Parameters estimates of models with and without interaction terms.

Parameters estimates of models with and without interaction terms.</p

Optimizing Virtual Nature for Psychological and Physiological Well-Being: A Systematic Review of the Moderating Effects of Duration, Nature Type, Sample Characteristics, and Immersiveness and Potential Risks of Bias

Virtual nature research has emerged as a prominent and captivating study area, gaining much attention for its profound potential to enhance well-being. This literature review aimed to expand prior reviews of virtual nature experiences on psychological and physiological well-being in two ways: summarizing how four factors may moderate the beneficial effects of virtual nature and reporting the risk of bias in this body of literature. Searches for peer-reviewed research articles were conducted in Web of Science and Scopus and manually identified, returning 78 relevant empirical studies published between 2010 and 2023. The assessment of bias was conducted utilizing Cochrane’s RoB 2 and ROBINS-I tools. The four moderators examined were duration of exposure (i.e., ≤5 min, 5–10 min, ≥10 min), type of virtual nature (i.e., green space, blue space), sample characteristics (i.e., age, health status), and immersion level (i.e., virtual reality [VR], 2D screens). We found limited evidence for the impact of the first three moderators but stronger evidence for higher levels of immersion showing stronger benefits. All studies were found to have a moderate to high risk of bias, mostly related to the subjective measurement of outcomes. Future research should prioritize studying tailored virtual nature interventions and their long-term effects among diverse participants and different types of virtual environments, as well as investigating the influence of presence and immersion levels in virtual settings. These efforts will provide further insights into the underlying mechanisms of the benefits derived from virtual nature exposure.</p

Variables incorporated in statistical analysis.

Studies have investigated various aspects of how the COVID-19 pandemic has impacted college students’ well-being. However, the complex relationships between stress and its correlates have received limited attention. Thus, the main objective of this study is to evaluate multiplicative associations between stress and demographic, lifestyle, and other negative emotion factors during the pandemic. We used data from a survey with 2,534 students enrolled in seven U.S. universities and analyzed such data with generalized additive Tobit models and pairwise interaction terms. The results highlighted associations and interactions between myriad factors such as students’ social class, income, parental education, body mass index (BMI), amount of exercise, and knowing infected people in the student’s communities. For instance, we found that the associations between feeling irritable and sad due to the pandemic were interactive, resulting in higher associated stress for students with higher levels of parents’ education. Furthermore, associations between taking precautionary actions (i.e., avoiding travel and large gatherings) and stress varied with the intensity of negative feelings (i.e., sadness and irritability). Considering these interaction terms, the results highlighted a great inequality in pandemic-related stress within low income, lower social class, and higher BMI students. This study is among the earliest that employed a stratified approach with numerous interaction terms to better understand the multiplicative associations between different factors during the COVID-19 pandemic.</div

Summary of the complex relationships between college students’ stress from the COVID-19 pandemic (left hand side) and associations with myriad factors (right hand side); positive and negative signs show whether associations with more stress are positive or negative.

Summary of the complex relationships between college students’ stress from the COVID-19 pandemic (left hand side) and associations with myriad factors (right hand side); positive and negative signs show whether associations with more stress are positive or negative.</p

Data_Sheet_5_Leveraging and exercising caution with ChatGPT and other generative artificial intelligence tools in environmental psychology research.DOCX

Generative Artificial Intelligence (GAI) is an emerging and disruptive technology that has attracted considerable interest from researchers and educators across various disciplines. We discuss the relevance and concerns of ChatGPT and other GAI tools in environmental psychology research. We propose three use categories for GAI tools: integrated and contextualized understanding, practical and flexible implementation, and two-way external communication. These categories are exemplified by topics such as the health benefits of green space, theory building, visual simulation, and identifying practical relevance. However, we also highlight the balance of productivity with ethical issues, as well as the need for ethical guidelines, professional training, and changes in the academic performance evaluation systems. We hope this perspective can foster constructive dialogue and responsible practice of GAI tools.</p

Data_Sheet_2_Leveraging and exercising caution with ChatGPT and other generative artificial intelligence tools in environmental psychology research.DOCX

Generative Artificial Intelligence (GAI) is an emerging and disruptive technology that has attracted considerable interest from researchers and educators across various disciplines. We discuss the relevance and concerns of ChatGPT and other GAI tools in environmental psychology research. We propose three use categories for GAI tools: integrated and contextualized understanding, practical and flexible implementation, and two-way external communication. These categories are exemplified by topics such as the health benefits of green space, theory building, visual simulation, and identifying practical relevance. However, we also highlight the balance of productivity with ethical issues, as well as the need for ethical guidelines, professional training, and changes in the academic performance evaluation systems. We hope this perspective can foster constructive dialogue and responsible practice of GAI tools.</p

Data_Sheet_4_Leveraging and exercising caution with ChatGPT and other generative artificial intelligence tools in environmental psychology research.DOCX

Generative Artificial Intelligence (GAI) is an emerging and disruptive technology that has attracted considerable interest from researchers and educators across various disciplines. We discuss the relevance and concerns of ChatGPT and other GAI tools in environmental psychology research. We propose three use categories for GAI tools: integrated and contextualized understanding, practical and flexible implementation, and two-way external communication. These categories are exemplified by topics such as the health benefits of green space, theory building, visual simulation, and identifying practical relevance. However, we also highlight the balance of productivity with ethical issues, as well as the need for ethical guidelines, professional training, and changes in the academic performance evaluation systems. We hope this perspective can foster constructive dialogue and responsible practice of GAI tools.</p

Table_1_Leveraging and exercising caution with ChatGPT and other generative artificial intelligence tools in environmental psychology research.DOCX

Generative Artificial Intelligence (GAI) is an emerging and disruptive technology that has attracted considerable interest from researchers and educators across various disciplines. We discuss the relevance and concerns of ChatGPT and other GAI tools in environmental psychology research. We propose three use categories for GAI tools: integrated and contextualized understanding, practical and flexible implementation, and two-way external communication. These categories are exemplified by topics such as the health benefits of green space, theory building, visual simulation, and identifying practical relevance. However, we also highlight the balance of productivity with ethical issues, as well as the need for ethical guidelines, professional training, and changes in the academic performance evaluation systems. We hope this perspective can foster constructive dialogue and responsible practice of GAI tools.</p

Data_Sheet_7_Leveraging and exercising caution with ChatGPT and other generative artificial intelligence tools in environmental psychology research.DOCX

Generative Artificial Intelligence (GAI) is an emerging and disruptive technology that has attracted considerable interest from researchers and educators across various disciplines. We discuss the relevance and concerns of ChatGPT and other GAI tools in environmental psychology research. We propose three use categories for GAI tools: integrated and contextualized understanding, practical and flexible implementation, and two-way external communication. These categories are exemplified by topics such as the health benefits of green space, theory building, visual simulation, and identifying practical relevance. However, we also highlight the balance of productivity with ethical issues, as well as the need for ethical guidelines, professional training, and changes in the academic performance evaluation systems. We hope this perspective can foster constructive dialogue and responsible practice of GAI tools.</p

Data_Sheet_6_Leveraging and exercising caution with ChatGPT and other generative artificial intelligence tools in environmental psychology research.DOCX

Generative Artificial Intelligence (GAI) is an emerging and disruptive technology that has attracted considerable interest from researchers and educators across various disciplines. We discuss the relevance and concerns of ChatGPT and other GAI tools in environmental psychology research. We propose three use categories for GAI tools: integrated and contextualized understanding, practical and flexible implementation, and two-way external communication. These categories are exemplified by topics such as the health benefits of green space, theory building, visual simulation, and identifying practical relevance. However, we also highlight the balance of productivity with ethical issues, as well as the need for ethical guidelines, professional training, and changes in the academic performance evaluation systems. We hope this perspective can foster constructive dialogue and responsible practice of GAI tools.</p