Sensitivity to change of the Beck Depression Inventory versus the Inventory of Depressive Symptoms

Background: In a previous study which made a comparison between disorder-specific and generic instruments to assess outcome of treatments for depression, the Beck Depression Inventory, Second Edition (BDI-II) seemed to be more sensitive to change than the Inventory of Depressive Symptoms- Self Rating (IDS-SR). Methods: A set with longitudinal data from Routine Outcome Monitoring (n=144) were analyzed with multilevel models with random intercepts. The sensitivity to change of two disorder-specific instruments, the BDI-II and the IDS-SR, were compared head to head. Results: The BDI-II was more sensitive to change when measuring treatment outcome compared to the IDS-SR. The BDI-II decreases significantly more over time than the IDS-SR: the average decrease per week for the IDS-SR is -.012 (95%CI -0.015, -0.009) and for the BDI-II it is -.017 (95%CI -0.021, -0.014). Limitations: Conclusions can only be preliminary due to a small sample size. Conclusions: Treatment outcomes measured with questionnaires may differ depending on the degree of sensitivity to change of the instruments

Support for the higher-order factor structure of the WHODAS 2.0 self-report version in a Dutch outpatient psychiatric setting

PURPOSE: Previous studies of the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) interview version suggested a second-order model, with a general disability factor and six factors on a lower level. The goal of this study is to investigate if we can find support for a similar higher-order factor structure of the 36-item self-report version of the WHODAS 2.0 in a Dutch psychiatric outpatient sample. We aim to give special attention to the differences between the non-working group sample and the working group sample. Additionally, we intend to provide preliminary norms for clinical interpretation of the WHODAS 2.0 scores in psychiatric settings. METHODS: Patients seeking specialized ambulatory treatment, primarily for depressive or anxiety symptoms, completed the WHODAS 2.0 as part of the initial interview. The total sample consisted of 770 patients with a mean age of 37.5 years (SD = 13.3) of whom 280 were males and 490 were females. Several factorial compositions (i.e., one unidimensional model and two second-order models) were modeled using confirmatory factor analysis (CFA). Descriptive statistics, model-fit statistics, reliability of the (sub)scales, and preliminary norms for interpreting test scores are reported. RESULTS: For the non-working group, the second-order model with a general disability factor and six factors on a lower level, provided an adequate fit. Whereas, for the working group, the second-order model with a general disability factor and seven factors on a lower level seemed more appropriate. The WHODAS 2.0 36-item self-report form showed adequate levels of reliability. Percentile ranks and normalized T-scores are provided to aid clinical evaluations. CONCLUSION: Our results lend support for a factorial structure of the WHODAS 2.0 36-item self-report version that is comparable to the interview version. While we conjecture that a seven-factor solution might give a better reflection of item content and item variance, further research is needed to assess the clinical relevance of such a model. At this point, we recommend using the second-order structure with six factors that matches past findings of the interview form

Web-based guided self-help cognitive behavioral therapy–enhanced versus treatment as usual for binge-eating disorder: a randomized controlled trial protocol

Binge-eating disorder (BED) is a psychiatric disorder characterized by recurrent episodes of eating a large amount of food in a discrete period of time while experiencing a loss of control. Cognitive behavioral therapy-enhanced (CBT-E) is a recommended treatment for binge-eating disorder and is typically offered through 20 sessions. Although binge-eating disorder is highly responsive to CBT-E, the cost of treating these patients is high. Therefore, it is crucial to evaluate the efficacy of low-intensity and low-cost treatments for binge-eating disorder that can be offered as a first line of treatment and be widely disseminated. The proposed noninferiority randomized controlled trial aims to determine the efficacy of web-based guided self-help CBT-E compared to treatment-as-usual CBT-E. Guided self-help will be based on a self-help program to stop binge eating, will be shorter in duration and lower intensity, and will require fewer therapist hours. Patients with binge-eating disorder (N = 180) will be randomly assigned to receive guided self-help or treatment-as-usual. Assessments will take place at baseline, mid-treatment, at the end of treatment, and at 20- and 40-weeks post-treatment. Treatment efficacy will be measured by examining the reduction in binge-eating days in the previous 28 days between baseline and the end of treatment between groups, with a noninferiority margin (Δ) of 1 binge-eating day. Secondary outcomes will include full remission, body shape dissatisfaction, therapeutic alliance, clinical impairment, health-related quality of life, attrition, and an economic evaluation to assess cost-effectiveness and cost-utility. The moderators examined will be baseline scores, demographic variables, and body mass index. It is expected that guided self-help is noninferior in efficacy compared to treatment-as-usual. The proposed study will be the first to directly compare the efficacy and economically evaluate a low-intensity and low-cost binge-eating disorder treatment compared to treatment-as-usual. If guided self-help is noninferior to treatment-as-usual in efficacy, it can be widely disseminated and used as a first line of treatment for patients with binge-eating disorder. The Dutch trial register number is R21.016. The study has been approved by the Medical Research Ethics Committees United on May 25th, 2021, case number NL76368.100.21

Norms and T-scores for screeners of alcohol use, depression and anxiety in the population of Suriname

BackgroundThere is a considerable gap between care provision and the demand for care for common mental disorders in low-and-middle-income countries. Screening for these disorders, e.g., in primary care, will help to close this gap. However, appropriate norms and threshold values for screeners of common mental disorders are lacking.MethodsIn a survey study, we gathered data on frequently used screeners for alcohol use disorders, (AUDIT), depression, (CES-D), and anxiety disorders (GAD-7, ACQ, and BSQ) in a representative sample from Suriname, a non-Latin American Caribbean country. A stratified sampling method was used by random selection of 2,863 respondents from 5 rural and 12 urban resorts. We established descriptive statistics of all scale scores and investigated unidimensionality. Furthermore, we compared scores by gender, age-group, and education level with t-test and Mann–Whitney U tests, using a significance level of p < 0.05.ResultsNorms and crosswalk tables were established for the conversion of raw scores into a common metric: T-scores. Furthermore, recommended cut-off values on the T-score metric for severity levels were compared with international cut-off values for raw scores on these screeners.DiscussionThe appropriateness of these cut-offs and the value of converting raw scores into T-scores are discussed. Cut-off values help with screening and early detection of those who are likely to have a common mental health disorder and may require treatment. Conversion of raw scores to a common metric in this study facilitates the interpretation of questionnaire results for clinicians and can improve health care provision through measurement-based care

Distinguishing symptom dimensions of depression and anxiety: An integrative approach

AbstractBackgroundClark and Watson developed the tripartite model in which a symptom dimension of ‘negative affect’ covers common psychological distress that is typically seen in anxious and depressed patients. The ‘positive affect’ and ‘somatic arousal’ dimensions cover more specific symptoms. Although the model has met much support, it does not cover all relevant anxiety symptoms and its negative affect dimension is rather unspecific. Therefore, we aimed to extend the tripartite model in order to describe more specific symptom patterns with unidimensional measurement scales.Method1333 outpatients provided self report data. To develop an extended factor model, exploratory factor analysis (EFA) was conducted in one part of the data (n=578). Confirmatory factor analysis (CFA) was conducted in the second part (n=755), to assess model-fit and comparison with other models. Rasch analyses were done to investigate the unidimensionality of the factors.ResultsEFA resulted in a 6-factor model: feelings of worthlessness, fatigue, somatic arousal, anxious apprehension, phobic fear and tension. CFA in the second sample showed that a 6-factor model with a hierarchical common severity factor fits the data better than alternative 1- and 3-factor models. Rasch analyses showed that each of the factors and the total of factors can be regarded as unidimensional measurement scales.LimitationsThe model is based on a restricted symptom-pool: more dimensions are likely to exist.ConclusionThe extended tripartite model describes the clinical state of patients more specifically. This is relevant for both clinical practice and research

Workplace Stress, Presenteeism, Absenteeism, and Resilience Amongst University Staff and Students in the COVID-19 Lockdown

Background: This study explored how the COVID-19 outbreak and arrangements such as remote working and furlough affect work or study stress levels and functioning in staff and students at the University of York, UK. Methods: An invitation to participate in an online survey was sent to all University of York staff and students in May-June 2020. We measured stress levels [VAS-scale, Perceived Stress Questionnaire (PSQ)], mental health [anxiety (GAD-7), depression (PHQ-9)], physical health (PHQ-15, chronic medical conditions checklist), presenteeism, and absenteeism levels (iPCQ). We explored demographic and other characteristics as factors which may contribute to resilience and vulnerability for the impact of COVID-19 on stress. Results: One thousand and fifty five staff and nine hundred and twenty five students completed the survey. Ninety-eight per cent of staff and seventy-eight per cent of students worked or studied remotely. 7% of staff and 10% of students reported sickness absence. 26% of staff and 40% of the students experienced presenteeism. 22-24% of staff reported clinical-level anxiety and depression scores, and 37.2 and 46.5% of students. Staff experienced high stress levels due to COVID-19 (66.2%, labeled vulnerable) and 33.8% experienced low stress levels (labeled resilient). Students were 71.7% resilient vs. 28.3% non-resilient. Predictors of vulnerability in staff were having children [OR = 2.23; CI (95) = 1.63-3.04] and social isolation [OR = 1.97; CI (95) = 1.39-2.79] and in students, being female [OR = 1.62; CI (95) = 1.14-2.28], having children [OR = 2.04; CI (95) = 1.11-3.72], and social isolation [OR = 1.78; CI (95) = 1.25-2.52]. Resilience was predicted by exercise in staff [OR = 0.83; CI (95) = 0.73-0.94] and in students [OR = 0.85; CI (95) = 0.75-0.97]. Discussion: University staff and students reported high psychological distress, presenteeism and absenteeism. However, 33.8% of staff and 71.7% of the students were resilient. Amongst others, female gender, having children, and having to self-isolate contributed to vulnerability. Exercise contributed to resilience. Conclusion: Resilience occurred much more often in students than in staff, although psychological distress was much higher in students. This suggests that predictors of resilience may differ from psychological distress per se. Hence, interventions to improve resilience should not only address psychological distress but may also address other factors

Effectiveness of a multi-facetted blended eHealth intervention during intake supporting patients and clinicians in Shared Decision Making : A cluster randomised controlled trial in a specialist mental health outpatient setting

OBJECTIVE: To investigate the effectiveness of a multi-facetted blended eHealth intervention, called SDM-Digital Intake (SDM-DI), in which patients and clinicians are supported in Shared Decision Making during the intake process. METHODS: The study is a two-arm matched-paired cluster Randomised Controlled Trial in a specialist mental health outpatient setting with two conditions: SDM-DI and Intake As Usual (IAU). Four intake teams were allocated to each arm. All patients who followed an intake, were asked to participate if they were capable to complete questionnaires. Decisional Conflict (DC), referring to patients' engagement and satisfaction with clinical decisions, was the primary outcome. Secondary outcomes were patient participation, applying Shared Decision Making (SDM), working alliance, treatment adherence and symptom severity. Effects were measured at two weeks (T1) and two months (T2) after intake. Multilevel regression and intention-to-treat analyses were used. Additionally, the influence of subgroups and intervention adherence on DC were explored. RESULTS: At T1, 200 patients participated (47% intervention, 53% control), and at T2 175 patients (47% intervention, 53% control). At T1 and T2, no differences were found between conditions on DC. Subgroup analyses showed that effects of SDM-DI on DC were not modified by primary diagnoses mood, anxiety and personality disorders. Compared to IAU, at T2, patients reported positive effects of SDM-DI on SDM (β 7.553, p = 0.038, 95%CI:0.403-14.703, d = 0.32) and reduction of symptoms (β -7.276, p = 0.0497, 95%CI:-14.544--0.008, d = -0.43). No effects were found on patient participation, working alliance and treatment adherence. Exploratory analyses demonstrated that if SDM was applied well, patients reported less DC (β = -0.457, p = 0.000, 95%CI:-0.518--0.396, d = -1.31), which was associated with better treatment outcomes. CONCLUSION: Although, this trial fails to demonstrate that SDM-DI by itself is sufficient to reduce DC, the results are encouraging for further efforts in improving and implementing the SDM intervention

Comparative responsiveness of generic versus disorder-specific instruments for depression:An assessment in three longitudinal datasets

Background: Routine outcome monitoring (ROM) may enhance individual treatment and is also advocated as a means to compare the outcome of different treatment programs or providers. There is debate on the optimal instruments to be used for these separate tasks.  Methods: Three sets with longitudinal data from ROM were analyzed with correlational analysis and repeated measures ANOVAs, allowing for a head-to-head comparison of measures regarding their sensitivity to detect change. The responsiveness of three disorder-specific instruments, the Beck Depression Inventory, the Inventory of Depressive Symptoms, and the Mood and Anxiety Symptoms Questionnaire, was compared to three generic instruments, the Symptom Checklist (SCL-90), the Outcome Questionnaire (OQ-45), and the Brief Symptom Inventory, respectively.  Results: In two of the three datasets, disorder-specific measures were more responsive compared to the total score on generic instruments. Subscale scores for depression embedded within generic instruments are second best and almost match disorder-specific scales in responsiveness. No evidence of a desynchronous response on outcome measures was found.  Limitations: The present study compares measures head-to-had, and responsiveness is not assessed against an external criterion, such as clinical recovery.  Discussion: Disorder-specific measures yield the most precise assessment for individual treatment and are recommended for clinical use. Generic measures may allow for comparisons across diagnostic groups and their embedded subscales approach the responsiveness of disorder-specific measures

Shared decision-making in mental health care using routine outcome monitoring : results of a cluster randomised-controlled trial

PURPOSE: To investigate the effects of Shared Decision-Making (SDM) using Routine Outcome Monitoring (ROM) primary on patients' perception of Decisional Conflict (DC), which measures patients' engagement in and satisfaction with clinical decisions, and secondary on working alliance and treatment outcomes. METHOD: Multi-centre two-arm matched-paired cluster randomised-controlled trial in Dutch specialist mental health care. SDM using ROM (SDMR) was compared with Decision-Making As Usual (DMAU). Outcomes were measured at baseline (T0) and 6 months (T1). Multilevel regression and intention-to-treat analyses were used. Post hoc analyses were performed on influence of subgroups and application of SDMR on DC. RESULTS: Seven teams were randomised to each arm. T0 was completed by 186 patients (51% intervention; 49% control) and T1 by 158 patients (51% intervention, 49% control). DC, working alliance, and treatment outcomes reported by patients did not differ significantly between two arms. Post hoc analyses revealed that SDMR led to less DC among depressed patients (p = 0.047, d =- 0.69). If SDMR was applied well, patients reported less DC (SDM: p = 0.000, d = - 0.45; ROM: p = 0.021, d = - 0.32), which was associated with better treatment outcomes. CONCLUSION: Except for patients with mood disorders, we found no difference between the arms for patient-reported DC. This might be explained by the less than optimal uptake of this generic intervention, which did not support patients directly. Regarding the positive influence of a higher level of applying SDM and ROM on less DC and better treatment outcomes, the results are encouraging for further investments in patient-oriented development and implementation of SDMR