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Anxiety and insomnia can be treated with internet-delivered Cognitive Behavioural 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. A randomized, controlled study. Students were randomly allocated to intervention ('Anxiety Relief': n= 43; 'Insomnia Relief': n= 48; control: n = 47). Interventions lasted 6 weeks. Outcome measures were the State-Trait Anxiety Inventory and the Pittsburgh Sleep Quality Index. Significant within-group reductions in anxiety (t(31) = 2.00, p=.03) with moderate between-group (compared to control) effect size (d=.64) and increases in sleep quality (t(31) = 3.46, p= .002) with a moderate between-group 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-group effect size (d=.51). Findings support the use of iCBT for anxiety and insomnia in students, and indicate that further research is needed.
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Anxiety, Stress, & Coping: An
International Journal
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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:
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Downloaded by [University of Sheffield] at 05:59 22 July 2015
Internet-delivered cognitive behavior therapy for anxiety and
insomnia in a higher education context
Joanna Morris
, Ashlyn Firkins
, Abigail Millings
, Christine Mohr
, Paul Redford
Angela Rowe
School of Experimental Psychology, University of Bristol, 12a Priory Road, Bristol BS8 1TU, UK;
Department of
Psychology, University of Shefeld, Western Bank, Shefeld S10 2TP, UK;
Institut de psychologie, Université de
Lausanne, Quartier UNIL-Dorigny, Bâtiment Anthropole, CH-1015 Lausanne, Switzerland;
Department of
Psychology, University of the West of England, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK
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
efcacy 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: Signicant 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.
Signicant 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.
Received 5 September 2013
Revised 27 May 2015
Accepted 28 May 2015
Internet-delivered Cognitive
Behavior Therapy; CCBT;
student mental health;
undergraduates; anxiety;
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;, 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
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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%
(; 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-specic 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
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 signicantly 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 replacedby 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 ndings will extend to a student population, where so far, tested programs have been for a
single, situation-specic 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 efciency 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 efciency, sleep diary outcomes, daytime outcomes, and sleepwake 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 benets of iCBT for improving access to treatment among students specically 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 condentiality 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 upstudents 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 identied themselves, or have been identied (or indicated to be) at-risk or symptomatic.
While the distinction between universalversus selective,orindicatedapproaches 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 efcacy 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 rst
turn to an off-the-shelfprogram.
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.
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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 yers, 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
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 specically 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 = 1834 years) attended the
introductory session and were included in the study.
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 allto 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 testretest 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 specic 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
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
efciency, sleep disturbances, use of sleeping medication, and daytime dysfunction) which are
summed to generate a global sleep score (021). Higher scores indicate poorer sleep quality
(Buysse et al., 1989). The PSQI has good testretest 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 allto 3 very much(e.g. I am not discour-
aged about my futureand I feel my future is hopeless and will only get worse) and scores range
from 0 to 63. This inventory also has good testretest reliability r= .93 and internal reliability α= .91
(Beck et al., 1996).
iCBT interventions
The interventions used were commercially available programs –“Insomnia Reliefand 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 exible, on demanduse. However in order to standardize the delivery of the treatment for the
Figure 1. Content and structure of interventions.
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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.
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
rst 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 conrming 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 identication of indi-
viduals who did not meet these criteria.
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 signicant, 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 efcacy, we rst 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 specication (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
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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 ow through the
study is detailed in Figure 2.
A chi square showed that the groups did not differ in gender distribution, X
(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-signicant but the time X group interaction was signicant (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.
No signicant 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 signicant (compared to the non-signicant
increase in the Insomnia Relief group and the signicant 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-signicant increase for the Insomnia Relief condition, t(229)
=.595, p= .552. In comparison there was a non-signicant 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-specic 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 signicant 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
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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 signicance and effect size of within-group and
between-groups comparisons.
Anxiety Relief (n= 43) Insomnia Relief (n= 48) Control (n= 47)
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
Pre Post
Pre Post Wthn. dFWthn. Btwn. Wthn. Btwn.
.94 48.44
0.51* 0.64* 45.92
0.15 0.53 50.16
0.41 3.41*
.70 7.00
0.87* 0.55* 7.03
1.04* 0.51* 6.84
0.08 5.01*
.89 11.34
0.81 0.59 11.89
0.58 0.49 13.43
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.
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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 signi-
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 signicant 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 signicant improvement in sleep quality, t(79) = 4.615,
p< .001. There was a smaller, yet still signicant improvement in sleep quality in the Anxiety Relief
condition, t(755) = 3.192, p= .001. No signicant change in sleep quality was evident in the control
condition, t(13526) = .742, p= .458.
Figure 2. Participant and procedure ow.
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Change in self-reported depression as a function of the intervention group
For depression, there was a signicant effect of time (F(1, 109) = 13.32, p= .001) but no signicant
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
We also conducted the same analysis for individuals (n= 55) who had high stress symptomology,
dened as comorbid high anxiety (STAI scores > 38, Spielberger et al., 1970) and poor sleep quality
(PSQI scores > 5, Buysse et al., 1989). These cutoffs reect scores that were higher than the
average reported for female students, due to the majority female composition of our sample. We
found a signicant 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.
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STAI (p= .039) and PSQI (p= .024) scores, representing the same pattern of results as the main nd-
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 identied 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-signicant 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.
The main aim of this study was to examine the efcacy 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.
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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 ndings in turn, followed by a discussion of the limitations of our
study design, and the implications of our ndings 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 signicant stress in the student population. While this
nding 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 reect the light touchnature 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-
specic 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-specic 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 nding is in line with Okajima et al.s(2011) meta-analysis, which found mod-
eratelarge effect sizes for CBT (rather than iCBT) for insomnia. Our effect size compared to control
was moderate, and tallies with studies nding 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 ndings 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 signicant 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 benet 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 Ladouceurs(2004)nding that group CBT for generalized anxiety dis-
order signicantly reduced insomnia severity among anxiety patients. To the best of our knowledge,
our study is the rst to report this similar effect for iCBT in a student population.
We did not nd 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 specicity 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 specically 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 sufciently foregrounded to be potent.
Our nal 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
nding may relate to the time of year in which we conducted the study the pre-examination period.
Conceivably, as the examination period drew nearer, individualsperceived 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, specic programs
were effective for specic 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 ndings 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 specied or complete the
post-intervention measures, based on our expectation of nding 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 signicant
results despite the study being signicantly underpowered suggests that conducting a full, ade-
quately powered trial with the interventions we tested, preferably in more real-worldconditions,
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 reects 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 signicant results in a non-selective sample of students
indicate that the benets of universal approaches to treatment availability in this population could be
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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 signicantly reduced anxiety and insomnia symptoms
and that those in the Insomnia Relief group had signicantly 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 monthsfollow-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
moderatelarge, 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 specic
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 universitys 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 ndings 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 conrms the potential utility of two such programs, for
anxiety and insomnia specically. 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 Reliefand Insomnia Relief. The corresponding author is a minor shareholder in this company.
This work was supported by the European Commission Seventh Framework Programme [grant number 248544 (known
as Optimi)].
1. Approximately 20% of these texts received a reply, mainly to conrm 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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... To date, there have been 5 RCTs of CBT-I with university students [14,[37][38][39][40]. Four of these studies used interventions designed for general adult populations, which do not consider the unique circumstances of university students [14,[38][39][40]. ...
... To date, there have been 5 RCTs of CBT-I with university students [14,[37][38][39][40]. Four of these studies used interventions designed for general adult populations, which do not consider the unique circumstances of university students [14,[38][39][40]. University students' living circumstances (in student dorms, share houses, or with their family of origin) often mean that they spend large amounts of time in their bedroom [41], this is contrary to typical sleep hygiene recommendations. ...
... Other limitations of the available research include the use of small sample sizes [37,40], brief interventions designed for nonclinical populations [38], and low adherence to treatment [14]. Although speculative, it is possible that the use of generic programs that are not tailored to the needs of university students could have contributed to low adherence rates. ...
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Background Many university students have difficulties with sleep; therefore, effective psychological treatments are needed. Most research on psychological treatments to improve sleep has been conducted with middle-aged and older adults, which means it is unclear whether existing psychological treatments are helpful for young adult university students. Objective This study aimed to discover university student preferences for a psychological intervention to improve sleep quality. Methods Focus groups were conducted over 3 stages to examine students’ views regarding content, format, and session duration for a psychological intervention to improve sleep. A thematic analysis was conducted to analyze participant responses. ResultsIn total, 30 participants attended small focus group discussions. Three key themes were identified: (1) program development, (2) help-seeking, and (3) student sleep characteristics. Program development subthemes were program format, program content, and engagement facilitators. Help-seeking subthemes were when to seek help, where to access help, stigma, and barriers. Student sleep characteristics subthemes were factors disturbing sleep and consequences of poor sleep. Conclusions Students emphasized the need for a sleep intervention with an in-person and social component, individualized content, and ways to monitor their progress. Participants did not think there was a stigma associated with seeking help for sleep problems. Students identified the lack of routine in their lifestyle, academic workload, and the pressure of multiple demands as key contributors to sleep difficulties.
... Childhood anxiety disorders are associated with an increased risk of school underachievement (Hughes et al., 2008;Woodward & Fergusson, 2001), school refusal behavior (Gonzálvez et al., 2021;Kearney, 2001), school dropout (Duchesne et al., 2008), and lack of pursuit of higher education (Morris et al., 2016). Nail et al. (2015) found that nearly half of the parents of anxious youth (ages 7-11 years) reported youth impairment in terms of concentrating on schoolwork, giving oral reports, and taking exams (Nail et al., 2015). ...
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Introduction Although effortful control—the ability to restrain impulsive reactions in favor of more adaptive responses—has been identified as a protective factor for childhood anxiety, the protective effects of effortful control in terms of anxious children’s social/adaptive functioning remains unexplored. The present study examined the moderating role of effortful control in the association between anxiety symptom severity and social/adaptive functioning in a sample of clinically anxious youth. Method One hundred and five clinically anxious youth (M = 10.07 years, SD = 1.22; 57% female; 61% ethnic minority) and their clinically anxious mothers (M = 39.35 years, SD = 7.05) completed questionnaires assessing effortful control, anxiety symptoms, and social/adaptive functioning as part of a baseline assessment. Results Greater effortful control was statistically significantly associated with better individual social/adaptive functioning scores and lower anxiety scores. Moderation analyses revealed that greater anxiety symptom severity was associated with poorer peer relationships among youth with lower (vs. higher) effortful control. Discussion Greater effortful control was associated with better social/adaptive functioning and lower anxiety among anxious youth. The negative effects of anxiety on the peer relationships of clinically anxious youth were also buffered by better effortful control. Clinical implications and directions for future research are discussed.
... Due to its structured nature, CBT is easily translatable to a digital format (Newman, Consoli, & Taylor, 1997). Internet-based CBT has demonstrated effectiveness at reducing symptoms of depression (Day, McGrath, & Wojtowicz, 2013;Harrer et al., 2018;Newman, Szkodny, Llera, & Przeworski, 2011a;Rackoff et al., 2022), anxiety (Day et al., 2013;LaFreniere & Newman, 2016, 2023Mackinnon, Griffiths, & Christensen, 2008;Newman, Przeworski, Consoli, & Taylor, 2014;Newman et al., 2011a), stress (Harrer et al., 2018;Newman, Jacobson, Rackoff, Jones Bell, & Taylor, 2021;Rackoff et al., 2022), eating disorders (Fitzsimmons-Craft et al., 2022;Fitzsimmons-Craft et al., 2020;Linardon, Shatte, Rosato, & Fuller-Tyszkiewicz, 2022), insomnia (Morris et al., 2016), alcohol, drug use, and smoking (Newman, Szkodny, Llera, & Przeworski, 2011b), and somatic symptoms (Hennemann et al., 2022) in college students. Additionally, several studies have documented high usability and acceptability of internet-based CBT among college students (Currie, McGrath, & Day, 2010;Nitsch et al., 2016). ...
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BACKGROUND: Mental health problems are increasing in prevalence among college students, yet few students receive treatment due to barriers such as insufficient resources in college counseling centers. Digital mental health interventions (DMHIs) have potential to overcome barriers and offer accessible, evidence-based care to college students. However, to evaluate the true public health impact of evidence-based DMHIs, it is important to assess the reach and uptake rates of DMHIs on college campuses. OBJECTIVES: We conducted a systematic review to examine the reach (i.e., % of invited students who express interest) and uptake (i.e., % of enrolled participants who initiate an intervention) of DMHIs based on cognitive-behavioral therapy (CBT) for college students. METHODS: Eight databases were searched. Inclusion criteria included: (1) college population; (2) experimental design; (3) CBT-based intervention; (4) intervention targeting specific mental health conditions; and (5) digital intervention. Reach and uptake rates were calculated from data reported. A systematic narrative review framework was used to synthesize results. RESULTS: Of 10,315 articles screened, 90 were included. Seventeen studies (19%) reported sufficient data to calculate reach; 35 studies (39%) reported uptake rates. Of studies that reported reach or uptake, most evaluated unguided (n = 20) or guided (n = 16) self-help programs. Measurement methods varied widely. Overall reach was low, whereas uptake was high among enrolled participants. DISCUSSION: Despite evidence that improving reach and uptake can increase the public health impact of DMHIs, most studies did not report on either outcome. Suggested practices to improve these outcomes, and their reporting, are discussed.
... A total of 11 studies (73.33 %) each had two arms as the ICBT intervention was compared to a passive (k = 8) or an active control group (k = 3). Meanwhile, three studies (20 %) were three-armed RCTs because in each, in addition to a passive control group, the ICBT intervention was compared to ICBT for insomnia (Morris et al., 2016), an online peer-support intervention (Ellis et al., 2011), and a face-to-face CBT (Botella et al., 2010). Only one four-armed RCT was found (k = 1; Sethi et al., 2010). ...
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University years are marked by multiple stressors. Consequently, university students often report anxiety symptoms or disorders, but most remain untreated. Internet-delivered cognitive behavioral therapy (ICBT) has been proposed as an alternative to address known help-seeking barriers, which were aggravated during the COVID-19 pandemic. This meta-analysis aims to evaluate the efficacy of ICBT for university students with anxiety. A systematic search on three databases, EBSCOhost, PubMed, and Web of Science, and a manual search were performed. Fifteen studies were identified, including a total of 1619 participants. Seven studies evaluated ICBT treatment for both anxiety and depression, three for social anxiety, two for generalized anxiety, while the remaining (k = 3) only targeted anxiety, test anxiety, and comorbidity between anxiety and insomnia. Analyses were performed based on a random-effects model using the metafor package in R. The results indicated that ICBT had a significant and positive effect on university students with anxiety compared to controls at post-test (g = − 0.48; 95 % CI: − 0.63, − 0.27; p < .001, I 2 = 67.30 %). Nevertheless, more research is required to determine the intervention components that are more relevant for therapeutic change, how much guidance is required to produce better outcomes, and how patient engagement can be improved.
Üniversite öğrencilerinde ruh sağlığı problemleri yaygındır. Ancak damgalama, ulaşım, yüksek maliyet, uzmana ulaşamama, uzun bekleme listeleri gibi nedenlerden dolayı üniversite öğrencilerinin psikolojik yardım alma eğilimleri düşüktür. Üniversite öğrencilerinin psikolojik yardım alma engellerini ortadan kaldırabilecek alternatif psikolojik destek müdahaleleri geliştirilmektedir. Teknolojik gelişmelerin psikoloji alanına yansımasının bir sonucu olan internet tabanlı müdahaleler farklı gruplar ve farklı problem alanlarında etkililiği kanıtlanmış müdahalelerdir. Çeşitli kuramsal yaklaşımlara dayalı olarak geliştirilebilen internet tabanlı müdahaleler bilgisayar ya da mobil cihazlar yolu ile sunulmaktadır. İnternet tabanlı müdahaleler yüz yüze sunulan psikolojik yardım sürecine yardımcı bir araç olarak ya da tek başına bir müdahale olarak kullanılabilmektedir. Müdahaleler bir uzman desteği eşliğinde ya da kullanıcının yalnız başına kullanacağı şekilde dizayn edilebilmektedir. Bu derleme çalışmasının ilk bölümünde internet tabanlı müdahalelerin tanımı, kapsamı, türleri, etkililiği, avantajları ile birlikte internet tabanlı müdahalelerde katılım ve erken bırakma, olumsuz/yan etkiler ele alınmıştır. İkinci bölümde internet tabanlı müdahalelerin farklı problem alanları üzerindeki etkililiği üniversite öğrencileri üzerindeki çalışmalar bağlamında değerlendirilmiştir. Son bölümde Türkiye’deki mevcut durum ele alınmış ve bu alanda çalışacak araştırmacılara önerilerde bulunulmuştur.
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Background: College students report disturbed sleep patterns that can negatively impact their wellbeing and academic performance. Objectives: This study examined the effect of a 4-week sleep hygiene program that included sleep education and actigraph sleep trackers (FITBITs) on improving sleep quality and reducing psychological worry without control group. Design, settings, and participants: A pilot quasi-experimental design, participants were randomly selected medical and health sciences from a university students in the United-Arab-Emirates. Methods: Students were asked to wear FITBITs and log their daily sleep data and completed the Pittsburgh Sleep Quality Index (PSQI) and Penn State Worry Questionnaire (PSWQ). Extensive sleep hygiene education was delivered via lectures, a WhatsApp group, and the Blackboard platform. In total, 50 students completed pre-and post-assessments and returned FITBIT data. Results: There was a significant difference in the prevalence of good sleep postintervention compared with pre-intervention (46% vs. 28%; p = 0.0126). The mean PSQI score was significantly lower post-intervention compared with pre-intervention (6.17 ± 3.16 vs. 7.12.87; p = 0.04, Cohen’s d 0.33). After the intervention, subjective sleep quality, sleep latency, and daytime dysfunction were significantly improved compared with pre-intervention (p < 0.05). In addition, FITBIT data showed total sleep time and the number of restless episodes per night were significantly improved postintervention compared with pre-intervention (p = 0.013). The mean PSWQ score significantly decreased from pre-intervention to p = 0.049, Cohen’ d = 0.25. The correlation between PSQI and PSWQ scores was significant post-intervention (β = 0.40, p = 0.02). Conclusion: Our results may inform university educational policy and curricular reform to incorporate sleep hygiene awareness programs to empower students and improve their sleep habits
Objective: This project examines students' experiences using a mental health mobile application (app) as part of a class assignment developed to support student well-being. Participants: Data was collected from 265 undergraduate students enrolled in a psychology course during the COVID-19 pandemic. Methods: Students developed a self-care goal and used an app to support progress toward it. Thematic analysis was applied to students' written reflections about their experiences using the app and practicing self-care. Results: Students reported using an app for self-care was 1) more helpful than expected for improving focus, productivity, motivation, sleep, and mental health symptoms; 2) challenging due to loss of interest, slow improvement, difficulty integrating into routine, or negative feelings triggered; and 3) influenced by the pandemic and transition to remote learning. Conclusions: A classroom assignment designed to promote self-care using a mental health app shows promise. Future research is needed to better understand engagement and impact.
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Introduction Many university students have poor mental health, and co-occurring health risk behaviors. Targeting health behavior change in this population may improve mental health outcomes. This scoping review describes the extent and range of randomized controlled trials (RCT) evaluating interventions targeting health risk behaviors and measuring a mental health outcome, among university students. Methods Six electronic databases were searched for RCTs published until the 18 th May 2021. Eligible RCTs included university students, evaluated interventions that promoted health behavior change (i.e., dietary intake, physical activity, sedentary behavior, alcohol and drug use, smoking, and sleep), and measured a mental health-related outcome. Results Fifty-nine RCTs met the inclusion criteria that were published from 2000 to 2021, and over half ( n = 33) were conducted in the United States. Interventions evaluated within the RCTs ( n = 92) predominantly targeted changes to dietary intake ( n = 41 interventions), physical activity ( n = 39), or alcohol intake ( n = 35). Most interventions targeted one ( n = 51) or two ( n = 27) health behaviors only. Included RCTs considered mental ill health outcomes ( n = 24), psychological wellbeing outcomes ( n = 20), or both ( n = 15). Discussion This scoping review identified a moderate volume of experimental research investigating the impact of health behavior interventions on university students' mental health. There is scope for further research examining health behavior interventions targeting university students, particularly interventions taking a multi-behavioral approach.
Test anxiety and attrition are prevalent in nursing programs. Efforts should be made to assist nursing students in obtaining coping mechanisms to reduce anxiety. The aim of this quantitative, quasi-experimental, before and after study was to determine the impact of a cognitive-behavioral intervention on test anxiety during first-semester nursing courses. Thirty bachelor of science in nursing students in a rural university participated in a cognitive-behavioral intervention and completed the Cognitive Test Anxiety Scale. A two-tailed, dependent-samples t-test determined a statistically significant decrease in test anxiety scores (p < .001), supporting interventions aimed at reducing both the physical and mental effects of test anxiety.
University is a time of significant transitions during a young adult's life, with delayed and shortened sleep and poor mental health a common occurrence. This systematic review and meta-analysis examined the effect of both multi-component and single-component sleep interventions on improving university students’ sleep and mental health. Five databases (MEDLINE, PsycINFO, Embase, CINAHL and Cochrane Library) were searched for relevant literature published until April 2022. Treatment studies including university students aged 18–24 years, participating in a sleep intervention (multi-component, e.g., CBT-I, or single-component, e.g., sleep hygiene) were eligible. Comparator groups were either active, i.e., alternative intervention, or passive, i.e., waitlist control or treatment-as-usual, with study outcomes to include measures of sleep and mental health. Of 3435 references screened, 11 studies involving 5267 participants, with and without insomnia symptoms, were included for a narrative synthesis on intervention designs and methodology. Six studies eligible for meta-analyses showed a moderate effect of sleep interventions in reducing sleep disturbance (SMD=−0.548 [CI: -0.837, −0.258]) at post-treatment, alongside a small effect in improving anxiety (SMD=−0.226 [CI: -0.421, −0.031]) and depression (SMD=−0.295 [CI: -0.513, −0.077]). Meta-regression examining study and intervention characteristics identified subpopulation (experiencing insomnia or not) as a significant moderator for effects on sleep (p=0.0003) and depression (p=0.0063), with larger effects in studies with participants experiencing insomnia. Comparison group type (active or passive) was also a significant moderator (p=0.0474), with larger effects on sleep in studies using passive comparison groups. Study type, delivery format, and intervention duration were not identified as significant moderators. At follow-ups, small but significant effects were sustained for anxiety and depression. Protecting and promoting sleep amongst university students may help safeguard and advance mental health.
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This review summarizes the current meta-analysis literature on treatment outcomes of CBT for a wide range of psychiatric disorders. A search of the literature resulted in a total of 16 methodologically rigorous meta-analyses. Our review focuses on effect sizes that contrast outcomes for CBT with outcomes for various control groups for each disorder, which provides an overview of the effectiveness of cognitive therapy as quantified by meta-analysis. Large effect sizes were found for CBT for unipolar depression, generalized anxiety disorder, panic disorder with or without agoraphobia, social phobia, posttraumatic stress disorder, and childhood depressive and anxiety disorders. Effect sizes for CBT of marital distress, anger, childhood somatic disorders, and chronic pain were in the moderate range. CBT was somewhat superior to antidepressants in the treatment of adult depression. CBT was equally effective as behavior therapy in the treatment of adult depression and obsessive-compulsive disorder. Large uncontrolled effect sizes were found for bulimia nervosa and schizophrenia. The 16 meta-analyses we reviewed support the efficacy of CBT for many disorders. While limitations of the meta-analytic approach need to be considered in interpreting the results of this review, our findings are consistent with other review methodologies that also provide support for the efficacy CBT.
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University students are a high risk population for mental health problems, yet few seek professional help when experiencing problems. This study explored the potential role of an online intervention for promoting wellbeing in university students, by investigating students’ help-seeking behaviour, intention to use online interventions and student content preference for such interventionss; 254 university students responded to an online survey designed for this study. As predicted, students were less likely to seek help as levels of psychological distress increased. Conversely, intention to use an online intervention increased at higher levels of distress, with 39.1%, 49.4% and 57.7% of low, moderate and severely distressed students respectively indicating they would use an online program supporting student well-being. Results suggest that online interventions may be a useful way to provide help to students in need who otherwise may not seek formal help.
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The evidence base for computerised cognitive behavioural therapy (CCBT) for common mental health problems has expanded rapidly in recent years. Reviews and meta-analyses have produced promising findings with regard to CCBT's effectiveness and acceptability, but developing and supporting effective and sustainable models of CCBT service implementation remains a challenge. This paper considers CCBT usage and explores the uptake of, and engagement with, CCBT. Recent literature on the topic of engagement with CCBT is summarised. Factors relating to discontinuation of use or 'drop-out' are also explored. Drawing on this evidence base we propose a simple '4 Ps' model of engagement factors: the programme, the problem, the person and the provider. We highlight some actions that researchers, service developers and providers can take that might increase uptake and engagement within the CCBT services that they provide. Managing expectations and promoting hope in both service users and providers are emphasised.
There are increasing concerns globally about the mental health of students (Kadison, & Digeronimo, 2004). In the UK, the actual incidence of mental disturbance is unknown, although university counselling services report increased referrals (Association of University & College Counselling, 2011). This study assesses the levels of mental illness in undergraduate students to examine whether widening participation in education has resulted in increases as hypothesized by the UK Royal College of Psychiatrists (2003, 2011). Patterns of disturbance across years are compared to identify where problems arise. Students (N = 1197) completed the General Health Questionnaire-28 either on day one at university or midway through the academic year for first, second and third year students. Rates of mental illness in students equalled those of the general population but only 5.1% were currently receiving treatment. Second year students reported the most significant increases in psychiatric symptoms. Factors contributing to the problem are discussed.