Physical activity barriers in the workplace : an exploration of factors contributing to non-participation in a UK workplace physical activity intervention

Purpose– The purpose of this paper is to explore factors contributing to non-participation in a workplace physical activity (PA) intervention in a large UK call centre. Design/methodology/approach – In total, 16 inactive individuals (nine male/seven female), aged 27±9 years, who had not taken part in the intervention were interviewed to explore their perceptions of PA, the intervention and factors which contributed to their non-participation. Transcripts were analysed using thematic analysis. Findings – Six superordinate themes were identified: self-efficacy for exercise; attitudes towards PA; lack of time and energy; facilities and the physical environment; response to the PA programme and PA culture. Barriers occurred at multiple levels of influence, and support the use of ecological or multilevel models to help guide future programme design/delivery. Research limitations/implications – The 16 participants were not selected to be representative of the workplace gender or structure. Future intentions relating to PA participation were not considered and participants may have withheld negative opinions about the workplace or intervention despite use of an external researcher. Practical implications – In this group of employees education about the importance of PA for young adults and providing opportunities to gain social benefits from PA would increase perceived benefits and reduce perceived costs of PA. Workplace cultural norms with respect to PA must also be addressed to create a shift in PA participation. Originality/value – Employees’ reasons for non-participation in workplace interventions remain poorly understood and infrequently studied. The study considers a relatively under-studied population of employed young adults, providing practical recommendations for future interventions

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The 6-vertex model of hydrogen-bonded crystals with bond defects

It is shown that the percolation model of hydrogen-bonded crystals, which is a 6-vertex model with bond defects, is completely equivalent with an 8-vertex model in an external electric field. Using this equivalence we solve exactly a particular 6-vertex model with bond defects. The general solution for the Bethe-like lattice is also analyzed.Comment: 13 pages, 6 figures; added references for section

Validation of chronic obstructive pulmonary disease recording in the Clinical Practice Research Datalink (CPRD-GOLD)

Objectives: The optimal method of identifying people with chronic obstructive pulmonary disease (COPD) from electronic primary care records is not known. We assessed the accuracy of different approaches using the Clinical Practice Research Datalink, a UK electronic health record database. Setting: 951 participants registered with a CPRD practice in the UK between 1 January 2004 and 31 December 2012. Individuals were selected for ≥1 of 8 algorithms to identify people with COPD. General practitioners were sent a brief questionnaire and additional evidence to support a COPD diagnosis was requested. All information received was reviewed independently by two respiratory physicians whose opinion was taken as the gold standard. Primary outcome measure: The primary measure of accuracy was the positive predictive value (PPV), the proportion of people identified by each algorithm for whom COPD was confirmed. Results: 951 questionnaires were sent and 738 (78%) returned. After quality control, 696 (73.2%) patients were included in the final analysis. All four algorithms including a specific COPD diagnostic code performed well. Using a diagnostic code alone, the PPV was 86.5% (77.5-92.3%) while requiring a diagnosis plus spirometry plus specific medication; the PPV was slightly higher at 89.4% (80.7-94.5%) but reduced case numbers by 10%. Algorithms without specific diagnostic codes had low PPVs (range 12.2-44.4%). Conclusions: Patients with COPD can be accurately identified from UK primary care records using specific diagnostic codes. Requiring spirometry or COPD medications only marginally improved accuracy. The high accuracy applies since the introduction of an incentivised disease register for COPD as part of Quality and Outcomes Framework in 2004

Brownian motion of solitons in a Bose-Einstein Condensate

For the first time, we observed and controlled the Brownian motion of solitons. We launched solitonic excitations in highly elongated $^{87}\rm{Rb}$ BECs and showed that a dilute background of impurity atoms in a different internal state dramatically affects the soliton. With no impurities and in one-dimension (1-D), these solitons would have an infinite lifetime, a consequence of integrability. In our experiment, the added impurities scatter off the much larger soliton, contributing to its Brownian motion and decreasing its lifetime. We describe the soliton's diffusive behavior using a quasi-1-D scattering theory of impurity atoms interacting with a soliton, giving diffusion coefficients consistent with experiment.Comment: 4 figure

Effect of extreme data loss on long-range correlated and anti-correlated signals quantified by detrended fluctuation analysis

We investigate how extreme loss of data affects the scaling behavior of long-range power-law correlated and anti-correlated signals applying the DFA method. We introduce a segmentation approach to generate surrogate signals by randomly removing data segments from stationary signals with different types of correlations. These surrogate signals are characterized by: (i) the DFA scaling exponent $\alpha$ of the original correlated signal, (ii) the percentage $p$ of the data removed, (iii) the average length $\mu$ of the removed (or remaining) data segments, and (iv) the functional form of the distribution of the length of the removed (or remaining) data segments. We find that the {\it global} scaling exponent of positively correlated signals remains practically unchanged even for extreme data loss of up to 90%. In contrast, the global scaling of anti-correlated signals changes to uncorrelated behavior even when a very small fraction of the data is lost. These observations are confirmed on the examples of human gait and commodity price fluctuations. We systematically study the {\it local} scaling behavior of signals with missing data to reveal deviations across scales. We find that for anti-correlated signals even 10% of data loss leads to deviations in the local scaling at large scales from the original anti-correlated towards uncorrelated behavior. In contrast, positively correlated signals show no observable changes in the local scaling for up to 65% of data loss, while for larger percentage, the local scaling shows overestimated regions (with higher local exponent) at small scales, followed by underestimated regions (with lower local exponent) at large scales. Finally, we investigate how the scaling is affected by the statistics of the remaining data segments in comparison to the removed segments

Fractional derivatives of random walks: Time series with long-time memory

We review statistical properties of models generated by the application of a (positive and negative order) fractional derivative operator to a standard random walk and show that the resulting stochastic walks display slowly-decaying autocorrelation functions. The relation between these correlated walks and the well-known fractionally integrated autoregressive (FIGARCH) models, commonly used in econometric studies, is discussed. The application of correlated random walks to simulate empirical financial times series is considered and compared with the predictions from FIGARCH and the simpler FIARCH processes. A comparison with empirical data is performed.Comment: 10 pages, 14 figure

Common humpback whale (Megaptera novaeangliae) sound types for passive acoustic monitoring

Author Posting. © Acoustical Society of America, 2011. This article is posted here by permission of Acoustical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Acoustical Society of America 129 (2011): 476-482, doi:10.1121/1.3504708.Humpback whales (Megaptera novaeangliae) are one of several baleen whale species in the Northwest Atlantic that coexist with vessel traffic and anthropogenic noise. Passive acoustic monitoring strategies can be used in conservation management, but the first step toward understanding the acoustic behavior of a species is a good description of its acoustic repertoire. Digital acoustic tags (DTAGs) were placed on humpback whales in the Stellwagen Bank National Marine Sanctuary to record and describe the non-song sounds being produced in conjunction with foraging activities. Peak frequencies of sounds were generally less than 1 kHz, but ranged as high as 6 kHz, and sounds were generally less than 1 s in duration. Cluster analysis distilled the dataset into eight groups of sounds with similar acoustic properties. The two most stereotyped and distinctive types (“wops” and “grunts”) were also identified aurally as candidates for use in passive acoustic monitoring. This identification of two of the most common sound types will be useful for moving forward conservation efforts on this Northwest Atlantic feeding ground.This paper was funded by the National Oceanic and Atmospheric Administration (NOAA)’s National Marine Sanctuaries Program. It was also sponsored in part by the University of Hawaii Sea Grant College Program, School of Ocean and Earth Science and Technology, under Institutional Grant No. NA05OAR4171048 from the NOAA Office of Sea Grant, Department of Commerce

Quantifying signals with power-law correlations: A comparative study of detrended fluctuation analysis and detrended moving average techniques

Detrended fluctuation analysis (DFA) and detrended moving average (DMA) are two scaling analysis methods designed to quantify correlations in noisy non-stationary signals. We systematically study the performance of different variants of the DMA method when applied to artificially generated long-range power-law correlated signals with an {\it a-priori} known scaling exponent $\alpha_{0}$ and compare them with the DFA method. We find that the scaling results obtained from different variants of the DMA method strongly depend on the type of the moving average filter. Further, we investigate the optimal scaling regime where the DFA and DMA methods accurately quantify the scaling exponent $\alpha_{0}$, and how this regime depends on the correlations in the signal. Finally, we develop a three-dimensional representation to determine how the stability of the scaling curves obtained from the DFA and DMA methods depends on the scale of analysis, the order of detrending, and the order of the moving average we use, as well as on the type of correlations in the signal.Comment: 15 pages, 16 figure