Although excessive and compulsive shopping has been increasingly placed within the behavioral addiction paradigm in recent years, items in existing screens arguably do not assess the core criteria and components of addiction. To date, assessment screens for shopping disorders have primarily been rooted within the impulse-control or obsessive-compulsive disorder paradigms. Furthermore, existing screens use the terms ‘shopping,’ ‘buying,’ and ‘spending’ interchangeably, and do not necessarily reflect contemporary shopping habits. Consequently, a new screening tool for assessing shopping addiction was developed. Initially, 28 items, four for each of seven addiction criteria (salience, mood modification, conflict, tolerance, withdrawal, relapse, and problems), were constructed. These items and validated scales (i.e., Compulsive Buying Measurement Scale, Mini-International Personality Item Pool, Hospital Anxiety and Depression Scale, Rosenberg Self-Esteem Scale) were then administered to 23,537 participants (M_age = 35.8 years, SD_age = 13.3). The highest loading item from each set of four pooled items reflecting the seven addiction criteria were retained in the final scale, The Bergen Shopping Addiction Scale (BSAS). The factor structure of the BSAS was good (RMSEA = 0.064, CFI = 0.983, TLI = 0.973) and coefficient alpha was 0.87. The scores on the BSAS converged with scores on the Compulsive Buying Measurement Scale (CBMS; 0.80), and were positively correlated with extroversion and neuroticism, and negatively with conscientiousness, agreeableness, and intellect/imagination. The scores of the BSAS were positively associated with anxiety, depression, and low self-esteem and inversely related to age. Females scored higher than males on the BSAS. The BSAS is the first scale to fully embed shopping addiction within an addiction paradigm. A recommended cutoff score for the new scale and future research directions are discussed.

Objectives: No systematic review or meta-analysis concerning the prevalence of shift work disorder (SWD) has been conducted so far. The aim was thus to review prevalence studies of SWD, to calculate an overall prevalence by a random effects meta-analysis approach and investigate correlates of SWD prevalence using a random-effects meta-regression.Methods: Systematic searches were conducted in ISI Web of Science, PsycNET, PubMed, and Google Scholar using the search terms “shift work disorder” and “shift work sleep disorder.” No restrictions in terms of time frame were used. Included studies had to present original data on the prevalence of SWD in an occupational sample published in English. A total of 349 unique hits were made. In all, 29 studies were finally included from which two authors independently extracted data using predefined data fields. The meta-regression included four predictors (diagnostic criteria, study country, type of workers, and sample size).Results: The overall prevalence of SWD was 26.5% (95% confidence interval = 21.0–32.8). Cochran Q was 1,845.4 (df = 28, p < 0.001), and the I² was 98.5%, indicating very high heterogeneity across the observed prevalence estimates. Diagnostic criteria (International Classification of Sleep Disorders-2 = 0, International Classification of Sleep Disorders-3 = 1) and sample size were inversely related to SWD prevalence.Conclusions: The prevalence of SWD was high across the included studies. The between-study disparity was large and was partly explained by diagnostic criteria and sample size. In order to facilitate comparative research on SWD, there is a need for validation and standardization of assessment methodology as well as agreement in terms of sample restrictions.

Shift work is associated with negative health effects. Increased prevalence of several cardiovascular risk factors among shift workers/night workers compared with day workers have been shown resulting in increased risk of cardiovascular events among shift workers and night workers. Previous studies have taken a dichotomous approach to the comparison between day and night workers. The present study uses a continuous approach and provides such a new perspective to the negative effects of night work load as a possible risk factor for undesirable health effects. This cross sectional study (The SUrvey of Shift work, Sleep and Health (SUSSH)) uses data collected from December 2008 to March 2009. The study population consists of Norwegian nurses. The study collected information about demographic and lifestyle factors: Body Mass Index (BMI), smoking habits, alcohol consumption, caffeine consumption and exercise habits. The lifestyle parameters were evaluated using multiple hierarchical regression and binary logistic regression. Number of night shifts worked last year (NNL) was used as operationalization of night work load. Adjustment for possible confounders were made. Obesity was defined as BMI > 30. Alcohol Consumption was evaluated using the short form of the Alcohol Use Disorders Identification Test Consumption (AUDIT-C). Data were analyzed using SPSS version 22. We had data from 2059 nurses. NNL was significantly and positively associated with BMI, both when evaluated against BMI as a continuous parameter (Beta = .055, p < .05), and against obesity (OR = 1.01, 95 % CI = 1.00-1.01). The AUDIT-C score was significantly and positively associated with hours worked per week (OR = 1.03, 95 % CI = 1.01-1.05). We found a positive significant association between night work load and BMI. This suggests that workers with a heavy night work load might need special attention and frequent health checks due to higher risk of undesirable health effects.