This study analyses the determinants of cycling expenditure by means of a Tobit regression analysis, based on a dataset of 5,157 cyclists. Using a heterodox economic framework, 23 different variables are combined into two commonly used variable groups (socio-demographics, sports intensity variables) and two rarely investigated variables groups (socio-economic cycling capital, psychographics). With all variables included in the Tobit regression, gender, trip duration, frequency, number of cycling variants practiced, visiting cycling websites, and practicing road-cycling or mountain bike are positive determinants of cycling expenditure. A negative association is found with competitive riding and cycling drop out. Marketeers of cycling services and cycling apparel should meet the cyclists need for identification instead of focusing solely on socio-demographic factors.
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BACKGROUND: Due to complex processes of implementation of innovations aimed at persons with intellectual disabilities in healthcare organizations, lifestyle interventions are not used as intended or not used at all. In order to provide insight into determinants influencing this implementation, this study aims to ascertain if the Measurement Instrument for Determinants of Innovations (MIDI) is useful for objectively evaluating implementation.METHOD: With semi-structured interviews, data concerning determinants of implementation of lifestyle interventions were aggregated. These data were compared to the determinants questioned in the MIDI. Adaptations to the MIDI were made in consultation with the author of the MIDI.RESULTS: All determinants of the MIDI, except for that concerning legislation and regulations, were represented in the interview data. Determinants not represented in the MIDI were the level of intellectual disabilities, suitability of materials and physical environment, multi-levelness of interventions and several persons who could be involved in the intervention, such as direct support persons (DSPs), a therapist or family, and the communication between these involved persons.CONCLUSION: The present authors suggested making adjustments to existing questions of the MIDI in order to improve usability for deployment in organizations that provide care to persons with intellectual disabilities. The adjustments need to be tested with other interventions.
ABSTRACT Purpose: To gain insight into determinants of physical activity in wheelchair users with spinal cord injury or lower limb amputation, from the perspective of both wheelchair users and rehabilitation professionals. Methods: Seven focus groups were conducted: five with wheelchair users (n=25) and two with rehabilitation professionals (n¼11). The transcripts were analysed using a sequential coding strategy, in which the reported determinants of physical activity were categorized using the Physical Activity for people with a Disability (PAD) model. Results: Reported personal determinants of physical activity were age, general health status, stage of life, demotivation due to difficulty burning calories, available time and energy, balance in daily life, attitude, and history of a physically active lifestyle. Reported environmental determinants were professional guidance, inconvenient exercise times, accessibility of facilities, costs, transportation difficulties, equipment difficulties, and social support. Conclusions: Important, changeable determinants of physical activity that might be influenced in future lifestyle interventions for wheelchair users are: balance in daily life leading to more time and energy to exercise, attitude towards physical activity, professional guidance, accessibility of facilities (providing information on how and where to find accessible facilities), and social support (learning how to get this)
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).
The main objective of DEDIPAC is to understand the determinants of dietary, physical activity and sedentary behaviours and to translate this knowledge into a more effective promotion of a healthy diet and physical activity.The DEDIPAC KH is a multidisciplinary consortium of scientists from 68 research centers in 12 countries across Europe.
The main objective of DEDIPAC is to understand the determinants of dietary, physical activity and sedentary behaviours and to translate this knowledge into a more effective promotion of a healthy diet and physical activity.The DEDIPAC KH is a multidisciplinary consortium of scientists from 68 research centers in 12 countries across Europe.
