Speech and language therapy mobile health application

1. Speech and language therapy mobile health application Roger Eglin

2. Speech and language therapy m-Health • Background: A speech and language therapy m-Health (mobile health) application has been developed for people with Parkinson’s and dysarthria. • Approximately 70% of people with Parkinson’s encounter difficulties with communication. The majority of these will have lost the implicit cues that enable them to monitor speech volume, and speech becomes progressively quieter. • They are referred to Speech Therapy, but therapists’ time is finite and there can typically be six or 12 months between scheduled appointments. • Additionally, the current treatment with the most efficacy data, Lee Silverman Voice Treatment (LSVT), is very time-intensive, requiring 16 hourly treatment sessions spread over four weeks.

3. The application • The application provides volume feedback visually, so that users have an explicit cue to aid volume regulation. • There are two application components: one that simply provides visual feedback and nothing else; the other is a training element providing content for vocal practice, and encouragement to speak louder. • The previous successful study’s carried out in 2011-2013 objectives were to test the application with people with Parkinson’s, and to examine volume outcomes and engagement with the application over an extended usage period. • The prediction was that people with Parkinson’s, generally older adults, can learn to use a Smartphone application as part of their therapy.

4. Methods and results • Methods: Fifty-nine people with Parkinson’s were introduced to the application and to either an iPhone or iPod device running it. They were then left with the device and application, for an average of 9.2 days, before being met again for collection. Participants were advised that they might like to use the application three times per day, but that they could use it as much or as little as they wanted. Usage data was then collected by the application itself, tracking whenever it was used, how long for, and the mean volume for each usage trial. Results: There was no difference between usage of the application’s two components . Combining usage of both components the application was used a mean of 3.09 times per day. The results revealed some application development issues with capturing all available data created during usage, including some volume information. Some data loss was of volume information recorded by the application. Available data showed a significant volume increase between the first and second halves of the period a participant had the device.

5. Conclusions • Use of this vocal loudness feedback application led to increased speech volume in people with Parkinson’s while using this method. • The participants became more proficient using the application over time. • The combined usage of the application components was at roughly the rate suggested by the researchers, which is encouraging for future engagement with remote therapy. • Data collected by the application could also provide Speech Therapists with insight into patient progress between appointments, highlighting potential support needs. • We wish to automate the data collection and make it easy to view and share. The data will be owned and collected by the user. This will reduce many of our ethical and legal issues by making the data user centred. • Over 100 users to date

6. Overview of the wider picture of the work we have done so far: • We have developed (in collaboration with NHS at Kings College London and the Parkinson’s UK PUK charity) a mobile app for people with Parkinson’s who suffer from dysarthria. • The support app can be used in a hospital for therapy with a clinician or self directed at home by patient unsupervised. • The advantage of this app is that it does not require lengthy and expensive training to use as other commercial approaches do (e.g. LSVT). • We have tested the app in a clinical location with and without a Clinician present. We have also extended this study “in the home” location with about 60 people with Parkinson’s (PWP). This study was sponsored by PUK and received UoP and NHS ethical approval. • The PUK charity has asked that we add their name to the app as they wish to endorse the app and be associated with it. • They have also asked if they can use it on their conference stand at the Parkinson’s worldwide conference this September 2013. This conference will be attended by PWP, clinicians and other device specialists and drug companies.

7. Overview of the wider picture of the work we have done so far: • The NHS has also contacted us to ask if it can be promoted on their website for “NHS supported apps” as part of their technology innovation drive policy. • We have also been approached by Michael J. Fox foundation in the USA who has offered to place the app on their recommended app list. • We have received positive feedback from our studies on the app and its development. • However, we see that the real benefit of what we have done so far is a web link to show the app usage and its use as a data support tool and data management tool.

8. Future goal • We wish to commercialise the app in this regards, allowing this data to be viewed by the user. In tandem we would also like to have a clinician view. In accordance with our user centred approach to ownership of data, the clinician view will be enabled by the user so that the PWP grant user privileges to the clinician. This may obviate many of the data confidentiality issues that might otherwise be encountered. • This will allow clinicians to engage with PWP and manage their client bank in a much more efficient manner. • We can sell this as a package. Users can also enable the data to be accessed to allow further research. • It will also allow us to generate both income and data to support research. Many other organisations e.g. PUK and KCH would like to have research into data “at home” (telemedicine) for usage and therapy.

9. Questions?

10. Engaging with game Generating data 6 – e.g. participating in the game ethos without playing and 3 – e.g. thinking about 1 - Generating data acceptance of the game the game and rules, goals and so on generating data 5 - Fully participating in the game (data generating, observing and ethos) 2 – e.g. observing 4 – e.g. observing former players the data being generated 7 – e.g. perceiving data generation, data in the game by players, potential players, former players Receiving feedback