Revisiting the exercise heart rate-music tempo preference relationship

In the present study, we investigated a hypothesized quartic relationship (meaning three inflection points) between exercise heart rate (HR) and preferred music tempo. Initial theoretical predictions suggested a positive linear relationship (Iwanaga, 1995a, 1995b); however, recent experimental work has shown that as exercise HR increases, step changes and plateaus that punctuate the profile of music tempo preference may occur (Karageorghis, Jones, & Stuart, 2008). Tempi bands consisted of slow (95–100 bpm), medium (115–120 bpm), fast (135–140 bpm), and very fast (155–160 bpm) music. Twenty-eight active undergraduate students cycled at exercise intensities representing 40, 50, 60, 70, 80, and 90% of their maximal HR reserve while their music preference was assessed using a 10-point scale. The Exercise Intensity x Music Tempo interaction was significant, F(6.16, 160.05) = 7.08, p < .001, ηp 2 =.21, as was the test for both cubic and quartic trajectories in the exercise HR–preferred-music-tempo relationship (p < .001). Whereas slow tempo music was not preferred at any exercise intensity, preference for fast tempo increased, relative to medium and very fast tempo music, as exercise intensity increased. The implications for the prescription of music in exercise and physical activity contexts are discussed

A common spatial factor analysis model for measured neighborhood-level characteristics: The Multi-Ethnic Study of Atherosclerosis

The purpose of this study was to reduce the dimensionality of a set of neighborhood-level variables collected on participants in the Multi-Ethnic Study of Atherosclerosis (MESA) while appropriately accounting for the spatial structure of the data. A common spatial factor analysis model in the Bayesian setting was utilized in order to properly characterize dependencies in the data. Results suggest that use of the spatial factor model can result in more precise estimation of factor scores, improved insight into the spatial patterns in the data, and the ability to more accurately assess associations between the neighborhood environment and health outcomes

Pregnancy exposure to common-detect organophosphate esters and phthalates and maternal thyroid function

Background: Contemporary human populations are exposed to elevated concentrations of organophosphate esters (OPEs) and phthalates. Some metabolites have been linked with altered thyroid function, however, inconsistencies exist across thyroid function biomarkers. Research on OPEs is sparse, particularly during pregnancy, when maintaining normal thyroid function is critical to maternal and fetal health. In this paper, we aimed to characterize relationships between OPEs and phthalates exposure and maternal thyroid function during pregnancy, using a cross-sectional investigation of pregnant women nested within the Norwegian Mother, Father, and Child Cohort (MoBa). Methods: We included 473 pregnant women, who were euthyroid and provided bio-samples at 17 weeks' gestation (2004–2008). Four OPE and six phthalate metabolites were measured from urine; six thyroid function biomarkers were estimated from blood. Relationships between thyroid function biomarkers and log-transformed concentrations of OPE and phthalate metabolites were characterized using two approaches that both accounted for confounding by co-exposures: co-pollutant adjusted general linear model (GLM) and Bayesian Kernal Machine Regression (BKMR). Results: We restricted our analysis to common-detect OPE and phthalate metabolites (>94%): diphenyl phosphate (DPHP), di-n-butyl phosphate (DNBP), and all phthalate metabolites. In GLM, pregnant women with summed di-isononyl phthalate metabolites (∑DiNP) concentrations in the 75th percentile had a 0.37 ng/μg lower total triiodothyronine (TT3): total thyroxine (TT4) ratio (95% credible interval: [−0.59, −0.15]) as compared to those in the 25th percentile, possibly due to small but diverging influences on TT3 (−1.99 ng/dL [−4.52, 0.53]) and TT4 (0.13 μg/dL [−0.01, 0.26]). Similar trends were observed for DNBP and inverse associations were observed for DPHP, monoethyl phthalate, mono-isobutyl phthalate, and mono-n-butyl phthalate. Most associations observed in co-pollutants adjusted GLMs were attenuated towards the null in BKMR, except for the case of ∑DiNP and TT3:TT4 ratio (−0.48 [−0.96, 0.003]). Conclusions: Maternal thyroid function varied modestly with ∑DiNP, whereas results for DPHP varied by the type of statistical models

Air pollution exposure estimation using dispersion modelling and continuous monitoring data in a prospective birth cohort study in the Netherlands

Previous studies suggest that pregnant women and children are particularly vulnerable to the adverse effects of air pollution. A prospective cohort study in pregnant women and their children enables identification of the specific effects and critical periods. This paper describes the design of air pollution exposure assessment for participants of the Generation R Study, a population-based prospective cohort study from early pregnancy onwards in 9778 women in the Netherlands. Individual exposures to PM10 and NO2 levels at the home address were estimated for mothers and children, using a combination of advanced dispersion modelling and continuous monitoring data, taking into account the spatial and temporal variation in air pollution concentrations. Full residential history was considered. We observed substantial spatial and temporal variation in air pollution exposure levels. The Generation R Study provides unique possibilities to examine effects of short- and long-term air pollution exposure on various maternal and childhood outcomes and to identify potential critical windows of exposure

Automated time activity classification based on global positioning system (GPS) tracking data

<p>Abstract</p> <p>Background</p> <p>Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data.</p> <p>Methods</p> <p>We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model.</p> <p>Results</p> <p>Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data.</p> <p>Conclusions</p> <p>Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns.</p

Particulate Matter Exposure Exacerbates High Glucose-Induced Cardiomyocyte Dysfunction through ROS Generation

Diabetes mellitus and fine particulate matter from diesel exhaust (DEP) are both important contributors to the development of cardiovascular disease (CVD). Diabetes mellitus is a progressive disease with a high mortality rate in patients suffering from CVD, resulting in diabetic cardiomyopathy. Elevated DEP levels in the air are attributed to the development of various CVDs, presumably since fine DEP (<2.5 µm in diameter) can be inhaled and gain access to the circulatory system. However, mechanisms defining how DEP affects diabetic or control cardiomyocyte function remain poorly understood. The purpose of the present study was to evaluate cardiomyocyte function and reactive oxygen species (ROS) generation in isolated rat ventricular myocytes exposed overnight to fine DEP (0.1 µg/ml), and/or high glucose (HG, 25.5 mM). Our hypothesis was that DEP exposure exacerbates contractile dysfunction via ROS generation in cardiomyocytes exposed to HG. Ventricular myocytes were isolated from male adult Sprague-Dawley rats cultured overnight and sarcomeric contractile properties were evaluated, including: peak shortening normalized to baseline (PS), time-to-90% shortening (TPS90), time-to-90% relengthening (TR90) and maximal velocities of shortening/relengthening (±dL/dt), using an IonOptix field-stimulator system. ROS generation was determined using hydroethidine/ethidium confocal microscopy. We found that DEP exposure significantly increased TR90, decreased PS and ±dL/dt, and enhanced intracellular ROS generation in myocytes exposed to HG. Further studies indicated that co-culture with antioxidants (0.25 mM Tiron and 0.5 mM N-Acetyl-L-cysteine) completely restored contractile function in DEP, HG and HG+DEP-treated myocytes. ROS generation was blocked in HG-treated cells with mitochondrial inhibition, while ROS generation was blocked in DEP-treated cells with NADPH oxidase inhibition. Our results suggest that DEP exacerbates myocardial dysfunction in isolated cardiomyocytes exposed to HG-containing media, which is potentially mediated by various ROS generation pathways

The Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort study: Assessment of environmental exposures

The Canadian Healthy Infant Longitudinal Development birth cohort was designed to elucidate interactions between environment and genetics underlying development of asthma and allergy. Over 3600 pregnant mothers were recruited from the general population in four provinces with diverse environments. The child is followed to age 5 years, with prospective characterization of diverse exposures during this critical period. Key exposure domains include indoor and outdoor air pollutants, inhalation, ingestion and dermal uptake of chemicals, mold, dampness, biological allergens, pets and pests, housing structure, and living behavior, together with infections, nutrition, psychosocial environment, and medications. Assessments of early life exposures are focused on those linked to inflammatory responses driven by the acquired and innate immune systems. Mothers complete extensive environmental questionnaires including time-activity behavior at recruitment and when the child is 3, 6, 12, 24, 30, 36, 48, and 60 months old. House dust collected during a thorough home assessment at 3–4 months, and biological specimens obtained for multiple exposure-related measurements, are archived for analyses. Geo-locations of homes and daycares and land-use regression for estimating traffic-related air pollution complement time-activity-behavior data to provide comprehensive individual exposure profiles. Several analytical frameworks are proposed to address the many interacting exposure variables and potential issues of co-linearity in this complex data set