Learning to be Proficient? A Structural Model of User Dynamic Engagement in eHealth Interventions

44 Pages Posted: 21 Apr 2022 Last revised: 7 Jun 2023

See all articles by Tongxin Zhou

Tongxin Zhou

Arizona State University - W. P. Carey School of Business - Department of Information Systems

Yingfei Wang

University of Washington - Michael G. Foster School of Business

Lu (Lucy) Yan

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies

Yong Tan

University of Washington - Michael G. Foster School of Business

Date Written: March 24, 2022

Abstract

eHealth interventions have transformed how individuals manage their health by offering prompt and accessible support for modifying their lifestyles. In this study, we investigate how individuals dynamically learn about the effectiveness of eHealth interventions to shed light on their continued participation. To capture the potential changes in individuals’ perceptions and associated behavior dynamics, we establish a hierarchical Bayesian learning framework to structurally characterize individuals’ decision-making processes. Our model addresses several unique characteristics of learning in the health-management setting, such as correlations between intervention choices and heterogeneity in delivered health signals. Through analysis of a 4-month dataset of users’ intervention participation in an online weight-loss platform, we demonstrate that individuals’ learning performance may vary significantly across intervention types. Our analysis reveals that individuals’ learning performance tends to be suboptimal for interventions with ambiguous instructions or those that prioritize short-term health improvements. Given that individuals’ learning performance is largely influenced by the level of noise contained in their intervention experiences, we identified possible noise sources and proposed several denoising strategies to improve user engagement and learning outcomes, with their efficacy analyzed through counterfactual analysis. The proposed strategies can be easily integrated into platform design to generate a positive impact on user engagement. Our empirical estimation also leads to interesting findings regarding heterogeneity in intervention experiences and users’ preferences for descriptive intervention features. These findings provide various implications for the personalization and design of online healthcare interventions.

Keywords: personal health management, wellness promotion, lifestyle change, eHealth interventions, online healthcare platforms, dynamic participation, Bayesian learning

Suggested Citation

Zhou, Tongxin and Wang, Yingfei and Yan, Lu (Lucy) and Tan, Yong, Learning to be Proficient? A Structural Model of User Dynamic Engagement in eHealth Interventions (March 24, 2022). Available at SSRN: https://ssrn.com/abstract=4066017 or http://dx.doi.org/10.2139/ssrn.4066017

Tongxin Zhou (Contact Author)

Arizona State University - W. P. Carey School of Business - Department of Information Systems ( email )

Tempe, AZ
United States

Yingfei Wang

University of Washington - Michael G. Foster School of Business ( email )

Box 353200
Seattle, WA 98195-3200
United States

Lu (Lucy) Yan

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies ( email )

Department of Operations and Decision Technologies
1309 E. Tenth Street
Bloomington, IN 47401
United States

Yong Tan

University of Washington - Michael G. Foster School of Business ( email )

Box 353226
Seattle, WA 98195-3226
United States

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