Behavior Change and Persuasion in Mobile Health ...
Transcript of Behavior Change and Persuasion in Mobile Health ...
Behavior Change and Persuasion in Mobile Health Interventions:
A Critical Literature Review
Karim Zahed, Arjun H. Rao, Farzan Sasangohar
Department of Industrial & Systems Engineering, Texas A&M University, College Station, Texas, USA
About 1.9 billion people worldwide are considered obese;
costing the world economy $147 billion and putting those
people at a higher risk of mortality (Centers for Disease Control
and Prevention, 2017; World Health Organization, 2018).
Obesity as well as many of the chronic conditions are a result
of a sedentary lifestyle and an unhealthy diet, among other
factors (Lin et al., 2012). Adoption of new and healthy
behaviors is a necessary condition to address this global
challenge (Hilliard, Riekert, Ockene, & Pbert, 2018).
Behavior change interventions have been investigated to
provide additional support for individuals to adopt healthier
behaviors (Hardcastle et al., 2015). Persuasion to engage in a
particular action requires triggers at the optimal moment when
the recipient is both capable and motivated to perform a new
behavior (Fogg, 2009). Mobile Health (mHealth) apps serve as
a popular platform for this purpose (Zhao, Freeman, & Li,
2016). Mobile phones are used by more than 68% of the world’s
population, and can facilitate non-intrusive data collection (e.g.
solicit readings, monitor user activity) as well as initiating
interactions with users (e.g. reminders, educational messages)
in a timely manner.
Recent research has found that users are receptive to such
interventions (Loescher, Rains, Kramer, Akers, & Moussa,
2018); however it is not clear how to keep users engaged
effectively over long periods of time—a critical requirement to
transform healthy behaviors into habits (Lally, van Jaarsveld,
Potts, & Wardle, 2010).
To address this issue, we conducted a literature review to
understand what has been studied in the realm of behavior
change intervention on mHealth platforms. Studies were
retrieved by searching PubMed, Scopus, Compendex, and
PsycInfo databases. After removing duplicates, studies were
reviewed by title and abstract in order to apply the inclusion and
exclusion criteria. Studies were included if they promoted
persuasive design, used or developed a behavior change theory
or intervention. Studies were excluded if they were not in
English, did not involve mobile phones, or if they were not
studied within the healthcare domain. A thematic analysis for
latent concepts was then performed for the resulting 47 articles,
which rendered 4 key themes: Conceptual Framework, Content
Tailoring, Opportune Timing, and Adaptive Capabilities.
A Conceptual Framework constitutes the backbone of an
intervention, guiding design via behavioral theories, digital
behavior change models and behavior change techniques. By
understanding the user’s current and target behavior, the system
can be made to interact with the user in a way to achieve the
required change (Mohr, Schueller, Montague, Burns, &
Rashidi, 2014). Content Tailoring stresses the need to
personalize any interaction with the user by including the user’s
name and accounting for characteristics such as personality and
persuasion profile. To make an interaction more effective, it
needs to be initiated at the right time. Opportune Timing is thus
another crucial design characteristic that relies on input to
gather information about the user’s current state in order to
generate a timely interaction when needed. Here, the just-in-
time concept is utilized to send customized messages that
encourage users to engage with the system. However, if a user’s
engagement level drops, then there is no clear approach to know
the underlying reasons and to tackle them appropriately.
Consequently, the need for Adaptive Capabilities arises in order
to account for any external or internal changes that affect the
user’s behavior and adapt accordingly by changing set goals or
targeting the specific bottlenecks.
Our results show that adaptive capabilities of behavior
change interventions is in its infancy (Danaher, Brendryen,
Seeley, Tyler, & Woolley, 2015) and a unified framework
including all these themes needs to be developed and validated
in order to achieve maximum potential for effectiveness. It
seems that when a user fails to engage as required, little data is
available to understand the contributing factors. It is important
to further explore the factors influencing user engagement with
an intervention and attempting to predict this relationship in
real-time.
REFERENCES Centers for Disease Control and Prevention. (2017). Adult Obesity Causes &
Consequences. Retrieved February 16, 2019, from
https://www.cdc.gov/obesity/adult/causes.html
Danaher, B. G., Brendryen, H., Seeley, J. R., Tyler, M. S., & Woolley, T. (2015). From black box to toolbox: Outlining device functionality,
engagement activities, and the pervasive information architecture
of mHealth interventions. Internet Interventions, 2(1), 91–101. https://doi.org/10.1016/j.invent.2015.01.002
Fogg, B. (2009). A behavior model for persuasive design. 4th International
Conference on Persuasive Technology, 1. https://doi.org/10.1145/1541948.1541999
Hardcastle, S. J., Hancox, J., Hattar, A., Maxwell-Smith, C., Thøgersen-
Ntoumani, C., & Hagger, M. S. (2015). Motivating the unmotivated: How can health behavior be changed in those
unwilling to change? Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.00835
Hilliard, M. E., Riekert, K. A., Ockene, J. K., & Pbert, L. (Eds.). (2018). The
handbook of health behavior change (5th edition). New York, NY: Springer Publishing Company, LLC.
Lally, P., van Jaarsveld, C. H. M., Potts, H. W. W., & Wardle, J. (2010). How
are habits formed: Modelling habit formation in the real world. European Journal of Social Psychology, 40(6), 998–1009.
https://doi.org/10.1002/ejsp.674
Lin, C.-C., Li, C.-I., Liu, C.-S., Lin, W.-Y., Fuh, M. M.-T., Yang, S.-Y., … Li, T.-C. (2012). Impact of Lifestyle-Related Factors on All-Cause
and Cause-Specific Mortality in Patients With Type 2 Diabetes:
The Taichung Diabetes Study. Diabetes Care, 35(1), 105–112. https://doi.org/10.2337/dc11-0930
Loescher, L. J., Rains, S. A., Kramer, S. S., Akers, C., & Moussa, R. (2018).
A Systematic Review of Interventions to Enhance Healthy Lifestyle Behaviors in Adolescents Delivered via Mobile Phone
Text Messaging. American Journal of Health Promotion, 32(4),
865–879. https://doi.org/10.1177/0890117116675785 Mohr, D. C., Schueller, S. M., Montague, E., Burns, M. N., & Rashidi, P.
(2014). The Behavioral Intervention Technology Model: An
Proceedings of the Human Factors and Ergonomics Society 2019 Annual Meeting
Cop
yrig
ht 2
019
by H
uman
Fac
tors
and
Erg
onom
ics
Soci
ety.
DO
I 10.
1177
/107
1181
3196
3126
2
1697
Integrated Conceptual and Technological Framework for eHealth
and mHealth Interventions. Journal of Medical Internet Research,
16(6), e146. https://doi.org/10.2196/jmir.3077
World Health Organization,. (2018). Obesity and Overweight. Retrieved August 12, 2018, from World Health Organization website:
http://www.who.int/news-room/fact-sheets/detail/obesity-and-
overweight
Zhao, J., Freeman, B., & Li, M. (2016). Can Mobile Phone Apps Influence
People’s Health Behavior Change? An Evidence Review. Journal
of Medical Internet Research, 18(11), e287.
https://doi.org/10.2196/jmir.5692
Proceedings of the Human Factors and Ergonomics Society 2019 Annual Meeting 1698