New 'app' tracks Parkinson’s symptoms for clinical trial
Posted by
16-Nov-2015
When a new medicine or medical device is being tested in people with long-term illnesses, such as Parkinson’s disease, it is essential that patients’ symptoms are recorded so that doctors can study how patients are doing.
Patient diaries are an established way to record symptoms of long-term illnesses and usually come in paper form. However, paper-based patient diaries are imperfect.
For a start, experience shows that patients often do not stick with their diaries. They may begin a trial with great enthusiasm for recording their symptoms but, with the best will in the world, their commitment to diary-keeping tends to fade over time. Patients can lose interest and diaries can get lost, meaning data is not collected.
There are also concerns about accuracy. Patient diary entries are not always made in real time. When patients fill in a diary in retrospect, the risk of error is higher.
At UCB, we are always looking for innovative solutions that solve problems and give us scope to do more than ever before. That is why we are using a very simple but custom-made patient diary app to monitor Parkinson’s symptoms as part of a clinical study.
The study will assess the feasibility of a prototype medical device (the MC10 wearable sensor patch) that will monitor physiological parameters, such as limb movement. The device is being developed to measure and detect patterns in motor symptoms of patients suffering from Parkinson’s disease.
This is the first time UCB has used an app in this way. Not only does it overcome many of the limitations of paper-based diaries, it allows us to do things that were never before possible. For example, the app can include reminders and instructional videos on how to complete the diary, to support patients during the clinical trial.
The data captured by the patient diary app, together with some more traditional physician-scored symptom measures, will enable the development of intelligent algorithms that will be able to extract clinical symptoms from the data generated by the wearable sensor patch.
The potential for more accurate, real-time data collection by the patient in the real-world, enhances the quality of data feeding the algorithms, and thus the ability of the sensor to more accurately detect and monitor specific motor symptoms.
The user-friendly diary app can be carried by the patient throughout the study and they can enter information either when prompted or when they notice a change in their symptoms.
In addition to being more timely and accurate than paper-based diaries, the beauty of this new patient diary app is that it “brings the trial to the patient”. Rather than patients having input only when they visit their healthcare provider, they are participating throughout the process thanks to this easy-to-use technology.
Better quality diary information will lead to better quality clinical data and smarter decision-making. At UCB, we believe that this can ultimately improve outcomes for patients – and that is what we are here to do.
For more on UCB’s collaboration with MC10, read our articles on wearable sensors and how real-time data can make medicine smarter.
Patient diaries are an established way to record symptoms of long-term illnesses and usually come in paper form. However, paper-based patient diaries are imperfect.
For a start, experience shows that patients often do not stick with their diaries. They may begin a trial with great enthusiasm for recording their symptoms but, with the best will in the world, their commitment to diary-keeping tends to fade over time. Patients can lose interest and diaries can get lost, meaning data is not collected.
There are also concerns about accuracy. Patient diary entries are not always made in real time. When patients fill in a diary in retrospect, the risk of error is higher.
At UCB, we are always looking for innovative solutions that solve problems and give us scope to do more than ever before. That is why we are using a very simple but custom-made patient diary app to monitor Parkinson’s symptoms as part of a clinical study.
The study will assess the feasibility of a prototype medical device (the MC10 wearable sensor patch) that will monitor physiological parameters, such as limb movement. The device is being developed to measure and detect patterns in motor symptoms of patients suffering from Parkinson’s disease.
This is the first time UCB has used an app in this way. Not only does it overcome many of the limitations of paper-based diaries, it allows us to do things that were never before possible. For example, the app can include reminders and instructional videos on how to complete the diary, to support patients during the clinical trial.
The data captured by the patient diary app, together with some more traditional physician-scored symptom measures, will enable the development of intelligent algorithms that will be able to extract clinical symptoms from the data generated by the wearable sensor patch.
The potential for more accurate, real-time data collection by the patient in the real-world, enhances the quality of data feeding the algorithms, and thus the ability of the sensor to more accurately detect and monitor specific motor symptoms.
The user-friendly diary app can be carried by the patient throughout the study and they can enter information either when prompted or when they notice a change in their symptoms.
In addition to being more timely and accurate than paper-based diaries, the beauty of this new patient diary app is that it “brings the trial to the patient”. Rather than patients having input only when they visit their healthcare provider, they are participating throughout the process thanks to this easy-to-use technology.
Better quality diary information will lead to better quality clinical data and smarter decision-making. At UCB, we believe that this can ultimately improve outcomes for patients – and that is what we are here to do.
For more on UCB’s collaboration with MC10, read our articles on wearable sensors and how real-time data can make medicine smarter.
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