Longitudinal deprivation trajectories and risk of cardiovascular disease in New Zealand

We used longitudinal information on area deprivation status to explore the relationship between residential-deprivation mobility and Cardiovascular Disease (CVD). Data from 2,418,397 individuals who were: enrolled in any Primary Health Organisation within New Zealand (NZ) during at least 1 of 34 calendar quarters between 1st January 2006 and 30th June 2014; aged between 30 and 84 years (inclusive) at the start of the study period; had no prior history of CVD; and had recorded address information were analysed. Including a novel trajectory analysis, our findings suggest that movers are healthier than stayers. The deprivation characteristics of the move have a larger impact on the relative risk of CVD for younger movers than for older movers. For older movers any kind of move is associated with a decreased risk of CVD

Quantifying human movement using the Movn smartphone app: validation and field study

BACKGROUND: The use of embedded smartphone sensors offers opportunities to measure physical activity (PA) and human movement. Big data-which includes billions of digital traces-offers scientists a new lens to examine PA in fine-grained detail and allows us to track people\u27s geocoded movement patterns to determine their interaction with the environment. OBJECTIVE: The objective of this study was to examine the validity of the Movn smartphone app (Moving Analytics) for collecting PA and human movement data. METHODS: The criterion and convergent validity of the Movn smartphone app for estimating energy expenditure (EE) were assessed in both laboratory and free-living settings, compared with indirect calorimetry (criterion reference) and a stand-alone accelerometer that is commonly used in PA research (GT1m, ActiGraph Corp, convergent reference). A supporting cross-validation study assessed the consistency of activity data when collected across different smartphone devices. Global positioning system (GPS) and accelerometer data were integrated with geographical information software to demonstrate the feasibility of geospatial analysis of human movement. RESULTS: A total of 21 participants contributed to linear regression analysis to estimate EE from Movn activity counts (standard error of estimation [SEE]=1.94 kcal/min). The equation was cross-validated in an independent sample (N=42, SEE=1.10 kcal/min). During laboratory-based treadmill exercise, EE from Movn was comparable to calorimetry (bias=0.36 [-0.07 to 0.78] kcal/min, t82=1.66, P=.10) but overestimated as compared with the ActiGraph accelerometer (bias=0.93 [0.58-1.29] kcal/min, t89=5.27, P<.001). The absolute magnitude of criterion biases increased as a function of locomotive speed (F1,4=7.54, P<.001) but was relatively consistent for the convergent comparison (F1,4=1.26, P<.29). Furthermore, 95% limits of agreement were consistent for criterion and convergent biases, and EE from Movn was strongly correlated with both reference measures (criterion r=.91, convergent r=.92, both P<.001). Movn overestimated EE during free-living activities (bias=1.00 [0.98-1.02] kcal/min, t6123=101.49, P<.001), and biases were larger during high-intensity activities (F3,6120=1550.51, P<.001). In addition, 95% limits of agreement for convergent biases were heterogeneous across free-living activity intensity levels, but Movn and ActiGraph measures were strongly correlated (r=.87, P<.001). Integration of GPS and accelerometer data within a geographic information system (GIS) enabled creation of individual temporospatial maps. CONCLUSIONS: The Movn smartphone app can provide valid passive measurement of EE and can enrich these data with contextualizing temporospatial information. Although enhanced understanding of geographic and temporal variation in human movement patterns could inform intervention development, it also presents challenges for data processing and analytics

An Introduction to ATLAS Pixel Detector DAQ and Calibration Software Based on a Year's Work at CERN for the Upgrade from 8 to 13 TeV

An overview is presented of the ATLAS pixel detector Data Acquisition (DAQ) system obtained by the author during a year-long opportunity to work on calibration software for the 2015-16 Layer‑2 upgrade. It is hoped the document will function more generally as an easy entry point for future work on ATLAS pixel detector calibration systems. To begin with, the overall place of ATLAS pixel DAQ within the CERN Large Hadron Collider (LHC), the purpose of the Layer-2 upgrade and the fundamentals of pixel calibration are outlined. This is followed by a brief look at the high level structure and key features of the calibration software. The paper concludes by discussing some difficulties encountered in the upgrade project and how these led to unforeseen alternative enhancements, such as development of calibration “simulation” software allowing the soundness of the ongoing upgrade work to be verified while not all of the actual readout hardware was available for the most comprehensive testing

Correlations between the IMD, its Domains, with rates of smoking and household poverty.

<p>Correlations between the IMD, its Domains, with rates of smoking and household poverty.</p

Weights for ranked health indicators in the Health Domain.

<p>Weights for ranked health indicators in the Health Domain.</p

Developing the IMD: An overview.

<p>Adapted from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0181260#pone.0181260.g002" target="_blank">Fig 2</a> SIMD 2012 Methodology, in Scottish Index of Multiple Deprivation 2012. Edinburgh: Scottish Government (Crown Copyright 2012, See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0181260#pone.0181260.s001" target="_blank">S1 Fig</a>). [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0181260#pone.0181260.ref036" target="_blank">36</a>] Reproduced with Permission (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0181260#pone.0181260.s002" target="_blank">S1 File</a>)</p

Weights of ranked education indicators in the Education Domain.

<p>Weights of ranked education indicators in the Education Domain.</p