Alcohol Biosensor Monitoring for Alcoholic Liver Disease
Phase: NA
Conditions: Alcohol Use, Unspecified
Sponsor: University of Pittsburgh

 
Alcohol Biosensor Monitoring for Alcohol Related Liver Disease 
 
NCT03533660 
 
June 15, 2022 
 
Study Protocol and Statistical Analysis Plan 
 
 

A. Specific Aims - Alcohol Biosensor Monitoring for Alcohol Related Liver Disease 
 
Successful treatment of alcoholic liver disease (ALD) depends primarily on total abstinence from 
alcohol.  Patients are instructed to maintain complete abstinence in order to have the best health outcomes 
and prevent ALD sequela or worsening of their liver disease. Different than addiction treatment where 
abstinence or even harm reduction may be an eventual goal with less need for immediate adherence, the 
complete abstinence requirement in this patient population makes it a useful model in which to test new 
strategies for efficient monitoring of patient behavior. Currently monitoring of ALD patients’ alcohol use is 
primarily done through clinical interview and conventional laboratory alcohol testing. Optimally patients would 
be interviewed frequently and randomly tested, although in real world clinical settings this is difficult to 
accomplish.  Continuous monitoring of alcohol use by wearable biosensors is possible but to date these 
devices have not been used in clinical medical settings to monitor adherence to medical directives.  For ALD 
patients such devices could facilitate monitoring of alcohol abstinence providing both patients and clinicians 
with real-time information that may be critical for development and adjustment alcohol treatment plans.   
 
From our prior work (K23 AA00257 and R01 DK066266) we determined that most discoveries of 
alcohol use in ALD patients are not made until months after the initiation of consumption and were largely 
captured based on prospective repeated multi-method research measures not routinely employed in a clinical 
setting.  Continuous alcohol monitoring in the months following a hospitalization for ALD could provide more 
accurate appraisal of alcohol use that when combined with feedback may motivate patients to change drinking 
habits and engage in appropriate treatment intervention.  Whether biosensor devices would facilitate 
identification beyond self-reports, be integrated into the clinical workflow, accepted by clinicians and patients, 
and improve outcomes requires investigation. Recent technology advancements expand continuous biosensor 
performance while reducing overall cost.  It could be assumed that monitoring with a biosensor would be more 
convenient and more desirable, but there could be disadvantages.  Patients may feel uncomfortable using the 
device or feel it is an intrusion.  Clinicians may find additional data cumbersome or not useful to care delivery.   
 
Therefore feasibility of integrating such technology into routine clinical care requires examining both 
patients’ and clinicians’ perceived potential benefits (e.g., patient convenience, clinician confidence in data and 
its value, potential improvement in care delivery, facilitation of developing/adjusting treatment plans, reduction 
in staff burden) and possible barriers (e.g. patient resistance to adoption, disruption of clinician workflow or 
established communication).  These process considerations are essential to guide the development of a full 
scale trial and future translation into clinical practice. Thus the convenience, feasibility and acceptability of such 
devices require vetting by patients and clinicians alike. Our R21 proposal targets NIAAA PA 15-301 Alcohol 
Use Disorders: Behavioral Treatment, Services and Recovery Research soliciting proposals for co-morbid 
medical and alcohol use disorders, specifically proposals using technology to develop and validate 
assessments capturing real-time data to use in clinical treatment paradigms.  
 
We propose a 3 month RCT pilot of alcohol biosensor monitoring (ABM)(WrisTAS-see methods) for 
patients with a recent hospital discharge for ALD decompensation. Patients who intend to stop drinking will 
wear the ABM and be randomized to receive ABM personalized feedback on the data retrieved from the device 
(ABM) vs. an enhanced usual care group that will wear the ABM device but not receive information about 
device data (EUC).  Considering future translation into medical practice we chose a monthly assessment 
schedule aligned with the typical ALD clinical follow up and chose the EUC group to most closely resemble a 
typical medical encounter where generic questions on alcohol use are asked. The study will also include 
quantitative and qualitative data collected from participants and 15 clinical stakeholders (e.g. physicians, nurse 
practitioners, care coordinators) who will provide opinions on ABM usability, acceptability, feasibility, and 
potential to change clinical care and outcomes. Qualitative methods are especially useful for understanding 
patient and clinician cultures and perspectives to determine the perceived needs, barriers, and preferences for 
monitoring alcohol use and are essential for future translation of this technology into clinical practice.   
     Specific Aim 1. To determine usability, acceptability, feasibility and efficiency of ABM for ALD patients. 
     Specific Aim 2. To determine whether ABM plus feedback improves outcomes for ALD patients who intend 
to stop alcohol use compared to EUC. Hypotheses 1-5: ABM will 1) reduce alcohol use (primary outcome), and 
2) improve readiness for alcohol abstinence, 3) improve self-efficacy, 4) improve initiation of addiction 
treatment and 5) reduce ALD related hospital admissions and ER visits (secondary outcomes).  
     Specific Aim 3. To explore with clinicians whether ABM data is useful, acceptable, feasible and efficient.  
 
This study will inform our development of a full scale project by 1) determining the potential for and value to 
ABM healthcare integration 2) identifying barriers to ABM implementation 3) determining long term wearability 
of ABM devices and 4) determine whether identified patient outcome effect sizes appear clinically meaningful 
to justify ABM monitoring and evaluation in a full-scale randomized controlled trial in the clinical environment. 

 
D. Approach: Research Design and Methods 
 
We address the NIH mandate for rigor and transparency (NOT-OD-16-011) in the proposal and Table1. 
Sample: We have an ample study population. In the past 15 months >600 ALD patients were treated at the 
UPMC Center for Liver Diseases (CLD); >130 discharged from an ALD decompensated hospitalization who 
lived ~30 miles of our clinic.  Such patients require close CLD follow up for medical care, typically monthly after  
hospitalization. Our center’s ALD population is comparable to regional and national data not only for gender 
and age, but race/ethnicity as well, aiding generalizability. We will recruit for 15 months with planned 4-5 
enrollees/month. We have had good recruitment (<10% refusals) and retention (80-90%) for prior ALD cohorts 
and Drs. Dunn and Jakicic have had >75% consistent wearing of biosensors in prior studies.47,54  If required to 
assure assessments’ completion we will ask participants to return for a study visit or make home visits.  
Recruitment: Patient Participants-Patients followed at the CLD clinic who are hospitalized for decom- 
pensated ALD will be approached for recruitment before discharge. 
In keeping with NIAAA initiatives for data harmonization we use 
NIAAA alcoholic hepatitis consortium definitions for alcohol 
consumption history, liver disease diagnosis, and associated liver 
enzyme profile.55  Patients must have a history of excessive alcohol 
consumption based on quantity/frequency/duration defined as > 40 
grams of ethanol per day for women or > 60 grams ethanol per day 
for men56 for six months or more.55 Patients can be drinking up to 
admission but not have sustained sobriety (>3 months).  Diagnosis 
of decompensated ALD will be established by a CLD hepatologist 
as defined above.2-4 Patients may have hepatitis C for which 
curative therapies exist. Clinician participants-15 CLD clinicians caring for ALD patients will be recruited to 
participate in Aim 3 qualitative and quantitative interviews. Clinicians will be selected from a pool of 25 CLD 
hepatologists, certified clinical nurses, and nurse practitioners (ages 27-70, all > 5 years practice, 50% female).  
 
Screening: Patients with ALD will be asked if they intend to stop drinking/remain abstinent and if so will 
be screened for recruitment. Patients will be screened using 7 items on the recognition subscale of the Stages 
of Change Readiness and Treatment Eagerness Scale (SOCRATES V.8)57 and the Montreal Cognitive 
Assessment (MOCA)58 to screen for cognitive impairment. Patients will be asked about current other drug use.   
 
Inclusion: > 18 years old, willing to accept randomization, and agree to wear device for 3 months. 
Consistent with findings from alcohol treatment seeking individuals in NIAAA’s Project MATCH59 we will enroll 
ALD patients with SOCRATES problem recognition subscale score >26 (scores <26 indicate very low 
recognition). Maisto60 suggests individuals with low recognition in the face of secondary alcohol complications 
may have pathological denial or cognitive impairment and these individuals are unlikely to intend to stop use. 
 
Exclusions: Non-English 
speaking (<5% CLD); persisting 
encephalopathy, MOCA scores <21 
(moderate cognitive impairment) or 
neurologic diseases (e.g. 
Parkinson’s), conditions which would 
affect survey assessments; patients 
with unresponsive acute alcoholic 
hepatitis, multi-organ failure, 
fulminant hepatic failure, or MELD 
score >21(all with 3-month mortality 
>20%),61,62 cancer/terminal illness, 
non-cirrhotic conditions; those 
unable to wear a wrist monitor (e.g., 
edema); and those currently abusing 
drugs (checked by urine toxicology 
screen), lacking a residence, or 
unable to identify a contact person (if 
lost to follow-up).  
 
Study design/methods for 
patient: (see Table 2 procedures): We will screen and enroll using above inclusion/exclusion criteria. We will 
Table 1. Scientific Rigor, Reproducibility  & 
Transparency Study Plans 
● Scientific premise based on prevalence of ALD and 
need for novel strategies to address ALD alcohol use 
●Enhanced reproducibility by harmonizing with NIAAA 
initiatives for standard definitions and common data 
elements for clinical trials by:   
   -Using NIAAA Project Match outcomes instruments  
   -Using NIAAA alcoholic hepatitis consortium definition 
of sufficient alcohol exposure, baseline data, outcomes  
●Recruitment efforts to ensure sex distribution as in ALD 
●Well-designed data safety & monitoring plan (sect 4.1.)  
●Registering on ClinicalTrials.gov 
●Resource sharing plan for NIH/qualified investigators  
● CONSORT Guidelines in reporting the study findings 
Table 2.  STUDY PROCEDURES   
Recruitment, screening 
• Prior to discharge from ALD hospitalization patients will be approached by CLD staff 
• After CLD staff receive verbal consent, study coordinator will contact and screen patients (SOCRATES 
recognition subscale >26). If subject meets screening criteria they will be consented and enrolled 
Randomization and  baseline assessment 
• Subjects will be randomly assigned 1:1 ratio ABM with feedback or EUC (permuted blocks to limit 
assignment imbalances across enrollment). All subjects wear ABM (WrisTAS) 
• Random assignment implemented using sequential, opaque, numbered envelopes opened by the study 
coordinator who will inform the subject. Investigators/assessors blinded to allocation 
• Baseline assessments completed–Form90 AI, SOCRATES, AASE, AWR, DrInC, DPI (Table 3) 
Monthly ABM feedback and EUC procedures (see Appendix A) and data collection (Table 3)  
• ABM –receives monthly typical CLD medical appointment clinic questions plus personalized feedback on 
downloaded ABM drinking data, risk/referral information, and patient brochures if alcohol use reported 
• EUC –(attention control) monthly typical CLD medical appointment clinic questions including self-report 
alcohol questions, risk/referral information and patient brochure provided if alcohol use reported  
• Issues with comfort or adjustment of WrisTAS will be addressed 
• See methods & 4.1.5.data safety monitoring for quality control procedures monitoring intervention fidelity 
• 6 week mid-point assessment on study outcomes-Form90 AI, SOCRATES, AASE, AWR, DrInC, DPI 
Final Assessment procedures  
• Repeat of baseline measures–Form90AF, SOCRATES, AASE, AWR, DrInC, DPI (Table 3), TAM2 
• Qualitative interview on feasibility, acceptability usability (Appendix B) 
• 15 CLD clinicians complete TAM2 and INT questionnaires and have qualitative interview on feasibility, 
acceptability, usability, applicability to health care (Appendix B) 

demonstrate the ABM device and note it must be worn at all times. We will use a 2-arm parallel groups design; 
subjects randomized 1:1 to either receive personalized feedback on the ABM data or receive enhanced usual 
care (EUC). The EUC group will have ABM data down-loaded for alcohol outcomes assessment but will not 
receive feedback on the data. At each of 3 consecutive monthly sessions both groups will be asked standard 
medical questions (e.g. medications, symptoms).  In addition the ABM feedback group will receive 
personalized feedback on downloaded data including graphical displays of alcohol use (Appendix A). Our 
feedback script is based on standard personalized feedback interventions using key elements: personalized 
alcohol use information, comparison to normative cohort, risks of use, and resources for help.52,63,64 At the first 
visit (and at subsequent visits if use continues) both groups will receive information on negative health 
consequences, local alcohol addiction treatment referral information, and 2 NIAAA brochures on health 
consequences and treatment help (see scripted protocols Appendix A). EUC is “enhanced” because beyond 
generic alcohol use questions scripted information on risks and resources is not commonly provided.  A 
research assistant blinded to assignment will perform outcome assessments at baseline, 6 weeks and final 
interview. Final assessments include quantitative and qualitative questions on perceived usability and device 
satisfaction, acceptability, lifestyle compatibility, and feasibility of long term use. So as not to pair participant 
payment with intervention, the payment schedule aligns with assessments but includes compensation for 
wearing the device.  
For ultimate medical setting dissemination we use non-physician personnel because we see this as the 
future of such interventions.  Medical technicians, increasingly used to extend clinical staff, are ideal for 
feedback delivery. Our study uses medical technicians who will be assigned to only one arm of the study and 
separately trained to prevent cross-contamination.  Drs. DiMartini and Dew will train them on protocol delivery 
using lectures, role playing, audiotaping and training cases using the designed feedback delivery sheet/printed 
materials provided to participants. During training we will elicit and address any challenges to the maintenance 
of fidelity. Dr. DiMartini will assess competence based on ratings following principles of treatment fidelity 
evaluation,65,66,67,68 to focus on adherence and competence to the structured, didactic style of sessions. To 
maintain session and assessment fidelity refresher training will occur midway through the enrollment period, 
and adherence, competence and reliability will be rated.  For certification a random 20% of audiotaped 
sessions will be reviewed with retraining for deficiencies (see also DSMP 4.1.5.a Fidelity Development, 
Training, and Monitoring). Completed assessment and feedback sheets will be inspected for errors. 
Clinician participants: At study completion 15 CLD clinicians will review de-identified ABM data from 
the study and be asked a range of quantitative/qualitative questions on feasibility, acceptability, usability, 
graphical display of data, workflow integration, and potential health impacts (Appendix B) and be paid. 
 
 
E. Analytic Plan 
 
Specific Aims 1 & 3. To determine usability, acceptability, feasibility and efficiency of ABM for patients 
and clinicians. Qualitative data coding and analysis:  De-identified transcribed interviews will be thematically 
coded107 and a codebook developed for patients and clinicians. The constant comparison method will be used, 
and subsequent interviews may be modified/informed by findings from previous interviews to explore emerging 
themes as analysis proceeds. After initial consensus is reached among two coders, interviews will be coded by 
one coder and reviewed by at least one additional coder. Coding discrepancies will be resolved through 
consensus.  Periodic interviews (expected rate, 1 in 8) will be coded by two coders independently as another 
check for inter-coder reliability.  Further, interviews that have already been coded will be reviewed as more 
codes are identified. Dr. Dew will monitor for coding drift by periodically contrasting recent vs. earlier coded 
interviews. For SA1&3 a summary of themes extracted from patient and clinician interviews will be prepared, 
with examples of interview quotes.  We will also examine descriptive statistics on WrisTAS data including 
amount of time worn vs. removed and examine data for evidence of tampering to use as variable examining 
wearability and feasibility.   
 
Specific Aim 2. Analyses will follow an intent-to-treat principal. We will first examine univariate 
distributions on baseline characteristics and on each outcome. We will examine the data for extent and pattern 
of missingness across outcome assessments. Potential violations of assumptions of planned parametric 
methods of analysis will be explored and resolved, e.g., variables will be transformed as needed; 
nonparametric methods will be employed if transformations are inadequate.  Baseline demographic and clinical 
characteristics of the two study groups will be examined via x2 tests for categorical variables and t-tests for 
continuous variables (or Wilcoxon Rank Sum tests if necessary) in order to (a) describe the sample and (b) 
evaluate whether the randomization process effectively balanced the groups.  If imbalances are observed, we 

may need to adjust (covary) for significant and/or large baseline differences in the evaluation of hypotheses.  
 
The primary outcome is alcohol use (H1), which we will test via 2 measures: % of days drinking; total 
quantity/frequency during period between downloads. Secondary outcomes (H2-4) are readiness for alcohol 
abstinence, self-efficacy, initiation with addiction treatment will be analyzed using SOCRATES subscales and 
AASE/AWR (continuous variables), H5 Form 90AF hospital stays and ER visits (% of days hospitalized and no. 
of hospital admissions+ER visits). For each primary/secondary outcome, we will use a linear mixed effects 
model with fixed effects for intervention group (2 groups) and the repeated measures factor, time (pre-
intervention, 6 weeks, and 3 mons post-intervention); and random effects for individual participant and 
intercept.  
 
Statistical Power: Ours is an exploratory study; the ESs we might observe are unknown. For the 
simple effects we will compute as described above, with 30 participants in each of 2 groups being compared in 
a given test and alpha at .05, we would have power at 80% to detect an ES, d>0.76. Brief feedback 
interventions can produce moderate to large ESs for reducing alcohol use and consequences (d of 0.6-
0.8).81,111-113 Even if 5-8 participants are lost to attrition, we should have sufficient power to detect simple 
effects within the range found in previous feedback studies. Again, however, our interest will be to examine 
feasibility issues and ESs as opposed to testing for statistical significance.