Addictive behaviors最新文献

Objective

Study of the association between smoking and pain intensity has produced conflicting results; with less focus on pain interference. Different pain constructs could have varying associations with smoking behaviors. This study sought to investigate the association between smoking history and not only pain intensity, but also pain interference, symptom distribution and physical function.

Methods

Smoking history (current, past, or none), pain interference (Pain, Enjoyment of Life, and General Activity scale), symptom distribution and physical function scores were extracted from medical records of patients seen in physical therapy for common sites of musculoskeletal pain (lumbar and cervical spine, knee, or hip). Generalized linear models assessed the relationship between smoking history and pain/function.

Results

833 patients from an integrated healthcare system were included (mean: 57.6 years, SD=16.3; 43 % male). After controlling for several variables, current smokers had significantly higher baseline pain interference scores compared to never and former smokers (beta [B]: 0.65, 95 %CI: 0.13 to 1.18, P=.02). Smoking was not a significant predictor of symptom distribution at baseline [B: 0.17, 95 %CI −0.06 to 0.42, P=.16] or physical function scores at discharge [B: -0.03, 95 %CI: −0.08 to 0.02, P=.25].

Conclusion

Smokers experienced a greater impact of pain at baseline. However, symptom distribution at intake and function upon discharge were similar between all smoking groups. These findings suggest smoking cessation and abstinence may be important recommendations to help curb pain interference.

Objective

Polysubstance use among adults has been a public health concern in the U.S. and is associated with adverse consequences. This study aimed to identify the longitudinal trajectory of polysubstance use and test whether internalizing and externalizing problems predict it.

Methods

Data of adults aged 18 and older (N = 15076) were extracted from the Waves 1–5 Population Assessment of Tobacco and Health Study (2013–2019). Group-Based Trajectory Modeling was performed to identify the trajectory of polysubstance use. Examined substances included use of cigarettes, e-cigarettes, excessive alcohol, cannabis, painkillers, and cocaine in past 30 days from all waves. Weighted multinomial logistic regressions were conducted to investigate the associations between internalizing and externalizing problems and the trajectory of polysubstance use, controlling for demographic variables.

Results

Five trajectory groups were identified: (1) No to minimal polysubstance use risk (45.6 %); (2) Polysubstance use—low risk (10.7 %); (3) Cigarette-leading polysubstance use—high risk (23.5 %); (4) Cigarette-cannabis co-leading polysubstance use—high risk (12.3 %); and (5) Cannabis-leading polysubstance use—high risk (7.8 %). Compared with Group 1, higher internalizing problems predicted the membership of Group 3 [Relative risk ratio (RRR) range: 1.07–1.17] and Group 4 (RRR range: 1.04–1.21). Compared with Group 1, higher externalizing problems predicted the membership of Group 5 (RRR range: 1.01–1.10).

Conclusions

Prevention efforts should consider internalizing problems and associated trajectories of high-risk polysubstance use (e.g., cigarette-leading and cigarette-cannabis co-leading) as well as externalizing problems and associated trajectories of high-risk polysubstance use (e.g., cannabis-leading), when designing interventions to prevent polysubstance use.

Background

Health warnings about alcohol consumption could inform consumers and discourage alcohol consumption, but little is known about what topics these warnings should address. We sought to identify promising topics for alcohol warnings.

Methods

In January 2024, we recruited a convenience sample of 2,522 US adults ages ≥ 21 years. In an online within-subjects experiment, participants viewed messages about 6 topics (5 warning topics selected from a pool of 16 topics [e.g., liver disease, liver cancer] and 1 control topic [i.e., recycling or reselling products]) shown in random order. For each topic, participants viewed and rated 2 statements about that topic on perceived message effectiveness (primary outcome) and reactance (secondary outcome).

Results

The 16 warning topics elicited higher perceived message effectiveness than the control topic (ps < 0.001). Among the warning topics, liver disease, most cancer types, dementia or mental decline, and hypertension elicited the highest perceived message effectiveness, while breast cancer, sleep, and drinking guidelines elicited the lowest. Fourteen of the 16 warning topics (all except for fetal harms and impaired driving) elicited more reactance than the control topic (ps < 0.001). Warning topics that elicited high perceived message effectiveness generally elicited high reactance, except for messages about liver disease and liver cancer, which elicited high perceived message effectiveness but only moderate reactance.

Discussion

Warning messages about a variety of topics hold promise for discouraging alcohol consumption. Messages about liver disease, most cancer types, dementia or mental decline, and hypertension are perceived as especially effective; however, none of these topics are included in the current US alcohol warning.

Background

Cognitive impairments are common in patients with AUD and worsen the prognosis of addiction management. There are no clear guidelines for screening cognitive impairments in hospitalized patients with AUD.

Methods

Fifty-seven patients with an AUD history who were admitted to an acute hospital and assessed by the addiction care team were included. Those patients were screened for cognitive impairments using the Montreal Cognitive Assessment (MoCA) test. We collected clinical information regarding addiction history, comorbidities, and current treatments. Chi-square tests, t-tests, and Mann-Whitney tests were performed to determine factors associated with a pathological MoCA score (<26).

Results

A pathological MoCA score was positively associated with spatial–temporal disorientation, difficulty in recalling addiction history, patient underreporting of AUD and a date of last alcohol consumption lower than 11 days ago, and negatively associated with a reason for hospitalization due to alcohol-related health issues. No medication was associated with cognitive impairments.

Conclusions

Clinical elements from assessment by the addiction care team allow for relevant indication for screening cognitive impairments.

Co-use of alcohol and cannabis is prevalent and linked with heightened risk for substance-related harms. The current study investigated the role of substance-related pleasure as a reinforcing factor for co-use relative to alcohol or cannabis use. Specifically, we used data from a 21-day diary study of college students to examine day-level associations between co-use and self-reported substance-related pleasure (any, level of pleasure). Participants were 237 college students (65 % female sex at birth, ages 18–24) who reported 1+ alcohol and cannabis co-use occasion. Participants completed daily surveys across 21 consecutive days about yesterday’s substance use and experiences of pleasure, yielding 2,086 daily surveys involving alcohol and/or cannabis use. Multilevel models indicated that odds of substance-related pleasure were higher on days with co-use relative to days with single-substance use, and level of pleasure was higher on co-use days relative to cannabis but not alcohol use days. Pleasure may serve as a reinforcing property of co-use that may be related to continued use despite experience of negative consequences. Intensity of pleasure related to co-use appears to be largely driven by use of alcohol. However, given mixed findings concerning level of pleasure, individuals may report co-use increases feelings of pleasure but do not actually experience more pleasure. Pleasure may serve as a viable target in future prevention and intervention programming targeting co-use.

Background

Co-use of cannabis and tobacco poses greater risks than use of either substance individually and may be becoming more prevalent with increasing cannabis medicalization and legalization. We aimed to assess trends in the prevalence of cannabis and tobacco co-use between 2002–2019 and identify the updated prevalence and correlates of co-use in 2021 among US adults.

Methods

This study used data from the 2002–2021 National Survey on Drug Use and Health, a nationally-representative, cross-sectional survey in the US. We assessed prevalence trends in past 30-day cannabis and tobacco co-use, exclusive cannabis use, and exclusive tobacco use overall and by sociodemographic group using joinpoint regression. Additionally, multinomial models identified correlates of co-use in 2021.

Results

In 2021, the weighted prevalence of cannabis and tobacco co-use was 6.38 %, the weighted prevalence of exclusive cannabis use was 7.28 %, and the weighted prevalence of exclusive tobacco use was 15.01 %. From 2002 to 2019, the prevalence of past 30-day co-use of cannabis and tobacco increased significantly (annual percentage change [APC]: 1.9 [1.4–2.4], P<0.05) among the overall US population. All subgroups of sex, race/ethnicity, and age also saw significant increases in co-use, other than young adults ages 18–25, for whom co-use was stagnant between 2002–2014 and then decreased significantly between 2014–2019.

Conclusion

 This study identified increasing cannabis and tobacco co-use overall and among most sociodemographic strata in the US. As cannabis policy changes rapidly, co-use requires closer surveillance, clinical screening, and dedicated research.

Background

Impaired cognitive control has been linked to weakened self-regulatory processes underlying compulsive substance intake. Previous research has provided evidence for impaired task performance in substance-abusing groups during Stroop and Go/No-Go tasks. Mechanisms of distractor suppression in visual search might also involve overlapping regulatory components that support goal-directed behavior by resolving the attentional competition between distractors and the target of search. However, the efficiency of learning-dependent distractor suppression has not been examined in the context of drug abuse and a direct comparison between cognitive control and distractor suppression is lacking.

Method

A total of 84 participants were assigned either to the heavy drinking group (ALC, n = 42) or the control group (CTL, n = 42) based on self-reported substance use. Participants completed the Alcohol Use Disorders Identification Test (AUDIT) and Barratt Impulsiveness Scale (BIS). After that, participants completed a computerized version of the Stroop task, Go/No-go task, and a visual search task measuring learning-dependent distractor suppression.

Results

The Stroop effect and the frequency of no-go errors did not differ between groups. However, learned distractor suppression was significantly blunted in the ALC group compared to the control group. Across participants, performance on the Stroop and Go/No-go task were correlated, while the magnitude of distractor suppression was related to neither.

Conclusions

Our findings support a divergence of mechanistic processes underlying cognitive control and attentional control, and demonstrate impaired learning-dependent distractor suppression in heavy drinkers relative to a control group. Impaired distractor suppression offers new insight into why drug cues can be difficult to ignore.

The use of Medicinal Cannabis (MC) for treating chronic pain is increasing, globally, yet the definition of problematic MC use remains unclear. Defining problematic use of cannabis in medical and non-medical contexts may be fundamentally different since individuals prescribed MC often experience physical dependence, which do not necessarily imply pathology. We aimed to conceptualize problematic use of MC and develop a brief questionnaire for identifying and quantifying problematic MC use. Content validation used a broad definition of problematic substance use, adapting and compiling an initial list of 36 items from various sources: (a) screening tools for assessing problematic prescription opioid medication use; (b) screening tools for problematic recreational cannabis use and (c) qualitative interviews with MC patients. 390 American self-identified chronic pain patients holding a MC card rated each item from the initial list on a 5-point frequency scale and filled out questionnaires assessing various clinical outcomes. Following initial item titration, a multi‐group measurement invariance comparison strategy, using two external indicators: alcohol-related problems and depression, resulted a final eight-item list that met fit quality in a baseline model, presented excellent internal consistency reliability (α = 0.929), and significantly correlated with anxiety and low quality of life. Items in the final list related predominantly to negative consequences of MC use. Problematic use of MC is characterized by negative physiological, social, emotional and functional consequences. The final eight-item list was named the Medicinal Cannabis Negative Consequences Scale (MCNCS), emerging as a brief measure for problematic MC use and demonstrating preliminary reliability and validity, which could aid clinicians and researchers.

Introduction

Alcohol and cannabis use are common during young adulthood. Less is known regarding correlates of alcohol-cannabis use patterns and related problematic health outcomes.

Methods

Using longitudinal survey data (Fall 2018, 2019, 2020) from 2,194 young adults (YAs; ages 18–34), bivariate analyses and multivariable logistic regressions examined: (1) Wave 1 (W1) sociodemographics and psychosocial factors (i.e., adverse childhood experiences [ACEs], depressive symptoms, personality traits, parent and peer alcohol and cannabis use) in relation to W3 past-month use group (i.e., use of neither, alcohol only, cannabis only, both/co-use); and (2) W3 use group in relation to W5 problematic alcohol use (Alcohol Use Disorder Identification Test), problematic cannabis use (Cannabis Use Disorder Identification Test), and depressive/anxiety symptoms (Patient Health Questionnaire – 4 item).

Results

Overall, 42.3% reported W3 alcohol-only use, 34.9% co-use, 17.8% no use, and 5.0% cannabis-only use. Those reporting W3 co-use reported greater W1 extraversion, openness, friend alcohol/cannabis use, and were more likely to report parent cannabis use (vs. no use); reported less conscientiousness, greater friend cannabis use, and were more likely to report depressive symptoms and parent cannabis use (vs. alcohol-only use); and reported greater friend alcohol use, and were more likely to report parent alcohol use (vs. cannabis-only use). W3 co-use was associated with higher odds of W5 problematic alcohol use (vs. alcohol-only use) and problematic cannabis use (vs. cannabis-only use).

Conclusions

Substance use messaging and interventions should consider YAs’ alcohol-cannabis co-use and the unique correlates of such use.

Vaping is one of the most common forms of substance use among adolescents. Social influences play a key role in the decision to use substances and frequency of use during adolescence, and vaping is no exception. Using a sample of 891 adolescents across two time points (M_age = 15.1 and M_age = 17.2) in this pre-registered study, we explored whether the frequency of vaping nicotine and the frequency of vaping marijuana at age 17 were related to concurrent reports of resistance to peer influence (RPI), perceptions of friends vaping, and perceptions of classmates vaping. Then, we investigated whether resistance to peer influence reported at age 15 was similarly related to age 17 vaping of both substances. Higher RPI at both ages 15 and 17 was associated with a higher probability of abstaining from vaping both substances but was not related to the frequency of vaping among those who vaped. Perceiving that a higher proportion of friends − but not classmates − vaped was associated with a lower probability of abstaining and a greater frequency of vaping among those who vape (for both substances). Higher RPI had an attenuating effect on the relation between perceptions of vaping among peers and an adolescent’s own vaping behavior, but that differed by age and peer group (e.g., friends vs. classmates). Overestimation about the prevalence of classmate vaping may have played a role in the findings, but our results suggest that interventions to strengthen peer resistance across adolescence are warranted.