Technological Innovations Enabling Automatic, Context-Sensitive Ecological Momentary Assessment
Abstract
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.
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As technology becomes more sophisticated, it is becoming possible to collect large amounts of information automatically, perhaps without people's knowledge, raising novel privacy and confidentiality concerns.
“As the sophistication of technology continues to improve, it is becoming possible to collect large amounts of information automatically, perhaps even without people's knowledge (e.g., physiological measures, environmental sensors; Gandy et al., 2000; Intille, 2007), raising novel privacy and confidentiality concerns.”
From Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments (2010) · cites this paper as Intille, 2007
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Tracking Mobile Workers’ Daily Activities with the Contextual Activity Sampling System
Muukkonen, H., Hakkarainen, K., Li, S., & Vartiainen, M. A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07626-3_27 · Cited by 2 · see citing works
Emerging Technologies for Dementia Patient Monitoring
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A Research Framework for the Smartphone-Based Contextual Study of Mobile Knowledge Work
Heiskala, M., Palomäki, E., Vartiainen, M. A., Hakkarainen, K., & Muukkonen, H. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07626-3_23 · Cited by 1 · see citing works
Real-time measures of context to improve fall-detection models
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Social Psychological Methods Outside the Laboratory
Osling, A. D. G. (2010) · Cited by 1 · see citing works
Messung interindividueller Unterschiede in der Emotionsregulation
Holt, D. V., Aguilar‐Raab, C., & Pruessner, L. (2020) · Springer eBooks · doi:10.1007/978-3-662-60280-5_4 · Cited by 1 · see citing works
Methods to Study Everyday Activities in a Mobile Work Context – A Literature Overview
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Social Psychological Methods Outside the
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Secure Mobile Automation of Ecological Momentary Assessments (EMA) For Structured Querying
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Emerging Technologies for Dementia Patient Monitoring
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Tang, Y., He, J., Gierc, M. S. H., Ruissen, G. R., & Faulkner, G. (2024) · International Review of Sport and Exercise Psychology · doi:10.1080/1750984x.2024.2416964
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Self-citation
- Authors
- Intille, S. S.
- Corresponding author
- Intille, Stephen S
- ORCID
- Intille, Stephen S
- Venue
- The Science of Real-Time Data Capture
- Publisher
- Oxford University PressNew York, NY
- Published
- 2007-04-19
- Pages
- 308-360
- OpenAlex
- W2169384369
- Cited by
- 72
- References
- 56
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 1 · 2023: 2 · 2022: 2 · 2021: 3 · 2020: 3 · 2019: 4 · 2018: 7 · 2017: 6 · 2016: 7 · 2015: 6 · 2014: 5 · 2013: 4 · 2012: 4
- FWCI
- 12.70
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- ecological momentary assessment, automated data collection, real-time data acquisition, memory bias, retrospective recall, direct observation, self-report, health behavior, subjective states, naturalistic observation
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