Background: Transcranial direct-current stimulation (tDCS) is an easy-to-apply, cheap, and safe technique capable of affecting cortical brain activity. However, its effectiveness has not been proven for many clinical applications. Objective: The aim of this systematic review was to determine whether the effect of different strategies for gait training in patients with neurological disorders can be enhanced by the combined application of tDCS compared to sham stimulation. Additionally, we attempted to record and analyze tDCS parameters to optimize its efficacy. Methods: A search in Pubmed, PEDro, and Cochrane databases was performed to find randomized clinical trials that combined tDCS with gait training. A chronological filter from 2010 to 2018 was applied and only studies with variables that quantified the gait function were included. Results: A total of 274 studies were found, of which 25 met the inclusion criteria. Of them, 17 were rejected based on exclusion criteria. Finally, 8 trials were evaluated that included 91 subjects with stroke, 57 suffering from Parkinson’s disease, and 39 with spinal cord injury. Four of the eight assessed studies did not report improved outcomes for any of its variables compared to the placebo treatment. Conclusions: There are no conclusive results that confirm that tDCS can enhance the effect of the different strategies for gait training. Further research for specific pathologies, with larger sample sizes and adequate follow-up periods, are required to optimize the existing protocols for applying tDCS.

Estimation of surface intramuscular coherence has been used to indirectly assess pyramidal tract activity following spinal cord injury (SCI), especially within the 15-30 Hz bandwidth. However, change in higher frequency (>40 Hz) muscle coherence during SCI has not been characterised. Thus, the objective of this study was to identify change of high and low frequency intramuscular Tibialis Anterior (TA) coherence during incomplete subacute SCI. Fifteen healthy subjects and 22 subjects with motor incomplete SCI (American Spinal Injury Association Impairment Scale, AIS, C or D grade) were recruited and tested during 4 sessions performed at 2-week intervals up to 8 months after SCI. Intramuscular TA coherence estimation was calculated within the 10–60 Hz bandwidth during controlled maximal isometric and isokinetic foot dorsiflexion. Maximal voluntary dorsiflexion torque, gait function measured with the WISCI II scale, and TA motor evoked potentials (MEP) were recorded. During subacute SCI, significant improvement in total lower limb manual muscle score, TA muscle strength and gait function were observed. No change in TA MEP amplitude was identified. Significant increase in TA coherence was detected in the 40–60 Hz, but not the 15–30 Hz bandwidth. The spasticity syndrome was associated with lower 15-30 Hz TA coherence during maximal isometric dorsiflexion and higher 10–60 Hz coherence during fast isokinetic movement (p < 0.05). Longitudinal estimation of neurophysiological and clinical measures during subacute SCI suggest that estimation of TA muscle coherence during controlled movement provides indirect information regarding adaptive and maladaptive motor control mechanisms during neurorehabilitation.

Background: Devices for applying transcutaneous electrical nerve stimulation (TENS) use numerous frequency modulation patterns for decreasing habituation to currents. However, there is no evidence supporting the use of an optimal pattern instead of the others, or even modulated frequencies instead of a fixed frequency. Objective: The objectives of this study were to determine the effects of 3 TENS patterns (fixed frequency, random modulation, and 6-second–6-second [6s-6s] modulation) on habituation, to examine the subjective perception of habituation and comfort, and to determine their effects on the mechanical pain threshold (MPT). Design: This study was a randomized, double-blind, sham-controlled crossover trial. Setting: The study took place in a university research laboratory under attenuated noise conditions at a regulated temperature of 22°C to 26°C. Participants: Thirty-nine volunteers who were healthy participated in this trial. Intervention: The participants received 4 different TENS interventions (fixed frequency, random modulation, 6s-6s modulation, and sham) in random order, with a 24-hour washout period, on the radial nerve. Measurements: The main outcome—habituation—was quantified by the increase in current density and the number of times the intensity had to be increased during the session. Secondary outcome variables were subjective perception of habituation and comfort and MPT. Results: Random modulation reduced the number of times the intensity had to be increased because of habituation compared with no modulation (1.6 times; 95% confidence interval [CI] = 0.7–2.6) and 6s-6s modulation (0.8 times; 95% CI = 0.01–1.6). No differences were observed between interventions in terms of an increase in current density, self-perceived habituation, or self-perceived comfort. MPT increased during the intervention in the random-modulation group (4.4 N; 95% CI = 1.8–7.0) and the no-modulation group (5.9 N; 95% CI = 1.9–10.0); there were no significant changes in the other 2 groups. Limitations: The success of masking or blinding procedures of the participants was not assessed. Conclusions: Randomly modulated frequencies caused less habituation than nonmodulated frequencies or 6s-6s modulation. Further research on more prolonged (hours-long) interventions in participants with pain is required.

Identifying environmental factors that influence bone health is crucial for developing effective intervention strategies that maximize peak bone mass. The aim of this study was to estimate the relationship between milk consumption and bone mineral density (BMD) in young adults, and to examine whether this relationship is mediated by body mass index (BMI) and total lean and fat mass. A cross-sectional study involving college students (n = 239) from a Spanish public university was performed. Data on milk consumption and anthropometric and body composition variables were collected. The Pearson correlation coefficients among total body BMD, body composition variables, and milk consumption ranged from −0.111 to −1.171, most of them statistically significant (p < 0.05). The ANCOVA (analysis of covariance) models showed that those with higher regular milk consumption had less total body BMD than those with lower regular milk consumption (p < 0.05), even after controlling for different sets of confounders. In the mediation analysis, BMI and lean and fat mass turned out to act as full mediators of the relationship between regular milk consumption and total body BMD (z = −1.7148, −1.3208, and −1.8549, respectively; p ≤ 0.05). In conclusion, milk consumption, per se, does not seem to have a direct effect on bone development, because its association seems to be fully mediated by body composition variables in young adults.

Fibromyalgia is a chronic disease that causes pain and affects patients' quality of life. Current treatments focus on pharmacological therapies for pain reduction. However, patients' psychological well-being is also affected, with depression and pain catastrophizing being common. This research addresses the clinicians' need to assess the influence of mental health factors on FM severity compared to pain factors. A co-development study between FM clinicians and data scientists analyzed data from 166 FM-diagnosed patients to assess the influence of mental health factors on FM severity in comparison to pain factors. The study used the Polysymptomatic Distress Scale (PDS) and Fibromyalgia Impact Questionnaire (FIQ) as FM severity indicators and collected 15 variables including regarding demographics, pain intensity perceived, and mental health factors. The team used an author's developed framework to identify the optimal FM severity classifier and explainability by selecting a number of features that lead to obtaining the best classification result. Machine learning classifiers employed in the framework were: decision trees, logistic regression, support vector machines, random forests, AdaBoost, extra trees, and RUSBoost. Explainability analyses were conducted using the following explainable AI techniques: SHapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Mean Decrease Impurity (MDI). A balanced random forest with 6 features achieved the best performance with PDS (AUC_ROC, mean = 0.81, std = 0.07). Being FIQ the target variable, due to the imbalance in FM severity levels, a binary and a multiclass classification approaches were considered achieving the optimal performance, respectively, a logistic regression classifier (AUC_ROC, mean = 0.83, std = 0.08) with 6 selected features, and a random forest (AUC_ROC, mean = 0.91, std = 0.04) with 8 selected features. Next, the explainability analysis determined mental health factors were found to be more relevant than pain perceived factors for FM severity. This study's findings, validated by clinicians, are potentially aligned with FM international guidelines that promote non-pharmacological interventions such as promoting mental well-being of FM patients.