Regular physical activity (PA) is a major factor in maintaining health in aging populations. This study examines the influences of sociodemographic, health, and environmental characteristics on older adults’ walking behaviors, and the role physicians can play in promoting physical activity. Online and paper surveys (n = 272) were distributed to community-dwelling older (age ≥ 60) adults from a large integrated healthcare system in two counties in Central Texas. Descriptive statistics were utilized to characterize participant’s walking behaviors and places. Multivariate logistic regression was employed to predict being: 1) a frequent walker (i.e., walking at least three times a week); and 2) meeting the Centers for Disease Control and Prevention (CDC) PA recommendation through walking (i.e., walking ≥150 min per week), while considering sociodemographic, health, and environmental factors. Individuals had a median age of 69 years, were of both genders (50.37 % female), and were primarily non-Hispanic White (84.87 %). While the majority (59.55 %) walked at least three times a week, only 27.86 % walked ≥150 min a week. Factors associated with a lower likelihood of being frequent walkers included experiencing poor mental health in the past month (OR = 0.345, 95 % CI = 0.185–0.645) and residing in areas with low or moderate (versus high) perceived neighborhood cohesion (OR = 0.471, 95 % CI = 0.228–0.974), while those in Census Tracts reflecting populations with a lower median age were more likely to report frequent walking behavior (OR = 1.799, 95 % CI = 1.034–3.131). Factors associated with a lower likelihood of meeting the CDC PA recommendation included being 60–69 years (versus 70 years or older) (OR = 0.538, 95 % CI = 0.290–0.997), experiencing poor mental health in the past month (OR = 0.432, 95 % CI = 0.198–0.944), and lacking social support for walking (OR = 0.383, 95 % CI = 0.154–0.957). Given the health benefits, PA promotion must be seen as a national responsibility. In particular, physicians have a major role to play in communicating the importance of PA to their older patients and making discussions about strategies for overcoming barriers to walking an integral part of their clinical encounter with these patients.

Fear of falling is a substantial barrier to walking and has been associated with increased fall risks. This study examines neighborhood environmental risk factors related to fear of outdoor falling in middle-aged and older adults. A total of 394 participants aged 50 years or older living independently in the community were recruited between 2013 and 2014 from an integrated health care network serving Central Texas. Fear of outdoor falling and perceived neighborhood environmental variables were assessed using self-reported questionnaires. Logistic regression identified perceived neighborhood environmental variables associated with fear of outdoor falling. Sixty-nine (17.9%) of 385 participants reported having a fear of outdoor falling. Compared to those who did not report a fear of outdoor falling, those who reported having a fear of outdoor falling were more likely to be adults aged 65 years or older (odds ratio [OR] = 2.974, 95% confidence interval [CI] = 1.247–7.094), be female (OR = 4.423, 95% CI = 1.830–10.689), have difficulty with walking for a quarter of a mile (OR = 2.761, 95% CI = 1.124–6.782), and have had a fall in the past year (OR = 4.720, 95% CI = 1.472–15.137). Among the neighborhood environmental characteristics examined, low traffic speed on streets (OR = 0.420, 95% CI = 0.188–0.935), drainage ditches (OR = 2.383, 95% CI = 1.136–5.000), and broken sidewalks (OR = 3.800, 95% CI = 1.742–8.288) were associated with the odds of having a fear of outdoor falling. In addition to the individual factors, findings from this study suggest the importance of addressing the environmental risk factors in identifying and reducing fear of outdoor falling among middle-aged and older adults.

Studies have assessed neighborhood walkability responding to increasing interest in academics, practitioners, and policy makers. While Walk Score is a well-operationalized and efficient measure of neighborhood walkability, it is not supported in the Asian context. We thus developed a “Walkability Score” for use in Seoul, South Korea, following the Walk Score algorithm and then conducted a logistic regression for a satisfied versus dissatisfied binary outcome in order to conduct an empirical test. Results showed a significant association between Walkability Score and pedestrian satisfaction. We also explored sample locations and found some discrepancies between Walkability Score and the degree of pedestrian satisfaction. While some sample locations near parks, and rivers and waterfront spaces (but without destinations for walking trips such as banking or shopping) revealed low Walkability Scores, the degree of pedestrian satisfaction was relatively high in these areas. Some samples located in residential areas with well-designed sidewalks and greenery revealed relatively high pedestrian satisfaction, but Walkability Scores were insufficient. This study’s Walkability Score was somewhat valid for approaching the issue, but further research is needed to complete this measure, especially in high-density areas with well-equipped pedestrian infrastructures (e.g., crosswalks, pedestrian streets).

The accurate estimation of real estate value helps the development of real estate policies that can respond to the complexities and instability of the real estate market. Previously, statistical methods were used to estimate real estate value, but machine learning methods have gained popularity because their predictions are more accurate. In contrast to existing studies that use various machine learning methods to estimate the transactions or list prices of real estate properties without separating the building and land prices, this study estimates land price using a large amount of land-use information obtained from various land- and building-related datasets. The random forest and XGBoost methods were used to estimate 52,900 land prices in Seoul, South Korea, from January 2017 to December 2020. The models were also separately trained for different land uses and different time periods. Overall, the results revealed that XGBoost yields a higher prediction accuracy. Whereas the XGBoost models were more accurate on the 2020 data than on the 2017–2020 data when analyzing residential areas, the random forest models were more accurate on the 2017–2020 data than on the 2020 data. Further analysis will extend the prediction model to consider submarkets determined by price volatility and locality.