ArticleIPSN : [proceedings]. IPSN (Conference)2014
Identifying Drug (Cocaine) Intake Events from Acute Physiological Response in the Presence of Free-living Physical Activity.
Article in IPSN : [proceedings]. IPSN (Conference), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02915341 (Towards Detecting Cocaine Use Using Smartwatches in the NIDA Clinical Trials Network), which is not on this map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Towards Detecting Cocaine Use Using Smartwatches in the NIDA Clinical Trials Network
Who cites it
28 citing papers in PubMed, 35 citations in OpenAlex.
- Sense2Stop: A micro-randomized trial using wearable sensors to optimize a just-in-time-adaptive stress management intervention for smoking relapse prevention.Contemporary clinical trials · 2021Trial
- Mobile Health Interventions for Substance Use Disorders.Annual review of clinical psychology · 2024Review
- Discriminating cocaine use from other sympathomimetics using wearable electrocardiographic (ECG) sensors.Drug and alcohol dependence · 2023Article
- mRisk: Continuous Risk Estimation for Smoking Lapse from Noisy Sensor Data with Incomplete and Positive-Only Labels.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2022Article
- OpiTrack: A Wearable-based Clinical Opioid Use Tracker with Temporal Convolutional Attention Networks.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2021Article
- I feel good? Anhedonia might not mean "without pleasure" for people treated for opioid use disorder.Journal of abnormal psychology · 2021Article
- An Examination of the Feasibility of Detecting Cocaine Use Using Smartwatches.Frontiers in psychiatry · 2021Article
- The application of digital health to the assessment and treatment of substance use disorders: The past, current, and future role of the National Drug Abuse Treatment Clinical Trials Network.Journal of substance abuse treatment · 2020Article
- Methods to reduce the incidence of false negative trial results in substance use treatment research.Current opinion in psychology · 2019Review
- Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs.Circulation. Arrhythmia and electrophysiology · 2019Article
- Towards detecting cocaine use using smartwatches in the NIDA clinical trials network: Design, rationale, and methodology.Contemporary clinical trials communications · 2019Article
- Caregiver Daily Reporting of Symptoms in Autism Spectrum Disorder: Observational Study Using Web and Mobile Apps.JMIR mental health · 2019Article
- Combining ecological momentary assessment with objective, ambulatory measures of behavior and physiology in substance-use research.Addictive behaviors · 2018Review
- mHealth for the Detection and Intervention in Adolescent and Young Adult Substance Use Disorder.Current addiction reports · 2018Article
- Exacerbated Craving in the Presence of Stress and Drug Cues in Drug-Dependent Patients.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2018Observational
- Context and craving during stressful events in the daily lives of drug-dependent patients.Psychopharmacology · 2017Article
- Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2017Article
- Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research.Journal of psychiatric research · 2017Review
- iSurvive: An Interpretable, Event-time Prediction Model for mHealth.Proceedings of machine learning research · 2017Article
- Domain Adaptation Methods for Improving Lab-to-field Generalization of Cocaine Detection using Wearable ECG.Proceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference) · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 6 institutions in 1 country.
Funding
Abstract
A variety of health and behavioral states can potentially be inferred from physiological measurements that can now be collected in the natural free-living environment. The major challenge, however, is to develop computational models for automated detection of health events that can work reliably in the natural field environment. In this paper, we develop a physiologically-informed model to automatically detect drug (cocaine) use events in the free-living environment of participants from their electrocardiogram (ECG) measurements. The key to reliably detecting drug use events in the field is to incorporate the knowledge of autonomic nervous system (ANS) behavior in the model development so as to decompose the activation effect of cocaine from the natural recovery behavior of the parasympathetic nervous system (after an episode of physical activity). We collect 89 days of data from 9 active drug users in two residential lab environments and 922 days of data from 42 active drug users in the field environment, for a total of 11,283 hours. We develop a model that tracks the natural recovery by the parasympathetic nervous system and then estimates the dampening caused to the recovery by the activation of the sympathetic nervous system due to cocaine. We develop efficient methods to screen and clean the ECG time series data and extract candidate windows to assess for potential drug use. We then apply our model on the recovery segments from these windows. Our model achieves 100% true positive rate while keeping the false positive rate to 0.87/day over (9+ hours/day of) lab data and to 1.13/day over (11+ hours/day of) field data.
Indexed as
Identifiers
25531010PMC4269159W2114493575What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.