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Introduction Aging is a natural, cumulative, and irreversible process that culminates in various chronic conditions, such as cognitive decline and decrease in handgrip strength (HGS). Both conditions share not only [...], Objective. The aim of this study was to analyze the bidirectional association between handgrip strength (HGS) and cognitive performance in different cognitive functions in a European population and to evaluate the predictive validity of HGS for the risk of future cognitive impairment in aging individuals. Methods. This was a prospective cohort study conducted using data on individuals over 50 years of age from the Survey of Health, Aging and Retirement in Europe (SHARE). HGS measures and scores in numeracy, recall, and verbal fluency were repeated and analyzed biannually for 4 years and were used in generalized estimating equations to test the bidirectional association, categorized by sex. Results. Of the 8236 individuals included, 55.73% were women with a mean age of 67.55 (standard deviation [SD] = 8.4) years and 44.27% were men with a mean age of 68.42 (SD =7.7) years. HGS predicted cognitive decline in both sexes, except for numeracy in men, even after adjustments. The strongest association with HGS in women was in verbal fluency ([beta] =.094; 95% CI =0.039 to 0.151), whereas the strongest association with HGS in men was in delayed verbal recall ([beta] =.095; 95% CI =0.039 to 0.151). Conversely, the greatest cognitive predictor of HGS decline was verbal fluency in men ([beta] = .796; 95% CI =0.464 to 1.128), and in women ([beta] = .801; 95% CI= 0.567 to 1.109). Conclusion. There is a significant and bidirectional association between HGS and different cognitive functions in a European multicentric population. This bidirectional association differed between sexes. Impact. Both men and women who presented with cognitive decline also showed early changes in their HGS measures, and vice versa, but there still were differences between the sexes. These findings reinforce that HGS may be a simple and inexpensive method to identify early signs of cognitive decline, and that studies and rehabilitation strategies should be more sex specific.

To assess the effectiveness of an educational program leveraging technology‐enhanced learning and retrieval practice to teach trainees how to correctly identify interictal epileptiform discharges (IEDs). This was a bi‐institutional prospective randomized controlled educational trial involving junior neurology residents. The intervention consisted of three video tutorials focused on the six IFCN criteria for IED identification and rating 500 candidate IEDs with instant feedback either on a web browser (intervention 1) or an iOS app (intervention 2). The control group underwent no educational intervention (“inactive control”). All residents completed a survey and a test at the onset and offset of the study. Performance metrics were calculated for each participant. Twenty‐one residents completed the study: control (n= 8); intervention 1 (n= 6); intervention 2 (n= 7). All but two had no prior EEG experience. Intervention 1 residents improved from baseline (mean) in multiple metrics including AUC (.74; .85; p< .05), sensitivity (.53; .75; p< .05), and level of confidence (LOC) in identifying IEDs/committing patients to therapy (1.33; 2.33; p< .05). Intervention 2 residents improved in multiple metrics including AUC (.81; .86; p< .05) and LOC in identifying IEDs (2.00; 3.14; p< .05) and spike–wave discharges (2.00; 3.14; p< .05). Controls had no significant improvements in any measure. This program led to significant subjective and objective improvements in IED identification. Rating candidate IEDs with instant feedback on a web browser (intervention 1) generated greater objective improvement in comparison to rating candidate IEDs on an iOS app (intervention 2). This program can complement trainee education concerning IED identification.

We created a framework to assess the competency‐based EEG curriculum, outlined by the International League Against Epilepsy (ILAE) through a video‐based online educational resource (“Roadmap to EEGs”) and assessed its effectiveness and feasibility in improving trainees' knowledge. Ten video‐based e‐learning modules addressed seven key topics in EEG and epileptology (normal EEG, normal variants, EEG artifacts, interictal epileptiform discharges (IED), focal seizures, idiopathic generalized epilepsy (IGE), and developmental and epileptic encephalopathies (DEE)). We posted the educational videos on YouTube for free access. Pre‐ and post‐tests, each comprising 20 multiple‐choice questions, were distributed to institution leadership and advertised on social media platforms to reach a global audience. The tests were administered online to assess the participants' knowledge. Pre‐ and post‐test questions showed different EEG samples to avoid memorization and immediate recall. After completing the post‐test, participants were asked to respond to 7 additional questions assessing their confidence levels and recommendations for improvement. A total of 52 complete and matched pre‐ and post‐test responses were collected. The probability of a correct response was 73% before teaching (95% CI: 70%–77%) and 81% after teaching (95% CI: 78%–84%). The odds of a correct response increased significantly by 59% (95% CI: 28%–98%, p< .001). For participants having >4 weeks of EEG training, the probability of a correct response was 76% (95% CI: .72–.79) and 81% after teaching (95% CI: .78–.84). The odds of answering correctly increased by 44% (95% CI: 15%–80%, p= .001). Participants felt completely confident in independently interpreting and identifying EEG findings after completing the teaching modules (17.1% before vs. 37.8% after, p‐value < .0001). 86.5% of participants expressed a high likelihood of recommending the module to other trainees. The video‐based online educational resource allows participants to acquire foundational knowledge in EEG/epilepsy, and participants to review previously learned EEG/epilepsy information.

We published a list of “must‐know” routine EEG (rEEG) findings for trainees based on expert opinion. Here, we studied the accuracy and inter‐rater agreement (IRA) of these “must‐know” rEEG findings among international experts. A previously validated online rEEG examination was disseminated to EEG experts. It consisted of a survey and 30 multiple‐choice questions predicated on the previously published “must‐know” rEEG findings divided into four domains: normal, abnormal, normal variants, and artifacts. Questions contained de‐identified 10–20‐s epochs of EEG that were considered unequivocal examples by five EEG experts. The examination was completed by 258 international EEG experts. Overall mean accuracy and IRA (AC1) were 81% and substantial (0.632), respectively. The domain‐specific mean accuracies and IRA were: 76%, moderate (0.558) (normal); 78%, moderate (0.575) (abnormal); 85%, substantial (0.678) (normal variants); 85%, substantial (0.740) (artifacts). Academic experts had a higher accuracy than private practice experts (82% vs. 77%; p= .035). Country‐specific overall mean accuracies and IRA were: 92%, almost perfect (0.836) (U.S.); 86%, substantial (0.762) (Brazil); 79%, substantial (0.646) (Italy); and 72%, moderate (0.496) (India). In conclusion, collective expert accuracy and IRA of “must‐know” rEEG findings are suboptimal and heterogeneous. We recommend the development and implementation of pragmatic, accessible, country‐specific ways to measure and improve the expert accuracy and IRA.