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Background: Addressing modifiable risk factors such as physical inactivity and social isolation could reduce risk of Alzheimer's disease and all-cause dementia, but little is known about which factors individuals are most willing to address or how they prefer to address them. Objective: To examine and describe behavior change goals set by participants during the Systematic Multi-domain Alzheimer's Risk Reduction Trial (SMARRT). Methods: In SMARRT, older adults worked with a health coach and nurse over 2 years to set incremental, personalized goals to reduce dementia risk. We performed quantitative analyses to summarize the numbers of goals per risk factor and qualitative content analyses of health coach and nurse notes to describe types of goals and useful strategies. Results: 82 dementia-free adults (70 to 89 years) with at least two dementia risk factors participated in the SMARRT intervention arm (mean age, 76 ± 5 years; 72% women; 11% Black/African American, 4% Asian, 3% Hispanic, 7% another non-White race). Participants set a median of 12 health coach goals and 1 nurse goals. The risk factors participants chose to work on most frequently were physical activity (95%), hypertension (72%), and cognitive activity (60%). Participants reported that the most useful strategies included support and accountability from the health coach/nurse, setting small goals, and learning to manage setbacks. Conclusions: When given support, older adults at increased risk for dementia set a wide range of goals to reduce dementia risk. A flexible, personalized approach that focuses on setting feasible goals and managing setbacks provides a useful framework for dementia risk reduction. [ABSTRACT FROM AUTHOR]

Background: The Alcohol Use Disorders Identification Test‐Consumption (AUDIT‐C) is a three‐item screening measure of unhealthy alcohol use that is widely used in healthcare settings. Evidence shows high test–retest reliability of the AUDIT‐C in research samples, but most studies had limited external validity and used small samples that could not be used to evaluate reliability across demographic subgroups and/or screening modalities. This study evaluates the test–retest reliability of the AUDIT‐C completed in routine care in a large primary care sample, including across demographic subgroups defined by age, sex, race, ethnicity, and screening modality (i.e., completed in‐clinic or online). Methods: We used electronic health record (EHR) data from Kaiser Permanente Washington. The sample included 18,491 adult primary care patients who completed two AUDIT‐C screens 1–21 days apart as part of routine care in 2021. Test–retest reliability was evaluated for AUDIT‐C total scores (0–12) and for a binary measure indicating unhealthy alcohol use (scores ≥3 women, ≥4 men). Using previously established cutoffs, we interpreted reliability coefficients >0.75 as indicating "excellent" reliability. Results: AUDIT‐C screens completed in routine care and documented in EHRs had excellent test–retest reliability for total scores (ICC = 0.87, 95% CI: 0.87–0.87) and the binary indicator of unhealthy alcohol use (κ = 0.79, 95% CI: 0.78–0.80). Reliability coefficients were good to excellent across all demographic groups and for in‐clinic and online modalities. Higher reliability was seen when both screens were completed through online patient portals (ICC = 0.93, 95% CI: 0.93–0.93) versus in‐clinic (ICC = 0.81, 95% CI: 0.79–0.82) or when one screen was completed using each modality (ICC = 0.83, 95% CI: 0.82–0.83). Lower reliability was seen in American Indian/Alaska Native (ICC = 0.82, 95% CI: 0.75–0.87) and multiracial individuals (ICC = 0.82, 95% 0.80–0.84). Conclusions: In real‐world routine care conditions, AUDIT‐C screens have excellent test–retest reliability across demographic subgroups and modalities (online and in‐clinic). Future research should examine why reliability varies slightly across modalities and demographic subgroups. [ABSTRACT FROM AUTHOR]

Background: The National Institute on Alcohol Abuse and Alcoholism (NIAAA) recommends the paper-based or computerized Alcohol Symptom Checklist to assess alcohol use disorder (AUD) symptoms in routine care when patients report high-risk drinking. However, it is unknown whether Alcohol Symptom Checklist response characteristics differ when it is administered online (eg, remotely via an online electronic health record [EHR] patient portal before an appointment) versus in clinic (eg, on paper after appointment check-in). Objective: This study evaluated the psychometric performance of the Alcohol Symptom Checklist when completed online versus in clinic during routine clinical care. Methods: This cross-sectional, psychometric study obtained EHR data from the Alcohol Symptom Checklist completed by adult patients from an integrated health system in Washington state. The sample included patients who had a primary care visit in 2021 at 1 of 32 primary care practices, were due for annual behavioral health screening, and reported high-risk drinking on the behavioral health screen (Alcohol Use Disorder Identification Test–Consumption score ≥7). After screening, patients with high-risk drinking were typically asked to complete the Alcohol Symptom Checklist—an 11-item questionnaire on which patients self-report whether they had experienced each of the 11 AUD criteria listed in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) over a past-year timeframe. Patients could complete the Alcohol Symptom Checklist online (eg, on a computer, smartphone, or tablet from any location) or in clinic (eg, on paper as part of the rooming process at clinical appointments). We examined sample and measurement characteristics and conducted differential item functioning analyses using item response theory to examine measurement consistency across these 2 assessment modalities. Results: Among 3243 patients meeting eligibility criteria for this secondary analysis (2313/3243, 71% male; 2271/3243, 70% White; and 2014/3243, 62% non-Hispanic), 1640 (51%) completed the Alcohol Symptom Checklist online while 1603 (49%) completed it in clinic. Approximately 46% (752/1640) and 48% (764/1603) reported ≥2 AUD criteria (the threshold for AUD diagnosis) online and in clinic (P =.37), respectively. A small degree of differential item functioning was observed for 4 of 11 items. This differential item functioning produced only minimal impact on total scores used clinically to assess AUD severity, affecting total criteria count by a maximum of 0.13 criteria (on a scale ranging from 0 to 11). Conclusions: Completing the Alcohol Symptom Checklist online, typically prior to patient check-in, performed similarly to an in-clinic modality typically administered on paper by a medical assistant at the time of the appointment. Findings have implications for using online AUD symptom assessments to streamline workflows, reduce staff burden, reduce stigma, and potentially assess patients who do not receive in-person care. Whether modality of DSM-5 assessment of AUD differentially impacts treatment is unknown. [ABSTRACT FROM AUTHOR]

This paper considers methodological approaches that can help better understand inequity in healthcare, focusing on five key domains: availability, patient-centeredness, access, effectiveness, and implementation. We present conceptual definitions of each of these domains, example research questions pertaining to inequity in each domain, and methodological approaches that can contribute to research about health inequities. We discuss the role of multilevel, participatory, and longitudinal research, particularly using representative, real-world data. We propose alternatives to randomized controlled trials that better suit questions regarding inequities in healthcare. We discuss challenges and considerations for research on inequities in healthcare alongside opportunities for innovation and prioritization of methodologies that are underutilized in epidemiology and health services research. We emphasize throughout that each of these five domains are interconnected and essential to be understood jointly if we are to improve our understanding of the role of healthcare in perpetuating health inequities., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Medical cannabis is commonly used for chronic pain, but little is known about differences in characteristics, cannabis use patterns, and perceived helpfulness among primary care patients who use cannabis for pain versus nonpain reasons., Methods: Among 1688 patients who completed a 2019 cannabis survey administered in a health system in Washington state, where recreational use is legal, participants who used cannabis for pain (n = 375) were compared with those who used cannabis for other reasons (n = 558) using survey and electronic health record data. We described group differences in participant characteristics, use patterns, and perceptions and applied adjusted multinomial logistic and modified Poisson regression., Results: Participants who used cannabis for pain were significantly more likely to report using applied (50.7% vs 10.6%) and beverage cannabis products (19.2% vs 11.6%), more frequent use (47.1% vs 33.1% for use ≥2 times per day; 81.6% vs 69.7% for use 4 to 7 days per week), and smoking tobacco cigarettes (19.2% vs 12.2%) than those who used cannabis for other reasons. They were also significantly more likely to perceive cannabis as very/extremely helpful (80.5% vs 72.7%), and significantly less likely to use cannabis for nonmedical reasons (4.8% vs 58.8%) or report cannabis use disorder symptoms (51.7% vs 61.1%)., Discussion: Primary care patients who use cannabis for pain use it more frequently, often in applied and ingested forms, and have more co-use of tobacco, which may differentially impact safety and effectiveness. These findings suggest the need for different approaches to counseling in clinical care., Competing Interests: Conflict of interest: Ms. Ford, Dr. Lapham, Dr. Matson, Ms. Luce, and Ms. Oliver have no conflicts of interest to declare. Dr. Binswanger received royalties from UptoDate (Wolters Kluwer) and is employed by the Colorado Permanente Medical Group, which provides primary care and specialty care. She has no other conflicts of interest to declare., (© Copyright by the American Board of Family Medicine.)

Background: Digital interventions, such as smartphone apps, can be effective in treating alcohol use disorders (AUD). However, efforts to integrate digital interventions into primary care have been challenging. To inform successful implementation, we sought to understand how patients and clinicians preferred to use apps in routine primary care., Methods: This study combined user-centered design and qualitative research methods, interviewing 18 primary care patients with AUD and nine primary care clinicians on topics such as prior experiences with digital tools, and design preferences regarding approaches for offering apps for AUD in primary care. Interviews were recorded and transcribed for template analysis whereby a priori codes were based on interview topics and refined through iterative coding. New codes and cross-cutting themes emerged from the data., Results: Patient participants with AUD indicated they would be more likely to engage in treatment if primary care team members were involved in their use of apps. They also preferred to see clinicians "invested" and recommended that clinicians ask about app use and progress during follow-up appointments or check-ins. Clinician participants valued the opportunity to offer apps to their patients but noted that workflows would need to be tailored to individual patient needs. Time pressures, implementation complexity, and lack of appropriate staffing were cited as barriers. Clinicians proposed concrete solutions (e.g., education, tools, and staffing models) that could improve their ability to use apps within the constraints of primary care and suggested that some patients could potentially use apps without clinician support., Conclusions: A user-centered approach to engaging patients in digital alcohol interventions in primary care may require personalized support for both initiation and follow-up. Meeting patients' needs likely require increased staffing and efficient workflows in primary care. Health systems should consider offering multiple pathways for enrolling patients in apps to accommodate individual preferences and contextual barriers., Plain Language Summary: Healthcare systems have begun using app-based treatments to help patients manage their health conditions, including alcohol use disorders. Some apps have been tested in research studies and appear to be effective. However, it is difficult for healthcare teams to offer apps to patients. Clinicians must engage in new activities that they have not done before, such as "teaching" patients to use apps and checking in on their use of the apps. Identifying how to use apps in routine healthcare is critical to their successful implementation. This study interviewed 27 people, including healthcare providers and patients in primary care, to uncover the most optimal ways to offer apps to patients with alcohol use disorders. The interviews combined the use of qualitative research methods and user-centered design. Results suggest that to use to address alcohol use disorders, primary care teams should be prepared to offer personalized support to patients. Both patient and clinician interviewees said that the steps required to use apps must be intuitive and simple. Patients could gain more benefits if clinicians introduced the apps and guided patients to use them, as opposed to making apps available for patients to download and use on their own. However, the exact approach to offering apps would depend on a given patient's preferences and the extent that staffing was available in the clinic to support patients. Health systems should be prepared to offer and support patients in their use of apps, which should accommodate patient preferences and the constraints of the clinic., Competing Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Several authors (JEG, RMC, KAB) are investigators on a trial funded by the National Institute on Drug Abuse (R01DA047954; PI: Glass), which evaluates the implementation of reSET and reSET-O, which are digital therapeutics for substance use disorder marketed by Pear Therapeutics, Inc. Pear Therapeutics Inc provided digital therapeutic prescriptions at no cost to Kaiser Permanente Washington during a quality improvement pilot study that preceded the implementation trial. JEG is a coinvestigator on a Small Business Innovation Award funded awarded to Pear Therapeutics, Inc by the National Institute on Drug Abuse, which evaluates potential improvements to reSET-O (R44DA042652). This study is informing our research on the implementation of digital therapeutics, but Pear Therapeutics, Inc was not involved in this study and does not provide funding to the authors, (© The Author(s) 2022.)