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Anxiety, Stress, & Coping: An
International Journal
Publication details, including instructions for authors and
subscription information:
http://www.tandfonline.com/loi/gasc20
Internet-delivered cognitive behavior
therapy for anxiety and insomnia in a
higher education context
Joanna Morrisa, Ashlyn Firkinsa, Abigail Millingsb, Christine Mohrc,
Paul Redfordd & Angela Rowea
a School of Experimental Psychology, University of Bristol, 12a
Priory Road, Bristol BS8 1TU, UK
b Department of Psychology, University of Sheffield, Western
Bank, Sheffield S10 2TP, UK
c Institut de psychologie, Université de Lausanne, Quartier UNIL-
Dorigny, Bâtiment Anthropole, CH-1015 Lausanne, Switzerland
d Department of Psychology, University of the West of England,
Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK
Accepted author version posted online: 16 Jun 2015.Published
online: 20 Jul 2015.
To cite this article: Joanna Morris, Ashlyn Firkins, Abigail Millings, Christine Mohr, Paul Redford
& Angela Rowe (2015): Internet-delivered cognitive behavior therapy for anxiety and insomnia
in a higher education context, Anxiety, Stress, & Coping: An International Journal, DOI:
10.1080/10615806.2015.1058924
To link to this article: http://dx.doi.org/10.1080/10615806.2015.1058924
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Internet-delivered cognitive behavior therapy for anxiety and
insomnia in a higher education context
Joanna Morris
a
, Ashlyn Firkins
a
, Abigail Millings
b
, Christine Mohr
c
, Paul Redford
d
and
Angela Rowe
a
a
School of Experimental Psychology, University of Bristol, 12a Priory Road, Bristol BS8 1TU, UK;
b
Department of
Psychology, University of Sheffield, Western Bank, Sheffield S10 2TP, UK;
c
Institut de psychologie, Université de
Lausanne, Quartier UNIL-Dorigny, Bâtiment Anthropole, CH-1015 Lausanne, Switzerland;
d
Department of
Psychology, University of the West of England, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK
ABSTRACT
Background and Objectives: Anxiety and insomnia can be treated with
internet-delivered Cognitive Behavioral Therapy (iCBT). iCBT may be
well-suited to students who are known to be poor help-seekers and
suffer these symptoms. iCBT can offer easy access to treatment and
increase service availability. The aim of this study was to evaluate the
efficacy of anxiety and insomnia iCBT programs in students. Design:A
randomized, controlled study. Methods: Students were randomly
allocated to intervention (“Anxiety Relief”:n= 43; “Insomnia Relief”:n=
48; control: n= 47). Interventions lasted six weeks. Outcome measures
were the State-Trait Anxiety Inventory and the Pittsburgh Sleep Quality
Index. Results: Significant within-group reductions in anxiety (t(31) =
2.00, p= .03) with moderate between-groups (compared to control)
effect size (d= .64) and increases in sleep quality (t(31) = 3.46, p= .002)
with a moderate between-groups effect size (d= .55) were found for
completers of the anxiety program from pre- to post-intervention.
Significant within-group increases in sleep quality were found for
completers of the insomnia program from pre- to post-intervention
(t(35) = 4.28, p> .001) with a moderate between-groups effect size
(d= .51). Conclusions: Findings support the use of iCBT for anxiety and
insomnia in students, and indicate that further research is needed.
ARTICLE HISTORY
Received 5 September 2013
Revised 27 May 2015
Accepted 28 May 2015
KEYWORDS
Internet-delivered Cognitive
Behavior Therapy; CCBT;
student mental health;
undergraduates; anxiety;
insomnia
Introduction
Student mental ill-health is an increasingly large problem in the UK (Cooke, Bewick, Barkham, Bradley,
& Audin, 2006). Two recent UK student surveys have highlighted that (i) more than 50% reported poor
sleep/insomnia and (ii) up to 55% reported anxiety (Kerr, 2013; StudentBeans.com, 2011). Indeed,
insomnia and anxiety are common manifestations of student psychological distress (Webb,
Ashton, Kelly, & Kamali, 1996). Anxiety and insomnia are frequently comorbid, have a bidirectional
predictive relationship, and are also both predictors of later depression (Jansson-Fröjmark & Lind-
blom, 2008). Both problems have academic as well as mental health implications.
Erratic sleep patterns are common among students (Brown, Buboltz, & Soper, 2002), and poor
sleep quality is a global problem, with a large proportion of students meeting the commonly used
criteria for poor sleep, scoring > 5 on the Pittsburgh Sleep Quality Index (PSQI –Buysse, Reynolds,
Monk, Berman, & Kupfer, 1989). While rates vary across countries, the proportion of students with
poor sleep according to the PSQI criteria is generally concerning, with rates as high as 60% in the
© 2015 Taylor & Francis
CONTACT Abigail Millings a.millings@sheffield.ac.uk
ANXIETY, STRESS, & COPING
http://dx.doi.org/10.1080/10615806.2015.1058924
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USA (Lund, Reider, Whiting, & Prichard, 2010). To the best of our knowledge no published data exist
on the prevalence of poor sleep in UK students according to the PSQI criteria, but rates of UK students
reporting sleep problems in surveys are comparable, ranging from 24% (Webb et al., 1996) to 61%
(Studentbeans.com; Kerr, 2013).
Sleep problems are related to academic performance, and students with sleep problems are over-
represented among students at risk of academic failure (Gaultney, 2010).
Insomnia is also linked with other mental health problems. Sleep problems are prevalent in indi-
viduals with anxiety and depression throughout the adult lifespan (Taylor, Lichstein, Durrence, Reidel,
& Bush, 2005) but also represent an increased risk for the subsequent development of these disorders
(Baglioni et al., 2011). Associations between poor sleep quality and depression have also been found
in student samples (Moo-Estrella, Perez-Benitez, Solis-Rodriguez, & Arankowsky-Sandoval, 2005), and
poor sleep, anxiety, and depression are frequently comorbid and bi-directional (Jansson-Fröjmark &
Lindblom, 2008). Student sleep problems may therefore not only be threatening to academic per-
formance but could also be a risk factor for the development of mental health problems.
Like insomnia, anxiety among students is common and potentially academically damaging. In UK
undergraduates, Andrews and Wilding (2004) found that 20% of previously symptom-free students
became clinically anxious between one month prior to starting university and midway through
their course. Furthermore Macaskill (2012) found that 97% of UK undergraduates who met clinical
caseness criteria on the General Health Questionnaire (Goldberg & Williams, 1991) did so for
anxiety. Anxiety is a known risk factor for depression onset (Parker et al., 1999) and often temporally
precedes depression (de Graaf, Bijl, Spijker, Beekman, & Vollebergh, 2003). Among students, test
anxiety, a situation-specific anxiety (Eysenck, Derakshan, Santos, & Calvo, 2007), has deleterious
effects on academic performance, with those high in test anxiety performing worse than those
who are low in test anxiety (Cassady & Johnson, 2002; Chapell et al., 2005). There is a clear need
for widely available, affordable student mental health service provision to tackle insomnia and
anxiety.
iCBT for anxiety and insomnia
Classical Cognitive Behavior Therapy (CBT, Beck, 1967,1987) is a widely used therapeutic approach
effective in the treatment of common mental health problems (Butler, Chapman, Forman, & Beck,
2006). Meta-analyses have found CBT to be effective at treating anxiety (Morin et al., 2006; Norton
& Price, 2007) and insomnia (Harvey & Tang, 2003; Okajima, Komada, & Inoue, 2011) in a range of
populations. Over the past decade, the Internet has increasingly been used to deploy CBT-based
self-help for common mental health problems including anxiety (Paxling et al., 2011) and insomnia
(Ritterband et al., 2009). Such treatments are referred to as “internet-delivered CBT”(iCBT). The
nature of iCBT means that interventions can be made widely available, all of the time, without
waiting lists (Andersson, 2010; Cavanagh & Millings, 2013).
Researchers have found iCBT to be effective at reducing anxiety. Titov et al. (2009) found that par-
ticipants receiving therapist-assisted iCBT for anxiety improved significantly relative to control. Kiro-
poulos et al. (2008) found that iCBT for panic yielded comparable effectiveness to face-to-face CBT,
and Klein, Richards, and Austin (2006) found that iCBT with email contact had superior effects on
panic over a CBT manual with therapist assistance. Furthermore, Cuijpers et al.’s(2009) meta-analysis
found that computer-assisted psychotherapy was as effective for anxiety disorders as face-to-face
psychotherapy, but there was a negative association between the amount of therapist time that
was “replaced”by the computer and the effect size of improvements. Despite this caution, the evi-
dence base broadly supports the use of iCBT for anxiety disorders. As yet, it is unknown whether
these findings will extend to a student population, where so far, tested programs have been for a
single, situation-specific anxiety (Orbach, Lindsay, & Grey, 2007) rather than more generalized
anxiety symptoms.
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Similar successes are described for iCBT for insomnia. Ritterband, et al. (2009) found that an inter-
net-delivered program was effective at reducing insomnia in a US community sample of insomnia
sufferers. Ström, Pettersson, and Andersson (2004) found that an iCBT program for insomnia was
effective at improving sleep time, total wake time in bed, and sleep efficiency in a Swedish commu-
nity sample of insomnia sufferers, albeit with small effect sizes. Furthermore, Vincent and Lewycky
(2009) found an online insomnia program to be effective for patients undergoing treatment for
chronic insomnia, while in a randomized placebo-controlled trial, Espie et al. (2012) found improve-
ments in sleep efficiency, sleep diary outcomes, daytime outcomes, and sleep–wake functioning
among adults with insomnia disorder using web-provided CBT versus imagery relief therapy. Simi-
larly, in a comparison between Internet and pen and paper self-help CBT, Lancee, van den Bout,
van Straten, and Spoormaker (2012) found that both formats were effective compared to a wait-
list control at improving daily sleep, insomnia, depression, and anxiety. Finally, among students,
Trockel, Manber, Chang, Thurston, and Tailor (2011) found that emailed PDFs containing CBT self-
help reading material for insomnia were effective at reducing symptoms among those who had
sleep problems. However, the extent to which commercially available, fully automated and online
self-help programs can improve sleep quality in a student population is yet to be examined.
The potential benefits of iCBT for improving access to treatment among students specifically is
vast because despite high levels of need, up to 85% of students with mental health problems do
not seek help from available services (Eisenberg, Golberstein, & Gollust, 2007). The use of internet-
delivered interventions can help to address access convenience and confidentiality concerns, both
of which have been cited as barriers to help-seeking among medical students (Givens & Tjia,
2002). Preliminary research has found that although more distressed students are less likely to
seek help, such students reported greater intentions to use a hypothetical online program for stu-
dents than did those who were less psychologically distressed (Ryan, Shochet, & Stallman, 2010).
This suggests that online interventions have the potential to “scoop up”students in need of
mental health services who may otherwise not receive them. Poor help-seeking in the student popu-
lation might therefore be addressed by offering services universally, rather selectively to students
who have identified themselves, or have been identified (or indicated to be) at-risk or symptomatic.
While the distinction between “universal”versus “selective”,or“indicated”approaches usually applies
to prevention rather than treatment (Muñoz, Cuijpers, Smit, Barrera, & Leykin, 2010), in a population
where prevalence of symptoms is widespread and help-seeking is low, universal approaches to the
availability of treatment might be appropriate.
The current study
Given the Royal College of Psychiatrists’(2011) recommendation to increase the availability of iCBT in
the UK student population, it is important to examine the effectiveness of this mode of treatment for
common student mental health complaints. In this paper we describe an initial efficacy study exam-
ining the potential of two commercially available iCBT programs separately targeting insomnia and
anxiety for use in a UK student sample. We examined commercially available products in order to
emulate what might occur in student mental health services, where providers are likely to first
turn to an “off-the-shelf”program.
We hypothesized that (1) anxious symptoms would be reduced by iCBT for anxiety compared to
control and (2) sleep quality would be increased by iCBT for insomnia compared to control. Because
of the known correlational and predictive relationships between anxiety, insomnia, and depression
(Jansson-Fröjmark & Lindblom, 2008) as well as the potential for transfer gains from CBT targeting
one set of symptoms to also improve a related set of symptoms (e.g. Trockel et al., 2011, found
that emailed CBT for insomnia improved both insomnia and depression) we also assess depression,
and additionally hypothesize that (3) sleep quality might be increased by iCBT for anxiety; (4) anxious
symptoms might be reduced by iCBT for insomnia; and (5) depressive symptoms might be reduced
by either iCBT for anxiety or iCBT for insomnia.
ANXIETY, STRESS, & COPING 3
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Method
Participants
This study, which was not registered as a trial, received ethical approval from the University of Bristol
Human Research Ethics Committee prior to participant recruitment. Participants were recruited by an
email campaign, posters, and flyers, circulated at the University of Bristol prior to the summer exam-
ination revision period, inviting undergraduate students who were experiencing stress to take part in
the study and learn techniques used to manage stress. Inclusion criteria were: interest and self-refer-
ral to the study (participants had to contact us in response to our advertising); being a native or
advanced English reader, having Internet access, and being willing to attend a one-hour introductory
session.
Power analysis
A power analysis was conducted a priori for analysis of variance (ANOVA) repeated measures
between factors (3 groups and 2 time points), using an effect size of f= .25 (a medium ES), which
was selected because although studies have shown that iCBT can yield large effect sizes, these
tend to be found with clinical populations (Titov et al., 2009; Vincent & Lewycky, 2009), or when tar-
geting examination anxiety specifically in a student population (Orbach et al., 2007).We anticipated
that in our non-clinical population, using more general programs, any effects would be more conser-
vative. The power analysis revealed that a sample size of 147 would be required to detect a medium
effect size. This number was increased to 180, to account for attrition rates of between 4% and 35%
found in previous research using iCBT (Orbach, et al., 2007, Ritterband et al., 2009; Ström et al., 2004;
Trockel et al., 2011; Vincent & Lewycky, 2009). Of the original 180 students who contacted the exper-
imenters, 138 (n= 93 women, mean age of 20.5 years, SD = 1.95, range = 18–34 years) attended the
introductory session and were included in the study.
Measures
State-Trait Anxiety Inventory-state (STAI-S)
The STAI-S (Spielberger, Gorsuch, & Lushene, 1970) is a 20-item questionnaire, measuring feelings of
tension, worry, and apprehension. Participants rate each item on a 4-point scale, based on how much
they agree with the statement, ranging from 1 “not at all”to 4 “very much”, for example, “Iam
worried”. All items are summed to provide a total score ranging from 20 to 80. Higher scores indicate
greater anxiety. Previous research has found a test–retest reliability of r= 0.54 and an internal
reliability of α= .92 (Spielberger, 1983). The STAI-S can be used to evaluate how respondents feel
in the present moment, or how they have been feeling recently, in relation to a specific stressor.
This is done by revising the instructions (Spielberger et al., 1970). In the current study participants
were asked to indicate how they had been feeling in the last week about their end-of-year
examinations.
The Pittsburgh Sleep Quality Index (PSQI)
The PSQI (Buysse et al., 1989) consists of 19 items assessing sleep quality over the preceding month.
These form seven components (subjective sleep quality, sleep latency, sleep duration, habitual sleep
efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction) which are
summed to generate a global sleep score (0–21). Higher scores indicate poorer sleep quality
(Buysse et al., 1989). The PSQI has good test–retest reliability r= 0.85 and internal reliability α=
0.83 (Baker & Sederer, 2002). Due to the brief intervention period, instructions for the PSQI were
revised and participants were asked to report on events from the last two weeks.
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The Beck Depression Inventory –second edition (BDI-II)
The Beck Depression Inventory –second edition (BDI-II) (Beck, Steer, & Brown, 1996) is a 21-item ques-
tionnaire which measures depressive symptoms like prolonged sadness, self-criticism, and suicidal
thoughts in the preceding two weeks. Items are rated on a 4-point scale, based on how much par-
ticipants agree with the statement, ranging from 0 “not at all”to 3 “very much”(e.g. “I am not discour-
aged about my future”and “I feel my future is hopeless and will only get worse”) and scores range
from 0 to 63. This inventory also has good test–retest reliability r= .93 and internal reliability α= .91
(Beck et al., 1996).
iCBT interventions
The interventions used were commercially available programs –“Insomnia Relief”and “Anxiety
Relief”, both retailed by Ultrasis UK Ltd. Both programs are unguided internet-delivered self-help
programs featuring CBT components broken into seven and six modules, respectively (detailed
in Figure 1), and contained monitoring tools depicting graphical feedback. Both programs were
designed with subclinical symptoms in mind (Ultrasis, personal communication). Bulk site licenses
are currently provided to universities and colleges (as part of a package which includes other pro-
grams for different symptoms) for less than £0.5 per student, per year. Both programs are designed
for flexible, “on demand”use. However in order to standardize the delivery of the treatment for the
Figure 1. Content and structure of interventions.
ANXIETY, STRESS, & COPING 5
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purposes of this study, participants were provided with instructions regarding the areas of the
program to be completed, and completion order during the six-week intervention period.
Because evidence suggests that the outcomes for iCBT are improved by support provision in
some form (Gellatly et al., 2007; Newman, Erickson, Przeworski, & Dzus, 2003), each participant
received low-intensity support in the form of standardized texts and emails that were manually
sent each week, to remind them what module(s) they should have completed that week and
provide encouragement.
1
Procedure
Participants were enrolled seven weeks prior to the start of their examination period. Participants
gave fully informed consent to take part in the study and subsequently completed the pre-interven-
tion measures. Participants were then informed of the group to which they had been randomly allo-
cated. Randomization was carried out using a random number generator in Excel, operated by the
first two authors. This was done in blocks of 30 participants, to maximize the probability that
groups would be largely comparable in sample size. Participants allocated to the intervention
groups (either Insomnia Relief or Anxiety Relief) received the appropriate instructions and were
then sent an email confirming their online access details for the program. Participants allocated to
the control group were informed that they would be able to have access to the programs six
weeks later, after the post-intervention session.
Six weeks later, all participants completed the post-intervention measures, followed by an open-
ended feedback questionnaire about the programs (data to be presented elsewhere).
Participants were compensated for their time at a rate of £10 per hour if they completed all
aspects of the study. Those in the control condition were required to complete all pre- and post-
measures, resulting in a compensation of £10, while those in the intervention conditions were also
required to engage with their allocated iCBT program for a minimum of 20 minutes per week, result-
ing in a compensation of £50. Usage data stored by the program enabled the identification of indi-
viduals who did not meet these criteria.
2
Data analysis
In order to examine whether the target symptoms (anxiety and insomnia) were reduced by the
interventions, separate three (group: control, anxiety, and insomnia) by two (time: pre-intervention
and post-intervention) mixed ANOVAs were conducted with each target symptom score as the
dependent variable, with repeated measures pre- to post-intervention. Where time X group inter-
actions were significant, paired-sample t-tests were used to examine the interaction. To explore the
possibility of transfer gains in depressive symptoms, the same analysis was conducted for
depression scores. Because the CONSORT checklist has dropped the requirement for intention-
to-treat (ITT) analyses in favor of a clear description of the inclusion criteria for each analysis con-
ducted (Moher et al., 2010), and following guidance by Armijo-Olivo, Warren, and Magee (2009)
regarding the evaluation of treatment efficacy, we first undertook Completer Only analyses, and
then supplemented this with ITT analyses for the sake of comparison. We conducted ITT using mul-
tiple imputations in SPSS version 20, which uses a fully conditional specification (van Buuren,
Brand, Groothuis-Oudshoorn, & Rubin, 2006) method using an iterative Markov chain Monte
Carlo procedure. In this approach, multiple imputations of the data are generated and analyzed
and the pooled results are used to compare against the original data set. Finally, we conducted
additional analyses featuring comorbidity and symptomology severity to ascertain whether the
pattern of results differed according to severity and comorbidity of symptomology, and whether
our results were driven by only a subgroup of comorbid participants with more severe
symptomology.
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Results
Sample characteristics
Forty-three (31.2%) participants were allocated to receive “Anxiety Relief”, 48 (34.1%) participants
were allocated to receive “Insomnia Relief”, and 47 (34.8%) participants were allocated to the
control group. The gender distribution is detailed in Table 1, and the participant flow through the
study is detailed in Figure 2.
A chi square showed that the groups did not differ in gender distribution, X
2
(2, N= 138) = 1.67,
p= .44). One-way ANOVA showed that the groups were comparable in age (F(2, 135) = .490, p= .62),
and did not differ on anxiety, sleep quality, or depression at the baseline (STAI-S: F(2, 135) = .83,
p= .44; PSQI: F(2, 135) = .65, p= .53; BDI-II: F(2, 135) = .45, p= .64) (Table 1).
Adherence and attrition
Most participants (n= 112, 81.2%) completed the study. In the intervention groups, 7 (5.1%) formally
withdrew, 8 (5.8%) did not complete enough of the iCBT to meet the study criteria of engaging with
their allocated program for 20 minutes per week, and 8 (5.8%) participants failed to complete the
post-intervention measures. Independent t-tests indicated that non-completers were comparable
to those who completed the study on the self-report measures at the pre-intervention session
(STAI-S: t(136) = .12, p= .91, PSQI: t(136) = −.50, p= .62, BDI-II: t(136) = −.46, p= .65) (Table 1).
Change in self-reported anxiety as a function of the intervention group
For anxiety, the main effects of time (F(1, 109) = .24, p= .627) and group (F(2, 109) = 2.98, p= .055)
were non-significant but the time X group interaction was significant (F(2, 109) = 3.41, p= .037). Post
hoc paired-sample t-tests were conducted on the pre-intervention versus post-intervention anxiety
scores. These revealed that students in the Anxiety Relief group had lower anxiety symptom
scores at post-intervention (t(31) = 2.00, p= .03, one tailed), with moderate within-group (d= .51)
and between-groups (d= .64) effect sizes.
3
No significant reduction in anxiety symptoms was
found for those in the Insomnia Relief group (t(35) = −.62, p= .54) or for those in the control
group (t(43) = −1.92, p= .06) (Figure 3). In these analyses, there were two noteworthy trend
results: (i) the main effect of group, indicating that the control group had higher STAI scores to
begin with (p=.055) and (ii) the post hoc paired-sample t-test for the control group (p= .06), indicat-
ing that increase in STAI score for this group was almost significant (compared to the non-significant
increase in the Insomnia Relief group and the significant decrease in the Anxiety Relief group). The
imputed data demonstrated similar patterns, with an increase in anxiety for the control condition, t
(1711) = –2.066, p= .039 and a slight, non-significant increase for the Insomnia Relief condition, t(229)
=–.595, p= .552. In comparison there was a non-significant reduction in anxiety in the Anxiety Relief
condition, t(1987) = 1.352, p= .177.
These results indicate that while anxiety scores differed pre-intervention, they were reduced in the
Anxiety Relief group, while they increased in the other two groups. Anxiety Relief therefore worked as
a condition-specific intervention program, and no transfer gains occurred for the insomnia interven-
tion program.
Change in self-reported sleep quality as a function of the intervention group
For sleep quality, there was a main effect of time (F(1, 109) = 23.86, p< .001) and a significant time X
group interaction (F(2, 109) = 5.01, p= .008) but no main effect of group (F(2, 109) = .98, p= .38). The
main effect demonstrates that sleep quality improved from pre-intervention (M6.95, SD 2.92) to post-
intervention (M5.76, SD 2.82) (higher scores indicate more sleep problems) for all groups. To explore
ANXIETY, STRESS, & COPING 7
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Table 1. Descriptive data (Means and SDs) for STAI, PSQI, and BDI scores at pre- and post-intervention for each of the three intervention groups, and significance and effect size of within-group and
between-groups comparisons.
Anxiety Relief (n= 43) Insomnia Relief (n= 48) Control (n= 47)
Gender
MAge (SD) 12 (27%) Males, 31 (72.1%) Females
20.56 (1.54)
19 (39.6%) Males, 29 (60.4%) Females
20.69 (2.61)
14 (29.8%) Males, 33 (70.2%) Females
20.27 (1.56)
αPre Post
d
Pre Post
d
Pre Post Wthn. dFWthn. Btwn. Wthn. Btwn.
STAI-S
M(SD)
.94 48.44
(9.89)
45.81
(11.43)
0.51* 0.64* 45.92
(12.03)
46.89
(12.24)
−0.15 0.53 50.16
(11.19)
53.09
(11.25)
−0.41 3.41*
PSQI
M(SD)
.70 7.00
(2.47)
5.16
(1.95)
0.87* 0.55* 7.03
(3.23)
5.17
(2.5)
1.04* 0.51* 6.84
(3.03)
6.68
(3.37)
0.08 5.01*
BDI-II
M(SD)
.89 11.34
(6.56)
8.38
(5.98)
0.81 0.59 11.89
(7.23)
9.25
(5.09)
0.58 0.49 13.43
(8.40)
12.59
(8.19)
0.19 1.36
Note: Wthn, Within-group effect size comparing pre- to post-intervention; Btwn, Between-groups effect size at post-intervention compared to the control group; STAI-S, State-Trait Anxiety Inventory-
State; PSQI, Pittsburgh Sleep Quality Index; BDI-II, Beck Depression Inventory II; α, internal reliability.
*p<.05.
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the interaction, paired-sample t-tests were conducted on the pre-intervention versus post-interven-
tion sleep quality scores. These revealed that students who completed Insomnia Relief had signifi-
cantly higher sleep quality (represented by lower PSQI score) at post-intervention (t(35) = 4.28, p
< .001), with a large within-group effect size (d= 1.04) and a moderate between-groups effect size
(d= .51). Similarly, those who completed Anxiety Relief also had higher sleep quality at post-interven-
tion (t(31) = 3.46, p= .002), with a large within-group effect size (d= 0.87) and a moderate between-
groups effect size (d= .55). There was no significant improvement for those in the control group (t
(43) = .39, p= .70) (Figure 4). The imputed data demonstrated similar patterns, with the Insomnia
Relief condition demonstrating the largest significant improvement in sleep quality, t(79) = 4.615,
p< .001. There was a smaller, yet still significant improvement in sleep quality in the Anxiety Relief
condition, t(755) = 3.192, p= .001. No significant change in sleep quality was evident in the control
condition, t(13526) = .742, p= .458.
Figure 2. Participant and procedure flow.
ANXIETY, STRESS, & COPING 9
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Change in self-reported depression as a function of the intervention group
For depression, there was a significant effect of time (F(1, 109) = 13.32, p= .001) but no significant
effects were found for group (F(2, 109) = 2.59, p= .079) or the time X group interaction (F(2, 109)
= 1.36, p= .261). A main effect of time shows that depression scores were generally lower post-inter-
vention (M10.31, SD 6.90) when compared to pre-intervention (M12.34, SD 7.52) for all groups. The
imputed data demonstrated similar patterns, with both treatment conditions demonstrating a
reduction in depression (Anxiety Relief condition, t(128) = 2.54, p= .012; Insomnia Relief condition,
t(69) = 2.48, p= .016), yet no effect for the control condition, t(4292) = .991, p= .322.
Change in self-reported anxiety or insomnia as a function of comorbidity or symptom
severity
We also conducted the same analysis for individuals (n= 55) who had high stress symptomology,
defined as comorbid high anxiety (STAI scores > 38, Spielberger et al., 1970) and poor sleep quality
(PSQI scores > 5, Buysse et al., 1989). These cutoffs reflect scores that were higher than the
average reported for female students, due to the majority female composition of our sample. We
found a significant time × group interaction using repeated measures 2 × 3 mixed ANOVA for the
Figure 3. Changes in anxiety scores from pre- to post-intervention as a function of group.
Note: Figure depicts pre- and post-intervention STAI-S (State-Trait Anxiety Inventory-State) scores for each group with error bars
showing 2 SE.
10 J.MORRISETAL.
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STAI (p= .039) and PSQI (p= .024) scores, representing the same pattern of results as the main find-
ings, for these comorbid participants. Post hoc paired-sample t-tests showed that Anxiety Relief pro-
duced a trend of reduction in anxiety levels (p= .058, d= .41) as well as improving sleep quality ( p
< .001, d= 1.57), whereas, Insomnia Relief only improved sleep quality (p= .001, d= 1.12).
To determine whether individuals with high symptomology might be driving our main results, we
also conducted 2 (high and comorbid symptomology status: the group identified for the above analy-
sis, and those not in that group (all other participants) × 2 (Time: pre- and post-intervention) × 3
(Group: Anxiety Relief, Insomnia Relief & Control) ANOVAs for both the STAI and PSQI. We found
non-significant 3-way interactions for both the STAI (p= .478) and PSQI (p= .358), which indicates
that high symptomology status does not affect the effectiveness of the interventions. Together,
these analyses indicate that our results were not moderated by the severity of symptomology or
symptom comorbidity.
Discussion
The main aim of this study was to examine the efficacy of two iCBT programs, Anxiety Relief and
Insomnia Relief, compared to a wait-list control group, in treating self-reported anxiety and insomnia
symptoms in students at a particularly stressful time in the academic calendar. We hypothesized that
Figure 4. Changes in sleep quality scores from pre- to post-intervention as a function of group.
Note: Figure depicts pre- and post-intervention PSQI (Pittsburgh Sleep Quality Index) scores for each group with error bars showing
2 SE.
ANXIETY, STRESS, & COPING 11
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each program would be effective at reducing their target symptoms (H1 and H2). A secondary aim
was to identify whether either program led to transfer gains from one set of targeted symptoms
(anxiety or insomnia) to the other (H3 and H4), or to depression (H5). We found evidence supporting
the effectiveness of both programs at reducing their target symptoms, thus supporting H1 and H2.
We also found evidence of transfer gains from Anxiety Relief, which improved sleep quality, thus sup-
porting H3. H4 (that Insomnia Relief might improve anxiety) and H5 (that either program might
improve depression) were not supported. Regarding depression, all three groups improved over
time. We discuss each of these findings in turn, followed by a discussion of the limitations of our
study design, and the implications of our findings both for future research and for mental health ser-
vices in the Higher Education sector.
Participants allocated to the Anxiety Relief program had lower STAI scores at post-intervention
compared with pre-intervention (6 weeks previously), suggesting that the program helped to allevi-
ate symptoms during a time associated with significant stress in the student population. While this
finding is in keeping with Cuijpers et al.’s(2009) meta-analysis, which found that iCBT for anxiety dis-
orders is effective, the effect size compared to control for our study was moderate while that found in
the meta-analysis was large. Cuijpers et al. (2009) found that the reduction in time spent with a thera-
pist in computer-aided therapies as compared to face-to-face therapies was negatively associated
with effect size. Our smaller effect size might therefore reflect the “light touch”nature of the
support we provided to participants, which came in the form of texted reminders to persevere
with the program, rather than any direct contact or therapeutic content.
Another explanation is that previous studies have examined clinical anxiety rather than situation-
specific anxiety around examinations. A more dramatic reduction can be expected in a clinical
sample, because the anxiety level is higher to start off with and without treatment would remain
high over time. However, the current study focuses on situation-specific anxiety seven weeks prior
to the exam period. As exams get closer anxiety levels would be expected to rise. It is therefore
not surprising that our effect sizes were more conservative because of this.
Participants allocated to the Insomnia Relief program improved in sleep quality from pre-interven-
tion to post-intervention (six weeks later), indicating that the program was effective at alleviating its
target symptom. This finding is in line with Okajima et al.’s(2011) meta-analysis, which found mod-
erate–large effect sizes for CBT (rather than iCBT) for insomnia. Our effect size compared to control
was moderate, and tallies with studies finding that iCBT can be effective for reducing insomnia in
both community (Ritterband et al., 2009; Ström et al., 2004) and patient (Vincent & Lewycky, 2009)
populations, with effect sizes ranging from small to moderate (Ström et al., 2004) and small to
large (Vincent & Lewycky, 2009). Among students, Trockel et al. (2011) found that emailed PDFs con-
taining self-help CBT for insomnia were effective against a control treatment comprising a relaxation
intervention delivered in the same format. Our findings suggest that a commercially available, inter-
active, fully automated online program accessed via secure login, and supported by brief text mess-
ages, is also effective at improving sleep quality among students.
Moreover the significant results were not driven by individuals with high symptomology or comor-
bidity. This suggests that a universal approach was an appropriate method for recruitment and that
all individuals, even those with low symptom levels, could benefit from using these iCBT programs.
With regard to transfer gains, we found that Anxiety Relief had a moderate effect in reducing insom-
nia symptoms. Given that anxious symptoms such as worry can interfere with sleep quality (Watts,
Coyle, & East, 1994), our Anxiety Relief effects on insomnia are unsurprising. Indeed, they tally with
Bélanger, Morin, Langlois, and Ladouceur’s(2004)finding that group CBT for generalized anxiety dis-
order significantly reduced insomnia severity among anxiety patients. To the best of our knowledge,
our study is the first to report this similar effect for iCBT in a student population.
We did not find transfer gains from Insomnia Relief to anxious symptoms. Our rationale for
hypothesizing this possible effect was that CBT for anxiety and insomnia both feature cognitive
restructuring, which is a core component of CBT. In line with transdiagnostic approaches (Barlow,
Allen, & Choate, 2004) we therefore anticipated that iCBT for insomnia might also help anxiety. A
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possible reason why this transfer gain was not observed may be the specificity of some of the com-
ponents in Insomnia Relief. For instance, the components of stimulus control therapy and sleep
restriction therapy are directly related to sleep, and do not offer transferrable skills that might
target anxiety. Other components of Insomnia Relief, such as cognitive restructuring, relaxation,
and guided imagery, while common across programs targeted for both insomnia and anxiety, may
not have been potent enough to elicit transfer gains without more specifically anxiety-focused com-
ponents. That is to say, while there are a common set of techniques that comprise CBT, perhaps those
present in Insomnia Relief are not sufficiently foregrounded to be potent.
Our final hypothesis (H5) was that either Anxiety Relief or Insomnia Relief might evidence transfer
gains on depressive symptoms, but we found no such effects. Again, this lack of transfer gains could
indicate that the skills learned in these programs were not generalizable to depression. Additionally,
some researchers have proposed that certain psychological problems, such as depression, are par-
ticular challenges to engagement with iCBT, due to the characteristic symptoms of hopelessness
and poor concentration (Cavanagh & Millings, 2013). Such challenges may mean that programs
intended for a different target disorder are even less likely to invoke transfer gains.
We found that depression generally improved over time, regardless of the intervention group. This
finding may relate to the time of year in which we conducted the study –the pre-examination period.
Conceivably, as the examination period drew nearer, individuals’perceived control, a factor known to
be protective against depression in students (Ruthig, Haynes, Stupnisky, & Perry, 2009), may have
increased, along with the knowledge that this stressful period would soon be over. Alternatively,
we may have simply observed Hawthorne effects, whereby knowledge of research participation
results in improvements in symptoms (De Amici, Klersy, Ramajoli, Brustia, & Politi, 2000).
While novel in its approach and topic, our study is not without limitations. For pragmatic reasons,
participants were paid for taking part, and the intervention groups received more money than the
control group. This may have had an impact on treatment adherence, such that in our sample,
those in the intervention groups may have adhered better than an unpaid sample. Additionally,
one could argue that the greater payment of those in the intervention groups compared to the
control group may have resulted in their superior treatment effects. However, specific programs
were effective for specific symptoms, and not universally effective. That is to say, we found differential
effects of the programs we tested, which cannot be accounted for by the fact that participants
received payments because payments were equivalent across intervention conditions. That said,
future research should seek to replicate our findings without incentivizing research participation.
A further limitation of our study was that following the exclusion of participants who either with-
drew or failed to engage with the iCBT programs for the minimum duration specified or complete the
post-intervention measures, based on our expectation of finding a medium effect size, the study was
underpowered (a post hoc analysis showed that power was .68). Additionally, with hindsight, a power
calculation based on a small effect size may have been more appropriate, due to the iCBT being
unguided (no therapist input beyond reminder text messages). Unguided iCBT consistently produces
small rather than medium effect sizes (Johansson & Andersson, 2012). That we found significant
results despite the study being significantly underpowered suggests that conducting a full, ade-
quately powered trial with the interventions we tested, preferably in more “real-world”conditions,
is warranted and should be addressed in future studies.
Another aspect of our study which may be viewed as a limitation is that we did not employ a
screening procedure to identify and include only those individuals who were symptomatic.
However, this reflects our decision to emulate a model of universal treatment availability. Because
mental health problems are common, and help-seeking is poor among students, we would
contend that universal approaches are more appropriate than indicated or selective approaches. Fur-
thermore, given that we did not select students who were symptomatic, effect sizes may have been
lower than in a selected sample. Indeed, our significant results in a non-selective sample of students
indicate that the benefits of universal approaches to treatment availability in this population could be
widespread.
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Our study is limited by not having follow-ups. While we found that at the end of the intervention
period, those in the Anxiety Relief group had significantly reduced anxiety and insomnia symptoms
and that those in the Insomnia Relief group had significantly reduced insomnia symptoms, we do not
know what happened to symptom levels in the post-intervention period. Cuijpers et al.’s(2009) meta-
analysis of computer-aided psychotherapy for anxiety found evidence of stability in anxiety from
post-intervention to between 1 and 12 months’follow-up. Okajima et al.’s(2011) meta-analysis of
CBT for insomnia indicates that improvements in sleep at post-intervention and at follow-up were
moderate–large, also suggesting stability over the post-intervention period (between 1 and 12
months). Further studies could examine whether this holds true with Anxiety Relief and Insomnia
Relief applied to a student population. Future studies could also test for the emergence of transfer
gains over time as practice over prolonged periods could increase the generalizability of the specific
skills learned, to potentially reduce related sets of symptoms.
Future research can seek to build upon this study by employing a naturalistic design which makes
iCBT programs available to the entire student body from within a participating university’s webpages,
without requiring students to contact the research team for access and without offering payment for
participation. Multiple follow-ups would allow for tracking the extent to which improvements in
symptomology last over time as well as whether some improvements versus others take longer to
materialize (e.g. transfer gains on depression might be observable over a longer time frame). Such
a trial would enable analytic approaches to include ITT and economic evaluations, which were
beyond the scope of the current study.
In conclusion our findings suggest that commercially available iCBT programs are effective at
reducing the symptoms of anxiety and insomnia in a student population during what is widely
viewed as the most stressful period in the academic calendar. Following Ryan et al.’s(2010) study
reporting a positive relationship between level of student distress and likelihood of using online inter-
ventions, and the Royal College of Psychiatrists’(2011) recommendation to increase the availability of
iCBT in the UK student population, our study confirms the potential utility of two such programs, for
anxiety and insomnia specifically. We hope that our study paves the way for a larger trial of iCBT for
common mental health problems in student services both in the UK and internationally.
Disclosure statement
At the time this research was undertaken, the corresponding author an employee of Ultrasis UK Ltd., which makes the
products ‘Anxiety Relief’and ‘Insomnia Relief’. The corresponding author is a minor shareholder in this company.
Funding
This work was supported by the European Commission Seventh Framework Programme [grant number 248544 (known
as “Optimi”)].
Notes
1. Approximately 20% of these texts received a reply, mainly to confirm continued engagement with the program.
2. Usage data detailed the pages accessed, but not the length of time spent on the last page accessed in an individual
session. As such, it was possible to distinguish between those who cannot have spent 20 minutes engaging, from
those who probably did. Precise usage data was not stored by the program, due to the exit time only being recorded
if users logged out rather than simply closed their browser.
3. Between-groups effect sizes are between the intervention group in question and the control group.
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