Wearable and Wireless mHealth Technologies for Substance Use Disorder

An Autonomous Implantable Device for the Prevention of Death from Opioid Overdose

Opioid overdose accounts for nearly 75,000 deaths per year in the United States, representing a leading cause of mortality amongst the prime working age population (25-54 years). At overdose levels, opioid-induced respiratory depression becomes fatal without timely administration of the rescue drug naloxone. Currently, overdose survival relies entirely on bystander intervention, requiring a nearby person to discover and identify the overdosed individual, and have immediate access to naloxone to administer. Government efforts have focused on providing naloxone in abundance but do not address the equally critical component for overdose rescue: a willing and informed bystander. To address this unmet need, we developed the Naloximeter: a class of life-saving implantable devices that autonomously detect and treat overdose, with the ability to simultaneously contact first-responders. We present three Naloximeter platforms, for both fundamental research and clinical translation, all equipped with optical sensors, drug delivery mechanisms, and a supporting ecosystem of technology to counteract opioid-induced respiratory depression. In small and large animal studies, the Naloximeter rescues from otherwise fatal opioid overdose within minutes. This work introduces life-changing, clinically translatable technologies that broadly benefit a susceptible population recovering from opioid use disorder.

Digital interventions targeting excessive substance use and substance use disorders: a comprehensive and systematic scoping review and bibliometric analysis

Addictive substances are prevalent world-wide, and their use presents a substantial and persistent public health problem. A wide range of digital interventions to decrease use and negative consequences thereof have been explored, differing in approach, theoretical grounding, use of specific technologies, and more. The current study was designed to comprehensively map the recent (2015-2022) extant literature in a systematic manner, and to identify neglected and emerging knowledge gaps. Four major databases (Medline, Web of Science Core Collection, and PsychInfo) were searched using database-specific search strategies, combining terms related to clinical presentation (alcohol, tobacco or other drug use), technology and aim. After deduplication, the remaining n=13,917 unique studies published were manually screened in two stages, leaving a final n=3,056 studies, the abstracts of which were subjected to a tailored coding scheme. Findings revealed an accelerating rate of publications in this field, with randomized trials being the most common study type. Several meta-analyses on the topic have now been published, revealing promising and robust effects. Digital interventions are being offered on numerous levels, from targeted prevention to specialized clinics. Detailed coding was at times made difficult by inconsistent use of specific terms, which has important implications for future meta-analyses. Moreover, we identify several gaps in the extant literature - few health economic assessments, unclear descriptions of interventions, weak meta-analytic support for some type of interventions, and limited research on many target groups, settings and new interventions like video calls, chatbots and artificial intelligence - that we argue are important to address in future research.

mHealth Self-Monitoring Model for Medicine Adherence of Patients With Diabetes in Resource-Limited Countries: Structural Equation Modeling Approach

BACKGROUND

The COVID-19 pandemic has led to serious challenges and emphasized the importance of using technology for health care operational transformation. Consequently, the need for technological innovations has increased, thus empowering patients with chronic conditions to tighten their adherence to medical prescriptions.

OBJECTIVE

This study aimed to develop a model for a mobile health (mHealth) self-monitoring system for patients with diabetes in rural communities within resource-limited countries. The developed model could be based on the implementation of a system for the self-monitoring of patients with diabetes to increase medical adherence.

METHODS

This study followed a quantitative approach, in which data were collected from health care providers using a questionnaire with close-ended questions. Data were collected from district hospitals in 3 South African provinces that were selected based on the prevalence rates of diabetes and the number of patients with diabetes treated. The collected data were analyzed using smart partial least squares to validate the model and test the suggested hypotheses.

RESULTS

Using variance-based structural equation modeling that leverages smart partial least squares, the analysis indicated that environmental factors significantly influence all the independent constructs that inform patients' change of behavior toward the use of mHealth for self-monitoring of medication adherence. Technology characteristics such as effort expectancy, self-efficacy, and performance expectancy were equally significant; hence, their hypotheses were accepted. In contrast, the contributions of culture and social aspects were found to be insignificant, and their hypotheses were rejected. In addition, an analysis was conducted to determine the interaction effects of the moderating variables on the independent constructs. The results indicated that with the exception of cultural and social influences, there were significant interacting effects on other independent constructs influencing mHealth use for self-monitoring.

CONCLUSIONS

On the basis of the findings of this study, we conclude that behavioral changes are essential for the self-monitoring of chronic diseases. Therefore, it is important to enhance those effects that stimulate the behavior to change toward the use of mHealth for self-monitoring. Motivational aspects were also found to be highly significant as they triggered changes in behavior. The developed model can be used to extend the research on the self-monitoring of patients with chronic conditions. Moreover, the model will be used as a basic architecture for the implementation of fully fledged systems for self-monitoring of patients with diabetes.

Evaluating the Impact of Brave Technology Co-op's Novel Drug Overdose Detection and Response Devices in North America: a Retrospective Study

Assess the preliminary impact of Brave Technology Co-op's overdose detection devices that have been implemented in housing, medical, social service facilities, and several private settings in North America. Administrative data was collected by Brave on their Buttons and Sensors during several proof-of-concept projects and full installations in Canada and United States (US) between December 2018 and July 2022. Data analyzed provided insights on the number of overdoses detected and reversed (averted overdose deaths) using Brave Sensors and Buttons, along with other programmatic and session-specific indicators. Implementation of 486 Brave Buttons and 148 Brave Sensors in Canada has detected and prevented 108 overdose deaths (100 using Buttons and 8 using Sensors) whereas implementation of 170 Buttons in the US has averted 2 overdose deaths to date, with the potential to save many more lives. Brave's devices hold promise for increasing rates of overdose detection and preventing overdose deaths.

Digital Biomarker Applications Across the Spectrum of Opioid Use Disorder

Opioid use disorder (OUD) is one of the most pressing public health problems of the past decade, with over eighty thousand overdose related deaths in 2021 alone. Digital technologies to measure and respond to disease states encompass both on- and off-body sensors. Such devices can be used to detect and monitor end-user physiologic or behavioral measurements (i.e. digital biomarkers) that correlate with events of interest, health, or pathology. Recent work has demonstrated the potential of digital biomarkers to be used as a tools in the prevention, risk mitigation, and treatment of opioid use disorder (OUD). Multiple physiologic adaptations occur over the course of opioid use, and represent potential targets for digital biomarker based monitoring strategies. This review explores the current evidence (and potential) for digital biomarkers monitoring across the spectrum of opioid use. Technologies to detect opioid administration, withdrawal, hyperalgesia and overdose will be reviewed. Driven by empirically derived algorithms, these technologies have important implications for supporting the safe prescribing of opioids, reducing harm in active opioid users, and supporting those in recovery from OUD.

A systematic review of remotely delivered contingency management treatment for substance use

BACKGROUND

Substance use and related consequences (e.g., impaired driving, injuries, disease transmission) continue to be major public health concerns. Contingency management (CM) is a highly effective treatment for substance use disorders. Yet CM remains vastly underutilized, in large part due to implementation barriers to in-person delivery. If feasible and effective, remote delivery of CM may reduce barriers at both the clinic- and patient-level, thus increasing reach and access to effective care. Here, we summarize data from a systematic review of studies reporting remote delivery of CM for substance use treatment.

METHODS

We conducted a systematic review, reported according to PRISMA guidelines. The study team identified a total of 4358 articles after deduplication. Following title and abstract screening, full-text screening, and reference tracking, 39 studies met the eligibility criteria. We evaluated the methodological quality of the included studies using the Effective Public Health Practice Project Quality tool.

RESULTS

Of 39 articles included in the review, most (n = 26) targeted cigarette smoking, with others focusing on alcohol (n = 9) or other substance use or targeting multiple substances (n = 4). Most remotely delivered CM studies focused on abstinence (n = 29), with others targeting substance use reduction (n = 2), intervention engagement (n = 5), and both abstinence and intervention engagement (n = 3). CM was associated with better outcomes (either abstinence, use reduction, or engagement), with increasingly more remotely delivered CM studies published in more recent years. Studies ranged from moderate to strong quality, with the majority (57.5 %) of studies being strong quality.

CONCLUSIONS

Consistent with in-person CM, remotely delivered CM focusing on abstinence or use reduction from substances or engagement in substance use treatment services improves outcomes at the end of treatment compared to control conditions. Moreover, remotely delivered CM is feasible across a variety of digital delivery platforms (e.g., web, mobile, and wearable), with acceptability and reduced clinic and patient burden as technological advancements streamline monitoring and reinforcer delivery.

A scoping review of mHealth technologies for opioid overdose prevention, detection and response

ISSUES

Opioid overdose kills over 100,000 people each year globally. Mobile health (mHealth) technologies and devices, including wearables, with the capacity to prevent, detect or respond to opioid overdose exist in early form, or could be re-purposed or designed. These technologies may particularly help those who use alone. For technologies to be successful, they must be effective and acceptable to the at-risk population. The aim of this scoping review is to identify published studies on mHealth technologies that attempt to prevent, detect or respond to opioid overdose.

APPROACH

A systematic scoping review of literature was conducted up to October 2022. APA PsychInfo, Embase, Web of Science and Medline databases were searched.

INCLUSION CRITERIA

articles had to report on (i) mHealth technologies that deal with (ii) opioid (iii) overdose.

KEY FINDINGS

A total of 348 records were identified, with 14 studies eligible for this review across four domains: (i) technologies that require intervention/response from others (four); (ii) devices that use biometric data to detect overdose (five); (iii) devices that automatically respond to an overdose with administration of an antidote (three); (iv) acceptability/willingness to use overdose-related technologies/devices (five).

IMPLICATIONS

There are multiple routes in which these technologies may be deployed, but several factors impact acceptability (e.g., discretion or size) and accuracy of detection (e.g., sensitive parameter/threshold with low false positive rate).

CONCLUSION

mHealth technologies for opioid overdose may play a crucial role in responding to the ongoing global opioid crises. This scoping review identifies vital research that will determine the future success of these technologies.

Overdose Alert and Response Technologies: State-of-the-art Review

BACKGROUND

Drug overdose deaths, particularly from opioids, are a major global burden, with 128,000 deaths estimated in 2019. Opioid overdoses can be reversed through the timely administration of naloxone but only if responders are able to administer it. There is an emerging body of research and development in technologies that can detect the early signs of an overdose and facilitate timely responses.

OBJECTIVE

Our aim was to identify and classify overdose-specific digital technologies being developed, implemented, and evaluated.

METHODS

We conducted a "state-of-the-art review." A systematic search was conducted in MEDLINE, Embase, Web of Science, Scopus, ACM, IEEE Xplore, and SciELO. We also searched references from articles and scanned the gray literature. The search included terms related to telehealth and digital technologies, drugs, and overdose and papers published since 2010. We classified our findings by type of technology and its function, year of publication, country of study, study design, and theme. We performed a thematic analysis to classify the papers according to the main subject.

RESULTS

Included in the selection were 17 original research papers, 2 proof-of-concept studies, 4 reviews, 3 US government grant registries, and 6 commercial devices that had not been named in peer-reviewed literature. All articles were published between 2017 and 2022, with a marked increase since 2019. All were based in or referred to the United States or Canada and concerned opioid overdose. In total, 39% (9/23) of the papers either evaluated or described devices designed to monitor vital signs and prompt an alert once a certain threshold indicating a potential overdose has been reached. A total of 43% (10/23) of the papers focused on technologies to alert potential responders to overdoses and facilitate response. In total, 48% (11/23) of the papers and 67% (4/6) of the commercial devices described combined alert and response devices. Sensors monitor a range of vital signs, such as oxygen saturation level, respiratory rate, or movement. Response devices are mostly smartphone apps enabling responders to arrive earlier to an overdose site. Closed-loop devices that can detect an overdose through a sensor and automatically administer naloxone without any external intervention are still in the experimental or proof-of-concept phase. The studies were grouped into 4 themes: acceptability (7/23, 30%), efficacy or effectiveness (5/23, 22%), device use and decision-making (3/23, 13%), and description of devices (6/23, 26%).

CONCLUSIONS

There has been increasing interest in the research and application of these technologies in recent years. Literature suggests willingness to use these devices by people who use drugs and affected communities. More real-life studies are needed to test the effectiveness of these technologies to adapt them to the different settings and populations that might benefit from them.

Challenges and future trends in wearable closed-loop neuromodulation to efficiently treat methamphetamine addiction

Achieving abstinence from drugs is a long journey and can be particularly challenging in the case of methamphetamine, which has a higher relapse rate than other drugs. Therefore, real-time monitoring of patients' physiological conditions before and when cravings arise to reduce the chance of relapse might help to improve clinical outcomes. Conventional treatments, such as behavior therapy and peer support, often cannot provide timely intervention, reducing the efficiency of these therapies. To more effectively treat methamphetamine addiction in real-time, we propose an intelligent closed-loop transcranial magnetic stimulation (TMS) neuromodulation system based on multimodal electroencephalogram-functional near-infrared spectroscopy (EEG-fNIRS) measurements. This review summarizes the essential modules required for a wearable system to treat addiction efficiently. First, the advantages of neuroimaging over conventional techniques such as analysis of sweat, saliva, or urine for addiction detection are discussed. The knowledge to implement wearable, compact, and user-friendly closed-loop systems with EEG and fNIRS are reviewed. The features of EEG and fNIRS signals in patients with methamphetamine use disorder are summarized. EEG biomarkers are categorized into frequency and time domain and topography-related parameters, whereas for fNIRS, hemoglobin concentration variation and functional connectivity of cortices are described. Following this, the applications of two commonly used neuromodulation technologies, transcranial direct current stimulation and TMS, in patients with methamphetamine use disorder are introduced. The challenges of implementing intelligent closed-loop TMS modulation based on multimodal EEG-fNIRS are summarized, followed by a discussion of potential research directions and the promising future of this approach, including potential applications to other substance use disorders.

Safety in solitude? Competing risks and drivers of solitary drug use among women who inject drugs and implications for overdose detection

BACKGROUND AND AIMS

Solitary drug use (SDU) can amplify risks of fatal overdose. We examined competing risks and drivers of SDU, as well as harm reduction strategies implemented during SDU episodes, among women who inject drugs (WWID).

DESIGN

A cross-sectional qualitative study, including telephone and face-to-face in-depth interviews.

SETTING

Baltimore City, MD, USA.

PARTICIPANTS

Twenty-seven WWID (mean age = 39 years, 67% white, 74% injected drugs daily) recruited via outreach and street intercept (April-September 2021).

MEASUREMENTS

Interviews explored the physical (i.e. indoor/private, outdoor/public) and social (i.e. alone, accompanied) risk environments in which drug use occurred. Guided by the principles of emergent design, we used thematic analysis to interrogate textual data, illuminating women's preferences/motivations for SDU and strategies for minimizing overdose risks when using alone.

FINDINGS

Many participants reported experiences with SDU, despite expressed preferences for accompanied drug use. SDU motivations clustered around three primary drivers: (1) avoiding opioid withdrawal, (2) preferences for privacy when using drugs and (3) safety concerns, including threats of violence. Participants nevertheless acknowledged the dangers of SDU and, at times, took steps to mitigate overdose risk, including naloxone possession, communicating to peers when using alone ('spotting') and using drugs in public spaces.

CONCLUSIONS

WWID appear to engage frequently in SDU due to constraints of the physical and social environments in which they use drugs. They express a preference for accompanied drug use in most cases and report implementing strategies to mitigate their overdose risk, especially when using drugs alone.

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.

Perceptions on wearable sensor-based interventions for monitoring of opioid therapy: A qualitative study

Prescription opioid use is a risk factor for the development of opioid use disorder. Digital solutions, including wearable sensors, represent a promising opportunity for health monitoring, risk stratification and harm reduction in this treatment space. However, data on their usability and acceptability in individuals using opioids is limited. To address this gap, factors that impact usability and acceptability of wearable sensor-based opioid detection were qualitatively studied in participants enrolled in a wearable sensor-based opioid monitoring research study. At the conclusion of the monitoring period, participants were invited to take part in semi-structured interviews developed based on the technology acceptance model. Thematic analysis was conducted first using deductive, then inductive coding strategies. Forty-four participants completed the interview; approximately half were female. Major emergent themes include sensor usability, change in behavior and thought process related to sensor use, perceived usefulness in sensor-based monitoring, and willingness to have opioid use patterns monitored. Overall acceptance for sensor-based monitoring was high. Aesthetics, simplicity, and seamless functioning were all reported as key to usability. Perceived behavior changes related to monitoring were infrequent while perceived usefulness in monitoring was frequently projected onto others, requiring careful consideration regarding intervention development and targeting. Specifically, care must be taken to avoid stigma associated with opioid use and implied misuse. The design of sensor systems targeted for opioid use must also consider the physical, social, and cognitive alterations inherent in the respective disease processes compared to routine daily life.

Leveraging mHealth and Wearable Sensors to Manage Alcohol Use Disorders: A Systematic Literature Review

Background: Alcohol use disorder (AUD) is a condition prevalent in many countries around the world, and the public burden of its treatment is close to $130 billion. mHealth offers several possible interventions to assist in the treatment of AUD. Objectives: To analyze the effectiveness of mHealth and wearable sensors to manage AUD from evidence published over the last 10 years. Methods: Following the Kruse Protocol and PRISMA 2020, four databases were queried (PubMed, CINAHL, Web of Science, and Science Direct) to identify studies with strong methodologies (n = 25). Results: Five interventions were identified, and 20/25 were effective at reducing alcohol consumption. Other interventions reported a decrease in depression and an increase in medication compliance. Primary barriers to the adoption of mHealth interventions are a requirement to train users, some are equally as effective as the traditional means of treatment, cost, and computer literacy. Conclusion: While not all mHealth interventions demonstrated statistically significant reduction in alcohol consumption, most are still clinically effective to treat AUD and provide a patient with their preference of a technologically inclined treatment Most interventions require training of users and some technology literacy, the barriers identified were very few compared with the litany of positive results.

Selection of Optimal Physiological Features for Accurate Detection of Stress

Stress is an established risk factor in the development of addiction and in reinstating drug seeking. Substance use disorder (SUD) is a dangerous epidemic that affects the brain and behavior. Despite this growing epidemic and its subsequent consequences, there are limited management and treatment options, pharmacotherapies and psychosocial treatments available. To this end, there is a need for new and improved personalized devices and treatments for the detection and management of SUD. Based on documented negative effects of stress in SUD, in this paper, our objective was to select a few significant physiological features from a set of 8 features collected by a chest-worn RespiBAN Professional in 15 individuals. We used three machine learning classifiers on these optimal physiological features to detect stress. Our results indicate that best accuracies were achieved when electrodermal activity (EDA), body temperature and chest-worn accelerometer were considered as features for the classification. Challenges, implications and applications were discussed. In the near future, the proposed methods will be replicated in individuals with SUD.

Exploring Whether Addictions Counselors Recommend That Their Patients Use Websites, Smartphone Apps, or Other Digital Health Tools to Help Them in Their Recovery: Web-Based Survey

Background

Hundreds of smartphone apps or websites claiming to help those with addictions are available, but few have been tested for efficacy in changing clinically relevant addictions outcomes. Although most of these products are designed for self-facilitation by users struggling with addictions, counselors and other addictions treatment providers will likely play a critical role in facilitating adoption by integrating their use into counseling or recommending them to their patients. Yet, few studies have explored the practices of addictions counselors in using or recommending addictions-focused digital health tools in their work.

Objective

The aim of this study was to understand whether addiction counselors are recommending that their patients use addictions-focused apps to help them in their recovery, and the factors that affect their desire to do so.

Methods

Licensed addiction counselors practicing in the United States (N=112) were recruited from professional and scientific organizations of alcohol or drug counselors to complete a web-based survey.

Results

In total, 74% (83/112) of counselors had recommended that their patients use a website or smartphone app to assist them in recovery, and those that had done so reported recommending an app with an average of 54% of their patients. The most commonly recommended app or website was SMARTRecovery.org (9%), I am Sober (8%), In the Rooms (7%), Insight Timer (4%), Calm (4%), Sober Tool (4%), Recovery Box (3%), and Sober Grid (3%). The most important reason that counselors recommended the websites or apps was that colleagues or patients told them they found it helpful (55%), followed by their workplaces recommending it (20%) and professional organizations recommending it (10%). Counselors’ intentions to recommend a hypothetical app were strongest for apps that had been tested in rigorous, scientific studies that showed they helped users stay sober or reduce their substance use; 94% (105/112) reported that they would “definitely” or “probably” use such an app.

Conclusions

Most addictions counselors surveyed are already recommending that their patients use apps or websites to help them in their recovery, despite the paucity of available products that have evidence supporting their efficacy for addictions outcomes. One way that product developers could increase adoption among addictions treatment providers is to make efficacy testing a priority and to disseminate results through professional organizations and clinics.

Futures-oriented drugs policy research: Events, trends, and speculating on what might become

One concern in the field of drugs policy is how to make research more futures-oriented. Tracing trends and events with the potential to alter drug futures are seen as ways of becoming more prepared. This challenge is made complex in fast evolving drug markets which entangle with shifting social and material relations at global scale. In this analysis, we argue that drugs policy research orientates to detection and discovery based on the recent past. This narrows future-oriented analyses to the predictable and probable, imagined as extensions of the immediate and local present. We call for a more speculative approach; one which extends beyond the proximal, and one which orientates to possibilities rather than probabilities. Drawing on ideas on speculation from science and technology and futures studies, we argue that speculative research holds potential for more radical alterations in drugs policy. We encourage research approaches which not only valorise knowing in relation to what might happen but which conduct experiments on what could be. Accordingly, we trace how speculative research makes a difference by altering the present through making deliberative interventions on alternative policy options, including policy scenarios which make a radical break with the present. We look specifically at the 'Big Event' and 'Mega Trend' as devices of speculative intervention in futures-oriented drugs policy research. We illustrate how the device of Mega Trend helps to trace as well as to speculate on some of the entangling elements affecting drug futures, including in relation to climate, environment, development, population, drug production, digitalisation, biotechnology, policy and discourse.

Views of Practitioners and Researchers on the Use of Virtual Reality in Treatments for Substance Use Disorders

Virtual Reality Therapy (VRT) has been shown to be effective in treating anxiety disorders and phobias, but has not yet been widely tested for Substance Use Disorders (SUDs) and it is not known whether health care practitioners working with SUDs would use VRT if it were available. We report the results of an interview study exploring practitioners’ and researchers’ views on the utility of VRT for SUD treatment. Practitioners and researchers with at least two years’ experience delivering or researching and designing SUD treatments were recruited (n = 14). Interviews were thematically analyzed, resulting in themes relating to the safety and realism of VRT, and the opportunity for the additional insight it could offer to during SUD treatment. Participants were positive about employing VRT as an additional treatment for SUD. VRT was thought suitable for treating adults and people with mental health issues or trauma, provided that risks were appropriately managed. Subsequent relapse, trauma and over-confidence in the success of treatment were identified as risks. The opportunity VRT offered to include other actors in therapy (via avatar use), and observe reactions, were benefits that could not currently be achieved with other forms of therapy. Overall, VRT was thought to offer the potential for safe, realistic, personalized and insightful exposure to diverse triggering scenarios, and to be acceptable for integration into a wide range of SUD treatments.