Aim The aim of this study is to gain more insight into child and environmental factors that influence gross motor development (GMD) of healthy infants from birth until reaching the milestone of independent walking, based on longitudinal research. Background A systematic search was conducted using Scopus, PsycINFO, MEDLINE and CINAHL to identify studies from inception to February 2020. Studies that investigated the association between child or environmental factors and infant GMD using longitudinal measurements of infant GMD were eligible. Two independent reviewers extracted key information and assessed risk of bias of the selected studies, using the Quality in Prognostic Studies tool (QUIPS). Strength of evidence (strong, moderate, limited, conflicting and no evidence) for the factors identified was described according to a previously established classification. Results In 36 studies, six children and 11 environmental factors were identified. Five studies were categorized as having low risk of bias. Strong evidence was found for the association between birthweight and GMD in healthy full-term and preterm infants. Moderate evidence was found for associations between gestational age and GMD, and sleeping position and GMD. There was conflicting evidence for associations between twinning and GMD, and breastfeeding and GMD. No evidence was found for an association between maternal postpartum depression and GMD. Evidence for the association of other factors with GMD was classified as ‘limited’ because each of these factors was examined in only one longitudinal study. Conclusion Infant GMD appears associated with two child factors (birthweight and gestational age) and one environmental factor (sleeping position). For the other factors identified in this review, insufficient evidence for an association with GMD was found. For those factors that were examined in only one longitudinal study, and are therefore classified as having limited evidence, more research would be needed to reach a conclusion.
Objective: This study aims to assess the comparative effectiveness of a conventional splitting needle or a peelable cannula vs. the modified Seldinger technique (MST) by utilizing a dedicated micro-insertion kit across various clinically significant metrics, including insertion success, complications, and catheter-related infections. Methods: We conducted a retrospective observational cohort study using an anonymized data set spanning 3 years (2017-2019) in a large tertiary-level neonatal intensive care unit in Qatar. Results: A total of 1,445 peripherally inserted central catheter (PICC) insertion procedures were included in the analysis, of which 1,285 (89%) were successful. The primary indication for insertion was mainly determined by the planned therapy duration, with the saphenous vein being the most frequently selected blood vessel. The patients exposed to MST were generally younger (7 ± 15 days vs. 11 ± 26 days), but exhibited similar mean weights and gestational ages. Although not statistically significant, the MST demonstrated slightly higher overall and first-attempt insertion success rates compared to conventional methods (91 vs. 88%). However, patients undergoing conventional insertion techniques experienced a greater incidence of catheter-related complications (p < 0.001). There were 39 cases of catheter-related bloodstream infections (CLABSI) in the conventional group (3.45/1,000 catheter days) and eight cases in the MST group (1.06/1,000 catheter days), indicating a statistically significant difference (p < 0.001). Throughout the study period, there was a noticeable shift toward the utilization of the MST kit for PICC insertions. Conclusion: The study underscores the viability of MST facilitated by an all-in-one micro kit for neonatal PICC insertion. Utilized by adept and trained inserters, this approach is associated with improved first-attempt success rates, decreased catheter-related complications, and fewer incidences of CLABSI. However, while these findings are promising, it is imperative to recognize potential confounding factors. Therefore, additional prospective multicenter studies are recommended to substantiate these results and ascertain the comprehensive benefits of employing the all-in-one kit.
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Anxiety among pregnant women can significantly impact their overall well-being. However, the development of data-driven HCI interventions for this demographic is often hindered by data scarcity and collection challenges. In this study, we leverage the Empatica E4 wristband to gather physiological data from pregnant women in both resting and relaxed states. Additionally, we collect subjective reports on their anxiety levels. We integrate features from signals including Blood Volume Pulse (BVP), Skin Temperature (SKT), and Inter-Beat Interval (IBI). Employing a Support Vector Machine (SVM) algorithm, we construct a model capable of evaluating anxiety levels in pregnant women. Our model attains an emotion recognition accuracy of 69.3%, marking achievements in HCI technology tailored for this specific user group. Furthermore, we introduce conceptual ideas for biofeedback on maternal emotions and its interactive mechanism, shedding light on improved monitoring and timely intervention strategies to enhance the emotional health of pregnant women.
Het aantal alarmen dat afgaat op een Neonatale Intensive Care Unit (NICU) is hoog omdat de vitale fysiologische parameters van de neonaten als vanzelfsprekend continu gemonitord worden door medische apparatuur. Dit leidt tot een enorme alarmdruk bij NICU-verpleegkundigen, want elk alarm moet beoordeeld worden. Echter, slechts 20% van de klinische alarmen is relevant, wat niet alleen leidt tot inefficiënte werkprocessen, maar ook tot alarmmoeheid en daarmee bedreiging van patiëntveiligheid. Literatuur- en praktijkonderzoek door studenten HBO-ICT en onderzoekers van het lectoraat ICT-innovaties in de Zorg (Hogeschool Windesheim) op de NICU van Isala ziekenhuis in Zwolle laat zien dat er winst lijkt te behalen in het slim combineren van alarmen en het aanpassen van grenswaarden. Hier kan uiteraard niet zomaar mee geëxperimenteerd worden in de werkelijke klinische setting. Isala heeft daarom behoefte aan een testomgeving waarin de impact van alarmaanpassingen op alarmreductie gemeten kan worden zonder dat patiëntveiligheid daarmee in gevaar komt. Een digital twin kan hier een oplossing bieden. Dit is een replica van de fysieke, dynamische NICU-setting waarin data van patiënten, apparaten en hun onderlinge interacties gesimuleerd kunnen worden en artificial intelligence voorspellingen kan doen over de impact van veranderingen. In de gezondheidszorg wordt de potentie van digital twins de laatste twee jaar gezien en het aantal publicaties en best practices neemt toe, maar toepassingen op de intensive care-setting zijn nog dun gezaaid. Dit project, waarvoor Windesheim, Isala en data science agency Little Rocket de krachten bundelen, levert hier een bijdrage aan
