Using wearable technology to detect prescription opioid self-administration. Issue 2 (14th June 2021)

• Record Type: Journal Article
• Authors: Salgado García, Francisco I.; Indic, Premananda; Stapp, Joshua; Chintha, Keerthi K.; He, Zhaomin; Brooks, Jeffrey H.; Carreiro, Stephanie; Derefinko, Karen J.
• Is Part Of: Pain. Volume 163:Issue 2(2022)
• Page Start: e357
• Page End: e367
• Publication Date: 2021-06-14
• Subjects: Wearable technology -- mHealth -- Machine learning -- Detection -- Opioids -- Dental surgery; Pain -- Periodicals; Douleur -- Périodiques; Anesthésie -- Périodiques; Pain; Electronic journals; Periodicals; Electronic journals; 616.0472
• DOI: 10.1097/j.pain.0000000000002375
• Languages: English
• ISSNs: 0304-3959

Abstract

Abstract : Results from machine learning indicated that opioid self-administration could be identified with reasonable accuracy, suggesting that wearable technology can be for prevention and treatment. 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.