Using universal kriging to improve neighborhood physical disorder measurement.
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ABSTRACT:
SUBMITTER: Mooney SJ
PROVIDER: S-EPMC8330519 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This study examined how buffer type (shape), size, and the allocation of activity bouts inside buffers that delineate the neighborhood spatially produce different estimates of neighborhood-based physical activity. A sample of 375 adults wore a global positioning system (GPS) data logger and accelerometer over 2 weeks under free-living conditions. Analytically, the amount of neighborhood physical activity measured objectively varies substantially, not only due to buffer shape and size, but by how GPS-based activity bouts are identified with respect to containment within neighborhood buffers. To move the "neighborhood-effects" literature forward, it is critical to delineate the spatial extent of the neighborhood, given how different ways of measuring GPS-based activity containment will result in different levels of physical activity across different buffer types and sizes.
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Project description:Neighborhood physical disorder, or the deterioration of urban environments, is associated with negative mental and physical health outcomes. Eleven trained raters used CANVAS, a web-based system for conducting reliable virtual street audits, to collect data on nine indicators of physical disorder using Google Street View imagery of 532 block faces in New York City, New York, USA. We combined the block face indicator data into a disorder scale using item response theory; indicators ranged in severity from presence of litter, a weak indicator of disorder, to abandoned cars, a strong indicator. Using this scale, we estimated disorder at the center point of each sampled block. We then used ordinary kriging to interpolate estimates of disorder levels throughout the city. The resulting map condenses a complex estimation process into an interpretable visualization of the spatial distribution of physical disorder in New York City.
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Project description:Neighborhood physical disorder-the visual indications of neighborhood deterioration-may inhibit outdoor physical activity, particularly among older adults. However, few previous studies of the association between neighborhood disorder and physical activity have focused on this sensitive population group, and most have been cross-sectional. We examined the relationship between neighborhood physical disorder and physical activity, measured using the Physical Activity Scale for the Elderly (PASE), in a three-wave longitudinal study of 3497 New York City residents aged 65-75 at baseline weighted to be representative of the older adult population of New York City. We used longitudinal mixed linear regression controlling for a number of individual and neighborhood factors to estimate the association of disorder with PASE score at baseline and change in PASE score over 2 years. There were too few subjects to assess the effect of changes in disorder on activity levels. In multivariable mixed regression models accounting for individual and neighborhood factors; for missing data and for loss to follow-up, each standard deviation increase in neighborhood disorder was associated with an estimated 2.0 units (95% CI 0.3, 3.6) lower PASE score at baseline, or the equivalent of about 6 min of walking per day. However, physical disorder was not related to changes in PASE score over 2 years of follow-up. In this ethnically and socioeconomically diverse population of urban older adults, residents of more disordered neighborhoods were on average less active at baseline. Physical disorder was not associated with changes in overall physical activity over time.
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Project description:Neighborhood physical disorder is thought to affect mental and physical health, but it has been difficult to measure objectively and reliably across large geographical areas or multiple locales. Virtual street audits are a novel method for assessing neighborhood characteristics. We evaluated the ecometric properties of a neighborhood physical disorder measure constructed from virtual street audit data. Eleven trained auditors assessed 9 previously validated items developed to capture physical disorder (e.g., litter, graffiti, and abandoned buildings) on 1,826 block faces using Google Street View imagery (Google, Inc., Mountain View, California) dating from 2007-2011 in 4 US cities (San Jose, California; Detroit, Michigan; New York, New York; and Philadelphia, Pennsylvania). We constructed a 2-parameter item response theory scale to estimate latent levels of disorder on each block face and defined a function using kriging to estimate physical disorder levels, with confidence estimates, for any point in each city. The internal consistency reliability of the resulting scale was 0.93. The final measure of disorder was positively correlated with US Census data on unemployment and housing vacancy and negatively correlated with data on owner-occupied housing. These results suggest that neighborhood physical disorder can be measured reliably and validly using virtual audits, facilitating research on possible associations between physical disorder and health.
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Project description:There is evidence that simply measuring physical activity alone can increase self-reported physical activity behaviour. The aim of this review was to describe changes in objectively-measured physical activity within control groups in primary care physical activity intervention studies. Five electronic databases (PubMed, MEDLINE, SPORTDiscus, PsychINFO and CINAHL) were searched from inception to February 2019. Physical activity controlled intervention studies objectively measuring physical activity in primary care with adults were included and meta-analyses were completed. Thirty studies were eligible and 22 studies were included in the meta-analysis. No statistically significant change in steps.day-1, counts.day-1 and counts.minute-1 were found in the meta-analyses within control groups. Moderate-to-vigorous physical activity minutes.day-1 significantly decreased (-3.97; 95% CI -6.31 to -1.64; P < 0.001). Sub-analyses revealed there was a trend for steps.day-1 to increase in participants < 50 years old (504; 95% CI -20 to 1029; P = 0.06). Noteworthy increases (≥10%) in objectively-measured physical activity within control groups were found in 17% of studies. Noteworthy increases were reported in studies with younger participants, one-third of the pedometer studies, one-third of studies with participants at risk of chronic disease and in studies with a shorter duration between measurements. No control group improvements were found in participants with chronic disease. Overall, no significant improvements in objectively-measured physical activity were found within control groups in primary care. Further investigation of noteworthy increases in control group physical activity levels is indicated, particularly in certain sub-groups of participants as this may effect physical activity research and interventions in these populations.
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Project description:PURPOSE:This study aimed to evaluate methods for infants' physical activity measurement based on accelerometry, including the minimum number of measurement days and placement of a wrist or ankle device. We also evaluated the acceptability of the device among infants and mothers. METHODS:A cross-sectional mixed-methods study was conducted on a convenience sample of 90 infants. Physical activity was measured using the Actigraph GT3X+ accelerometer placed on the wrist and/or ankle for 7 consecutive days (worn for 24 h), and a qualitative interview was performed to verify acceptability. The intraclass correlation coefficient (ICC) method and the Bland and Altman's dispersion diagram were used to verify the minimum number of measurement days. All analyses were stratified by walking status. RESULTS:The mean (SD) age was 12.9 (1.70) months; the mean acceleration varied between 25.8 mg (95% confidence interval (CI), 14.3-52.7) and 27.3 mg (95% CI, 17.9-44.5) using the wrist placement, and between 24.9 mg (95% CI, 10.6-48.4) and 26.2 mg (95% CI, 11.7-65.6) using the ankle placement. The ICC results showed a lower acceleration variability between days among infants incapable of walking; they achieved an ICC of 0.80 with 1 d of measurement in both placements. Among those capable of walking, the minimum number of days to achieve an ICC of 0.80 was 2 d measured at the wrist (0.85; 95% CI, 0.71-0.93) and 3 d measured at the ankle (0.92; 95% CI, 0.84-0.96). The qualitative results pointed to the wrist placement as the preferred placement among the overall sample. CONCLUSIONS:Two and three measurement days with the accelerometer placed on the wrist and ankle, respectively, seemed to adequately represent a week of measurement. The accelerometer placed on the wrist had better acceptance by the infants and mothers.
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Project description:Background: Obesity is a worldwide public health problem with increasing prevalence and affects 80% of diabetes mellitus type 2 cases. Zebrafish (Danio rerio) are an established model organism for studying obesity and diabetes including diabetic microvascular complications. We aimed to determine whether physical activity is an appropriate tool to examine training effects in zebrafish and to analyse metabolic and transcriptional processes in trained zebrafish. Methods: A 2- and 8-weeks experimental training phase protocol with adult zebrafish in a swim tunnel system was established. We examined zebrafish basic characteristics before and after training such as body weight, body length and maximum speed and considered overfeeding as an additional parameter in the 8-weeks training protocol. Ultimately, the effects of training and overfeeding on blood glucose, muscle core metabolism and liver gene expression using RNA-Seq were investigated. Results: Zebrafish maximum speed was correlated with body length and was significantly increased after 2 weeks of training. Maximum swim speed further increased after 8 weeks of training in both the normalfed and the overfed groups, but training was found not to be sufficient in preventing weight gain in overfed fish. Metabolome and transcriptome profiling in trained fish exhibited increased blood glucose levels in the short-term and upregulated energy supply pathways in the long-term. Conclusion: Swim training is a valuable tool to study effects of physical activity in zebrafish, which is accompanied by metabolic and transcriptional adaptations.
2021-12-06 | GSE183023 | GEO
Project description:Epidemiological studies increasingly rely on exposure prediction models. Predictive performance of satellite data has not been evaluated in a combined land-use regression/spatial smoothing context. We performed regionalized national land-use regression with and without universal kriging on annual average NO2 measurements (1990-2012, contiguous U.S. EPA sites). Regression covariates were dimension-reduced components of 418 geographic variables including distance to roadway. We estimated model performance with two cross-validation approaches: using randomly selected groups and, in order to assess predictions to unmonitored areas, spatially clustered cross-validation groups. Ground-level NO2 was estimated from satellite-derived NO2 and was assessed as an additional regression covariate. Kriging models performed consistently better than nonkriging models. Among kriging models, conventional cross-validated R(2) (R(2)cv) averaged over all years was 0.85 for the satellite data models and 0.84 for the models without satellite data. Average spatially clustered R(2)cv was 0.74 for the satellite data models and 0.64 for the models without satellite data. The addition of either kriging or satellite data to a well-specified NO2 land-use regression model each improves prediction. Adding the satellite variable to a kriging model only marginally improves predictions in well-sampled areas (conventional cross-validation) but substantially improves predictions for points far from monitoring locations (clustered cross-validation).
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Project description:Real-time recognition of risky driving behavior and aggressive drivers is a promising research domain, thanks to powerful machine learning algorithms and the big data provided by in-vehicle and roadside sensors. However, since the occurrence of aggressive drivers in real traffic is infrequent, most machine learning algorithms treat each sample equally and prone to better predict normal drivers rather than aggressive drivers, which is our real interest. This paper aims to test the advantage of imbalanced class boosting algorithms in aggressive driver recognition using vehicle trajectory data. First, a surrogate measurement of collision risk, called Average Crash Risk (ACR), is proposed to calculate a vehicle's crash risk. Second, the driver's driving aggressiveness is determined by his/her ACR with three anomaly detection methods. Third, we train classification models to identify aggressive drivers using partial trajectory data. Three imbalanced class boosting algorithms, SMOTEBoost, RUSBoost, and CUSBoost, are compared with cost-sensitive AdaBoost and cost-sensitive XGBoost. Additionally, we try two resampling techniques with AdaBoost and XGBoost. Among all algorithms tested, CUSBoost achieves the highest or the second-highest Area Under Precision-Recall Curve (AUPRC) in most datasets. We find the discrete Fourier coefficients of gap as the key feature to identify aggressive drivers.
| S-EPMC7177658 | biostudies-literature
Project description:ABSTRACT Background: We determined the association between neighborhood socio-environmental factors and insomnia symptoms in a nationally representative sample of US adults aged >50 years. Methods: Data were analyzed from two waves (2006 and 2010) of the Health and Retirement Study using 7,231 community-dwelling participants (3,054 men and 4,177 women) in the United States. Primary predictors were neighborhood physical disorder (e.g. vandalism/graffiti, feeling safe alone after dark, and cleanliness) and social cohesion (e.g. friendliness of people, availability of help when needed, etc.); outcomes were insomnia symptoms (trouble falling asleep, night awakenings, waking too early, and feeling unrested). Results: After adjustment for age, income, race, education, sex, chronic diseases, body mass index, depressive symptoms, smoking, and alcohol consumption, each one-unit increase in neighborhood physical disorder was associated with a greater odds of trouble falling asleep (odds ratio (OR) = 1.09, 95% confidence interval (CI): 1.04-1.14), waking too early (OR = 1.05, 95% CI: 1.00-1.10), and, in adults aged ?69 years (adjusting for all variables above except age), feeling unrested in the morning (OR = 1.11, 95% CI: 1.02-1.22 in 2006). Each one-unit increase in lower social cohesion was associated with a greater odds of trouble falling asleep (OR = 1.06, 95% CI: 1.01-1.11) and feeling unrested (OR = 1.09, 95% CI: 1.04-1.15). Conclusions: Neighborhood-level factors of physical disorder and social cohesion are associated with insomnia symptoms in middle-aged and older adults. Neighborhood-level factors may affect sleep, and consequently health, in our aging population.
| S-EPMC4362806 | biostudies-literature