Prevalence of sleep disturbance among adolescents with substance use: a systematic review and meta-analysis

Phiri, Doreen; Amelia, Vivi Leona; Muslih, Muhammad; Dlamini, Lindelwa Portia; Chung, Min-Huey; Chang, Pi-Chen

doi:10.1186/s13034-023-00644-5

Cited by 5 publications

(3 citation statements)

References 98 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Poor sleep quality can intensify stress and emotional instability, increasing the likelihood that adolescents will turn to substance use. This observation is consistent with international studies that describe a cyclical relationship between sleep disturbances and substance use, where each condition may exacerbate or trigger the other [ 24 ]. In the Philippines, this indicates a need for interventions that address not only substance use but also underlying factors such as sleep hygiene.…”

Section: Discussionsupporting

confidence: 91%

Predictors and Prevalence of Alcohol and Cannabis Co-use Among Filipino Adolescents: Evidence From a School-based Student Health Survey

Adebisi,

Lucero-Prisno III,

Ogaya

et al. 2024

JPMPH

View full text Add to dashboard Cite

Objectives: This study explored the prevalence and predictors of alcohol and cannabis co-use among 9263 Filipino adolescents, using data from the 2019 Global School-based Student Health Survey (GSHS).Methods: We conducted a cross-sectional secondary analysis of the GSHS, targeting adolescents aged 13-17 years and excluding cases with incomplete data on alcohol and cannabis use. Our analysis employed the bivariate chi-square test of independence and multivariable logistic regression using Stata version 18 to identify significant predictors of co-use, with a p-value threshold set at 0.05.Results: The weighted prevalence of co-users was 4.2% (95% confidence interval [CI], 3.4 to 5.3). Significant predictors included male sex (adjusted odds ratio [aOR], 4.50; 95% CI, 3.31 to 6.10; p<0.001) and being in a lower academic year, specifically grade 7 (aOR, 4.08; 95% CI, 2.39 to 6.99; p<0.001) and grade 8 (aOR, 2.20; 95% CI, 1.30 to 3.72; p=0.003). Poor sleep quality was also a significant predictor (aOR, 1.77; 95% CI, 1.29 to 2.44; p<0.001), as was a history of attempted suicide (aOR, 5.31; 95% CI, 4.00 to 7.06; p<0.001). Physical inactivity was associated with lower odds of co-use (aOR, 0.45; 95% CI, 0.33 to 0.62; p<0.001). Additionally, non-attendance of physical education classes (aOR, 1.48; 95% CI, 1.06 to 2.05; p=0.021), infrequent unapproved parental checks (aOR, 1.37; 95% CI, 1.04 to 1.80; p=0.024), and lower parental awareness of free-time activities (aOR, 0.63; 95% CI, 0.45 to 0.87; p=0.005) were associated with higher odds of co-use. Factors not significantly linked to co-use included age group, being in grade 9, always feeling lonely, having no close friends, being bullied outside school, and whether a parent or guardian understood the adolescent’s worries.Conclusions: The findings highlight the critical need for comprehensive interventions in the Philippines, addressing not only physical inactivity and parental monitoring but also focusing on sex, academic grade, participation in physical education classes, sleep quality, and suicide attempt history, to effectively reduce alcohol and cannabis co-use among adolescents.

show abstract

Section: Discussionsupporting

confidence: 91%

Predictors and Prevalence of Alcohol and Cannabis Co-use Among Filipino Adolescents: Evidence From a School-based Student Health Survey

Adebisi,

Lucero-Prisno III,

Ogaya

et al. 2024

JPMPH

View full text Add to dashboard Cite

show abstract

“…First, these symptom-based disorder classifications often share similar symptoms within their criteria, making it difficult to distinguish between them. For example, impaired sleep is common to several disorders including attention-deficit/hyperactivity disorder (ADHD), anxiety disorder, autism spectrum disorder (ASD), mood disorders, substance use disorder, and post-traumatic stress disorder (PTSD) ( 37 – 39 ). Consequently, it is common for patients to be comorbid across multiple disorders, rather than having symptoms that only align with one disorder ( 40 , 41 ).…”

Section: Challenges and Complexities Of Identifying Causal Drivers Of...mentioning

confidence: 99%

Leveraging big data for causal understanding in mental health: a research framework

Newson,

Bala,

Giedd

et al. 2024

Front. Psychiatry

View full text Add to dashboard Cite

Over the past 30 years there have been numerous large-scale and longitudinal psychiatric research efforts to improve our understanding and treatment of mental health conditions. However, despite the huge effort by the research community and considerable funding, we still lack a causal understanding of most mental health disorders. Consequently, the majority of psychiatric diagnosis and treatment still operates at the level of symptomatic experience, rather than measuring or addressing root causes. This results in a trial-and-error approach that is a poor fit to underlying causality with poor clinical outcomes. Here we discuss how a research framework that originates from exploration of causal factors, rather than symptom groupings, applied to large scale multi-dimensional data can help address some of the current challenges facing mental health research and, in turn, clinical outcomes. Firstly, we describe some of the challenges and complexities underpinning the search for causal drivers of mental health conditions, focusing on current approaches to the assessment and diagnosis of psychiatric disorders, the many-to-many mappings between symptoms and causes, the search for biomarkers of heterogeneous symptom groups, and the multiple, dynamically interacting variables that influence our psychology. Secondly, we put forward a causal-orientated framework in the context of two large-scale datasets arising from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term study of brain development and child health in the United States, and the Global Mind Project which is the largest database in the world of mental health profiles along with life context information from 1.4 million people across the globe. Finally, we describe how analytical and machine learning approaches such as clustering and causal inference can be used on datasets such as these to help elucidate a more causal understanding of mental health conditions to enable diagnostic approaches and preventative solutions that tackle mental health challenges at their root cause.

show abstract

“…To begin with, cannabinoid use has been linked to polysubstance use and illicit substance use disorders when initiated during adolescence, 14,15 and the use of cannabis, alcohol, and smoking have been associated with insomnia and sleep disturbances in adolescents. 16,17 Furthermore, cannabinoid use has been shown to induce psychotic-like symptoms, such as thought disorder, delusions, and visual disturbances, 18 and is thought to be a risk factor for the development of schizophrenia. 19 This is notable because psychosis has been associated with sleep disorders.…”

Section: Introductionmentioning

confidence: 99%

Cannabinoid Use and Obstructive Sleep Apnea: A Retrospective Cohort Study

Veitch,

Jairam,

Gurges

et al. 2024

Can. J. Neurol. Sci.

View full text Add to dashboard Cite

Background: Obstructive sleep apnea (OSA) is a sleep disorder with no widely accepted pharmacological therapy. Cannabinoids have been suggested to reduce OSA severity in small human studies. The purpose of this retrospective cohort study was to explore the association of self-reported cannabis use on OSA severity and sleep parameters in a large cohort of adults undergoing in-laboratory polysomnography. Methods: Sleep and medication data were collected for all consecutive adults who completed diagnostic polysomnography at Sunnybrook Health Sciences Centre from 2010 to 2022. Multivariable linear regression models were employed that adjusted for age, sex, and BMI (minimally adjusted model), as well as medication and comorbidity data (maximally adjusted model). An exploratory subgroup analysis was additionally run in patients with moderate to severe OSA. Results: Of 6,958 individuals (mean age 54.7 ± 16.3, BMI 29.1 ± 6.8, 51.0% female), 71 reported cannabis use. In our minimally adjusted models, cannabis use predicted a reduced respiratory disturbance index (RDI) (β: −4.8 [95% CI: −9.4, −0.2]; p = 0.042); this association became nonsignificant in the fully adjusted models. In an exploratory analysis of patients with moderate to severe OSA (n = 613), cannabis use (n = 7) predicted increased stage N3 sleep (β: 33.5 [95% CI: 15.6, 51.4]; p < 0.001) and decreased REM sleep (β: 16.0 [95% CI: 0.3, 31.7]; p = 0.046). Conclusion: Self-reported cannabis use was not associated with OSA severity after adjusting for confounders. In an exploratory subgroup analysis of patients with moderate to severe OSA, cannabis use impacted sleep architecture. Future studies should further explore these findings.

show abstract

Prevalence of sleep disturbance among adolescents with substance use: a systematic review and meta-analysis

Cited by 5 publications

References 98 publications

Predictors and Prevalence of Alcohol and Cannabis Co-use Among Filipino Adolescents: Evidence From a School-based Student Health Survey

Predictors and Prevalence of Alcohol and Cannabis Co-use Among Filipino Adolescents: Evidence From a School-based Student Health Survey

Leveraging big data for causal understanding in mental health: a research framework

Cannabinoid Use and Obstructive Sleep Apnea: A Retrospective Cohort Study

Contact Info

Product

Resources

About