Problem Behavior Syndrome and its Influence on the Transition from Experimental to Daily Smoking Among Adolescents in the US

2010 ◽  
Vol 21 (2-3) ◽  
pp. 114-122 ◽  
Author(s):  
Sunhee Park ◽  
Daniel Romer
2010 ◽  
Author(s):  
Alexander T. Vazsonyi ◽  
Pan Chen ◽  
Dusty D. Jenkins ◽  
Esra Burcu ◽  
Ginesa Torrente ◽  
...  

2011 ◽  
pp. 2171-2180
Author(s):  
Kristina Childs ◽  
Christopher Sullivan

2018 ◽  
pp. 2880-2891
Author(s):  
Kristina Childs ◽  
Christopher Sullivan

2008 ◽  
Author(s):  
Alexander T. Vazsonyi ◽  
Pan Chen ◽  
Maureen Young ◽  
Dusty Jenkins ◽  
Sara Browder ◽  
...  

2005 ◽  
Vol 7 (3) ◽  
pp. 197-221 ◽  
Author(s):  
Nadine Lambert

The investigation capitalizes on a 28-year prospective longitudinal study of hyperactive (ADHD) and age mate control participants identified from among 5212 elementary school children in randomly sampled classrooms from grades kindergarten through 5. The participants were followed from childhood through adolescence and interviewed 3 times in adulthood to document their life histories and the ages and use of licit and illicit substances over the developmental course. The Children’s Attention and Adjustment survey provided parent and teacher rating scales of the cardinal symptoms of ADHD—inattention, impulsivity and hyperactivity—as well as ratings of conduct problems. The ratings were available at baseline, making possible the classification of all of the participants by research diagnostic proxies for DSM-IV ADHD. The substance use data included the age of initiation into tobacco, alcohol, marijuana, cocaine, and amphetamines, daily smoking and lifetime use of the substances, and DSM-III-R diagnoses of psychoactive substance use disorder at an average age of 26. Survival analysis of the age of regular smoking showed that the severity of ADHD symptoms lowered the survival rate for regular smoking. Severity of conduct problems also lowered the survival rate. Stimulant treatment affected lower survival rates, and when participants were classified by the age when stimulant treatment stopped, a protective effect was evident: Regular smoking did not begin until stimulant treatment ended. But the protective effect was short-lived. Those who had been treated with stimulants were significantly more likely to be daily smokers in adulthood. Chi-square analysis of ADHD, problem behavior and stimulant treatment showed a significant association between ADHD and between stimulant treatment and DSM-III-R diagnoses of tobacco dependence, and cocaine dependence. ADHD was also significantly associated with amphetamine dependence. Childhood conduct problems were significantly associated only with tobacco dependence. ADHD and stimulant treatment were each significantly associated with daily smoking in adulthood. Stimulant treatment was associated as well with lifetime use of amphetamine, and conduct problems only with the lifetime use of marijuana. Logistic regression was used to model the prediction of psychoactive substance dependence and lifetime use. Being initiated into tobacco by age 13 increased the odds of dependence on all of the substances in the investigation. Severity of ADHD increased the odds of dependence on tobacco, cocaine, amphetamine, and cocaine/amphetamine when the contribution of other variables in the analysis was accounted for. Stimulant treatment increased the odds of dependence on tobacco, cocaine, and cocaine/amphetamine. The logistic regressions for lifetime use as the dependent variable showed that being initiated into tobacco by age 13 increased the risk for lifetime use of all of the substances. Having been treated with stimulants increased the odds of adult daily smoking and lifetime use of amphetamine and cocaine/amphetamines. ADHD and problem behavior did not increase the odds of either daily smoking or lifetime use of any of the substances. The study supported hypotheses that tobacco serves as a gateway substance for dependence and lifetime use of all of the substances investigated. Self-medication, problem behavior, and sensitization hypotheses were discussed as possible explanations for the findings.


Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 370
Author(s):  
Zhaobo K. Zheng ◽  
John E. Staubitz ◽  
Amy S. Weitlauf ◽  
Johanna Staubitz ◽  
Marney Pollack ◽  
...  

Autism Spectrum Disorder (ASD) impacts 1 in 54 children in the US. Two-thirds of children with ASD display problem behavior. If a caregiver can predict that a child is likely to engage in problem behavior, they may be able to take action to minimize that risk. Although experts in Applied Behavior Analysis can offer caregivers recognition and remediation strategies, there are limitations to the extent to which human prediction of problem behavior is possible without the assistance of technology. In this paper, we propose a machine learning-based predictive framework, PreMAC, that uses multimodal signals from precursors of problem behaviors to alert caregivers of impending problem behavior for children with ASD. A multimodal data capture platform, M2P3, was designed to collect multimodal training data for PreMAC. The development of PreMAC integrated a rapid functional analysis, the interview-informed synthesized contingency analysis (IISCA), for collection of training data. A feasibility study with seven 4 to 15-year-old children with ASD was conducted to investigate the tolerability and feasibility of the M2P3 platform and the accuracy of PreMAC. Results indicate that the M2P3 platform was well tolerated by the children and PreMAC could predict precursors of problem behaviors with high prediction accuracies.


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