Choosing the right time granularity for analysis of digital biomarker trajectories. Issue 1 (18th December 2020)

• Record Type: Journal Article
• Authors: Wakim, Nicole I.; Braun, Thomas M.; Kaye, Jeffrey A.; Dodge, Hiroko H.
• Is Part Of: Alzheimer's & dementia. Volume 6:Issue 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-18
• Subjects: dementia -- exploratory analysis -- high‐frequency data -- longitudinal data -- repeated measures; Dementia -- Periodicals; Dementia -- Treatment -- Periodicals; Alzheimer's disease -- Treatment -- Periodicals; Alzheimer's disease -- Periodicals; 616.831
• DOI: 10.1002/trc2.12094
• Languages: English
• ISSNs: 2352-8737

Abstract

Abstract: Introduction: The use of digital biomarker data in dementia research provides the opportunity for frequent cognitive and functional assessments that was not previously available using conventional approaches. Assessing high‐frequency digital biomarker data can potentially increase the opportunities for early detection of cognitive and functional decline because of improved precision of person‐specific trajectories. However, we often face a decision to condense time‐stamped data into a coarser time granularity, defined as the frequency at which measurements are observed or summarized, for statistical analyses. It is important to find a balance between ease of analysis by condensing data and the integrity of the data, which is reflected in a chosen time granularity. Methods: In this paper, we discuss factors that need to be considered when faced with a time granularity decision. These factors include follow‐up time, variables of interest, pattern detection, and signal‐to‐noise ratio. Results: We applied our procedure to real‐world data which include longitudinal in‐home monitored walking speed. The example shed lights on typical problems that data present and how we could use the above factors in exploratory analysis to choose an appropriate time granularity. Discussion: Further work is required to explore issues with missing data and computational efficiency.