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.

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Proceedings of the Human Factors and Ergonomics Society 2019 Annual Meeting

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Proceedings of the Human Factors and Ergonomics Society 2019 Annual Meeting 1698