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. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improving+the+quality+of+early+childhood+care+at+scale:+The+effects+of+"From+Zero+to+Forever".">Improving the quality of early childhood care at scale: The effects of "From Zero to Forever".</a> World Development. 118, 91-105 (2019).</li><li>American Psychiatric Association. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Diagnostic and Statistical Manual of Mental Disorders (DSM-5). (5th Edition). , (2013).</li></ol>

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 develop...

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://jove.unicartagenaproxy.elogim.com/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://jove.unicartagenaproxy.elogim.com/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 Intellige...

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 ...

measuring-functional-abilities-children-aged-3-6-years-old-with

Universidad de Cartagena UDEC

Research

JoVE Journal

Behavior

9.0K 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

Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

The main goal of this study was to assess the usability of a tablet-computer-based application (EmoCogMeter) in investigating the effects of age on cognitive functions across the lifespan in a sample of 378 healthy subjects (age range 18-89 years). Consistent with previous findings we found an age-related cognitive decline across a wide range of neuropsychological domains (memory, attention, executive functions), thereby proving the usability of our tablet-based application. Regardless of prior computer experience, subjects of all age groups were able to perform the tasks without instruction or feedback from an experimenter. Increased motivation and compliance proved to be beneficial for task performance, thereby potentially increasing the validity of the results. Our promising findings underline the great clinical and practical potential of a tablet-based application for detection and monitoring of cognitive dysfunction.

We tested the usability of a tablet-computer-based application (EmoCogMeter) in investigating the effects of age on cognition. Results show an age-related cognitive decline, thereby proving the usability of our application. Findings underline the great clinical and practical potential of a tablet-based application for detection and monitoring of cognitive dysfunction.

The main goal of this study was to assess the usability of a tablet-computer-based application (EmoCogMeter) in investigating the effects of age on ...

Development of a Virtual Reality Assessment of Everyday Living Skills

Cognitive impairments affect the majority of patients with schizophrenia and these impairments predict poor long term psychosocial outcomes.&#160; Treatment studies aimed at cognitive impairment in patients with schizophrenia not only require demonstration of improvements on cognitive tests, but also evidence that any cognitive changes lead to clinically meaningful improvements.&#160; Measures of &#8220;functional capacity&#8221; index the extent to which individuals have the potential to perform skills required for real world functioning. &#160;Current data do not support the recommendation of any single instrument for measurement of functional capacity.&#160; The Virtual Reality Functional Capacity Assessment Tool (VRFCAT) is a novel, interactive gaming based measure of functional capacity that uses a realistic simulated environment to recreate routine activities of daily living. Studies are currently underway to evaluate and establish the VRFCAT&#8217;s sensitivity, reliability, validity, and practicality.&#160;This new measure of functional capacity is practical, relevant, easy to use, and has several features that improve validity and sensitivity of measurement of function in clinical trials of patients with CNS disorders.

A challenge for proving treatment efficacy for cognitive impairments in schizophrenia is finding the optimizing measurement of skills related to everyday functioning.&#160; The Virtual Reality Functional Capacity Assessment Tool (VRFCAT) is an interactive gaming based computerized measure aimed at skills associated with everyday functioning, including baseline impairments and treatment related changes.

Cognitive impairments affect the majority of patients with schizophrenia and these impairments predict poor long term psychosocial outcomes.&#160; ...

Practical Methodology of Cognitive Tasks Within a Navigational Assessment

This paper describes an approach for measuring navigation accuracy relative to cognitive skills. The methodology behind the assessment will thus be clearly outlined in a step-by-step manner. Navigational skills are important when trying to find symbols within a speech-generating device (SGD) that has a dynamic screen and taxonomical organization. The following skills have been found to impact children&#8217;s ability to find symbols when navigating within the levels of an SGD: sustained attention, categorization, cognitive flexibility, and fluid reasoning1,2. According to past studies, working memory was not correlated with navigation1,2.
The materials needed for this method include a computerized tablet, an augmentative and alternative communication application, a booklet of symbols, and the Leiter International Performance Scale-Revised (Leiter-R)3. This method has been used in two previous studies. Robillard, Mayer-Crittenden, Roy-Charland, Minor-Corriveau and B&#233;langer1 assessed typically developing children, while Rondeau, Robillard and Roy-Charland2 assessed children and adolescents with a diagnosis of Autism Spectrum Disorder. The direct observation of this method will facilitate the replication of this study for researchers. It will also help clinicians that work with children who have complex communication needs to determine the children&#8217;s ability to navigate an SGD with taxonomical categorization.

Children who have complex communication needs can benefit from the use of a speech-generating device (SGD). Cognitive skills were identified as having an impact on the ability to navigate between levels of SGDs. This protocol describes the steps needed for Speech-language Pathologists to assess cognitive skills and navigational abilities.

This paper describes an approach for measuring navigation accuracy relative to cognitive skills. The methodology behind the assessment will thus be ...

A Method to Quantify Visual Information Processing in Children Using Eye Tracking

Visual problems that occur early in life can have major impact on a child's development. Without verbal communication and only based on observational methods, it is difficult to make a quantitative assessment of a child's visual problems. This limits accurate diagnostics in children under the age of 4 years and in children with intellectual disabilities. Here we describe a quantitative method that overcomes these problems. The method uses a remote eye tracker and a four choice preferential looking paradigm to measure eye movement responses to different visual stimuli. The child sits without head support in front of a monitor with integrated infrared cameras. In one of four monitor quadrants a visual stimulus is presented. Each stimulus has a specific visual modality with respect to the background, e.g., form, motion, contrast or color. From the reflexive eye movement responses to these specific visual modalities, output parameters such as reaction times, fixation accuracy and fixation duration are calculated to quantify a child's viewing behavior. With this approach, the quality of visual information processing can be assessed without the use of communication. By comparing results with reference values obtained in typically developing children from 0-12 years, the method provides a characterization of visual information processing in visually impaired children. The quantitative information provided by this method can be advantageous for the field of clinical visual assessment and rehabilitation in multiple ways. The parameter values provide a good basis to: (i) characterize early visual capacities and consequently to enable early interventions; (ii) compare risk groups and follow visual development over time; and (iii), construct an individual visual profile for each child.

A method is described to quantify the quality of visual information processing based on reflexive eye movements in response to specific visual modalities. Reaction times and fixation output parameters are used to characterize visual performance in children with and without visual impairments from 6 months of age.

Visual problems that occur early in life can have major impact on a child's development. Without verbal communication and only based on observational ...

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses

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 ...

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

Progressive and irreversible muscle atrophy characterizes Spinal Muscular Atrophy (SMA) and other similar muscle disorder diseases. Objective assessment of muscle functions is an essential and important, although challenging, prerequisite for successful clinical trials. Current clinical rating scales restrain the movement abnormalities to certain predefined coarse-grained individual items. The Kinect 3-D sensor has emerged as a low-cost and portable motion sensing technology used to capture and track people&#39;s movement in many medical and research fields. A novel approach using this 3-D sensor was developed and a game-like test was designed to objectively measure the upper limb function of patients with SMA. The prototype test targeted joint movement capability. While sitting in a virtual scene, the patient was instructed to extend, flex, and lift the whole arm in order to reach and place some objects. Both kinematic and spatiotemporal characteristics of upper limb movement were extracted and analyzed, e.g., elbow extension and flexion angles, hand velocity, and acceleration. The first study included a small cohort of 18 ambulant SMA patients and 19 age- and gender-matched healthy controls. A comprehensive analysis of arm movement was achieved; however, no significant difference between the groups were found due to the mismatch of patient&#39;s capability and the test difficulty. Based on this experience, a second version of the test consisting of a modified version of the first game with increased difficulties and a second game targeting muscle endurance were designed and implemented. The new test has not been conducted in any patient groups yet. Our work has demonstrated the potential capability of the 3-D sensor in assessing such muscle function and suggested an objective approach to complement the clinical rating scales.

An objective measure of muscle functions is challenging especially in children. Based on a commercially available digital 3-D sensor, a child-friendly gaming test was developed to assess upper limb function for clinical trials.

Progressive and irreversible muscle atrophy characterizes Spinal Muscular Atrophy (SMA) and other similar muscle disorder diseases. Objective assessment ...

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics (BM-PROMA)

Learning mathematics is a complex process that requires the development of multiple domain-general and domain-specific skills. It is therefore not unexpected that many children struggle to stay at grade level, and this becomes especially difficult when several abilities from both domains are impaired, as in the case of mathematical learning disabilities (MLD). Surprisingly, although MLD is one of the most common neurodevelopmental disorders affecting schoolchildren, most of the diagnostic instruments available do not include assessment of domain-general and domain-specific skills. Furthermore, very few are computerized. To the best of our knowledge, there is no tool with these features for Spanish-speaking children. The purpose of this study was to describe the protocol for the diagnosis of Spanish MLD children using the BM-PROMA multimedia battery. BM-PROMA facilitates the evaluation of both skill domains, and the 12&#160;tasks included for this purpose are empirically evidence-based. The strong internal consistency of BM-PROMA and its multidimensional internal structure are demonstrated. BM-PROMA proves to be an appropriate tool for diagnosing children with MLD during primary education. It provides a broad cognitive profile for the child, which will be relevant not only for diagnosis but also for individualized instructional planning.

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics BM-PROMA

BM-PROMA is a valid and reliable multimedia diagnostic tool that can provide a complete cognitive profile of children with mathematical learning disabilities.

Learning mathematics is a complex process that requires the development of multiple domain-general and domain-specific skills. It is therefore not ...

Using Brain Activation (nir-HEG/Q-EEG) and Execution Measures (CPTs) in a ADHD Assessment Protocol

Attention Deficit Hyperactivity Disorder (ADHD) is a problem that impacts academic performance and has serious consequences that result in difficulties in scholastic, social and familial contexts. One of the most common problems in the identification of this disorder relates to the apparent over diagnosis of the disorder due to the absence of global protocols for assessment. The research group of School Learning, Difficulties and Academic Performance (ADIR) from the University of Oviedo, has developed a complete protocol that suggests the existence of certain patterns of cortical activation and executive control for identifying ADHD more objectively. This protocol takes into consideration some of the hypothetical determinants of ADHD, including the relationship between activation of selected areas of the brain, and differences in performance on various aspects of executive functioning such as omissions, commissions or response times, using innovative tools of Continuous Performance Testing (based on Virtual Reality CPT and Traditional CPT) and brain activation measures (two different tools, based on Hemoencephalography- nirHEG; and Quantified Electroencephalography --Q-EEG, respectively). This model of assessment aims to provide an effective assessment of ADHD symptomatology in order to design an accurate intervention and make appropriate recommendations for parents and teachers.

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

This work presents a new protocol for the assessment of Attention Deficit Hyperactivity Disorder (ADHD) by providing a more objective diagnostic procedure for this developmental disorder based on the use of innovative tools. It also analyzes the relationship between activation measures and executive function measures.

Attention Deficit Hyperactivity Disorder (ADHD) is a problem that impacts academic performance and has serious consequences that result in difficulties in ...

Neuroscience

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Statistical learning, a fundamental skill to extract regularities in the environment, is often considered a core supporting mechanism of the first language development. While many studies of statistical learning are conducted within a single domain or modality, recent evidence suggests that this skill may differ based on the context in which the stimuli are presented. In addition, few studies investigate learning as it unfolds in real-time, rather focusing on the outcome of learning. In this protocol, we describe an approach for identifying the cognitive and neural basis of statistical learning, within an individual, across domains (linguistic vs. non-linguistic) and sensory modalities (visual and auditory). The tasks are designed to cast as little cognitive demand as possible on participants, making it ideal for young school-aged children and special populations. The web-based nature of the behavioral tasks offers a unique opportunity for us to reach more representative populations nationwide, to estimate effect sizes with greater precision, and to contribute to open and reproducible research. The neural measures provided by the functional magnetic resonance imaging (fMRI) task can inform researchers about the neural mechanisms engaged during statistical learning, and how these may differ across individuals on the basis of domain or modality. Finally, both tasks allow for the measurement of real-time learning, as changes in reaction time to a target stimulus is tracked across the exposure period. The main limitation of using this protocol relates to the hour-long duration of the experiment. Children might need to complete all four statistical learning tasks in multiple sittings. Therefore, the web-based platform is designed with this limitation in mind so that tasks may be disseminated individually. This methodology will allow users to investigate how the process of statistical learning unfolds across and within domains and modalities in children from different developmental backgrounds.

Presented here is a protocol introducing a set of child-friendly statistical learning tasks geared towards examining children&#8217;s learning of temporal statistical patterns across domains and sensory modalities. The developed tasks collect behavioral data using the web-based platform and task-based functional magnetic resonance imaging (fMRI) data for examining neural engagement during statistical learning.