Systematic review of smartphone-based passive sensing for health and wellbeing

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.

[1]  Yvonne Rogers,et al.  HCI Theory: Classical, Modern, and Contemporary , 2012, HCI Theory.

[2]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[3]  H. Riper,et al.  Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study , 2016, Journal of medical Internet research.

[4]  Deborah Estrin,et al.  Small data, where n = me , 2014, Commun. ACM.

[5]  Kyung-Sup Kwak,et al.  The Internet of Things for Health Care: A Comprehensive Survey , 2015, IEEE Access.

[6]  Parisa Rashidi,et al.  The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions , 2014, Journal of medical Internet research.

[7]  Christine Cheng,et al.  Uncovering patterns of technology use in consumer health informatics , 2013, Wiley interdisciplinary reviews. Computational statistics.

[8]  Oscar Mayora-Ibarra,et al.  Mobile phones as medical devices in mental disorder treatment: an overview , 2014, Personal and Ubiquitous Computing.

[9]  C. Pollak,et al.  The role of actigraphy in the study of sleep and circadian rhythms. , 2003, Sleep.

[10]  Victor M. Montori,et al.  Minimally Disruptive Medicine: A Pragmatically Comprehensive Model for Delivering Care to Patients with Multiple Chronic Conditions , 2015, Healthcare.

[11]  Oscar Mayora-Ibarra,et al.  Smartphone-Based Recognition of States and State Changes in Bipolar Disorder Patients , 2015, IEEE Journal of Biomedical and Health Informatics.

[12]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement , 2009, BMJ : British Medical Journal.

[13]  P. Schulz,et al.  Mapping mHealth Research: A Decade of Evolution , 2013, Journal of medical Internet research.

[14]  Sjaak Brinkkemper,et al.  The sociability score: App-based social profiling from a healthcare perspective , 2016, Comput. Hum. Behav..

[15]  Mike Thelwall,et al.  Online Interventions for Social Marketing Health Behavior Change Campaigns: A Meta-Analysis of Psychological Architectures and Adherence Factors , 2011, Journal of medical Internet research.

[16]  Jung A Kim The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care , 2011 .

[17]  Wazir Zada Khan,et al.  Mobile Phone Sensing Systems: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[18]  Andrew T. Campbell,et al.  Mobile Behavioral Sensing for Outpatients and Inpatients With Schizophrenia. , 2016, Psychiatric services.

[19]  Mirza Mansoor Baig,et al.  Mobile healthcare applications: system design review, critical issues and challenges , 2014, Australasian Physical & Engineering Sciences in Medicine.

[20]  Robert A. Greenes,et al.  White Paper: Audacious Goals for Health and Biomedical Informatics in the New Millennium , 1998, J. Am. Medical Informatics Assoc..

[21]  Thomas Stütz,et al.  Smartphone Based Stress Prediction , 2015, UMAP.

[22]  David C. Mohr,et al.  Realizing the Potential of Behavioral Intervention Technologies , 2013 .

[23]  C. Mascolo,et al.  A Context-Sensing Mobile Phone App (Q Sense) for Smoking Cessation: A Mixed-Methods Study , 2016, JMIR mHealth and uHealth.

[24]  Hamed Abedtash,et al.  Systematic review of the effectiveness of health-related behavioral interventions using portable activity sensing devices (PASDs) , 2017, J. Am. Medical Informatics Assoc..

[25]  Sudhansu Chokroverty,et al.  Is There a Clinical Role For Smartphone Sleep Apps? Comparison of Sleep Cycle Detection by a Smartphone Application to Polysomnography. , 2015, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[26]  Mark Weiser The computer for the 21st century , 1991 .

[27]  F. Mair,et al.  Thinking about the burden of treatment , 2014, BMJ : British Medical Journal.

[28]  L. Rubenstein Falls in older people: epidemiology, risk factors and strategies for prevention. , 2006, Age and ageing.

[29]  V. Natale,et al.  Monitoring sleep with a smartphone accelerometer , 2012 .

[30]  N. Schork,et al.  The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? , 2011, Personalized medicine.

[31]  Konrad Paul Kording,et al.  Distributed under Creative Commons Cc-by 4.0 the Relationship between Mobile Phone Location Sensor Data and Depressive Symptom Severity , 2022 .

[32]  Nicholas D. Gilson,et al.  Measuring and Influencing Physical Activity with Smartphone Technology: A Systematic Review , 2014, Sports Medicine.

[33]  Russell A. McCann,et al.  mHealth for mental health: Integrating smartphone technology in behavioral healthcare. , 2011 .

[34]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[35]  H. Christensen,et al.  Smartphones for Smarter Delivery of Mental Health Programs: A Systematic Review , 2013, Journal of medical Internet research.

[36]  Oscar Mayora-Ibarra,et al.  Automatic Stress Detection in Working Environments From Smartphones’ Accelerometer Data: A First Step , 2015, IEEE Journal of Biomedical and Health Informatics.

[37]  Scout Calvert,et al.  Opportunities and challenges in the use of personal health data for health research , 2016, J. Am. Medical Informatics Assoc..

[38]  I. Olkin,et al.  Using pedometers to increase physical activity and improve health: a systematic review. , 2007, JAMA.

[39]  Eric J Topol,et al.  Can mobile health technologies transform health care? , 2013, JAMA.

[40]  Gregory D. Abowd,et al.  Charting past, present, and future research in ubiquitous computing , 2000, TCHI.

[41]  C. Robson,et al.  Real World Research: A Resource for Social Scientists and Practitioner-Researchers , 1993 .

[42]  David W. Bates,et al.  White Paper: Personal Health Records: Definitions, Benefits, and Strategies for Overcoming Barriers to Adoption , 2006, J. Am. Medical Informatics Assoc..

[43]  D. Mohr,et al.  Harnessing Context Sensing to Develop a Mobile Intervention for Depression , 2011, Journal of medical Internet research.

[44]  Ruzena Bajcsy,et al.  Real-Time Tele-Monitoring of Patients with Chronic Heart-Failure Using a Smartphone: Lessons Learned , 2016, IEEE Transactions on Affective Computing.

[45]  Brian Caulfield,et al.  Automatic Prediction of Health Status Using Smartphone-Derived Behavior Profiles , 2017, IEEE Journal of Biomedical and Health Informatics.

[46]  Tanzeem Choudhury,et al.  Automated Personalized Feedback for Physical Activity and Dietary Behavior Change With Mobile Phones: A Randomized Controlled Trial on Adults , 2015, JMIR mHealth and uHealth.

[47]  Wanda Pratt,et al.  Healthcare in the pocket: Mapping the space of mobile-phone health interventions , 2012, J. Biomed. Informatics.

[48]  Konrad Paul Kording,et al.  Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study , 2015, Journal of medical Internet research.

[49]  T. Trull,et al.  Ambulatory Assessment : An Innovative and Promising Approach for Clinical Psychology , 2009 .

[50]  Blaine Reeder,et al.  Health at hand: A systematic review of smart watch uses for health and wellness , 2016, J. Biomed. Informatics.

[51]  Wendy Nilsen,et al.  Dynamic Models of Behavior for Just-in-Time Adaptive Interventions , 2014, IEEE Pervasive Computing.

[52]  F. Wahle,et al.  Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild , 2016, JMIR mHealth and uHealth.

[53]  Bin Xu,et al.  Infer Daily Mood Using Mobile Phone Sensing , 2014, Ad Hoc Sens. Wirel. Networks.

[54]  Alison Richardson,et al.  Rethinking the patient: using Burden of Treatment Theory to understand the changing dynamics of illness , 2014, BMC Health Services Research.

[55]  Steve Wheeler,et al.  How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX , 2011, Biomedical engineering online.

[56]  W. Rössler,et al.  Using Smartphones to Monitor Bipolar Disorder Symptoms: A Pilot Study , 2016, JMIR mental health.

[57]  Vicente Pelechano,et al.  Inferring loneliness levels in older adults from smartphones , 2015, J. Ambient Intell. Smart Environ..

[58]  Andrew T. Campbell,et al.  Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health. , 2015, Psychiatric rehabilitation journal.

[59]  D. Ben-Zeev,et al.  Strategies for mHealth Research: Lessons from 3 Mobile Intervention Studies , 2015, Administration and Policy in Mental Health and Mental Health Services Research.

[60]  Emiliano Miluzzo,et al.  A survey of mobile phone sensing , 2010, IEEE Communications Magazine.

[61]  Camille Nebeker,et al.  Acceptance of Mobile Health in Communities Underrepresented in Biomedical Research: Barriers and Ethical Considerations for Scientists , 2017, JMIR mHealth and uHealth.

[62]  Neil C. Evans,et al.  Integrating patient voices into health information for self-care and patient-clinician partnerships: Veterans Affairs design recommendations for patient-generated data applications , 2016, J. Am. Medical Informatics Assoc..

[63]  Richard J. Holden,et al.  The Technology Acceptance Model: Its past and its future in health care , 2010, J. Biomed. Informatics.

[64]  Garrett Mehl,et al.  H_pe for mHealth: More "y" or "o" on the horizon? , 2013, Int. J. Medical Informatics.

[65]  Tanzeem Choudhury,et al.  Automatic detection of social rhythms in bipolar disorder , 2016, J. Am. Medical Informatics Assoc..

[66]  Richard J Holden,et al.  The patient work system: an analysis of self-care performance barriers among elderly heart failure patients and their informal caregivers. , 2015, Applied ergonomics.

[67]  Heyoung Lee,et al.  The SAMS: Smartphone Addiction Management System and Verification , 2013, Journal of Medical Systems.

[68]  E. Diener,et al.  Experience Sampling: Promises and Pitfalls, Strengths and Weaknesses , 2003 .