Background 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. Objective 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. 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 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. Results 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. Conclusions 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.

Total number of words in manuscript: 8155. Total number of words in abstract: 178 AbstractRecent advancements in portable technology have opened new avenues in the study of human cognition outside research laboratories. This flexibility in methodology has led to the publication of several Electroencephalography (EEG) studies recording brain responses in real world scenarios such as cycling and walking outside. In the present study, we wanted to test the classic oddball task event related potentials (ERPs) while participants moved around a running track using an electric skateboard. This novel approach allows for the study of attention in motion while virtually removing body movement. Using the auditory oddball paradigm, we were able to measure the P3 and MMN-N2b components elicited by this task. We also found that compared to resting state, alpha power is attenuated in frontal and parietal regions during skateboarding. We also tested for the effect of stance preference in terms of P3 and alpha magnitude and found no differences in for either of these. By replicating the findings of the classic oddball task under such a novel environment this study extends our knowledge of brain function in highly ecologically valid scenarios. Attention in motion 3

For decades, the study of cognitive electrophysiology using electroencephalography (EEG) has taken place inside highly controlled research facilities as EEG signals are easily contaminated by a myriad of environmental factors (Luck, 2014).EEG research has informed our understanding of human attention, yet this knowledge generally comes from paradigms that isolate participants in faraday cages to avoid electromagnetic fields and other sources of noise that can compromise data quality (Puce & Hämäläinen, 2017). Over recent years, developments in minicomputers such as the Raspberry Pi (https:// www.raspb errypi.org/) and mobile phones have allowed such studies to move outside the lab and into the real world, resulting in a growth of mobile EEG studies within ecologically

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.

In this study, we used an oddball EEG bicycle paradigm to study how changes in urban environments elicit changes in EEG markers. Participants completed an auditory oddball task while riding in three different cycling lane environments. A low traffic condition where participants rode in a fully separated bike lane alongside a quiet residential street, an intermediate traffic condition where participants rode alongside a busy residential street in a painted lane, and a heavy traffic condition where participants rode alongside fast/heavy traffic on a shared-use path. Relative to the low traffic, heavy traffic was associated with faster reaction time and a trend towards reduced accuracy, and increased N1 amplitude evoked by the standard tones. We attribute this difference in N1 amplitude to different attentional demands evoked by the different traffic conditions. In this fashion, heavy traffic requires greater auditory filtering. Furthermore, we found no differences in P3 amplitude associated with the traffic conditions. We discuss the implications of mobile paradigms to study attention in real-world settings.