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      Long-term Effectiveness and Predictors of Transdiagnostic Internet-Delivered Cognitive Behavioral Therapy for Emotional Disorders in Specialized Care: Secondary Analysis of a Randomized Controlled Trial

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          Abstract

          Background

          Transdiagnostic internet-delivered cognitive behavioral therapy (iCBT) for emotional disorders has been shown to be effective in specialized care in the short term. However, less is known about its long-term effects in this specific setting. In addition, predictors of long-term effectiveness may help to identify what treatments are more suitable for certain individuals.

          Objective

          This study aimed to analyze the long-term effectiveness of transdiagnostic iCBT compared with that of treatment as usual (TAU) in specialized care and explore predictors of long-term effectiveness.

          Methods

          Mixed models were performed to analyze the long-term effectiveness and predictors of transdiagnostic iCBT (n=99) versus TAU (n=101) in public specialized mental health care. Outcomes included symptoms of depression and anxiety, health-related quality of life (QoL), behavioral inhibition and behavioral activation, comorbidity, and diagnostic status (ie, loss of principal diagnosis) from baseline to 1-year follow-up. Sociodemographic characteristics (sex, age, and education) and clinical variables (principal diagnosis, comorbidity, and symptom severity at baseline) were selected as predictors of long-term changes.

          Results

          Compared with baseline, transdiagnostic iCBT was more effective than TAU in improving symptoms of depression ( b=–4.16, SE 1.80, 95% CI –7.68 to –0.67), health-related QoL ( b=7.63, SE 3.41, 95% CI 1.00-14.28), diagnostic status ( b=–0.24, SE 0.09, 95% CI –1.00 to –0.15), and comorbidity at 1-year follow-up ( b=–0.58, SE 0.22, 95% CI –1.00 to –0.15). From pretreatment assessment to follow-up, anxiety symptoms improved in both transdiagnostic iCBT and TAU groups, but no significant differences were found between the groups. Regarding the predictors of the long-term effectiveness of transdiagnostic iCBT compared with that of TAU, higher health-related QoL at follow-up was predicted by a baseline diagnosis of anxiety, male sex, and the use of psychiatric medication; fewer comorbid disorders at follow-up were predicted by older age and higher baseline scores on health-related QoL; and fewer depressive symptoms at follow-up were predicted by baseline diagnosis of depression. However, this pattern was not observed for baseline anxiety diagnoses and anxiety symptoms.

          Conclusions

          The results suggest that transdiagnostic iCBT is more effective than TAU to target depressive symptoms among patients with emotional disorders. Anxiety symptoms remained stable at 1-year follow-up, with no differences between the groups. Results on predictors suggest that some groups of patients may obtain specific gains after transdiagnostic iCBT. Specifically, and consistent with the literature, patients with baseline depression improved their depression scores at follow-up. However, this pattern was not found for baseline anxiety disorders. More studies on the predictor role of sociodemographic and clinical variables in long-term outcomes of transdiagnostic iCBT are warranted. Future studies should focus on studying the implementation of transdiagnostic iCBT in Spanish public specialized mental health care.

          Trial Registration

          ClinicalTrials.gov NCT02345668; https://clinicaltrials.gov/ct2/show/NCT02345668

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          Most cited references65

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          Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication.

          Little is known about lifetime prevalence or age of onset of DSM-IV disorders. To estimate lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the recently completed National Comorbidity Survey Replication. Nationally representative face-to-face household survey conducted between February 2001 and April 2003 using the fully structured World Health Organization World Mental Health Survey version of the Composite International Diagnostic Interview. Nine thousand two hundred eighty-two English-speaking respondents aged 18 years and older. Lifetime DSM-IV anxiety, mood, impulse-control, and substance use disorders. Lifetime prevalence estimates are as follows: anxiety disorders, 28.8%; mood disorders, 20.8%; impulse-control disorders, 24.8%; substance use disorders, 14.6%; any disorder, 46.4%. Median age of onset is much earlier for anxiety (11 years) and impulse-control (11 years) disorders than for substance use (20 years) and mood (30 years) disorders. Half of all lifetime cases start by age 14 years and three fourths by age 24 years. Later onsets are mostly of comorbid conditions, with estimated lifetime risk of any disorder at age 75 years (50.8%) only slightly higher than observed lifetime prevalence (46.4%). Lifetime prevalence estimates are higher in recent cohorts than in earlier cohorts and have fairly stable intercohort differences across the life course that vary in substantively plausible ways among sociodemographic subgroups. About half of Americans will meet the criteria for a DSM-IV disorder sometime in their life, with first onset usually in childhood or adolescence. Interventions aimed at prevention or early treatment need to focus on youth.
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            Random effects structure for confirmatory hypothesis testing: Keep it maximal.

            Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F 1 and F 2 tests, and in many cases, even worse than F 1 alone. Maximal LMEMs should be the 'gold standard' for confirmatory hypothesis testing in psycholinguistics and beyond.
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              Psychosocial impact of COVID-19

              Background Along with its high infectivity and fatality rates, the 2019 Corona Virus Disease (COVID-19) has caused universal psychosocial impact by causing mass hysteria, economic burden and financial losses. Mass fear of COVID-19, termed as “coronaphobia”, has generated a plethora of psychiatric manifestations across the different strata of the society. So, this review has been undertaken to define psychosocial impact of COVID-19. Methods Pubmed and GoogleScholar are searched with the following key terms- “COVID-19”, “SARS-CoV2”, “Pandemic”, “Psychology”, “Psychosocial”, “Psychitry”, “marginalized”, “telemedicine”, “mental health”, “quarantine”, “infodemic”, “social media” and” “internet”. Few news paper reports related to COVID-19 and psychosocial impacts have also been added as per context. Results Disease itself multitude by forced quarantine to combat COVID-19 applied by nationwide lockdowns can produce acute panic, anxiety, obsessive behaviors, hoarding, paranoia, and depression, and post-traumatic stress disorder (PTSD) in the long run. These have been fueled by an “infodemic” spread via different platforms social media. Outbursts of racism, stigmatization, and xenophobia against particular communities are also being widely reported. Nevertheless, frontline healthcare workers are at higher-risk of contracting the disease as well as experiencing adverse psychological outcomes in form of burnout, anxiety, fear of transmitting infection, feeling of incompatibility, depression, increased substance-dependence, and PTSD. Community-based mitigation programs to combat COVID-19 will disrupt children's usual lifestyle and may cause florid mental distress. The psychosocial aspects of older people, their caregivers, psychiatric patients and marginalized communities are affected by this pandemic in different ways and need special attention. Conclusion For better dealing with these psychosocial issues of different strata of the society, psychosocial crisis prevention and intervention models should be urgently developed by the government, health care personnel and other stakeholders. Apt application of internet services, technology and social media to curb both pandemic and infodemic needs to be instigated. Psychosocial preparedness by setting up mental organizations specific for future pandemics is certainly necessary.
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                Author and article information

                Contributors
                Journal
                JMIR Ment Health
                JMIR Ment Health
                JMH
                JMIR Mental Health
                JMIR Publications (Toronto, Canada )
                2368-7959
                October 2022
                31 October 2022
                : 9
                : 10
                : e40268
                Affiliations
                [1 ] Department of Psychology and Sociology Universidad de Zaragoza Teruel Spain
                [2 ] Department of Psychology Universidad Villanueva Madrid Spain
                [3 ] Department of Basic and Clinical Psychology, and Psychobiology Universitat Jaume I Castellón de la Plana Spain
                [4 ] CIBER Fisiopatología Obesidad y Nutrición (CIBERObn) Instituto Carlos III Madrid Spain
                Author notes
                Corresponding Author: Alberto González-Robles gonzaleza@ 123456unizar.es
                Author information
                https://orcid.org/0000-0002-3749-3756
                https://orcid.org/0000-0001-9815-4902
                https://orcid.org/0000-0003-4398-4014
                https://orcid.org/0000-0001-9250-8714
                https://orcid.org/0000-0001-8783-6959
                Article
                v9i10e40268
                10.2196/40268
                9664329
                36315227
                a378a0ba-f6f4-412b-afff-f3965da12c5a
                ©Alberto González-Robles, Pablo Roca, Amanda Díaz-García, Azucena García-Palacios, Cristina Botella. Originally published in JMIR Mental Health (https://mental.jmir.org), 31.10.2022.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on https://mental.jmir.org/, as well as this copyright and license information must be included.

                History
                : 14 June 2022
                : 18 July 2022
                : 1 August 2022
                : 3 August 2022
                Categories
                Original Paper
                Original Paper

                transdiagnostic,anxiety,depression,long term,predictors
                transdiagnostic, anxiety, depression, long term, predictors

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