Motivating Patients in Cardiac Rehabilitation Programs: A Multicenter Randomized Controlled Trial

Concerns have been raised about motivation and psychological distress when implementing telerehabilitation in patients with heart failure. The current study compared conventional and telerehabilitation in two groups (n=67; n=70) of patients with heart failure at 0, 6, and 12 months on measures of motivation (Self-Determination Theory measures) and psychological distress (Hospital Anxiety and Depression scale). We found no significant changes in motivation across groups, although our telerehabilitation group had a slightly lower level of controlled motivation and higher levels of relatedness. In addition, there were no differences between groups with regard to psychological distress. This study demonstrates that telerehabilitation motivates patients with heart failure to the same degree as conventional rehabilitation, and that telerehabilitation is not associated with increased psychological distress. As such, telerehabilitation offers an alternative to conventional rehabilitation and addresses some of the barriers for participating in rehabilitation identified in the literature

Governance Struggles and Policy Processes in Disaster Risk Reduction: A Case Study from Nepal

In the neo-liberal climate of reduced responsibility for the state, alongside global platforms established to implement the Hyogo Framework for Action, a new arena opens for a multitude of stakeholders to engage in disaster risk reduction (DRR). The key role that the state can play in instituting effective DRR tends to receive little attention, yet in situations where the state apparatus is weak, such as in Nepal, it becomes evident that integrating DRR into development is a particularly challenging task. Due to the political situation in Nepal, progress has been stalled in providing a legislative context conducive to effective DRR. This paper traces the evolution of key DRR initiatives that have been developed in spite of the challenging governance context, such as the National Strategy for Disaster Risk Management and the Nepal Risk Reduction Consortium. Informed by in-depth interviews with key informants, the argument is made that the dedicated efforts of national and international non-governmental organisations, multilateral agencies and donors in mainstreaming DRR demonstrate that considerable progress can be made even where government departments are protective of their own interests and are slow to enact policies to support DRR. The paper suggests however, that without stronger engagement of key political actors the prospects for further progress in DRR may be limited. The findings have implications for other post-conflict countries or weak states engaging in DRR

Local control on precipitation in a fully coupled climate-hydrology model

The ability to simulate regional precipitation realistically by climate models is essential to understand and adapt to climate change. Due to the complexity of associated processes, particularly at unresolved temporal and spatial scales this continues to be a major challenge. As a result, climate simulations of precipitation often exhibit substantial biases that affect the reliability of future projections. Here we demonstrate how a regional climate model (RCM) coupled to a distributed hydrological catchment model that fully integrates water and energy fluxes between the subsurface, land surface, plant cover and the atmosphere, enables a realistic representation of local precipitation. Substantial improvements in simulated precipitation dynamics on seasonal and longer time scales is seen for a simulation period of six years and can be attributed to a more complete treatment of hydrological sub-surface processes including groundwater and moisture feedback. A high degree of local influence on the atmosphere suggests that coupled climate-hydrology models have a potential for improving climate projections and the results further indicate a diminished need for bias correction in climate-hydrology impact studies

Data assimilation in integrated hydrological modeling using ensemble Kalman filtering:evaluating the effect of ensemble size and localization on filter performance

Groundwater head and stream discharge is assimilated using the ensemble transform Kalman filter in an integrated hydrological model with the aim of studying the relationship between the filter performance and the ensemble size. In an attempt to reduce the required number of ensemble members, an adaptive localization method is used. The performance of the adaptive localization method is compared to the more common distance-based localization. The relationship between filter performance in terms of hydraulic head and discharge error and the number of ensemble members is investigated for varying numbers and spatial distributions of groundwater head observations and with or without discharge assimilation and parameter estimation. The study shows that (1) more ensemble members are needed when fewer groundwater head observations are assimilated, and (2) assimilating discharge observations and estimating parameters requires a much larger ensemble size than just assimilating groundwater head observations. However, the required ensemble size can be greatly reduced with the use of adaptive localization, which by far outperforms distance-based localization. The study is conducted using synthetic data only

Data assimilation in integrated hydrological modelling in the presence of observation bias

The use of bias-aware Kalman filters for estimating and correcting observation bias in groundwater head observations is evaluated using both synthetic and real observations. In the synthetic test, groundwater head observations with a constant bias and unbiased stream discharge observations are assimilated in a catchment-scale integrated hydrological model with the aim of updating stream discharge and groundwater head, as well as several model parameters relating to both streamflow and groundwater modelling. The coloured noise Kalman filter (ColKF) and the separate-bias Kalman filter (SepKF) are tested and evaluated for correcting the observation biases. The study found that both methods were able to estimate most of the biases and that using any of the two bias estimation methods resulted in significant improvements over using a bias-unaware Kalman filter. While the convergence of the ColKF was significantly faster than the convergence of the SepKF, a much larger ensemble size was required as the estimation of biases would otherwise fail. Real observations of groundwater head and stream discharge were also assimilated, resulting in improved streamflow modelling in terms of an increased Nash–Sutcliffe coefficient while no clear improvement in groundwater head modelling was observed. Both the ColKF and the SepKF tended to underestimate the biases, which resulted in drifting model behaviour and sub-optimal parameter estimation, but both methods provided better state updating and parameter estimation than using a bias-unaware filter