Are Suggested Hiking Times Accurate? A Validation of Hiking Time Estimations for Preventive Measures in Mountains

Abstract

Background and Objectives: 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. Materials and Methods: 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. Results: 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 p < 0.001). In contrast, MOVE showed no statistically significant difference (ΔWM: -0.27 ± 65.72 min; p = 0.278), providing the most accurate predictions. Conclusions: 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.

Keywords: GPS; hiking; mountain; outdoor; physical activity; safety; trail.

Figure 1

The figure illustrates the geographical distribution of 25 selected hiking trails across Italy that meet the study’s inclusion criteria. The trails are numbered and labeled with their respective names and locations. The map highlights the Veneto region in red, with several trails concentrated in this area, while others are distributed across various regions of northern and central Italy. Each trail is connected to its respective location on the map via dashed red lines, with the trail names listed in corresponding boxes.

Figure 2

The figure displays the mean differences in hiking time estimates (in minutes) between actual hiking times and those provided by four methods: Komoot (ΔWK, green bar), Outdooractive (ΔWO, black bar), mountain signage (ΔWS, red bar), and MOVE (ΔWM, blue bar). Error bars represent the standard deviation of the differences. The results indicate that Komoot, Outdooractive, and mountain signage consistently overestimate hiking times, whereas MOVE shows minimal deviation from actual times, demonstrating higher accuracy.

Figure 3

Differences in MOVE’s hiking time estimates (ΔWM, in minutes) across various subgroup analyses. MOVE underestimates hiking times for women while slightly overestimating for men (p < 0.001) (a). Low and high difficulty trails showing positive deviations while average difficulty trails being nearly accurate (p = 0.060) (b). Trails shorter than 10 km show positive mild overestimation, while those longer than or equal to 10 km exhibit slight underestimation (p = 0.229) (c). Trails with elevation gain below 600 m show overestimated hiking times, whereas trails with elevation gain equal to or greater than 600 m are underestimated (p < 0.001) (d).