Introduction Poor adherence is a common challenge in self-directed mental health interventions. Research findings indicate that telephone prompts may be useful to increase adherence. Method Due to poor adherence in a randomized controlled trial evaluating an Internet-based aftercare program for women with bulimia nervosa we implemented regular short telephone prompts into the study protocol halfway through the trial period. Of the 126 women in the intervention group, the first 63 women were not prompted by telephone (unprompted group) and compared with 63 women who subsequently enrolled into the study and were attempted to prompt bimonthly by a research assistant (telephone prompt group). Completed telephone calls took less than 5 min and did not include any symptom-related counseling. Results Most of the women in the telephone prompt group (67%) were reached only once or twice during the intervention period. However, overall adherence in the telephone prompt group was significantly higher than in the unprompted group (T = − 3.015, df = 124, p = 0.003). Conclusion Our findings from this secondary analysis suggest that telephone prompts can positively affect adherence to an Internet-based aftercare intervention directed at patients with bulimia nervosa.

Die in der Reihe theorie.org erschienene Streitschrift Kritik des Familismus. Theorie und soziale Realität eines ideologischen Gemäldes von Gisela Notz diskutiert kritisch die Entwicklung des bundesdeutschen Familienbilds. Dabei entlarvt die Autorin dieses als ein ideologisch aufgeladenes und individuelle Freiheitsrechte einschränkendes Konstrukt, das äußerst persistent Geschlechter- und Arbeitsverhältnisse prägt.

Graphs are used to model a wide range of systems from different disciplines including social network analysis, biology, and big data processing. When analyzing these constantly changing dynamic graphs at a high frequency, performance is the main concern. Depending on the graph size and structure, update frequency, and read accesses of the analysis, the use of different data structures can yield great performance variations. Even for expert programmers, it is not always obvious, which data structure is the best choice for a given scenario. In previous work, we presented an approach for handling the selection of the most efficient data structures automatically using a compile-time approach well-suited for constant workloads. We extend this work with a measurement study of seven data structures and use the results to fit actual cost estimation functions. In addition, we evaluate our approach for the computations of seven different graph metrics. In analyses of real-world dynamic graphs with a constant workload, our approach achieves a speedup of up to 5.4× compared to basic data structure configurations. Such a compile-time based approach cannot yield optimal results when the behavior of the system changes later and the workload becomes non-constant. To close this gap we present a run-time approach which provides live profiling and facilitates automatic exchanges of data structures during execution. We analyze the performance of this approach using an artificial, non-constant workload where our approach achieves speedups of up to 7.3× compared to basic configurations.

The mystery behind the origin of the pain and the difficulty to propose methodologies for its quantitative characterization fascinated philosophers (and then scientists) from the dawn of our modern society. Nowadays, studying patterns of information flow in mesoscale activity of brain networks is a valuable strategy to offer answers in computational neuroscience. In this paper, complex network analysis was performed on the time-varying brain functional connectomes of a rat model of persistent peripheral neuropathic pain, obtained by means of local field potential and spike train analysis. A wide range of topological network measures (14 in total, the code is publicly released at: https://github.com/biomedical-cybernetics/topological_measures_wide_analysis) was employed to quantitatively investigate the rewiring mechanisms of the brain regions responsible for development and upkeep of pain along time, from three hours to 16 days after nerve injury. The time trend (across the days) of each network measure was correlated with a behavioural test for rat pain, and surprisingly we found that the rewiring mechanisms associated with two local topological measure, the local-community-paradigm and the power-lawness, showed very high statistical correlations (higher than 0.9, being the maximum value 1) with the behavioural test. We also disclosed clear functional connectivity patterns that emerged in association with chronic pain in the primary somatosensory cortex (S1) and ventral posterolateral (VPL) nuclei of thalamus. This study represents a pioneering attempt to exploit network science models in order to elucidate the mechanisms of brain region re-wiring and engram formations that are associated with chronic pain in mammalians. We conclude that the local-community-paradigm is a model of complex network organization that triggers a local learning rule, which seems associated to processing, learning and memorization of chronic pain in the brain functional connectivity. This rule is based exclusively on the network topology, hence was named epitopological learning.