Psychosocial mediators of change in physical activity in the Welsh national exercise referral scheme: secondary analysis of a randomised controlled trial

Objective: While an increasing number of randomised controlled trials report impacts of exercise referral schemes (ERS) on physical activity, few have investigated the mechanisms through which increases in physical activity are produced. This study examines whether a National Exercise Referral Scheme (NERS) in Wales is associated with improvements in autonomous motivation, self-efficacy and social support, and whether change in physical activity is mediated by change in these psychosocial processes.<p></p> Methods: A pragmatic randomised controlled trial of NERS across 12 LHBs in Wales. Questionnaires measured demographic data and physical activity at baseline. Participants (N = 2160) with depression, anxiety or CHD risk factors were referred by health professionals and randomly assigned to control or intervention. At six months psychological process measures were collected by questionnaire. At 12 months physical activity was assessed by 7 Day PAR telephone interview. Regressions tested intervention effects on psychosocial variables, physical activity before and after adjusting for mediators and socio demographic patterning.<p></p> Results: Significant intervention effects were found for autonomous motivation and social support for exercise at 6 months. No intervention effect was observed for self-efficacy. The data are consistent with a hypothesis of partial mediation of the intervention effect by autonomous motivation. Analysis of moderators showed significant improvements in relative autonomy in all subgroups. The greatest improvements in autonomous motivation were observed among patients who were least active at baseline.<p></p> Discussion: The present study offered key insights into psychosocial processes of change in an exercise referral scheme, with effects on physical activity mediated by autonomous motivation. Findings support the use of self-determination theory as a framework for ERS. Further research is required to explain socio-demographic patterning in responses to ERS, with changes in motivation occurring among all sub-groups of participants, though not always leading to higher adherence or behavioural change. This highlights the importance of socio-ecological approaches to developing and evaluating behaviour change interventions, which consider factors beyond the individual, including conditions in which improved motivation does or does not produce behavioural change

Comparison of modelling techniques for milk-production forecasting

peer-reviewedThe objective of this study was to assess the suitability of 3 different modeling techniques for the prediction of total daily herd milk yield from a herd of 140 lactating pasture-based dairy cows over varying forecast horizons. A nonlinear auto-regressive model with exogenous input, a static artificial neural network, and a multiple linear regression model were developed using 3 yr of historical milk-production data. The models predicted the total daily herd milk yield over a full season using a 305-d forecast horizon and 50-, 30-, and 10-d moving piecewise horizons to test the accuracy of the models over long- and short-term periods. All 3 models predicted the daily production levels for a full lactation of 305 d with a percentage root mean square error (RMSE) of ≤12.03%. However, the nonlinear auto-regressive model with exogenous input was capable of increasing its prediction accuracy as the horizon was shortened from 305 to 50, 30, and 10 d [RMSE (%) = 8.59, 8.1, 6.77, 5.84], whereas the static artificial neural network [RMSE (%) = 12.03, 12.15, 11.74, 10.7] and the multiple linear regression model [RMSE (%) = 10.62, 10.68, 10.62, 10.54] were not able to reduce their forecast error over the same horizons to the same extent. For this particular application the nonlinear auto-regressive model with exogenous input can be presented as a more accurate alternative to conventional regression modeling techniques, especially for short-term milk-yield predictions

Heathers: The Musical (February 23-25, 2017)

Program for Heathers: The Musical (February 23-25, 2017). To view the photos from this production of Heathers, please click here

Phase transition of the susceptible-infected-susceptible dynamics on time-varying configuration model networks

We present a degree-based theoretical framework to study the susceptible-infected-susceptible (SIS) dynamics on time-varying (rewired) configuration model networks. Using this framework, we provide a detailed analysis of the stationary state that covers, for a given structure, every dynamic regimes easily tuned by the rewiring rate. This analysis is suitable for the characterization of the phase transition and leads to three main contributions. (i) We obtain a self-consistent expression for the absorbing-state threshold, able to capture both collective and hub activation. (ii) We recover the predictions of a number of existing approaches as limiting cases of our analysis, providing thereby a unifying point of view for the SIS dynamics on random networks. (iii) We reinterpret the concept of hub-dominated phase transition. Within our framework, it appears as a heterogeneous critical phenomenon : observables for different degree classes have a different scaling with the infection rate. This leads to the successive activation of the degree classes beyond the epidemic threshold.Comment: 14 pages, 11 figure

The impacts of commercial lease structures on landlord and tenant leasing behaviours and experiences

The commercial property market in New Zealand is characterized by two standard but distinct lease environments. In Auckland, the commercial core of the economy, net leases dominate, whereas in Wellington, the political capital, gross leases are dominant. These different lease environments have the potential to strongly influence the nature of landlord and tenant relationships in these markets. Using in-depth interviews with key industry personnel, this study examines the perceptions, behaviours, experiences and key issues confronting landlords and tenants under net and gross leases. The paper examines how different lease structures affect the behavioural and attitudinal characteristics of landlords and tenants including: landlord/tenant perceptions of a lease, the operation and maintenance procedures, landlord-tenant relationship, and ultimately, overall satisfaction

A rank based social norms model of how people judge their levels of drunkenness whilst intoxicated

Background: A rank based social norms model predicts that drinkers’ judgements about their drinking will be based on the rank of their breath alcohol level amongst that of others in the immediate environment, rather than their actual breath alcohol level, with lower relative rank associated with greater feelings of safety. This study tested this hypothesis and examined how people judge their levels of drunkenness and the health consequences of their drinking whilst they are intoxicated in social drinking environments. Methods: Breath alcohol testing of 1,862 people (mean age = 26.96 years; 61.86 % male) in drinking environments. A subset (N = 400) also answered four questions asking about their perceptions of their drunkenness and the health consequences of their drinking (plus background measures). Results: Perceptions of drunkenness and the health consequences of drinking were regressed on: (a) breath alcohol level, (b) the rank of the breath alcohol level amongst that of others in the same environment, and (c) covariates. Only rank of breath alcohol level predicted perceptions: How drunk they felt (b 3.78, 95 % CI 1.69 5.87), how extreme they regarded their drinking that night (b 3.7, 95 % CI 1.3 6.20), how at risk their long-term health was due to their current level of drinking (b 4.1, 95 % CI 0.2 8.0) and how likely they felt they would experience liver cirrhosis (b 4.8. 95 % CI 0.7 8.8). People were more influenced by more sober others than by more drunk others. Conclusion: Whilst intoxicated and in drinking environments, people base judgements regarding their drinking on how their level of intoxication ranks relative to that of others of the same gender around them, not on their actual levels of intoxication. Thus, when in the company of others who are intoxicated, drinkers were found to be more likely to underestimate their own level of drinking, drunkenness and associated risks. The implications of these results, for example that increasing the numbers of sober people in night time environments could improve subjective assessments of drunkenness, are discussed

Deep learning of contagion dynamics on complex networks

Forecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically or computationally tractable, these models must rely on simplifying assumptions, thereby limiting the quantitative accuracy of their predictions and the complexity of the dynamics they can model. Here, we propose a complementary approach based on deep learning where effective local mechanisms governing a dynamic on a network are learned from time series data. Our graph neural network architecture makes very few assumptions about the dynamics, and we demonstrate its accuracy using different contagion dynamics of increasing complexity. By allowing simulations on arbitrary network structures, our approach makes it possible to explore the properties of the learned dynamics beyond the training data. Finally, we illustrate the applicability of our approach using real data of the COVID-19 outbreak in Spain. Our results demonstrate how deep learning offers a new and complementary perspective to build effective models of contagion dynamics on networks

Deep learning of contagion dynamics on complex networks

Forecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically or computationally tractable, these models must rely on simplifying assumptions, thereby limiting the quantitative accuracy of their predictions and the complexity of the dynamics they can model. Here, we propose a complementary approach based on deep learning where the effective local mechanisms governing a dynamic on a network are learned from time series data. Our graph neural network architecture makes very few assumptions about the dynamics, and we demonstrate its accuracy using different contagion dynamics of increasing complexity. By allowing simulations on arbitrary network structures, our approach makes it possible to explore the properties of the learned dynamics beyond the training data. Finally, we illustrate the applicability of our approach using real data of the COVID-19 outbreak in Spain. Our results demonstrate how deep learning offers a new and complementary perspective to build effective models of contagion dynamics on networks