Association between behavioral phenotypes and response to a physical activity intervention using gamification and social incentives: Secondary analysis of the STEP UP randomized clinical trial

PLOS ONE RESEARCH ARTICLE Association between behavioral phenotypes and response to a physical activity intervention using gamification and social incentives: Secondary analysis of the STEP UP randomized clinical trial Xisui Shirley Chen ID1*, Sujatha Changolkar2, Amol S. Navathe1,3, Kristin A. Linn4, Gregory Reh5, Gregory Szwartz5, David Steier5, Sarah Godby5, Mohan Balachandran2, Joseph D. Harrison2, Charles A. L. Rareshide2, Mitesh S. Patel1,2,3 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Department of Medicine, University of Pennyslvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America, 2 Penn Medicine Nudge Unit, University of Pennyslvania, Philadelphia, Pennsylvania, United States of America, 3 Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, United States of America, 4 Department of Biostatistics, Epidemiology and Informatics, University of Pennyslvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America, 5 Deloitte Consulting, Philadelphia, Pennsylvania, United States of America * OPEN ACCESS Citation: Chen XS, Changolkar S, Navathe AS, Linn KA, Reh G, Szwartz G, et al. (2020) Association between behavioral phenotypes and response to a physical activity intervention using gamification and social incentives: Secondary analysis of the STEP UP randomized clinical trial. PLoS ONE 15 (10): e0239288. https://doi.org/10.1371/journal. pone.0239288 Editor: Rebecca A. Krukowski, University of Tennessee Health Science Center, UNITED STATES Received: April 24, 2020 Accepted: September 1, 2020 Published: October 14, 2020 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0239288 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Abstract Participants often vary in their response to behavioral interventions, but methods to identify groups of participants that are more likely to respond are lacking. In this secondary analysis of a randomized clinical trial, we used baseline characteristics to group participants into distinct behavioral phenotypes and evaluated differential responses to a physical activity intervention. Latent class analysis was used to segment participants based on baseline participant data including demographics, validated measures of psychosocial variables, and physical activity behavior. The trial included 602 adults from 40 U.S. states with body mass index �25 who were randomized to control or one of three gamification interventions (supportive, collaborative, or competitive) to increase physical activity. Daily step counts were monitored using a wearable device for a 24-week intervention with 12 weeks of follow-up. The model segmented participants into three classes named for key defining traits: Class 1, extroverted and motivated; Class 2, less active and less social; Class 3, less motivated and at-risk. Adjusted regression models were used to test for differences in intervention response relative to control within each behavioral phenotype. In Class 1, only participants in the competitive arm increased their mean daily steps during the intervention (adjusted difference, 945; 95% CI, 352–1537; P = .002), but it was not sustained during follow-up. In Class 2, participants in all three gamification arms significantly increased their mean daily steps compared to control during the intervention (supportive arm adjusted difference 1172; 95% CI, 363–1980; P = .005; collaborative arm adjusted difference 1119; 95% CI, 319– 1919; P = .006; competitive arm adjusted difference 1179; 95% CI, 400–1957; P = .003) and all three had sustained impact during follow-up. In Class 3, none of the interventions had a significant effect on physical activity. Three behavioral phenotypes were identified, each PLOS ONE | https://doi.org/10.1371/journal.pone.0239288 October 14, 2020 1 / 13 PLOS ONE Data Availability Statement: All relevant data are within the manuscript and will be included in its Supporting Information files. Funding: This study was funded by the University of Pennsylvania Health System through the Penn Medicine Nudge Unit. The University of Pennsylvania co-authors conducted all analyses, and drafted and submitted the manuscript. The Deloitte co-authors had the opportunity to review and provide comments on the manuscript. The University of Pennsylvania co-authors had full authority on whether or not to incorporate those comments. The Penn Medicine Nudge Unit provided support in the form of salaries for authors SC and CALR for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of all authors are articulated in the ‘author contributions’ section. Competing interests: Dr. Patel was supported by career development awards from the Department of Veterans Affairs HSR&D and the Doris Duke Charitable Foundation. Dr. Patel is founder of Catalyst Health, a technology and behavior change consulting firm that has received consulting income from Deloitte, not related to this project. The following authors are or were employees of Deloitte: Gregory Reh, Gregory Szwartz, David Steier, and Sarah Godby. This does not alter our adherence to PLOS ONE policies on sharing data and materials. We declare no patents, products in development, or marketed products related to this study at this time. Participant behavioral phenotypes and response to a physical activity intervention with a different response to the interventions. This approach could be used to better target behavioral interventions to participants that are more likely to respond to them. Introduction Modifiable behavioral risk factors such as poor diet, low physical activity, and tobacco use account for 40% of premature mortality in the United States, highlighting the need for effective behavior change interventions [1, 2]. Many behavior modification strategies appear promising, but systematic reviews and meta-analyses of randomized control trials indicate significant heterogeneity in outcomes that is poorly understood [3–5]. In particular, little is known about which interventions are effective for population sub-groups. While demographic attributes have been correlated with health behaviors [6], they often do not fully explain differences in intervention effectiveness [7–9]. Similarly, comparisons within studies suggest that baseline participant traits such as age, sex, race/ethnicity and health status are not relia (...truncated)