Mobile Health

1. Front Matter

   Pages i-xl

   PDF

2. mHealth Applications and Tools

   1. Front Matter

      Pages 1-1

      PDF

   2. Introduction to Part I: mHealth Applications and Tools

      • Santosh Kumar, James M. Rehg, Susan A. Murphy

      Pages 3-6

   3. StudentLife: Using Smartphones to Assess Mental Health and Academic Performance of College Students

      • Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor et al.

      Pages 7-33

   4. Circadian Computing: Sensing, Modeling, and Maintaining Biological Rhythms

      • Saeed Abdullah, Elizabeth L. Murnane, Mark Matthews, Tanzeem Choudhury

      Pages 35-58

   5. Design Lessons from a Micro-Randomized Pilot Study in Mobile Health

      • Shawna N. Smith, Andy Jinseok Lee, Kelly Hall, Nicholas J. Seewald, Audrey Boruvka, Susan A. Murphy et al.

      Pages 59-82

   6. The Use of Asset-Based Community Development in a Research Project Aimed at Developing mHealth Technologies for Older Adults

      • David H. Gustafson, Fiona McTavish, David H. Gustafson Jr., Scott Gatzke, Christa Glowacki, Brett Iverson et al.

      Pages 83-99

   7. Designing Mobile Health Technologies for Self-Monitoring: The Bite Counter as a Case Study

      • Eric R. Muth, Adam Hoover

      Pages 101-120

   8. mDebugger: Assessing and Diagnosing the Fidelity and Yield of Mobile Sensor Data

      • Mahbubur Rahman, Nasir Ali, Rummana Bari, Nazir Saleheen, Mustafa al’Absi, Emre Ertin et al.

      Pages 121-143

3. Sensors to mHealth Markers

   1. Front Matter

      Pages 145-145

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   2. Introduction to Part II: Sensors to mHealth Markers

      • Santosh Kumar, James M. Rehg, Susan A. Murphy

      Pages 147-150

   3. Challenges and Opportunities in Automated Detection of Eating Activity

      • Edison Thomaz, Irfan A. Essa, Gregory D. Abowd

      Pages 151-174

   4. Detecting Eating and Smoking Behaviors Using Smartwatches

      • Abhinav Parate, Deepak Ganesan

      Pages 175-201

   5. Wearable Motion Sensing Devices and Algorithms for Precise Healthcare Diagnostics and Guidance

      • Yan Wang, Mahdi Ashktorab, Hua-I Chang, Xiaoxu Wu, Gregory Pottie, William Kaiser

      Pages 203-218

   6. Paralinguistic Analysis of Children’s Speech in Natural Environments

      • Hrishikesh Rao, Mark A. Clements, Yin Li, Meghan R. Swanson, Joseph Piven, Daniel S. Messinger

      Pages 219-238

   7. Pulmonary Monitoring Using Smartphones

      • Eric C. Larson, Elliot Saba, Spencer Kaiser, Mayank Goel, Shwetak N. Patel

      Pages 239-264

   8. Wearable Sensing of Left Ventricular Function

      • Omer T. Inan

      Pages 265-287

   9. A New Direction for Biosensing: RF Sensors for Monitoring Cardio-Pulmonary Function

      • Ju Gao, Siddharth Baskar, Diyan Teng, Mustafa al’Absi, Santosh Kumar, Emre Ertin

      Pages 289-312

   10. Wearable Optical Sensors

       • Zachary S. Ballard, Aydogan Ozcan

       Pages 313-342

4. Markers to mHealth Predictors

   1. Front Matter

      Pages 343-343

      PDF

This volume provides a comprehensive introduction to mHealth technology and is accessible to technology-oriented researchers and practitioners with backgrounds in computer science, engineering, statistics, and applied mathematics. The contributing authors include leading researchers and practitioners in the mHealth field.
 
The book offers an in-depth exploration of the three key elements of mHealth technology: the development of on-body sensors that can identify key health-related behaviors (sensors to markers), the use of analytic methods to predict current and future states of health and disease (markers to predictors), and the development of mobile interventions which can improve health outcomes (predictors to interventions). Chapters are organized into sections, with the first section devoted to mHealth applications, followed by three sections devoted to the above three key technology areas. Each chapter can be read independently, but the organizationof the entire book provides a logical flow from the design of on-body sensing technology, through the analysis of time-varying sensor data, to interactions with a user which create opportunities to improve health outcomes. This volume is a valuable resource to spur the development of this growing field, and ideally suited for use as a textbook in an mHealth course.

• mobile health
• wearable sensors
• mobile computing
• health data analytics
• low-power sensing and computing
• behavioral medicine
• health interventions
• mHealth
• chronic diseases and conditions
• mental health
• machine learning
• data mining
• reinforcement learning
• control systems engineering
• just-in-time adaptive interventions
• fitness trackers

• College of Computing, Georgia Institute of Technology, Atlanta, USA

  James M. Rehg

• Department of Statistics, University of Michigan, Ann Arbor, USA

  Susan A. Murphy

• Department of Computer Science, University of Memphis, Memphis, USA

  Santosh Kumar

James M. Rehg is a Professor of Computer Science at the Georgia Institute of Technology where he directs the Center for Behavioral Imaging and co-directs the Computational Perception Lab. Dr. Rehg’s research focuses on computer vision, machine learning, and mobile health, with an emphasis on the analysis of video captured by wearable cameras for mobile health applications. He was the lead PI on an NSF Expedition to develop computational methods for measuring, modeling, and analyzing social and communicative behavior, with applications to developmental disorders such as autism. He is currently the Deputy Director of the NIH Center of Excellence on Mobile Sensor Data-to-Knowledge (MD2K), where he leads the Data Science Research Core.


Susan Murphy is the H.E. Robbins Distinguished University Professor of Statistics at the University of Michigan. Dr. Murphy’s research focuses on improving sequential, individualized, decision making in health, in particular onclinical trial design and data analysis to inform the development of adaptive interventions (e.g. treatment policies). She currently works, as part of the MD2K team and other interdisciplinary teams, to develop clinical trial designs and learning algorithms in mobile health. She is a Fellow of the College on Problems in Drug Dependence, a former editor of the Annals of Statistics, President-Elect of the Bernoulli Society, a member of the US National Academy of Science, a member of the US National Academy of Medicine and a 2013 MacArthur Fellow.


Santosh Kumar is a Professor of Computer Science at the University of Memphis where he holds the Lillian & Morrie Moss Chair of Excellence. Dr. Kumar’s research focusses on mobile health, with an emphasis on developing computational models to infer human health and behavior such as stress, conversation, smoking, and drug use from wearable sensor data. He is director of the NIH Center of Excellence on Mobile Sensor Data-to-Knowledge (MD2K), that involves over 20 scientists from in computing, engineering, behavioral science, and medicine. He was named one of America’s ten most brilliant scientists under the age of 38 by Popular Science in 2010.

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