Technological Innovations Enabling Automatic, Context-Sensitive Ecological Momentary Assessment

Health-related behavior, subjective states, cognitions, and interpersonal experiences are inextricably linked to context. Context includes information about location, time, past activities, interaction with other people and objects, and mental, physiological, and emotional states. Most real-time dat...

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Bibliographic Details
Main Author: Intille, Stephen S. (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Architecture (Contributor)
Format: Article
Language:English
Published: Oxford University Press, 2014-05-23T18:02:50Z.
Subjects:
Online Access:Get fulltext
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100 1 0 |a Intille, Stephen S.  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Architecture  |e contributor 
100 1 0 |a Intille, Stephen S.  |e contributor 
245 0 0 |a Technological Innovations Enabling Automatic, Context-Sensitive Ecological Momentary Assessment 
260 |b Oxford University Press,   |c 2014-05-23T18:02:50Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/87152 
520 |a Health-related behavior, subjective states, cognitions, and interpersonal experiences are inextricably linked to context. Context includes information about location, time, past activities, interaction with other people and objects, and mental, physiological, and emotional states. Most real-time data collection methodologies require that subjects self-report information about contextual influences, notwithstanding the difficulty they have identifying the contextual factors that are influencing their behavior and subjective states. Often these assessment methodologies ask subjects to report on their activities or thoughts long after the actual events, thereby relying on retrospective recall and introducing memory biases. The "gold standard" alternative to these self-report instruments is direct observation. Direct observation in a laboratory setting, however, artificially constrains behavior. Direct observation is also typically too costly and invasive for long-term, large-sample-size studies of people in their natural environments. 
520 |a National Science Foundation (U.S.) (NSF ITR grant #0112900) 
520 |a Massachusetts Institute of Technology (House_n Consortium) 
546 |a en_US 
655 7 |a Article 
773 |t The science of real-time data capture: self-reports in health research