BookJames M. Rehg, Susan A. Murphy, Santosh Kumar, editors ; foreword by Deborah Estrin.
Summary: 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 organization of 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.
Contents:
Introduction to Section 1: mHealth Applications and Tools
StudentLife: Using Smartphone to Assess Mental Health and Academic Performance of College Students
Circadian Computing: Sensing, Modeling, and Maintaining Biological Rhythms
Design Lessons from a Micro-Randomized Pilot Study in Mobile Health
The Use of Asset-Based Community Development in a Research Project Aimed at Developing mHealth Technologies for Older Adults
Designing Mobile Health Technologies for Self-Monitoring: The Bit Counter as a Case Study
mDebugger: Assessing and Diagnosing the Fidelity and Yield of Mobile Sensor Data
Introduction to Section II: Sensors to mHealth Markers
Challenges and Opportunities in Automated Detection of Eating Activity
Detecting Eating and Smoking Behavior Using Smartwatches
Wearable Motion Sensing Devices and Algorithms for Precise Healthcare Diagnostics and Guidance
Paralinguistic Analysis of Children's Speech in Natural Environments
Pulmonary Monitoring Using Smartphones
Wearable Sensing of Left Ventricular Function
A new direction for Biosensing: RF sensors for monitoring cardio-pulmonary function
Wearable Optical Sensors
Introduction to Section III: Markers to mHealth Predictors
Exploratory Visual Analytics of Mobile Health Data: Sensemaking Challenges and Opportunities
Learning Continuous-Time Hidden Markov Models for Event Data
Time-series Feature Learning with Applications to Healthcare Domain
From Markers to Interventions: The Case of Just-in-Time Stress Intervention
Introduction to Section IV: Predictors to mHealth Interventions
Modeling Opportunities in mHealth Cyber-Physical Systems
Control Systems Engineering for Optimizing Behavioral mHealth Interventions
From Ads to Interventions: Contextual Bandits in Mobile Health
Towards Health Recommendation Systems: An Approach for Providing Automated Personalized Health Feedback from Mobile Data.