Association between Performance Fatigability and GPS Measured Community Mobility

INTRODUCTION

Community mobility has been defined as one’s ability to access and interact with different areas within their larger spatial environment.¹ Diminished community mobility has been associated with deleterious health outcomes including mortality.² Understanding associations between modifiable risk factors and community mobility may inform interventions to maintain independence in older adults.

Fatigability can be operationalized as either perceived or performance related.³ Greater perceived fatigability (what one thinks they can do) has been associated with lower community mobility when using a validated self-reported life-space questionnaire.^4,5 We assessed whether performance fatigability, the quantification of one’s slowing down due to fatigue, was associated with Global Positioning System (GPS) assessed community mobility.

METHODS

Study Participants

Participants (n = 142) were drawn from the Program to Improve Mobility in Aging (PRIMA) (N=249) randomized intervention trial (Supplementary Methods S1). Details on PRIMA design, participants and GPS protocols were published.^6,7 Briefly, PRIMA assessed the effects of a standard physical performance program (control) and the standard program plus a coordination and timing program (treatment) on mobility.⁶ Data presented here were from the baseline, pre-intervention visit.

Measures

Exposure: Pittsburgh Performance Fatigability Index (PPFI)

The Pittsburgh Performance Fatigability Index (PPFI)⁸ is an accelerometer-based performance fatigability measure that quantifies performance decrement (i.e., slowing down) by comparing the area under the observed cadence-time curve to a hypothetical area under the curve in the absence of fatigue (higher PPFI scores=greater fatigability) during a walking task.⁸ In PRIMA, PPFI was applied to a 6-minute walk test (Supplementary Methods S1).

Outcome: GPS Assessed Community Mobility

From July 2016 to October 2019, participants were asked to carry a GPS device (iBlue 747: TSI: Hsinchu, Taiwan or Columbus V990: Columbus: Germany; <5% carried the iBlue device) for 7 consecutive days.⁷ To assess attributes of community mobility, GPS data were used to calculate time-weighted standard deviational ellipses (SDE) (higher area=greater community mobility), median hours outside of home (TOH), percent TOH, median maximum distance from home (MDH), and overall MDH.⁷

Statistical Analysis

First, we assessed baseline characteristics across tertiles of SDE area using chi-square tests for categorical variables and analysis of variance (ANOVA) tests for continuous variables. Next, ordinal logistic regression models quantified the association between 1% higher PPFI score and tertiles of GPS measures adjusted for self-reported age and sex. Parallel regression assumptions were assessed using the Brant test. All associations were considered statistically significant if P < 0.05. All analyses were conducted in R version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria).

RESULTS

Participants were 68% women, 85% White, 10% Black, 50% had higher than college education, and mean (±standard deviation) age of 77.0±6.5 years (Table 1). PPFI scores ranged from 0–12.7%. For each 1% higher PPFI score, odds of being in a higher tertile of SDE area was lower by 18% [Odds Ratio (OR)=0.82 (95% Confidence Interval (CI): 0.72, 0.94)] and odds of being in a higher tertile of median maximum distance from home was lower by 12% [OR=0.88 (95% CI: 0.77, 0.99)] (Figure 1). PPFI was not associated with median hours outside the home, overall percentage of time outside the home, or overall maximum distance from home (Figure 1).

Table 1.

Baseline Program to Improve Mobility in Aging (PRIMA) Characteristics Stratified by Tertiles of Standard Deviational Ellipse (N = 142)

	Standard Deviational Ellipse, km2
	Total	Tertile 1	Tertile 2	Tertile 3
		≥ 0.00 & <0.88	≥ 0.88 & ≤ 3.85	> 3.85 & ≤ 1185.37
Characteristic	(N = 142)	(n = 48)	(n = 47)	(n = 47)	P-value*
PPFI, %	2.8 ± 2.6	3.6 ± 3.1	2.6 ± 2.2	2.1 ± 2.0	0.02
Age, years	77.0 ± 6.5	77.6 ± 7.1	77.0 ± 6.1	76.5 ± 6.3	0.72
Sex, Women	96 (67.6)	34 (70.8)	31 (66)	31 (66)	0.84
Race					0.38
White	121 (85.2)	41 (85.4)	38 (80.9)	42 (89.4)
Black or African American	14 (9.9)	5 (10.4)	4 (8.5)	5 (10.6)
Hispanic	2 (1.4)	0 (0)	2 (4.3)	0 (0)
Asian	1 (0.7)	0 (0)	1 (2.1)	0 (0)
Educational Attainment					0.85
Postgraduate	71 (50)	22 (45.8)	25 (53.2)	24 (51.1)
College	44 (31)	17 (35.4)	15 (31.9)	12 (25.5)
High School	23 (16.2)	7 (14.6)	6 (12.8)	10 (21.3)
Other	4 (2.8)	2 (4.2)	1 (2.1)	1 (2.1)
Usual Gait Speed, m/s **	1.09 ± 0.16	1.04 ± 0.18	1.09 ± 0.14	1.12 ± 0.16	0.06
Global Positioning System (GPS) Measures
Median hours outside home	3.6 ± 2.2	2.0 ± 1.6	3.3 ± 1.3	5.4 ± 2.2	<.001
Overall percent time outside home	18.8 ± 10.7	12.1 ± 6.9	17.2 ± 7.4	27.1 ± 11.4	<.001
Median maximum distance from home	6.9 ± 12.0	2.4 ± 1.6	6.0 ± 3.0	12.5 ± 19.3	<.001
Overall maximum distance from home	36.0 ± 97.7	33.4 ± 133.6	21.8 ± 36.2	52.8 ± 96.0	0.30

Time-weighted standard deviational ellipses (SDE) represent the participant’s activity space in square kilometers (higher area = greater community mobility).

^*P-value for continuous variables from the One-way ANOVA and Chi-Sq. goodness of fit test for categorical variables across standard deviational ellipse tertiles.

^**Usual gait speed was assessed on a 14-foot instrumented walkway system (Zeno Walkway, Protokinetics LLC, Havertown, PA).

Data are presented as mean ± SD for continuous variables and n (%) for categorical variables.

Figure 1.

Odds of being in a higher tertile of GPS measures for each 1% higher PPFI score in PRIMA (N=142)

Odds are from ordinal logistic regression. PRIMA = Program to Improve Mobility on Aging; GPS = Global Positioning System; PPFI = Pittsburgh Performance Fatigability Index (higher PPFI score = greater fatigability); SDE = standard deviational ellipses (higher area = greater activity space)

DISCUSSION

We provided preliminary, novel evidence that greater performance fatigability, measured by PPFI, was associated with lesser spatial area of activity and distance traveled from one’s home, but not the time spent outside of the home. Further, a measure that represents daily habits (median maximum distance from home) was associated with performance fatigability, but a measure representing a single day of farthest travel was not (overall maximum distance from home which may be skewed by single days with a large distance traveled).⁷ Current findings support and strengthen prior evidence that greater perceived fatigability was also associated with more restricted community mobility.^4,5 Our results suggest that performance fatigability may be primarily restricting the day-to-day distance traveled into one’s community.

Generalizability of our findings is somewhat limited as the included sample was primarily White and well-educated. Also, data regarding participants’ driving ability was not assessed.⁷ Another limitation was the exclusion of almost one-third of GPS users from analysis due to logistical or compliance issues. Future work should explore facilitators and barriers to effective use. Strengths of this study include use of objective data collection methods with GPS technology and accelerometer-derived performance fatigability.

Our findings reveal emerging cross-sectional evidence that greater performance fatigability may be a unique clinical indicator of less real-world community mobility. Future work should be conducted in a larger, more diverse sample controlling for demographic, health and social characteristics related to community mobility.

Supplementary Material

Supinfo

Supplementary Methods S1: Derivation of the Analytic Sample from baseline enrollment in the Program to Improve Mobility in Aging (PRIMA)

Supplementary Methods S1: Calculation of the Pittsburgh Performance Fatigability Index (PPFI) for the 6-minute Walk Test