Using wearable technology to detect prescription opioid self-administration

García, Francisco I. Salgado; Indic, Premananda; Stapp, Joshua; Chintha, Keerthi Kumar; He, Zhaomin; Brooks, Jeffrey H; Carreiro, Stephanie; Derefinko, Karen J.

doi:10.1097/j.pain.0000000000002375

Cited by 9 publications

(9 citation statements)

References 38 publications

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“…Using DPS technology to better understand the contextual basis of PrEP adherence and nonadherence may help provide support to individuals who struggle with PrEP adherence at key junctures of risk. The emerging use of wearable devices and collection of smartphonebased digital phenotyping data may provide insight into key events where nonadherence is likely and the delivery of proactive, personalized adherence support may mitigate nonadherence [22][23][24][25]. Contrary to perceptions that individuals with substance use may be less accepting of the collection of personal data via mobile devices and other systems, the degree of substance use in our subsample was not associated the willingness of MSM on PrEP to interact with ancillary devices or text message-based queries to contextualize DPS-detected adherence data.…”

Section: Discussionmentioning

confidence: 99%

Acceptance of digital phenotyping linked to a digital pill system to measure PrEP adherence among men who have sex with men with substance use

Albrechta,

Goodman,

Oginni

et al. 2024

PLOS Digit Health

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Once-daily oral HIV pre-exposure prophylaxis (PrEP) is an effective strategy to prevent HIV, but is highly dependent on adherence. Men who have sex with men (MSM) who use substances face unique challenges maintaining PrEP adherence. Digital pill systems (DPS) allow for real-time adherence measurement through ingestible sensors. Integration of DPS technology with other digital health tools, such as digital phenotyping, may improve understanding of nonadherence triggers and development of personalized adherence interventions based on ingestion behavior. This study explored the willingness of MSM with substance use to share digital phenotypic data and interact with ancillary systems in the context of DPS-measured PrEP adherence. Adult MSM on PrEP with substance use were recruited through a social networking app. Participants were introduced to DPS technology and completed an assessment to measure willingness to participate in DPS-based PrEP adherence research, contribute digital phenotyping data, and interact with ancillary systems in the context of DPS-based research. Medical mistrust, daily worry about PrEP adherence, and substance use were also assessed. Participants who identified as cisgender male and were willing to participate in DPS-based research (N = 131) were included in this subsample analysis. Most were White (76.3%) and non-Hispanic (77.9%). Participants who reported daily PrEP adherence worry had 3.7 times greater odds (95% CI: 1.03, 13.4) of willingness to share biometric data via a wearable device paired to the DPS. Participants with daily PrEP adherence worry were more likely to be willing to share smartphone data (p = 0.006) and receive text messages surrounding their daily activities (p = 0.003), compared to those with less worry. MSM with substance use disorder, who worried about PrEP adherence, were willing to use DPS technology and share data required for digital phenotyping in the context of PrEP adherence measurement. Efforts to address medical mistrust can increase advantages of this technology for HIV prevention.

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Section: Discussionmentioning

confidence: 99%

Acceptance of digital phenotyping linked to a digital pill system to measure PrEP adherence among men who have sex with men with substance use

Albrechta,

Goodman,

Oginni

et al. 2024

PLOS Digit Health

Self Cite

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“…Since that study was published, there have been technological developments in this area to meet the pressing need that arose during the COVID-19 pandemic. Specifically, new wearable sensors have been developed that monitor sweat, heart rate, and temperature and that predict overdose in patients [7][8][9][10][11][12][13]. Several studies have evaluated the application of wearable opioid sensors for detecting overdose by detecting changes in temperature, movement, respiratory rate, heart rate, and electrodermal activity [8,[11][12][13][14][15][16][17].…”

Section: Overviewmentioning

confidence: 99%

Users' Acceptability and Perceived Efficacy of mHealth for Opioid Use Disorder: Scoping Review

Lyzwinski,

Elgendi,

Menon

2024

JMIR Mhealth Uhealth

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Background The opioid crisis continues to pose significant challenges to global public health, necessitating the development of novel interventions to support individuals in managing their substance use and preventing overdose-related deaths. Mobile health (mHealth), as a promising platform for addressing opioid use disorder, requires a comprehensive understanding of user perspectives to minimize barriers to care and optimize the benefits of mHealth interventions. Objective This study aims to synthesize qualitative insights into opioid users’ acceptability and perceived efficacy of mHealth and wearable technologies for opioid use disorder. Methods A scoping review of PubMed (MEDLINE) and Google Scholar databases was conducted to identify research on opioid user perspectives concerning mHealth-assisted interventions, including wearable sensors, SMS text messaging, and app-based technology. Results Overall, users demonstrate a high willingness to engage with mHealth interventions to prevent overdose-related deaths and manage opioid use. Users perceive mHealth as an opportunity to access care and desire the involvement of trusted health care professionals in these technologies. User comfort with wearing opioid sensors emerged as a significant factor. Personally tailored content, social support, and encouragement are preferred by users. Privacy concerns and limited access to technology pose barriers to care. Conclusions To maximize benefits and minimize risks for users, it is crucial to implement robust privacy measures, provide comprehensive user training, integrate behavior change techniques, offer professional and peer support, deliver tailored messages, incorporate behavior change theories, assess readiness for change, design stigma-reducing apps, use visual elements, and conduct user-focused research for effective opioid management in mHealth interventions. mHealth demonstrates considerable potential as a tool for addressing opioid use disorder and preventing overdose-related deaths, given the high acceptability and perceived benefits reported by users.

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“…This wearable device is primarily used for research in fields such as psychology, neuroscience, and physiology. It is also used for monitoring stress and emotional states, as well as for tracking sleep patterns and physical activity in chronic pain rehabilitation [ 67 ], and for monitoring opioid use in patients with pain conditions [ 68 ].…”

Section: Neurophysiology-based Pain Detectionmentioning

confidence: 99%

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives

Cascella,

Schiavo,

Cuomo

et al. 2023

Pain Research and Management

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Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.

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Using wearable technology to detect prescription opioid self-administration

Abstract: Results from machine learning indicated that opioid self-administration could be identified with reasonable accuracy, suggesting that wearable technology can be for prevention and treatment.

Cited by 9 publications

References 38 publications

Acceptance of digital phenotyping linked to a digital pill system to measure PrEP adherence among men who have sex with men with substance use

Acceptance of digital phenotyping linked to a digital pill system to measure PrEP adherence among men who have sex with men with substance use

Users' Acceptability and Perceived Efficacy of mHealth for Opioid Use Disorder: Scoping Review

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives

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