14 research outputs found

    The Feasibility and Utility of Harnessing Digital Health to Understand Clinical Trajectories in Medication Treatment for Opioid Use Disorder: D-TECT Study Design and Methodological Considerations

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    Introduction: Across the U.S., the prevalence of opioid use disorder (OUD) and the rates of opioid overdoses have risen precipitously in recent years. Several effective medications for OUD (MOUD) exist and have been shown to be life-saving. A large volume of research has identified a confluence of factors that predict attrition and continued substance use during substance use disorder treatment. However, much of this literature has examined a small set of potential moderators or mediators of outcomes in MOUD treatment and may lead to over-simplified accounts of treatment non-adherence. Digital health methodologies offer great promise for capturing intensive, longitudinal ecologically-valid data from individuals in MOUD treatment to extend our understanding of factors that impact treatment engagement and outcomes. Methods: This paper describes the protocol (including the study design and methodological considerations) from a novel study supported by the National Drug Abuse Treatment Clinical Trials Network at the National Institute on Drug Abuse (NIDA). This study (D-TECT) primarily seeks to evaluate the feasibility of collecting ecological momentary assessment (EMA), smartphone and smartwatch sensor data, and social media data among patients in outpatient MOUD treatment. It secondarily seeks to examine the utility of EMA, digital sensing, and social media data (separately and compared to one another) in predicting MOUD treatment retention, opioid use events, and medication adherence [as captured in electronic health records (EHR) and EMA data]. To our knowledge, this is the first project to include all three sources of digitally derived data (EMA, digital sensing, and social media) in understanding the clinical trajectories of patients in MOUD treatment. These multiple data streams will allow us to understand the relative and combined utility of collecting digital data from these diverse data sources. The inclusion of EHR data allows us to focus on the utility of digital health data in predicting objectively measured clinical outcomes. Discussion: Results may be useful in elucidating novel relations between digital data sources and OUD treatment outcomes. It may also inform approaches to enhancing outcomes measurement in clinical trials by allowing for the assessment of dynamic interactions between individuals\u27 daily lives and their MOUD treatment response. Clinical Trial Registration: Identifier: NCT04535583

    Patient Engagement in a Multimodal Digital Phenotyping Study of Opioid Use Disorder

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    Background: Multiple digital data sources can capture moment-to-moment information to advance a robust understanding of opioid use disorder (OUD) behavior, ultimately creating a digital phenotype for each patient. This information can lead to individualized interventions to improve treatment for OUD. Objective: The aim is to examine patient engagement with multiple digital phenotyping methods among patients receiving buprenorphine medication for OUD. Methods: The study enrolled 65 patients receiving buprenorphine for OUD between June 2020 and January 2021 from 4 addiction medicine programs in an integrated health care delivery system in Northern California. Ecological momentary assessment (EMA), sensor data, and social media data were collected by smartphone, smartwatch, and social media platforms over a 12-week period. Primary engagement outcomes were meeting measures of minimum phone carry (≥8 hours per day) and watch wear (≥18 hours per day) criteria, EMA response rates, social media consent rate, and data sparsity. Descriptive analyses, bivariate, and trend tests were performed. Results: The participants’ average age was 37 years, 47% of them were female, and 71% of them were White. On average, participants met phone carrying criteria on 94% of study days, met watch wearing criteria on 74% of days, and wore the watch to sleep on 77% of days. The mean EMA response rate was 70%, declining from 83% to 56% from week 1 to week 12. Among participants with social media accounts, 88% of them consented to providing data; of them, 55% of Facebook, 54% of Instagram, and 57% of Twitter participants provided data. The amount of social media data available varied widely across participants. No differences by age, sex, race, or ethnicity were observed for any outcomes. Conclusions: To our knowledge, this is the first study to capture these 3 digital data sources in this clinical population. Our findings demonstrate that patients receiving buprenorphine treatment for OUD had generally high engagement with multiple digital phenotyping data sources, but this was more limited for the social media data. International Registered Report Identifier (IRRID): RR2-10.3389/fpsyt.2022.87191

    Augmenting project ECHO for opioid use disorder with data-informed quality improvement

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    Abstract Background National opioid-related overdose fatalities totaled 650,000 from 1999 to 2021. Some of the highest rates occurred in New Hampshire, where 40% of the population lives rurally. Medications for opioid use disorder (MOUD; methadone, buprenorphine, and naltrexone) have demonstrated effectiveness in reducing opioid overdose and mortality. Methadone access barriers disproportionally impact rural areas and naltrexone uptake has been limited. Buprenorphine availability has increased and relaxed regulations reduces barriers in general medical settings common in rural areas. Barriers to prescribing buprenorphine include lack of confidence, inadequate training, and lack of access to experts. To address these barriers, learning collaboratives have trained clinics on best-practice performance data collection to inform quality improvement (QI). This project sought to explore the feasibility of training clinics to collect performance data and initiate QI alongside clinics’ participation in a Project ECHO virtual collaborative for buprenorphine providers. Methods Eighteen New Hampshire clinics participating in a Project ECHO were offered a supplemental project exploring the feasibility of performance data collection to inform QI targeting increased alignment with best practice. Feasibility was assessed descriptively, through each clinic’s participation in training sessions, data collection, and QI initiatives. An end-of-project survey was conducted to understand clinic staff perceptions of how useful and acceptable they found the program. Results Five of the eighteen health care clinics that participated in the Project ECHO joined the training project, four of which served rural communities in New Hampshire. All five clinics met the criteria for engagement, as each clinic attended at least one training session, submitted at least one month of performance data, and completed at least one QI initiative. Survey results showed that while clinic staff perceived the training and data collection to be useful, there were several barriers to collecting the data, including lack of staff time, and difficulty standardizing documentation within the clinic electronic health record. Conclusions Results suggest that training clinics to monitor their performance and base QI initiatives on data has potential to impact clinical best practice. While data collection was inconsistent, clinics completed several data-informed QI initiatives, indicating that smaller scale data collection might be more attainable

    Implementation of substance use screening in rural federally-qualified health center clinics identified high rates of unhealthy alcohol and cannabis use among adult primary care patients

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    Abstract Background Screening for substance use in rural primary care clinics faces unique challenges due to limited resources, high patient volumes, and multiple demands on providers. To explore the potential for electronic health record (EHR)-integrated screening in this context, we conducted an implementation feasibility study with a rural federally-qualified health center (FQHC) in Maine. This was an ancillary study to a NIDA Clinical Trials Network study of screening in urban primary care clinics (CTN-0062). Methods Researchers worked with stakeholders from three FQHC clinics to define and implement their optimal screening approach. Clinics used the Tobacco, Alcohol, Prescription Medication, and Other Substance (TAPS) Tool, completed on tablet computers in the waiting room, and results were immediately recorded in the EHR. Adult patients presenting for annual preventive care visits, but not those with other visit types, were eligible for screening. Data were analyzed for the first 12 months following implementation at each clinic to assess screening rates and prevalence of reported unhealthy substance use, and documentation of counseling using an EHR-integrated clinical decision support tool, for patients screening positive for moderate-high risk alcohol or drug use. Results Screening was completed by 3749 patients, representing 93.4% of those with screening-eligible annual preventive care visits, and 18.5% of adult patients presenting for any type of primary care visit. Screening was self-administered in 92.9% of cases. The prevalence of moderate-high risk substance use detected on screening was 14.6% for tobacco, 30.4% for alcohol, 10.8% for cannabis, 0.3% for illicit drugs, and 0.6% for non-medical use of prescription drugs. Brief substance use counseling was documented for 17.4% of patients with any moderate-high risk alcohol or drug use. Conclusions Self-administered EHR-integrated screening was feasible to implement, and detected substantial alcohol, cannabis, and tobacco use in rural FQHC clinics. Counseling was documented for a minority of patients with moderate-high risk use, possibly indicating a need for better support of primary care providers in addressing substance use. There is potential to broaden the reach of screening by offering it at routine medical visits rather than restricting to annual preventive care visits, within these and other rural primary care clinics

    Technology-Based Interventions in Substance Use Treatment to Promote Health Equity Among People Who Identify as African American/Black, Hispanic/Latinx, and American Indian/Alaskan Native: Protocol for a Scoping Review

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    BackgroundTechnology-based interventions (TBIs; ie, web-based and mobile interventions) have the potential to promote health equity in substance use treatment (SUTx) for underrepresented groups (people who identify as African American/Black, Hispanic/Latinx, and American Indian/Alaskan Native) by removing barriers and increasing access to culturally relevant effective treatments. However, technologies (emergent and more long-standing) may have unintended consequences that could perpetuate health care disparities among people who identify as a member of one of the underrepresented groups. Health care research, and SUTx research specifically, is infrequently conducted with people who identify with these groups as the main focus. Therefore, an improved understanding of the literature at the intersection of SUTx, TBIs, and underrepresented groups is warranted to avoid exacerbating inequities and to promote health equity. ObjectiveThis study aims to explore peer-reviewed literature (January 2000-March 2021) that includes people who identify as a member of one of the underrepresented groups in SUTx research using TBIs. We further seek to explore whether this subset of research is race/ethnicity conscious (does the research consider members of underrepresented groups beyond their inclusion as study participants in the introduction, methods, results, or discussion). MethodsFive electronic databases (MEDLINE, Scopus, Cochrane Library, CINAHL, and PsycInfo) were searched to identify SUTx research using TBIs, and studies were screened for eligibility at the title/abstract and full-text levels. Studies were included if their sample comprised of people who identify as a member of one of the underrepresented groups at 50% or more when combined. ResultsTitle/abstract and full-text reviews were completed in 2021. These efforts netted a sample of 185 studies that appear to meet inclusionary criteria. Due to the uniqueness of tobacco relative to other substances in the SUTx space, as well as the large number of studies netted, we plan to separately publish a scoping review on tobacco-focused studies that meet all other criteria. Filtering for tobacco-focused studies (n=31) netted a final full-text sample for a main scoping review of 154 studies. The tobacco-focused scoping review manuscript is expected to be submitted for peer review in Spring 2022. The main scoping review data extraction and data validation to confirm the accuracy and consistency of data extraction across records was completed in March 2022. We expect to publish the main scoping review findings by the end of 2022. ConclusionsResearch is needed to increase our understanding of the range and nature of TBIs being used in SUTx research studies with members of underrepresented groups. The planned scoping review will highlight research at this intersection to promote health equity. International Registered Report Identifier (IRRID)DERR1-10.2196/3450

    Towards detecting cocaine use using smartwatches in the NIDA clinical trials network: Design, rationale, and methodology

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    Cocaine use in clinical trials is often measured via self-report, which can be inaccurate, or urine drug screens, which can be intrusive and burdensome. Devices that can automatically detect cocaine use and can be worn conveniently in daily life may provide several benefits. AutoSense is a wearable, physiological-monitoring suite that can detect cocaine use, but it may be limited as a method for monitoring cocaine use because it requires wearing a chestband with electrodes. This paper describes the design, rationale, and methodology of a project that seeks to build upon and extend previous work in the development of methods to detect cocaine use via wearable, unobtrusive mobile sensor technologies. To this end, a wrist-worn sensor suite (i.e., MotionSense HRV) will be developed and evaluated. Participants who use cocaine (N = 25) will be asked to wear MotionSense HRV and AutoSense for two weeks during waking hours. Drug use will be assessed via thrice-weekly urine drug screens and self-reports, and will be used to isolate periods of cocaine use that will be differentiated from other drug use. The present study will provide information on the feasibility and acceptability of using a wrist-worn device to detect cocaine use
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