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Additional file1: Table S1. County specific lithium prevalences. All BD patients. Table S2. County specific lithium prevalences for BD I, BD II, and BD NOS. Table S3. Logistic regression analyses for patients with BD I, BD II, or BD NOS only year 2015. OR estimating differences for patients in counties with high lithium prevalence compared to counties with low lithium prevalence. Adjusted for age, sex and psychiatric comorbidity. Table S4. Logistic regression analyses for patients with BD I only year 2015. OR estimating differences for patients in counties with high lithium prevalence compared to counties with low lithium prevalence. Adjusted for age, sex and psychiatric comorbidity. Figure S1. Inclusion and exclusion flow chart.

Additional file 1. Supplementary Tables and Figures.

Current management of bipolar disorder relies on self-reported questionnaires and interviews with clinicians. The development of objective measures of deteriorating mood may also allow for early interventions to take place to avoid transitions into depressive states. The objective of this study was to use acceleration data recorded from smartphones to predict levels of depression in a population of participants diagnosed with bipolar disorder. Data were collected from 52 participants, with a mean of 37 weeks of acceleration data with a corresponding depression score recorded per participant. Time varying hidden Markov models were used to extract weekly features of activity, sleep and circadian rhythms. Personalised regression achieved mean absolute errors of 1.00(0.57) from a possible scale of 0 to 27 and was able to classify depression with an accuracy of 0.84(0.16). The results demonstrate features derived from smartphone accelerometers are able to provide objective markers of depression. Low barriers for uptake exist due to the widespread use of smartphones, with personalised models able to account for differences in the behaviour of individuals and provide accurate predictions of depression., Comment: 8 pages, 3 figures

Additional file 1:. SPIRIT 2013 Checklist: Recommended items to address in a clinical trial protocol and related documents*

Insomnia is common in people experiencing psychosis. It has been identified as a contributory cause of paranoia, but any causal relationship with hallucinations has yet to be established. We tested the hypotheses that insomnia i) has a cross-sectional association with hallucinations ii) predicts new inceptions of hallucinations and iii) that these associations remain after controlling for depression, anxiety, and paranoia. Data from the second (2000, N=8580) and third (2007, N=7403) British Psychiatric Morbidity Surveys were used to assess cross-sectional associations between insomnia and hallucinations. The 2000 dataset included an 18 month follow up of a subsample (N=2406) used to test whether insomnia predicted new inceptions of hallucinations. Insomnia was associated with hallucinations in both cross-sectional datasets. Mild sleep problems were associated with 2–3 times greater odds of reporting hallucinations, whilst chronic insomnia was associated with four times greater odds. Insomnia was also associated with increased odds of hallucinations occurring de novo over the next 18 months. These associations remained significant, although with smaller odds ratios, after controlling for depression, anxiety and paranoia. This is the first longitudinal evidence that insomnia is associated with the development of hallucinatory experiences. Effective treatment of insomnia may lessen the occurrence of hallucinations., Highlights • Insomnia was associated with hallucinatory experiences in both the 2000 and 2007 datasets. • Initial insomnia was associated with new hallucinatory experiences 18 months later. • The above associations remained significant after controlling for negative affect and paranoia.

ObjectivesCognitive impairment is known to occur in bipolar disorder (BD), even in euthymic patients, with largest effect sizes often seen in Verbal Learning and Memory Tasks (VLT). However, comparisons between BD Type-I and Type-II have produced inconsistent results partly due to low sample sizes.MethodsThis study compared the performance of 183 BDI with 96 BDII out-patients on an adapted version of the Rey Verbal Learning Task. Gender, age, years of education, mood scores and age at onset were all used as covariates. Current medication and a variety of illness variables were also investigated for potential effects on VLT performance.ResultsBDI patients were significantly impaired relative to BDII patients on all five VLT outcome measures after controlling for the other variables [Effect Sizes=.13–.17]. The impairments seem to be unrelated to drug treatment and largely unrelated to illness variables, although age of onset affected performance on three outcome measures and number of episodes of mood elevation affected performance on one.LimitationsThis study used historical healthy controls. Analysis of potential drug effects was limited by insufficient participants not being drug free. Cross-sectional nature of the study limited the analysis of the potential effect of illness variables.ConclusionsThis study replicates earlier findings of increased verbal learning impairment in BDI patients relative to BDII in a substantially larger sample. Such performance cannot be wholly explained by medication effects or illness variables. Thus, the cognitive impairment is likely to reflect a phenotypic difference between bipolar sub-types.

Background: We recently described a new questionnaire to monitor mood called mood zoom (MZ). MZ comprises 6 items assessing mood symptoms on a 7-point Likert scale; we had previously used standard principal component analysis (PCA) to tentatively understand its properties, but the presence of multiple nonzero loadings obstructed the interpretation of its latent variables. Objective: The aim of this study was to rigorously investigate the internal properties and latent variables of MZ using an algorithmic approach which may lead to more interpretable results than PCA. Additionally, we explored three other widely used psychiatric questionnaires to investigate latent variable structure similarities with MZ: (1) Altman self-rating mania scale (ASRM), assessing mania; (2) quick inventory of depressive symptomatology (QIDS) self-report, assessing depression; and (3) generalized anxiety disorder (7-item) (GAD-7), assessing anxiety. Methods: We elicited responses from 131 participants: 48 bipolar disorder (BD), 32 borderline personality disorder (BPD), and 51 healthy controls (HC), collected longitudinally (median [interquartile range, IQR]: 363 [276] days). Participants were requested to complete ASRM, QIDS, and GAD-7 weekly (all 3 questionnaires were completed on the Web) and MZ daily (using a custom-based smartphone app). We applied sparse PCA (SPCA) to determine the latent variables for the four questionnaires, where a small subset of the original items contributes toward each latent variable. Results: We found that MZ had great consistency across the three cohorts studied. Three main principal components were derived using SPCA, which can be tentatively interpreted as (1) anxiety and sadness, (2) positive affect, and (3) irritability. The MZ principal component comprising anxiety and sadness explains most of the variance in BD and BPD, whereas the positive affect of MZ explains most of the variance in HC. The latent variables in ASRM were identical for the patient groups but different for HC; nevertheless, the latent variables shared common items across both the patient group and HC. On the contrary, QIDS had overall very different principal components across groups; sleep was a key element in HC and BD but was absent in BPD. In GAD-7, nervousness was the principal component explaining most of the variance in BD and HC. Conclusions: This study has important implications for understanding self-reported mood. MZ has a consistent, intuitively interpretable latent variable structure and hence may be a good instrument for generic mood assessment. Irritability appears to be the key distinguishing latent variable between BD and BPD and might be useful for differential diagnosis. Anxiety and sadness are closely interlinked, a finding that might inform treatment effects to jointly address these covarying symptoms. Anxiety and nervousness appear to be amongst the cardinal latent variable symptoms in BD and merit close attention in clinical practice.

Additional file 1. Questionnaire: Information seeking in bipolar disorder.

PReDiCT SPIRIT Checklist v0.2 6 Jan 2017. (DOCX 49 kb)

PReDiCT SPIRIT Checklist v0.2 6 Jan 2017. (DOCX 49 kb)

As with the two preceding guidelines of this series, these practice guidelines for the pharmacological maintenance treatment of bipolar disorder were developed by an international task force of the World Federation of Societies of Biological Psychiatry (WFSBP). Their purpose is to supply a systematic overview of all scientific evidence relating to maintenance treatment. The data used for these guidelines were extracted from a MEDLINE and EMBASE search, from recent proceedings from key conferences and various national and international treatment guidelines. The scientific justification of support for particular treatments was categorised into four levels of evidence (A-D). As these guidelines are intended for clinical use, the scientific evidence was not only graded, but also reviewed by the experts of the task force to ensure practicality.