Acne vulgaris is considered one of the most common medical skin conditions globally, affecting approximately 85% of individuals worldwide. While acne is most prevalent among adolescents between 15 to 24 years old, it is not uncommon in adults either. Acne addresses a number of different challenges, causing a multidimensional disease burden. These challenges include clinical sequelae, such as post inflammatory hyperpigmentation (PIH) and the chance of developing lifelong disfiguring scars, psychological aspects such as deficits in health related quality of life, chronicity of acne, economic factors, and treatment-related issues, such as antimicrobial resistance. The multidimensionality of the disease burden stipulates the importance of an effective and timely treatment in a well organised care system. Within the Netherlands, acne care provision is managed by several types of professional care givers, each approaching acne care from different angles: (I) general practitioners (GPs) who serve as ‘gatekeepers’ of healthcare within primary care; (II) dermatologists providing specialist medical care within secondary care; (III) dermal therapists, a non-physician medical professional with a bachelor’s degree, exclusively operating within the Australian and Dutch primary and secondary health care; and (IV) beauticians, mainly working within the cosmetology or wellness domain. However, despite the large variety in acne care services, many patients experience a delay between the onset of acne and receiving an effective treatment, or a prolonged use of care, which raises the question whether acne related care resources are being used in the most effective and (cost)efficient way. It is therefore necessary to gain insights into the organization and quality of Dutch acne health care beyond conventional guidelines and protocols. Exploring areas of care that may need improvement allow Dutch acne healthcare services to develop and improve the quality of acne care services in harmony with patient needs.
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OBJECTIVE: Juvenile dermatomyositis (DM) is an inflammatory myopathy in which the immune system targets the microvasculature of the skeletal muscle and skin, leading to significant muscle weakness and exercise intolerance, although the precise etiology is unknown. The goal of this study was to investigate the changes in exercise capacity in children with myositis during active and inactive disease periods and to study the responsiveness of exercise parameters.METHODS: Thirteen children with juvenile DM (mean+/-SD age 11.2+/-2.6 years) participated in this study. Patients performed a maximal exercise test using an electronically braked cycle ergometer and respiratory gas analysis system. Exercise parameters were analyzed, including peak oxygen uptake (VO2peak), peak work rate (Wpeak), and ventilatory anaerobic threshold (VAT). All children were tested during an active period of the disease and during a remission period. From these data, 4 different response statistics were calculated.RESULTS: The children performed significantly better during a remission period compared with a period of active disease. Most exercise parameters showed a very large response. The 5 most responsive parameters were Wpeak, Wpeak (percent predicted), oxygen pulse, VO2peak, and power at the VAT.CONCLUSION: We found in our longitudinal study that children with active juvenile DM had significantly reduced exercise parameters compared with a remission period. Moreover, we found that several parameters had very good responsiveness. With previously established validity and reliability, exercise testing has been demonstrated to be an excellent noninvasive instrument for the longitudinal followup of children with myositis.
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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.
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