Convergent Validity of a Wearable Sensor System for Measuring Sub-Task Performance during the Timed Up-and-Go Test

Abstract

Background: The timed-up-and-go test (TUG) is one of the most commonly used tests of physical function in clinical practice and for research outcomes. Inertial sensors have been used to parse the TUG test into its composite phases (rising, walking, turning, etc.), but have not validated this approach against an optoelectronic gold-standard, and to our knowledge no studies have published the minimal detectable change of these measurements. Methods: Eleven adults performed the TUG three times each under normal and slow walking conditions, and 3 m and 5 m walking distances, in a 12-camera motion analysis laboratory. An inertial measurement unit (IMU) with tri-axial accelerometers and gyroscopes was worn on the upper-torso. Motion analysis marker data and IMU signals were analyzed separately to identify the six main TUG phases: sit-to-stand, 1st walk, 1st turn, 2nd walk, 2nd turn, and stand-to-sit, and the absolute agreement between two systems analyzed using intra-class correlation (ICC, model 2) analysis. The minimal detectable change (MDC) within subjects was also calculated for each TUG phase. Results: The overall difference between TUG sub-tasks determined using 3D motion capture data and the IMU sensor data was <0.5 s. For all TUG distances and speeds, the absolute agreement was high for total TUG time and walk times (ICC > 0.90), but less for chair activity (ICC range 0.5–0.9) and typically poor for the turn time (ICC < 0.4). MDC values for total TUG time ranged between 2–4 s or 12–22% of the TUG time measurement. MDC of the sub-task times were higher proportionally, being 20–60% of the sub-task duration. Conclusions: We conclude that a commercial IMU can be used for quantifying the TUG phases with accuracy sufficient for clinical applications; however, the MDC when using inertial sensors is not necessarily improved over less sophisticated measurement tools.

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

“…However, the body-worn sensors and motion capture agreed better than the App and video with respect to average gait speed (-0.017 m/s vs. -0.103 m/s, respectively). Unfortunately, Beyea et al [15] did not report 95% LOA, so we are unable to compare the random error observed here with theirs.…”

“…Like the present study, Beyea et al [15] additionally investigated the sit-to-stand time, gait speed, turn duration, and stand-tosit time. While the differences were statistically significant, the App here exhibited less mean difference from video analysis, compared with the difference between body-worn sensors and motion capture investigated by Beyea et al [15] for the sit-to-stand time (-0.09 s vs. -0.14 s, respectively), turn duration (-0.10 s vs. -0.23 s, respectively), and the stand-to-sit time (-0.14 s vs -0.21 s, respectively). However, the body-worn sensors and motion capture agreed better than the App and video with respect to average gait speed (-0.017 m/s vs. -0.103 m/s, respectively).…”

“…Several others have studied the agreement between two methods for the assessment of the total time taken to complete the 8-ft Up-and-Go [11] or some version of the Timed Up-and-Go [15][16][17][18]. The mean difference between the App and video here (0.08 s) was as good or better than what has been reported elsewhere [11,15,18]. Dubois et al [16] reported a negligible systematic bias between body-worn sensors and a stopwatch (-0.001 s), but the 95% LOA were wide relative to ours (-2.242 to 2.240 s vs. -0.459 to 0.305 s).…”

“…Both devices reporting the same information is not evidence that either is reporting valid information. Importantly, larger differences have been observed when comparing body-worn sensors to other technologies [11,15,16,18].…”

“…It has become critical to adapt our paradigm of healthcare from the treatment of existing diseases to one of disease prevention and the promotion of health and wellness. In order to do this, we need to better understand the charac-CrossMark ← Click for updates doi: 10.7243/2055-2386- [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] teristics of healthy aging so that we know which ones have the biggest impact on quality of life. As we collect information about strength, balance, gait, and functional mobility in this population, it has become important to develop tests and measures with normative data and criterion-referenced standards that are reliable and valid, but also easy to administer at relatively low cost [3].…”