ArticleThe International journal on drug policy2021
Decision-making by laypersons equipped with an emergency response smartphone app for opioid overdose.
Article in The International journal on drug policy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06466005 (UnityPhilly Response App for Overdose Reversal), which is not on this map. Cited by 2 papers.
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.
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.
UnityPhilly Response App for Overdose Reversal: Assessing Citywide Effectiveness and Sustainability
Who cites it
2 citing papers in PubMed, 12 citations in OpenAlex.
- Overdose Alert and Response Technologies: State-of-the-art Review.Journal of medical Internet research · 2023Review
- Overdose Detection Technologies to Reduce Solitary Overdose Deaths: A Literature Review.International journal of environmental research and public health · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundTargeted naloxone distribution to potential lay responders increases the timeliness of overdose response and reduces mortality. Little is known, however, about the patterns of decision-making among overdose lay responders. This study explored heuristic decision-making among laypersons equipped with an emergency response smartphone app.
methodsUnityPhilly, a smartphone app that connects lay responders equipped with naloxone to overdose victims, was piloted in Philadelphia from March 2019 to February 2020. Participants used the app to signal overdose alerts to peer app users and emergency medical services, or respond to alerts by arriving at overdose emergency sites. This study utilised in-depth interviews, background information, and app use data from a sample of 18 participants with varying histories of opioid use and levels of app use activity.
resultsThe sample included 8 people who used opioids non-medically in the past 30 days and 10 people reporting no opioid misuse. Three prevailing, not mutually exclusive, heuristics were identified. The heuristic of unconditional signalling ("Always signal for help or backup") was used by 7 people who valued external assistance and used the app as a replacement for a 911 call; this group had the highest number of signalled alerts and on-scene appearances. Nine people, who expressed confidence in their ability to address an overdose themselves, followed a heuristic of conditional signalling ("Rescue, but only signal if necessary"); these participants had the highest frequency of prior naloxone administrations. Eleven participants used the heuristic of conditional responding ("Assess if I can make a difference"), addressing an alert if they carried naloxone, were nearby, or received a signal before dark hours.
conclusionThe deployment of specific heuristics was influenced by prior naloxone use and situational factors. Success of overdose prevention interventions assisted by digital technologies may depend on the involvement of people with diverse overdose rescue backgrounds.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.