Sedentary lifestyle is an escalating epidemic. Little is known about whether or how social media can be used to design a cost-effective solution for sedentary lifestyle. In this article we describe the data from a randomized controlled trial (RCT) that evaluated two prominent strategies for conducting exercise interventions using elements of social media: motivational media campaigns and online peer networks. The data file includes 217 participants' basic demographic information, number of exercise class enrollments over 13 weeks, and self-reported number of days for exercise activities in the previous 7 days at baseline. Among the 217, 164 also have data on self-reported number of days for exercise activities at the post-program. Data are supplied with this article. The interpretation of these data can be found in the research article published by the authors in Preventive Medicine Reports in 2015 [1].

Objective

To identify what features of social media - promotional messaging or peer networks - can increase physical activity.

Method

A 13-week social media-based exercise program was conducted at a large Northeastern university in Philadelphia, PA. In a randomized controlled trial, 217 graduate students from the University were randomized to three conditions: a control condition with a basic online program for enrolling in weekly exercise classes led by instructors of the University for 13 weeks, a media condition that supplemented the basic program with weekly online promotional media messages that encourage physical activity, and a social condition that replaced the media content with an online network of four to six anonymous peers composed of other participants of the program, in which each participant was able to see their peers' progress in enrolling in classes. The primary outcome was the number of enrollments in exercise classes, and the secondary outcomes were self-reported physical activities. Data were collected in 2014.

Results

Participants enrolled in 5.5 classes on average. Compared with enrollment in the control condition (mean = 4.5), promotional messages moderately increased enrollment (mean = 5.7, p = 0.08), while anonymous social networks significantly increased enrollment (mean = 6.3, p = 0.02). By the end of the program, participants in the social condition reported exercising moderately for an additional 1.6 days each week compared with the baseline, which was significantly more than an additional 0.8 days in the control condition.

Conclusion

Social influence from anonymous online peers was more successful than promotional messages for improving physical activity.

Clinical trial registration

ClinicalTrials.gov: NCT02267369.

To identify what features of online social networks can increase physical activity, we conducted a 4-arm randomized controlled trial in 2014 in Philadelphia, PA. Students (n = 790, mean age = 25.2) at an university were randomly assigned to one of four conditions composed of either supportive or competitive relationships and either with individual or team incentives for attending exercise classes. The social comparison condition placed participants into 6-person competitive networks with individual incentives. The social support condition placed participants into 6-person teams with team incentives. The combined condition with both supportive and competitive relationships placed participants into 6-person teams, where participants could compare their team's performance to 5 other teams' performances. The control condition only allowed participants to attend classes with individual incentives. Rewards were based on the total number of classes attended by an individual, or the average number of classes attended by the members of a team. The outcome was the number of classes that participants attended. Data were analyzed using multilevel models in 2014. The mean attendance numbers per week were 35.7, 38.5, 20.3, and 16.8 in the social comparison, the combined, the control, and the social support conditions. Attendance numbers were 90% higher in the social comparison and the combined conditions (mean = 1.9, SE = 0.2) in contrast to the two conditions without comparison (mean = 1.0, SE = 0.2) (p = 0.003). Social comparison was more effective for increasing physical activity than social support and its effects did not depend on individual or team incentives.

Objective

We sought to test whether participation in an online group including IUD users influenced IUD-related knowledge, attitudes, and behavior among IUD non-users, as a proof-of-concept evaluation of information dissemination for less commonly used or novel contraceptives.

Study design

We conducted a blinded, randomized controlled trial on the effect of online communication with IUD users within an online program called Birth Control Connect. Participants were women age 18-45 living in the United States who had never used an IUD. We invited participants randomized to the intervention to join two-week, nine-member discussion groups including four satisfied IUD users and five IUD non-users; we invited control participants to groups including nine IUD non-users. We performed chi-squared tests on IUD knowledge, information-seeking, informational support and use in immediate post-surveys, and t-tests comparing change in IUD attitudes and frequency of logins to discussion groups.

Results

We invited 488 IUD non-users and enrolled them into 70 groups between October 2015 and April 2016. We found increased positive attitudes towards the IUD in the intervention arm (0.65-point increase between pre- and post-surveys, versus 0.05 mean change for control arm, p = 0.03 for hormonal IUD, with a trend in the same direction for the non-hormonal IUD). Informational support also increased, with 70.3% of intervention arm participants self-reporting that they gained a better idea of what the IUD would be like, compared to 51.3% in control arm (p < 0.01). Of intervention participants, 63.3% versus 51.3% of control participants reported gaining new information from their group (p = 0.03). There were no differences in correct responses to knowledge items or information-seeking between groups.

Conclusions

Online exposure to IUD users increased positive attitudes toward the IUD and informational support for decision-making about the IUD among non-users.

Implications statement

Online spaces provide a promising environment for the exchange of accurate, useful contraceptive information based on real user experiences. Interventions aiming to harness social communication through structured online conversations (e.g., on existing social media platforms) about user experiences with lesser-known contraceptive methods such as the IUD may be worthwhile.

Human groups are able to converge to more accurate beliefs through deliberation, even in the presence of polarization and partisan bias --- a phenomenon known as the ``wisdom of partisan crowds.'' Large Language Models (LLMs) are increasingly being used to simulate human collective behavior, yet few benchmarks exist for evaluating their dynamics against the behavior of human groups. In this paper, we examine the extent to which the wisdom of partisan crowds emerges in groups of LLM-based agents that are prompted to role-play as partisan personas (e.g., Democrat or Republican). We find that they not only display human-like partisan biases, but also converge to more accurate beliefs through deliberation, as humans do. We then identify several factors that interfere with convergence, including the use of chain-of-thought prompting and lack of details in personas. Conversely, fine-tuning on human data appears to enhance convergence. These findings show the potential and limitations of LLM-based agents as a model of human collective intelligence.

Accurately simulating human opinion dynamics is crucial for understanding a variety of societal phenomena, including polarization and the spread of misinformation. However, the agent-based models (ABMs) commonly used for such simulations often over-simplify human behavior. We propose a new approach to simulating opinion dynamics based on populations of Large Language Models (LLMs). Our findings reveal a strong inherent bias in LLM agents towards producing accurate information, leading simulated agents to consensus in line with scientific reality. This bias limits their utility for understanding resistance to consensus views on issues like climate change. After inducing confirmation bias through prompt engineering, however, we observed opinion fragmentation in line with existing agent-based modeling and opinion dynamics research. These insights highlight the promise and limitations of LLM agents in this domain and suggest a path forward: refining LLMs with real-world discourse to better simulate the evolution of human beliefs.