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Background Neck and back pain are common musculoskeletal conditions in university students and associated with unhealthy lifestyle behaviors such as physical inactivity, unhealthy dietary habits, smoking and risky alcohol use. Cohort studies have investigated the effect of a healthy lifestyle including several lifestyle behaviors on the risk and prognosis for neck and back pain. Studies of an association between an unhealthy lifestyle and musculoskeletal conditions in university students are lacking. This study aimed to assess the association between an unhealthy lifestyle and incident activity-limiting neck/back problems (ALNBP) in university students. Methods Participants enrolled in the Sustainable UNiversity Life (SUN) study who did not report baseline ALNBP in the past three months were included ( n  = 3492). The baseline web-survey assessed unhealthy lifestyle behaviors (physical inactivity, high sitting time, meal skipping and risky use of alcohol, tobacco, and illicit drugs) with valid instruments and single-item questions. Participants with ≥ 3 unhealthy lifestyle behaviors were categorized as exposed to an unhealthy lifestyle. Responses to web-based follow-up surveys every third month until first reporting ALNBP or to the end of the one-year follow-up were used. The outcome ALNBP was defined as reporting limitations in daily activities due to a neck, low back and/or midback problem the past three months, assessed with a modified version of the Nordic Musculoskeletal Questionnaire. Crude and adjusted Cox regression models were built to assess the association between an unhealthy lifestyle and incident ALNBP, reported as a hazard rate ratio (HRR) with a 95% confidence interval (CI). Results Sixty percent of the participants were women, and the mean age was 24.5 ± 6.0 years. A total of 574 participants (16%) were exposed to an unhealthy lifestyle at baseline. Having an unhealthy lifestyle generated an adjusted HRR of 1.35 (95% CI 1.12, 1.63) for incident ALNBP, compared to not having an unhealthy lifestyle. Conclusions An unhealthy lifestyle, categorized as ≥ 3 unhealthy lifestyle behaviors, is associated with incident ALNBP in university students.

Adolescent alcohol use has significant consequences for concurrent and longitudinal health and wellbeing, with sexual minority youth consistently reporting higher levels of alcohol use than their heterosexual peers. Understanding how individual-difference variables like aggressive behavior are associated with variability in sexual minority adolescents’ higher levels of alcohol use offers novel theoretical insight into this vulnerability. The 81,509 participants were drawn from the Profiles of Student life: Attitudes and Behavior Study . They were ages 14–17 years (M = 15.38, SD = 1.09) and 50.1% were cisgender girls. For sexual identity, 88% were heterosexual, 5% were mostly heterosexual, were 4% bisexual, were 1% mostly gay or lesbian, and were 1% being gay or lesbian. Participants reported on alcohol use, aggressive behavior, and sexual identity. Gay/lesbian and mostly gay/lesbian adolescents who reported higher aggressive behavior had higher levels of alcohol use than their heterosexual peers who also reported higher aggressive behavior. The way in which aggressive behavior amplified the link between mostly gay/lesbian and gay/lesbian identities and alcohol use suggests the need for more research examining how this trait may heighten both exposure and reaction to minority stressors among some subgroups of sexual minority youth.

There is a continuum between gambling and investing behaviors, with speculative investment instruments positioned in the middle. Cryptocurrencies, being significantly more volatile than traditional investment tools, have increasingly been linked to gambling disorder (GD). This study aims to examine the relationship between cryptocurrency trading behavior and GD, high-risk substance use, high-risk alcohol use, and tobacco dependence among healthcare professionals in Türkiye. A total of 192 healthcare professionals were assessed using the Problematic Cryptocurrency Trading Scale (PCTS), Gambling Disorder Screening Test (GDST), and the Addiction Profile Index Risk Screening Form (APIRS) (Alcohol and Drug Scales). Categorical data comparisons between two independent groups were conducted using Chi-square or Fisher's Exact tests. Spearman correlation coefficients were used to examine relationships between PCTS scores and APIRS/GDST scores. Additionally, linear regression models assessed the predictive relationships between PCTS scores and APIRS/GDST scores. Among the participants, 25.5% reported engaging in cryptocurrency trading, 41.7% had tobacco dependence, 15.1% reported high-risk alcohol use, 5.7% had high-risk substance use, and 8.9% met the criteria for GD. Cryptocurrency traders demonstrated higher rates of substance use (  = 0.033), tobacco dependence (  < 0.001), and GD (  = 0.043). Additionally, the severity of problematic cryptocurrency trading behavior was positively correlated with the severity of substance use (  = 0.172,  = 0.017) and GD (  = 0.455,  < 0.001). The findings indicate a significant relationship between cryptocurrency trading behavior and addiction. Further research with clinical interviews and larger sample sizes is required to validate these findings. The high rates of alcohol, substance, tobacco, and gambling addictions observed among healthcare professionals underscore the need for targeted preventive measures and interventions in this population.

Objective: Our study aimed to evaluate the effectiveness of an intervention based on Motivational Interviewing (MI) performed by healthcare professionals in Primary Care (PC) patients with risky alcohol use through a multicenter, two-arm parallel, cluster-randomized, open-label controlled clinical trial. Methods: PC professionals were randomized into two groups: an Experimental Group (EG) and a Control Group (CG). The study was carried out in PC centers of the Andalusian Health Service, located in Cordoba, Spain. An MI-based approach was implemented with patients recruited in the EG, while health advice was provided to those included in the CG. The follow-up period was 12 months, with five visits scheduled. The consumption of standard drinking units per week was quantified, and risky alcohol use was estimated using the Alcohol Use Disorders Identification Test (AUDIT). An intention-to-treat statistical analysis was performed. Relative risk (RR), absolute risk reduction (ARR) and the number of subjects needed to treat (NNT) were used to estimate the intervention effect size. Results: A total of 268 patients were included, 148 in the EG and 120 in the CG. Considering the quantification of risky alcohol use, the ARR at 12 months after baseline visit was 16.46% (95% CI: 5.37–27.99), with an NNT of 6 (95% CI: 4–19). According to the AUDIT, the ARR at 12 months was 13.15% (95% CI: 2.73–24.24%), and the NNT was 8 (95% CI: 4–37). Conclusions: We concluded that MI is more effective than the usual health advice in decreasing risky alcohol use in patients treated in PC.

The authors examined the perception of college students from Greek organizations on the effectiveness of an alcohol intervention program that included gender‐specific programming. Significant reductions in risky alcohol use were found in men who attended and evaluated the program as helpful.

Background While brief intervention (BI) for risky alcohol use generally yields positive effects among those identified by screening, effect sizes are small and there is unexplained heterogeneity in outcome. The heterogeneity may be related to differences in intervention style and content, including elements of motivational interviewing (MI). To date, it has been difficult to interpret the role of MI in BI and these gaps in knowledge interfere with efforts to train, disseminate and implement BI that retains and maximizes efficacy. This study sought to develop BI protocols with varying doses of MI and test their differentiability. Differentiable BI protocols could allow for future studies that prospectively evaluate the role MI plays in affecting BI outcome. Methods We developed three intervention protocols: brief advice, standard BI (NIAAA Clinician’s Guide), and MI-enhanced BI and administered them to 45 primary care patients who reported exceeding recommended drinking limits. We then rated the BI sessions for fidelity to the assigned protocol as well as MI consistency based on Motivational Interviewing Treatment Integrity (MITI) scale scores. The differentiability of BI protocols was determined by calculating fidelity to assigned protocols and comparing MITI scores using pairwise, Tukey-adjusted comparisons of least squares mean scores. Results High rates of fidelity to each protocol were achieved. The three BI protocols were also highly differentiable based on MITI scores. Conclusions The three interventions can be used in future trials to prospectively examine the role MI has in determining BI outcome. Trial registration clinicaltrials.gov NCT02978027, retrospectively registered 11/28/16

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University entrance test-takers are a vulnerable group of students transitioning from adolescence to youth, a peak time for adopting unhealthy behaviors. However, limited studies investigate substance use behaviors among this group. This study aimed to investigate the current (past 12 months) prevalence, associated risk factors, and geographic distribution of substance use behaviors, employing spatial analysis to identify regional disparities. A cross-sectional study was conducted utilizing convenience sampling in February 2024 among university entrance test-takers at Jahangirnagar University, Dhaka, Bangladesh ( N  = 1485). Participants provided information on socio-demographics, admission-related, depression (PHQ-9), anxiety (GAD-7), and substance use. Chi-square tests and logistic regression were employed to explore significant associations, with spatial analysis using ArcGIS to map substance use across districts. About 20.8% reported smoking, 10.1% reported drug use, and 9.9% reported alcohol consumption over the past 12 months. Males exhibited higher rates of smoking (23.1% vs. 17.6%) and alcohol consumption (10% vs. 9.7%), while females reported slightly higher rates of drug use (10.2% vs. 10%). Being female (AOR = 0.56; 95% CI: 0.40–0.79; p  = 0.001), living in rural areas (AOR = 0.68; 95% CI: 0.49–0.94; p  = 0.020), having a higher monthly family income (AOR = 1.72; 95% CI: 1.01–2.91; p  = 0.042), a history of physical illness (AOR = 2.65; 95% CI: 1.87–3.76; p  < 0.001), being a repeat test-taker (AOR = 1.49; 95% CI: 1.08–2.05; p  = 0.014), and satisfaction with previous mock tests (AOR = 1.66; 95% CI: 1.20–2.28; p  = 0.002) were significant risk factors for smoking. For drug use, belonging to a joint family (AOR = 1.71; 95% CI: 1.08–2.71; p  = 0.021), having a history of physical illness (AOR = 2.57; 95% CI: 1.64–4.02; p  < 0.001), and satisfaction with previous mock tests (AOR = 1.84; 95% CI: 1.20–2.83; p  = 0.005) increased the risk. Additionally, a history of physical illness (AOR = 5.12; 95% CI: 3.27–8.02; p  < 0.001) and satisfaction with previous mock tests (AOR = 1.78; 95% CI: 1.13–2.80; p  = 0.013) were contributing factors to alcohol consumption. Spatial analyses indicated higher rates of substance use in border districts. The findings warrant targeted interventions, such as mental health support and substance use prevention programs, particularly in high-risk regions. By understanding the factors driving substance use, tailored strategies can be developed to reduce risk behaviors among university entrance test-takers, ultimately enhancing public health outcome in this vulnerable population.

Background Alcohol use and risky drinking are significant public health problem globally. Young people, including university students, are among the most affected populations. We conducted the study to determine the prevalence and correlates of alcohol use and risky drinking among undergraduate students in the Faculty of Health Sciences at the University of the Witwatersrand, South Africa. Methods We conducted a cross-sectional study using an anonymous, self-administered online survey in REDCap. The survey questionnaire consisted of socio demographic, and alcohol use questions using the risky drinking identification screening tool (AUDIT-C). We performed descriptive statistics, bivariate and multivariable logistic regression to determine factors associated with alcohol use and risky drinking. The p-value of < 0.05 was considered statistically significant. Results The response rate was 15.7%. Most participants were female (69.6%) and majority of the participants were White (38.1%). The prevalence of lifetime use of alcohol was 79.1%, and among the lifetime users; 70.2% reported alcohol use in the last 12-months, 37.1% reported alcohol use in the last 30 days. The prevalence of risky drinking was 54.8% among lifetime drinkers. Factors significantly associated with current alcohol use were siblings alcohol use (aOR = 1.79, 95% CI: 1.02–3.15) and parents alcohol use (aOR = 2.58, 95% CI: 1.39–4.80), white race (aOR = 5.70, 95% CI: 3.12–10.41), and always or daily exposure to alcohol marketing in the media (aOR = 3.31, 95% CI: 1.07–10.24). Factors associated with risky drinking were: Indian/Asian race (aOR = 2.82, 95% CI: 1.09–7.31), White race (aOR = 2.15, 95% CI: 1.14–4.04), and exposure to alcohol marketing in the media as follows, most of the time (aOR = 3.42, 95% CI: 1.29–9.04) and Always/daily exposure (aOR = 3.31, 95% CI: 1.07–10.24). Conclusion The reported alcohol use and risky drinking were common amongst undergraduate students at Wits university. There is an urgent need to design, pilot and adapt targeted interventions for this population group.

Primary care settings are uniquely positioned to reach individuals at risk of alcohol use disorder through technology-delivered behavioral health interventions. Despite emerging effectiveness data, few efforts have been made to summarize the collective findings from these delivery approaches. The aim of this study was to review recent literature on the use of technology to deliver, enhance, or support the implementation of alcohol-related interventions in primary care. We focused on addressing questions related to (1) categorization or target of the intervention, (2) descriptive characteristics and context of delivery, (3) reported efficacy, and (4) factors influencing efficacy. We conducted a comprehensive search and systematic review of completed studies at the intersection of primary care, technology, and alcohol-related problems published from January 2000 to December 2018 within EBSCO databases, ProQuest Dissertations, and Cochrane Reviews. Of 2307 initial records, 42 were included and coded independently by 2 investigators. Compared with the years of 2000 to 2009, published studies on technology-based alcohol interventions in primary care nearly tripled during the years of 2010 to 2018. Of the 42 included studies, 28 (64%) were randomized controlled trials. Furthermore, studies were rated on risk of bias and found to be predominantly low risk (n=18), followed by moderate risk (n=16), and high risk (n=8). Of the 24 studies with primary or secondary efficacy outcomes related to drinking and drinking-related harms, 17 (71%) reported reduced drinking or harm in all primary and secondary efficacy outcomes. Furthermore, of the 31 studies with direct comparisons with treatment as usual (TAU), 13 (42%) reported that at least half of the primary and secondary efficacy outcomes of the technology-based interventions were superior to TAU. High efficacy was associated with provider involvement and the reported use of an implementation strategy to deliver the technology-based intervention. Our systematic review has highlighted a pattern of growth in the number of studies evaluating technology-based alcohol interventions in primary care. Although these interventions appear to be largely beneficial in primary care, outcomes may be enhanced by provider involvement and implementation strategy use. This review enables better understanding of the typologies and efficacy of these interventions and informs recommendations for those developing and implementing technology-based alcohol interventions in primary care settings.