Age-related mechanical work expenditure during normal walking: The Baltimore Longitudinal Study of Aging

Abstract

The aim of this cross sectional study was to delineate age-associated kinematic and kinetic gait patterns of normal walking, and to test the hypothesis that older adults exhibit gait patterns that reduce generative mechanical work expenditures (MWEs). We studied 52 adult Baltimore Longitudinal Study of Aging participants (means age 72 ± 9, from 60 to 92 years) who could walk 4-meters unaided. 3-dimensional kinematic and kinetic parameters assessed during rotation-defined gait periods were used to estimate MWEs for the rotation of lower extremities about the medial-lateral (ML) and anterior-posterior (AP) axes of proximal joints, which represent MWEs in the AP and ML sides, respectively. Relationships between gait parameters and age were examined using regression analysis with adjustments for walking speed, sex, height, and weight. Older age was associated with slower self-selected walking speed (p < 0.001), shorter stride length (p < 0.001), and greater propensity of landing flat-footed (p = 0.003). With older age, hip generative MWE for thigh rotation was lower about the AP axis (hip abduction and adduction) during stance (p = 0.010) and higher about the ML axis (hip extension and flexion) during late stance (p < 0.001). Knee absorptive MWE for shank rotation about the AP axis (knee abduction and adduction) during early stance was also lower with older age (p < 0.003). These age-related gait patterns may represent a compensatory effort to maintain balance and may also reflect mobility limitations.

Age-related differences in sagittal-plane knee function at heel-strike of walking are increased in osteoarthritic patients

“…48-49 Interestingly, while the standing posture only revealed differences with the severe OA group, differences between the younger and older asymptomatic groups and differences between the moderate and severe OA groups were detected during walking. This higher sensitivity during a dynamic activity supported the idea that decrease in motor control, 9,12,29,46 muscle strength 24,37 and soft-tissue properties 27,45 could be primary factors for the functional knee differences observed in this study. The importance of these three factors over swelling or pain was further strengthened by the fact that extension differences were noticed between two asymptomatic groups, which were equally free of swelling and pain.…”

“…So it is also possible that they offer more options for adaptation since both feet are in contact with the ground during that time, thus reducing the consequences of functional adaptations on balance and gait performance. 9,46 …”

“…This analysis showed consistent group differences for both speed conditions, indicating that there were functional differences among the four groups beyond the differences in walking speed. Future research is necessary to identify the causes of these functional differences, but these results suggested that age-related decrease in motor control, 9,12,29,46 muscle strength 24,37 and soft-tissue properties 27,45 are probably key factors because they can affect both the movement of the knee joint and walking speed.…”

The effects of gait strategy on metabolic rate and indicators of stability during downhill walking

“…Alternatively, humans may be able to choose more conservative gait strategies in which greater active control of leg motion is used, decreasing metabolic economy but increasing stability (lowering the risk of falling). Indeed, several investigators have suggested that the decreased metabolic economy of walking in many clinical populations is partially due to an increased focus on maintaining stability (Zamparo et al, 1995;Hoffman et al, 1997;Donelan et al, 2001;Mian et al, 2006;Ko et al, 2009;Reisman et al, 2009), which would correspond to a more conservative gait pattern on our theoretical figure.…”

Human Gait Kinematic Measurement

“…Although there are studies that show the principles and phases present in the walk of a person [1] [2] [3] [4], it is affected in every person by numerous factors, including health [5] [6], age [7] [8] [9], the physical state [10], and gender [11]. Being able to measure and quantify the variables involved in the move-ments of the human body is justified in three main research categories [12]: physiology and health, computational, and robotics.…”