03035nas a2200409   4500000000100000008004100001260002200042653003400064653003400098653002300132653001800155100001100173700001300184700001500197700001400212700001600226700001400242700001100256700001700267700001300284700001600297700002400313700001100337700001200348700001400360700002500374700001200399700001500411700001300426700001600439245020400455856006700659300002000726490000600746520184800752022002502600       2022                            d  bSAGE Publications10aHealth Information Management10aComputer Science Applications10aHealth Informatics10aHealth Policy1 aTran V1 aGwenzi F1 aMarongwe P1 aRutsito O1 aChatikobo P1 aMurenje V1 aHove J1 aMunyaradzi T1 aRogers Z1 aTshimanga M1 aSidile-Chitimbire V1 aXaba S1 aNcube G1 aMasimba L1 aMakunike-Chikwinya B1 aHolec M1 aBarnhart S1 aWeiner B1 aFeldacker C00aREDCap mobile data collection: Using implementation science to explore the potential and pitfalls of a digital health tool in routine voluntary medical male circumcision outreach settings in Zimbabwe  uhttps://journals.sagepub.com/doi/pdf/10.1177/20552076221112163  a2055207622111210 v83 a<p>Background Digital data collection tools improve data quality but are limited by connectivity. ZAZIC, a Zimbabwean consortium focused on scaling up male circumcision (MC) services, provides MC in outreach settings where both data quality and connectivity is poor. ZAZIC implemented REDCap Mobile app for data collection among roving ZAZIC MC nurses. To inform continued scale-up or discontinuation, this paper details if, how, and for whom REDCap improved data quality using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. Methods Data were collected for this retrospective, cross-sectional study for nine months, from July 2019 to March 2020, before COVID-19 paused MC services. Data completeness was compared between paper- and REDCap-based tools and between two ZAZIC partners using two sample, one-tailed t-tests. Results REDCap reached all roving nurses who reported 26,904 MCs from 1773 submissions. REDCap effectiveness, as measured by data completeness, decreased from 89.2% in paper to 76.6% in REDCap app for Partner 1 ( p &lt; 0.001, 95% CI: −0.24, −0.12) but increased modestly from 86.2% to 90.3% in REDCap for Partner 2 ( p = 0.05, 95% CI: -.007, 0.12). Adoption of REDCap was 100%; paper-based reporting concluded in October 2019. Implementation varied by partner and user. Maintenance appeared high. Conclusion Although initial transition from paper to REDCap showed mixed effectiveness, post-hoc analysis from service resumption found increased REDCap data completeness across partners, suggesting locally-led momentum for REDCap-based data collection. Staff training, consistent mentoring, and continued technical support appear critical for continued use of digital health tools for quality data collection in rural Zimbabwe and similar low connectivity settings.</p>
  a2055-2076, 2055-2076