Victor P Cornet1, Richard J Holden2. 1. Department of Human Centered Computing, Indiana University School of Informatics and Computing, Indianapolis, IN, USA. 2. Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indianapolis, IN, USA; Indiana University Center for Aging Research, Regenstrief Institute, Inc., Indianapolis, IN, USA. Electronic address: rjholden@iupui.edu.
Abstract
OBJECTIVE: To review published empirical literature on the use of smartphone-based passive sensing for health and wellbeing. MATERIAL AND METHODS: A systematic review of the English language literature was performed following PRISMA guidelines. Papers indexed in computing, technology, and medical databases were included if they were empirical, focused on health and/or wellbeing, involved the collection of data via smartphones, and described the utilized technology as passive or requiring minimal user interaction. RESULTS: Thirty-five papers were included in the review. Studies were performed around the world, with samples of up to 171 (median n = 15) representing individuals with bipolar disorder, schizophrenia, depression, older adults, and the general population. The majority of studies used the Android operating system and an array of smartphone sensors, most frequently capturing accelerometry, location, audio, and usage data. Captured data were usually sent to a remote server for processing but were shared with participants in only 40% of studies. Reported benefits of passive sensing included accurately detecting changes in status, behavior change through feedback, and increased accountability in participants. Studies reported facing technical, methodological, and privacy challenges. DISCUSSION: Studies in the nascent area of smartphone-based passive sensing for health and wellbeing demonstrate promise and invite continued research and investment. Existing studies suffer from weaknesses in research design, lack of feedback and clinical integration, and inadequate attention to privacy issues. Key recommendations relate to developing passive sensing strategies matching the problem at hand, using personalized interventions, and addressing methodological and privacy challenges. CONCLUSION: As evolving passive sensing technology presents new possibilities for health and wellbeing, additional research must address methodological, clinical integration, and privacy issues. Doing so depends on interdisciplinary collaboration between informatics and clinical experts.
OBJECTIVE: To review published empirical literature on the use of smartphone-based passive sensing for health and wellbeing. MATERIAL AND METHODS: A systematic review of the English language literature was performed following PRISMA guidelines. Papers indexed in computing, technology, and medical databases were included if they were empirical, focused on health and/or wellbeing, involved the collection of data via smartphones, and described the utilized technology as passive or requiring minimal user interaction. RESULTS: Thirty-five papers were included in the review. Studies were performed around the world, with samples of up to 171 (median n = 15) representing individuals with bipolar disorder, schizophrenia, depression, older adults, and the general population. The majority of studies used the Android operating system and an array of smartphone sensors, most frequently capturing accelerometry, location, audio, and usage data. Captured data were usually sent to a remote server for processing but were shared with participants in only 40% of studies. Reported benefits of passive sensing included accurately detecting changes in status, behavior change through feedback, and increased accountability in participants. Studies reported facing technical, methodological, and privacy challenges. DISCUSSION: Studies in the nascent area of smartphone-based passive sensing for health and wellbeing demonstrate promise and invite continued research and investment. Existing studies suffer from weaknesses in research design, lack of feedback and clinical integration, and inadequate attention to privacy issues. Key recommendations relate to developing passive sensing strategies matching the problem at hand, using personalized interventions, and addressing methodological and privacy challenges. CONCLUSION: As evolving passive sensing technology presents new possibilities for health and wellbeing, additional research must address methodological, clinical integration, and privacy issues. Doing so depends on interdisciplinary collaboration between informatics and clinical experts.
Authors: Elizabeth O Lillie; Bradley Patay; Joel Diamant; Brian Issell; Eric J Topol; Nicholas J Schork Journal: Per Med Date: 2011-03 Impact factor: 2.512
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: Sohrab Saeb; Mi Zhang; Christopher J Karr; Stephen M Schueller; Marya E Corden; Konrad P Kording; David C Mohr Journal: J Med Internet Res Date: 2015-07-15 Impact factor: 5.428
Authors: William T Riley; April Oh; Will M Aklin; Joel T Sherrill; Dana L Wolff-Hughes; Augie Diana; James A Griffin; Rebecca A Campo Journal: J Pediatr Psychol Date: 2019-04-01
Authors: Richard J Holden; Noll L Campbell; Ephrem Abebe; Daniel O Clark; Denisha Ferguson; Kunal Bodke; Malaz A Boustani; Christopher M Callahan Journal: Res Social Adm Pharm Date: 2019-02-26
Authors: Lisa A Marsch; Aimee Campbell; Cynthia Campbell; Ching-Hua Chen; Emre Ertin; Udi Ghitza; Chantal Lambert-Harris; Saeed Hassanpour; August F Holtyn; Yih-Ing Hser; Petra Jacobs; Jeffrey D Klausner; Shea Lemley; David Kotz; Andrea Meier; Bethany McLeman; Jennifer McNeely; Varun Mishra; Larissa Mooney; Edward Nunes; Chrysovalantis Stafylis; Catherine Stanger; Elizabeth Saunders; Geetha Subramaniam; Sean Young Journal: J Subst Abuse Treat Date: 2020-03
Authors: Carmen Rosa; Lisa A Marsch; Erin L Winstanley; Meg Brunner; Aimee N C Campbell Journal: Contemp Clin Trials Date: 2020-11-17 Impact factor: 2.226