Journal of aging and physical activity · 2026
Walking Speed Reserve in Community-Dwelling Older Adults: An Integrative Biokinesiology and Ecokinesiology Approach.
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Medina-González Paul
Аннотация
BACKGROUND/OBJECTIVES: Walking speed is a functional biomarker, but clinical thresholds often miss community demands. This study quantified mechanical reserve (walking speed functional reserve, %FRWS) and physiological reserve (heart rate functional reserve, %FRHR) in Near- and Far-ecokinesiological groups and examined their alignment with ecological thresholds.
METHODS: Observational cross-sectional secondary analysis of a deidentified cohort. STROBE-compliant. Seventy older adults (≥60 years) completed a 40-m elliptical-circuit test.
EXCLUSIONS: Neurological disease, contraindication to exertion, lower-limb pain, or Mini-Mental State Examination < 13.
MEASURES: Self-selected and maximal walking speeds (MWS); %FRWS and %FRHR derived from heart rate work. Heart rate reserve was computed to monitor effort during testing. Ecokinesiology (EK) status: Near-EK (self-selected walking speed 0.80-1.19 m/s) or Far-EK (≥1.20 m/s). Quadrant profiling combined %FRWS (x) and %FRHR (y).
RESULTS: Near-EK participants were older with higher body mass/body mass index (p = .006; p = .009; p = .003). Far-EK showed higher self-selected walking speed and MWS (both p < .001). Reserves diverged: %FRWS was higher in Near-EK (18.4 ± 6.3% vs. 14.0 ± 6.8%, p = .008), whereas %FRHR and its increment were higher in Far-EK (56.5 ± 21.6% vs. 45.9 ± 18.1%, p = .027; 18.7 ± 7.6% vs. 15.1 ± 9.9%, p = .016). Quadrant allocation was heterogeneous but nonsignificant (Near-EK χ2 = 5.40, p = .15; Far-EK χ2 = 2.04, p = .62), revealing capacity-context mismatches.
CONCLUSION: Ecological mobility cannot be inferred from reserve metrics alone. Integrating indicators with ecological thresholds enables stratification and exposes mismatches in Near- and Far-EK. Significance/Implications: This approach supports personalized assessment, exercise prescription, and environment-informed configurations for healthy aging.
METHODS: Observational cross-sectional secondary analysis of a deidentified cohort. STROBE-compliant. Seventy older adults (≥60 years) completed a 40-m elliptical-circuit test.
EXCLUSIONS: Neurological disease, contraindication to exertion, lower-limb pain, or Mini-Mental State Examination < 13.
MEASURES: Self-selected and maximal walking speeds (MWS); %FRWS and %FRHR derived from heart rate work. Heart rate reserve was computed to monitor effort during testing. Ecokinesiology (EK) status: Near-EK (self-selected walking speed 0.80-1.19 m/s) or Far-EK (≥1.20 m/s). Quadrant profiling combined %FRWS (x) and %FRHR (y).
RESULTS: Near-EK participants were older with higher body mass/body mass index (p = .006; p = .009; p = .003). Far-EK showed higher self-selected walking speed and MWS (both p < .001). Reserves diverged: %FRWS was higher in Near-EK (18.4 ± 6.3% vs. 14.0 ± 6.8%, p = .008), whereas %FRHR and its increment were higher in Far-EK (56.5 ± 21.6% vs. 45.9 ± 18.1%, p = .027; 18.7 ± 7.6% vs. 15.1 ± 9.9%, p = .016). Quadrant allocation was heterogeneous but nonsignificant (Near-EK χ2 = 5.40, p = .15; Far-EK χ2 = 2.04, p = .62), revealing capacity-context mismatches.
CONCLUSION: Ecological mobility cannot be inferred from reserve metrics alone. Integrating indicators with ecological thresholds enables stratification and exposes mismatches in Near- and Far-EK. Significance/Implications: This approach supports personalized assessment, exercise prescription, and environment-informed configurations for healthy aging.