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dc.contributor.authorIntille, Stephen S.
dc.date.accessioned2014-05-23T18:02:50Z
dc.date.available2014-05-23T18:02:50Z
dc.date.issued2007
dc.date.submitted2006-12
dc.identifier.isbn0195178718
dc.identifier.isbn9780195178715
dc.identifier.urihttp://hdl.handle.net/1721.1/87152
dc.description.abstractHealth-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.en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (NSF ITR grant #0112900)en_US
dc.description.sponsorshipMassachusetts Institute of Technology (House_n Consortium)en_US
dc.language.isoen_US
dc.publisherOxford University Pressen_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceOxford University Pressen_US
dc.titleTechnological Innovations Enabling Automatic, Context-Sensitive Ecological Momentary Assessmenten_US
dc.typeArticleen_US
dc.identifier.citationIntille, Stephen S. "Technological innovations enabling automatic, context-sensitive ecological momentary assessment ." (Eds.) Arthur Stone et al. The science of real-time data capture: self-reports in health research. Oxford: Oxford University Press, 2007. p. 308-337.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Architectureen_US
dc.contributor.mitauthorIntille, Stephen S.en_US
dc.relation.journalThe science of real-time data capture: self-reports in health researchen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/BookItemen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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