Background Sleep and circadian rhythm disturbances in schizophrenia are common, but incompletely characterized. We aimed to describe and compare the magnitude and heterogeneity of sleep-circadian alterations in remitted schizophrenia and compare them with those in interepisode bipolar disorder. Methods EMBASE, Medline, and PsycINFO were searched for case–control studies reporting actigraphic parameters in remitted schizophrenia or bipolar disorder. Standardized and absolute mean differences between patients and controls were quantified using Hedges’ g, and patient–control differences in variability were quantified using the mean-scaled coefficient of variation ratio (CVR). A wald-type test compared effect sizes between disorders. Results Thirty studies reporting on 967 patients and 803 controls were included. Compared with controls, both schizophrenia and bipolar groups had significantly longer total sleep time (mean difference [minutes] [95% confidence interval {CI}] = 99.9 [66.8, 133.1] and 31.1 [19.3, 42.9], respectively), time in bed (mean difference = 77.8 [13.7, 142.0] and 50.3 [20.3, 80.3]), but also greater sleep latency (16.5 [6.1, 27.0] and 2.6 [0.5, 4.6]) and reduced motor activity (standardized mean difference [95% CI] = −0.86 [−1.22, −0.51] and −0.75 [−1.20, −0.29]). Effect sizes were significantly greater in schizophrenia compared with the bipolar disorder group for total sleep time, sleep latency, and wake after sleep onset. CVR was significantly elevated in both diagnoses for total sleep time, time in bed, and relative amplitude. Conclusions In both disorders, longer overall sleep duration, but also disturbed initiation, continuity, and reduced motor activity were found. Common, modifiable factors may be associated with these sleep-circadian phenotypes and advocate for further development of transdiagnostic interventions that target them.

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BackgroundThere is growing interest in the potential for wearable and mobile devices to deliver clinically relevant information in real-world contexts. However, there is limited information on their acceptability and barriers to long-term use in people living with psychosis.ObjectiveThis study aimed to describe the development, implementation, feasibility, acceptability, and user experiences of the Sleepsight platform, which harnesses consumer wearable devices and smartphones for the passive and unobtrusive capture of sleep and rest-activity profiles in people with schizophrenia living in their homes.MethodsA total of 15 outpatients with a diagnosis of schizophrenia used a consumer wrist-worn device and smartphone to continuously and remotely gather rest-activity profiles over 2 months. Once-daily sleep and self-rated symptom diaries were also collected via a smartphone app. Adherence with the devices and smartphone app, end-of-study user experiences, and agreement between subjective and objective sleep measures were analyzed. Thresholds for acceptability were set at a wear time or diary response rate of 70% or greater.ResultsOverall, 14 out of 15 participants completed the study. In individuals with a mild to moderate symptom severity at baseline (mean total Positive and Negative Syndrome Scale score 58.4 [SD 14.4]), we demonstrated high rates of engagement with the wearable device (all participants meeting acceptability criteria), sleep diary, and symptom diary (93% and 86% meeting criteria, respectively), with negative symptoms being associated with lower diary completion rate. The end-of-study usability and acceptability questionnaire and qualitative analysis identified facilitators and barriers to long-term use, and paranoia with study devices was not a significant barrier to engagement. Comparison between sleep diary and wearable estimated sleep times showed good correspondence (ρ=0.50, P<.001).ConclusionsExtended use of wearable and mobile technologies are acceptable to people with schizophrenia living in a community setting. In the future, these technologies may allow predictive, objective markers of clinical status, including early markers of impending relapse.

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In this selected population, the initial neutropenia was unlikely to be related to clozapine in a substantial proportion of cases. This group was successfully rechallenged following careful consideration of the risks and benefits, and lithium and G-CSF contributed to allowing continued clozapine therapy. In addition to black patients, other ethnic groups can have persistently low ANC unrelated to clozapine.

The number of trials aimed at evaluating treatments for autism spectrum disorder has been increasing progressively. However, it is not clear which outcome measures should be used to assess their efficacy, especially for treatments which target core symptoms. The present review aimed to provide a comprehensive overview regarding the outcome measures used in clinical trials for people with autism spectrum disorder. We systematically searched the Web of KnowledgeSM database between 1980 and 2016 to identify published controlled trials investigating the efficacy of interventions in autism spectrum disorder. We included 406 trials in the final database, from which a total of 327 outcome measures were identified. Only seven scales were used in more than 5% of the studies, among which only three measured core symptoms (Autism Diagnostic Observation Schedule, Childhood Autism Rating Scale, and Social Responsiveness Scale). Of note, 69% of the tools were used in the literature only once. Our systematic review has shown that the evaluation of efficacy in intervention trials for autism spectrum disorder relies on heterogeneous and often non-specific tools for this condition. The fragmentation of tools may significantly hamper the comparisons between studies and thus the discovery of effective treatments for autism spectrum disorder. Greater consensus regarding the choice of these measures should be reached.

The rapidly expanding field of mobile health (mHealth) seeks to harness increasingly affordable and ubiquitous mobile digital technologies including smartphones, tablets, apps and wearable devices to enhance clinical care. Accumulating evidence suggests that mHealth interventions are increasingly being adopted and valued by people living with serious mental illnesses such as schizophrenia and bipolar disorder, as a means of better understanding and managing their condition. We draw on experiences from three geographically and methodologically distinct mHealth studies to provide a pragmatic overview of the key challenges and considerations relating to the process of developing digital interventions for this population. the Promise And chAllenges of mobile heAlth (mheAlth) in PsychiAtryThe opportunities presented by digital technologies including smartphones, apps and wearable devices for delivering new paradigms of care in people experiencing serious mental illness (SMI) have stimulated a surge of interest. 1The portable, connected nature of such devices enables the longitudinal, remote and high-resolution capture of clinical variables in ecologically valid settings-both actively, for example, via self-rated assessments, and passively, using sensors to sample objective markers of social, emotional and cognitive states, with low user burden. Information can be fed back to the patient and their clinical teams in real time, offering the opportunity to facilitate self-management, and trigger timely, preventative interventions. Mobile platforms may promote communication between patients and clinicians, and allow the delivery of therapies tailored to each user's clinical status. The increasing ubiquity, affordability and ownership of digital technologies, 2 including in populations with SMI 3-5 has the potential to address the disparities in healthcare provision in underserved populations with SMI globally, including members of ethnic minorities, low-income groups, and individuals living in rural and low-resource settings.6 Several lines of evidence now indicate that people experiencing SMI already use, 3 5 or are interested in using, 7 mobile devices and web-based technology to manage their conditions, and that these are acceptable across a range of age, sex, educational level and clinical characteristics. As the field proliferates and establishes itself as a discipline in its own right, unique challenges emerge.9 10 A rush to implementation, with insufficient attention to design and usability during development, may be detrimental to the longer-term adoption of an intervention by patients and clinicians. Most interventions have been tested in small studies that report short-term feasibility and acceptability, largely with positive findings, 11 12 raising questions around their performance over longer durations, in more heterogeneous patient groups. Evidence gathered through rigorous evaluation frameworks 13-15 will be required to ensure purported benefits are realised, and that these outweigh potential harms ...

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