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  • The study sample consisted of 764 adult patients, all of which had a principal diagnosis of SAD and had undergone treatment as part of routine outpatient psychiatric care at a specialized public ICBT clinic at the time of data extraction (i.e. patients having completed treatment between October 2010 and October 2014). Patients were admitted to treatment either through self-referral or through referral from psychiatric or primary care. All patients first completed an on-line screening battery of self-report questionnaires. Then, clinician administered assessments were conducted before admittance where the Mini-International Neuropsychiatric Interview (MINI) [28] diagnostic interview was used by psychiatrists (or resident physicians supervised by psychiatrists) to diagnose Axis I DSM-IV disorders. The study, including its consent procedure, was approved by the Regional Ethical Review Board in Stockholm, Sweden (no 2011/2091-31/3). Since this research was conducted as a retrospective cohort study, active informed consent was not required and therefore not obtained. However, as required by the ethics committee, a letter explaining the study was sent to all participants prior to data collection providing a choice of participation. Thus, participation in the study was obtained through an opt-out methodology. This approach (i.e. passive consent) is considered to be the most efficient procedure for registry data that does not violate the option of providing choice [29] and also increases the likelihood of having a more representative sample [30]. Over the study period, approximately 25 different psychologists delivered the treatments. These were trained in CBT and either licensed or occasionally resident psychologists under clinical supervision. Therapists followed a treatment manual originally developed by Andersson and colleagues [31] with documented effects in several randomized controlled trials [32] adhering to Clark and Wells’ [33] cognitive model of social phobia developed for individual therapy of SAD. Non-inferiority of the treatment compared to conventional group-based CBT has previously been documented within the context of routine psychiatric care [34]. Treatment modules consisted of psycho-educative text and supporting figures, complemented with homework assignments. This is similar to self-help books used in bibliotherapy, with the difference that the material was accessed online after logging in to a secure treatment website. In ICBT, treatment modules are the equivalence of sessions in conventional CBT. However, since the treatment content was presented in text form, patients were able to learn at their own convenience. Similar to sessions in CBT, modules were administered sequentially; after completion of a module and its homework assignment, the subsequent module was activated by the therapist. This meant that patients had to complete a module in order to get access to the following module. The first module introduced patients to CBT for SAD providing rationale for the treatment. This was followed by exercises in cognitive restructuring to help patients understand how SAD is maintained and learn how to recognize maladaptive or negative automatic thoughts with the aid of a detailed review of Clark & Wells’ cognitive model. Patients were also taught how to challenge negative thoughts and encouraged to reflect on treatment goals. In the next phase of treatment, behavioural experiments were introduced, including gradual exposure to feared situations. During these modules, patients learned about the function of safety behaviours and experimented with attentional refocussing to enhance the effect of exposure. The final modules contained material on developing communication and social skills and relapse prevention strategies. The last module encouraged patients to review what they had learned throughout the treatment, and to make a plan for continued improvements and how to manage setbacks. The role of the therapists was to guide patients through each treatment module. This was conducted through online written conversations within the secure treatment platform. This included not only providing feedback on homework assignments, but prompting patients when they were inactive or helping them with problem solving when experiencing difficulties. Thus, more therapist time tended to be allocated to patients having a greater need for support and guidance. The primary outcome measure was the self-rated version of the Liebowitz Social Anxiety Scale, LSAS-SR [35]. The LSAS-SR has reported strong psychometric properties with an internal consistency of α =. 95 and a 12-week test-retest reliability of r =. 83 [35,36]. The LSAS has also been evaluated for internet administration with a reported internal consistency of α =. 94 [37]. For the current sample, Cronbach's alpha of the LSAS-SR at pre-treatment was. 95. Besides at pre- and post-treatment, social anxiety was measured weekly throughout the active treatment phase. The secondary outcome measure was treatment adherence, operationalized as the number of activated modules. All potential predictors of symptomatic change and adherence were measured at baseline except for treatment program factors, which were measured during treatment (e.g. treatment credibility was assessed during the second week) or after treatment (e.g. adherence). Each variable was assigned to one of the following four domains: socio-demographic variables, clinical characteristics, family history of mental illness and treatment-related factors. This domain comprised 6 variables: age, gender, level of education, employment status (dichotomized; i.e. working part- or full-time or student vs. unemployed, part- or full-time sick leave or disability retirement), cohabitation status (dichotomized; i.e. single, divorced, widowed, or separated vs. married or living with partner) and having children. The clinical domain consisted of 14 variables: Clinical Global Impression—Severity Scale [38] which was rated on a 7-point scale; co-morbid depressive symptoms were measured at baseline using the self-rated Montgomery-Åsberg Depression Rating Scale (MADRS-S) [39]; the Alcohol Use Disorders Identification Test (AUDIT) [40] was used prior to treatment to screen for alcohol problems and the Drug Use Disorders Identification Test (DUDIT) [41] was used to screen for drug misuse; the Adult ADHD Self-Report Scale-V1.1 (ASRS) was used as a short screening instrument for adult ADHD-like symptoms [42]; the number of co-morbid diagnoses was assessed during the clinician diagnostic interview using the MINI; the Global Assessment of Functioning (GAF) was used to assess social, occupational, and psychological functioning; years since onset of symptoms was assessed during the clinical interview; age of onset of symptoms was calculated by subtracting the reported number of years since onset of symptoms from the patient’s age at the time of treatment; generalized beliefs of self-efficacy was assessed using the General Self-Efficacy Scale (GSES) [43]; concurrent psychotropic medication, history of depression, history of inpatient psychiatric care and history of attempted suicide were assessed during the diagnostic interview. Family history of mental illness: The family history domain consisted of 14 variables reporting occurrence of family history of mental illness: family history of social anxiety disorder, social anxiety disorder-like symptoms, anxiety, depression, minor depression, panic disorder, neuropsychiatric condition, psychosis, bipolar disorder, dependence / substance abuse, suicide attempts and family history of completed suicide. These were assessed during the diagnostic interview by a clinician prior to treatment as part of the intake assessment process at the clinic in the form of a checklist. As such, the measure of family history was created for this study as a categorical variable. The treatment domain consisted of 9 variables: adherence, operationalized as the number of activated treatment modules; treatment credibility, operationalized as the total score of the credibility/expectancy scale [44] where patients’ attitudes to the credibility of the treatment and expectancy regarding treatment effectiveness were rated on a 10-point scale (0 = not at all to 10 = very much) after the first week of the treatment; therapist time (hours); therapist time per module (minutes); patients’ logged time online; patients’ number of logins; patients’ number of mouse clicks; patients’ number of sent messages and patients’ number of posted messages on a common discussion forum within the treatment platform. Multilevel model of symptomatic change: When analysing predictors of symptomatic improvement during the course of treatment, a longitudinal multilevel framework was applied using the linear mixed-effects models procedure in SPSS version 21. All available data was used in a full intent-to-treat analysis approach with full maximum likelihood estimation procedures for all multilevel models. The objective of using multilevel modelling was to model change in social anxiety over the treatment period and to examine the influence of predictors on this trajectory. This was studied by testing the interaction effect of each predictor × time product; i.e. whether the effect of time on social anxiety varied depending on the value of the predictor variable. Due to the relatively large number of potential predictor variables, these were grouped into the four domains described above. As such, all variables were first tested in separate models before building a final model of change that included significant variables from all domains. Each domain of predictors were explored in a stepwise manner following the procedure adopted by Fournier et al. [27] and Amir et al. [11]. In Step 1, each model therefore included all variables of that domain, which were then screened in regard to their significance levels. Since the main interest of the study was in the factors that have an effect on the rate of symptomatic improvement, potential prognostic variables were required to have a significant interaction with time in order to progress to the next step. However, each consecutive model still included both the main effect (i.e. the effect on post-treatment level of social anxiety) of each added predictor as well as its interaction with time (i.e. the effect on the rate of change). Those that were found to be significant at p <. 2 in Step 1 were tested again in a second model (Step 2). Increasingly stricter significance levels were applied at each step and this screening procedure was repeated at p <. 10 (Step 3) and at p <. 05 (Step 4). Only those variables that remained significant at Step 4 (p <. 05) within each domain were included in a final model where the effect of each predictor was tested while simultaneously controlling for the effects of the others. Each multilevel model consisted of two levels, where Level 1 was the repeated measurements describing the linear change of social anxiety within patients over the course of treatment (a quadric change trajectory was also tested, but because it did not significantly describe the shape of change over time it was not included in the models) and Level 2 consisted of the patients with their socio-demographic, family history, clinical and treatment-related characteristics. In line with the approach described by Smits and colleagues [12], the time variable was re-centred at post-treatment; estimates of the intercept therefore reflect the predicted level of social anxiety at post-treatment. We also chose to recode the time variable between -1and 0 as proposed by Heck et al. [45]. This coding has the benefit that the estimated coefficients reflect the magnitude of change in social anxiety achieved over the entire treatment period (as opposed to the amount of weekly change). Therefore, since there were 12 measurement occasions, the distance between each step was 0.083; the time variable was therefore coded as -1, -0.92, -0.83, -0.67, -0.58, -0.50, -0.42, -0.33, -0.25, -0.17, -0.08 and 0. Finally, to facilitate comparison of coefficients for predictors measured on different scales, we followed the approach by Smits and colleagues [12] where predictor variables were standardized into z-scores prior to analysis. In the case of analysing adherence as an outcome, selection of variables to enter into the mixed-effects regression model was based on the same stepwise procedure described above. However, in the treatment factors domain, only treatment credibility and therapist time per module were tested since patient activity such as number of logins, mouse clicks, messages and time online were variables highly correlated with adherence (p <. 001).
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