Analysis of human gait requires accurate measurement of foot-ground contact, often determined using either foot-ground reaction force thresholds or kinematic data. This study examined the differences in calculating event times across five vertical force thresholds and validated a vertical acceleration-based algorithm as a measure of heel contact and toe-off. The experiment also revealed the accuracy in determining heel contact and toe-off when raw displacement/time data were smoothed using a range of digital filter cutoff frequencies. Four healthy young participants completed 10 walking trials: 5 at normal speed (1.2 m/s) and 5 at fast speed (1.8 m/s). A 3D optoelectric system was synchronized with a forceplate to measure the times when vertical force exceeded (heel contact) or fell below (toe-off) 10, 20, 30, 40, and 50 N. These were then compared and subsequently used to validate an acceleration-based method for calculating heel contact and toe-off with the displacement/time data filtered across a range of four cutoff frequencies. Linear regression analyses showed that during both normal and fast walking, any force threshold within 0 to 50 N could be used to predict heel-contact time. For estimating toe-off low force thresholds, 10 N or less should be used. When raw data were filtered with the optimal cutoff frequency, the absolute value (AbsDt) of the difference between the forceplate event times obtained using a 10-N threshold and the event times of heel contact and toe-off using the acceleration-based algorithms revealed average AbsDt of 10.0 and 16.5 ms for normal walking, and 7.4 and 13.5 ms for fast walking. Data smoothing with the non-optimal cutoff frequencies influenced the event times computed by the algorithms and produced greater AbsDt values. Optimal data filtering procedures are, therefore, essential for ensuring accurate measures of heel contact and toe-off when using the acceleration-based algorithms.

The gene SMART (genes and the Skeletal Muscle Adaptive Response to Training) Study aims to identify genetic variants that predict the response to both a single session of High-Intensity Interval Exercise (HIIE) and to four weeks of High-Intensity Interval Training (HIIT). While the training and testing centre is located at Victoria University, Melbourne, three other centres have been launched at Bond University, Queensland University of Technology, Australia, and the University of Brighton, UK. Currently 39 participants have already completed the study and the overall aim is to recruit 200 moderately-trained, healthy Caucasians participants (all males 18–45 y, BMI < 30). Participants will undergo exercise testing and exercise training by an identical exercise program. Dietary habits will be assessed by questionnaire and dietitian consultation. Activity history is assessed by questionnaire and current activity level is assessed by an activity monitor. Skeletal muscle biopsies and blood samples will be collected before, immediately after and 3 h post HIIE, with the fourth resting biopsy and blood sample taken after four weeks of supervised HIIT (3 training sessions per week). Each session consists of eight to fourteen 2-min intervals performed at the pre-training lactate threshold (LT) power plus 40 to 70% of the difference between pre-training lactate threshold (LT) and peak aerobic power (Wpeak). A number of muscle and blood analyses will be performed, including (but not limited to) genotyping, mitochondrial respiration, transcriptomics, protein expression analyses, and enzyme activity. The participants serve as their own controls. Even though the gene SMART study is tightly controlled, our preliminary findings still indicate considerable individual variability in both performance (in-vivo) and muscle (in-situ) adaptations to similar training. More participants are required to allow us to better investigate potential underlying genetic and molecular mechanisms responsible for this individual variability.