In India, Community Healthcare Workers (CHWs) serve as critical intermediaries between the state and beneficiaries, including pregnant mothers and children. Effective planning and prioritization of care and services necessitate the collection of accurate health data from the community. Crowdsourcing child anthropometric data through CHWs could establish a valuable repository for evidence-based decision-making and service planning. However, existing platforms often fail to maintain CHWs' engagement over time and across different spatial contexts, resulting in spatially misrepresented and outdated data. This study addresses these challenges by conducting a co-design exercise to develop innovative methods for collecting anthropometric data over time and space. The exercise involved analyzing data to create hotspot and density distribution maps. We implemented a trial of the developed game with two groups (n=94 per group) from various states across India, comparing the game-based and non-game-based data collection methods. Our findings reveal that the game-based approach significantly improved measuring efficiency (p<0.05) and demonstrated superior engagement and retention compared to the non-game-based method. This research contributes to the expanding literature on co-design and Research through Design (RtD) methodologies for developing geospatial games, highlighting their potential to enhance data collection practices and improve engagement among CHWs.

翻译：在印度，社区卫生工作者（CHWs）作为国家与受益者（包括孕妇和儿童）之间的关键中介，发挥着重要作用。有效的护理和服务规划需要从社区收集准确的健康数据。通过社区卫生工作者众包儿童人体测量数据，可建立基于证据的决策和服务规划数据库。然而，现有平台往往无法在不同时空背景下持续维持社区卫生工作者的参与度，导致数据存在空间代表性不足和时效滞后问题。本研究通过开展协同设计实践，开发创新方法以持续收集时空人体测量数据。研究团队通过数据分析生成热点图与密度分布图，并针对印度各邦两组参与者（每组n=94）实施游戏化方案试验，比较基于游戏与非游戏数据收集方法的差异。研究结果表明，基于游戏的方法显著提升了测量效率（p<0.05），并在参与度和留存率方面优于非游戏方法。本研究拓展了地理空间游戏协同设计与"通过设计研究"（RtD）方法论的相关文献，揭示了该类游戏在提升数据采集实践和增强社区卫生工作者参与度方面的潜力。