Concussion Knowledge and Prior Concussion Experience of Theater Personnel

In Volume 3, Issue 1 of the JSMAHS you will find Professional research abstracts, as well as Under Graduate student research abstracts, case reports, and critically appraised topics. Thank you for viewing this 3rd Annual OATA Special Edition

A multicentre, randomised controlled trial to compare the clinical and cost-effectiveness of Lee Silverman Voice Treatment versus standard NHS Speech and Language Therapy versus control in Parkinson’s disease: a study protocol for a randomised controlled trial

Abstract: Background: Parkinson’s disease (PD) affects approximately 145,519 people in the UK. Speech impairments are common with a reported prevalence of 68%, which increase physical and mental demands during conversation, reliance on family and/or carers, and the likelihood of social withdrawal reducing quality of life. In the UK, two approaches to Speech and Language Therapy (SLT) intervention are commonly available: National Health Service (NHS) SLT or Lee Silverman Voice Treatment (LSVT LOUD®). NHS SLT is tailored to the individuals’ needs per local practice typically consisting of six to eight weekly sessions; LSVT LOUD® comprises 16 sessions of individual treatment with home-based practice over 4 weeks. The evidence-base for their effectiveness is inconclusive. Methods/design: PD COMM is a phase III, multicentre, three-arm, unblinded, randomised controlled trial. Five hundred and forty-six people with idiopathic PD, reporting speech or voice problems will be enrolled. We will exclude those with a diagnosis of dementia, laryngeal pathology or those who have received SLT for speech problems in the previous 2 years. Following informed consent and completion of baseline assessments, participants will be randomised in a 1:1:1 ratio to no-intervention control, NHS SLT or LSVT LOUD® via a central computer-generated programme, using a minimisation procedure with a random element, to ensure allocation concealment. Participants randomised to the intervention groups will start treatment within 4 (NHS SLT) or 7 (LSVT LOUD®) weeks of randomisation. Primary outcome: Voice Handicap Index (VHI) total score at 3 months. Secondary outcomes include: VHI subscales, Parkinson’s Disease Questionnaire-39; Questionnaire on Acquired Speech Disorders; EuroQol-5D-5 L; ICECAP-O; resource utilisation; adverse events and carer quality of life. Mixed-methods process and health economic evaluations will take place alongside the trial. Assessments will be completed before randomisation and at 3, 6 and 12 months after randomisation. The trial started in December 2015 and will run for 77 months. Recruitment will take place in approximately 42 sites around the UK. Discussion: The trial will test the hypothesis that SLT is effective for the treatment of speech or voice problems in people with PD compared to no SLT. It will further test whether NHS SLT or LSVT LOUD® provide greater benefit and determine the cost-effectiveness of both interventions. Trial registration: International Standard Randomised Controlled Trials Number (ISRCTN) Registry, ID: 12421382. Registered on 18 April 2016

A multicentre, randomised controlled trial to compare the clinical and cost-effectiveness of Lee Silverman Voice Treatment versus standard NHS Speech and Language Therapy versus control in Parkinson’s disease: a study protocol for a randomised controlled trial

Abstract: Background: Parkinson’s disease (PD) affects approximately 145,519 people in the UK. Speech impairments are common with a reported prevalence of 68%, which increase physical and mental demands during conversation, reliance on family and/or carers, and the likelihood of social withdrawal reducing quality of life. In the UK, two approaches to Speech and Language Therapy (SLT) intervention are commonly available: National Health Service (NHS) SLT or Lee Silverman Voice Treatment (LSVT LOUD®). NHS SLT is tailored to the individuals’ needs per local practice typically consisting of six to eight weekly sessions; LSVT LOUD® comprises 16 sessions of individual treatment with home-based practice over 4 weeks. The evidence-base for their effectiveness is inconclusive. Methods/design: PD COMM is a phase III, multicentre, three-arm, unblinded, randomised controlled trial. Five hundred and forty-six people with idiopathic PD, reporting speech or voice problems will be enrolled. We will exclude those with a diagnosis of dementia, laryngeal pathology or those who have received SLT for speech problems in the previous 2 years. Following informed consent and completion of baseline assessments, participants will be randomised in a 1:1:1 ratio to no-intervention control, NHS SLT or LSVT LOUD® via a central computer-generated programme, using a minimisation procedure with a random element, to ensure allocation concealment. Participants randomised to the intervention groups will start treatment within 4 (NHS SLT) or 7 (LSVT LOUD®) weeks of randomisation. Primary outcome: Voice Handicap Index (VHI) total score at 3 months. Secondary outcomes include: VHI subscales, Parkinson’s Disease Questionnaire-39; Questionnaire on Acquired Speech Disorders; EuroQol-5D-5 L; ICECAP-O; resource utilisation; adverse events and carer quality of life. Mixed-methods process and health economic evaluations will take place alongside the trial. Assessments will be completed before randomisation and at 3, 6 and 12 months after randomisation. The trial started in December 2015 and will run for 77 months. Recruitment will take place in approximately 42 sites around the UK. Discussion: The trial will test the hypothesis that SLT is effective for the treatment of speech or voice problems in people with PD compared to no SLT. It will further test whether NHS SLT or LSVT LOUD® provide greater benefit and determine the cost-effectiveness of both interventions. Trial registration: International Standard Randomised Controlled Trials Number (ISRCTN) Registry, ID: 12421382. Registered on 18 April 2016

Insights into environmental drivers of acoustic angular response using a self-organising map and hierarchical clustering

Acoustic backscatter from the seafloor is a complex function of signal frequency, seabed roughness, grain size distribution, benthos, bioturbation, volume reverberation, and other factors. Angular response is the variation in acoustic backscatter with incident angle and is considered be an intrinsic property of the seabed. An unsupervised classification technique combining a self-organising map (SOM) and hierarchical clustering was used to create an angular response facies map and explore the relationships between acoustic facies and ground truth data. Cluster validation routines indicated that a two cluster solution was optimal and separated sediment dominated environments from mixtures of sediment and hard ground. Low cluster separation limited cluster validation routines from identifying fine cluster structure visible with an AR density plot. Cluster validation, aided by a visual comparison with an AR density plot, indicated that a 14 cluster solution was also a suitable representation of the input dataset. Clusters that were a mixture of hard and unconsolidated substrates displayed an increase in backscatter with an increase in the occurrence of hard ground and highlighted the sensitivity of AR curves to the presence of even modest amounts of hard ground. Remapping video observations and sediment data onto the SOM matrix is innovative and depicts the relationship between ground truth data and cluster structure. Mapping environmental variables onto the SOM matrix can show broad trends and localised peaks and troughs and display the variability of ground truth data within designated clusters. These variables, when linked to AR curves via clusters, can indicate how environmental factors influence the shape of the curves. Once these links are established they can be incorporated into improved geoacoustic models that replicate field observations

Predictive modelling of seabed sediment parameters using multibeam acoustic data: a case study on the Carnarvon Shelf, Western Australia

Seabed sediment textural parameters such as mud, sand and gravel content can be useful surrogates for predicting patterns of benthic biodiversity. Multibeam sonar mapping can provide near-complete spatial coverage of high-resolution bathymetry and backscatter data that are useful in predicting sediment parameters. Multibeam acoustic data collected across a ∼1000 km2 area of the Carnarvon Shelf, Western Australia, were used in a predictive modelling approach to map eight seabed sediment parameters. Four\ud machine learning models were used for the predictive modelling: boosted decision tree, random forest decision tree, support vector machine and generalised regression neural network. The results indicate overall satisfactory statistical performance, especially for %Mud, %Sand, Sorting, Skewness and Mean Grain Size. The study also demonstrates that predictive modelling using the combination of machine learning models has provided the ability to generate prediction uncertainty maps. However, the single models were shown to have overall better prediction performance than the combined models.\ud Another important finding was that choosing an appropriate set of explanatory variables, through a manual feature selection process, was a critical step for optimising\ud model performance. In addition, machine learning models were able to identify important explanatory variables, which are useful in identifying underlying environmental processes and checking predictions against the existing knowledge of the study area.\ud The sediment prediction maps obtained in this study provide reliable coverage of key physical variables that will be incorporated into the analysis of covariance of physical\ud and biological data for this area