Risk Stratification and Early Alerting Regarding COVID-19 Hospitalization
Conditions: Covid19
Sponsor: Current Health

 
 
Risk Stratification and Early 
Alerting Regarding COVID-19 
Hospitalization 
 
NCT04709068 
11-March-2021 

 
Version 4.0 11.Mar.2021  
  
  
Protocol  
January 2021  
    
 Risk Stratification and Early  
Alerting Regarding COVID-19  
Hospitalization (Risk SEARCH)    
  
  
  
  
STUDY TEAM: Christopher McCann, Stewart Whiting, Adam  
Wolfberg, Samuel Moreland, Juliana Pugmire, Hope Watson, Matt  
Wilkes, Nicole Zahradka  
  
  
   
  
  
  
   
  
   
  
  
  
Contents  
  
Overview.................................................................................................................................. 4 
Background ............................................................................................................................. 5 
Research Objective and Outcomes.......................................................................................... 7 
Methods ................................................................................................................................... 7 
Study Design ........................................................................................................................ 7 

3  
  
Inclusion and Exclusion Criteria ......................................................................................... 11 
Pilot Phase ......................................................................................................................... 12 
Algorithmic Approach – Bayesian Network ......................................................................... 12 
Stage 1............................................................................................................................... 13 
Stage 2............................................................................................................................... 13 
Recruitment Strategies ....................................................................................................... 15 
Data Analysis ..................................................................................................................... 16 
Substudy ............................................................................................................................ 16 
Research governance ............................................................................................................ 17 
Ethical issues ..................................................................................................................... 17 
Data Management & Security ............................................................................................. 18 
Device Decontamination .................................................................................................... 18 
Timeframe .......................................................................................................................... 19 
Dissemination..................................................................................................................... 19 
References ......................................................................................................................... 19 
Appendix A. Simplified Bayesian Network.............................................................................. 20 
Appendix B. Sample Size Calculations with Multiple Considerations ..................................... 21 
Appendix C. Telehealth Usability Questionnaire .................................................................... 23 
Appendix D. RiskSEARCH Interview Schedule ...................................................................... 25 
 
  
   
  
Risk Stratification and Early Alerting Regarding COVID-19 
Hospitalization (Risk SEARCH)  
  
  
Risk Stratification and Early Alerting Regarding COVID-19 
Hospitalization (Risk SEARCH)  
  
  

4  
  
Study team:    
 
Co-Principal Investigators: Adam Wolfberg, Matt Wilkes1 & Juliana  
Pugmire1  
 
Trial Manager: Nicole Zahradka1  
 
Co-Investigators: Christopher McCann1, Stewart Whiting1, Samuel  
Moreland1, Hope Watson1, Ashley Cecere2, Marie Wax2  
Affiliations:   
1 Current Health   
2 Biomedical Advanced Research and Development Authority, U.S.  
Department of Health and Human Services   
 
 
Sponsor:   
Current Health, 500 5th Ave, New York, NY, 10017, USA  
 
 
 
Funder:   
 
Biomedical Advanced Research and Development Authority   
 
  
 
 
 
U.S. Department of Health and Human Services  
Short title:    
Risk SEARCH  
Overview:  
  
 
 
 
In the proposed research, we will use Current Health’s remote 
monitoring wearable device to create novel algorithms to help us 
predict the progression and severity of COVID-19 in individuals that 
have recently become infected in the community. Risk scores based 
on demographics, or for patients in hospital, already exist. However, 
by using continuous remote patient monitoring, in combination with 
machine learning and data analysis, we hope to both predict and 
detected changes in the risk of an individual infected with COVID-19 
requiring hospitalization, intensive care unit (ICU) treatment, or death 
based on their vital signs while still in the community.  
 
  
Risk Stratification and Early Alerting Regarding COVID-19 
Hospitalization (Risk SEARCH)  
Overview  
In the proposed research, we will use Current Health’s remote monitoring wearable device to 
create novel algorithms to help us predict the progression and severity of COVID-19 in 
individuals that have recently become infected in the community. Risk scores based on 
demographics, or for patients in hospital, already exist. However, by using continuous 
remote patient monitoring, in combination with machine learning and data analysis, we hope 
to predict the risk of an individual infected with COVID-19 requiring hospitalization, intensive 
care unit (ICU) treatment, or death based on their vital signs while still in the community.  

5  
  
We plan to use a pilot and two-stage approach, including an observational stage to ‘train’ a 
predictive algorithm and a validation stage to test its efficacy. In the pilot stage, we will recruit 
up to 50 participants to test the recruitment pathway, trial logistics, and to begin model 
building. In stage one, the training stage, we plan to recruit 1000 study participants, who will 
contribute information from the remote monitoring device, demographic data, and daily 
questionnaires on symptoms they experience. This information will help our data scientists 
train a dataset using machine learning and algorithm development techniques. In the second 
stage, we will validate or test that algorithm on a different sample of up to 1000 study 
participants. Based on current epidemiological data, the recruitment of this number of 
participants in each stage should provide us with 100 events/hospitalizations in each cohort 
(see Graph 2, below).   
Using a hospital-grade home-based, remote monitoring wearable gives us the opportunity to 
create a unique and extremely useful tool in predicting and managing the clinical effects of 
the COVID-19 pandemic. Our algorithm might bridge the gap between demographic models 
and in-hospital models of disease progression by providing real-time data from the 
community. If we are successful, this tool could be made available immediately to enable 
better resource allocation for patients after diagnosis. In addition, it enables patient-centered 
management, increasing confidence for low and high risk patients, and those managing their 
care. The results of this research will provide a database that can be used in future trials to 
assess treatment efficacy and offer the potential for rapid algorithm iteration in the event of a 
future pandemic.  
Background   
Landscape of SARS-CoV-2/COVID-19  
SARS-CoV-2/COVID-19 is a highly contagious virus that has rapidly spread across the 
globe, with the United States among the countries consistently reporting the highest number 
of cases.1 Emerging reports of risk factors for hospitalization and death in patients infected 
with SARS-CoV-2/COVID-19 include increasing age, BMI > 40, being male, and having 
diabetes, among others.2,3  CDC data from the first month of the SARS-CoV-2/COVID-19 
outbreak in the United States showed that the absolute number of hospitalizations per age 
group is similar between the ages of 20 and 75. However, deaths and ICU admissions tend 
to increase with age.4 (Graph 1).  
 Graph 1. Absolute number of hospitalizations, ICU admissions, and deaths  
 in the United States as of March 16, 2020, CDC MMWR  
  

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Cases and hospitalizations were on the decline at the end of summer 2020.5 However, the 
overall weekly hospitalization rates in the United States have increased since the end of 
September (MMWR week 39, see Graph 2), driven mostly by adults over 18 years of age.5  
           
Graph 2. Weekly COVID-19-associated hospitalization rates by age group –  COVID-
NET, March 1 – October 24, 2020  
 
  
Time from symptom onset to death   
Data assessing the time course from onset of symptoms to death or time to recovery remain 
limited and generally do not include vital sign measures in the community.6 After onset of 
symptoms, the highest probability of death occurs just under 12 days, so there may be time 
after symptom onset to predict the likelihood of clinical decline and guide clinical intervention 
in many patients.6   
Currently, predictors of hospitalization across the lifespan remain general, demographic and 
largely intuitive.7 For example, individuals with advanced disease or multi-morbidity are at 
increased risk for poor outcomes when infected with COVID-19. A major goal of this work is 
to identify novel digital biomarkers that will enable early identification of patients who are 
likely to have severe disease while still in the community. This early identification could allow 
for more timely and aggressive supportive care, potentially reducing more severe disease 
and poor outcomes.   
    
Digital biomarkers and algorithm development using large databases  
There is a body of research utilizing commercially available wearable devices (e.g., Fitbit) 
that seeks to identify biomarkers that could predict infection ahead of noticeable 
symptoms.8,9 There is another avenue of research utilizing large, extensive preexisting 
databases to find and validate risk prediction algorithms that can estimate hospital 
admissions and death in COVID-19 infected adults.3,7 We plan to utilize already existing 
population-based algorithms and combine these with continuously collected vital signs from 
our medical-grade, remote monitoring wearable device. Unlike Fitbit or other commercially 
available wearables, the quality of observations from Current Health’s remote monitoring 
device is much higher than so it can be used like a hospital monitor in the community. The 
Current Health device also stores the raw waveforms, allowing for retrospective audit of 
observation quality.  
  

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To our knowledge, this will be the first effort to apply these advanced models utilizing 
continuously collected, high-quality vital signs in the community, for the prediction of COVID19 
disease progression.      
  
Research Objective and Outcomes  
The overall objective of this contract is to train, develop, validate and release a digital 
biomarker-based algorithm that, in COVID-19 PCR or antigen positive patients, within 30 days 
of a positive test:  
  
1. Primary Outcome: predicts likelihood of requiring a stay in hospital of 24 hours or 
longer  
  
2. Secondary Outcomes:  
a. Predicts likelihood of being admitted to ICU for either non-invasive (CPAP, 
BiPAP, high flow nasal oxygen), invasive ventilation or vasopressor support.  
b. Predicts likelihood of death.  
  
3. Tertiary Outcomes: Provides successful delivery, setup, daily interface 
engagement, and compliance with wearing of the device  
  
  
Methods  
Study Design  
This study will be broken down into a pilot phase, followed by two stages of data collection. 
We will recruit, consent, and enroll approximately 2050 participants with a COVID-19 positive 
test over the course of the study. In the pilot phase and stages one and two, the following 
methods will apply.  
Participants will be asked to wear the Current Health remote patient monitoring device for up 
to 30 days. Participants will also be provided with a Current Health tablet with an application 
installed. The tablet is not password protected, but no responses are stored locally, and they 
are associated with vital signs via the Current Health dashboard. The participant will be 
asked to complete a welcome survey on the first day of participation. The tablet will remind 
the participant each day to answer a brief series of questions about their symptoms and 
decisions on the tablet. The welcome and daily surveys include the content below:    

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Welcome Survey: Welcome!  Today is your first day in Community by Current  
Health’s COVID-19 Study.  We need to collect some information about you. We will  
only ask these questions one time. This survey should take less than 5 minutes  
to complete.  
  
Thank you again for taking part in this study. With your help our researchers will 
gain a better understanding of COVID-19 and be able to predict recovery and 
progression.  
  
1) What is your date of birth?  
2) What is your sex? Male/Female/other or prefer not to specify  
  
If Female  
1) To your own knowledge are you currently pregnant?  
2) Do you have menstrual periods?  
3) Are you:  
- 
Pregnant or breastfeeding  
- 
Taking hormonal birth control  
- 
Undergoing menopause or post-menopausal -  Highly active 
(working out, training, fitness, etc.) 4) When was your last period?  
3) What is your occupational category? Healthcare (doctor, nurse, dentist, etc.)  
Service industry (food service, grocery, mechanic, plumber, etc.) 
Retail  
Office – able to work from home  
Office – unable to work from home  
Currently unemployed  
Student – remote  
Student – attending class in person 4) 
What is your household make-up?  
Living alone  
Living with people  
5) Do other members of your household currently have COVID-19? Y/N  
6) Are any other members of your household enrolled in Community by Current 
Health’s COVID-10 Study? Y/N  
7) What is your self-identified race/ethnicity?  
White Hispanic  

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White Non-Hispanic  
Black or African American  
Asian  
American Indian or Alaska Native 
Native Hawaiian and Pacific Islander 8) 
What is your height?  
9) What is your weight (in pounds)?  
10) Do you have asthma? Y/N  
11) Do you have cancer? Y/N  
12) Do you have chronic obstructive pulmonary disease (COPD)? Y/N  
13) Do you have diabetes? Y/N  
14) Do you have a heart condition? Y/N  
15) Do you have high blood pressure? Y/N  
16) Do you have kidney disease or reduced kidney function? Y/N  
17) Have you ever had an organ transplant? Y/N  
18) Do you have sickle cell disease? Y/N  
19) Are you a smoker? Y/N  
20) Do you take beta blockers? Beta blockers are medicines designed to reduce heart rate,  
which can affect vital sign readings. For example: Tenormin, Cardiacor, Emcor, Betaloc, Inderal, or Angilol. 
Y/N  
21) Please enter any feedback about this survey:  
  
Thank you for filling out your welcome survey! Your responses have been recorded.  
  
Daily Survey:  
Each day Current Health will ask you a brief series of questions about  symptoms 
and decisions that may be related to COVID-19. Your daily response and 
information collected from the wearable device will create the most accurate picture 
of your health. This survey should take less than 5 minutes to complete.  
  
1) In the past 24 hours, did you have (or continue to have) any of the following 
symptoms:   
  
 
loss of smell or taste  
 
dry cough  
 
sore throat   
 
fever or feeling feverish  
 
chills  
 
muscle aches  
 
diarrhea  
 
nausea  
 
other, please specify (free text)  
  
[FOR EACH YES] Is your <symptom> better or worse than yesterday?    
 
Categorical scale: Much worse, Worse, The same, Better, Much  
better  

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2) Due to how you are feeling today, do you plan to speak to a health care 
provider?    
  
3) Due to how you are feeling today, do you plan to go to the hospital?  
  
4) Do you feel as though you have completely recovered from COVID-19 as of 
today?    
  
Thank you for completing your daily survey! Your responses have been recorded.  
  
  
For our study participants, data will be collected “blind”. The participant will not be able to 
see their data in real time, and no data will be reviewed or acted upon by the study team 
unless there is a clinically indicated trigger elicited by the system. These triggers exist to 
prompt the capture of study-specific information, not to direct patient care.  
1. Reduction in SpO2 below 88% for 60 minutes  
2. Increase in pulse rate to more than 90 beats per minute for 60 minutes with minimal 
body movement (Current Health motion score < 2)  
3. Increase in respiratory rate to more than 25 breaths per minute with minimal body 
movement (Current Health motion score < 2)  
  
In these instances, participants will be contacted and asked the following two to three 
questions:   
   
  
  We cannot provide you with any medical advice but would like to ask you a few 
questions.  First, have you experienced any new signs or symptoms including:  
   
  
Shortness of breath  
Chest pain or pressure  
   
New or worsening cough  
   
Fever or feeling feverish  
   
Chills  
   
Headache  
   
Sore throat   
   
Muscle aches or joint pain  
   
Abdominal pain  
   
Diarrhea  
   
Nausea  
   
Other, please specify  
  
 
   
If yes, to any symptom:  
  
• 
Do you consider the symptom to be mild, moderate, or severe (worst ever)?    

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• 
Do you intend to seek medical care at an emergency room, urgent care, or 
hospital in the near future?    
  
If no:  
  
• 
Has your device been working properly, or has it been damaged in any way?  
  
Throughout the study, if a participant believes they have recovered from COVID-19 for any of 
the reasons listed below and tells the study team they will be resuming normal life and would 
like to exit the study, we will consider these participants fully compliant. COVID-19 recovery 
would be considered in any of the following situations:  
1. Participant receives a follow-up COVID-19 PCR test that is negative   
2. Participant was asymptomatic and isolated for 14 days without symptoms   
3. Participant was symptomatic and onset of symptoms was at least 14 days prior with 
the last three days symptom free  
Conversely, if a participant feels that they are experiencing severe symptoms related to 
SARS-CoV-2/COVID-19 that warrant a visit to the hospital or urgent care facility (which will 
be also evident by our questionnaire), the participant will be instructed to contact their 
healthcare provider, contact a nearby hospital to arrange for appropriate assessment, or call 
911 if an emergency. We will not provide medical advice, or a real-time monitoring service, 
as this will likely bias the outcomes of the trial. This will be made explicitly clear to 
participants in the Informed Consent Form and process of consent. The outcome of that visit 
will be captured by a study team member.  
  
A study participant will be considered fully adherent to study protocol if they:  
• 
Wear the device for at least 20 hours a day, at least 6 days a week up to 30 days  
• 
Answer the questions on the tablet at least 6 days a week up to 30 days  
• 
Return the wearable device kit and tablet  
  
Inclusion and Exclusion Criteria  
Table 1. Criteria for study participants  
  
Inclusion   
Exclusion   
  
1) Living in the United States (contiguous 
states)  
2) Aged 21 years or older  
 
1) Under the age of 21  
2) Received a positive test result more 
than 48 hours prior to contacting study 
staff or has a pending test for 
COVID19  
3) Able to provide documentation of +PCR 
or +antigen test for COVID-19 within the 
past 48 hours   
4) Self-reports that comfortable and willing 
to wear the wearable device and interact 
with a tablet-based interface daily  
5) Able to provide a next of kin/designated 
person who can be contacted in the 
event of hospitalization for follow up.  
  
3) Cannot confirm a PCR or antigen 
positive test for COVID-19  
4) Is unable to read English  

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5) Is unwilling to wear the device 24 
hours/day except for showering/bathing 
or interact with a tablet-based interface 
for daily questionnaire  
6) Is unwilling or unable to provide 
baseline data required for entry into the 
study  
7) Is unable to provide a next of 
kin/designated person who can be 
contacted in the event of 
hospitalization for follow-up  
8) Heavy tattooing on both upper arms  
9) Known atrial fibrillation (permanent or 
paroxysmal)  
10) Has taken/is taking part in a COVID-19 
vaccine or treatment trial  
  
 
  
  
Pilot Phase  
The pilot phase study with a goal of 50 participants will test the feasibility of recruitment, trial 
logistics, observed patient events, timeline to deterioration, and environmental test settings. 
The primary aim is to make any revisions to recruitment strategy and to the final number of  
“critical events” (e.g., hospitalization) required. The secondary aim is to produce descriptive 
statistics on pilot data and understand user issues that could affect study data. Descriptive 
statistics will include univariate analysis such as frequency, box plots, or histograms, and 
where appropriate, bivariate analysis on covariates using methods such as regression, Fisher’s 
exact tests, or chi-squared analyses.   
  
Algorithmic Approach – Bayesian Network   
The proposed methodical approach will use a Bayesian network to predict risk/probability of 
the study outcomes. There are four levers to consider in model development that will be 
elaborated on separately: 1) variables in the model 2) characterization of variables 3) 
distribution of variables and 4) provided prior direction of effect. The latter two levers lend 
themselves more to Bayesian methods. Variables that will be collected in the study are 
outlined below, however each variable will need to be analyzed to determine statistical 
significance and interaction effects to determine if it belongs in the final model.   
The characterization of variables refers to how they will be categorized, binned, or otherwise 
transformed, such as using multiple variables to make an aggregated score variable that 
acts as input to the risk model. For example, a continuous BMI variable may be taken and 
grouped it into clinical categories (e.g., normal, overweight, obese, etc.), then further 
combine the variable with a patient’s self-reported health to generate a “wellness score”.  
These variable transformations will be done iteratively in the training stage. The distribution of 
the variable is the percent in each category/bin. Traditional machine learning methods 
require that the distribution of a variable comes only from study data. However, in Bayesian 
methods if a value of interest is unknown or will not be able to be explicitly measured in the 
study, a prior can be provided to make inference; this is especially helpful in smaller sample 
sizes. For reference, a prior, of an uncertain quantity is the probability distribution that would 
express one's beliefs about this quantity before some evidence is taken into account. An 
example of how prior probabilities from literature maybe incorporated in the study is to state 
the probabilities of false positives and false negative rates in positive COVID-19 nasal swab 
PCR testing. Direction of effect refers to the causal flow of Bayesian networks. These are 
prespecified by existing literature, clinical knowledge, and subject matter knowledge to 

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consider how one event precedes another. In this study design, such causal flow is able to 
explicitly state that infection precedes the onset of symptoms.   
Finally, Bayesian networks are more transparent than other models, as they rely on 
traditional conditional probabilities, that is, how the likelihood of having a characteristic 
affects the likelihood of having another characteristic or the probability experiencing an 
event. Model transparency will likely help with clinical adoption and utility.  
  
Stage 1  
Training Stage – Model Creation & Training Algorithm   
The overall aim of Stage 1 is to collect enough data to perform machine learning analytics on 
data from the Current Health device to create a Bayesian network algorithm that successfully 
predicts the risk/probability of no severe disease, hospitalization, ICU admission, and death 
following SARS-CoV-2/COVID-19 infection. The outcomes are not mutually exclusive 
categories, and can be hierarchical, i.e., when a patient first goes to hospital and then is 
admitted to the ICU. Conversely, a patient at risk could experience death without ever being 
hospitalized. This will be accounted for by calculating probabilities of each outcome and 
compare the conditional probabilities analyzing the risk of ICU admission, given that 
hospitalization has already occurred.  One thousand participants will be recruited for the 
training stage.    
A Bayesian network will first be developed using all 1000 participants in the training set. A  
Bayesian network illustrated (Appendix A, Fig1) has “nodes” which represent the variables 
and “arc” or “edge” which is the directional arrow drawn connecting the nodes. The 
probability of a “base model” is where no participant observations have been made, so each 
variable is the marginal (independent) probability. When information is input for each 
participant, I.e., in base model probability of sex is 50% male, 50% female, but once it is 
known a participant is male, the probability will be updated to 100% male. This in turn 
updates the risk probability of no severe disease, hospitalization, ICU, and death, now 
knowing the participant is male.10   
Once the Bayesian network model is constructed, to account for model variation, that is a 
model’s parameters change depending on the data the model is being given, nested 
crossvalidation will be performed to ensure model stability and reduce issues with overfitting.10 
Sensitivity analysis will be conducted to determine variables that have most effect and are 
statistically significant to outcome variable also called a “target node". If necessary, to further 
check data stability bootstrapping resampling will be explored.   
Not all readings from the wearable are perfect, meaning some data from the wearable can 
be considered noise from factors such as wearing device in the wrong orientation 
(upsidedown) or large amounts of movement inhibiting reading. The study team will instruct 
participant on correct use of the wearable during onboarding then check the signal 
robustness to ensure noisy data is either transformed to be used or filtered out.   
  
Stage 2  
Testing Stage – Expansion for Model Creation or Validation  
To assess the algorithm performance, algorithm testing on a total of 1,000 participants will be 
conducted. This cohort of 1,000 testing participants will be held entirely separate from the 

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training participants to minimize data snooping bias in the study. In machine learning, it is 
typical to obtain initial testing cohort results to determine if the model created from the 
training data is performing well. After the first 100 participants in the testing cohort are 
monitored for 30 days, the algorithm will be tested for understanding initial performance. As 
more participants are recruited into the study, follow-up algorithm testing will occur for each 
100 participants that are added to the testing cohort.   
Although the outcome of the participants in the validation set is known, the outcomes will be 
initially ignored, as if researchers did not know participant outcome. This simulates what 
researchers know about a participant, preceding the participant’s outcome. The testing 
participants’ collected variables (demographic, medical history, continuous data from 
wearable, and symptom data) will be run through the developed Bayes network algorithm 
and comparison models. The algorithm will return probabilities of the study outcomes. The 
algorithm’s probabilities will then be compared with the true outcome of the participant 
recorded by the study team.  
The differences between the algorithm prediction and participant’s true outcome will be used 
to calculate performance metrics such as positive predictive value (PPV), area under the 
curve/receiver operating characteristic (AUC/ROC), and Brier scores. The final part of 
algorithm validation will be calibration analysis, sensitivity analysis, and determining any 
subpopulations that may have had poor predictive performance.   
The benchmarks of model performance will be as follows: area under curve between 0.50 
and 0.55 is poor, 0.55 to 0.65 is moderate, 0.65 to 0.75 is acceptable, and >0.75 is 
excellent.11 Brier scores will be used to verify the accuracy of the event prediction during 
calibration, where a perfect score is 0 and imperfect score is 1. Different specificity and 
sensitivity levels will need to be considered when creating a feature that automatically 
triggers notification of study staff that a participant is likely (probability of hospitalization 
>50%) to reduce alarm and alarm fatigue. In addition, the duration of time a patient spends 
in a high-risk state needs to be considered. For example, if a participant’s oxygen saturation 
levels (SpO2) levels decrease for a brief period, then regain to normal levels, we will 
evaluate this to determine if it is an indicative of patient deterioration or not.  
We hypothesize that our model will achieve an accuracy of 75% or higher.  
  
  
 
 
 
 
 
 
 
 
 
 
   

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Table 2. Variables collected for analysis   
  
 
Continuously acquired variables  
  
1. Respiration rate  
2. Pulse rate  
3. Oxygen saturation  
4. Skin temperature  
5. Step count  
6. Activity levels  
  
Categorical variables  
7. Symptoms  
8. Days when reporting for employment  
9. Outcomes of any SARS-CoV-2/COVID- 
19 testing   
10. Admission to hospital, ICU, death  
  
Categorical variables (continued)  
  
13. Age  
14. Sex  
15. Occupation  
16. Household make-up (living alone or with 
people)  
17. Other household members with 
COVID19  
18. Other household members enrolled in 
this study  
19. Menopausal state and time since last 
menstrual cycle / Pregnancy status  
20. Race/Ethnicity  
21. Zip code  
22. BMI (height & weight)  
23. Comorbidities  
24. Acute and Chronic Medication  
  
  
Recruitment Strategies  
The primary recruitment tactics will involve digital marketing through social media (Facebook 
Advertising), and Google Advertising. We will use CDC data to target areas experiencing 
outbreaks of SARS-CoV-2/COVID-19. Additional recruitment tactics include email content 
and circulation of physical materials such as flyers, posters, or bulletin boards and direct 
mailers. All marketing material will direct to the website, including study team contact 
information, for initial screening. Throughout the enrollment process, Current Health will 
communicate updates on the clinical trial and/or enrolment process via phone calls, text 
messages, or emails. All materials will be developed by Current Health, reviewed by BARDA 
funders, and approved by the Advarra Institutional Review Board before becoming 
patientfacing. No communications via email or text message will contain protected health 
information.  
We plan to create a website to serve as the primary communication method with participants 
prior to enrollment in the clinical trial. This website (https://community.currenthealth.com) sits 
as a subdomain on the Current Health corporate website, which is secure and HIPAA 
compliant. We have included the website wording, and sample screenshots as appendices.  
  
On the website, we articulate:  
  
• 
The goals of the clinical trials  
• 
Qualification criteria for participants  
• 
What is expected of participants as part of the clinical trial  
• 
What compensation the participant can expect to receive  
• 
Support contact information for participants enrolled in the clinical trial  

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• 
Information regarding Current Health  
  
Once participants have progressed through the website, their details will be passed to a 
study team member. The study team member will contact the participant via email or text 
message and send a copy of the Informed Consent Form. The study team member and 
participant will arrange a mutually convenient time to speak via telephone or Zoom. 
Together, they will review the Informed Consent Form and engage with the process of 
consent. Once consent has been obtained via secure electronic means, the participant will 
be shipped a Current Health device by a logistics partner (Seko).  
  
Data Analysis  
By recruiting participants that have already been identified as being SARS-CoV-2/COVID-19 
positive, we expect that approximately of 10% of individuals from each age group will 
eventually develop severe or critical COVID-19 symptoms and require hospitalization or 
treatment in an intensive care unit. Currently there are not sufficiently rigorous statistical 
models for predicting statistical power for machine learning studies.  Some of the issues 
related to sample size for machine learning models have been recently reviewed in the 
scientific literature. Machine learning models can be trained to find an effect, but it is not 
clear if the effect is the result of over-fitting and/or poor sample selection. Nested 
crossvalidation and calibration have been proposed to mitigate these issues.  Based on 
extensive prior experience of our group, we expect that 100 events in continuously monitored 
participants will be sufficient to assess the training performance of the model for early 
identification of novel digital biomarkers indicative of disease severity as well future trajectory 
of the severity of disease. Using the 100 events and approximate 10% event rate, testing 
and training cohorts of 1,000 participants (2,000 participants total) were determined.   
Although not required in observational clinical trials due to no directly measured effect size, 
sample size calculations were conducted under different circumstances to ensure sample is 
sufficiently large for a clinical prediction model.12  
  
Substudy  
Objectives  
The overall objective of this RiskSEARCH sub-study is to understand the participant 
experience of joining the study and participating, by wearing the Current Health 
wearable device 24 hours/day and answering daily surveys on the tablet.  Research 
Objectives  
1. To explore recruitment and retention for the RiskSEARCH (COVID-19) study  
2. To explore feasibility, acceptability, and usability of the intervention, i.e., the Current 
Health wearable device and tablet  
3. To explore barriers and facilitators of study compliance  
  
Interviews with participants and data analysis  

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We will interview up to 20 participants (depending on data saturation) and ask them to take a 
modified Telehealth Usability Questionnaire that assesses usability of telehealth 
implementation and service.13 (see Appendix C) Participants will be purposively sampled for 
a range of characteristics (e.g., level of device use, age, gender, socioeconomic status by 
zip code). We will also specifically seek to interview those who did not take up the 
intervention to explore the reasons for this. A semi-structured interview guide (Appendix D) 
will be used to explore participant insights related to acceptability and usability of the website 
and Current Health device/tablet, patterns of usage, and barriers to use.   
Interviews will be completed by trained study team members via telephone or internet-based 
call. Interviews will be audio recorded and transcribed verbatim with the help of 
HIPAAcompliant software (e.g., Trint). Participants will have the opportunity to request sole 
interviewers and handwritten note taking of responses. A separate informed consent process 
will take place for the qualitative interviews.  
Qualitative data analysis will explore the acceptability of the intervention, the extent to which 
participants engaged with it, and contextual factors that may have influenced engagement.  
The study team will analyse data using qualitative and quantitative methodologies. Qualitative 
methods using software (e.g., NVivo) will include analysis to identify emergent themes that 
could provide direction for future design improvements to make the device more comfortable 
or engaging. Quantitative methods will include the use of descriptive statistics to report 
frequencies of responses.   
  
Research governance  
  
Ethical issues  
As this study involves human subjects, we will submit an application to the Advarra 
Institutional Review Board. Participation in this study has a low risk of harms and 
discomforts: the Current Health device is a widely deployed wearable that has been 
subjected to rigorous usability testing. Potential participants will be made aware of potential 
harms and discomforts in the Informed Consent Form. During the consenting process one of 
our study team members will be available to answer any concerns or questions. Participants 
will be compensated for their time during the study, contingent on their wearing the device 
for the duration of the study and completing the daily survey and returning the device at the 
end. Participants may leave the study at any time. If a participant does experience any 
adverse event, this will be investigated using the Current Health Post Market Surveillance 
process.  
  
As with many studies, there is a chance that confidentiality could be compromised; however, 
we are taking precautions to minimize this risk. All our participant data will be de-identified 
for analysis.   
  
FDA Clearance  
Current Health has 510k FDA regulatory clearance for use in the USA. The clearance covers 
both the physical wearable and relevant cloud-based software components of the platform 
for wearable vital signs monitoring and alarming of patients both in the hospital and at home. 

18  
  
The 510k clearance was obtained for Generation 1 (G1) of the wearable device. Participants 
in the study will be using the newer iteration, Generation 2 (G2). G2 uses the same sensing 
optics and has an identical technical specification to G1, but is smaller and lighter, improving 
patient comfort. G2 is being released under the FDA’s Enforcement Policy for Non-Invasive 
Remote Monitoring Devices Used to Support Patient Monitoring During the Coronavirus 
Disease 2019 (COVID-19) Public Health Emergency (Revised), while formal 510k is 
concurrently sought. Further information is available in the Current Health Outline 
Regulatory Strategy (200911) if required.  
  
  
Data Management & Security  
After participants are identified as being eligible for this protocol, they will be assigned a 
randomized and unique identifier under which all subsequent data will be stored.  The only 
mechanism for linking this number to the participant will be through a key that is stored 
within the Current Health Firewall.  Only study team members will have access to the key. 
Consequently, all participant data collected by the Current Health Device will be stored 
under this anonymized number, and any information linking to participant data collected by 
Current Health study team members will also be anonymized under this number. The key 
will be destroyed at the end of the data analysis portion of this study.  For the qualitative 
substudy, all de-identified interviews will be recorded and stored on a HIPAA compliant 
platform until transcribed. Once the data are analyzed, audio recordings will be destroyed.  
Participant data (including daily survey data) are sent to the Current Health cloud-based 
system when participants are near the transmitter located within their homes. De-identified 
welcome survey data are collected and stored in Jotform’s HIPAA compliant database. No 
Protected Health Information (PHI) data is transmitted via the wearable, only anonymized 
raw waveform data. The device will not attempt to transmit data under unregistered/free 
access wifi systems and will store data for up to ten hours in the absence of the home-based 
hotspot. The servers (SOC-2 certified AWS data centers) and solutions housed by Current 
Health meet stringent guidelines for HIPAA security. The Current Health Information Security 
Management System is ISO 27001 certified, and also 21 CFR Part 11 and HIPAA 
Compliant.       
Current Health has robust security standards that meet HIPAA compliance standards and no 
identified, identifying data, or key will be shared or transferred.   
Internally, all data collected, stored or analyzed at Current Health will be done behind the 
Current Health firewall.  As noted above, no data used for analyses will be identifiable other 
than by the unique, random participant number assigned at the time eligibility is determined.  
All computing systems and servers at Current Health used for these study data are HIPAA 
compliant.  Shared drives are password protected, backed up daily on secure servers, and 
ensure version control.  
  
Device Decontamination  
Current Health has stringent post-use device reprocessing and decontamination methods for 
medical facilities using methods that meet or exceed both ISO and FDA guidelines for the 
decontamination of reusable medical devices.  Their pre-existing decontamination strategies 
have already been shown to be effective at eliminating SARS-CoV-2/COVID-19.    

19  
  
Current Health works with chosen medical logistics partners who are experienced in 
handling, decontaminating and recycling medical equipment. Each kit is shipped back to 
Current Health in specific medical bags to ensure safety of courier staff as well as staff 
employed by their medical logistics partners.   
  
Timeframe  
The award was granted 30 September 2020. Enrollment for the pilot study will begin in January 
2021 will be conducted over eight months with a final report submitted in August 2021.  
  
Dissemination  
The primary dissemination method will be the preparation of a report for the Biomedical 
Advanced Research and Development Authority (BARDA) of the U.S. Department of Health 
and Human Services. A draft final report will be prepared for BARDA for review and 
comment and the study team will revise as required for the final report. The final report for 
this project will provide a comprehensive overview of the work undertaken and results 
obtained. This will then be prepared for publication in an academic journal.   
  
References  
1. Weekly epidemiological update - 3 November 2020. Accessed November 10, 2020.  
https://www.who.int/publications/m/item/weekly-epidemiological-update---3-november-2020  
2. Killerby ME. Characteristics Associated with Hospitalization Among Patients with COVID-19 — 
Metropolitan Atlanta, Georgia, March–April 2020. MMWR Morb Mortal Wkly Rep. 2020;69. 
doi:10.15585/mmwr.mm6925e1  
3. Docherty AB, Harrison EM, Green CA, et al. Features of 20 133 UK patients in hospital with covid-19 
using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study. 
BMJ. 2020;369. doi:10.1136/bmj.m1985  
4. CDC. Severe Outcomes Among Patients with Coronavirus Disease 2019 (COVID-19) — United 
States, February 12–March 16, 2020. MMWR Morb Mortal Wkly Rep. 2020;69.  
doi:10.15585/mmwr.mm6912e2  
5. CDC. COVIDView, Key Updates for Week 43. Centers for Disease Control and Prevention. Published 
October 30, 2020. Accessed November 10, 2020.  
https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/past-reports/10302020.html  
6. Verity R, Okell LC, Dorigatti I, et al. Estimates of the severity of coronavirus disease 2019: a model-
based analysis. Lancet Infect Dis. 2020;20(6):669-677. doi:10.1016/S14733099(20)30243-7  
7. Clift AK, Coupland CAC, Keogh RH, et al. Living risk prediction algorithm (QCOVID) for risk of hospital 
admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study. 
BMJ. Published online October 20, 2020:m3731. doi:10.1136/bmj.m3731  
8. Quer G, Radin JM, Gadaleta M, et al. Wearable sensor data and self-reported symptoms for COVID-
19 detection. Nat Med. Published online October 29, 2020:1-5. doi:10.1038/s41591-0201123-x  
9. Weizman Y, Tan AM, Fuss FK. Use of wearable technology to enhance response to the Coronavirus 
(COVID-19) pandemic. Public Health. 2020;185:221-222.  
doi:10.1016/j.puhe.2020.06.048  
10. Vabalas A, Gowen E, Poliakoff E, Casson AJ. Machine learning algorithm validation with a limited 
sample size. PloS One. 2019;14(11):e0224365. doi:10.1371/journal.pone.0224365  

20  
  
11. Sun H, Jain A, Leone MJ, et al. CoVA: An Acuity Score for Outpatient Screening that Predicts COVID-
19 Prognosis. J Infect Dis. Published online October 24, 2020:jiaa663.  
doi:10.1093/infdis/jiaa663  
12. Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical 
prediction model. BMJ. 2020;368:m441. doi:10.1136/bmj.m441  
13. Parmanto B, Lewis AN, Graham KM, Bertolet MH. Development of the Telehealth Usability 
Questionnaire (TUQ). Int J Telerehabilitation. 2016;8(1):3-10. doi:10.5195/ijt.2016.6196  
  
  
  
  
Appendix A. Simplified Bayesian Network10  
  
A simplified Bayesian network. This is an example of a “base model”, where no observations 
specific to a patient have been specified.  
  
  
  
  
  
  

21  
  
Appendix B. Sample Size Calculations with Multiple 
Considerations12  
  
Calculation 1: Sample size required for precise estimation of the overall outcome 
probability in the target population. In this study a 10% event rate has been estimated. 
The number of participants required under the assumption this is a true event rate:  
  
To estimate the required number of participants to properly observe 10% event rate at 
margin error of ≤0.05:   
  
This calculation does not represent the main goal of the study, rather is the lowest bar for 
number of participants for the state event observation rate.  
Calculation 2: Sample size required to help ensure a developed prediction model of a 
binary outcome will have a small mean absolute error in predicted probabilities when 
applied in other targeted individuals.   
Given that we do not know the predictive power of a model, this estimate is conservative to 
apply a model to new individuals. The calculation below is given with an event rate of 0.1 
(10%) (ϕ) and approximately 30 model parameters (P) based upon our 21 variables listed in 
the study.   
  
  

22  
  
  
  
 

23  
  
Appendix C. Telehealth Usability Questionnaire13  
Telehealth Usability Questionnaire  
 
 
 
 
 
 
Components  Question  
7  6  5  4  3  2  1  
Usefulness  
 
 
 
 
 
 
1  Current Health improves my access to healthcare 
services  
  
  
  
  
  
  
  
2  Current Health saves me time traveling to a hospital or 
specialist clinic  
  
  
  
  
  
  
  
3  Current Health provides for my healthcare needs  
  
  
  
  
  
  
  
Ease of Use & Learnability  
 
 
 
 
 
 
1  It was simple to use the Current Health kit  
  
  
  
  
  
  
  
2  It was easy to learn to use the Current Health kit  
  
  
  
  
  
  
  
3  I believe I could become productive quickly using the 
Current Health kit  
  
  
  
  
  
  
  
Interface Quality  
 
 
 
 
 
 
1  The way I interact with the Current Health kit is pleasant  
  
  
  
  
  
  
  
2  I like using the Current Health kit  
  
  
  
  
  
  
  
3  The Current Health kit is simple and easy to understand  
  
  
  
  
  
  
  
4  This Current Health kit is able to do everything I would 
want it to be able to do  
  
  
  
  
  
  
  
Interaction Quality  
 
 
 
 
 
 
1  I could easily talk to the clinician using the Current 
Health tablet  
  
  
  
  
  
  
  
2  I could hear the clinician clearly using the Current Health 
tablet  
  
  
  
  
  
  
  
3  I felt I was able to express myself effectively  
  
  
  
  
  
  
  
4  Using the Current Health tablet, I could see the clinician 
as well as if we met in person  
  
  
  
  
  
  
  
Reliability  
 
 
 
 
 
 
1  I think the visits provided through Current Health are the 
same as in-person visits  
  
  
  
  
  
  
  
2  Whenever I made a mistake using Current Health, I 
could recover easily and quickly  
  
  
  
  
  
  
  
3  The Current Health tablet gave error messages that 
clearly told me how to fix problems  
  
  
  
  
  
  
  
Satisfaction and Future Use  
 
 
 
 
 
 
1  I feel comfortable communicating with the clinician using 
the Current Health tablet  
  
  
  
  
  
  
  
2  Current Health is an acceptable way to receive 
healthcare services  
  
  
  
  
  
  
  

24  
  
3  I would use Current Health services again  
  
  
  
  
  
  
  
4  Overall, I am satisfied with Current Health   
  
  
  
  
  
  
  
  
7: Strongly Agree; 6: Agree; 5: Somewhat Agree; 4: Neutral; 3: Somewhat Disagree; 2: Disagree; 1: Strongly 
Disagree  
  
*Adapted from the Telehealth Usability Questionnaire13  
  
  
  
 

25  
  
Appendix D. RiskSEARCH Interview Schedule   
   
Thank you for agreeing to speak to me today about your experience in the study. In this 
interview I would like to discuss your views of the COVID-19 study which you have been taking 
part in, how you found the experience, and what impact it had on you.  
  
The interview should take about 40 minutes, but if you want to take a break or stop at any 
time just tell me and we can stop immediately. You don’t have to answer any question you 
don’t want to, so if that is the case, just say so and we can move on to something else. 
There are no right or wrong answers.  
  
I’d like to reassure you, again, of confidentiality. Any information you give us will be used 
anonymously - your name will not be attached to the transcripts of this interview or included 
in any reports of our findings from these interviews.   
  
Please feel free to add anything that you think is important but which I may not ask you 
about. It is only by talking to people in this way, can we develop a monitor that is really 
useful to you and other people who are in a similar situation to you in the future.  
  
I’d like to make sure you have had an opportunity to read the Informed Consent Form which 
we sent you by email. Do you have any question about the Informed Consent Form? Have 
you read it and do you have questions about it? Do you consent to being interviewed? In 
particular, are you happy that your anonymous data can be stored and shared with other 
genuine researchers (this is not necessary for interview to take place)? Are you happy to 
proceed with the interview?  
  
Context  
1. What led you to sign up for the study?   
  
Study  
2. We are interested in hearing about your views on the enrollment process, for example, 
navigating the website, completing the online eligibility questionnaire, talking to a study 
team member to give consent, getting the device and setting it up, etc. How was this 
process for you? (Prompt: barriers)  
  
3. Are you still involved with/participating in the COVID-19 study? If not, why?   

26  
  
  
4. Have there been any negative effects of taking part in the study?  
  
5. What did you think about the following COVID-19 study stuff?  
a. Website  
b. Emails from the study team   
c. Calls with the study team  
d. $100 for participating  
e. Other?  
6. What was the best and the worst thing about taking part in the study?  
  
Community by Current Health website  
7. When you used the Community by Current Health website, could you tell me more 
about when and how often?   
a. How long did you use it for?  
b. If you stopped, why?   
c. What ideas do you have for encouraging people to keep using it?  
8. Tell me about your experience using the website.   
a. Was the information helpful?  
b. Was the layout usable?  
c. What would you like to have seen?  
d. What were your patterns of use?  
e. Were there issues with the design?  
9. Aside from this study, can you tell us about anything else you’ve used or done to 
contribute to COVID-19 research?  
  
Wearable Device  
10. Have you had any challenges in receiving, setting up the device or wearing it?  
11. Have you had any technical issues?  
a. If so, have you received the support you needed to resolve the issues?  
12. If you have had periods of time you have not worn the device, could you tell me why 
that is? (prompt: times of days or activities that led to removing the device)  
13. What have you liked about wearing the device?  
14. What have you disliked about wearing the device?  
15. What could make using and wearing the device easier or more engaging?  
  

27  
  
Tablet  
16. Have you had any challenges in using the tablet?  
17. Have you had days you missed answering the survey and if so, what contributed to 
these?  
18. What have you liked about the tablet?  
19. What could make using the tablet easier to use or more engaging?  
20. When setting up the kit, did you follow the printed guide, the on-tablet guide, or both?  
21. Were the instructions on the tablet to fill in the questionnaire clear? Are there any 
improvements that we should consider?  
22. Are there any other features you would like to see added to the tablet device?  
23. We’re about to finish up. Now is your opportunity to share any other feedback or 
thoughts you have about the study.  
  
Thank you for your time today. We will send payment to you through PayPal.

 
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Risk SEARCH Statistical Analysis Plan  
 
  
TRIAL FULL TITLE  
Risk Stratification and Early Alerting Regarding COVID-
19 Hospitalization (Risk SEARCH)  
Document Number  
900002  
SAP VERSION  
3  
SAP VERSION 
DATE  
February 10th, 2021  
TRIAL 
STATISTICIAN  
Hope Watson  
Protocol 
Version 
(SAP 
associated 
with)  
3  
TRIAL PRINCIPAL 
INVESTIGATORS  
Juliana Pugmire 
Matt Wilkes   
SAP AUTHOR(s)  
Hope Watson, Juliana Pugmire  
  
  
  
  
  
    
 

 
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1.Administrative Information   
  
1.1. SAP Version   
SAP Version   
Date  
1  
December 14th, 2020  
2  
January 11th  2021  
3  
February 10th, 2021  
  
1.2. Protocol Version  
Protocol Version   
Date  
1  
November 23rd, 2020  
2  
December 18th, 2020  
3  
January 21st, 2021  
  
1.3. SAP Revisions   
  
The revisions in this draft SAP have been redlined with track changes and dates in addition to 
previous versions kept in a version controlled cloud environment.   
  
1.4. Roles and Responsibilities   
Names   
Affiliations   
Role  
Hope Watson  
Current Health  
Data Scientist   
Juliana Pugmire  
Current Health   
Principal Investigator  
Matt Wilkes  
Current Health  
Principal Investigator  
James Zhou   
BARDA   
Statistical Reviewer  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  

 
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1.5. SAP Signatures   
  
I give my approval for the attached SAP entitled Risk Stratification and Early Alerting Regarding 
COVID-19 Hospitalization (Risk SEARCH) dated February 9th, 2021.   
  
Names   
Role  
Signature  
Date  
Hope 
Watson  
Data Scientist/  
Statistician 
(Author)   
  
10/2/2021 
Juliana 
Pugmire  
Principal 
Investigator  
 
  
11/2/2021 
Matt Wilkes Principal 
Investigator  
 
  
11/2/2021 
James 
Zhou   
Statistical 
Reviewer  
 
  
10/2/2021 
  
  
  
  
  
    
 
 
 
 
  
  
  
  

 
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2.Table of Contents  
1. 
Administrative Information  
1.1. 
SAP Version  
1.2. 
Protocol Version  
1.3. 
SAP Revisions    
1.4. 
Roles and Responsibilities  
1.5. 
SAP Signatures  
2. 
Table of Contents   
3. 
Abbreviations and Definitions 
4. 
Introduction  
4.1. 
Preface   
4.2. 
Scope of the analyses 
5. 
Study Objectives and Endpoints and Hypotheses 
5.1. 
Study Objectives, Endpoints, and Outcomes   
5.2. 
Statistical Hypotheses  
6. 
Study Methods  
6.1. 
General Trial Design and Plan  
6.2. 
Randomization and Blinding  
6.3. 
Sample Size  
6.4. 
Statistical Analyses and Stopping Guidance  
6.5. 
Timing of Final Analysis  
6.6. 
Timing of Outcome Assessments  
7. 
Statistical Principles  
7.1. 
Consideration for Bayesian Methods  
  
7.2. 
Confidence Intervals and P values  
  
7.3. 
Adherence and Protocol Deviations  
 
7.3.1. 
Full Analysis Population   
7.3.2. 
Per Protocol Population   
8. 
Trial Population  
8.1. 
Recruitment and Screening  
8.2. 
Eligibility  
8.3. 
CONSORT flow diagram  
 
Follow-Up  
  
 
Allocation  
  
 
Enrollment  
  
 
Analysis  
  
8.4. 
Withdrawal and Follow-up  
8.5. 
Baseline Participant Characteristics  
8.6. 
Continuous Vital Signs  
8.7. 
Derived Variables  

 
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9. 
Analysis  
9.1. 
Outcome definitions  
9.2. 
Study Stages 
10. 
Summary of Study Data  
11. 
Reporting Conventions 
12. 
Summary of Changes to the SAP  
13. 
References  
    
 
3. Abbreviations and Definitions  
  
BMI  
Body Mass Index   
BN  
Bayesian Network   
CH  
Current Health   
CRA   
Clinical Research Associate   
CRF  
Case Report Form  
DP  
Designated Person  
IMP  
Investigational Medical Product  
NOK  
Next of Kin   
Risk SEARCH  
Risk Stratification and Early Alerting 
Regarding  
COVID-19 Hospitalization  
  
SAP  
Statistical Analysis Plan  
  
4. Introduction  
4.1. Preface  
In the proposed research, we will use Current Health’s remote monitoring wearable device to 
create novel algorithms to help us predict the progression and severity of COVID-19 in 
individuals that have recently become infected in the community. Risk scores based on 
demographics, or for patients in hospital, already exist. However, by using continuous remote 
patient monitoring, in combination with machine learning and data analysis, we hope to predict 
the risk of an individual infected with COVID-19 requiring hospitalization, intensive care unit 
(ICU) treatment, or death based on their vital signs while still in the community.   
Using a hospital-grade home-based, remote monitoring wearable gives us the opportunity to 
create a unique and extremely useful tool in predicting and managing the clinical effects of the 
COVID-19 pandemic. Our algorithm might bridge the gap between demographic models and in-
hospital models of disease progression by providing real-time data from the community. If we are 
successful, this tool could be made available immediately to enable better resource allocation for 
patients after diagnosis. In addition, it enables patient-centered management, increasing 
confidence for low and high-risk patients, and those managing their care. The results of this 
research will provide a database that can be used in future trials to assess treatment efficacy and 
offer the potential for rapid algorithm iteration in the event of a future pandemic.   

 
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4.2. Scope of the analyses  
These analyses will assess the model robustness and predictive performance for primary and 
secondary outcomes (5.1) in COVID-19 PCR or antigen positive participants.   
5. Study Objectives and Endpoints and Hypotheses   
5.1. Study Objectives, Endpoints, and Outcomes  
The overall objective of this study is to train, develop, validate and release a digital biomarker-
based algorithm that, in COVID-19 PCR or antigen positive participants, within 30 days of a 
positive test:   
  
1. Primary Outcomes:  
a. Predicts likelihood of requiring a stay in hospital of at least 24 hours   
b. Predicts likelihood of recovery from COVID-19 defined as:  
i. For asymptomatic participants: 14 days without symptoms following 
positive test  
ii. For symptomatic: at least 14 days since onset of symptoms, with the 
last three days symptom free  
2. Secondary Outcomes:  
a. Predicts likelihood of being admitted to ICU for either non-invasive (CPAP, 
BiPAP, high flow nasal oxygen), invasive ventilation or vasopressor support.  
b. Predicts likelihood of death.   
3. Tertiary Outcomes: Successful delivery, setup, daily interface engagement, and 
compliance with wearing of the device  
  
5.2. Statistical Hypotheses   
These study methods are designed to create an algorithm, specifically a Bayesian Network 
(BN), and then test the statistical accuracy of the developed Bayesian Network and 
comparison models on the primary and secondary outcomes. The model acceptance criteria 
and stated hypothesis is the model’s ability to predict the primary outcome of hospitalization 
of study participants with 75% accuracy. The secondary outcomes will be more exploratory 
with less strict acceptance criteria. The ability to successfully predict secondary outcomes will 
largely depend on the number of secondary outcomes observed and if it warrants adequate 
power.      
  
The benchmarks of model performance will be as follows: AUC between 0.50 and 0.55 is poor, 
0.55 to 0.65 is moderate, 0.65 to 0.75 is acceptable, and >0.75 is excellent. Brier scores will be 
used to verify the accuracy of the event prediction during calibration, where a perfect score is 0 
and imperfect score is 1. Brier scores will be calculated for each individual prediction, 
representing one study participant, and an aggregate Brier score for the sequence of predictions 
of the participant set.   
Different specificity and sensitivity levels will need to be considered when creating a feature that 
automatically triggers notification of study staff to reduce number of unnecessary alarms and 
alarm fatigue. In addition, the duration of time a patient spends in a high-risk state needs to be 
considered. For example, if a participant’s oxygen saturation levels (SpO2) levels decrease for a 
brief period, then regain to normal levels, we will evaluate this to determine if it is an indicative of 

 
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patient deterioration or not. This will likely be undertaken by generating a rolling average or 
median of a certain number of time periods, t, to ascertain a confident deterioration event.   
If the model performs statistically significantly better than 75% (AUC >0.75) with reasonable 
specificity, sensitivity, and calibration the model will be finalized and deployed.  
  
6. Study Methods  
  
6.1. General Trial Design and Plan  
General study design characteristics:  
  
● 
Longitudinal observational cohort trial   
● 
Virtual trial (in contrast to a single or multicenter design)  
● 
Level and method of blinding: Participants will be aware of wearing the medical 
device, therefore there is no blinding to actual device wearing. However, 
participants will be blinded from seeing their own data being captured by the 
device.   
● 
Sequence and duration of all study periods: screening, baseline, active treatment, 
follow-up.  
6.2. Randomization and Blinding  
No randomization will be conducted in this experiment, as it is a longitudinal observational cohort 
trial.  
  
For our study participants, data will be collected “blind”. The participant will not be able to see 
their data in real time, and no data will be reviewed or acted upon by the study team unless there 
is a clinically indicated trigger elicited by the system. These triggers exist to prompt the capture 
of studyspecific information, not to direct participant care.  
  
6.3. Sample Size  
By recruiting participants that have already been identified as SARS-CoV-2/COVID-19 positive, 
we expect that approximately 10% of individuals from each age group will eventually develop 
severe or critical COVID-19 symptoms and require hospitalization or treatment in an intensive 
care unit. Currently there are not sufficiently rigorous statistical models for predicting statistical 
power for machine learning studies.  Some of the issues related to sample size for machine 
learning models have been recently reviewed in the scientific literature. Machine learning models 
can be trained to find an effect, but it is not clear if the effect is the result of over-fitting and/or 
poor sample selection. Nested cross-validation and calibration have been proposed to mitigate 
these issues.  Based on extensive prior experience of our group, we expect that 100 events in 
continuously monitored participants will be sufficient to assess the training performance of the 
model for early identification of novel digital biomarkers indicative of disease severity as well as 
future trajectory of the severity of disease. Using the 100 events and approximate 10% event 

 
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rate, we determined that testing and training cohorts of 1,000 participants each (2,000 
participants total) will be sufficient.   
Although not required in observational clinical trials due to no directly measured effect size, 
sample size calculations were conducted under different circumstances to ensure sample is 
sufficiently large for a clinical prediction model. 1  
  
Calculation 11: Sample size required for precise estimation of the overall outcome probability in 
the target population. In this study a 10% event rate has been estimated. The number of 
participants required under the assumption this is a true event rate where n is the required 
sample size, 𝜙 is the anticipated outcome proportion, and 𝛿 is the absolute margin of error.   
  
1.96 2 
𝑛 = (
) 𝜙 (1 − 𝜙 )  
𝛿 
  
To estimate the required number of participants to properly observe 10% event rate at margin 
error of ≤0.05:    
1.96 2 
𝑛 = (
) 0.1(1 − 0.1) = 139 𝑝𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑛𝑡𝑠  
0.05 
  
This calculation does not represent the main goal of the study, rather is the lowest bar for 
number of participants for the stated event observation rate.   
  
Calculation 21: Sample size required to help ensure a developed prediction model of a binary 
outcome will have a small mean absolute error in predicted probabilities when applied in other 
targeted individuals.  MAPE is the mean absolute prediction error, P is the number of candidate 
predictor parameters, and the constants are derived from simulations in Smeden et al.2 The goal 
of using this formula is to avoid the reductionistic “rule of 10” that would indicate the sample size 
required needs to be 10 times the number of parameters (in this study parameters are variables). 
However, using only the rule of thumb fails to account for number of categories and frequency of 
the category within the parameters.   
  
−0.508 + 0.259 ln(𝜙) + 0.504ln(𝑃) − ln (𝑀𝐴𝑃𝐸) 
 
𝑛 = 𝑒𝑥𝑝 (
)   
0.544 
  
  
Given that we do not know the predictive power of a model, this estimate is conservative to apply 
a model to new individuals. The calculation below is given with an event rate of 0.1 (10%) (ϕ) and 
approximately 30 model parameters (P) based upon our 21 variables listed in the study.    
  
 
𝑛 = 𝑒𝑥𝑝 (
) 
= 756 𝑝𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑛𝑡𝑠  
  
  

 
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6.4. Statistical Analyses and Stopping Guidance   
Analyses will be conducted in three distinct parts of the trial. Analysis following the pilot study of 
50 participants including feasibility, adherence rates, univariate and bivariate statistics, and 
informing structural design of the Bayesian Network. Descriptive statistics will include univariate 
analysis such as frequency, box plots, or histograms, and where appropriate, bivariate analysis 
on covariates using methods such as regression, Fisher’s exact tests, or chi-squared analyses.  
  
Stages 1 and 2 are covered in more depth (9.1) but generally following a training and testing 
machine learning design, respectively. Stopping guidance is determined by both recruitment 
feasibility and model performance. After a total of 2050 participants are reported, the recruitment 
will stop.  If the model performs statistically significantly better than 75% (AUC >0.75) with 
reasonable specificity, sensitivity, and calibration, the model will be finalized and deployed. 
Benchmarks were established as reasonable for similar outcomes in literature.3,4 If the model 
does not meet this standard, we will conduct further analysis into where performance failures 
were most pervasive to decide how performance could best be improved. Adjustments to 
significance levels due to analysis stages are not relevant to Bayesian methods.   
6.5. Timing of Final Analysis   
We will perform the final analysis after 1000 participants in the validation phase have been 
observed. Final analysis will include machine learning validation and reporting. The current 
timeline aims to have this completed by July 30th, 2021 but is subject to changes due to study 
recruitment delays.   
6.6. Timing of Outcome Assessments   
  
The frequency and timing of all the relevant study assessments is considered from the time the 
participant CH kit arrives to 30 days of observation or a met outcome.   
  
Study Task   
Timeline  
Kit Setup   
Day 1   
Participant Intake Survey  
Day 1 to Day 3  
*Daily Survey  
Daily   
CRA Adherence Checks   
Day 1 to Day 30 ongoing   
Vital signs captured by CH wearable   
Day 1 to Day 30 ongoing  
Outcome Verification   
Day 1 to Day 30 ongoing and 30 days after 
observation period ends   
*Daily symptom questionnaire will close if not completed within a given number of hours. Participant will still be 
able to fill out the following day’s daily symptom questionnaire.  
   
Variables collected from study assessments will be generally as follows:   
• 
Demographic – Categorical   
• 
Medical history and medication – Boolean   
• 
Symptom presence – Boolean (“Much better” to “Much worse”)  
• 
Symptom severity – Ordinal categorical   
  
  

 
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7.  Statistical Principles   
7.1. Consideration for Bayesian Methods   
Both Bayesian and frequentist statistics will be used in this analysis. In the Bayesian view, a 
probability is assigned to a hypothesis, while with the frequentist view, a hypothesis is tested 
without being assigned a probability. The reporting interpretation and semantics of the different 
methods will be explicitly stated in comparing confidence versus credible intervals.   
  
7.2. Confidence Intervals and P values   
The level of statistical significance will be set at α=0.05 threshold.   
  
As this study is primarily Bayesian focused, 95% credible intervals will be used for single 
parameters, and credible regions will be used for multivariate parameters.5   
  
Where confidence intervals are appropriate, 95% confidence will be used.   
7.3. Adherence and Protocol Deviations   
A study participant will be considered fully adherent to study protocol if they:   
  
Wear the CH device for at least 20 hours a day, at least 6 
days a week;  
  
Answer the questions on the tablet at least 6 days a week;   
  
Return wearable kit and device.   
  
Adherence metrics will be presented in absolute and percentage terms. Both measures will 
account for how long a participant is in the study. For example, a participant that is observed the 
entire 30 days will have a possible total of 720 hours (30 days x 24 hours) and 30 answered 
tablet symptom surveys. This participant's minimum adherence level would be 600 hours of 
device wear and at least 26 completed symptom surveys. A participant that meets an outcome 
much earlier due to hospitalization after 7 days would still be considered adherent if prior to 
hospitalization they wore the device 140 hours and completed 6 symptom surveys.   
  
A summary table will generally be presented with the following variables per participant:   
Participant ID  
Time in trial (hours)  
Hours of CH device observation  
Wearable adherence percentage  
Time in trial (days)  
Number of surveys completed   
Survey adherence percentage   
Overall study adherence (Y/N)  
  
Following collection all participants this information will be aggregated to the CONSORT flow 
chart metrics.   

 
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7.3.1. Full Analysis Population   
● All subjects who receive CH kit  
● The participants will be represented in flow diagram and analyzed for where 
participants systematically dropped out or did not adhere to study protocol. 
Participant feedback will also be captured and analyzed on this population.   
7.3.2. Per Protocol Population  
● All subjects who adhere to the major criteria in the protocol as defined by adherence 
of wearing the CH device for 20 hours a day, 6 days a week, answer tablet surveys 
at least 6 days a week, and return the CH wearable device kit and tablet.   
● The adherent per protocol population will be used for BN training and testing.     
8. Trial Population      
8.1. Recruitment and Screening  
Participant recruitment will primarily use social media (Facebook) and internet advertisements. 
The study population may contain biases such as a younger audience and those “tech savy” 
enough to wear a medical device for the duration of the study compared to the general 
population. These potential limitations will be considered in the study’s scope of generalizability. 
For screening, participants who are more likely to fit inclusion criteria could be correlated with 
other social determinants of health, which will also be in the study’s scope of generalizability. 
Where population demographic information deviates far from the general population expected 
this will be highlighted.   
  
  
  
 

 
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8.2. Eligibility   
  
Inclusion and Exclusion Criteria   
Table 1. Criteria for study participants   
   
Inclusion    
Exclusion    
1. 
Living in the United States   
2. 
Aged 21 years or older   
3. 
Able to provide documentation of 
+PCR or +antigen test for COVID-19 
within the past 48 hours    
4. 
Self-reports that comfortable and 
willing to wear the wearable device 
and interact with a tablet-based 
interface daily   
5. 
Able to provide a next of 
kin/designated person who can be 
contacted in the event of 
hospitalization for follow up.   
 
1. Under the age of 21   
2. Received a positive test result more 
than 48 hours prior to contacting 
study staff or has a pending test for 
COVID-19  
3. Cannot confirm a PCR or antigen 
positive test for COVID-19. 
4. Is unable to read English 
5. Is unwilling to wear the device 
24 hours/day except for 
showering/bathing or interact 
with a tablet-based interface for 
daily questionnaire   
6. Is unwilling or unable to provide 
baseline data required for entry into 
the study   
7. Is unable to provide a next of 
kin/designated person who can be 
contacted 
in 
the 
event 
of 
hospitalization for follow-up   
8. Heavy tattooing on both upper arms 
9. Known atrial fibrillation (permanent 
or paroxysmal) 
10. Has taken/is taking part in a 
COVID19 vaccine or treatment 
study   
     
   

 
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8.3. CONSORT flow diagram   
A CONSORT diagram modified for the observational design of the trial will be outlined as 
follows. Upon study completion each n= will be provided and reasons for ineligibility and loss of 
follow-up will be noted.   
  
    
Assessed for eligibility (n=  )   
Excluded  (n=   )   
     Not meeting inclusion criteria (n=  )   
     Declined to participate (n=  )   
      Other reasons (n=  )   
Analysed  (n=  )   
   Excluded from analysis (reasons) (n=  )  
Lost to follow - up (reasons) (n=  )   
Discon tinued intervention (give reasons) (n=  )  
Allocated to  CH device kit   ( n=   )  
   Received allocated intervention (n=  )  
   Did not receive allocated intervention  
) 
( reasons) (n=     
Allocation   
Analysis   
Follow - Up   
Enrollment   

 
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8.4. Withdrawal and Follow-up  
Participants may withdrawal at any time from the study if they no longer wish to wear the CH 
device and not respond to tablet based questions. The level of withdrawal, non-adherence, and 
loss of follow-up will be analyzed separately from modeling (7.3). Where possible CRA will 
capture reasons for withdrawal such as discomfort of continuous wear of device on upper arm. 
Withdrawal and loss of follow-up will be presented in the modified CONSORT flow diagram.   
  
8.5. Baseline Participant Characteristics   
Characteristics will be presented in a descriptive summary table — continuous variables as 
mean and standard deviation or median and quartiles, and categorical and binary variables as 
absolute and relative frequencies.  The analysis population will be produced for those who 
complete the trial to a study outcome including recovery. Surveys will be administered via tablet, 
which is part of the CH device kit a participant receives. The list of baseline participant 
characteristics and description of how they will be recorded are captured in the following table. 
The variables planned to be captured represent the most recent literature findings of COVID-19 
risk factors.6  
  
  
Description   
Variable  
Variable Type   
*Participant Intake Survey   
 
 
Date of birth  
dob  
Date (MMDDYYY)  
Sex  
sex  
Categorical: Male, Female,  
Other or prefer not to 
specify  
Pregnant  
pregnant   
Binary: Yes, No   
Presence of menstrual 
periods  Absence of 
menstrual periods reason  
menstrual_periods  
Binary: Yes, No   
no_periods_reason  
Categorical: Pregnant or 
breastfeeding, Taking 
hormonal birth control, 
Undergoing menopause or 
post-menopausal, High 
active,  
Other   
Date of last period  
last_period  
Date (MMDDYYY)  
Occupational category   
occupational_cat  
Categorical: Healthcare,  
Service industry, Retail, 
Office  
- able to work from home, 
Office - unable to work from 
home, Currently 
unemployed, Student - 
remote, Student - attending 
class in person  
Household make-up  
household_makeup  
Binary: Living alone, Living 
with people  
Do other members of 
your household currently 
have COVID-19?  
household_covid  
Binary: Yes, No   

 
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Are any other members of 
your household enrolled in 
Current Health's COVID-19  
household_study  
Binary: Yes, No   
study?  
What is your self-identified 
race/ethnicity?  
race_ethnicity  
Categorical: White Hispanic,  
White Non-Hispanic, Black 
or  
African American, Asian,  
American Indian or Alaska  
Native, Native Hawaiian and  
Pacific Islander  
 
Height   
height  
Discrete (user input)   
Weight (in pounds)  
weight  
Continuous: (pounds or kg 
user input)   
Asthma  
asthma  
Binary: Yes, No   
Cancer  
cancer  
Binary: Yes, No   
Chronic 
obstructive 
pulmonary disease (COPD)  
copd  
Binary: Yes, No   
Diabetes  
diabetes  
Binary: Yes, No   
Heart condition  
heart_condition  
Binary: Yes, No   
High blood pressure  
hbp  
Binary: Yes, No   
Kidney disease or reduced 
kidney function  
kidney_disease  
Binary: Yes, No   
Organ transplant  
organ_transplant  
Binary: Yes, No   
Sickle cell disease  
sickle_cell_disease  
Binary: Yes, No   
Smoker  
smoker  
Binary: Yes, No   
Intake survey feedback  
intake_survey_feedbacbk  
Long free text   
  
 
 
†Daily Survey   
 
 
Loss of smell or taste  
loss_smell_taste  
Binary: Yes, No   
Is your loss of smell or taste 
better or worse than 
yesterday?  
loss_smell_taste_likert  
Categorical: Much worse,  
Worse, The same, Better, 
Much better  
Dry cough  
dry_cough  
Binary: Yes, No   
Is your dry cough better or 
worse than yesterday?  
dry_cough_likert  
Categorical: Much worse,  
Worse, The same, Better, 
Much better  
Sore throat  
sore_throat  
Binary: Yes, No   
Is your sore throat better or 
worse than yesterday?  
sore_throat_likert  
Categorical: Much worse,  
Worse, The same, Better, 
Much better  
Fever or feeling feverish  
fever  
Binary: Yes, No   
Is your fever better or worse 
than yesterday?  
fever_likert  
Categorical: Much worse,  
Worse, The same, Better, 
Much better  
Chills  
chills  
Binary: Yes, No   

 
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Are your chills better or 
worse than yesterday?  
chills_likert  
Categorical: Much worse,  
Worse, The same, Better, 
Much better  
Muscle aches  
muscle_aches  
Binary: Yes, No   
Are your muscle aches better 
or worse than yesterday?  
muscle_aches_likert  
Categorical: Much worse, 
Worse, The same, Better,  
Much better  
Diarrhea  
diarrhea  
Binary: Yes, No   
Is your diarrhea better or 
worse than yesterday?  
diarrhea_likert  
Categorical: Much worse,  
Worse, The same, Better, 
Much better  
Nausea  
nausea  
Binary: Yes, No   
Is your nausea better or 
worse than yesterday?  
nausea_likert  
Categorical: Much worse,  
Worse, The same, Better, 
Much better  
Are there any other 
symptoms or differences in 
health you would like to 
report?  
daily_survey_feedback  
Long free text   
Will you speak to a 
healthcare provider due to 
worsening symptoms today?  
self_reported_provider_care  Categorical: Yes, No, 
Maybe  
Will you go to the hospital 
due to worsening symptoms 
today?  
self_reported_hospital   
Categorical: Yes, No, 
Maybe  
Do you feel as though you 
have completely recovered 
from COVID-19 as of today?  
self_reported_recovery  
Categorical: Yes, No, 
Maybe  
*Participant Intake Survey questions are to be completed by participant one time.  
†Daily Survey questions are asked once a day at the same time each day.   
  
8.6. Continuous Vital Signs   
Continuous vital signs are as follows and are reported as continuous and discrete data:  
Variable   
Variable Type  
Respiration rate   
Continuous numeric  
Respiration rate   
Continuous numeric  
Pulse rate   
Continuous numeric  
Oxygen saturation   
Continuous numeric  
Skin temperature   
Continuous numeric  
Step count   
Discrete numeric  
Activity levels  
Discrete numeric  
  
8.7. Derived Variables  
In order for CH to ship a kit to a participant zip code will be collected. Zip code may be used 
during analysis to determine potential social determinants of health such as median household 

 
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income in an area. BMI will be derived from height and weight measurements. Date of birth will 
be used to calculate age and age may be used to derive age groups.   
9. Analysis  
9.1. Outcome definitions  
Study outcomes will be ascertained by the CRA within the study observational period of 30 days 
or the following 30 days if the participant has not definitively experienced other outcomes of 
recovery or death.  
  
Primary and secondary outcomes are presented in (5.1) and will be presented as follows.   
Description   
Variable  
Variable Type   
Recovery from COVID-19  
recovered  
Binary: Yes, No  
Hospital stay of 24 hours or longer  hospitalized  
Binary: Yes, No  
ICU admission  
icu_admission  
Binary: Yes, No  
Death  
death  
Binary: Yes, No  
  
No calculations are necessary to derive primary or secondary outcomes, they all will be directly 
stated in binary terms.   
9.2. Study Stages   
Study stages will be broken into three sections: pilot, stage 1 training, and stage 2 testing and 
validation.   
  
Pilot   
The pilot phase study with 50 participants will test the feasibility of recruitment, trial logistics, 
observed participant events, timeline to deterioration, and environmental test settings. The 
primary aim is to make any revisions to recruitment strategy and to the final number of “critical 
events” (e.g., hospitalization) required. The secondary aims are to produce descriptive statistics 
on pilot data, understand user issues that could affect study data, and begin formulating the 
structure of the Bayesian network. Descriptive statistics will include univariate analysis such as 
frequency, box plots, or histograms, and where appropriate, bivariate analysis on covariates 
using methods such as regression, Fisher’s exact tests, or chi-squared analyses.   
Stage 1   
The overall aim of Stage 1 is to collect enough data to perform machine learning analytics on 
data from the Current Health device to create a Bayesian network algorithm that successfully 
predicts the risk/probability of no severe disease, hospitalization, ICU admission, and death 
following SARS-CoV2/COVID-19 infection. The outcomes are not mutually exclusive categories, 
and can be hierarchical, i.e., when a participant first goes to hospital and then is admitted to the 
ICU. Conversely, a participant at risk could experience death without ever being hospitalized. 
This will be accounted for by calculating probabilities of each outcome and compare the 
conditional probabilities analyzing the risk of ICU admission, given that hospitalization has 
already occurred.  One thousand participants will be recruited for the training stage.    

 
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Once the Bayesian network model is constructed, to account for model variation, that is a 
model’s parameters change depending on the data the model is being given, cross-validation 
will be performed to ensure model stability and reduce issues with overfitting. Cross validation 
will only be conducted in the training data, as any cross validation with testing data has been 
shown in instances not to aid overfitting issues.7   
  
Stage 2  
To assess the algorithm performance, algorithm testing on a total of 1,000 participants will be 
conducted. This cohort of 1,000 testing participants will be held entirely separate from the 
training participants to avoid any possible data snooping or data mining bias in the study. In 
machine learning it is typical to obtain initial testing cohort results to determine if the model 
created from the training data is performing well. After the first 100 participants in the testing 
cohort are monitored for 30 days, the algorithm will be tested for understanding initial 
performance. As more participants are recruited into the study, follow up algorithm testing will 
occur for each 100 participants that are added to the testing cohort. To avoid any potential 
issues with multiple testing, only the most recent testing metrics (AUC/ROC, PPV, Brier Scores, 
etc.) will be reported. Therefore, the unique full test set will only be run once.   
Although the outcome of the participants in the validation set is known, the outcomes will be 
initially ignored, as if researchers did not know participant outcome. This simulates what 
researchers know about a participant preceding the participant’s outcome. The testing 
participants’ collected variables (demographic, medical history, continuous data from wearable, 
and symptom data) will be run through the developed algorithm. The algorithm will return 
probabilities of the study outcomes. The algorithm’s probabilities will then be compared with the 
true outcome of the participant recorded by the CRA.   
10.  Summary of Study Data  
All continuous variables will be summarized using the following descriptive statistics: n (non 
missing sample size), mean, standard deviation, median, maximum and minimum. The 
frequency and percentages (based on the non-missing sample size) of observed levels will be 
reported for all categorical measures.   
Major reporting aspects of the study will be the CONSORT diagram to understand participant 
recruiting funnel, baseline participant characteristics tables, the Bayesian network model and 
its performance features, and participant predict risk scores and probabilities as outputs from 
the model.  
11.  Reporting Conventions  
Describe reporting conventions, for example the precision used for reporting p-values and 
other numeric values.  Example:  
P-values ≥0.001 will be reported to 3 decimal places; p-values less than 0.001 will be 
reported as “<0.001”. The mean, standard deviation, and any other statistics other than 
quantiles, will be reported to one decimal place greater than the original data. Quantiles, such 
as median, or minimum and maximum will use the same number of decimal places as the 
original data. Estimated parameters, not on the same scale as raw observations (i.e. 
regression coefficients) will be reported to 3 significant figures.   

 
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12. Summary of Changes to the SAP  
In version 2, the hypothesis was more explicitly stated with specific target metrics that need to 
be met for model standards and deployment. Additional information regarding structural and 
parameter learning were added for further context. Information about incorporation of temporal 
data was added. In Version 3 information about how to randomize the for data modeling if study 
recruitment is not met is considered in section (9.5)   
 
13. References  
  
1. 
Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a 
clinical prediction model. BMJ. 2020;368:m441. doi:10.1136/bmj.m441  
2. 
van Smeden M, Moons KG, de Groot JA, et al. Sample size for binary logistic prediction 
models: Beyond events per variable criteria. Stat Methods Med Res. 2019;28(8):2455-2474.  
doi:10.1177/0962280218784726  
3. 
Sun H, Jain A, Leone MJ, et al. CoVA: An Acuity Score for Outpatient Screening that 
Predicts Coronavirus Disease 2019 Prognosis. J Infect Dis. 2021;223(1):38-46. 
doi:10.1093/infdis/jiaa663  
4. 
Clift AK, Coupland CAC, Keogh RH, et al. Living risk prediction algorithm (QCOVID) for risk 
of hospital admission and mortality from coronavirus 19 in adults: national derivation and 
validation cohort study. BMJ. 2020;371:m3731. doi:10.1136/bmj.m3731  
5. 
Marcot BG. Metrics for evaluating performance and uncertainty of Bayesian network 
models. Ecol Model 230 50-62. 2012;230:50-62.  
6. 
CDC. COVID-19 and Your Health. Centers for Disease Control and Prevention. Published 
February 11, 2020. Accessed January 7, 2021. 
https://www.cdc.gov/coronavirus/2019ncov/need-extra-precautions/evidence-table.html  
7. 
Vabalas A, Gowen E, Poliakoff E, Casson AJ. Machine learning algorithm validation with a 
limited sample size. PLOS ONE. 2019;14(11):e0224365. doi:10.1371/journal.pone.0224365  
8. 
Fenton NE, Neil M. Risk Assessment and Decision Analysis with Bayesian Networks. CRC Press, 
Taylor & Francis Group; 2019.  
9. 
Crump C, Saxena S, Wilson B, Farrell P, Rafiq A, Silvers CT. Using Bayesian Networks 
and RuleBased Trending to Predict Patient Status in the Intensive Care Unit. AMIA Annu 
Symp Proc. 2009;2009:124-128.  
10. Park E, Chang H, Nam HS. A Bayesian Network Model for Predicting Post-stroke 
Outcomes With Available Risk Factors. Front Neurol. 2018;9. doi:10.3389/fneur.2018.00699  
11. CDC. National Diabetes Statistics Report, 2020. Centers for Disease Control and 
Prevention.  

 
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Published February 11, 2020. Accessed January 7, 2021.  
https://www.cdc.gov/diabetes/library/features/diabetes-stat-report.html  
12. Hoens TR, Polikar R, Chawla NV. Learning from streaming data with concept drift and 
imbalance: an overview. Prog Artif Intell. 2012;1(1):89-101. doi:10.1007/s13748-011-0008-0  
13. Asia Bayesian network | Live demo. Accessed January 7, 2021. 
https://www.bayesserver.com/examples/networks/asia  
14. Alarm Bayesian network | Live demo. Accessed January 7, 2021.  
https://www.bayesserver.com/examples/networks/alarm  
15. Fenton N, McLachlan S, Lucas P, et al. A privacy-preserving Bayesian network model for 
personalised COVID19 risk assessment and contact tracing. medRxiv. Published online July 
19, 2020:2020.07.15.20154286. doi:10.1101/2020.07.15.20154286 
 
 

 
Current Health / Protocol Number 2002  
Page 48 of 57  
  
  
  
 Adam Wolfberg, MD, MPH  
Advarra IRB Approved Version 22 Dec 2020  
  
  
 
INFORMED CONSENT FORM AND   
AUTHORIZATION TO USE AND DISCLOSE PROTECTED HEALTH INFORMATION  
  
 
Sponsor / Study Title:  
   
Current Health / “Risk Stratification and Early Alerting  
Regarding COVID-19 Hospitalization (Risk SEARCH)”  
 
Protocol Number:  
  
2002  
 
Principal Investigator:  
(Study Doctor)  
  
Adam Wolfberg, MD, MPH  
 
Telephone:  
  
(415) 941-5734 (24 Hours)  
 
Address:  
Current Health  
500 5th Ave  
New York, NY    10017  
  
  
Key Study Information  
This section provides a brief summary of the study. It is important for you to understand why 
the research is being done and what it will involve before you decide whether or not to take 
part. Please take the time to read the entire information sheet carefully and talk to a study team 
member before making your decision. You should not sign and date the consent if you have any 
questions that have not been answered.  
  
  
It’s Your Choice  
This is a research study. Being in this research study is your choice 
and voluntary; you do not have to participate. If you decide to join, 
you can still stop at any time. You should only participate if you want 
to do so. You will not lose any services, benefits or rights you would 
normally have if you choose not to take part.  
  
  
Research Purpose  
The purpose of this research is to study ways to remotely monitor 
participants who have tested positive for COVID-19 to learn more 
about progression and recovery from the disease. You have been 
asked to take part in this research because you have received a 
positive test for COVID19.  

 
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What’s Involved  
Study participation involves wearing the Current Health Wearable 
Health Monitoring System each day for up to 30 days. You will also 
be provided with a Current Health tablet with an application installed. 
The tablet will remind you to answer a brief series of questions each 
day.  
  
 
Steps for taking part in this study:  
You will be asked to   
1. Review, sign and date Informed Consent Form   
2. Review, sign and date the Authorization to Use and Disclose 
Protected Health Information form  
3. Set up your Current Health wearable study device when you 
receive it in the mail   
4. Wear the study device every day except when you shower or 
bathe 
5. Answer the tablet questions daily  
6. Return the wearable study device kit and tablet when the 
study is over (you will not have to pay for shipping)  
  
Risks  
There are minimal risks related to participation in this research study.  
As always, there is a chance that wearing a device could cause 
irritation or discomfort. There is also a chance that confidentiality 
could be compromised; however, we take precautions to minimize 
this risk.  
  
  
Benefits  
This study is being done for research and to advance our 
understanding of COVID-19 disease. There is no direct benefit to you 
from your participation in the study. Information learned from the 
study may help other people in the future. You will be paid up to $100 
for your time and effort for taking part in this study.  
Healthcare  
This study will not provide you with healthcare or replace your 
standard healthcare. The study results will not be shared with your 
healthcare provider.  
  
  
Learn More  
If you are interested in learning more about this study, please read 
the rest of this form carefully. The information in this form will help 
you decide if you want to participate in this research or not. A study 
team member will talk with you about taking part in this study before 
you sign and date the consent form to take part. If you have 
questions at any time, please ask us.  
  
Please read this form carefully. Take your time to ask the study team as many questions about 
the study as you would like. The study team can explain words or information that you do not 
understand. Reading this form and talking to the study team may help you decide whether to 

 
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take part or not. If you decide to take part in this study, you must sign your name at the end of 
this form and date it.   
  
BACKGROUND AND PURPOSE   
The purpose of this research is to study ways to remotely monitor participants who have tested 
positive for COVID-19, to learn more about progression and recovery from the disease.  
  
You have been asked to take part in this research because you have received a positive test for 
COVID-19 in the last 48 hours.  
  
About 2050 participants will participate in this study.   
  
WHAT WILL HAPPEN DURING THE STUDY?  
If you decide to participate, after reading the Informed Consent Form and providing a digital 
signature, the study team will send you a Current Health wearable study device and kit within 24 
hours with instructions about how to set up and use the study device. Then you will wear the 
study device each day you are in the study up to 30 days. The study device tracks your pulse 
rate, respiratory (breathing) rate, skin temperature, oxygen saturation, step count, and activity 
levels.  Each day, you will be reminded to answer a brief series of questions on a tablet 
provided with the study device.  
  
The study team will contact you about every two weeks to ask how you are feeling and how the 
equipment is working.  
  
Additionally, the Current Health system can send the study team notifications if it is not receiving 
data. Study team members may call you on occasion to make sure your devices are functioning 
properly and to remind you to answer the daily questionnaire on your tablet.  
  
Participation in this study does not provide you access to medical care for COVID-19. Instead, it 
allows us to learn what body functions change with the disease and once we learn that we may 
be able to help future patients with these devices. If you have new or worsening symptoms, you 
should contact your primary care provider or go to the local emergency department for 
evaluation and management.   
  
If at any time you feel your health is rapidly declining, do not hesitate to seek care from your health 
care provider or call 911 if it is an emergency.  
  
At the end of participation, the study team will call you to agree on a collection time for the 
device. You will also be reminded to answer a brief series of questions before mailing the 
system back.  
  

 
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EXPECTATIONS   
If you participate in this study, you will be expected to:   
• 
Provide some basic information about yourself when signing up for the study, including 
contact details.  
• 
Talk to a study team member and sign and date an electronic consent form.  
• 
Receive and set up the Current Health wearable study device – the study device is 
straightforward to set up and full instructions are provided.  
• 
Wear the Current Health study device on either upper arm for up to 30 consecutive days. 
As a participant, you will not be able to see your vital sign data collected by the study 
device. These go directly to the study team.  
• 
Answer a series of brief questions on the tablet provided every day.  
• 
Return the study device kit when the study is over or when you have stopped 
participating.   
  
RISKS, SIDE EFFECTS, AND/OR DISCOMFORTS   
The study will take up some of your time to wear the study device, charge it occasionally and 
answer questions on the study device tablet.   
  
The study device may cause some irritation and/or discomfort when worn for prolonged periods 
of time. If experiencing any irritation and/or discomfort when wearing the device, you may 
remove the study device to give your skin a rest, swap arms and/or change the fit of the study 
device strap. If the irritation and/or discomfort persist, you should stop wearing the study device 
and contact the study team.  
  
As with all research, there is a chance that confidentiality could be compromised; however, we 
take precautions to minimize this risk. All your data will be de-identified for analysis. Your 
equipment for the study will be mailed via a separate external vendor from Current Health. You 
may be emailed an electronic copy of this signed and dated consent form. There may be risks of 
loss of privacy and confidentiality if the electronic copy of this consent form is viewed and/or 
stored on a personal electronic device (PED), especially if that PED is shared with other users 
or is lost, hacked, or subject to a search warrant or subpoena. Also, the electronic copy of the 
consent may not be able to be permanently removed from a PED.  
  
ALTERNATIVES TO PARTICIPATION   
This research study is for research purposes only. The only alternative is to not participate in 
this study.  
  

 
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NEW FINDINGS   
Any new important information that is discovered during the study and which may influence your 
willingness to continue participation in the study will be provided to you by email notification and 
a call from the study team.  
  
BENEFITS  
This study is being undertaken to find out if this study device can help predict the progression 
and severity of COVID-19 progression in individuals so the people that need it can receive 
earlier intervention and better care in the future. This study is being done for research and to 
advance our understanding of COVID-19 disease. There is no direct benefit to you from your 
participation in the study. Information learned from the study may help other people in the 
future.  
  
COMPENSATION FOR PARTICIPATION   
You will be paid up to a total of $100.00 if you complete this study. We would like to emphasize 
that wearing the study device every day is essential for the best study results. However, you will 
be paid for the weeks you wear the study device and answer the tablet questions according to 
the following schedule:  
• 
$25.00 for week 1, wearing the study device at least 6 days for at least 20 hours of each 
day and answering the tablet questions at least 6 days.   
• 
$25.00 for week 2, wearing the study device at least 6 days for at least 20 hours of each 
day and answering the tablet questions at least 6 days.   
• 
$25.00 for week 3, wearing the study device at least 6 days for at least 20 hours of each 
day and answering the tablet questions at least 6 days.   
• 
$25.00 for week 4, wearing the study device at least 6 days for at least 20 hours of each 
day and answering the tablet questions at least 6 days.   
  
If you do not complete the study, for any reason, you will be paid for each study week you do 
complete.   
  
You will be paid at the end of the 30-day study period once the Current Health wearable study 
device kit and tablet have been returned.   
  
If you have any questions regarding your compensation for participation, please contact the 
study team.    
  
FINANCIAL DISCLOSURE  
Investigators on this study have an ownership interest, and some of the investigators hold 
executive positions, in Current Health, the company sponsoring this research study. As a result, 

 
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the investigators may benefit financially from a successful study. Please speak with your study 
doctor if you have questions about this.  
  
CONFIDENTIALITY    
Records of your participation in this study will be held confidential except when sharing the 
information is required by law or as described in this informed consent. The study doctor, the 
sponsor or persons working on behalf of the sponsor, and under certain circumstances, the 
United States Food and Drug Administration (FDA) and the Institutional Review Board (IRB) will 
be able to inspect and copy confidential study-related records which identify you by name. This 
means that absolute confidentiality cannot be guaranteed. If the results of this study are 
published or presented at meetings, you will not be identified.  
  
A description of this clinical trial will be available on http://www.ClinicalTrials.gov, as required by 
U.S. Law. This website will not include information that can identify you. At most, the website 
will include a summary of the results. You can search this website at any time.  
  
COMPENSATION FOR INJURY   
If you become ill or are injured while you are in the study, get the medical care that you need 
right away. You should inform the healthcare professional treating you that you are participating 
in this study. If you tell the study team that you think you have been injured as a result of this 
study, then they will help you get the care you need.   
  
If you are injured as a result from procedures done for the purpose of this study, the sponsor will 
pay for those medical expenses necessary to treat your injury that are not covered by your 
medical insurance or any other third-party coverage. By signing and dating this document, you 
will not lose any of your legal rights or release anyone involved in the research from 
responsibility for mistakes.  
  
To pay medical expenses, the sponsor will need to know some information about you like your 
name, date of birth, and Medicare Beneficiary Identifier (MBI). This is because the sponsor has 
to check to see if you receive Medicare and if you do, report the payment it makes to Medicare.   
  
COSTS   
There will be no charge to you for your participation in this study. The Current Health wearable 
study device and study-related procedures, like shipping the study device to you and back, will 
be provided at no charge to you or your insurance company.  
  

 
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FUTURE RESEARCH STUDIES   
Identifiers might be removed from your identifiable private information collected during this study 
and could then be used for future research studies or distributed to another investigator for future 
research studies without additional informed consent.  
  
WHOM TO CONTACT ABOUT THIS STUDY  
During the study, if you experience any medical problems, suffer a research-related injury, or 
have questions, concerns or complaints about the study, please contact the study doctor at the 
telephone number listed on the first page of this consent document. If you seek emergency 
care, or hospitalization is required, alert the treating physician that you are participating in this 
research study.   
  
An institutional review board (IRB) is an independent committee established to help protect the 
rights of research participants. If you have any questions about your rights as a research 
participant, and/or concerns or complaints regarding this research study, contact:  
• 
By mail:  
Study Subject Adviser  
Advarra IRB  
6940 Columbia Gateway Drive, Suite 110 
Columbia, MD 21046  
• 
or call toll free:    877-992-4724  
• 
or by email:          adviser@advarra.com  
  
Please reference the following number when contacting the Study Subject Adviser:  
Pro00047371.  
  
VOLUNTARY PARTICIPATION / WITHDRAWAL   
Your decision to participate in this study is voluntary. You may choose to not participate, or you 
may withdraw from the study for any reason without penalty or loss of benefits to which you are 
otherwise entitled and without any effect on your future medical care. However, please note that 
the FDA requires that any information collected up to the point of your withdrawal cannot be 
removed from the study.  
  
The study doctor or the sponsor can stop your participation at any time without your consent for 
any of the following reasons:  
• 
If it is in your best interest  
• 
If you don’t follow the study procedures  
• 
If it is discovered that you do not meet the study requirement  
• 
If the study is canceled  

 
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We will tell you about any new information that may affect your willingness to stay in the 
research study.  
  
  
CONSENT   
I have read and understand the information in this informed consent document. I have had an 
opportunity to ask questions and all of my questions have been answered to my satisfaction. I 
voluntarily agree to participate in this study until I decide otherwise. I do not give up any of my 
legal rights by signing and dating this consent document. I will receive a copy of this signed and 
dated consent document.  
  
  
________________________________________  
Participant’s Printed Name   
 
 
 
 
________________________________________   
___________  
Participant’s Signature  
  
  
  
 
 
 
Date  
________________________________________   
 
Printed Name of the Person Conducting the   
Consent Discussion     
 
 
 
________________________________________   
___________  
Signature of the Person Conducting the   
  
 
Date    
Consent Discussion     
  
  
  
 
 
  
  
  
  
  
 
 

 
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AUTHORIZATION TO USE AND DISCLOSE PROTECTED HEALTH INFORMATION  
  
If you decide to be in this study, the study team will use and share health data about you to 
conduct the study. Health data may include:  
• 
Your name.  
• 
Address.  
• 
Phone number.  
• 
Date of birth.  
• 
Medical, medication and family history.  
• 
Information from the Current Health wearable device  
• 
Information from your study questionnaires.   
  
Health data may come from your study records or from existing records kept by your doctor or 
other health care workers.   
  
For this study, the study team may share health data about you with authorized users.  
Authorized users may include:  
• 
Representatives of Current Health.  
• 
Representatives of Advarra IRB (an Institutional Review Board that reviews this study).  
• 
The Food and Drug Administration (FDA) and other US federal and state agencies.  
• 
Government agencies to whom certain diseases (like HIV, hepatitis, and STDs) must be 
reported.  
• 
Outside individuals and companies, such as laboratories and data storage companies, 
that work with the researchers and sponsor and need to access your information to 
conduct this study.  
• 
Other research doctors and medical centers participating in this study, if applicable.   
• 
The Institutional Review Board that oversees the research.  
• 
Federal and State agencies (such as the Food and Drug Administration, the Department 
of Health and Human Services, the National Institutes of Health and other United States 
agencies) or government agencies in other countries that oversee or review research.  
• 
Current Health, the sponsor of this study, and the people or groups it hires to help 
perform this research.  
  
Your health data will be used to conduct and oversee the research, including for instance:  
• 
For research activities related to the Current Health wearable device.  
  
Once your health data has been shared with authorized users, it may no longer be protected by 
federal privacy law and could possibly be used or disclosed in ways other than those listed here.   
  

 
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 of 10  
Your permission to use and share health data about you will end in 50 years unless you revoke it 
(take it back) sooner.   
  
You may revoke (take back) your permission to use and share health data about you at any time 
by writing to the study team at the address listed on the first page of this form. If you do this, you 
will not be able to stay in this study. No new health data that identifies you will be gathered after 
your written request is received. However, health data about you that has already been gathered 
may still be used and given to others as described in this form.   
  
Your right to access your health data in the study records will be suspended during the study to 
keep from changing the study results. When the study is over, you can access your study health 
data.  
  
If you decide not to sign and date this form, you will not be able to take part in the study.  
  
STATEMENT OF AUTHORIZATION  
I have read this form and its contents were explained. My questions have been answered. I 
voluntarily agree to allow study team to collect, use and share my health data as specified in this 
form. I will receive a signed and dated copy of this form for my records. I am not giving up any of 
my legal rights by signing and dating this form.   
  
__________________________________  
Printed Name of Participant  
  
  
__________________________________    
__________  
Signature of Participant  
  
  
  
Date