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Article ; Online: Validation of the Health Protective Sexual Communication Scale Among Young Adults in the United Kingdom and Spain.

Alonso-Martínez, Laura / Forrest, Simon / Heras-Sevilla, Davinia / Sáiz-Manzanares, María Consuelo / Puente-Alcaraz, Jesús / Fernández-Hawrylak, María

Journal of nursing measurement

2024

Abstract: Background and Purpose: ...

Abstract	Background and Purpose:
Language	English
Publishing date	2024-01-09
Publishing country	United States
Document type	Journal Article
ZDB-ID	1180408-7
ISSN	1945-7049 ; 1061-3749
ISSN (online)	1945-7049
ISSN	1061-3749
DOI	10.1891/JNM-2022-0113
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Article: Use of Digitalisation and Machine Learning Techniques in Therapeutic Intervention at Early Ages: Supervised and Unsupervised Analysis.

Sáiz-Manzanares, María Consuelo / Solórzano Mulas, Almudena / Escolar-Llamazares, María Camino / Alcantud Marín, Francisco / Rodríguez-Arribas, Sandra / Velasco-Saiz, Rut

Children (Basel, Switzerland)

2024 Volume 11, Issue 4

Abstract: Advances in technology and artificial intelligence (smart healthcare) open up a range of possibilities for precision intervention in the field of health sciences. The objectives of this study were to analyse the functionality of using supervised ( ... ...

Abstract	Advances in technology and artificial intelligence (smart healthcare) open up a range of possibilities for precision intervention in the field of health sciences. The objectives of this study were to analyse the functionality of using supervised (prediction and classification) and unsupervised (clustering) machine learning techniques to analyse results related to the development of functional skills in patients at developmental ages of 0-6 years. We worked with a sample of 113 patients, of whom 49 were cared for in a specific centre for people with motor impairments (Group 1) and 64 were cared for in a specific early care programme for patients with different impairments (Group 2). The results indicated that in Group 1, chronological age predicted the development of functional skills at 85% and in Group 2 at 65%. The classification variable detected was functional development in the upper extremities. Two clusters were detected within each group that allowed us to determine the patterns of functional development in each patient with respect to functional skills. The use of smart healthcare resources has a promising future in the field of early care. However, data recording in web applications needs to be planned, and the automation of results through machine learning techniques is required.
Language	English
Publishing date	2024-03-22
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2732685-8
ISSN	2227-9067
ISSN	2227-9067
DOI	10.3390/children11040381
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Article: A Systematic Review of the Use of T-Pattern and T-String Analysis (TPA) With Theme: An Analysis Using Mixed Methods and Data Mining Techniques.

Sáiz-Manzanares, María Consuelo / Alonso-Martínez, Laura / Marticorena-Sánchez, Raúl

Frontiers in psychology

2022 Volume 13, Page(s) 943907

Abstract: In recent years, research interest in human and non-human behavioral analysis has increased significantly. One key element in the resulting studies is the use of software that facilitates comparative analysis of behavioral patterns, such as using T- ... ...

Abstract	In recent years, research interest in human and non-human behavioral analysis has increased significantly. One key element in the resulting studies is the use of software that facilitates comparative analysis of behavioral patterns, such as using T-Pattern and T-String analysis -TPA- with THEME. Furthermore, all these studies use mixed methods research. Results from these studies have indicated a certain amount of similarity between the biological, temporal, and spatial patterns of human social interactions and the interactions between the contents of their constituent cells. TPA has become an important, widely-used technique in applied behavioral science research. The objectives of the current review were: (1) To identify the results of research over the last 4 years related to the concepts of T-Pattern, TPA, and THEME, since it is in this period in which more publications on these topics have been detected (2) To examine the key concepts and areas in the selected articles with respect to those concepts, applying data and text mining techniques. The results indicate that, over the last 4 years, 20% of the studies were laboratory focused with non-humans, 18% were in sports environments, 9% were in psychological therapy environments and 9% were in natural human contexts. There were also indications that TPA is beginning to be used in workplace environments, which is a very promising setting for future research in this area.
Language	English
Publishing date	2022-07-22
Publishing country	Switzerland
Document type	Systematic Review
ZDB-ID	2563826-9
ISSN	1664-1078
ISSN	1664-1078
DOI	10.3389/fpsyg.2022.943907
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Article ; Online: Improvements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0-6 Years.

Sáiz-Manzanares, María Consuelo / Marticorena-Sánchez, Raúl / Arnaiz-González, Álvar

International journal of environmental research and public health

2022 Volume 19, Issue 11

Abstract: Technological advances together with machine learning techniques give health science disciplines tools that can improve the accuracy of evaluation and diagnosis. The objectives of this study were: (1) to design a web application based on cloud technology ...

Abstract	Technological advances together with machine learning techniques give health science disciplines tools that can improve the accuracy of evaluation and diagnosis. The objectives of this study were: (1) to design a web application based on cloud technology (eEarlyCare-T) for creating personalized therapeutic intervention programs for children aged 0-6 years old; (2) to carry out a pilot study to test the usability of the eEarlyCare-T application in therapeutic intervention programs. We performed a pilot study with 23 children aged between 3 and 6 years old who presented a variety of developmental problems. In the data analysis, we used machine learning techniques of supervised learning (prediction) and unsupervised learning (clustering). Three clusters were found in terms of functional development in the 11 areas of development. Based on these groupings, various personalized therapeutic intervention plans were designed. The variable with most predictive value for functional development was the users' developmental age (predicted 75% of the development in the various areas). The use of web applications together with machine learning techniques facilitates the analysis of functional development in young children and the proposal of personalized intervention programs.
MeSH term(s)	Child ; Child, Preschool ; Cluster Analysis ; Humans ; Infant ; Infant, Newborn ; Machine Learning ; Pilot Projects ; Software
Language	English
Publishing date	2022-05-27
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph19116558
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Article ; Online: Perceived satisfaction of university students with the use of chatbots as a tool for self-regulated learning

Sáiz-Manzanares, María Consuelo / Marticorena-Sánchez, Raúl / Martín-Antón, Luis Jorge / González Díez, Irene / Almeida, Leandro

Heliyon. 2023 Jan., v. 9, no. 1 p.e12843-

2023

Abstract: Chatbots are a promising resource for giving students feedback and helping them deploy metacognitive strategies in their learning processes. In this study we worked with a sample of 57 university students, 42 undergraduate and 15 Master's degree students ...

Abstract	Chatbots are a promising resource for giving students feedback and helping them deploy metacognitive strategies in their learning processes. In this study we worked with a sample of 57 university students, 42 undergraduate and 15 Master's degree students in Health Sciences. A mixed research methodology was applied. The quantitative study analysed the influence of the variables educational level (undergraduate vs. master's degree) and level of prior knowledge on the frequency of chatbot use (low vs. average), learning outcomes, and satisfaction with the chatbot's usefulness. In addition, we examined whether the frequency of chatbot use depended on students' metacognitive strategies. The qualitative study analysed the students' suggestions for improvement to the chatbot and the type of questions it used. The results indicated that the level of degree being studied influenced the frequency of chatbot use and learning outcomes, with Master's students exhibiting higher levels of both, but levels of prior knowledge only influenced learning outcomes. Significant differences were also found in students' perceived satisfaction with the use of the chatbot, with Master's students scoring higher, but not with respect to the level of prior knowledge. No conclusive results were found regarding frequency of chatbot use and the levels of students' metacognitive strategies. Further studies are needed to guide this research based on the students' suggestions for improvement.
Keywords	educational status ; qualitative analysis ; research methods ; Chatbot ; Prior knowledge ; Metacognitive strategies ; Higher education ; Effective learning
Language	English
Dates of publication	2023-01
Publishing place	Elsevier Ltd
Document type	Article ; Online
Note	Use and reproduction
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2023.e12843
Database	NAL-Catalogue (AGRICOLA)

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Article: Perceived satisfaction of university students with the use of chatbots as a tool for self-regulated learning.

Sáiz-Manzanares, María Consuelo / Marticorena-Sánchez, Raúl / Martín-Antón, Luis Jorge / González Díez, Irene / Almeida, Leandro

Heliyon

2023 Volume 9, Issue 1, Page(s) e12843

Abstract	Chatbots are a promising resource for giving students feedback and helping them deploy metacognitive strategies in their learning processes. In this study we worked with a sample of 57 university students, 42 undergraduate and 15 Master's degree students in Health Sciences. A mixed research methodology was applied. The quantitative study analysed the influence of the variables educational level (undergraduate vs. master's degree) and level of prior knowledge on the frequency of chatbot use (low vs. average), learning outcomes, and satisfaction with the chatbot's usefulness. In addition, we examined whether the frequency of chatbot use depended on students' metacognitive strategies. The qualitative study analysed the students' suggestions for improvement to the chatbot and the type of questions it used. The results indicated that the level of degree being studied influenced the frequency of chatbot use and learning outcomes, with Master's students exhibiting higher levels of both, but levels of prior knowledge only influenced learning outcomes. Significant differences were also found in students' perceived satisfaction with the use of the chatbot, with Master's students scoring higher, but not with respect to the level of prior knowledge. No conclusive results were found regarding frequency of chatbot use and the levels of students' metacognitive strategies. Further studies are needed to guide this research based on the students' suggestions for improvement.
Language	English
Publishing date	2023-01-13
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2023.e12843
Database	MEDical Literature Analysis and Retrieval System OnLINE

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

María Consuelo Sáiz-Manzanares / Raúl Marticorena-Sánchez / Álvar Arnaiz-González

International Journal of Environmental Research and Public Health, Vol 19, Iss 6558, p

2022 Volume 6558

Abstract	Technological advances together with machine learning techniques give health science disciplines tools that can improve the accuracy of evaluation and diagnosis. The objectives of this study were: (1) to design a web application based on cloud technology (eEarlyCare-T) for creating personalized therapeutic intervention programs for children aged 0–6 years old; (2) to carry out a pilot study to test the usability of the eEarlyCare-T application in therapeutic intervention programs. We performed a pilot study with 23 children aged between 3 and 6 years old who presented a variety of developmental problems. In the data analysis, we used machine learning techniques of supervised learning (prediction) and unsupervised learning (clustering). Three clusters were found in terms of functional development in the 11 areas of development. Based on these groupings, various personalized therapeutic intervention plans were designed. The variable with most predictive value for functional development was the users’ developmental age (predicted 75% of the development in the various areas). The use of web applications together with machine learning techniques facilitates the analysis of functional development in young children and the proposal of personalized intervention programs.
Keywords	early care ; web application ; machine learning techniques ; precision therapeutic program ; personalized intervention ; disabilities ; Medicine ; R
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Usability of a Virtual Learning Environment in Down Syndrome Adult Learning

María Consuelo Sáiz-Manzanares / Cristina Arranz Barcenilla / Sara Gutiérrez-González / Lourdes Alameda Cuenca-Romero

Sustainability, Vol 15, Iss 23, p

2023 Volume 16404

Abstract: The use of virtual learning environments (VLEs) is becoming increasingly common in teaching. Nevertheless, analysis of how effective these prove to be for the learning of persons with disabilities remains scarce. In this study, we work with a sample of ... ...

Abstract	The use of virtual learning environments (VLEs) is becoming increasingly common in teaching. Nevertheless, analysis of how effective these prove to be for the learning of persons with disabilities remains scarce. In this study, we work with a sample of 34 people aged between 16 and 44 (14 women and 20 men) who have Down Syndrome. The aims of the study were to (1) explore whether there were any significant differences before and after teaching when using a VLE; (2) determine whether the frequency of use and time spent on the VLE impacted learning outcomes; (3) examine clusters vis à vis learning behaviour in the VLE; and (4) gauge perceived user satisfaction with the use of the VLE. Significant differences in learning outcomes before and after teaching using a VLE were found. The frequency and time spent using the VLE were seen to have no impact on learning outcomes. Three clusters were identified in terms of VLE behaviour, and perceived user satisfaction with the VLE was high. There is a need to increase the number of studies addressing the impact of VLEs on learning in persons with different disabilities.
Keywords	virtual learning environment ; educational data mining ; Down Syndrome ; monitoring ; personalised learning ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
Subject code	370
Language	English
Publishing date	2023-11-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Effectiveness of Blended Learning in Nursing Education.

Sáiz-Manzanares, María Consuelo / Escolar-Llamazares, María-Camino / Arnaiz González, Álvar

International journal of environmental research and public health

2020 Volume 17, Issue 5

Abstract: Currently, teaching in higher education is being heavily developed by learning management systems that record the learning behaviour of both students and teachers. The use of learning management systems that include project-based learning and hypermedia ... ...

Abstract	Currently, teaching in higher education is being heavily developed by learning management systems that record the learning behaviour of both students and teachers. The use of learning management systems that include project-based learning and hypermedia resources increases safer learning, and it is proven to be effective in degrees such as nursing. In this study, we worked with 120 students in the third year of nursing degree. Two types of blended learning were applied (more interaction in learning management systems with hypermedia resources vs. none). Supervised learning techniques were applied: linear regression and k-means clustering. The results indicated that the type of blended learning in use predicted 40.4% of student learning outcomes. It also predicted 71.9% of the effective learning behaviors of students in learning management systems. It therefore appears that blended learning applied in Learning Management System (LMS) with hypermedia resources favors greater achievement of effective learning. Likewise, with this type of Blended Learning (BL) a larger number of students were found to belong to the intermediate cluster, suggesting that this environment strengthens better results in a larger number of students. BL with hypermedia resources and project-based learning increase students´ learning outcomes and interaction in learning management systems. Future research will be aimed at verifying these results in other nursing degree courses.
MeSH term(s)	Adult ; Computer-Assisted Instruction/methods ; Education, Nursing, Baccalaureate/methods ; Educational Measurement ; Female ; Humans ; Learning ; Linear Models ; Male ; Models, Educational ; Students, Nursing/psychology ; Young Adult
Language	English
Publishing date	2020-03-01
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1660-4601
ISSN (online)	1660-4601
DOI	10.3390/ijerph17051589
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Evaluation of Functional Abilities in 0-6 Year Olds: an Analysis with the eEarlyCare Computer Application.

Sáiz-Manzanares, María Consuelo / Marticorena-Sánchez, Raúl / Arnaiz-González, Álvar

International journal of environmental research and public health

2020 Volume 17, Issue 9

Abstract: The application of Industry 4.0 to the field of Health Sciences facilitates precise diagnosis and therapy determination. In particular, its effectiveness has been proven in the development of personalized therapeutic intervention programs. The objectives ...

Abstract	The application of Industry 4.0 to the field of Health Sciences facilitates precise diagnosis and therapy determination. In particular, its effectiveness has been proven in the development of personalized therapeutic intervention programs. The objectives of this study were (1) to develop a computer application that allows the recording of the observational assessment of users aged 0-6 years old with impairment in functional areas and (2) to assess the effectiveness of computer application. We worked with a sample of 22 users with different degrees of cognitive disability at ages 0-6. The eEarlyCare computer application was developed with the aim of allowing the recording of the results of an evaluation of functional abilities and the interpretation of the results by a comparison with "normal development". In addition, the Machine Learning techniques of supervised and unsupervised learning were applied. The most relevant functional areas were predicted. Furthermore, three clusters of functional development were found. These did not always correspond to the disability degree. These data were visualized with distance map techniques. The use of computer applications together with Machine Learning techniques was shown to facilitate accurate diagnosis and therapeutic intervention. Future studies will address research in other user cohorts and expand the functionality of their application to personalized therapeutic programs.
MeSH term(s)	Activities of Daily Living ; Child ; Child Development ; Child, Preschool ; Cognition Disorders/diagnosis ; Developmental Disabilities/diagnosis ; Female ; Humans ; Infant ; Infant, Newborn ; Machine Learning ; Male ; Software
Language	English
Publishing date	2020-05-09
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1660-4601
ISSN (online)	1660-4601
DOI	10.3390/ijerph17093315
Database	MEDical Literature Analysis and Retrieval System OnLINE

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