Impulsivity-Compulsivity Axis: Evidence of Its Clinical Validity to Individually Classify Subjects on the Use/Abuse of Information and Communication Technologies
The compulsive habit model proposed by Everitt and Robbins has accumulated important empirical evidence. One of their proposals is the existence of an axis, on which each a person with a particular addiction can be located depending on the evolutionary moment of his/her addictive process. The object...
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| Published in | Frontiers in psychology Vol. 12; p. 647682 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
Frontiers Media S.A
06.04.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1664-1078 1664-1078 |
| DOI | 10.3389/fpsyg.2021.647682 |
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| Abstract | The compulsive habit model proposed by Everitt and Robbins has accumulated important empirical evidence. One of their proposals is the existence of an axis, on which each a person with a particular addiction can be located depending on the evolutionary moment of his/her addictive process. The objective of the present study is to contribute in addressing the identification of such axis, as few studies related to it have been published to date. To do so, the use/abuse of Information and Communication Technologies (ICT) was quantified on an initial sample of 807 subjects. Questionnaires were also delivered to measure impulsivity, compulsivity and symptoms of prefrontal dysfunction. Evidence of the existence of the proposed axis was obtained by means of Machine Learning techniques, thus allowing the classification of each subject along the continuum. The present study provides preliminary evidence of the existence of the Impulsivity-Compulsivity axis, as well as an IT tool so that each patient that starts getting treatment for an addiction can be statistically classified as “impulsive” or “compulsive.” This would allow the matching of each person with the most appropriate treatment depending on his/her moment in the addiction/abuse process, thus facilitating the individualized design of each therapeutic process and a possible improvement of the results of the treatment. |
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| AbstractList | The compulsive habit model proposed by Everitt and Robbins has accumulated important empirical evidence. One of their proposals is the existence of an axis, on which each a person with a particular addiction can be located depending on the evolutionary moment of his/her addictive process. The objective of the present study is to contribute in addressing the identification of such axis, as few studies related to it have been published to date. To do so, the use/abuse of Information and Communication Technologies (ICT) was quantified on an initial sample of 807 subjects. Questionnaires were also delivered to measure impulsivity, compulsivity and symptoms of prefrontal dysfunction. Evidence of the existence of the proposed axis was obtained by means of Machine Learning techniques, thus allowing the classification of each subject along the continuum. The present study provides preliminary evidence of the existence of the Impulsivity-Compulsivity axis, as well as an IT tool so that each patient that starts getting treatment for an addiction can be statistically classified as “impulsive” or “compulsive.” This would allow the matching of each person with the most appropriate treatment depending on his/her moment in the addiction/abuse process, thus facilitating the individualized design of each therapeutic process and a possible improvement of the results of the treatment. The compulsive habit model proposed by Everitt and Robbins has accumulated important empirical evidence. One of their proposals is the existence of an axis, on which each a person with a particular addiction can be located depending on the evolutionary moment of his/her addictive process. The objective of the present study is to contribute in addressing the identification of such axis, as few studies related to it have been published to date. To do so, the use/abuse of Information and Communication Technologies (ICT) was quantified on an initial sample of 807 subjects. Questionnaires were also delivered to measure impulsivity, compulsivity and symptoms of prefrontal dysfunction. Evidence of the existence of the proposed axis was obtained by means of Machine Learning techniques, thus allowing the classification of each subject along the continuum. The present study provides preliminary evidence of the existence of the Impulsivity-Compulsivity axis, as well as an IT tool so that each patient that starts getting treatment for an addiction can be statistically classified as "impulsive" or "compulsive." This would allow the matching of each person with the most appropriate treatment depending on his/her moment in the addiction/abuse process, thus facilitating the individualized design of each therapeutic process and a possible improvement of the results of the treatment.The compulsive habit model proposed by Everitt and Robbins has accumulated important empirical evidence. One of their proposals is the existence of an axis, on which each a person with a particular addiction can be located depending on the evolutionary moment of his/her addictive process. The objective of the present study is to contribute in addressing the identification of such axis, as few studies related to it have been published to date. To do so, the use/abuse of Information and Communication Technologies (ICT) was quantified on an initial sample of 807 subjects. Questionnaires were also delivered to measure impulsivity, compulsivity and symptoms of prefrontal dysfunction. Evidence of the existence of the proposed axis was obtained by means of Machine Learning techniques, thus allowing the classification of each subject along the continuum. The present study provides preliminary evidence of the existence of the Impulsivity-Compulsivity axis, as well as an IT tool so that each patient that starts getting treatment for an addiction can be statistically classified as "impulsive" or "compulsive." This would allow the matching of each person with the most appropriate treatment depending on his/her moment in the addiction/abuse process, thus facilitating the individualized design of each therapeutic process and a possible improvement of the results of the treatment. |
| Author | Cassú-Ponsatí, Daniel Morales-Alonso, Sara Ruiz-Sánchez de León, José María Pedrero-Pérez, Eduardo J. |
| AuthorAffiliation | 3 Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Faculty of Psychology, Complutense University of Madrid , Madrid , Spain 2 Training and Research Unit, Evaluation and Quality Department, Madrid Salud , Madrid , Spain 1 Data Analysis and Science, Operations Unit, Banco Santander , Madrid , Spain |
| AuthorAffiliation_xml | – name: 3 Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Faculty of Psychology, Complutense University of Madrid , Madrid , Spain – name: 2 Training and Research Unit, Evaluation and Quality Department, Madrid Salud , Madrid , Spain – name: 1 Data Analysis and Science, Operations Unit, Banco Santander , Madrid , Spain |
| Author_xml | – sequence: 1 givenname: Daniel surname: Cassú-Ponsatí fullname: Cassú-Ponsatí, Daniel – sequence: 2 givenname: Eduardo J. surname: Pedrero-Pérez fullname: Pedrero-Pérez, Eduardo J. – sequence: 3 givenname: Sara surname: Morales-Alonso fullname: Morales-Alonso, Sara – sequence: 4 givenname: José María surname: Ruiz-Sánchez de León fullname: Ruiz-Sánchez de León, José María |
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| Copyright | Copyright © 2021 Cassú-Ponsatí, Pedrero-Pérez, Morales-Alonso and Ruiz-Sánchez de León. Copyright © 2021 Cassú-Ponsatí, Pedrero-Pérez, Morales-Alonso and Ruiz-Sánchez de León. 2021 Cassú-Ponsatí, Pedrero-Pérez, Morales-Alonso and Ruiz-Sánchez de León |
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| Keywords | impulsivity ICT—information and communication technologies compulsivity machine learning addiction |
| Language | English |
| License | Copyright © 2021 Cassú-Ponsatí, Pedrero-Pérez, Morales-Alonso and Ruiz-Sánchez de León. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. cc-by |
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