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Previous research has highlighted the role of stress in substance misuse and addiction, particularly for relapse risk. Mobile health interventions that incorporate real-time monitoring of physiological markers of stress offer promise for delivering tailored interventions to individuals during high-risk states of heightened stress to prevent alcohol relapse. Before such interventions can be developed, measurements of these processes in ambulatory, real-world settings are needed.
This research is a proof-of-concept study to establish the feasibility of using a wearable sensor device to continuously monitor stress in an ambulatory setting. Toward that end, we first aimed to examine the quality of 2 continuously monitored physiological signals—electrodermal activity (EDA) and heart rate variability (HRV)—and show that the data follow standard quality measures according to the literature. Next, we examined the associations between the statistical features extracted from the EDA and HRV signals and self-reported outcomes.
Participants (N=11; female: n=10) were asked to wear an Empatica E4 wearable sensor for continuous unobtrusive physiological signal collection for up to 14 days. During the same time frame, participants responded to a daily diary study using ecological momentary assessment of self-reported stress, emotions, alcohol-related cravings, pain, and discomfort via a web-based survey, which was conducted 4 times daily. Participants also participated in structured interviews throughout the study to assess daily alcohol use and to validate self-reported and physiological stress markers. In the analysis, we first used existing artifact detection methods and physiological signal processing approaches to assess the quality of the physiological data. Next, we examined the descriptive statistics for self-reported outcomes. Finally, we investigated the associations between the features of physiological signals and self-reported outcomes.
We determined that 87.86% (1,032,265/1,174,898) of the EDA signals were clean. A comparison of the frequency of skin conductance responses per minute with previous research confirmed that the physiological signals collected in the ambulatory setting were successful. The results also indicated that the statistical features of the EDA and HRV measures were significantly correlated with the self-reported outcomes, including the number of stressful events marked on the sensor device, positive and negative emotions, and experienced pain and discomfort.
The results demonstrated that the physiological data collected via an Empatica E4 wearable sensor device were consistent with previous literature in terms of the quality of the data and that features of these physiological signals were significantly associated with several self-reported outcomes among a sample of adults diagnosed with alcohol use disorder. These results suggest that ambulatory assessment of stress is feasible and can be used to develop tailored mobile health interventions to enhance sustained recovery from alcohol use disorder.
A well-established literature describes the important role of stress in addiction and the risk of relapse. For example, laboratory studies have shown that acute stressors increase drug-seeking behaviors in animals [
A common approach to monitoring stress is to analyze physiological signals, such as electroencephalography, blood volume pulse (BVP), heart rate variability (HRV), galvanic skin response, electrodermal activity (EDA), and electromyography [
HRV refers to the variability of the time interval between consecutive heartbeats in individuals and can be computed from BVP signal readings. Previous research has established HRV as an objective measure of individual differences in emotional responses. In particular, HRV provides information about the flexibility of the ANS, the ease with which an individual can transition between high and low arousal states [
To date, most research studies that have used physiological signals to detect stress have been conducted in controlled laboratory settings [
To address these gaps, this study used a multimodal approach to investigate the associations between 2 physiological signals (EDA and HRV) and self-reported outcomes, including alcohol use, heightened stress, positive and negative emotions, alcohol-related cravings, pain, and discomfort, among adults seeking treatment for AUD during a 2-week uncontrolled data collection. Specifically, the aims of this study are 3-fold: (1) to assess the quality of the physiological signals collected from an unobtrusive wearable sensor device, (2) to examine the associations between EDA and self-reported outcomes, and (3) to examine the associations between HRV and self-reported outcomes.
A convenience sample of 11 participants (10 females) was recruited from adults seeking care at a mental health facility in a Western state in the United States. Potential participants were identified from 2 points in the consort flow of a larger study examining the effectiveness of contingency management treatment among adults with co-occurring serious mental illness and moderate to severe AUD. First, we recruited participants among those who did not meet the primary inclusion criteria of the larger study: Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) diagnosis of a serious mental illness or DSM-5 diagnosis of moderate to severe AUD. Participants were also identified from among those who did not meet the secondary eligibility criteria, after an induction phase and before randomization to the contingency management conditions. These individuals either failed to achieve an average urine ethyl glucuronide level that indicated recent heavy drinking (>349 ng/mL) or failed to attend at least one study visit during the last week of the 4-week induction phase.
Participants in this study met the following inclusion criteria: (1) aged 18-65 years and (2) self-reported consumption of 4 or more standard drinks on 5 or more occasions in the past 60 days. Participants were also required to own a smartphone with a data plan that allowed them to respond to the ecological momentary assessment (EMA) survey (described in the following section). Exclusion criteria included (1) current DSM-5 diagnosis of a severe drug use disorder, (2) inability to demonstrate competency to provide consent on the MacArthur Competence Assessment Tool for Clinical Research, (3) risk of medically dangerous alcohol withdrawal (ie, seizure within the last 12 months and concern by participant or clinician regarding a potentially dangerous withdrawal), (4) previous diagnosis of dementia, and (5) determination (by the principal investigator of the larger study) that participation would be medically or psychiatrically unsafe.
This study included 3 components: (1) a daily diary study using EMAs of self-reported emotions, cravings, and stress via a web-based survey, prompted 4 times daily; (2) a wearable sensor device (Empatica E4 wristband) that captured continuous physiological markers of stress, including heart rate (HR), skin temperature, bodily movement, HRV, and skin conductance; and (3) structured qualitative interviews to assess daily alcohol use, using a timeline follow-back calendar, and to validate self-reported and physiological markers of stress.
Each participant was asked to wear an Empatica E4 wristband to record continuous, real-time physiological measures of stress in their daily lives (
The study staff provided instructions about proper handling and wear of the E4 wristband during a scheduled meeting after the individuals agreed to participate in the study. For example, participants were instructed to remove the wristband each night while sleeping and at other times during which the device may be damaged (eg, in the shower or bath) and to wear the device on the same wrist throughout the study. The training session also included instructions on how to use the
Empatica E4 sensor.
Data collection for the EMA component was performed using a mobile phone–based survey. The survey assessed the perceptions of positive and negative emotions, alcohol-related cravings, and experiences of pain and discomfort. Participants responded to 4 timed signals throughout the day that corresponded to early morning (waking), noon, late afternoon, and bedtime for up to 14 consecutive days.
The measurement of
Measurement of
Throughout the study, participants attended up to 6 short follow-up sessions to meet with study staff on an every-other-day basis (eg, Monday, Wednesday, and Friday). During the follow-up sessions, the study staff ascertained whether the participants were experiencing any problems or difficulties with either the wearable wristband device or completing the EMA surveys on their cell phones. The follow-up sessions also included administration of a timeline follow-back measure of recent alcohol use [
We conducted a series of statistical analyses in 4 steps. First, we assessed the quality of the physiological signals, EDA and BVP. As described in the following sections, we used the recommended tools and procedures of the Empatica 4 guidelines to remove artifacts and extract features of the EDA and BVP signals for use in further analyses. Empatica E4 assesses the HR and IBI from the BVP signal using a proprietary algorithm [
Physiological signals such as EDA are prone to noise and artifacts, especially when acquired in uncontrolled real-life scenarios. Therefore, we preprocessed the EDA signal to remove the most common artifacts, including environmental, sensor motion, and muscle movement artifacts. We used EDA Explorer public scripts to perform automatic artifact and noise detection [
Trough-to-peak (TTP) and continuous decomposition analysis (CDA) are 2 commonly used analyses to assess the quality of EDA signals. We performed these analyses using the LedaLab toolbox (MATLAB program [MathWorks] suggested by the Empatica Manual for signal processing). In the TTP analysis, we set the sample rate to 1 Hz and the minimum amplitude threshold to 0.01 µS. In the CDA analysis, the EDA signal was decomposed into phasic and tonic components to increase the temporal precision. LedaLab provides information on the number of skin conductance responses (SCRs) and the SCR onset for each TTP and CDA analysis of the EDA signal. We downsampled the data from 4 Hz to 1 Hz and then computed the average SCRs per minute for all 11 participants to compare the results of the CDA and TTP analysis with previous research by plotting the frequency of the SCRs per minute [
The next step was to extract the features that captured the patterns in the EDA signal. The EDA peak detection analysis provides a set of features corresponding to each EDA peak. We used EDA Explorer public scripts to detect the EDA peaks [
An example of an electrodermal activity peak. EDA: electrodermal activity; SCR: skin conductance response.
Description of the features that are extracted from the electrodermal activity peaks.
Feature | Description |
EDAa | The EDA value at apex of the peak |
Rise time | Time (microseconds) taken by the EDA peak to reach its maximum value |
Maximum derivative | The maximum |
Amplitude | The amplitude |
Decay time | Time (microseconds) taken by the signal to drop from the apex to the minimum of the peak |
Skin conductance response width | The width of the peak (number of the samples) |
AUCb | 2D area under the EDA peak curve |
aEDA: electrodermal activity.
bAUC: area under the curve.
Measuring HR is a routine part of a clinical examination. The resting HR (RHR) of individuals reflects their overall health. We measured the RHR of the participant using IBI derived from the photoplethysmography sensor to identify any irregularities in the HR data. We note that the IBI data for this study were provided by Empatica and represent the time in milliseconds between two successive heartbeats (the R-R interval). This proprietary algorithm [
Time domain analysis and frequency domain analysis are 2 standard methods for investigating HRV. In the time domain analysis, HRV measures were directly extracted from the IBI or RR interval signals. Frequency domain analysis extracts HRV measures from the power spectrum of the Fourier transform of the RR interval signals. In this study, we focus on the time domain HRV measures, including the mean of the RR interval (MRR), the SD of the RR interval (STDRR), the root mean square successive differences of the RR intervals (RMSSDs), and the coefficient of variance of the RR intervals (CVRR) [
The mean age of the participants was 40.27 years (SD 3.66; range 27-60 years). The majority of the sample was White, non-Hispanic (n=9). One participant identified as White, Hispanic, and one participant identified as American Indian or Alaskan Native. Of the 11 participants, 10 (91%) identified as female, and 1 (9%) participant identified as male.
Previous research suggests that the collected physiological signals must maintain a number of quality measures to be considered valid for data analysis. For this research, we examined the quality of the collected data across several dimensions, including (1) the number of clean signals after artifact removal, (2) the distribution of the SCR values, using TTP and CDA analyses, and (3) the distribution of the HRV values. On the basis of the results of artifact detection, out of 1,174,898 EDA signal measurements, 1,032,265 (87.86%), 108,208 (9.21%), and 34,424 (2.93%) collected in total from all the participants were clean, noisy, and questionable, respectively. In both the TTP and CDA analyses, the EDA data were downsampled to a sampling rate of 1 Hz, and a minimum amplitude threshold of 0.01 µS was considered.
As expected, in both analyses, the SCRs per minute were positively skewed with most SCR values near or at 0.0 per minute. The results from the TTP analysis show a peak at 0 SCRs per minute, with a continuous decrease of up to 17.5 SCRs per minute. The results from the CDA analysis show a peak at 0 SCRs per minute, followed by a sharp decline at 1 SCRs per minute and later by a small increase from 15.0 to 17.5 SCRs. Thus, our data demonstrated results similar to those of a previous study that used the same analyses on 8 participants to assess the quality of EDA and HRV data collected with the E4 [
Total numbers and percentages of participants’ heart rate (bpm) across low, normal, and high ranges during the study period.
Subject | Total, N | Low (40–59 bpma), n (%) | Normal (60-100 bpm), n (%) | High (101-200 bpm), n (%) |
Participant 1 | 402,257 | 3626 (0.91) | 290,316 (72.17) | 108,267 (26.91) |
Participant 2 | 715,794 | 9599 (1.34) | 605,916 (84.65) | 100,146 (13.99) |
Participant 3 | 473,283 | 2601 (0.55) | 386,090 (81.58) | 84,554 (17.87) |
Participant 4 | 606,614 | 15,592 (2.57) | 533,903 (88.01) | 50,999 (8.41) |
Participant 5 | 738,475 | 953 (0.13) | 555,673 (75.25) | 181,685 (24.6) |
Participant 6 | 650,860 | 14,739 (2.26) | 551,797 (84.78) | 84,276 (12.95) |
Participant 7 | 340,036 | 399 (0.12) | 263,814 (77.58) | 75,801 (22.29) |
Participant 8 | 388,833 | 6394 (1.64) | 340,905 (87.67) | 41,496 (10.67) |
Participant 9 | 535,352 | 3592 (0.67) | 467,507 (87.33) | 64,213 (11.99) |
Participant 10 | 547,746 | 2487 (0.45) | 472,416 (86.25) | 72,789 (13.29) |
Participant 11 | 692,901 | 2164 (0.31) | 310,876 (44.87) | 379,119 (54.71) |
abpm: beats per minute.
Means and SDs for the statistical features extracted from the heart rate signal.
Subject | Mean value of the heart rate, mean (SD) | Minimum value of the heart rate, mean (SD) | Maximum value of the heart rate, mean (SD) | SD of the heart rate, mean (SD) |
Participant 1 | 104.72 (9.81) | 51.17 (19.27) | 167.32 (17.9) | 10.21 (1.74) |
Participant 2 | 87.05 (6.15) | 45.08 (5.01) | 186.11 (14.61) | 12.1 (2.86) |
Participant 3 | 88.83 (5.29) | 51.26 (6.18) | 182.97 (14.71) | 11.28 (3.15) |
Participant 4 | 71.27 (6.15) | 35.64 (6.15) | 159.71 (27.55) | 9.21 (2.90) |
Participant 5 | 86.3 (8.03) | 45.92 (11.18) | 171.66 (21.96) | 9.85 (2.27) |
Participant 6 | 74.11 (7.88) | 37.40 (4.73) | 185.45 (12.29) | 14.97 (3.94) |
Participant 7 | 91.07 (7.84) | 45.48 (5.06) | 168.99 (19.72) | 11.72 (2.91) |
Participant 8 | 78.54 (9.48) | 38.53 (4.52) | 180.56 (12.34) | 10.37 (2.23) |
Participant 9 | 79.04 (6.88) | 48.22 (4.28) | 169.47 (30.22) | 9.87 (1.54) |
Participant 10 | 84.79 (3.38) | 42.88 (5.47) | 166.88 (13.08) | 12.54 (0.98) |
Participant 11 | 95.19 (8.14) | 44.29 (9.58) | 180.7 (29.88) | 22.53 (8.45) |
Means and SDs for the statistical features extracted from the heart rate variability measures.
Subject | Mean value of all of the RR intervals, mean (SD) | SD of the RR interval, mean (SD) | Root mean square, mean (SD) | Covariance of SD, mean (SD) | Covariance of all the RR intervals, mean (SD) |
Participant 1 | 0.58 (0.05) | 0.06 (0.01) | 0.06 (0.01) | 0.11 (0.02) | 0.10 (0.02) |
Participant 2 | 0.70 (0.05) | 0.00 (0.02) | 0.07 (0.02) | 0.10 (0.02) | 0.13 (0.02) |
Participant 3 | 0.69 (0.04) | 0.08 (0.02) | 0.07 (0.02) | 0.11 (0.03) | 0.12 (0.02) |
Participant 4 | 0.86 (0.07) | 0.10 (0.01) | 0.07 (0.01) | 0.08 (0.02) | 0.12 (0.03) |
Participant 5 | 0.71 (0.06) | 0.08 (0.01) | 0.07 (0.01) | 0.10 (0.03) | 0.11 (0.02) |
Participant 6 | 0.84 (0.08) | 0.13 (0.03) | 0.11 (0.02) | 0.13 (0.03) | 0.15 (0.05) |
Participant 7 | 0.68 (0.06) | 0.09 (0.02) | 0.07 (0.02) | 0.10 (0.03) | 0.13 (0.03) |
Participant 8 | 0.78 (0.10) | 0.10 (0.02) | 0.09 (0.02) | 0.11 (0.01) | 0.12 (0.03) |
Participant 9 | 0.78 (0.08) | 0.09 (0.01) | 0.07 (0.01) | 0.09 (0.02) | 0.12 (0.01) |
Participant 10 | 0.72 (0.03) | 0.10 (0.01) | 0.09 (0.01) | 0.13 (0.02) | 0.14 (0.01) |
Participant 11 | 0.68 (0.05) | 0.13 (0.01) | 0.09 (0.01) | 0.13 (0.02) | 0.19 (0.06) |
Descriptive statistics of the self-reported outcomes.
Self-reported outcomes | Mean (SD) | Median | Minimum | Maximum | 1st quartile | 3rd quartile |
Stress events | 3 (2.9) | 2 | 0 | 17 | 1 | 3.5 |
Alcohol use | 2.8 (5.2) | 0.0 | 0.0 | 18.0 | 0.0 | 3.0 |
Alcohol cravings | 2.9 (0.9) | 3.0 | 1.0 | 4.8 | 2.3 | 3.5 |
Positive emotion | 2.5 (0.6) | 2.5 | 1.3 | 3.9 | 2.1 | 2.9 |
Negative emotion | 2.3 (0.8) | 2.5 | 1.0 | 2.9 | 1.5 | 2.9 |
Discomfort | 2.3 (1.0) | 2.3 | 1.0 | 5.0 | 1.0 | 3.0 |
Pain | 2.2 (1.1) | 2.5 | 1.0 | 5.0 | 1.0 | 3.0 |
Bivariate correlation coefficient values of the self-reported outcomes.
Self-reported outcomes. | Stress events | Alcohol use | Alcohol cravings | Positive emotion | Negative emotion | Discomfort | Pain |
Stress events | —a | — | — | — | — | — | — |
Alcohol use | −0.11 | — | — | — | — | — | — |
Alcohol cravings | −0.001 | 0.46 | — | — | — | — | — |
Positive emotion | 0.07 | 0.001 | −0.16 | — | — | — | — |
Negative emotion | 0.21 | −0.38 | 0.02 | −0.15 | — | — | — |
Discomfort | 0.04 | −0.44 | −0.20 | −0.02 | 0.49 | — | — |
Pain | 0.18 | −0.45 | −0.25 | −0.05 | 0.48 | 0.93 | — |
aNot applicable.
Spearman correlation coefficients between the daily electrodermal activity features and the daily self-reported outcomes.
Feature | Self-reported outcome | ||||||
|
Stress | Alcohol | Cravings | Positive emotion | Negative emotion | Discomfort | Pain |
Electrodermal activity | −0.09 | −0.03 | 0.01 | −0.14 | 0.17 | −0.06 | −0.07 |
Rise time | 0.11 | −0.06 | 0.10 | 0.23a | 0.28b | −0.12 | −0.12 |
Maximum derivative | 0.04 | 0.01 | 0.16 | 0.09 | 0.11 | −0.07 | −0.07 |
Amplitude | 0.26b | −0.02 | 0.16 | 0.20c | 0.41d | 0.01 | 0.01 |
Decay time | −0.20a | 0.18c | 0.12 | 0.17c | −0.10 | −0.18c | −0.22a |
Skin conductance response width | −0.34d | 0.06 | 0.15 | −0.01 | 0.02 | −0.12 | −0.14 |
Area under the curve | −0.32d | 0.04 | 0.17 | 0.01 | 0.05 | −0.07 | −0.08 |
Counts | 0.27b | 0.05 | 0.09 | 0.09 | 0.09 | 0.10 | 0.10 |
aSignificance code .05.
bSignificance code .01.
cSignificance code .10.
dSignificance code .001.
Spearman correlation coefficients between the daily heart rate variability measures and the daily self-reported outcomes.
Feature | Self-reported outcome | ||||||
|
Stress | Alcohol | Cravings | Positive | Negative | Discomfort | Pain |
Mean value of all the RR intervals | 0.22a | −0.07 | 0.00 | 0.26a | 0.18b | −0.21a | −0.18b |
SD of the RR interval | 0.22a | −0.07 | 0.04 | 0.21a | 0.22a | −0.06 | −0.06 |
Root mean squared SD | 0.08 | −0.04 | 0.02 | 0.26a | 0.15 | −0.10 | −0.12 |
Covariance of SD | 0.12 | 0.00 | 0.05 | 0.24a | 0.22a | −0.10 | −0.13 |
Covariance of all the RR intervals | 0.27c | −0.05 | 0.04 | 0.20a | 0.29c | −0.08 | −0.08 |
Mean value of heart rate | 0.28c | 0.01 | 0.03 | 0.26c | 0.33d | −0.22a | −0.20b |
SD of the heart rate | 0.21a | −0.02 | 0.04 | 0.14 | 0.32c | −0.07 | −0.07 |
aSignificance code .05.
bSignificance code .10.
cSignificance code .01.
dSignificance code .001.
The overall goal of this study is to examine the feasibility of using 2 physiological markers of stress, EDA and HRV, obtained from an unobtrusive wristband device in an ambulatory setting, as a first step toward developing mobile health (mHealth) interventions to help prevent alcohol relapse. To achieve this goal, we first assessed the quality of the continuous monitoring of physiological signals using an Empatica E4 wearable sensor device. We determined that 87.88% (1,032,265/1,174,898) of the EDA signals were clean, whereas only 9.21% (108,208/1,174,898) of the EDA signals could be considered noise. A comparison of the distribution of the EDA SCRs also demonstrated high correspondence between our study and previous studies that used similar techniques [
In the next steps, we examined associations between features of the EDA and HRV signals with a number of self-reported outcomes, including daily tallies of stress events and alcoholic drinks as well as aggregated levels of alcohol-related cravings, positive and negative moods, and experiences of pain and discomfort. In total, 2 features of the EDA signal (amplitude and peak counts) were positively associated with the number of stress events reported each day, whereas 3 features (decay time, SCR width, and area under the curve) were negatively associated with daily tallies of stress events. These results were as expected and suggest that participants experienced more and higher EDA peaks on days of heightened stress and that the EDA signal varied more rapidly during more stressful intervals. The results also showed that some (but not all) EDA features were positively associated with self-reported positive and negative emotions. In general, EDA features were not significantly correlated with the daily use of alcohol or alcohol-related cravings or with experiences of pain or discomfort.
However, almost all HRV features were significantly and positively associated with daily tallies of stress events. These results were as expected and indicated that participants experienced greater HRV on days characterized by higher levels of stress. Nearly all HRV features were also significantly and positively associated with the aggregate levels of positive and negative emotions. Thus, the results suggest an increased recovery ability of the participants in stressful situations or when feeling excessive positive or negative moods. Similar to the findings for the EDA features, HRV features were generally not significantly associated with daily use of alcohol or alcohol-related cravings or experiences of pain or discomfort.
This study was designed to establish the feasibility of assessing physiological data in an ambulatory setting to inform the development of a future mHealth intervention. Thus, the major limitation of this pilot study is the small sample size, which limited our ability to test hypotheses regarding associations between physiological signals and self-reported outcomes with full statistical power. Future studies that include larger and more representative samples are needed to replicate our findings. We also note that our analyses did not account for demographic characteristics of the participants, such as age, personality characteristics, race, or gender. Future work that examines how these and other demographic characteristics might play a role in these processes is needed.
We further acknowledge that although the uncontrolled nature of the data collection in ambulatory settings was a strength of this study, this may have introduced some subjective bias into the assessment of stress events. Future research in ambulatory settings should also consider how HRV is affected by sleep and circadian processes [
We investigated the associations of physiological signals, EDA and HRV, with self-reported outcomes among adults diagnosed with AUD in a 14-day uncontrolled data collection. The results demonstrated that the physiological data collected via an Empatica E4 wearable sensor device were useful and that features of these physiological signals were significantly associated with several self-reported outcomes, including identification of stress events, daily use of alcohol, negative and positive emotions, and pain and discomfort. Future research is needed to further validate these findings to develop tailored mHealth interventions to enhance sustained recovery from AUD.
autonomic nervous system
alcohol use disorder
beats per minute
blood volume pulse
continuous decomposition analysis
coefficient of variance of the RR intervals
covariance of SD
electrodermal activity
ecological momentary assessment
heart rate
heart rate variability
interbeat interval
mobile health
mean value of the heart rate
minimum value of the heart rate
mean of the RR interval
maximum value of the heart rate
Positive and Negative Affect Scale
root mean square successive differences of the RR interval
skin conductance response
SD of the RR interval
trough-to-peak
This project was funded by the Alcohol and Drug Research Program of Washington State University through grant funding to MJC. This investigation was supported in part by funds provided for medical and biological research by the State of Washington Initiative Measure (number 171).
MJC and HG conceived and designed the study. MJC and SP acquired the data. PA and RKS analyzed and interpreted the data. PA and MJC prepared the manuscript. MM, SP, and PP contributed to project administration and reviewing and editing the manuscript. All authors read and approved the final manuscript.
None declared.