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

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.