Continuous in-the-field measurement of heart rate: Correlates of drug use, craving, stress, and mood in polydrug users
Introduction
As mobile electronic devices become nearly ubiquitous in most of the world, the field of mobile health (mHealth) burgeons, bringing the potential for remote assessments and interventions—if sufficient empirical data are collected first (Collins, 2012). One especially good target for mHealth interventions is drug addiction. Because the risk of relapse persists for years after treatment, there is need for proactive aftercare that does not require heavy continuous use of “brick and mortar” resources. Also, the actual content of addiction treatment is usually amenable to delivery on mobile devices. Efforts are already underway to develop desktop-computer delivery of cognitive-behavioral therapy for addiction (Carroll et al., 2014, Marsch et al., 2014) and internet delivery of contingency management for addiction (Dallery et al., 2013).
As for mobile technology in addiction, researchers have already embraced it as an assessment tool in the form of ecological momentary assessment (EMA; Epstein et al., 2009, Waters et al., 2014). Using EMA, our research clinic has demonstrated that both cocaine craving and exposure to drug-use triggers increase in the hours before cocaine use, and that craving ratings increase as stress ratings increase (Epstein et al., 2009, Preston et al., 2009, Preston and Epstein, 2011). We are now also using real-time geolocation data collected with global positioning system (GPS) devices to assess environmental influences on addiction (Epstein et al., 2014).
The participant burden of EMA, the occasional resultant sparsity of EMA data, and the possibility that some important biological events (such as unconscious physiological responses to stressors) might not be amenable to self-report (Epstein et al., 2014) have led us to augment EMA with continuous physiological monitoring. At least one of the leading theories of addiction posits that behavior can be driven by “unconscious emotions” (Berridge and Winkielman, 2003). Regardless of whether one accepts that term, there is clear evidence that biologically and socially relevant environmental events (e.g., images of facial expressions) that occur too quickly to be consciously detectable can produce measurable physiological responses (Dimberg, 1990). And regardless of whether one accepts that such physiological responses have important consequences for health or behavior when unaccompanied by subjective responses, the fact remains that physiological monitoring can be done continuously without requiring participants to stop and provide reports. Continuous monitoring increases the likelihood that some data will be available from any given moment of interest, such as a lapse to drug use or an encounter with a stressor. Also, physiological data collected in the field will almost certainly have greater ecological validity than those that have been collected in behavioral-pharmacology laboratories, because ethical and practical considerations limit the doses and combinations of drugs that can be given in a laboratory, as well as the activities that can occur.
In this study, we supplemented EMA with a wireless physiological-monitoring suite called AutoSense (Ertin et al., 2011). AutoSense provides continuous measurements of heart rate, heart-rate variability, respiration, skin conductance, ambient temperature, and physical activity. In prior studies by some of the current authors, AutoSense was used to collect a week's worth of ambulatory physiological data from smokers and social drinkers (Rahman et al., 2012). Here, we report on a field test of AutoSense in 40 illicit-drug users during outpatient treatment. As we discussed in a separate report, each type of physiological data requires extensive processing for quality control (Rahman et al., 2012). For the present analyses, we focused only on heart-rate data. Here we present, for the first time, data on heart-rate changes associated with EMA reports of mood, drug craving, and stress, as well as cocaine and heroin use in polydrug users.
Section snippets
Participants
Opioid-dependent treatment seekers underwent screening for medical, psychiatric, and drug-use histories, physical examination, standard laboratory tests, and a battery of assessment instruments, including the Addiction Severity Index (ASI; McLellan et al., 1985), Structured Clinical Interview for DSM-IV (SCID; First et al., 2007), and the Diagnostic Interview Schedule (DIS-IV; Robins et al., 1995). Inclusion criteria were: age 18–75; evidence of physical dependence on opioids (by self-report
Participant characteristics and urine drug screen results
Forty (73%) of 57 participants who signed consent provided 3 or more weeks of AutoSense data and were considered completers. Of the 17 who did not complete: 3 were dropped from the parent study for noncompliance; 2 were dropped from the AutoSense study for noncompliance; 1 was incarcerated; 6 withdrew from the AutoSense study after starting it (2 because participation was incompatible with their jobs and 4 for unspecified reasons); 5 withdrew from the AutoSense study before starting it (3 for
Discussion
Our most important finding, immediately apparent in each of the figures, was that our AutoSense device captured ambulatory measures of heart rate that clearly changed in the expected directions with real-time self-reported changes in mood, craving, stress, and drug use. The robustness of the associations supports the credibility of both our ambulatory heart-rate monitoring and the real-time self-report data with which we compared it.
Our intention is a practical one: to use these findings as the
Role of funding source
This research was supported by the National Institute on Drug Abuse Intramural Research Program and NSF grants CNS-0910878 (funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)), CNS-1212901, IIS-1231754, and by NIH Grants U01DA023812 under Genes Environment and Health Initiative (GEI) and R01DA035502 under the Basic Behavioral and Social Sciences Research Opportunity Network (OppNet). The authors had sole responsibility for the design and conduct of the study, the
Contributors
A.P.K, M.L.J., and D.A. helped design the study and participated in data collection; D.H.E. helped design the study, performed statistical analyses, and contributed to drafting the manuscript; M.T. participated in data management and statistical analyses; K.A.P. helped design the study and oversaw clinical aspects of data collection; A.A.A., R.B., S.M.H., K.H., M.M.R., and E.E. developed the AutoSense hardware and data collection, transmission, and storage software, and analyzed data; S.K.
Conflict of interest
No conflict declared.
Acknowledgments
This research was supported by the National Institute on Drug Abuse Intramural Research Program and NSF grants CNS-0910878 (funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)), CNS-1212901, IIS-1231754, and by NIH Grants U01DA023812 under Genes Environment and Health Initiative (GEI) and R01DA035502 under the Basic Behavioral and Social Sciences Research Opportunity Network (OppNet).
References (40)
- et al.
Real-time tracking of neighborhood surroundings and mood in urban drug misusers: application of a new method to study behavior in its geographical context
Drug Alcohol Depend.
(2014) - et al.
Cardiovascular effects of cocaine in humans: laboratory studies
Drug Alcohol Depend.
(1995) - et al.
Reduced heart rate variability in chronic alcohol abuse: relationship with negative mood, chronic thought suppression, and compulsive drinking
Biol. Psychiatry
(2003) - et al.
Emotion identification using extremely low frequency components of speech feature contours
Sci. World J.
(2014) - et al.
Web-based behavioral treatment for substance use disorders as a partial replacement of standard methadone maintenance treatment
J. Subst. Abuse Treat.
(2014) - et al.
Heart rate variability predicts alcohol craving in alcohol dependent outpatients: further evidence for HRV as a psychophysiological marker of self-regulation
Drug Alcohol Depend.
(2013) - et al.
An approach to artifact identification: application to heart period data
Psychophysiology
(1990) - et al.
What is an unconscious emotion? (The case for unconscious liking)
Cogn. Emot.
(2003) - et al.
Computer-assisted delivery of cognitive-behavioral therapy: efficacy and durability of CBT4CBT among cocaine-dependent individuals maintained on methadone
Am. J. Psychiatry
(2014) - et al.
Cue-reactivity and the future of addiction research
Addiction
(1999)