Evaluation of a digital tool for detecting stress and craving in SUD recovery: An observational trial of accuracy and engagement.

BACKGROUND: Digital health interventions offer opportunities to expand access to substance use disorder (SUD) treatment, collect objective real-time data, and deliver just-in-time interventions: however implementation has been limited. RAE (Realize, Analyze, Engage) Health is a digital tool which uses continuous physiologic data to detect high risk behavioral states (stress and craving) during SUD recovery. METHODS: This was an observational study to evaluate the digital stress and craving detection during outpatient SUD treatment. Participants were asked to use the RAE Health app, wear a commercial-grade wrist sensor over a 30-day period. They were asked to self-report stress and craving, at which time were offered brief in-app de-escalation tools. Supervised machine learning algorithms were applied retrospectively to wearable sensor data obtained to develop group-based digital biomarkers for stress and craving. Engagement was assessed by number of days of utilization, and number of hours in a given day of connection. RESULTS: Sixty percent of participants (N=30) completed the 30-day protocol. The model detected stress and craving correctly 76 % and 69 % of the time, respectively, but with false positive rates of 33 % and 28 % respectively. All models performed close to previously validated models from a research grade sensor. Participants used the app for a mean of 14.2 days (SD 10.1) and 11.7 h per day (SD 8.2). Anxiety disorders were associated with higher mean hours per day connected, and return to drug use events were associated with lower mean hours per day connected. CONCLUSIONS: Future work should explore the effect of similar digital health systems on treatment outcomes and the optimal dose of digital interventions needed to make a clinically significant impact.

Towards Device Agnostic Detection of Stress and Craving in Patients with Substance Use Disorder.

Novel technologies have great potential to improve the treatment of individuals with substance use disorder (SUD) and to reduce the current high rate of relapse (i.e. return to drug use). Wearable sensor-based systems that continuously measure physiology can provide information about behavior and opportunities for real-time interventions. We have previously developed an mHealth system which includes a wearable sensor, a mobile phone app, and a cloud-based server with embedded machine learning algorithms which detect stress and craving. The system functions as a just-in-time intervention tool to help patients de-escalate and as a tool for clinicians to tailor treatment based on stress and craving patterns observed. However, in our pilot work we found that to deploy the system to diverse socioeconomic populations and to increase usability, the system must be able to work efficiently with cost-effective and popular commercial wearable devices. To make the system device agnostic, methods to transform the data from a commercially available wearable for use in algorithms developed from research grade wearable sensor are proposed. The accuracy of these transformations in detecting stress and craving in individuals with SUD is further explored.

Ethics Roundtable: Advance Directives, Autonomy, and Gastrostomy Placement.

CL is a 94-year-old male who is brought to the Emergency Department from an assisted living facility due to a new onset of weakness and altered mental status. He was in his usual state of moderately compensated health, requiring assistance for most activities of daily living due to medical frailty and a previous right brain CVA with residual mild left sided weakness. On the day of admission, the staff found him lethargic and disoriented. The family requested a surgical consult for percutaneous, endoscopic, gastrostomy (PEG) tube placement. A review of the patient's records showed that the patient had refused a PEG tube during his last two hospitalizations. During the last admission, the hospitalist documented that the patient repeatedly refused nutritional support stating "if it's my time, I've lived a full life. I'm ready to die and join my wife." There was no advance care plan ("living will"), but CL did sign a "Selection of Surrogate Decision-maker" form previously, assigning his nephew as primary surrogate. Under pressure from multiple family members, including the designated surrogate, the attending requested a surgical consultation. The surgical team determined that the patient did not have capacity and scheduled CL for PEG tube placement. The care team had concerns regarding the conflict between the patient's previously (and consistently) stated desires and the family's wishes; an ethics consult was requested.

Establishing a Pre-COVID-19 Hospital Biopreparedness Initiative.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)/coronavirus disease 2019 (COVID-19) pandemic has amplified the role of hospitals in infectious disease response and capacity building. In efforts to respond to the growing volume of cases, hospitals have become a microcosm for US pandemic response. The COVID-19 outbreak has highlighted that hospital preparedness for biological events, such as a pandemic, are often inadequate and dependent on leadership investment in biopreparedness. This article discusses the proactive decision, before COVID-19, that a Phoenix-based hospital system made to invest in high-consequence disease (HCD) preparedness. Within these efforts, a gap analysis was performed, which led to creation of an HCD subcommittee and corresponding efforts to address vulnerabilities and opportunities for improvement. From establishing enhanced personal protective equipment (PPE) and infectious disease training for frontline staff, to building an outbreak tracking mechanism for travel alerts within the electronic medical record, the HCD efforts of this hospital system created a stronger foundation to respond to biological events like the COVID-19 pandemic.

Digital Health Interventions for Mental Health, Substance Use, and Co-occurring Disorders in the Criminal Justice Population: A Scoping Review.

BACKGROUND: Substance use disorder (SUD), mental health disorders (MHD), and co-occurring mental health and substance use disorders are common among criminal justice populations. Digital health interventions (DHI) represent an opportunity to expand co-occurring disorder treatment for justice involved populations, but efficacy data are lacking. OBJECTIVES: The current scoping review aims to address this gap via following objectives: (1) Describe trends involving DHIs for MHD, SUD, or co-occurring disorders studied in criminal justice settings; and (2) review available evidence for the impact of DHIs on criminal justice-, substance-, and mental health-related outcomes. METHODS: PubMed was searched for relevant articles that met the follow inclusion criteria: (1) focus on criminal justice-involved individuals; (2) description of an intervention focused on SUD, MHD, or co-occurring disorders; and (3) use of DHI. Articles were assessed using standardized data abstraction and quality assessment tools. RESULTS: Four-hundred unique articles were identified on initial search, and 19 were included in the final review. The most common focus of the intervention was SUDs. The most common modalities were telehealth and computer assisted interventions, with most utilized as an adjunct to treatment as usual. No DHIs used wearable devices, and one included justice involved youth. Feasibility and acceptability were high, and the studies that measured substance and mental health-related outcomes reported equivocal or positive results. No studies focused on long-term justice-related outcomes. CONCLUSIONS: Literature on DHIs for criminal justice involved populations diagnosed with SUD, MHD and co-occurring disorders is limited, and largely focuses on telehealth or eHealth, with less data on mHealth approaches. Future research should focus on the inclusion of diverse populations and include objective monitoring tools.

Current reporting of usability and impact of mHealth interventions for substance use disorder: A systematic review.

BACKGROUND: Connected interventions use data collected through mobile/wearable devices to trigger real-time interventions and have great potential to improve treatment for substance use disorder (SUD). This review aims to describe the current landscape, effectiveness and usability of connected interventions for SUD. METHODS: A systematic review was conducted to identify articles evaluating connected health interventions for SUD in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Three databases (PubMed, IEEE, and Scopus) were searched over a five-year period. Included articles described a connected health intervention targeting SUD and provided outcomes data. Data were extracted using a standardized reporting tool. RESULTS: A total of 1676 unique articles were identified during the initial search, with 32 articles included in the final analysis. Seven articles of the 32 were derived from two large studies. The most commonly studied SUD was alcohol use disorder. Sixteen articles reported at least one statistically significant result with respect to reduced craving and/or substance use. The majority of articles used ecological momentary assessment to trigger interventions, while four used biologic/physiologic data. Two articles used a wearable device. Common intervention types included craving management, coping assistance, and tailored feedback. Twenty-three articles measured usability factors, and acceptability was generally reported as high. CONCLUSION: Identified themes included a focus on AUD, use of smart phones, use of EMA for intervention delivery, positive effects on SUD related outcomes, and overall high acceptability. Wearables that directly monitor biologic data and predictive analytics using integrated data streams represent understudied opportunities for new research.

Medication adherence and older renal transplant patients' perceptions of electronic medication monitoring.

This study evaluated older renal transplant recipients' perceptions of electronic medication monitoring and the influence of these perceptions on medication adherence. A sample of 73 older adult renal transplant recipients who used the Medication Event Monitoring System (MEMS(®)) TrackCaps for 12 months provided their perceptions of device use. Participants perceived that the MEMS had a neutral effect on their medication-taking routine (65%), believed the MEMS was practical (56%), and could not describe any instances in which using the MEMS was difficult (56%). No significant difference in medication adherence was found between those who perceived the MEMS's influence negatively/neutrally and those who perceived the MEMS positively (p = 0.22). Medication adherence data from older adult renal transplant recipients can be used regardless of their perceptions of the MEMS's influence on their medication taking without biasing medication adherence data.

Identifying Gaps in Frontline Healthcare Facility High-Consequence Infectious Disease Preparedness.

Hospital infection disease preparedness gaps were brought to the forefront during the 2013-2016 Ebola virus disease (EVD) outbreak. The ability of US hospitals to rapidly identify, isolate, and manage patients with potentially high-consequence pathogens is a critical component to health security. Since the EVD cases in Dallas, Texas, the continuity of hospital preparedness has been questionable. While certain hospitals were designated as EVD treatment facilities, the readiness of most American hospitals remains unknown. A gap analysis of a hospital system in Phoenix, Arizona, underscores the challenges of maintaining infectious disease preparedness in the existing US healthcare system.