<![CDATA[Digital technologies such as wearable devices, unmanned aerial vehicles (UAVs), and e-learning platforms are increasingly applied to enhance safety and emergency response in outdoor and mountain environments. However, evidence regarding their effectiveness, feasibility, and implementation remains limited and scattered across disciplines. This systematic review aimed to synthesize current findings on digital technologies supporting emergency preparedness and response in outdoor activities. Following the PRISMA 2020 guidelines, a comprehensive search was conducted across PubMed/MEDLINE, Scopus dan sciencedirect, including grey literature, for studies published up to October 30, 2025. Eligible studies examined (a) wearable or mobile-based fall detection systems, (b) UAV-based search-and-rescue (SAR) applications, or (c) e-learning interventions for rescue personnel. Two reviewers independently performed study selection and data extraction. Risk of bias was appraised using appropriate tools according to study design (QUADAS-2, JBI, and MMAT). Due to heterogeneity and the small number of studies, a narrative synthesis approach following the SWiM framework was applied. Out of 161 records identified, three studies met the inclusion criteria. One simulation-based study on wearable fall detection reported 97.9% sensitivity and 99.9% specificity in recognizing fall events with real-time location tracking. A feasibility study demonstrated the operational potential of UAVs in avalanche search-and-rescue scenarios but lacked standardized performance indicators. An e-learning intervention among rescuers showed significant improvement in knowledge retention, especially among professionals compared to volunteers after 12 months. Overall, the methodological quality of included studies was moderate to low. Digital technologies show promising potential to improve emergency preparedness and response in outdoor and mountain settings. Nonetheless, the evidence remains preliminary due to limited sample sizes, heterogeneous designs, and simulation-based validation.]]>
