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. 2015 Dec;39(12):186.
doi: 10.1007/s10916-015-0337-9. Epub 2015 Oct 21.

iMStrong: Deployment of a Biosensor System to Detect Cocaine Use

Affiliations

iMStrong: Deployment of a Biosensor System to Detect Cocaine Use

Stephanie Carreiro et al. J Med Syst. 2015 Dec.

Abstract

Biosensor systems are increasingly promoted for use in behavioral interventions. Portable biosensors might offer advancement over self-report use and can provide improved opportunity for detection and intervention in patients undergoing drug treatment programs. Fifteen participants wore a biosensor wristband capable of detecting multiple physiologic markers of sympathetic nervous system (SNS) arousal for 30 days. Urine drug screening and drug use self-report were obtained twice per week. A parameter trajectory description method was applied to capture abrupt changes in magnitude of three measures of SNS activity: Electrodermal activity (EDA), skin temperature and motion. Drug use events detected by the biosensor were verified using a triad of parameters: the biosensor data, urine drug screens, and patient self-report of substance use. Twelve positive cocaine urine screens were identified. Thirteen self-reported episodes of cocaine use were recorded. Distinct episodes with biometric parameters consistent with cocaine use were identified on biosensor data. Eleven potential cocaine use episodes were identified by biosensors that were missed by both self-report and drug screening. Study participants found mobile biosensors to be acceptable, and compliance with the protocol was high. Episodes of cocaine use, as measured by supraphysiologic changes in biophysiometric parameters, were detected by analysis of biosensor data in instances when self-report or drug screening or both failed. Biosensors have substantial potential in detecting substance abuse, in understanding the context of use in real time, and in evaluating the efficacy of behavioral interventions for drug abuse.

Keywords: Biosensor system; Drug abuse; Mobile biosensing; Parameter trajectory; mHealth.

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Figures

Fig. 1
Fig. 1
Affectiva Q sensor a On participant’s wrist b Back of sensor, showing skin electrodes
Fig. 2
Fig. 2
Analytical approach to assess iMStrong biosensor system in drug use detection
Fig. 3
Fig. 3
Demographics
Fig. 4
Fig. 4
Drug use detection by iMstrong biosensor system (Note: Red dashed lines represent urine screen dates of interest, red dots represent drug use episodes of interest) a Biosensor captured two drug use episodes reflected in urine screen but not self-reported b Biosensor captured drug use episode self-reported but not reflected in urine screen c Biosensor captured suspicious drug use episode neither self-reported nor reflected in urine screen
Fig. 5
Fig. 5
Biosensor user experience

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