Forest trail difficulty levels must be investigated to promote their safe usage. However, exercise-based physiological changes are difficult to estimate in a forest environment. Hence, studies comparing indoor and outdoor environmental activities are required. We aimed to investigate the physiological traits associated with different usage grades of national forest trails in the Republic of Korea. We conducted both field and indoor experiments to measure different variables of 20 healthy participants aged 40–50 years to evaluate the exercise intensity and physiological effects of hiking on these trails. Significant variations in physiological responses were observed based on trail difficulty, with the highest heart rate and energy expenditure recorded during the most challenging uphill segments. Specifically, the heart rate increased from 115.4 ± 13.7 bpm in easy segments to 140.3 ± 15.6 bpm in difficult segments, whereas energy expenditure ranged from 404.1 ± 112.2 kcal/h to 518.1 ± 131.0 kcal/h. This study highlights the importance of considering both objective trail difficulty and users’ physiological reactions, including perceived exertion, to enhance user safety and optimize the health benefits of national forest trails. These findings provide essential data for developing comprehensive exercise programs and improving the national forest trail usage grade system.

Predicting forest trail visitation is essential for sustainable management and policy development, including infrastructure planning, safety operations, and conservation. However, due to numerous informal access points and complex external influences, accurately monitoring visitor numbers remains challenging. This study applied random forest, gradient boosting, and LightGBM models with Bayesian optimization to predict daily visitor counts across six sections of the National Daegwallyeong Forest Trail, incorporating variables such as weather conditions, social media activity, COVID-19 case counts, tollgate traffic volume, and local festivals. SHAP analysis revealed that tollgate traffic volume and weekends consistently increased visitation across all sections. The impact of temperature varied by section: higher temperatures increased visitation in Kukmin Forest, whereas lower temperatures were associated with higher visitation at Seonjaryeong Peak. COVID-19 cases demonstrated negative effects across all sections. By integrating diverse variables and conducting section-level analysis, this study identified detailed visitation patterns and provided a practical basis for adaptive, section- and season-specific management strategies. These findings support flexible measures such as seasonal staffing, congestion mitigation, and real-time response systems and contribute to the advancement of data-driven regional tourism management frameworks in the context of evolving nature-based tourism demand.

Accurate information on trail difficulty is essential for ensuring safety and enhancing the effectiveness of forest-based health and recreational activities. This study examined the physiological responses of middle-aged adults to varying trail difficulty levels across both controlled indoor and natural outdoor walking environments. A total of ten healthy individuals aged 40–50 years participated in walking tasks across three designated trail difficulty levels: Moderate, Difficult, and Very Difficult. Physiological indicators assessed included step speed (SS), step count (SC), rate of perceived exertion (RPE), heart rate (HR), oxygen saturation (OS), energy expenditure (EE), metabolic equivalents (MET), and oxygen consumption (VO₂). As trail difficulty increased, HR, RPE, VO₂, EE, and MET consistently showed upward trends, whereas SS and SC demonstrated significant decreases. Additionally, the outdoor setting imposed generally greater physiological demands compared to the indoor condition, suggesting that terrain complexity and elevation changes amplify physical exertion during real-world trail use. The findings contribute valuable empirical evidence for the design of individualized exercise programs, improved trail difficulty classifications, and the advancement of forest-based health promotion policies.

National Forest Trails (NFTs), a key component of forest welfare infrastructure, increasingly require a shift from government-led management to citizen-participatory governance. This study examined the structural relationships among volunteer motivation, activity satisfaction, perceived quality-of-life (QoL) change, and behavioral intention in the context of NFT volunteering. A survey was conducted with 217 adults who had participated in forest trail volunteering programs in Korea, and the data were analyzed using structural equation modeling (SEM). The results showed that volunteer motivation had significant positive effects on reward importance, activity satisfaction, and perceived QoL change. Activity satisfaction positively influenced both Future Participation Intention and Active Participation Intention, whereas perceived QoL change had a significant positive effect only on Active Participation Intention. In addition, activity satisfaction and perceived QoL change mediated the relationship between volunteer motivation and Active Participation Intention. These findings suggest that forest trail volunteers are not merely supplementary labor for trail management, but active participants in forest governance who both contribute to and benefit from the environments they help sustain. Overall, the study indicates that sustainable NFT volunteering depends not only on motivation itself, but also on the quality and personal meaning of the volunteer experience. The findings highlight the importance of experience-centered program design, appropriate recognition systems, and greater attention to participant-centered well-being outcomes in sustainable forest trail governance.

Increasing forest-trail use requires empirical identification of patterns that conventional surveys may miss, including prolonged stationary periods that may be associated with camping or backpacking. This study examined activity records from the Seonjaryeong section of Korea’s Daegwallyeong Forest Trail, combining GPS movement data with geotagged photographs linked at the activity-record level. Of 6505 GPS records collected in 2021, 3105 remained after cleaning. Stay point detection and K-means clustering identified three user types: short-distance (89.5%), long-distance (9.1%), and long-stay (1.4%). We also analyzed 1410 geolocated photographs using the Google Cloud Vision API. The long-stay type had an exceptionally long mean rest time (930 min) and distinct movement behavior. No significant difference in sex composition was detected, whereas age composition (with a small effect) and seasonal composition differed significantly among types. In the available photographs, forest and vegetation dominated object labels for all three types; second-ranked categories differed but rest on small, untested subsamples, two dominated by single contributors. The long-stay group, identified from GPS trajectories alone and defined by prolonged stationary time rather than verified on-site stays, represents a pattern current management systems do not explicitly address; the photographs add exploratory information on where some records stopped.