Seasonality in physical activity: should this be a concern in all settings?

This study examines the relationship between weather conditions and overall and domain-specific physical activity in adults living in a city with a temperate, stable climate. Objective and self-reported physical activity levels were measured in 1754 adults participating in RESIDE, a longitudinal study undertaken in Perth, Australia. Steps per week and self-reported minutes of domain-specific physical activity were compared with date-stamped weather data. Weather conditions were relatively constant across all seasons, showing little impact on physical activity behaviour. Variation in weather conditions had modest explanatory power (<6%) for predicting overall and domain-specific physical activity engagement in this sample. Weather variations observed in this study were of insufficient magnitude to impact on physical activity levels. This has implications for study designs and exploration of other factors associated with physical activity in these settings.     

Myocardial infarction on snow days: incidence, procedure, use and outcomes

BACKGROUND: Snowfall can cause chaos in urban centres and put considerable stress on health care systems. Given that myocardial infarction (MI) is a condition that may be triggered or aggravated by stress, and that health system stress could influence the typical care provided to patients with MI, a study was conducted comparing 'snow days' with 'nonsnow days', specifically assessing the incidence of MI, the use of acute procedures and in-hospital mortality. METHODS: Hospital discharge data were used on all patients discharged after MI. These data were merged with data from Environment Canada to determine the amount of snowfall that occurred on any given day. The use of acute procedures was determined by linking to data from the Alberta Provincial PRoject for Outcomes Assessment in Coronary Heart disease (APPROACH). Snow days were defined as days when at least 5 cm of snow fell, and the two subsequent days were included because of the lingering effect of 'urban chaos' that can ensue after significant snowfall. The average incidence of MIs on snow days versus nonsnow days was then determined. Risk-adjusted odds ratios for the use of direct percutaneous coronary intervention and in-hospital mortality were also determined. RESULTS: There were 61 snow days and 575 nonsnow days. The incidence of MI (incidence density ratio of 1.08, 95% CI 0.82 to 3.10) and the use of direct percutaneous coronary intervention (adjusted OR=1.07, 95% CI 0.74 to 1.54) were slightly higher on snow days. In-hospital mortality trended toward being lower (adjusted OR=0.54, 95% CI 0.28 to 1.04) for patients admitted on snow days, although none of these differences were statistically significant. CONCLUSION: Despite the potential for the significant adverse effects of snow days on the incidence of MI, the use of acute procedures and outcomes, these findings suggest only minor effects, if any.     

A time-series analysis of any short-term effects of meteorological and air pollution factors on preterm births in London, UK

Although much is known about the incidence and burden of preterm birth, its biological mechanisms are not well understood. While several studies have suggested that high levels of air pollution or exposure to particular climatic factors may be associated with an increased risk of preterm birth, other studies do not support such an association. To determine whether exposure to various environmental factors place a large London-based population at higher risk for preterm birth, we analyzed 482,568 births that occurred between 1988 and 2000 from the St. Mary's Maternity Information System database. Using an ecological study design, any short-term associations between preterm birth and various environmental factors were investigated using time-series regression techniques. Environmental exposures included air pollution (ambient ozone and PM(10)) and climatic factors (temperature, rainfall, sunshine, relative humidity, barometric pressure, and largest drop in barometric pressure). In addition to exposure on the day of birth, cumulative exposure up to 1 week before birth was investigated. The risk of preterm birth did not increase with exposure to the levels of ambient air pollution or meteorological factors experienced by this population. Cumulative exposure from 0 to 6 days before birth also did not show any significant effect on the risk of preterm birth. This large study, covering 13 years, suggests that there is no association between preterm births and recent exposure to ambient air pollution or recent changes in the weather.     

Associations between weather conditions and clinical symptoms in patients with hip osteoarthritis: A 2-year cohort study

The goal of this study was to assess whether there is an association between ambient weather conditions and patients’ clinical symptoms in patients with hip osteoarthritis (OA). The design was a cohort study with a 2-year follow-up and 3-monthly measurements and prospectively collected data on weather variables. The study population consisted of 222 primary care patients with hip OA. Weather variables included temperature, wind speed, total amount of sun hours, precipitation, barometric pressure, and relative humidity. The primary outcomes were severity of hip pain and hip disability as measured with the Western Ontario and McMasters University Osteoarthritis Index (WOMAC) pain and function subscales. Associations between hip pain and hip disability and the weather variables were assessed using crude and multivariate adjusted linear mixed-model analysis for repeated measurements. On the day of questionnaire completion, mean relative humidity was associated with WOMAC pain (estimate 0.1; 95% confidence interval = 0.0–0.2; P = .02). Relative humidity contributed ?1% to the explained within-patient variance and between-patient variance of the WOMAC pain score. Mean barometric pressure was associated with WOMAC function (estimate 0.1; 95% confidence interval = 0.0–0.1; P = .02). Barometric pressure contributed ?1% to the explained within-patient variance and between-patient variance of the WOMAC function score. The other weather variables were not associated with the WOMAC pain or function score. Our results support the general opinion of OA patients that barometric pressure and relative humidity influence perceived OA symptoms. However, the contribution of these weather variables (?1%) to the severity of OA symptoms is not considered to be clinically relevant.     

Climatic and geographic temporal patterns of pain in the Multicenter Study of Hydroxyurea

No multi-site comparisons have tested whether seasonally cold temperature or climate exacerbate pain intensity in sickle cell disease (SCD). We examined seasonal SCD pain intensity and frequency patterns and compared them with concurrent climate conditions (temperature and barometric pressure) and geography of patient residence in the Multicenter Study of Hydroxyurea (MSH). We conducted a time series analysis of the monthly average daily pain intensity (0–9 scale) and pain frequency of the 299 MSH patients from December 1991 to December 1994. We used both an unobserved component model (UCM) and a nonparametric local regression (LOESS) to probe for a cycle and/or trend associated with the time series. We also examined base mixed regression models of season, monthly average temperature and barometric pressure, and geographic region as stand-alone predictors of pain intensity and frequency. Expanded models included additional predictor variables. UCM and LOESS analyses showed a cyclic pattern of pain intensity and frequency with peaks in late Fall/early Winter and troughs in Spring. Base regression models showed colder seasons were significantly associated with greater pain intensity (p = .0035) but not frequency (p = .07); higher monthly temperatures were significantly associated with both lower pain intensity and pain frequency, but higher monthly barometric pressures were significantly associated with greater pain intensity and frequency (all p’s < .0001); and northern sites had nonsignificantly higher pain intensity (p = .40) and frequency (p = .07) than southern sites. This pattern of results did not change in expanded models including other predictors. Our results suggest that seasonably colder temperatures exacerbate sickle cell-related pain, but low barometric pressure does not, and geographic region of residence is not significantly related to pain in this sample.     

Disparities by race in heat-related mortality in four US cities: The role of air conditioning prevalence

Daily mortality is typically higher on hot days in urban areas, and certain population groups experience disproportionate risk. Air conditioning (AC) has been recommended to mitigate heat-related illness and death. We examined whether AC prevalence explained differing heat-related mortality effects by race. Poisson regression was used to model daily mortality in Chicago, Detroit, Minneapolis, and Pittsburgh. Predictors included natural splines of time (to control seasonal patterns); mean daily apparent temperature on the day of death, and averaged over lags 1–3; barometric pressure; day of week; and a linear term for airborne particles. Separate, city-specific models were fit to death counts stratified by race (Black or White) to derive the percent change in mortality at 29°C, relative to 15°C (lag 0). Next, city-specific effects were regressed on city-and race-specific AC prevalence. Combined effect estimates across all cities were calculated using inverse variance-weighted averages. Prevalence of central AC among Black households was less than half that among White households in all four cities, and deaths among Blacks were more strongly associated with hot temperatures. Central AC prevalence explained some of the differences in heat effects by race, but room-unit AC did not. Efforts to reduce disparities in heat-related mortality should consider access to AC.     

A Deep Spatio-Temporal Forecasting Model for Multi-Site Weather Prediction Post-Processing

In this paper, we propose a deep spatio-temporal forecasting model (DeepSTF) for multi-site weather prediction post-processing by using both temporal and spatial information. In our proposed framework, the spatio-temporal information is modeled by a CNN (convolutional neural network) module and an encoder-decoder structure with the attention mechanism. The novelty of our work lies in that our model takes full account of temporal and spatial characteristics and obtain forecasts of multiple meteorological stations simultaneously by using the same framework. We apply the DeepSTF model to short-term weather prediction at 226 meteorological stations in Beijing. It significantly improves the short-term forecasts compared to other widely-used benchmark models including the Model Output Statistics method. In order to evaluate the uncertainty of the model parameters, we estimate the confidence intervals by bootstrapping. The results show that the prediction accuracy of the DeepSTF model has strong stability. Finally, we evaluate the impact of seasonal changes and topographical differences on the accuracy of the model predictions. The results indicate that our proposed model has high prediction accuracy.     

Implicit learning on a probabilistic classification task in adults and adolescents with Bulimia Nervosa

BackgroundDysfunction in frontostriatal circuits likely contributes to impaired regulatory control in Bulimia Nervosa (BN), resulting in binge-eating and purging behaviors that resemble maladaptive habits. Less is known about the implicit learning processes of these circuits, which may contribute to habit formation.MethodsWe compared 52 adolescent and adult females with BN to 55 healthy matched-controls during performance of a probabilistic classification learning task, one form of implicit learning. Groups were compared in accuracy and response times, using mixed-models with block, age, and diagnosis as predictors, corrected for multiple comparisons with confounds covaried.ResultsBN participants showed differences in performance on a probabilistic classification learning task that varied by age. Adolescents with BN initially performed as accurately as healthy adolescents, but showed poorer perseverance over time. Adults with BN initially performed less accurately than healthy adults, but improved to perform equivalently. Symptom severity was associated with poorer accuracy in both adults and adolescents with BN.ConclusionsFrontostriatal dysfunction may underlie abnormalities in regulatory control and probabilistic classification learning in BN, likely contributing to the dysregulation of implicitly learned, maladaptive binge-eating and purging behaviors. Such dysfunction in BN may progress with increasing age, first manifesting in poor regulatory control over behaviors and then expanding to implicit learning processes that may underlie habitual behaviors.