Evidence supports the importance of physical activity (PA) in the prevention of chronic diseases (1–4). Despite the known benefits of PA, only about half of adults meet the Surgeon General’s guideline of 150 minutes of moderate PA weekly (4). Many public health interventions exist to increase PA; however, engagement depends on various factors (5). Personal motivation and activity choice play a major role in behavior change, and methods to increase motivation is a large field of study (6).

Individuals are more likely to be active when they are able to select the types of activities they perform (7). This ability to choose makes the activity more pleasurable and increases the likelihood that a routine will be established (7). In interventions to increase PA, providing more choices promotes the inclusion of people with less common preferences (8).

Website-delivered PA interventions can improve PA among users relative to nonusers (9), and tracking of PA is associated with increased PA (10). Tracking activities may encourage individuals to exercise by helping them to visualize their progress. Numerous consumer technologies allow individuals to track their PA, and these technologies are increasing general awareness about PA engagement (11). The proliferation of exercise technology, such as wearables and other PA tracking smartphone applications, has created a pool of users interested in and conscious about their PA.

Although PA can be an individual activity, group-based PA incorporates a social element to promote engagement (12). Interaction with others can increase engagement in PA because it keeps people encouraged, motivated, and accountable (5,13). The objectives of this study were to 1) examine use of the Walk Georgia platform according to whether the user was registered as an individual or group user and 2) identify factors associated with increased use of the platform among users.

From February 1, 2015 through June 21, 2017, 13,902 accounts were created in Walk Georgia. Of these 13,902 accounts, 58.6% were individual users and 41.4% were group users. Of the 6,639 (47.8%) accounts with at least one logged activity, 218,766 activities were logged, accounting for 15,119,249 minutes of PA, 592,714 miles, and 41,858,446 points. Of these 6,639 users, 2,271 (34.2%) were individual users and 4,368 (65.8%) were group users. Approximately 95% (4,133 of 4,368) of group users were affiliated with a group when they registered. Among the 233 group users who initially registered as individual users, the average time for them to join a group was 200.4 days. Overall, 21.9% of users resided in rural counties, and a significantly larger proportion of individual users (27.7%) than group users (19.0%) resided in rural areas.

Among the 6,639 accounts with logged activity, on average, group users had created accounts more recently (591 [median, 513] days) than individual users (700 [median, 750] days) (Table 1). However, group users logged significantly more activities (40 [median, 18] activities) than individual users (20 [median, 4] activities). On average, compared with individual users, group users engaged in significantly more minutes of PA, earned significantly more points, rated higher difficulty levels of PA, and logged more weeks with 150 minutes or more of PA. Additionally, the percentage of weeks with 150 minutes or more of logged PA (for weeks with logged activity) was higher among group users (53%) than among individual users (32%).

The leading 20 activities for each set of users comprised 24 unique activities (Table 2). Overall, 218,766 activities were logged, of which 79.5% were logged by group users. The 6 most frequently logged activities for both groups were walking, running or jogging, cleaning, weight lifting, active stretching, and cardio sessions. Walking was the leading activity for both sets of users. Four activities were unique to individual users (ballet, CrossFit, sweeping, and calisthenics), and 4 activities were unique to group users (stair climbing, child care, squats, and Zumba).

Overall, 571 groups were created with an average of 7.6 members per group (range, 1– 912) (Table 3). The largest proportion (55%) of group users were affiliated with workplace groups (2,405 users; 177 groups; average number of members, 13.6), followed by school groups (791 users; 159 groups; average number of members, 5.0). On average, workplace, county, and city group users logged the most activities in the system, but school group members logged the most minutes. City, faith-based, and workplace group users logged 150 minutes or more of PA for the most number of weeks, on average. However, the proportion of weeks with 150 minutes or more of PA logged was highest among community, city, faith-based, and workplace group users.

In the regression model that examined factors associated with more logging activity, users affiliated with a group (B [standard error (SE)] = 21.80 [1.87]; P < .001) and users who had accounts for more days (B = 0.02 [0]; P < .001), on average, had significantly more PA logging activity. In the regression model that examined factors associated with larger proportions of weeks with 150 minutes or more of logged PA, on average, users affiliated with a group (B = 20.47 [1.03]; P < .001) and users who logged activities with higher perceived difficulty (B = 4.45 [0.69], P < .001) had significantly larger proportions of weeks with 150 minutes or more of logged PA.

Our results show that group affiliation was associated with more logging activity and more weeks with 150 minutes or more of logged PA. Our findings are consistent with those of other studies that report social motivation contributes to greater participation in and adherence to exercise programs (16–18). Although individual users recorded substantial levels of PA, most Walk Georgia users were group users and logged substantially more activity. Because the Walk Georgia system relies on users to self-report and proactively enter their PA into the system, we do not know whether all PA was accurately logged. Nor do we know whether group users were actually more physically active than individual users or group users were more diligent in their logging activity. Furthermore, users who did not join groups were solely responsible for tracking their activity, whereas group users could designate an individual to log activity for all group members. Regardless, our findings suggest that levels of social accountability (for actual PA and/or system logging) may have been higher among group users than among individual users. Future programs should incorporate the use of wearable PA trackers to more objectively measure the effects of group-based involvement on PA levels and online logging. Additionally, efforts are needed to integrate data collected daily from wearable PA trackers into existing online platforms such as Walk Georgia.

An innovative aspect of Walk Georgia is its assignment of points based on MET estimations (15,16). Users may view points as an incentive for logging activity, and organizations can use points to track and reward users and groups. Individual and group users can visualize and track their weekly and monthly progress and set personal goals to help motivate them to improve performance (18,19). Among group users, an additional incentive exists because each team member can compete to be the leading point earner on their team, and groups can compete with other groups to earn the highest point total. Generally, a point system promotes competition among users and serves as a reward to users (18). The use of a standardized equation that incorporates METs allows all users to engage in the competition equally, not just the walkers, runners, or other users who engage in distance-based activities. This standardized equation also allows those who engage primarily in one type of PA (eg, swimming) to be on a team with those who engage in different activities (eg, tennis, weightlifting, housework). However, MET estimates, supplied by the 2011 Compendium of Physical Activities (16), were intended to standardize MET intensities for PA but were not intended to be precise PA energy costs per individual (ie, they did not account for “differences in body mass, adiposity, age, sex, efficiency of movement, geographic and environmental conditions in which the activities are performed”) (16).

Given that group users used the Walk Georgia system more than individual users (and had fewer days since accounts were created), we recommend that Walk Georgia and similar programs expand their proportion of group users. This expansion could use strategies to bring together existing members into groups or purposively recruit new users that may inherently join in groups. One strategy could be to identify and recruit naturally occurring groups, organized by location (eg, county, city), affiliation (eg, workplace, school, faith-based), or social group (eg, friends, clubs). Beyond groups such as walking or biking clubs, settings that show promise for purposive Walk Georgia recruitment (as a stand-alone intervention or a component of an existing PA intervention) are faith-based organizations (20), schools (21–23), and worksites (24,25). For example, in schools, Walk Georgia may be particularly attractive because of its interactive map of Georgia, online lesson plans (www.walkgeorgia.org/lesson-plans.php), point calculations, and information on county resources (www.walkgeorgia.org/resources/county-resources.php). These features can be integrated into other curriculum subjects (eg, physical education, math, science, history, social science) (26).

This study had limitations. It was a naturalistic inquiry in which the only data collected were data entered into the Walk Georgia system, which is a tracking system, not a formal intervention. Thus, users were not required to attend sessions, and we do not know whether group users physically or virtually met to engage in PA. Furthermore, data were not collected from users before they registered; thus we could not assess changes over time. Another possible limitation is the limitation of self-reported data: it is important to reiterate that increased activity logging in the system does not necessarily equate to increased engagement in PA (participants could engage in more or less activity than what is being logged in the system). Another limitation was the absence of users’ sociodemographic information (eg, age, sex, race/ethnicity, socioeconomic); such information would allow for comparisons across individual and group users and users in different group types. This limitation prevents us from generalizing our findings beyond this sample. Walk Georgia is beginning to collect these data from users routinely. Structured and comprehensive data collection is recommended for online tracking systems. A small number of groups and organizations, primarily those who received local county Extension sub-awards ($500 awards, which required local matching funds), provided incentives to users. Incentives and recruitment strategies implemented by groups and organizations, not simply group affiliation, could have been the true driver of activity logging. This possible confounder in our study requires further investigation. Future studies should also investigate the activity preferences within group types and identify the influence of the type of PA in which users engage on logging frequency and PA levels (eg, walking vs swimming vs biking). Finally, we do not know whether group users actually competed within or across groups and whether or not this competition influenced their activity logging behavior. Moving forward, Walk Georgia plans to integrate a “leader board” to allow groups and users to view the logged PA of others and formally or informally compete with other users.

Our findings highlight the potential benefit of online PA tracking platforms like Walk Georgia to encourage PA for health promotion and disease prevention. Our analyses suggest such platforms are used more by group users than by individual users; thus, we recommend purposive group user recruitment strategies. The easy-to-use online/mobile-friendly system makes it attractive for diverse audiences despite geographic location. Overall, Walk Georgia provides a way of connecting groups for the common goal of being more physically active. The platform is sufficient to engage users as a proactive activity; however, the system could be enhanced by integrating other passive technologies such as those available via wearables and smartphones.