An Interactive Voice Response System to Increase Physical Activity and Prevent Cancer in the Rural Alabama Black Belt: Design and Usability Study
Bibliographic record
Abstract
BACKGROUND: Increased physical activity (PA) levels are associated with reduced risk and improved survival for several cancers; however, most Americans engage in less than the recommended levels of PA. Using interactive voice response (IVR) systems to provide personalized health education and counseling may represent a high-reach, low-cost strategy for addressing physical inactivity and cancer disparities in disproportionately burdened rural regions. However, there has been a paucity of research conducted in this area to date. OBJECTIVE: The aim of this study is to design, develop, and test the usability of an IVR system aimed at increasing PA levels in the rural Alabama Black Belt. METHODS: A pilot version of the IVR system was used to assess initial feasibility and acceptability. Detailed exit interviews were conducted to elicit participant feedback, which helped inform the development of a substantially upgraded in-house IVR system. This refined IVR system was then subjected to a sequential explanatory mixed methods evaluation. Participating rural county coordinators and research staff (N=10) tested the usability of the IVR system features for 2 weeks and then completed the System Usability Scale and qualitative semistructured interviews. RESULTS: The study sample comprised mostly African American people, women, rural county coordinators, and research staff (N=10). Participants rated the IVR system with a mean score of 81 (SD 5) on the System Usability Scale, implying excellent usability. In total, 5 overarching themes emerged from the qualitative interviews: likes or dislikes of the intervention, barriers to or facilitators of PA, technical difficulties, quality of calls, and suggestions for intervention improvement. Message framing on step feedback, call completion incentives, and incremental goal-setting challenges were areas identified for improvement. The positive areas highlighted in the interviews included the personalized call schedules, flexibility to call in or receive a call, ability to make up for missed calls, narration, and PA tips. CONCLUSIONS: The usability testing and feedback received from the rural county coordinators and research staff helped inform a final round of refinement to the IVR system before use in a large randomized controlled trial. This study stresses the importance of usability testing of all digital health interventions and the benefits it can offer to the intervention.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".