Paolo Fraccaro1,2, Anna Beukenhorst3, Matthew Sperrin1, Simon Harper4, Jasper Palmier-Claus5,6, Shôn Lewis5, Sabine N Van der Veer1,3,7, Niels Peek1,7,8. 1. Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom. 2. Hartree Centre STFC Laboratory, IBM Research UK, Warrington, United Kingdom. 3. Centre for Epidemiology, Division of Musculoskeletal & Dermatological Sciences, University of Manchester, Manchester, United Kingdom. 4. School of Computer Science, University of Manchester, Manchester, United Kingdom. 5. Division of Psychology & Mental Health, University of Manchester, Manchester, United Kingdom. 6. Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom. 7. National Institute of Health Research Greater Manchester Patient Safety Translational Research Centre, University of Manchester, Manchester, United Kingdom. 8. National Institute of Health Research Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom.
Abstract
OBJECTIVE: The study sought to explore to what extent geolocation data has been used to study serious mental illness (SMI). SMIs such as bipolar disorder and schizophrenia are characterized by fluctuating symptoms and sudden relapse. Currently, monitoring of people with an SMI is largely done through face-to-face visits. Smartphone-based geolocation sensors create opportunities for continuous monitoring and early intervention. MATERIALS AND METHODS: We searched MEDLINE, PsycINFO, and Scopus by combining terms related to geolocation and smartphones with SMI concepts. Study selection and data extraction were done in duplicate. RESULTS: Eighteen publications describing 16 studies were included in our review. Eleven studies focused on bipolar disorder. Common geolocation-derived digital biomarkers were number of locations visited (n = 8), distance traveled (n = 8), time spent at prespecified locations (n = 7), and number of changes in GSM (Global System for Mobile communications) cell (n = 4). Twelve of 14 publications evaluating clinical aspects found an association between geolocation-derived digital biomarker and SMI concepts, especially mood. Geolocation-derived digital biomarkers were more strongly associated with SMI concepts than other information (eg, accelerometer data, smartphone activity, self-reported symptoms). However, small sample sizes and short follow-up warrant cautious interpretation of these findings: of all included studies, 7 had a sample of fewer than 10 patients and 11 had a duration shorter than 12 weeks. CONCLUSIONS: The growing body of evidence for the association between SMI concepts and geolocation-derived digital biomarkers shows potential for this instrument to be used for continuous monitoring of patients in their everyday lives, but there is a need for larger studies with longer follow-up times.
OBJECTIVE: The study sought to explore to what extent geolocation data has been used to study serious mental illness (SMI). SMIs such as bipolar disorder and schizophrenia are characterized by fluctuating symptoms and sudden relapse. Currently, monitoring of people with an SMI is largely done through face-to-face visits. Smartphone-based geolocation sensors create opportunities for continuous monitoring and early intervention. MATERIALS AND METHODS: We searched MEDLINE, PsycINFO, and Scopus by combining terms related to geolocation and smartphones with SMI concepts. Study selection and data extraction were done in duplicate. RESULTS: Eighteen publications describing 16 studies were included in our review. Eleven studies focused on bipolar disorder. Common geolocation-derived digital biomarkers were number of locations visited (n = 8), distance traveled (n = 8), time spent at prespecified locations (n = 7), and number of changes in GSM (Global System for Mobile communications) cell (n = 4). Twelve of 14 publications evaluating clinical aspects found an association between geolocation-derived digital biomarker and SMI concepts, especially mood. Geolocation-derived digital biomarkers were more strongly associated with SMI concepts than other information (eg, accelerometer data, smartphone activity, self-reported symptoms). However, small sample sizes and short follow-up warrant cautious interpretation of these findings: of all included studies, 7 had a sample of fewer than 10 patients and 11 had a duration shorter than 12 weeks. CONCLUSIONS: The growing body of evidence for the association between SMI concepts and geolocation-derived digital biomarkers shows potential for this instrument to be used for continuous monitoring of patients in their everyday lives, but there is a need for larger studies with longer follow-up times.
Authors: Ian Barnett; John Torous; Patrick Staples; Luis Sandoval; Matcheri Keshavan; Jukka-Pekka Onnela Journal: Neuropsychopharmacology Date: 2018-02-22 Impact factor: 7.853
Authors: Dror Ben-Zeev; Rachel Brian; Rui Wang; Weichen Wang; Andrew T Campbell; Min S H Aung; Michael Merrill; Vincent W S Tseng; Tanzeem Choudhury; Marta Hauser; John M Kane; Emily A Scherer Journal: Psychiatr Rehabil J Date: 2017-04-03
Authors: Dror Ben-Zeev; Rui Wang; Saeed Abdullah; Rachel Brian; Emily A Scherer; Lisa A Mistler; Marta Hauser; John M Kane; Andrew Campbell; Tanzeem Choudhury Journal: Psychiatr Serv Date: 2015-12-15 Impact factor: 3.084
Authors: Ellen Frank; Isabella Soreca; Holly A Swartz; Andrea M Fagiolini; Alan G Mallinger; Michael E Thase; Victoria J Grochocinski; Patricia R Houck; David J Kupfer Journal: Am J Psychiatry Date: 2008-10-01 Impact factor: 18.112
Authors: Agnes Grünerbl; Amir Muaremi; Venet Osmani; Gernot Bahle; Stefan Ohler; Gerhard Tröster; Oscar Mayora; Christian Haring; Paul Lukowicz Journal: IEEE J Biomed Health Inform Date: 2014-07-25 Impact factor: 5.772
Authors: Andrew I Gumley; Simon Bradstreet; John Ainsworth; Stephanie Allan; Mario Alvarez-Jimenez; Maximillian Birchwood; Andrew Briggs; Sandra Bucci; Sue Cotton; Lidia Engel; Paul French; Reeva Lederman; Shôn Lewis; Matthew Machin; Graeme MacLennan; Hamish McLeod; Nicola McMeekin; Cathy Mihalopoulos; Emma Morton; John Norrie; Frank Reilly; Matthias Schwannauer; Swaran P Singh; Suresh Sundram; Andrew Thompson; Chris Williams; Alison Yung; Lorna Aucott; John Farhall; John Gleeson Journal: Health Technol Assess Date: 2022-05 Impact factor: 4.106
Authors: Michelle L Byrne; Monika N Lind; Sarah R Horn; Kathryn L Mills; Benjamin W Nelson; Melissa L Barnes; George M Slavich; Nicholas B Allen Journal: Digit Health Date: 2021-08-27