An Examination of the Feasibility of Detecting Cocaine Use Using Smartwatches.
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ABSTRACT:
SUBMITTER: Ertin E
PROVIDER: S-EPMC8264124 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Cocaine use in clinical trials is often measured via self-report, which can be inaccurate, or urine drug screens, which can be intrusive and burdensome. Devices that can automatically detect cocaine use and can be worn conveniently in daily life may provide several benefits. AutoSense is a wearable, physiological-monitoring suite that can detect cocaine use, but it may be limited as a method for monitoring cocaine use because it requires wearing a chestband with electrodes. This paper describes the design, rationale, and methodology of a project that seeks to build upon and extend previous work in the development of methods to detect cocaine use via wearable, unobtrusive mobile sensor technologies. To this end, a wrist-worn sensor suite (i.e., MotionSense HRV) will be developed and evaluated. Participants who use cocaine (N?=?25) will be asked to wear MotionSense HRV and AutoSense for two weeks during waking hours. Drug use will be assessed via thrice-weekly urine drug screens and self-reports, and will be used to isolate periods of cocaine use that will be differentiated from other drug use. The present study will provide information on the feasibility and acceptability of using a wrist-worn device to detect cocaine use.
| S-EPMC6582185 | biostudies-literature
Project description:BackgroundCocaine use is associated with an increased risk of cerebrovascular accidents. Small vessel pathology has been linked to the risk of stroke in cocaine users, but can be challenging to detect on conventional magnetic resonance (MR) scans. Fluid-attenuated inversion recovery (FLAIR) scans permit better resolution of small vessel lesions.ObjectivesFLAIR scans are currently only acquired based on the subjective judgement of abnormalities on MR scans at face value. We sought to evaluate this practice and the added value of FLAIR scans for patients with cocaine use disorder (CUD), by comparing microbleeds detected by MR and FLAIR scans. We hypothesised that microbleeds are more pronounced in CUD patients, particularly so in participants who had been selected for a FLAIR scan by radiographers.MethodsSixty-four patients with CUD and 60 control participants underwent a brain scan. The MR of 20 CUD patients and 16 control participants showed indicators of cerebral infarction at face value and were followed up by a FLAIR scan. We determined the volume of microbleeds in both MR and FLAIR scans and examined associations with various risk factors.ResultsWhile MR lesion volumes were significantly increased in CUD patients, no significant differences in lesion volume were found in the subgroup of individuals who received a FLAIR.ConclusionThe current practice of subjectively evaluating MR scans as a basis for the follow-up FLAIR scans to detect vascular pathology may miss vulnerable individuals. Hence, FLAIR scans should be included as a routine part of research studies.
| S-EPMC8867820 | biostudies-literature
Project description:Frequent spontaneous facial self-touches, predominantly during outbreaks, have the theoretical potential to be a mechanism of contracting and transmitting diseases. Despite the recent advent of vaccines, behavioral approaches remain an integral part of reducing the spread of COVID-19 and other respiratory illnesses. The aim of this study was to utilize the functionality and the spread of smartwatches to develop a smartwatch application to identify motion signatures that are mapped accurately to face touching. Participants (n = 10, five women, aged 20-83) performed 10 physical activities classified into face touching (FT) and non-face touching (NFT) categories in a standardized laboratory setting. We developed a smartwatch application on Samsung Galaxy Watch to collect raw accelerometer data from participants. Data features were extracted from consecutive non-overlapping windows varying from 2 to 16 s. We examined the performance of state-of-the-art machine learning methods on face-touching movement recognition (FT vs. NFT) and individual activity recognition (IAR): logistic regression, support vector machine, decision trees, and random forest. While all machine learning models were accurate in recognizing FT categories, logistic regression achieved the best performance across all metrics (accuracy: 0.93 ± 0.08, recall: 0.89 ± 0.16, precision: 0.93 ± 0.08, F1-score: 0.90 ± 0.11, AUC: 0.95 ± 0.07) at the window size of 5 s. IAR models resulted in lower performance, where the random forest classifier achieved the best performance across all metrics (accuracy: 0.70 ± 0.14, recall: 0.70 ± 0.14, precision: 0.70 ± 0.16, F1-score: 0.67 ± 0.15) at the window size of 9 s. In conclusion, wearable devices, powered by machine learning, are effective in detecting facial touches. This is highly significant during respiratory infection outbreaks as it has the potential to limit face touching as a transmission vector.
| S-EPMC8513006 | biostudies-literature
Project description:Background and objectivesClinicians commonly prescribe assistive devices such as walkers or canes to reduce older adults' fall risk. However, older adults may not consistently use their assistive device, and measuring adherence can be challenging due to self-report bias or cognitive deficits. Because walking patterns can change while using an assistive device, we hypothesized that smartphones and smartwatches, combined with machine-learning algorithms, could detect whether an older adult was walking with an assistive device.Research design and methodsOlder adults at an Adult Day Center (n = 14) wore an Android smartphone and Actigraph smartwatch while completing the six-minute walk, 10-meter walk, and Timed Up and Go tests with and without their assistive device on five separate days. We used accelerometer data from the devices to build machine-learning algorithms to detect whether the participant was walking with or without their assistive device. We tested our algorithms using cross-validation.ResultsSmartwatch classifiers could accurately detect assistive device use, but smartphone classifiers performed poorly. Customized smartwatch classifiers, which were created specifically for one participant, had greater than 95% classification accuracy for all participants. Noncustomized smartwatch classifiers (ie, an "off-the-shelf" system) had greater than 90% accuracy for 10 of the 14 participants. A noncustomized system performed better for walker users than cane users.Discussion and implicationsOur approach can leverage data from existing commercial devices to provide a deeper understanding of walker or cane use. This work can inform scalable public health monitoring tools to quantify assistive device adherence and enable proactive fall interventions.
| S-EPMC6476414 | biostudies-literature
Project description:The indirect dopamine agonist methylphenidate remediates cognitive deficits in psychopathology, but the individual characteristics that determine its effects on the brain are not known.We aimed to determine whether targeted dopaminergically modulated traits and individual differences could predict neural response to methylphenidate across multiple functional magnetic resonance imaging (fMRI) procedures.We combined neural measures from three separate procedures (two inhibitory control tasks differing in their degree of emotional salience and resting-state functional connectivity) during methylphenidate (20 mg oral, versus randomized and counterbalanced placebo) and correlated these aggregated responses with cocaine use disorder diagnosis (22 cocaine abusers, 21 controls), symptoms of attention deficit hyperactivity disorder, and working memory capacity.Cocaine abusers, relative to controls, had a lower response in the dorsolateral prefrontal cortex to methylphenidate across all three procedures, driven by responses to the two inhibitory control tasks; reduced methylphenidate fMRI response in this region further correlated with more frequent cocaine use.Cocaine abuse (and its frequency), associated with lower tonic dopamine levels, correlated with a reduction in activation to methylphenidate (versus placebo). These initial results provide feasibility to the idea that multimodal fMRI tasks can be meaningfully aggregated, and that these aggregated procedures show a common disruption in addiction in a highly anticipated region relevant to cognitive control. Results also suggest that drug use frequency may represent an important modulatory variable in interpreting the efficacy of pharmacologically enhanced cognitive interventions in addiction.
| S-EPMC4916842 | biostudies-literature
Project description:BackgroundWearables provide opportunities for frequent health data collection and symptom monitoring. The feasibility of using consumer cellular smartwatches to provide information both on symptoms and contemporary sensor data has not yet been investigated.ObjectiveThis study aimed to investigate the feasibility and acceptability of using cellular smartwatches to capture multiple patient-reported outcomes per day alongside continuous physical activity data over a 3-month period in people living with knee osteoarthritis (OA).MethodsFor the KOALAP (Knee OsteoArthritis: Linking Activity and Pain) study, a novel cellular smartwatch app for health data collection was developed. Participants (age ?50 years; self-diagnosed knee OA) received a smartwatch (Huawei Watch 2) with the KOALAP app. When worn, the watch collected sensor data and prompted participants to self-report outcomes multiple times per day. Participants were invited for a baseline and follow-up interview to discuss their motivations and experiences. Engagement with the watch was measured using daily watch wear time and the percentage completion of watch questions. Interview transcripts were analyzed using grounded thematic analysis.ResultsA total of 26 people participated in the study. Good use and engagement were observed over 3 months: most participants wore the watch on 75% (68/90) of days or more, for a median of 11 hours. The number of active participants declined over the study duration, especially in the final week. Among participants who remained active, neither watch time nor question completion percentage declined over time. Participants were mainly motivated to learn about their symptoms and enjoyed the self-tracking aspects of the watch. Barriers to full engagement were battery life limitations, technical problems, and unfulfilled expectations of the watch. Participants reported that they would have liked to report symptoms more than 4 or 5 times per day.ConclusionsThis study shows that capture of patient-reported outcomes multiple times per day with linked sensor data from a smartwatch is feasible over at least a 3-month period.International registered report identifier (irrid)RR2-10.2196/10238.
| S-EPMC7016619 | biostudies-literature
Project description:ObjectivesCocaine use is on the rise and it is comorbid with marijuana use. We examined the association between lifetime cocaine + marijuana polysubstance use (CM PSU) versus cocaine only and lifetime cocaine use disorder (CocUD) and examined the potential mediation by cocaine use patterns. Methods: A total of 2,968 lifetime cocaine users were identified from the National Epidemiologic Survey on Alcohol and Related Conditions-III. Mediation analysis was utilized to examine cocaine use quantity, frequency, and duration as potential mediators in the association between CM PSU and CocUD. A parallel multiple mediator model and a structural equation model were used, respectively, to examine: (1) the individual contribution of cocaine use quantity, frequency, and duration, and (2) combined contribution as a set specified by a latent variable. Results: Cocaine users were divided into 2,782 (93.7%) CM polysubstance users and 186 (6.3%) cocaine only users. CM PSU was associated with decreased risk of CocUD, but after including the mediators, the association was no longer significant. Examined separately, only quantity was found to be a significant mediator over and above frequency and duration, while the latent variable with three cocaine use pattern indicators explained 56.6% of the total association between CM PSU and CocUD. Conclusions: Compared to cocaine only users, CM polysubstance users were less likely to use cocaine heavily; this lower intensity of cocaine use was in turn associated with decreased risk of CocUD. Future research is warranted to determine the nature of the association between CM PSU and reduced CocUD.
| S-EPMC7902454 | biostudies-literature
Project description:BackgroundThe autobiographical Implicit Association Test (aIAT) is a novel application of the implicit association concept for detecting life events. It has been used to reveal concealed knowledge in clinical and forensic settings, including detecting drug use. In this study, we aimed to explore the functionality of the aIAT to identify drug use in real-world settings.MethodsThe study used mixed methodology with known groups of drug users and nonusers. Recreational cocaine users (n = 23) and non-users (n = 23) were recruited through ethnographic methodology and assessed using a bespoke brief aIAT for cocaine use. An identical aIAT test for heroin detection was also administered to a sub-sample of 10 cocaine users and 13 nonusers. The accuracy of the cocaine aIAT was measured through ROC analysis. Paradoxical aIAT results were explored by integrating craving, consumption measures and life-story interviews into the analysis.ResultsWhilst the two brief aIATs showed good concurrent validity for cocaine users by accurately detecting drug using status for 18 of the 23 users (78.3%), the test falsely reported 61% cocaine users in the non-user comparison group. The average D-scores were 0.257±0.246 for the cocaine users and 0.134±0.367 for the non-users, showing no discriminatory power (t(44) = 1.339, p = 0.187; AUC = 0.605, p = 0.223). Results were independent from craving and recent cocaine use. The comparison group's cocaine and heroin aIAT scores correlated significantly (r(13) = 0.776, p = 0.002) whilst an accurate absence of such relationship was evidenced in the cocaine using sample (r(10) = 0.061, p = 0.866). Triangulation with life-story interviews suggests that in the absence of an autobiographical event, this test may measure an alternative cognitive construct linked to the Self-concept.ConclusionThe aIAT is a variant of an attitude measure and can be better rationalized if propositional thinking is implied to explain outcomes. The Relational Frame and Social Knowledge Structure theories can perhaps provide a more plausible theoretical background. Further work is required to clarify which factors underlie this testing technique's functioning. Reappraisal is advised before further forensic use of the instrument to ensure that general associations not related to autobiographical memory do not confound results.
| S-EPMC3685584 | biostudies-literature
Project description:More than 12 billion COVID-19 vaccination shots have been administered as of August 2022, but information from active surveillance about vaccine safety is limited. Surveillance is generally based on self-reporting, making the monitoring process subjective. We study participants in Israel who received their second or third Pfizer BioNTech COVID-19 vaccination. All participants wore a Garmin Vivosmart 4 smartwatch and completed a daily questionnaire via smartphone. We compare post-vaccination smartwatch heart rate data and a Garmin-computed stress measure based on heart rate variability with data from the patient questionnaires. Using a mixed effects panel regression to remove participant-level fixed and random effects, we identify considerable changes in smartwatch measures in the 72 h post-vaccination even among participants who reported no side effects in the questionnaire. Wearable devices were more sensitive than questionnaires in determining when participants returned to baseline levels. We conclude that wearable devices can detect physiological responses following vaccination that may not be captured by patient self-reporting. More broadly, the ubiquity of smartwatches provides an opportunity to gather improved data on patient health, including active surveillance of vaccine safety.
| S-EPMC9461410 | biostudies-literature
Project description:BackgroundRelapse is a two-component process consisting of a highly motivated drug-seeking phase that, if successful, is followed by a drug-using phase resulting in temporary satiation. In rodents, cue-induced drug seeking requires transient synaptic potentiation (t-SP) of cortical glutamatergic synapses on nucleus accumbens core medium spiny neurons, but it is unknown how achieving drug use affects this plasticity. We modeled the two phases of relapse after extinction from cocaine self-administration to assess how cocaine use affects t-SP associated with cue-induced drug seeking.MethodsRats were trained to self-administer cocaine (n = 96) or were used as yoked-saline control animals (n = 21). After extinction, reinstatement was initiated by 10 minutes of cue-induced drug seeking, followed by 45 minutes with contingent cocaine access, after which cocaine was discontinued and unreinforced lever pressing ensued. Three measures of t-SP were assayed during reinstatement: dendritic spine morphology, alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) to N-methyl-D-aspartate (NMDA) ratios, and matrix metalloproteinase activity.ResultsWe found that cocaine use for 10 minutes collapsed all three measures of cue-potentiated t-SP back to baseline. Moreover, when cocaine use was discontinued 45 minutes later, dendritic spine morphology and AMPA to NMDA ratios were restored as animals became motivated to engage unrewarded lever pressing. Nonreinforced drug seeking was positively correlated with changes in spine morphology, and cocaine access reversed this relationship.ConclusionsUsing a novel modification of the reinstatement paradigm, we show that achieving cocaine use reversed the synaptic plasticity underpinning the motivation to seek the drug.
| S-EPMC5346331 | biostudies-literature