Harnessing Mobile Devices for Nervous System Disorders Proceedings of a Workshop (2018) / Chapter Skim
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3 Transforming Digital Data into Insight: Collection, Analysis, Standardization, and Validation
3 Transforming Digital Data into Insight: Collection, Analysis, Standardization, and Validation
Pages 9-20
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From page 9... ...
. • A higher level of data validation involves correlating digital measures to gold-standard measures or to "ground truth" (Brunner, Marks)
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From page 10... ...
that would transform aggregated digital data into knowledge that would help patients through the development of predictive algorithms. However, Onnela noted that integrating raw data collected from different kinds of devices would be extremely challenging because of the difficulty of convincing device manufacturers and researchers to share their data as well as the complex statistical approaches needed.
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From page 11... ...
Concepts derived from Manji et al., 2014. While recognizing the challenges associated with collecting and analyzing digital data, Onnela noted that digital phenotyping has three distinct advantages for research: it facilitates the inclusion of many participants, reduces the burden on participants by enabling the passive collection of data, and enables researchers to conduct large population-level studies with data over a long period of time, including before and after an event or intervention occurs.
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From page 12... ...
Onnella described that in a pilot study designed to demon strate the face validity of the approach, the researchers studied pain in a group of patients after spine surgery. They showed that the patient's subjective rating of pain on a scale of 0 to 10 was significantly associated with reduced mobility assessed using global positioning system summary statistics.
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From page 13... ...
For example, if a researcher wants to study various sleep parameters, combining data from a wearable and a bed sensor may provide a solution. Determining Which Data to Collect and How to Get Them William Marks noted that one of the advantages of wearable devices is that they enable the unobtrusive collection of continuous or nearcontinuous digital data at home in a person's normal environment or elsewhere during the normal course of the day.
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From page 14... ...
. While these platforms are designed to enable people to stay connected with others, build new connections, and share information about their lives, Munmun De Choudhury, assistant professor in the School of Interactive Computing at Georgia Tech, said they also provide rich data about people's behaviors and moods, and thus may be helpful in assessing mental health, identifying early warning signals and risk factors, and even may enable early diagnosis.
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From page 15... ...
She went on to show evidence from one study suggesting that social media data could be used to efficiently develop an index for depression at the population level. In a subsequent study, De Choudhury and colleagues mined data from the social media platform Reddit to identify markers of suicidal ideation (De Choudhury et al., 2016)
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From page 16... ...
Alternatives to tying these measures to ground truth may be to look at how they relate to some disordered behavior or diagnosable illness, said Hyman, or how they may help select interventions or make decisions regarding incremental care, said Deborah Estrin, professor of computer science at Cornell Tech. Connecting digital measures with what is known about the biology of neuropsychiatric disorders is key to making these technologies useful, said Tanzeem Choudhury, associate professor in computing and information sciences at Cornell University.
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From page 17... ...
Even companies developing proprietary interventions can benefit from shared data, he said. Representing complex multidimensional data -- for example, data from sensors, clinical assessments, and other outcome measures -- in a format that is easy to understand may be accomplished using visual approaches that transform group data using dimensionality reduction and identifying clusters that represent certain features of a population (e.g., persons with a certain condition)
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From page 18... ...
Smartphones, for example, combine multiple integrated sensors that detect light, touch, movement, position, connectivity, sound, and other data that may be relevant to an individual's health status, said Matos. At Roche, they are conducting the FLOODLIGHT trial2 using a mobile smartphone app that aims to use passive remote monitoring combined with active tests to monitor disease activity for 1 year in 60 patients with multiple sclerosis 2For more information, see https://floodlightopen.com (accessed July 2, 2018)
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From page 19... ...
FIGURE 3-3 FLOODLIGHT Digital Biomarker analysis from adherence to augmentation. Smartphone data collected in the FLOODLIGHT trial have been shown to improve adherence, correlate with standard clinical scales, and provide a much more complete view of the progression of multiple sclerosis symptoms in comparison to assessments conducted only at sporadic clinic visits.
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