Observational Study for the Improvement of a Digital Health Platform for Remote Monitoring of Patients With Heart Failure
Conditions: Heart Failure
Sponsor: humanITcare

Study Protocol 
 
NCT05708846 
13th November 2023 

Clinical Study Protocol
Protocol Title:
“Observational study for the improvement of a digital
health platform for remote monitoring of patients with
heart failure.”
Study type:
Observational study with software as a medical device.
Protocol Version Date:
5th version, 13th November 2023
Principal Investigator:
Julio César Blázquez, MD
Hospital de Torrevieja, Chief of Internal Medicine Unit
blazquez_jul@gva.es
Sponsor:
FollowHealth, S.L.
C/Aristides Maillol, 17
08028, Barcelona
Investigation product:
●
HumanITcare digital platform software
●
In-platform alarm-based system
Class of device:
CE marked - Class I (MDD) at the beginning of the study
Indication:
Heart Failure
Confidentiality:
This is a confidential document. It contains proprietary
information
of
FollowHealth,
S.L..
Any
viewing
or
disclosure of such information that is not authorized in
writing
by
the
Sponsor
is
strictly
prohibited.
Such
information may be used solely for the purpose of
reviewing or performing this study.
1

SIGNATURE PAGE(S)
Protocol number: FOLLOWHEALTH-2023-01
Protocol title: “Observational study for the improvement of a digital health platform for
remote monitoring of patients with heart failure.”
Principal Investigator of the study:
I have read and understood this trial protocol and agree to conduct the trial as set out in this
study protocol, the current version of the World Medical Association Declaration of Helsinki,
ICH-GCP guidelines or ISO 14155 norm and the local legally applicable requirements.
2

SYNOPSIS
Sponsor:
FollowHealth, S.L.
C/Aristides Maillol, 17
08028 Barcelona
Study Title:
“Observational study for the improvement of a digital health
platform for remote monitoring of patients with heart failure.”
Protocol Version:
5th version, 13th November 2023
Study Design:
Multicenter, Observational study
Indication:
Heart Failure
Background and
Rationale:
This study is being conducted in the framework of a European
project promoted by the European Innovation Council. An earlier
version of the platform was validated in a study conducted in 2020
at Hospital de Torrevieja focused on Heart Failure. The rationale for
this study is in line with HumanITcare’s goal of incorporating
artificial intelligence tools to optimize the digital platform.
Study Objectives:
Primary endpoint
A. Create a labeled and diverse dataset with the following data:
-
Physiological parameters: heart rate, calories consumed,
steps count, physical activity, oxygen saturation, heart rate
variability measurements, stress levels, sleep tracking data,
systolic and diastolic blood pressure, weight, BMI
-
Socio-demographic
data:
sex,
age,
ethnic
group,
employment, level of education, number of people living
with the patient, caretaker
-
Risk
factors:
comorbidities,
diabetes,
hypertension,
smoking, alcohol consumption, heart disease history
-
Medication
tracking:
lipid-lowering
medication,
PCSK9
inhibitors,
beta-blockers,
anticoagulation
agents,
ACE
inhibitors, anti-arrhythmic drugs, anti-inflammatory drugs,
aldosterone
antagonists,
angiotensin
receptor/neprilysin
inhibitor, diuretics, antidiabetic agents, Vericiguat, SGLT2
inhibitors, Ivabradina
-
Symptomatology questionnaire for patients
-
NYHA-class
-
Left Ventricular Ejection Fraction (LVEF)
-
Clinical
interventions:
hospital
readmission,
mortality,
emergency department visits, non-fatal HF events
-
Health questionnaire answers: self-care, quality of life
-
Classified
alarms
with their respective timestamp and
annotation by the MD
-
Measurement ranges for each personalized alarm and their
changes
B. Implementation of machine learning models to improve the
current alarm-based system to provide a relevance level for each
new alarm by reducing the number of irrelevant alarms and thus
fostering personalized follow-up.
3

Secondary endpoints
Development
and
implementation
of
ML
event
prediction
algorithms that will add new self-generated alarms to the system.
These alarms should forecast:
●
Untracked
hospital
interventions,
such
as
emergency
department visits or hospital readmissions.
●
Changes of medication with their particular estimated dose.
●
Clinical events, such as mortality.
Assessment of patient and medical professional’s satisfaction with
and usability of the digital platform at the end of the study by
means of the “System Usability Scale” (SUS) and the “Post-Study
Usability Questionnaire” (PSSUQ).
Study Population:
Approximately, 500 patients
Eligibility criteria:
Inclusion criteria
●
Heart failure (HF) patients with NYHA Functional Class >= II
(according to 2021 EU guidelines).
●
Patients who have suffered an acute decompensation of HF
(first and recurrent) in the 30 days prior to enrollment in the
study.
An acute decompensation of HF is defined as a new
episode of symptoms and signs that requires intravenous
decongestive treatment or an increase in oral diuretics,
either on an outpatient basis (e.g., hospitalization for HF
during the day or at home) or in the emergency service or
that requires an unplanned hospital admission.
●
NT-pro BNP ≥300 pg/mL or BNP ≥100 pg/mL at the time of
inclusion in the study for patients without ongoing atrial
fibrillation/flutter. If there is ongoing atrial fibrillation/flutter,
NT-pro BNP should be ≥600 pg/mL
●
Patients must have had an echocardiogram during their HF
hospitalization or the previous 12 months.
●
Prior to the initiation of any procedures, the hospital will
ensure that an informed consent document completed by
the patient is obtained.
●
All patients will be eligible regardless of the level of LVEF:
HFrEF, HFmrEF, and HFpEF.
Exclusion criteria
●
Oncology patients with metastasis or with chemotherapy
treatment ongoing
●
Patients participating in other studies or trials.
●
Patients not willing to participate.
●
Patients over 150 kg
4

●
Patients
who
do
not
use
Catalan,
Spanish,
English,
Portuguese, Italian, Dutch, German, Swedish, Hungarian,
Romanian, or French.
●
Patients without a mobile phone
●
Patients without internet connection
●
Patients with moderate or severe cognitive impairment
without a competent caregiver.
●
Patients with severe psychiatric illness.
●
Patients with planned cardiac surgery.
●
Patients with planned heart transplant or LVAD implant.
Recruitment
strategy:
When a patient is eligible according to the criteria, he/she must
complete the informed consent provided on day 0. Each hospital
will decide when to include their patients according to their
particular clinical practice (either in the process of discharge
planning or during the first follow-up visit, i.e.. 1 or 2 weeks after
discharge). Patients will be monitored for 6 months, with a
recruiting period of 6 months. The last subject included will then
finish the study after one year from the first day of the study.
Data Analysis:
There is no power calculation associated with the study since it is
an observational study. Once the algorithms are developed, model
performance in terms of accuracy will be evaluated by means of C
statistic, area under the receiver operating characteristic curve, and
creation of a calibration plot. Furthermore, the models will be
evaluated in terms of fairness and potential bias using metrics
including statistical parity, group fairness, equalized odds, and
predictive equality.
Study Schedule:
February - March 2023: CEIm approval
April 2023: study kick-off
October 2023: recruitment period completed.
April 2024: last patient, last visit
May 2024: results analysis and final report draft
Clinical
Procedures:
Phase 1: Hospitals selection and coordination. CEIm approvals.
Phase 2: Study execution:
Day 0. Patient enrollment: study explanation, informed consent
completion, patient data completion, provision of medical devices
and access credentials, and first questionnaires.
Day 0 until the end of the study:
-
Physiological constants monitored.
-
Patient answers to questionnaires.
-
Control calls in case of missing data
-
Report every hospital readmission, mortality, emergency
department visit or non-fatal HF event of each patient.
-
Report changes of medication and their doses by the
medical professional.
-
Patient in-person follow-up: at day 0, at day 7, at month 3,
at month 6.
Last day. Follow-up visit. Completion of last questionnaires. Return
5

the medical devices.
Questionnaires:
●
Minnesota Living with Heart Failure Questionnaire (MLHF)
(Bilbao et al., 2016)
●
European
Heart
Failure
Self-care
Behaviour
Scale
(EHFScB scale) (Jaarsma et al., 2009)
●
Symptom report questionnaire: seven questions to capture
the worsening of the symptoms of heart disease, mainly
worsening of the HF, and one question to capture general
deterioration.
●
“System Usability Scale” (SUS) (Brooke, J. (1986) SUS—A
Quick and Dirty Usability Scale. Usability Evaluation in
Industry,
189-194.
-
References
- Scientific Research
Publishing,
n.d.)
and
the
“Post-Study
Usability
Questionnaire” (PSSUQ) (Lewis, 1992) will be delivered at
the end of the study.
●
Mobile Device Proficiency Questionnaire (Roque & Boot,
2018)
6

Table of contents
1 Identifier
8
2 Administrative section
8
2.1 Sponsor
8
2.2 Principal Investigator(s)
8
2.3 Ethics Committee
8
2.4 Clinical centers
8
3 Regulatory status and activities
8
3.1 Regulatory / ethics status
8
3.2 Informed consent
8
3.3 Data confidentiality
9
3.3 Study registration
11
3.4 Scientific advice / protocol assistance
11
3.5 Early termination of the study
11
3.6 Protocol amendments
11
4 Background and rationale
12
4.1 Background and rationale
12
4.2 Evidence to Date
12
5 Study design and endpoints
13
5.1 Study design
13
5.2 Primary and secondary endpoints
13
6 Subjects/population(s)
14
6.1 Eligibility criteria
14
6.2 Recruitment and screening
15
6.3 Criteria for withdrawal/discontinuation of participants
15
7 Statistical analysis plan and power calculation
16
8 Conduct
17
8.1 Summary of the study schedule
17
8.2 Study management, study monitoring, data and sample management
17
8.2.1 Study management and clinical procedures.
17
8.2.2 Adverse event reporting
19
8.2.3 Data collection
19
7

1 Identifier
“Observational study for the improvement of a digital health platform for remote monitoring of
patients with heart failure.”
2 Administrative section
2.1 Sponsor
FollowHealth, S.L.
C/Aristides Maillol, 17, 08028 Barcelona
2.2 Principal Investigator(s)
Julio César Blázquez, MD
Hospital de Torrevieja, Chief of Internal Medicine Unit
Mail: blazquez_jul@gva.es
2.3 Ethics Committee
CEIm Hospital General Universitario de Alicante, Spain
2.4 Clinical centers
The study will comprise a total of 500 patients, approximately.
3 Regulatory status and activities
3.1 Regulatory / ethics status
The study will be performed in compliance with the Declaration of Helsinki (version in force;
currently Fortaleza, Brazil, October 2013), and is to be conducted in accordance with Good
Clinical Practice (CPMP/ICH/135/95).
The study will be conducted in accordance with the protocol and with the relevant legal
requirements, following Law 14/2007 of July 3, 2007, on Biomedical Research.
The device class is CE marked - Class I (MDD) at the beginning of the study.
3.2 Informed consent
Participants will be informed of the anonymity and confidentiality of their data. They will be
informed of the study, its objectives and methods, orally and by means of an information
leaflet. Those patients who wish to participate in the study will receive an informed consent
form that they will have to sign in order to continue in the study. The investigators will
preserve patient anonymity, using the data collected only for the purposes indicated in this
document and also, keeping anonymized data to be able to develop artificial intelligence.
Sociodemographic and clinical data and the scales administered will be stored in coded
form, and only the investigators will have access to them.
Informed consent will be requested and collected by a research assistant prior to the
subject's inclusion in the study.
8

3.3 Data confidentiality
The processing, communication, and transfer of personal data of all participants will comply
with the EU Regulation 2016/679 of the European Parliament and of the Council of 27 April
2016 on the protection of natural persons with regard to the processing of personal data and
the free movement of data, being mandatory as of May 25, 2018. The legal basis justifying
the processing of your data is the consent given at the beginning of the study, in accordance
with the provisions of Article 9 of EU Regulation 2016/679.
The data collected for these studies will be collected only by a code identification, so no
information will be included to identify the participants. Only the study physician and
collaborators with the right of access to the source data (medical record), will be able to
relate the data collected in the study to the patient's medical record. The identity of the
participants will not be available to any other person except in the case of a medical
emergency or legal requirement.
Access to the identified personal information may be granted to the health authorities, the
Research Ethics Committee and personnel authorized by the study sponsor, when
necessary to verify data and study procedures, but always maintaining confidentiality in
accordance with current legislation.
Only coded data will be transferred to third parties and other countries, which in no case will
contain information that can directly identify the participant (such as name and surname,
initials, address, social security number, etc.). In the event of such a transfer, it would be for
the same purpose of the study described above and guaranteeing confidentiality, and after
participant’s consent.
If a transfer of coded data were to take place outside the EU, either to entities related to the
hospital center where the patient participates, to service providers or to researchers
collaborating with us, the data of the participants will be protected by safeguards such as
contracts or other mechanisms established by the data protection authorities.
As technology providers of the project, we commit ourselves to perform data processing in
accordance with EU Regulation 2016/679 and, therefore, to keep a record of the processing
activities we carry out and to perform a risk assessment of the processing we perform, in
order to know what measures, we will have to implement and how to do it.
In addition to the rights already provided for in the previous legislation (access, modification,
opposition and cancellation of data, deletion in the new Regulation), participants can now
also limit the processing of data collected for the project that are incorrect, request a copy or
that they be transferred to a third party (portability). To exercise these rights, they should
contact the principal investigator of the study or the Data Protection Officer of the Hospital as
data controllers. They also have the right to contact the Data Protection Agency if they are
not satisfied.
Data cannot be deleted, even if a patient leaves the study, in order to guarantee the validity
of the research and to comply with legal duties and drug authorization requirements.
9

The Investigator and the Sponsor are obliged to retain the data collected for the study for at
least 1 year after completion of the study. Thereafter, personal information will only be
retained by the facility for health care purposes and by the Sponsor for other scientific
research purposes if the patient has given consent to do so, and if permitted by applicable
law and ethical requirements.
a) Data Protection Considerations
FOLLOWHEALTH S.L. has cloud servers located in Ireland that comply with the General
Data Protection Regulation of 25/05/2018 and the Data Protection Act (LOPD). In addition,
the participant's data is encrypted both on the access platform for the research assistant as
well as for the technical (IT) administrators of the study and on the server.
Furthermore, FITBIT and/or its suppliers comply with the Privacy shield policy.
b) Data pseudonymity
Participant data encryption: each participant is assigned a randomly generated 8-character
participant ID (e.g., "d4w192bg"), and all participant data is connected only to that ID.
Other pseudonymity data: there are 4 types of information that are processed using the
industry-standard SHA-256 hash algorithm that ensure pseudonymization:
●
Incoming and outgoing call phone numbers.
●
Incoming and outgoing text message phone numbers.
●
MAC addresses of nearby Wi-Fi routers
●
MAC addresses of nearby Bluetooth devices
●
The server where the data is to be referenced must be identified.
"Hashing" this data means that each phone number and MAC address is converted into a
random string of numbers and letters of 32 characters, but a certain phone number is always
transformed into the same random string. It is also impossible (under current mathematical
theory) to undo a hash.
Let's take as an example that participant d4maaw called the phone number 617-123-4567
once on Monday and then received two calls from that same number on Tuesday. A
researcher analyzing the data obtained from the HumanITcare platform can see when those
three calls occur and can tell that they all involve the same phone number but will not be
able to tell what that phone number is.
"Hashing" the MAC addresses of Wi-Fi routers has the same effect. A researcher analyzing
platform data could say that a participant was near a certain Wi-Fi router at 10 am every
morning, Monday through Friday, and therefore could assume that the participant was
probably in the same room at 10 am every morning, Monday through Friday, but the data
would not reveal the actual MAC address of the router.
c)
Data encryption
All data on the phones, on the server and in transit uses industry standard encryption
techniques. The phone also uses asymmetric encryption, which means that even the phone
cannot read its own data; data recorded on the phone can only be read on the server.
The fact that the server and the platform are encrypted makes it impossible to identify the
user. In addition, FOLLOWHEALTH S.L. has a technical team that monitors and protects the
cloud, the server and the database.
10

d) Possible data anonymity breaches
Only one type of data can contain personally identifiable information: GPS data (which
record location).
The GPS data in the app provides sufficient detail to identify individual buildings or street
addresses with some degree of confidence, although a fair amount of analysis will be
required to transform a set of GPS coordinates into a residential address for a participant.
The smartphone app is completely customizable when it comes to data collection, so a
particular study could disable GPS data collection from a study, if desired, if it is not part of
the research question.
3.3 Study registration
The study is intended to be registered before its initiation in the WHO primary register
(ClinicalTrials.gov).
3.4 Scientific advice / protocol assistance
The study design is based on the previous study focused on Heart Failure with Hospital de
Torrevieja and has been adapted to the present context by discussing the design with the
multiple participating hospitals.
The technical scope of the study has been designed with the support of Dr. Karim Lekadir,
Director of the Artificial Intelligence in Medicine Lab at the Universitat de Barcelona
(BCN-AIM), and Dr. Polyxeni Gkontra, tenure-track lecturer at the Universitat de Barcelona
and researcher at BCN-AIM lab.
3.5 Early termination of the study
The sponsor and other competent authority may terminate the study at any time according to
certain circumstances, such as:
●
ethical concerns
●
when the safety of the participants is questionable or at risk, respectively
●
alterations in accepted clinical practice that make it inadvisable to continue a clinical
trial
●
early evidence of harm from the experimental intervention
3.6 Protocol amendments
The content of the protocol may face corrections and updates according to the suggestions
received by the sponsor and principal investigator after an evaluation process. Such
suggestions
should
be
delivered
before the ethics committee approval. Substantial
amendments are only applied after the approval of the ethics committee.
11

4 Background and rationale
4.1 Background and rationale
Heart Failure (HF) is a prevalent and fatal clinical syndrome that affects the quality of life of
millions of people worldwide. Between 17% and 45% of patients suffering from HF die within
the first year and the remaining die within 5 years (Tripoliti et al., 2017). Furthermore, those
patients have a high risk of rehospitalization, their associated healthcare costs are huge, and
the higher the life expectancy, the higher the disease’s prevalence (McDonagh et al., 2021).
HF symptoms commonly include shortness of breath, excessive tiredness, and leg swelling
which may be worsened with decompensation (Schiff et al., 2003), and thus displacement to
medical centers represents a handicap for such individuals. Remote monitoring technologies
provide a feasible solution that allows earlier decompensation identification and better
adherence to lifestyle changes and medication (Brahmbhatt & Cowie, 2019). Although
telemonitoring by smartphones showed the potential to reduce both the frequency and the
duration of HF hospitalizations (Koulaouzidis et al., 2016; Scherr et al., 2009), there was no
association with the reduction of all-cause mortality (Koehler et al., 2011). Thus, it indicates
there is a need to look for more effective and precise methodologies. In recent years, the
use of wearable devices that allow daily monitoring of patient’s physiological data combined
with
Artificial
Intelligence
(AI)
has
shown
immense
potential
in
predicting
cardiovascular-related diseases, their adverse events and patient’s health status (Lee et al.,
2022), including that of patients with HF (Guidi et al., 2015).
HumanITcare has implemented a cloud platform and an alarm-based system that delivers
health alarms when a patient’s biomedical measurement is out of a predefined range. The
platform relieves clinicians and caretakers of going through each patient’s data to check for
anomalies, accelerating the decision-making process and reducing hospital consultations.
However, the system is creating many straightforward alarms that are finally being discarded
after evaluation by the medical professional. In the present project, we propose to run an
observational study in order to create a huge dataset with patients' clinical data that will
contain annotations regarding the relevance of each alarm. With the annotated dataset we
will be able to implement and train Machine Learning (ML) models that will improve the
alarm-based system by making it more robust, trustworthy and reliable.
This study is being conducted in the framework of a European project promoted by the
European Innovation Council (EIC). An earlier version of the platform was validated in a
study conducted in 2020 at Hospital de Torrevieja focused on Heart Failure. The rationale for
this study is in line with HumanITcare’s goal of incorporating artificial intelligence tools to
optimize the digital platform. While this study is focused on the creation of a diverse and
labeled dataset and on the development of artificial intelligence event-prediction algorithms,
a forthcoming second study will focus on the validation of the algorithms to assess their
clinical effectiveness.
4.2 Evidence to Date
A single-center study was performed in 2020 in Hospital de Torrevieja in which an early
version of the digital platform was validated. A total of 32 patients participated, 16 as a
control group and 16 as a study group. The study group was monitored remotely using the
HumanITcare digital platform and wearables (smart scale, blood pressure monitor, pulse
12

oximeter, and smartwatch) for 3 months, while the control group proceeded with the normal
clinical praxis.
The results showed that patients monitored by the HumanITcare platform are 33% less likely
to return to the emergency department. Besides, the study group showed a 40% reduction in
hospital readmissions, and 92% of patients would recommend using the platform. Finally,
100% of clinical professionals agree that the platform improves health care and services,
saves time in visits, and satisfies remote medical care, that it is an easy-to-use system and,
moreover, easy to learn how to use it. In addition, the professionals state that the way of
interacting with the system is pleasant and that the platform fulfills all the expected tasks.
5 Study design and endpoints
5.1 Study design
This is an observational study involving a European network of hospitals. The study consists
of continuous remote patient monitoring using HumanITcare’s digital platform and the
supplied devices described below. For 6 months, a total of 500 patients suffering from Heart
Failure (HF) will have their physiological constants monitored. The recruitment period is
stated as 6 months since the beginning of the study.
The main objective is to create a diverse and labeled dataset of patients’ parameters to
nourish ML prediction algorithms and optimize the in-platform alarm-based system.
5.2 Primary and secondary endpoints
Primary endpoints
●
Creation of a labeled and diverse dataset from a multicenter perspective. The dataset
will contain the following data:
○
Physiological
parameters:
heart
rate,
calories consumed, steps count,
physical activity, oxygen saturation, heart rate variability measurements,
stress levels, sleep tracking data, systolic and diastolic blood pressure,
weight, BMI
○
Socio-demographic data: sex, age, ethnic group, employment, level of
education, number of people living with the patient, caretaker.
○
Risk
factors:
comorbidities,
diabetes,
hypertension,
smoking,
alcohol
consumption, heart disease history
○
Medication
tracking:
lipid-lowering
medication,
PCSK9
inhibitors,
beta-blockers, anticoagulation agents, ACE inhibitors, anti-arrhythmic drugs,
anti-inflammatory
drugs,
aldosterone
antagonists,
angiotensin
receptor/neprilysin inhibitor, other diuretics
○
Symptomatology questionnaire for patients
○
NYHA-class
○
Left Ventricular Ejection Fraction (LVEF)
○
Clinical interventions: hospital readmission, mortality, UCI visits
○
Health questionnaire answers: self-care, quality of life
○
Classified alarms with their respective timestamp and annotation by the
medical professionals
○
Measurement ranges for each personalized alarm and their changes
13

●
Implementation of ML models to improve the current alarm-based system using the
dataset created. The models should:
○
Provide a relevance level for each new alarm by reducing the number of
irrelevant alarms and thus fostering personalized follow-up.
○
Be robust across different new hospitals and reliable and fair across different
target populations, considering the diverse sociodemographic data that will be
available in the dataset.
Secondary endpoints
●
Development and implementation of ML event prediction algorithms that will add new
self-generated alarms to the system. These alarms should forecast:
○
Untracked hospital interventions, such as UCI visits or hospital readmissions.
○
Changes of medication with their particular estimated dose.
○
Clinical events, such as mortality.
●
Assessment of patient and medical professional’s satisfaction with and usability of
the digital platform at the end of the study by means of the “System Usability Scale”
(SUS) (Brooke, 1986) and the “Post-Study Usability Questionnaire” (PSSUQ).
6 Subjects/population(s)
6.1 Eligibility criteria
Subjects participating in the study must be diagnosed with Heart Failure according to the
ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure.
2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure
(McDonagh et al 2021)
Inclusion criteria
●
Heart failure (HF) patients with NYHA Functional Class >= II (according to 2021 EU
guidelines).
●
Patients who have suffered an acute decompensation of HF (first and recurrent) in
the 30 days prior to enrollment in the study.
An acute decompensation of HF is defined as a new episode of symptoms and signs
that requires intravenous decongestive treatment or an increase in oral diuretics,
either on an outpatient basis (e.g., hospitalization for HF during the day or at home)
or in the emergency service or that requires an unplanned hospital admission.
●
NT-pro BNP ≥300 pg/mlL or BNP ≥100 pg/mL at the time of inclusion in the study for
patients
without
ongoing
atrial
fibrillation/flutter.
If
there
is
ongoing
atrial
fibrillation/flutter, NT-pro BNP should be ≥600 pg/mL
●
Patients must have had an echocardiogram during their HF hospitalization or the
previous 12 months.
●
Prior to the initiation of any procedures, the hospital will ensure that an informed
consent document completed by the patient is obtained.
●
All patients will be eligible regardless of the level of LVEF: HFrEF, HFmrEF, and
HFpEF.
14

Exclusion criteria
●
Oncology patients with metastasis or with chemotherapy treatment ongoing
●
Patients participating in other studies or trials.
●
Patients not willing to participate.
●
Patients over 150 kg
●
Patients who do not use Catalan, Spanish, English, Portuguese, Italian, Dutch,
German, Swedish, Hungarian, Romanian, or French.
●
Patients without a mobile phone
●
Patients without internet connection
●
Patients
with
moderate or severe cognitive impairment without a competent
caregiver.
●
Patients with severe psychiatric illness.
●
Patients with planned cardiac surgery.
●
Patients with planned heart transplant or LVAD implant.
6.2 Recruitment and screening
For the creation of the dataset, many hospitals have been contacted. Patients will be
included in the study based on the eligibility criteria mentioned and must complete the
informed consent provided on day 0. Patients will be monitored for 6 months after being
included in the study on day 0. Each hospital will decide when to include their patients
according to their particular clinical practice (either in the process of discharge planning or
during the first follow-up visit, i.e.. 1 or 2 weeks after discharge).
The recruitment period is defined as 6 months. That means patients will be incorporated into
the study from its start until the sixth month. The last subject included in the study will then
finish the study after one year from the first day of the study (study KICKOFF: first patient -
first day).
Medical professionals from each hospital will be in charge of recruiting the participants. The
recruitment rate is specific for each hospital, and it may vary depending on the month.
If the recruitment target is not reached for a specific center at the end of the recruitment
period (6 months), the latter may be extended until the accorded number of participants
recruited is satisfied under the hospital decision.
6.3 Criteria for withdrawal/discontinuation of participants
There are several situations in which the participant may be withdrawn from the study,
including the development of an exclusion criterion during the execution of the study,
withdrawal of informed consent, and when the participant is unwilling to continue the study.
The withdrawn participants do not have to be replaced.
If the participant withdrawals from the study, his/her data must be anonymized at the end of
the study. This anonymization is not performed if the participant gives explicit consent. For
the protection of the withdrawn participant, he/she must be provided with the standard
follow-up care designated by his/her respective medical professional.
15

7 Statistical analysis plan and power calculation
Machine learning models are data-hungry models. This means that the performance of the
model will potentially increase with the increase of the dataset and classified events in each
classification class. We could simplify the classification task by categorizing alarms as
relevant or irrelevant, thus reducing it to a binary classification problem. We will probably
have to deal with an unbalanced problem as we might receive a lot of irrelevant classified
alarms and few relevant ones. In an unbalanced classification problem as in our case, the
best measurements to assess model performance are precision and sensitivity. Precision is
defined as the fraction of well-classified instances from class A among all instances that
were classified as class A. Sensitivity instead is defined as the fraction of instances from
class A that were classified as pertaining to class A. A recent study by Juba and Le (Juba
and Le, 2019) proved that even using data augmentation techniques to deal with an
unbalanced dataset, adding more training data is the best solution for measuring precision
and recall.
Furthermore, we pretend to train the algorithm not just to solve the binary classification
problem but also to be able to give specific recommendations in terms of changes in
medication, hospital readmissions, and others. Thus, the sample size might have to be big
enough to cover a huge range of possible outcomes.
For instance, a similar study (Koulaouzidis et al., 2016) with 308 HF patients telemonitored
for around 9 months predicted whether patients would undergo hospital readmission during
the following day based on the day-to-day physiological measurements of each patient.
Performance accuracy was high. However, more specific measures such as the sensitivity
were very low, putting patients at a high risk of misclassification.
During model development and training, k-fold cross-validation will be implemented to
fine-tune the model parameters and ensure robustness. Additionally, part of the data won’t
be used during training and validation in order to test model performance on new data never
seen by the algorithm. After the implementation of the AI system into the platform, a
randomized controlled trial will be carried out in hospitals that didn’t participate in the present
observational study to externally validate the ML models. In this future study, a quantitative
sample size evaluation will be possible by comparing clinical outcomes between the control
and study group.
Therefore, the sample size for the present study has been determined qualitatively taking
into consideration the reasons mentioned above, that during the 3 first months after HF
decompensation is when patients present more harmful events, and the budget available for
the project. The plan is to include the maximum number of participants while accomplishing
a relevant socio-demographic diversity in the final cohort. We consider that a total of 500
patients will give sufficient data diversity and classified events (alarms + unexpected
interventions) to ensure a robust model performance.
Once the algorithms are developed, model performance in terms of accuracy will be
evaluated by means of C statistic, the area under the receiver operating characteristic curve,
and by creating a calibration plot. Furthermore, we will evaluate the models in terms of
fairness and potential bias using metrics such as statistical parity, group fairness, equalized
odds, and predictive equality.
16

8 Conduct
8.1 Summary of the study schedule
September 2022
Kick-off. First meeting with Hospital de Torrevieja, the
study leaders.
September - December 2022
Partners selection and coordination.
Discussion of protocol design and alarm system
configuration.
January - March 2023
CEIms approvals
April 2023
Study kick-off. Register investigators on the platform.
Initiate patient recruitment.
April 2023
First patient - first visit
October 2023
First patient - last visit
October 2023
Last patient - first visit
April 2024
Last patient - last visit
May 2024
Results analysis and final report draft
June 2024
Improvement of the ML models
Please note that these are preliminary dates that may be subject to change during the
execution of the clinical study.
8.2 Study management, study monitoring, data and sample management
8.2.1 Study management and clinical procedures.
●
Recruitment phase: once the CEIm protocol has been approved for each hospital,
the assigned medical professionals responsible for the study will be in charge of
including patients in the study, following the inclusion criteria. They will explain the
study characteristics to the patient and ask if they are willing to be included in the
study. The patient will sign the informed consent that includes the acceptance to use
active and passive data. Once accepted, the medical professionals will register the
user into the platform, fill in all the data required about their socio-demographics and
risk factors, and will train the patient on the proper use of the app and devices.
The specific calendar for each hospital will depend on its particular involvement and
protocol approval date.
●
Execution of the study: during the study, patients and medical professionals will
evolve as mentioned below.
17

○
Day 0: First visit and study enrollment either before or after HF hospital
discharge or HF decompensation. This first visit will include,
■
Explanation of the study to the patient and doubt clarification
■
Explanation of the terms and conditions, and signature of the informed
consent
■
Medical professionals will register the patient to the HumanITcare
platform together with their personal credentials. They will train
patients on the proper use of the app and medical devices.
■
Patients will be given a blood pressure monitor, a smartwatch and, a
pulse oximeter. Devices will be synchronized with the app (or with the
provider’s
app
in
some
cases).
The
patient
should
wear
the
smartwatch during the entire study period, charging its battery
whenever it is about to run out and putting it back on just after it is fully
charged.
■
Registration of the following patient variables into the platform by the
medical professional:
●
Socio-demographic data: Age, sex, ethnic group, employment,
education level, caretaker, number of people living with the
patient
●
Risk factors: Comorbidities, diabetes, hypertension, smoking,
alcohol consumption, heart disease history
●
Height
●
New York Heart Association (NYHA) Functional Classification
●
Left Ventricular Ejection Fraction (LVEF)
●
Medication use and doses
■
Patients will answer first the European Heart Failure Self-Care
Behaviour Scale (EHFScB scale), the Quality-of-life SF-12 and the
Mobile Device Proficiency questionnaires through the app.
○
Day 0 until the end of the study: Registration of measurements and
questionnaires: Patients will be notified through the app whenever they need
to register a measurement or answer a health questionnaire.
Weight, blood pressure (systolic and diastolic) and oxygen saturation (through
the pulse oximeter) will be registered daily.
Times
for
each
registration
are
specified
below.
Smartwatch
device
measurements are automatically registered once synchronized with the app.
Health questionnaires will be delivered periodically through the app to the
patient. Specifically, the symptomatology questionnaire will be delivered twice
a week (on Mondays and Thursdays) and the self-care and quality-of-life
questionnaires will be answered periodically (specified below).
○
Day 0 until the end of the study: Medical professionals will follow up on
patient's adherence to the study by checking their measurements and health
questionnaire answers through the platform. Clinicians will make a control call
in case of missing data.
○
Day 0 until the end of the study: Medical professionals are required to
annotate each alarm whenever they receive it and check its relevance. If they
are unable to evaluate it at the time of appearance, they are allowed to do it
later. However, we strongly recommend annotating the alarms, if there are
18

any, on a daily basis. Annotations will include adding information about the
relevance of an alarm by assessing the health status of the patient.
○
Day 0 until the end of the study: Medical professionals must report every
hospital readmission, mortality, emergency department visit or non-fatal HF
decompensation of each patient whenever it happens through the platform. If
they are unable to register the event at the moment, events can be registered
later on through the platform with their corresponding date/s and duration.
○
Day 0 until the end of the study: Medical professionals are required to
report every medication change through the platform, both due to an
alarm-derived decision or derived from a clinical evaluation.
○
Day 7 to 10: First follow-up visit: Medical professionals will cite the patients
for in-person follow-up visits.
○
Day 90 (3 months): Second follow-up visit: Medical professionals will cite the
patients for in-person follow-up visits.
○
Day 180 (6 months): Last follow-up visit and end of the study: The patient
monitoring will finish after 6 months from inclusion to the study (first hospital
discharge). Patients should answer the European Heart Failure Self-Care
Behaviour Scale (EHFScB scale), the Minnesota Living with Heart Failure
questionnaire, the Mobile Device Proficiency Questionnaire, the “System
Usability
Scale”
(SUS)
and
the
“Post-Study
Usability
Questionnaire”
(PSSUQ). Then, they will be required to return the medical devices and
medical professionals will change the status of the patient to “finished” on the
platform.
Medical professionals will be asked to answer the SUS and PSSUQ
questionnaires at the end of the whole study period.
○
Medical professionals may cite patients for more in-person visits depending
on the specific clinical practice of each center.
8.2.2 Adverse event reporting
HumanITcare will provide technical support to patients and medical professionals during the
whole period of the study. Bugs and technical inquiries will be delivered through the same
platform or by telephone call or email to the project manager of the company.
In all cases, the information regarding adverse events needs to be reported to the company
and recorded.
8.2.3 Data collection
A full description of the data that will be collected and the frequency of registry is presented
below in Table 1.
19

Table 1: Data collected
Class
Input
measurement
(units)
Units
Frequency of
registry
Device
(Brand)
Collection
Physiological
measurements
Heart rate
Beats per minute
(bpm)
Continuous. One
registry every
minute.
Smartwatch
(Garmin, Fitbit)
API or SDK.
Patients
through the
app
Calories
Kilocalories (kcal)
Steps count
Steps
Physical activity
Minutes (min)
Oxygen saturation Percent (%)
Heart rate
variability
Miliseconds (ms)
Stress levels
Scale from 0 to
100
Accelerometer
Array of x,y,z
values in
millig-units (mG)
Sleep
Minutes (min)
Systolic blood
pressure
Millimetre of
mercury (mmHg)
Daily. Once 2h
after taking the
medication after
breakfast.
Blood pressure
monitor
(Beurer, Lifevit)
API or SDK.
Patients
through the
app
Diastolic blood
pressure
Weight
Kilogram (kg)
Daily. Once always
at the same time
and with the same
clothes.
Scale
(Lifevit)
API or SDK
or manual
regitsry.
Patients
through the
app
Height
Meter (m)
Initial data
At the time
of inclusion.
Medical
professional
s through
the platform
BMI
Kilogram per
squared meter
(kg/m²)
Daily, depending
on weight
measurement.
Computed
automaticall
y
Oxygen saturation Percent (%)
Daily. Once 2h
after taking the
medication after
breakfast.
Pulse oximeter
(Beurer, Lifevit)
API or SDK.
Patients
through the
app
Pulse rate
Beats per minute
(bpm)
Socio-demographi
cs
Sex
Integer
Initial data
At the time
of inclusion.
Medical
professional
s through
the platform
Age
Years
Ethnic group
White, Black,
Asian, Pacific
Islander or
Latino-South
American
20

Employment
Employed/Self-em
ployed, retired,
unemployed,
doing unpaid or
voluntary work,
student, none
Education
Primary,
secondary,
higher-level
Caretaker
Yes or not
nº of people living
with patient
Integer
Risk factors
Comorbidities
Text separated by
commas
Initial data
At the time
of inclusion.
Medical
professional
s through
the platform
Diabetes
Yes or not
Hypertension
Smoking
Alcohol use
Heart disease
history
NYHA-class
Scale from 1 to 4
Initial data and if
modified
At the time
of inclusion.
Medical
professional
s through
the platform
Level of Ejection
Fraction (LVEF)
Percent (%)
Initial data and if
modified
At the time
of inclusion.
Medical
professional
s through
the platform
Medication
Lipid lowering
medication
Milligrams per day
(mg/day) or grams
per day (g/day)
Initial data and if
modified
Medical
professional
s through
the platform
PCSK9 inhibitors
Beta blockers
Anticoagulation
agents
ACE inhibitors /
Angiotensin
receptor-neprilysin
inhibitor
Anti-arrhythmic
drugs
Anti-inflammatory
drugs
Aldosterone
antagonists
Antidiabetic
21

agents
Vericiguat
Diuretics
SGLT2 inhibitor
(Dapaglifrocina/e
mpagliflocina)
Ivabradina
Symptoms
Chest pain
Yes or not
2 times per week.
On Mondays and
Thursdays
Patients
through app
questionnair
e
Shortness of
breath
Oedema
Clinical events
Hospital
readmission
Number and dates
At occurrence
Medical
professional
s through
the platform
Non-fatal HF
event
Emergency
department visits
Death
Yes/No and date
Questionnaire
European Heart
Failure Self-Care
Behaviour Scale
(EHFScB scale)
From 0 to 100
4-5 times: when
included in the
study, 7 days after
discharge, 1
month, 3 months, 6
months
Patients
through app
questionnair
e
Minnesota living
with heart failure
questionnaire
(MLHF)
Mobile Device
Proficiency
Questionnaire
From 8 to 40
2 times: when
included in the
study and at the
end of the study
Morinsky-Green
medication
adherence 4-item
questionnaire
From 0 to 4
4 times: when
included in the
study and once
every month
The questionnaires delivered to the patients are specified below and added in the Annex 1:
●
Minnesota Living with Heart Failure Questionnaire (MLHF) (Bilbao et al., 2016)
●
European Heart Failure Self-care Behaviour Scale (EHFScB scale) (Jaarsma et al.,
2009)
●
Symptom report questionnaire: six questions to capture the worsening of the
symptoms of heart disease, mainly worsening of the HF, and one question to capture
general deterioration.
●
“System Usability Scale” (SUS) (Brooke, J. (1986) SUS—A Quick and Dirty Usability
Scale. Usability Evaluation in Industry, 189-194. - References - Scientific Research
Publishing,
n.d.)(Brooke,
1986)
and
the
“Post-Study
Usability
Questionnaire”
(PSSUQ) (Lewis, 1992) will be delivered at the end of the study.
●
Mobile Device Proficiency Questionnaire (MDPQ) (Roque & Boot, 2018)(Roque and
Boot 2018)
22

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24

ANNEX 1:
Symptom report questionnaire
1.
My feet or legs are more swollen than usual. Yes/No
2.
I feel more fatigued, tired or with a sensation of choking. Y/N
3.
I had bad nights because of shortness of breath or the sensation of choking. Y/N
4.
I needed more pillows to breathe better at night lying on my bed. Y/N
5.
I felt weaker or more dizzy than usual. Y/N
6.
I had more chest pain than usual. Y/N
7.
In general, I feel worse than usual. Y/N
25