Viewing Study NCT05958641



Ignite Creation Date: 2024-05-06 @ 7:16 PM
Last Modification Date: 2024-10-26 @ 3:04 PM
Study NCT ID: NCT05958641
Status: NOT_YET_RECRUITING
Last Update Posted: 2023-07-24
First Post: 2023-07-16

Brief Title: Construction of Early Warning Model for the New Psychoactive Substances Using in Adolescents
Sponsor: Wei XIA PhD
Organization: Sun Yat-sen University

Study Overview

Official Title: Early Warning Model Construction on Cognition Resistance and Using Risk of the New Psychoactive Substances in Adolescents Based on Behavioral Expression of Addiction Susceptibility Genes and Adverse Childhood Experience
Status: NOT_YET_RECRUITING
Status Verified Date: 2023-07
Last Known Status: None
Delayed Posting: No
If Stopped, Why?: Not Stopped
Has Expanded Access: False
If Expanded Access, NCT#: N/A
Has Expanded Access, NCT# Status: N/A
Acronym: None
Brief Summary: Based on the biological-psychology-sociological medicine pattern this study aims to construction an early warning model of the New Psychoactive Substances NPS using for adolescents aged 14-35 years old This study intends to obtain the data related to the behavioral expression of addiction susceptibility genes adverse childhood experience cognition resistance and the use of NPS in adolescents by questionnaire survey sample size 200 and then use logistic regression and machine learning to construct an early warning model
Detailed Description: Based on the biological-psychology-sociological medicine pattern this study aims to construction an early warning model on the use of the New Psychoactive Substances using for adolescents aged 14-35 years old This study intends to conduct a questionnaire survey sample size 200 to obtain data related to the behavioral expression of addiction susceptibility genes childhood adversity experience cognition resistance and the use of NPS in adolescents from different regions and different populations Through data analysis this study aims to get the weight of different risk factors on NPS use and then use logistic regression and machine learning to construct an early warning model so as to screen out high-risk groups At the same time the expert group was consulted to establish a risk early warning system to provide a basis for intervention

Study Oversight

Has Oversight DMC: None
Is a FDA Regulated Drug?: False
Is a FDA Regulated Device?: False
Is an Unapproved Device?: None
Is a PPSD?: None
Is a US Export?: None
Is an FDA AA801 Violation?: None