Usability and Adoption of a Mobile Health Decision Support Tool Among Community Health Workers in Rural Kenya: A Mixed-Methods Evaluation
Bibliographic record
Abstract
Background: Mobile health (mHealth) decision support tools have the potential to improve community health worker (CHW) performance in low-resource settings, but evidence on real-world usability and adoption remains limited. In Kenya, CHWs serve as the frontline of primary healthcare, yet they often work with minimal supervision and limited access to clinical guidelines. Objective: This study evaluated the usability, adoption patterns, and barriers to use of a mobile health decision support tool deployed among CHWs in rural Kenya. The tool provided algorithm-based guidance for integrated community case management (iCCM) of childhood illnesses. Methods: We conducted a mixed-methods evaluation involving 85 CHWs across 12 community health units in Machakos County, Kenya, between January and December 2023. Quantitative data included System Usability Scale (SUS) scores, automated usage logs, and pre-post knowledge assessments. Qualitative data included 20 in-depth interviews and 4 focus group discussions. Results: Mean SUS score was 72.4 (SD 8.3), indicating acceptable usability. Sustained use was achieved by 58% of CHWs. Knowledge scores improved from 68% to 84% (P<.001). Key barriers included technical challenges (41%), perceived redundancy among experienced CHWs (32%), and accuracy concerns (28%). Facilitators included perceived time savings (67%), improved credibility (54%), and peer support (45%). CHWs under 40 years were more likely to sustain use (OR 2.8, P=.02). Conclusions: The mobile decision support tool demonstrated acceptable usability and was associated with improved knowledge, but adoption varied substantially. Implementation strategies should address technical barriers, engage experienced CHWs, and leverage peer support networks. Keywords: mHealth, community health workers, usability, adoption, implementation science, Kenya, digital health, iCCM
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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 | 0.056 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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 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".