Digital Health Applications in the Area of Mental Health.

BACKGROUND: The introduction of digital health applications (DiGA) is a fundamental innovation in Germany. In the field of mental health, numerous applications are already available whose efficacy has been tested in clinical trials. We investigated whether, and to what extent, the use of DiGA can be recommended on the basis of the available evidence. METHODS: In this scoping review, we summarize the evidence supporting the use of DiGA in the mental health field through an examination of relevant publications that were retrieved by a systematic literature search. We provide an annotated tabular listing and discuss the current advantages of, and obstacles to, the care of mentally ill patients with the aid of DiGA. RESULTS: We identified 17 DiGA for use in depression, anxiety disorders, addiction disorders, sleep disorders, stress/burnout, vaginismus, and chronic pain. These DiGA have been evaluated to date in 3 meta-analyses, 39 randomized controlled trials (RCTs), and two single-armed intervention trials. 23 of the 36 published trials were carried out with the direct participation of the manufacturers. 29 of the 39 RCTs were not blinded or contained no information regarding blinding. Active controls were used in 6 of the 39 RCTs. The reported effect sizes, with the exclusion of pre-post analyses, ranged from 0.16 to 1.79. CONCLUSION: Most of the published studies display a high risk of bias, both because of the manufacturers' participation and because of methodological deficiencies. DiGA are an increasingly important therapeutic modality in psychiatry. The available evidence indicates that treatment effects are indeed present, but prospective comparisons with established treatments are still entirely lacking.

Individual Differences in Response to Antidepressants: A Meta-analysis of Placebo-Controlled Randomized Clinical Trials.

Importance: Antidepressants are commonly used to treat major depressive disorder (MDD). Antidepressant outcomes can vary based on individual differences; however, it is unclear whether specific factors determine this variability or whether it is at random. Objective: To investigate the assumption of systematic variability in symptomatic response to antidepressants and to assess whether variability is associated with MDD severity, antidepressant class, or study publication year. Data Sources: Data used were updated from a network meta-analysis of treatment with licensed antidepressants in adults with MDD. The Cochrane Central Register of Controlled Trials, CINAHL, Embase, LILACS database, MEDLINE, MEDLINE In-Process, and PsycInfo were searched from inception to March 21, 2019. Additional sources were international trial registries and sponsors, drug companies and regulatory agencies' websites, and reference lists of published articles. Data were analyzed between June 8, 2020, and June 13, 2020. Study Selection: Analysis was restricted to double-blind, randomized placebo-controlled trials with depression scores available at the study's end point. Data Extraction and Synthesis: Baseline means, number of participants, end point means and SDs of total depression scores, antidepressant type, and publication year were extracted. Main Outcomes and Measures: Log SDs (bln σ̂) were derived for treatment groups (ie, antidepressant and placebo). A random-slope mixed-effects model was conducted to estimate the difference in bln σ̂ between treatment groups while controlling for end point mean. Secondary models determined whether differences in variability between groups were associated with baseline MDD severity; antidepressant class (selective serotonin reuptake inhibitors and other related drugs; serotonin and norepinephrine reuptake inhibitors; norepinephrine-dopamine reuptake inhibitors; noradrenergic agents; or other antidepressants); and publication year. Results: In the 91 eligible trials (18 965 participants), variability in response did not differ significantly between antidepressants and placebo (bln σ̂, 1.02; 95% CI, 0.99-1.05; P = .19). This finding is consistent with a range of treatment effect SDs (up to 16.10), depending on the association between the antidepressant and placebo effects. Variability was not associated with baseline MDD severity or publication year. Responses to noradrenergic agents were 11% more variable than responses to selective serotonin reuptake inhibitors (bln σ̂, 1.11; 95% CI, 1.01-1.21; P = .02). Conclusions and Relevance: Although this study cannot rule out the possibility of treatment effect heterogeneity, it does not provide empirical support for personalizing antidepressant treatment based solely on total depression scores. Future studies should explore whether individual symptom scores or biomarkers are associated with variability in response to antidepressants.

On the treatment effect heterogeneity of antidepressants in major depression: A Bayesian meta-analysis and simulation study.

BACKGROUND: The average treatment effect of antidepressants in major depression was found to be about 2 points on the 17-item Hamilton Depression Rating Scale, which lies below clinical relevance. Here, we searched for evidence of a relevant treatment effect heterogeneity that could justify the usage of antidepressants despite their low average treatment effect. METHODS: Bayesian meta-analysis of 169 randomized, controlled trials including 58,687 patients. We considered the effect sizes log variability ratio (lnVR) and log coefficient of variation ratio (lnCVR) to analyze the difference in variability of active and placebo response. We used Bayesian random-effects meta-analyses (REMA) for lnVR and lnCVR and fitted a random-effects meta-regression (REMR) model to estimate the treatment effect variability between antidepressants and placebo. RESULTS: The variability ratio was found to be very close to 1 in the best fitting models (REMR: 95% highest density interval (HDI) [0.98, 1.02], REMA: 95% HDI [1.00, 1.02]). The between-study standard deviation τ under the REMA with respect to lnVR was found to be low (95% HDI [0.00, 0.02]). Simulations showed that a large treatment effect heterogeneity is only compatible with the data if a strong correlation between placebo response and individual treatment effect is assumed. CONCLUSIONS: The published data from RCTs on antidepressants for the treatment of major depression is compatible with a near-constant treatment effect. Although it is impossible to rule out a substantial treatment effect heterogeneity, its existence seems rather unlikely. Since the average treatment effect of antidepressants falls short of clinical relevance, the current prescribing practice should be re-evaluated.

Lithium Treatment Over the Lifespan in Bipolar Disorders.

Lithium has been the treatment of choice for patients with bipolar disorder (BD) for nearly 70 years. It is recommended by all relevant guidelines as a first-line treatment for maintenance therapy. In this review, we outline the current state of evidence for lithium in the treatment of BD over the lifespan. First, we summarize the evidence on efficacy in general, from relapse prevention to acute anti-manic treatment and its role in treating mood episodes with mixed features and bipolar depression. As patients are often treated for many years and different aspects have to be considered in different phases of life, we discuss the particularities of lithium in the treatment of paediatric BD, in older aged individuals and in pregnant women. Lastly, we discuss the evidence on lithium's proposed suicide-preventive effects, the dangers of rapid discontinuation and lithium's adverse effects, particularly with regard to long-term treatment.

Bassoon proteinopathy drives neurodegeneration in multiple sclerosis.

Multiple sclerosis (MS) is characterized by inflammatory insults that drive neuroaxonal injury. However, knowledge about neuron-intrinsic responses to inflammation is limited. By leveraging neuron-specific messenger RNA profiling, we found that neuroinflammation leads to induction and toxic accumulation of the synaptic protein bassoon (Bsn) in the neuronal somata of mice and patients with MS. Neuronal overexpression of Bsn in flies resulted in reduction of lifespan, while genetic disruption of Bsn protected mice from inflammation-induced neuroaxonal injury. Notably, pharmacological proteasome activation boosted the clearance of accumulated Bsn and enhanced neuronal survival. Our study demonstrates that neuroinflammation initiates toxic protein accumulation in neuronal somata and advocates proteasome activation as a potential remedy.