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Opioid overdose detection using smartphones.

Rajalakshmi Nandakumar1, Shyamnath Gollakota2, Jacob E Sunshine3

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Smartphone algorithms can detect opioid overdose by monitoring breathing patterns. This early detection system, using phone-based sonar, could help prevent fatal overdoses and facilitate rapid intervention.

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Area of Science:

  • Biomedical Engineering
  • Public Health
  • Toxicology

Background:

  • Opioid overdose, particularly from fentanyl, causes respiratory depression and death.
  • Early detection and intervention are critical for preventing fatalities.
  • Current detection methods are limited, necessitating novel approaches.

Purpose of the Study:

  • To develop and validate smartphone-based algorithms for early detection of opioid overdose.
  • To assess the system's effectiveness in real-world settings and simulated overdose events.
  • To explore the potential of this technology as a harm reduction intervention.

Main Methods:

  • Developed contactless algorithms running on smartphones to act as short-range active sonar.
  • Utilized frequency shifts to detect respiratory depression, apnea, and motor movements.
  • Tested the system in a supervised injection facility (SIF) and an operating room (OR) with simulated overdoses.

Main Results:

  • In the SIF, the system achieved 96% sensitivity and 98% specificity for detecting central apnea and 87% sensitivity/89% specificity for respiratory depression.
  • The algorithm successfully identified 19 out of 20 simulated overdose events in the OR.
  • Key events preceding overdose were reliably detected by the smartphone system.

Conclusions:

  • Smartphone-based sonar technology can effectively detect precursors to fatal opioid overdose.
  • This low-barrier intervention holds potential for harm reduction by enabling timely alerts to naloxone providers or EMS.
  • Further development could significantly impact overdose prevention strategies.