Intra and Inter-Device Reliabilities of the Instrumented Timed-Up and Go Test Using Smartphones in Young Adult Population.

The Timed-Up and Go (TUG) test is widely utilized by healthcare professionals for assessing fall risk and mobility due to its practicality. Currently, test results are based solely on execution time, but integrating technological devices into the test can provide additional information to enhance result accuracy. This study aimed to assess the reliability of smartphone-based instrumented TUG (iTUG) parameters. We conducted evaluations of intra- and inter-device reliabilities, hypothesizing that iTUG parameters would be replicable across all experiments. A total of 30 individuals participated in Experiment A to assess intra-device reliability, while Experiment B involved 15 individuals to evaluate inter-device reliability. The smartphone was securely attached to participants' bodies at the lumbar spine level between the L3 and L5 vertebrae. In Experiment A, subjects performed the TUG test three times using the same device, with a 5 min interval between each trial. Experiment B required participants to perform three trials using different devices, with the same time interval between trials. Comparing stopwatch and smartphone measurements in Experiment A, no significant differences in test duration were found between the two devices. A perfect correlation and Bland-Altman analysis indicated good agreement between devices. Intra-device reliability analysis in Experiment A revealed significant reliability in nine out of eleven variables, with four variables showing excellent reliability and five showing moderate to high reliability. In Experiment B, inter-device reliability was observed among different smartphone devices, with nine out of eleven variables demonstrating significant reliability. Notable differences were found in angular velocity peak at the first and second turns between specific devices, emphasizing the importance of considering device variations in inertial measurements. Hence, smartphone inertial sensors present a valid, applicable, and feasible alternative for TUG assessment.

Smartphone-based evaluation of static balance and mobility in long-lasting COVID-19 patients.

Background: SARS-CoV-2 infection can lead to a variety of persistent sequelae, collectively known as long COVID-19. Deficits in postural balance have been reported in patients several months after COVID-19 infection. The purpose of this study was to evaluate the static balance and balance of individuals with long COVID-19 using inertial sensors in smartphones. Methods: A total of 73 participants were included in this study, of which 41 had long COVID-19 and 32 served as controls. All participants in the long COVID-19 group reported physical complaints for at least 7 months after SARS-CoV-2 infection. Participants were evaluated using a built-in inertial sensor of a smartphone attached to the low back, which recorded inertial signals during a static balance and mobility task (timed up and go test). The parameters of static balance and mobility obtained from both groups were compared. Results: The groups were matched for age and BMI. Of the 41 participants in the long COVID-19 group, 22 reported balance impairment and 33 had impaired balance in the Sharpened Romberg test. Static balance assessment revealed that the long COVID-19 group had greater postural instability with both eyes open and closed than the control group. In the TUG test, the long COVID-19 group showed greater acceleration during the sit-to-stand transition compared to the control group. Conclusion: The smartphone was feasible to identify losses in the balance motor control and mobility of patients with long-lasting symptomatic COVID-19 even after several months or years. Attention to the balance impairment experienced by these patients could help prevent falls and improve their quality of life, and the use of the smartphone can expand this monitoring for a broader population.

The hand tremor spectrum is modified by the inertial sensor mass during lightweight wearable and smartphone-based assessment in healthy young subjects.

Tremors are common disorders characterized by an involuntary and relatively rhythmic oscillation that can occur in any part of the body and may be physiological or associated with some pathological condition. It is known that the mass loading can change the power spectral distribution of the tremor. Nowadays, many instruments have been used in the evaluation of tremors with bult-in inertial sensors, such as smartphones and wearables, which can significantly differ in the device mass. The aim of this study was to compare the quantification of hand tremor using Fourier spectral techniques obtained from readings of accelerometers built-in a lightweight handheld device and a commercial smartphone in healthy young subjects. We recruited 28 healthy right-handed subjects with ages ranging from 18 to 40 years. We tested hand tremors at rest and postural conditions using lightweight wearable device (5.7 g) and smartphone (169 g). Comparing both devices at resting tremor, we found with smartphone the power distribution of peak ranging 5 and 12 Hz in both hands. With wearable, the result was similar but less evident. When comparing both devices in postural tremor, there were significant differences in both frequency ranges in peak frequency and peak amplitude in both hands. Our main findings show that in resting condition the hand tremor spectrum had a higher peak amplitude in the 5-12 Hz range when the tremor was recorded with smartphones, and in postural condition there was a significantly (p < 0.05) higher peak power spectrum and peak frequency in the dominant hand tremors recorded with smartphones compared to those obtained with lightweight wearable device. Devices having different masses can alter the features of the hand tremor spectrum and their mutual comparisons can be prejudiced.

Wearable Devices and Smartphone Inertial Sensors for Static Balance Assessment: A Concurrent Validity Study in Young Adult Population.

Falls represent a public health issue around the world and prevention is an important part of the politics of many countries. The standard method of evaluating balance is posturography using a force platform, which has high financial costs. Other instruments, such as portable devices and smartphones, have been evaluated as low-cost alternatives to the screening of balance control. Although smartphones and wearables have different sizes, shapes, and weights, they have been systematically validated for static balance control tasks. Different studies have applied different experimental configurations to validate the inertial measurements obtained by these devices. We aim to evaluate the concurrent validity of a smartphone and a portable device for the evaluation of static balance control in the same group of participants. Twenty-six healthy and young subjects comprised the sample. The validity for static balance control evaluation of built-in accelerometers inside portable smartphone and wearable devices was tested considering force platform recordings as a gold standard for comparisons. A linear correlation (r) between the quantitative variables obtained from the inertial sensors and the force platform was used as an indicator of the concurrent validity. Reliability of the measures was calculated using Intraclass correlation in a subsample (n = 14). Smartphones had 11 out of 12 variables with significant moderate to very high correlation (r > 0.5, p < 0.05) with force platform variables in open eyes, closed eyes, and unipedal conditions, while wearable devices had 8 out of 12 variables with moderate to very high correlation (r > 0.5, p < 0.05) with force platform variables under the same task conditions. Significant reliabilities were found in closed eye conditions for smartphones and wearables. The smartphone and wearable devices had concurrent validity for the static balance evaluation and the smartphone had better validity results than the wearables for the static balance evaluation.

Validity and Reliability of Smartphone App for Evaluating Postural Adjustments during Step Initiation.

The evaluation of anticipatory postural adjustments (APAs) requires high-cost and complex handling systems, only available at research laboratories. New alternative methods are being developed in this field, on the other hand, to solve this issue and allow applicability in clinic, sport and hospital environments. The objective of this study was to validate an app for mobile devices to measure the APAs during gait initiation by comparing the signals obtained from cell phones using the Momentum app with measurements made by a kinematic system. The center-of-mass accelerations of a total of 20 healthy subjects were measured by the above app, which read the inertial sensors of the smartphones, and by kinematics, with a reflective marker positioned on their lumbar spine. The subjects took a step forward after hearing a command from an experimenter. The variables of the anticipatory phase, prior to the heel-off and the step phase, were measured. In the anticipatory phase, the linear correlation of all variables measured by the two measurement techniques was significant and indicated a high correlation between the devices (APAonset: r = 0.95, p < 0.0001; APAamp: r = 0.71, p = 0.003, and PEAKtime: r = 0.95, p < 0.0001). The linear correlation between the two measurement techniques for the step phase variables measured by ques was also significant (STEPinterval: r = 0.56, p = 0.008; STEPpeak1: r = 0.79, p < 0.0001; and STEPpeak2: r = 0.64, p < 0.0001). The Bland−Altman graphs indicated agreement between instruments with similar behavior as well as subjects within confidence limits and low dispersion. Thus, using the Momentum cell phone application is valid for the assessment of APAs during gait initiation compared to the gold standard instrument (kinematics), proving to be a useful, less complex, and less costly alternative for the assessment of healthy individuals.

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.