costly further investigations (like polysomnography [PSG]). This can be achieved by a test with a high specificity, mostly at the expense of reduced sensitivity, so accepting some missed cases. In addition, a confirmation test (in OSAS a sleep study) in subjects with positive test results should be performed. The Study Objectives: To develop and evaluate a screening questionnaire and a two-step screening strategy for obstructive sleep apnea syndrome (OSAS) in healthy workers. Methods: This is a cross-sectional study of 1,861 employees comprising healthy blue-and white-collar workers in two representative plants in the Netherlands from a worldwide consumer electronic company who were approached to participate. Employees were invited to complete various sleep questionnaires, and undergo separate single nasal flow recording and home polysomnography on two separate nights. Results: Of the 1,861 employees, 249 provided informed consent and all nasal flow and polysomnography data were available from 176 (70.7%). OSAS was diagnosed in 65 (36.9%). A combination of age, absence of insomnia, witnessed breathing stops, and three-way scoring of the Berlin and STOPBANG questionnaires best predicted OSAS. Factor analysis identified a six-factor structure of the resulting new questionnaire: snoring, snoring severity, tiredness, witnessed apneas, sleep quality, and daytime well-being. Subsequently, some questions were removed, and the remaining questions were used to construct a new questionnaire. A scoring algorithm, computing individual probabilities of OSAS as high, intermediate, or low risk, was developed. Subsequently, the intermediate risk group was split into low and high probability for OSAS, based on nasal flow recording. This two-step approach showed a sensitivity of 63.1%, and a specificity of 90.1%. Specificity is important for low prevalence populations. Conclusion: A two-step screening strategy with a new questionnaire and subsequent nasal flow recording is a promising way to screen for OSAS in a healthy worker population. Keywords: home recording, polysomnography, questionnaire, screening, sleep apnea syndrome Clinical Trial Registration: Development and validation of a screening instrument for obstructive sleep apnea syndrome in healthy workers. Netherlands Trial Register (www.trailregister.nl), number: NTR2675. Citation: Eijsvogel MM, Wiegersma S, Randerath W, Verbraecken J, Wegter-Hilbers E, van der Palen J. Obstructive sleep apnea syndrome in company workers: development of a two-step screening strategy with a new questionnaire. J Clin Sleep Med 2016;12(4):555-564. I NTRO DUCTI O NObstructive sleep apnea syndrome (OSAS) is a prevalent and treatable disease with often impaired daytime performance and increased cardiovascular and metabolic risks. Recently, increasing awareness for these consequences is reflected in a growing interest in screening for OSAS.1-3 Screening for OSAS can be important in a hospital setting (preoperative patients), primary care, work environment or in specific groups such as commercial...

Therapeutic Change Process Research (TCPR) connects within-therapeutic change processes to outcomes. The labour intensity of qualitative methods limit their use to small scale studies. Automated text-analyses (e.g. text mining) provide means for analysing large scale text patterns. We aimed to provide an overview of the frequently used qualitative text-based TCPR methods and assess the extent to which these methods are reliable and valid, and have potential for automation. We systematically reviewed PsycINFO, Scopus, and Web of Science to identify articles concerning change processes and text or language. We evaluated the reliability and validity based on replicability, the availability of code books, training data and inter-rater reliability, and evaluated the potential for automation based on the example- and rule-based approach. From 318 articles we identified four often used methods: Innovative Moments Coding Scheme, the Narrative Process Coding Scheme, Assimilation of Problematic Experiences Scale, and Conversation Analysis. The reliability and validity of the first three is sufficient to hold promise for automation. While some text features (content, grammar) lend themselves for automation through a rule-based approach, it should be possible to automate higher order constructs (e.g. schemas) when sufficient annotated data for an example-based approach are available.

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Background: Identifying and addressing hotspots is a key element of imaginal exposure in Brief Eclectic Psychotherapy for PTSD (BEPP). Research shows that treatment effectiveness is associated with focusing on these hotspots and that hotspot frequency and characteristics may serve as indicators for treatment success. Objective: This study aims to develop a model to automatically recognize hotspots based on text and speech features, which might be an efficient way to track patient progress and predict treatment efficacy. Method: A multimodal supervised classification model was developed based on analog tape recordings and transcripts of imaginal exposure sessions of 10 successful and 10 nonsuccessful treatment completers. Data mining and machine learning techniques were used to extract and select text (e.g. words and word combinations) and speech (e.g. speech rate, pauses between words) features that distinguish between 'hotspot' (N = 37) and 'nonhotspot' (N = 45) phases during exposure sessions. Results: The developed model resulted in a high training performance (mean F 1-score of 0.76) but a low testing performance (mean F 1-score = 0.52). This shows that the selected text and speech features could clearly distinguish between hotspots and non-hotspots in the current data set, but will probably not recognize hotspots from new input data very well. Conclusions: In order to improve the recognition of new hotspots, the described methodology should be applied to a larger, higher quality (digitally recorded) data set. As such this study should be seen mainly as a proof of concept, demonstrating the possible application and contribution of automatic text and audio analysis to therapy process research in PTSD and mental health research in general.

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Background Text mining and machine learning are increasingly used in mental health care practice and research, potentially saving time and effort in the diagnosis and monitoring of patients. Previous studies showed that mental disorders can be detected based on text, but they focused on screening for a single predefined disorder instead of multiple disorders simultaneously. Objective The aim of this study is to develop a Dutch multi-class text-classification model to screen for a range of mental disorders to refer new patients to the most suitable treatment. Methods On the basis of textual responses of patients (N=5863) to a questionnaire currently used for intake and referral, a 7-class classifier was developed to distinguish among anxiety, panic, posttraumatic stress, mood, eating, substance use, and somatic symptom disorders. A linear support vector machine was fitted using nested cross-validation grid search. Results The highest classification rate was found for eating disorders (82%). The scores for panic (55%), posttraumatic stress (52%), mood (50%), somatic symptom (50%), anxiety (35%), and substance use disorders (33%) were lower, likely because of overlapping symptoms. The overall classification accuracy (49%) was reasonable for a 7-class classifier. Conclusions A classification model was developed that could screen text for multiple mental health disorders. The screener resulted in an additional outcome score that may serve as input for a formal diagnostic interview and referral. This may lead to a more efficient and standardized intake process.

In dit onderzoek wordt gekeken naar de relatie tussen het hebben van een tuin bij huis, de hoeveelheid groen in die tuin en de mate waarin bepaalde aandoeningen voorkomen, zoals bekend bij de huisarts. Van circa 800.000 in een bebouwde kom wonende mensen zijn gegevens beschikbaar over zowel hun gezondheid als over hun tuinbezit en de hoeveelheid tuingroen daarin. In de analyses wordt rekening gehouden met zaken zoals de sociaaleconomische positie van het individu en de woonbuurt, maar ook met de lokale luchtkwaliteit en geluidsbelasting. Het resultaat is dat voor vrij veel aandoeningen een gunstig verband wordt gevonden met het hebben van een tuin en de hoeveelheid groen in die tuin: tuinbezit, en met name meer tuingroen, gaat gepaard met lagere prevalenties.This study investigates the association between having a domestic garden or not and the amount of greenery in this garden, and the prevalence of several types of diseases and disorders, as known by one's general practitioner. Data on garden ownership and the amount of garden greenery, as well as on health, are available for about 800,000 people, all living within city limits. In the statistical analyses, the associations are corrected for, among other things, the socioeconomic status of the individual and its neighbourhood, but also for the local air quality and noise exposure. The results show that for a large number of diseases and disorders having a garden, and especially having more greenery in that garden, is associated with a lower prevalence.Trefwoorden: groen, tuin, woonomgeving, gezondheid, prevalentie, morbiditeit, zorgregistratie Dit rapport is gratis te downloaden van https://doi.org/10.18174/586989 of op www.wur.nl/environmentalresearch (ga naar 'Wageningen Environmental Research' in de grijze balk onderaan). Wageningen Environmental Research verstrekt geen gedrukte exemplaren van rapporten.

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