The sampled population was identified as healthy, they were motivated to volunteer for a research project and their fundamental gait parameters were comparable to previous studies with similar age-group samples. Doyo et al. [15], for example, also observed a reduction in step length with age, based on a sample of 2006 community dwelling citizens in Japan. They also reported similar age-related changes in spatio-temporal parameters in the 70 s age range, such as reduced step length.
Two important findings emerged from the MFC analysis. First, contrary to previous findings [4, 6, 8] age effects on MFC height were found because mean MFC height was significantly lower in the 60 s and 70 s than the 50 s group. Most previous studies compared young (e.g. 18–35 yrs.) with a single older group (e.g. > 60 yrs.) [6, 17] but our analysis from a considerably larger sample, incorporating three sub-groups, revealed that MFC height may reduce prior to the 60 years age range. Inconsistent MFC height has been identified as the primary ageing effect on foot trajectory control, leading to higher tripping risk [4]. Our MFC findings here suggest that MFC height may begin to fall from the 60 s, while significantly more variable MFC may appear later, in the 70 s age group.
Pearson’s correlations indicated that across all three age groups more symmetrical gait, reflected in a lower SI, was generally associated with elevated MFC height. Previous work [8, 11, 12, 18] suggested that reduced leg strength with ageing leads to higher asymmetry, causing loss of symmetrical gait control and increased tripping risk. In contrast to Alcock et al. [10], we did not find significant correlations between step length and MFC height but they examined both preferred speed and fast walking, revealing increased MFC due to higher velocity, while the current experiment was conducted only at preferred speed. It may, however, be reasonable to suggest that decreased step length associated with ageing-related declines in walking speed [19, 20] is causally related to reduced MFC height with ageing.
While lower MFC height was identified from the 60 s, correlation analysis revealed that ankle control may decline later, from the 70 s. This age group showed a positive correlation between mean MFC and SD of MFC, such that the positive effects of elevated MFC were counteracted by increased MFC variability [22]. With ageing, the loss of finely coordinated ankle movement may require a greater contribution from the knee and hip, but these joints are less adapted to precise swing foot control [13, 23]. Increased Foot Contact Angle was also linked to reduced MFC height only in the 70 s group, also demonstrating impaired ankle action. Heel contact was associated with dorsiflexion but correlation results suggested that attempts to achieve increased foot contact angle may have caused reduced MFC in the 70 s participants.
Reduced MFC height was seen from age 60 years while MFC variability increased from 70 years. While each decade showed different strategies to control MFC, in general, less variable and more symmetrical gait optimises MFC control. Exercise interventions may help in maintaining foot elevation and reducing tripping risk and in addition to maintaining ankle dorsiflexion, particular at mid-swing close to MFC [13], exercises for older people should emphasise symmetrical walking. Treadmill-based gait training with real-time biofeedback, for example, can increase MFC height while reducing variability [21] and gait-feedback provided by “smart footwear” systems may also reduce tripping risk by alerting the wearer to asymmetrical gait control [25, 26].
Precise gait measures obtained using motion capture will more reliably identify age-associated changes to mobility than more commonly used assessments, such as the 6 m-walk test [7, 16]. Large-sample community-based gait screening could also be practically undertaken using a force-sensitive commercial gait assessment system (e.g. GaitRite mat) that does not require specialised skills. For a comprehensive understanding of ground clearance, however, 3D analysis is required. Our study used motion capture apparatus, but larger samples could be tested more efficiently using markerless motion capture suits or footwear-mounted wearable sensors. As far as we know, there have been no previous attempts to use 3D motion capture to examine mobility within an everyday community.
This study was conducted as part of Konosu City’s health promotion initiative and advances in remote gait monitoring, i.e. gait measurement outside the laboratory, will encourage future falls prevention and physical activity initiatives. This early-stage gait assessment scheme should, therefore, be viewed as a community model with the potential to be adopted by other cities to maintain the mobility and safety of their valuable and deserving senior citizens.