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Lyzwinski LN, Elgendi M, Menon C. Users' Acceptability and Perceived Efficacy of mHealth for Opioid Use Disorder: Scoping Review. JMIR Mhealth Uhealth 2024; 12:e49751. [PMID: 38602751 PMCID: PMC11046395 DOI: 10.2196/49751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 11/14/2023] [Accepted: 11/29/2023] [Indexed: 04/12/2024] Open
Abstract
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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Affiliation(s)
- Lynnette Nathalie Lyzwinski
- Menrva Research Group, School of Mechatronics Systems Engineering and Engineering Science, Simon Fraser University, Vancouver, BC, Canada
| | - Mohamed Elgendi
- ETH Biomedical and Mobile Health Technology Lab, Zurich, Switzerland
| | - Carlo Menon
- Menrva Research Group, School of Mechatronics Systems Engineering and Engineering Science, Simon Fraser University, Vancouver, BC, Canada
- ETH Biomedical and Mobile Health Technology Lab, Zurich, Switzerland
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Mesa JC, MacLean MD, Ms M, Nguyen A, Patel R, Diemer T, Lim J, Lee CH, Lee H. A Wearable Device Towards Automatic Detection and Treatment of Opioid Overdose. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2024; 18:396-407. [PMID: 37938943 DOI: 10.1109/tbcas.2023.3331272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Opioid-induced overdose is one of the leading causes of death among the US population under the age of 50. In 2021 alone, the death toll among opioid users rose to a devastating number of over 80,000. The overdose process can be reversed by the administration of naloxone, an opioid antagonist that rapidly counteracts the effects of opioid-induced respiratory depression. The idea of a closed-loop opioid overdose detection and naloxone delivery has emerged as a potential engineered solution to mitigate the deadly effects of the opioid epidemic. In this work, we introduce a wrist-worn wearable device that overcomes the portability issues of our previous work to create a closed-loop drug-delivery system, which includes (1) a Near-Infrared Spectroscopy (NIRS) sensor to detect a hypoxia-driven opioid overdose event, (2) a MOSFET switch, and (3) a Zero-Voltage Switching (ZVS) electromagnetic heater. Using brachial artery occlusion (BAO) with human subjects (n = 8), we demonstrated consistent low oxygenation events. Furthermore, we proved our device's capability to release the drug within 10 s after detecting a hypoxic event. We found that the changes in the oxyhemoglobin, deoxyhemoglobin and oxygenation saturation levels ( SpO2) were different before and after the low-oxygenation events ( 0.001). Although additional human experiments are needed, our results to date point towards a potential tool in the battle to mitigate the effects of the opioid epidemic.
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Albrechta H, Goodman GR, Oginni E, Mohamed Y, Venkatasubramanian K, Dumas A, Carreiro S, Lee JS, Glynn TR, O'Cleirigh C, Mayer KH, Fisher CB, Chai PR. Acceptance of digital phenotyping linked to a digital pill system to measure PrEP adherence among men who have sex with men with substance use. PLOS DIGITAL HEALTH 2024; 3:e0000457. [PMID: 38386618 PMCID: PMC10883553 DOI: 10.1371/journal.pdig.0000457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 02/01/2024] [Indexed: 02/24/2024]
Abstract
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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Affiliation(s)
- Hannah Albrechta
- The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America
| | - Georgia R Goodman
- The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Elizabeth Oginni
- The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America
| | - Yassir Mohamed
- The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America
| | - Krishna Venkatasubramanian
- Department of Computer Science and Statistics, The University of Rhode Island, Kingston, Rhode Island, United States of America
| | - Arlen Dumas
- Department of Computer Science and Statistics, The University of Rhode Island, Kingston, Rhode Island, United States of America
| | - Stephanie Carreiro
- Department of Emergency Medicine, University of Massachusetts Chan Medical School
| | - Jasper S Lee
- The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Tiffany R Glynn
- The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Conall O'Cleirigh
- The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Kenneth H Mayer
- The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
| | - Celia B Fisher
- Center for Ethics Education, Fordham University, New York City, New York, United States of America
| | - Peter R Chai
- The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute, Boston, Massachusetts, United States of America
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
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Rigatti M, Chapman B, Chai PR, Smelson D, Babu K, Carreiro S. Digital Biomarker Applications Across the Spectrum of Opioid Use Disorder. COGENT MENTAL HEALTH 2023; 2:2240375. [PMID: 37546179 PMCID: PMC10399596 DOI: 10.1080/28324765.2023.2240375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 07/17/2023] [Indexed: 08/08/2023]
Abstract
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.
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Affiliation(s)
- Marc Rigatti
- Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, USA
| | - Brittany Chapman
- Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, USA
| | - Peter R Chai
- Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA
| | - David Smelson
- Department of Psychiatry, UMass Chan Medical School, Worcester, MA, USA
| | - Kavita Babu
- Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, USA
| | - Stephanie Carreiro
- Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, USA
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Cascella M, Schiavo D, Cuomo A, Ottaiano A, Perri F, Patrone R, Migliarelli S, Bignami EG, Vittori A, Cutugno F. Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives. Pain Res Manag 2023; 2023:6018736. [PMID: 37416623 PMCID: PMC10322534 DOI: 10.1155/2023/6018736] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 02/03/2023] [Accepted: 04/20/2023] [Indexed: 07/08/2023]
Abstract
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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Affiliation(s)
- Marco Cascella
- Division of Anesthesia and Pain Medicine, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Naples 80131, Italy
| | - Daniela Schiavo
- Division of Anesthesia and Pain Medicine, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Naples 80131, Italy
| | - Arturo Cuomo
- Division of Anesthesia and Pain Medicine, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Naples 80131, Italy
| | - Alessandro Ottaiano
- SSD-Innovative Therapies for Abdominal Metastases, Istituto Nazionale Tumori di Napoli IRCCS “G. Pascale”, Via M. Semmola, Naples 80131, Italy
| | - Francesco Perri
- Head and Neck Oncology Unit, Istituto Nazionale Tumori IRCCS-Fondazione “G. Pascale”, Naples 80131, Italy
| | - Renato Patrone
- Dieti Department, University of Naples, Naples, Italy
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS, Fondazione Pascale-IRCCS di Napoli, Naples, Italy
| | - Sara Migliarelli
- Department of Pharmacology, Faculty of Medicine and Psychology, University Sapienza of Rome, Rome, Italy
| | - Elena Giovanna Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Alessandro Vittori
- Department of Anesthesia and Critical Care, ARCO ROMA, Ospedale Pediatrico Bambino Gesù IRCCS, Rome 00165, Italy
| | - Francesco Cutugno
- Department of Electrical Engineering and Information Technologies, University of Naples “Federico II”, Naples 80100, Italy
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