Smartphone Ownership, Smartphone Utilization, and Interest in Using Mental Health Apps to Address Substance Use Disorders: Literature Review and Cross-sectional Survey Study Across Two Sites

Technology-based interventions for tobacco smoking prevention and treatment: a 20-year bibliometric analysis (2003-2022)

BACKGROUND

Substance abuse, particularly tobacco smoking, is a significant global public health concern. Efforts have been made to reduce smoking prevalence and promote cessation, but challenges, such as nicotine addiction, marketing tactics by tobacco industry, and cultural acceptability hinder progress. Technology has emerged as a potential tool to address these challenges by providing innovative scalable interventions. The objective of the study was to analyze and map scientific literature on technology-based intervention for tobacco prevention and treatment.

METHODS

A bibliometric methodology was conducted. Scopus database was used to retrieve relevant research articles published between 2003 and 2022. The analysis included publication trends, key contributors, research hotspots, research themes, the most impactful articles, and emerging research topics.

RESULTS

A total of 639 articles were found, with a slow and fluctuating growth pattern observed after 2011. The Journal of Medical Internet Research was the most prominent journal in the field. The United States was the leading country in the field, followed up by the United Kingdom, and the Netherlands. Research hotspots included smoking cessation, randomized controlled trials, and technology-based methods such as internet, mHealth, smartphone apps, text messages, and social media. Four primary research themes were identified: development of smartphone applications, efficacy of text messaging interventions, acceptance and effectiveness of smartphone applications, and interventions targeting young adults and students using mobile phone and social media platforms. The top 10 cited articles demonstrated effectiveness of digital interventions in promoting smoking cessation rates and reducing relapse rates. Emerging research topics included the use of virtual reality interventions, interventions for specific populations through personalized tools, and technology-based interventions in non-Western countries.

CONCLUSIONS

The findings of the current study highlight the potential of technology to address the challenges associated with tobacco smoking. Further future research in this area is warranted to continue advancing the field and developing effective and evidence-based interventions to combat tobacco smoking.

Preference for hotline versus mobile application/countdown-based mobile overdose response services: a qualitative study

BACKGROUND

In response to the exacerbated rates of morbidity and mortality associated with the overlapping overdose and COVID-19 epidemics, novel strategies have been developed, implemented, operationalized and scaled to reduce the harms resulting from this crisis. Since the emergence of mobile overdose response services (MORS), two strategies have aimed to help reduce the mortality associated with acute overdose including staffed hotline-based services and unstaffed timer-based services. In this article, we aim to gather the perspectives of various key interest groups on these technologies to determine which might best support service users.

METHODS

Forty-seven participants from various interested groups including people who use substances who have and have not used MORS, healthcare workers, family members, harm reduction employees and MORS operators participated in semi-structured interviews. Transcripts were coded and analyzed using a thematic analysis approach.

RESULTS

Four major themes emerged regarding participant perspectives on the differences between services, namely differences in connection, perceived safety, privacy and accessibility, alongside features that are recommended for MORS in the future.

CONCLUSIONS

Overall, participants noted that individuals who use substances vary in their desire for connection during a substance use session offered by hotline and timer-based service modalities. Participants perceived hotline-based approaches to be more reliable and thus potentially safer than their timer-based counterparts but noted that access to technology is a limitation of both approaches.

Precision psychiatry and Research Domain Criteria: Implications for clinical trials and future practice

Psychiatric disorders are associated with significant social and economic burdens, many of which are related to issues with current diagnosis and treatments. The coronavirus (COVID-19) pandemic is estimated to have increased the prevalence and burden of major depressive and anxiety disorders, indicating an urgent need to strengthen mental health systems globally. To date, current approaches adopted in drug discovery and development for psychiatric disorders have been relatively unsuccessful. Precision psychiatry aims to tailor healthcare more closely to the needs of individual patients and, when informed by neuroscience, can offer the opportunity to improve the accuracy of disease classification, treatment decisions, and prevention efforts. In this review, we highlight the growing global interest in precision psychiatry and the potential for the National Institute of Health-devised Research Domain Criteria (RDoC) to facilitate the implementation of transdiagnostic and improved treatment approaches. The need for current psychiatric nosology to evolve with recent scientific advancements and increase awareness in emerging investigators/clinicians of the value of this approach is essential. Finally, we examine current challenges and future opportunities of adopting the RDoC-associated translational and transdiagnostic approaches in clinical studies, acknowledging that the strength of RDoC is that they form a dynamic framework of guiding principles that is intended to evolve continuously with scientific developments into the future. A collaborative approach that recruits expertise from multiple disciplines, while also considering the patient perspective, is needed to pave the way for precision psychiatry that can improve the prognosis and quality of life of psychiatric patients.

Access challenges to opioid use disorder treatment among individuals experiencing homelessness: Voices from the streets

BACKGROUND

Achieving equitable access to medications for opioid use disorder (MOUD) such as buprenorphine is a pressing issue. Evidence suggests disparities in MOUD access based on race and socioeconomic status, further exacerbated by the COVID-19 pandemic. However, the drivers behind this access gap remain poorly understood. This study explores barriers to treatment access among individuals with opioid use disorder (OUD) experiencing homelessness.

METHODS

We interviewed 28 individuals in and around the Boston Public Health Commission (BPHC) Engagement Center, an area known for its high density of active substance use and homelessness. We asked about people's experiences, perceptions, and attitudes toward OUD treatment. We conducted a thematic analysis of our interview data.

RESULTS

Fifty-four percent of participants sampled were not prescribed MOUD. None of the participants reported having an active prescription of sublingual buprenorphine or buprenorphine/naloxone. White participants were more likely to have been prescribed buprenorphine in the past compared to participants of other races even in this socioeconomically homogeneous sample. Themes that emerged in our data included challenges to accessing MOUD due to reduced services during the COVID-19 pandemic, lost or stolen medications, fewer inpatient withdrawal management beds for women, transportation challenges, fear of adverse effects of MOUD, the perception that taking MOUD replaces one addiction for another, and community disapproval of MOUD. Participants also reported stigma and discrimination based on race, gender, and socioeconomic status.

CONCLUSION

Systems and individual-level factors contribute to the MOUD treatment gap across race and socioeconomic status. The COVID-19 pandemic posed additional access challenges. This study provides important, actionable insights about the barriers faced by a particularly vulnerable population of individuals with OUD experiencing homelessness.

Engagement with mHealth Alcohol Interventions: User Perspectives on an App or Chatbot-Delivered Program to Reduce Drinking

Research suggests participant engagement is a key mediator of mHealth alcohol interventions' effectiveness in reducing alcohol consumption among users. Understanding the features that promote engagement is critical to maximizing the effectiveness of mHealth-delivered alcohol interventions. The purpose of this study was to identify facilitators and barriers to mHealth alcohol intervention utilization among hazardous-drinking participants who were randomized to use either an app (Step Away) or Artificial Intelligence (AI) chatbot-based intervention for reducing drinking (the Step Away chatbot). We conducted semi-structured interviews from December 2019 to January 2020 with 20 participants who used the app or chatbot for three months, identifying common facilitators and barriers to use. Participants of both interventions reported that tracking their drinking, receiving feedback about their drinking, feeling held accountable, notifications about high-risk drinking times, and reminders to track their drinking promoted continued engagement. Positivity, personalization, gaining insight into their drinking, and daily tips were stronger facilitator themes among bot users, indicating these may be strengths of the AI chatbot-based intervention when compared to a user-directed app. While tracking drinking was a theme among both groups, it was more salient among app users, potentially due to the option to quickly track drinks in the app that was not present with the conversational chatbot. Notification glitches, technology glitches, and difficulty with tracking drinking data were usage barriers for both groups. Lengthy setup processes were a stronger barrier for app users. Repetitiveness of the bot conversation, receipt of non-tailored daily tips, and inability to self-navigate to desired content were reported as barriers by bot users. To maximize engagement with AI interventions, future developers should include tracking to reinforce behavior change self-monitoring and be mindful of repetitive conversations, lengthy setup, and pathways that limit self-directed navigation.

Analysis of Wastewater Samples to Explore Community Substance Use in the United States: Pilot Correlative and Machine Learning Study

BACKGROUND

Substance use disorder and associated deaths have increased in the United States, but methods for detecting and monitoring substance use using rapid and unbiased techniques are lacking. Wastewater-based surveillance is a cost-effective method for monitoring community drug use. However, the examination of the results often focuses on descriptive analysis.

OBJECTIVE

The objective of this study was to explore community substance use in the United States by analyzing wastewater samples. Geographic differences and commonalities of substance use were explored.

METHODS

Wastewater was sampled across the United States (n=12). Selected drugs with misuse potential, prescriptions, and over-the-counter drugs and their metabolites were tested across geographic locations for 7 days. Methods used included wastewater assessment of substances and metabolites paired with machine learning, specifically discriminant analysis and cluster analysis, to explore similarities and differences in wastewater measures.

RESULTS

Geographic variations in the wastewater drug or metabolite levels were found. Results revealed a higher use of methamphetamine (z=-2.27, P=.02) and opioids-to-methadone ratios (oxycodone-to-methadone: z=-1.95, P=.05; hydrocodone-to-methadone: z=-1.95, P=.05) in states west of the Mississippi River compared to the east. Discriminant analysis suggested temazepam and methadone were significant predictors of geographical locations. Precision, sensitivity, specificity, and F1-scores were 0.88, 1, 0.80, and 0.93, respectively. Finally, cluster analysis revealed similarities in substance use among communities.

CONCLUSIONS

These findings suggest that wastewater-based surveillance has the potential to become an effective form of surveillance for substance use. Further, advanced analytical techniques may help uncover geographical patterns and detect communities with similar needs for resources to address substance use disorders. Using automated analytics, these advanced surveillance techniques may help communities develop timely, tailored treatment and prevention efforts.

Use of Mobile Applications in Smoking, Alcohol and Substance Use Disorders

Currently, technology and mobile health applications are developing and being applied in different fields every day. Especially the use of mobile technology in many health fields offers various opportunities for the use of mobile applications in smoking, alcohol and substance use disorders, which is an important public mental health problem. The aim of this review study was to examine 16 mobile applications designed for the treatment and monitoring of smoking, alcohol and substance use. It was concluded that self-monitoring, notification and warning systems, location services, peer and expert support services were frequently used in the analyzed mobile applications. As a result of the studies, the use of mobile applications as a preventive intervention in the prevention of addiction and as an additional treatment option in addiction treatment is promising. However, this situation brings along various harms, risks and obstacles. This review of the relevant literature provides a critical update of mobile applications used in smoking, alcohol and substance use disorders. This review is expected to be an important guide for mental health professionals and mobile application designers working on addiction.

Prospective acceptability of digital phenotyping among pregnant and parenting people with opioid use disorder: A multisite qualitative study

Background

While medications for opioid use disorder (MOUD) effectively treat OUD during pregnancy and the postpartum period, poor treatment retention is common. Digital phenotyping, or passive sensing data captured from personal mobile devices, namely smartphones, provides an opportunity to understand behaviors, psychological states, and social influences contributing to perinatal MOUD non-retention. Given this novel area of investigation, we conducted a qualitative study to determine the acceptability of digital phenotyping among pregnant and parenting people with opioid use disorder (PPP-OUD).

Methods

This study was guided by the Theoretical Framework of Acceptability (TFA). Within a clinical trial testing a behavioral health intervention for PPP-OUD, we used purposeful criterion sampling to recruit 11 participants who delivered a child in the past 12  months and received OUD treatment during pregnancy or the postpartum period. Data were collected through phone interviews using a structured interview guide based on four TFA constructs (affective attitude, burden, ethicality, self-efficacy). We used framework analysis to code, chart, and identify key patterns within the data.

Results

Participants generally expressed positive attitudes about digital phenotyping and high self-efficacy and low anticipated burden to participate in studies that collect smartphone-based passive sensing data. Nonetheless, concerns were noted related to data privacy/security and sharing location information. Differences in participant assessments of burden were related to length of time required and level of remuneration to participate in a study. Interviewees voiced broad support for participating in a digital phenotyping study with known/trusted individuals but expressed concerns about third-party data sharing and government monitoring.

Conclusion

Digital phenotyping methods were acceptable to PPP-OUD. Enhancements in acceptability include allowing participants to maintain control over which data are shared, limiting frequency of research contacts, aligning compensation with participant burden, and outlining data privacy/security protections on study materials.