Emerging Opportunities Provided by Technology to Advance Research in Child Health Globally

Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks

BACKGROUND

The Face-to-Face Still-Face (FFSF) task is a validated and commonly used observational measure of mother-infant socio-emotional interactions. With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos, could be coded with a high degree of accuracy by deep neural networks (DNNs). The primary objective of this study was to test the accuracy of four DNN image classification models against the coding of infant engagement conducted by two trained independent manual raters.

METHODS

68 mother-infant dyads completed the FFSF task at three timepoints. Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect.

RESULTS

Training four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993.

LIMITATIONS

This study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample.

CONCLUSIONS

Based on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. DNN image classification models may also have the potential to improve the efficiency of coding all observational tasks with applications across multiple fields of human behavior research.

Digital Health Technologies for Maternal and Child Health in African and other LMICs: A Cross-disciplinary Scoping Review with Stakeholder Consultation (Preprint)

BACKGROUND

Maternal and child health (MCH) is a global health concern, especially impacting low- and middle-income countries (LMIC). Digital health technologies are creating opportunities to address the social determinants of MCH by facilitating access to information and providing other forms of support throughout the maternity journey. Previous reviews in different disciplines have synthesized digital health intervention outcomes in LMIC. However, contributions in this space are scattered across publications in different disciplines and lack coherence in what digital MCH means across fields.

OBJECTIVE

This cross-disciplinary scoping review synthesized the existing published literature in 3 major disciplines on the use of digital health interventions for MCH in LMIC, with a particular focus on sub-Saharan Africa.

METHODS

We conducted a scoping review using the 6-stage framework by Arksey and O'Malley across 3 disciplines, including public health, social sciences applied to health, and human-computer interaction research in health care. We searched the following databases: Scopus, PubMed, Google Scholar, ACM Digital Library, IEEE Xplore, Web of Science, and PLOS. A stakeholder consultation was undertaken to inform and validate the review.

RESULTS

During the search, 284 peer-reviewed articles were identified. After removing 41 duplicates, 141 articles met our inclusion criteria: 34 from social sciences applied to health, 58 from public health, and 49 from human-computer interaction research in health care. These articles were then tagged (labeled) by 3 researchers using a custom data extraction framework to obtain the findings. First, the scope of digital MCH was found to target health education (eg, breastfeeding and child nutrition), care and follow-up of health service use (to support community health workers), maternal mental health, and nutritional and health outcomes. These interventions included mobile apps, SMS text messaging, voice messaging, web-based applications, social media, movies and videos, and wearable or sensor-based devices. Second, we highlight key challenges: little attention has been given to understanding the lived experiences of the communities; key role players (eg, fathers, grandparents, and other family members) are often excluded; and many studies are designed considering nuclear families that do not represent the family structures of the local cultures.

CONCLUSIONS

Digital MCH has shown steady growth in Africa and other LMIC settings. Unfortunately, the role of the community was negligible, as these interventions often do not include communities early and inclusively enough in the design process. We highlight key opportunities and sociotechnical challenges for digital MCH in LMIC, such as more affordable mobile data; better access to smartphones and wearable technologies; and the rise of custom-developed, culturally appropriate apps that are more suited to low-literacy users. We also focus on barriers such as an overreliance on text-based communications and the difficulty of MCH research and design to inform and translate into policy.

Time Perception is a Focal Symptom of Attention-Deficit/Hyperactivity Disorder in Adults

Attention-Deficit/Hyperactivity Disorder (ADHD) is classically associated with symptoms that include inattentiveness, hyperactivity, and impulsivity together with a variety of other observable externalized symptoms. ADHD has also been associated with specific internalized cognitive symptoms, including restlessness and emotional impulsivity. This disorder has been recognized as a lifelong condition and can be recognized by a variety of unique cognitive phenomena. In addition to the frequently ignored affective symptoms exhibited by individuals diagnosed with ADHD, problems with time perception have been noted, although these are considered to be secondary issues. Temporal shifts in cognitive processing, however, may be at the very root of ADHD-related symptoms, given the importance of coordinated signal translation in the construction of behavior. In this review, we consider the evidence that suggests that differences in time perception are a central symptom in adults with ADHD. Some of these differences include the feeling of time moving faster, which causes difficulties in prospective time tasks and inaccuracies in time estimation tasks. We analyze the literature from both neurological and psychological perspectives and include an assessment of tools that can be administered via computer to measure time perception. We also suggest several computer-based methods that might be used to address problems with time perception in both children and adults. We strongly recommend the inclusion of ADHD symptoms associated with time perception in the next revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM) published by the American Psychiatric Association.