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

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

Gamified Wearable Fitness Tracker for Physical Activity: A Comprehensive Literature Review

“…Social-based gamification theme includes collaboration, cooperation, sharing, competition, and recognition. Firstly, several studies found that collaboration and teamwork were common methods in social-based gamification [31,34,43,44]. Engaging in a group-based PA, individuals learn how to set health goals and interact with peers.…”

Association between behavioral phenotypes and sustained use of smartphones and wearable devices to remotely monitor physical activity

“…While these findings suggest that smartphones might lead to higher sustained use overall, there may be differences for different groups of patients. In a previous study, subgroups of participants distinguished by personality traits, social support, risk-taking attitudes, and baseline physical activity levels displayed varying responses to interventions targeting changes in physical activity 23 . Moreover, adoption and use of remote activity monitoring technologies have been shown to vary not only by sociodemographic characteristics such as age, gender, and income 21 , 24 , but also by physical activity levels 25 and social and behavioral traits.…”

“…Variables were selected for inclusion in LCA model construction based on established associations with physical activity intervention responsiveness and success of remote health monitoring 21 , 23 – 28 . Indicators with insufficient variability were excluded given that they are unlikely to aid in identifying subgroups.…”

Does gamification improve physical activity? A systematic review and meta-analysis

“…In addition, these subgroups and characteristics may not explain all the variance of the interventions. Indeed, demographic data often do not fully explain differences in the effectiveness of interventions [69,70] and more precise socio-psychological variables such as personality traits or motivational factors could explain this poorly understood significant heterogeneity. Moreover, the risk of bias analysis and the sunset funnel plot showed substantial differences in the qualities of included studies that can influence the heterogeneity.…”

“…Future studies should explicitly discuss motivational theory and systematically test the effect of gamification on psychological outcomes known to be involved in behavior change (e.g., self-efficacy, attitudes, intention), in order to better understand its mechanisms. As introduced before, the consideration of personality traits and psychological variables to determine behavioral phenotypes [69] are a promising way to evaluate participant's responses to interventions.…”