Telelactation with a Mobile App: User Profile and Most Common Queries

Final publication is available from Mary Ann Liebert, Inc., publishers https://doi.org/10.1089/bfm.2020.0269Background: Mobile applications related to health issues are currently expanding. Different uses of new technologies have produced positive results regarding breastfeeding support. Breastfeeding applications are increasing. Objective: We conducted a descriptive analysis of a mobile application for breastfeeding (LactApp) to study the user profile and the most frequent queries. Materials and Methods: This was a retrospective, comparative, and descriptive ecological time-series study of LactApp from 2016 to 2019. Google Analytics and the app itself were used for data collection. The data were analyzed in Excel, and for the time series, Prais–Winsten autoregressions were applied based on the Durbin–Watson method in Stata. Results: A total of 115,830 users and 71,780 infants were registered in the application. A total of 1.91% of these users obtained the medical version. The application was used for both queries and surveys and for users to interact through chat. A total of 30.17% of the responses were related with “baby's sleep” (8.94%), 8.91% were related to “preservation of milk,” 6.16% were related to “breastfeeding crisis,” and 6.15% were related to “physiological evolution of breastfeeding,” all with an increasing trend. Conclusion: LactApp is a resource for breastfeeding that is widely downloaded and used by a substantial number of individuals. The most recurring topics were baby's sleep, milk extraction and preservation, breastfeeding crisis and physiological evolution of breastfeeding

Affective Medicine: a review of Affective Computing efforts in Medical Informatics

Background: Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as “computing that relates to, arises from, or deliberately influences emotions”. AC enables investigation and understanding of the relation between human emotions and health as well as application of assistive and useful technologies in the medical domain. Objectives: 1) To review the general state of the art in AC and its applications in medicine, and 2) to establish synergies between the research communities of AC and medical informatics. Methods: Aspects related to the human affective state as a determinant of the human health are discussed, coupled with an illustration of significant AC research and related literature output. Moreover, affective communication channels are described and their range of application fields is explored through illustrative examples. Results: The presented conferences, European research projects and research publications illustrate the recent increase of interest in the AC area by the medical community. Tele-home healthcare, AmI, ubiquitous monitoring, e-learning and virtual communities with emotionally expressive characters for elderly or impaired people are few areas where the potential of AC has been realized and applications have emerged. Conclusions: A number of gaps can potentially be overcome through the synergy of AC and medical informatics. The application of AC technologies parallels the advancement of the existing state of the art and the introduction of new methods. The amount of work and projects reviewed in this paper witness an ambitious and optimistic synergetic future of the affective medicine field

Concept and considerations of a medical device:the active noise cancelling incubator

Background: An increasingly 24/7 connected and urbanised world has created a silent pandemic of noise-induced hearing loss. Ensuring survival to children born (extremely) preterm is crucial. The incubator is a closed medical device, modifying the internal climate, and thus providing an environment for the child, as safe, warm, and comfortable as possible. While sound outside the incubator is managed and has decreased over the years, managing the noise inside the incubator is still a challenge.Method: Using active noise cancelling in an incubator will eliminate unwanted sounds (i.e., from the respirator and heating) inside the incubator, and by adding sophisticated algorithms, normal human speech, neonatal intensive care unit music-based therapeutic interventions, and natural sounds will be sustained for the child in the pod. Applying different methods such as active noise cancelling, motion capture, sonological engineering. and sophisticated machine learning algorithms will be implemented in the development of the incubator. Projected Results: A controlled and active sound environment in and around the incubator can in turn promote the wellbeing, neural development, and speech development of the child and minimise distress caused by unwanted noises. While developing the hardware and software pose individual challenges, it is about the system design and aspects contributing to it. On the one hand, it is crucial to measure the auditory range and frequencies in the incubator, as well as the predictable sounds that will have to be played back into the environment. On the other, there are many technical issues that have to be addressed when it comes to algorithms, datasets, delay, microphone technology, transducers, convergence, tracking, impulse control and noise rejection, noise mitigation stability, detection, polarity, and performance.Conclusion: Solving a complex problem like this, however, requires a de-disciplinary approach, where each discipline will realise its own shortcomings and boundaries, and in turn will allow for innovations and new avenues. Technical developments used for building the active noise cancellation-incubator have the potential to contribute to improved care solutions for patients, both infants and adults. Code available at: 10.3389/fped.2023.1187815.</p

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Offspring outcomes when a parent experiences one or more major psychiatric disorder(s): a clinical review

We sought evidence on quantifiable offspring outcomes, including problems, needs and strengths, associated with their experience of major parental psychiatric disorder(s), focusing on schizophrenia, affective illnesses and personality disorder(s). We were motivated by the absence of any systematic exploration of the needs of offspring of parents in secure hospitals. Seven electronic databases were searched to identify systematic reviews of studies quantifying offspring outcomes when a parent, or parent surrogate, has major psychiatric disorder(s). Our search (updated in February 2018) identified seven high-quality reviews, which incorporated 291 unique papers, published in 1974–2017. The weight of evidence is of increased risk of poor offspring outcomes, including psychiatric disorder and/or behavioural, emotional, cognitive or social difficulties. No review explored child strengths. Potential moderators and mediators examined included aspects of parental disorder (eg, severity), parent and child gender and age, parenting behaviours, and family functioning. This clinical review is the first review of systematic reviews to focus on quantifiable offspring problems, needs or strengths when a parent has major psychiatric disorder(s). It narratively synthesises findings, emphasising the increased risk of offspring problems, while highlighting limits to what is known, especially the extent to which any increased risk of childhood problems endures and the extent to which aspects of parental disorder moderate offspring outcomes. The absence of the reviews’ consideration of child strengths and protective factors limits opportunity to enhance offspring resilience

Ubiquitous Technologies for Emotion Recognition

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions