Influence of age, height, weight, body mass index, grip strength, and incline and decline on ground reaction forces during the gait cycle

Background The demographic trend in Germany is characterized by an ageing society and is expected to shrink by around 16% by 2050 (BUNDESAMT, 2015). Accordingly, an increase of 38% in the over-65s and 156% in the over-80s is forecast (PETERS et al., 2010). The ageing of people reduces their physiological ability to balance, leads to gait disorders and thus increases the risk of falling (EWAN et al., 2019). In this context, the analysis of gait patterns using sensor shoe insoles (baropedography) is possible to investigate the behavior and activities of people in daily life and of patients during the rehabilitation phase. Despite the popularity of baropedography, the characteristic effects of anthropometric and other individual parameters on the ground reaction forces of the gait cycle are not yet known. The aim of this study was therefore to evaluate the relationship or effect of age, body weight, height, handgrip strength, and the slope and inclination of a gait plane on ground reaction forces or plantar pressure progression during the physiological gait cycle. Methods The data of the present study on load and gait analysis were collected in the sense of a prospective intervention study in a cross-sectional design. Gait data (n= 40) and anthropometric data (age, height, body weight, BMI) as well as maximum grip strength (n= 37) were collected from healthy subjects who were able to walk without assistance. The gait data were collected using calibrated sensor insoles with 16 pressure sensors per sole and a recording frequency of 100 Hz. The participants walked on a treadmill at 4 km/h for one minute at the following inclines: -20, -15, -10, -5, 0, 5, 10, 15 and 20 %. The resulting raw data was processed for further data processing using a specially developed algorithm for stance recognition based on the typical M-shaped gait cycle curve with 9 different established parameters compared to (LARSEN et al., 2008) and checked, formatted and interpolated together with the anthropometric data of the test subjects. The data were statistically analyzed using linear regression and correlation analyses. Results The mean age of the test participants was 43.65 ± 17.59 years. The mean height of the participants was 173.70 ± 11.22 cm, the mean body weight was 79.81 ± 27.85 kg. The maximum grip strength on the dominant arm was on average 35.41 ± 12.46 kg and the subjects had a BMI of 22.78 ± 7.04 kg/m². Age showed a negative correlation with the initial slope of the gait cycle curve (loading slope). Body height correlated with the force between the start of the loading phase and one of the maximum points in the gait cycle curve (Fmeanload) and the loading slope. Body weight and BMI correlated with all analyzed parameters, with the exception of loading slope. Handgrip strength correlated with changes in the second half of the stance phase and had no influence on the first half of the stance phase, which is probably due to a stronger push-off during the gait cycle. However, only up to 46 % of the variability can be explained by age, body weight, height, BMI and handgrip strength. There must therefore be other factors influencing the gait cycle curve than the parameters analyzed in this study. In addition, the slope and incline of the gait level were found to cause significant changes in the loading forces during the initial slope and the slope at the end of the gait cycle curve (loading and unloading slope) (each p <0.001). Conclusion The present study demonstrated that age, height, body weight, BMI and handgrip strength influence the gait cycle curve in a characteristic way, but explain only 46 % of the variability of a gait cycle. In addition, characteristic changes in the plantar pressure distribution during the gait cycle curve were identified, which for the first time characterize uphill and downhill walking. This allows changes in the gait cycle curve to be systematically recorded and individualized. In the future, this could also be used prospectively for clinical application in patients with altered gait patterns, for example after lower limb injuries. To this end, automated annotation and continuous analysis of gait data should be used to develop improved rehabilitation and feedback systems for the prevention and treatment of patients in the future. A combination of known regression statistics in the context of heuristics paired with methods of artificial intelligence are necessary to further exploit the potential of these promising applications.