Faculty of Health Sciences Degree of Master of Science in Physiotherapy 9601486g / A study was conducted to determine the optimum position of the handlebar on the Johnny G. Spinning® bicycle to reduce low back pain in cyclists participating in a Spinning® class. A three period open label cross over design, involving thirty six subjects was conducted. Each subject participated in three Spinning® classes with a different handlebar height at each session. The saddle angle, saddle post height and fore/aft position of the saddle remained fixed to eliminate variability. The cyclists’ pain perception was measured via the Visual Analogue Scale, Lickert Scale and the McGill Pain Questionnaire. The results were analysed with respect to the change in the mean and standard deviation of the Visual Analogue Scale, the Lickert scale and the McGill Pain Questionnaire. The significance of the study was set at the 0.05 level. A zero value was recorded with respect to pain experienced by the cyclists during a Spinning® class on the VAS, Lickert scale and on the McGill Pain scale, when the handlebars were placed in the high handlebar height position on the Spinning® bicycle; this is the most important outcome of the study conducted. In conclusion, there is a statistically meaningful difference (p<0.001) between the mean values of pain recorded by participants of the low handlebar height compared to the normal handlebar height, with the normal handlebar height being the better position. The standard deviation remains relatively constant. No pain was recorded on the VAS, Lickert scale and on the McGill Pain scale when the handlebars were placed in the high handlebar height position on the Spinning® bicycle. The results of the study indicate that the high handlebar height position is the best position for participants in a Spinning® class.

The study of cycling biomechanics typically requires measurement of pedal loads through force transducer instrumentation. However, analysis of seated cycling often necessitates the additional measurement of the loads exerted on the rider by the seat and handlebars. A stationary exercise bicycle was instrumented with two commercial six-axis force transducers at both the seat and handlebar locations via a custom designed mounting system. The system was tested by applying known forces and moments to the fixtures and proved capable of accurately measuring the loads. Additional data collected from cycling tests were compared to values from the literature to add supporting evidence to the validity of the system. The instrumented stationary bicycle was further used to study the accuracy of modeling seated cycling in OpenSim. Five participants cycled at a moderate resistance level for three trials. Force and moment data were collected by seat, handlebar, and pedal load cells, while kinematic data were collected by an optical motion capture system. Participant data were analyzed with the OpenSim residual reduction algorithm (RRA) tool excluding seat and handlebar loads. The RRA pelvic residual was then compared to an experimentally determined handlebar and seat equivalent (HBSE) calculated from respective load cell data. Graphical comparisons of the RRA and HBSE results showed strong correlations in Anterior-Posterior (A-P) and Superior-Inferior (S-I) force directions and to a lesser degree, Medial-Lateral (M-L) force and S-I moment directions. M-L and A-P moment plots showed the least correlation between RRA and HBSE. Statistical comparisons showed RRA errors likely within 5.2% body weight (BW) for forces and 2.4% BW*height for moments. Considering the average participant height and weight of 167.7 cm and 63.6 kg, respectively, recommended error ranges for RRA are roughly ±4.0% BW for forces and ±7.2% BW*height for moments. This indicates that the OpenSim RRA tool can be used for cycling analysis.

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