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.
1 Maternity services across Europe during the pandemic has undergone changes to limit virus transmission; however, many changes are not evidence-based. 2 Although these changes were introduced to keep women, babies and healthcare staff safe, the exclusion of companions and the separation of mothers and babies is particularly antithetical to a human rights-based approach to quality care. 3 A poll of COST Action 18211 network members showed that inconsistency in the application of restrictions was high, and there were significant deviations from the recommendations of authoritative bodies. 4 Concerns have emerged that restrictions in practice may have longer term negative impacts on mothers and their families and, in particular, may impact on the long-term health of babies. 5 When practice changes deviate from evidence-based frameworks that underpin quality care, they must be monitored, appraised and evaluated to minimise unintended iatrogenic effects.
Kinderen met een lage sociaaleconomische status (SES) hebben een verhoogd risico op een suboptimale start in het leven met hogere kosten voor de gezondheidszorg. Deze studie onderzoekt de effecten van SES op individueel (maandelijks huishoudinkomen) en contextuele SES (huishoudinkomen en buurtdeprivatie), en perinatale morbiditeit op de zorgkosten in het vroege leven (0-3 jaar). Conclusie: Meer buurtdeprivatie was direct gerelateerd aan hogere zorgkosten bij jonge kinderen. Bovendien was een lager huishoudinkomen consistent en onafhankelijk gerelateerd aan hogere zorgkosten. Door de omstandigheden voor lage SES-populaties te optimaliseren, kan de impact van lage SES-omstandigheden op hun zorgkosten positief worden beïnvloed.
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