An Experimental Study on the Validity and Reliability of a Smartphone Application to Acquire Temporal Variables during the Single Sit-to-Stand Test with Older Adults

System for Tracking and Evaluating Performance (Step-App®): validation and clinical application of a mobile telemonitoring system in patients with knee and hip total arthroplasty. A prospective cohort study

BACKGROUND

Technological advances and digital solutions have been proposed to overcome barriers to sustainable rehabilitation programs in patients with musculoskeletal disorders. However, to date, standardized telemonitoring systems able to precisely assess physical performance and functioning are still lacking.

AIM

To validate a new mobile telemonitoring system, named System for Tracking and Evaluating Performance (Step-App®), to evaluate physical performance in patients undergone knee and hip total arthroplasty.

DESIGN

Prospective cohort study.

METHODS

A consecutive series of older adults with knee and hip total arthroplasty participated in a comprehensive rehabilitation program. The Step-App®, a mobile telemonitoring system, was used to remotely monitor the effects of rehabilitation, and the outcomes were assessed before (T0) and after the rehabilitation treatment (T1). The primary outcomes were the 6-Minute Walk Test (6MWT), the 10-Meter Walk Test (10MWT), and the 30-Second Sit-To-Stand Test (30SST).

RESULTS

Out of 42 patients assessed, 25 older patients were included in the present study. The correlation analysis between the Step-App® measurements and the traditional in-person assessments demonstrated a strong positive correlation for the 6MWT (T0: r2=0.9981, P<0.0001; T1: r2=0.9981, P<0.0001), 10MWT (T0: r2=0.9423, P<0.0001; T1: r2=0.8634, P<0.0001), and 30SST (T0: r2=1, P<0.0001; T1: r2=1, P<0.0001). The agreement analysis, using Bland-Altman plots, showed a good agreement between the Step-App® measurements and the in-person assessments.

CONCLUSIONS

Therefore, we might conclude that Step-App® could be considered as a validated mobile telemonitoring system for remote assessment that might have a role in telemonitoring personalized rehabilitation programs for knee and hip replacement patients.

CLINICAL REHABILITATION IMPACT

Our findings might guide clinicians in remote monitoring of physical performance in patients with musculoskeletal conditions, providing new insight into tailored telerehabilitation programs.

Relationship between Acceleration in a Sit-To-Stand Movement and Physical Function in Older Adults

Acceleration parameters in sit-to-stand (STS) movements are useful for measuring lower-limb function in older adults. The purpose of this study was to examine the relationship between acceleration in STS movements and physical function and the test-retest reliability of acceleration parameters in older adults. We performed cross-sectional analyses on 244 older adults including 107 men (mean age: 77.4 ± 4.7) and 137 women (mean age: 75.6 ± 5.3). Four acceleration parameters were measured in STS movements: maximum acceleration (MA), maximum velocity (MV), maximum power (MP), and stand-up time (ST). Good intraclass correlation coefficients (ICC > 0.70) were observed for all parameters. For the acceleration parameters, MA, MV, and MP were relatively strongly associated with the 5-time STS test (men: r = -0.36~-0.47; women: r = -0.37~-0.45) and the timed up and go test (men: r = -0.39~0.47, women: r = -0.43~-0.51): MP was also strongly associated with grip strength (men: r = 0.48, women: r = 0.43). All acceleration parameters were poorer in participants reporting mobility limitations than in those reporting no mobility limitations. These findings support the usefulness of sensor-based STS measurement. The system is expected to be useful in various settings where care prevention is addressed.

Concurrent and discriminant validity and reliability of an Android App to assess time, velocity and power during sit-to-stand test in community-dwelling older adults

INTRODUCTION

Nowadays, smartphones are equipped with the most sophisticated hardware which provides the opportunity to develop specific smartphone apps to analyze kinetic and kinematic parameters during sit-to-stand test in a clinical setting. The aims were to ascertain whether a new Android video-analysis based-App is comparable to the previously validated Apple-App for measuring time, velocity and power during sit-to-stand test, to determine its reliability and discriminant validity.

METHODS

One-hundred sixty-one older adults (61-86 years) were recruited from an elderly social center. Sit-to-stand variables were simultaneously recorded through the Android and Apple-App. Their validity and inter-rater, intra-rater, and test-retest reliability was tested using an intraclass correlation coefficient (ICC_2-1). Low gait speed (< 1.0 m/s), low physical performance (Short Physical Performance Battery < 10 points), and sarcopenia (EWGSOP2 guideline) were used to determine discriminant validity which was reported as the area under the curves (AUC) and their effect sizes (Hedges' g) for independent sample t-test.

RESULTS

Excellent reproducibility (ICC_2-1 > 0.85) and strong agreement (ICC_2-1 > 0.90) between operating systems for sit-to-stand variables derived from the App was found. Older adults classified as sarcopenic (11.2%), low physical performance (15.5%), or reduced gait speed (14.3%) showed worse sit-to-stand time, velocity and power with large effect sizes (Hedges' g: > 0.8) compared to their respective counterpart. These variables showed the acceptable-to-excellent ability to identify low gait speed, low physical performance, and sarcopenic older adults (AUC-range: 0.73-0.82).

CONCLUSION

The new Sit-to-Stand App running on the Android operating system is comparable to the previously validated Apple App. Excellent reproducibility and acceptable-to-excellent discriminant validity were found.

Test-retest of the Subjective Visual Vertical Test performed using a mobile application with the smartphone anchored to a turntable

PURPOSE

The alterations of the Subjective visual vertical test are related to vestibular pathology. Our previously validated method to distinguish between healthy and pathological individuals measures the deviation from the Subjective visual vertical using a mobile application installed on a smartphone fixed to a turntable anchored to the wall. The aim of this study was evaluating the intra-observer reliability of our method in individuals with or without vestibular pathology.

METHODS

Participants were recruited consecutively. In each individual two measurements with an interval of 2 h were made. Both tests were performed by the same examiner. A total of 91 patients were included in this study, of which 25 were healthy and 66 diseased. Intra-observer reliability was evaluated using the intraclass correlation coefficient (ICC). To assess the clinical accuracy of the measurement, we calculated the standard error of the measurement (SEM) and the minimum detectable change (MDC) with a 95% confidence interval.

RESULTS

Intra-observer reliability was excellent with an ICC 0.95 (0.92-0.97) in the whole sample, in healthy patients 0.91 (0.80-0.96) and in pathological patients 0.92 (0.87-0.95). The SEM was calculated to be 0.59 for the whole sample (0.26 in the "healthy" group, and 0.67 in the pathological group). Likewise, the sample's MDC was 1.16, being 0.52 and 1.36 for the healthy and the pathological group, respectively.

CONCLUSIONS

Considering the results, our method presents an excellent intraobserver reliability. Furthermore, changes in deviation greater than 0.52 in healthy individuals and 1.36 in pathological individuals can be considered a real change in deviation.

Mobile Technology for Falls Prevention in Older Adults

Falls are the leading cause of accidental death in older adults that result from a complex interplay of risk factors. Recently, the need for person-centered approach utilizing personalization, prediction, prevention and participation, known as the P4 model, in fall prevention has been highlighted. Features of mobile technology make it a suitable technological infrastructure to employ such an approach. This narrative review aims to review the evidence for using mobile technology for personalized fall risk assessment and prevention since 2017 in older adults. We aim to identify lessons learned and future directions for using mobile technology as a fall risk assessment and prevention tool. Articles were searched in PubMed and Web of Science with search terms related to older adults, mobile technology, and falls prevention. A total of 23 articles were included. Articles were identified as those examining aspects of the P4 model including prediction (measurement of fall risk), personalization (usability), prevention, and participation. Mobile technology appears to be comparable to gold-standard technology in measuring well-known fall risk factors including static and dynamic balance. Seven applications were developed to measure different fall risk factors and tested for personalization, and/or participation aspects, and four were integrated into a falls prevention program. Mobile health technology offers an innovative solution to provide tailored fall risk screening, prediction, and participation. Future studies should incorporate multiple, objective fall risk measures and implement them in community settings to determine if mobile technology can offer tailored and scalable interventions.

Estimation of Functional Aerobic Capacity Using the Sit-to-Stand Test in Older Adults with Heart Failure with Preserved Ejection Fraction

Background: The 6-Min Walking Test (6MWT) has been proposed to assess functional aerobic capacity in patients with heart failure, but many older adults with heart failure cannot complete it. The adequacy of the 5-repetition Sit-To-Stand (5-STS), a simpler test than 6MWT, to assess the functional aerobic capacity in older adults with heart failure has not been evaluated. Objectives: This study aimed to assess the usefulness of 5-STS in estimating maximal oxygen uptake (VO2 peak) in older adults with heart failure with preserved ejection fraction (HFpEF). Methods: A cross-sectional study was carried out. Patients 70 years and older with HFpEF were included. A bivariant Pearson correlation and subsequent multivariate linear regression analysis were used to analyze the correlations between the 5-STS and the estimated VO2 peak. Results: Seventy-six patients (80.74 (5.89) years) were recruited. The 5-STS showed a moderate and inversely correlation with the estimated VO2 peak (r = −0.555, p < 0.001). The 5-STS explained 40.4% of the variance in the estimated VO2 peak, adjusted by age, sex, and BMI. When older adults were stratified by BMI, the 5-STS explained 70% and 31.4% of the variance in the estimated VO2 peak in older adults with normal weight and overweight/obesity, respectively. Conclusions: The 5-STS may be an easy tool to assess functional aerobic capacity in older adults with HFpEF, especially for those with normal weight.

Can the Eight Hop Test Be Measured with Sensors? A Systematic Review

Rehabilitation aims to increase the independence and physical function after injury, surgery, or other trauma, so that patients can recover to their previous ability as much as possible. To be able to measure the degree of recovery and impact of the treatment, various functional performance tests are used. The Eight Hop Test is a hop exercise that is directly linked to the rehabilitation of people suffering from tendon and ligament injuries on the lower limb. This paper presents a systematic review on the use of sensors for measuring functional movements during the execution of the Eight Hop Test, focusing primarily on the use of sensors, related diseases, and different methods implemented. Firstly, an automated search was performed on the publication databases: PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Secondly, the publications related to the Eight-Hop Test and sensors were filtered according to several search criteria and 15 papers were finally selected to be analyzed in detail. Our analysis found that the Eight Hop Test measurements can be performed with motion, force, and imaging sensors.

Review-Emerging Portable Technologies for Gait Analysis in Neurological Disorders

The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments.

Day-to-Day Variability and Year-to-Year Reproducibility of Accelerometer-Measured Free-Living Sit-to-Stand Transitions Volume and Intensity among Community-Dwelling Older Adults

(1) Background: The purpose of this study was to evaluate the day-to-day variability and year-to-year reproducibility of an accelerometer-based algorithm for sit-to-stand (STS) transitions in a free-living environment among community-dwelling older adults. (2) Methods: Free-living thigh-worn accelerometry was recorded for three to seven days in 86 (women n = 55) community-dwelling older adults, on two occasions separated by one year, to evaluate the long-term consistency of free-living behavior. (3) Results: Year-to-year intraclass correlation coefficients (ICC) for the number of STS transitions were 0.79 (95% confidence interval, 0.70-0.86, p < 0.001), for mean angular velocity-0.81 (95% ci, 0.72-0.87, p < 0.001), and maximal angular velocity-0.73 (95% ci, 0.61-0.82, p < 0.001), respectively. Day-to-day ICCs were 0.63-0.72 for number of STS transitions (95% ci, 0.49-0.81, p < 0.001) and for mean angular velocity-0.75-0.80 (95% ci, 0.64-0.87, p < 0.001). Minimum detectable change (MDC) was 20.1 transitions/day for volume, 9.7°/s for mean intensity, and 31.7°/s for maximal intensity. (4) Conclusions: The volume and intensity of STS transitions monitored by a thigh-worn accelerometer and a sit-to-stand transitions algorithm are reproducible from day to day and year to year. The accelerometer can be used to reliably study STS transitions in free-living environments, which could add value to identifying individuals at increased risk for functional disability.

Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks

Pneumonia caused by COVID-19 is a severe health risk that sometimes leads to fatal outcomes. Due to constraints in medical care systems, technological solutions should be applied to diagnose, monitor, and alert about the disease's progress for patients receiving care at home. Some sleep disturbances, such as obstructive sleep apnea syndrome, can increase the risk for COVID-19 patients. This paper proposes an approach to evaluating patients' sleep quality with the aim of detecting sleep disturbances caused by pneumonia and other COVID-19-related pathologies. We describe a non-invasive sensor network that is used for sleep monitoring and evaluate the feasibility of an approach for training a machine learning model to detect possible COVID-19-related sleep disturbances. We also discuss a cloud-based approach for the implementation of the proposed system for processing the data streams. Based on the preliminary results, we conclude that sleep disturbances are detectable with affordable and non-invasive sensors.