Mobile footprinting: linking individual distinctiveness in mobility patterns to mood, sleep, and brain functional connectivity

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...

Full description

Saved in:
Bibliographic Details
Published in:Neuropsychopharmacology (New York, N.Y.) N.Y.), 2022-08, Vol.47 (9), p.1662-1671
Main Authors: Xia, Cedric Huchuan, Barnett, Ian, Tapera, Tinashe M, Adebimpe, Azeez, Baker, Justin T, Bassett, Danielle S, Brotman, Melissa A, Calkins, Monica E, Cui, Zaixu, Leibenluft, Ellen, Linguiti, Sophia, Lydon-Staley, David M, Martin, Melissa Lynne, Moore, Tyler M, Murtha, Kristin, Piiwaa, Kayla, Pines, Adam, Roalf, David R, Rush-Goebel, Sage, Wolf, Daniel H, Ungar, Lyle H, Satterthwaite, Theodore D
Format: Article
Language:English
Subjects:
Accelerometers
Adolescent
Adult
Affect
Behavior
Brain
Brain mapping
Brain research
Circadian rhythm
Circadian rhythms
Epidemiology
Female
Footprinting
Global positioning systems
GPS
Humans
Informatics
Medicine
Mental disorders
Mobility
Nervous system
Neural networks
Neurosciences
Personality disorders
Psychiatry
Psychopathology
Sleep
Smartphone
Smartphones
Teenagers
Young Adult
Young adults
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 
ISSN:0893-133X
1740-634X
DOI:10.1038/s41386-022-01351-z