li>Record observations throughout the week in different natural environments that reflect the daily life of the students at the center (Table 1) (e.g., while playing, etc.).
	<ol>
		<li>Let the teacher or the therapist directly attending the children at the educational institutions record the observations. Gather the observations for each functional area. 
		NOTE: Prior to data observation, three training sessions should be conducted for teachers or therapists. These sessions must be conducted by a psychologist or a professional with experience in early-childhood assessment.</li>
	</ol>
	</li>
	<li>Input the results into the software for the assessment of functional abilities in early childhood between 0-6 years old. The details on how to use the software is provided in step 3.</li>
</ol>
3. Experimental procedure
<ol>
	<li>Assigning a student to a teacher in the software 
	NOTE: This step has to be carried out by the director or coordinator of the center where the intervention takes place.
	<ol>
		<li>Log in to the software package with a username (professional) and password and select the language: English or Spanish.</li>
		<li>Enter the student data information from the records held by the director of the center.</li>
		<li>Fill in the following fields for each student: Name, Surname, Code, Sex, Date of Birth, Developmental Age, Primary Diagnosis, Secondary Diagnosis, where appropriate, and observations that are relevant to medication data, allergies, and other information of interest to the management of the center.</li>
		<li>Enter the teacher or therapist information from the records held at the center.</li>
		<li>Assign the students to a group with a teacher (or therapist) by clicking on Classroom. Go to the column Students, choose the student to be assigned to the classroom, and click on Teachers and select one.</li>
		<li>Allocate each group of students and their teacher (or therapist) to a classroom by clicking on Teachers, input dates, name, surname, identification code, mail, passwords and observations. Click Accept. 
		NOTE: The director or coordinator of the center assigns a role for each teacher or therapist to carry out the evaluations of each student or patient (as explained above, it depends on the type of center). The director or coordinator of the center can also consult the evaluations that the teacher or therapist has made.</li>
	</ol>
	</li>
	<li>Use of the software by the teacher 
	NOTE: The teacher or therapist performing the assessment can then select an academic year and/or a term. The scale offers the possibility of selecting different functional areas for each term (Table of Materials).
	<ol>
		<li>Log in to the software with the username (professional) and the password previously assigned by the director of the center.</li>
		<li>Enter the results of the assessments completed in natural environments for each participant assigned to the classroom.</li>
		<li>Choose a student in the software by clicking on his/her name, and start the assessment of the different functional areas (detailed in step 2.3.1). 
		NOTE: The teachers or therapists can stop the assessment of each student at any time and continue at another time, having saved the data that they have registered.</li>
		<li>Perform the comparative analysis between the development of each user and the expected development at that chronological age.
		<ol>
			<li>Once the data is registered, select the column Evaluation by right clicking. Then select the year and the trimester.</li>
			<li>Select the column Students and select the students of a class of whom to make the evaluation. Select the column Areas clicking on the area or subarea that needs to be evaluated.</li>
			<li>Click on the Maximum tab. It gets the information about the students and the comparison with the development expected for their age. 
			NOTE: This offers a user (student or patient) or development profile for a given functional area with a comparative link to the highest expected scores for the chronological age of the user (student or patient).</li>
		</ol>
		</li>
		<li>Compare the different functional areas of each user from the classroom. An analysis of the functional areas of each user can be performed with the software. Following step: once the data is registered, select the column Evaluation by right clicking. Then select the year and the trimester. Select the column Students and select the students of all classes. Select the column Areas and click on the area or subarea to evaluate.
		<ol>
			<li>Click on the Maximum tab. It gets the development of the students and the comparison with the development expected for their age.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Exporting data from the software
	<ol>
		<li>Select the user data and functional areas, and export the database. Select the column Excel to get the database. Export the database in the statistics program or library of choice.</li>
		<li>Import the data into statistical packages and libraries such as SPSS, Weka, Python&#39;s scikit-learn, etc. and perform clustering analysis. Here, analysis with SPSS is detailed as below. 
		NOTE: Clustering or cluster analysis is an &#8216;unsupervised&#8217; machine-learning technique, and, within k-means, it is a grouping method, which aims to partition a set of n observations into k groups, in which each observation belongs to the group with the closest mean value. In this experiment, k-means clustering was used to check the clusters of children and their functional development measured with SFA.
		<ol>
			<li>Select the option Analyze and Classify followed by the option k-means cluster in the statistical package.</li>
			<li>Select crosstabs under Descriptive Statistics, and the following two variables: the cluster variable of &#8216;belonging to a group&#8217; and the degree variable (&#8216;moderate&#8217; or &#8216;severe&#8217;). 
			NOTE: This correspondence is important, because it offers the teacher or therapist information on the homogeneous functional development of users that goes beyond the diagnosis, providing information to propose similar intervention programs in some areas of functional development. This option is expected to facilitate full use of the educational or therapeutic service and its personal and material resources.</li>
			<li>Select the Descriptive Statistics option and select crosstabs and the Cohen Kappa Coefficient option. 
			NOTE: The Cohen Kappa coefficient is a statistic which measures inter-rater agreement for qualitative (categorical) items. It is generally thought to be a more robust measure than simple percent agreement calculations, as k accounts for the possibility of the agreement occurring by chance. This coefficient provides an indicator of the reliability of the relationship found between the diagnostic classification in moderate-to-severe intellectual disabilities and the clustering of the developmental results obtained with the software.</li>
		</ol>
		</li>
		<li>Use the spreadsheet to generate the spider chart and specific bar graphs for the groups of children with moderate and severe intellectual disabilities.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>S&#225;iz, M. C., G&#243;mez, A., Viguer, P., Cantero, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intervenci&#243;n+cognitiva+en+ni&#241;os+peque&#241;os+[Cognitive+intervention+in+early+children].">Intervenci&#243;n cognitiva en ni&#241;os peque&#241;os [Cognitive intervention in early children].</a> Intervenci&#243;n Temprana: Desarrollo &#243;ptimo de 0 a 6 a&#241;os [Early Intervention: Optimal development from 0-to-6 years old]. , 117-133 (2003).</li><li>Bluma, M. S., Shearer, M. S., Frohman, A. H., Hilliard, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Portage Guide to Early Education (2nd Edition). , (1978).</li><li>Josse, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Escala+de+desarrollo+psicomotor+de+la+primera+infancia+Brunet-L&#233;zine+Revisado+[Scale+of+psychomotor+development+of+early+childhood+(Brunet-L&#233;zine-Revised)].">Escala de desarrollo psicomotor de la primera infancia Brunet-L&#233;zine Revisado [Scale of psychomotor development of early childhood (Brunet-L&#233;zine-Revised)].</a> Psymt&#233;c. , (1997).</li><li>Newborg, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Battelle Developmental Inventory, 2nd Edition: Examiner's manual. , (2005).</li><li>Whitebread, D., Basilio, M. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Emergencia+y+desarrollo+temprano+de+la+autorregulaci&#243;n+en+ni&#241;os+preescolares+[The+emergence+and+early+development+of+self-regulation+in+preschool+children].">Emergencia y desarrollo temprano de la autorregulaci&#243;n en ni&#241;os preescolares [The emergence and early development of self-regulation in preschool children].</a> Profesorado. Revista de Curr&#237;culum y Formaci&#243;n del Profesorado. 16 (1), 1-20 (2012).</li><li>P&#233;rez, Y., S&#225;iz, M. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Escala para la medici&#243;n de habilidades funcionales en edades 0-6 a&#241;os (EHFI) [Scale for the measurement of functional abilities in 0-to-6-year olds (SFA)]. Registration number 00/2019/4253. , (2018).</li><li>Haley, S. M., Coster, W. J., Ludlow, L. H., Haltiwanger, J. T., Andrellos, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The Pediatric Evaluation of Disability Inventory (PEDI) (2nd ed.). , (2012).</li><li>Bronson, M. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Self-regulation In Early Childhood. , (2000).</li><li>S&#225;iz, M. C., Carbonero, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metacognitive+Precursors:+An+Analysis+in+Children+with+Different+Disabilities.">Metacognitive Precursors: An Analysis in Children with Different Disabilities.</a> Brain Science. 7 (10), 1-14 (2017).</li><li>S&#225;iz, M. C., Marticorena, R., Arnaiz-Gonz&#225;lez, J. F., D&#237;ez-Pastor, <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> eEarlyCare Computer application. , (2019).</li><li>Chowdhury, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Windows Presentation Foundation Development Cookbook. (1st Edition). , (2018).</li><li>Nogal, F., Marticorena, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> UBUGrades 2.0. Doctoral Degree dissertation. , (2018).</li><li>S&#225;iz, M. C., Marticorena, R., Arnaiz-Gonzalez, &. #. 1. 9. 3. ;., D&#237;ez-Pastor, J. F., Rodr&#237;guez-Arribas, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computer+application+for+the+registration+and+automation+of+the+correction+of+a+functional+abilities+detection+scale+in+early+care.">Computer application for the registration and automation of the correction of a functional abilities detection scale in early care.</a> Proceeding of INTED2019: 13th annual International Technology, Education and Development Conference. , 5322-5328 (2019).</li><li>Hartson, R., Pyla, P. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The UX book: Process and guidelines for ensuring a quality user experience. , (2012).</li><li>Bernal, R., Ram&#237;rez, S. 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The authors declare that they have no competing financial interests.

When using the software, it is recommended that the teacher or therapist record the development of each child&#39;s functional abilities over at least for two sessions. The scale contains 114 items, 11 functional areas, which are divided into 33 functional subareas and the assessment should be the result of observation in natural contexts. In addition, the software allows early-care professionals to record evaluation data with ease and to analyze the data displayed in individual and group graphs, highlighting the development of the users, students or patients, assigned to the classroom in all functional areas and subareas.
The software also facilitates data exportation from a center and classroom to text files (e.g., .csv, .json,) and other spreadsheet formats (e.g., .xlsx), facilitating their transfer to statistical packages, and specific Machine Learning libraries, where it is possible to perform other more complex analyses, such as cluster analysis, mean differences with parametric and/or non-parametric statistics, and reliability analyses of the software for that sample, among many others. In this case, we applied non-parametric statistics with statistical software packages.
The use of the software was only tested at one center, so the work is underway to extend its application to other service centers for users with disabilities. Similarly, and from a perspective of continuous improvement, work is also underway to develop a second phase of the software, in which the possibility will be offered of implementing individualized early stimulation programs, based on the detection of the most affected functional area or areas&#160;of development that will be automatically generated. These programs will include the behaviors to be implemented in hierarchical order of acquisition need, as well as specific guidelines for intervention, the necessary materials, time and space for implementation and generalization activities, and a monitoring and evaluation template similar to the one presented for the assessment phase.
It is worth noting that the use of a protocol for the observation of functional abilities that offered no automated processing of the information to be interpreted by the professional, could neither obtain the results for their interpretation in real time, nor conduct the statistical analyses that would guide the subsequent practice of therapeutic intervention.
As a general conclusion, it may be noted that the use of the software has facilitated both the recording and the interpretation of the results by the therapists. Those functional aspects were highly valued in a very positive light, both by the director of the center and the therapists participating in this study.
This method has very few limitations on its implementation, in terms of both material resources (computer characteristics, application requirements) and personal resources (degree of prior knowledge for use of the software application and for the interpretation of the results by the professional recording the observations). The use of the application is not complicated. The most important difficulty can be found in the processing of the database with statistical packages, at which point more sophisticated data calculation and computation abilities are required.
Similarly, and from the perspective of continuous improvement, work is underway to develop a second phase of the software, in which the possibility will be offered of implementing individualized early stimulation programs based on the detection of the most affected functional area or areas of development that will be automatically generated. These programs will include the behaviors to be implemented in hierarchical order of acquisition need, as well as specific guidelines for intervention, necessary materials, time and space for the implementation, and the generalization of activities, and a monitoring and evaluation template similar to the one presented in the assessment phase.
The use of the tool presented here and the proposed improvements in addition to the field work in institutions that work with children with some development affectation will serve the work of researchers in this field and also for programmed use from government institutions, since it facilitates both the registration and the interpretation of the results.

Observation at early ages: what and how to observe
Early childhood assessment in ordinary family contexts and at school is performed using the observational method. Hence, the evaluator must adhere to a precise observational process, the key to an accurate diagnosis and, therefore, to a successful training1. There are many development inventories that provide guidelines for assessment: The Portage Guide2, the Brunet L&#233;zine Scale3, and the Battelle Developmental Inventory4, among others. These tools are based on internationally agreed standards set by the scientific community in the field of human evolutionary development. Although these tools analyze developmental areas (Psychomotor, Cognitive, Communication and Language, and Autonomy and Socialization), recent studies5 have proposed new tools that can also analyze these areas. These studies point out that the observational method from birth provides pointers of immense utility to early intervention and for the early detection of pathologies. However, the observational processes at these ages are complex, as they depend on behavioral observations recorded in natural contexts, which are not always easy to carry out.
Within this framework, the assessment of the acquisition of functional abilities at early ages is of great interest to parents, educators, and therapists alike. Any such assessment is of relevance for children who have been diagnosed or who are at risk of developing some disability. Early detection of developmental disorders is essential for the early diagnosis and intervention. Observational study from birth will provide indicators of this early detection and intervention5. Currently there are various tools (development inventories, scales, tests, etc.) for measuring development at those ages. The instruments that can currently be applied are developmental inventories, some of which are standardized. However, some of those instruments may require knowledge of psychometric techniques and the results are not automatically displayed on screen. For this reason, it is important to develop other tools that are easier to use and interpret.
&#160; &#160;
Preparation of software for recording and interpreting data from the processes of contextual observation processes at early ages
The software development was, therefore, considered of relevance, which would assist the observers (therapist, educator, etc.) to record and to interpret the results of their observations. This protocol and software, eEarlyCare, can be used both in educational centers that work with children with disabilities and in therapeutic intervention centers aimed at this group. This is why from now on the term &#34;user &#34;will be used, which includes both students and patients, depending on the place where the intervention is carried out. In particular, a software that could facilitate the recording and the interpretation of data gathered in natural contexts from the observation of functional abilities among children from 0 to 6 years old. This software, eEarlyCare, is based on the functional abilities scale6 [Scale for the measurement of Functional Abilities in children between 0-6 years old] (SFA); this scale includes the measurement of 11 areas of development (Food Autonomy, Personal Care and Hygiene, Independently and dress and undresses, Sphincter control, Functional mobility, Communication and Language, Interactive symbolic play, Daily life routines, Adaptive behavior). Also, it was in turn, inspired by the Portage Guide2, the Pediatric Evaluation of Disability Inventory (PEDI)7, and the works of Bronson8, as well as Whitebread and Basilio4 on social skills at ages 0-6, the Brunet-L&#233;zine Scale3, developmental inventories for children aged 0-67, and the Assessment Scale of the precursors9 to social skills. This tool is a computer application that is used for registering the results of each user assessment in longitudinal follow-ups (quarterly, monthly, annual, etc.). It is a referential aspect for the therapist with regard to the intervention, and for other professionals who work with children in early childhood with suspected dysfunctionalities. In addition, the software10 can automatically produce comparisons between the development of the functional abilities of different users, regardless of whether they are at the same intervention center, thereby facilitating the definition of common aspects for collaborative work.
In particular, this software is based on mainstream technologies (e.g., Windows Presentation Foundation Development -WPF-11), a technological innovation that integrates advanced graphics to produce accurate graphic results12 and a positive computer user-experience. The quality of the graphs improves the visualizations and the interactivity available with other tools such as spreadsheets. The application can store the data locally on relational databases and upload the information onto the cloud to be shared. In addition, the classic client-server architecture is also supported. These features make it easy to record the data that are collected from the observations and to process the results for visualization. Also, once registered, it is very easy to export the data. This allows the data to be used in powerful statistical packages to apply data mining techniques, such as supervised (classification and/or regression) and unsupervised (grouping) machine learning.
In this study, the classification techniques are of specific utility for personalized learning13. The architecture can be seen in Figure 1 and Figure 2. In Figure 1, the functionality of cloud storage is used as a safeguard, in case of data security problems and potential loss and corruption of data when exchanged between applications. Additionally, the software can also operate in a classic network-based client-server architecture with a database (Figure 2) with all data exchanges taking place between clients (these are concepts that are used in the field of computer science). These platforms provide authentication mechanisms and restricted access, which ensure privacy and data protection, while facilitating interaction with fully developed applications. The final result is an application interface that is designed14 for early-care professionals, so that they can use Learning Analytics techniques in a simple way and refer to them, in order to follow up the degree of each student&#8217;s development in each evaluation area of the scale15.
The application also offers a general profile of each user in each of the functional areas and subareas (Table 1). It also produces a comparison between all users at any one center. In brief, it produces a personalized analysis of the intervention needs of the different users. In addition, it helps early-care professionals with their intervention programs, as it can flag up areas in which users might or might not show similar developmental patterns. All these results guide the type of intervention programs that can be used together, rather than those that have to be designed individually. The data that are linked to this interface are the SFA scores of the users that are measured on a Likert scale from 1 to 5. Those scores can be compared with the maximum developmental age scores linked to each SFA dimension. The software can also link the chronological age of each user to the developmental age of each SFA dimension; a relevant aspect for the detection of intervention areas from among which to prioritize the areas for treatment.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Functional area</td>
			<td>Functional sub-area</td>
		</tr>
		<tr>
			<td rowspan="2">1. Food Autonomy</td>
			<td>1. Food Texture</td>
		</tr>
		<tr>
			<td>2. Use of utensils</td>
		</tr>
		<tr>
			<td rowspan="5">2. Personal Care and Hygiene</td>
			<td>3. Dental Hygiene</td>
		</tr>
		<tr>
			<td>4. Hairstyle</td>
		</tr>
		<tr>
			<td>5. Nasal Care</td>
		</tr>
		<tr>
			<td>6. Handwashing</td>
		</tr>
		<tr>
			<td>7. Face and body wash</td>
		</tr>
		<tr>
			<td rowspan="2">3. Independently dresses and undresses</td>
			<td>8. Dressing and undressing (waist upwards)</td>
		</tr>
		<tr>
			<td>Dressing and undressing (waist downwards)</td>
		</tr>
		<tr>
			<td>4. Sphincter control</td>
			<td>9. Sphincter control</td>
		</tr>
		<tr>
			<td rowspan="8">5. Functional mobility</td>
			<td>10. Upper extremity</td>
		</tr>
		<tr>
			<td>11. Transfers in WC</td>
		</tr>
		<tr>
			<td>12. Transfers on a chair</td>
		</tr>
		<tr>
			<td>13. Mobility and bed transfers</td>
		</tr>
		<tr>
			<td>14. Mobility in the bathtub</td>
		</tr>
		<tr>
			<td>15. Mobility indoors</td>
		</tr>
		<tr>
			<td>16. Transport of objects</td>
		</tr>
		<tr>
			<td>17. Outdoor Mobility</td>
		</tr>
		<tr>
			<td rowspan="3">6. Communication and Language</td>
			<td>18. Word comprehension</td>
		</tr>
		<tr>
			<td>19. Understanding phrases</td>
		</tr>
		<tr>
			<td>20. Functional use of communication</td>
		</tr>
		<tr>
			<td rowspan="2">7. Resolution of tasks in social contexts</td>
			<td>21. Solving Problems</td>
		</tr>
		<tr>
			<td>22. Information about yourself</td>
		</tr>
		<tr>
			<td rowspan="3">8. Interactive and symbolic play</td>
			<td>23. Interactive Play</td>
		</tr>
		<tr>
			<td>24. Interaction with the peer group</td>
		</tr>
		<tr>
			<td>25. Play with objects</td>
		</tr>
		<tr>
			<td rowspan="2">9. Daily life routines</td>
			<td>26. Guidance in time</td>
		</tr>
		<tr>
			<td>27. Homework</td>
		</tr>
		<tr>
			<td rowspan="5">10. Adaptive behavior</td>
			<td>28. Self-harm behavior</td>
		</tr>
		<tr>
			<td>29. heteroagressiveness (harming others)</td>
		</tr>
		<tr>
			<td>30. Destruction of objects</td>
		</tr>
		<tr>
			<td>31. Disruptive behaviour (cry, shout, laugh without reason)</td>
		</tr>
		<tr>
			<td>32. Stereotypes</td>
		</tr>
		<tr>
			<td>11. Attention</td>
			<td>33. Attention</td>
		</tr>
	</tbody>
</table>
Table 1: List of functional areas and subareas.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60247/60247fig01.jpg" /> 
Figure 1: Architecture of the proposal of automation of the correction of the Scale functionality of cloud storage. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60247/60247fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60247/60247fig02.jpg" /> 
Figure 2: Architecture of the proposal of automation of the correction of the Scale functionality classic network. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60247/60247fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Therapists and intervention professionals can use this assessment protocol and the software implementation for the assessment of functional abilities and their development in early childhood between 0 and 6 years of development. The software can be used with children within that age range, although it is especially useful for children with suspected impaired development of functional abilities. It is likewise especially useful at Special Education Centers. The Research question is whether, having observed the functional abilities of children, the use of a computer tool will facilitate the recording and the interpretation of the results for the therapist.

<table>
	<tbody>
		<tr>
			<td>eEarlyCare software</td>
			<td>Authors and University of Burgos. Register number 00/2019/3855</td>
			<td></td>
			<td>Computer application to implement SFA</td>
		</tr>
		<tr>
			<td>Scale for the measurement of functional abilities in 0-6 years old (SFA)</td>
			<td>Authors and University of Burgos. Register number 00/2019/4253</td>
			<td></td>
			<td>Scale for the measurement of functional abilities in 0-6 year olds</td>
		</tr>
	</tbody>
</table>

measuring the functional abilities of children aged 3-6 years old with observational methods and computer tools

The analysis of functional abilities and their development in early childhood (0-6 years old) are fundamental aspects among young children with certain types of developmental difficulties that can facilitate prevention, through programmed interventions adapted to the needs of each user (student or patient). There are, however, few investigations to date, that have analyzed the use of automated tools for recording and interpreting the results of the initial assessment. Here, a protocol is presented to examine the functional abilities in early childhood in young children, aged between 3-6 years old, with intellectual disabilities, but the protocol can also be used for ages 0 to 6 years. The protocol makes use of a computer application, eEarlyCare, that facilitates the interpretation of the results of systematic observations, which are recorded in natural environments by professionals trained in early intervention. The software can be used to analyze 11 functional areas (Food Autonomy, Personal Care and Hygiene, Dressing and Undressing Independently, Sphincter Control, Functional Mobility, Communication and Language, Daily Life Routines, Adaptive Behavior and Attention) and a total of 114 different behaviors. Its use facilitates the analysis of the observed abilities and greatly assists early intervention. Compared to other observational methods, it allows a more efficient use of personal and material resources. The use of the computer application facilitates the recording of the observation results, which helps with organization and reflection on the observations. The software displays the observation results on-screen compared to normal developmental parameters. This information can be referred to for decision-making about the most suitable intervention program for each user (student or patient). Likewise, clustering techniques are applied to analyze the relation between the type of intellectual disabilities and functional development identified with the software, a relation that is intended to serve as a guide for early-care professional intervention.

The present study recruited 11 participants with confirmed diagnoses [both moderate (development quotient = DQ 40-65) and severe (DQ 39-60) intellectual disabilities]. The protocol was tested in a pilot project over 20 months at a Special Education Center. All the diagnoses for this study had been confirmed by a multidisciplinary team in accordance with DSM-5, using the Brunet L&#233;zine Scale, as the degree of affectation was beyond the scope of other scales such as the Wechsler Preschool and Primary Scale of Intelligence (WPPSI). However, the ages of the participants slightly exceeded the chronological age of 6 (Table 2). The actions carried out and the application times in this study can be found in Table 3.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Functional area</td>
			<td>Time</td>
			<td>Location</td>
			<td>Responsible*</td>
		</tr>
		<tr>
			<td>Food Autonomy</td>
			<td>1 week</td>
			<td>During meals in natural contexts.</td>
			<td>Teacher or Therapist</td>
		</tr>
		<tr>
			<td rowspan="2">Personal Care and Hygiene</td>
			<td rowspan="2">1 week</td>
			<td>In care and hygiene activities in natural contexts.</td>
			<td rowspan="2">Teacher or Therapist</td>
		</tr>
		<tr>
			<td>(center or home)</td>
		</tr>
		<tr>
			<td rowspan="2">Dressing and undressing independently</td>
			<td rowspan="2">1 week</td>
			<td>During the task of dressing and undressing in natural contexts.</td>
			<td rowspan="2">Teacher or Therapist</td>
		</tr>
		<tr>
			<td>(center or home)</td>
		</tr>
		<tr>
			<td rowspan="2">Control of sphincters</td>
			<td rowspan="2">1 week</td>
			<td>During the daytime in the natural context.</td>
			<td rowspan="2">Teacher or Therapist</td>
		</tr>
		<tr>
			<td>(center or home)</td>
		</tr>
		<tr>
			<td rowspan="2">Functional mobility</td>
			<td rowspan="2">1 week</td>
			<td>During the activities of daily life in natural contexts.</td>
			<td rowspan="2">Teacher or Therapist</td>
		</tr>
		<tr>
			<td>(center or home)</td>
		</tr>
		<tr>
			<td rowspan="2">Communication and language</td>
			<td rowspan="2">1 week</td>
			<td>During the activities of daily life in natural contexts.</td>
			<td rowspan="2">Teacher or Therapist</td>
		</tr>
		<tr>
			<td>(center or home)</td>
		</tr>
		<tr>
			<td rowspan="2">Resolution of tasks in social contexts</td>
			<td rowspan="2">1 week</td>
			<td>During activities of daily living in natural contexts</td>
			<td rowspan="2">Teacher or Therapist</td>
		</tr>
		<tr>
			<td>(center or home)</td>
		</tr>
		<tr>
			<td rowspan="2">Interactive and symbolic play</td>
			<td rowspan="2">1 week</td>
			<td>Situations of symbolic play in natural contexts.,</td>
			<td rowspan="2">Teacher or Therapist</td>
		</tr>
		<tr>
			<td>(center or home)</td>
		</tr>
		<tr>
			<td rowspan="2">Daily life routines</td>
			<td rowspan="2">1 week</td>
			<td>During the activities of daily life in natural contexts.</td>
			<td rowspan="2">Teacher or Therapist</td>
		</tr>
		<tr>
			<td>(center or home)</td>
		</tr>
		<tr>
			<td rowspan="2">Adaptive behavior</td>
			<td rowspan="2">1 week</td>
			<td>During the activities of daily life in natural contexts.</td>
			<td rowspan="2">Teacher or Therapist</td>
		</tr>
		<tr>
			<td>(center or home)</td>
		</tr>
		<tr>
			<td rowspan="2">Attention</td>
			<td rowspan="2">1 week</td>
			<td>During the activities of daily life in natural contexts.</td>
			<td rowspan="2">Teacher or Therapist</td>
		</tr>
		<tr>
			<td>(center or home)</td>
		</tr>
		<tr>
			<td colspan="4">NOTE: *Either the teacher or the therapist will contrast the results of the observations gathered at the center with the information gathered from the family, to see whether there are any discrepancies. Prior agreement and consent were always sought from the family, before observation at home in natural contexts that were recorded in video format for subsequent analysis. The number of observations was established according to the type of functional ability, establishing the criterion of different observations during one week in a natural context (center or home).</td>
		</tr>
	</tbody>
</table>
Table 3: Process to observation in different functional areas.
A comparative analysis can be performed once the professional (teacher or therapist) enters the data in the software. The development of users (students or patients) from the same classroom on the different dimensions of the scale is shown.
In this study, we present some examples of data analysis with the observational protocol coupled with the use of the software. We first performed a cluster analysis, in order to check whether the diagnoses of both medium and severe intellectual disabilities of the children with special educational needs corresponded to their actual development. Using the k-means method, we found 2 clusters belonging to either group. Cluster 1 and Cluster 2 integrated 55% and 45% of the sample, respectively. A cross-table was then constructed to study the relationship between membership of a group cluster and the variable &#8216;degree of disability&#8217;. It was found that all the subjects grouped in Cluster 1 belonged to group a) (moderate intellectual disabilities) and Cluster 2 included all the students belonging to group b) (severe intellectual disabilities) and there was only one with moderate intellectual disabilities (Table 4). A Cohen Kappa coefficient = .820, p = .006 was obtained.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="2" rowspan="2"></td>
			<td colspan="2">Cluster case number</td>
			<td rowspan="2">Total</td>
		</tr>
		<tr>
			<td>1</td>
			<td>2</td>
		</tr>
		<tr>
			<td rowspan="2">Disabilities</td>
			<td>a</td>
			<td>5</td>
			<td>0</td>
			<td>5</td>
		</tr>
		<tr>
			<td>b</td>
			<td>1</td>
			<td>5</td>
			<td>6</td>
		</tr>
		<tr>
			<td colspan="2">Total</td>
			<td>6</td>
			<td>5</td>
			<td>11</td>
		</tr>
	</tbody>
</table>
Table 4: Cross Table Disability Cluster Case Number.
The software can, therefore, record the development of the functional abilities of the children under treatment at the same (education or therapy) center and can determine similar and different levels of development. This aspect is very important, as it will facilitate the application of similar intervention programs among children with similar needs, which will in turn facilitate personalized intervention and efficient use of resources.
Progressing with this analysis, a study can be performed of the abilities of the participants in each functional group (disability moderate vs. disability severe). Both Spider and specific bar graphs were used for this purpose see Figure 3 and Figure 4.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60247/60247fig03.jpg" /> 
Figure 3: Comparative analysis between development of each user (student or patient) and the expected development in children with several intellectual disabilities. 
(A) Food Autonomy, (B) Personal Care and Hygiene, (C) Independently dress and undresses, (D) Sphincter control, (E) Functional mobility, (F) Communication and Language, (G) Interactive and symbolic play, (H) Daily life routines, (I) Adaptive behavior. Blue line indicates skill acquisition score. Garnet line indicates maximum score. Error bars included in A, B, C, D, E, F, G, H and I. The error bars are graphical representations of data variability, an error bar indicates the uncertainty of a value. The x-axis represents the participants (with several intellectual disabilities) and the y-axis represents the scores they have obtained in each functional area. The spider graph shows the comparative development of the participants in each functional area. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60247/60247fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/60247/60247fig04.jpg" /> 
Figure 4: Comparative analysis between development of each user (student or patient) and the expected development in children with moderate disabilities. 
(A) Food Autonomy, (B) Personal Care and Hygiene, (C) Independently dress and undresses, (D) Sphincter control, (E) Functional mobility, (F) Communication and Language, (G) Interactive and symbolic play, (H) Daily life routines, (I) Adaptive behavior. Blue line indicates skill acquisition score. Garnet line indicates maximum score. Error bars included in A, B, C, D, E, F, G, H and I. The error bars are graphical representations of data variability, an error bar indicates the uncertainty of a value. The x-axis represents the participants (with moderate intellectual disabilities) and the y-axis represents the scores they have obtained in each functional area. The spider graph shows the comparative development of the participants in each functional area. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60247/60247fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools at JoVE.com

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

We present a protocol to use a computational tool to record and analyze the functional abilities of children aged 3-6 years old. The protocol facilitates the comparison of these abilities throughout their development and can be used to assess developmental difficulties.

The analysis of functional abilities and their development in early childhood (0-6 years old) are fundamental aspects among young children with certain ...

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8.9K Views.  Universidad de Burgos. We present a protocol to use a computational tool to record and analyze the functional abilities of children aged 3-6 years old. The protocol facilitates the comparison of these abilities throughout their development and can be used to assess developmental difficulties.

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

Quantitative Assessment of Cortical Auditory-tactile Processing in Children with Disabilities

Objective and easy measurement of sensory processing is extremely difficult in nonverbal or vulnerable pediatric patients. We developed a new methodology to quantitatively assess children&#39;s cortical processing of light touch, speech sounds and the multisensory processing of the 2 stimuli, without requiring active subject participation or causing children discomfort. To accomplish this we developed a dual channel, time and strength calibrated air puff stimulator that allows both tactile stimulation and sham control. We combined this with the use of event-related potential methodology to allow for high temporal resolution of signals from the primary and secondary somatosensory cortices as well as higher order processing. This methodology also allowed us to measure a multisensory response to auditory-tactile stimulation.

Objective and easy measurement of sensory processing is extremely difficult in nonverbal or vulnerable pediatric patients. We developed a new methodology to quantitatively assess infants and children&#39;s cortical processing of light touch, speech sounds, and the multisensory processing of the 2 stimuli, without requiring active subject participation or causing discomfort in vulnerable patients.

Objective and easy measurement of sensory processing is extremely difficult in nonverbal or vulnerable pediatric patients. We developed a new methodology ...

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Kinesthetic awareness is important to successfully navigate the environment. When we interact with our daily surroundings, some aspects of movement are deliberately planned, while others spontaneously occur below conscious awareness. The deliberate component of this dichotomy has been studied extensively in several contexts, while the spontaneous component remains largely under-explored. Moreover, how perceptual processes modulate these movement classes is still unclear. In particular, a currently debated issue is whether the visuomotor system is governed by the spatial percept produced by a visual illusion or whether it is not affected by the illusion and is governed instead by the veridical percept. Bistable percepts such as 3D depth inversion illusions (DIIs) provide an excellent context to study such interactions and balance, particularly when used in combination with reach-to-grasp movements. In this study, a methodology is developed that uses a DII to clarify the role of top-down processes on motor action, particularly exploring how reaches toward a target on a DII are affected in both deliberate and spontaneous movement domains.

It is unclear how top-down signals from the ventral visual stream affect movement. We developed a paradigm to test motor behavior towards a target on a 3D depth inversion illusion. Significant differences are reported in both deliberate, goal-directed movements and automatic actions under illusory and veridical viewing conditions.

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Measuring Attentional Biases for Threat in Children and Adults

Investigators have long been interested in the human propensity for the rapid detection of threatening stimuli. However, until recently, research in this domain has focused almost exclusively on adult participants, completely ignoring the topic of threat detection over the course of development. One of the biggest reasons for the lack of developmental work in this area is likely the absence of a reliable paradigm that can measure perceptual biases for threat in children. To address this issue, we recently designed a modified visual search paradigm similar to the standard adult paradigm that is appropriate for studying threat detection in preschool-aged participants. Here we describe this new procedure. In the general paradigm, we present participants with matrices of color photographs, and ask them to find and touch a target on the screen. Latency to touch the target is recorded. Using a touch-screen monitor makes the procedure simple and easy, allowing us to collect data in participants ranging from 3 years of age to adults. Thus far, the paradigm has consistently shown that both adults and children detect threatening stimuli (e.g., snakes, spiders, angry/fearful faces) more quickly than neutral stimuli (e.g., flowers, mushrooms, happy/neutral faces). Altogether, this procedure provides an important new tool for researchers interested in studying the development of attentional biases for threat.

Here we describe a touch-screen visual search paradigm that can be used to study threat detection across the lifespan. The paradigm has already been used in various studies demonstrating that both children and adults detect threatening stimuli like snakes, spiders, and angry faces faster than non-threatening stimuli.

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Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

There are several advantages that functional near-infrared spectroscopy (fNIRS) presents in the study of the neural control of human movement. It is relatively flexible with respect to participant positioning and allows for some head movements during tasks. Additionally, it is inexpensive, light weight, and portable, with very few contraindications to its use. This presents a unique opportunity to study functional brain activity during motor tasks in individuals who are typically developing, as well as those with movement disorders, such as cerebral palsy. An additional consideration when studying movement disorders, however, is the quality of actual movements performed and the potential for additional, unintended movements. Therefore, concurrent monitoring of both blood flow changes in the brain and actual movements of the body during testing is required for appropriate interpretation of fNIRS results. Here, we show a protocol for the combination of fNIRS with muscle and kinematic monitoring during motor tasks. We explore gait, a unilateral multi-joint movement (cycling), and two unilateral single-joint movements (isolated ankle dorsiflexion, and isolated hand squeezing). The techniques presented can be useful in studying both typical and atypical motor control, and can be modified to investigate a broad range of tasks and scientific questions.

Monitoring brain activity during upright motor tasks is of great value when investigating the neural source of movement disorders. Here, we demonstrate a protocol that combines functional near infrared spectroscopy with continuous monitoring of muscle and kinematic activity during 4 types of motor tasks.

There are several advantages that functional near-infrared spectroscopy (fNIRS) presents in the study of the neural control of human movement. It is ...

Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach

Mouse is the preferred model organism for testing drugs designed to increase sociability. We present a method to quantify mouse sociability in which the test mouse is placed in a standardized apparatus and relevant behaviors are assessed in three different sessions (called session I, II, and III).
The apparatus has three compartments (see Figure 1), the left and right compartments contain an inverted cup which can house a mouse (called &#34;stimulus mouse&#34;).
In session I, the test mouse is placed in the cage and its mobility is characterized by the number of transitions made between compartments. In session II, a stimulus mouse is placed under one of the inverted cups and the sociability of the test mouse is quantified by the amounts of time it spends near the cup containing the enclosed stimulus mouse vs. the empty inverted cup. In session III, the inverted cups are removed and both mice interact freely. The sociability of the test mouse in session III is quantified by the number of social approaches it makes toward the stimulus mouse and by the number of times it avoids a social approach by the stimulus mouse.
The automated evaluation of the movie detects the nose of the test mouse, which allows the determination of all described sociability measures in session I and II (in session III, approaches are identified automatically but classified manually). To find the nose, the image of an empty cage is digitally subtracted from each frame of the movie and the resulting image is binarized to identify the mouse pixels. The mouse tail is automatically removed and the two most distant points of the remaining mouse are determined; these are close to nose and base of tail. By analyzing the motion of the mouse and using continuity arguments, the nose is identified.
<img alt="Figure 1" src="/files/ftp_upload/52508/52508fig1.jpg" /> 
Figure 1. Assessment of Sociability During 3 sessions. Session I (top): Acclimation of test mouse to the cage. Session II (middle): Test mouse moving freely in the cage while the stimulus mouse is enclosed in an inverted cup. Session III (bottom): Both test mouse and stimulus mouse are allowed to move freely and interact with each other.

This protocol describes a method to quantify mouse sociability. Mice are videotaped as they move and interact in a special cage. Movie processing allows for the automated quantification of sociability with excellent accuracy and reliability.

Mouse is the preferred model organism for testing drugs designed to increase sociability. We present a method to quantify mouse sociability in which the ...

Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder

In addition to impairments in social communication and the presence of restricted interests and repetitive behaviors, deficits in sensory processing are now recognized as a core symptom in autism spectrum disorder (ASD). Our ability to perceive and interact with the external world is rooted in sensory processing. For example, listening to a conversation entails processing the auditory cues coming from the speaker (speech content, prosody, syntax) as well as the associated visual information (facial expressions, gestures). Collectively, the &#8220;integration&#8221; of these multisensory (i.e., combined audiovisual) pieces of information results in better comprehension. Such multisensory integration has been shown to be strongly dependent upon the temporal relationship of the paired stimuli. Thus, stimuli that occur in close temporal proximity are highly likely to result in behavioral and perceptual benefits &#8211; gains believed to be reflective of the perceptual system&#39;s judgment of the likelihood that these two stimuli came from the same source. Changes in this temporal integration are expected to strongly alter perceptual processes, and are likely to diminish the ability to accurately perceive and interact with our world. Here, a battery of tasks designed to characterize various aspects of sensory and multisensory temporal processing in children with ASD is described. In addition to its utility in autism, this battery has great potential for characterizing changes in sensory function in other clinical populations, as well as being used to examine changes in these processes across the lifespan.

We describe how to implement a battery of behavioral tasks to examine the processing and integration of sensory stimuli in children with ASD. The goal is to characterize individual differences in temporal processing of simple auditory and visual stimuli and relate these to higher order perceptual skills like speech perception.

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A Treatment Package without Escape Extinction to Address Food Selectivity

Feeding difficulties and feeding disorders are a commonly occurring problem for young children, particularly children with developmental delays including autism. Behavior analytic interventions for the treatment of feeding difficulties oftentimes include escape extinction as a primary component of treatment. The use of escape extinction, while effective, may be problematic as it is also associated with the emergence of challenging behavior (e.g., extinction burst). Such challenging behavior may be an acceptable side effect in treatment cases where feeding problems are severe and chronic (e.g., failure to thrive). However, in more acute cases (e.g., selective eating), the negative side effect may be unwarranted and undesired. More recent research on the behavioral treatment of food selectivity has begun to evaluate treatments for feeding difficulties that do not include escape extinction (e.g., demand fading, behavioral momentum), with some success. However, research to date reveals individual differences in responsiveness to such treatments and no clear preferable treatment has emerged. This manuscript describes a multi-component treatment package that includes shaping, sequential presentation and simultaneous presentation, for the treatment of food selectivity in four young children with developmental delays. This treatment package extends the literature on the behavioral treatment for food selectivity and offers a multi-component treatment protocol that may be clinically applicable across a range of treatment scenarios and settings.

Feeding difficulties are a common problem for children with developmental disorders, including autism, and behavioral interventions often include escape extinction. Recent research has begun to evaluate treatments that do not include escape extinction. This manuscript describes a multicomponent treatment package that does not use escape extinction to treat feeding difficulties.

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Collecting reliable behavioral data from toddlers and preschoolers is challenging. As a result, there are significant gaps in our understanding of human auditory development for these age groups. This paper describes an observer-based procedure for measuring hearing sensitivity with a two-interval, two-alternative forced-choice paradigm. Young children are trained to perform a play-based, motor response (e.g., putting a block in a bucket) whenever they hear a target signal. An experimenter observes the child&#39;s behavior and makes a judgment about whether the signal was presented during the first or second observation interval; the experimenter is blinded to the true signal interval, so this judgment is based solely on the child&#39;s behavior. These procedures were used to test 2 to 4 year-olds (n = 33) with no known hearing problems. The signal was a 1,000 Hz warble tone presented in quiet, and the signal level was adjusted to estimate a threshold corresponding to 71%-correct detection. A valid threshold was obtained for 82% of children. These results indicate that the two-interval procedure is both feasible and reliable for use with toddlers and preschoolers. The two-interval, observer-based procedure described in this paper is a powerful tool for evaluating hearing in young children because it guards against response bias on the part of the experimenter.

This paper describes a procedure for measuring hearing sensitivity in 2 to 4 year-old children. Children are trained to perform play-based responses when they hear a target signal. Thresholds are then estimated in a two-interval, two-alternative forced-choice paradigm, based on observations of the child&#39;s behaviors.

Collecting reliable behavioral data from toddlers and preschoolers is challenging. As a result, there are significant gaps in our understanding of human ...

Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children &#8211; Working Memory (CABC-WM)

The Comprehensive Assessment Battery for Children - Working Memory (CABC-WM) is a computer-based battery designed to assess different components of working memory in young school-age children. Working memory deficits have been identified in children with language-based learning disabilities, including dyslexia1,2 and language impairment3,4, but it is not clear whether these children exhibit deficits in subcomponents of working memory, such as visuospatial or phonological working memory. The CABC-WM is administered on a desktop computer with a touchscreen interface and was specifically developed to be engaging and motivating for children. Although the long-term goal of the CABC-WM is to provide individualized working memory profiles in children, the present study focuses on the initial success and utility of the CABC-WM for measuring central executive, visuospatial, phonological loop, and binding constructs in children with typical development. Immediate next steps are to administer the CABC-WM to children with specific language impairment, dyslexia, and comorbid specific language impairment and dyslexia.

Working memory predicts a significant amount of variance for a variety of cognitive tasks, including speaking, reading, and writing. However, few tools are available to assess working memory in children. We present an innovative, computer-based battery that comprehensively assesses different components of working memory in school-age children.

The Comprehensive Assessment Battery for Children - Working Memory (CABC-WM) is a computer-based battery designed to assess different components of ...

Classical Short-Delay Eyeblink Conditioning in One-Year-Old Children

Classical eyeblink conditioning (EBC) refers to the learned association between a conditioned stimulus (an auditory tone) and an unconditioned stimulus (a puff of air to the cornea). Eyeblink conditioning is often used experimentally to detect abnormalities in cerebellar-dependent learning and memory that underlies this type of associative learning. While experiments in adults and older children are relatively simple to administer using commercial equipment, eyeblink conditioning in infants is more challenging due to their poor compliance, which makes correct positioning of the equipment difficult. To achieve conditioning in one-year-old infants, a custom-made or an adapted commercial system can be used to deliver the air puff to the infant's cornea. The main challenge lies in successfully detecting and classifying the behavioral responses. We report that automated blink detection methods are unreliable in this population, and that conditioning experiments should be analyzed using frame-by-frame analysis of supplementary video camera recordings. This method can be applied to study developmental changes in eyeblink conditioning and to examine whether this paradigm can detect children with neurological disorders.

This protocol describes an eyeblink conditioning paradigm appropriate for experiments with one-year-old infants. Commercial or custom-made equipment can be used to deliver the stimuli, and data collection and analysis should be performed on the video recordings.

Classical eyeblink conditioning (EBC) refers to the learned association between a conditioned stimulus (an auditory tone) and an unconditioned stimulus (a ...