iPhone Sensors in Tracking Outcome Variables of the 30-Second Chair Stand Test and Stair Climb Test to Evaluate Disability: Cross-Sectional Pilot Study

Abstract

BackgroundPerformance tests are important to characterize patient disabilities and functional changes. The Osteoarthritis Research Society International and others recommend the 30-second Chair Stand Test and Stair Climb Test, among others, as core tests that capture two distinct types of disability during activities of daily living. However, these two tests are limited by current protocols of testing in clinics. There is a need for an alternative that allows remote testing of functional capabilities during these tests in the osteoarthritis patient population.ObjectiveObjectives are to (1) develop an app for testing the functionality of an iPhone’s accelerometer and gravity sensor and (2) conduct a pilot study objectively evaluating the criterion validity and test-retest reliability of outcome variables obtained from these sensors during the 30-second Chair Stand Test and Stair Climb Test.MethodsAn iOS app was developed with data collection capabilities from the built-in iPhone accelerometer and gravity sensor tools and linked to Google Firebase. A total of 24 subjects performed the 30-second Chair Stand Test with an iPhone accelerometer collecting data and an external rater manually counting sit-to-stand repetitions. A total of 21 subjects performed the Stair Climb Test with an iPhone gravity sensor turned on and an external rater timing the duration of the test on a stopwatch. App data from Firebase were converted into graphical data and exported into MATLAB for data filtering. Multiple iterations of a data processing algorithm were used to increase robustness and accuracy. MATLAB-generated outcome variables were compared to the manually determined outcome variables of each test. Pearson’s correlation coefficients (PCCs), Bland-Altman plots, intraclass correlation coefficients (ICCs), standard errors of measurement, and repeatability coefficients were generated to evaluate criterion validity, agreement, and test-retest reliability of iPhone sensor data against gold-standard manual measurements.ResultsApp accelerometer data during the 30-second Chair Stand Test (PCC=.890) and gravity sensor data during the Stair Climb Test (PCC=.865) were highly correlated to gold-standard manual measurements. Greater than 95% of values on Bland-Altman plots comparing the manual data to the app data fell within the 95% limits of agreement. Strong intraclass correlation was found for trials of the 30-second Chair Stand Test (ICC=.968) and Stair Climb Test (ICC=.902). Standard errors of measurement for both tests were found to be within acceptable thresholds for MATLAB. Repeatability coefficients for the 30-second Chair Stand Test and Stair Climb Test were 0.629 and 1.20, respectively.ConclusionsApp-based performance testing of the 30-second Chair Stand Test and Stair Climb Test is valid and reliable, suggesting its applicability to future, larger-scale studies in the osteoarthritis patient population.

Validity and feasibility of a cell phone application for the assessment of gait and other functional fitness outcomes

“…The mean difference in the number of chair stands between the App and video in the present study (0.1 stands) was smaller than the difference between body-worn sensors and manual counting (0.5 stands) reported by Salvatore et al [11] and the difference between an automated cell phone application and manual counting (-0.6 stands) reported by Adusumilli et al [4]. More importantly, our 95% LOA (-0.6 to 0.8 stands) were much narrower than both studies (-3.0 to 2.0 stands, and -3.9 to 2.6 stands, respectively).…”

“…This allows researchers and clinicians to analyze the subcomponents of functional activity tests in order to determine which ones are having the largest impact on performance of the overall tasks. While many of these new technologies have proven to be reliable and valid for measuring components of gait and functional mobility tasks [4][5][6][7][8][9][10], their feasibility of use varies widely. Some of the equipment used for instrumented testing is cost-prohibitive, or requires a dedicated research space and specially-trained testers.…”

Remote Assessment of Functional Mobility and Strength in Older Cancer Survivors: Protocol for a Validity and Reliability Study

“…The past decade has seen tremendous advances in the development and testing of wearable sensors to evaluate important outcomes like physical performance. However, to date, wearable sensors are either very expensive [ 15 - 18 ], proprietary [ 19 , 20 ], require technicians present for testing [ 15 , 17 , 21 ], and/or involve complex programming code to process the data [ 22 ]. For example, commercially available systems that include wearable sensors, such as LEGSys or the Opal, and software to administer and score standard gait tests (eg, Timed Up and Go, or TUG test), provide a range of useful gait parameters [ 23 , 24 ].…”

Digital Analysis of Sit-to-Stand in Masters Athletes, Healthy Old People, and Young Adults Using a Depth Sensor

“…This work exclusively focused on the use of depth sensor technology due to its ability to track human motion without any physical anatomical landmarks, sensors, or devices being placed on the participant’s body. There are several studies that have utilised wearable technology (i.e., mobile devices, accelerometer, and gravity sensors) to track balance and sit-to-stand motions successfully; however, they are only capable of providing outcomes in relation to where the device/marker has been located, which means that the body itself is not being assessed, and they are expensive to implement widely [ 32 , 33 , 34 ]. However, future work should explore uniting both modalities to provide a holistic overview of the execution of balance and sit-to-stand motions.…”