Association Between Objectively Sleep Pattern and Obesity in the Elderly

Background: Previous studies on the relationship between sleep patterns and obesity in the elderly are limited and have conflicting results. Moreover, few studies have measured sleep patterns objectively. In this study, we investigated objective sleep patterns and their relationship with obesity in the elderly in Tehran, Iran.Methods: In this cross-sectional study, 88 elderly (≥60 years old) who were members of health homes of zone 5 in Tehran, Iran, were included by simple random sampling method in 2014. Sleep patterns were objectively assessed using waist actigraphy for a mean of 4.3 ± 1.7 days). Height, weight, and waist circumference (WC) were measured by standard methods and body mass index (BMI) was calculated. Data entry and statistical analyses were performed using SPSS version 21.Results: Mean actigraphy-assessed sleep duration, sleep efficiency (percentage of time in bed spent sleeping), and sleep latency (time required to fall asleep) were 427 ± 62 min, 71.3 ± 18%, and 14.2 ± 3.8 min, respectively. A negative relationship was found between BMI and sleep duration (r = −0.2, p = 0.03), BMI and sleep efficiency (r = −0.3, p=0.01), and WC and sleep efficiency (r = −0.2, p = 0.04). Also, a positive association was observed between BMI and sleep latency (r = 0.4, p = 0.006).Conclusions: In the elderly, actigraphy-assessed sleep duration was associated with obesity and the sleep efficiency was poor in obese participants. It seems that sleep patterns and BMI are correlated with each other. However, there is a need for prospective studies to affirm causal relationships between these constructs

Association Between Objectively Sleep Pattern and Obesity in the Elderly

Background: Previous studies on the relationship between sleep patterns and obesity in the elderly are limited and have conflicting results. Moreover, few studies have measured sleep patterns objectively. In this study, we investigated objective sleep patterns and their relationship with obesity in the elderly in Tehran, Iran.Methods: In this cross-sectional study, 88 elderly (≥60 years old) who were members of health homes of zone 5 in Tehran, Iran, were included by simple random sampling method in 2014. Sleep patterns were objectively assessed using waist actigraphy for a mean of 4.3 ± 1.7 days). Height, weight, and waist circumference (WC) were measured by standard methods and body mass index (BMI) was calculated. Data entry and statistical analyses were performed using SPSS version 21.Results: Mean actigraphy-assessed sleep duration, sleep efficiency (percentage of time in bed spent sleeping), and sleep latency (time required to fall asleep) were 427 ± 62 min, 71.3 ± 18%, and 14.2 ± 3.8 min, respectively. A negative relationship was found between BMI and sleep duration (r = −0.2, p = 0.03), BMI and sleep efficiency (r = −0.3, p=0.01), and WC and sleep efficiency (r = −0.2, p = 0.04). Also, a positive association was observed between BMI and sleep latency (r = 0.4, p = 0.006).Conclusions: In the elderly, actigraphy-assessed sleep duration was associated with obesity and the sleep efficiency was poor in obese participants. It seems that sleep patterns and BMI are correlated with each other. However, there is a need for prospective studies to affirm causal relationships between these constructs

The Association between Obesity and Quality of Life among the Elderly

Background: The prevalence of obesity in the elderly is increasing worldwide. Obesity greatly impacts quality of life(QoL). The present study aimed to investigate the association between obesity and QoL among the elderly, in Tehran, Iran.Methods: This cross-sectional study (observational) was conducted in 2014 in Tehran, Iran. A total of 421 elderly people aged ≥ 60 years old were recruited using simple random sampling methods. Height, weight, and waist circumference were measured by standard methods; body mass index (BMI) was calculated from height and weight. QoL was evaluated by the Persian language version of the SF-36 questionnaire.The alpha value was set at 0.05 to indicate the statistical significant level. Independent samples t-tests and Chi-square tests were used for comparing the quantitative and categorical variables, respectively. One-way ANOVA, followed by Tukeys’ post-hoc test, was used to compare mean scores of SF-36 scales between BMI groups. Pearson correlation coefficients were used for investigating the relationship between SF-36 scores and anthropometric parameters.Results: The mean age of participants was 77.6 ± 8.6 years. The frequency of obesity and overweight (BMI ≥ 25 kg/m2) was 59.4% (57.2% in males and 60.6% in females). Except for the mental health scale, for all other SF-36 scale mean scores, participants with overweight or obesity had lower scores compared to their normal weight counterparts (p < 0.05). Additionally, subjects with underweight had significantly lower scores for the vitality scale (p < 0.05).Conclusions: The results of present study persist on importance of preserving normal weight on improving quality of life in elderly. Although the observed association in this study was bidirectional and prospective studies are needed to investigate the cause and effect relationship.

The Association between Obesity and Quality of Life among the Elderly

Background: The prevalence of obesity in the elderly is increasing worldwide. Obesity greatly impacts quality of life(QoL). The present study aimed to investigate the association between obesity and QoL among the elderly, in Tehran, Iran.Methods: This cross-sectional study (observational) was conducted in 2014 in Tehran, Iran. A total of 421 elderly people aged ≥ 60 years old were recruited using simple random sampling methods. Height, weight, and waist circumference were measured by standard methods; body mass index (BMI) was calculated from height and weight. QoL was evaluated by the Persian language version of the SF-36 questionnaire.The alpha value was set at 0.05 to indicate the statistical significant level. Independent samples t-tests and Chi-square tests were used for comparing the quantitative and categorical variables, respectively. One-way ANOVA, followed by Tukeys’ post-hoc test, was used to compare mean scores of SF-36 scales between BMI groups. Pearson correlation coefficients were used for investigating the relationship between SF-36 scores and anthropometric parameters.Results: The mean age of participants was 77.6 ± 8.6 years. The frequency of obesity and overweight (BMI ≥ 25 kg/m2) was 59.4% (57.2% in males and 60.6% in females). Except for the mental health scale, for all other SF-36 scale mean scores, participants with overweight or obesity had lower scores compared to their normal weight counterparts (p < 0.05). Additionally, subjects with underweight had significantly lower scores for the vitality scale (p < 0.05).Conclusions: The results of present study persist on importance of preserving normal weight on improving quality of life in elderly. Although the observed association in this study was bidirectional and prospective studies are needed to investigate the cause and effect relationship.

Identifying source of dust aerosol using a new framework based on remote sensing and modelling

Dust particles are transported globally. Dust storms can adversely impact both human health and the environment, but they also impact transportation infrastructure, agriculture, and industry, occasionally severely. The identification of the locations that are the primary sources of dust, especially in arid and semi-arid environments, remains a challenge as these sites are often in remote or data-scarce regions. In this study, a new method using state-of-the-art machine-learning algorithms – random forest (RF), support vector machines (SVM), and multivariate adaptive regression splines (MARS) – was evaluated for its ability to spatially model the distribution of dust-source potential in eastern Iran. To accomplish this, empirically identified dust-source locations were determined with the ozone monitoring instrument aerosol index and the Moderate-Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol optical thickness methods. The identified areas were divided into training (70%) and validation (30%) sets. Measurements of the conditioning factors (lithology, wind speed, maximum air temperature, land use, slope angle, soil, rainfall, and land cover) were compiled for the study area and predictive models were developed. The area-under-the-receiver operating characteristics curve (AUC) and true-skill statistics (TSS) were used to validate the maps of the models' predictions. The results show that the RF algorithm performed best (AUC = 89.4% and TSS = 0.751), followed by the SVM (AUC = 87.5%, TSS = 0.73) and the MARS algorithm (AUC = 81%, TSS = 0.69). The results of the RF indicated that wind speed and land cover are the most important factors affecting dust generation. The region of highest dust-source potential that was identified by the RF is in the eastern parts of the study region. This model can be applied to other arid and semi-arid environments that experience dust storms to promote management that prevents desertification and reduces dust production

Scrutinizing relationships between submarine groundwater discharge and upstream areas using thermal remote sensing: A case study in the northern Persian gulf

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Nutrient input through submarine groundwater discharge (SGD) often plays a significant role in primary productivity and nutrient cycling in the coastal areas. Understanding relationships between SGD and topo-hydrological and geo-environmental characteristics of upstream zones is essential for sustainable development in these areas. However, these important relationships have not yet been completely explored using data-mining approaches, especially in arid and semi-arid coastal lands. Here, Landsat 8 thermal sensor data were used to identify potential sites of SGD at a regional scale. Relationships between the remotely-sensed sea surface temperature (SST) patterns and geoenvironmental variables of upland watersheds were analyzed using logistic regression model for the first time. The accuracy of the predictions was evaluated using the area under the receiver operating characteristic curve (AUC-ROC) metric. A highly accurate model, with the AUC-ROC of 96.6%, was generated. Moreover, the results indicated that the percentage of karstic lithological formation and topographic wetness index were key variables influencing SGD phenomenon and spatial distribution in the northern coastal areas of the Persian Gulf. The adopted methodology and applied metrics can be transferred to other coastal regions as a rapid assessment procedure for SGD site detection. Moreover, the results can help planners and decision-makers to develop efficient environmental management strategies and the design of comprehensive sustainable development policies

Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNNEC methods

Although estimating the uncertainty of models used for modelling nitrate contamination of groundwater is essential in groundwater management, it has been generally ignored. This issue motivates this research to explore the predictive uncertainty of machine-learning (ML) models in this field of study using two different residuals uncertainty methods: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Prediction-interval coverage probability (PICP), the most important of the statistical measures of uncertainty, was used to evaluate uncertainty. Additionally, three state-of-the-art ML models including support vector machine (SVM), random forest (RF), and k-nearest neighbor (kNN) were selected to spatially model groundwater nitrate concentrations. The models were calibrated with nitrate concentrations from 80 wells (70% of the data) and then validated with nitrate concentrations from 34 wells (30% of the data). Both uncertainty and predictive performance criteria should be considered when comparing and selecting the best model. Results highlight that the kNN model is the best model because not only did it have the lowest uncertainty based on the PICP statistic in both the QR (0.94) and the UNEEC (in all clusters, 0.85–0.91) methods, but it also had predictive performance statistics (RMSE = 10.63, R2 = 0.71) that were relatively similar to RF (RMSE = 10.41, R2 = 0.72) and higher than SVM (RMSE = 13.28, R2 = 0.58). Determining the uncertainty of ML models used for spatially modelling groundwater-nitrate pollution enables managers to achieve better risk-based decision making and consequently increases the reliability and credibility of groundwater-nitrate predictions

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis

Flood is considered to be the most common natural disaster worldwide during the last decades. Flood hazard potential mapping is required for management and mitigation of flood. The present research was aimed to assess the efficiency of analytical hierarchical process (AHP) to identify potential flood hazard zones by comparing with the results of a hydraulic model. Initially, four parameters via distance to river, land use, elevation and land slope were used in some part of the Yasooj River, Iran. In order to determine the weight of each effective factor, questionnaires of comparison ratings on the Saaty's scale were prepared and distributed to eight experts. The normalized weights of criteria/parameters were determined based on Saaty's nine-point scale and its importance in specifying flood hazard potential zones using the AHP and eigenvector methods. The set of criteria were integrated by weighted linear combination method using ArcGIS 10.2 software to generate flood hazard prediction map. The inundation simulation (extent and depth of flood) was conducted using hydrodynamic program HEC-RAS for 50- and 100-year interval floods. The validation of the flood hazard prediction map was conducted based on flood extent and depth maps. The results showed that the AHP technique is promising of making accurate and reliable prediction for flood extent. Therefore, the AHP and geographic information system (GIS) techniques are suggested for assessment of the flood hazard potential, specifically in no-data regions