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dc.contributor.authorSalgado Garcia, Francisco I.
dc.contributor.authorIndic, Premananda
dc.contributor.authorStapp, Joshua
dc.contributor.authorChintha, Keerthi K.
dc.contributor.authorHe, Zhaomin
dc.contributor.authorBrooks, Jeffrey H.
dc.contributor.authorCarreiro, Stephanie
dc.contributor.authorDerefinko, Karen J.
dc.date2022-08-11T08:08:28.000
dc.date.accessioned2022-08-23T15:56:27Z
dc.date.available2022-08-23T15:56:27Z
dc.date.issued2022-02-01
dc.date.submitted2022-01-31
dc.identifier.citation<p>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. <a href="https://doi.org/10.1097/j.pain.0000000000002375">Link to article on publisher's site</a></p>
dc.identifier.issn0304-3959 (Linking)
dc.identifier.doi10.1097/j.pain.0000000000002375
dc.identifier.pmid34270522
dc.identifier.urihttp://hdl.handle.net/20.500.14038/29970
dc.description.abstractAppropriate 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.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=34270522&dopt=Abstract">Link to Article in PubMed</a></p>
dc.relation.urlhttps://doi.org/10.1097/j.pain.0000000000002375
dc.subjectWearable technology
dc.subjectmHealth
dc.subjectMachine learning
dc.subjectDetection
dc.subjectOpioids
dc.subjectDental surgery
dc.subjectBiomedical Devices and Instrumentation
dc.subjectHealth Information Technology
dc.subjectPain Management
dc.subjectTherapeutics
dc.titleUsing wearable technology to detect prescription opioid self-administration
dc.typeJournal Article
dc.source.journaltitlePain
dc.source.volume163
dc.source.issue2
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/faculty_pubs/2171
dc.identifier.contextkey27891368
atmire.contributor.authoremailstephanie.carreiro@umassmed.edu
html.description.abstract<p>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.</p>
dc.identifier.submissionpathfaculty_pubs/2171
dc.contributor.departmentDepartment of Emergency Medicine
dc.source.pagese357-e367


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