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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



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.
Cancers 2022, 14, 974.
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
, Aida Flix-Valle
, Ana Rodríguez-Ortega
, Rosa Hernández-Ribas
María Lleras de Frutos
and Cristian Ochoa-Arnedo
E-Health ICOnnecta’t and Psycho-Oncology Services, Institut Català d’Oncologia, L’Hospitalet de
Llobregat, 08908 Barcelona, Spain; (J.C.M.); (A.F.-V.); (A.R.-O.); (R.H.-R.); (M.L.d.F.)
Department of Psychology and Education Sciences, Universitat Oberta de Catalunya, 08018 Barcelona,
Department of Clinical Psychology and Psychobiology, Universitat de Barcelona, 08035 Barcelona, Spain
Psycho-Oncology and Digital Health, Health Services Research in Cancer, Institut d’Investigació Biomèdica
de Bellvitge (IDIBELL), L’Hospitalet del Llobregat, 08908 Barcelona, Spain
Department of Psychiatry, Hospital Universitari de Bellvitge, 08908 Barcelona, Spain
Centro de Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain
* Correspondence:; 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.
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
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Copyright: © 2022 by the authors. Licen-
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under the terms and conditions of the
Creative Commons Attribution (CC BY)
license (
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 (; 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-
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-
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
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
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-
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
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)
atients contacted
139 patients excluded
- Not interested (n = 107)
- No internet access (n = 25)
- Not fluent in Spanish (n = 7)
ants acce
ted enrollmen
141 users 48 non-users
6 o
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
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
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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... Part of that process is to create a table that lists the characteristics of each study analyzed: participants, intervention, results, medical outcomes, and study design (see Table 1: PICOS). The 33 studies are broken down into the following years: 2012(0), 2013(0), 2014(1) [19], 2015(2) [20,21], 2016(1) [22], 2017(4) [23][24][25][26], 2018(4) [27][28][29][30], 2019(1) [31], 2020(7) [32][33][34][35][36][37][38], 2021(8) [39][40][41][42][43][44][45][46], 2022(5) [47][48][49][50][51]. All studies involved adults as participants. ...
... Thirteen themes and four individual observations were identified by the reviewers for a total of 111 occurrences in the literature. The theme most often observed was "improved mental health", which occurred 16/111 (14%) occurrences [19,23,34,36,39,40,46,49,50]. This theme combined observations of anxiety, distress, fear of reoccurrence, depression, optimism, self-efficacy, and self-actualization. ...
... Although nausea and vomiting are highly correlated, they are not synonymous, so reviewers chose to report them separately, but they appeared together in two studies. Two themes appeared in 3/111 (3%) of the occurrences: improved global health/return to baseline functioning [22,35,43] and improved social support, and questions were answered by providers [21,29,50]. Two themes occurred in 2/111 (2%) of the occurrences: improved arm symptoms/upper limb functionality [37,48], and the app provided education and answered questions [32,42]. ...
Full-text available
Background: Breast cancer affects 2.3 million women and kills 685,000 globally, making it the most prevalent cancer. The telemedicine modality has been used to treat the symptoms associated with breast cancer recovery. Objectives: To analyze the effectiveness of telemedicine to help women recover from the treatment-associated effects and promote overall recovery from breast cancer. Methods: Four databases were queried for published literature from the last 10 years. The systematic literature review was conducted in accordance with the Kruse Protocol and reported in accordance with PRISMA 2020. Results: Five interventions were identified in the literature, with the most dominant being eHealth and mHealth. The other interventions were telephone, video teleconference, and a combination of eHealth and mHealth. There were positive effects of these telemedicine interventions in 88% of the studies analyzed. Telemedicine is shown to positively affect physical and mental health, sleep outcomes, quality of life, and body image. The largest barriers to the adoption of telemedicine interventions are training, cost, workflow, time of providers, and low reimbursement. Conclusion: Telemedicine offers promise to both providers and breast cancer survivors to improve the physical and mental health detriments of both cancer and its associated treatments. It also helps women develop healthy habits to reduce the risk of reoccurrence.
... ICOnnecta't is a stepped eHealth ecosystem led from a public, monographic, oncological hospital located in Southern Europe and supported by Horizon 2020, through the European Institute of Innovation and Technology, which pursues the deployment of new digital tools in cancer care. The objective of this program applied to BC patients is to support them by, first, monitoring their symptoms and assessing psychosocial risk through a mobile application, and, if needed, offering them educational information and psychosocial care [21,22]. Therefore, ICOnnecta't is structured in four stepped levels: (1) screening and monitoring, (2) education resources, (3) peer-support community, and (4) online-group psychotherapy. ...
... Indeed, the ecosystem was created to democratize psychosocial care and health education in cancer. Recently, ICOnnecta't has proven to be a successful eHealth tool to monitor symptoms and psychosocial needs, facilitating access to guided early interventions [22]. Digital psychosocial education resources may promote equity in the access of knowledge, and therefore result in an increased quality of life of BC patients In ICOnnecta't, BC patients can access the second care level, education resources, both autonomously (directly from the app) or prescribed by a professional when they detect distress during the screening and monitoring (first care level of the ecosystem). ...
... It is organized into four levels of care, offered progressively, according to patients' psychosocial needs. All patients join the program in the first level and they retain access to the previous levels if they step up [22,24]. ...
Full-text available
Health education and psychosocial interventions prevent emotional distress, and the latter has been shown to have an impact on survival. In turn, digital health education interventions may help promote equity by reaching a higher number of cancer patients, both because they avoid journeys to the hospital, by and having a better efficiency. A total of 234 women recently diagnosed with breast cancer in a comprehensive cancer center used the digital ecosystem ICOnnecta’t from March 2019 to March 2021. ICOnnecta’t consists of four care levels, provided to patients according to their level of distress. The second level of this intervention consists of an educational campus, which was analyzed to track users’ interests and their information-seeking behavior. Overall, 99 out of 234 women (42.3%) used the educational campus. There were no significant differences in sociodemographic and clinical variables between the campus users and non-users. Among users, the median number of resources utilized per user was four (interquartile range: 2–9). Emotional and medical resources were the contents most frequently viewed and the audiovisual format the most consulted (p < 0.01). Resources were used mainly within the first three months from enrolment. Users who were guided to visit the virtual campus were more active than spontaneous users. Offering an early holistic health educational platform inside a digital cancer ecosystem, with health professionals involved, can reach more patients, promoting equity in the access of cancer information and prevention, from the very beginning of the disease.
... The tiered intervention has four levels, starting with screening and monitoring of psychosocial needs and offering more intensive interventions as patients need them, and the last level is group psychotherapy. This has shown promising early results in breast cancer (Ciria-Suarez et al., 2022;Medina et al., 2022) and the potential to address the psychosocial needs in lung cancer (Graves et al., 2007). It is anticipated that a stepped, tailored, psychosocial eHealth intervention based on this model will detect distress in patients with lung cancer quickly and facilitate the provision of personalized solutions through psychological counseling, health literacy, and social support in the community. ...
... However, existing research has usually centered on physical symptoms, not the emotional and spiritual concerns of patients. Furthermore, patients benefit most from psychosocial interventions when they have elevated baseline emotional distress (Jansen et al., 2019;Medina et al., 2022;Nissen et al., 2021), stressing the importance of individualized strategies that allocate resources to patients with at-risk profiles. ...
Full-text available
Background: Receiving a diagnosis of lung cancer is an emotional event, not least because it is usually diagnosed at advanced stages with limited life expectancy. Although evidence-based educational, emotional, and social interventions exist, they reach few patients and usually when it is too late. Objective: This project will be carried out in a comprehensive center for cancer care and health research, aiming to study the efficacy, costs, and utility of an eHealth ecosystem to meet the psychosocial needs of patients with advanced lung cancer. Method: We will enroll 76 patients with advanced lung cancer into an eHealth ecosystem of stepped and personalized psychosocial care for 9 months. These patients will be compared with another 76 receiving usual care in a non-inferiority randomized controlled trial. The following main outcomes will be measured every 3 months: emotional distress, spirituality, demoralization, quality of life, and medication adherence. Secondary outcomes will include symptomatology, health education, cost-utility analyses, usability and satisfaction with the platform, and time to detect emotional needs and provide care. Baseline differences between groups will be measured with the Student t-test or chi-square test, as appropriate. We will then compare the main outcomes between groups over time using multilevel linear models, report effect sizes (Hedges' g), and assess non-inferiority. The cost-utility of both interventions will be considered in terms of quality adjusted life years and quality of life given the costs of providing each treatment. Discussion: This randomized controlled trial should provide new evidence on the efficacy and cost-utility of an eHealth ecosystem to deliver personalized and timely psychosocial care to patients with advanced lung cancer. Trial registration: ID "NCT05497973".
... During the ongoing coronavirus disease 2019 (COVID-19) pandemic, telemedicine-based psychosocial intervention appears to be a promising option for BC patients. It not only maintains BC patients' access and continuity of care, optimizes face-to-face services, and minimizes infectious transmission, but also caters to patients with individualized needs and social stigma [69,70]. Based on the evidence provided in this review, the use of telemedicine-based psychosocial interventions has revealed some effectiveness and highlights several directions for future research. ...
Full-text available
Objectives This review aimed to synthesize the available evidence on the effectiveness of telemedicine-based psychosocial interventions among breast cancer (BC) patients regarding quality of life (QOL), depression, anxiety, distress, fatigue, sleep disorders, sexual function, and fear of cancer recurrence (FCR). Methods A search of 10 databases was conducted to identify RCTs of the effects of telemedicine-based psychosocial interventions on outcomes. Selection of studies, quality appraisal, and data extraction were performed by two reviewers independently. GRADE and Cochrane risk of bias assessment tools were used for quality appraisal. Heterogeneity was determined by I², standardized mean differences (SMD) were used to determine intervention effects, and meta-analyses, subgroup analysis, and sensitivity analysis were performed. Results In total, 29 RCTs were included. Telemedicine-based psychosocial interventions improved the primary outcomes of QOL (SMD = 0.32), distress (SMD = − 0.22), and anxiety (SMD = − 0.16) in BC patients with moderate effect size. There were some improvements in the secondary outcomes of sleep disorders (SMD = − 056), sexual function (SMD = 0.19), and FCR (SMD = − 0.41). After sensitivity analysis, the effect size of fatigue was moderate (SMD = − 0.24). Conclusion Telemedicine-based psychosocial interventions are superior to usual care in BC patients with improved QOL, sexual function, and less distress, anxiety, fatigue, sleep disorders, and FCR. Due to the heterogeneity of the results for QOL, anxiety, fatigue, sleep disturbance, and FCR, these results should be interpreted cautiously. In the future, more rigorous RCTs need to be designed to identify better delivery models and intervention times to further test their effectiveness.
... Qualitative research offers a solution to the gap in the literature on PS as it can further our understanding of the patient's experience of participating in PS. Specifically, meta-ethnography, a well-known method to synthesize qualitative research, is useful in the area of analyzing individuals' experiences (Adams et al., 2011;Atkins et al., 2008;Wanat et al., 2016), which can then be used to understand patient needs, as information and support are frequently sought by patients (Ciria-Suarez et al., 2022;Medina et al., 2022). To the best of our knowledge, no previous meta-ethnography has been conducted to explore the experience of PS in BC. ...
Full-text available
Breast cancer is associated with adverse physical and psychological consequences. Although research has identified the various benefits linked to psychosocial interventions, mixed results have been found in relation to peer support. The aim of the present systematic review and meta-ethnography is to explore the qualitative evidence on the experience of breast cancer survivors in peer support. A systematic search of the literature was conducted until June 2023, and a meta-ethnographic approach was used to synthesize the included papers. Eleven articles were included, collecting the experience of 345 participants. The following four core areas involved in peer support implementation were identified from the synthesis: Peer support can create understanding and a mutual therapeutic and emotional connection; peer support can facilitate an educational and supportive patient-centered journey ; peer support should monitor group members for unpleasant emotional experiences; peer support should have professional supervision of recruitment and Derek Clougher and Laura Ciria-Suarez contributed equally to this work.
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Background: The COVID-19 pandemic has disrupted medical care, increased isolation, and exacerbated anxiety in breast cancer patients. Since March 2020, experienced a sustained surge in requested pandemic-related information and support. To characterize the pandemic-related experiences of breast cancer patients, we surveyed the Community early in the COVID-19 era. Methods: Community members were invited to complete an online questionnaire regarding their experience during the pandemic. Self-reported data on demographics, comorbidities, care disruptions, anxiety, coping ability, telemedicine use, and satisfaction with care were collected. Results were analyzed using Stata 16.0 (Stata Corp., Inc). Results: Included were 568 current and previous breast cancer patients, primarily with U.S. residence. Overall, 43.8% reported at least one comorbidity associated with severe COVID-19 illness and 61.9% experienced care delays. Moderate to extreme anxiety about contracting COVID-19 was reported by 36.5%, increasing with number of comorbidities (33.0% vs. 55.4%, p = 0.021), current breast cancer diagnosis (30.4% vs. 42.5%, p = 0.011), and poorer coping ability (15.5% vs. 53.9%, p < 0.0001). Moderate to extreme anxiety about cancer care disruptions was reported by 29.1%, increasing with current breast cancer diagnosis (19.1% vs. 38.9%, p < 0.0001), actual delayed care (18.9% vs. 35.3%, p < 0.0001), and poorer coping ability (13.1% vs. 57.7%, p < 0.0001). Most utilized telehealth and found it helpful, but also expressed increased anxiety and subjectively expressed that these were less preferable. Conclusion: Early in the COVID-19 pandemic, anxiety was reported by a large proportion of breast cancer patients, with increased prevalence in those with risk factors. Attention to mental health is critical, as emotional distress not only harms quality of life but may also compromise outcomes.
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Cancer is a very distressing disease, not only for the patients themselves, but also for their family members and relatives. Therefore, patients are regularly monitored to decide whether psychological treatment is necessary and applicable. However, such monitoring processes are costly in terms of required staff and time. Mobile data collection is an emerging trend in various domains. The medical and psychological field benefits from such an approach, which enables experts to quickly collect a large amount of individual health data. Mobile data collection applications enable a more holistic view of patients and assist psychologists in taking proper actions. We developed a mobile application, FeelBack, which is designed to support data collection that is based on well-known and approved psychological instruments. A controlled pilot evaluation with 60 participants provides insights into the feasibility of the developed platform and it shows the initial results. 31 of these participants received paper-based questionnaire and 29 followed the digital approach. The results reveal an increase of the overall acceptance by 58.5% in the mean when using a digital screening as compared to the paper-based. We believe that such a platform may significantly improve cancer patients’ and relatives’ psychological treatment, as available data can be used to optimize treatment.
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Background Electronic symptom self-reporting systems (e-SRS) have been shown to improve symptoms and survival in patients with cancer. However, patient engagement in using e-SRS for voluntary symptom self-reporting is less optimal. Multiple factors can potentially affect patients’ acceptance and engagement in using home-based e-SRS. However, such factors have not been fully explored in cancer populations. Objective The aim of this study is to understand the acceptance and use of home-based e-SRS by patients with cancer and identify associated facilitators and barriers. Methods PubMed, CINAHL, Scopus, and PsycINFO (January 2010 to March 2020) were searched using a combination of Medical Subject Headings (MeSH) terms and keywords such as symptom self-reporting, electronic/technology, cancer, and their synonyms. Included studies focused on the use of home-based e-SRS by patients with cancer and their families. Studies on patients’ use of e-SRS in clinical settings only were excluded. Of the 3740 papers retrieved, 33 were included in the final review. Factors associated with patient acceptance and use of e-SRS were extracted and synthesized. Results Most e-SRS were web based (22/33, 66%) or mobile app based (9/33, 27%). The e-SRS initial acceptance, represented by patient enrollment rates, ranged from 40% (22/55) to 100% (100/100). High e-SRS acceptance was rated by 69% (59/85) to 77.6% (337/434) of the patients after they used the system. The e-SRS use, measured by patients’ response rates to questionnaires (ranging from 1596/3521, 45.33% to 92%) or system log-on rates (ranging from 4/12, 33% to 99/100, 99%), declined over time in general patterns. Few studies (n=7) reported e-SRS use beyond 6 months, with the response rates ranging from 62% (40/64) to 85.1% (541/636) and the log-on rates ranging from 63.6% (103/162) to 77% (49/64). The availability of compatible devices and technical support, interactive system features, information accessibility, privacy, questionnaire quality, patient physical/psychosocial status, and age were associated with patient acceptance and use of home-based e-SRS. Conclusions Acceptance and use of home-based e-SRS by patients with cancer varied significantly across studies, as assessed by a variety of approaches. The lack of access to technology has remained a barrier to e-SRS adoption. Interactive system features and personalized questionnaires may increase patient engagement. More studies are needed to further understand patients’ long-term use of home-based e-SRS behavior patterns to develop personalized interventions to support symptom self-management and self-reporting of patients with cancer for optimal health outcomes.
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Introduction Psychosocial interventions for patients with breast cancer (BC) have demonstrated their effectiveness at reducing emotional distress and improving quality of life. The current digitisation of screening, monitoring and psychosocial treatment presents the opportunity for a revolution that could improve the quality of care and reduce its economic burden. The objectives of this study are, first, to assess the effectiveness of an e-health platform with integrated and stepped psychosocial services compared with usual psychosocial care, and second, to examine its cost–utility. Methods and analysis This study is a multicentre randomised controlled trial with two parallel groups: E-health intervention with integrated and stepped psychosocial services vs usual psychosocial care. An estimated sample of 338 patients with BC in the acute survival phase will be recruited from three university hospitals in Catalonia (Spain) and will be randomly assigned to one of two groups. All participants will be evaluated at the beginning of the study (T1: recruitment), 3 months from T1 (T2), 6 months from T1 (T3) and 12 months from T1 (T4). Primary outcome measures will include number of clinical cases detected, waiting time from detection to psychosocial intervention and proportion of cases successfully treated in the different steps of the intervention, as well as outcomes related to emotional distress, quality of life, post-traumatic stress and growth, treatment adherence and therapeutic alliance. Secondary outcomes will include the acceptability of the platform, patients’ satisfaction and usability. For the cost–utility analysis, we will assess quality-adjusted life years and costs related to healthcare utilisation, medication use and adherence, work absenteeism and infrastructure-related and transport-related costs. Ethics and dissemination This study was approved by the Ethics committee of the Institut Català d’Oncologia network in Hospitalet, Spain. Findings will be disseminated through peer-reviewed journals, reports to the funding body, conferences among the scientific community, workshops with patients and media press releases. Trial registration number Online Psychosocial Cancer Screening, Monitoring and Stepped Treatment in Cancer Survivors (ICOnnectat-B),NCT04372459.
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Background: eHealth mindfulness-based programs (eMBPs) are on the rise in complex oncology and palliative care. However, we are still at the beginning of answering the questions of how effective eMBPs are and for whom, and what kinds of delivery modes are the most efficient. Objective: This systematic review aims to examine the feasibility and efficacy of eMBPs in improving the mental health and well-being of patients with cancer, to describe intervention characteristics and delivery modes of these programs, and to summarize the results of the included studies in terms of moderators, mediators, and predictors of efficacy, adherence, and attrition. Methods: In total, 4 databases (PubMed, PsycINFO, Scopus, and Web of Knowledge) were searched using relevant search terms (eg, mindfulness, program, eHealth, neoplasm) and their variations. No restrictions were imposed on language or publication type. The results of the efficacy of eMBPs were synthesized through the summarizing effect estimates method. Results: A total of 29 published papers describing 24 original studies were included in this review. In general, the results indicate that eMBPs have the potential to reduce the levels of stress, anxiety, depression, fatigue, sleep problems, and pain, and improve the levels of mindfulness, posttraumatic growth, and some parameters of general health. The largest median of Cohen d effect sizes were observed in reducing anxiety and depression (within-subject: median −0.38, IQR −0.62 to −0.27; between-group: median −0.42, IQR −0.58 to −0.22) and facilitating posttraumatic growth (within-subject: median 0.42, IQR 0.35 to 0.48; between-group: median 0.32, IQR 0.22 to 0.39). The efficacy of eMBP may be comparable with that of parallel, face-to-face MBPs in some cases. All studies that evaluated the feasibility of eMBPs reported that they are feasible for patients with cancer. Potential moderators, mediators, and predictors of the efficacy, attrition, and adherence of eMBPs are discussed. Conclusions: Although the effects of the reviewed studies were highly heterogeneous, the review provides evidence that eMBPs are an appropriate way for mindfulness practice to be delivered to patients with cancer. Thus far, existing eMBPs have mostly attempted to convert proven face-to-face mindfulness programs to the eHealth mode. They have not yet fully exploited the potential of eHealth technology.
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Background: Chronic disease represents a large and growing burden to the health care system worldwide. One method of managing this burden is the use of app-based interventions; however attrition, defined as lack of patient use of the intervention, is an issue for these interventions. While many apps have been developed, there is some evidence that they have significant issues with sustained use, with up to 98% of people only using the app for a short time before dropping out and/or dropping use down to the point where the app is no longer effective at helping to manage disease. Objective: Our objectives are to systematically appraise and perform a meta-analysis on dropout rates in apps for chronic disease and to qualitatively synthesize possible reasons for these dropout rates that could be addressed in future interventions. Methods: MEDLINE (Medical Literature Analysis and Retrieval System Online), PubMed, Cochrane CENTRAL (Central Register of Controlled Trials), and Embase were searched from 2003 to the present to look at mobile health (mHealth) and attrition or dropout. Studies, either randomized controlled trials (RCTs) or observational trials, looking at chronic disease with measures of dropout were included. Meta-analysis of attrition rates was conducted in Stata, version 15.1 (StataCorp LLC). Included studies were also qualitatively synthesized to examine reasons for dropout and avenues for future research. Results: Of 833 studies identified in the literature search, 17 were included in the review and meta-analysis. Out of 17 studies, 9 (53%) were RCTs and 8 (47%) were observational trials, with both types covering a range of chronic diseases. The pooled dropout rate was 43% (95% CI 29-57), with observational studies having a higher dropout rate (49%, 95% CI 27-70) than RCTs in more controlled scenarios, which only had a 40% dropout rate (95% CI 16-63). The studies were extremely varied, which is represented statistically in the high degree of heterogeneity (I2>99%). Qualitative synthesis revealed a range of reasons relating to attrition from app-based interventions, including social, demographic, and behavioral factors that could be addressed. Conclusions: Dropout rates in mHealth interventions are high, but possible areas to minimize attrition exist. Reducing dropout rates will make these apps more effective for disease management in the long term. Trial registration: International Prospective Register of Systematic Reviews (PROSPERO) CRD42019128737;
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Purpose The aim was to evaluate the effects of co‐created internet‐based stepped care (iCAN‐DO) on anxiety, depression, posttraumatic stress, and health‐related quality of life (HRQoL) in individuals with cancer and self‐reported anxiety and/or depression symptoms, compared with standard care. Patients and methods Clinically recruited individuals with breast, colorectal, or prostate cancer underwent online screening with the Hospital Anxiety and Depression Scale (HADS). Those with anxiety and/or depression symptoms (> 7 on any of the HADS subscales) were randomized to iCAN‐DO or standard care. iCAN‐DO comprised psychoeducation and self‐care strategies (step 1) and internet‐based cognitive behavioral therapy (iCBT, step 2). Data were collected before randomization and at 1, 4, 7, and 10 months and analyzed with ITT regression analysis and randomization tests. Results Online screening identified 245 (27%) of 909 individuals who reported anxiety and/or depression symptoms. They were randomized to iCAN‐DO (n=124) or standard care (n=121). Of them 49% completed the 10‐month assessment, and in the iCAN‐DO group 85% accessed step 1 and 13% underwent iCBT. iCAN‐DO decreased the levels of symptoms of depression (‐0.54, 95% CI: (‐1.08)– (‐0.01), p<.05) and the proportion of individuals with symptoms of depression (p<.01) at 10 months, compared with standard care, according to HADS. There were no significant effects on anxiety, posttraumatic stress, or HRQoL. Conclusion Internet‐based stepped care improves symptoms of depression in individuals with cancer. Further studies are needed to gain knowledge on how to optimize and implement internet‐based support in oncology care. This article is protected by copyright. All rights reserved.
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Objective This study assesses the effectiveness of face‐to‐face group positive psychotherapy for cancer survivors (PPC) compared to its online adaptation, online group positive psychotherapy for cancer survivors (OPPC), which is held via videoconference. A two‐arm, pragmatic RCT was conducted to examine the effects of both interventions on emotional distress, posttraumatic stress (PTSS) and posttraumatic growth (PTG) among cancer survivors and analyze attrition to treatment. Methods Adult women with a range of cancer diagnoses were invited to participate if they experienced emotional distress at the end of their primary oncological treatment. Emotional distress, PTSS and PTG were assessed at baseline, immediately after treatment and three months after treatment. Intention‐to‐treat analyses were carried out using general linear mixed models to test the effect of the interventions overtime. Logistic regressions were performed to test differential adherence to treatment and retention to follow‐up. Results A total of 269 individuals participated. The observed treatment effect was significant in both modalities, PPC and OPPC. Emotional distress (b = − 2.24, 95%CI = ‐3.15‐ −1.33) and PTSS (b = − 3.25, 95%CI = ‐4.97‐ −1.53) decreased significantly over time, and PTG (b = 3.08, 95%CI = 0.38‐5.78) increased significantly. Treatment gains were sustained across outcomes and over time. Analyses revealed no significant differences between modalities of treatment, after adjusting for baseline differences, finding that OPPC is as effective and engaging as PPC. Conclusions The OPPC treatment was found to be effective and engaging for female cancer early survivors. These results open the door for psycho‐oncology interventions via videoconference, which are likely to lead to greater accessibility and availability of psychotherapy. This article is protected by copyright. All rights reserved.
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Objective: The study sought to determine which patient characteristics are associated with the use of patient-facing digital health tools in the United States. Materials and methods: We conducted a literature review of studies of patient-facing digital health tools that objectively evaluated use (eg, system/platform data representing frequency of use) by patient characteristics (eg, age, race or ethnicity, income, digital literacy). We included any type of patient-facing digital health tool except patient portals. We reran results using the subset of studies identified as having robust methodology to detect differences in patient characteristics. Results: We included 29 studies; 13 had robust methodology. Most studies examined smartphone apps and text messaging programs for chronic disease management and evaluated only 1-3 patient characteristics, primarily age and gender. Overall, the majority of studies found no association between patient characteristics and use. Among the subset with robust methodology, white race and poor health status appeared to be associated with higher use. Discussion: Given the substantial investment in digital health tools, it is surprising how little is known about the types of patients who use them. Strategies that engage diverse populations in digital health tool use appear to be needed. Conclusion: Few studies evaluate objective measures of digital health tool use by patient characteristics, and those that do include a narrow range of characteristics. Evidence suggests that resources and need drive use.
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Introduction: Comorbid posttraumatic stress disorder (PTSD) in patients with chronic pain may have a negative effect on the course and outcome of both disorders. Nevertheless, the co-occurrence of the two conditions is often overlooked in clinical settings. Further, little is known about how PTSD is associated with biopsychosocial characteristics in this patient group. The first objective was to assess the prevalence of posttraumatic stress symptoms (PTSS) in patients with chronic pain in a Norwegian university hospital outpatient pain clinic. The second objective was to investigate possible associations between PTSS and adverse outcomes such as pain intensity, disability, and distress. The third objective was to compare the PTSS prevalence rates between primary versus secondary pain conditions. Materials and methods: Six hundred and ninety-two patients meeting for pain assessment completed self-report questionnaires about PTSS and possibly associated factors. The Life Events Checklist and the Stressful Life Events Screening Questionnaire were used to screen for potentially traumatic life events. The Impact of Events Scale - Revised and the PTSD Checklist for DSM-5 were used to assess PTSS. Differences between patients with and without severe PTSS on the possibly associated variables were analyzed by chi-squared-, and t-tests. Results: 20.7% of the participants reported a level of PTSS qualifying for a PTSD diagnosis. These patients reported higher levels of pain intensity, pain bothersomeness, disability, and psychological distress, as well as lower levels of self-efficacy. They also reported higher levels of pain catastrophizing, perceived injustice, fatigue, and sleep difficulties. Finally, there was not a significant difference in prevalence rates between primary and secondary pain conditions. Discussion: PTSS are frequent in patients with chronic pain, and a range of psychological characteristics is associated with a high level of such symptoms in this patient group. Patients with both conditions report a significantly higher symptom load, and the potential impact on the individual's life is major. In terms of pain condition, there were no differences in PTSS between primary pain conditions and secondary pain conditions in this pain population. This study emphasizes the importance of increased attention on PTSS when seeing patients with chronic pain conditions in clinical practice.
The increasing use of eHealth has ushered in a new era of patient-centred cancer care that moves beyond the traditional in-person care model to real-time, dynamic, and technology-assisted assessments and interventions. eHealth has the potential to better the delivery of cancer care through improved patient–provider communication, enhanced symptom and toxicity assessment and management, and optimised patient engagement across the cancer care continuum. In this Review, we provide a brief, narrative appraisal of the peer reviewed literature over the past 10 years related to the uses of patient-centred eHealth to improve cancer care delivery. These uses include the addressal of symptom management, health-related quality of life, and other patient-reported outcomes across cancer care. In addition, we discuss the challenges of, and opportunities for, accessibility, scalability, and implementation of these technologies, important areas for further development, and future research directions.