Abstract
Mapping individual differences in behavior is fundamental to personalized neuroscience, but quantifying complex behavior in real world settings remains a challenge. While mobility patterns captured by smartphones have increasingly been linked to a range of psychiatric symptoms, existing research has not specifically examined whether individuals have person-specific mobility patterns. We collected over 3000 days of mobility data from a sample of 41 adolescents and young adults (age 17–30 years, 28 female) with affective instability. We extracted summary mobility metrics from GPS and accelerometer data and used their covariance structures to identify individuals and calculated the individual identification accuracy—i.e., their “footprint distinctiveness”. We found that statistical patterns of smartphone-based mobility features represented unique “footprints” that allow individual identification (p < 0.001). Critically, mobility footprints exhibited varying levels of person-specific distinctiveness (4–99%), which was associated with age and sex. Furthermore, reduced individual footprint distinctiveness was associated with instability in affect (p < 0.05) and circadian patterns (p < 0.05) as measured by environmental momentary assessment. Finally, brain functional connectivity, especially those in the somatomotor network, was linked to individual differences in mobility patterns (p < 0.05). Together, these results suggest that real-world mobility patterns may provide individual-specific signatures relevant for studies of development, sleep, and psychopathology.
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Code availability
The code for GPS data preprocessing, mobility metric extraction, individual identification, additional analysis, and data visualization is available in R on github: https://github.com/PennLINC/footprinting. Code notebook is available at: https://pennlinc.github.io/footprinting/.
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Funding
This study was supported by the AE foundation, a Young Investigator Grant from the Brain & Behavior Research Foundation, as well as grants from National Institute of Health: R01MH107703, R01MH112847, R01MH113550, R37MH125829, R01EB022573, R01MH120482, RF1MH116920, R01MH116884, Intramural Research Program, and K01DA047417. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.
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CHX, IB, and TDS made substantial contributions to the conception or design of the work; CHX, IB, TMT, AA, JTB, DSB, MAB, MEC, ZC, EL, SL, DML, MLM, TMM, KM, KP, AP, DRR, SRG, DHW, LHU and TDS contributed substantially to the acquisition, analysis, or interpretation of data for the work. All authors contributed to drafting the work, revising it critically for important intellectual content, and gave final approval of the version to be published. All authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
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Xia, C.H., Barnett, I., Tapera, T.M. et al. Mobile footprinting: linking individual distinctiveness in mobility patterns to mood, sleep, and brain functional connectivity. Neuropsychopharmacol. 47, 1662–1671 (2022). https://doi.org/10.1038/s41386-022-01351-z
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DOI: https://doi.org/10.1038/s41386-022-01351-z
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