Overdose Alert and Response Technologies: State-of-the-art Review
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,013 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».