TY - GEN
T1 - Self-experimentation for behavior change
T2 - 2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017
AU - Lee, Jisoo
AU - Walker, Erin
AU - Burleson, Winslow
AU - Kay, Matthew
AU - Buman, Matthew
AU - Hekler, Eric B.
N1 - Funding Information:
This work was supported by a Google Research Faculty Award (PI: Hekler). Thanks to Bob Evans from Google for being our sponsor, for support on the early conceptualization of this work, and for providing the tool, Paco, for our use.
Publisher Copyright:
© 2017 ACM.
PY - 2017/5/2
Y1 - 2017/5/2
N2 - Desirable outcomes such as health are tightly linked to behaviors, thus inspiring research on technologies that support people in changing those behaviors. Many behavior-change technologies are designed by HCI experts but this approach can make it difficult to personalize support to each user's unique goals and needs. This paper reports on the iterative design of two complementary support strategies for helping users create their own personalized behavior-change plans via self-experimentation: One emphasized the use of interactive instructional materials, and the other additionally introduced context-aware computing to enable user creation of "just in time" home-based interventions. In a formative trial with 27 users, we compared these two approaches to an unstructured sleep education control. Results suggest great promise in both strategies and provide insights on how to develop personalized behavior-change technologies. Copyright is held by the owner/author(s). Publication rights licensed to ACM.
AB - Desirable outcomes such as health are tightly linked to behaviors, thus inspiring research on technologies that support people in changing those behaviors. Many behavior-change technologies are designed by HCI experts but this approach can make it difficult to personalize support to each user's unique goals and needs. This paper reports on the iterative design of two complementary support strategies for helping users create their own personalized behavior-change plans via self-experimentation: One emphasized the use of interactive instructional materials, and the other additionally introduced context-aware computing to enable user creation of "just in time" home-based interventions. In a formative trial with 27 users, we compared these two approaches to an unstructured sleep education control. Results suggest great promise in both strategies and provide insights on how to develop personalized behavior-change technologies. Copyright is held by the owner/author(s). Publication rights licensed to ACM.
KW - Behavior change
KW - Context-aware computing
KW - Just-in-time interventions
KW - Self-experimentation
UR - http://www.scopus.com/inward/record.url?scp=85027443036&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027443036&partnerID=8YFLogxK
U2 - 10.1145/3025453.3026038
DO - 10.1145/3025453.3026038
M3 - Conference contribution
AN - SCOPUS:85027443036
T3 - Conference on Human Factors in Computing Systems - Proceedings
SP - 6837
EP - 6849
BT - CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 6 May 2017 through 11 May 2017
ER -