Loss Aversion Explains Physical Activity Changes in a Behavioral Gamification Trial.

Introduction: Loss aversion when using gamification is incompletely understood. The aim of this study was therefore to examine how participants alter their behavior vis-a-vis meeting a daily step goal based on the prospect of losing or gaining a gamification level. Methods: We enrolled 602 participants across four arms who were given pedometers. In the three experimental arms, participants began at the medium level and were allocated 70 points each week, losing 10 points each day they did not meet their step goal. Having at least 40 points at the end of the week resulted in a level increase, otherwise they lost a level. We fit a generalized estimating equation, clustered on participants, modeling step goal attainment on day 7. Our primary predictor was a categorical variable simultaneously indicating what level the participants began the week in and whether they had more than, less than, or exactly 40 points after 6 days. Results: Participants at risk of losing the highest level were 18.40% (confidence interval [95% CI]: 18.26-19.90) more likely to meet their step goal than those who had secured the highest level. Participants who could potentially move from the low to the medium level were 10.61% (95% CI: 9.98-11.24) more likely to meet their step goal than those in the Control group. Those in the Medium group were similarly more likely to achieve their step goal on day 7 (10.00%, 95% CI: 9.15-10.85) than those who had already secured an increase to the high level. Discussion: We find that participants in this trial generally exhibit loss aversion so long as the loss relates to something that was earned rather than endowed. This knowledge can be incorporated in future interventions using gamification by requiring participants to earn all levels as they progress. ClinicalTrials.gov identifier: NCT03311230.

Association of Behavioral Phenotypes With Changes in Sleep During a Workplace Wellness Program.

PURPOSE: Examine changes in sleep duration by 3 behavioral phenotypes during a workplace wellness program with overweight and obese adults. DESIGN: Secondary analysis of a randomized clinical trial. SETTING: Remotely monitored intervention conducted across the United States. SUBJECTS: 553 participants with a body mass index ≥25. INTERVENTION: Participants were randomized to 1 of 4 study arms: control, gamification with support, gamification with collaboration, and gamification with competition to increase their physical activity. All participants were issued a wrist-worn wearable device to record their daily physical activity and sleep duration. MEASURES: The primary outcome was change in daily sleep duration from baseline during the 24 week intervention and follow-up period by study arm within behavioral phenotype class. ANALYSIS: Linear mixed effects regression. RESULTS: Participants who had a phenotype of less physically active and less social at baseline, in the gamification with collaboration arm, significantly increased their sleep duration during the intervention period (30.2 minutes [95% CI 6.9, 53.5], P = 0.01), compared to the control arm. There were no changes in sleep duration among participants who were more extroverted and motivated or participants who were less motivated and at-risk. CONCLUSIONS: Changes in sleep during a physical activity intervention varied by behavioral phenotype. Behavioral phenotypes may help to precisely identify who is likely to improve sleep duration during a physical activity intervention.

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.

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 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.

Effectiveness of Behaviorally Designed Gamification Interventions With Social Incentives for Increasing Physical Activity Among Overweight and Obese Adults Across the United States: The STEP UP Randomized Clinical Trial.

Importance: Gamification, the use of game design elements in nongame contexts, is increasingly being used in workplace wellness programs and digital health applications. However, the best way to design social incentives in gamification interventions has not been well examined. Objective: To assess the effectiveness of support, collaboration, and competition within a behaviorally designed gamification intervention to increase physical activity among overweight and obese adults. Design, Setting, and Participants: This 36-week randomized clinical trial with a 24-week intervention and 12-week follow-up assessed 602 adults from 40 states with body mass indexes (calculated as weight in kilograms divided by height in meters squared) of 25 or higher from February 12, 2018, to March 17, 2019. Interventions: Participants used a wearable device to track daily steps, established a baseline, selected a step goal increase, were randomly assigned to a control (n = 151) or to 1 of 3 gamification interventions (support [n = 151], collaboration [n = 150], and competition [n = 150]), and were remotely monitored. The control group received feedback from the wearable device but no other interventions for 36 weeks. The gamification arms were entered into a 24-week game designed using insights from behavioral economics with points and levels for achieving step goals. No gamification interventions occurred during follow-up. Main Outcomes and Measures: The primary outcome was change in mean daily steps from baseline through the 24-week intervention period. Results: A total of 602 participants (mean [SD] age, 39 [10] years; mean [SD] body mass index, 30 [5]; 427 [70.9%] male) were included in the study. Compared with controls, participants had a significantly greater increase in mean daily steps from baseline during the intervention in the competition arm (adjusted difference, 920; 95% CI, 513-1328; P < .001), support arm (adjusted difference, 689; 95% CI, 267-977; P < .001), and collaboration arm (adjusted difference, 637; 95% CI, 258-1017; P = .001). During follow-up, physical activity remained significantly greater in the competition arm than in the control arm (adjusted difference, 569; 95% CI, 142-996; P = .009) but was not significantly greater in the support (adjusted difference, 428; 95% CI, 19-837; P = .04) and collaboration (adjusted difference, 126; 95% CI, -248 to 468; P = .49) arms than in the control arm. Conclusions and Relevance: All 3 gamification interventions significantly increased physical activity during the 24-week intervention, and competition was the most effective. Physical activity was lower in all arms during follow-up and only remained significantly greater in the competition arm than in the control arm. Trial Registration: ClinicalTrials.gov identifier: NCT03311230.

Social incentives to encourage physical activity and understand predictors (STEP UP): Design and rationale of a randomized trial among overweight and obese adults across the United States.

BACKGROUND: Less than half of adults in the United States (US) obtain the recommended level of physical activity. Social incentives, the influences that impact individuals to adjust their behaviors based on social ties or connections, are ubiquitous and could be leveraged within gamification interventions to provide a scalable, low-cost approach to increase engagement. Gamification, or the use of game design in non-game situations, is commonly used in the real world, but in most cases has not appropriately leveraged principles from theories of health behavior. METHODS: We are conducting a four-arm, randomized, controlled trial of 602 overweight and obese adults to evaluate the effectiveness of gamification interventions that leverage insights from behavioral economics to enhance either supportive, competitive, or collaborative social incentives. Daily step counts are monitored using wearable devices that transmit data to the study platform. Participants established a baseline step count, selected a step goal increase, and then were randomly assigned to control or one of three interventions for a 24-week intervention and 12-week follow-up period. To understand predictors of strong or poor performance, we had participants complete validated questionnaires on a range of areas including their personality, risk preferences, social network, and habits relating to physical activity, eating, and sleep. Trial enrollment was conducted in partnership with Deloitte Consulting and included employees from 40 states across the US. CONCLUSION: The STEP UP Trial represents a scalable model and interventions found to be effective could be deployed more broadly to increase physical activity. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT03311230.