INTERSECTING SELF-REPORTED MOBILITY AND GAIT SPEED TO CREATEA MULTI-DIMENSIONAL MEASURE OF AMBULATION: THE “AMBULATION SPEED-ENDURANCE” (ASE) TYPOLOGY

Abstract

Background

Assessing mobility through readily available and affordable protocols may help advance public health by providing early detection and implementing intervention therapies aimed at mitigating the progression from physiological vitality to disability at older ages. Until now, little attention has been given to how self-reported mobility (SRM) and gait speed can be combined in a categorization scheme.

Objectives

The specific aim of this report is to introduce the Ambulation Speed-Endurance (ASE) Typology to the literature—a classification system that intersects SRM and gait speed to create a multi-dimensional measure of ambulation.

Design

Cross-sectional. Setting: Community-dwelling older adults in the United States.

Participants

Evidence is provided from the National Health and Aging Trends Study (NHATS) that community-dwelling older adults (n=5,403) may be found in each of the ASE Typologies. The discussion is complimented by investigating the cross-sectional predictors of a “Discrepancy Score” (measure of gap between speed and endurance) amongst those with gait speeds < 0.99 m/sec (n=4,521).

Results

Multivariable linear regression results indicate level of severity in speed-endurance discrepancy is higher amongst: non-Latino-Blacks (β=0.48); Latinos (β=0.42); older ages; and lower educated. Models also show that severity in speed-endurance discrepancy is lower amongst: females (β=−0.38); those with higher body mass index; with more chronic health conditions; and poorer self-rated health.

Conclusion

Research should continue to investigate how to optimize SRM.

Introduction

Assessing health objectively is typically framed as being superior to determining health status through self-report—despite the fact that evidence over the years has shown that both objective and subjective measures of health provide unique and important insight on the health status of older adults. Although often relegated to the undesirable, subjective assessment of health is crucial for understanding health, as the 1985 Constitution of the World Health Organization (WHO) defined health as “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity.” In other words, both physiological constitution and perceived well being contribute to an individual’s “health” as defined by WHO—neither should be though as superior to the other.

Interest in the difference between subjective and objective measures of health began more than half a century ago, when Suchman and colleagues (1) investigated how self-reported health (SRH) was related with physical health. Their work shows that perceived general health status varies as a function of physiology and other factors:

$$\text{SRH}=\int (\text{Physiological}\text{Health},\dots ,\mathrm{n}(\text{th}))$$

They argued that SRH may have “greater validity for certain purposes” (1). Guralnik and colleagues later extended this line of research by explaining that self-reported mobility (SRM) and observed performance “may complement each other in providing useful information about functional status” (2). Their work and research by other (6) provides evidence that SRM varies by physiological capacity for mobility and other factors:

$$\text{SRM}=\int (\text{Performace}\text{Based}\text{Mobility},\dots \mathrm{n}(\text{th}))$$

While observed performance for walking may only be done over short distances (best case scenario 400 meters when space allows), SRM over long distances (e.g., ½ mile or 1 mile) is used in both research and clinical settings (3). In recent years, Seidel and colleagues (4) provided evidence that variance in self-reported function can be explained by actual functional capacity—suggesting that SRM is influenced by physiological conditions. Authors have clearly argued over the years that there is a physiologic basis for SRM (5).

Arguments have been presented for why performance task only offer measures of functional capacity at the low-end (short distance) of the functional spectrum (6, 7). In comparison, SRM is not restricted by study protocols for safety and allows researchers and clinicians the ability to assess perceived function at the high-end (long distance) of the functional spectrum (3). While endurance with performance measures have been measured over 400 meters in adults aged 65 and older (8), “level of ease” in SRM over 1 mile (≈1,609 meters) is available in studies like the widely respected Health, Aging, and Body Composition (Health ABC) Study. Measured walking speed is considered by some an adequate and surrogate measure for gait and motor function in ambulation (9). Walking distance is considered by others as a proxy for endurance (6–8). Neither gait speed over short distances (e.g., <20 meters) nor self-reported ability to walk may indicate cardiorespiratory fitness (10, 11).

In general, an argument may be made that although both objective and subjective measures of mobility provide unique insight into an individual’s health, SRM is typically framed as being ‘less well-measured’ than observed and measured ambulation (e.g., gait speed). In other words, despite the fact that research shows that both subjective and objective measures of mobility capture information on the functional ability of older adults, a debate remains over the meaning of SRM—with most favoring observed performance over SRM.

Researchers interested in aging and mobility should begin by asking: Is there evidence that “fast walkers” can self-report not having the ability to walk over long distances? Or that “slow walkers” can self-report having the ability to walk over long distances? Both hypothetical scenarios present speed-endurance discrepancies: the first because ambulatory endurance is absent in the presence of normal gait; and the second because ambulatory endurance is present despite the presence of abnormal gait. At the core of ascertaining these potential “speed-endurance discrepancies” is the idea that presence of high gait speed over short distances may not always be accompanied by ability to walk over long distances (i.e., endurance).

In theory, while observed gait speed helps assess speed of ambulation over short distances, SRM over long-distances provides a proxy measure of ambulatory endurance. Because gait speed has an “endurance limitation” and SRM a “speed limitation”; when possible, they should be combined (“intersected”) to build a detailed profile of mobility in the analytic sample. The specific aim of the current study is to present the Ambulation Speed-Endurance (ASE) Typology. The ASE Typology provides an approach for categorizing individuals by gait speed and walking endurance. The study presents empirical evidence (with 5,403 observations) that individuals can be classified into each of the ASE Typologies. The main goal is complimented by investigating the predictors of “speed-endurance discrepancies” amongst those with abnormal gait (n=4,521).

Methods

Data

The analysis uses baseline (“Round 1”) data from the National Health and Aging Trends Study (NHATS). The panel study of persons aged > 65 was funded by the National Institute on Aging (NIA) as a successor to the National Long Term Care Survey (12). The NHATS was designed to investigate physical function in later life. The current study uses data collected during 2011 from a group of individuals in the Medicare enrollment file who were selected by using a stratified and multistage sampling design. “Medicare” is a national health care insurance program used by many older adults in the United States (US). At baseline, 8,245 out of 11,637 contacts agreed to participate in the study. NHATS participants are Medicare beneficiaries living in the continental US. NHATS data is readily available to anyone with an email address and internet connection. The author registered as a secondary data user to obtain access to de-identified public NHATS data. NHATS personnel have explained that written informed consent was obtained from NHATS participants and that the original study protocol was approved by the Johns Hopkins University Institutional Review Board.

Sample

The analysis only includes Latinos, non-Latino-Blacks, and non-Latino-Whites that completed the 3-meter walk test without the use of any assistive device or person (13). The main goal of the quantitative analysis is to provide evidence for the presence of individuals over each of the ASE Typologies. A total of 5,403 NHATS participants are used in the analysis. The main goal is complimented by providing regression results that include 4,521 of these observation—which represent people with “abnormal gait” (defined below).

3-Meter Walk at Usual Pace

The 3-meter gait speed was used as part of the short physical performance battery (SPPB) test (14). This is the first NHATS publication making use of the item as an individual factor separate from the SPPB score. Physical performance was assessed at the participant’s home by a trained interviewer. Gait speed was measured by instructing participants to “walk at their usual pace” over a 3-meter course (distance measured using a 5 meter colored chain). Participants started from a standing position and time was marked when the last foot crossed over the 3-meter mark on the link-chain. Participants were allowed to attempt the 3-meter walk two times. The current analysis computes each trial and takes the average of the two—or the score of one if only one trial was completed.

The total number of seconds and hundreds of a second were added from NHATS Round 1 data to produce the “total number of seconds” used to walk 3 meters. Gait speed is computed as “meters per-second” (m/sec) as follows: (3÷total number of seconds). The first and second trial are combined to estimate “average meters per second” as follows: {[(trial one)+(trial two)]÷2}. For example, if a person walked the 3 meters the first time in 4.9 seconds and the second time in 4.2 seconds then: 1st trial=(3÷3.6=0.83); 2nd trial=(3÷3.8=0.79); averages=[(0.83+0.79) ÷2=0.81]. Contrast this hypothetical scenario with scores for a “slower” walker—where 1st time is 5.3 seconds and the 2nd time is 4.9 seconds: 1st trial=(3÷5.3=0.56); 2nd trial=(3÷5.1=0.59); averages=[(0.56+0.59) ÷2=0.58]. Thus, as the gait speed score increases, the individual is said to have a faster gait speed over a short distance when using their usual pace. When measured as m/sec, gait speed scores indicate how many meters the person covers per-second—the approach allows for a standardize measure that can help inter-study comparisons.

Self-Reported Ability to Walk Blocks

“Ambulatory endurance” is said to be present in this analysis when a person self-reports to have the ability to walk 6 blocks. Ability to walk 6 blocks was asked in NHATS as follows: In the last month, were you able to... walk 6 blocks, or about half a mile, by yourself and without your {cane/walker/cane or walker}? (13). Their binary response (yes/no) to this question is used to measure Self-Reported Mobility (SRM). Ambulatory endurance is said to be present when person reports being able to walk 6 blocks. The study assumes that SRM reflects “true ambulatory endurance”—a noteworthy postulate as SRM may vary as a function of person characteristics. “False ambulatory endurance” is discussed in closing.

Ambulation Speed-Endurance (ASE) Typology

The basic scheme for the Ambulation Speed-Endurance (ASE) Typology is formally presented in Table 1. Three basic factors are used to categorize individuals over 24 ASE Typologies: gait category (normal vs abnormal); endurance (present vs absent); and gait speed. The labeling of gait speed and thresholds are loosely informed by seminal work on the topic (15).

Table 1.

Basic elements used in creating Ambulation Speed-Endurance (ASE) Typology and distribution of 5,403 NHATS observations over each of the typologies

ASE#	Gait Category	Endurance	Gait Speed m/sec	n	%
1	Normal	Yes	≥1.5	22	0.4%
2	Normal	Yes	1.40–1.49	16	0.3%
3	Normal	Yes	1.30–1.39	36	0.7%
4	Normal	Yes	1.20–1.29	123	2.3%
5	Normal	Yes	1.10–1.19	230	4.3%
6	Normal	Yes	1.00–1.09	402	7.4%
7	Normal	No	≥1.5	8	0.2%
8	Normal	No	1.40–1.49	2	0.0%
9	Normal	No	1.30–1.39	1	0.0%
10	Normal	No	1.20–1.29	2	0.0%
11	Normal	No	1.10–1.19	13	0.2%
12	Normal	No	1.00–1.09	27	0.5%
13	Abnormal	Yes	0.90–0.99	616	11.4%
14	Abnormal	Yes	0.80–0.89	729	13.5%
15	Abnormal	Yes	0.70–0.79	652	12.1%
16	Abnormal	Yes	0.60–0.69	454	8.4%
17	Abnormal	Yes	0.50–0.59	298	5.5%
18	Abnormal	Yes	≥0.49	210	3.9%
19	Abnormal	No	0.90–0.99	68	1.3%
20	Abnormal	No	0.80–0.89	152	2.8%
21	Abnormal	No	0.70–0.79	219	4.1%
22	Abnormal	No	0.60–0.69	333	6.2%
23	Abnormal	No	0.50–0.59	292	5.4%
24	Abnormal	No	≥0.49	498	9.2%

Table 2 lists detailed descriptions for each of the ASE Typologies. Those with normal gait speed are divided into: superb (gait > 1.5 m/sec); highest-normal (gait 1.40–1.49 m/sec); high-normal (gait 1.30–1.39 m/sec); mid-normal (gait 1.20–1.29 m/sec); low-normal (gait 1.10–1.19 m/sec); and lowest-normal (gait 1.00–1.09 m/sec). Note those with “abnormal” gait speed are divided into: semi-slow (gait 0.90–0.99 m/sec); moderately slow (gait 0.80–0.89 m/sec); slow gait (gait 0.70–0.79 m/sec); very slow (gait 0.60–0.69 m/sec); super slow (gait 0.50-0.50 m/sec); and slowest (gait < 0.49 m/sec). Each of these 12 categories are further divided by presence or absence of endurance (i.e., SRM=yes or no). Table 2 also offers a list of “simple categorization” labels. The simple categorization labels divide the 24 ASE Typologies into four main groups: normal gait and NO speed-endurance discrepancy (ASE 1–6); normal gait WITH speed-endurance discrepancy (ASE 7–12; abnormal gait and NO speed-endurance discrepancy (ASE 13–18); and abnormal gait WITH speed-endurance discrepancy (ASE 19–24).

Table 2.

Detailed description of Ambulation Speed-Endurance (ASE) Typologies

ASE#	Gait	Speed-Endurance Discrepancy Present?	Detailed Description
1	Normal	No	Superb gait speed and reports ambulatory endurance
2	Normal	No	Highest-normal gait speed and reports ambulatory endurance
3	Normal	No	High-normal gait speed and reports ambulatory endurance
4	Normal	No	Mid-normal gait speed and reports ambulatory endurance
5	Normal	No	Low-normal gait speed and reports ambulatory endurance
6	Normal	No	Lowest-normal gait speed and reports ambulatory endurance
7	Normal	Yes	Superb gait speed and reports no ambulatory endurance
8	Normal	Yes	Highest-normal gait speed and reports no ambulatory endurance
9	Normal	Yes	High-normal gait speed and reports no ambulatory endurance
10	Normal	Yes	Mid-normal gait speed and reports no ambulatory endurance
11	Normal	Yes	Low-normal gait speed and reports no ambulatory endurance
12	Normal	Yes	Lowest-normal gait speed and reports no ambulatory endurance
13	Abnormal	Yes	Semi-slow gait speed and reports ambulatory endurance
14	Abnormal	Yes	Moderately slow gait speed and reports ambulatory endurance
15	Abnormal	Yes	Slow gait speed and reports ambulatory endurance
16	Abnormal	Yes	Very slow gait speed and reports ambulatory endurance
17	Abnormal	Yes	Super slow gait speed and reports ambulatory endurance
18	Abnormal	Yes	Slowest gait speed and reports ambulatory endurance
19	Abnormal	No	Semi-slow gait speed and reports no ambulatory endurance
20	Abnormal	No	Moderately slow gait speed and reports no ambulatory endurance
21	Abnormal	No	Slow gait speed and reports no ambulatory endurance
22	Abnormal	No	Very slow gait speed and reports no ambulatory endurance
23	Abnormal	No	Super slow gait speed and reports no ambulatory endurance
24	Abnormal	No	Slowest gait speed and reports no ambulatory endurance

“Discrepancy Score” amongst those with Abnormal Gait

In order to model what predicts ‘level of severity’ in the speed-endurance discrepancy amongst those with abnormal gait speed, the Discrepancy Score is created by using ASE Typologies from 13 to 24. ASE Typologies 19–24 are treated “0” on the scale and the following as follows: ASE#13=1; ASE#14=2; ASE#15=3; ASE#16=4; ASE#17=5; and ASE#18=6. On one end of the Discrepancy Score (where it equals zero), we have individuals with abnormal gait speed who report no endurance (i.e., inability to walk 6 blocks). As the Discrepancy Score increases, the level of severity in the speed-endurance discrepancy increases: where “Discrepancy Score=1” signals gap between speed and endurance is smallest; and where “Discrepancy Score=6” signals gap between speed and endurance is the largest.

Predictors

The models account for race, ethnicity, sex, and age. NHATS participants are divided into: non-Latino-Whites (reference group); non-Latino-Blacks; and Latinos. Because age is only made available in the data with the following age ranges: 65–69; 70–74; 75–79; 80–84; 85–89; and >90 (reference group). Educational attainment is control for with the following categories: > junior high school (>1st–8th grade); high school (includes 9th–12th grade and high school graduate); some college (includes: vocational, technical, business, or trade school certificate or diploma beyond high school level); and college graduate (includes: some college but no degree; associate’s degree; bachelor’s degree; and master’s, professional, or doctoral degree)—reference group.

Health factors include BMI, SRH, and count of chronic conditions. BMI was measured with self-reported weight in pounds and self-reported height in inches. BMI was thus computed as follows: BMI=(pounds/inches²)×703. Subjectively estimated BMI was divided into the following categories: BMI < 18.49 kg/m²; BMI 18.5–24.99 kg/m² (reference group); BMI 25–27.99 kg/m²; BMI 28–30.99 kg/m²; BMI 31–33.99 kg/m²; and BMI > 34 kg/m². Previous work has argued that self-reported height and weight provide an acceptable method for estimating BMI (16, 17). Self-reported health was also included as a continuous variable: 0=excellent (reference group); 1=very good; 2=good; 3=fair; and 4=poor. A count of “number of chronic conditions” was computed by adding the presence of each of the following conditions based on self-report: heart attack; heart decease; high blood pressure; arthritis; osteoporosis; diabetes; lung decease; stroke; dementia; and cancer.

Statistical Approach

Descriptive statistics are presented from NHATS data to show individuals may be categorized into each of the ASE Typologies by using 5,403 observations. Multivariable linear regression model predictors of Discrepancy Scores amongst those with abnormal gait (n=4,521). Three models scale predictors by: race-ethnicity (Model-1); then adding sex and age (Model-2); and finally including educational attainment and health measures in the complete model (Model-3). Special focus is given to race-ethnic predictors as previous work has shown racial-minorities (e.g., non-Latino-Blacks) may SRM at lower levels than racial-majorities like non-Latino-Whites (31). All data was managed in SAS 9.3® and only coefficients statistically significant at a 0.05 level of below are discussed in the findings section.

Results

Table 1 shows how 5,403 observations form NHATS are distributed over each of the ASE Typologies. As can be seen, most individuals with normal gait speed also report being able to walk 6 blocks (ASE# 1–6). Only a few individuals with normal gait speed report being unable to walk 6 blocks (ASE# 7–12)—i.e., have a speed-endurance discrepancy. However, most of those with abnormal gait report being able to walk 6 blocks (ASE#13–18)—have a speed-endurance discrepancy. Amongst those with abnormal gait speed, some report being unable to walk 6 blocks (ASE# 19–24). As can be seen, NHATS has participants that fit into each of the ASE Typologies.

Figure 1 graphs the “%” column displayed in Table 1. As can be readily appreciated from this visual representation of NHATS data, few speed-endurance discrepancies are present amongst those with normal gait speed. In contrast, there are many speed-endurance discrepancies’ amongst those with abnormal gait speed. ASE Typologies 15 through 18 (red bars in graph) may be said to represent the most severe level of discrepancy between speed and endurance—very slow walkers reporting being able to walk 6 blocks. The graph clearly shows that speed-endurance discrepancies may be most concentrated amongst those with abnormal gait speed.

Figure 1.

Distribution of 5,403 observations by Ambulation Speed-Endurance (ASE) typology

Table 3 shows the descriptive statistics for the 4,521 individuals with abnormal gait speed. The mean Discrepancy Score (i.e., outcome in models) is 1.90—which means that the majority have no discrepancy (n=1,562) or of discrepancies are “mild” (where gait speed is between 0.80–0.99 m/sec) (n=1,345)—with 1,614 having a severe discrepancy (i.e., ASE# 15–18). Most of the sample (72%) is non-Latino-White and more than half (58%) of the 4,521 observations in the regression analysis are female. About 64% of the observations are between the ages of 70 and 84 and 56% have a high school education or below. About 55% of the sample in the regression models has a BMI between 18.5 and 24.99. On average, individuals have about 3 chronic health conditions and self-rate their health as good.

Table 3.

Descriptive statistics for 4,521 observations in regression analysis

	Mean	SD	Min	Max
Discrepancy Score	1.90	1.84	0	6
Demographics
Non-Latino-White	0.72	0.45	0	1
Non-Latino-Black	0.22	0.41	0	1
Latino/Hispanic	0.06	0.24	0	1
Female	0.58	0.49	0	1
Age 65–69 years	0.19	0.39	0	1
Age 70–74 years	0.22	0.41	0	1
Age 75–79 years	0.21	0.41	0	1
Age 80–84 years	0.21	0.41	0	1
Age 85–89 years	0.11	0.32	0	1
Age ≥90 years	0.06	0.12	0	1
Up to Junior High School	0.11	0.31	0	1
High School	0.44	0.50	0	1
Some College	0.20	0.40	0	1
College Graduate	0.25	0.43	0	1
Body Mass Index
BMI ≤18.49 kg/m2	0.02	0.14	0	1
BMI 18.5–24.99 kg/m2	0.31	0.46	0	1
BMI 25–27.99 kg/m2	0.24	0.43	0	1
BMI 28–30.99 kg/m2	0.18	0.39	0	1
BMI 31–33.99 kg/m2	0.11	0.31	0	1
BMI ≥34 kg/m2	0.12	0.32	0	1
Health
Number of chronic conditions	2.54	1.53	0	9
Self-rated health	1.79	1.05	0	4

SD: Standard deviation; Min: Minimum; Max: Maximum

Results from Models 1–3 (presented in Table 4) indicate that a greater level of severity in the speed-endurance discrepancy is associated with: race-ethnic minorities; those at older ages; lower educational attainment; and with higher BMI. For example, from the full model, the non-Latino-Black (β=0.48) and Latino (β=0.42) status is associated with higher Discrepancy Scores when compared to non-Latino-White status—holding all other factors constant. Being age 65–69 is associated with a higher (β=0.67) Discrepancy Score when compared to those aged > 90. In similar fashion, those with > junior high school education are associate with a higher (β=0.21) Discrepancy Score when compared to college graduates—ceteris paribus.

Table 4.

Models predicting Discrepancy Score for those with abnormal gait

	Model 1 β	Model 2 β	Model 3 β
Demographics
Non-Latino-White	Ref.	Ref.	Ref.
Non-Latino-Black	0.37‡	0.34‡	0.48‡
Latino/Hispanic	0.34‡	0.33‡	0.42‡
Female		−0.43‡	−0.38‡
Age 65–69 years		0.54‡	0.67‡
Age 70–74 years		0.66‡	0.82‡
Age 75–79 years		0.58‡	0.74‡
Age 80–84 years		0.61‡	0.75‡
Age 85–89 years		0.52‡	0.63‡
Age ≥90 years		Ref.	Ref.
Up to Junior High School			0.21‡
High School			0.18†
Some College			0.09
College Graduate			Ref
Body Mass Index
BMI ≤18.49 kg/m2			0.18
BMI 18.5–24.99 kg/m2			Ref.
BMI 25–27.99 kg/m2			−0.02
BMI 28–30.99 kg/m2			−0.15*
BMI 31–33.99 kg/m2			−0.37‡
BMI ≥34 kg/m2			−0.49‡
Health
Number of chronic conditions			−0.17
Self-rated health			−0.26

^*α <0.05;

^†α <0.01;

^‡α <0.001

Results from Models 1–3 indicate that a lower level of severity in the speed-endurance discrepancy is associated with: females; higher BMIs; morbidity; and poorer SRH. For example, from Model-3, females are associated with a having a lower (β=−0.38) Discrepancy Score when compared to males. When compared to those with a BMI of 18.5–24.99, those with a BMI > 34 are associate with a having a lower (β=−0.49) Discrepancy Score. Each increase in the count of chronic conditions is associated with a 0.17 decrease on the Discrepancy Score. Each increase in SRH (towards poor ratings) is associated with a decrease of 0.26 on the Discrepancy Score—ceteris paribus.

Conclusions

The study has introduced the Ambulation Speed-Endurance (ASE) Typology and provided empirical evidence that study participants can be identified over each of the categories by using 5,403 observations. In doing so, the study provides support for the view that “speed-endurance discrepancies”: do exist; vary as a function of person characteristics; and may be most heavily concentrated amongst those with abnormal gait speed.

The main goal of the present study was to introduce the ASE Typology in the literature. However, it should be mentioned that speed-endurance discrepancies may be the product of ‘true’ or ‘false’ events. True differences, like having an abnormal gait speed and truly being able to walk 6 blocks when gait < 0.99 m/sec and ambulatory endurance is present, may be referred to as “spontaneous assessment” (18)—cases where SRM reflects an individual’s physiological capacity for ambulation. False differences, like having an abnormal gait speed and truly being able to walk 6 blocks—when gait < 0.99 m/sec and ambulatory endurance is absent—may be related to “enduring self-concept” (18) where SRM does not reflect an individual’s physiological capacity for ambulation. While differences in SRH have been studied by race (19–21), nation of residence (22), and by functional limitation (23), no studies have explored the mechanism by which mismatches between SRM and gait speed arise.

In most cases SRM over 6 blocks, ½ mile, or 1 mile is likely to be unaccompanied by a performance test observing gait over the same length of distance. This limits the ability to comprehensively evaluate if, when, and how speed-endurance discrepancies reflect ‘true’ or ‘false’ events. Limiting research to performance test would relegate measures of mobility to the low-end of the spectrum (i.e., mobility over short distances). Until devices that employ the Global Positioning System (GPS) are more readily embrace by health researchers, SRM will remain the only source for measuring mobility at the high-end of the spectrum (i.e., mobility over long distances). In theory, tracking ambulation over physical space with GPS devices will provide time-and-place specific (geodetic) points to estimate the ‘organic gait’ of outdoor ambulation in daily living—unlike performance measures taking place in non-organic settings (32–33).

Beyond measurement issues is the fact that advances in measures of SRM may help advance public health by providing the means for assessing the presence and incidence of “preclinical” mobility disability (24–30). Until ambulation is more frequently measured with GPS devices, researchers should continue to investigate how to optimize self-reported mobility as it is widely used in survey research and clinical settings. In doing so, we should advance the sophistication of our classification schemes—for example, by adopting the use of the ASE Typology—to ascertain how objective and subjective measures of mobility intersect and delineate ambulation through a multi-dimensional lens.