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Objective: We examine role of ACEs and pathways to risk of opioid misuse among young adults. Participants and Methods: A cross-sectional survey of validated measures of ACEs, risk of opioid misuse, and health conditions with a sample of 1,402 students from a large public university followed by multivariate logistic regression and pathway analysis. Results: Majority (61%) of participants reported at least one ACE. A dose-response relationship between numbers of ACEs with risk for opioid misuse was present. Compared to participants with no ACEs, participants with [greater than or equal to]4 ACEs and 0-3 ACEs were 2.93 (95% CI: 1.95, 4.39; p < 0.001) and 1.96 (95% CI: 1.46, 2.65; p < 0.001) times more likely to be at risk for opioid misuse, respectively. Having at least one existing or past health condition significantly mediated the association. Conclusions: Our findings suggest need to include assessment of ACEs as a screening criterion for opioid prescription and administration among college-aged individuals.

Objective: The proliferation of m-health interventions has led to a growing research area of app analysis. We derived RACE (Review, Assess, Classify, and Evaluate) framework through the integration of existing methodologies for the purpose of analyzing m-health apps, and applied it to study opioid apps.Materials and Methods: The 3-step RACE framework integrates established methods and evidence-based criteria used in a successive manner to identify and analyze m-health apps: the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, inter-rater reliability analysis, and Nickerson-Varshney-Muntermann taxonomy.Results: Using RACE, 153 opioid apps were identified, assessed, and classified leading to dimensions of Target Audience, Key Function, Operation, Security & Privacy, and Impact, with Cohen's kappa < 1.0 suggesting subjectivity in app narrative assessments. The most common functions were education (24%), prescription (16%), reminder-monitoring-support (13%), and treatment & recovery (37%). A majority are passive apps (56%). The target audience are patients (49%), healthcare professionals (39%), and others (12%). Security & Privacy is evident in 84% apps.Discussion: Applying the 3-step RACE framework revealed patterns and gaps in opioid apps leading to systematization of knowledge. Lessons learned can be applied to the study of m-health apps for other health conditions.Conclusion: With over 350 000 existing and emerging m-health apps, RACE shows promise as a robust and replicable framework for analyzing m-health apps for specific health conditions. Future research can utilize the RACE framework toward understanding the dimensions and characteristics of existing m-health apps to inform best practices for collaborative, connected and continued care. [ABSTRACT FROM AUTHOR]

Despite significant declines in the use of cigarettes, a significant proportion of adults smoke. This study explores the association between smoking and health‐related quality of life (HRQoL) by age. The 2016 Behavioral Risk Factor Surveillance System survey was administered to adults in 50 states and District of Columbia (n = 437,195). Physically unhealthy days (PUDs) and mentally unhealthy days (MUDs)) were regressed on age strata (18–24, 25–34, 35–44, 45–54, 55–64, ≥ 65 years) and smoking status (never, former, someday, and everyday) using negative binomial regression models with adjustment for sociodemographic covariates. For each age group, everyday smoking highly predicted PUDs and MUDs. Predicted PUDs increased with age; predicted MUDs decreased with age. Among adults aged 45–54 and 55–64 years, 3‐day difference in PUDs was observed between never smokers and everyday smokers. Among young adults (18–24 years), a 4.3‐day difference in MUDs was observed between everyday and never smokers. The discrepancies were nonlinear with age. The observed relationship between smoking and HRQoL provides novel information about the need to consider age when designing community‐based interventions. Additional research can provide needed depth to understanding the relationship between smoking and HRQoL in specific age groups. [ABSTRACT FROM AUTHOR]