Home-administered pre-surgical psychological intervention for knee osteoarthritis (HAPPiKNEES): study protocol for a randomised controlled trial

Background: Knee replacement surgery reduces pain for many people with osteoarthritis (OA). However, surgical outcomes are partly dependent on patients’ moods, and those with depression or anxiety have worse outcomes. Approximately one-third of people with OA have mood problems. Cognitive behavioural therapy (CBT), a psychological therapy, is recommended by the National Institute for Health and Care Excellence for improving mood. However, evidence for the effectiveness of CBT before knee surgery in improving pain, mood, and quality of life following this surgery for people with knee OA is lacking. Methods/Design: This is a multi-centre, mixed-methods feasibility randomised controlled trial to compare treatment as usual (TAU) plus a brief CBT-based intervention with a TAU-only control, for people with knee OA. We will recruit 50 patients with knee OA, listed for knee replacement surgery, with high levels of distress (assessed using a mood questionnaire), and who consent to take part. Participants will be randomly allocated to receive TAU plus intervention or TAU. Up to 10 sessions of CBT will be offered on an individual basis by a psychologist. The assessments and interventions will be completed before surgery. Repeat assessments at 4 and 6 months after randomisation will be sent and received by post. Two patient-partners will conduct feedback interviews with some participants to assess what aspects of the intervention were helpful or unhelpful, the acceptability of randomisation, the experience of being in a control group, and the appropriateness of the measures used. Interviews will be audio-recorded, transcribed, and analysed using the framework approach. We will examine the feasibility and acceptability of patient-partners conducting the interviews by also interviewing the patient-partners. Discussion: Findings from this study will be used to design a definitive study that will examine the clinical and cost-effectiveness of the CBT intervention in improving patient outcomes following knee surgery

Assessing uncertainty and sensor biases in passive microwave data across High Mountain Asia

Snowfall comprises a significant percentage of the annual water budget in High Mountain Asia (HMA), but snow water equivalent (SWE) is poorly constrained due to lack of in-situ measurements and complex terrain that limits the efficacy of modeling and observations. Over the past few decades, SWE has been estimated with passive microwave (PM) sensors with generally good results in wide, flat, terrain, and lower reliability in densely forested, complex, or high-elevation areas. In this study, we use raw swath data from five satellite - sensors the Special Sensor Microwave/Imager (SSMI) and Special Sensor Microwave Imager/Sounder (SSMIS) (1987-2015, F08, F11, F13, F17), Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E, 2002-2011), AMSR2 (2012-2015), and the Global Precipitation Measurement (GPM, 2014-2015) - in order to understand the spatial and temporal structure of native sensor, topographic, and land cover biases in SWE estimates in HMA. We develop a thorough understanding of the uncertainties in our SWE estimates by examining the impacts of topographic parameters (aspect, relief, hillslope angle, and elevation), land cover, native sensor biases, and climate parameters (precipitation, temperature, and wind speed). HMA, with its high seasonality, large topographic gradients and low relief at high elevations provides an excellent context to examine a wide range of climatic, land-cover, and topographic settings to better constrain SWE uncertainties and potential sensor bias. Using a multi-parameter regression, we compare long-term SWE variability to forest fraction, maximal multiyear snow depth, topographic parameters, and long-term average wind speed across both individual sensor time series and a merged multi-sensor dataset. In regions where forest cover is extensive, it is the strongest control on SWE variability. In those regions where forest density is low (<5%), maximal snow depth dominates the uncertainty signal. In our regression across HMA, we find that forest fraction is the strongest control on SWE variability (75.8%), followed by maximal multi-year snow depth (7.82%), 90th percentile 10-m wind speed of a 10-year December-January-February (DJF) time series (5.64%), 25th percentile DJF 10-m wind speed (5.44%), and hillslope angle (5.24%). Elevation, relief, and terrain aspect show very low influence on SWE variability (<1%). We find that the GPM sensor provides the most robust regression results, and can be reliably used to estimate SWE in our study region. While forest cover and elevation have been integrated into many SWE algorithms, wind speed and long-term maximal snow depth have not. Our results show that wind redistribution of snow can have impacts on SWE, especially over large, flat, areas. Using our regression results, we have developed an understanding of sensor specific SWE uncertainties and their spatial patterns. The uncertainty maps developed in this study provide a first-order approximation of SWE-estimate reliability for much of HMA, and imply that high-fidelity SWE estimates can be produced for many high-elevation areas. (C) 2016 Elsevier Inc. All rights reserved

Effect of Acting Experience on Emotion Expression and Recognition in Voice: Non-Actors Provide Better Stimuli than Expected

Both in the performative arts and in emotion research, professional actors are assumed to be capable of delivering emotions comparable to spontaneous emotional expressions. This study examines the effects of acting training on vocal emotion depiction and recognition. We predicted that professional actors express emotions in a more realistic fashion than non-professional actors. However, professional acting training may lead to a particular speech pattern; this might account for vocal expressions by actors that are less comparable to authentic samples than the ones by non-professional actors. We compared 80 emotional speech tokens from radio interviews with 80 re-enactments by professional and inexperienced actors, respectively. We analyzed recognition accuracies for emotion and authenticity ratings and compared the acoustic structure of the speech tokens. Both play-acted conditions yielded similar recognition accuracies and possessed more variable pitch contours than the spontaneous recordings. However, professional actors exhibited signs of different articulation patterns compared to non-trained speakers. Our results indicate that for emotion research, emotional expressions by professional actors are not better suited than those from non-actors