Literature Collection

IMPORTANCE: The demand for medications for opioid use disorder (MOUD) in rural US counties far outweighs their availability. Novel approaches to extend treatment capacity include telemedicine (TM) and mobile treatment on demand; however, their combined use has not been reported or evaluated. OBJECTIVE: To evaluate the use of a TM mobile treatment unit (TM-MTU) to improve access to MOUD for individuals living in an underserved rural area. DESIGN, SETTING, AND PARTICIPANTS: This quality improvement study evaluated data collected from adult outpatients with a diagnosis of OUD enrolled in the TM-MTU initiative from February 2019 (program inception) to June 2020. Program staff traveled to rural areas in a modified recreational vehicle equipped with medical, videoconferencing, and data collection devices. Patients were virtually connected with physicians based more than 70 miles (112 km) away. Data analysis was performed from June to October 2020. INTERVENTION: Patients received buprenorphine prescriptions after initial teleconsultation and follow-up visits from a study physician specialized in addiction psychiatry and medicine. MAIN OUTCOMES AND MEASURES: The primary outcome was 3-month treatment retention, and the secondary outcome was opioid-positive urine screens. Exploratory outcomes included use of other drugs and patients' travel distance to treatment. RESULTS: A total of 118 patients were enrolled in treatment, of whom 94 were seen for follow-up treatment predominantly (at least 2 of 3 visits [>50%]) on the TM-MTU; only those 94 patients' data are considered in all analyses. The mean (SD) age of patients was 36.53 (9.78) years, 59 (62.77%) were men, 71 (75.53%) identified as White, and 90 (95.74%) were of non-Hispanic ethnicity. Fifty-five patients (58.51%) were retained in treatment by 3 months (90 days) after baseline. Opioid use was reduced by 32.84% at 3 months, compared with baseline, and was negatively associated with treatment duration (F = 12.69; P = .001). In addition, compared with the nearest brick-and-mortar treatment location, TM-MTU treatment was a mean of 6.52 miles (range, 0.10-58.70 miles) (10.43 km; range, 0.16-93.92 km) and a mean of 10 minutes (range, 1-49 minutes) closer for patients. CONCLUSIONS AND RELEVANCE: These data demonstrate the feasibility of combining TM with mobile treatment, with outcomes (retention and opioid use) similar to those obtained from office-based TM MOUD programs. By implementing a traveling virtual platform, this clinical paradigm not only helps fill the void of rural MOUD practitioners but also facilitates access to underserved populations who are less likely to reach traditional medical settings, with critical relevance in the context of the COVID-19 pandemic.

Dual-task paradigms that combine cognitive and motor tasks offer a valuable lens for detecting subtle impairments in cognitive and physical functioning, especially in older adults. This study used artificial neural network (ANN) modeling to predict clinical, cognitive, and psychosocial outcomes from integrated gait, speech-linguistic, demographic, physiological, and psychological data collected during single- and dual-task conditions. Forty healthy adults (ages 20-84) completed physical, cognitive, and psychosocial assessments and a dual-task walking task involving cell phone use. ANN models were optimized using hyperparameter tuning and k-fold cross-validation to predict outcomes such as the Montreal Cognitive Assessment (MOCA), Trail Making Tests (TMT A and B), Activities-Specific Balance Confidence (ABC) Scale, Geriatric Depression Scale (GDS), and measures of memory, affect, and social support. The models achieved high accuracy for MOCA (100%), ABC (80%), memory function (80%), and social support satisfaction (75%). Feature importance analyses revealed key predictors such as speech-linguistic markers and sensory impairments. First-person plural pronoun used and authenticity of internal thoughts during dual-task emerged as strong predictors of MOCA and memory. Models were less accurate for complex executive tasks like TMT A and B. These findings support the potential of ANN models for the early detection of cognitive and psychosocial changes.