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Abstract Background Substance use disorders (SUDs) represent major public health concerns and are linked to enhanced risk of legal consequences. Unresolved legal issues may prevent individuals with SUD from completing treatment. Interventions aimed at improving SUD treatment outcomes are limited. Filling that gap, this randomized controlled trial (RCT) tests the ability of a technology-assisted intervention to increase SUD treatment completion rates and improve post-treatment health, economic, justice-system, and housing outcomes. Methods A randomized controlled trial with a two-year administrative follow-up period will be conducted. Eight hundred Medicaid eligible and uninsured adults receiving SUD treatment will be recruited at community-based non-profit health care clinics in Southeast, Michigan, USA. Using an algorithm embedded in a community-based case management system, we randomly assign all eligible adults to one of two groups. The treatment/intervention group will receive hands-on assistance with a technology aimed at resolving unaddressed legal issues and the control group receives no treatment. Upon enrollment into the intervention, both treatment ( n = 400) and control groups ( n = 400) retain traditional options to resolve unaddressed legal issues, such as hiring an attorney, but only the treatment group is targeted the technology and offered personalized assistance in navigating the online legal platform. To develop baseline and historical contexts for participants, we collect life course history reports from all participants and intend to link those in each group to administrative data sources. In addition to the randomized controlled trial (RCT), we used an exploratory sequential mixed methods and participatory-based design to develop, test, and administer our life course history instruments to all participants. The primary objective is to test whether targeting no-cost online legal resources to those experiencing SUD improves their long-term recovery and decreases negative health, economic, justice-system, and housing outcomes. Discussion Findings from this RCT will improve our understanding of the acute socio-legal needs faced by those experiencing SUD and provide recommendations to help target resources toward the areas that best support long-term recovery. The public health impact includes making publicly available a deidentified, longitudinal dataset of uninsured and Medicaid eligible clients in treatment for SUD. Data include an overrepresentation of understudied groups including African American and American Indian Alaska Native persons documented to experience heightened risk for SUD-related premature mortality and justice-system involvement. Within these data, several intended outcome measures can inform the health policy landscape: (1) health, including substance use, disability, mental health diagnosis, and mortality; (2) financial health, including employment, earnings, public assistance receipt, and financial obligations to the state; (3) justice-system involvement, including civil and criminal legal system encounters; (4) housing, including homelessness, household composition, and homeownership. Trial registration Retrospectively registered # NCT05665179 on December 27, 2022.
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Identifying Biomarkers for Accurate Detection of Stress
Substance use disorder (SUD) is a dangerous epidemic that develops out of recurrent use of alcohol and/or drugs and has the capability to severely damage one’s brain and behaviour. Stress is an established risk factor in SUD’s development of addiction and in reinstating drug seeking. Despite this expanding epidemic and the potential for its grave consequences, there are limited options available for management and treatment, as well as pharmacotherapies and psychosocial treatments. To this end, there is a need for new and improved devices dedicated to the detection, management, and treatment of SUD. In this paper, the negative effects of SUD-related stress were discussed, and based on that, a few significant biomarkers were selected from a set of eight features collected by a chest-worn device, RespiBAN Professional, on fifteen individuals. We used three machine learning classifiers on these optimal biomarkers to detect stress. Based on the accuracies, the best biomarkers to detect stress and those considered as features for classification were determined to be electrodermal activity (EDA), body temperature, and a chest-worn accelerometer. Additionally, the differences between mental stress and physical stress, as well as different administrations of meditation during the study, were identified and analysed. Challenges, implications, and applications were also discussed. In the near future, we aim to replicate the proposed methods in individuals with SUD.
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- Award ID(s):
- 2042203
- PAR ID:
- 10406404
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 22
- Issue:
- 22
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 8703
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s22228703
