UMass Chan Medical School Faculty Publications

Title

Using wearable technology to detect prescription opioid self-administration

UMMS Affiliation

Department of Emergency Medicine

Publication Date

2022-02-01

Document Type

Article

Disciplines

Biomedical Devices and Instrumentation | Health Information Technology | Pain Management | Therapeutics

Abstract

Appropriate monitoring of opioid use in patients with pain conditions is paramount, yet it remains a very challenging task. The current work examined the use of a wearable sensor to detect self-administration of opioids after dental surgery using machine learning. Participants were recruited from an oral and maxillofacial surgery clinic. Participants were 46 adult patients (26 female) receiving opioids after dental surgery. Participants wore Empatica E4 sensors during the period they self-administered opioids. The E4 collected physiological parameters including accelerometer x-, y-, and z-axes, heart rate, and electrodermal activity. Four machine learning models provided validation accuracies greater than 80%, but the bagged-tree model provided the highest combination of validation accuracy (83.7%) and area under the receiver operating characteristic curve (0.92). The trained model had a validation sensitivity of 82%, a specificity of 85%, a positive predictive value of 85%, and a negative predictive value of 83%. A subsequent test of the trained model on withheld data had a sensitivity of 81%, a specificity of 88%, a positive predictive value of 87%, and a negative predictive value of 82%. Results from training and testing model of machine learning indicated that opioid self-administration could be identified with reasonable accuracy, leading to considerable possibilities of the use of wearable technology to advance prevention and treatment.

Keywords

Wearable technology, mHealth, Machine learning, Detection, Opioids, Dental surgery

DOI of Published Version

10.1097/j.pain.0000000000002375

Source

Salgado García FI, Indic P, Stapp J, Chintha KK, He Z, Brooks JH, Carreiro S, Derefinko KJ. Using wearable technology to detect prescription opioid self-administration. Pain. 2022 Feb 1;163(2):e357-e367. doi: 10.1097/j.pain.0000000000002375. PMID: 34270522. Link to article on publisher's site

Related Resources

Link to Article in PubMed

Journal/Book/Conference Title

Pain

PubMed ID

34270522

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