健康
数据收集
母乳喂养
心理学
公民科学
日志文件系统
干预(咨询)
心理干预
计算机科学
应用心理学
医学
护理部
统计
植物
数据文件
数学
病理
数据库
生物
作者
Abigail E. Page,Emily H Emmott,Rebecca Sear,Nilushka Perera,Matthew Black,Jack Elgood-Field,Sarah Myers
标识
DOI:10.31234/osf.io/gmdhb
摘要
Breastfeeding rates in the UK have remained stubbornly low despite long-term intervention efforts. Social support is a key, theoretically grounded intervention target in both cases, yet they have a weak evidence base. Understanding of the dynamics between infant feeding, maternal wellbeing 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 the development of a data collection methodology as a case-study, designed to address these challenges. We coproduced and piloted a mobile health (mHealth) data collection methodology linked to a pre-existing pregnancy and parenting app (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-methods 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 predicted 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.
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