Development and assessment of a mobile app for veterinary herd health management

Thesis Abstract

Effective management of herd health is critical to enhancing productivity and ensuring the well-being of livestock, yet many veterinary practitioners and farmers face challenges related to timely data collection, disease monitoring, and decision-making due to limited access to integrated management tools. This study addresses these issues by developing and rigorously assessing a mobile application designed to facilitate veterinary herd health management (VHHM). The specific objectives include designing an intuitive, user-friendly mobile app integrated with current veterinary guidelines, evaluating its usability and functionality among end-users, and determining its impact on herd health management practices, disease detection, and record accuracy. The research adopts a mixed-methods approach, combining qualitative and quantitative techniques. The study population comprises 150 cattle farmers and 20 veterinary practitioners operating within a semi-urban agricultural region with high livestock density. A purposive sampling technique selects participants to ensure relevance and diversity of expertise. The development phase of the mobile app employs user-centered design principles, incorporating feedback from focus group discussions with 30 farmers and 10 veterinary professionals to inform features and interface design. Software testing follows, including alpha and beta testing phases, to refine the application's functionality. Quantitative data collection utilizes structured questionnaires and app usage logs over a six-month trial period. The questionnaires assess user satisfaction, perceived ease of use, and behavioral changes in herd health practices, with Likert scales providing quantifiable data. App usage data, such as login frequency, feature utilization, and data entries, are captured automatically. Data analysis involves descriptive statistics to profile user demographics and app engagement, followed by inferential techniques such as multiple regression analysis to examine the relationship between app usage and improvements in herd health indicators. Additionally, thematic analysis of qualitative feedback from user interviews provides insights into usability challenges and contextual factors affecting adoption. Expected findings suggest that the mobile app significantly enhances herd health management by streamlining record-keeping, facilitating early detection of diseases, and improving communication between farmers and veterinary services. Users are anticipated to report high satisfaction levels and increased confidence in managing herd health due to the app’s functionalities. Statistical analyses are projected to demonstrate a positive correlation between app utilization and key herd health outcomes, such as reduced disease incidence and improved vaccination compliance. This study contributes to veterinary medicine by providing empirical evidence on the effectiveness of mobile health (mHealth) interventions in livestock management, aligning with the theoretical framework of the Technology Acceptance Model (TAM) and the Diffusion of Innovations theory. It advances understanding of digital health adoption in veterinary contexts and offers a replicable model for similar settings. The main conclusion underscores the potential of tailored mobile applications to transform herd health practices in resource-limited environments. Recommendations include integrating the app into existing veterinary extension services, providing ongoing training for users, and continuously updating the application based on user feedback. Future research should explore the long-term sustainability of mHealth interventions and their scalability across different livestock sectors. Overall, the study affirms that strategically designed mobile health tools can serve as effective, scalable solutions for improving herd health management, thereby contributing to increased livestock productivity and economic resilience.