Community Engagement to Optimize the Use of Web-Based and Wearable Technology in a Cardiovascular Health and Needs Assessment Study: A Mixed Methods Approach
Bibliographic record
Abstract
BACKGROUND: Resource-limited communities in Washington, D.C. have high rates of obesity-related cardiovascular disease in addition to inadequate physical activity (PA) facilities and limited Internet access. Engaging community members in the design and implementation of studies to address these health disparities is essential to the success of community-based PA interventions. OBJECTIVE: The objective of the study was to use qualitative and quantitative methods to evaluate the feasibility and acceptability of PA-monitoring wristbands and Web-based technology by predominantly African American, church-based populations in resource-limited Washington, D.C. neighborhoods. METHODS: To address cardiovascular health in at-risk populations in Washington, D.C., we joined community leaders to establish a community advisory board, the D.C. Cardiovascular Health and Obesity Collaborative (D.C. CHOC). As their first initiative, the Washington, D.C. Cardiovascular Health and Needs Assessment intends to evaluate cardiovascular health, social determinants of health, and PA-monitoring technologies. At the recommendation of D.C. CHOC members, we conducted a focus group and piloted the proposed PA-monitoring system with community members representing churches that would be targeted by the Cardiovascular Health and Needs Assessment. Participants (n=8) agreed to wear a PA-monitoring wristband for two weeks and to log cardiovascular health factors on a secure Internet account. Wristbands collected accelerometer-based data that participants uploaded to a wireless hub at their church. Participants agreed to return after two weeks to participate in a moderated focus group to share experiences using this technology. Feasibility was measured by Internet account usage, wristband utilization, and objective PA data. Acceptability was evaluated through thematic analysis of verbatim focus group transcripts. RESULTS: Study participants (5 males, 3 females) were African American and age 28-70 years. Participant wristbands recorded data on 10.1±1.6 days. Two participants logged cardiovascular health factors on the website. Focus group transcripts revealed that participants felt positively about incorporating the device into their church-based populations, given improvements were made to device training, hub accessibility, and device feedback. CONCLUSIONS: PA-monitoring wristbands for objectively measuring PA appear to be a feasible and acceptable technology in Washington, D.C., resource-limited communities. User preferences include immediate device feedback, hands-on device training, explicit instructions, improved central hub accessibility, and designation of a church member as a trained point-of-contact. When implementing technology-based interventions in resource-limited communities, engaging the targeted community may aid in early identification of issues, suggestions, and preferences. TRIAL REGISTRATION: ClinicalTrials.gov NCT01927783; https://clinicaltrials.gov/ct2/show/NCT01927783 (Archived by WebCite at http://www.webcitation.org/6f8wL117u).
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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 | 0.043 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".