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. Author manuscript; available in PMC: 2022 Dec 8.
Published in final edited form as: J Nutr Health Aging. 2020;24(8):906–913. doi: 10.1007/s12603-020-1432-2

COMPARISON OF A MULTI-COMPONENT PHYSICAL FUNCTION BATTERY TO USUAL WALKING SPEED FOR ASSESSING LOWER EXTREMITY FUNCTION AND MOBILITY LIMITATION IN OLDER ADULTS

C RIWNIAK 1, JE SIMON 1,2, NP WAGES 1,3, LA CLARK 1,3,4, TM MANINI 5, DW RUSS 1,6, BC CLARK 1,3,7
PMCID: PMC9731178  NIHMSID: NIHMS1852310  PMID: 33009544

Abstract

Objectives:

To compare a composite measure of physical function that comprises locomotor and non-locomotor tests (i.e., the Mobility Battery Assessment (MBA)) with traditional measures of mobility (4-m usual gait speed (UGS), six-minute walk (6MW) gait speed, and short physical performance battery (SPPB) score) for assessing lower extremity function and discriminating community dwelling older adults with and without mobility limitations.

Design:

Cross-sectional, observational study.

Setting:

Laboratory-based.

Participants:

89 community-dwelling older adults (74.9±6.7).

Measurements:

Using principal component analysis we derived an MBA score for 89 community-dwelling older adults, and quantified 4-m UGS, 6MW gait speed, and SPPB score. The MBA score was based on five lab-based tests. We also quantified self-reported lower extremity function/mobility using the Neuro-QOL Lower Extremity Function-Mobility instrument. Based on this data a continuous score was derived and subjects were classified as “mobility limited” or “non-mobility limited”. Correlations between the mobility measures and the Neuro-QOL score were calculated, and ROC curves were constructed to determine the AUC for the mobility measures ability to predict mobility limitations.

Results:

The MBA had the largest AUC (0.92) for discriminating mobility limitations and exhibited the strongest correlation (0.73) with the Neuro-QOL Lower Extremity Function-Mobility Scale. The worst performing predictors were the 4-meter UGS and stair climb power both with an AUC of 0.8 for discriminating mobility limitations, and a low correlation with Neuro-QOL Lower Extremity Function Scale of 0.39 and 0.46, respectively.

Conclusion:

The MBA score moderately improves the magnitude of correlation and discrimination of mobility limitation in older adults than singular, standard tests of mobility.

Keywords: Physical function, mobility, sarcopenia, gait, assessment, strength, balance

Introduction

Optimal mobility, defined as relative ease and freedom of movement in all of its forms, is central to independent living and overall healthy aging (1). Unfortunately, mobility limitations are common, affecting ~35% of adults over 70 years, and the majority of adults over 85 years (2-4). Mobility limitations are strongly associated with fall risk, disability, increased dependency, hospitalization, and mortality (5-10). As a result, mobility limitations are a significant burden, not only to individuals, but also to the public with annual health care costs of approximately $42 billion in the U.S. (11). Older persons judge mobility and independence, not longevity, as their most important health goal (12, 13). The culmination of increased life expectancy and disease burden in the United States threatens the mobility independence of older Americans.

Assessing gait speed over both short (e.g., 4-8 m) and long distance (e.g., 400 m) has become a common way of quantifying mobility in both clinical and research settings. Because slow usual gait speed (UGS) is strongly negatively associated with survival in both men and women (7), it has been suggested that UGS serve as a “sixth vital sign” for older adults (14, 15). However, there is more to the construct of mobility than gait speed, leading investigators to postulate that a composite score derived from performance on a series of physical tasks may better capture mobility (16, 17). For example, the short physical performance battery (SPPB) assesses short distance UGS along with chair rise performance and static balance to derive a composite score (18). However, Guralnik et al. (2000) concluded that the UGS component of the SPPB alone performed as well as the full SPPB in predicting incident disability, despite a slight (3–5%) difference favoring the SPPB between Area Under the Curves (AUCs) of the full SPPB and the UGS alone (19). More recently, Seino and colleagues (2012) reported that a composite score derived from performance in upper and lower extremity function tests was only marginally better than short distance UGS alone at discriminating self-reported functional capacity and mobility limitations (17). These results suggest that a composite score does not yield additional value beyond 4-meter UGS.

The counter argument is that ambulating, particularly only a short-distance in a straight line, does not activate all the physiological systems required to meet mobility challenges faced in the "real-world" (e.g., muscle strength, aerobic capacity, muscle fatigue-resistance, musculoskeletal range of motion, balance and postural control, motor planning, motor initiation, motor sequencing, motor recall, etc.). Ergo, it is very possible that a composite score derived from a combination of locomotor and non-locomotor tests that collectively challenge more of the above-mentioned physiological components may better assess mobility capacity than 4-meter UGS alone.

The purpose of this study was to compare a composite measure of physical function that comprises five different locomotor and non-locomotor tests with traditional measures of mobility (UGS alone, six-minute walk gait speed, and SPPB score) for assessing lower extremity function and discriminating community dwelling older adults with and without mobility limitations. Specifically, we derived a composite score from the following lab-based tests: 1) six-minute walk gait speed (with 180° turns every 30-meters), 2) time to complete the four square step test, 3) 5x chair rise time, 4) stair climb power, and 5) time to complete a complex functional task involving rising from the floor and then lifting and carrying a laundry basket to a table. We postulated that this combination of tests would be a robust representation of mobility as characterized by “ease and freedom of movement in all of its forms”. We henceforth refer to this composite score as the “Mobility Battery Assessment”, or MBA, score.

Methods

Study Participants

89 community-dwelling older adults (63-92 years of age, 74.9±6.7) participated in this study, which was part of a larger study examining the neurological mechanisms of age-related muscle weakness (i.e., The UNCODE Study; NCT02505529). To be eligible for the study, individuals had to be > 60-years of age, have a body mass index (BMI) between 18-40 kg/m2, live independently, and be free of major musculoskeletal, neurological, cardiac, pulmonary, renal, psychiatric, and cognitive disease or disorders (See Supplementary Table 1 for a complete list of inclusion and exclusion criteria).

To characterize and describe the study participants we measured the following as previously described: body composition (including estimates of lean mass) using dual-energy x-ray absorptiometry (DEXA) (20), moderate intensity physical activity via accelerometry (21), neuropsychological status via the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) (22), comorbidities via the Charlson Comorbidity Index (23), depression via the Center for Epidemiologic Studies Depression Scale (24), participants opinion about their knees and associated problems via the Knee Injury and Osteoarthritis Outcome Score (25), isokinetic leg extension strength (max torque at 60 degrees/sec relative to body weight) via dynamometry (26), and how much and to what spatial extent is the person’s typical life space defined via the Life Space Survey (27). This study was reviewed and approved by the Ohio University Institutional Review Board, and all participants gave written informed consent to participate.

Mobility Battery Assessment (MBA)

Our composite MBA score was derived from a combination of 1) six-minute walk gait speed, 2) time to complete the four square step test, 3) 5x chair rise time, 4) stair climb power, and 5) time to complete a complex functional task involving rising from the floor and then lifting and carrying a laundry basket to a table. These tests were selected based on the theoretical framework that they included both locomotor and non-locomotor tasks that would challenge the key physiological systems required to meet mobility challenges in the “real world setting”, such as muscle strength, aerobic capacity, muscle fatigue-resistance, musculoskeletal range of motion, balance and postural control, motor planning, motor initiation, motor sequencing, motor recall, etc. Below we describe each component of the MBA.

Six-Minute Walk Gait Speed

Participants were instructed to walk as quick and as far as possible in 6 minutes (on a 60-meter course). Specifically, participants were instructed to walk in a straight line for 30-meters, complete a 180° left turn around a cone and walk back to the starting line, which was an additional 30 meters. This constituted one lap of the walk course. Once participants arrived at the starting line, they turned 180° to the left around an additional cone and proceeded to repeat this sequence as many times as possible in the 6-minute time period. Markers were set every 3 meters so that the research team could accurately determine the distance (m) completed in 6-minutes. Participants were given the remaining time after each 60 m lap and verbal encouragement was provided. This task was performed once. The average gait speed over the entirety of the 6 min walk was then calculated (m/sec).

Four Square Step Test Time

Participants were required to step in a predetermined sequence over four 76-cm-long pieces of white tape, placed in a cross configuration on the ground over dark colored carpet. The participants started in square 1, facing forward. The subject steps right laterally into square 2, backwards to square 3, left laterally to square 4, forward to square 1, backwards to square 4, right laterally to square 3, forward to square 2, and left laterally to square 1. Participants were instructed to complete the sequence as fast as possible without touching the pieces of white tape and placing both feet in each square. Time taken to complete the sequence was recorded by the research staff. The test was performed three times with at least 30-secs rest between trials and the mean of the trials determined. In the event that a subject performed the test incorrectly (e.g., sequencing was incorrect, stepped on the line, lost their balance, etc.) the trial was discontinued. Up to one additional trial was permitted for averaging in these instances.

5x Chair Rise Time

Participants were asked to sit in a chair with a pan height of 45 cm (height included 4 cm of padding). Participants were instructed to cross their arms and place their hands on the opposite shoulder. Starting from an upright seated position, they were instructed to stand up and then sit back down five times consecutively keeping both feet on the floor throughout the test. Subjects were given as much time as necessary to complete the test, and all subjects were able to complete the test. The task was manually timed and performed once.

Stair Climb Power

Participants were instructed to climb a flight of stairs (8 steps) as quickly as possible. They were instructed not to use the handrail unless they were unable to climb the stairs without its use. The time required to complete the test was measured using switch mats (Lafayette Instruments Model 63516A) interfaced with a digital timer (Lafayette Instruments Model 54060). Stair climb power was subsequently calculated using Equation 1.

Power (Watts)=((Body Weight in kg)x(9.8msec2)x(stair height in meters))(stair climb time in seconds). (Eq. 1.)

The task was perfonned twice, and the results were averaged between trials.

Complex Functional Task Tune

Participants were asked to sit upright on the floor with their legs extended in front of them with their knees bent to —45°. A laundry basket that weighed a total of 4.5 kg was positioned in front of their feet. Participants were permitted to place their hands on the ground behind themselves if needed or preferred. Starting from the seated position, participants were instructed to stand, lift the laundry basket from standing, walk 1.52 meters, and place the basket on a 0.76 meter high table. The time to complete the task was measured manually. For participants who were unable to complete the task or those who took >30 seconds to complete the task, a time value of 30 seconds was assigned. The task was performed twice, and the results were averaged. If the participant was unable to perform the test a second time, the first attempt was used to represent their value.

Calculation of MBA Score

Based on the performance of all of the above tasks we calculated the MBA score using principal component analysis similar to that described by Seino et al. (17). The equation (Eq. 2) for the MBA resulted in a composite score in which all five tasks are loaded onto one component. The MBA scores have a distribution of a mean of 0 and a standard deviation of 1.0, where X1=six minute walk gait speed [m/sec], X2= time to complete the four square step test [sec], X3=5x chair rise time [sec], X4= stair climb power [Watts], and X5= time to complete the complex functional task [sec].

MBA Score=-0.26X1+0.26X2+0.23X3-0.20X4+0.24X5 (Eq. 2.)

The equation was constructed in a weighted manner using the coefficients from the principal component scores obtained from analysis. This analysis includes the first component which accounts for the largest variance among the extracted components and is a useful tool combining all explanatory variables into a single score (28, 29). Since all five tasks loaded onto the first component, this will represent a linear combination of 6-min walk gait speed, time to complete the four-square step test, 5x chair rise time, stair climb power, and time to complete a complex functional task, which can be used as an overall measure of mobility. A positive MBA score indicates better mobility whereas, as negative mobility score indicates worse mobility.

SPPB Score and Usual Gait Speed

We conducted the SPPB in a similar manner as previously described (18). In brief, we assessed 1) 5x repeated chair stand time (note the 5x chair rise time was only assessed once and as such the same values were used in the MBA and SPPB), 2) a series of static balance tests (side-by-side, semi-tandem, and tandem balance tests), and a 4-meter UGS. The scores from the 3 events were summed together with a maximum score of 12.

Neuro-QOL Lower Extremity Function-Mobility

The Neuro-QOL Lower Extremity Function-Mobility instrument is a self-report survey developed to assess the domain of lower extremity function-mobility (30). The domain definition is “one’s ability to carry out various activities involving the trunk region and increasing degrees of bodily movement, ambulation, balance or endurance” (30). Subjects completed the 19 item Adult form where higher scores indicate better self-reported health related to the domain. The Neuro-QoL items, item banks, and scales are the result of a rigorous development process that included literature review, qualitative and cognitive interviewing, general population and clinical population testing, and state-of-the-art item response theory analyses (31). Internal consistency and 1 week test-retest reliability of the Neuro-QoL is high with Cronbach’s alphas ranging from 0.78 to 0.94 and Intraclass Correlations ranging from 0.57 to 0.89 (31). Content validity of the Neuro-QoL has been shown using focus groups (32) and construct validity has been shown in a variety of clinical populations (33-35). Normative values of the Neuro-QOL also exist for general and clinical populations indicating the use in a healthy older adult sample (31). For more details about the development and validation of the Neuro-QOL instruments please see (31). In addition to the raw score being calculated (range 0-76), we also discretely classified subjects as either “mobility limited” or “non-mobility limited”. Here, a subject was classified as mobility limited if they reported difficulty on either of two questions related to difficulty with walking or difficulty climbing stairs. This operational definition is similar to that previously used (2, 17). Specifically, a participant was classified as mobility limited if they reported “some difficulty”, “with much difficulty”, or “unable to do” for either of the two following Neuo-QOL Lower Extremity Function-Mobility questions: 1) “How much difficulty do you currently have climbing stairs step over step without a handrail?” or “How much difficulty do you currently have taking a 20-minute brisk walk, without stopping to rest?”.

Statistical Analysis

Pearson correlations between each predictor variable and the Neuro-QOL Lower Extremity Function Scale were calculated. Correlations were interpreted as negligible (0.00 to 0.29), low (0.29-0.49), moderate (0.50-0.69), strong (0.70 to 0.89), and very strong (0.90 to 1.0) (36). To compare between correlations the comparison of correlations for dependent samples was used according to Eid et al. (37) in which a z-statistic and p-value are calculated. It should be noted that assumptions of linearity were evaluated and met in order to conduct the Pearson correlations and Z-tests. Additionally, a receiver operator characteristic (ROC) curve was constructed to determine the AUC with 95% confidence interval for the MBA, 4-meter usual gait speed, SPPB, and each component of the MBA individually for predicting mobility limited and non-mobility limited individuals. A perfect model would have an AUC=1.0 and random guessing would be expected to produce an AUC=0.5. An AUC between 0.7 - 0.8 is considered acceptable discrimination, between 0.8 - 0.9 is considered excellent discrimination, and > 0.9 is considered outstanding discrimination (38). Youden’s Index with corresponding optimal cut points were calculated for each ROC curve with corresponding sensitivity and specificity (39). To compare between AUCs the comparison of AUCs for dependent samples was used according to DeLong et al. (40) in which a z-statistic and p-value are calculated (41). Alpha level was set at μ=0.05 for all analyses.

Results

Descriptive characteristics of the study participants are provided in Table 1. Table 2 contains all AUCs, Youden Indices with corresponding optimal cut points, sensitivity and specificity values, and correlations for all predictor variables with the Neuro-QOL. All predictor variables were significantly different from an AUC of 0.5 and had excellent discriminating ability with AUCs >0.8. Specifically, the MBA had the largest AUC (0.92) for discriminating mobility limitations and exhibited the strongest correlation (0.73, moderate) with the Neuro-QOL Lower Extremity Function-Mobility Scale. The SPPB, six minute walk test, 4SST, chair rise, and CFT had an AUC of 0.89, 0.88, 0.86, 0.86, and 0.87 for discriminating mobility limitations, respectively, and moderate correlations with the Neuro-QOL Lower Extremity Function Scale of 0.57, 0.64, −0.68, −0.59, and −0.63 respectively. The worst performing predictors were the 4-meter UGS and stair climb power both with an AUC of 0.8 for discriminating mobility limitations, and a low correlation with Neuro-QOL Lower Extremity Function Scale of 0.39 and 0.46, respectively.

Table 1.

Characteristics and descriptive statistics of the study participants (n=89)

Characteristic Mean ± SD or n (%)
Age, years 74.9 ± 6.7
Males 29 (33.7%)
Females 57 (66.3%)
Height (cm) 164.3 ± 10.0
Weight (kg) 74.2 ± 15.9
BMI 27.4 ± 5.0
Morbid Obesity (% BMI ≥ 35) 5 (5.6%)
Appendicular Lean Mass (kg/height2) 6.7 ± 1.2
Body fat % 35.6 ±7.9
4-Meter Usual Gait Speed (m/s) 1.0 ± 0.20
Short Physical Performance Battery (score) 10.7 ± 2.1
Chair Rise (s) 11.1 ± 4.4
Complex Functional Task (s) 10.9 ± 9.2
6-Min Walk Gait Speed (m/s) 1.3 ± 0.30
Unloaded Stair Climb Power (Watts) 257.7 ± 88.8
Four Square Step Test (s) 9.6 ± 3.7
Accelerometry minutes per week of moderate activity 103.6 ± 52.9
RBANS Score 106.5 ± 12.2
Charlson Comorbidity Index (% 10-yr survival) 4.05 ± 0.9
Center for Epidemiological Studies Depression Score 7.2 ± 6.7
Knee Injury and Osteoarthritis Outcome Score 88.9 ± 13.7
LifeSpace Score 80.3 ± 29.5

cm: centimeters; kg: kilograms; BMI: Body Mass Index (weight in kg/height in meters squared); RBANS: Repeatable Battery for the Assessment of Neuropsychological Status.

Table 2.

Pearson correlations between the laboratory-based assessments of mobility and the Neuro-QOL Lower Extremity Function-Mobility score, and the area under the receiving operator curve (AUC) and associated AUC data for the laboratory-based assessments of mobility for discriminating mobility limited older adults

Correlation with Neuro-QOL
Lower Extremity-Mobility
Function
AUC (95% CI) Optimal Cut Point
(Youden’s Index)
Specificity Sensitivity
MBA 0.73* 0.92 (0.83, 0.99)* 0.33 (0.74) 0.87 0.87
4 Meter Usual Gait Speed 0.39* 0.80 (0.66, 0.94)* 0.85 (0.65) 0.90 0.75
SPPB 0.57* 0.89 (0.79, 0.99)* 10.50 (0.62) 0.87 0.75
6-Min Walk 0.64* 0.88 (0.78, 0.98)* 1.14(0.63) 0.88 0.75
4SST −0.68* 0.86 (0.76, 0.96)* 9.87 (0.64) 0.78 0.86
Chair Rise −0.59* 0.86 (0.75, 0.98)* 12.13 (0.67) 0.79 0.88
Stair Climb 0.46* 0.80 (0.66, 0.94)* 171.04(0.61) 0.93 0.68
CFT −0.63* 0.87 (0.76, 0.98)* 8.93 (0.63) 0.79 0.84

MBA: Mobility battery assessment; SPPB: Short physical performance battery

*

Denotes statistically significant at p≤0.05

For the specific Youden Indices when taking into consideration both sensitivity and specificity the MBA cutoff score performed the best. Stair climb had the highest specificity (0.93) but had the lowest sensitivity (0.68). Therefore, the stair climb power is the best at identifying individuals who are not classified as mobility limited; however, the MBA is good at identifying individuals who are and who are not classified as mobility limited with a specificity of 0.87 and sensitivity of 0.87. Table 3 contains the comparisons between the correlations of each variable and the Neuro-QOL (z-statistic and p-value for each comparison). Table 4 contains the comparisons between the AUC values (z-statistic and p-value for each comparison). The MBA AUC was significantly different from the AUCs of 4SST, chair rise, stair climb power, and 4-meter UGS, with the MBA performing better. Additionally, the 6-minute walk gait speed and 4SST AUC was significantly different from the AUC of stair climb, with stair climb performing worse. All other AUC comparisons were not statistically different from each other. Overall, the MBA appeared to discriminate mobility limited older adults better than the 4-meter UGS test, 4SST, chair rise, stair climb, and CFT, but were similar to the 6-min walk gait speed and SPPB. However, 6MWT and SPPB had larger confidence intervals around the AUC when compared to the MBA, and therefore, are potentially less precise. For the correlation comparisons, the MBA had a statistically significant stronger correlation with the Neuro-QOL when compared to the 4m UGS, 6MWT, SPPB, chair rise, stair climb, and CFT (i.e., all tests except the 4SST) (Tables 2 and 3).

Table 3.

Statistical comparisons between the correlations for the laboratory-based assessments of mobility and the Neuro-QOL Lower Extremity Function-Mobility score

MBA 6-Min Walk 4SST Chair Rise Stair Climb CFT 4m UGS SPPB
MBA
6-Min Walk Z=2.45, p=0.01*
4SST Z=1.45, p=0.15 Z=−0.71, p=0.47
Chair Rise Z=2.61, p=0.01* Z=0.66, p=0.51 Z=1.28, p=0.19
Stair Climb Z=3.99, p=0.01* Z=2.18, p=0.03* Z=2.68, p=0.01* Z=1.26, p=0.21
CFT Z=2.04, p=0.04* Z=0.15, p=0.88 Z=0.79, p=0.43 Z=−0.5, p=0.62 Z=−1.66, p=0.09
4m UGS Z=3.54, p=0.01* Z=2.56, p=0.01* Z=3.62, p=0.01* Z=2.21, p=0.01* Z=0.72, p=0.47 Z=2.57, p=0.01*
SPPB Z=1.77, p=0.04* Z=0.44, p=0.33 Z=1.91, p=0.02* Z=0.38, p=0.70 Z=1.25, p=0.21 Z=0.88, p=0.38 Z=2.12, p=0.01*
*

Denotes statistically significant at p≤0.05

Table 4.

Statistical comparisons between the area under the receiving operator curves (AUCs) for the laboratory-based assessments of mobility for discriminating mobility limited older adults

MBA 6-Min Walk 4SST Chair Rise Stair Climb CFT 4m UGS SPPB
MBA
6-Min Walk Z=0.96, p=0.33
4SST Z=2.25, p=0.02* Z=0.81, p=0.42
Chair Rise Z=2.09, p=0.03* Z=0.14, p=0.89 Z=0.12, p=0.90
Stair Climb Z=2.25, p=0.02* Z=1.63, p=0.05* Z=1.72, p=0.04* Z=0.76, p=0.45
CFT Z=1.71, p=0.08 Z=0.21, p=0.83 Z=0.13, p=0.89 Z=0.02, p=0.98 Z=0.98, p=0.32
4m UGS Z=2.11, p=0.03* Z=1.19, p=0.23 Z=1.23, p=0.21 Z=0.84, p=0.39 Z=0.06, p=0.95 Z=1.31, p=0.19
SPPB Z=0.46, p=0.65 Z=0.14, p=0.88 Z=0.42, p=0.67 Z=0.76, p=0.44 Z=1.09, p=0.27 Z=0.29, p=0.77 Z=1.29, p=0.19
*

Denotes statistically significant at p≤0.05

Discussion

The purpose of this study was to compare a composite measure of physical function that comprises five different locomotor and non-locomotor tests (i.e., the MBA) with traditional measures of mobility (UGS alone, six-minute walk gait speed, and SPPB score) for assessing lower extremity function and discriminating community dwelling older adults with and without mobility limitations. To this end, we found that the MBA was moderately more associated with self-reported lower extremity function-mobility than 6-min walk gait speed and SPPB score, but substantially more correlated than 4-meter UGS. The MBA was also better than 4-meter UGS at discriminating mobility limitations in older adults; however, it did not outperform 6-min walk gait speed or the SPPB in this regard. These findings have implications for both scientific research as well as clinical practice, which are discussed in more detail below.

We believe our findings have strong implications for scientific research. Our motivations for this study were driven by our desire to develop a better way to quantify mobility in the laboratory setting, which we conceptualize as the ‘relative ease and freedom of movement in all of its forms’ (1), than the standard measures relating to gait speed. We reasoned that a battery of tests that assess performance by challenging many of the physiological components required for mobility, including muscle strength, aerobic capacity, muscle fatigue-resistance, musculoskeletal range of motion, balance and postural control, motor planning, motor initiation, motor sequencing, and motor recall, would yield a superior approach to quantifying mobility than standard tests of mobility. We chose to include some of the standard mobility measures in our composite MBA score (5x chair rise time, six-minute walk gait speed, and stair climb power) that we assert mainly challenge muscle strength, aerobic capacity, and muscle fatigue-resistance. However, we also included some more novel measures, such as the four square step test and our complex functional task. Here, we sought to incorporate tasks that challenged other broad aspects of “motor function” (i.e., the ability to learn, or to demonstrate, the skillful and efficient assumption, maintenance, modification, and control of voluntary postures and movement patterns (42)). Accordingly, we included tasks that were challenging, yet doable by most older adults. For initial proof-of-concept testing we examined the relationship of this composite MBA score in relation to self-reported measures of lower extremity function-mobility.

We did not expect to observe dramatic differences between the MBA and other classic measures of mobility per se; however, we did anticipate modest improvements, which is obviously important to clinical researchers interested in assessing mobility. As expected, we did observe a significantly stronger association between the MBA score and self-reported lower extremity function-mobility in comparison to the associations observed for all singular tests except the four square step test. For instance, the MBA explained 53% of the between-subject variance in self-reported lower extremity function-mobility, which is significantly more than the 15% explained variance noted for 4-meter UGS, and the 40% explained variance for 6-min walk gait speed. The MBA also demonstrated a significantly larger AUC than the 4-meter UGS for discriminating mobility limitations; however, it should be noted that the MBA’s ability to discriminate mobility limitations were not statistically better than 6-min walk gait speed or the SPPB. We should also note that the 6-min walk gait speed test used in this study (in both the MBA as well as the singular test) was performed in a hallway that required a 180° turn every 30-meters. As such, this tests conceptually taxes other systems than that of straight line ambulation.

Our findings are in contrast to some prior work suggesting that short distance UGS is comparable to more comprehensive, composite scores based on a series of laboratory-based tests (17, 19). For instance, in 2000 Guralnik et al. reported data on a cohort of older adults who were tracked longitudinally and reported that the receiver operating characteristic curves showed that gait speed alone performed almost as well as the full battery of SPPB tests in predicting incident disability (19). Similarly, a composite score derived from both upper (i.e., hand-grip strength, manipulating pegs in a pegboard, and functional reach) and lower extremity (i.e., tandem stance, chair stand, alternate up-and-go) performance measures was only marginally better than UGS alone at discriminating self-reported functional capacity and mobility limitations (17). However, our findings are consistent with others that have reported combining two tests of mobility were better at predicting incident disability (43) and falls (44) than a single test in isolation. For instance, it was recently reported that combining two tests, the 5x chair rise and timed up and go, yielded higher sensitivity for detecting the development of disability than each test alone (43). Our findings suggest that the MBA score yields a better assessment of mobility than 4-meter UGS, and a slightly better assessment of mobility than 6-min walk gait speed and the SPPB. While the improvement above the 6-min walk gait speed and SPPB is only moderate this difference is likely meaningful in the research setting where obtaining a more precise quantitative measure of mobility is of highest priority.

There are several limitations of the present study that should be noted. Namely, this work represents proof-of-concept evidence for the MBA and should only be interpreted in that regard. To this end, the reader should clearly recognize that the endpoint outcomes for this study were 1) self-reported lower extremity function-mobility and 2) self-reported mobility limitations (as opposed to endpoints such as disability). It should also be noted that these data were generated in a cross-sectional design, and thus do not provide insights regarding decline over time. Lastly, the sample tested may not be generalizable to the entire older adult population, and thus the external validity is somewhat limited. Additional work is needed to further validate and/or refine the MBA as tools for assessing mobility.

In conclusion, we sought to determine whether a composite score comprised of five different tests relevant to locomotor and non-locomotor function (i.e., the MBA score) was more strongly associated with self-reported lower extremity function-mobility, and better at discriminating mobility limitations than standard mobility indices in a cohort of community-dwelling older adults. Our findings indicated that the MBA was more strongly associated with self-reported lower extremity function-mobility than 4-meter UGS, six-min walk gait speed, and the SPPB. The MBA was also better than 4-meter UGS at discriminating mobility limitations; however, it did not outperform 6-min walk gait speed (on a course requiring 180 degree turns every 30 meters) or the SPPB in this regard. Collectively, these findings suggest that a composite score of objectively measured, laboratory-based assessments of mobility moderately improves the magnitude of correlation and discrimination of mobility limitation in older adults than singular, standard tests of mobility.

Supplementary Material

Supplementary table 1

Acknowledgements:

This work was supported, in part, by a grant from the National Institute of Health (NIH) (NIA R01AG044424 to BC Clark).

Footnotes

Conflicts of Interest: In the past 5-years, Brian Clark has received research funding from the NIH, Regeneron Pharmaceuticals, Astellas Pharma Global Development, Inc., RTI Health Solutions, and the Osteopathic Heritage Foundations. In the past 5-years, Brian Clark has received consulting fees from Regeneron Pharmaceuticals, Abbott Laboratories, and the Gerson Lehrman Group. Additionally, Brian Clark is co-founder with equity, and serves as the Chief of Aging Research, of AEIOU Scientific, LLC. In the past 5-years, Todd Manini has received research funding from the NIH, Regeneron Pharmaceuticals and Sanofi Pharmaceuticals for contracted studies that involved muscle related research. Todd Manini also owns publicly traded stock in Abbott Laboratories and Amgen Inc who both make muscle health related products. Purchase of these stocks occurred prior to the beginning of the current research. Dave Russ has received research funding from the NIH and Abbott Laboratories. All other authors declare no conflicts of interest, financial or otherwise.

Ethical Standards: The study was approved by the Ohio University IRB and we followed Good Clinical Practice procedures.

References

  • 1.Satariano WA, et al. , Mobility and aging: new directions for public health action. Am J Public Health, 2012. 102(8): p. 1508–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Musich S, et al. , The impact of mobility limitations on health outcomes among older adults. Geriatr Nurs, 2018. 39(2): p. 162–169. [DOI] [PubMed] [Google Scholar]
  • 3.Shumway-Cook A, et al. , Mobility limitations in the Medicare population: prevalence and sociodemographic and clinical correlates. J Am Geriatr Soc, 2005. 53(7): p. 1217–21. [DOI] [PubMed] [Google Scholar]
  • 4.Cummings SR, Studenski S, and Ferrucci L, A diagnosis of dismobility--giving mobility clinical visibility: a Mobility Working Group recommendation. JAMA, 2014. 311(20): p. 2061–2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Vermeulen J, et al. , Predicting ADL disability in community-dwelling elderly people using physical frailty indicators: a systematic review. BMC Geriatr, 2011. 11: p. 33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Cawthon PM, et al. , Do muscle mass, muscle density, strength, and physical function similarly influence risk of hospitalization in older adults? J Am Geriatr Soc, 2009. 57(8): p. 1411–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Studenski S, et al. , Gait speed and survival in older adults. JAMA, 2011. 305(1): p. 50–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Newman AB, et al. , Association of long-distance corridor walk performance with mortality, cardiovascular disease, mobility limitation, and disability. JAMA, 2006. 295(17): p. 2018–26. [DOI] [PubMed] [Google Scholar]
  • 9.Gill TM, et al. , The dynamic nature of mobility disability in older persons. J Am Geriatr Soc, 2006. 54(2): p. 248–54. [DOI] [PubMed] [Google Scholar]
  • 10.Simonsick EM, et al. , Mobility limitation in self-described well-functioning older adults: importance of endurance walk testing. J Gerontol A Biol Sci Med Sci, 2008. 63(8): p. 841–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Hardy SE, et al. , Ability to walk 1/4 mile predicts subsequent disability, mortality, and health care costs. J Gen Intern Med, 2011. 26(2): p. 130–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Song M and Kong EH, Older adults’ definitions of health: A metasynthesis. Int J Nurs Stud, 2015. 52(6): p. 1097–106. [DOI] [PubMed] [Google Scholar]
  • 13.Goins RT, et al. , Older Adults’ Perceptions of Mobility: A Metasynthesis of Qualitative Studies. Gerontologist, 2015. 55(6): p. 929–42. [DOI] [PubMed] [Google Scholar]
  • 14.Middleton A, Fritz SL, and Lusardi M, Walking speed: the functional vital sign. J Aging Phys Act, 2015. 23(2): p. 314–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Fritz S and Lusardi M, White paper: “walking speed: the sixth vital sign”. J Geriatr Phys Ther, 2009. 32(2): p. 46–9. [PubMed] [Google Scholar]
  • 16.Cooper R, et al. , Objective measures of physical capability and subsequent health: a systematic review. Age Ageing, 2011. 40(1): p. 14–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Seino S, et al. , Is a composite score of physical performance measures more useful than usual gait speed alone in assessing functional status? Arch Gerontol Geriatr, 2012. 55(2): p. 392–8. [DOI] [PubMed] [Google Scholar]
  • 18.Guralnik JM, et al. , A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol, 1994. 49(2): p. M85–94. [DOI] [PubMed] [Google Scholar]
  • 19.Guralnik JM, et al. , Lower extremity function and subsequent disability: consistency across studies, predictive models, and value of gait speed alone compared with the short physical performance battery. J Gerontol A Biol Sci Med Sci, 2000. 55(4): p. M221–31. [DOI] [PubMed] [Google Scholar]
  • 20.Tavoian D, et al. , Changes in DXA-derived lean mass and MRI-derived cross-sectional area of the thigh are modestly associated. Sci Rep, 2019. 9(1): p. 10028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Corbett DB, et al. , Evaluating Walking Intensity with Hip-Worn Accelerometers in Elders. Med Sci Sports Exerc, 2016. 48(11): p. 2216–2221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Randolph C, et al. , The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): preliminary clinical validity. J Clin Exp Neuropsychol, 1998. 20(3): p. 310–9. [DOI] [PubMed] [Google Scholar]
  • 23.Charlson ME, et al. , A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis, 1987. 40(5): p. 373–83. [DOI] [PubMed] [Google Scholar]
  • 24.Lewinsohn PM, et al. , Center for Epidemiologic Studies Depression Scale (CES-D) as a screening instrument for depression among community-residing older adults. Psychol Aging, 1997. 12(2): p. 277–87. [DOI] [PubMed] [Google Scholar]
  • 25.Roos EM and Toksvig-Larsen S, Knee injury and Osteoarthritis Outcome Score (KOOS) - validation and comparison to the WOMAC in total knee replacement. Health Qual Life Outcomes, 2003. 1: p. 17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Manini TM, et al. , Knee extension strength cutpoints for maintaining mobility. J Am Geriatr Soc, 2007. 55(3): p. 451–7. [DOI] [PubMed] [Google Scholar]
  • 27.Stalvey B, et al. , The life space questionnaire: a measure of the extent of mobility of older adults. Journal of Applied Gerontology, 1999. 18: p. 479–498. [Google Scholar]
  • 28.Nakamura E, Miyao K, and Ozeki T, Assessment of biological age by principal component analysis. Mech Ageing Dev, 1988. 46(1-3): p. 1–18. [DOI] [PubMed] [Google Scholar]
  • 29.Nakamura E and Miyao K, Sex differences in human biological aging. J Gerontol A Biol Sci Med Sci, 2008. 63(9): p. 936–44. [DOI] [PubMed] [Google Scholar]
  • 30.NINDS., N.I.o.N.D.a.S., User Manual for the Quality of Life in Neurological Disorders (Neuro-QOL) Measures. 2015. [Google Scholar]
  • 31.Celia D Neuro-QOL Technical Report. 2015. [Google Scholar]
  • 32.Perez L, et al. , Using focus groups to inform the Neuro-QOL measurement tool: exploring patient-centered, health-related quality of life concepts across neurological conditions. J Neurosci Nurs, 2007. 39(6): p. 342–53. [DOI] [PubMed] [Google Scholar]
  • 33.Nowinski CJ, et al. , Neuro-QoL health-related quality of life measurement system: Validation in Parkinson’s disease. Mov Disord, 2016. 31(5): p. 725–33. [DOI] [PubMed] [Google Scholar]
  • 34.Miller DM, et al. , Validating Neuro-QoL short forms and targeted scales with people who have multiple sclerosis. Mult Scler, 2016. 22(6): p. 830–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Victorson D, et al. , Validity of the Neurology Quality-of-Life (Neuro-QoL) measurement system in adult epilepsy. Epilepsy Behav, 2014. 31: p. 77–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.EL DE, Wiersma W, and Jurs SG, Applied statistics for the behavioral sciences. 5th ed. 2003, Boston: Houghton Mifflin. [Google Scholar]
  • 37.Eid M, Gollwitzer M, and Schmitt M, Statistik und Forschungsmethoden Lehrbuch. 2011, Weinheim, Germany: Beltz. [Google Scholar]
  • 38.Hosmer DW, Applied Logistic Regression. 2nd ed. 2000, New York: John Wiley & Sons, Inc. [Google Scholar]
  • 39.Youden WJ, Index for rating diagnostic tests. Cancer, 1950. 3(1): p. 32–5. [DOI] [PubMed] [Google Scholar]
  • 40.DeLong ER, DeLong DM, and Clarke-Pearson DL, Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics, 1988. 44(3): p. 837–45. [PubMed] [Google Scholar]
  • 41.Robin X, et al. , pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinfoimatics, 2011. 12: p. 77. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Association, A.P.T., Guide to Physical Therapist Practice. 3.0 ed. 2014, Alexandria, VA. [Google Scholar]
  • 43.Makizako H, et al. , Predictive Cutoff Values of the Five-Times Sit-to-Stand Test and the Timed “Up & Go” Test for Disability Incidence in Older People Dwelling in the Community. Phys Ther, 2017. 97(4): p. 417–424. [DOI] [PubMed] [Google Scholar]
  • 44.Tiedemann A, et al. , The comparative ability of eight functional mobility tests for predicting falls in community-dwelling older people. Age Ageing, 2008. 37(4): p. 430–5. [DOI] [PubMed] [Google Scholar]

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Supplementary table 1

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