Leveraging the Black Girls Run Web-Based Community as a Supportive Community for Physical Activity Engagement: Mixed Methods Study
Bibliographic record
Abstract
Background About 59%-73% of Black women do not meet the recommended targets for physical activity (PA). PA is a key modifiable lifestyle factor that can help mitigate risk for chronic diseases such as obesity, diabetes, and hypertension that disproportionately affect Black women. Web-based communities focused on PA have been emerging in recent years as web-based gathering spaces to provide support for PA in specific populations. One example is Black Girls Run (BGR), which is devoted to promoting PA in Black women. Objective The purpose of this study was to describe the content shared on the BGR public Facebook page to provide insight into how web-based communities engage Black women in PA and inform the development of web-based PA interventions for Black women. Methods Using Facebook Crowdtangle, we collected posts (n=397) and associated engagement data from the BGR public Facebook page for the 6-month period between June 1, 2021, and December 31, 2021. We pooled data in Dedoose to analyze the qualitative data and conducted a content analysis of qualitative data. We quantified types of posts, post engagement, and compared post types on engagement: “like,” “love,” “haha,” “wow,” “care,” “sad,” “angry,” “comments,” and “shares.” Results The content analysis revealed 8 categories of posts: shout-outs to members for achievements (n=122, 31%), goals or motivational (n=65, 16%), announcements (n=63, 16%), sponsored or ads (n=54, 14%), health related (n=47, 11%), the lived Black experience (n=23, 6%), self-care (n=15, 4%), and holidays or greetings (n=8, 2%). The 397 posts attracted a total of 55,354 engagements (reactions, comments, and shares). Associations between the number of engagement and post categories were analyzed using generalized linear models. Shout-out posts (n=22,268) elicited the highest average of total user engagement of 181.7 (SD 116.7), followed by goals or motivational posts (n=11,490) with an average total engagement of 160.1 (SD 125.2) and announcements (n=7962) having an average total engagement of 129.9 (SD 170.7). Significant statistical differences were found among the total engagement of posts (χ72=80.99, P<.001), “like” (χ72=119.37, P<.001), “love” (χ72=63.995, P<.001), “wow” (χ72=23.73, P<.001), “care” (χ72=35.06, P<.001), “comments” (χ72=80.55, P<.001), and “shares” (χ72=71.28, P<.001). Conclusions The majority of content on the BGR Facebook page (n=250, 63%) was focused on celebrating member achievements, motivating members to get active, and announcing and promoting active events. These types of posts attracted 75% of total post engagement. BGR appears to be a rich web-based community that offers social support for PA as well as culturally relevant health and social justice content. Web-based communities may be uniquely positioned to engage minoritized populations in health behavior. Further research should explore how and if web-based communities such as BGR can be interwoven into health interventions and health promotion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".