University of New South Wales, Sydney, Australia
Institute of Psychiatry, Kings College London
Universities of Leeds and Sheffield
Institute of Psychiatry, Kings College London
Imperial College, London
Institute of Psychiatry, Kings College London, UK
Correspondence: Dr Judith Proudfoot, Centre for General Practice Integration Studies, School of Public Health and Community Medicine, University of New South Wales, Sydney, NSW 2052, Australia. E-mail: j.proudfoot{at}unsw.edu.au
Declaration of interest J.P. and J.A.G. are minority partners in the commercial exploitation of Beating the Blues, and D.G. and D.A.S. are occasional consultants to Ultrasis plc.
See pp. 5562,
this issue.
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ABSTRACT |
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Aims To determine, in an expanded sample, the dependence of the efficacy of this therapy upon clinical and demographic variables.
Method A sample of 274 patients with anxiety and/or depression were randomly allocated to receive, with or without medication, computerised CBT or treatment as usual, with follow-up assessment at 6 months.
Results The computerised therapy improved depression, negative attributional style, work and social adjustment, without interaction with drug treatment, duration of preexisting illness or severity of existing illness. For anxiety and positive attributional style, treatment interacted with severity such that computerised therapy did better than usual treatment for more disturbed patients. Computerised therapy also led to greater satisfaction with treatment.
Conclusions Computer-delivered CBT is a widely applicable treatment for anxiety and/or depression in general practice.
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INTRODUCTION |
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METHOD |
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Treatment
Beating the Blues (Ultrasis;
http://www.ultrasis.com)
is an interactive, multimedia, computerised cognitivebehavioural
therapy package consisting of a 15 min introductory videotape, followed by
eight therapy sessions (Fig.
1). Each weekly session lasts about 50 min, with
homework projects between the sessions. Sessions and homework
projects are customised to the patients specific needs and each session
builds on the one before. A report of the patients progress, including
whether the patient has expressed any suicidal intent, is printed out for the
patient and the general practitioner at the end of each session. As part of
the research protocol, a practice nurse checked that the patient had logged on
successfully at the beginning of each session. The nurse then left the room,
having indicated where she was to be found if something went wrong (for
example, if the patient had difficulties with the program, or the printer ran
out of paper). At the end of the session, the nurse checked that the patient
had the necessary print-outs (session summary, homework tasks and progress
report) and booked the next session. Nurses were instructed to spend no more
than 5 min with each patient at the start and at the end of each session (i.e.
up to a total of 80 min over the eight sessions). Patients randomised to the
computerised therapy could also receive pharmacotherapy if the general
practitioner wished to prescribe it, and/or general support and practical or
social help, but not face-to-face counselling or psychological intervention.
Patients allocated to usual treatment received whatever therapy the general
practitioner prescribed. In order to replicate natural conditions in primary
care, we did not randomise drug treatments, nor did we constrain the
interventions received by patients allocated to usual treatment. The latter
included (besides any medication, discussion of problems with the doctor,
provision of practical and social help and further physical investigation
available also to the intervention group) referral to a counsellor, practice
nurse or mental health professional (psychologist, psychiatrist, community
psychiatric nurse or counsellor).
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Response variables
Each of the following four instruments was administered on five occasions
throughout the trial: pre-treatment, 2 months later (following completion of
the 9-week therapy program) and at three follow-up assessments, 1 month, 3
months and 6 months later.
The primary outcome measure was the Beck Depression Inventory II (BDI;
Beck et al, 1996).
This is an established 21-item measure of depression. The internal
consistency, measured by Cronbachs at pre-treatment in our
data-set, was 0.88.
The Beck Anxiety Inventory (BAI; Beck & Steer, 1990) is a 21-item symptom checklist rated on a four-point scale (03). The internal consistency of the scale at intake in this study was 0.88.
The Work and Social Adjustment Scale (WSA; Mundt et al, 2002) measures the degree to which the patients problems interfere with ability to work, home management, social life, private leisure and relationships. Each of the five indices is measured by a single-item Likert scale of 0 to 8 with 8 indicating severe impairment. The overall scale had an internal consistency of 0.85 in this study.
The Attributional Style Questionnaire (ASQ;
Peterson et al, 1982)
presents six negative and six positive hypothetical situations, for which
respondents are asked to supply a cause and then rate their cause along three
attributional dimensions. Scoring of the questionnaire yields a composite
index for the negative situations (CoNeg) and one for the positive situations
(CoPos). In this study, =0.77 for CoNeg and
=0.66 for CoPos.
A fifth measure, satisfaction with treatment, was administered 2 months after randomisation. It was measured with a single item, How satisfied are you with the treatment you have had for your anxiety/depression in this study?, which was rated on a nine-point scale ranging from 0 (not at all) to 8 (totally satisfied).
Since the ASQ yields two response variables (CoNeg and CoPos), there were therefore six variables in all. Missing or incomplete data for the BDI and BAI were imputed with the average score of the completed items when no more than four items were missing. The same procedure was applied to missing items in the WSA, but here only one missing item was permitted for imputation owing to the smaller size of the scale. In addition, demographic information (Table 1) was collected from all participants prior to randomisation.
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Design and statistical methods
Scores on the primary outcome measure (the BDI) and the BAI, WSA, CoNeg and
CoPos were individually submitted to two analyses. A summary measure analysis
was first applied to the four post-random values on each measure, using the
mean of available values as the summary measure for each participant. This
approach to the analysis of longitudinal data from a clinical trial is
described by Everitt & Pickles
(2000). The summary measure
approach, however, tells us nothing about how an outcome measure changes over
time in each treatment group, or how the response is related to other
variables of interest. Consequently, a further analysis was performed, which
involved fitting linear mixed effects models. These are essentially regression
models in which random effects are included to model possible subject
heterogeneity in intercepts and slopes of the outcome measures over time, thus
allowing the probable lack of independence of the repeated measurements to be
taken into account. Full details of such models are given by (for example)
Pinheiro & Bates (2000)
and Everitt (2002). The lme
function in S-PLUS (Everitt &
Rabe-Hesketh, 2001) was used to fit the models.
For each outcome variable a random intercept and slope model (see Everitt, 2002) was fitted using the following covariates (preliminary analyses showed that age and gender were not needed in these models):
Patient recruitment took place in two phases
(Fig. 2): in seven surgeries in
phase 1 and in four surgeries in phase 2. The results of phase 1 assessed by
the BDI, BAI and WSA have already been reported
(Proudfoot et al,
2003). Since a secondary aim of our study was to determine the
replicability of these results, these three variables were first analysed with
the inclusion of phase as a further fixed effect factor. A series of fitted
models allowing for possible phase or phase x treatment effects
disclosed none that approached conventional significance levels. Consequently
it was considered appropriate to undertake more detailed analyses of the
combined data-set. All response variables were therefore analysed with the
patients from all 11 surgeries combined. Power calculations, based on
independent t-tests of the change scores (pre-treatment to
post-treatment) between groups in two previous studies
(Selmi et al, 1990;
Mynors-Wallis et al,
1995), showed that to detect a difference of 1 standard deviation
in change scores at 80% power and with =0.05, a total sample size of
200 would be needed. All analyses were intention-to-treat, by which we mean
that patients were analysed as randomised rather than by treatment actually
received. Within each intention-to-treat group, patients were not included in
the model-fitting process described above if they had missing values on any of
the covariates used (pre-treatment value, drugs, duration of episode) or if
all four post-randomisation values of the response being analysed were
missing. Consequently, all the available post-randomisation values of the
response were included, rather than only those obtained from patients with all
four values recorded. Table 2 presents a breakdown of the patient groups according to drug treatment, length
of pre-existing illness and the surgery in which they were treated.
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RESULTS |
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There was a clear difference between the two treatments on the BDI: patients given computerised therapy scored on average 27 points lower than those given treatment as usual. A clear effect was also seen on the WSA, with scores on average between just above 1 and just below 6 points lower in the computerised therapy group than in the usual treatment group. The effect of computerised therapy on the BAI was in the same direction but just failed to reach the conventional 5% significance level (P=0.06). The two measures from the ASQ confirmed this picture: the effect of the intervention was to decrease CoNeg by about 614 points and to increase CoPos by about 18 points.
Linear mixed effects models
One of the assumptions of the random effects models described in this
section is that missing values are missing at random (see
Everitt, 2002). If this
assumption is invalid, scores at a particular visit for patients who missed a
subsequent visit would differ from the scores of patients who attended the
subsequent visit. There was little difference in the BDI scores of those
attending and those not attending their next scheduled visit; consequently,
the missing at random assumption seems to be justified.
The means and standard deviations for all response variables in each treatment group at each time of measurement are shown in Table 4; for the primary outcome measure, the BDI, the means and standard errors are also shown in Fig. 3. We present full details of the analysis only for the BDI; results for the other variables are summarised below (further information available from the authors upon request).
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(A further model considered was one in which random effects were included to model possible differences between surgeries, and surgery x treatment interactions; the model provided no improvement in fit over that reported.) In the refitted model the estimated regression coefficient for computerised treatment is -4.62 with a standard error of 1.12, giving a 95% CI for the treatment effect adjusted for the remaining covariates of 2.436.82, which is similar to the confidence interval calculated from the summary measure approach.
Other response variables
Fitting the same initial model as for the BDI, the results for the BAI are
similar, with again no significant interaction of treatment with time
(P=0.17), drugs (P=0.68) or length of illness
(P=0.43), but in this case a significant
treatmentxpre-randomisation BAI score interaction (P=0.005). A
relatively informal investigation of this interaction suggested that, below a
pre-randomisation value of approximately 18 on the BAI, there was no
difference between the intervention and treatment as usual, but above this
value the intervention resulted in an estimated average decrease of 4.04 (95%
CI 0.447.64). For the WSA the four interaction effects were again
totally non-significant (treatmentxpre-randomisation WSA score,
P=0.81; treatment x time, P=0.88;
treatmentxlength, P=0.98; treatmentxdrugs;
P=0.69). The adjusted treatment effect confidence interval of
1.084.68 found from fitting the model with phase and the interaction
terms removed is again very similar to that calculated from the simpler
summary measure method. For CoNeg all the interaction terms were
non-significant (treatmentxtime, P=0.30;
treatmentxpre-randomisation CoNeg score, P=0.58;
treatmentxdrugs, P=0.44; treatmentxlength,
P=0.44). The estimated treatment effect from the refitted model
excluding these terms was 11.16 (95% CI 7.7514.57), similar to that
given by the summary measure approach. For CoPos the model-fitting procedure
revealed a significant treatmentxpre-randomisation score interaction
(P=0.02). Informal investigation of the reasons for this interaction
suggested that above a value of approximately 100 on pre-randomisation CoPos
there was no treatment effect, but below 100 the intervention therapy
increased average CoPos by an estimated 5.22 (95% CI 1.389.06) points
relative to treatment as usual.
Satisfaction with treatment
Finally, a multiple regression model fitted to satisfaction with treatment
(151 completed responses) showed that treatment, drug and age were predictive
of this variable. Average satisfaction in the computerised therapy group was
1.68 (95% CI 0.822.54) points higher than in the treatment-as-usual
group; for those given drugs compared with those not given drugs, the
corresponding figure was 1.28 (95% CI 0.631.94). For age, the estimated
regression coefficient was 0.028 (95% CI 0.00450.052).
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DISCUSSION |
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Applicability of computerised therapy
Our unusually large sample for a randomised controlled trial of
psychological treatment enabled robust estimation of the extent to which the
use of a computerised cognitive-behavioural therapy program gives rise to
clinical improvement irrespective of other treatments and of patient
characteristics. The overall conclusion is clear: computerised
cognitive-behavioural therapy is a generally suitable treatment across the
range of patients presenting with anxiety and depression in primary care,
including those with mild depression or mixed anxiety and depression: mild
(representing 34% of our computerised therapy group at intake:
Table 1) as well as those with
moderate and severe depression. The observed effects of the program did not
interact with prescribed drug treatment, which itself appeared effective in
reducing depression and negative attributions and in increasing satisfaction
with treatment; nor did they interact with duration of pre-randomisation
illness, although this was independently associated with increased depression
and anxiety, decreased work and social adjustment, and decreased positive
attributions; nor with time-clinical improvement was manifested by the end of
treatment with computerised therapy and persisted undiminished until the end
of follow-up 6 months later. As an exception to the general trend, however,
the effects of the intervention were moderated by two measures of
pre-randomisation clinical state: first, anxiety was reduced only in patients
whose starting BAI score was above about 18 (this is an approximate threshold
value, simply judged graphically from an appropriate plot); and second,
positive attributions were increased only in patients whose starting CoPos
score was below about 100 (again an approximate value). In respect of both
variables, therefore, the efficacy of the intervention therapy was greater in
patients whose initial clinical state was worse. Given that there was no
interaction between treatment with computerised therapy and pre-randomisation
clinical state on the other outcome measures, this therapy appears to be
appropriate to patients across the whole range of clinical severity
encountered in general practice, and irrespective of duration of pre-existing
illness.
This inference is confirmed by the findings that, over the entire duration of the trial, depression was worse, the higher the levels of pre-randomisation BDI and BAI scores, and work and social adjustment was poorer, the higher the level of the pre-randomisation WSA score (data not shown), yet these factors did not influence the efficacy of the intervention as measured by either the BDI or the WSA. On the ASQ, the CoNeg (which measures negative attribution style) showed the same pattern. For both groups of patients, negative attributions were greater with higher pre-randomisation levels of CoNeg or BAI scores (data not shown); yet the reduction in CoNeg produced by computerised therapy was independent of these factors. As further evidence of the wide range of applicability of this type of therapy, the additive effects of the intervention and drug treatment on some measures (BDI, CoNeg and overall satisfaction with treatment), as well as their lack of interaction with one another in their effects on any measure, indicate that this form of treatment can provide clinical benefit whether administered on its own or in conjunction with pharmacotherapy. In all these respects, our findings confirm the earlier report by Proudfoot et al (2003), limited to 167 phase 1 patients and to only three of the response measures (BDI, BAI and WSA). The analyses allowing effects of study phase on these three measures further demonstrated the robustness of our results, in that no significant effect of this variable, either alone or in interaction with others, was found.
Acceptability of computerised therapy
Familiarity with computers was not an inclusion criterion for entry into
the trial. Yet, in phase 1 of this study
(Proudfoot et al,
2003), the rate of withdrawal from this therapy was only 35%,
similar to rates reported for face-to-face cognitivebehavioural therapy
(Watkins & Williams,
1998). In phase 2 of our study the rate of withdrawal was reduced
to 12 out of 55 patients randomised to computerised therapy (22%), of whom
only slightly over half (7 of 12) quit for reasons of dissatisfaction with
treatment. This reduction probably reflects the considerable improvements in
program reliability made since the inception of phase 1. Satisfaction with
treatment was, in fact, significantly higher among computerised therapy than
treatment-asusual patients. Thus, computer-delivered
cognitivebehavioural therapy is acceptable to patients, clinically
effective, of wide suitability in general practice and, as we report in a
companion paper (McCrone et al,
2004, this issue), cost-effective. It is well suited, therefore,
to help supply the unmet need arising from the limited and geographically
inequitable availability in the National Health Service of
cognitivebehavioural therapists
(Shapiro et al,
2003). Thus, our results warrant further research to compare the
clinical efficacy of computerised and face-to-face cognitivebehavioural
therapy (see report of the National
Institute for Clinical Excellence, 2002).
Role of attributional change
Attributional style correlates with susceptibility to clinical depression
and physical illness, risk of relapse in depression, low motivation and poor
achievement (Seligman, 1991).
Individuals who typically attribute their failures to internal, stable and
global factors (high CoNeg) and their successes to external, temporary and
specific causes (low CoPos) are most vulnerable to problems of depression and
its cognitive, behavioural and motivational correlates. Face-to-face
cognitivebehavioural therapy has been shown to modify attributional
style (Seligman et al,
1988) and produce enduring therapeutic benefit in depression and
other psychiatric conditions (Hawton et
al, 1989). Our results here demonstrate that computerised
cognitivebehavioural therapy brings about attributional change
commensurate with that achieved by face-to-face therapy
(Proudfoot et al,
1997), along with concomitant improvement in clinical symptoms, as
well as in work and social functioning.
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Clinical Implications and Limitations |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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Received for publication February 28, 2003. Revision received October 22, 2003. Accepted for publication January 24, 2004.
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