BackgroundHarm reduction strategies for opioid use disorder (OUD) emphasize pragmatic, evidence-based approaches that reduce overdose risk, relapse, and other adverse outcomes without requiring abstinence. Medication for opioid use disorder (MOUD) and structured psychotherapy represent core harm-reduction modalities, yet their real-world comparative effectiveness, alone and in combination, remains underexplored at scale.MethodsA retrospective cohort study was conducted using the TriNetX Research Network, comprising de-identified electronic health records from 112 U.S. health systems. 18,047 adults aged 18–45 were identified with a diagnosis of opioid dependence (ICD-10 F11.20) between 2016 and 2025. Subjects were assigned to eight mutually exclusive treatment cohorts: no treatment (Cohort 1); buprenorphine alone (Cohort 2); methadone alone (Cohort 3); psychotherapy alone (30 minutes (Cohort 4), 45 minutes (Cohort 5), or 60 minutes (Cohort 6)); buprenorphine + psychotherapy (Cohort 7); and methadone + psychotherapy (Cohort 8), with combination treatments defined within a ±30-day window. Cox proportional hazards models estimated adjusted hazard ratios (aHRs) for remission (F11.21, F11.11) within 12 months.ResultsBuprenorphine (aHR = 2.33; 95% CI: 1.85–2.94), methadone (aHR = 2.50; 95% CI: 2.05–3.04), and psychotherapy (30 min: aHR = 2.18; 45 min: aHR = 2.38) were each independently associated with significantly higher remission compared to no treatment. The combination of buprenorphine + psychotherapy yielded the strongest effect (aHR = 5.26; 95% CI: 2.68–10.32). Anxiety diagnoses and gabapentinoid prescriptions were positively associated with remission; benzodiazepine co-prescription was negatively associated.ConclusionsIn this first national-scale, multi–health-care-organization analysis, both pharmacologic and psychosocial harm-reduction interventions were independently associated with improved OUD remission, with additive benefit when integrated. These findings underscore the value of embedding comprehensive, multimodal harm-reduction services within routine care and support policies promoting equitable access to both MOUD and behavioral health supports across diverse health systems.
How adolescent cannabis use reshapes the developing brain — a systematic review
Background and hypothesisCannabis use initiation during adolescence has increased globally, raising concerns about neurodevelopmental consequences during this critical period when the brain undergoes extensive remodeling in cannabinoid receptor-rich regions.Study designThis systematic review examines neurodevelopmental consequences of adolescent cannabis use, focusing on structural brain changes, cognitive impacts, addiction vulnerability, and long-term outcomes. We searched PubMed, EMBASE, PsycINFO, and Web of Science (2000-2025) for studies examining cannabis effects in adolescent populations. Following PRISMA guidelines, two reviewers screened 3,421 records and assessed 156 full-text articles, including studies with neuroimaging, cognitive assessments, or longitudinal follow-up.Study resultsThirty-six studies involving 8,432 participants met criteria: 23 longitudinal cohorts (62.2%), 8 cross-sectional (22.2%), 4 RCTs (11.1%), and 1 case-control study (2.8%). Neuroimaging revealed dose-dependent alterations including reduced prefrontal cortical and hippocampal/amygdala volumes, accelerated cortical thinning in longitudinal studies, and impaired white matter connectivity correlating with initiation age. Cognitive findings were mixed — some showed persistent deficits after prolonged abstinence in adolescent-onset users, others found no effects after controlling for confounders. Epidemiological studies consistently showed elevated addiction risk (ORs 3.9–7.2) in adolescents versus adults. Long-term associations included educational difficulties, mental health problems, and functional impairment, though causal relationships remained unclear.ConclusionsAdolescent cannabis use associates with structural brain changes, elevated addiction risk, and variable cognitive effects, suggesting greater vulnerability versus adult-onset use. However, methodological limitations including confounders, heterogeneous definitions, and observational designs limit causal inference. Findings support age-specific prevention and specialized interventions while highlighting needs for rigorous longitudinal research establishing causality.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifierCRD420251165329.
Adapted RPM-08 for Substance Use Disorder in Pakistan
Conditions: Substance Use Disorder (SUD); Drug Abuse; Relapse
Interventions: Behavioral: Adapted Relapse Prevention Module (RPM-08); Other: Treatment as Usual (TAU)
Sponsors: Universiti Sains Malaysia
Completed
Interventions: Behavioral: Adapted Relapse Prevention Module (RPM-08); Other: Treatment as Usual (TAU)
Sponsors: Universiti Sains Malaysia
Completed
A longitudinal inquiry into the vicious cycle of social media addiction and self-injury: the moderating role of resilience
BackgroundThe reciprocal relationship between social networking addiction (SNA) and non-suicidal self-injury (NSSI) represents a critical, yet poorly understood, feedback loop in adolescent psychopathology. This study aimed to longitudinally test a “vicious cycle” model, examining the bidirectional effects between SNA and NSSI, and to investigate psychological resilience as a potential protective factor that could disrupt this harmful dynamic.MethodsA three-wave longitudinal study was conducted with a large cohort of 2,628 Chinese high school students (mean age = 16.1 years; 53.1% female) over a 12-month period. Participants completed measures of SNA, NSSI frequency, and psychological resilience at each wave. A cross-lagged panel model (CLPM) was used to examine the reciprocal, prospective relationships between SNA and NSSI. A multi-group CLPM was then employed to test the moderating role of resilience.ResultsThe CLPM revealed significant, positive, and reciprocal cross-lagged effects. SNA at T1 and T2 prospectively predicted increases in NSSI at T2 and T3, respectively (βs = .19 and.17). Conversely, NSSI at T1 and T2 prospectively predicted increases in SNA at T2 and T3 (βs = .14 and.12), providing robust evidence for a vicious cycle. Furthermore, resilience significantly moderated the pathway from SNA to NSSI. For adolescents with low resilience, the effect was strong and significant (β = .25), whereas for those with high resilience, the effect was rendered non-significant (β = .07).ConclusionsSocial networking addiction and non-suicidal self-injury are not merely comorbid but are locked in a mutually reinforcing developmental spiral over time. However, this dangerous cycle is not deterministic. Psychological resilience acts as a powerful protective buffer, effectively uncoupling the link from addictive social media use to self-harm. These findings underscore the urgent need for integrated, dual-focus interventions that address both online and offline maladaptive behaviors, while championing resilience-building as a primary strategy for prevention.
Rational causal induction from events in time.
Psychological Review, Vol 133(3), Apr 2026, 584-618; doi:10.1037/rev0000570
A longstanding focus in the causal learning literature has been on inferring causal relations from contingencies, where these abstract away from time by collating independent instances or by aggregating over regularly demarcated trials. In contrast, individual causal learners encounter events in their daily lives that occur in a continuous temporal flow with no such demarcation. Consequently, the process of learning causal relationships in naturalistic environments is comparatively less understood. In this article, we lay out a rational framework that foregrounds the role of time in causal learning. We work within the Bayesian rational analysis tradition, starting by considering how causal relations induce dependence between events in continuous time and how this can be modeled by stochastic processes from the Poisson–Gamma distribution family. We derive the qualitative signatures of causal influence and the general computations needed to infer structure from temporal patterns. We show that this rational account can parsimoniously explain the human preference for causal models that invoke shorter, more reliable, and more predictable causal influences. Furthermore, we show this provides a unifying explanation for human judgments across a wide variety of tasks in the reanalysis of seven experimental data sets. We anticipate the framework will help researchers better understand the many manifestations of continuous-time causal learning across human cognition and the tasks that probe it, from explicit causal structure induction settings to implicit associative or reinforcement learning settings. (PsycInfo Database Record (c) 2026 APA, all rights reserved)
The temporal stability of core symptoms of social media addiction and their comorbidity with anxiety and depression in adolescents: a longitudinal network analysis
IntroductionSocial media addiction (SMA) is often comorbid with anxiety and depression. This study examined the temporal stability of core SMA symptoms and the bridging symptoms with anxiety and depression.MethodsA total of 1,240 adolescents (179 males, 1,061 females; mean age = 15.46 ± 0.63 years, age range: 14 – 18) completed the Bergen Social Media Addiction Scale (BSMAS), the Patient Health Questionnaire–9 (PHQ–9), and the Generalized Anxiety Disorder–7 (GAD–7) on two separate occasions in 2023 (T1) and 2024 (T2). The four symptom networks, including the BSMAS networks, two comorbidity networks (the BSMAS–GAD and the BSMAS–PHQ), and the integrated BSMAS–GAD–PHQ network, were estimated using Gaussian graphical models. Core symptom centrality was assessed using Expected Influence (EI), whereas bridge symptoms were identified using Bridge Expected Influence (BEI).Results1) Although SMA, anxiety, and depression levels of respondents rose significantly over the year, all four networks showed strong temporal stability, with the edge weights (r = .892 –.973, p < .001), the EI (r = .806 – .961, p ≤ .002), and the BEI (r = .699 – .804, p ≤ .008) highly correlated between T1 and T2; network comparison tests showed no significant changes in overall structures of all four networks, with most edges showing stable weights. 2) Within the BSMAS network, BSMAS2 (tolerance) and BSMAS6 (conflict) exhibited the highest EI at both time points. 3) In the comorbidity networks, BSMAS3 (mood modification), BSMAS5 (withdrawal), and BSMAS6 (conflict) consistently served as bridge symptoms on the SMA side at both T1 and T2. 4) Across both time points, PHQ1 (anhedonia) and PHQ7 (concentration problems) exhibited the highest BEI on the depression side, whereas GAD1 (nervousness) and GAD5 (restlessness) did so on the anxiety side. 5) These bridge symptoms were also confirmed in the integrated network.DiscussionThese findings illuminate the temporal persistence and development of symptom relationships, offering a more dynamic understanding of SMA–depression–anxiety comorbidity in adolescents.
Internet addiction among nursing students: application of latent profile analysis and network analysis
BackgroundInternet addiction is widely reported and heterogeneous among nursing students. However, variable-centered approaches may not fully capture profile differences and core symptom patterns, potentially limiting precise interventions. Therefore, identifying distinct profiles and key symptoms is important for informing effective prevention.ObjectiveThis study aims to identify distinct internet addiction profiles among nursing students, explore the characteristics and core symptoms of these profiles, and investigate the factors associated with their variation.MethodsA cross-sectional survey was conducted among undergraduate nursing students from September to November 2025. Latent profile analysis (LPA) and network analysis were performed to characterize the patterns of problematic internet use across identified profiles.ResultLatent Profile Analysis revealed four distinct problematic internet use profiles: No-Problematic Internet Use Profile (17.895%), Low-Problematic Internet Use Profile (41.957%), Moderate-Problematic Internet Use Profile (26.676%), and High-Problematic Internet Use Profile (13.472%). Multinomial logistic regression identified gender, monthly household income, and physical activity as significant factors associated with profile membership. Network analysis highlighted central symptoms specific to each profile: Health-related problems (RP-IH) and compulsive internet use and withdrawal symptoms (Sym-C & Sym-W) exhibited the highest centrality within the Moderate- and High-Problematic Internet Use Profiles.ConclusionInternet addiction among undergraduate nursing students is a heterogeneous phenomenon that can be categorized into four distinct profiles. Our findings clarify key associated factors and identify central symptoms specific to each profile, potentially providing an empirical basis for nursing educators to develop targeted psychological interventions.
Correction: Promoting Sustained Real-Life Benefits of Virtual Reality–Based Interventions in People With Mental Health and Substance Use Disorders: Qualitative Study
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Effect of low-intensity focused ultrasound on hippocampus of alcohol addicted mice: a preliminary study
Alcohol addiction is a chronic relapsing brain disorder characterized by significant neurobiological changes, particularly within the hippocampus, which mediates emotional regulation and reward-seeking behavior. Previous studies have shown that alcohol-induced neuronal injury contributes to withdrawal-associated anxiety and persistent alcohol preference. This study investigated the therapeutic effects of low-intensity focused ultrasound (LIFU) on the hippocampus in a mouse model of alcohol addiction. Twenty-six male C57BL/6 mice were allocated to an alcohol-exposed group (n = 20) and a control group (n = 6). Following a 28-day modeling period, the alcohol group was randomly subdivided into a therapy group and a sham group. The therapy group received LIFU treatment, while the sham group underwent an identical procedure with the ultrasound transducer powered off. After seven days of treatment, the therapy group exhibited less severe anxiety symptoms upon alcohol withdrawal and a reduced preference for alcohol compared to the sham group. The brain-derived neurotrophic factor (BDNF) concentration was significantly lower in the therapy group than in the sham group, but did not differ significantly from the control group. Hippocampal HE staining revealed more pronounced degeneration and apoptosis of granule cells in the dentate gyrus (DG) region in the sham group relative to the therapy group. These preliminary findings suggest that LIFU may modulate alcohol addiction by mitigating hippocampal neuronal injury.
The patterns of relapse and abstinence: using machine learning to identify a multidimensional signature of long-term outcome after inpatient alcohol withdrawal treatment
AimsA machine learning approach to identify a multidimensional signature associated with relapse and long-term outcome in alcohol dependence treatment.DesignIn this observational naturalistic study, inpatients with alcohol dependence received qualified detoxification plus CBT (Cognitive Behavioral Therapy) and were followed up 6-months after discharge to assess abstinence and drinking behavior. Cross-validated multivariate sparse partial least squares analysis (SPLS) was used to investigate the relationship between clinical features and four long-term outcome variables.SettingGermany.Participants152 patients (on average 47.8 years old, 72% male) with alcohol dependence, who received inpatient qualified detoxification plus CBT.Measurements35 clinical features were used to cover all three phases of inpatient treatment (pre-, within-, post-treatment). Among these, sociodemographic characteristics, ICD-10 psychiatric diagnoses, previous detoxification treatments, and somatic measurements as well as inpatient treatment setting such as withdrawal medication, liver ultrasound, further information about the patients´ stay, and post-inpatient care were assessed. The four outcome dimensions included: continuous abstinence, abstinence at follow up, daily alcohol consumption, and days of abstinence after discharge.FindingsSix months after withdrawal treatment 46% of the patients achieved continuous abstinence. Socioeconomic, clinical and somatic features across the treatment timeline were analyzed and summarized into a multivariate signature associated with long-term treatment outcome. Thereby, the SPLS algorithm identified regular completion of withdrawal treatment, higher education, and employment status to be most strongly associated with a positive outcome. Alcohol-related hepatic and hematopoietic damage, number of previous withdrawal treatments and living in a shelter were most profoundly associated with a negative outcome.ConclusionConceiving treatment outcome as a multidimensional signature and moving beyond simple binary classifications of relapse versus abstinence may improve the understanding of relapse pathways and support more individualized treatment strategies.