Department of Economics, University of Zurich, Zurich, Switzerland.
College of Medicine, University of Malawi, Lilongwe, Malawi.
JMIR Public Health Surveill. 2021 Mar 5;7(3):e23154. doi: 10.2196/23154.
Multimodal approaches have been shown to be a promising way to collect data on child development at high frequency, combining different data inputs (from phone surveys to signals from noninvasive biomarkers) to understand children's health and development outcomes more integrally from multiple perspectives.
The aim of this work was to describe an implementation study using a multimodal approach combining noninvasive biomarkers, social contact patterns, mobile surveying, and face-to-face interviews in order to validate technologies that help us better understand child development in poor countries at a high frequency.
We carried out a mixed study based on a transversal descriptive analysis and a longitudinal prospective analysis in Malawi. In each village, children were sampled to participate in weekly sessions in which data signals were collected through wearable devices (electrocardiography [ECG] hand pads and electroencephalography [EEG] headbands). Additionally, wearable proximity sensors to elicit the social network were deployed among children and their caregivers. Mobile surveys using interactive voice response calls were also used as an additional layer of data collection. An end-line face-to-face survey was conducted at the end of the study.
During the implementation, 82 EEG/ECG data entry points were collected across four villages. The sampled children for EEG/ECG were 0 to 5 years old. EEG/ECG data were collected once a week. In every session, children wore the EEG headband for 5 minutes and the ECG hand pad for 3 minutes. In total, 3531 calls were sent over 5 weeks, with 2291 participants picking up the calls and 984 of those answering the consent question. In total, 585 people completed the surveys over the course of 5 weeks.
This study achieved its objective of demonstrating the feasibility of generating data through the unprecedented use of a multimodal approach for tracking child development in Malawi, which is one of the poorest countries in the world. Above and beyond its multiple dimensions, the dynamics of child development are complex. It is the case not only that no data stream in isolation can accurately characterize it, but also that even if combined, infrequent data might miss critical inflection points and interactions between different conditions and behaviors. In turn, combining different modes at a sufficiently high frequency allows researchers to make progress by considering contact patterns, reported symptoms and behaviors, and critical biomarkers all at once. This application showcases that even in developing countries facing multiple constraints, complementary technologies can leverage and accelerate the digitalization of health, bringing benefits to populations that lack new tools for understanding child well-being and development.
多模态方法已被证明是一种很有前途的方法，可以高频收集儿童发展数据，结合不同的数据输入（从电话调查到无创生物标志物信号），从多个角度更全面地了解儿童的健康和发展结果。
本研究旨在描述一项使用多模态方法的实施研究，该方法结合了无创生物标志物、社会接触模式、移动调查和面对面访谈，以验证有助于我们更频繁地了解贫困国家儿童发展的技术。
我们在马拉维进行了一项混合研究，基于横断面描述性分析和纵向前瞻性分析。在每个村庄，抽取儿童参加每周一次的会议，通过可穿戴设备（心电图[ECG]手垫和脑电图[EEG]头带）收集数据信号。此外，还在儿童及其照顾者之间部署了可穿戴式接近传感器来引出社交网络。还使用交互式语音应答电话进行移动调查作为额外的数据收集层。在研究结束时进行了面对面的终线调查。
在实施过程中，在四个村庄共采集了 82 个 EEG/ECG 数据录入点。接受 EEG/ECG 采样的儿童年龄在 0 至 5 岁之间。每周采集一次 EEG/ECG 数据。在每次会议中，儿童佩戴 EEG 头带 5 分钟，佩戴 ECG 手垫 3 分钟。总共发送了 3531 个电话，有 2291 名参与者接听了电话，其中 984 人回答了同意问题。在 5 周的时间里，共有 585 人完成了调查。
本研究实现了其目标，即在马拉维展示了通过前所未有的多模态方法跟踪儿童发展的可行性，马拉维是世界上最贫穷的国家之一。儿童发展的动态不仅复杂，而且没有任何单一数据流可以准确描述它，即使结合在一起，不频繁的数据也可能错过关键的转折点以及不同条件和行为之间的相互作用。相反，以足够高的频率结合不同的模式可以让研究人员通过同时考虑接触模式、报告的症状和行为以及关键生物标志物来取得进展。本应用展示了即使在面临多种限制的发展中国家，互补技术也可以利用和加速健康的数字化，为缺乏了解儿童福祉和发展新工具的人群带来益处。
