JMIR Publications

ABSTRACT

Background:

Multi-modal approach has been shown a promising alternative for high-frequency monitoring, combining different inputs of non-invasive biomarkers creating multiple angles to understand health and clinical outcomes. These data signals include not only biomarkers but also other types of data streams that can help understanding more integrally the different aspects in each patient or subject.

Objective:

The objective of this work is to describe a pilot study using a multi-modal approach to combine 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.

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 use wearable devices (ECG hand pads and headbands). Additionally, wearable proximity sensors to elicit the social network were deployed in children and caregivers. Mobile surveys using Interactive Voice Response calls and text messages were sent serving as an additional layer for data collection. The end line face-to-face survey was conducted by the end of experiments.

Results:

During the pilot, 82 EEG/ECG data entry points were collected in the four villages. The sampled children for EEG/ECG are 0-5 years old. EEG/ECG data was collected weekly, which means that health workers use the wearable device to collect data on the children once a week. For every collection session, the children need to wear the EEG headband for 5 minutes, while they need to wear the ECG hand pad for 3 minutes. In total 3,531 calls were sent over the 5 weeks the project was live. 2,291 people picked up the call, and 984 answered the consent question. In total, 585 people completed the surveys over the 5 weeks.

Conclusions:

The present study achieved expectations in validating and generating preliminary data in the unprecedented use of a multi-modal approach for collecting data related to child development in Malawi settings. The complexity and innovation of the study can be perceived by the scarcity of literature that describes the methods adopted with precision and reproducibility. On the other hand, several studies applied in developing countries have already proven the feasibility of using wearables to understand how diseases behave and affect certain populations, including children. In addition to this, we understand that it is a good time for developing countries, even those that are in a critical scarcity scenario, to use this opportunity to leverage and accelerate the digitalization of health, bringing benefits to populations that lack new tools to understanding, mainly, of child well-being and development.