Lattari, E, Vieira, LAF, Oliveira, BRR, Unal, G, Bikson, M, de Mello Pedreiro, RC, Marques Neto, SR, Machado, S, and Maranhão-Neto, GA. Effects of transcranial direct current stimulation with caffeine intake on muscular strength and perceived exertion. J Strength Cond Res 33(5): 1237–1243, 2019—The aim of this study was to investigate the acute effects of transcranial direct current stimulation (tDCS) associated with caffeine intake on muscular strength and ratings of perceived exertion (RPE). Fifteen healthy young males recreationally trained (age: 25.3 ± 3.2 years, body mass: 78.0 ± 6.9 kg, height: 174.1 ± 6.1 cm) were recruited. The experimental conditions started with the administration of caffeine (Caff) or placebo (Pla) 1 hour before starting the anodal tDCS (a-tDCS or sham). There was an intake of 5 mg·kg⁻¹ of Caff or 5 mg·kg⁻¹ of Pla. After the intake, a-tDCS or sham was applied in the left dorsolateral prefrontal cortex with intensity of 2 mA and 20 minutes of duration. The experimental conditions were defined as Sham + Pla, a-tDCS + Pla, Sham + Caff, and a-tDCS + Caff. After the conditions, muscular strength and RPE were verified. Muscular strength was determined by volume load performed in bench press exercise. Muscular strength in Sham + Pla condition was lower compared with all others conditions (p < 0.05). The RPE in the Sham + Pla was greater compared with a-tDCS + Caff (p < 0.05). Muscular strength was greater in all experimental conditions, and a-tDCS + Caff had lower RPE compared with placebo. When very little gains in muscle strength are expected, both caffeine and tDCS were effective in increasing muscle strength. Besides, the improvement in RPE of the caffeine associated with a-tDCS could prove advantageous in participants experienced in strength training. In fact, coaches and applied sport scientists quantitating the intensity of training based on RPE.

Background/Objectives: Parkinson’s disease (PD) is a neurodegenerative disorder characterized by tremors, balance impairments, and mobility limitations. Innovative approaches like combining transcranial direct current stimulation (tDCS) with exercise show promise in addressing these symptoms. This study investigates the effects of exercise combined with tDCS on mobility and tremor management in PD patients. Methods: Twenty-five individuals aged 60−75 (66.6 ± 7.33), diagnosed with PD (Hoehn and Yahr stage 2−3), were assigned to three groups in a randomized controlled design: exercise with active tDCS (n = 8), exercise with sham tDCS (n = 8), and a control group (n = 9). Dual-task training sessions focusing on walking speed, balance, and force control were conducted over ten sessions. Results: No significant differences were detected across the groups for grip strength or force control measures (p > 0.05). Significant improvements were observed in the intervention group: the Timed Up and Go (TUG) test showed a significant reduction in time (mean difference = 2.498 s, p < 0.001, ηp² = 0.331); anterior–posterior displacement significantly increased (mean difference = 21.375 mm, p = 0.0269, ηp² = 0.303); and force-tremor decoupling improved, with coherence in the 1−4 Hz band significantly decreasing (p = 0.0067). Finally, changes in TUG from post- to pre-treatment values were significantly positively correlated with the changes in coherence (R = 0.468, p = 0.018). Conclusions: Combining tDCS with exercise enhances mobility and tremor management in PD patients. These findings support the potential for such interventions to improve functional outcomes and quality of life for individuals with PD.

Background: Accurate differentiation of Parkinson’s disease (PD) from healthy aging is crucial for timely intervention and effective management. Postural sway abnormalities are prominent motor features of PD. Quantitative stabilometry and machine learning (ML) offer a promising avenue for developing objective markers to support the diagnostic process. This study aimed to develop and validate high-performance ML models to classify individuals with PD and age-matched healthy older adults (HOAs) using a comprehensive set of stabilometric parameters. Methods: Thirty-seven HOAs (mean age 70 ± 6.8 years) and 26 individuals with idiopathic PD (Hoehn and Yahr stages 2–3, on medication; mean age 66 years ± 2.9 years), all aged 60–80 years, participated. Stabilometric data were collected using a force platform during quiet stance under eyes-open (EO) and eyes-closed (EC) conditions, from which 34 parameters reflecting the time- and frequency-domain characteristics of center-of-pressure (COP) sway were extracted. After data preprocessing, including mean imputation for missing values and feature scaling, three ML classifiers (Random Forest, Gradient Boosting, and Support Vector Machine) were hyperparameter-tuned using GridSearchCV with three-fold cross-validation. An ensemble voting classifier (soft voting) was constructed from these tuned models. Model performance was rigorously evaluated using 15 iterations of stratified train–test splits (70% train and 30% test) and an additional bootstrap procedure of 1000 iterations to derive reliable 95% confidence intervals (CIs). Results: Our optimized ensemble voting classifier achieved excellent discriminative power, distinguishing PD from HOAs with a mean accuracy of 0.91 (95% CI: 0.81–1.00) and a mean Area Under the ROC Curve (AUC ROC) of 0.97 (95% CI: 0.92–1.00). Importantly, feature analysis revealed that anteroposterior sway velocity with eyes open (V-AP) and total sway path with eyes closed (TOD_EC, calculated using COP displacement vectors from its mean position) are the most robust and non-invasive biomarkers for differentiating the groups. Conclusions: An ensemble ML approach leveraging stabilometric features provides a highly accurate, non-invasive method to distinguish PD from healthy aging and may augment clinical assessment and monitoring.

Cardiorespiratory fitness (CRF) is associated with several health outcomes. Some non-exercise equations are available for CRF estimation. However, little is known about the validation of these equations among elderly. The aim of this study was to exam the validity of non-exercise equations with self-reported information in elderly. Participants (n= 93) aged 60 to 91 years measured CRF using maximal cardiopulmonary exercise test. Five non-exercise equations were selected. Data included in the equations (age, sex, weight, height, body mass index, physical activity and smoking) were self-reported. Coefficient of determination (R2) of linear regressions with laboratory-measured VO2 peak ranged from 0.04 to 0.64. The Bland-Altman plots showed higher agreement between achieved and predicted CRF obtained by Jackson and colleagues, and Wier and colleagues equations. On the other hand, the other equations showed lower agreement and overestimation. Our findings provide evidences that two non-exercise equations, previously developed, could be used on the prediction of CRF among elderly.