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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
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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 1and Cristian Ochoa-Arnedo 1,3,4,*
1
E-Health ICOnnecta’t and Psycho-Oncology Services, Institut Catalàd’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, 08908 Barcelona, Spain
3Department of Clinical Psychology and Psychobiology, Universitat de Barcelona, 08035 Barcelona, Spain
4Psycho-Oncology and Digital Health, Health Services Research in Cancer, Institut d’InvestigacióBiomèdica
de Bellvitge (IDIBELL), L’Hospitalet del Llobregat, 08908 Barcelona, Spain
5Department of Psychiatry, Hospital Universitari de Bellvitge, 08908 Barcelona, Spain
6Centro 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 unaddressed,
although effective interventions exist. Nowadays, eHealth solutions like ICOnnecta’t offer new tools
to overcome these limitations and improve access to treatment. This digital ecosystem has been
proved to be feasible to implement, reaching good acceptability, use, and satisfaction between users.
In addition, it allowed symptom monitoring in real time, facilitating preventive early interventions.
Overall, fostering social support appears as a key to facilitate a resilient 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 potential
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. ICOnnecta’t consists
of four care levels, provided according to users’ distress: screening and monitoring, psychoeducation
campus, peer-support community, and online-group psychotherapy. Descriptive analyses were
conducted to assess the platform’s implementation, while multilevel linear 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. ICOnnecta’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.
Keywords:
breast cancer; cancer survivors; internet-based intervention; patient monitoring; patient
reported outcomes measures; psychosocial intervention; stepped-care
Cancers 2022,14, 974. https://doi.org/10.3390/cancers14040974 https://www.mdpi.com/journal/cancers
Cancers 2022,14, 974 2 of 13
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
treatment. Apart from the shortage of psycho-oncologists, other factors that have been
highlighted are long wait lists; time or mobility restrictions; or poor early detection [
1
]. This
situation is especially alarming given the availability of effective psychosocial interventions
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 studies
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 number
of levels, characteristics, professionals involved, and criteria to step up. In consequence,
they all show variable results [6].
Another possibility is to improve access to psychosocial attention through Information
and Communication Technologies (ICT), developing internet-based health (i.e., eHealth) in-
terventions. 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 professionals, 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 acceptance
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 interventions
in cancer range between 40–57% [
10
]. Regarding use, its definition and calculation accu-
mulates 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 commu-
nication 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% depending 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 mon-
itoring, and online psychosocial treatments. However, only very few research groups
have recently explored internet-based psychosocial SC interventions with special focus in
prevention, showing heterogeneous results regarding psychological outcomes [20].
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
Cancers 2022,14, 974 3 of 13
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 target
users during its first-year implementation. Secondary aims were to assess the psychosocial
status of patients and measure their evolution in the first months within the program.
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 accor-
dance 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 cancer
experience. It consists of 4 levels of care ordered by psychosocial complexity (see Figure 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 protocol 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, participants
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) [
24
].
Green and yellow lights (CTCAE’s grade 1 and 2, respectively) mean low risk symptoms
(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 recommenda-
tions for symptom management), while for red lights it also shows the emergency services
contact details. The health feedback messages were exhaustively developed 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.
Cancers 2022,14, 974 4 of 13
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 Campus
are prescribed, tailoring the intervention to each of them. The same monitoring procedure
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
resources 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 ICOnnecta’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-
oncology.
ICOnnecta’t was developed through the cooperation between the hospital’s Informa-
tion Technology Unit and technological providers, after the signature of a legal agreement
to define data management and privacy standards compliant with the European General
Data Protection Regulation (GDPR; EC/2016/679). Patients’ sensitive personal data re-
mained 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
Cancers 2022, 14, x FOR PEER REVIEW 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
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 acceptance
rate of the digital platform was operationalized as percentage of enrolled patients, that is,
number of participants who accepted the eHealth program divided by number 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 informed
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
moderate 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 Disorder
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].
Cancers 2022,14, 974 6 of 13
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
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
usability 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 after
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 participants
(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 institution
(i.e., medical and nursing follow-up visits, referral to psychological care only if distress is
inferred by health professionals).
2.7. Statistical Analyses
The Rsoftware 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 ttests
as appropriate. Then, the platform’s functioning was assessed according to the number 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 imme-
diately 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
afterwards.
Models were built parsimoniously and considered maximum likelihood as estimation
method. We always started by the simplest meaningful model with fixed intercept and time,
and increased complexity progressively in nested models supported by likelihood ratio
tests (LRT). In these subsequent models, we added social support at baseline and sociode-
mographic 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.
Cancers 2022,14, 974 7 of 13
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
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 outcome 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.
Cancers 2022, 14, x FOR PEER REVIEW 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
X2
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)
Unknown
76 (53.90)
36 (75.00)
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) reported
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 noticed
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) tX2p
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
Cancers 2022,14, 974 8 of 13
Table 1. Cont.
Users (n= 141) Non-Users (n= 48) tX2p
Primary or no studies 5 (3.55) 2 (4.17)
Secondary 17 (12.06) 3 (6.25)
Tertiary 43 (30.50) 7 (14.58)
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
average 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
interval 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 experiencing
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
contrary, 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 so-
cial support in the MOS-SSS, with an average exceeding by ten points the mean estimations
reported in the literature [
40
,
41
]. Mean scores obtained by participants in all instruments
can be seen in Table 2.
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.
Cancers 2022,14, 974 9 of 13
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
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: Medical Outcomes Study—Social
Support Survey.
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 previous
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
platform 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 satisfaction
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 relationships, 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.
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
Cancers 2022,14, 974 10 of 13
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
patients to score below the population cut-off point at baseline [
35
], findings that must be
interpreted considering that participants were starting their primary oncological treatments.
This condition impacts on several areas covered by the EQ-5D-3L, such as the feeling 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
consistently 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 participants
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
]. Consequently,
in our sample a steady trend means most users remain free from clinical symptoms, 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 receive
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 continued
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 expected to
contribute to this line of research. Similarly, although both patients and practitioners 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 implementation.
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 2to 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 influence is still mild.
5. Conclusions
In conclusion, this study supports the use of eHealth in BC healthcare, with preliminary
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
saving costs for health systems. While many users show a resilient response to their
diagnosis, many others do not. Consequently, it will be clinically relevant to refine the
individualized 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
Cancers 2022,14, 974 11 of 13
replicate these results and to extend ICOnnecta’t to other diagnoses and countries, making
its services 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’. Fi-
nally, 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 Pro-
gramme/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 CATALÀD’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
corresponding author upon reasonable request.
Acknowledgments:
Authors want to express their gratitude to all BC patients who generously
accepted to participate in this project.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Travado, L.; Reis, J.C.; Watson, M.; Borràs, J. Psychosocial oncology care resources in Europe: A study under the European
Partnership for Action Against Cancer (EPAAC). Psychooncology 2017,26, 523–530. [CrossRef] [PubMed]
2.
McFarland, D.C.; Shaffer, K.M.; Tiersten, A.; Holland, J. Prevalence of Physical Problems Detected by the Distress Thermometer
and Problem List in Patients with Breast Cancer. Psychooncology 2018,27, 1394–1403. [CrossRef] [PubMed]
3.
Giese-Davis, J.; Collie, K.; Rancourt, K.M.S.; Neri, E.; Kraemer, H.C.; Spiegel, D. Decrease in Depression Symptoms Is Associated
With Longer Survival in Patients With Metastatic Breast Cancer: A Secondary Analysis. J. Clin. Oncol.
2015
,29, 413–420.
[CrossRef] [PubMed]
4.
Faller, H.; Schuler, M.; Richard, M.; Heckl, U.; Weis, J.; Küffner, R. Effects of Psycho-Oncologic Interventions on Emotional Distress
and Quality of Life in Adult Patients With Cancer: Systematic Review and Meta-Analysis. J. Clin. Oncol.
2013
,31, 782–793.
[CrossRef]
5.
Singer, S.; Danker, H.; Roick, J.; Einenkel, J.; Briest, S.; Spieker, H.; Dietz, A.; Hoffmann, I.; Papsdorf, K.; Meixensberger, J.; et al.
Effects of stepped psychooncological care on referral to psychosocial services and emotional well-being in cancer patients: A
cluster-randomized phase III trial. Psychooncology 2017,26, 1675–1683. [CrossRef]
6.
Jansen, F.; Lissenberg-Witte, B.I.; Krebber, A.M.H.; Cuijpers, P.; de Bree, R.; Becker-Commissaris, A.; Smit, E.F.; van Straten, A.;
Eeckhout, G.M.; Beekman, A.T.F.; et al. Stepped care targeting psychological distress in head and neck cancer and lung cancer
patients: Which groups specifically benefit? Secondary analyses of a randomized controlled trial. Support. Care Cancer
2019
,27,
4543–4553. [CrossRef]
7.
Corbett, T.; Singh, K.; Payne, L.; Bradbury, K.; Foster, C.; Watson, E.; Richardson, A.; Little, P.; Yardley, L. Understanding
acceptability of and engagement with Web-based interventions aiming to improve quality of life in cancer survivors: A synthesis
of current research. Psychooncology 2018,27, 22–33. [CrossRef]
8.
Penedo, F.J.; Oswald, L.B.; Kronenfeld, J.P.; Garcia, S.F.; Cella, D.; Yanez, B. The increasing value of eHealth in the delivery of
patient-centred cancer care. Lancet Oncol. 2020,21, e240–e251. [CrossRef]
Cancers 2022,14, 974 12 of 13
9.
Schobel, J.; Volz, M.; Hörner, K.; Kuhn, P.; Jobst, F.; Schwab, J.D.; Ikonomi, N.; Werle, S.D.; Fürstberger, A.; Hoenig, K.; et al.
Supporting Medical Staff from Psycho-Oncology with Smart Mobile Devices: Insights into the Development Process and First
Results. Int. J. Environ. Res. Public Health 2021,18, 5092. [CrossRef]
10.
Cho, Y.; Zhang, H.; Harris, M.R.; Gong, Y.; Smith, E.L.; Jiang, Y. Acceptance and use of home-based electronic symptom
self-reporting systems in patients with cancer: Systematic review. J. Med. Internet Res. 2021,23, e24638. [CrossRef]
11.
Cox, A.; Lucas, G.; Marcu, A.; Piano, M.; Grosvenor, W.; Mold, F.; Maguire, R.; Ream, E. Cancer survivors’ experience with
telehealth: A systematic review and thematic synthesis. J. Med. Internet Res. 2017,19, e11. [CrossRef]
12.
Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F. User Acceptance of Information Technology: Toward a Unified View. MIS Q.
2003,27, 425–478. [CrossRef]
13.
O’Connor, S.; Hanlon, P.; O’Donnell, C.A.; Garcia, S.; Glanville, J.; Mair, F.S. Understanding factors affecting patient and public
engagement and recruitment to digital health interventions: A systematic review of qualitative studies. BMC Med. Inform. Decis.
Mak. 2016,16, 120. [CrossRef]
14. Eysenbach, G. The law of attrition. J. Med. Internet Res. 2005,7, e11. [CrossRef]
15.
Matis, J.; Svetlak, M.; Slezackova, A.; Svoboda, M.; Šumec, R. Mindfulness-Based Programs for Patients With Cancer via eHealth
and Mobile Health: Systematic Review and Synthesis of Quantitative Research. J. Med. Internet Res.
2020
,22, e20709. [CrossRef]
16.
Meyerowitz-Katz, G.; Ravi, S.; Arnolda, L.; Feng, X.; Maberly, G.; Astell-Burt, T. Rates of attrition and dropout in app-based
interventions for chronic disease: Systematic review and meta-analysis. J. Med. Internet Res. 2020,22, e20283. [CrossRef]
17.
Lleras de Frutos, M.; Casellas-Grau, A.; Sumalla, E.C.; Gracia, M.; Borràs, J.M.; Ochoa Arnedo, C. A systematic and comprehensive
review of internet use in cancer patients: Psychological factors. Psychooncology 2020,29, 6–16. [CrossRef]
18. Wang, Y.; Lin, Y.; Chen, J.; Wang, C.; Hu, R.; Wu, Y. Effects of Internet-based psycho-educational interventions on mental health
and quality of life among cancer patients: A systematic review and meta-analysis. Support. Care Cancer
2020
,28, 2541–2552.
[CrossRef]
19.
Xu, A.; Wang, Y.; Wu, X. Effectiveness of e-health based self-management to improve cancer-related fatigue, self-efficacy and
quality of life in cancer patients: Systematic review and meta-analysis. J. Adv. Nurs. 2019,75, 3434–3447. [CrossRef]
20.
Hauffman, A.; Alfonsson, S.; Bill-Axelson, A.; Bergkvist, L.; Forslund, M.; Mattsson, S.; von Essen, L.; Nygren, P.; Igelström,
H.; Johansson, B. Cocreated internet-based stepped care for individuals with cancer and concurrent symptoms of anxiety and
depression: Results from the U-CARE AdultCan randomized controlled trial. Psychooncology 2020,29, 2012–2018. [CrossRef]
21.
Chan, A.-W.; Tetzlaff, J.M.; Altman, D.G.; Laupacis, A.; Gøtzsche, P.C.; Krleža-Jeri´c, K.; Hróbjartsson, A.; Mann, H.; Dickersin, K.;
Berlin, J.A.; et al. SPIRIT 2013 Statement: Defining Standard Protocol Items for Clinical Trials. Ann. Intern. Med.
2013
,158, 200.
[CrossRef]
22.
Ochoa-Arnedo, C.; Medina, J.C.; Flix-Valle, A.; Anastasiadou, D. E-health ecosystem with integrated and stepped psychosocial
services for breast cancer survivors: Study protocol of a multicentre randomised controlled trial. BMJ Open
2021
,11, e041548.
[CrossRef]
23.
Ochoa-Arnedo, C.; Flix-Valle, A.; Medina, J.C.; Escriche, E.; Rodríguez, A.; Villanueva, C.; Sumalla, E.C.; Alabèrnia-Segura, M.;
Prats, C.; Corral, M.J.; et al. Programa E-Health ICOnnecta’t: Un ecosistema para fomentar el bienestar en cáncer a través de la
propuesta europea ONCOMMUN. Psicooncología2020,17, 41–58. [CrossRef]
24.
National Cancer Institute (U.S.). Common Terminology Criteria for Adverse Events (CTCAE); Version 5.0; U.S. Department of Health
and Human Services: Washington, DC, USA, 2017. Available online: https://ctep.cancer.gov/protocoldevelopment/electronic_
applications/docs/ctcae_v5_quick_reference_5x7.pdf (accessed on 5 February 2022).
25.
Harju, E.; Michel, G.; Roser, K. A systematic review on the use of the emotion thermometer in individuals diagnosed with cancer.
Psychooncology 2019,28, 1803–1818. [CrossRef]
26.
Gil, F.; Grassi, L.; Travado, L.; Tomamichel, M.; Gonzalez, J.R.; Zanotti, P.; Lluch, P.; Hollenstein, M.F.; Maté, J.; Magnani, K.; et al.
Use of distress and depression thermometers to measure psychosocial morbidity among southern European cancer patients.
Support. Care Cancer 2005,13, 600–606. [CrossRef]
27.
Requena, G.C.; Martín, X.P.; Baró, M.S.; Moncayo, F.L.G. Discriminación del malestar emocional en pacientes oncológicos
utilizando la escala de ansiedad y depresión hospitalaria (HADS). Ansiedad Estres 2009,15, 217–229.
28.
Terol-Cantero, M.C.; Cabrera-Perona, V.; Martín-Aragón, M. Revisión de estudios de la Escala de Ansiedad y Depresión
Hospitalaria (HAD) en muestras españolas. An. Psicol. 2015,31, 494. [CrossRef]
29.
Ochoa Arnedo, C.; Casellas-Grau, A. Positive Psychotherapy in Cancer: Facilitating Posttraumatic Growth in Assimilation
and Accommodation of Traumatic Experience. In Comprehensive Guide to Post-Traumatic Stress Disorder; Springer International
Publishing: Cham, Switzerland, 2015; pp. 1–14.
30.
Blevins, C.A.; Weathers, F.W.; Davis, M.T.; Witte, T.K.; Domino, J.L. The Posttraumatic Stress Disorder Checklist for DSM-5
(PCL-5): Development and Initial Psychometric Evaluation. J. Trauma. Stress 2015,28, 489–498. [CrossRef]
31.
Linnemørken, L.T.B.; Granan, L.-P.; Reme, S.E. Prevalence of Posttraumatic Stress Symptoms and Associated Characteristics
Among Patients With Chronic Pain Conditions in a Norwegian University Hospital Outpatient Pain Clinic. Front. Psychol.
2020
,
11, 1–14. [CrossRef]
32.
Costa-Requena, G.; Gil Moncayo, F.L. Crecimiento postraumático en pacientes oncológicos. Análisis Modif. Conduct.
2007
,33, 148.
[CrossRef]
Cancers 2022,14, 974 13 of 13
33.
Mazor, Y.; Gelkopf, M.; Mueser, K.T.; Roe, D. Posttraumatic Growth in Psychosis. Front. Psychiatry
2016
,7, 1–11. [CrossRef]
[PubMed]
34.
Badia, X.; Schiaffino, A.; Alonso, J.; Herdman, M. Using the EuroQoI 5-D in the Catalan general population: Feasibility and
construct validity. Qual. Life Res. 1998,7, 311–322. [CrossRef]
35.
Szende, A.; Janssen, B.; Cabases, J. (Eds.) Self-Reported Population Health: An International Perspective based on EQ-5D; Springer
Netherlands: Dordrecht, The Netherlands, 2014; ISBN 978-94-007-7595-4.
36.
Costa Requena, G.; Salamero, M.; Gil, F. Validación del cuestionario MOS-SSS de apoyo social en pacientes con cáncer. Med. Clin.
2007,128, 687–691. [CrossRef] [PubMed]
37. R Core Team, R. A Language and Environment for Statistical Computing 2020; R Core Team: Vienna, Austria, 2020.
38.
Carlson, L.E.; Waller, A.; Groff, S.L.; Giese-Davis, J.; Bultz, B.D. What goes up does not always come down: Patterns of distress,
physical and psychosocial morbidity in people with cancer over a one year period. Psychooncology
2013
,22, 168–176. [CrossRef]
[PubMed]
39. Syrowatka, A.; Motulsky, A.; Kurteva, S.; Hanley, J.A.; Dixon, W.G.; Meguerditchian, A.N.; Tamblyn, R. Predictors of distress in
female breast cancer survivors: A systematic review. Breast Cancer Res. Treat. 2017,165, 229–245. [CrossRef] [PubMed]
40. Pais-Ribeiro, J.L.; Silva, A.C. Social Support Survey With a Sample of Aged People. Psicol. Saúde Doenças 2009,10, 163–174.
41. Sherbourne, C.D.; Stewart, A.L. The MOS social support survey. Soc. Sci. Med. 1991,32, 705–714. [CrossRef]
42.
Nouri, S.S.; Adler-Milstein, J.; Thao, C.; Acharya, P.; Barr-Walker, J.; Sarkar, U.; Lyles, C. Patient characteristics associated with
objective measures of digital health tool use in the United States: A literature review. J. Am. Med. Inform. Assoc.
2020
,27, 834–841.
[CrossRef]
43.
Swartzman, S.; Booth, J.N.; Munro, A.; Sani, F. Posttraumatic stress disorder after cancer diagnosis in adults: A meta-analysis.
Depress. Anxiety 2017,34, 327–339. [CrossRef]
44.
Lleras de Frutos, M.; Medina, J.C.; Vives, J.; Casellas-Grau, A.; Marzo, J.L.; Borràs, J.M.; Ochoa-Arnedo, C. Video conference
vs face-to-face group psychotherapy for distressed cancer survivors: A randomized controlled trial. Psychooncology
2020
,29,
1995–2003. [CrossRef]
45.
Ho, P.J.; Gernaat, S.A.M.; Hartman, M.; Verkooijen, H.M. Health-related quality of life in Asian patients with breast cancer: A
systematic review. BMJ Open 2018,8, e020512. [CrossRef]
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