BACKGROUND Hazardous environmental exposures are recognized risk factors for falls among older adults. However, the gender differences in the associations of falls with indoor and outdoor environmental hazards are scarce. This study examined the indoor and outdoor environmental risk factors for falls and compared the data for men and women among U.S. older adults using nationally representative data. METHODS We used the 2011 National Health and Aging Trends Study (NHATS) for a cross-sectional analysis of 6680 community-dwelling adults aged ≥65 years in the United States. A series of logistic regressions was used to identify the indoor and outdoor environmental hazards associated with falls stratified by gender after adjusting for sociodemographic, health, and behaviors. We also tested for significant interactions with gender. RESULTS Compared to men, women had a higher prevalence of falls. In the model adjusted for sociodemographic, health, and behavioral conditions, there were gender differences in the association of falls with the presence of indoor and outdoor environmental hazards. Gender-specific analyses showed that women with the presence of indoor environmental hazards (OR = 1.37, 95% CI = 1.04.-1.79) had higher odds of falls, whereas for men, the presence of outdoor environmental hazards (OR = 1.34, 95% CI = 1.02-1.75) was associated with falls. We also found a significant interaction term between outdoor environmental hazards and gender (OR = 0.65, 95% CI = 0.47-0.90). The interaction plot indicated that the presence of outdoor environmental hazards increased the risks of falling in men but not in women. CONCLUSIONS Significant gender differences exist in the association of falls with indoor and outdoor environmental hazards among older men and women. Our findings suggest that gender-tailored prevention programs to increase awareness of the environmental hazards and gender-specific environmental interventions are needed to help prevent falls.

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Falls associated with indoor and outdoor environmental hazards among community-dwelling older adults between men and women.

Little is known regarding the role of gender as a possible modulator in determining the associations between lifestyle risk factors and depression in older adults.This study examined whether gender modulates the relationship between depression and lifestyle risk factors in Korean adults aged 65 years and older (n = 3700).Data were obtained from the 2016 and 2018 Korea National Health and Examination Survey. The primary outcome was depression, assessed with the patient health questionnaire-9. As exposures, smoking habits, at-risk alcohol consumption, and physical inactivity were assessed with a standardized questionnaire. In addition, mean adequacy ratio (MAR) as an indicator of overall nutritional inadequacy was assessed with dietary intakes of macro- and micronutrients.In men only, either two or three and more risk factors were significantly associated with higher depression risk (OR (95% confidence interval, CI) = 2.886 (1.003-8.299) and OR (95% CI) = 3.109 (1.064-9.097), respectively). In women only, either two or three and more risk factors were also significantly associated with higher depression risk (OR (95% CI) = 1.505 (1.067-2.124) and OR (95% CI) = 2.828 (1.527-5.239), respectively). In particular, the presence of smoking habits and MAR were the major determinants of depression (OR (95% CI) = 1.835 (1.09-3.10) and OR (95% CI) = 1.585 (1.125-2.233), respectively) in women only. Finally, a moderation analysis with the Hayes PROCESS Macro showed a significant moderating effect of gender (β (95% CI) = 0.633 (0.206 ~ 1.060)) on the relationship between risk factors and depression. In addition, the slope of the relationship was much steeper in women than in men.Current findings suggest that lifestyle risk factors are more closely associated with depression risk in women than in men. 

https://bmcgeriatr.biomedcentral.com/counter/pdf/10.1186/s12877-021-02729-2

The relationship between lifestyle risk factors and depression in Korean older adults: a moderating effect of gender

We analyzed the association between individual frailty-related factors and depression in older adults.A total of 796 older adults who underwent geriatric assessments were included in this cross-sec...

Association Between Frailty-Related Factors and Depression among Older Adults

Reconstruction of long-term strain data for structural health monitoring with a hybrid deep-learning and autoregressive model considering thermal effects

Temperature-induced response reconstruction method based on DL-AR model and attention mechanism

Gender differences in the effect of depression and cognitive impairment on risk of falls among community-dwelling older adults.

This study proposes a long-term strain prediction model for concrete structures using weather data. In the proposed model, the relationship between weather and the strain data for a concrete structure is defined by a convolutional neural network (CNN), which is a machine learning technique, based on the strong correlation between the two types of data. The weather data collected from a weather station located near the monitored structure are used in the input layer of the CNN; the strain data measured by fiber-optic sensors (FOSs) at the structure are used in the output layer of the CNN. The trained CNN can predict the strain using only weather data in the case of sensor malfunctions or data loss. Various types of weather data, including the air temperature, relative humidity, and wind speed, are used to determine the environmental factors that are valid for predicting the long-term deformation of concrete structures. Six prediction models are proposed, in which the three types of weather data are used individually or jointly in the input layer of the CNN. The proposed models are applied to predict the strain of a footbridge located at Princeton University. To build the prediction models, the strain data measured at the bridge over a long-term period and the weather data obtained from a nearby local weather station are used. The performance of the prediction models is verified through long-term strain prediction. Furthermore, the prediction performance of the analyzed models is compared, and the weather data types that are significant for predicting the long-term deformation of concrete structures are elucidated. 

Long-term structural response prediction models for concrete structures using weather data, fiber-optic sensing, and convolutional neural network

Multi-objective green design model for prestressed concrete slabs in long-span buildings

An automatic decision model for optimal noise barrier plan in terms of health impact, productivity, and cost aspects

ABSTRACT Repetitive and severe thermal loads and load changes affect not only the physical properties of the coke drum but also its dynamic properties, possibly leading to structural failure. In particular, the operation weight of the drum, which increases up to 10 times or more, affects the dynamic properties of the structure and may threaten safety. Therefore, a vibration safety evaluation model for coke drums in operation is proposed to provide critical feedback on damage accumulation and to evaluate the vibration stability. A monitoring system was also developed to validate the model predictions by measuring the vibration patterns and corresponding modal parameters of the coke drums in operation. The proposed technique was applied to two coke drums in operation, and the vibration safety and factors causing vibration in the target structures were analyzed. The maximum vibration pattern was observed in the target structures at a feeding ratio of 71%. It exceeded the vibration criteria of the vibration safety evaluation model by up to 406%. Moreover, the analysis determined that the rapid increase in operating weight of the coke drums due to the influx of heavy crude oil, and the down-shift in their mode frequency induced superimposition and resonance with the structural mode, causing vibration.

https://www.tandfonline.com/doi/pdf/10.1080/13467581.2022.2085719?needAccess=true

Dong Eun Lee

Papers

Gender differences in the effect of depression and cognitive impairment on risk of falls among community-dwelling older adults.

Long-term structural response prediction models for concrete structures using weather data, fiber-optic sensing, and convolutional neural network

Multi-objective green design model for prestressed concrete slabs in long-span buildings

An automatic decision model for optimal noise barrier plan in terms of health impact, productivity, and cost aspects

Vibration safety evaluation model and sensor network-based monitoring system for coke drums in operation