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How to evaluate technologies for health behavior change in HCI research
Abstract
New technologies for encouraging physical activity, healthy diet, and other types of health behavior change now frequently appear in the HCI literature. Yet, how such technologies should be evaluated within the context of HCI research remains unclear. In this paper, we argue that the obvious answer to this question - that evaluations should assess whether a technology brought about the intended change in behavior - is too limited. We propose that demonstrating behavior change is often infeasible as well as unnecessary for a meaningful contribution to HCI research, especially when in the early stages of design or when evaluating novel technologies. As an alternative, we suggest that HCI contributions should focus on efficacy evaluations that are tailored to the specific behavior-change intervention strategies (e.g., self-monitoring, conditioning) embodied in the system and studies that help gain a deep understanding of people's experiences with the technology.
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“First, they focus on clinical outcomes, which are often more distal to the behavioral targets of specific BIT interventions and are unlikely to change within the timeframe necessary to inform design considerations or to submit BITs to rapid development, evaluation, and iteration [4].”
From The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions (2014) · cites this paper as [4]
In the citing paper
“First, they focus on clinical outcomes, which are often more distal to the behavioral targets of specific BIT interventions and are unlikely to change within the timeframe necessary to inform design considerations or to submit BITs to rapid development, evaluation, and iteration [4].”
From The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions (2014) · cites this paper as [4]
In the citing paper
“Much of the evaluation of BITs has focused on efficacy, which limits the growth in our knowledge regarding the mechanisms by which BITs achieve both their proximal intervention aims as well as the ultimate treatment goals [4].”
From The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions (2014) · cites this paper as [4]
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Adler, D. A., Yang, Y., Viranda, T., Meter, A. V., McGinty, E. E., & Choudhury, T. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713481 · Cited by 6
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Metadata
- Authors
- Klasnja, P., Consolvo, S., & Pratt, W.
- Affiliations
- University of Washington (US)
- Intel (United States) (US)
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2011-05-07
- Pages
- 3063-3072
- OpenAlex
- W2003659858
- Cited by
- 414
- References
- 52
- Citations by year
- 2026: 14 · 2025: 23 · 2024: 24 · 2023: 21 · 2022: 24 · 2021: 38 · 2020: 24 · 2019: 26 · 2018: 36 · 2017: 33 · 2016: 43 · 2015: 40 · 2014: 34 · 2013: 21 · 2012: 11
- FWCI
- 16.21
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- health behavior change, technology evaluation, behaviour change interventions, self-monitoring, conditioning, physical activity, healthy eating, user experience, preliminary design
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