A Pilot Study of Ambulatory Heart Rate Variability Biofeedback for Substance Use Disorder
Phase: NA
Conditions: Substance Use Disorders
Sponsor: Massachusetts General Hospital

 
Version 2021.06.10 
Page 1 of 17 
 
 
 
Detailed Protocol 
 
 
Principal Investigator: 
David Eddie, Ph.D. 
 
Project Title: 
A pilot study of ambulatory heart rate variability biofeedback for 
substance use disorder 
 
Version Date: 
11/27/2023 
 
 
NCT Number: 
NCT05454657 
 
 
1. 
 Background and Significance 
In early SUD recovery, everyday experiences of negative affect and alcohol and other drug cues 
often serve as triggers for substance use that appear to be automatic in the sense that they 
bypass patients’ cognitive plans and conscious intentions not to use. A central goal of first-line 
cognitive-behavioral SUD treatments is to strengthen affective and cognitive control to increase 
individuals’ ability to override impulses to use alcohol and other drugs (AOD) 1, 2. Yet, automatic 
physiological processes compromised by SUD interact with internal affective states and 
environmental cues to undermine effortful cognitive control and outcompete goals to avoid 
substance use 3.  
 
In particular, evidence suggests that the baroreflex—the central autonomic network’s (CAN) 
cardiovascular regulatory system that is integral to emotion regulation and goal-oriented 
behavior—directly participates in substance use maintenance or desistence through its influence 
on attention capture by provocative cues, cue salience, and visceral reaction to cues 4-6. 
Ineffective or maladaptive functioning of the baroreflex is believed to set in motion a cascade of 
neurocardiac events that alter affect, emotional arousal, and stress response 4, 7, 8 thereby 
increasing the likelihood that susceptible individuals will seek or use AOD, even following 
extended periods of abstinence 3, 9, 10. Moreover, baroreflex inefficiency is strongly interrelated 
with SUD drivers and corollaries including anxiety, depression, and stress 11-14. 
 
Heart Rate Variability Biofeedback (HRVB) is a biobehavioral intervention involving rhythmic 
breathing at resonance frequency (RF) that stimulates the baroreflex to offset these 
psychophysiological deficits. The autonomic normalization effected by RF breathing is believed 
to bolster cognitive control efforts by interrupting or dampening automatic-visceral reactions that 
can unintentionally undermine treatment gains, and in doing so support better decision-making, 
motivation, reductions in craving, and shifts in attention allocation 15. HRVB’s capacity to help 
individuals better regulate affect and bolster cognitive control has garnered excitement in 
addiction clinical science 3, 16, 17, and preliminary studies suggest HRVB could be a valuable 
addendum to first-line addiction treatments 15, 18-24. 
 
Studies of first-generation HRVB focused on positive cognitive and behavioral effects that accrue 
over weeks or months of regular, daily RF breathing practice. However, recent studies have 
demonstrated that a brief exposure to RF breathing in anticipation of psychosocial stress, or 
during induced stress, helps to control physiological arousal, reduce state anxiety, and improve 
cognitive performance 15, 25, 26. Our clinical observations provide further support for in-the-moment 

Mass General Brigham Institutional Review Board 
Intervention/Interaction Detailed Protocol 
 
Version 2021.06.10 
 
Page 2 of 17 
RF breathing. We have observed many HRVB study participants intuitively use RF breathing to 
manage intense, momentary negative affect, even though they were not explicitly instructed to do 
so 18, 24. We posit that such in-the-moment bursts of HRVB practice could be an SUD treatment 
tool that helps individuals buffer salient triggers and urges to use AOD in-the-moment. 
 
Recent advances in the field have given rise to small, lightweight, wearable biosensors that can 
allow wearers to do HRVB on-the-go. These second-generation HRVB devices can also function 
as a just-in-time interventions by prompting in-the-moment HRVB practice when autonomic 
hyperarousal is detected, to buffer salient triggers and urges to use AOD. In addition to their 
potential for direct clinical benefit, the advent of these second-generation, ambulatory, HRVB 
devices has implications for the accessibility and scalability of HRVB because these devices are 
affordable, accessible, integrate with the end-user’s smartphone, and do not require a provider to 
administer the intervention. 
 
This study builds on a body of preliminary work speaking to HRVB’s potential as an addendum to 
first-line SUD treatments. We will be using the ‘Lief Smart Patch’ device in this research 
(https://getlief.com). The Lief Smart Patch is a small, wearable, electrocardiogram (ECG)-based 
heart rate monitor that is attached to the body utilizing skin safe ECG electrodes. ECG technology 
is known to be safe and carries no substantial risk. The device monitors heart rate variability 
(HRV) as an index of autonomic nervous system arousal and can prompt the wearer to engage 
in brief bursts of HRVB practice via a connected smartphone app when stress is inferred. Wearers 
are also able to engage in regularly scheduled HRVB practice, and practice ad libitum. The Lief 
biofeedback device is being used in this study to deliver a clinical intervention and collect 
physiological data; the effectiveness of the device itself is not being tested. 
 
 
2. 
Specific Aims and Objectives  
The aim of this pilot study is to administer heart rate variability biofeedback (HRVB) to adults 
with substance use disorder who are receiving outpatient treatment as usual (TAU) and 
compare them to a control group receiving TAU only on intervention outcomes that include 
alcohol/drug use and negative affect. The study’s specific aims are as follows: 
 
Aim 1:  Assess ambulatory HRVB’s uptake by individuals with SUD in terms of practice time, 
and perceived barriers to uptake based on qualitative participant feedback (proof-of-
concept) 
Aim 2: Test effects of HRVB practice on affect and substance use 
a.  
Test day-level effects of HRVB practice on affective states and substance use  
b. 
Test the accumulative, person-level effects of scheduled daily HRVB practice vs. in-the-
moment HRVB practice, vs. their interaction on substance use during the 8-week 
intervention period 
 
We hypothesize that participants receiving HRVB + TAU will have a lower alcohol/drug use 
lapse rate and demonstrate greater reductions in negative affect compared to controls receiving 
TAU only. 
 
 
3. 
General Description of Study Design 
Design Overview  

Mass General Brigham Institutional Review Board 
Intervention/Interaction Detailed Protocol 
 
Version 2021.06.10 
 
Page 3 of 17 
We will recruit 120 adults with substance use disorder (SUD) in their first year of a current SUD 
recovery attempt. Participants will be randomized using the SAS statistical program to either the 
experimental (n= 70) or control group (n= 50). We will also recruit 10 experimental group 
participants to participate in focus groups (n= 5 in each). 
 
Experimental group participants will participate in 8 weeks of HRVB training using the Lief 
HRVB biopatch and smartphone app, and 2x daily ecological momentary assessment of affect 
and substance use. Participants will also be encouraged to self-initiate ecological momentary 
assessment surveys when feeling high levels of stress or craving. Experimental group 
participants will be encouraged to wear the Lief HRVB biopatch for at least 8 hours per day and 
use it as needed to regulate negative affect in-the-moment with a minimum of 5mins daily ad 
hoc practice, and 5min of scheduled HRVB practice twice daily, for a total of 15min daily 
practice.  
 
The control group will participate in 8 weeks of 2x daily ecological momentary assessment of 
affect and substance use. Participants will also be encouraged to self-initiate ecological 
momentary assessment surveys when feeling high levels of stress or craving. Both groups will 
be allowed to continue with whatever SUD treatment they are independently engaged in. In 
other words, we will not ask participant to desist any existing treatments they are utilizing.  
 
4. 
Subject Selection 
Participants 
Participants will be 120 adults ≥18 years of age in the first year of a current SUD recovery 
attempt. To reduce the likelihood of acute substance withdrawal symptoms influencing affect 
and physiological indices, participants will be required to have at least two weeks of abstinence 
from alcohol and other drugs immediately prior to study participation. To allow for recruitment of 
a representative sample of individuals in early recovery from SUD, commonly co-occurring 
psychological conditions such as affective disorders and PTSD will be allowed. Medications will 
be allowed. However, individuals with severe cardiac arrhythmias which would confound HRV 
Volunteers phone screened for study eligibility    
Volunteers randomized  
(n= 120)   
Heart rate variability biofeedback (HRVB) + 
Treatment as usual (TAU)
(n= 70)
Treatment as usual (TAU) only       
(n= 50)   

Mass General Brigham Institutional Review Board 
Intervention/Interaction Detailed Protocol 
 
Version 2021.06.10 
 
Page 4 of 17 
analyses will be excluded, as will persons with active psychosis. 
 
Recruitment Procedures 
Participants will be recruited via Mass General Brigham HealthCare’s RSVP for Health 
Recruitment system, as well as flyers placed throughout Mass General Brigham Healthcare 
sites (e.g., Massachusetts General Hospital, Brigham and Women’s Hospital, McLean Hospital). 
Advertisements will also be placed on social media sites such as In the Rooms and Facebook 
with chat and comment features disabled in order to protect privacy. Individuals who are 
interested in participation will be invited to call the study researchers who will explain the study 
procedures in detail, including the potential benefits and risks and the expected duration and 
time commitment of their participation. Potential participants will also be able to complete a 
brief, online, pre-screening questionnaire delivered via REDCap. Individuals who are potentially 
eligible based on their responses to this form will be contacted by study staff for full phone 
screening (see ‘Subject Enrolment’ section below). 
 
 
 
5. 
Subject Enrollment 
Individuals interested in participating in the study will complete a brief phone interview (~10min) 
to determine their study eligibility. Those deemed eligible will be scheduled for a virtual study 
intake session conducted over a Mass General Brigham Zoom and will be randomized to either 
the experimental (HRVB + TAU) or control group (TAU only). On the study intake Zoom call, 
participants will be invited to provide informed consent using the REDCap e-Consent Module. 
 
The consent form will include language indicating experimental group participants will be invited 
to participate in a focus group following the 8-week course of HRVB. 
 
 
6. 
STUDY PROCEDURES 
On the study intake Zoom call, after providing informed consent (~10min), participants will 
complete a series of questionnaires via a secure REDCap link provided in the Zoom chat-box 
(~45min). Following this, participants will be guided through the process of installing the 
TigerAware ecological momentary assessment app on their smartphone and instructed how to 
use it (~5min). Finally, for experimental group participants, the researcher will provide the study 
rationale and give brief instruction in paced breathing (~30min). Immediately following the intake 
call, the experimenter will arrange for a Lief device to be mailed to the participant by Lief 
Therapeutics, which will come with instructions for setting up the device and downloading the 
accompanying smartphone app. 
 
To maximize ecological validity in this study, the same HRVB phone coaching offered to regular 
Lief subscribers will be available to the experimental group participants. The Lief coaches are 
experts in HRVB and can help with any technical issues with the equipment or queries 
pertaining to the participant’s HRVB practice. Coaches will reach out to study participants to 
schedule a 20-minute onboarding call when the Lief equipment is ordered. During the 
orientation call, the Lief coach will cover the following topics: 
 
o 
Why HRV matters for your mental and physical health 
o 
How Lief's HRV tracking and biofeedback training will help you achieve your 
health and wellness goals  

Mass General Brigham Institutional Review Board 
Intervention/Interaction Detailed Protocol 
 
Version 2021.06.10 
 
Page 5 of 17 
o 
How to do a Downtime practice (a.k.a. HRV biofeedback exercise) 
o 
Understanding how Lief's autodosing program works  
o 
The coach’s role: "As your coach, I will support you as a sensemaker of your 
data to optimize your use of the Lief so we help you self-regulate your stress 
response more effectively"  
o 
Communication expectations: Mostly in the app chat box, I'll respond on 
weekdays. Schedule 20-minute calls every 2 weeks in the app so we can review 
your progress together.  
  
Then, after experimental participants receive their device, they will have the option of 
communicating as often as they want with their coach in the Lief app's chat box. Coaches aim to 
respond within a business day (asynchronous communication). Participants will also have the 
option of scheduling 20min, ad hoc coaching sessions with one of Lief’s coaches. Importantly, 
these HRVB phone coaching sessions are not psychotherapy, nor are they designed to replace 
psychotherapy, and Lief’s coaches are trained to only offer support related to HRVB.  
 
Throughout the 8-week study protocol, all study participants will have as needed access via 
phone and text to the study research coordinator, should issues with the ecological momentary 
assessment application arise, or the participant has any other questions or concerns. This will 
be a convenient channel through which participants can contact research staff for technical 
assistance, however, research staff may contact participants to check apps are functioning 
properly, for instance, in the event a participant has missed a lot of ecological momentary 
assessment surveys. We will use the MGB Cisco Jabber messaging app to create a unique 
study contact number at which the study research coordinator can be reached.  
 
The heart rate variability biofeedback intervention 
The HRVB equipment in this study includes a wearable ECG monitor (known as the Lief Smart 
Patch) and a smartphone app. 
 
The Lief Smart Patch is a wearable, ECG-based heart rate monitor. It is attached to the body 
utilizing skin-safe ECG electrodes. ECG technology is known to be safe and carries no 
substantial risk. The Smart Patch senses stress and anxiety in real time based on HRV and 
breathing patterns, and then provides a breath-based heart rate variability biofeedback 
intervention. The Smart Patch collects heart rate, heart rate variability, and accelerometer data 
through continuous monitoring. These data are synced with the end-user’s smartphone via 
Bluetooth Low Energy, then encrypted and sent through their wireless network to Lief’s HIPAA-
compliant server. This device does not collect data on geo-location, nor does it have any 
participant information stored on it. If a device is lost, there is no risk to participants of a privacy 
breach. 
 
Experimental group participants will use the complementary Lief smartphone app to engage in 
the 8 weeks of HRVB. The Lief app is compatible with iOS and Android devices and provides a 
visual interface that supports HRVB practice. It also includes architecture that allows end-users 
to schedule ad hoc phone coaching sessions. 
 
Focus groups 
Experimental group participants (N= 10) will participate in 60-minute, audio-recorded focus 
groups moderated by the PI. There will be two groups of 5. These focus groups will gather 
feedback on participant interest in ambulatory HRVB, perceived benefits, and barriers to 
practice/device use.  

Mass General Brigham Institutional Review Board 
Intervention/Interaction Detailed Protocol 
 
Version 2021.06.10 
 
Page 6 of 17 
 
Participant Compensation 
To encourage study completion, participants (N= 120) will be compensated with an Advarra gift 
card loaded in installments. 
 
The compensation breaks down as follows: Participants will be compensated $50 for completing 
the study intake/orientation and baseline questionnaire measures. They will also be 
compensated $100 for completing the ecological momentary assessment component of the 
study (defined as completing surveys every week of the 8-week study period), with a $100 
bonus payment for achieving ≥90% ecological momentary assessment survey completion over 
the whole 8-week study period. Also, to motivate the return of ECG monitors and give the 
experimental group extra compensation for their additional work in the study, the experimental 
group will also receive a $100 bonus for return of study equipment. Participants who participate 
in the focus group will also be compensated $50. 
 
The $50 for completing the study intake session will be provided immediately following the 
intake session. The remainder of the compensation will be electronically added to participants’ 
Advarra gift card at the end of the 8-week study period. 
 
Sending and Receiving Data 
Lief Therapeutics will provide the PI Dr. David Eddie with the participants’ physiology data, 
including heart rate variability, wear time, and paced-breathing practice time. This physiology 
data will be available for download from the Lief participant dashboard. The deidentified data file 
will be sent to the study email for direct download and saved to the study drive for analysis. 
Participant data that the study team cannot directly download will be retrieved by Lief 
Therapeutics and shared to the study team using a secure, MGB DropBox account. 
 
Participant data will not be shared with Lief Therapeutics or TigerAware. Deidentified data will 
be shared with Co-I Dr. Noah Emery at Colorado State University in the form of datasets. 
Datasets will be shared using a secure, MGB DropBox account. 
 
 
7. 
Risks and Discomforts 
We believe there to be only minimal risks associated with this study. We will ensure proper 
confidentiality of information by training staff about the critical importance of confidentiality and 
providing study ID numbers for participants’ study information. Identifying information will not be 
kept with collected assessment data. Research staff will be trained appropriately and undergo 
human research subjects protection training. Secure soft and hard data storage mechanisms 
will be in place (i.e., locked offices/suites, password-protected databases). Although we will 
reimburse subjects for their time and efforts, we do not believe the amounts to be coercive. 
 
Out of necessity, we will provide Lief Therapeutics with experimental group participants’ name, 
address, and phone number so equipment and phone coaching can be provided. This is a 
commensurate level of individual-level information Lief securely hold for thousands of 
subscribers. No additional participant information will be shared with Lief Therapeutics. Lief 
Therapeutics will not have access to participants’ demographic, questionnaire, or ecological 
momentary assessment data.  
 

Mass General Brigham Institutional Review Board 
Intervention/Interaction Detailed Protocol 
 
Version 2021.06.10 
 
Page 7 of 17 
The risk to participants in this study is relatively low because it does not involve invasive 
procedures or the administration of medication, and we will not be asking participants to modify 
their day-to-day behaviors in profound ways. Potential risks in the study are considered minimal 
and include, 1) potential discomfort related to completing questionnaires about sensitive topics 
such as psychological problems, and 2) potential breach of confidentiality and/or privacy.  
 
Though potential risks of study participation are considered minimal, it is important to have 
participant protection plans in place. Participants will be informed they are free to withdraw from 
the study at any time. Also, we hope that any potential subjective discomfort from completing 
questionnaires will be minimized by assurances that participants can refuse to answer any 
question that they do not feel comfortable answering. In the event, however, that discomfort or 
distress do arise during any aspect of the laboratory visits, participants will receive support. 
 
Participant names and contact information will be maintained in a secure recruitment/enrollment 
database in REDCap. Signed consent forms will be electronically stored on Mass General 
Brigham’s REDCap e-Consent secure server.  
 
All ecological momentary assessment data collected using the TigerAware app will be saved on 
TigerAware’s encrypted, HIPPA compliant server, with copies of this data stored on Mass 
General Brigham’s secure server in a designated study folder. No identifying participant 
information will be shared with TigerAware. Participants will only be identified by a unique study 
number in the TigerAware system, and not by their name. The TigerAware system does not 
require participant information such as an email address or phone number to function, nor does 
it require participants to sign an end-user license agreement. 
 
In the event we encounter issues running the TigerAware app on a participant’s smartphone, we 
will use REDCap’s ecological momentary assessment feature as a backup. This feature works 
by linking REDCap with Twilio (an MGB approved texting app). Participants receive a text which 
links them to a REDCap survey through their smartphone’s browser. Ecological momentary 
assessment collected this way will be stored with study questionnaire data also being collected 
through REDCap. 
 
All ECG and HRVB digital practice records from the Lief app will be saved on Lief’s encrypted, 
HIPPA compliant server, with copies of this data stored on Mass General Brigham’s secure 
server in a designated study folder. Though Lief will need participants’ name, mailing address, 
and phone number to send them equipment and provide phone coaching, research staff will not 
share participants’ demographic, questionnaire, or ecological momentary assessment data with 
them.  
 
In the event researchers observe a potentially serious cardiac arrythmia during post processing 
of ECG recordings, the participant will be notified via phone call by the study PI who will 
encourage them to consult with their doctor. 
 
All study data will also be backed up on a Mass General Brigham issued, password protected, 
server and a desktop computer housed in secure office at the MGH Recovery Research 
Institute. Breach of confidentiality via final study datasets is highly unlikely because all study 
data will be identified with a numeric code, will contain no identifying information, and will be 
stored on HIPPA compliant servers. 
 

Mass General Brigham Institutional Review Board 
Intervention/Interaction Detailed Protocol 
 
Version 2021.06.10 
 
Page 8 of 17 
All research staff at the MGH Recovery Research Institute are fully trained in relevant ethical 
principles and procedures, including maintaining confidentiality, and are certified by 
Collaborative Institutional Training Initiative (CITI) to conduct research with human subjects. 
During the study, we will notify officials, as mandated by law, if a participant reports intentions to 
harm himself/herself or others, or reports child or elder abuse. Participants will be informed of 
these limits to confidentiality during the consenting process. Serious adverse events will be 
reported to the MGH Institutional Review Board immediately by telephone and by written report 
within 24 hours of our receipt of information regarding the event.  
 
 
 
8. 
Benefits 
A potential participant benefit for the experimental group is improved substance use and 
affective outcomes. A potential participant benefit for both the experimental and control groups 
may be increased awareness of personal patterns in affect through the conduct of ecological 
momentary assessment.  
 
There are also considerable benefits to society associated with this study. Substance problems 
carry a tremendous emotional, medical, interpersonal, and societal cost. The knowledge gained 
in this study may lead to the adoption of a novel SUD treatment tool that bridges gaps in 
existing treatment approaches. The minimal risks associated with this research are reasonable 
given the potential benefits of this study.  
 
 
9. 
Statistical Analysis 
Because this is a pilot study with limited power to detect statistical significance, we will focus on 
interpreting effect sizes to inform a subsequent R01 trial. We note however, certain design 
strengths that will allow us to maximize statistical power. For instance, in our regression models, 
rather than just looking at main effects of group, we will also assess Heart Rate Variability 
Biofeedback (HRVB) practice time as a continuous measure with controls scored as 0. 
Additionally, power to test day-level associations is boosted by the repeated measures design 
with up to 84 repeated, momentary assessments per participant. 
 
Please note that we use the term “day-level” below to denote effects at the level of individual 
ecological momentary assessment (EMA) surveys. From this perspective we have up to 84 
repeated, momentary assessments of affect and substance use.  
Aim 1.  Assess ambulatory HRVB’s uptake by individuals with SUD in terms of practice 
time, and perceived barriers to uptake based on qualitative participant feedback 
We will explore ambulatory HRVB engagement using HRVB practice time (engagement with 
regular daily practice; engagement with unscheduled, in-the-moment practice). As a general 
benchmark of intervention uptake success that can be used to guide the subsequent R01 design, 
we will accept ≥50% engagement/adherence to scheduled practice (with consideration given to 
both number of days practiced, and time practiced each day). In-the-moment practice is harder 
to assess given we cannot know exactly when participants are experiencing increases/high levels 
of negative affect outside of each EMA survey, however, as a heuristic, we will accept ≥50% in-
the-moment practice as indicative of engagement/adherence, based on the ratio of just-in-time 
practice prompts responded to vs. ignored, and the number of days participants engaged in in-
the-moment HRVB practice. 

Mass General Brigham Institutional Review Board 
Intervention/Interaction Detailed Protocol 
 
Version 2021.06.10 
 
Page 9 of 17 
 
We will also explore perceived barriers to ambulatory HRVB uptake based on qualitative 
participant feedback provided from the focus groups. Focus group recordings will be transcribed. 
We will then conduct a systematic qualitative analysis of the transcripts using approaches we 
have used previously to contextualize findings and offer insights into participants’ experiences. 
 
Aim 1 Power 
Based on our previous research of a similar nature, we anticipate that engaging 10 participants 
in focus groups will allow for sufficient diversity of opinion to stimulate discussion and will provide 
ample time for each participant to share their perspective. We are confident that this sample size 
will give us the information we need to refine our intervention and research procedures for the 
subsequent R01.   
 
Aim 2a.  Test day-level effects of HRVB practice on affective states and substance use.  
We will create indices of EMA recorded positive affect ([happiness + calm + energy] ÷ 3) and 
negative affect ([sadness + guilt + nervousness + stress + craving] ÷ 5) for each EMA survey (2 
surveys per day with up to 84 surveys per participant), while also exploring craving scores as a 
stand-alone measure. 
 
Using multilevel models where days (L1) are nested within persons (L2), we will explore the 
effects of day-level HRVB practice earlier in the day (i.e., lagged) on subsequent day-level 
negative affect (Model 1), craving (Model 2), positive affect (Model 3), and substance use 
(yes/no; Model 4), while also testing whether HRVB decouples the anticipated relationship of day-
level negative affect (Model 5) and craving on subsequent substance use (Model 6). 
 
In models 1-3 (multilevel linear regression models), the dependent variables will be day-level 
negative affect scores, day-level craving scores, and day-level positive affect scores, with lagged 
day-level minutes of scheduled practice, and lagged in-the-moment HRVB practice (with controls’ 
HRVB practice coded as 0), and their interaction term (lagged scheduled practice × lagged in-
the-moment practice) as the independent variables, controlling for age, sex, nicotine use (yes/no), 
medications known to affect the cardiovascular system (yes/no), and baseline past-month AOD 
use (percent days abstinent). 
 
A priori, we anticipate that both greater daily scheduled HRVB practice and in-the-moment HRVB 
practice will predict lower subsequent day-level negative affect and craving, and that their 
interaction will indicate additive benefits of combining scheduled and in-the-moment practice. The 
relationship between positive affect and HRVB has not previously been explored, so we will treat 
this aspect of the analysis as exploratory. Our benchmark for preliminary demonstration of 
treatment benefit will be semi-partial R2’s for practice time of ≥ .10 (medium effect size).  
Models 1-3 
Negative affect = day-level scheduled HRVB practice t–1  +  day-level in-the-moment 
HRVB practice t–1   +  [day-level scheduled HRVB practice t–1  * day-
level in-the-moment HRVB practice t–1]  +  age  +  sex  + nicotine use  
+  medications known to affect the cardiovascular system  +  
baseline past-month AOD use  +  error 
Craving = day-level scheduled HRVB practice t–1  +  day-level in-the-moment HRVB 
practice t–1   +  [day-level scheduled HRVB practice t–1  * day-level in-the-
moment HRVB practice t–1]  +  age  +  sex  +  nicotine use  +  medications 

Mass General Brigham Institutional Review Board 
Intervention/Interaction Detailed Protocol 
 
Version 2021.06.10 
 
Page 10 of 17 
known to affect the cardiovascular system  +  baseline past-month AOD use  
+  error 
Positive affect = day-level scheduled HRVB practice t–1  +  day-level in-the-moment 
HRVB practice t–1   +  [day-level scheduled HRVB practice t–1  * day-
level in-the-moment HRVB practice t–1]  +  age  +  sex  +  nicotine use  
+  medications known to affect the cardiovascular system  +  baseline 
past-month AOD use  +  error 
In Model 4 (multilevel logistic regression), the dependent variable will be day-level substance use 
(yes/no), with lagged day-level minutes of scheduled, and lagged day-level minutes of in-the-
moment HRVB practice (with controls’ HRVB practice coded as 0), and their interaction term 
(scheduled practice × in-the-moment practice) as the independent variables, controlling age, sex, 
nicotine use (yes/no), medications known to affect the cardiovascular system (yes/no), and 
baseline past-month AOD use (percent days abstinent). We will also test this model with group 
as independent variable instead of HRVB practice. 
 
In Models 5-6 (multilevel logistic regression), we will use a model building approach, where we 
will systematically increase model complexity in steps to sequentially examine hypotheses while 
maintaining a parsimonious model. Likelihood ratio tests will be used to examine the relative fit of 
nested models (i.e., the difference in –2 log-likelihood statistics between pairs of base and 
expanded models).  
 
In Model 5, the dependent variable will be day-level substance use (yes/no), with combined 
lagged day-level scheduled and lagged in-the-moment HRVB practice (with controls’ HRVB 
practice coded as 0), lagged day-level negative affect, and the lagged HRVB practice × lagged 
negative affect interaction term as the independent variables, controlling for age, sex, nicotine 
use (yes/no), medications known to affect the cardiovascular system (yes/no), and baseline past-
month AOD use (percent days abstinent). We will also test this model with group as independent 
variable instead of HRVB practice. 
 
In Model 6, lagged day-level craving will be added to Model 5. As with the previous model, the 
dependent variable will be day-level substance use (yes/no), with lagged day-level scheduled and 
lagged in-the-moment HRVB practice combined (with controls’ HRVB practice coded as 0), 
lagged day-level negative affect, lagged day-level craving, and the lagged HRVB practice × 
lagged craving interaction term as the independent variables, controlling for age, sex, nicotine 
use (yes/no), medications known to affect the cardiovascular system (yes/no), and baseline past-
month AOD use (percent days abstinent). We will also test this model with group as independent 
variable instead of HRVB practice. 
 
We anticipate that greater HRVB practice and lower negative affect and craving will be associated 
with lower probability of substance use at the subsequent EMA survey, and that the HRVB 
practice × negative affect/craving interaction terms will indicate that HRVB practice mitigates the 
deleterious relationship between negative affect and craving, and subsequent substance use. 
McFadden’s R2 ≥ .10 (medium effect size) will be our benchmark for preliminary demonstration of 
day-level treatment benefit.  
Models 4-6 
Substance use = day-level scheduled HRVB practice t–1  +  day-level in-the-moment 
HRVB practice t–1   +  [day-level scheduled HRVB practice t–1  * day-

Mass General Brigham Institutional Review Board 
Intervention/Interaction Detailed Protocol 
 
Version 2021.06.10 
 
Page 11 of 17 
level in-the-moment HRVB practice t–1]  +  age  +  sex  +  nicotine use  
+  medications known to affect the cardiovascular system  +  baseline 
past-month AOD use  +  error 
 
Substance use = total day-level HRVB practice t–1  +  day-level negative affect t–1  +  
[total day-level HRVB practice t–1 * day-level negative affect t–1]  +  age  
+  sex  +  nicotine use  +  medications known to affect the 
cardiovascular system  +  baseline past-month AOD use  +  error 
 
Substance use = total day-level HRVB practice t–1 +  day-level negative affect t–1  +  day-
level craving t–1  + [total day-level HRVB practice t–1 *  day-level 
negative affect t–1] +  [total day-level HRVB practice t–1 * day-level 
craving t–1]  +  age  +  sex  +  nicotine use  +  medications known to 
affect the cardiovascular system  +  baseline past-month AOD use  +  
error 
Aim 2a Power 
With N= 100, and 42 days of EMA monitoring with 2 self-reports per day, the multilevel models 
exploring day-level relationships between HRVB practice, affect, and substance use will include 
8,300 clustered observations. Given 5% alpha and a substance use/HRVB practice association 
of r= 0.15, and assuming conditional independence, we will have >99% power to predict a main 
effect of HRVB practice in Models 1-6. 
 
 
Aim 2b.  Test the accumulative, person-level effects of scheduled daily HRVB practice vs. 
in-the-moment HRVB practice, vs. their interaction on substance use during the 
8-week intervention period 
To begin to tease out the aggregated effects of scheduled vs. in-the-moment HRVB practice on 
substance use, we will run a linear regression model with percent days abstinent (PDA) from 
alcohol and other drugs (AOD) over the 8-week study period as the dependent variable, with total 
aggregated scheduled HRVB practice time, total aggregated in-the-moment HRVB practice time, 
and their interaction term (scheduled practice × in-the-moment practice) as independent variables 
(with controls’ HRVB practice coded as 0), controlling for age, sex, nicotine use (yes/no), 
medications known to affect the cardiovascular system (yes/no), and baseline past-month AOD 
use (percent days abstinent; Model 7). We will also run versions of this model testing a main 
effect of group, and number of in-the-moment practice sessions rather than aggregated in-the-
moment HRVB practice time. 
 
A priori, we anticipate that both greater accumulative scheduled HRVB practice, and accumulative 
in-the-moment HRVB practice will predict greater PDA, and importantly, that their interaction will 
indicate additional benefits of combining scheduled and in-the-moment practice. A semi-partial 
R2 value for HRVB practice time ≥ .10 (medium effect size) will be our benchmark for preliminary 
demonstration of aggregate-level treatment benefit.  
 
An ancillary aim of this study is to explore the effects of differing HRVB practice times on treatment 
outcomes. We will conduct a post hoc regions-of-significance analysis to explore the moderation 
effect of practice time on the relationship between PDA and HRVB practice. This analysis will 
consider the effect for several alternate cutoff scores representing the 10th, 20th, 30th, 40th and 
50th percentiles of practice time. 
 

Mass General Brigham Institutional Review Board 
Intervention/Interaction Detailed Protocol 
 
Version 2021.06.10 
 
Page 12 of 17 
Another ancillary aim is to garner a better understanding of when HRVB effects come on-board 
and how stable the effects are once established. To accomplish this, we will explore how the 
effect of HRVB on affective states and substance use vary over the trail using time-varying effects 
modeling (TVEM). TVEM estimates a non-parametric spline regression that flexibly estimates how 
the association between a predictor and outcome across continuous time (e.g., days). The series 
of regression coefficients produced by TVEM are presented graphically with 95% confidence 
intervals to demonstrate the magnitude and direction of the association between predictor and 
outcome across time. The P-spline technique, which automatically selects the best coefficient 
function through balancing tradeoffs between model fit and parsimony, will be used for all models. 
 
Model #7 
PDA = total scheduled HRVB practice  +  total in-the-moment HRVB practice  +  [total 
scheduled HRVB practice * total in-the-moment HRVB practice]  +  age  +  sex  
+  nicotine use  +  medications known to affect the cardiovascular system  +  
baseline past-month AOD use  +  error 
 
 
 
 
 
 
 
 
 
 
Aim 2b Power  
Though our emphasis will be on effect size interpretation rather than statistical significance, we 
note that with N= 100, 80% power, and a 5% alpha level, the detectible effect size is R2= .10 
(small effect size). 
 
 
10.   Monitoring and Quality Assurance 
In an effort to meet the MGB policy for Data and Safety Monitoring, we have created a system 
for oversight of the project. Oversight of internal monitoring of the subjects’ safety is conducted 
by the PI, Dr. David Eddie.  
 
On a weekly basis, the PI and study staff will discuss the progress of the study, review data 
quality, recruitment, study retention and examine other factors that may affect the outcomes.  
We will review the rate of adverse events to determine any changes in participant risk. The 
investigators are available to meet outside of the weekly meetings, if necessary, due to 
concerns regarding a particular participant or any problems that may arise for participants.  
 
Mandated reporting procedures will be followed according to Massachusetts state law in cases 
of abuse or neglect. Study staff will monitor and ensure validity and integrity of the data and 
adherence to the IRB-approved protocol. During meetings with research staff, consenting 
procedures, data collection, security, and entry will be reviewed by the PI. Furthermore, study 
staff will conduct periodic, random checks on all consent forms and procedures. Any adverse 
events that are observed and/or reported by study staff will immediately be reported to Dr. 
Eddie. Serious adverse events are reported to the MGH IRB immediately by telephone and by 
written report within 24 hours of our receipt of information regarding the event. 
 

Mass General Brigham Institutional Review Board 
Intervention/Interaction Detailed Protocol 
 
Version 2021.06.10 
 
Page 13 of 17 
 
11.   Privacy and Confidentiality 
☒ Study procedures will be conducted in a private setting 
☒ Only data and/or specimens necessary for the conduct of the study will be collected 
☒ Data collected (paper and/or electronic) will be maintained in a secure location with appropriate 
protections such as password protection, encryption, physical security measures (locked 
files/areas) 
☒ Specimens collected will be maintained in a secure location with appropriate protections (e.g. 
locked storage spaces, laboratory areas) 
☒ Data and specimens will only be shared with individuals who are members of the IRB-approved 
research team or approved for sharing as described in this IRB protocol 
☒  Data and/or specimens requiring transportation from one location or electronic space to 
another will be transported only in a secure manner (e.g. encrypted files, password protection, 
using chain-of-custody procedures, etc.) 
☒   All electronic communication with participants will comply with Mass General Brigham secure 
communication policies 
☒ Identifiers will be coded or removed as soon as feasible and access to files linking identifiers 
with coded data or specimens will be limited to the minimal necessary members of the research 
team required to conduct the research 
☒ All staff are trained on and will follow the Mass General Brigham policies and procedures for 
maintaining appropriate confidentiality of research data and specimens 
☒ The PI will ensure that all staff implement and follow any Research Information Service Office 
(RISO) requirements for this research 
☐ Additional privacy and/or confidentiality protections 
 
 
 
12.   References 
1. 
Morgenstern J., Naqvi N.H., Debellis R., Breiter H.C. The contributions of cognitive 
neuroscience and neuroimaging to understanding mechanisms of behavior change in 
addiction. Psychology of Addictive Behaviors. 2013;27(2):336-350. Epub 2013/04/17. 
doi: 10.1037/a0032435. PubMed PMID: 23586452; PMCID: PMC3700582. 
2. 
McCrady B.S., Epstein E.E. Addictions: A Comprehensive Guidebook. 1 ed. New York, 
NY: Oxford University Press; 1999. 
3. 
Eddie D., Bates M.E., Buckman J.F. Closing the brain–heart loop: Towards more holistic 
models of addiction and addiction recovery. Addiction Biology. 2022;27(1):e12958. doi: 
10.1111/adb.12958. PubMed PMID: 32783345, NIHMSID: 1625494. 
4. 
Thayer J.F., Lane R.D. A model of neurovisceral integration in emotion regulation and 
dysregulation. Journal of Affective Disorders. 2000;61:201-216. doi: 10.1016/S0165-
0327(00)00338-4. PubMed PMID: 11163422. 
5. 
Garland E.L., Carter K., Ropes K., Howard M.O. Thought suppression, impaired 
regulation of urges, and Addiction-Stroop predict affect-modulated cue-reactivity among 
alcohol dependent adults. Biological Psychology. 2012;89(1):87-93. 
6. 
Garland E.L., Franken I.H., Sheetz J.J., Howard M.O. Alcohol attentional bias is 
associated with autonomic indices of stress-primed alcohol cue-reactivity in alcohol-
dependent patients. Experimental and Clinical Psychopharmacology. 2012;20:225-235. 
7. 
Hugdahl K. Cognitive influences on human autonomic nervous system function. Current 
Opinion in Neurobiology. 1996;6(2):252-258. PubMed PMID: 8725968. 

Mass General Brigham Institutional Review Board 
Intervention/Interaction Detailed Protocol 
 
Version 2021.06.10 
 
Page 14 of 17 
8. 
Porges S.W. The polyvagal theory: New insights into adaptive reactions of the 
autonomic nervous system. Cleveland Clinic Journal of Medicine. 2009;76 Supplement 
2:S86-S90. doi: 10.3949/ccjm.76.s2.17. PubMed PMID: 19376991; PMCID: 3108032. 
9. 
Tiffany S.T. The role of cognitive factors in reactivity to drug cues. In: Drummond DC, 
Tiffany ST, editors. Addictive Behaviour: Cue Exposure Theory and Practice. New York: 
Wiley; 1995. 
10. 
Robinson T.E., Berridge K.C. The incentive sensitization theory of addiction: Some 
current issues. Philosophical Transactions of the Royal Society of London - Series B: 
Biological Sciences. 2008;363(1507):3137-3146. PubMed PMID: 18640920. 
11. 
Agelink M.W., Boz C., Ullrich H., Andrich J. Relationship between major depression and 
heart rate variability: Clinical consequences and implications for antidepressive 
treatment. Psychiatry Research. 2002;113:139-149. doi: 10.1016/S0165-
1781(02)00225-1. PubMed PMID: 12467953. 
12. 
Hughes J.W., Stoney C.M. Depressed mood is related to high-frequency heart rate 
variability during stressors. Psychosomatic Medicine. 2000;62:796-803. 
13. 
Vrijkotte T.G.M., Van Doornen L.J.P., De Geus E.J.C. Effects of work stress on 
ambulatory blood pressure, heart rate, and heart rate variability. Hypertension. 
2000;35(4):880-886. PubMed PMID: 10775555. 
14. 
Yeragani V.K., Rao K.A., Smitha M.R., Pohl R.B., Balon R., Srinivasan K. Diminished 
chaos of heart rate time series in patients with major depression. Biological Psychiatry. 
2002;51(9):733-744. PubMed PMID: 11983187. 
15. 
Bates M.E., Lesnewich L.M., Uhouse S.G., Gohel S., Buckman J.F. Resonance-paced 
breathing alters neural response to visual cues: Proof-of-concept for a neuroscience-
informed adjunct to addiction treatments. Frontiers in Psychiatry. 2019;10:624. Epub 
2019/09/24. doi: 10.3389/fpsyt.2019.00624. PubMed PMID: 31543840; PMCID: 
PMC6739688. 
16. 
Leyro T.M., Buckman J.F., Bates M.E. Theoretical implications and clinical support for 
heart rate variability biofeedback for substance use disorders. Current Opinion in 
Psychology. 2019;30:92-97. Epub 2019/05/06. doi: 10.1016/j.copsyc.2019.03.008. 
PubMed PMID: 31055246; PMCID: PMC6774915. 
17. 
Eddie D., Vaschillo E.G., Vaschillo B., Lehrer P. Heart rate variability biofeedback: 
Theoretical basis, delivery, and its potential for the treatment of substance use disorders. 
Addiction Research & Theory. 2015;23(4):266-272. doi: 
10.3109/16066359.2015.1011625. PubMed PMID: 28077937; PMCID: 5222529. 
18. 
Eddie D., Kim C., Lehrer P., Deneke E., Bates M.E. A pilot study of brief heart rate 
variability biofeedback to reduce craving in young adult men receiving inpatient 
treatment for substance use disorders. Applied Psychophysiology and Biofeedback. 
2014;39(3):181-192. doi: 10.1007/s10484-014-9251-z. PubMed PMID: 25179673; 
PMCID: 4221295. 
19. 
Eddie D., Alayan N., Conway F., Bates M.E., editors. Anxiety, depression, and stress in 
a high-risk college environment: Improving heart rate variability and neurocardiac 
signaling to prevent relapse. Congress on Alcoholism and Stress: A Framework for 
Future Treatment Strategies; 2017; Volterra, Italy. 
20. 
Alayan N., Eddie D., Eller L., Bates M.E., Carmody D.P. Substance craving changes in 
university students receiving heart rate variability biofeedback: A longitudinal multilevel 
modeling approach. Addictive Behaviors. 2019;97:35-41. doi: 
10.1016/j.addbeh.2019.05.005. PubMed PMID: 31132527. 
21. 
Penzlin A.I., Barlinn K., Illigens B.M., Weidner K., Siepmann M., Siepmann T. Effect of 
short-term heart rate variability biofeedback on long-term abstinence in alcohol 
dependent patients - a one-year follow-up. BMC Psychiatry. 2017;17(1):325. Epub 

Mass General Brigham Institutional Review Board 
Intervention/Interaction Detailed Protocol 
Version 2021.06.10 
Page 15 of 17 
2017/09/07. doi: 10.1186/s12888-017-1480-2. PubMed PMID: 28874146; PMCID: 
PMC5585893. 
22.
Penzlin A.I., Siepmann T., Illigens B.M.-W., Weidner K., Siepmann M. Heart rate
variability biofeedback in patients with alcohol dependence: A randomized controlled
study. Neuropsychiatric Disease and Treatment. 2015;11:2619–2627. doi:
10.2147/NDT.S84798. PubMed PMID: 26557753; PMCID: 4624058
23.
Zucker T.L., Samuelson K.W., Muench F., Greenberg M.A., Gevirtz R.N. The effects of
respiratory sinus arrhythmia biofeedback on heart rate variability and post-traumatic
stress disorder symptoms: A pilot study. Applied Psychophysiology and Biofeedback.
2009;34:135-143.
24.
Eddie D., Conway F.N., Alayan N., Buckman J.F., Bates M.E. Assessing heart rate
variability biofeedback as an adjunct to college recovery housing programs. Journal of
Substance Abuse Treatment. 2018;92:70-76. doi: 10.1016/j.jsat.2018.06.014. PubMed
PMID: 30032947; PMCID: 6123018.
25.
Prinsloo G.E., Rauch H.G., Lambert M.I., Muench F., Noakes T.D., Derman W.E. The
effect of short duration heart rate variability (HRV) biofeedback on cognitive performance
during laboratory induced cognitive stress. Applied Cognitive Psychology. 2011;25:792-
801.
26.
Wells R., Outhred T., Heathers J.A.J., Quintana D.S., Kemp A.H. Matter over mind: A
randomised-controlled trial of single-session biofeedback training on performance
anxiety and heart rate variability in musicians. PloS One. 2012;7(10):e46597.