Domain matters: An examination of college student physical activity participation patterns by gender and race/ethnicity.

BACKGROUND: Physical activity (PA) consists of multiple domains, including leisure-time PA (LTPA), occupational PA (OPA), and transportation PA (TPA), though limited research has examined these domains among college students. METHODS: This cross sectional, online survey asked undergraduate students to self-report demographics (gender, race/ethnicity, employment) and PA (LTPA, TPA, and OPA). Participants were categorized as meeting/not meeting current aerobic PA recommendations with only LTPA and with all domains of PA. Analyses examined differences by domain and demographics. RESULTS: For participants (n = 3732) when only considering LTPA, 79% met recommendations, while considering all forms of PA resulted in 94% of students meeting recommendations. Gender and race/ethnicity differences in the odds of meeting PA recommendations were present with only LTPA, however when considering all PA domains, some disparities were no longer present. CONCLUSIONS: These findings highlight how different domains of activity contribute to overall PA and the relationship with gender and race/ethnicity.

Reducing LGBTQ+ Physical Activity Disparities Through Improved Measurement and Inclusion of Sexual Orientation in US National Data Sets.

Adequate participation in physical activity (PA) is effective in reducing negative health outcomes, including cardiovascular disease, stroke, type 2 diabetes, as well as stress, anxiety, and depression. However, 1 in 4 adults meet the PA guidelines, with lesbian, gay, bisexual, transgender, and queer (LGBTQ+) persons reporting increased rates of inactivity and higher rates of negative health outcomes. Limited research can be conducted on best methods to promote PA among LGBTQ+ adults as there is a lack of standardized measurements for both sexual orientation status and PA used in US national data set methodologies. A call to action is warranted to highlight the lack of uniform methodologies for collecting both sexual orientation and PA data in national data sets, with an overall goal of promoting inclusion and transparency of sexual orientation as a primary, secondary, and tertiary influence on PA. The current societal disconnect of national data sets collecting sexual orientation does not allow for proper extrapolation within the LGBTQ+ classifications. LGBTQ+ identities each report differing PA and health outcomes, promoting the need for proper sexual orientation measures. Without this inclusion, we will continue to see larger health disparities among LGBTQ+ persons due to outdated measurements in current US national data sets. This commentary provides sexual orientation status on health outcomes linked to physical inactivity, the need to include uniform sexual orientation measures in national data sets, and implications of the inclusion of this measure to conduct PA research as it relates to health outcomes.

Mapping prescription drug monitoring program data to self-report measures of non-medical prescription opioid use in community pharmacy settings.

BACKGROUND: Community pharmacists are well-positioned to identify patients engaged in non-medical prescription opioid use (NMPOU) through Prescription Drug Monitoring Program (PDMP) databases. Integrating patient-reported outcomes with PDMP data may improve the interpretability of PDMP information to support clinical decision-making. OBJECTIVE: This study linked patient-reported clinical measures of substance use with PDMP data to examine relationships between average daily opioid dose in morphine milligram equivalents (MME) and visits to multiple pharmacies/prescribers with self-reported NMPOU. METHODS: Data from a cross-sectional health assessment given to patients aged ≥18 years filling opioid prescriptions were linked to PDMP records. NMPOU in the past three months was assessed on a continuous scale (range 0-39) using an adapted version of the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST). PDMP measures included average daily MME and number of distinct pharmacies/prescribers visited in the past 180 days. Univariable and multivariable zero-inflated negative binomial models estimated associations between PDMP measures and any NMPOU and severity of use. RESULTS: The sample included 1421 participants. In multivariable models adjusted for sociodemographic, mental health, and physical health characteristics, any NMPOU was associated with higher average daily MME (adjusted OR = 1.22, 95% CI = 1.05-1.39) and number of distinct prescribers visited (adjusted OR = 1.15, 95% CI = 1.01-1.30). Higher average daily MME (adjusted mean ratio (MR) = 1.12, 95% CI = 1.08-1.15), number of distinct pharmacies visited (adjusted MR = 1.11, 95% CI = 1.04-1.18), and number of distinct prescribers visited (adjusted MR = 1.07, 95% CI = 1.02-1.11) were associated with increased NMPOU severity. CONCLUSIONS: We observed significant, positive associations between average daily MME and visits to multiple pharmacies/prescribers with any NMPOU and severity of use. This study demonstrates self-report clinical measures of substance use can be cross-walked to PDMP data and translated into clinically interpretable information.