Scaffolding the Mastery of Healthy Behaviors with Fittle+ Systems: Evidence-Based Interventions and Theory

Abstract

We present a series of mHealth applications and studies pursued as part of the Fittle+ project. This program of research has the dual aims of (1) bringing scalable evidence-based behavior-change interventions to mHealth and evaluating them and (2) developing theoretically based predictive models to better understand the dynamics of the impact of these interventions on achieving behavior-change goals. Our approach in the Fittle+ systems rests on the idea that to master the complex fabric of a new healthy lifestyle, one must weave together a new set of healthy habits that over-ride the old unhealthy habits. To achieve these aims, we have developed a series of mHealth platforms that provide scaffolding interventions: Behavior-change techniques and associated mHealth interactions (e.g., SMS reminders; chatbot dialogs; user interface functionality; etc.) that provide additional support to the acquisition and maintenance of healthy habits. We present experimental evidence collected so far for statistically significant improvements in behavior change in eating, exercise, and physical activity for the following scaffolding interventions: guided mastery, teaming, self-affirmation, and implementation intentions. We also present predictive computational ACT-R models of daily individual behavior goal success for data collected in guided mastery and implementation intention studies that address goal-striving and habit formation mechanisms.

Acknowledgments

We would like to thank those who collaborated on Fittle+ research: Shane Ahern, Victoria Bellotti, Nicole Crenshaw, Jacqueline LeBlanc, Pai Liu, Shiwali Mohan, Ashwin Ram, Frank Rolek, Jonathan Rubin, Michael Silva, Simon Tucker, Anusha Venkatakrishnan, Jesse Vig, Steve Whittaker, and Rong Yang. Fittle® is a registered trademark of Palo Alto Research Center, Inc.

Notes

¹ The notion of specific goals is consistent with the definition of SMART goals (specific, measurable, achievable, realistic, relevant and timed).

Additional information

Funding

This material is based upon work supported by the National Science Foundation, Directorate for 
Computer and Information Science and Engineering, Smart and Connected Health program, under Grant No. 1346066 to Peter Pirolli and Michael Youngblood.


Notes on contributors

Peter Pirolli

Peter Pirolli ([email protected]), is a cognitive psychologist; he is a Senior Research Scientist at the Institute for Human and Machine Cognition.

G. Michael Youngblood

G. Michael Youngblood ([email protected]) is a computer scientist; he is a Principal Scientist at the Palo Alto Research Center.

Honglu Du

Honglu Du ([email protected]) is a computer scientist; he is Staff User Research at the Ant Financial Services Group.

Artie Konrad

Artie Konrad ([email protected]) is a mixed methods researcher; he is a UX Researcher at Facebook.

Les Nelson

Les Nelson ([email protected]) is a mixed methods researcher; he is a Senior Researcher at the Palo Alto Research Center.

Aaron Springer

Aaron Springer ([email protected]) is a psychology doctoral student at the University of California, Santa cruz.