Are Suggested Hiking Times Accurate? A Validation of Hiking Time Estimations for Preventive Measures in Mountains
<i>Background and Objectives</i>: 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...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Medicina |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1648-9144/61/1/115 |
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| Summary: | <i>Background and Objectives</i>: 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. <i>Materials and Methods</i>: 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. <i>Results</i>: 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 <i>p</i> < 0.001). In contrast, MOVE showed no statistically significant difference (ΔWM: −0.27 ± 65.72 min; <i>p</i> = 0.278), providing the most accurate predictions. <i>Conclusions</i>: 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. |
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| ISSN: | 1010-660X 1648-9144 |