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Aims and methodTo assess physical health needs of adolescent in-patients by routine monitoring. A retrospective analysis of case notes was conducted on a 6-month intake to generic and secure adolescent mental health units in Greater Manchester, UK.ResultsFifty individuals were admitted (52% female, average age 15.84 years). Diagnoses varied and 66% were prescribed medications before admission. All had a physical health assessment, which identified various physical health risk factors. Average body mass index was 25.99 (range 15.8–44), and increased during in-patient treatment for 84% of individuals who had their body mass recorded more than once. A total of 28% of individuals smoked. Lipids and prolactin levels were elevated across the sample.Clinical implicationsThis evaluation strengthens the argument to optimise physical healthcare for adolescent in-patients and develop physical health interventions, particularly given that we observed elevated lipids and prolactin. Physical health and well-being may not be prioritised when assessing and managing young peoples' mental health, despite their increased vulnerability for comorbid conditions.

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We summarized and compared meta‐analyses of pharmacological and non‐pharmacological interventions targeting physical health outcomes among people with schizophrenia spectrum disorders. Major databases were searched until June 1, 2018. Of 3,709 search engine hits, 27 meta‐analyses were included, representing 128 meta‐analyzed trials and 47,231 study participants. While meta‐analyses were generally of adequate or high quality, meta‐analyzed studies were less so. The most effective weight reduction interventions were individual lifestyle counseling (standardized mean difference, SMD=–0.98) and exercise interventions (SMD=–0.96), followed by psychoeducation (SMD=–0.77), aripiprazole augmentation (SMD=–0.73), topiramate (SMD=–0.72), d‐fenfluramine (SMD=–0.54) and metformin (SMD=–0.53). Regarding waist circumference reduction, aripiprazole augmentation (SMD=–1.10) and topiramate (SMD=–0.69) demonstrated the best evidence, followed by dietary interventions (SMD=–0.39). Dietary interventions were the only to significantly improve (diastolic) blood pressure (SMD=–0.39). Switching from olanzapine to quetiapine or aripiprazole (SMD=–0.71) and metformin (SMD=–0.65) demonstrated best efficacy for reducing glucose levels, followed by glucagon‐like peptide‐1 receptor agonists (SMD=–0.39), dietary interventions (SMD=–0.37) and aripiprazole augmentation (SMD=–0.34), whereas insulin resistance improved the most with metformin (SMD=–0.75) and rosiglitazone (SMD=–0.44). Topiramate had the greatest efficacy for triglycerides (SMD=–0.68) and low‐density lipoprotein (LDL)‐cholesterol (SMD=–0.80), whereas metformin had the greatest beneficial effects on total cholesterol (SMD=–0.51) and high‐density lipoprotein (HDL)‐cholesterol (SMD=0.45). Lifestyle interventions yielded small effects for triglycerides, total cholesterol and LDL‐cholesterol (SMD=–0.35 to –0.37). Only exercise interventions increased exercise capacity (SMD=1.81). Despite frequent physical comorbidities and premature mortality mainly due to these increased physical health risks, the current evidence for pharmacological and non‐pharmacological interventions in people with schizophrenia to prevent and treat these conditions is still limited and more larger trials are urgently needed.

With over 10,000 mental health– and psychiatry-related smartphone apps available today and expanding, there is a need for reliable and valid evaluation of these digital tools. However, the updating and nonstatic nature of smartphone apps, expanding privacy concerns, varying degrees of usability, and evolving interoperability standards, among other factors, present serious challenges for app evaluation. In this article, we provide a narrative review of various schemes toward app evaluations, including commercial app store metrics, government initiatives, patient-centric approaches, point-based scoring, academic platforms, and expert review systems. We demonstrate that these different approaches toward app evaluation each offer unique benefits but often do not agree to each other and produce varied conclusions as to which apps are useful or not. Although there are no simple solutions, we briefly introduce a new initiative that aims to unify the current controversies in app elevation called CHART (Collaborative Health App Rating Teams), which will be further discussed in a second article in this series.

The rapid advances and adoption of smartphone technology presents a novel opportunity for delivering mental health interventions on a population scale. Despite multi-sector investment along with wide-scale advertising and availability to the general population, the evidence supporting the use of smartphone apps in the treatment of depression has not been empirically evaluated. Thus, we conducted the first meta-analysis of smartphone apps for depressive symptoms. An electronic database search in May 2017 identified 18 eligible randomized controlled trials of 22 smartphone apps, with outcome data from 3,414 participants. Depressive symptoms were reduced significantly more from smartphone apps than control conditions (g=0.38, 95% CI: 0.24-0.52, p<0.001), with no evidence of publication bias. Smartphone interventions had a moderate positive effect in comparison to inactive controls (g=0.56, 95% CI: 0.38-0.74), but only a small effect in comparison to active control conditions (g=0.22, 95% CI: 0.10-0.33). Effects from smartphone-only interventions were greater than from interventions which incorporated other human/computerized aspects along the smartphone component, although the difference was not statistically significant. The studies of cognitive training apps had a significantly smaller effect size on depression outcomes (p=0.004) than those of apps focusing on mental health. The use of mood monitoring softwares, or interventions based on cognitive behavioral therapy, or apps incorporating aspects of mindfulness training, did not affect significantly study effect sizes. Overall, these results indicate that smartphone devices are a promising self-management tool for depression. Future research should aim to distil which aspects of these technologies produce beneficial effects, and for which populations.