Increased Health Risk in Subjects with High Self-Reported Seasonality

Background: Seasonal variations in mood and behaviour, termed seasonality, are commonly reported in the general population. As a part of a large cross-sectional health survey in Hordaland, Norway, we investigated the relationship between seasonality, objective health measurements and health behaviours. Methodology/Principal Findings: A total of 11,545 subjects between 40–44 years old participated, completing the Global Seasonality Score, measuring seasonality. Waist/hip circumference, BMI and blood pressure were measured, and blood samples were analyzed for total cholesterol, HDL cholesterol, triglycerides and glucose. Subjects also completed a questionnaire on miscellaneous health behaviours (exercise, smoking, alcohol consumption). Hierarchical linear regression analyses were used to investigate associations between seasonality and objective health measurements, while binary logistic regression was used for analysing associations between seasonality and health behaviours. Analyses were adjusted for sociodemographic factors, month of questionnaire completion and sleep duration. Seasonality was positively associated with high waist-hip-ratio, BMI, triglyceride levels, and in men high total cholesterol. Seasonality was negatively associated with HDL cholesterol. In women seasonality was negatively associated with prevalence of exercise and positively associated with daily cigarette smoking. Conclusions/Significance: High seasonality was associated with objective health risk factors and in women also with health behaviours associated with an increased risk for cardiovascular disease

Nonlinear Analysis of Motor Activity Shows Differences between Schizophrenia and Depression: A Study Using Fourier Analysis and Sample Entropy

The purpose of this study has been to describe motor activity data obtained by using wrist-worn actigraphs in patients with schizophrenia and major depression by the use of linear and non-linear methods of analysis. Different time frames were investigated, i.e., activity counts measured every minute for up to five hours and activity counts made hourly for up to two weeks. The results show that motor activity was lower in the schizophrenic patients and in patients with major depression, compared to controls. Using one minute intervals the depressed patients had a higher standard deviation (SD) compared to both the schizophrenic patients and the controls. The ratio between the root mean square successive differences (RMSSD) and SD was higher in the schizophrenic patients compared to controls. The Fourier analysis of the activity counts measured every minute showed that the relation between variance in the low and the high frequency range was lower in the schizophrenic patients compared to the controls. The sample entropy was higher in the schizophrenic patients compared to controls in the time series from the activity counts made every minute. The main conclusions of the study are that schizophrenic and depressive patients have distinctly different profiles of motor activity and that the results differ according to period length analysed

Prevalence of objective health risk factors associated with cardiovascular disease in different seasonality groups.

<p>Each chart has prevalence of health risks on the vertical axis and seasonality group on the horizontal axis, and results for men and women are shown separately. GSS = Global Seasonality Score. Vertical lines depict the 95% Confidence intervals.</p

Effect of season on the associations between high seasonality and objective health risk factors.

<p>Analysis of variance using objective health measurements as dependent variables and seasonality group, season and the interaction term seasonality group*season as independent variables. Only significant effects are shown.</p><p><b>GSS = Global Seasonality Score.</b></p

Objective health measurements and health behaviours in different seasonality groups (n = 11,545).

<p>Means are compared by using ANOVA for normally distributed data and Kruskal-Wallis for non-normally distributed data. Standard Deviations are shown in parentheses.</p><p>GSS  =  Global Seasonality Score.</p>a<p>Kruskal-Wallis statistics.</p>b<p>ANOVA statistics.</p>c<p>Post-Hoc analysis (using Least Squares Difference and 0.05 significance level) were reported as follows: 1- Significant difference between the GSS <8 and GSS 8–10 groups, 2- Significant difference between the GSS <8 and GSS ≥11 groups and 3- Significant difference between the GSS 8–10 and GSS ≥11 groups.</p

The impact of the Global Seasonality Score on objective health risk factors.

<p>Hierarchic linear regression model controlled for month of questionnaire completion, marital status, income, education, living area and sleep duration (n = 11,544).</p><p>β: Standardized regression coefficient; NS: not significant.</p><p>*p<.05;</p><p>***p<.001.</p

The impact of High seasonality on health behaviours.

<p>Logistic regression analysis using Global Seasonality Score (GSS) as the predictor variable and objective health risk factors/health behaviours as criterion variables (n = 11,544). The analyses are adjusted for annual income, education, marital status, month of completing the questionnaire, urban/rural residence and sleep duration.</p><p>CI: Confidence Interval.</p><p>*P<0.05.</p><p>**P<0.01.</p