Engagement of African American Women With Fitness Trackers and Mobile Technology for Shared Physical Activity Goals: Mixed Methods Study
Bibliographic record
Abstract
Background: While there is growing evidence demonstrating the usefulness of integrating social features within mobile health approaches, little research has explored how African American women use mobile platforms to facilitate physical activity within the context of a group-based physical activity intervention. Objective: The primary aim of this study was to qualitatively describe how African American women used private group messaging boards on the Fitbit mobile app (eg, the type of social and motivational strategies) during a 10-week group-based physical activity intervention. The secondary aim of this study was to quantitatively test whether greater engagement on the Fitbit mobile app (number of posts per week) was associated with greater daily physical activity (ie, steps and total minutes of physical activity) across 10 weeks. Methods: Data were collected from 54 African American women who participated in the Together Everyone Achieves More Physical Activity trial (mean age 51.57, SD 13.89 y). Participants completed weekly in-person group sessions, set group-based weekly physical activity goals, and used the Fitbit mobile app for ongoing group communication and support, including posting in a private group. This study used a QUAN-qual mixed methods design to describe how participants used the private group messaging boards on the Fitbit mobile app and to evaluate whether engagement on the Fitbit app was associated with greater physical activity (ie, steps and total minutes of physical activity) across the 10-week intervention. Results: The mean number of posts per week ranged from 1.79 (SD 2.95) in week 1 to 1.11 (SD 2.49) in week 10, with a maximum of 5.06 (SD 7.62) posts in week 5. A thematic content analysis revealed that the private groups reflected numerous topics, including motivational strategies, cognitive strategies, group strategies, informal positive communication, and sharing pictures. The quantitative analyses revealed that participants who posted at least once per week engaged in more steps (P=.006) and total minutes of physical activity (P=.02). Conclusions: Participants engaged in ongoing social support, positive communication, and vicarious learning through the Fitbit app, suggesting several important directions for future research, including potential interpersonal mechanisms and best practices for enhancing social support and physical activity among African American women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".