Back

Process Evaluation for the Delivery of a Water, Sanitation and Hygiene Mobile Health Program: Randomized Controlled Trial of the PICHA7 Mobile Health Program

Sanvura, P.; Endres, K.; Bisimwa, J.-C.; Perin, J.; Cikomola, C.; Bengehya, J.; Maheshe, G.; Mwishingo, A.; Bisimwa, L.; Williams, C.; George, C. M.

2025-02-27 public and global health
10.1101/2025.02.26.25322956 medRxiv
Show abstract

In the Democratic Republic of the Congo (DRC) there are over 85 million diarrhea episodes annually. Effective and scalable water, sanitation, and hygiene (WASH) interventions are needed to reduce diarrheal diseases in the DRC. Mobile health (mHealth) reminders have been shown to reduce disease morbidity and increase health-protective behaviors. Therefore, WASH mHealth programs present a promising approach to improve WASH behaviors. The Preventative-Intervention-for-Cholera-for-7-days (PICHA7) program is a targeted WASH intervention combining of mHealth and in-person visits delivered to diarrhea patient households in DRC to reduce diarrheal diseases. During the randomized controlled trial (RCT) of PICHA7, 1196 participants received weekly PICHA7 mHealth program voice, interactive voice response (IVR) quiz, and text messages over 12 months. Outcome indicators included % of unique text, voice, and IVR messages received (fidelity) and % of unique messages fully listened to (dose) assessed using the engageSPARK mobile message platform, and program reach to households assessed through monthly follow-up visits. 84% of households received unique text messages and 90% of unique voice and IVR messages were answered. Households reported receiving a PICHA7 mHealth message in the past two weeks at 72% of surveillance visits (844/1177). 74% (309/418) of participants reported sharing a PICHA7 mHealth message with another person at least once. These findings show high fidelity, dose, and reach of mobile message delivery in the PICHA7 mHealth program. This study demonstrates the feasibility of delivering the PICHA7 mHealth program in eastern DRC and provides important insights for delivering WASH mHealth programing in low- and middle-income countries globally. Conflict of interestThe authors declare no conflicts of interest.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.