When investigating fractal phenomena, the following questions are fundamental for the applied researcher: (1) What are essential statistical properties of 1/f noise? (2) Which estimators are available for measuring fractality? (3) Which measurement instruments are appropriate and how are they applied? The purpose of this article is to give clear and comprehensible answers to these questions. First, theoretical characteristics of a fractal pattern (self-similarity, long memory, power law) and the related fractal parameters (the Hurst coefficient, the scaling exponent α, the fractional differencing parameter d of the autoregressive fractionally integrated moving average methodology, the power exponent β of the spectral analysis) are discussed. Then, estimators of fractal parameters from different software packages commonly used by applied researchers (R, SAS, SPSS) are introduced and evaluated. Advantages, disadvantages, and constrains of the popular estimators (dMathClass-op^ML, power spectral density, detrended fluctuation analysis, signal summation conversion) are illustrated by elaborate examples. Finally, crucial steps of fractal analysis (plotting time series data, autocorrelation, and spectral functions; performing stationarity tests; choosing an adequate estimator; estimating fractal parameters; distinguishing fractal processes from short-memory patterns) are demonstrated with empirical time series.

The present randomized controlled intervention study tested the hypothesis that a personally adaptable and realistic "just 1 more" goal would be more effective for increasing fruits and vegetables (FV) intake compared to the common "5 a day" goal. Study participants (N=84 students, 85% female) consumed less than 4 servings of FVs per day at recruitment. During the 1-week intervention, participants randomized to the 5 a day-group were asked to eat 5 servings of FVs/day; participants of the just 1 more-group were asked to eat 1 serving more of FVs than they usually did, and participants of the control group were instructed to eat as usual. Measurements were taken before (T1), directly following (T2), and 1 week after (T3) the intervention. Participants in the 5 a day-group increased their average FV intake significantly by about one serving from 2.49 at T1 to 3.45 servings/day at T3. At T3, only the 5 a day-group-not the just 1 more-group-had a significantly higher FV intake than the control group. Contrary to the hypothesis, the "5 a day" goal was more effective than "just 1 more" for increasing FV intake. Results of our study support the rationale of the "5 a day" campaign, at least in the short term.

Abstract. The present research investigated whether an intervention designed to increase the consumption of fruits and vegetables (FVs) would elicit reactance and explored the consequences of reactance. Eighty-four students were randomized to one of two intervention groups or a control group. During the 1-week intervention, which was accompanied by a food diary, the 5aday-group had to eat 5 portions of FVs per day, the just1more group had to eat 1 more portion of FVs than usual, and the control group had to eat as usual. Both intervention groups reported higher reactance than the control group immediately after the intervention (T2) and still 1 week later (T3) with high effect sizes. Trait-reactance had no effect on any of the study variables. Intervention-elicited reactance was associated with a lower FV intake at follow-up (T4), and this association was mediated by a more negative attitude toward eating 5aday assessed 1 week after the intervention (T3).

ObjectiveThe role of emotion dysregulation with regard to the psychopathology of anorexia nervosa (AN) is increasingly discussed. It is both assumed that AN symptoms have an impact on difficulties in tolerating aversive emotions and that—conversely—emotion dysregulation influences AN. To date, such conclusions are drawn on the basis of cross-sectional data not allowing for inferences on the temporal dynamics. The current study investigates the longitudinal interaction between emotional intolerance and core AN symptoms over the course of inpatient treatment by comparing patients with high (BMI<15 kg/m2) vs. low symptom severity (HSS vs. LSS).MethodThe study adopted a longitudinal, process-oriented design with N = 16 analysed electronic diaries. Throughout the course of their inpatient treatment, the patients answered questions daily about emotional intolerance and their AN-specific cognitions and behaviours. The temporal dynamics between emotional intolerance and these variables were analysed using a multivariate time series approach.ResultsThe time series of the processes under investigation adequately reflected the individual treatment courses. The majority of significant linear time trends was found for HSS patients. Most importantly, analysis revealed significant temporal interactions between emotional intolerance and AN symptoms in almost 70% of HSS patients. Thereby, up to 37% of variance in eating restraint and up to 23% in weight concern could be attributed to changes in emotional intolerance.ConclusionsThe findings support the notion that intolerable unpleasant emotions in severely affected AN patients influence their psychopathology. Additionally, time series analysis outlined the inter-individual heterogeneity of psychosomatic treatment courses of AN patients.

Understanding of interactional dynamics between several processes is one of the most important challenges in psychology and psychosomatic medicine. Researchers exploring behavior or other psychological phenomena mostly deal with ordinal or interval data. Missing values and consequential non-equidistant measurements represent a general problem of longitudinal studies from this field. The majority of process-oriented methodologies was originally designed for equidistant data measured on ratio scales. Therefore, the goal of this article is to clarify the conditions for satisfactory performance of longitudinal methods with data typical in psychological and psychosomatic research. This study examines the performance of the Johansen test, a procedure incorporating a set of sophisticated time series techniques, in reference to data quality utilizing a Monte Carlo method. The main results of the conducted simulation studies are: (1) Time series analyses require samples of at least 70 observations for an accurate estimation and inference. (2) Discrete data and failing equidistance of measurements due to irregular missing values appear unproblematic. (3) Relevant characteristics of stationary processes can be adequately captured using 5-or 7-point ordinal scales. (4) For trending processes, at least 10-point scales are necessary to ensure an acceptable quality of estimation and inference.