Many cancer survivors, facing the consequences of their disease and its treatment, have medical and supportive aftercare needs. However, limited knowledge exists regarding the relationship between support needs and survivors' self-management skills. The study aim is to explore factors contributing to cancer survivors' self-management skills. A cross-sectional study was conducted among cancer survivors (n = 277) of two outpatient oncology clinics at a university hospital in the Netherlands. Patients with head and neck cancer (n = 55) who had received radiotherapy and cisplatin or cetuximab were included, as well as patients who had undergone hematopoietic stem cell transplantation (n = 222). The primary outcome was self-management skills, assessed using the Partners in Health Scale (PIH), which comprises two subscales: knowledge and coping (PIH-KC), and recognition and management of symptoms, and adherence to treatment (PIH-MSA). Secondary outcomes were quality of life (EORTC QLQ-C30), self-efficacy (SECD6), patient-centered care (CAPHS), and social support (HEIQ). Machine learning-based Random Forest models were employed to construct associative models. Feature Importance (FI) was used to express the contribution to the model. High emotional quality of life (FI = 33.1%), increased self-efficacy (FI = 22.2%), and greater social support (FI = 18.2%) were identified as key factors contributing to cancer survivors' self-management knowledge (PIH-KC). Furthermore, greater support from professionals (FI = 36.1%) and higher self-efficacy (FI = 18.2%) were found to benefit participants' recognition and management, and therapy adherence (PIH-MSA). A patient-centered relationship between nurses and cancer survivors is essential for therapy adherence and the management of aftercare needs. Training to provide this holistic self-management support is required.

In adolescents with non-pathological and pathological joint hypermobility, gait deviations have been associated with pain and fatigue. It remains unclear what distinguishes the non-pathological form of joint hypermobility (JH) from pathological forms (i.e. hypermobile Ehlers-Danlos syndrome (hEDS) or hypermobility spectrum disorders (HSD). Our objective was to identify discriminative clinical characteristics and biomechanical gait features between adolescents with hEDS/HSD, JH, and healthy controls (HC). Thirty-two adolescents were classified into three subgroups (hEDS/HSD=12, JH=5, HC=15). Clinical characteristics (e.g. pain intensity and surface, fatigue, functional disability) were inventoried. The gait pattern was assessed using a three-dimensional, eight-camera VICON MX1.3 motion capture system, operating at a sample rate of 100 Hz (VICON, Oxford, UK). Spatiotemporal parameters, joint angles (sagittal plane), joint work, joint impulse, ground reaction force and gait variability expressed as percentage using Principal Component Analysis (PCA) were assessed and analysed using multivariate analysis. Multivariate analysis data is expressed in mean differences(MD), standard error(SE) and P-values. The hEDS/HSD-group had significantly higher fatigue score (+51.5 points, p = <0.001) and functional disability (+1.6, p < .001) than the HC-group. Pain intensity was significantly higher in the hEDS/HSD-group than the other subgroups (JH; +37 mm p = .004, HC; +38 mm, p = .001). The hEDS/HSD-group showed significantly more gait variability (JH; +7.2(2.0)% p = .003, HC; + 7.8(1.4)%, p = <0.001) and lower joint work (JH; -0.07(0.03)J/kg, p = .007, HC; - 0.06(0.03)J/kg, p = .013) than the other subgroups. The JH-group showed significantly increased ankle dorsiflexion during terminal stance (+5.0(1.5)degree, p = .001) compared to hEDS/HSD-group and knee flexion during loading response compared to HC-group (+5.7(1.8) degree, p = .011). A distinctive difference in gait pattern between adolescents with non-pathological and pathological joint hypermobility is found in gait variability, rather than in the biomechanical features of gait. This suggests that a specific gait variability metric is more appropriate than biomechanical individual joint patterns for assessing gait in adolescents with hEDS/HSD.

Artificial intelligence, the simulation of human intelligence by computers and machines, has found its way into healthcare, helping surgeons, doctors, radiologists, and many more. However, over 80% of healthcare professionals consists of people working in allied health professions such as nurses, physiotherapists, and midwives. Considering the aging of the general population around the world, the workforce shortages in these occupations are especially crucial. As the COVID-19 pandemic demonstrated, globally, most healthcare systems are strained, and there is a consensus that current healthcare systems are not sustainable with the increasing challenges. AI is often viewed as one of the potential solutions for not only reducing the strain on the healthcare workforce, but also to sustain the current workforce. Still, most AI applications are being developed for the medical community and often allied health is overlooked or not even considered despite comprising a large proportion of the total workforce. In addition, the interest of the private sector to invest specifically in the allied health workforce is low since the financial incentive is low. This paper provides examples of AI solutions for seven important allied health professions. To increase the uptake of AI solutions in allied healthcare, AI companies need to connect more with professional associations and be as patient-oriented as many claim to be. There also needs to be more AI schooling for allied healthcare professionals to increase adoption of these AI solutions.