TY - JOUR
T1 - Building new computational models to support health behavior change and maintenance
T2 - new opportunities in behavioral research
AU - Spruijt-Metz, Donna
AU - Hekler, Eric
AU - Saranummi, Niilo
AU - Intille, Stephen
AU - Korhonen, Ilkka
AU - Nilsen, Wendy
AU - Rivera, Daniel E.
AU - Spring, Bonnie
AU - Michie, Susan
AU - Asch, David A.
AU - Sanna, Alberto
AU - Salcedo, Vicente Traver
AU - Kukakfa, Rita
AU - Pavel, Misha
PY - 2015
Y1 - 2015
N2 - Adverse and suboptimal health behaviors and habits are
responsible for approximately 40 % of preventable deaths,
in addition to their unfavorable effects on quality of
life and economics. Our current understanding of human
behavior is largely based on static "snapshots" of human
behavior, rather than ongoing, dynamic feedback loops of
behavior in response to ever-changing biological, social,
personal, and environmental states. This paper first
discusses how new technologies (i.e., mobile sensors,
smartphones, ubiquitous computing, and cloud-enabled
processing/computing) and emerging systems modeling
techniques enable the development of new, dynamic, and
empirical models of human behavior that could facilitate
just-in-time adaptive, scalable interventions. The paper
then describes concrete steps to the creation of robust
dynamic mathematical models of behavior including: (1)
establishing "gold standard" measures, (2) the creation
of a behavioral ontology for shared language and
understanding tools that both enable dynamic theorizing
across disciplines, (3) the development of data sharing
resources, and (4) facilitating improved sharing of
mathematical models and tools to support rapid
aggregation of the models. We conclude with the
discussion of what might be incorporated into a
"knowledge commons," which could help to bring together
these disparate activities into a unified system and
structure for organizing knowledge about behavior.
AB - Adverse and suboptimal health behaviors and habits are
responsible for approximately 40 % of preventable deaths,
in addition to their unfavorable effects on quality of
life and economics. Our current understanding of human
behavior is largely based on static "snapshots" of human
behavior, rather than ongoing, dynamic feedback loops of
behavior in response to ever-changing biological, social,
personal, and environmental states. This paper first
discusses how new technologies (i.e., mobile sensors,
smartphones, ubiquitous computing, and cloud-enabled
processing/computing) and emerging systems modeling
techniques enable the development of new, dynamic, and
empirical models of human behavior that could facilitate
just-in-time adaptive, scalable interventions. The paper
then describes concrete steps to the creation of robust
dynamic mathematical models of behavior including: (1)
establishing "gold standard" measures, (2) the creation
of a behavioral ontology for shared language and
understanding tools that both enable dynamic theorizing
across disciplines, (3) the development of data sharing
resources, and (4) facilitating improved sharing of
mathematical models and tools to support rapid
aggregation of the models. We conclude with the
discussion of what might be incorporated into a
"knowledge commons," which could help to bring together
these disparate activities into a unified system and
structure for organizing knowledge about behavior.
KW - Mobile health
KW - mHealth
KW - Connected health
KW - Health-related behavior
KW - Just-in-time adaptive interventions
KW - Real-time interventions
KW - Computational models of behavior
U2 - 10.1007/s13142-015-0324-1
DO - 10.1007/s13142-015-0324-1
M3 - Article
SN - 1869-6716
VL - 5
SP - 335
EP - 346
JO - Translational Behavioral Medicine
JF - Translational Behavioral Medicine
IS - 3
ER -