The role of sex and handedness in the performance of the smartphone-based Finger-Tapping Test.

The Finger Tapping Test (FTT) is a classical neuropsychological test that assesses motor functioning, and recently it has been employed using smartphones. For classical protocols, it has been observed that sex and handedness influence the performance during the test. By assessing the influence of sex and handedness on the test, it is possible to adjust the performance measurements to ensure the validity of test results and avoid sex- and handedness-related bias. The present study aimed to evaluate the influence of sex and handedness on smartphone-based FTT performance. We developed an Android application for the FTT and recruited 40 males and 40 females to carry out three spatial designs on it (protocols I, II, and III). Participants' performance was measured using the global, temporal, and spatial parameters of the FTT. We observed that for the performance in protocol I, handedness had a significant influence on global and temporal variables, while the interaction between handedness and sex had a greater influence on spatial variables. For protocols II and III, we observed that handedness had a significant influence on global, temporal, and spatial variables compared to the other factors. We concluded that the smartphone-based test is partly influenced by handedness and sex, and in clinical implications, these factors should be considered during the evaluation of the smartphone-based FTT.

Comparison of Static Balance Control in Infected Htlv-1 Subjects with Different Tsp/Ham Diagnosis.

(1) Background: Tropical spastic paraparesis (TSP/HAM) associated with the T cell lymphotropic virus in type I humans (HTLV-1) is a slow, chronic, and progressive disease that causes balance changes. TSP/HAM diagnosis can be classified as probable, possible, and definite. We compared the static balance control of HTLV-1-infected patients with different TSP/HAM diagnosis. (2) Methods: Our sample consisted of 13 participants infected with HTLV-1 and 16 healthy participants. The center of pressure was recorded using a force platform with open and closed eyes. We divided the recordings into three intervals, period T1 (corresponds to the first 10 s); period T2 (from 10 to 45 s); period T3 (from 45 to 55 s). (3) Results: Eight participants infected with HTLV-1 were classified as probable TSP/HAM and five participants infected with HTLV-1 were classified as definite TSP/HAM. There was a significant increase in postural instability in patients with definite PET/MAH considering the structural and global variables of body sway compared to the control and the probable TSP/HAM. (4) Conclusions: We concluded that the severity of balance is directly related to the degree of signs and symptoms of TSP/HAM.

Gait patterns in ischemic and hemorrhagic post-stroke patients with delayed access to physiotherapy.

OBJECTIVES: (1) To assess the effects of a conventional, delayed physiotherapy protocol used by Ischemic Stroke (IS) and Hemorrhagic Stroke (HS) post-stroke patients, in their electromyographic activation patterns during hemiparetic gait; and (2) to study whether this protocol may improve the functional abilities in this population. METHODS: This is an observational, descriptive, and analytical quasi-experimental trial. Forty patients with unilateral IS ( n = 25 ) and HS ( n = 15 ) stroke were recruited; the stroke involved the motor cortex or sub-cortical areas, and the patients were able to walk independently. Interventions with standard protocols of physiotherapy were carried out. Evaluations (clinical and gait assessment) were performed at the time of admission and at the end of the protocol. Outcome measures include Stroke Impact Scale, Timed Up and Go Test, and gait electromyographic evaluation. RESULTS: Only IS patients (with an average of 124 . 4 ± 45 . 4 months delayed access to physiotherapy rehabilitation) had improvements in Timed Up and Go Test (change in speed =- 8 . 0 seg p < 0 . 05 ) and presented an anticipation of the onset in Upper leg muscles after the intervention. BF ( p = 0 . 05 ), ST ( p = 0 . 001 ), and RF ( p = 0 . 024 ), started their recruitment (onset) earlier at the swing phase of the gait cycle, which is more similar to the normal pattern (grey shadow). IS and HS ( 120 . 4 ± 28 . 4 months since last stroke) patients presented higher electromyographic activation, after physiotherapy, of the posterior leg muscles (gastrocnemius, semitendinosus and biceps femoris) during stance phase ( p < 0 . 05 ). CONCLUSION: IS patients had improvements after delayed conventional physiotherapy. For HS limited response to intervention was observed.

Assessing Interlimb Jump Asymmetry in Young Soccer Players: The My Jump 2 App.

BACKGROUND: Jumps are important evaluation tools for muscle strength and power and for interlimb asymmetries. Different jump tests are well related to athletic performance, prediction of injury risk, and common motor gestures of several sports such as soccer. Low-cost mobile applications (apps) have gained popularity for this measure. The authors hypothesized that the My Jump 2 app would be a valid tool to assess drop-jump performance and interlimb asymmetry in soccer players. METHODS: Eleven male soccer players took part in this study (18.2 [1.3] y, 69.9 [9.5] kg, 174 [6.6] cm). The athletes performed each test twice on a force plate (gold-standard method), while the jumps were recorded through the mobile app. Measures with the My Jump 2 app were applied by 2 evaluators, independently and in duplicate (interrater and intrarater reliability). The agreement analysis between both evaluations was done using an intraclass correlation coefficient and Bland-Altman plots. RESULTS: Compared with the force platform, the app tested showed excellent reliability for the drop jump's flight time and interlimb asymmetry (intraclass correlation coefficient > .98). For interlimb contact-time asymmetry, the values were 18.4 (9.9) and 19.1 (9.9) milliseconds for the My Jump 2 app and the force platform, respectively (P = .88). For flight-time asymmetries, the values were 389.7 (114.3) and 396.8 (112.5) milliseconds for the My Jump 2 app and the force platform, respectively (P = .88). CONCLUSION: The My Jump 2 app is a valid tool to assess drop-jump and interlimb asymmetry in soccer players.

Hand Resting Tremor Assessment of Healthy and Patients With Parkinson's Disease: An Exploratory Machine Learning Study.

The aim of this study is comparing the accuracies of machine learning algorithms to classify data concerning healthy subjects and patients with Parkinson's Disease (PD), toward different time window lengths and a number of features. Thirty-two healthy subjects and eighteen patients with PD took part on this study. The study obtained inertial recordings by using an accelerometer and a gyroscope assessing both hands of the subjects during hand resting state. We extracted time and temporal frequency domain features to feed seven machine learning algorithms: k-nearest-neighbors (kNN); logistic regression; support vector classifier (SVC); linear discriminant analysis; random forest; decision tree; and gaussian Naïve Bayes. The accuracy of the classifiers was compared using different numbers of extracted features (i.e., 272, 190, 136, 82, and 27) from different time window lengths (i.e., 1, 5, 10, and 15 s). The inertial recordings were characterized by oscillatory waveforms that, especially in patients with PD, peaked in a frequency range between 3 and 8 Hz. Outcomes showed that the most important features were the mean frequency, linear prediction coefficients, power ratio, power density skew, and kurtosis. We observed that accuracies calculated in the testing phase were higher than in the training phase. Comparing the testing accuracies, we found significant interactions among time window length and the type of classifier (p < 0.05). The study found significant effects on estimated accuracies, according to their type of algorithm, time window length, and their interaction. kNN presented the highest accuracy, while SVC showed the worst results. kNN feeding by features extracted from 1 and 5 s were the combination with more frequently highest accuracies. Classification using few features led to similar decision of the algorithms. Moreover, performance increased significantly according to the number of features used, reaching a plateau around 136. Finally, the results of this study suggested that kNN was the best algorithm to classify hand resting tremor in patients with PD.