Elsevier

Drug and Alcohol Dependence

Volume 151, 1 June 2015, Pages 159-166
Drug and Alcohol Dependence

Continuous in-the-field measurement of heart rate: Correlates of drug use, craving, stress, and mood in polydrug users

https://doi.org/10.1016/j.drugalcdep.2015.03.024Get rights and content

Highlights

  • High-quality heart-rate data can be obtained from drug users in the field.

  • Drug craving is associated with increased heart rate in the natural environment.

  • Dose-related effects of cocaine on heart rate were detectable in the field data.

Abstract

Background

Ambulatory physiological monitoring could clarify antecedents and consequences of drug use and could contribute to a sensor-triggered mobile intervention that automatically detects behaviorally risky situations. Our goal was to show that such monitoring is feasible and can produce meaningful data.

Methods

We assessed heart rate (HR) with AutoSense, a suite of biosensors that wirelessly transmits data to a smartphone, for up to 4 weeks in 40 polydrug users in opioid-agonist maintenance as they went about their daily lives. Participants also self-reported drug use, mood, and activities on electronic diaries. We compared HR with self-report using multilevel modeling (SAS Proc Mixed).

Results

Compliance with AutoSense was good; the data yield from the wireless electrocardiographs was 85.7%. HR was higher when participants reported cocaine use than when they reported heroin use (F(2,9) = 250.3, p < .0001) and was also higher as a function of the dose of cocaine reported (F(1,8) = 207.7, p < .0001). HR was higher when participants reported craving heroin (F(1,16) = 230.9, p < .0001) or cocaine (F(1,14) = 157.2, p < .0001) than when they reported of not craving. HR was lower (p < .05) in randomly prompted entries in which participants reported feeling relaxed, feeling happy, or watching TV, and was higher when they reported feeling stressed, being hassled, or walking.

Conclusions

High-yield, high-quality heart-rate data can be obtained from drug users in their natural environment as they go about their daily lives, and the resultant data robustly reflect episodes of cocaine and heroin use and other mental and behavioral events of interest.

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).

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