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• Childbearing age women with epilepsy (WWE) are at increased risk to epilepsy harm. • The free EpSMon mobile-phone app allows self-monitoring of seizure related risks. • This is a 5 year prospective real-world cohort study of 2158 WWE accessing EpSMon. • The baseline awareness to risks is low suggesting current clinical models are failing. • Repeated EpSMon use increased SUDEP awareness but not pregnancy related epilepsy risk. Women with epilepsy (WWE) are vulnerable in pregnancy, with increased risks to mother and baby including teratogenic risks, especially from valproate. The free EpSMon mobile-phone app allows self-monitoring to afford patient-centred feedback on seizure related risks, such as sudden death in epilepsy (SUDEP) to its users. We sought to generate insights into various seizure related risks and its treatments in WWE of childbearing age (16 to 60 years) using EpSMon. The study utilizes a prospective real-world cohort of 5.5 years. Patient reported data on demographics, medication taken, diagnoses, seizure types and recognised biological, psychological, and social factors of seizure related harm were extracted. Data was stratified according to frequent and infrequent users and those scoring lower and higher risk scores. Multivariate logistic regression and different statistical tests were conducted. Data from 2158 WWE of childbearing age encompassing 4016 self-assessments were analysed. Overall risk awareness was 25.3% for pregnancy and 54.1% for SUDEP. Frequent users were more aware of pregnancy risks but not of SUDEP. Repeated EpSMon use increased SUDEP awareness but not pregnancy risks. Valproate was used by 11% of WWE, ranging from 6.5% of younger to 31.5% of older women. The awareness to risks to pregnancy, SUDEP and valproate is low. Valproate is being used by a significant minority. It is imperative risk communication continues for WWE based on their individual situation and need. This is unlikely to be delivered by current clinical models. Digital solutions hold promise but require work done to raise implementation and acceptability. [ABSTRACT FROM AUTHOR]

Objectives: Exercise is reported to improve function for people with ankylosing spondylitis (AS) but it is not clear if this effect is causal or if patients with milder disease find it easier to exercise. This study examines the effect of exercise and motivation to exercise on function, while controlling for disease severity. Methods: Participants who were members of an existing AS cohort were asked about physical activity, motivation to exercise, function, and disease severity. Path analysis on STATA was used to examine the correlation between factors associated with function at time of exercise and with function after 3 months of follow-up. Results: The response rate to the questionnaire was 88% (326/371). Improvement in function was greatest for people with higher physical activity levels and those who were more motivated to exercise—this was especially the case for patients with the most severe disease activity. The effect of motivation to exercise not only had a direct effect on function, but also an indirect effect of improving activity levels thereby improving both current and future function. People with high intrinsic motivation (driven by pleasure) had the greatest benefit to activity and function. Conclusions: Exercise does improve function, especially for those with severe disease. In addition, motivation alone improves function as much as exercising itself. Therefore, interventions targeting motivation to exercise would have as much effect on improving function as interventions offering exercise opportunities. In addition, any intervention that both improves motivation and increases opportunities to exercise would have a 2-fold influence on function. [Copyright &y& Elsevier]

Objectives: To examine if people with ankylosing spondylitis (AS) are at higher risk of acute myocardial infarction (MI) or stroke compared to those without AS. Methods: Primary care records were linked with all hospital admissions and deaths caused by MI or stroke in Wales for the years 1999-2010. The linked data were then stratified by AS diagnosis and survival analysis was used to obtain the incidence rate of MI and separately cerebrovascular disease (CVD)/stroke. Cox regression was used to adjust for gender and age. Logistic regression was used to examine prevalence of diabetes, hypertension, or hyperlipidemia for those with AS compared to those without. Results: There were 1686 AS patients (75.9% male, average age 46.1 years) compared to 1,206,621 controls (48.9% male, average age 35.9 years). Age- and gender-adjusted hazard ratios for MI were 1.28 (95% CI: 0.93 to 1.74) P = 0.12, and for CVD/stroke 1.0 (95% CI: 0.73 to 1.39) P = 0.9, in AS compared to controls. The prevalence of diabetes and hypertension, but not hyperlipidemia/hypercholesterolemia, was higher in AS. Conclusions: There is no increase in the MI or CVD/stroke rates in patients with AS compared to those without AS, despite higher rates of hypertension, which may be related to nonsteroidal anti-inflammatory drug use. [ABSTRACT FROM AUTHOR]

The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challenging task. In addition, it is well-known that due to non-stationarity based covariate shifts, the input data distributions of EEG-based BCI systems change during inter- and intra-session transitions, which poses great difficulty for developments of online adaptive data-driven systems. Ensemble learning approaches have been used previously to tackle this challenge. However, passive scheme based implementation leads to poor efficiency while increasing high computational cost. This paper presents a novel integration of covariate shift estimation and unsupervised adaptive ensemble learning (CSE-UAEL) to tackle non-stationarity in motor-imagery (MI) related EEG classification. The proposed method first employs an exponentially weighted moving average model to detect the covariate shifts in the common spatial pattern features extracted from MI related brain responses. Then, a classifier ensemble was created and updated over time to account for changes in streaming input data distribution wherein new classifiers are added to the ensemble in accordance with estimated shifts. Furthermore, using two publicly available BCI-related EEG datasets, the proposed method was extensively compared with the state-of-the-art single-classifier based passive scheme, single-classifier based active scheme and ensemble based passive schemes. The experimental results show that the proposed active scheme based ensemble learning algorithm significantly enhances the BCI performance in MI classifications. [ABSTRACT FROM AUTHOR]

Abstract: Modular neural network is a popular neural network model which has many successful applications. In this paper, a sequential Bayesian learning (SBL) is proposed for modular neural networks aiming at efficiently aggregating the outputs of members of the ensemble. The experimental results on eight benchmark problems have demonstrated that the proposed method can perform information aggregation efficiently in data modeling. [Copyright &y& Elsevier]