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.

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.

It was compared smartphone-based measurements of static balance control and mobility of elderly population with and without type 2 diabetes mellitus (DM2). The present cross-sectional study investigated 73 participants grouped in a control group (n = 36) and a DM2 group (n = 37). Smartphone’s built in inertial sensors were used to record inertial changes of the participants during static balance and mobility (Timed Up and Go test – TUG) tasks. The inertial variations as a function of the time were analyzed and compared between groups. Both groups were matched in age, body mass index, male-female proportion, but DM2 group had significant larger fasting glucose than control group. Additionally, DM2 group had worst static balance control with open and closed eyes than the controls (p < 0.05) as well as they also had longer duration to execute the different events of the mobility test than the controls (p < 0.05). DM2 patients had decline of motor functions compared to controls and the use of bult-in sensors of smartphones was feasible to identify these functional impairments. The easy access of smartphones could be improving the screening of functional impairments in DM2 patients.

Recommendation systems try to guide the users in carrying out a task providing them with useful information about it. Considering the context of software development, programs are ever-increasing, making it difficult to conduct a detailed verification and validation. Automated static analyzers help to detect possible faults on software products earlier and quickly but, in general, the issue maybe a false-positive warning. In this sense, this work presents and evaluates a recommendation system, called WarningsFIX (WFX), which combines several static analyzers aim at: i) Expand the possible fault domain approached by each static analysis tool increasing the range of warnings types covered, allowing the concentration of a higher number of true-positive warnings. ii) Establish different prioritization strategies of warnings aiming at suggesting for reviewers first analyze the ones with a higher chance of being true-positive. WFX organizes the warnings information via treemaps considering four levels of abstraction: program, package, class, and line. The nodes of the treemap on each level may be classified by three different prioritization strategies based on the number of warnings, the number of tools, and the suspicions rate. The use of these strategies enables the reviewer to handle the set of warnings in a coordinated way depending on the cost and time constraint available. We perform a feasibility study to evaluate the WFX effectiveness whose results shown that: i) WFX was able to improve the results obtained from combined static analyzers to 44% of the analyzed programs, concentrating for them a greater number of true-positives. ii) WFX, depending on the adopted prioritization strategy, improved from 67.5% to 55% the ranking of lines with real bugs when compared with the list of warnings provided by the automated static analyzers without the WFX support.