Improving Oral Hygiene Skills by Computer-Based Training: A Randomized Controlled Comparison of the Modified Bass and the Fones Techniques

Background: Gingivitis and other plaque-associated diseases have a high prevalence in western communities even though the majority of adults report daily oral hygiene. This indicates a lack of oral hygiene skills. Currently, there is no clear evidence as to which brushing technique would bring about the best oral hygiene skills. While the modified Bass technique is often recommended by dentists and in textbooks, the Fones technique is often recommended in patient brochures. Still, standardized comparisons of the effectiveness of teaching these techniques are lacking. Methodology/Principal Findings: In a final sample of n=56 students, this multidisciplinary, randomized, examiner-blinded, controlled study compared the effects of parallel and standardized interactive computer presentations teaching either the Fones or the modified Bass technique. A control group was taught the basics of tooth brushing alone. Oral hygiene skills (remaining plaque after thorough oral hygiene) and gingivitis were assessed at baseline and 6, 12, and 28 weeks after the intervention. We found a significant group×time interaction for gingivitis (F(4/102)=3.267; p=0.016; e=0.957; ?2=0.114) and a significant main effect of group for oral hygiene skills (F(2/51)=7.088; p=0.002; ?2=0.218). Fones was superior to Bass; Bass did not differ from the control group. Group differences were most prominent after 6 and 12 weeks. Conclusions/Significance: The present trial indicates an advantage of teaching the Fones as compared to the modified Bass technique with respect to oral hygiene skills and gingivitis. Future studies are needed to analyze whether the disadvantage of teaching the Bass technique observed here is restricted to the teaching method employed. Trial Registration: German Clinical Trials Register http://www.drks.de/DRKS0000348

Latent class analysis reveals clinically relevant atopy phenotypes in 2 birth cohorts

Background: Phenotypes of childhood-onset asthma are characterized by distinct trajectories and functional features. For atopy, definition of phenotypes during childhood is less clear. Objective: We sought to define phenotypes of atopic sensitization over the first 6 years of life using a latent class analysis (LCA) integrating 3 dimensions of atopy: allergen specificity, time course, and levels of specific IgE (sIgE). Methods: Phenotypes were defined by means of LCA in 680 children of the Multizentrische Allergiestudie (MAS) and 766 children of the Protection against allergy: Study in Rural Environments (PASTURE) birth cohorts and compared with classical nondisjunctive definitions of seasonal, perennial, and food sensitization with respect to atopic diseases and lung function. Cytokine levels were measured in the PASTURE cohort. Results: The LCA classified predominantly by type and multiplicity of sensitization (food vs inhalant), allergen combinations, and sIgE levels. Latent classes were related to atopic disease manifestations with higher sensitivity and specificity than the classical definitions. LCA detected consistently in both cohorts a distinct group of children with severe atopy characterized by high seasonal sIgE levels and a strong propensity for asthma; hay fever; eczema; and impaired lung function, also in children without an established asthma diagnosis. Severe atopy was associated with an increased IL-5/IFN-gamma ratio. A path analysis among sensitized children revealed that among all features of severe atopy, only excessive sIgE production early in life affected asthma risk. Conclusions: LCA revealed a set of benign, symptomatic, and severe atopy phenotypes. The severe phenotype emerged as a latent condition with signs of a dysbalanced immune response. It determined high asthma risk through excessive sIgE production and directly affected impaired lung function.Peer reviewe

Predictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing

Predictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments