The Body Appreciation Scale-2 (BAS-2) is a widely used measure of a core facet of the positive body image construct. However, extant research concerning measurement invariance of the BAS-2 across a large number of nations remains limited. Here, we utilised the Body Image in Nature (BINS) dataset - with data collected between 2020 and 2022 - to assess measurement invariance of the BAS-2 across 65 nations, 40 languages, gender identities, and age groups. Multi-group confirmatory factor analysis indicated that full scalar invariance was upheld across all nations, languages, gender identities, and age groups, suggesting that the unidimensional BAS-2 model has widespread applicability. There were large differences across nations and languages in latent body appreciation, while differences across gender identities and age groups were negligible-to-small. Additionally, greater body appreciation was significantly associated with higher life satisfaction, being single (versus being married or in a committed relationship), and greater rurality (versus urbanicity). Across a subset of nations where nation-level data were available, greater body appreciation was also significantly associated with greater cultural distance from the United States and greater relative income inequality. These findings suggest that the BAS-2 likely captures a near-universal conceptualisation of the body appreciation construct, which should facilitate further cross-cultural research.

Exposure to natural environments offers an array of mental health benefits. Virtual reality provides simulated experiences of being in nature when outdoor access is limited. Previous studies on virtual nature have focused mainly on single "doses" of virtual nature. The effects of repeated exposure remain poorly understood. Motivated by this gap, we studied the influence of a daily virtual nature intervention on symptoms of anxiety, depression, and an underlying cause of poor mental health: rumination. Forty college students (58% non-Hispanic White, median age = 19) were recruited from two U.S. universities and randomly assigned to the intervention or control group. Over several weeks, anxious arousal (panic) and anxious apprehension (worry) decreased with virtual nature exposure. Participants identifying as women, past VR users, experienced with the outdoors, and engaged with the beauty in nature benefited particularly strongly from virtual nature. Virtual nature did not help symptoms of anhedonic depression or rumination. Further research is necessary to distinguish when and for whom virtual nature interventions impact mental health outcomes.

Total knee arthroplasty (TKA) is one of the most common joint surgeries, with over a million procedures performed annually in the US. Over 70% of patients report moderate to high pain and anxiety surrounding TKA surgery, and 96% are discharged with an opioid prescription. This population requires special attention as approximately 90% of TKA patients are older adults and one of the riskiest groups prone to misusing opioids. This study aimed to develop and compare the efficacy of nature-based virtual reality (VR) with heart rate variability biofeedback (HRV_BF) to mitigate surgical pain and anxiety. This randomized control trial recruited 30 patients (mean age = 66.3 ± 8.2 years, 23 F, 7 M) undergoing TKA surgery and randomly assigned to a control, 2D video with HRV_BF, or VR with HRV_BF group. A visual analog scale (VAS) was used to measure pain levels before and after the intervention. In addition, a second VAS and the State-Trait Anxiety Inventory (STAI) were used to measure anxiety before and after the intervention. Electrocardiogram (ECG) was used to continuously measure HRV and respiration rate in preoperative and postoperative settings. VR and 2D-video with HRV_BF decreased pain and anxiety post-intervention compared with the control group, p's <.01. On analyzing physiological signals, both treatment groups showed greater parasympathetic activity levels, and VR with HRV_BF reduced pain more than the 2D video, p < .01. Nature-based VR and 2D video with HRV_BF can mitigate surgical pain and anxiety. However, VR may be more efficacious than 2D video in reducing pain.

According to the World Health Organization, maternal mortality rates remain a critical public health issue, with 94% of maternal deaths occurring in low- and middle-income countries (LMICs), where the rates reached 430 per 100,000 live births in 2020 compared to 13 in high-income countries. Despite this difference, only a few studies have investigated whether sparse data and features such as vital signs can effectively predict maternal health risks. This study addresses this gap by evaluating the predictive capability of vital sign data using machine learning models trained on a dataset of 1014 pregnant women from rural Bangladesh. This study developed multiple machine learning models using a dataset containing age, blood pressure, temperature, heart rate, and blood glucose of 1014 pregnant women from rural Bangladesh. The models’ performance were evaluated using regular, random and stratified sampling techniques. Additionally, we developed a stacking ensemble machine learning model combining multiple methods to evaluate predictive accuracy. A key contribution of this study is developing a stacking ensemble model combined with stratified sampling, an approach not previously considered in maternal health risk prediction. The ensemble model using stratified sampling achieved the highest accuracy (87.2%), outperforming CatBoost (84.7%), XGBoost (84.2%), random forest (81.3%) and decision trees (80.3%) without stratified sampling. Observations from our study demonstrate the feasibility of using sparse data and features for maternal health risk prediction using algorithms. By focusing on data from resource-constrained settings, we show that machine learning offers a convenient and accessible solution to improve prenatal care and reduce maternal deaths in LMICs.