BACKGROUND
Multi-modal 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 non-invasive bio-markers – to understand children’s health and development outcomes more integrally, from multiple perspectives.
OBJECTIVE
The objective of this work is to describe an implementation study using a multi-modal approach combining non-invasive 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 high frequency.
METHODS
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 collecting through wearable devices (ECG hand pads and EEG headbands). Additionally, wearable proximity sensors to elicit the social network were deployed in 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.
RESULTS
During the implementation, 82 EEG/ECG data entry points were collected across the four villages. The sampled children for EEG/ECG were 0-5 years old. EEG/ECG data were collected one a week. In every session, children worn the EEG headband for 5 minutes, and the ECG hand pad for 3 minutes. In total, 3,531 calls were sent over 5 weeks. 2,291 participants picked up the calls, and 984 of those answered the consent question. In total, 585 people completed the surveys over the course of the 5 weeks.
CONCLUSIONS
The present study achieved its objectives in demonstrating the feasibility of generating data through an unprecedented use of a multi-modal approach for tracking child development in Malawi, one of the poorest countries in the world. Above and beyond its multiple dimensions, the dynamics of child development are complex: not only it is the case 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, and at 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 developed countries facing multiple constraints, complementary technologies can leverage and accelerate the digitalization of health, bringing benefits to populations that lack new tools to understanding, mainly, of child well-being and development.
CLINICALTRIAL
Using Wearable Devices for Non-invasive, Inexpensive Physiological Data Collection
