A significant proportion of adolescents with chronic musculoskeletal pain (CMP) experience difficulties in physical functioning, mood and social functioning, contributing to diminished quality of life. Generalized joint hypermobility (GJH) is a risk factor for developing CMP with a striking 35-48% of patients with CMP reporting GJH. In case GJH occurs with one or more musculoskeletal manifestations such as chronic pain, trauma, disturbed proprioception and joint instability, it is referred to as generalized hypermobility spectrum disorder (G-HSD). Similar characteristics have been reported in children and adolescents with the hypermobile Ehlers-Danlos Syndrome (hEDS). In the management of CMP, a biopsychosocial approach is recommended as several studies have confirmed the impact of psychosocial factors in the development and maintenance of CMP. The fear-avoidance model (FAM) is a cognitive-behavioural framework that describes the role of pain-related fear as a determinant of CMP-related disability. Pubmed was used to identify existing relevant literature focussing on chronic musculoskeletal pain, generalized joint hypermobility, pain-related fear and disability. Relevant articles were cross-referenced to identify articles possibly missed during the primary screening. In this paper the current state of scientific evidence is presented for each individual component of the FAM in hypermobile adolescents with and without CMP. Based on this overview, the FAM is proposed explaining a possible underlying mechanism in the relations between GJH, pain-related fear and disability. It is assumed that GJH seems to make you more vulnerable for injury and experiencing more frequent musculoskeletal pain. But in addition, a vulnerability for heightened pain-related fear is proposed as an underlying mechanism explaining the relationship between GJH and disability. Further scientific confirmation of this applied FAM is warranted to further unravel the underlying mechanism. In explaining disability in individuals with G-HSD/hEDS, it is important to focus on both the physical components related to joint hypermobility, in tandem with the psychological components such as pain-related fear, catastrophizing thoughts and generalized anxiety.
Cervical dystonia (CD) is a neurological movement disorder characterized by involuntary muscle contractions causing abnormal postures and/or twisting movements of the head and neck.Patients may also experience non-motor symptoms including pain, anxiety and depression. The main treatment option is botulinum toxin (BoNT) injections in affected muscles to improve head postures and reduce pain. In addition to BoNT treatment, patients are often referred for physical therapy (PT), but there is little evidence regarding the long-term effectiveness.Despite remarkable improvements during the last decades, there are still many unmet needs that remain open in the treatment of cervical dystonia (CD). The first goal of this thesis was to assess clinical issues in BoNT treatment that need further improvement and to define clinical recommendations for clinicians. The second goal was to explore which determinants play an important role in disability of CD patients and the third goal was to develop a specialized PT program and to evaluate its effects on disability.Results showed that BoNT treatment can be further improved despite all the evidence for its effectiveness. Further research is needed towards optimal treatment intervals, dose equivalence between different BoNT formulations, the use of supportive techniques like electromyography or ultrasound and managing side effects. Secondly, we found that psychological factors are important determinants of disability. Finally, we found that PT is a valuable addition to BoNT treatment to improve disability and pain. Based on these findings, a multidisciplinary treatment approach to further improve the treatment and quality of life for CD patients is recommended.
Background: Persons with an intellectual disability are at a higher risk of experiencing adversities. The concept of resilience offers promising insights into facilitating personal growth after adversity. The current study aims at providing an overview of the current research on resilience and the way this can contribute to quality of life in people with intellectual disability. Method: A literature review was conducted in the databases PsycINFO and Web of Science. To evaluate the quality of the studies, the Mixed Method Appraisal Tool (MMAT) was used. Results: The themes, autonomy, self-acceptance and physical health, were identified as internal sources of resilience. External sources of resilience can be found within the social network and daily activities. Conclusion: The current overview shows promising results to address resilience in adults with intellectual disability. More research is needed to identify the full range of resiliency factors.
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
