As mental health issues increase worldwide, nature-based experiences are being recognized as alternative treatments for improving health and well-being. Increasing urbanization precludes many people from accessing green spaces owing to time or physical limitations. Therefore, opportunities to connect with nature through virtual technology is being encouraged. We conducted a systematic review of studies on the psychological effects of experiencing nature using virtual technology. We searched the academic databases PubMed, Web of Science, and Scopus for relevant studies and assessed their quality using Cochrane’s RoB 2 and ROBINS-I tools. Twenty-one studies were included and the psychological outcomes were negatively synthesized by the intervention characteristics (duration, observation position of the landscape, interaction, environment description, and sensory type). Psychological outcomes were classified into emotional recovery, cognitive recovery, stress reduction, and other indicators. Emotional recovery was most consistently presented, and virtual natural contact alleviated negative emotions more than it elicited positive emotions. Additionally, virtual nature interventions lasting more than 10 min showed more consistent effects than those of less than 10 min. Moreover, an open field of view led to significant emotional recovery and an in-forest view led to significant cognitive recovery. Despite some limitations, our findings will contribute to the development of virtual forest experiences to improve human well-being.

Over the past decade, clinical trials of forest-based interventions have increased, leading to their recognition as preventive medicine. However, little is known about the differences in health effects according to the activity characteristics of interventions. This study aimed to understand the types of activities and their associated health effects to identify differences in health effects between activities. PubMed, PsycINFO, Web of Science, and Scopus databases were searched, and methodological quality was assessed using Cochrane ROB2. A total of 32 randomized controlled trials (RCTs) met the eligibility criteria. Health outcomes were collected from 6264 participants aged 6–98 years, and the sample size was 12–585. The Interventions were walking (n = 21), staying (n = 7), exercise (n = 4), indirect exposure (n = 4), and the activity time was between 10 and 240 min. Overall, walking showed consistent positive health effects, and there were differences in effects on anxiety and depression, cognitive function, stress hormone, and inflammation according to the activity. However, most of the included studies had a high risk of bias, and interventions were limited to specific activities, durations, and frequencies. Although a few limitations remain, the findings in this study are of great significance in providing the basis for the design of forest-based interventions.

In the existing phytoncide-prediction process, solar radiation and photosynthetically active radiation (PAR) are difficult microclimate factors to measure on site. We derived a phytoncide-prediction technique that did not require field measurements. Visual indicators extracted from forest images and statistical analysis were used to determine appropriate positioning for forest environment photography to improve the accuracy of the new phytoncide-prediction formula without using field measurements. Indicators were selected from the Automatic Mountain Meteorology Observation System (AMOS) of the Korea Forest Service to replace on-site measured climate data and the phytoncide-prediction equation was derived using them. Based on regression analyses, we found that forest density, leaf area, and light volume above the horizon could replace solar radiation and PAR. In addition, AMOS data obtained at 2 m altitudes yielded suitable variables to replace microclimate data measured on site. The accuracy of the new equation was highest when the surface area in the image accounted for 25% of the total. The new equation was found to have a higher prediction accuracy (71.1%) compared to that of the previous phytoncide-prediction equation (69.1%), which required direct field measurements. Our results allow the public to calculate and predict phytoncide emissions more easily in the future.