There's No Place Like Home…Feasibility of Remote Physiological Monitoring in Childhood Heart Failure
Conditions: Heart Failure
Sponsor: University of Alberta

 
 
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There’s No Place like Home…Feasibility of Remote Physiological Monitoring in 
Childhood Heart Failure 
Short title: WeRoaM: Wearable Remote Monitoring in Heart Failure 
 
Principal Investigators: 
Jennifer Conway – Stollery Children’s Hospital 
Aamir Jeewa – The Hospital for Sick Children (SickKids)  
 
Co-Investigators: 
Aine Lynch, New York-Presbyterian Morgan Stanley Children's Hospital, The Hospital for Sick 
Children 
Seema Mital, The Hospital for Sick Children 
Samantha Anthony, The Hospital for Sick Children 
Kathryn Armstrong, British Columbia Children’s Hospital 
Nassiba Alami Laroussi, CHU Sainte-Justine, 
Mjaye Mazwi, Seattle Children's Hospital, The Hospital for Sick Children 
 
Study Sites: 
Stollery Children’s Hospital, Edmonton, AB 
The Hospital for Sick Children, Toronto, ON 
British Columbia Children’s Hospital, Vancouver, BC 
CHU Sainte-Justine, Montreal, QC 
 
Collaborators: 
Matthew Waddington 
Michelle Waddington 
 
Research Coordinator: 
Tara Pidborochynski 
 
 
Funding: Canadian Institute of Health Research 
 
In-Kind Support: 
Myant 
mmHg 
 
Protocol/version #: 2.0 
 
Current Version Date:  02 Apr 2024 
 
Previous REB Approved Versions Dates:  
Version 1: 20 Nov 2023 
 
 
 

 
 
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1 Introduction and Background 
Remote physiologic monitoring (RPM) refers to utilizing non-invasive medical devices to obtain 
physiologic data, such as pulse oximetry, blood pressure, weight, and electrocardiography at 
home. Recent advances in RPM have facilitated improved home-based care, reduced 
hospitalizations, and improved quality of life in adult heart failure populations1–3. Additionally, the 
integration of multiple physiologic variables acquired through remote physiologic monitoring into 
machine learning algorithms has been shown to predict hospitalization for heart failure4. Previous 
paediatric studies have demonstrated the utility of incorporating multiple physiologic variables into 
risk prediction algorithms5 for cardiac events. This is particularly important in heart failure cohorts 
where limits of acceptability are often nuanced and patient specific. The use of multiple 
physiologic parameters creates a more comprehensive insight into the complex pathophysiologic 
changes in heart failure. 
However, the use of RPM devices in children remains limited due to a lack of validated devices 
and uncertainty about the acceptability and uptake of such interventions. This is partly due to 
challenges developing devices that can be easily applied, and digital platforms suited to the wider 
range and variability in body sizes and physiologic parameters. Thus, care of children with heart 
failure continues to rely on hospital-based models, where tertiary heart failure centers serve large 
geographically and socio-economically diverse populations. A validated paediatric virtual home 
monitoring system using RPM to predict clinical deterioration could safely facilitate earlier 
discharge, reduce the need for outpatient hospital visits, and potentially improve outcomes while 
minimizing family social disruption and school absence. 
The overarching goal of this project is to assess the feasibility of RPM in paediatrics and validate 
a RPM based risk prediction model for paediatric patients with or at-risk of heart failure, with a 
view to facilitating safe home-based care across geographically and socially diverse urban, rural, 
and remote communities. To achieve this goal, this study proposes to utilize a wearable Bluetooth 
enabled textile (Skiin Device) that can monitor heart rate and rhythm, respiratory rate and activity, 
together with additional home-based monitoring of blood pressure, oxygen saturations and 
weight. The textile, developed by Myant (Toronto, ON) has completed pilot6 testing at SickKids to 
assess its validity in an outpatient setting (NCT04305340). The Skiin textile will be paired to its 
software solution, the Myant Health Platform (MHP), which comprises the Skiin Connected Life 
App (phone application), the Myant Back End (cloud storage of data) and the Myant Virtual Clinical 
Portal (internet browser visualization of data collected). The Skiin Connected Life App will be used 
for collection of ECG, heart rate, body temperature, and physical activity throughout the day, and 
can generate the following average metric for each night when the device is used: resting heart 
rate, respiratory rate, resting heart rate variability, sleep duration, body temperature.  
The MHP will be paired with another RPM platform, SphygmoTM (mmHG Inc). The Sphygmo™ 
platform consists of a smartphone App (Android, iOS), which can be linked with Bluetooth-
compatible devices for automated uploading of measurements to a clinician portal. This platform, 
originally developed for adults, has the ability to connect with blood pressure, heart rate, weight, 
and oxygen saturation devices. This platform will be used for collection of additional physiologic 
data as above and is currently under study at Stollery Children’s Hospital. The two systems will 
have a single-sign on feature allowing their integrated use by the patient, their families, and the 
research team. We will leverage descriptive and predictive analytics to augment clinician 
monitoring by defining trajectories and longitudinally predicting risk of key adverse outcomes. 
 

 
 
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2 Rationale 
Advances in technology now offer opportunities for RPM of heart failure patients with a virtual 
platform. However, to date this technology remains untested in pediatrics and care continues to 
rely on a hospital-based model. This model presents challenges in providing equitable access to 
care particularly to those with lower socio-economic status or living in remote communities. For 
those with increased risk of life-threatening complications, frequent hospital visits are needed for 
monitoring, and are associated with a financial burden and negative impact on quality of life of 
both patient and family. Tools that predict clinical deterioration with heart failure using a virtual 
home monitoring system could facilitate safe care in the home irrespective of geographical 
location, fewer in-person outpatient visits, better communication with care providers and 
potentially improved outcomes. As a result, telemonitoring technology specifically tailored for 
pediatric populations with machine-based algorithms to predict deterioration is needed to take the 
vital next step towards the equitable provision of safe, home-based care. 
 
3 Study Objectives 
3.1 Primary Objectives 
1. 
To investigate the feasibility of a remote physiological monitor using a textile smart garment 
(Skiin devices) using the Skiin Connected Life App along with additional standard home 
monitoring tools (BP monitor, weigh scales) that are paired with a Bluetooth enabled app 
(SphygmoTM)  
 
2. 
To test the acceptability of a remote physiological monitor using a textile smart garment (Skiin 
devices) along with the acceptability of a Bluetooth enabled app (SphygmoTM) 
 
3.2 Secondary Objectives 
1. To leverage analytical methods to develop descriptive and predictive tools using RPM that 
augment detection of clinical deterioration in pediatric patients as measured by admission, 
adverse cardiac events and patient reported outcomes within 6-months post intervention.  
 
4 Study Duration 
Patients will be recruited over a 2-year period. 
 
5 Study Design 
We will conduct a prospective, longitudinal multi-centre observational study of feasibility of a 
virtual RPM system. We will then define population vital sign behavior in pediatric outpatients with 
heart failure syndromes or at-risk of heart failure. The use of normative vital sign ranges derived 
from healthy children in abnormal physiological states has limited utility when the relevant 
question is how unusual an individual patient’s behavior is within a population of patients who all 
have abnormal vital signs. Longitudinal data collected in this study will generate a foundational 
dataset that will be stratified by age, sex and diagnosis to determine ‘normative’ patient behavior 
in the context of a compensated heart failure syndrome in pediatric outpatients. All of the data 
points sampled will be used to generate distributions of vital sign behavior both at the level of the 
individual patient and the population (cohort) as a whole utilizing a validated methodology we 
have previously applied to analyze similar inpatient vital signs7.  

 
 
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6 Study Procedures 
 
6.1 Study Population 
This study will enroll 100 paediatric outpatients (8 to 18 years old) with diagnosis of 
cardiomyopathy (with systolic or diastolic dysfunction), subjects with congenital heart disease with 
biventricular or univentricular physiology plus systemic ventricular dysfunction from 4 tertiary heart 
failure care centres.  
6.2 Eligibility Criteria 
1. 
Patients from 8-18 years of age who are outpatients at time of study enrollment. 
2. 
Patients with a chest size of at least 69.85 cm in perimeter/circumference (as measured under 
the pectoral muscles) 
3. 
Patients at-risk for heart failure and with American Heart Association (AHA) Stage B-D Heart 
Failure will be included in this study irrespective of heart failure medication use. 
4. 
HF etiologies include: congenital cardiac malformation with systemic ventricular systolic 
dysfunction, idiopathic cardiomyopathy, familial/inherited and/or genetic cardiomyopathy, 
history of myocarditis with persistent ventricular dysfunction, neuromuscular disorder, inborn 
error of metabolism, mitochondrial disorder, acquired (chemotherapy, iatrogenic, infection, 
rheumatic, or nutritional), ischemic (e.g., Kawasaki disease and post-operative HF), and left 
ventricular non-compaction, restrictive cardiomyopathy and HCM with systolic or diastolic 
dysfunction. 
6.3 Exclusion Criteria 
1. Patients within 3 months of a surgery. 
2. Patients supported by ventricular assist device at study onset. 
3. Inability to use technology due to physical or cognitive impairment in the patient or caregiver.  
4. Non-English speaking.  
5. Patients who have an implantable cardiac defibrillator or pacemaker 
6. Patients whose chest size is too large or small to fit available sizes of the Skiin device. 
Eligible patients will be identified by a study team member. Patients may be approached for 
consent in cardiology outpatient clinics or at time of discharge from inpatient stay. Written consent 
will be obtained from the participant or substitute decision maker prior to any study activities being 
performed on the patient. 
6.4 Withdrawal Criteria 
A participant can choose to withdraw from the study at any time by informing the study team. Data 
can be withdrawn from the study up until the point of statistical data analysis. 
 
7 Assessments 
7.1. Medical History 
Medical records will be accessed to collect baseline characteristics including date of birth, sex, 
gender, postal code, diagnoses, surgeries, current heart failure medications, and procedures. The 

 
 
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number of pediatric cardiology clinic visits, hospitalisations, ED visits, and cardiac investigations 
in the last 2 years will be collected to describe the patients’ requirements for access to cardiac 
care. To satisfy our secondary objective, we will collect data on patient outcomes 6-months post 
intervention. This data will include: admissions, adverse cardiac events and patient reported 
outcomes In addition, the family will be asked to complete a demographic form that compiles 
information about parents’ ethnicity, education level, socio-economic status, access to Wi-Fi and 
iOS/Android devices in the home. 
7.2 Anthropometric Measurements 
These will consist of body weight, height/length, and body surface area obtained as per standard 
hospital policy.  
Additional measurements may be obtained of the chest perimeter at the sub-pectoral level, to 
select the appropriate size of the Skiin chest band (Bluetooth enabled smart textiles device). 
7.3 Comparator Data 
Routine blood pressure, heart rate, average respiratory rate, body temperature, ECG at enrolment 
(if available) or within the preceding 6 months prior to enrolment, and oxygen saturation (if 
available) will be obtained at clinic visit prior to commencing study per the standard of care for 
comparison with measures obtained with the Skiin device.  
 
7.4 Textile Style Selection and Placement 
The Skiin comprises a textile chest band that will be appropriately sized for patient size. A Skiin 
biometric pod will be charged ahead of the visit and positioned in the garment. The family will be 
provided with equipment to reload the Skiin pod, and launder the smart textile, and be shown how 
to do so. 
7.5 Skiin Connected Life App (SCLA) 
Following consent families will be asked to download the Skiin Connected Life App (SCLA) onto 
their iOS (13+) or Android (9+) device using the Wi-Fi in the clinic. For patients that do not have 
access to one of these devices, a Samsung A Tablet will be provided for study purposes. The 
families will be shown how to register and pair the Skiin pod to the SCLA and a test run will be 
performed prior to leaving the clinic (e.g., report a mock symptom and check electrocardiogram 
signal).  
The SCLA collects real time heart rate, body temperature, and ECG. It also collects resting heart 
rate, resting heart rate variability, resting respiratory rate and sleep duration over-night. Device 
real time results will be compared to vitals taken at the same time in the clinic setting. 
Discrepancies in measurements, including any reasons for the discrepancies will be documented. 
In addition, written instructions with pictures will be provided for the families to take home.  
7.6 SphygmoTM App 
Following consent families will be asked to download the SphygmoTM App onto their iOS or 
Android device using the Wi-Fi in clinic. For patients that do not have access to one of these 
devices, a Samsung A Tablet \will be provided for study purposes. The families will be shown how 
to pair the Bluetooth devices to the SphygmoTM App and a test run for each of the devices will 
be performed prior to leaving the clinic. Device results will be compared to vitals taken at the same 
time in the clinic setting. Discrepancies in measurements, including any reasons for the 
discrepancies will be documented. In addition, written instructions with pictures will be provided 
for the families to take home. 

 
 
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7.7 Patient-reported Outcomes 
To assess quality of life, participants will complete the Pediatric Quality of Life (PedsQL) Quality 
of Life Module 4.08 as baseline, end of intervention (12 weeks) and post-intervention (6 months 
post intervention). This is a self-reporting instrument that consists of 23 items in four domains: 
physical functioning (8 items), emotional functioning (5 items), social functioning (5 items), and 
school functioning (5 items). It has been designed for multiple age groups, including a parent 
Proxy report if required. For participants 8-12 years of age, parents will complete the Parent Proxy 
report as well.  
Participants will also complete at baseline, end of intervention (12 weeks) and post-intervention 
(6 months post intervention) the Patient Global Impression of Severity (PGIS). This instrument 
will be used to capture the patients’ current health status relative to their heart failure over a 7-
days recall period. This is a global index that is used to rate the severity of a specific condition. It 
is a simple, direct, and easy to use scale that is understandable to clinicians. The PGIS uses a 5-
point patient evaluation scale (none, mild, moderate, severe, very severe) for patient self-report 
in those aged ≥7 years. 
At the end of the study, participants will complete the MedTech 209,10 which is a standardized tool 
measuring how medical devices affect people’s general well-being and assess users’ experiences 
of benefits of any medical technology product used by patients with disability/disease. The 
Questionnaire consists of 20 statements of attributes of patient benefit of medical devices. The 
statements are grouped into 4 domains as follows: sense of security, social participation, integrity, 
and convenience, with each domain including five attributes. When answering the questionnaire, 
participants are asked to select to what extent they agree with statement, for the product in 
question, on a 7-point scale; from (disagree) to (completely agree). In addition, participants can 
choose a not relevant response if the attribute is of no relevance for the product in question. 
Hence, there are 8 answer options. Based on the responses, an index (0-1) describing the 
product’s benefit can be calculated, with an index closer to 1 representing a larger general patient-
reported benefit from the product of concern 
New York Heart Association Failure class will be completed at baseline, end of intervention (12 
weeks) and post-intervention (6 months post intervention) to assess extent of limitation in 
physical activity due to heart failure symptoms11. 
7.8 Data Collection 
7.8.1 Data collection with the Skiin devices 
Data will be collected during a 12-week period. Study participants will be asked to wear the Skiin 
chest band daily and to keep in proximity their Bluetooth enabled device (smartphone or tablet).  
Participants will wear the Skiin chest band continuously for the first 48 hours of study participation. 
Following that and for the remaining duration while in the study participants will be asked to only 
wear the device for a 12-hour period between 8pm and 8am keeping their bluetooth enabled 
devices (smartphone or tablet) in close proximity (e.g., in pocket or backpack during the day, in 
the bedroom during the night) to capture a maximum of real-world situations. This will provide 
continuous data and for the research team to understand the feasibility and value of the 
continuous remote monitoring of this population through the Skiin garments.  

 
 
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The biometric pod within the Skiin chest band is designed to transfer data continuously to a paired 
Bluetooth enabled device with SCLA. When not connected (e.g., when too far from the 
smartphone or tablet), the Skiin pod can store ECG data for up-to 45-minutes. 
7.8.2 Data collection with the Sphygmo devices 
Additional physiologic parameters will be collected daily through devices compatible with the 
SphygmoTM App (BP, oxygen saturation, weight). These devices will include: 
- 
Blood Pressure: A&D Life Sciences BP Monitor: UA-615BLE – License #94858 
- 
Oxygen Saturation: Nonin SpO2: Model 3230 – License #68959 or Contec SpO2: Model 
CMS50D-BT - License #90629. 
- 
Weigh Scale: A&D Medical/LifeSource UC-352BLE – This is not represented or used as 
a medical device 
Transmission of data to the clinical portal designed for the SphygmoTM App and SCLA will be 
monitored by the study coordinator and a secure message will be sent at the six-week mark to 
remind families that they are at the mid-way point of the study and how to contact the study 
coordinator if they are having any difficulties. Study personnel will be available throughout the 
duration of the study for troubleshooting. Participants will be asked to transmit data daily. Pre-
paid packages will be provided to courier back the equipment at the end of the 12-week period 
including tablets.  
7.9 Data Handling and Storage 
All paper case report forms will be kept in a locked filing cabinet in a locked office, only accessible 
by the study team. De-identified data will be entered and stored on a REDCap database hosted 
on a secure server through the Women and Children’s Health Research Institute at the University 
of Alberta. Only study team members and authorized personnel will have access to this database.  
Data generated by the Skiin device will be housed on the Myant Backend, which is part of the 
Myant Health Platform (MHP, Class 2 software medical device) platform and data from other 
clinical devices (weights, saturations, BP) will be housed on the physician portal of the 
Sphygmo™ platforms and can be uploaded to redcap or downloaded to a CSV file. The 
Collaborator, Mazwi, has developed a novel time series database that will facilitate archival and 
retrieval of higher frequency patient physiological recordings12. 
Study data will be stored for up to 10 years in accordance with Health Canada policies for device 
studies. 
7.10 Confidentiality 
The study team will take all necessary precautions to ensure the confidentiality of participants. 
Participants in this study will be assigned a study specific ID that will be linked to their personal 
identifying information, maintained on a password protected and encrypted master list, stored on 
a secure AHS server. Identifying data is required to verify participant information, accessed only 
by members of the research team. De-identified data will be extracted from the REDCap database 
for analysis. 
Specific study team members will have access to the Participants’ identifying information and 
Skiin data (ECG, heart rate, steps, posture, overnight average metrics) through the Myant Virtual 
Clinic Portal (MVCP, part of the MHP). The MHP is hosted on a secure server that implements 

 
 
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HIPAA compliant data security procedures throughout the clinical workflow process from data 
collection to server storage and viewing.  
Myant staff will not have access to the MVCP nor to any identified health information. Myant staff 
may have access to de-identified dataset without any Personal Identifying information if the study 
team shares with them the Skiin pod number and time of usage. The study team may share with 
Myant non-identifying health information such as sex, BMI, medical history, and age which may 
be required for algorithm development and validation. Access to these research datasets will be 
limited to Clinical and Data Sciences team members involved in this study (i.e., on a need-to-
access basis). 
The Sphygmo platform implements HIPAA compliant data security procedures throughout the 
clinical workflow process from data collection to server storage and viewing. After the patient 
transmits their blood pressure and vitals readings into the app, the data is stored locally on the 
smartphone using 256-bit advanced encryption standard (AES) protocol. Next, the data is 
transmitted to the server using hypertext transfer protocol secure (HTTPS) transport layer security 
(TLS) as the encryption protocol where it is stored securely on the server using 256-bit AES 
protocol and is visualized by clinical users via HTTPS.  
All data that is collected is region specific, and in Canada is stored on Server Cloud Canada 
servers, which acts as the cloud server storage center for the Sphygmo remote patient monitoring 
platform. Data does not leave Canada. Server Cloud Canada is SOC 2, and 3 compliant.  
7.11 Device Storage and Use 
Devices provided by Myant will be stored in a secure location to which only team members have 
access. Myant textiles are designed to be used multiple times by/for the same individual and 
sustain between 30 and 50 washes at their commercial stage. For the purpose of the present 
study, each device will be used by a single patient.  All devices will be returned to Myant at the 
end of the study. 
8 Analysis Plan 
8.1 Study Endpoints 
Primary Outcomes: Feasibility will be assessed by recruitment rates (the number of participants 
recruited; retention rate (the number of participants who complete measures at baseline and at 
follow up post intervention), percentage of people with access to Wi-Fi and smart devices,  
Acceptability will be assessed by compliance defined as >80% of the physiologic monitoring 
requested has been captured twice daily for a period of 12 weeks; Medtech 20 will also inform on 
acceptability. 
Secondary Outcomes: For the secondary outcome we will use a composite end-point. Given the 
low prevalence and rarity of events such as death or transplant from chronic heart failure in 
paediatrics, there is limited potential for large outcome trials. As a result, we have elected to use 
a composite end-point designed for the PANORAMA-HF study and based on Packer’s composite 
score which has been extensively used in adult heart failure studies13–15. 
Patients will be ranked within 5 categories from worst to best based on heart failure events such 
as death, transplant listing, need for mechanical circulatory supports, as well as progression of 
heart failure symptoms by NYHA or Ross Class and patient reported outcomes as assessed by 
the PedsQL and PGIS.  

 
 
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Patients who experience a clinical event during follow up will be classified into category 1 and 2 
as described in Table 1. Patients without a clinical event during follow up will be divided into 3, 4 
and 5 based on change from baseline symptoms and functional capacity (Table 1).    
 
 
 
Table 1 
Category 
Sub- 
Category 
Description 
 
1 
A 
Death, listing for transplant, mechanical 
circulatory support, mechanical ventilation 
required for life support at study end-point 
 
2 
B 
Worsening heart failure with ICU admission 
 
 
C 
Worsening heart failure with hospital 
admission (no ICU) 
 
 
D 
Worsening heart failure with no hospital 
admission 
 
3 
E 
Worsened NYHA/Ross or P-GIS on last 
available assessment compared to baseline 
If no change in 
NYHA/ROSS or 
PGIS, further rank 
by PedsQL 
4 
F 
Unchanged NYHA/Ross or P-GIS on last 
available assessment compared to baseline 
If no change in 
NYHA/ROSS or 
PGIS, further rank 
by PedsQL 
5 
G 
Improved NYHA/Ross or P-GIS improved 
(neither can be worse) 
based on last available assessment 
compared to baseline 
If no change in 
NYHA/ROSS or 
PGIS, further rank 
by PedsQL 
ICU, intensive care unit; NYHA, New York Heart Association; PedsQL, Pediatric Quality of Life 
Inventory; PGIS, Patient Global Impression of Severity 
8.2 Statistical Analysis 
Baseline and demographic characteristics will be analyzed using descriptive statistics. Adherence 
with the protocol will be determined by the mean number of requested assessments performed 
and the proportion of patients who completed >80% of measurements.  
Physiological data will be analyzed as discussed in the study plan. Descriptive statistics including 
mean, variance, median and quartiles will then be used to characterize the population distribution 
in a manner that facilitates detection of outlier behaviour in individual patients. Correlation of 

 
 
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outlier behavior with outcomes of interest allows identification of ‘undesirable’ trajectories 
associated with adverse events and ‘desirable’ trajectories that can become targets for goal 
directed management.  This approach permits mapping vital sign behaviour to categories of 
specific risk of adverse events as well as other actionable clinical outcomes, such as the need to 
titrate 
medical 
therapy.  
 
We intend to augment this risk monitoring approach by incorporating change point detection for 
both individual vital signs and patient trajectory to facilitate earlier detection of deterioration and 
individualize risk stratification further. Finally, as the cohort grows in size we will probabilistically 
forecast the vital sign time series by leveraging neural networks (RNN) to learn the temporal 
dependence of the data and Long Short-Term Memory (LSTM) to deal with missing or irregularly 
sampled data. For patients with multiple vital sign observations for each timepoint multivariate 
Multi-Layer Perceptron (MLP) models will be employed to forecast a vector (magnitude and 
direction) of patient behavior. study duration, study methods, analysis plan, and timelines) 
8.3 Enrolment Justification for Aim 2: 
Based on the preliminary unpublished data from the Panorama study as described above: 
Category 1: 13.6%; Category 2: 9.07%; Category 3-5: 77.34%  
We set statistical power at 80% and p-value <0.05, and a prevalence of ~20% for Category 1 and 
2, a sample size of 100 would be able to detect a change in sensitivity between 80-90% and a 
specificity of 70-90%. As this is a screening test for worsening HF, we will prioritize sensitivity 
over specificity16. 
 
9 Study Devices 
9.1 Skiin Textile Device 
The Skiin chest band and biometric pod (electronics for recording and sending signals) are 
approved as a Class II medical device by Health Canada (License Number 106352).  
Skiin devices collect real time ECG, heart rate, and body temperature and overnight average 
resting heart rate, resting heart rate variability, and sleep duration. The Skiin textile will be paired 
to its software solution, the Myant Health Platform (MHP), which comprises the Skiin Connected 
Life App (phone application), the Myant Back End (cloud storage of data) and the Myant Virtual 
Clinical Portal (internet browser visualization of data collected). 
 
 
 
 
 
 
 

 
 
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Figure 1: Skiin textile chest band, Pod and USB charging kit. 
 

 
 
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Figure 2: Skiin Connected Life app 
 
 

 
 
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9.2 SpyhgmoTM 
This RPM system was developed by mmHg Inc, a local physician led, digital health company. The 
Sphygmo™ platform consists of a smartphone App (Android, iOS), which can be linked with 
Bluetooth-compatible devices for automated uploading of measurements to a clinician portal. This 
platform, originally developed for adults, has the ability to monitor blood pressure, heart rate, 
weight, and oxygen saturations. Sphygmo™ has multiple features that lends its applicability to a 
broad patient population including those with limited resources. 
 
10 Infection Control Measures 
Sensors which are disposable will be removed from the participants immediately after testing and 
disposed of as per institutional approach to disposal of personal healthcare products (e.g., gel 
electrodes).  
The textile equipment made by Myant (providing the innovative measurement being evaluated 
against reference device) will be single-use and returned to Myant in a sealed bag to be returned 
to Myant. The electronic pod placed in the textile, which does not come in direct contact with the 
participant, can easily be sanitized using a standard disinfecting wipe. 
 
11 Assessment of Safety and Adverse Events 
11.1 Risk Assessment 
There is no known risk of any harm directly from the measurement or the outlined protocol.  
Subjects or their guardians will reserve the right to withdraw from the study at any time should 
they feel uncomfortable or believe that the protocol is unmanageable in their physical state – 
which will also be noted by researchers when determining subject participation. Slight skin 
irritation may be experienced at sites of electrode placement from skin preparation or adhesives 
used to attach.  Participants and/or their guardians will be asked about any possible allergies to 
adhesives to prevent reactions from occurring.   
Note that the use of adhesive is related to the routine hemodynamic and physiologic assessment 
of the vital signs, while the innovative devices developed by Myant will be simply worn, strapped 
on, or laid on without using adhesives.  
11.2 Safety Monitoring 
Clinical treating team will be instructed to contact the study team if they feel the device is 
disrupting clinical care or causing irritation to the patient. Each reported event will be reviewed by 
Study Investigator within 24 hours. Notes contained in the electronic medical record will also be 
reviewed for the study period to assess important developmental and clinical activities. 
11.2 Documentation and Reporting of Adverse Events and Reactions 
The study participants will be monitored for adverse events by the study team and by the treating 
clinical team which may be unaffiliated with the study while the study device is in contact with the 
patient. When the Skiin device has been removed from the participant, adverse event monitoring 
for study purposes will cease. Adverse events that may be attributable to the Skiin Device will be 
monitored until resolved. 
Only adverse events that, in the opinion of the treating clinical team, may be attributable to the 
Skiin Device as documented in clinical recorded in the study participant adverse events log. These 

 
 
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events will then be reassessed by a physician on the study team for relatedness to the Skiin 
Device, severity, and seriousness.   
 
 

 
 
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12  References 
1.  
Boubacar HA, Rahim M, Al-Hamoud G, et al. HeartPredict algorithm: Machine intelligence 
for the early detection of heart failure. Intell Med. 2021;5:100044.  
2.  
D’Amario D, Canonico F, Rodolico D, et al. Telemedicine, artificial intelligence and 
humanisation of clinical pathways in heart failure management: Back to the future and 
beyond. Card Fail Rev. 2020;3:e16. 
3.  
Croon PM, Selder JL, Allaart CP, et al. Current state of artificial intelligence-based 
algorithms for hospital admission prediction in patients with heart failure: A scoping review. 
Eur Heart J. 2022;3:415-425. 
4.  
Stehlik J, Schmalfuss C, Bozkurt B, et al. Continuous wearable monitoring analytics predict 
heart failure hospitalization: The LINK-HF Multicenter Study. Circ Hear Fail. 
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