This technology has promise, and I had recently the pleasure of discussing opportunities for this approach in Alzheimer’s disease with Steve Friend. With the onset of Alzheimer’s, patients go out less and their world begins to shrink. This change in behavior could be neatly captured with any GPS device. Another big advantage is that this kind of data collection is passive, which when working with Alzheimer’s patients is an advantage. In the mild stages of the disease, active interaction with a digital device would still be achievable but, as dementia progresses, anything beyond simple or passive data collection will be a challenge.
Data sharing is a major asset in uncovering more about the consequences of the disease. The success of the ADNI and AIBL studies is a good indicator of data-sharing benefits. Clearly, there are issues of privacy. One revelation of meeting with Apple representatives last year was the extraordinary amount of information about one’s activities that just carrying a GPS-enabled device can yield. As always with digital information, there will need to be clear safeguards in place to protect privacy.
This seems to me an avenue of research well worth pursuing. Last year at the European Games for Health event, I organized a symposium on digital initiatives in CNS indications. The consensus from our discussion was that this approach may yield interesting information about patient behavior, as well as their responses to treatment.
This type of app has potential in other neurodegenerative diseases such as Alzheimer’s. The smartphone, with all its sensors, is a very powerful data-collection tool. We are excited to see this approach used and validated broadly, since it is related to some of our research going back to 2010. At Ginger.io, we have collected data on, and studied, various diseases, with a core focus on mental health conditions such as depression and anxiety. We have partnered with more than 40 medical institutions in the United States and in all of these cases, data collection using a smartphone has been a central tenet that has appealed to researchers, data scientists, and the medical community.
In terms of potential drawbacks, for example getting people to stick with the app, there are always challenges to user engagement with this kind of approach. Making it useful for the users is important. We have found that for users to stay engaged for long periods and continue to enter data, they need to find the tool and self-reflection important in managing their condition. The advantage of a large-scale research data collection exercise like this one is that even if a subset of users don't stick around, the ones who do can provide valuable data.
With regard to the data-sharing model developed here, the researchers provided participants the option to choose between narrow sharing with just this research team and broad sharing with quality researchers worldwide. The study protocol was approved by an IRB. In particular, (i) the user's permission was obtained, (ii) user information seems to have been de-identified, and (iii) ethical guidelines were provided for other researchers. At first glance, these measures make the data sharing reasonable and alleviate privacy concerns because they offer users the opportunity to choose how their data is shared.
This could be a useful model for the research community to follow when collecting this kind of data. Many research groups have been using smartphones to gather data over the last few years, whether through surveys or sensor data. At Ginger.io, we have worked with several such researchers. Smartphones allow researchers to gather data at a scale that was not feasible in the past. As a result, the medical and research communities have a chance to leverage the power of these devices to advance science and impact people's lives.
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Metis Cognition Ltd.
This technology has promise, and I had recently the pleasure of discussing opportunities for this approach in Alzheimer’s disease with Steve Friend. With the onset of Alzheimer’s, patients go out less and their world begins to shrink. This change in behavior could be neatly captured with any GPS device. Another big advantage is that this kind of data collection is passive, which when working with Alzheimer’s patients is an advantage. In the mild stages of the disease, active interaction with a digital device would still be achievable but, as dementia progresses, anything beyond simple or passive data collection will be a challenge.
Data sharing is a major asset in uncovering more about the consequences of the disease. The success of the ADNI and AIBL studies is a good indicator of data-sharing benefits. Clearly, there are issues of privacy. One revelation of meeting with Apple representatives last year was the extraordinary amount of information about one’s activities that just carrying a GPS-enabled device can yield. As always with digital information, there will need to be clear safeguards in place to protect privacy.
This seems to me an avenue of research well worth pursuing. Last year at the European Games for Health event, I organized a symposium on digital initiatives in CNS indications. The consensus from our discussion was that this approach may yield interesting information about patient behavior, as well as their responses to treatment.
View all comments by John HarrisonGinger.io
This type of app has potential in other neurodegenerative diseases such as Alzheimer’s. The smartphone, with all its sensors, is a very powerful data-collection tool. We are excited to see this approach used and validated broadly, since it is related to some of our research going back to 2010. At Ginger.io, we have collected data on, and studied, various diseases, with a core focus on mental health conditions such as depression and anxiety. We have partnered with more than 40 medical institutions in the United States and in all of these cases, data collection using a smartphone has been a central tenet that has appealed to researchers, data scientists, and the medical community.
In terms of potential drawbacks, for example getting people to stick with the app, there are always challenges to user engagement with this kind of approach. Making it useful for the users is important. We have found that for users to stay engaged for long periods and continue to enter data, they need to find the tool and self-reflection important in managing their condition. The advantage of a large-scale research data collection exercise like this one is that even if a subset of users don't stick around, the ones who do can provide valuable data.
With regard to the data-sharing model developed here, the researchers provided participants the option to choose between narrow sharing with just this research team and broad sharing with quality researchers worldwide. The study protocol was approved by an IRB. In particular, (i) the user's permission was obtained, (ii) user information seems to have been de-identified, and (iii) ethical guidelines were provided for other researchers. At first glance, these measures make the data sharing reasonable and alleviate privacy concerns because they offer users the opportunity to choose how their data is shared.
This could be a useful model for the research community to follow when collecting this kind of data. Many research groups have been using smartphones to gather data over the last few years, whether through surveys or sensor data. At Ginger.io, we have worked with several such researchers. Smartphones allow researchers to gather data at a scale that was not feasible in the past. As a result, the medical and research communities have a chance to leverage the power of these devices to advance science and impact people's lives.
View all comments by Sai MoturuMake a Comment
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