Association between performance fatigability and GPS‐measured community mobility

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 the 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 (what one can do), the quantification of one's slowing down due to fatigue, was associated with Global Positioning System (GPS)-measured community mobility. Participants (n = 142) were drawn from the Program to Improve Mobility in Aging (PRIMA) Study (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. The Pittsburgh Performance Fatigability Index (PPFI)8 is an accelerometry-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 score = greater fatigability) during a walking task.8 In PRIMA, PPFI was applied to a 6-minute walk test (Supplementary Methods S1). 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 seven consecutive days.7 To assess the 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 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). 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% to 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). 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 were 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. The strengths of this study include the use of objective data collection methods with GPS technology and accelerometry-based 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. BTS—Conceptualization; formal analysis; writing—original draft; KDM—Conceptualization; formal analysis; writing—review and editing; YQ—Methodology; writing—review and editing; JSB—Funding acquisition; conceptualization; investigation; writing—review and editing; ALR—Funding acquisition; conceptualization; investigation; supervision; writing—review and editing; NWG—Methodology; supervision; writing—review and editing. The programming code to calculate PPFI for noncommercial purposes can be requested from the developer (NWG). The authors declare no conflicts of interest. This work was supported by the National Institute on Aging (NIA) at the National Institutes of Health (grant numbers R01AG057671 and R21AG054666 to ALR; R01AG045252 and K24AG057728 to JSB) and the Pittsburgh Pepper Center (NIA P30AG024827). The Epidemiology of Aging training grant at the University of Pittsburgh (NIA T32 AG000181) supported BTS and KDM. None. 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. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. 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