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People with Insomnia Disorder tend to underestimate their sleep compared with polysomnography or actigraphy, a phenomenon known as paradoxical insomnia or sleep‐state misperception. Previous studies suggested that night‐to‐night variability could be an important feature differentiating subtypes of misperception. This study aimed for a data‐driven definition of misperception subtypes revealed by multiple sleep features including night‐to‐night variability. We assessed features describing the mean and dispersion of misperception and objective and subjective sleep duration from 7‐night diary and actigraphy recordings of 181 people with Insomnia Disorder and 55 people without sleep complaints. A minimally collinear subset of features was submitted to latent class analysis for data‐driven subtyping. Analysis revealed three subtypes, best discriminated by three of five selected features: an individual’s shortest reported subjective sleep duration; and the mean and standard deviation of misperception. These features were on average 5.4, −0.0 and 0.5 hr in one subtype accommodating the majority of good sleepers; 4.1, −1.4 and 1.0 hr in a second subtype representing the majority of people with Insomnia Disorder; and 1.7, −2.2 and 1.5 hr in a third subtype representing a quarter of people with Insomnia Disorder and hardly any good sleepers. Subtypes did not differ on an individual’s objective sleep duration mean (6.9, 7.2 and 6.9 hr) and standard deviation (0.8, 0.8 and 0.9 hr). Data‐driven analysis of naturalistic sleep revealed three subtypes that markedly differed in misperception features. Future studies may include misperception subtype to investigate whether it contributes to the unexplained considerable individual variability in treatment response.

Background. There is a growing interest to unravel the genetic and brain structural underpinnings of personality by pooling data for mega- and meta-analyses within international consortia. Consequently, it is of increasing importance to evaluate the consistency of personality traits across assessment formats and languages. In this study, we evaluated the profile similarity framework as a means to compare personality profiles assessed using different inventories.Methods. We first assessed the psychometric properties of the Dutch translations of the 20-item Mini-IPIP and 120-item IPIP-NEO-120, both completed by 822 volunteers. We then applied the profile similarity framework to compare the personality factors and profiles obtained using the Mini-IPIP and the IPIP-NEO-120. Results. The psychometric properties and factor structure of the Dutch translations resembled the original English questionnaires well. The correlations between the personality profile scores were shown to be more robust and less format-dependent than the correlations between personality factors. This improvement was maintained when accounting for profile normativeness. Conclusions. These findings show the way to more consistent personality factors and profiles obtained across different assessment formats and languages. The method promises increased sensitivity and reliability in large-scale international collaborative studies on the underlying mechanisms of the five-factor personality model.

Persistent insomnia is among the most frequent complaints in general practice. To identify genetic factors for insomnia complaints, we performed a genome-wide association study (GWAS) and a genome-wide gene-based association study (GWGAS) in 113,006 individuals. We identify three loci and seven genes associated with insomnia complaints, with the associations for one locus and five genes supported by joint analysis with an independent sample (n = 7,565). Our top association (MEIS1, P < 5 × 10 -8) has previously been implicated in restless legs syndrome (RLS). Additional analyses favor the hypothesis that MEIS1 exhibits pleiotropy for insomnia and RLS and show that the observed association with insomnia complaints cannot be explained only by the presence of an RLS subgroup within the cases. Sex-specific analyses suggest that there are different genetic architectures between the sexes in addition to shared genetic factors. We show substantial positive genetic correlation of insomnia complaints with internalizing personality traits and metabolic traits and negative correlation with subjective well-being and educational attainment. These findings provide new insight into the genetic architecture of insomnia.

The authors would like to inform you that the name of the seventh author was incorrectly listed as “L. Hartescu” in their published article. Dr Hartescu's affiliation to Loughborough University was also missed. We apologise for these errors, which have been corrected above.

We investigated the associations between leg length and specific ballet movements in different skill groups. Volunteers were from an undergraduate dance programme (n=18), a pre-professional school (n=43) and from an elite classical ballet company (n=45). Individual data were collected for anthropometry, vertical jump, leg dexterity, and leg active and passive ROM. ANCOVA identified both main effects as significant with regard to vertical jump (gender P

Introduction Insomnia as a complaint is a complex, heterogeneous condition. It can be a symptom of medical, psychiatric, or substance abuse disorders or it can be a condition on its own. Even in the latter case, there appears to be considerable variability in complaints and patient characteristics. This heterogeneity makes progress in our understanding of insomnia difficult. A first logical step would be to determine well-defined insomnia subtypes and subsequently unravel the brain mechanisms underlying insomnia complaints in these homogenous subtypes. The need to determine these subtypes, and obtain a large sample to be able to do so, has been recognized by several researchers before (e.g. Edinger et al., 1996; Van Someren et al., 2009; Vgontzas et al., 2012; Roenneberg, 2013). Materials and methods Internet technology has profoundly changed survey methodology and moreover makes web-based cognitive performance testing possible. Freeware implementations of these technologies have been combined in the Sleep Registry (www.sleepregistry.org) to obtain multiple descriptive, subjective and performance parameters in a huge sample of individuals and families. The multidimensional variable space can be subjected to latent class analysis in order to yield homogeneous subgroups. Given the large amount of measurements that will be done in the Sleep Registry, participants are given the opportunity to fill out their set of questionnaires and computer-based tests voluntarily at their own pace. Feedback on performance is given as reward to promote adherence. The setup of the Sleep Registry site is very modular making it possible for individual labs to add questionnaires and tests, or to initiate parallel sampling using different languages. Results Currently, around 14.000 people signed up for the Sleep Registry in the Netherlands and filled out between one and 28 modules, where each module contains one or more validated questionnaires. In addition, modules are available to assess the Consensus Sleep Diary (Carney et al., 2012), as well as common tasks including overnight word pair learning, the psychomotor vigilance test (Dinges and Powell, 1985; Basner and Dinges, 2011) and the digit span. Two modules are available for extensive structured diagnosis of sleep disorders as well as somatic, psychiatric and neurological disorders and medication use. The Sleep Registry is now available in Dutch, English, Spanish, Portugese, German, French, Italian and Polish. Labs can identify their own URL to make sure that they can own the data of their recruited participants, and are free to pool data with colleagues. Conclusion The Sleep Registry thus provides a useful tool for efficiently and flexibly acquiring large samples of descriptive, subjective and performance measurements to facilitate sleep research and truly start a cooperative Human Sleep Project (Roenneberg, 2013). Labs interested to run their own Sleep Registry and cooperate are welcome: e.van.someren@nin.knaw.nl Dinges DF and Powell JW (1985) Microcomputer analyses of performance on a portable, simple visual RT task during sustained attention. Behav Res Meth Instr Comp 17:652–655. Edinger JD, Fins AI, Goeke JM, McMillan DK, Gersh TL, Krystal AD and McCall WV (1996) The empirical identification of insomnia subtypes: a cluster analytic approach. Sleep 19:398–411. Van Someren EJW, Pollmacher T, Leger D, Espie C, Bassetti C and Riemann D (2009) The European Insomnia Network. Front Neurosci 3:436. Basner M and Dinges DF (2011) Maximizing Sensitivity of the Psychomotor Vigilance Test (PVT) to Sleep Loss. Sleep 34:581–591. Carney CE, Buysse DJ, Ancoli-Israel S, Edinger JD, Krystal AD, Lichstein KL and Morin CM (2012) The consensus sleep diary: standardizing prospective sleep self-monitoring. Sleep 35:287–302. Vgontzas AN, Fernandez-Mendoza J, Bixler EO, Singareddy R, Shaffer ML, Calhoun SL, Liao D, Basta M and Chrousos GP (2012) Persistent insomnia: the role of objective short sleep duration and mental health. Sleep 35:61–68. Roenneberg T (2013) Chronobiology: the human sleep project. Nature 498:427–428. Acknowledgements Supported by the VICI Innovation Grant 453-07-001 of the Netherlands Organization of Scientific Research (NWO); The Hague, the Netherlands; co-funding of the Polish translation by an ENSRL travel grant of the ESRS to Michal Jarkiewicz; co-funding of the English translatop by Kevin Morgan, Sleep Research Centre, School of Sport, Exercise and Health Sciences, Loughborough University, Leicestershire LE11 3TU, UK.