Comparison of 3 accelerometer data reduction approaches, step counts, and 2 self-report measures for estimating physical activity in free-living adults

Background Accelerometers have become one of the most common methods of measuring physical activity (PA). Thus, validity of accelerometer data reduction approaches remains an important research area. Yet, few studies directly compare data reduction approaches and other PA measures in free-living samples. Objective To compare PA estimates provided by 3 accelerometer data reduction approaches, steps, and 2 self-reported estimates: Crouter's 2-regression model, Crouter's refined 2-regression model, the weighted cut-point method adopted in the National Health and Nutrition Examination Survey (NHANES; 2003-2004 and 2005-2006 cycles), steps, IPAQ, and 7-day PA recall. Methods A worksite sample (N = 87) completed online-surveys and wore ActiGraph GT1M accelerometers and pedometers (SW-200) during waking hours for 7 consecutive days. Daily time spent in sedentary, light, moderate, and vigorous intensity activity and percentage of participants meeting PA recommendations were calculated and compared. Results Crouter's 2-regression (161.8 +/- 52.3 minutes/day) and refined 2-regression (137.6 +/- 40.3 minutes/day) models provided significantly higher estimates of moderate and vigorous PA and proportions of those meeting PA recommendations (91% and 92%, respectively) as compared with the NHANES weighted cut-point method (39.5 +/- 20.2 minutes/day, 18%). Differences between other measures were also significant. Conclusions When comparing 3 accelerometer cut-point methods, steps, and self-report measures, estimates of PA participation vary substantially.

Design of a garment for data collection of toddler language and physical activity

Design process phases of development, evaluation and implementation were used to create a garment to simultaneously collect reliable data of speech production and intensity of movement of toddlers (18-36 months). A series of prototypes were developed and evaluated that housed accelerometer-based motion sensors and a digital transmitter with microphone. The approved test garment was a top constructed from loop-faced fabric with interior pockets to house devices. Extended side panels allowed for sizing. In total, 56 toddlers (28 male; 28 female; 16-36 months of age) participated in the study providing pilot and baseline data. The test garment was effective in collecting data as evaluated for accuracy and reliability using ANOVA for accelerometer data, transcription of video for type of movement, and number and length of utterances for speech production. The data collection garment has been implemented in various studies across disciplines.

Ray's Night Out

Research Background: The proliferation of technologically-based interventions and mHealth in particular have led to a need for innovative, relevant and engaging ways of presenting health messages to young people using technology. ‘Ray’s Night Out’ is a mobile health application co-designed with young people by an interdisciplinary team of researchers at Queensland University of Technology. Research Questions: The design, research, development and evaluation of ‘Ray’s Night Out’ addressed a number of research questions from across the fields of Psychology and Interactive and Visual Design. The specific design research questions addressed were: How can a mobile intervention be best designed to promote young people’s safety and wellbeing and minimise harm when consuming alcohol on a typical night out? Specifically, how can principles of interactive and visual design be effectively applied to develop innovative digital health communication solutions that empower young people as active participants in improving their health and wellbeing? Research Contribution: Innovation The mobile app, as a digital artifact, represents a new way of engaging young people in the issue of alcohol consumption and the pacing and self-care behaviours through unique interaction, visual and interface designs which resulted from the participant-led and iterative design research process. The design of the specific interactive and visual features of the app informed by participatory design data and by health research present a novel approach to preventing young people in crossing the ‘stupid line’ on a typical night out. Research Significance: The significance of the design research component within the larger interdisciplinary practices that have informed ‘Ray’s Night Out’ (e.g. field of psychology, reported through journal articles and other related outcomes), is the unique visual and interactive presentation of participant data and health concepts within the app interface and interaction design which improves and increases young people’s engagement with the health messages it contains. The global quality standard is further demonstrated by the launch on Apple iTunes: https://itunes.apple.com/us/app/rays-night-out/id978589497?mt=8 This demonstrates the application meets the high professional requirements for global release and international standards set by Apple AppStore.

'Music eScape': mHealth application

Research Background Young people’s avid use of mobile technologies in daily life has led to an increase in the design and research on mHealth (mobile health) interventions targeting young people. ‘Music eScape’ is a mobile based mood regulation app that uses an innovative approach to promoting young people’s wellbeing using music. Research Question The design, research, development and evaluation of ‘Music eScape’ addressed a number of research questions from across the fields of Psychology and Interactive and Visual Design. The specific design research question addressed was: How can interaction and visual design be utilized to promote and enable young people to effectively regulate their mood using music and how can the new design further promote their experience of empowerment, control and agency over actively directing their mood journey? Research Contribution Innovation and New Knowledge Through its unique visual interface design and interactivity, the application presents a novel approach to promoting young people’s wellbeing using music and a specific function that allows users to ‘draw’ their mood journey in order to generate a playlist. The mobile app is the first to contain a function that enables users to plan their mood journey and exercise a sense of agency, intentional choice and control over the mood shift and by extension, their wellbeing. The feature ‘drawing’ interface was designed by Oksana Zelenko using participatory design research and Russell’s circumplex model of affect (1980) to inform the key visual design concept and underpinning interaction design. Research Significance The significance of the design research component within the larger interdisciplinary practices that have informed ‘Music eScape’ (e.g. field of psychology, reported through journal articles and other related outcomes), is the unique visual and interactive presentation of participant data and music therapy research within the app interface and interaction design which improves and increases young people’s engagement with the health messages it contains. The industry quality standard is further demonstrated by the launch on Apple iTunes. This demonstrates the application meets the high professional requirements for national release and meets international standards. The app also creates a new benchmark for the quality of health apps on the market as it marks the industry release of a trialled evidence-based mHealth intervention co-designed with young people.

Bluetooth and Wi-Fi MAC address based crowd data collection and monitoring : benefits, challenges and enhancement

This paper firstly presents the benefits and critical challenges on the use of Bluetooth and Wi-Fi for crowd data collection and monitoring. The major challenges include antenna characteristics, environment’s complexity and scanning features. Wi-Fi and Bluetooth are compared in this paper in terms of architecture, discovery time, popularity of use and signal strength. Type of antennas used and the environment’s complexity such as trees for outdoor and partitions for indoor spaces highly affect the scanning range. The aforementioned challenges are empirically evaluated by “real” experiments using Bluetooth and Wi-Fi Scanners. The issues related to the antenna characteristics are also highlighted by experimenting with different antenna types. Novel scanning approaches including Overlapped Zones and Single Point Multi-Range detection methods will be then presented and verified by real-world tests. These novel techniques will be applied for location identification of the MAC IDs captured that can extract more information about people movement dynamics.

Human lens lipids differ markedly from those of commonly used experimental animals

Electrospray ionisation tandem mass spectrometry has allowed the unambiguous identification and quantification of individual lens phospholipids in human and six animal models. Using this approach ca. 100 unique phospholipids have been characterised. Parallel analysis of the same lens extracts by a novel direct-insertion electron-ionization technique found the cholesterol content of human lenses to be significantly higher (ca. 6 times) than lenses from the other animals. The most abundant phospholipids in all the lenses examined were choline-containing phospholipids. In rat, mouse, sheep, cow, pig and chicken, these were present largely as phosphatidylcholines, in contrast 66% of the total phospholipid in Homo sapiens was sphingomyelin, with the most abundant being dihydrosphingomyelins, in particular SM(d18:0/16:0) and SM(d18:0/24:1). The abundant glycerophospholipids within human lenses were found to be predominantly phosphatidylethanolamines and phosphatidylserines with surprisingly high concentrations of ether-linked alkyl chains identified in both classes. This study is the first to identify the phospholipid class (head-group) and assign the constituent fatty acid(s) for each lipid molecule and to quantify individual lens phospholipids using internal standards. These data clearly indicate marked differences in the membrane lipid composition of the human lens compared to commonly used animal models and thus predict a significant variation in the membrane properties of human lens fibre cells compared to those of other animals. © 2008 Elsevier B.V. All rights reserved.

Mortality following hip arthroplasty—inappropriate use of National Joint Registry (NJR) data

Mortality following hip arthroplasty is affected by a large number of confounding variables each of which must be considered to enable valid interpretation. Relevant variables available from the 2011 NJR data set were included in the Cox model. Mortality rates in hip arthroplasty patients were lower than in the age-matched population across all hip types. Age at surgery, ASA grade, diagnosis, gender, provider type, hip type and lead surgeon grade all had a significant effect on mortality. Schemper's statistic showed that only 18.98% of the variation in mortality was explained by the variables available in the NJR data set. It is inappropriate to use NJR data to study an outcome affected by a multitude of confounding variables when these cannot be adequately accounted for in the available data set.

Methodology for developing real-time motorway traffic risk identification models using individual-vehicle data

Most of existing motorway traffic safety studies using disaggregate traffic flow data aim at developing models for identifying real-time traffic risks by comparing pre-crash and non-crash conditions. One of serious shortcomings in those studies is that non-crash conditions are arbitrarily selected and hence, not representative, i.e. selected non-crash data might not be the right data comparable with pre-crash data; the non-crash/pre-crash ratio is arbitrarily decided and neglects the abundance of non-crash over pre-crash conditions; etc. Here, we present a methodology for developing a real-time MotorwaY Traffic Risk Identification Model (MyTRIM) using individual vehicle data, meteorological data, and crash data. Non-crash data are clustered into groups called traffic regimes. Thereafter, pre-crash data are classified into regimes to match with relevant non-crash data. Among totally eight traffic regimes obtained, four highly risky regimes were identified; three regime-based Risk Identification Models (RIM) with sufficient pre-crash data were developed. MyTRIM memorizes the latest risk evolution identified by RIM to predict near future risks. Traffic practitioners can decide MyTRIM’s memory size based on the trade-off between detection and false alarm rates. Decreasing the memory size from 5 to 1 precipitates the increase of detection rate from 65.0% to 100.0% and of false alarm rate from 0.21% to 3.68%. Moreover, critical factors in differentiating pre-crash and non-crash conditions are recognized and usable for developing preventive measures. MyTRIM can be used by practitioners in real-time as an independent tool to make online decision or integrated with existing traffic management systems.

Nutrition and physical activity policies and practices in family child care homes in oregon : baseline findings from the healthy home child care project

Baseline findings from the Healthy Home Child Care Project include data from Family Child Care Providers (FCCPs) in Oregon (n=53) who completed assessments of nutrition and physical activity policies and practices and BMI data for children in the care of FCCPs (n=205). Results show that a significant percentage of FCCPs failed to meet child care standards in several areas and that 26.8% of children under the care of FCCPs were overweight or obese. These data supported the development of an Extension-delivered intervention specific to FCCPs in Oregon and highlight areas of concern that should be addressed through targeted trainings of FCCPs.

The unfinished revolution : what is missing from the E&P industry’s move to “big data”

One cannot help but be impressed by the inroads that digital oilfield technologies have made into the exploration and production (E&P) industry in the past decade. Today’s production systems can be monitored by “smart” sensors that allow engineers to observe almost any aspect of performance in real time. Our understanding of how reservoirs are behaving has improved considerably since the dawn of this revolution, and the industry has been able to move away from point answers to more holistic “big picture” integrated solutions. Indeed, the industry has already reaped the rewards of many of these kinds of investments. Many billions of dollars of value have been delivered by this heightened awareness of what is going on within our assets and the world around them (Van Den Berg et al. 2010).