hindered due to lack of resources. The flexibility of iCBT and the fact
that it requires a low level of support makes it a suitable alternative to
be offered as a relapse prevention intervention within clinical services.
This would be especially relevant during the first 6 months after dis-
continuation of treatment, when most relapses occur (Ali et al.,
2017;
Klein et al.,
2018). In one study, an iCBT programme for relapse pre-
vention was offered to partially remitted patients and the protective
effects were observed up to 2 years later (Holländare et al.,
2013).
Therefore, the utilization of iCBT for relapse prevention holds promise
for reducing relapse rates in individuals who respond to an acute
phase of treatment.
Our study is not without limitations. We did not have a control
group at follow up, which prevents us attributing the observed main-
tenance of the effects solely to the iCBT intervention. However,
reductions of symptoms during follow-up have been shown in previ-
ous iCBT studies (Andersson et al.,
2018; Andrews et al., 2018). The
design of the study allowed us to follow up patients up to 9 months
post-treatment, which prevents us from comparing results at a later
time, when relapse rates might be higher. Future studies should
include longer follow ups to explore the durability of the effects up to
2 years. Finally, the measurements used to calculate relapse are self-
reported and no objective clinical assessment was completed with
participants beyond 3 months. However, routine outcome assessment
using the PHQ-9 and GAD-7 is common in these service settings, and
this study provides further support for how using them to continue to
acquire information about patients' symptoms beyond the end of
treatment can provide an important means for rapidly identifying both
remitters and relapsers.
5 | C O N C L U S I O N S
The ability to identify patients at risk of relapse is an important chal-
lenge, as recurrence of depression and anxiety may require further
treatment, which can lead to higher personal, economic and societal
costs. This paper adds to the literature on the durable effects of iCBT
and factors that may predict risk of relapse after an acute phase of
iCBT treatment. Results showed that during a 9-month period after
iCBT treatment, the relapse rates were comparable to face-to-face
CBT. Our findings on predictors suggest that remitters who have
residual symptoms of anxiety at post-treatment, who are young and
suffer from long-term conditions have a higher risk for relapse. Early
identification of these individuals and the provision of supplementary
iCBT sessions or other targeted relapse prevention support could help
to improve the likelihood that these individuals stay in remission after
successful treatment. The routine care setting for this trial supports
the ecological validity to the results as well as the potential to modify
care pathways to leverage iCBT in relapse prevention.
A C K N O W L E D G E M E N T S
We are grateful to the Berkshire NHS Foundation Trust for providing
assistance that allowed us to successfully complete the execution of
the original study which is the basis of this secondary analysis. Special
thanks to Judith Chapman and Sarah Sollesse for their constant
support. Open access funding provided by IReL.
C O N F L I C T O F I N T E R E S T
Authors Palacios, Enrique, Mooney, Farrell, Earley, Duffy, Eilert, Harty
and Richards are employees of SilverCloud Health and Timulak serves
as a research consultant for SilverCloud Health. SilverCloud Health is
a commercial entity that develops computerized psychological inter-
ventions for depression, anxiety, stress and comorbid long-term con-
ditions and sells these to Health Services globally. In England, the
SilverCloud service is delivered free to patients through the Improving
Access to Psychological Therapies (IAPT) programme. Commercial
departments within SilverCloud Health played no role in the analysis
or interpretation of data.
D A T A A V A I L A B I L I T Y S T A T E M E N T
Data supporting these findings, along with all coding and modelling
done as part of the statistical analysis, are available on request from
the corresponding author.
O R C I D
Jorge E. Palacios
https://orcid.org/0000-0002-2103-5507
Angel Enrique https://orcid.org/0000-0003-0585-4008
Olwyn Mooney https://orcid.org/0000-0002-3225-1571
Daniel Duffy https://orcid.org/0000-0001-9722-0437
Nora Eilert https://orcid.org/0000-0003-3184-3599
Siobhan Harty https://orcid.org/0000-0001-7560-9185
Ladislav Timulak https://orcid.org/0000-0003-2785-0753
Derek Richards https://orcid.org/0000-0003-0871-4078
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