Evidence map›Paper›PMID 36790860›Full record

ReviewJournal of medical Internet research2023

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

Alberto Oteo, Hadi Daneshvar, Alexander Baldacchino, Catriona Matheson

Abstract readReview
In one paragraph

Review in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed.

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  14. Perceptions of overdose response hotlines and applications among rural and remote individuals who use drugs in Canada: a qualitative study.Health promotion and chronic disease prevention in Canada : research, policy and practice · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Alberto OteoDigitAS, Populations and Behavioural Science Division, School of Medicine, University of St Andrews, St Andrews, Fife, United Kingdom.ORCID 0000-0002-4239-9529
Hadi Daneshvar *Salvation Army Centre for Addiction Services, Faculty of Social Sciences, University of Stirling, Stirling, United Kingdom.ORCID 0000-0002-2727-7646
Alexander BaldacchinoDigitAS, Populations and Behavioural Science Division, School of Medicine, University of St Andrews, St Andrews, Fife, United Kingdom.ORCID 0000-0002-0227-2849
Catriona MathesonFaculty of Social Sciences, University of Stirling, Stirling, United Kingdom.ORCID 0000-0002-7568-1125

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDrug 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.

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

methodsWe 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.

resultsIncluded 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%).

conclusionsThere 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.

Indexed as

Drug OverdoseTelemedicineAnalgesics, OpioidHumansNaloxoneTechnologyUnited StatesAnalgesics, OpioidNaloxoneapneadrug overdosemHealthmobile healthmobile phonenaloxoneopioidssensortechnologytelemedicine

Identifiers

PMID36790860
PMCPMC9978985

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.