Vecchiato Marco, Borasio Nicola, Scettri Emiliano, Franzoi Vanessa, Duregon Federica, Savino Sandro, Ermolao Andrea, Neunhaeuserer Daniel
Sports and Exercise Medicine Division, Department of Medicine, University of Padova, Via Giustiniani 2, 35128 Padova, Italy.
Institute of Mountain Emergency Medicine, EURAC Research, Viale Druso 1, 39100 Bolzano, Italy.
Medicina (Kaunas). 2025 Jan 14;61(1):115. doi: 10.3390/medicina61010115.
: Accurate hiking time estimate is crucial for outdoor activity planning, especially in mountainous terrains. Traditional mountain signage and online platforms provide generalized hiking time recommendations, often lacking personalization. This study aims to evaluate the variability in hiking time estimates from different methods and assess the potential of a novel algorithm, MOVE, to enhance accuracy and safety. : A cross-sectional analysis was conducted using data from 25 Italian loop trails selected via the Wikiloc platform, considering user-uploaded GPS data from at least 20 users per trail. Real-world hiking times were compared with estimations from Komoot, Outdooractive, mountain signage, and the MOVE algorithm, which incorporates individualized biological and trail characteristics. : Significant discrepancies were observed between actual hiking times and estimates from Komoot (ΔWK: -48.92 ± 57.16 min), Outdooractive (ΔWO: -69.13 ± 58.23 min), and mountain signage (ΔWS: -29.59 ± 59.90 min; all < 0.001). In contrast, MOVE showed no statistically significant difference (ΔWM: -0.27 ± 65.72 min; = 0.278), providing the most accurate predictions. : Current hiking time estimation methods show substantial variability and inaccuracy, which may pose safety risks. MOVE demonstrated superior accuracy, offering personalized hiking time predictions based on user-specific data and trail characteristics. Integrating such advanced tools into outdoor activity planning could enhance safety and accessibility, particularly for individuals with chronic conditions. Further studies should explore integrating real-time health data to refine these tools.
准确的徒步时间估计对于户外活动规划至关重要，尤其是在山区地形中。传统的山区标识和在线平台提供的是通用的徒步时间建议，往往缺乏个性化。本研究旨在评估不同方法得出的徒步时间估计的变异性，并评估一种新型算法MOVE提高准确性和安全性的潜力。
采用通过Wikiloc平台选择的25条意大利环形步道的数据进行横断面分析，考虑每条步道至少20名用户上传的GPS数据。将实际徒步时间与Komoot、Outdooractive、山区标识以及结合个体生物学和步道特征的MOVE算法的估计值进行比较。
实际徒步时间与Komoot（ΔWK：-48.92±57.16分钟）、Outdooractive（ΔWO：-69.13±58.23分钟）和山区标识（ΔWS：-29.59±59.90分钟；均P<0.001）的估计值之间存在显著差异。相比之下，MOVE没有显示出统计学上的显著差异（ΔWM：-0.27±65.72分钟；P=0.278），提供了最准确的预测。
当前的徒步时间估计方法显示出很大的变异性和不准确性，这可能带来安全风险。MOVE表现出卓越的准确性，基于用户特定数据和步道特征提供个性化的徒步时间预测。将此类先进工具整合到户外活动规划中可以提高安全性和可达性，特别是对于患有慢性病的个体。进一步的研究应探索整合实时健康数据以完善这些工具。
