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The impact of internet-delivered cognitive behavioural therapy for health anxiety on cyberchondria
Journal of Anxiety Disorders 69 (2020) 102150
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Journal of Anxiety Disorders journal homepage: www.elsevier.com/locate/janxdis
The impact of internet-delivered cognitive behavioural therapy for health anxiety on cyberchondria
T
Jill M. Newbya,b,*, Eoin McElroyc a
School of Psychology, UNSW Sydney, 1302 Mathews Building, Randwick, NSW, 2052, Australia Clinical Research Unit for Anxiety and Depression (CRUfAD), School of Psychiatry, UNSW Sydney at St Vincent’s Hospital, 390 Victoria Street Darlinghurst, NSW, Sydney, 2010, Australia c Department of Neuroscience, Psychology and Behaviour, University of Leicester, University Road, Leicester, LE1 7RH, UK b
A R T I C LE I N FO
A B S T R A C T
Keywords: Cyberchondria Health anxiety Illness anxiety disorder Online health information searching Somatic symptom disorder
Cyberchondria refers to an emotional-behavioural pattern whereby excessive online searches lead to increased anxiety about one’s own health status. It has been shown to be associated with health anxiety, however it is unknown whether existing cognitive behavioural therapy (CBT) interventions targeting health anxiety also improve cyberchondria. This study aimed to determine whether internet-delivered cognitive behavioural therapy (iCBT) for severe health anxiety led to improvements in self-reported cyberchondria and whether improvements in cyberchondria were associated with improvements in health anxiety observed during treatment. Methods: We analysed secondary data from a randomised controlled trial (RCT) comparing an iCBT group (n = 41) to an active control group who underwent psychoeducation, monitoring and clinical support (n = 41) in health anxious patients with a DSM-5 diagnosis of Illness Anxiety Disorder and/or Somatic Symptom Disorder. The iCBT group showed a significantly greater reduction in cyberchondria compared to the control group, with large differences at post-treatment on the Cyberchondria Severity Scale Total scale (CSS; Hedges g = 1.09), and the Compulsion, Distress, Excessiveness subscales of the CSS (g’s: 0.8–1.13). Mediation analyses showed improvements in health anxiety in the iCBT group were mediated by improvements in all of the CSS subscales, except for the Mistrust subscale. Conclusions: Internet CBT for health anxiety improves cyberchondria.
1. Introduction Experiencing some anxiety about one’s health can be normal and adaptive, but when health anxiety becomes persistent, excessive and preoccupying, it can have a negative impact on the individual, their loved ones and health professionals (Tyrer, Eilenberg, Fink, Hedman, & Tyrer, 2016), and society (Bobevski, Clarke, & Meadows, 2016; Tyrer, 2018). Health anxiety is thought to be dimensional in nature, ranging from normal, transient health worries to severe, and disabling health anxiety. In the DSM-5 it is categorised as Illness Anxiety Disorder (IAD) or Somatic Symptom Disorder (SSD) depending on the severity of the individual’s somatic symptoms (American Psychiatric Association, 2013). Severe and functionally impairing health anxiety affects between 3–5% of the population (Creed & Barsky, 2004; Sunderland, Newby, & Andrews, 2012). People with health anxiety are afraid that they either have, or will develop serious, often life-threatening illnesses. As a result of these fears, they often engage in a range of behaviours such as excessive
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reassurance-seeking from loved ones and health professionals about their health concerns, which may temporarily allay their fears, but maintain preoccupation with illness over the long term (Warwick & Salkovskis, 1990). With free, private, and easily accessible health information available online, it is not surprising that people with health anxiety frequently report searching the internet to seek reassurance about their symptoms and health concerns (Muse, McManus, Leung, Meghreblian, & Williams, 2012). Exposure to information during online searches, especially alarming, inaccurate and misleading information about life-threatening illnesses (White & Horvitz, 2009) can exacerbate existing worries about illnesses, and lead to new anxieties, causing an escalating pattern of distress and further excessive and repeated online searches for health information in a pattern labelled ‘cyberchondria’ (Starcevic and Berle, 2013). This phenomenon may be heightened by technical features of online search engines. The algorithms that determine search results are driven largely by the popularity of the available information, so that frequently accessed web pages are given priority in search results. Thus,
Corresponding author at: School of Psychology, 1302 Mathews Building, UNSW Australia, Kensington, NSW, 2052, Australia. E-mail address: [email protected] (J.M. Newby).
https://doi.org/10.1016/j.janxdis.2019.102150 Received 24 May 2019; Received in revised form 10 September 2019; Accepted 9 October 2019 Available online 31 October 2019 0887-6185/ © 2019 Elsevier Ltd. All rights reserved.
Journal of Anxiety Disorders 69 (2020) 102150
J.M. Newby and E. McElroy
health anxiety was outlined (the ‘Health Anxiety Program’) (Newby et al., 2016). The iCBT program included psychoeducation about health anxiety, and taught CBT skills to target the cognitive and behavioural processes that are proposed to maintain health anxiety (Warwick & Salkovskis, 1990), including cognitive therapy components to change maladaptive interpretations of bodily symptoms and beliefs about health and illness, and behavioural strategies to reduce excessive checking, reassurance-seeking and avoidance of situations that trigger illness fears (e.g., hospitals, conversations about illness). In addition to these strategies, the program educated participants about the role of online health information searching (‘Googling behaviours’) in exacerbating health anxiety and preoccupation with illness. It also taught participants to become more aware of, self-monitor, and reduce excessive online health information searching through behavioural experiments, activity scheduling, and other strategies to delay and prevent unhelpful online searching about symptoms and illness. We expected the iCBT program to be helpful at reducing the behavioural and emotional components of cyberchondria via several avenues. For example, this intervention aimed to increase awareness of the frequency of online searching and the unhelpful personal costs of online searching, and taught practical strategies to prevent, delay and stop online searching behaviour once it started. We expected these strategies to help health anxious participants to reduce the frequency and duration of online health information searching, and in turn also prevent escalations in distress that can occur through excessive and repeated searching. In addition, the iCBT program taught participants how to relate to symptoms and health information in a different way. For example, they developed new ways of thinking about unexplained bodily sensations and health information, as opposed to relying on catastrophic ‘worst case scenario’ thinking patterns and assumptions. Once an individual can develop their own less threatening interpretations of bodily sensations, and improve their ability to tolerate uncertainty about these sensations, we expected them to rely less on internet searches to alleviate anxiety. In addition, by learning how to regulate negative emotions including anxiety, coupled with the ability to think more clearly, rationally, and in less negatively biased interpretation style, we expected that they should be able to both prevent anxiety from escalating, and alleviate the anxiety more quickly after online searches. Through facilitating these changes, we expected health anxiety to reduce. We therefore expected reductions in cyberchondria would be associated with improvements in health anxiety symptoms. In a randomised controlled trial (Newby et al., 2018), we showed that iCBT for health anxiety had large and superior effects in reducing health anxiety compared to an active control group who received online anxiety psychoeducation, support and monitoring from a clinician. As part of that RCT, we also aimed to examine the impact of the iCBT program on cyberchondria, and administered the self-report Cyberchondria Severity Scale (CSS) before and after a 12-week treatment period to achieve this aim. The CSS contains five subscales that assess the multidimensional construct of cyberchondria. These include the Compulsion (the degree to which online health information interrupts daily activities), Distress (emotional distress associated with online health information searching), Excessiveness (seeking out repeated, and frequent sources of information), Reassurance (anxiety leading from online searches to seeking opinions from other experts of health professionals) and Mistrust of Health Professionals subscales (whether online information is trusted more than health professionals) subscales. Our primary aim in the current study was to explore whether iCBT for health anxiety improves cyberchondria (and specific subscale ratings) relative to the control group. Our second aim was to explore whether improvements in cyberchondria were associated with improvements in health anxiety using mediation analyses. We hypothesised that iCBT would outperform the control group in improving cyberchondria, and that improvements would be associated with changes in symptoms of health anxiety.
searches of relatively benign symptoms may be biased to produce information about alarming, rare, life-threatening conditions. For instance, White and Horvitz (White & Horvitz, 2009) found that the most common result from a search of the term ‘muscle twitch’ was amyotrophic lateral sclerosis, despite the fact that it has an annual incidence rate of 1 in every 55,000 persons. The way that cyberchondria has been defined and conceptualised has varied widely across studies. For the purposes of this study, we conceptualise cyberchondria as having both a behavioural and emotional component. The behavioural aspect of cyberchondria involves excessive, and repeated online searches for health information that may function similar to reassurance-seeking behaviour, where the individual seeks reassurance online as opposed to in-person. The individual may search online initially in an attempt to reduce distress, or to alleviate their fears of illness or uncertainty about unexplained physical sensations. Some searches may temporarily alleviate the individual’s anxiety, but serve to maintain anxiety long-term through maintaining the individual’s preoccupation with health concerns and difficulty tolerating uncertainty about unexplained sensations. Individuals may search online, but not feel reassured by the searches and instead experience heightened distress and anxiety. That is, the searching causes escalating distress, and prompts further online searching, which further escalates distress and future searches. The emotional component of cyberchondria is the distress, or anxiety caused by the searching, or the inability to control the searching behaviour. There is an ongoing debate as to whether cyberchondria is a core feature of health anxiety (Starcevic & Berle, 2013), or whether it constitutes a distinct concept in its own right (Fergus & Russell, 2016; Mathes, Norr, Allan, Albanese, & Schmidt, 2018). Cross-sectional studies have shown a strong association between cyberchondria and health anxiety (2014, Fergus, 2013; Mathes et al., 2018; Norr, Albanese, Oglesby, Allan, & Schmidt, 2015; Norr, Allan, Boffa, Raines, & Schmidt, 2015; Norr, Oglesby et al., 2015). A recent meta-analysis of 20 studies, including 7373 participants showing a positive correlation between health anxiety and cyberchondria (r = 0.62) (McMullan, Berle, Arnaez, & Starcevic, 2019). However, psychometric analyses have supported meaningful a distinction between cyberchondria and health anxiety (Fergus & Russell, 2016). Furthermore, the direction of causality between online health searches and health anxiety may also vary between individuals, meaning the behavioural cycle of cyberchondria cannot be considered, in all cases, purely the result of pre-existing health anxiety. Although people with elevated health anxiety report a greater frequency of online health information searches, along with greater resultant anxiety (Doherty-Torstrick, Walton, & Fallon, 2016; Muse et al., 2012; Singh & Brown, 2016), it is has been demonstrated that individuals without an existing tendency towards health worries can experience elevated levels of distress/anxiety as a result of searching behaviour (te Poel, Baumgartner, Hartmann, & Tanis, 2016). Such searches may be prompted by curiosity or the sudden emergence of an unexplained symptom (Starcevic, 2017). Together, these findings suggest that cyberchondria can be conceptualised as a unique pattern of behaviours and emotions that undoubtedly plays an important role in health anxiety, and therefore needs to be targeted in treatment. To our knowledge, there is no research on the treatment of cyberchondria, either alone or in the context of health anxiety (McMullan et al., 2019; Starcevic & Berle, 2013). The current study sought to address this gap by examining, for the first time, whether clinician-guided internet-delivered CBT for health anxiety improves cyberchondria, in a sample of participants who met criteria for DSM-5 diagnoses of IAD and/or SSD. Clinician-guided iCBT has previously been shown to be effective at improving health anxiety (Hedman et al., 2011, 2014; Hedman, Axelsson, Andersson, Lekander, & Ljótsson, 2016; Newby et al., 2016), mirroring the success of face-to-face CBT protocols (see Cooper, Gregory, Walker, Lambe, & Salkovskis, 2017; Thomson & Page, 2007 for reviews on CBT). In a previous paper, the development of a new iCBT program for 2
Journal of Anxiety Disorders 69 (2020) 102150
J.M. Newby and E. McElroy
2. Methods
but that this relief is often short-lived. It also taught how googling can lead to unintended negative effects, such as exacerbating existing fears, maintaining focus and preoccupation with anxieties, lead to new fears of illness, and lead to incorrect self-diagnoses. They were also taught about the variable quality of online health information, and how more highly ranked websites in search engines are ranked by popularity not necessarily their accuracy, and that ‘alarming and threatening, worst case scenario’ information, stories of suffering and mistaken diagnoses are often higher in the search results, rather than factual or benign information. Finally, the module tips to become an ‘expert searcher’. Tips included keeping track of, and learning to identify triggers for internet searching, and replacing triggers with alternative activities using Activity Planning; checking only credible websites, such as health experts, and government websites; avoiding searching for vague symptoms; and stopping searching when it exacerbates anxiety. Finally, they were encouraged to conduct a behavioural experiment to compare anxiety levels, and health preoccupation when they avoid searching versus when they search the internet. It also included a ‘checking prevention plan’ to develop strategies to prevent the participant from googling excessively including using behavioural activation and other distracting strategies, as well as behavioural experiments to test cognitions about googling about symptoms. Participants were encouraged to read the online module, and complete key skills practice exercises between the lessons. Participants in this group received email and/or phone contact with the clinician (JN, a PhD-level Clinical Psychologist) after lessons 1 and 2 to encourage progress, and after subsequent lessons clinician contact was made upon patient request or if the patient reported significant deterioration (increased distress) or suicidal ideation. If participants stopped logging in, they were sent up to two automated emails to remind them to complete their next lesson. In addition, the clinician attempted to contact the participant via email or phone if the participant did not respond to the automated emails, or login after they were prompted. The clinician spent on average 43.11 min per participant on email and telephone contact in the iCBT group (SD = 25.75, range = 13–116 minutes).
2.1. Participants Participants who met criteria for a diagnosis of DSM-5 IAD or SSD (participants with SSD must endorse criterion B2 for SSD ‘persistently high level of anxiety about health or symptoms’) who were on a stable dose of psychological and/or pharmacotherapy for the two months prior to intake assessment were eligible to participate. Applicants were not eligible to participate if they had a self-reported diagnosis of psychosis or bipolar disorder, currently used antipsychotic or regular benzodiazepine medications, or had severe depression (defined as scoring > 24 on the Patient Health Questionnaire 9-item [PHQ-9]). These exclusion criteria were set due to safety reasons, to maintain consistency with our previous trial protocols, and to minimise the possibility of concurrent pharmacotherapies interfering with the efficacy of the CBT techniques (e.g., exposure components). 2.2. Trial design Eligible participants were randomised to either iCBT or the control group (n = 45 for iCBT, and n = 41 for control group), and completed assessments at pre-, mid-, post-treatment and 3-month follow-up (iCBT group only, as the control group was crossed over to iCBT after posttreatment assessment). The CSS was only administered at pre and posttreatment. The SHAI was administered at each lesson to track changes in symptoms. The previous manuscript reporting the main RCT findings (Newby et al., 2018) reported the results for the iCBT and control groups on self-reported health anxiety severity, and a range of other outcomes, including generalised anxiety, depression, distress, body hypervigilance, and maladaptive cognitions. The CSS results have not been reported previously. 2.3. Ethics approval and informed consent All participants provided electronic informed consent to participate in the study. The study involving the clinical sample was approved by St Vincent’s Hospital Human Research Ethics Committee (HREC/14/SVH/ 294) and registered with the Australian and New Zealand Clinical Trials Registry (ACTRN12615000887572).
2.4.2. Control group Fact sheets (2–4 pages) were delivered online each fortnight on topics related to anxiety (e.g., The fight-or-flight response, Causes of anxiety, How to Manage Stress). Participants were offered clinician contact via email or phone. Phone contact was made if the participant reported significant deterioration in distress or depression symptoms, or suicidal ideation. The clinician spent an average of 23.20 min (SD = 13.74, range = 8–61) minutes per participant in the control group.
2.4. Interventions 2.4.1. Internet CBT The Health Anxiety Course is a 6-lesson illustrated comic-style online program delivered via the Virtual Clinic website (www.virtualclinic. org.au)1 over 12 weeks. After a participant completed a lesson, they were required to wait a minimum 5 days until the next lesson was released. Participants were encouraged to complete one lesson per week to fortnight, however, they were able to complete the program at their own pace, with a maximum of 12 weeks to complete the 6 lessons. The program delivered psychoeducation, and CBT skills including detecting and challenging negative thinking patterns about bodily symptoms and health, exposure to feared situations and sensations, behavioural strategies to reduce checking, reassurance-seeking and googling about symptoms, and relapse prevention. Importantly, strategies to reduce unhelpful and excessive googling of symptoms were included in Lesson 2. In this lesson, excessive googling was conceptualised as a form of unhelpful checking behaviour. Participants read psychoeducation about the unhelpful role of googling, and how it can exacerbate health anxiety, maintain preoccupation with illness fears, and trigger new fears. First, the lesson explained how googling may have lead to immediate, temporary relief from anxiety, 1
2.5. Diagnostic interview Participants were administered an abbreviated telephone-administered Anxiety Disorders Interview Schedule for DSM-5 (ADIS-5) (Brown & Barlow, 2014) prior to being included in the study to confirm whether they met criteria for an IAD, or SSD diagnosis or comorbid IAD and SSD (Brown & Barlow, 2014). 2.6. Clinical outcomes The primary outcome was health anxiety according to the 18-item Short Health Anxiety Inventory (SHAI) (Salkovskis, Rimes, Warwick, & Clark, 2002). Cyberchondria was assessed using the 33-item Cyberchondria Severity Scale (McElroy & Shevlin, 2014). 2.6.1. The Short Health Anxiety Inventory (SHAI) (Salkovskis et al., 2002) The SHAI is a validated 18-item self-report measure of the severity of health anxiety symptoms over the past week. The measure has good psychometric properties including good internal consistency, test-retest reliability and construct validity, and is sensitive to treatment
For a free demonstration of the course, contact the first author. 3
Journal of Anxiety Disorders 69 (2020) 102150
J.M. Newby and E. McElroy
cyberchondria (either the total scale score, or the separate subscale scores) as the mediator variable (M) using PROCESS (Hayes, 2013). This enabled us to test the indirect effects of treatment (where 0 = control, and 1= iCBT) on post-treatment health anxiety severity (SHAI scores) via changes in cyberchondria. Baseline SHAI scores were entered as a covariate in all analyses. A positive change score between pre and post-treatment evidenced an improvement in ratings. All 95% biascorrected bootstrapped confidence intervals for the indirect effects are presented in brackets.
(Abramowitz, Deacon, & Valentiner, 2007; Alberts, Hadjistavropoulos, Jones, & Sharpe, 2013). Participants are asked to rate each item with four response options to examine cognitive and behavioural features of health anxiety. Items are rated on a four point scale, ranging from 0 to 3 (for example, the first item is: 0 = I do not worry about my health, 1 = I occasionally worry about my health, 2 = I spend much of my time worrying about my health, and 3 = I spend most of my time worrying about my health). 2.6.2. Cyberchondria Severity Scale (CSS) (McElroy & Shevlin, 2014) The CSS is a validated 33 item self-report measure of cyberchondria. Items (e.g., “If I notice an unexplained bodily sensation I will search for it on the internet”) are rated on a 5-point scale, ranging from 1-5. The CSS has a total score, as well as five subscales: Compulsion scale, Distress subscale, Excessiveness subscale, Reassurance subscale, and a Mistrust of Medical Professionals subscale. The scale has been validated in undergraduate and community samples (Norr, Albanese et al., 2015; Norr, Allan et al., 2015; Norr, Oglesby et al., 2015) and shown to have high internal consistency, and good convergent and divergent validity, as demonstrated by higher correlations with measures of health anxiety (r = 0. 54 – 0.59; Fergus, 2014; Norr, Oglesby et al., 2015) compared with other forms of anxiety, e.g. OCD (r = 0.34 – 0.45; Norr, Oglesby et al., 2015) and generalised anxiety (r = 0.39; McElroy & Shevlin, 2014). Chronbach’s alpha’s in the current sample were 0.96 for the Total scale, 0.96 for the Compulsion scale, 0.81 on the Mistrust scale, 0.85 for the Reassurance, 0.91 for the Excessiveness subscale, 0.95 for the Distress subscale.
3. Results 3.1. Participants Forty-five iCBT participants, and 41 participants in the control group completed baseline questionnaires; of these data were collected from 37/45 participants at post-treatment in the iCBT group and 32/41 participants in the control group. 3.1.1. Demographic and sample characteristics Participants were 30 years on average (SD = 12 years, range = 18–65), the majority were female (87.2 %, n = 75), and half were either married or living in a de facto relationship (n = 43, 50 %). Most were either in part-time or full time paid work (n = 61, 70.9 %), spoke English as their main language (94.2 %, n = 81), and were well educated having completed year 12 or equivalent (n = 19, 22.1 %), or a tertiary degree (n = 37, 43 %). The mean score on the SHAI was 35.77 (SD = 7.21, range = 16–49), and the average age of onset of health anxiety was 21 years (SD = 9, range: 8–55). For further details of the sample, see Newby et al. (2018). In terms of DSM-5 diagnoses, there was an even split of IAD (n = 39, 45.3 %) and SSD (n = 39, 45.3 %), and 8 had comorbid IAD and SSD (17 %). On average, participants met criteria for 3.2 diagnoses (SD = 1.7). The most common comorbidity was generalised anxiety disorder (GAD) (n = 43, 50 %), then panic disorder (39.5%), agoraphobia (n = 31, 36.0 %), and major depressive disorder (MDD) (n = 31, 36 %). Fifteen participants were receiving concurrent counselling (17.4 %) with 3 (3.5%) receiving current CBT that had started at least 2 months prior to initial assessment, and 16 were on antidepressant medications (18.6 %). There were no significant differences between the two groups on any baseline characteristics or treatment expectancy.
2.7. Statistical analyses Treatment efficacy was evaluated using repeated measures analyses of variance (ANOVAs). Because the CSS was only measured at pre- and post-treatment in the two treatment groups, linear mixed models were judged not to be appropriate, given they would not have added additional value. Within- and between-groups effect sizes (Hedges g) were calculated and interpreted using standard guidelines: 0.2 = small effect, 0.5 = medium effect, 0.8 = large effect (Cohen 1988). Due to recent evidence showing that the cyberchondria Mistrust subscale does not load on the same factors as the other subscales of the CSS (Fergus, 2014), we chose to analyse the subscales separately, as well as the CSS Total score results. The analyses of the health anxiety outcomes have been reported previously in the original paper (Newby et al., 2018), and have also been included in the current paper for clarity of interpreting the results. The CSS results have not been reported previously. The CSS results were not included in the previous report because it was only administered at two time points, whereas the remaining measures were administered at multiple time points.
3.1.2. Primary outcomes: impact of internet CBT on health anxiety, and cyberchondria The time by group interactions were statistically significant for the SHAI, Total scores on the CSS as well as the Compulsion, Distress, Reassurance, and Excessiveness subscales (all p’s < .001, except for the Reassurance subscale where p < .05). The between-group differences on the SHAI, and CSS Compulsion, Distress and Excessiveness subscales were large ranging from 0.8 (Distress) to 1.15 (Compulsion). There were only moderate between-group differences on the Reassurance subscale favouring the iCBT group (g = 0.56). In contrast, the time by group interactions for the Mistrust subscale was not statistically significant (p > .05), and there were small non-significant differences favouring the control group (g=-0.1) on the Mistrust subscale. See Table 1 for results.
2.7.1. Mediation analyses Tests of the indirect effects (mediation) were conducted using PROCESS (Hayes, 2013). Estimates of indirect effects were generated using bootstrapping analysis (see Preacher & Hayes, 2004; Preacher, Rucker, & Hayes, 2007). Bootstrapping is a nonparametric resampling method that generates an estimate of the indirect effect, and does not require assumptions about the shape of the sampling distribution that underlie the Sobel test. In bootstrapping analysis, the most stringent test of an indirect effect (mediation) is if the 95% bias corrected and accelerated confidence intervals for the indirect effect do not include the value of 0. When zero is outside of the 95% confidence interval estimate, the indirect effect is declared statistically different from zero at p < .05 (two-tailed), indicating that the effect of the independent variable on the dependent variable is contingent upon the effect of the proposed mediator (Preacher & Hayes, 2004). In the current study, we estimated 5000 bias-corrected bootstrap 95% confidence intervals using PROCESS for SPSS (Hayes, 2012). We estimated several separate mediation models, with changes in
3.1.3. Mediation analyses Results of the mediation analysis indicated that the total effect of treatment on post-treatment SHAI scores was significant (B=-11.44, t=-6.13, SE = 1.86, p < .001). The indirect effect of treatment on post-treatment health anxiety via reductions in cyberchondria (CSS Total) scores between pre- and post-treatment was supported, as the indirect effect was statistically different from zero (95%CI: -8.18 to -2.60). In addition, the indirect effect of treatment on post-treatment 4
Journal of Anxiety Disorders 69 (2020) 102150
J.M. Newby and E. McElroy
Table 1 Observed means and standard deviations of the online cognitive behavioural therapy program and control group on health anxiety and cyberchondria severity scale subscales. Measure
iCBT Control iCBT Control iCBT Control iCBT Control iCBT Control iCBT Control iCBT Control iCBT Control
SHAI SHAI CSS Total CSS Total CSS Compulsion CSS Compulsion CSS Distress CSS Distress CSS Reassurance CSS Reassurance CSS Excessiveness CSS Excessiveness CSS Mistrust CSS Mistrust CSS Total (without mistrust subscale) CSS Total (without mistrust subscale)
Baseline
Post Mean
M
SD
M
SD
35.11 35.81 102.16 104.50 21.27 22.56 28.68 28.81 16.38 16.47 27.89 28.75 7.95 7.91 94.22 96.59
7.18 7.73 21.41 22.68 7.72 7.45 6.60 7.42 4.57 5.46 7.29 6.67 2.44 2.41 20.07 22.27
19.81 31.63 70.78 96.22 12.97 20.94 19.05 26.03 12.41 15.38 18.62 26.44 7.73 7.44 63.05 88.78
7.87 9.41 22.60 24.17 6.21 7.51 8.31 7.95 4.92 5.51 6.52 7.24 3.10 2.26 22.34 23.71
Within ES (95%CI) Baseline to Post
Between-group post-treatment ES (Hedge’s g) (95%CI) Post-treatment
F (time by group)
2.15 0.50 1.41 0.23 1.27 0.16 1.30 0.23 0.66 0.13 1.85 0.21 0.11 0.15 1.57 0.36
1.36 (0.83-1.88)
F(1,67) = 29.88, p < .001
1.09 (0.58-1.59)
F(1,67) = 22.64, p < .001
1.15 (0.64-1.66)
F(1,67) = 16.73, p < .001
0.80 (0.31-1.30)
F(1,67) = 17.56, p < .001
0.56 (0.08-1.05)
F(1,67) = 6.68, p < .05
1.13 (0.62-1.63)
F(1,67) = 25.07, p < .001
−0.10 (-0.58-0.36)
F(1,67) = 0.12, p = 0.73
1.11 (0.60-1.61)
F(1,67) = 25.41, p < .001
(1.58–2.72) (0.00–1.00) (0.90–1.92) (–0.26–0.72) (0.77–1.77) (−0.33–0.66) (0.38–1.81) (−0.26–0.73) (0.19–1.13) (−0.3–0.62) (1.31–2.40) (−0.28–0.70) (−0.35–0.56) (−0.34–0.64) (1.05–2.09) (−0.13–0.86)
Note. iCBT, n = 37, control: n = 32. CSS = Cyberchondria Severity Scale.
Shevlin (2014) originally developed this subscale based on the results of exploratory factor analysis, which revealed a five factor model (see Selvi, Turan, Sayin, Boysan, & Kandeger, 2018 for a replication of the five factor model). However, more recently, other researchers have questioned the utility of the Mistrust subscale (Fergus, 2014; Norr, Allan et al., 2015), in part because items on this scale (e.g., ‘I trust my GP/medical professional’s diagnosis over my online self-diagnosis’) are reverse scored, unlike the other subscales, and in part because the Mistrust subscale does not correlate as well with other cyberchondria subscales, or health anxiety (Barke, Bleichhardt, Rief, & Doering, 2016; Selvi et al., 2018). In addition, recent factor analytic studies found that the mistrust factor may be a separate and distinct factor to the other factors (Fergus, 2014; Norr, Allan et al., 2015), suggesting that the mistrust factor may not be central to cyberchondria (Norr, Allan et al., 2015). These findings again call into question the utility of the Mistrust subscale, consistent with Fergus (2014) who suggested ‘the Mistrust subscale should not be used when creating a CSS total score’ (p509, Fergus, 2014). However, Fergus (2014) found that this subscale explained unique variance in health anxiety scores in a community sample, beyond the other CSS subscales, suggesting it may be an important construct to assess, and may tap into a separate dysfunctional belief that is associated with health anxiety. In addition, although the scores were elevated on this scale (baseline mean of 7.9 on the Mistrust subscale out of a total possible score of 15), it is possible that the scores were not elevated enough to improve substantially from baseline to post-treatment. Nonetheless, iCBT did not appear to change scores on these items. Due to the length of the CSS (33 items) we only administered it at baseline and post-treatment, to minimise assessment burden on participants. Therefore, the findings of the mediation analysis need to be interpreted with caution because we cannot establish a causal relationship between the proposed mediator (cyberchondria) and the outcome (health anxiety) (Thoemmes, 2015). However, our preliminary results suggest that improvements in health anxiety in the iCBT group may be mediated by improvements in cyberchondria severity ratings, on all subscales except for the Mistrust subscale. Further research is required to test the causal relationship(s) between these two constructs during and after treatment. For instance, the integration of mediation analyses into experimental or quasi-experimental designs (i.e. the collection of data pre-trial, post-trial and at a later follow-up) would help determine whether reductions in cyberchondria lead to
health anxiety via reductions in CSS Compulsion subscale scores (B: -3.72; 95%CI: -7.13 to -1.65), Distress subscale scores (B: -3.35; 95%CI: -5.92 to -1.60), Excessiveness subscale scores (B: -4.24; 95%CI: -7.44 to -1.91), and Reassurance subscale scores (B: -2.29; 95%CI: -4.55 to -0.61) were all significant. However, the indirect effects via reductions in the Mistrust subscale scores was not significant (B: -.01; 95%CI: -.32 to 0.56). Together these results suggest that reductions in health anxiety were partly mediated by reductions in cyberchondria severity, except for Mistrust scores.
4. Discussion People who experience health anxiety often report excessive, and distressing online searches for health information, in a pattern called ‘cyberchondria’. This study is the first to our knolwedge to examine the impact of CBT for health anxiety in any modality (e.g., in-person, internet) on cyberchondria. In a clinical treatment-seeking sample of health anxious participants with DSM-5 Illness Anxiety Disorder and/or Somatic Symptom Disorder, we found that the clinician-guided iCBT group experienced large improvements in cyberchondria from pre- to post-treatment. The iCBT group also reported significantly lower cyberchondria severity at post-treatment relative to the control group who received online anxiety psychoeducation, clinical support and monitoring (Newby et al., 2018). These findings add to a growing body of literature supporting the positive effects of iCBT on health anxiety (Hedman et al., 2011, 2014; Hedman et al., 2016; Newby et al., 2016). We found a different pattern of results across the five subscales of the Cyberchondria Severity Scale (CSS) (McElroy & Shevlin, 2014), with large improvements observed in the iCBT group on the Distress, Excessiveness and Compulsion subscales, and moderate improvements on the Reassurance subscale. It is possible that these findings were due to positive response bias, or demand characteristics. However, these results suggest that iCBT may help health anxious individuals to reduce the excessive nature of online health information searches, the distress caused, and reduce the impact of online searching on daily activities. iCBT may also have a positive impact on the degree to which online searches motivate further reassurance-seeking from experts or health professionals (the Reassurance subscale), but this effect appears smaller than the other dimensions of cyberchondria. In contrast to these results, we failed to find any changes on the Mistrust of Medical Professionals subscale, in either group. McElroy and 5
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participant searches the internet for health information, to add further context to the meaning of the CSS scores. In addition, there are some other limitations of the CSS that are important to highlight. Scores on this scale are moderately correlated with health anxiety; therefore the positive changes we observed on this scale in the iCBT group may have been reflective of general improvements in health anxiety, rather than a unique or distinct process. This measure confounds the presence of unexplained bodily sensations and medical illnesses, with the behavioural (e.g., frequency of searching) and emotional (e.g., distress associated with searching) components of cyberchondria. It is not known whether the gains for cyberchondria are maintained long-term, although the improvements in self-reported health anxiety severity were maintained in the iCBT group at 3-month follow-up (Newby et al., 2018). Replication by independent research teams with a larger sample, weekly self-report and objective measures of online searching behaviour, and a longer-term follow-up assessment is needed.
subsequent reductions in health anxiety, or vice versa (Jose, 2016). Furthermore, the use of experience sampling methodologies (ESM) in the context of clinical trials may shed light on momentary processes. For instance, in future trials we could include regular measures of both cyberchondria and health anxiety immediately prior to each lesson to explore the time course of changes in these processes, and the causal role of changes in cyberchondria on health anxiety. Such research may help further our understanding of the degree to which cyberchondria and health anxiety are intertwined. Although brief versions of the CSS (12-item English version; McElroy et al., 2019) and a 15-item German version (Barke et al., 2016) have been developed in order to minimise participant burden, further refinement or new measurement approaches may be required in order to adequately capture cyberchondria in an ESM design. 4.1. Clinical implications Our findings have important implications for assessment, and treatment of cyberchondria. First, they suggest that CBT for health anxiety, which include components that directly target excessive online health information searching, can successfully improve cyberchondria severity, in treatment-seeking individuals with DSM-5 IAD and/or SSD diagnoses. It is not yet clear whether multicomponent CBT for health anxiety, or cyberchondria-specific interventions achieve better results. We also do not know which components of our existing program are most effective for reducing cyberchondria. We included component to reduce and target excessive googling behaviour in the second module of the program, which specifically educated participants about the potential role of online health information searching (‘Googling’) in worsening symptoms of health anxiety and attentional hypervigilance toward bodily symptoms and threatening sensations. However, the program also incorporated a range of other cognitive and behavioural therapy techniques which may have had an indirect, positive impact on online searching behaviour. For example, participants were taught to develop alternative (neutral) explanations for their symptoms to reduce catastrophic thinking, and strategies to reduce excessive body hypervigilance and body checking. These strategies may have reduced anxiety and fears of illness, in turn reducing the urge to search online about symptoms and feared illnesses. Future research could incorporate module-by-module assessment of cyberchondria and health anxiety, or test the specific effects of specific treatment components using dismantling designs, to determine which components are critical in reducing cyberchondria.
4.3. Conclusions This is the first study to show that clinician-guided internet CBT reduces cyberchondria in a sample of patients with DSM-5 diagnoses of Illness Anxiety Disorder and Somatic Symptom Disorder. Our findings provide support for the use of CBT to reduce cyberchondria in health anxious samples. Declaration of Competing Interest None. Acknowledgements Funding: This work was supported by the Australian National Health and Medical Research Council (NHMRC) Early Career Fellowship (grant number: 1037787) and Medical Research Future Fund Career Development Fellowship (grant number 1145382) awarded to Dr Jill Newby, and a St Vincent’s Clinic Foundation grant awarded to Dr Jill Newby. References Abramowitz, J., Deacon, B., & Valentiner, D. (2007). The short health anxiety inventory: Psychometric properties and construct validity in a non-clinical sample. Cognitive Therapy and Research, 31(6), 871–883. https://doi.org/10.1007/s10608-006-9058-1. Alberts, N. M., Hadjistavropoulos, H. D., Jones, S. L., & Sharpe, D. (2013). The short health anxiety inventory: A systematic review and meta-analysis. Journal of Anxiety Disorders, 27(1), 68–78. https://doi.org/10.1016/j.janxdis.2012.10.009. American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (fifth edition). Arlington, VA: American Psychiatric Association. Barke, A., Bleichhardt, G., Rief, W., & Doering, B. K. (2016). The cyberchondria severity scale (CSS): German validation and development of a short form. International Journal of Behavioral Medicine, 23(5), 595–605. https://doi.org/10.1007/s12529-0169549-8. Bobevski, I., Clarke, D. M., & Meadows, G. (2016). Health anxiety and its relationship to disability and service use: Findings from a large epidemiological survey. Psychosomatic Medicine, 78(1), 13–25. Brown, T. A., & Barlow, D. (2014). Anxiety and related disorders interview schedule for DSM5 (ADIS-5). New York: Oxford University Press. Cooper, K., Gregory, J. D., Walker, I., Lambe, S., & Salkovskis, P. M. (2017). Cognitive behaviour therapy for health anxiety: A systematic review and meta-analysis. Behavioural and Cognitive Psychotherapy, 45(2), 110–123. https://doi.org/10.1017/ s1352465816000527. Creed, F., & Barsky, A. (2004). A systematic review of the epidemiology of somatisation disorder and hypochondriasis. Journal of Psychosomatic Research, 56(4), 391–408. https://doi.org/10.1016/s0022-3999(03)00622-6. Doherty-Torstrick, E. R., Walton, K. E., & Fallon, B. A. (2016). Cyberchondria: Parsing health anxiety from online behavior. Psychosomatics, 57(4), 390–400. https://doi. org/10.1016/j.psym.2016.02.002. Fergus, T. A. (2013). Cyberchrondria and intolerance of uncertainty: Examining when individuals experience health anxiety in response to internet searches for medical information. Cyberpsychology, Behavior and Social Networking, 16(10), 735–739. https://doi.org/10.1089/cyber.2012.0671. Fergus, T. A. (2014). The Cyberchondria Severity Scale (CSS): An examination of
4.2. Limitations The results of this study need to be interpreted in the context of some limitations. All of the data was collected via online self-report measures, as there is currently no validated clinical interview to assess cyberchondria. The findings need to be replicated using objective measures of online searching behaviour. Second, the sample was motivated, treatment-seeking, and generally well educated; it is unknown whether the findings generalise to other samples, including whether iCBT would have as powerful effects for those who were not treatment seeking, or those convinced by medical explanations for their symptoms. Third, another limitation of this study was that we did not explicitly ask participants whether they used the internet to search for health information. Rather, we used the Cyberchondria Severity Scale (CSS), which is the most widely used measure of cyberchondria (McMullan et al., 2019) used to assess the severity of cyberchondria on a dimensional scale. For each of the 33 items of the CSS, participants choose a response from the following options: Never, Rarely, Sometimes, Often and Always. There were no participants who endorsed the ‘Never’ response option on all of the items, which suggests that these participants did engage in online health information searching. Nonetheless, future studies should ask explicitly about whether or not the 6
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Contents lists available at ScienceDirect
Journal of Anxiety Disorders journal homepage: www.elsevier.com/locate/janxdis
The impact of internet-delivered cognitive behavioural therapy for health anxiety on cyberchondria
T
Jill M. Newbya,b,*, Eoin McElroyc a
School of Psychology, UNSW Sydney, 1302 Mathews Building, Randwick, NSW, 2052, Australia Clinical Research Unit for Anxiety and Depression (CRUfAD), School of Psychiatry, UNSW Sydney at St Vincent’s Hospital, 390 Victoria Street Darlinghurst, NSW, Sydney, 2010, Australia c Department of Neuroscience, Psychology and Behaviour, University of Leicester, University Road, Leicester, LE1 7RH, UK b
A R T I C LE I N FO
A B S T R A C T
Keywords: Cyberchondria Health anxiety Illness anxiety disorder Online health information searching Somatic symptom disorder
Cyberchondria refers to an emotional-behavioural pattern whereby excessive online searches lead to increased anxiety about one’s own health status. It has been shown to be associated with health anxiety, however it is unknown whether existing cognitive behavioural therapy (CBT) interventions targeting health anxiety also improve cyberchondria. This study aimed to determine whether internet-delivered cognitive behavioural therapy (iCBT) for severe health anxiety led to improvements in self-reported cyberchondria and whether improvements in cyberchondria were associated with improvements in health anxiety observed during treatment. Methods: We analysed secondary data from a randomised controlled trial (RCT) comparing an iCBT group (n = 41) to an active control group who underwent psychoeducation, monitoring and clinical support (n = 41) in health anxious patients with a DSM-5 diagnosis of Illness Anxiety Disorder and/or Somatic Symptom Disorder. The iCBT group showed a significantly greater reduction in cyberchondria compared to the control group, with large differences at post-treatment on the Cyberchondria Severity Scale Total scale (CSS; Hedges g = 1.09), and the Compulsion, Distress, Excessiveness subscales of the CSS (g’s: 0.8–1.13). Mediation analyses showed improvements in health anxiety in the iCBT group were mediated by improvements in all of the CSS subscales, except for the Mistrust subscale. Conclusions: Internet CBT for health anxiety improves cyberchondria.
1. Introduction Experiencing some anxiety about one’s health can be normal and adaptive, but when health anxiety becomes persistent, excessive and preoccupying, it can have a negative impact on the individual, their loved ones and health professionals (Tyrer, Eilenberg, Fink, Hedman, & Tyrer, 2016), and society (Bobevski, Clarke, & Meadows, 2016; Tyrer, 2018). Health anxiety is thought to be dimensional in nature, ranging from normal, transient health worries to severe, and disabling health anxiety. In the DSM-5 it is categorised as Illness Anxiety Disorder (IAD) or Somatic Symptom Disorder (SSD) depending on the severity of the individual’s somatic symptoms (American Psychiatric Association, 2013). Severe and functionally impairing health anxiety affects between 3–5% of the population (Creed & Barsky, 2004; Sunderland, Newby, & Andrews, 2012). People with health anxiety are afraid that they either have, or will develop serious, often life-threatening illnesses. As a result of these fears, they often engage in a range of behaviours such as excessive
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reassurance-seeking from loved ones and health professionals about their health concerns, which may temporarily allay their fears, but maintain preoccupation with illness over the long term (Warwick & Salkovskis, 1990). With free, private, and easily accessible health information available online, it is not surprising that people with health anxiety frequently report searching the internet to seek reassurance about their symptoms and health concerns (Muse, McManus, Leung, Meghreblian, & Williams, 2012). Exposure to information during online searches, especially alarming, inaccurate and misleading information about life-threatening illnesses (White & Horvitz, 2009) can exacerbate existing worries about illnesses, and lead to new anxieties, causing an escalating pattern of distress and further excessive and repeated online searches for health information in a pattern labelled ‘cyberchondria’ (Starcevic and Berle, 2013). This phenomenon may be heightened by technical features of online search engines. The algorithms that determine search results are driven largely by the popularity of the available information, so that frequently accessed web pages are given priority in search results. Thus,
Corresponding author at: School of Psychology, 1302 Mathews Building, UNSW Australia, Kensington, NSW, 2052, Australia. E-mail address: [email protected] (J.M. Newby).
https://doi.org/10.1016/j.janxdis.2019.102150 Received 24 May 2019; Received in revised form 10 September 2019; Accepted 9 October 2019 Available online 31 October 2019 0887-6185/ © 2019 Elsevier Ltd. All rights reserved.
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health anxiety was outlined (the ‘Health Anxiety Program’) (Newby et al., 2016). The iCBT program included psychoeducation about health anxiety, and taught CBT skills to target the cognitive and behavioural processes that are proposed to maintain health anxiety (Warwick & Salkovskis, 1990), including cognitive therapy components to change maladaptive interpretations of bodily symptoms and beliefs about health and illness, and behavioural strategies to reduce excessive checking, reassurance-seeking and avoidance of situations that trigger illness fears (e.g., hospitals, conversations about illness). In addition to these strategies, the program educated participants about the role of online health information searching (‘Googling behaviours’) in exacerbating health anxiety and preoccupation with illness. It also taught participants to become more aware of, self-monitor, and reduce excessive online health information searching through behavioural experiments, activity scheduling, and other strategies to delay and prevent unhelpful online searching about symptoms and illness. We expected the iCBT program to be helpful at reducing the behavioural and emotional components of cyberchondria via several avenues. For example, this intervention aimed to increase awareness of the frequency of online searching and the unhelpful personal costs of online searching, and taught practical strategies to prevent, delay and stop online searching behaviour once it started. We expected these strategies to help health anxious participants to reduce the frequency and duration of online health information searching, and in turn also prevent escalations in distress that can occur through excessive and repeated searching. In addition, the iCBT program taught participants how to relate to symptoms and health information in a different way. For example, they developed new ways of thinking about unexplained bodily sensations and health information, as opposed to relying on catastrophic ‘worst case scenario’ thinking patterns and assumptions. Once an individual can develop their own less threatening interpretations of bodily sensations, and improve their ability to tolerate uncertainty about these sensations, we expected them to rely less on internet searches to alleviate anxiety. In addition, by learning how to regulate negative emotions including anxiety, coupled with the ability to think more clearly, rationally, and in less negatively biased interpretation style, we expected that they should be able to both prevent anxiety from escalating, and alleviate the anxiety more quickly after online searches. Through facilitating these changes, we expected health anxiety to reduce. We therefore expected reductions in cyberchondria would be associated with improvements in health anxiety symptoms. In a randomised controlled trial (Newby et al., 2018), we showed that iCBT for health anxiety had large and superior effects in reducing health anxiety compared to an active control group who received online anxiety psychoeducation, support and monitoring from a clinician. As part of that RCT, we also aimed to examine the impact of the iCBT program on cyberchondria, and administered the self-report Cyberchondria Severity Scale (CSS) before and after a 12-week treatment period to achieve this aim. The CSS contains five subscales that assess the multidimensional construct of cyberchondria. These include the Compulsion (the degree to which online health information interrupts daily activities), Distress (emotional distress associated with online health information searching), Excessiveness (seeking out repeated, and frequent sources of information), Reassurance (anxiety leading from online searches to seeking opinions from other experts of health professionals) and Mistrust of Health Professionals subscales (whether online information is trusted more than health professionals) subscales. Our primary aim in the current study was to explore whether iCBT for health anxiety improves cyberchondria (and specific subscale ratings) relative to the control group. Our second aim was to explore whether improvements in cyberchondria were associated with improvements in health anxiety using mediation analyses. We hypothesised that iCBT would outperform the control group in improving cyberchondria, and that improvements would be associated with changes in symptoms of health anxiety.
searches of relatively benign symptoms may be biased to produce information about alarming, rare, life-threatening conditions. For instance, White and Horvitz (White & Horvitz, 2009) found that the most common result from a search of the term ‘muscle twitch’ was amyotrophic lateral sclerosis, despite the fact that it has an annual incidence rate of 1 in every 55,000 persons. The way that cyberchondria has been defined and conceptualised has varied widely across studies. For the purposes of this study, we conceptualise cyberchondria as having both a behavioural and emotional component. The behavioural aspect of cyberchondria involves excessive, and repeated online searches for health information that may function similar to reassurance-seeking behaviour, where the individual seeks reassurance online as opposed to in-person. The individual may search online initially in an attempt to reduce distress, or to alleviate their fears of illness or uncertainty about unexplained physical sensations. Some searches may temporarily alleviate the individual’s anxiety, but serve to maintain anxiety long-term through maintaining the individual’s preoccupation with health concerns and difficulty tolerating uncertainty about unexplained sensations. Individuals may search online, but not feel reassured by the searches and instead experience heightened distress and anxiety. That is, the searching causes escalating distress, and prompts further online searching, which further escalates distress and future searches. The emotional component of cyberchondria is the distress, or anxiety caused by the searching, or the inability to control the searching behaviour. There is an ongoing debate as to whether cyberchondria is a core feature of health anxiety (Starcevic & Berle, 2013), or whether it constitutes a distinct concept in its own right (Fergus & Russell, 2016; Mathes, Norr, Allan, Albanese, & Schmidt, 2018). Cross-sectional studies have shown a strong association between cyberchondria and health anxiety (2014, Fergus, 2013; Mathes et al., 2018; Norr, Albanese, Oglesby, Allan, & Schmidt, 2015; Norr, Allan, Boffa, Raines, & Schmidt, 2015; Norr, Oglesby et al., 2015). A recent meta-analysis of 20 studies, including 7373 participants showing a positive correlation between health anxiety and cyberchondria (r = 0.62) (McMullan, Berle, Arnaez, & Starcevic, 2019). However, psychometric analyses have supported meaningful a distinction between cyberchondria and health anxiety (Fergus & Russell, 2016). Furthermore, the direction of causality between online health searches and health anxiety may also vary between individuals, meaning the behavioural cycle of cyberchondria cannot be considered, in all cases, purely the result of pre-existing health anxiety. Although people with elevated health anxiety report a greater frequency of online health information searches, along with greater resultant anxiety (Doherty-Torstrick, Walton, & Fallon, 2016; Muse et al., 2012; Singh & Brown, 2016), it is has been demonstrated that individuals without an existing tendency towards health worries can experience elevated levels of distress/anxiety as a result of searching behaviour (te Poel, Baumgartner, Hartmann, & Tanis, 2016). Such searches may be prompted by curiosity or the sudden emergence of an unexplained symptom (Starcevic, 2017). Together, these findings suggest that cyberchondria can be conceptualised as a unique pattern of behaviours and emotions that undoubtedly plays an important role in health anxiety, and therefore needs to be targeted in treatment. To our knowledge, there is no research on the treatment of cyberchondria, either alone or in the context of health anxiety (McMullan et al., 2019; Starcevic & Berle, 2013). The current study sought to address this gap by examining, for the first time, whether clinician-guided internet-delivered CBT for health anxiety improves cyberchondria, in a sample of participants who met criteria for DSM-5 diagnoses of IAD and/or SSD. Clinician-guided iCBT has previously been shown to be effective at improving health anxiety (Hedman et al., 2011, 2014; Hedman, Axelsson, Andersson, Lekander, & Ljótsson, 2016; Newby et al., 2016), mirroring the success of face-to-face CBT protocols (see Cooper, Gregory, Walker, Lambe, & Salkovskis, 2017; Thomson & Page, 2007 for reviews on CBT). In a previous paper, the development of a new iCBT program for 2
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2. Methods
but that this relief is often short-lived. It also taught how googling can lead to unintended negative effects, such as exacerbating existing fears, maintaining focus and preoccupation with anxieties, lead to new fears of illness, and lead to incorrect self-diagnoses. They were also taught about the variable quality of online health information, and how more highly ranked websites in search engines are ranked by popularity not necessarily their accuracy, and that ‘alarming and threatening, worst case scenario’ information, stories of suffering and mistaken diagnoses are often higher in the search results, rather than factual or benign information. Finally, the module tips to become an ‘expert searcher’. Tips included keeping track of, and learning to identify triggers for internet searching, and replacing triggers with alternative activities using Activity Planning; checking only credible websites, such as health experts, and government websites; avoiding searching for vague symptoms; and stopping searching when it exacerbates anxiety. Finally, they were encouraged to conduct a behavioural experiment to compare anxiety levels, and health preoccupation when they avoid searching versus when they search the internet. It also included a ‘checking prevention plan’ to develop strategies to prevent the participant from googling excessively including using behavioural activation and other distracting strategies, as well as behavioural experiments to test cognitions about googling about symptoms. Participants were encouraged to read the online module, and complete key skills practice exercises between the lessons. Participants in this group received email and/or phone contact with the clinician (JN, a PhD-level Clinical Psychologist) after lessons 1 and 2 to encourage progress, and after subsequent lessons clinician contact was made upon patient request or if the patient reported significant deterioration (increased distress) or suicidal ideation. If participants stopped logging in, they were sent up to two automated emails to remind them to complete their next lesson. In addition, the clinician attempted to contact the participant via email or phone if the participant did not respond to the automated emails, or login after they were prompted. The clinician spent on average 43.11 min per participant on email and telephone contact in the iCBT group (SD = 25.75, range = 13–116 minutes).
2.1. Participants Participants who met criteria for a diagnosis of DSM-5 IAD or SSD (participants with SSD must endorse criterion B2 for SSD ‘persistently high level of anxiety about health or symptoms’) who were on a stable dose of psychological and/or pharmacotherapy for the two months prior to intake assessment were eligible to participate. Applicants were not eligible to participate if they had a self-reported diagnosis of psychosis or bipolar disorder, currently used antipsychotic or regular benzodiazepine medications, or had severe depression (defined as scoring > 24 on the Patient Health Questionnaire 9-item [PHQ-9]). These exclusion criteria were set due to safety reasons, to maintain consistency with our previous trial protocols, and to minimise the possibility of concurrent pharmacotherapies interfering with the efficacy of the CBT techniques (e.g., exposure components). 2.2. Trial design Eligible participants were randomised to either iCBT or the control group (n = 45 for iCBT, and n = 41 for control group), and completed assessments at pre-, mid-, post-treatment and 3-month follow-up (iCBT group only, as the control group was crossed over to iCBT after posttreatment assessment). The CSS was only administered at pre and posttreatment. The SHAI was administered at each lesson to track changes in symptoms. The previous manuscript reporting the main RCT findings (Newby et al., 2018) reported the results for the iCBT and control groups on self-reported health anxiety severity, and a range of other outcomes, including generalised anxiety, depression, distress, body hypervigilance, and maladaptive cognitions. The CSS results have not been reported previously. 2.3. Ethics approval and informed consent All participants provided electronic informed consent to participate in the study. The study involving the clinical sample was approved by St Vincent’s Hospital Human Research Ethics Committee (HREC/14/SVH/ 294) and registered with the Australian and New Zealand Clinical Trials Registry (ACTRN12615000887572).
2.4.2. Control group Fact sheets (2–4 pages) were delivered online each fortnight on topics related to anxiety (e.g., The fight-or-flight response, Causes of anxiety, How to Manage Stress). Participants were offered clinician contact via email or phone. Phone contact was made if the participant reported significant deterioration in distress or depression symptoms, or suicidal ideation. The clinician spent an average of 23.20 min (SD = 13.74, range = 8–61) minutes per participant in the control group.
2.4. Interventions 2.4.1. Internet CBT The Health Anxiety Course is a 6-lesson illustrated comic-style online program delivered via the Virtual Clinic website (www.virtualclinic. org.au)1 over 12 weeks. After a participant completed a lesson, they were required to wait a minimum 5 days until the next lesson was released. Participants were encouraged to complete one lesson per week to fortnight, however, they were able to complete the program at their own pace, with a maximum of 12 weeks to complete the 6 lessons. The program delivered psychoeducation, and CBT skills including detecting and challenging negative thinking patterns about bodily symptoms and health, exposure to feared situations and sensations, behavioural strategies to reduce checking, reassurance-seeking and googling about symptoms, and relapse prevention. Importantly, strategies to reduce unhelpful and excessive googling of symptoms were included in Lesson 2. In this lesson, excessive googling was conceptualised as a form of unhelpful checking behaviour. Participants read psychoeducation about the unhelpful role of googling, and how it can exacerbate health anxiety, maintain preoccupation with illness fears, and trigger new fears. First, the lesson explained how googling may have lead to immediate, temporary relief from anxiety, 1
2.5. Diagnostic interview Participants were administered an abbreviated telephone-administered Anxiety Disorders Interview Schedule for DSM-5 (ADIS-5) (Brown & Barlow, 2014) prior to being included in the study to confirm whether they met criteria for an IAD, or SSD diagnosis or comorbid IAD and SSD (Brown & Barlow, 2014). 2.6. Clinical outcomes The primary outcome was health anxiety according to the 18-item Short Health Anxiety Inventory (SHAI) (Salkovskis, Rimes, Warwick, & Clark, 2002). Cyberchondria was assessed using the 33-item Cyberchondria Severity Scale (McElroy & Shevlin, 2014). 2.6.1. The Short Health Anxiety Inventory (SHAI) (Salkovskis et al., 2002) The SHAI is a validated 18-item self-report measure of the severity of health anxiety symptoms over the past week. The measure has good psychometric properties including good internal consistency, test-retest reliability and construct validity, and is sensitive to treatment
For a free demonstration of the course, contact the first author. 3
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cyberchondria (either the total scale score, or the separate subscale scores) as the mediator variable (M) using PROCESS (Hayes, 2013). This enabled us to test the indirect effects of treatment (where 0 = control, and 1= iCBT) on post-treatment health anxiety severity (SHAI scores) via changes in cyberchondria. Baseline SHAI scores were entered as a covariate in all analyses. A positive change score between pre and post-treatment evidenced an improvement in ratings. All 95% biascorrected bootstrapped confidence intervals for the indirect effects are presented in brackets.
(Abramowitz, Deacon, & Valentiner, 2007; Alberts, Hadjistavropoulos, Jones, & Sharpe, 2013). Participants are asked to rate each item with four response options to examine cognitive and behavioural features of health anxiety. Items are rated on a four point scale, ranging from 0 to 3 (for example, the first item is: 0 = I do not worry about my health, 1 = I occasionally worry about my health, 2 = I spend much of my time worrying about my health, and 3 = I spend most of my time worrying about my health). 2.6.2. Cyberchondria Severity Scale (CSS) (McElroy & Shevlin, 2014) The CSS is a validated 33 item self-report measure of cyberchondria. Items (e.g., “If I notice an unexplained bodily sensation I will search for it on the internet”) are rated on a 5-point scale, ranging from 1-5. The CSS has a total score, as well as five subscales: Compulsion scale, Distress subscale, Excessiveness subscale, Reassurance subscale, and a Mistrust of Medical Professionals subscale. The scale has been validated in undergraduate and community samples (Norr, Albanese et al., 2015; Norr, Allan et al., 2015; Norr, Oglesby et al., 2015) and shown to have high internal consistency, and good convergent and divergent validity, as demonstrated by higher correlations with measures of health anxiety (r = 0. 54 – 0.59; Fergus, 2014; Norr, Oglesby et al., 2015) compared with other forms of anxiety, e.g. OCD (r = 0.34 – 0.45; Norr, Oglesby et al., 2015) and generalised anxiety (r = 0.39; McElroy & Shevlin, 2014). Chronbach’s alpha’s in the current sample were 0.96 for the Total scale, 0.96 for the Compulsion scale, 0.81 on the Mistrust scale, 0.85 for the Reassurance, 0.91 for the Excessiveness subscale, 0.95 for the Distress subscale.
3. Results 3.1. Participants Forty-five iCBT participants, and 41 participants in the control group completed baseline questionnaires; of these data were collected from 37/45 participants at post-treatment in the iCBT group and 32/41 participants in the control group. 3.1.1. Demographic and sample characteristics Participants were 30 years on average (SD = 12 years, range = 18–65), the majority were female (87.2 %, n = 75), and half were either married or living in a de facto relationship (n = 43, 50 %). Most were either in part-time or full time paid work (n = 61, 70.9 %), spoke English as their main language (94.2 %, n = 81), and were well educated having completed year 12 or equivalent (n = 19, 22.1 %), or a tertiary degree (n = 37, 43 %). The mean score on the SHAI was 35.77 (SD = 7.21, range = 16–49), and the average age of onset of health anxiety was 21 years (SD = 9, range: 8–55). For further details of the sample, see Newby et al. (2018). In terms of DSM-5 diagnoses, there was an even split of IAD (n = 39, 45.3 %) and SSD (n = 39, 45.3 %), and 8 had comorbid IAD and SSD (17 %). On average, participants met criteria for 3.2 diagnoses (SD = 1.7). The most common comorbidity was generalised anxiety disorder (GAD) (n = 43, 50 %), then panic disorder (39.5%), agoraphobia (n = 31, 36.0 %), and major depressive disorder (MDD) (n = 31, 36 %). Fifteen participants were receiving concurrent counselling (17.4 %) with 3 (3.5%) receiving current CBT that had started at least 2 months prior to initial assessment, and 16 were on antidepressant medications (18.6 %). There were no significant differences between the two groups on any baseline characteristics or treatment expectancy.
2.7. Statistical analyses Treatment efficacy was evaluated using repeated measures analyses of variance (ANOVAs). Because the CSS was only measured at pre- and post-treatment in the two treatment groups, linear mixed models were judged not to be appropriate, given they would not have added additional value. Within- and between-groups effect sizes (Hedges g) were calculated and interpreted using standard guidelines: 0.2 = small effect, 0.5 = medium effect, 0.8 = large effect (Cohen 1988). Due to recent evidence showing that the cyberchondria Mistrust subscale does not load on the same factors as the other subscales of the CSS (Fergus, 2014), we chose to analyse the subscales separately, as well as the CSS Total score results. The analyses of the health anxiety outcomes have been reported previously in the original paper (Newby et al., 2018), and have also been included in the current paper for clarity of interpreting the results. The CSS results have not been reported previously. The CSS results were not included in the previous report because it was only administered at two time points, whereas the remaining measures were administered at multiple time points.
3.1.2. Primary outcomes: impact of internet CBT on health anxiety, and cyberchondria The time by group interactions were statistically significant for the SHAI, Total scores on the CSS as well as the Compulsion, Distress, Reassurance, and Excessiveness subscales (all p’s < .001, except for the Reassurance subscale where p < .05). The between-group differences on the SHAI, and CSS Compulsion, Distress and Excessiveness subscales were large ranging from 0.8 (Distress) to 1.15 (Compulsion). There were only moderate between-group differences on the Reassurance subscale favouring the iCBT group (g = 0.56). In contrast, the time by group interactions for the Mistrust subscale was not statistically significant (p > .05), and there were small non-significant differences favouring the control group (g=-0.1) on the Mistrust subscale. See Table 1 for results.
2.7.1. Mediation analyses Tests of the indirect effects (mediation) were conducted using PROCESS (Hayes, 2013). Estimates of indirect effects were generated using bootstrapping analysis (see Preacher & Hayes, 2004; Preacher, Rucker, & Hayes, 2007). Bootstrapping is a nonparametric resampling method that generates an estimate of the indirect effect, and does not require assumptions about the shape of the sampling distribution that underlie the Sobel test. In bootstrapping analysis, the most stringent test of an indirect effect (mediation) is if the 95% bias corrected and accelerated confidence intervals for the indirect effect do not include the value of 0. When zero is outside of the 95% confidence interval estimate, the indirect effect is declared statistically different from zero at p < .05 (two-tailed), indicating that the effect of the independent variable on the dependent variable is contingent upon the effect of the proposed mediator (Preacher & Hayes, 2004). In the current study, we estimated 5000 bias-corrected bootstrap 95% confidence intervals using PROCESS for SPSS (Hayes, 2012). We estimated several separate mediation models, with changes in
3.1.3. Mediation analyses Results of the mediation analysis indicated that the total effect of treatment on post-treatment SHAI scores was significant (B=-11.44, t=-6.13, SE = 1.86, p < .001). The indirect effect of treatment on post-treatment health anxiety via reductions in cyberchondria (CSS Total) scores between pre- and post-treatment was supported, as the indirect effect was statistically different from zero (95%CI: -8.18 to -2.60). In addition, the indirect effect of treatment on post-treatment 4
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Table 1 Observed means and standard deviations of the online cognitive behavioural therapy program and control group on health anxiety and cyberchondria severity scale subscales. Measure
iCBT Control iCBT Control iCBT Control iCBT Control iCBT Control iCBT Control iCBT Control iCBT Control
SHAI SHAI CSS Total CSS Total CSS Compulsion CSS Compulsion CSS Distress CSS Distress CSS Reassurance CSS Reassurance CSS Excessiveness CSS Excessiveness CSS Mistrust CSS Mistrust CSS Total (without mistrust subscale) CSS Total (without mistrust subscale)
Baseline
Post Mean
M
SD
M
SD
35.11 35.81 102.16 104.50 21.27 22.56 28.68 28.81 16.38 16.47 27.89 28.75 7.95 7.91 94.22 96.59
7.18 7.73 21.41 22.68 7.72 7.45 6.60 7.42 4.57 5.46 7.29 6.67 2.44 2.41 20.07 22.27
19.81 31.63 70.78 96.22 12.97 20.94 19.05 26.03 12.41 15.38 18.62 26.44 7.73 7.44 63.05 88.78
7.87 9.41 22.60 24.17 6.21 7.51 8.31 7.95 4.92 5.51 6.52 7.24 3.10 2.26 22.34 23.71
Within ES (95%CI) Baseline to Post
Between-group post-treatment ES (Hedge’s g) (95%CI) Post-treatment
F (time by group)
2.15 0.50 1.41 0.23 1.27 0.16 1.30 0.23 0.66 0.13 1.85 0.21 0.11 0.15 1.57 0.36
1.36 (0.83-1.88)
F(1,67) = 29.88, p < .001
1.09 (0.58-1.59)
F(1,67) = 22.64, p < .001
1.15 (0.64-1.66)
F(1,67) = 16.73, p < .001
0.80 (0.31-1.30)
F(1,67) = 17.56, p < .001
0.56 (0.08-1.05)
F(1,67) = 6.68, p < .05
1.13 (0.62-1.63)
F(1,67) = 25.07, p < .001
−0.10 (-0.58-0.36)
F(1,67) = 0.12, p = 0.73
1.11 (0.60-1.61)
F(1,67) = 25.41, p < .001
(1.58–2.72) (0.00–1.00) (0.90–1.92) (–0.26–0.72) (0.77–1.77) (−0.33–0.66) (0.38–1.81) (−0.26–0.73) (0.19–1.13) (−0.3–0.62) (1.31–2.40) (−0.28–0.70) (−0.35–0.56) (−0.34–0.64) (1.05–2.09) (−0.13–0.86)
Note. iCBT, n = 37, control: n = 32. CSS = Cyberchondria Severity Scale.
Shevlin (2014) originally developed this subscale based on the results of exploratory factor analysis, which revealed a five factor model (see Selvi, Turan, Sayin, Boysan, & Kandeger, 2018 for a replication of the five factor model). However, more recently, other researchers have questioned the utility of the Mistrust subscale (Fergus, 2014; Norr, Allan et al., 2015), in part because items on this scale (e.g., ‘I trust my GP/medical professional’s diagnosis over my online self-diagnosis’) are reverse scored, unlike the other subscales, and in part because the Mistrust subscale does not correlate as well with other cyberchondria subscales, or health anxiety (Barke, Bleichhardt, Rief, & Doering, 2016; Selvi et al., 2018). In addition, recent factor analytic studies found that the mistrust factor may be a separate and distinct factor to the other factors (Fergus, 2014; Norr, Allan et al., 2015), suggesting that the mistrust factor may not be central to cyberchondria (Norr, Allan et al., 2015). These findings again call into question the utility of the Mistrust subscale, consistent with Fergus (2014) who suggested ‘the Mistrust subscale should not be used when creating a CSS total score’ (p509, Fergus, 2014). However, Fergus (2014) found that this subscale explained unique variance in health anxiety scores in a community sample, beyond the other CSS subscales, suggesting it may be an important construct to assess, and may tap into a separate dysfunctional belief that is associated with health anxiety. In addition, although the scores were elevated on this scale (baseline mean of 7.9 on the Mistrust subscale out of a total possible score of 15), it is possible that the scores were not elevated enough to improve substantially from baseline to post-treatment. Nonetheless, iCBT did not appear to change scores on these items. Due to the length of the CSS (33 items) we only administered it at baseline and post-treatment, to minimise assessment burden on participants. Therefore, the findings of the mediation analysis need to be interpreted with caution because we cannot establish a causal relationship between the proposed mediator (cyberchondria) and the outcome (health anxiety) (Thoemmes, 2015). However, our preliminary results suggest that improvements in health anxiety in the iCBT group may be mediated by improvements in cyberchondria severity ratings, on all subscales except for the Mistrust subscale. Further research is required to test the causal relationship(s) between these two constructs during and after treatment. For instance, the integration of mediation analyses into experimental or quasi-experimental designs (i.e. the collection of data pre-trial, post-trial and at a later follow-up) would help determine whether reductions in cyberchondria lead to
health anxiety via reductions in CSS Compulsion subscale scores (B: -3.72; 95%CI: -7.13 to -1.65), Distress subscale scores (B: -3.35; 95%CI: -5.92 to -1.60), Excessiveness subscale scores (B: -4.24; 95%CI: -7.44 to -1.91), and Reassurance subscale scores (B: -2.29; 95%CI: -4.55 to -0.61) were all significant. However, the indirect effects via reductions in the Mistrust subscale scores was not significant (B: -.01; 95%CI: -.32 to 0.56). Together these results suggest that reductions in health anxiety were partly mediated by reductions in cyberchondria severity, except for Mistrust scores.
4. Discussion People who experience health anxiety often report excessive, and distressing online searches for health information, in a pattern called ‘cyberchondria’. This study is the first to our knolwedge to examine the impact of CBT for health anxiety in any modality (e.g., in-person, internet) on cyberchondria. In a clinical treatment-seeking sample of health anxious participants with DSM-5 Illness Anxiety Disorder and/or Somatic Symptom Disorder, we found that the clinician-guided iCBT group experienced large improvements in cyberchondria from pre- to post-treatment. The iCBT group also reported significantly lower cyberchondria severity at post-treatment relative to the control group who received online anxiety psychoeducation, clinical support and monitoring (Newby et al., 2018). These findings add to a growing body of literature supporting the positive effects of iCBT on health anxiety (Hedman et al., 2011, 2014; Hedman et al., 2016; Newby et al., 2016). We found a different pattern of results across the five subscales of the Cyberchondria Severity Scale (CSS) (McElroy & Shevlin, 2014), with large improvements observed in the iCBT group on the Distress, Excessiveness and Compulsion subscales, and moderate improvements on the Reassurance subscale. It is possible that these findings were due to positive response bias, or demand characteristics. However, these results suggest that iCBT may help health anxious individuals to reduce the excessive nature of online health information searches, the distress caused, and reduce the impact of online searching on daily activities. iCBT may also have a positive impact on the degree to which online searches motivate further reassurance-seeking from experts or health professionals (the Reassurance subscale), but this effect appears smaller than the other dimensions of cyberchondria. In contrast to these results, we failed to find any changes on the Mistrust of Medical Professionals subscale, in either group. McElroy and 5
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participant searches the internet for health information, to add further context to the meaning of the CSS scores. In addition, there are some other limitations of the CSS that are important to highlight. Scores on this scale are moderately correlated with health anxiety; therefore the positive changes we observed on this scale in the iCBT group may have been reflective of general improvements in health anxiety, rather than a unique or distinct process. This measure confounds the presence of unexplained bodily sensations and medical illnesses, with the behavioural (e.g., frequency of searching) and emotional (e.g., distress associated with searching) components of cyberchondria. It is not known whether the gains for cyberchondria are maintained long-term, although the improvements in self-reported health anxiety severity were maintained in the iCBT group at 3-month follow-up (Newby et al., 2018). Replication by independent research teams with a larger sample, weekly self-report and objective measures of online searching behaviour, and a longer-term follow-up assessment is needed.
subsequent reductions in health anxiety, or vice versa (Jose, 2016). Furthermore, the use of experience sampling methodologies (ESM) in the context of clinical trials may shed light on momentary processes. For instance, in future trials we could include regular measures of both cyberchondria and health anxiety immediately prior to each lesson to explore the time course of changes in these processes, and the causal role of changes in cyberchondria on health anxiety. Such research may help further our understanding of the degree to which cyberchondria and health anxiety are intertwined. Although brief versions of the CSS (12-item English version; McElroy et al., 2019) and a 15-item German version (Barke et al., 2016) have been developed in order to minimise participant burden, further refinement or new measurement approaches may be required in order to adequately capture cyberchondria in an ESM design. 4.1. Clinical implications Our findings have important implications for assessment, and treatment of cyberchondria. First, they suggest that CBT for health anxiety, which include components that directly target excessive online health information searching, can successfully improve cyberchondria severity, in treatment-seeking individuals with DSM-5 IAD and/or SSD diagnoses. It is not yet clear whether multicomponent CBT for health anxiety, or cyberchondria-specific interventions achieve better results. We also do not know which components of our existing program are most effective for reducing cyberchondria. We included component to reduce and target excessive googling behaviour in the second module of the program, which specifically educated participants about the potential role of online health information searching (‘Googling’) in worsening symptoms of health anxiety and attentional hypervigilance toward bodily symptoms and threatening sensations. However, the program also incorporated a range of other cognitive and behavioural therapy techniques which may have had an indirect, positive impact on online searching behaviour. For example, participants were taught to develop alternative (neutral) explanations for their symptoms to reduce catastrophic thinking, and strategies to reduce excessive body hypervigilance and body checking. These strategies may have reduced anxiety and fears of illness, in turn reducing the urge to search online about symptoms and feared illnesses. Future research could incorporate module-by-module assessment of cyberchondria and health anxiety, or test the specific effects of specific treatment components using dismantling designs, to determine which components are critical in reducing cyberchondria.
4.3. Conclusions This is the first study to show that clinician-guided internet CBT reduces cyberchondria in a sample of patients with DSM-5 diagnoses of Illness Anxiety Disorder and Somatic Symptom Disorder. Our findings provide support for the use of CBT to reduce cyberchondria in health anxious samples. Declaration of Competing Interest None. Acknowledgements Funding: This work was supported by the Australian National Health and Medical Research Council (NHMRC) Early Career Fellowship (grant number: 1037787) and Medical Research Future Fund Career Development Fellowship (grant number 1145382) awarded to Dr Jill Newby, and a St Vincent’s Clinic Foundation grant awarded to Dr Jill Newby. References Abramowitz, J., Deacon, B., & Valentiner, D. (2007). The short health anxiety inventory: Psychometric properties and construct validity in a non-clinical sample. Cognitive Therapy and Research, 31(6), 871–883. https://doi.org/10.1007/s10608-006-9058-1. Alberts, N. M., Hadjistavropoulos, H. D., Jones, S. L., & Sharpe, D. (2013). The short health anxiety inventory: A systematic review and meta-analysis. Journal of Anxiety Disorders, 27(1), 68–78. https://doi.org/10.1016/j.janxdis.2012.10.009. American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (fifth edition). Arlington, VA: American Psychiatric Association. Barke, A., Bleichhardt, G., Rief, W., & Doering, B. K. (2016). The cyberchondria severity scale (CSS): German validation and development of a short form. International Journal of Behavioral Medicine, 23(5), 595–605. https://doi.org/10.1007/s12529-0169549-8. Bobevski, I., Clarke, D. M., & Meadows, G. (2016). Health anxiety and its relationship to disability and service use: Findings from a large epidemiological survey. Psychosomatic Medicine, 78(1), 13–25. Brown, T. A., & Barlow, D. (2014). Anxiety and related disorders interview schedule for DSM5 (ADIS-5). New York: Oxford University Press. Cooper, K., Gregory, J. D., Walker, I., Lambe, S., & Salkovskis, P. M. (2017). Cognitive behaviour therapy for health anxiety: A systematic review and meta-analysis. Behavioural and Cognitive Psychotherapy, 45(2), 110–123. https://doi.org/10.1017/ s1352465816000527. Creed, F., & Barsky, A. (2004). A systematic review of the epidemiology of somatisation disorder and hypochondriasis. Journal of Psychosomatic Research, 56(4), 391–408. https://doi.org/10.1016/s0022-3999(03)00622-6. Doherty-Torstrick, E. R., Walton, K. E., & Fallon, B. A. (2016). Cyberchondria: Parsing health anxiety from online behavior. Psychosomatics, 57(4), 390–400. https://doi. org/10.1016/j.psym.2016.02.002. Fergus, T. A. (2013). Cyberchrondria and intolerance of uncertainty: Examining when individuals experience health anxiety in response to internet searches for medical information. Cyberpsychology, Behavior and Social Networking, 16(10), 735–739. https://doi.org/10.1089/cyber.2012.0671. Fergus, T. A. (2014). The Cyberchondria Severity Scale (CSS): An examination of
4.2. Limitations The results of this study need to be interpreted in the context of some limitations. All of the data was collected via online self-report measures, as there is currently no validated clinical interview to assess cyberchondria. The findings need to be replicated using objective measures of online searching behaviour. Second, the sample was motivated, treatment-seeking, and generally well educated; it is unknown whether the findings generalise to other samples, including whether iCBT would have as powerful effects for those who were not treatment seeking, or those convinced by medical explanations for their symptoms. Third, another limitation of this study was that we did not explicitly ask participants whether they used the internet to search for health information. Rather, we used the Cyberchondria Severity Scale (CSS), which is the most widely used measure of cyberchondria (McMullan et al., 2019) used to assess the severity of cyberchondria on a dimensional scale. For each of the 33 items of the CSS, participants choose a response from the following options: Never, Rarely, Sometimes, Often and Always. There were no participants who endorsed the ‘Never’ response option on all of the items, which suggests that these participants did engage in online health information searching. Nonetheless, future studies should ask explicitly about whether or not the 6
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