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Background
Wearables, as small portable computer systems worn on the body, can track user fitness and health data, which can be used to customize health insurance contributions individually. In particular, insured individuals with a healthy lifestyle can receive a reduction of their contributions to be paid. However, this potential is hardly used in practice.
Objective
This study aims to identify which barrier factors impede the usage of wearables for assessing individual risk scores for health insurances, despite its technological feasibility, and to rank these barriers according to their relevance.
Methods
To reach these goals, we conduct a ranking-type Delphi study with the following three stages. First, we collected possible barrier factors from a panel of 16 experts and consolidated them to a list of 11 barrier categories. Second, the panel was asked to rank them regarding their relevance. Third, to enhance the panel consensus, the ranking was revealed to the experts, who were then asked to re-rank the barriers.
Results
The results suggest that regulation is the most important barrier. Other relevant barriers are false or inaccurate measurements and application errors caused by the users. Additionally, insurers could lack the required technological competence to use the wearable data appropriately.
Conclusion
A wider use of wearables and health apps could be achieved through regulatory modifications, especially regarding privacy issues. Even after assuring stricter regulations, users’ privacy concerns could partly remain, if the data exchange between wearables manufacturers, health app providers, and health insurers does not become more transparent.
Injuries in professional soccer are a significant concern for teams, and they are caused amongst others by high training load. This cohort study describes the relationship between workload parameters and the occurrence of non-contact injuries, during weeks with high and low workload in professional soccer players throughout the season. Twenty-one professional soccer players aged 28.3 ± 3.9 yrs. who competed in the Iranian Persian Gulf Pro League participated in this 48-week study. The external load was monitored using global positioning system (GPS, GPSPORTS Systems Pty Ltd) and the type of injury was documented daily by the team's medical staff. Odds ratio (OR) and relative risk (RR) were calculated for non-contact injuries for high- and low-load weeks according to acute (AW), chronic (CW), acute to chronic workload ratio (ACWR), and AW variation (Δ-Acute) values. By using Poisson distribution, the interval between previous and new injuries were estimated. Overall, 12 non-contact injuries occurred during high load and 9 during low load weeks. Based on the variables ACWR and Δ-AW, there was a significantly increased risk of sustaining non-contact injuries (p < 0.05) during high-load weeks for ACWR (OR: 4.67), and Δ-AW (OR: 4.07). Finally, the expected time between injuries was significantly shorter in high load weeks for ACWR [1.25 vs. 3.33, rate ratio time (RRT)] and Δ-AW (1.33 vs. 3.45, RRT) respectively, compared to low load weeks. The risk of sustaining injuries was significantly larger during high workload weeks for ACWR, and Δ-AW compared with low workload weeks. The observed high OR in high load weeks indicate that there is a significant relationship between workload and occurrence of non-contact injuries. The predicted time to new injuries is shorter in high load weeks compared to low load weeks. Therefore, the frequency of injuries is higher during high load weeks for ACWR and Δ-AW. ACWR and Δ-AW appear to be good indicators for estimating the injury risk, and the time interval between injuries.
Background: Network models are useful tools for researchers to simplify and understand investigated systems. Yet, the assessment of methods for network construction is often uncertain. Random resampling simulations can aid to assess methods, provided synthetic data exists for reliable network construction.
Objectives: We implemented a new Monte Carlo algorithm to create simulated data for network reconstruction, tested the influence of adjusted parameters and used simulations to select a method for network model estimation based on real-world data. We hypothesized, that reconstructs based on Monte Carlo data are scored at least as good compared to a benchmark.
Methods: Simulated data was generated in R using the Monte Carlo algorithm of the mcgraph package. Benchmark data was created by the huge package. Networks were reconstructed using six estimator functions and scored by four classification metrics. For compatibility tests of mean score differences, Welch’s t-test was used. Network model estimation based on real-world data was done by stepwise selection.
Samples: Simulated data was generated based on 640 input graphs of various types and sizes. The real-world dataset consisted of 67 medieval skeletons of females and males from the region of Refshale (Lolland) and Nordby (Jutland) in Denmark.
Results: Results after t-tests and determining confidence intervals (CI95%) show, that evaluation scores for network reconstructs based on the mcgraph package were at least as good compared to the benchmark huge. The results even indicate slightly better scores on average for the mcgraph package.
Conclusion: The results confirmed our objective and suggested that Monte Carlo data can keep up with the benchmark in the applied test framework. The algorithm offers the feature to use (weighted) un- and directed graphs and might be useful for assessing methods for network construction.
We collect a network dataset of tenured economics faculty in Austria, Germany and Switzerland. We rank the 100 institutions included with a minimum violation ranking. This ranking is positively and significantly correlated with the Times Higher Education ranking of economics institutions. According to the network ranking, individuals on average go down about 23 ranks from their doctoral institution to their employing institution. While the share of females in our dataset is only 15%, we do not observe a significant gender hiring gap (a difference in rank changes between male and female faculty). We conduct a robustness check with the Handelsblatt and the Times Higher Education ranking. According to these rankings, individuals on average go down only about two ranks. We do not observe a significant gender hiring gap using these two rankings (although the dataset underlying this analysis is small and these estimates are likely to be noisy). Finally, we discuss the limitations of the network ranking in our context.
Environmental pollution by microplastics has become a severe problem in terrestrial and aquatic ecosystems and, according to actual prognoses, problems will further increase in the future. Therefore, assessing and quantifying the risk for the biota is crucial. Standardized short-term toxicological procedures as well as methods quantifying potential toxic effects over the whole life span of an animal are required. We studied the effect of the microplastic polystyrene on the survival and reproduction of a common freshwater invertebrate, the rotifer Brachionus calyciflorus, at different timescales. We used pristine polystyrene spheres of 1, 3, and 6 µm diameter and fed them to the animals together with food algae in different ratios ranging from 0 to 50% nonfood particles. As a particle control, we used silica to distinguish between a pure particle effect and a plastic effect. After 24 h, no toxic effect was found, neither with polystyrene nor with silica. After 96 h, a toxic effect was detectable for both particle types. The size of the particles played a negligible role. Studying the long-term effect by using life table experiments, we found a reduced reproduction when the animals were fed with 3 µm spheres together with similar-sized food algae. We conclude that the fitness reduction is mainly driven by the dilution of food by the nonfood particles rather than by a direct toxic effect.
Basic psychological needs theory postulates that a social environment that satisfies individuals’ three basic psychological needs of autonomy, competence, and relatedness leads to optimal growth and well-being. On the other hand, the frustration of these needs is associated with ill-being and depressive symptoms foremost investigated in non-clinical samples; yet, there is a paucity of research on need frustration in clinical samples. Survey data were compared between adult individuals with major depressive disorder (MDD; n = 115; 48.69% female; 38.46 years, SD = 10.46) with those of a non-depressed comparison sample (n = 201; 53.23% female; 30.16 years, SD = 12.81). Need profiles were examined with a linear mixed model (LMM). Individuals with depression reported higher levels of frustration and lower levels of satisfaction in relation to the three basic psychological needs when compared to non-depressed adults. The difference between depressed and non-depressed groups was significantly larger for frustration than satisfaction regarding the needs for relatedness and competence. LMM correlation parameters confirmed the expected positive correlation between the three needs. This is the first study showing substantial differences in need-based experiences between depressed and non-depressed adults. The results confirm basic assumptions of the self-determination theory and have preliminary implications in tailoring therapy for depression.
Background:
Children’s spontaneous focusing on numerosity (SFON) is related to numerical skills. This study aimed to examine (1) the developmental trajectory of SFON and (2) the interrelations between SFON and early numerical skills at pre-school as well as their influence on arithmetical skills at school.
Method:
Overall, 1868 German pre-school children were repeatedly assessed until second grade. Nonverbal intelligence, visual attention, visuospatial working memory, SFON and numerical skills were assessed at age five (M = 63 months, Time 1) and age six (M = 72 months, Time 2), and arithmetic was assessed at second grade (M = 95 months, Time 3).
Results:
SFON increased significantly during pre-school. Path analyses revealed interrelations between SFON and several numerical skills, except number knowledge. Magnitude estimation and basic calculation skills (Time 1 and Time 2), and to a small degree number knowledge (Time 2), contributed directly to arithmetic in second grade. The connection between SFON and arithmetic was fully mediated by magnitude estimation and calculation skills at pre-school.
Conclusion:
Our results indicate that SFON first and foremost influences deeper understanding of numerical concepts at pre-school and—in contrast to previous findings –affects only indirectly children’s arithmetical development at school.
Large-scale databases that report the inhibitory capacities of many combinations of candidate drug compounds and cultivated cancer cell lines have driven the development of preclinical drug-sensitivity models based on machine learning. However, cultivated cell lines have devolved from human cancer cells over years or even decades under selective pressure in culture conditions. Moreover, models that have been trained on in vitro data cannot account for interactions with other types of cells. Drug-response data that are based on patient-derived cell cultures, xenografts, and organoids, on the other hand, are not available in the quantities that are needed to train high-capacity machine-learning models. We found that pre-training deep neural network models of drug sensitivity on in vitro drug-sensitivity databases before fine-tuning the model parameters on patient-derived data improves the models’ accuracy and improves the biological plausibility of the features, compared to training only on patient-derived data. From our experiments, we can conclude that pre-trained models outperform models that have been trained on the target domains in the vast majority of cases.
Metabolic derangement with poor glycemic control accompanying overweight and obesity is associated with chronic low-grade inflammation and hyperinsulinemia. Macrophages, which present a very heterogeneous population of cells, play a key role in the maintenance of normal tissue homeostasis, but functional alterations in the resident macrophage pool as well as newly recruited monocyte-derived macrophages are important drivers in the development of low-grade inflammation. While metabolic dysfunction, insulin resistance and tissue damage may trigger or advance pro-inflammatory responses in macrophages, the inflammation itself contributes to the development of insulin resistance and the resulting hyperinsulinemia. Macrophages express insulin receptors whose downstream signaling networks share a number of knots with the signaling pathways of pattern recognition and cytokine receptors, which shape macrophage polarity. The shared knots allow insulin to enhance or attenuate both pro-inflammatory and anti-inflammatory macrophage responses. This supposedly physiological function may be impaired by hyperinsulinemia or insulin resistance in macrophages. This review discusses the mutual ambiguous relationship of low-grade inflammation, insulin resistance, hyperinsulinemia and the insulin-dependent modulation of macrophage activity with a focus on adipose tissue and liver.
The aim of this review was to describe and summarize the scientific literature on programming parameters related to jump or plyometric training in male and female soccer players of different ages and fitness levels. A literature search was conducted in the electronic databases PubMed, Web of Science and Scopus using keywords related to the main topic of this study (e.g., “ballistic” and “plyometric”). According to the PICOS framework, the population for the review was restricted to soccer players, involved in jump or plyometric training. Among 7556 identified studies, 90 were eligible for inclusion. Only 12 studies were found for females. Most studies (n = 52) were conducted with youth male players. Moreover, only 35 studies determined the effectiveness of a given jump training programming factor. Based on the limited available research, it seems that a dose of 7 weeks (1–2 sessions per week), with ~80 jumps (specific of combined types) per session, using near-maximal or maximal intensity, with adequate recovery between repetitions (<15 s), sets (≥30 s) and sessions (≥24–48 h), using progressive overload and taper strategies, using appropriate surfaces (e.g., grass), and applied in a well-rested state, when combined with other training methods, would increase the outcome of effective and safe plyometric-jump training interventions aimed at improving soccer players physical fitness. In conclusion, jump training is an effective and easy-to-administer training approach for youth, adult, male and female soccer players. However, optimal programming for plyometric-jump training in soccer is yet to be determined in future research.