Concurrent validity and minimum detectable change of Senior Fitness Test components: instrumented vs. manual assessment

Abstract

Background: The Senior Fitness Test is a reliable and valid battery of individual tests which collectively measure the strength, balance, and endurance necessary to perform activities of daily living. These tests require little equipment or cost to administer, and are commonly administered in both clinical and community settings. However, with recent and rapid advancements in technology, there is now frequent use of wearable sensors to assess and quantify mobility tasks. It was the primary aim of the present study to compare the relative and absolute test-retest reliability of body-worn sensors vs. manual assessment of Senior Fitness Test components. Methods: Participants (65 years and older) attended a single 2-hour test session at a university academic building, where they performed two blocks of 3 tests (30-Second Chair Stand, 8-Foot Up-and-Go, and 6-Minute Walk Test) with a 45 minute to one hour rest in between blocks. Ambulatory Parkinson's Disease Monitoring (APDM) Opal sensors and stopwatch methods simultaneously measured the intended outcomes for each test. Test-retest relative reliability for the fitness tests was assessed with intra-class correlation coefficients (ICC (2,1) ). The agreement between test and retest was assessed by calculating minimum detectable change, which reflects the smallest amount of absolute change from test to retest that can be considered real. Results: Participants included 17 females and 11 males (Age=70.6±5.4 years; Body Mass=70.6±15.3 kg; Height=1.66±0.08m). All manual 6-Minute Walk Test outcomes exhibited excellent test-retest reliability (ICC≥0.96). The same was true of APDM, except for turn duration (ICC>0.74) and the number of steps in turn (ICC>0.61). Test-retest reliability for the 30-Second Chair Stand and 8-Foot Up-and-Go were excellent for the manual method (ICC>0.83 and >0.88, respectively), but were poor for APDM (ICC>0.41 and >0.28, respectively). Minimum detectable change was similar between the APDM and manual methods for 6-Minute Walk Test cadence (6.8 vs. 6.6 steps/min, respectively) and gait speed (0.11 vs. 0.13 m/s, respectively). There were substantial differences between manual and APDM minimum detectable change for the 30-Second Chair Stand and 8-Foot Up-and-Go, with APDM requiring approximately twice as much change, compared with manual. Conclusions: Both the APDM system and manual methods reliably and validly assessed gait speed and cadence during the 6-Minute Walk Test, though it should be noted that APDM only successfully recorded and reported data for 2 out of every 3 participants. The manual method of assessing the 8-Foot Up-and-Go and 30-Second Chair Stand was more valid and reliable than the APDM method.

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).…”

“…The setup for the 400 m Walk Test is depicted in Figure 2 (adapted from Salvatore et al [11]). The course was 20 m long and had tape on the floor 2.5 m from each end, leaving a 15 m straightaway for the assessment of steady-state gait speed and cadence.…”

“…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).…”

“…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].…”

“…Some of the equipment used for instrumented testing is cost-prohibitive, or requires a dedicated research space and specially-trained testers. Some of the equipment can be subject to technical glitches that can impact data collection during testing [11]. Additionally, the technology needs to remain stable enough to be able to track an individual's change in function over time, or to collectively compare results at two different points in time in order to determine the effect of an intervention.…”