节奏
自感劳累
工作(物理)
心理学
搭便车
应用心理学
物理医学与康复
工程类
激励
医学
机械工程
血压
放射科
经济
微观经济学
心率
作者
Annika Laqua,Jan Schnee,Jo Pletinckx,Martin Meywerk
标识
DOI:10.1016/j.trf.2023.08.008
摘要
E-bike riding contributes to a more sustainable lifestyle by offering health and environmental benefits. Especially e-mountain bikes enjoy great popularity in hilly areas. Although the frequency of use depends substantially on the perceived riding experience, little is known about factors influencing the riding experience. The present work aimed to explore factors influencing the riding experience on e-mountain bikes using a two-step approach. Subjective and objective factors influencing the riding experience were determined through qualitative content analysis of data collected in an interview study (Study 1) with 23 active e-mountain bike riders. Results from Study 1 identified riding performance, consisting of perceived handling, exertion and motor performance as a subjective impact factor of the riding experience. Rider behaviour, which is composed of rider cadence and rider torque, motor power and riding dynamics such as speed were identified as objective impact factors of the riding experience. In a next step, the impact of the determined factors was examined with multiple linear regression analysis applied to data collected in a cycling study (Study 2) with 80 active mountain bikers. The data from Study 2 was recorded on a forest uphill track with e-mountain bikes that provided different motor assistance. Results from Study 2 confirmed that high motor performance, good handling and reduced exertion contributed to a positive riding experience (ad. R2=.68, F(3,446)=325.2, p<.001). Increased rider cadence and rider torque affected the riding experience negatively while increased motor power and speed affected the riding experience positively (ad. R2=.40, F(4,445)=75.89, p<.001). The findings of the present work help to improve the riding experience on e-mountain bikes by revealing influencing factors and their impact on the riding experience.
科研通智能强力驱动
Strongly Powered by AbleSci AI