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Cancers 2022, 14, 974. https://doi.org/10.3390/cancers14040974 www.mdpi.com/journal/cancers
Article
ICOnnecta’t: Development and Initial Results of a Stepped
Psychosocial eHealth Ecosystem to Facilitate Risk Assessment
and Prevention of Early Emotional Distress in Breast Cancer
Survivors’ Journey
Joan C. Medina
1,2,3,†
, Aida Flix-Valle
1,3,4,†
, Ana Rodríguez-Ortega
1,3
, Rosa Hernández-Ribas
1,3,4,5,6
,
María Lleras de Frutos
1
and Cristian Ochoa-Arnedo
1,3,4,
*
1
E-Health ICOnnecta’t and Psycho-Oncology Services, Institut Català d’Oncologia, L’Hospitalet de
Llobregat, 08908 Barcelona, Spain; jmedina1@uoc.edu (J.C.M.); aflixv@iconcologia.net (A.F.-V.);
crodriguez@iconcologia.net (A.R.-O.); mrhernandez@bellvitgehospital.cat (R.H.-R.);
mlleras@iconcologia.net (M.L.d.F.)
2
Department of Psychology and Education Sciences, Universitat Oberta de Catalunya, 08018 Barcelona,
Spain
3
Department of Clinical Psychology and Psychobiology, Universitat de Barcelona, 08035 Barcelona, Spain
4
Psycho-Oncology and Digital Health, Health Services Research in Cancer, Institut d’Investigació Biomèdica
de Bellvitge (IDIBELL), L’Hospitalet del Llobregat, 08908 Barcelona, Spain
5
Department of Psychiatry, Hospital Universitari de Bellvitge, 08908 Barcelona, Spain
6
Centro de Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain
* Correspondence: cochoa@iconcologia.net; Tel.: +34-93-2607800 (ext. 3821)
† These authors contributed equally to this work.
Simple Summary: In current clinical practice, between one third and a half of patients diagnosed
with cancer experience distress. Moreover, many of these psychosocial needs often remain un-
addressed, although effective interventions exist. Nowadays, eHealth solutions like ICOnnecta’t
offer new tools to overcome these limitations and improve access to treatment. This digital eco-
system has been proved to be feasible to implement, reaching good acceptability, use, and satis-
faction between users. In addition, it allowed symptom monitoring in real time, facilitating pre-
ventive early interventions. Overall, fostering social support appears as a key to facilitate a resili-
ent response after diagnosis.
Abstract: Psychosocial interventions prevent emotional distress and facilitate adaptation in breast
cancer (BC). However, conventional care presents accessibility barriers that eHealth has the po-
tential to overcome. ICOnnecta’t is a stepped digital ecosystem designed to build wellbeing and
reduce psychosocial risks during the cancer journey through a European-funded project. Women
recently diagnosed with BC in a comprehensive cancer center were offered the ecosystem. ICOn-
necta’t consists of four care levels, provided according to users’ distress: screening and monitor-
ing, psychoeducation campus, peer-support community, and online-group psychotherapy. De-
scriptive analyses were conducted to assess the platform’s implementation, while multilevel lin-
ear models were used to study users’ psychosocial course after diagnosis. ICOnnecta’t showed
acceptance, use and attrition rates of 57.62, 74.60, and 29.66%, respectively. Up to 76.19% of users
reported being satisfied with the platform and 75.95% informed that it was easy to use. A total of
443 patients’ needs were detected and responsively managed, leading 94.33% of users to remain
in the preventive steps. In general, strong social support led to a better psychosocial course. ICOn-
necta’t has been successfully implemented. The results showed that it supported the development
of a digital relation with healthcare services and opened new early support pathways.
Citation: Medina, J.C.; Flix-Valle, A.;
Rodríguez-Ortega, A.; Hernández-
Ribas, R.; Lleras de Frutos, M.; Ochoa-
Arnedo, C. ICOnnecta’t: Development
and Initial Results of a Stepped
Psychosocial eHealth Ecosystem to
Facilitate Risk Assessment and
Prevention of Early Emotional Distress
in Breast Cancer Survivors’ Journey.
Cancers 2022, 14, 974. https://doi.org/
10.3390/cancers14040974
Academic Editors: Tommaso Susini
and Laura Papi
Received: 15 January 2022
Accepted: 14 February 2022
Published: 15 February 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affili-
ations.
Copyright: © 2022 by the authors. Licen-
see MDPI, Basel, Switzerland. This arti-
cle is an open access article distributed
under the terms and conditions of the
Creative Commons Attribution (CC BY)
license (https://creativecommons.org/li-
censes/by/4.0/).
Cancers 2022, 14, 974 2 of 13
Keywords: breast cancer; cancer survivors; internet-based intervention; patient monitoring; patient
reported outcomes measures; psychosocial intervention; stepped-care
1. Introduction
It is widely agreed that comprehensive oncological treatments should consider early
educational and psychosocial preventive care [1] addressed to assess individualized psy-
chosocial risk and reduce the impact of cancer on mental health [2,3]. However, only a
minority of survivors are screened and referred to receive psychosocial support and treat-
ment. Apart from the shortage of psycho-oncologists, other factors that have been high-
lighted are long wait lists; time or mobility restrictions; or poor early detection [1]. This
situation is especially alarming given the availability of effective psychosocial interven-
tions in cancer [4].
Several actions have been proposed to improve accessibility to educational and psy-
chosocial care. One option is to restructure their contents and intensity, delivering services
progressively depending on the individualized needs detected. In this sense, recent stud-
ies have introduced psychosocial stepped-care (SC) interventions in breast cancer (BC) [5].
Although these alternatives showed high acceptance (between 51.8–84%) and tend to be
effective, comparing their results is complicated. Every SC program has a different num-
ber of levels, characteristics, professionals involved, and criteria to step up. In conse-
quence, they all show variable results [6].
Another possibility is to improve access to psychosocial attention through Infor-
mation and Communication Technologies (ICT), developing internet-based health (i.e.,
eHealth) interventions. Indeed, they have already shown their capacity to overcome many
limitations expressed for conventional care [7]. In the last decade, several web and mobile
platforms for screening, monitoring, or managing symptoms have been created, with BC
being a common focus [8]. These tools have provided faster, easier, more intense, and
convenient risk assessment means to identify warning signs [9]. Also, they have proved
to improve self-management, to promote communication between patients and profes-
sionals, and to develop peer support [10]. These functionalities, inherent to eHealth, are
facilitators of patients’ satisfaction and of improved acceptance and use rates [11].
According to the Unified Theory of Acceptance and Use of Technology, the ac-
ceptance of eHealth interventions could be described as participants’ intention to use the
digital tool [12]. A recent systematic review showed that acceptance rates for mobile in-
terventions in cancer range between 40–57% [10]. Regarding use, its definition and calcu-
lation accumulates less consensus. Even considering the several definitions of the concept,
Cho and colleagues [10] concluded that 70–92% of patients use eHealth solutions at least
once. It should be noted that interactive systems with professionals’ follow-up and direct
communication between patient and practitioner are the ones with the highest use [10,13].
Another relevant indicator is attrition, understood as the proportion of patients opting out
from treatment [14]. The eHealth research refers attrition rates between 13–60% depend-
ing on intervention characteristics [14–16].
Moving on to patient-reported outcomes, several meta-analyses and systematic re-
views in cancer survivors have shown that internet-based interventions could improve
emotional distress, quality of life (QoL), social support, and symptom self-management,
among other clinical variables [11,17–19]. However, it is generally recognized that more
solid evidence is needed.
Certainly, there is a good deal of research in cancer reporting proper feasibility and
clinical results for the independent use of SC interventions based on screening and moni-
toring, and online psychosocial treatments. However, only very few research groups have
recently explored internet-based psychosocial SC interventions with special focus in pre-
vention, showing heterogeneous results regarding psychological outcomes [20].
Cancers 2022, 14, 974 3 of 13
Building upon the evidence exposed, a European consortium has created a stepped
eHealth ecosystem (https://oncommun.eu/; accessed on 14 January 2022), named ICOn-
necta’t, that has the purpose of integrating screening and monitoring risk assessment tools
with early stepped educational and psychosocial interventions for BC survivors during
the acute phase of their illness (from diagnosis to the end of the primary treatments).
ICOnnecta’t was developed because, when its first design started in 2017, we did not find
any solution encompassing all these features and oriented to the specific population we
were interested in targeting. The project is part of a Horizon 2020 proposal, under the
name ONCOMMUN and funded by the European Institute of Innovation & Technology,
which pursued to deploy new digital tools in cancer care, featuring a strong commitment
to innovation.
The present study aimed to examine the feasibility of ICOnnecta’t in a sample of tar-
get users during its first-year implementation. Secondary aims were to assess the psycho-
social status of patients and measure their evolution in the first months within the pro-
gram.
2. Materials and Methods
2.1. Study Design
This pilot study follows a quasi-experimental single-group longitudinal design. The
inclusion of a control group was not considered at this stage, focused on feasibility. This
article has been written following the SPIRIT statement [21].
All procedures performed in this study involving human participants were in ac-
cordance with the ethical standards of the institutional and research committee and with
the 1964 Helsinki declaration and its later amendments. The protocol was approved by
the Clinical Research Ethics Committee of the leading institution on the 25th of October
2018 (PR343/18).
2.2. Participants
Participants were recruited from a public healthcare institution specialized in cancer,
located in north-eastern Spain. The first participant was recruited on the 15th of March
2019. Therefore, to analyze first-year results, the data tranche until the 14th of March 2020
was extracted. Eligibility criteria were: (1) adults (≥18 years), (2) diagnosed with a first
episode of BC in the previous 3 months, (3) internet access and user-level skills, and (4)
fluent in Catalan or Spanish. Patients were excluded and referred to more specialized care
in the same hospital if they showed: (1) major depressive disorder, psychosis, or substance
abuse; (2) autolytic ideation; or (3) impaired cognition.
2.3. Intervention
ICOnnecta’t provides a SC intervention tailored to each patient throughout their can-
cer experience. It consists of 4 levels of care ordered by psychosocial complexity (see Fig-
ure 1). All patients enter the program at the first level and, whenever they step up, they
retain access to the previous levels [22,23]. The details of the levels and the step-up proto-
col are outlined below:
-Level 1. Screening and monitoring symptoms and psychosocial risk assessment:
The first level is integrated in a central mobile application, named ICOnnecta’t, in which
patients may connect and communicate with their health professionals about their psy-
chosocial state and cancer’ symptoms, including treatments’ side effects. Thus, partici-
pants are monitored within ICOnnecta’t by health professionals with a traffic light system
devised to this aim.
Symptom management. When participants report a symptom, a traffic light turns on.
Its colors correspond to the symptom severity classification set by the National Cancer
Institute in the guideline Common Terminology Criteria for Adverse Events (CTCAE)
Cancers 2022, 14, 974 4 of 13
[24]. Green and yellow lights (CTCAE’s grade 1 and 2, respectively) mean low risk symp-
toms (e.g., hair loss), and orange and red lights (CTCAE’s grade 3 and 4, respectively)
mean high risk (e.g., high fever). In all cases, participants receive tailored automatic health
educational feedback from the system according to their symptom severity (i.e., health
recommendations for symptom management), while for red lights it also shows the emer-
gency services contact details. The health feedback messages were exhaustively devel-
oped by a working group composed of nurses, oncologists, nutritionists, and pharmacists
from the healthcare institution. Apart from these automatic responses, the nurses of our
team contact patients afterwards.
Psychosocial care. Patients are programmed psychometric questionnaires periodically
in this platform to screen and follow-up their psychosocial evolution. To step up to the
next intervention levels, the scores of an emotional thermometer (0–10 visual analogue
scale (VAS)) are considered. This thermometer is administered weekly, and its use has
been recommended in oncological settings to rapidly detect psychosocial morbidity [25],
with a sensitivity of 70% and specificity of 73% in southern Europe for a ≥6 cut-off [26].
These scores are interpreted as moderate emotional distress (i.e., orange light), while those
≥8 as high (i.e., red light). If any of these warnings is flagged for at least 2 weeks, the
Hospital Anxiety and Depression Scale (HADS) [27] is administered in addition to the
routine schedule of this instrument. This 2 week delay is intentionally introduced to give
the person time to explore the resources at each step. In case distress is confirmed (HADS
≥10) [28], a videoconference is scheduled to explore their needs and propose access to the
second level of care (i.e., Campus). If the patient agrees, specific contents within the Cam-
pus are prescribed, tailoring the intervention to each of them. The same monitoring pro-
cedure is followed at all four care levels, and whenever distress is identified in the routine
quarterly administrations of the HADS. Therefore, the patient journey in the ecosystem is
always guided and accompanied by the healthcare team.
-Level 2. Campus: The second level of care offers a wide variety of educational re-
sources through a virtual campus, across several topics that have been found relevant for
cancer patients (e.g., lifestyle, mood, social relationships). All videos, posts, infographics,
and articles have been selected and co-created with BC patients and health professionals.
This level is available to patients in an unguided manner from the beginning to facilitate
access to reliable information. Differently, when they step up to the second level of care,
their Campus access is guided and therefore tailored to their needs.
-Level 3. Communities: The social community hosted in the third level of ICOn-
necta’t, guided by health psychologists, is structured in 12 thematic areas mirroring the
Campus co-created topics. Its main objective is to foster peer emotional and social support
and to bridge the gap between users and their healthcare team, who stimulate debate and
solve specific doubts.
-Level 4. Group psychotherapy: The most intensive and specialized intervention in
our digital ecosystem consists of group psychotherapy delivered through videoconfer-
ence. It comprises eight weekly 90 min sessions, and is based on a positive psychology
approach [29]. These sessions are led by a clinical psychologist specialized in psycho-on-
cology.
ICOnnecta’t was developed through the cooperation between the hospital’s Infor-
mation Technology Unit and technological providers, after the signature of a legal agree-
ment to define data management and privacy standards compliant with the European
General Data Protection Regulation (GDPR; EC/2016/679). Patients’ sensitive personal
data remained hosted in the hospital’s secure servers in all cases, with communication
means point-to-point encrypted to maintain confidentiality and datasets anonymized.
Cancers 2022, 14, 974 5 of 13
Figure 1. ICOnnecta’t stepped model.
2.4. Acceptance, Use, and Attrition of the eHealth Intervention
Acceptance. Following reference guidelines [10,13], in the present study the ac-
ceptance rate of the digital platform was operationalized as percentage of enrolled pa-
tients, that is, number of participants who accepted the eHealth program divided by num-
ber of eligible patients to whom the program was proposed.
Use. The participants’ use rate was calculated based on the number of participants
that reported their psychosocial status at least in one of the platform’s instruments divided
by the total accepting participants [10].
Attrition. The attrition rate was expressed as the number of participants who in-
formed on their willingness to stop using the platform after the initial acceptance divided
by the total accepting participants. In other words, participants who opted out from the
program [14].
2.5. Instruments
Distress. The HADS is a self-reported instrument to measure distress in individuals
with a physical illness [27]. It is composed by two subscales, anxiety and depression. The
Spanish validation in oncological patients proved high reliability (α = 0.82 for anxiety and
α = 0.84 for depression). The HADS was administered routinely every three months and
its overall score was considered the primary outcome, with scores ≥10 interpreted as mod-
erate and ≥16 as high distress [28].
Post-traumatic stress. The Post-traumatic Stress Disorder Checklist for DSM-5 (PCL-5)
is a self-report to measure post-traumatic symptoms [30]. It includes 20 items and has
shown good reliability (α = 0.94). The official Spanish version has not been published yet,
but it was provided by the United States National Center for Post-Traumatic Stress Disor-
der for the purpose of this study. The PCL-5 was administered every three months, with
scores ≥33 interpreted as moderate and ≥45 as high stress [31].
Post-traumatic growth. The Post-traumatic Growth Inventory (PTGI) is a self-reported
measure for growth experiences following traumatic events [32]. It features 21 items and
has been validated in Spain with an oncological sample, proving high reliability (α = 0.95).
The PTGI was administered every three months, with scores ≥46 interpreted as high
growth [33].
Quality of life. The EuroQoL-5D-3L (EQ-5D-3L) is a brief instrument to measure QoL
[34]. It covers five dimensions related to both physical and mental health. The Spanish
Cancers 2022, 14, 974 6 of 13
version has shown appropriate convergent and construct validity, and was administered
every three months in our sample with time trade-off scores ≥90 interpreted as high QoL
[35].
Social support. Participants’ perceived social support was measured with the Medical
Outcomes Study—Social Support Survey (MOS-SSS) [36]. The Spanish version has been
validated with cancer patients and it showed excellent reliability (α = 0.94). No cut-off
scores are applicable to this instrument.
Satisfaction and usability. Satisfaction with the digital ecosystem and its perceived us-
ability were assessed three weeks after registration with a 0–10 VAS. No clear cut-offs
have been found in the literature, we interpreted their scores ≥5 as some and ≥8 as high
satisfaction/usability.
Sociodemographic and clinical data were collected from patients’ clinical records af-
ter obtaining their informed consent.
2.6. Procedure
All new patients treated in the BC unit of the recruiting institution were informed of
the study by their nurses. Those showing interest met with an ICOnnecta’t team member
to discuss about the program, check eligibility criteria, and sign the informed consent.
Then, participants were guided to install the digital ecosystem on their devices and were
offered a basic training to use it. Thereafter, the screening and monitoring of the partici-
pants (i.e., level 1) could start. Participants who accepted the enrollment but finally did
not make use of the eHealth platform received usual face-to-face care in the health insti-
tution (i.e., medical and nursing follow-up visits, referral to psychological care only if dis-
tress is inferred by health professionals).
2.7. Statistical Analyses
The R software was used [37]. First, descriptive analyses were conducted to appraise
the implementation of the digital ecosystem, covering its acceptance, use and attrition
rates, as well as user satisfaction and usability. Differences between participants who used
the system and those who did not were also estimated with Chi-squared and Student’s t
tests as appropriate. Then, the platform’s functioning was assessed according to the num-
ber of health education and psychosocial needs detected, and the time needed to provide
care, in addition to users’ distribution across its SC program.
Finally, we were interested in understanding patients’ psychosocial course immedi-
ately after diagnosis. Therefore, we conducted independent multilevel linear models
(MLM) for all outcomes of interest (i.e., HADS, PCL-5, PTGI, and EQ-5D-3L). For each
model, we included only those participants who provided at least one score during the
first month immediately after inclusion and analyzed their evolution in the 3 months af-
terwards.
Models were built parsimoniously and considered maximum likelihood as estima-
tion method. We always started by the simplest meaningful model with fixed intercept
and time, and increased complexity progressively in nested models supported by likeli-
hood ratio tests (LRT). In these subsequent models, we added social support at baseline
and sociodemographic variables (i.e., age, marital status, education, and work status) as
predictors. We were especially interested in social support since its role in psychological
adjustment after diagnosis has been repeatedly highlighted [38,39]. Results reported
herein are those for the best-fitting model for each outcome. The covariance structures
that best fitted our data were first-order autoregressive.
3. Results
3.1. Participant Characteristics
Up to 328 patients were referred to ICOnnecta’t from the BC unit of the recruiting
institution in the time frame of the study (i.e., one year). Among these, 189 patients were
Cancers 2022, 14, 974 7 of 13
finally enrolled, which entails an acceptance rate of 57.62%. Moreover, 141 participants
completed at least one of the scheduled instruments, which represents a use rate of
74.60%. The other 48 participants (25.40%) did not make use of the platform and no out-
come data could be extracted from them. In turn, the attrition among users was of 4.26%
(n = 6). All six participants lost interest in the ecosystem. Added together non-users and
those users who opted out, global attrition can be established at 29.66%. The participants’
flowchart can be seen in Figure 2.
Figure 2. Participants’ flowchart.
Finally, the average satisfaction level with the platform among users was 6.22 (SD =
3.17), although only 63 participants answered this instrument. Up to 76.19% (n = 48) re-
ported being satisfied, with 41.27% (n = 26) specifically very satisfied. The mean platform
usability perceived by users was 7.09 (SD = 3.77), estimated with 79 respondents. For
75.95% (n = 60), the ecosystem was easy to use. Particularly, most of them (68.35%, n = 54)
reported it as very usable.
Participants’ main sociodemographic and clinical characteristics are shown in Table
1. No significant differences existed between users and non-users. However, it can be no-
ticed that the former were slightly younger and more educated.
Table 1. Demographic and clinical characteristics of users and non-users.
Users (n = 141) Non-Users (n = 48) t
X
2 p
Age M (SD) 52.35 (8.57) 55.15 (9.55) 1.90 0.059
Marital status n (%) 0.87 0.929
Single 9 (6.38) 2 (4.17)
Married/partnered 101 (71.63) 33 (68.75)
Divorced/separated 6 (4.26) 3 (6.25)
Widowed 2 (1.42) 1 (2.08)
Unknown 23 (16.31) 9 (18.75)
Education n (%) 7.30 0.063
Primary or no studies 5 (3.55) 2 (4.17)
Secondary 17 (12.06) 3 (6.25)
Tertiary 43 (30.50) 7 (14.58)
328
p
atients contacted
139 patients excluded
- Not interested (n = 107)
- No internet access (n = 25)
- Not fluent in Spanish (n = 7)
189
p
artici
p
ants acce
p
ted enrollmen
t
141 users 48 non-users
6 o
p
ted out
Cancers 2022, 14, 974 8 of 13
Unknown 76 (53.90) 36 (75.00)
Work status n (%) 5.83 0.323
Active 54 (38.30) 12 (25.00)
Passive 13 (9.22) 6 (12.50)
Occupational disability 4 (2.84) 3 (6.25)
Work leave 21 (14.89) 5 (10.42)
Retired 9 (6.38) 6 (12.50)
Unknown 40 (28.37) 16 (33.33)
Cancer stage n (%) 3.77 0.438
0 16 (11.35) 3 (6.25)
I 53 (37.59) 22 (45.83)
II 52 (36.88) 15 (31.25)
III 15 (10.64) 4 (8.33)
IV 5 (3.55) 4 (8.33)
3.2. Symptom and Psychosocial Management
During this first year, 150 symptoms requiring attention (i.e., orange and red lights)
were detected from 61 participants. The average time elapsed from patients’ report and
professionals’ first response was 2.05 days (SD = 4.14). In turn, up to 293 psychosocial
warnings (i.e., orange and red lights) were identified from 71 individuals, with an average
of 5.91 days (SD = 7.83) from patients’ report and initial support provided by psychologists
in the team.
Most of these needs could be solved after this initial care, with only 43 symptoms and
48 psychosocial needs requiring further attention. These contacts were conducted on av-
erage 0.12 days (SD = 0.6) after the first response in case of symptoms, and 7.15 days (SD
= 11.1) for psychosocial warnings. This last estimation was extended by the two-week in-
terval set prior to decide whether to refer patients to higher levels of care.
Indeed, as for the escalation rates of the digital ecosystem, up to 102 (72.34%) of the
141 participants did not require further intervention beyond the first level of ICOnnecta’t.
For the remaining 39 individuals (27.66%), persistent distress was detected and were
given access to the second level of care (Campus). Then, 15 (10.64%) continued experienc-
ing distress at this level and were referred to the third (Communities) step. Finally, eight
(5.67%) reached the fourth and most intensive level (group psychotherapy).
3.3. Users’ Psychosocial Course in ICOnnecta’t after Diagnosis
Up to 14.74% of participants were found high and 31.58% moderate distress in the
HADS, whereas 13.33% scored high and 10.67% moderate stress in the PCL-5. On the con-
trary, 43.48% of participants already showed high growth in the PTGI, despite 57.28%
scored below the QoL cut-off score in the EQ-5D-3L. Finally, participants reported strong
social support in the MOS-SSS, with an average exceeding by ten points the mean estima-
tions reported in the literature [40,41]. Mean scores obtained by participants in all instru-
ments can be seen in Table 2.
Table 2. Participants’ mean scores and standard deviations after diagnosis.
Mean SD
HADS 9.89 6.52
PCL-5 24.6 15.6
PTGI 37.8 23.9
EQ-5D-3L 0.82 0.22
MOS-SSS 81.4 12.1
Cancers 2022, 14, 974 9 of 13
HADS: Hospital Anxiety and Depression Scale; PCL-5: Post-traumatic Stress Disorder Checklist
for DSM-5; PTGI: Post-traumatic Growth Inventory; EQ-5D-3L: EuroQoL-5D-3L; MOS-SSS: Medi-
cal Outcomes Study—Social Support Survey.
The MLM results for the HADS (n = 95) showed significant variance in its intercepts
across participants (χ2(1) = 24.85, p < 0.001), and included time and social support as fixed
predictors. Time did not yield a significant effect (b = −0.012, p = 0.077, 95% CI = −0.025 to
0.001), but social support did (b = −0.251, p < 0.001, 95% CI = −0.334 to −0.167), with lower
scores in the HADS associated with higher support.
For the PCL-5, the MLM (n = 75) showed again significant variance in participants’
intercepts (χ2(1) = 52.54, p < 0.001), and included time and social support as fixed. Time
was not significant (b = 0.017, p = 0.462, 95% CI = −0.028 to 0.061), but social support was
(b = −0.539, p < 0.001, 95% CI = −0.813 to −0.265), as high scores predicted lower levels in
the PCL-5.
Moving on to the PTGI model (n = 46), intercepts varied across participants (χ2(1) =
8.59, p = 0.003) and included time and social support as fixed predictors. Like in the pre-
vious models, time did not prove to be significant (b = 0.083, p = 0.265, 95% CI = −0.068 to
0.234). For the PTGI, social support was not either (b = -0.146, p = 0.704, 95% CI = −0.896 to
0.604).
Finally, in the EQ-5D-3L model (n = 103), intercepts did not show significant variance
χ2(1) = 1.46, p = 0.228), so it included fixed intercept and time, which again, was not a
significant predictor of EQ-5D-3L scores (b = −0.001, p = 0.127, 95% CI = −0.001 to 0.001).
4. Discussion
This study sought to report, for the first time, the implementation and initial results
of ICOnnecta’t, an eHealth ecosystem designed to deliver preventive education and psy-
chosocial care in cancer based in individualized risk assessment. The acceptance and use
rates were within the expected ranges, with around half of the patients accepting the plat-
form and, among these, three quarters actually using it [10]. In our sample, there was a
tendency in users to be slightly younger and to have higher studies than non-users, but
the non-significance of these results strengthen the idea that sociodemographic barriers
for eHealth use are vanishing [42].
In turn, attrition was relatively low and within the expected ranges, which is a
strength of the program, but needs to be tested with more diagnoses, since attrition in BC
tends to be particularly low [15]. In addition, most users reported high levels of both sat-
isfaction with, and usability of, the platform. Although not all of them provided their
views on these matters, ICOnnecta’t seems to tackle some of the main limitations found
among cancer survivors’ experience with telehealth through personalization, trusting re-
lationships, and patient autonomy favored by symptom self-management [11]. Indeed,
other recent eHealth interventions have obtained similar results in terms of satisfaction
and ease of use [9].
Regarding the detection of needs and the provision of appropriate care, the digital
ecosystem proved to facilitate patients’ follow-up as it has already been reported for
eHealth [8] and SC interventions [6]. An average of two days for health education, and
under six for psychosocial warnings, were established as waiting-times to be provided
with a professional first response. In addition, this fast management was also efficient
since most needs could be solved with a quick initial reply. This finding needs to be
merged with the fact that 94.33% of participants remained within the (preventive) first
three levels of the SC program. Therefore, ICOnnecta’t may be a valuable complement,
and even an alternative, to usual care. It is relevant to highlight in this regard that this
digital ecosystem does not seek to replace healthcare professionals, but to provide them
with more responsive means to deliver care. Indeed, the program pursues to foster the
collaboration within the patient-professional dyad, which articulates healthcare decisions
in all cases.
Cancers 2022, 14, 974 10 of 13
The prevalence of distress found among patients after diagnosis was aligned with
the literature [38] with moderate rather than high scores. Similarly, the low proportion of
stress was also coincident with extant research for BC [43], and the same applied for the
moderate levels of posttraumatic growth [44]. Regarding QoL, results proved most pa-
tients to score below the population cut-off point at baseline [35], findings that must be
interpreted considering that participants were starting their primary oncological treat-
ments. This condition impacts on several areas covered by the EQ-5D-3L, such as the feel-
ing of pain and the performance of daily routines [45].
Finally, strong social support was perceived by most participants. This finding is of
interest given its stress-buffering role [39,45]. Indeed, in our longitudinal analyses we con-
sistently found stronger social support to be associated with better results, underlining
the relevance of this variable. Fostering fulfilling and supportive relationships seems to
be a key factor for attaining a better course and, if assessed from the beginning, it may
anticipate psychosocial trajectories during treatments. This finding confirms that the peer
support community featured in ICOnnecta’t could be one of its main assets.
It is true that, unlike other similar studies [6,20], we have not found significant im-
provements with time. However, these previous proposals often included only partici-
pants who were already experiencing distress, while we offered the platform to all new
patients with a health promotion objective. The fact that the majority of users showed a
resilient response to cancer makes significant improvements unlikely to occur, as they are
typically found among patients with a poorer mental health status at baseline [6]. Conse-
quently, in our sample a steady trend means most users remain free from clinical symp-
toms, even when active treatments are still in play [39,45].
Apart from the small sample size and the short-term longitudinal data collected, the
present work has other limitations. Although this was a feasibility study, the absence of a
control group limits the interpretation of results. In addition, not all patients completed
all questionnaires, as they were reminded of the importance of doing so, but did not re-
ceive any kind of pressure given the real-world nature of the project. Also, we could not
extract any data from the non-users who accepted to participate, but finally did not make
use of the platform, who could not be provided care through it either. These patients con-
tinued to receive usual in-person care in the hospital, they attended their medical and
nursing periodic visits, but did not receive any psychosocial support unless the healthcare
team perceived distress during their appointments. However, since we could not measure
any outcome variable from them, no comparisons between the effect of ICOnnecta’t and
usual care could be made. The results of future randomized controlled studies are ex-
pected to contribute to this line of research. Similarly, although both patients and practi-
tioners were involved in the design of the eHealth solution, we focused on the former at
this stage and did not administer any instrument to the professionals involved in its im-
plementation. In the future, it may be relevant to study their insights as well, both in terms
of patients’ progress and regarding their own satisfaction with, and usability assigned to
the system. Finally, although we found no sociodemographic differences between users
and non-users, we must acknowledge that the 25 patients reported in Figure 2 to reject
participation due to no internet access might have changed these results. However, since
they represented only 7.62% of all individuals who were offered the ecosystem, such in-
fluence is still mild.
5. Conclusions
In conclusion, this study supports the use of eHealth in BC healthcare, with prelimi-
nary results suggesting the absence of sociodemographic barriers for their acceptance and
use. ICOnnecta’t allowed professionals to timely monitor and manage needs throughout
patients’ journey, intervening before the clinical course of physical and psychological
symptoms worsen. This feature has the capacity to prevent suffering in patients, also sav-
ing costs for health systems. While many users show a resilient response to their diagno-
Cancers 2022, 14, 974 11 of 13
sis, many others do not. Consequently, it will be clinically relevant to refine the individu-
alized risk assessment to identify and model differential trajectories among the whole
sample in the future, in order to feed more precise and personalized treatments and to
better estimate effectiveness and cost-utility [22]. Future studies will also aim to replicate
these results and to extend ICOnnecta’t to other diagnoses and countries, making its ser-
vices available to more patients who may benefit from them. Indeed, preliminary versions
of this digital ecosystem have already been developed in Portuguese and Polish, following
a careful adaptation process to each cultural background. We hope this work will lead to
increase the availability of comprehensive cancer care programs in more regions.
Author Contributions: Conceptualization, C.O.-A.; Data curation, J.C.M. and A.F.-V.; Formal anal-
ysis, J.C.M.; Funding acquisition, A.F.-V. and C.O.-A.; Investigation, J.C.M., A.F.-V., A.R.-O., R.H.-
R., M.L.d.F., and C.O.-A.; Methodology, J.C.M. and C.O.-A.; Project administration, J.C.M., A.F.-V.,
and C.O.-A.; Resources, C.O.-A.; Supervision, C.O.-A.; Writing—original draft, J.C.M. and A.F.-V.;
Writing—review and editing, A.R.-O., R.H.-R., M.L.d.F., and C.O.-A. All authors have read and
agreed to the published version of the manuscript.
Funding: This research was funded by the European Institute of Innovation and Technology (EIT)
(19046 [1st year], 20536 [2nd year]; ONCOMMUNITIES: Online Cancer Support Communities). This
work has also been supported by the Carlos III Health Institute under the FIS grant PI19/01880, co-
financed by the European Regional Development Fund (ERDF) ‘a way to build Europe’. Finally, the
Generalitat de Catalunya through the consolidated research group “Research in health services in
cancer” (2017SGR00735) has also partially funded this research. We thank CERCA Programme /
Generalitat de Catalunya for institutional support.
Institutional Review Board Statement: The study was conducted according to the guidelines of the
Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of
the INSTITUT CATALÀ D’ONCOLOGIA on the 25th of October 2018 (PR343/18).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the
study.
Data Availability Statement: The anonymized datasets of this study may be obtained from the cor-
responding author upon reasonable request.
Acknowledgments: Authors want to express their gratitude to all BC patients who generously ac-
cepted to participate in this project.
Conflicts of Interest: The authors declare no conflict of interest.
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