Towards Detecting Cocaine Use Using Smartwatches in the NIDA Clinical Trials Network (AutoSense)

• ClinicalTrials.gov Identifier: NCT02915341
• Recruitment Status: Completed
• First Posted: September 27, 2016
• Last Update Posted: July 18, 2018
• Sponsor: Dartmouth-Hitchcock Medical Center
• Collaborators: National Drug Abuse Treatment Clinical Trials Network; University of Memphis; Johns Hopkins University; National Institute on Drug Abuse (NIDA); Dartmouth College
• Information provided by (Responsible Party): Lisa A. Marsch, Dartmouth-Hitchcock Medical Center

Study Description

Brief Summary:

The overall objective of this study is to extend previous work in the development of methods to automatically detect the timing of cocaine use from cardiac interbeat interval and physical activity data derived from wearable, unobtrusive mobile sensor technologies. The specific objectives of this protocol are to characterize under which conditions high quality continuous interbeat interval data and physical activity data can be obtained from a specially developed smartwatch device in the natural field setting among a population of cocaine users. In addition to identifying common failure scenarios and understanding wearability/usage patterns when collecting interbeat interval from smartwatches, this study will extend previous work in the detection of cocaine use via interbeat interval and physical activity data that were previously obtained from wearable chestband sensors. Information from this study will contribute toward the adaptation of the investigators' existing computational model for detecting cocaine use via the chest sensors, so it can be applied to the interbeat and physical activity data obtained from less obtrusive smartwatches.

Condition or disease
Cocaine-Related Disorders

Detailed Description:

This study will evaluate a smartwatch device for the continuous field assessment of physiological measures, including cardiac interbeat interval and physical activity. These measures have been previously employed using wearable chest sensors to develop a model for the automatic in-the-field detection of the timing of cocaine use; computational models using physiological data of this type have been used in prior research to detect cocaine use and moment-by-moment stress using a mobile sensor suite called AutoSense. AutoSense is a chest-worn device used to collect measures of heart rate via a two-lead electrocardiograph (ECG) and physical activity via 3-axis accelerometers that can be transmitted wirelessly to an Android-based smartphone for initial processing and data storage. The adapted AutoSense protocol will incorporate smartwatches specially designed to continuously detect heart beat timings using optical photoplethysmogram (PPG) sensors rather than ECG leads.

Prior to the start of this protocol, investigators will optimize collection of cardiac interbeat interval data on the smartwatches via a preliminary ambulatory study (with Co-Investigator Ertin at the Ohio State University). The development of the smartwatch device and the initial smartwatch computational model is currently being supported separately (outside of this human subjects protocol) by the National Drug Abuse Treatment Clinical Trials Network (CTN). Investigators and research assistants at the Ohio State University will wear prototypes of the smartwatch devices and the AutoSense chest sensor during waking hours for five days to capture cardiac interbeat interval data as well as identify initial fit and usability problems with the prototype smartwatch devices and inform its subsequent refinements. This preliminary ambulatory study is a separate protocol being conducted at Ohio State University with oversight by that institution's Institutional Review Board (IRB), and thus is not considered part of the 0073-Ot human subjects protocol.

Once the preliminary study has concluded, investigators will conduct a field test during which the smartwatch and AutoSense chest sensors will be worn by 25 cocaine users for two weeks (5 participants will participate in pilot testing for two weeks each, after which the smartwatch device may undergo further refinements for improved wearability and/or data collection among the remaining participants). Outcomes of this study are to characterize the feasibility of the smartwatch device to continuously detect interbeat interval and physical activity data, and to characterize situations where data yield of sufficient quality for the application of more advanced computational models (e.g., cocaine detection) can take place in participants' natural field settings. Secondarily, data from the trial will be used to compare data yields from the two sensor suites being worn (the smartwatch devices and the AutoSense chest sensors). The data may also be useful for updating the computational models (e.g., cocaine detection) previously developed with the AutoSense chest sensors for data collected by the smartwatches. The results from this study may be used to inform future research of this type to investigate technological improvements and the situations during which using mobile sensors can unobtrusively characterize precipitants and use patterns (e.g., contextual) surrounding drug use events.

It is important to note that this study is not designed to assess the acceptability of the smartwatch among cocaine users, nor is it a study to evaluate the utility of using a smartwatch for measuring cocaine use outcomes as part of a clinical trial. Rather, investigators are intentionally recruiting participants who frequently use cocaine and compensating them to participate in this study designed to characterize the feasibility of using a smartwatch to collect reliable, continuous interbeat interval and physical activity data in the natural field setting, and to characterize under what conditions high quality data can be obtained from smartwatches. If results are promising, future research designs with larger sample sizes can explore some of these more clinically-relevant scientific questions.

Study Design

• Study Type: Observational
• Actual Enrollment: 24 participants
• Observational Model: Other
• Time Perspective: Other
• Official Title: Towards Detecting Cocaine Use Using Smartwatches in the NIDA Clinical Trials Network
• Actual Study Start Date: May 1, 2016
• Actual Primary Completion Date: April 24, 2018
• Actual Study Completion Date: April 24, 2018

Groups and Cohorts

Outcome Measures

Primary Outcome Measures :

1. Ambulatory physiological and activity data [ Time Frame: 14 days ]
   Data yield of ambulatory physiological and activity data will be used to characterize the feasibility of using smartwatches to collect reliable interbeat interval and physical activity data in the natural field setting, sensor usage patterns (e.g., hours per day of device wear, periods of device removal), and common failure scenarios (e.g., poor sensor data quality, missing data due to removal of the device, or other factors which may affect data yield). Ambulatory physiological and activity data will be collected using passive mobile sensor data collection platforms (AutoSense chest sensors and smartwatches). This data is collected via AutoSense sensors and will utilize participants' heart beats, which will be continually streamed into the database at the Mobile Sensor Data-to-Knowledge Center of Excellence (University of Memphis). This is one assessment with one unit of measurement (heart rate) that will inform the multiple aspects of this outcome.
2. Device wearability [ Time Frame: 14 days ]
   Device wearability (e.g., acceptability and burden) will be characterized via user questionnaires.
3. Cocaine use [ Time Frame: 14 days ]
   Real-time self-report of cocaine use via Ecological Momentary Assessment (EMA) questionnaire prompts via the smartphone device, retrospective recall of drug use via Timeline Followback (TLFB), and urine drug assay for cocaine and other drugs of abuse will be used to adapt the computational model, so that it can be applied to the interbeat and physical activity data obtained from smartwatches. These measurements of cocaine use will be aggregated to arrive at one reported value (cocaine use episodes) to inform the model.

Secondary Outcome Measures :

1. Device comparison [ Time Frame: Through study completion, an average of 18 months ]
   Data yield will be compared between the smartwatch wrist sensor platform and the AutoSense chest sensors to characterize the conditions under which reliable interbeat interval and physical activity data can be collected with the respective devices in the natural field setting. Comparisons between overall data yield, sensor usage patterns (e.g., hours per day of device wear, periods of device removal), and common failure scenarios (e.g., poor sensor data quality, missing data due to removal of the device, or other factors which may affect data yield) will be conducted between the AutoSense chest sensors and the smartwatch wrist sensor platform to determine if differences exist between the two sensor suites. To achieve this outcome data will be aggregated into one reported value (discrepancies between the devices).
2. Cocaine detection specificity [ Time Frame: Through study completion, an average of 18 months ]
   The degree of specificity of the cocaine detection model will be characterized relative to other stimulant use via precision and recall. To perform this comparison, rates of true positive and false positive cocaine detection events (obtained via physiological sensor data) will be compared when other stimulant drugs of abuse (e.g. amphetamines, methamphetamines) are determined to be present via urine assay for other drugs, real-time self-report of other amphetamine use via EMA, and retrospective recall of drug use via study assessments. Measures will be aggregated to arrive at one reported value, informing investigators of whether other stimulant use affects the model in the same way as cocaine.

Eligibility Criteria

• Ages Eligible for Study: 18 Years and older (Adult, Older Adult)
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: Yes
• Sampling Method: Non-Probability Sample

Study Population

The proposed study will be conducted with 25 cocaine users from the parent clinical trial who agree to wear the AutoSense smartwatches and chestband for two weeks. Participants in the parent clinical trial are unemployed, cocaine-using adults who inject drugs and are enrolled in opioid agonist treatment.

There are no study groups or randomization.

Criteria

Inclusion Criteria:

1. Be enrolled in the parent clinical trial for at least one week.
2. Be active in the Induction Period (days 8-35) of the parent clinical trial at the time of study intake.
3. Provide a cocaine-positive urine sample in the week prior (based on thrice-weekly urine samples collected as a part of the parent trial).
4. Be available for the duration of the study (16 days total) and able to attend weekday research check-in sessions at the recruitment site.

Exclusion Criteria:

There are no exclusion criteria specific to this study.

Contacts and Locations

Locations

United States, Maryland

Center for Learning and Health, Johns Hopkins University

Baltimore, Maryland, United States, 21205

Sponsors and Collaborators

Dartmouth-Hitchcock Medical Center

National Drug Abuse Treatment Clinical Trials Network

University of Memphis

Johns Hopkins University

National Institute on Drug Abuse (NIDA)

Dartmouth College

Investigators

• Principal Investigator: Lisa A. Marsch, PhD; Dartmouth College
• Principal Investigator: Santosh Kumar, PhD; University of Memphis

More Information

Publications:

Hossain SM, Ali AA, Rahman M, Ertin E, Epstein D, Kennedy A, Preston K, Umbricht A, Chen Y, Kumar S. Identifying Drug (Cocaine) Intake Events from Acute Physiological Response in the Presence of Free-living Physical Activity. IPSN. 2014;2014:71-82.

Ertin, E., Stohs, N., Kumar, S., Raij, A.B., al'Absi, M., Kwon, T., Mitra, S., Shah, S., & Jeong, J.W. (2011). AutoSense: Unobtrusively wearable sensor suite for inferencing of onset, causality, and consequences of stress in the field: Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems (SenSys) (pp. 274-287). New York, NY: Association for Computing Machinery.

Publications automatically indexed to this study by ClinicalTrials.gov Identifier (NCT Number):

Ertin E, Sugavanam N, Holtyn AF, Preston KL, Bertz JW, Marsch LA, McLeman B, Shmueli-Blumberg D, Collins J, King JS, McCormack J, Ghitza UE. An Examination of the Feasibility of Detecting Cocaine Use Using Smartwatches. Front Psychiatry. 2021 Jun 24;12:674691. doi: 10.3389/fpsyt.2021.674691. eCollection 2021.

• Responsible Party: Lisa A. Marsch, Professor, Dartmouth-Hitchcock Medical Center
• ClinicalTrials.gov Identifier: NCT02915341
• Other Study ID Numbers: CTN-0073-Ot
• First Posted: September 27, 2016
• Last Update Posted: July 18, 2018
• Last Verified: June 2018

Individual Participant Data (IPD) Sharing Statement:

• Plan to Share IPD: Yes
• Plan Description: De-identified data will be reviewed by IRB and shared with DSMB every 6 months, as per the monitoring protocol set forth by the CTN and this study.

Additional relevant MeSH terms:

Cocaine-Related Disorders; Substance-Related Disorders; Chemically-Induced Disorders; Mental Disorders