Introduction As individuals in recovery from substance use disorder (SUD) find initial stability, the next milestone is developing the emotional wellbeing that will offer greater quality of life, making affect a critical target. Many investigations into emotion during recovery, however, fail to account for the significant heterogeneity in daily affective experiences. The present study leverages advanced person-centered methods to capture that heterogeneity by identifying types of affect days in recovery and examines their place in daily recovery. Methods 129 individuals in SUD recovery were recruited remotely to complete a 10-day daily diary protocol (1,177 days). A mixed-indicator latent profile analysis was conducted on PANAS items to identify distinct profiles of daily affect. Latent profiles were examined in relation to prior-day meaning, next-day binary, and conditional craving, using robust standard errors to account for the repeated measures data structure. Results A 5-profile solution was selected: Prevalent NA (14% of affect days), Prevalent NA-Mild PA (30%), High Arousal Affect (11%), Intense PA (17%), and Mixed Affect (28%). Compared to other profiles, Intense PA and High Arousal Affect days were associated with higher prior-day meaning, lower odds of next-day craving, and lower levels of next-day craving among craving days. Conclusion Not only do results demonstrate the prevalence of NA-PA mixed days in recovery, but a second dimension – arousal – was also uncovered in affect patterns. Types of affect days differentially predicted acute recovery outcomes, with High Arousal Affect promoting recovery, warranting consideration of arousal in future studies.
Attentional bias toward substance-related cues is a key feature of addiction and may contribute to the continued use of substances, such as cannabis. However, the sensitivity of different paradigms for detecting this bias remains underexplored. This study compared three visual attentional bias paradigms: dot probe (DP), eye movement (EM), and MouseView.js (MV) in detecting cannabis-related attentional biases among cannabis users and non-users. Thirty-one participants (i.e., 16 users and 15 non-users) completed all measures: DP (reaction time to probes replacing cannabis or neutral stimuli), EM (measured via eye-tracking), and MV (cursor-based dwell time). Linear mixed-effects models examined the effects of stimulus type, group, craving, and cannabis use severity. The EM and MV tasks revealed significant stimulus effects, with non-users showing greater attention to neutral images, while cannabis users exhibited more evenly distributed attention across stimuli. In the EM task, a significant stimulus × severity interaction indicated reduced attentional differentiation with increased cannabis use severity. MV results followed a similar, but less precise pattern. In contrast, the DP task showed minimal group or interaction effects and lacked sensitivity to detect attentional differences. These findings suggest that EM-based measures are more sensitive for detecting cannabis-related attentional biases, particularly when accounting for cannabis use severity. MV showed some alignment with EM patterns but demonstrated weaker effects. The DP task, despite its widespread use, may be insufficient for capturing cannabis-related attentional differences. Results highlight the importance of task selection in attentional bias research and the potential influence of individual differences in cannabis use severity.
Objective Polysubstance use is a major public health concern in the U.S., yet longitudinal examination of patterns and transitions among adults remains limited. Understanding these transitions is important because it can reveal whether substance use patterns persist, escalate, or remit over time. This study aimed to examine polysubstance use patterns (i.e., concurrent use of multiple substances) and their transitions over time. Methods Data were drawn from Waves 4–7 (2016–2023) of the U.S. Population Assessment of Tobacco and Health Study (N = 10,433 adults). Participants reporting no substance use across all waves were excluded. Examined substance use behaviors included past 30-day cigarettes, e-cigarettes, binge drinking, cannabis, prescription drug misuse, and hard drug use. Latent class analysis identified substance use patterns at each wave, and latent transition analysis estimated transitions between classes over time. Results Three latent classes were identified: predominant cigarette use (Class 1), primary binge drinking (Class 2), and polysubstance use (Class 3). Class membership was highly stable across waves, with high probabilities of remaining in same class over time (Class 1: 90.82 %–99.64 %; Class 2: 97.45 %–98.92 %; Class 3: 96.18 %–100.00 %). A notable transition was observed from Class 1 to Class 3 between Waves 5 and 6 (6.22 %), whereas transitions from Class 3 to other classes were infrequent (≤2.40 %). Conclusion The substantial stability of substance use patterns among adults suggests that these behaviors may persist once established, highlighting the need for early intervention. Transitions from predominant cigarette use to polysubstance use underscore cigarette use as a key intervention target.
The present study aimed to address conceptual and measurement challenges in assessing problematic social media use by developing a scale grounded in the ICD-11 diagnostic framework for behavioral addictions. Item generation was based on core features including impaired control, prioritization of the behavior, continuation despite negative consequences, and functional impairment. Following expert review and pilot testing, data were collected in multiple phases. Exploratory and confirmatory factor analyses supported a multidimensional structure consistent with ICD-11 criteria. Measurement invariance was established across gender (female vs. male) and age groups (emerging adults vs. adults) in separate Multigroup Confirmatory Analyses. Item Response Theory analyses showed that the scale demonstrates higher discrimination at low to moderate levels of problematic use, highlighting its sensitivity for early detection. Overall, the findings suggest that the scale is a theoretically grounded and psychometrically sound instrument for assessing problematic social media use, with potential applications in research, screening, and early intervention contexts.
Background Alcohol use disorder (AUD) is a pervasive public health problem in the United States, and military veterans are at elevated risk. A prior wave of the National Health and Resilience in Veterans Study (NHRVS) estimated the prevalence of past-year probable AUD at 10.5 % in 2019–2020. Updated national surveillance data are needed given demographic shifts in the veteran population and evolving drinking patterns in the post-pandemic era. Methods Using data from the 2025–2026 NHRVS (N = 2,620), we estimated the weighted prevalence of probable AUD and compared it to that of the prior NHRVS wave. Multivariable analyses examined sociodemographic, military, trauma and mental health characteristics associated with probable AUD. Results The weighted prevalence of probable AUD in 2025–2026 was 12.1 %, compared with 10.5 % in 2019–2020 (difference, 1.6 percentage points; 95 % CI, −0.6 to 3.8), a difference that was not statistically significant. Independent correlates of probable AUD included younger age, unpartnered status, homelessness history, higher household income, and more adverse childhood experiences. When all lifetime psychiatric diagnoses were examined simultaneously, self-reported provider-diagnosed attention-deficit/hyperactivity disorder (ADHD) remained significantly associated with probable AUD (OR = 2.40). Veterans with probable AUD also had significantly greater odds of past-year suicidal ideation (OR = 2.02; 31.6 % vs. 11.7 %) and lifetime suicide attempt (OR = 1.67; 11.8 % vs. 4.0 %). Conclusions The prevalence of probable AUD among U.S. veterans remains elevated and is characterized by early adversity, co-occurring psychiatric conditions, and elevated suicide risk. Enhanced screening for social determinants of health and psychiatric concerns, including routine ADHD assessment in AUD treatment settings, alongside targeted outreach and integrated approaches are essential to address this complex and important problem.
