Lack of information regarding the level of fitness required to complete a hiking trail may create perceived and real health risks for inexperienced hikers. In this study, the link between current fitness levels of potential hikers and actual exertion on hiking trails is investigated. In particular, we investigated whether simple, pre-hike fitness tests (Step-up and Cooper tests) could be used to predict physical exertion on two graded hiking trails (Trail 1: graded easy; Trail 2: graded moderate). Fifty participants completed the pre-hike fitness tests and the two hiking trails. Correlations between relevant sets of variables were calculated, together with the associated p-value. Analysis of covariance (ANCOVA) models followed by model selection were used to investigate if the exertion levels on the two trails, as characterised by the minimum heart rate (HR), mean HR and maximum HR at the end of the trail, could be predicted by the pre-hike fitness tests. A statistical model was created that predicts the mean HR and maximum HR of hikers undertaking an easy and a moderate hike; the Step-up test best predicted mean and maximum HR on Trial 1, and maximum HR on Trail 2, while the combination of Step-up and Cooper tests best predicted mean HR on Trail 2. Park managers are continuously looking to implement new and novel techniques that will increase customer enjoyment, while simultaneously minimise customer risk. By using an accurate predictive model such as the one proposed, managers can improve users' experiences. Satisfied customers are more likely to return to these facilities and positive reviews may increase facility usage.

中文翻译：

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体能测试可预测分级远足路线上的体力消耗

缺乏有关完成徒步旅行所需的健康水平的信息可能会给没有经验的徒步旅行者带来明显的和真正的健康风险。在这项研究中，调查了潜在徒步旅行者当前的健康水平与徒步小径实际消耗量之间的联系。特别是，我们研究了是否可以使用简单的远足前体能测试（Step-up 和 Cooper 测试）来预测两条分级徒步路线（路线 1：轻松级别；路线 2：中等级别）的体力消耗。五十名参加者完成了远足前的体能测试和两条远足路线。计算相关变量组之间的相关性以及相关的 p 值。使用协方差分析 (ANCOVA) 模型和模型选择来研究两条路线的运动水平（以路线结束时的最小心率 (HR)、平均 HR 和最大 HR 为特征）是否可以预测通过徒步前的体能测试。创建了一个统计模型来预测进行轻松和适度徒步旅行的徒步旅行者的平均心率和最大心率； Step-up 测试最能预测试验 1 的平均和最大心率，以及试验 2 的最大心率，而 Step-up 和 Cooper 测试的组合最能预测试验 2 的平均心率。公园管理者不断寻求实施新的和新颖的方法技术将增加客户的享受，同时最大限度地降低客户风险。通过使用所提出的准确预测模型，管理者可以改善用户体验。满意的客户更有可能返回这些设施，正面评价可能会增加设施的使用率。