Page Abigail E, Emmott Emily H, Sear Rebecca, Perera Nilushka, Black Matthew, Elgood-Field Jake, Myers Sarah
Centre for Culture and Evolution, Brunel University London, London, UK.
London School of Hygiene and Tropical Medicine, Population Health, London, UK.
Int Breastfeed J. 2025 Apr 3;20(1):23. doi: 10.1186/s13006-025-00707-7.
Breastfeeding rates in the UK have remained stubbornly low despite long-term intervention efforts. Social support is a key, theoretically grounded intervention method, yet social support has been inconsistently related to improved breastfeeding. Understanding of the dynamics between infant feeding and social support is currently limited by retrospective collection of quantitative data, which prohibits causal inferences, and by unrepresentative sampling of mothers. In this paper, we present a case-study presenting the development of a data collection methodology designed to address these challenges.
In April-May 2022 we co-produced and piloted a mobile health (mHealth) data collection methodology linked to a pre-existing pregnancy and parenting app in the UK (Baby Buddy), prioritising real-time daily data collection about women's postnatal experiences. To explore the potential of mHealth in-app surveys, here we report the iterative design process and the results from a mixed-method (explorative data analysis of usage data and content analysis of interview data) four-week pilot.
Participants (n = 14) appreciated the feature's simplicity and its easy integration into their daily routines, particularly valuing the reflective aspect akin to journaling. As a result, participants used the feature regularly and looked forward to doing so. We find no evidence that key sociodemographic metrics were associated with women's enjoyment or engagement. Based on participant feedback, important next steps are to design in-feature feedback and tracking systems to help maintain motivation.
Reflecting on future opportunities, this case-study underscores that mHealth in-app surveys may be an effective way to collect prospective real-time data on complex infant feeding behaviours and experiences during the postnatal period, with important implications for public health and social science research.
尽管长期进行干预努力，但英国的母乳喂养率一直顽固地保持在较低水平。社会支持是一种关键的、有理论依据的干预方法，然而社会支持与母乳喂养改善之间的关系并不一致。目前，对婴儿喂养与社会支持之间动态关系的理解受到定量数据回顾性收集的限制，这种方法禁止进行因果推断，同时也受到母亲样本缺乏代表性的限制。在本文中，我们展示了一个案例研究，介绍了一种旨在应对这些挑战的数据收集方法的开发过程。
2022年4月至5月，我们共同制作并试点了一种移动健康（mHealth）数据收集方法，该方法与英国一款现有的怀孕和育儿应用程序（宝贝伙伴）相关联，重点是实时每日收集有关女性产后经历的数据。为了探索mHealth应用内调查的潜力，我们在此报告迭代设计过程以及一项为期四周的混合方法（使用数据的探索性数据分析和访谈数据的内容分析）试点的结果。
参与者（n = 14）赞赏该功能的简单性及其易于融入日常生活，尤其重视类似于写日记的反思方面。因此，参与者经常使用该功能并期待继续使用。我们没有发现关键社会人口统计学指标与女性的使用体验或参与度相关的证据。根据参与者反馈，接下来重要的步骤是设计功能内反馈和跟踪系统，以帮助维持积极性。
考虑到未来的机会，本案例研究强调，mHealth应用内调查可能是收集产后复杂婴儿喂养行为和经历的前瞻性实时数据的有效方式，对公共卫生和社会科学研究具有重要意义。
