Depression is a highly prevalent and seriously impairing disorder. Evidence suggests that music therapy can decrease depression, though the music therapy that is offered is often not clearly described in studies. The purpose of this study was to develop an improvisational music therapy intervention based on insights from theory, evidence and clinical practice for young adults with depressive symptoms. The Intervention Mapping method was used and resulted in (1) a model to explain how emotion dysregulation may affect depressive symptoms using the Component Process Model (CPM) as a theoretical framework; (2) a model to clarify as to how improvisational music therapy may change depressive symptoms using synchronisation and emotional resonance; (3) a prototype Emotion-regulating Improvisational Music Therapy for Preventing Depressive symptoms (EIMT-PD); (4) a ten-session improvisational music therapy manual aimed at improving emotion regulation and reducing depressive symptoms; (5) a program implementation plan; and (6) a summary of a multiple baseline study protocol to evaluate the effectiveness and principles of EIMT-PD. EIMT-PD, using synchronisation and emotional resonance may be a promising music therapy to improve emotion regulation and, in line with our expectations, reduce depressive symptoms. More research is needed to assess its effectiveness and principles.
Young professional dancers find themselves in a demanding environment. GJH within dancers is often seen as aesthetically beneficial and a sign of talent but was found to be potentially disabling. Moreover, high-performing adolescents and young adults (HPAA), in this specific lifespan, might be even more vulnerable to anxiety-related disability. Therefore, we examined the development of the association between the presence of Generalized Joint Hypermobility (GJH) and anxiety within HPAA with a one-year follow-up. In 52.3% of the HPAA, anxiety did not change significantly over time, whereas GJH was present in 28.7%. Fatigue increased significantly in all HPAA at one year follow-up (respectively, females MD (SD) 18(19), p < 0.001 and males MD (SD) 9(19), p < 0.05). A significantly lower odds ratio (ß (95% CI) 0.4 (0.2–0.9); p-value 0.039) for participating in the second assessment was present in HPAA with GJH and anxiety with a 55% dropout rate after one year. This confirms the segregation between GJH combined with anxiety and GJH alone. The fatigue levels of all HPAA increased significantly over time to a serious risk for sick leave and work disability. This study confirms the association between GJH and anxiety but especially emphasizes the disabling role of anxiety. Screening for anxiety is relevant in HPAA with GJH and might influence tailored interventions.
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.
