Background:Research evidence supports the efficacy of cognitive-behavioral therapy in the treatment of drugrefractory positive symptoms of schizophrenia. Although the cumulative evidence is strong, early controlled trials showed methodological limitations.

Background Although several large-scale randomised controlled trials have shown the efficacy of digital cognitive behavioural therapy for insomnia (dCBT-I), there is a need to validate widespread dissemination of dCBT-I using recommended key outcomes for insomnia. We investigated the effect of a fully automated dCBT-I programme on insomnia severity, sleep-wake patterns, sleep medication use, and daytime impairment.Methods We did a parallel-group superiority randomised controlled trial comparing dCBT-I with online patient education about sleep. The interventions were available through a free-to-access website, publicised throughout Norway, which incorporated automated screening, informed consent, and randomisation procedures, as well as outcome assessments. Adults (age ≥18 years) who had regular internet access and scored 12 or higher on the Insomnia Severity Index (ISI) were eligible for inclusion, and were allocated (1:1) to receive dCBT-I (consisting of six core interactive sessions to be completed over 9 weeks) or patient education (control group). Participants were masked to group assignment and had no contact with researchers during the intervention period. The primary outcome was the change in ISI score from baseline to 9-week follow-up, assessed in the intention-to-treat population. This trial is registered with ClinicalTrials.gov (NCT02558647) and is ongoing, with 2-year follow-up assessments planned.

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BackgroundUntil recently, actigraphy studies in bipolar disorders focused on sleep rather than daytime activity in mania or depression, and have failed to analyse mixed episodes separately. Furthermore, even those studies that assessed activity parameters reported only mean levels rather than complexity or predictability of activity. We identified cases presenting in one of three acute phases of bipolar disorder and examined whether the application of non-linear dynamic models to the description of objectively measured activity can be used to predict case classification.MethodsThe sample comprised 34 adults who were hospitalized with an acute episode of mania (n = 16), bipolar depression (n = 12), or a mixed state (n = 6), who agreed to wear an actiwatch for a continuous period of 24 h. Mean level, variability, regularity, entropy, and predictability of activity were recorded for a defined 64-min active morning and active evening period. Discriminant function analysis was used to determine the combination of variables that best classified cases based on phase of illness.ResultsThe model identified two discriminant functions: the first was statistically significant and correlated with intra-individual fluctuation in activity and regularity of activity (sample entropy) in the active morning period; the second correlated with several measures of activity from the evening period (e.g. Fourier analysis, autocorrelation, sample entropy). A classification table generated from both functions correctly classified 79% of all cases based on phase of illness (χ 2 = 36.21; df 4; p = 0.001). However, 42% of bipolar depression cases were misclassified as being in manic phase.ConclusionsThe findings should be treated with caution as this was a small-scale pilot study and we did not control for prescribed treatments, medication adherence, etc. However, the insights gained should encourage more widespread adoption of statistical approaches to the classification of cases alongside the application of more sophisticated modelling of activity patterns. The difficulty of accurately classifying cases of bipolar depression requires further research, as it is unclear whether the lower prediction rate reflects weaknesses in a model based only on actigraphy data, or if it reflects clinical reality i.e. the possibility that there may be more than one subtype of bipolar depression.Electronic supplementary materialThe online version of this article (doi:10.1186/s40345-017-0076-6) contains supplementary material, which is available to authorized users.