Objectives: Health problems in patients with heritable connective tissue disorders (HCTD) are diverse and complex and might lead to lower physical activity (PA) and physical fitness (PF). This study aimed to investigate the PA and PF of children with heritable connective tissue disorders (HCTD).Methods: PA was assessed using an accelerometer-based activity monitor (ActivPAL) and the mobility subscale of the Pediatric Evaluation of Disability Inventory Computer Adaptive Test (PEDI-CAT). PF was measured in terms of cardiovascular endurance using the Fitkids Treadmill Test (FTT); maximal hand grip strength, using hand grip dynamometry (HGD) as an indicator of muscle strength; and motor proficiency, using the Bruininks-Oseretsky Test of Motor Proficiency-2 (BOTMP-2).Results: A total of 56 children, with a median age of 11.6 (interquartile range [IQR], 8.8–15.8) years, diagnosed with Marfan syndrome (MFS), n = 37, Loeys-Dietz syndrome (LDS), n = 6, and genetically confirmed Ehlers-Danlos (EDS) syndromes, n = 13 (including classical EDS n = 10, vascular EDS n = 1, dermatosparaxis EDS n = 1, arthrochalasia EDS n = 1), participated. Regarding PA, children with HCTD were active for 4.5 (IQR 3.5–5.2) hours/day, spent 9.2 (IQR 7.6–10.4) hours/day sedentary, slept 11.2 (IQR 9.5–11.5) hours/day, and performed 8,351.7 (IQR 6,456.9–1,0484.6) steps/day. They scored below average (mean (standard deviation [SD]) z-score −1.4 (1.6)) on the PEDI-CAT mobility subscale. Regarding PF, children with HCTD scored well below average on the FFT (mean (SD) z-score −3.3 (3.2)) and below average on the HGD (mean (SD) z-score −1.1 (1.2)) compared to normative data. Contradictory, the BOTMP-2 score was classified as average (mean (SD) z-score.02 (.98)). Moderate positive correlations were found between PA and PF (r(39) = .378, p < .001). Moderately sized negative correlations were found between pain intensity and fatigue and time spent actively (r(35) = .408, p < .001 and r(24) = .395 p < .001, respectively).Conclusion: This study is the first to demonstrate reduced PA and PF in children with HCTD. PF was moderately positively correlated with PA and negatively correlated with pain intensity and fatigue. Reduced cardiovascular endurance, muscle strength, and deconditioning, combined with disorder-specific cardiovascular and musculoskeletal features, are hypothesized to be causal. Identifying the limitations in PA and PF provides a starting point for tailor-made interventions.
Heritable Connective Tissue Disorders (HCTD) show an overlap in the physical features that can evolve in childhood. It is unclear to what extent children with HCTD experience burden of disease. This study aims to quantify fatigue, pain, disability and general health with standardized validated questionnaires.METHODS: This observational, multicenter study included 107 children, aged 4-18 years, with Marfan syndrome (MFS), 58%; Loeys-Dietz syndrome (LDS), 7%; Ehlers-Danlos syndromes (EDS), 8%; and hypermobile Ehlers-Danlos syndrome (hEDS), 27%. The assessments included PROMIS Fatigue Parent-Proxy and Pediatric self-report, pain and general health Visual-Analogue-Scales (VAS) and a Childhood Health Assessment Questionnaire (CHAQ).RESULTS: Compared to normative data, the total HCTD-group showed significantly higher parent-rated fatigue T-scores (M = 53 (SD = 12), p = 0.004, d = 0.3), pain VAS scores (M = 2.8 (SD = 3.1), p < 0.001, d = 1.27), general health VAS scores (M = 2.5 (SD = 1.8), p < 0.001, d = 2.04) and CHAQ disability index scores (M = 0.9 (SD = 0.7), p < 0.001, d = 1.23). HCTD-subgroups showed similar results. The most adverse sequels were reported in children with hEDS, whereas the least were reported in those with MFS. Disability showed significant relationships with fatigue (p < 0.001, rs = 0.68), pain (p < 0.001, rs = 0.64) and general health (p < 0.001, rs = 0.59).CONCLUSIONS: Compared to normative data, children and adolescents with HCTD reported increased fatigue, pain, disability and decreased general health, with most differences translating into very large-sized effects. This new knowledge calls for systematic monitoring with standardized validated questionnaires, physical assessments and tailored interventions in clinical care.
Blended behavior change interventions combine therapeutic guidance with online care. This new way of delivering health care is supposed to stimulate patients with chronic somatic disorders in taking an active role in their disease management. However, knowledge about the effectiveness of blended behavior change interventions and how they should be composed is scattered. This comprehensive systematic review aimed to provide an overview of characteristics and effectiveness of blended behavior change interventions for patients with chronic somatic disorders.
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Everyone has the right to participate in society to the best of their ability. This right also applies to people with a visual impairment, in combination with a severe or profound intellectual and possibly motor disability (VISPIMD). However, due to their limitations, for their participation these people are often highly dependent on those around them, such as family members andhealthcare professionals. They determine how people with VISPIMD participate and to what extent. To optimize this support, they must have a good understanding of what people with disabilities can still do with their remaining vision.It is currently difficult to gain insight into the visual abilities of people with disabilities, especially those with VISPIMD. As a professional said, "Everything we can think of or develop to assess the functional vision of this vulnerable group will help improve our understanding and thus our ability to support them. Now, we are more or less guessing about what they can see.Moreover, what little we know about their vision is hard to communicate to other professionals”. Therefore, there is a need for methods that can provide insight into the functional vision of people with VISPIMD, in order to predict their options in daily life situations. This is crucial knowledge to ensure that these people can participate in society to their fullest extent.What makes it so difficult to get this insight at the moment? Visual impairments can be caused by a range of eye or brain disorders and can manifest in various ways. While we understand fairly well how low vision affects a person's abilities on relatively simple visual tasks, it is much more difficult to predict this in more complex dynamic everyday situations such asfinding your way or moving around during daily activities. This is because, among other things, conventional ophthalmic tests provide little information about what people can do with their remaining vision in everyday life (i.e., their functional vision).An additional problem in assessing vision in people with intellectual disabilities is that many conventional tests are difficult to perform or are too fatiguing, resulting in either no or the wrong information. In addition to their visual impairment, there is also a very serious intellectual disability (possibly combined with a motor impairment), which makes it even more complex to assesstheir functional vision. Due to the interplay between their visual, intellectual, and motor disabilities, it is almost impossible to determine whether persons are unable to perform an activity because they do not see it, do not notice it, do not understand it, cannot communicate about it, or are not able to move their head towards the stimulus due to motor disabilities.Although an expert professional can make a reasonable estimate of the functional possibilities through long-term and careful observation, the time and correct measurement data are usually lacking to find out the required information. So far, it is insufficiently clear what people with VZEVMB provoke to see and what they see exactly.Our goal with this project is to improve the understanding of the visual capabilities of people with VISPIMD. This then makes it possible to also improve the support for participation of the target group. We want to achieve this goal by developing and, in pilot form, testing a new combination of measurement and analysis methods - primarily based on eye movement registration -to determine the functional vision of people with VISPIMD. Our goal is to systematically determine what someone is responding to (“what”), where it may be (“where”), and how much time that response will take (“when”). When developing methods, we take the possibilities and preferences of the person in question as a starting point in relation to the technological possibilities.Because existing technological methods were originally developed for a different purpose, this partly requires adaptation to the possibilities of the target group.The concrete end product of our pilot will be a manual with an overview of available technological methods (as well as the methods themselves) for assessing functional vision, linked to the specific characteristics of the target group in the cognitive, motor area: 'Given that a client has this (estimated) combination of limitations (cognitive, motor and attention, time in whichsomeone can concentrate), the order of assessments is as follows:' followed by a description of the methods. We will also report on our findings in a workshop for professionals, a Dutch-language article and at least two scientific articles. This project is executed in the line: “I am seen; with all my strengths and limitations”. During the project, we closely collaborate with relevant stakeholders, i.e. the professionals with specific expertise working with the target group, family members of the persons with VISPIMD, and persons experiencing a visual impairment (‘experience experts’).
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).
Low back pain is the leading cause of disability worldwide and a significant contributor to work incapacity. Although effective therapeutic options are scarce, exercises supervised by a physiotherapist have shown to be effective. However, the effects found in research studies tend to be small, likely due to the heterogeneous nature of patients' complaints and movement limitations. Personalized treatment is necessary as a 'one-size-fits-all' approach is not sufficient. High-tech solutions consisting of motions sensors supported by artificial intelligence will facilitate physiotherapists to achieve this goal. To date, physiotherapists use questionnaires and physical examinations, which provide subjective results and therefore limited support for treatment decisions. Objective measurement data obtained by motion sensors can help to determine abnormal movement patterns. This information may be crucial in evaluating the prognosis and designing the physiotherapy treatment plan. The proposed study is a small cohort study (n=30) that involves low back pain patients visiting a physiotherapist and performing simple movement tasks such as walking and repeated forward bending. The movements will be recorded using sensors that estimate orientation from accelerations, angular velocities and magnetometer data. Participants complete questionnaires about their pain and functioning before and after treatment. Artificial analysis techniques will be used to link the sensor and questionnaire data to identify clinically relevant subgroups based on movement patterns, and to determine if there are differences in prognosis between these subgroups that serve as a starting point of personalized treatments. This pilot study aims to investigate the potential benefits of using motion sensors to personalize the treatment of low back pain. It serves as a foundation for future research into the use of motion sensors in the treatment of low back pain and other musculoskeletal or neurological movement disorders.
