Jiuling Li

Park audits can inform neighbourhood-scale urban design strategies that support health. However, interpreting park audit data is time-consuming and requires specialised expertise. Recent advances in large language models suggest potential for assisting with structured data interpretation, but their application to park audits remains unexplored. This study examined whether a large language model (ChatGPT-5) can assist in interpreting park audit data from public parks in Dhaka City, Bangladesh. Using a modified park audit tool, 59 parks were audited, of which 26 met the predefined completeness threshold and were included in the analysis. ChatGPT-5 and human experts received identical park audit datasets and instructions for interpretation. Outputs were evaluated using content analysis and a pre-defined scoring framework to assess accuracy and comprehensiveness. Paired t-tests compared ChatGPT-5 outputs with the expert benchmark. ChatGPT-5 showed no statistically significant difference from the expert benchmark in overall accuracy in this sample (p = 0.13). The unadjusted analysis showed a higher recommendation-match count for ChatGPT-5 (p < 0.05), but this result should be interpreted as exploratory because multiple criteria were tested. No statistically significant differences were detected across accuracy domains or other comprehensiveness criteria. These findings suggest that ChatGPT-5 may assist with preliminary interpretation of structured park audit data. However, the results do not establish equivalence with expert assessment. Expert review remains necessary to assess feasibility, contextual relevance, and alignment with local planning standards.

Background: Street layout is a key urban design element that influences a neighbourhood’s street connectivity, providing opportunities for physical activity, social interaction, and risk of environmental exposures. However, its effects across different socioeconomic contexts and varying durations of exposure to the neighbourhood are less explored. Methods: This study aims to address these gaps by employing ordinal logistic regression models to examine the associations between neighbourhood street patterns and residents’ physical and mental health. It also investigates whether these associations vary by neighbourhood socioeconomic status (SES) and length of residence. Results: The findings showed that grid street patterns were positively associated with self-assessed physical health, particularly in low SES neighbourhoods and amongst longer-term residents. However, no significant associations were observed between street pattern and self-assessed mental health. Conclusions: Our findings underscore the importance of considering both the social context and duration of residence when investigating the influence of urban design on health.

Perceptions of the neighbourhood environment can play an important role in promoting public health, yet modifying perceptions is challenging. Adjusting the built environment may be a pathway to influence perceptions. In addition to the physical environment, intrapersonal factors may shape perceptions. This study analysed data from several Japanese major cities to explore the association between objective and perceived neighbourhood environment attributes, stratified by age and gender. Perceived neighbourhood environment measures were adapted from established scales, while objective measures were derived from participants’ geographic address data. Multivariate linear regression was employed to assess these associations. All objective measures were positively associated with overall neighbourhood environment perception, and destination diversity presented the strongest association. Perceptions among those 65–69 were more strongly influenced by the physical environments of their neighbourhood, whether positively or negatively. Objective environmental measures have a greater positive impact on perception for females than for males, while males are more negatively affected in terms of perceptions of crime and traffic safety. These findings highlight how objective built environment attributes may shape residents’ perceptions across different demographic groups.

Background: The worldwide rise in the prevalence of noncommunicable diseases has increased the recognition of the need to identify modifiable risk factors for preventing and managing these diseases. The office worker, as a representative group of physically inactive workers, is exposed to risk factors for metabolic syndrome, which is a primary driver of noncommunicable diseases. The use of virtual reality (VR) exergames may offer a potential solution to the problem of increasing noncommunicable disease prevalence, as it can help individuals increase their physical activity levels while providing a more immersive experience. Objective: This exploratory study aims to examine the interventional efficacy of at-home immersive VR exergames on metabolic syndrome biomarkers among office workers. Additionally, it seeks to investigate the impacts of at-home immersive VR exergames on the active and sedentary behaviors of office workers. Methods: A 3-arm, single-blinded pilot randomized controlled trial will be conducted to examine the therapeutic effects of at-home immersive VR exergames. A total of 120 Chinese office workers, engaging in less than 150 minutes per week of moderate to vigorous intensity physical activity, will be recruited via a convenience sampling method. The participants, who will be tested over a 12-week period, will be randomly assigned to one of three groups: (1) the VR exergame intervention group, (2) the regular physical activity control group, and (3) the nonexercise control group. Throughout the 12-week trial, three categories of variables will be collected across the three groups: clinical risk factors associated with metabolic syndrome, active and sedentary behaviors, and demographics. To analyze variance among the groups, a mixed linear model will be applied to assess the efficacy of each group. Differences in metabolic syndrome clinical risk factors among all groups will be used to evaluate the effects of at-home immersive VR exergames. Changes in active and sedentary behaviors will also be used to determine the impacts of VR exergames on metabolic syndrome. Results: The ethics committee of Guangzhou University, China, approved this study on September 25, 2024. Participant recruitment will begin in early 2025 and continue for approximately 3 months. Data will be analyzed after the 12-week trial is completed, with full results expected to be presented in early 2026. Conclusions: This study explores an emerging topic by applying an at-home immersive VR exergame intervention, potentially contributing to understanding the effects of an exergame program on metabolic syndrome risk among office workers. Trial Registration: ClinicalTrials.gov NCT06556784; https://clinicaltrials.gov/study/NCT06556784 International Registered Report Identifier (IRRID): PRR1-10.2196/64560

BACKGROUND The worldwide rise in the prevalence of noncommunicable diseases has increased the recognition of the need to identify modifiable risk factors for preventing and managing these diseases. The office worker, as a representative group of physically inactive workers, is exposed to risk factors for metabolic syndrome, which is a primary driver of noncommunicable diseases. The use of virtual reality (VR) exergames may offer a potential solution to the problem of increasing noncommunicable disease prevalence, as it can help individuals increase their physical activity levels while providing a more immersive experience. OBJECTIVE This exploratory study aims to examine the interventional efficacy of at-home immersive VR exergames on metabolic syndrome biomarkers among office workers. Additionally, it seeks to investigate the impacts of at-home immersive VR exergames on the active and sedentary behaviors of office workers. METHODS A 3-arm, single-blinded pilot randomized controlled trial will be conducted to examine the therapeutic effects of at-home immersive VR exergames. A total of 120 Chinese office workers, engaging in less than 150 minutes per week of moderate to vigorous intensity physical activity, will be recruited via a convenience sampling method. The participants, who will be tested over a 12-week period, will be randomly assigned to one of three groups: (1) the VR exergame intervention group, (2) the regular physical activity control group, and (3) the nonexercise control group. Throughout the 12-week trial, three categories of variables will be collected across the three groups: clinical risk factors associated with metabolic syndrome, active and sedentary behaviors, and demographics. To analyze variance among the groups, a mixed linear model will be applied to assess the efficacy of each group. Differences in metabolic syndrome clinical risk factors among all groups will be used to evaluate the effects of at-home immersive VR exergames. Changes in active and sedentary behaviors will also be used to determine the impacts of VR exergames on metabolic syndrome. RESULTS The ethics committee of Guangzhou University, China, approved this study on September 25, 2024. Participant recruitment will begin in early 2025 and continue for approximately 3 months. Data will be analyzed after the 12-week trial is completed, with full results expected to be presented in early 2026. CONCLUSIONS This study explores an emerging topic by applying an at-home immersive VR exergame intervention, potentially contributing to understanding the effects of an exergame program on metabolic syndrome risk among office workers. CLINICALTRIAL ClinicalTrials.gov NCT06556784; https://clinicaltrials.gov/study/NCT06556784 INTERNATIONAL REGISTERED REPORT PRR1-10.2196/64560