Background Eating behaviour of older adults is influenced by a complex interaction of determinants. Understanding the determinants of a specific target group is important when developing targeted health-promoting strategies. The aim of this study was to explore interpersonal determinants of eating behaviours in older adults living independently in a specific neighbourhood in the Netherlands. Methods In the neighbourhood of interest, populated by relatively many older adults, fifteen semi-structured interviews were conducted with independently living older adults (aged 76.9 ± 6.4y). Interviews were complemented with observations among the target group: three occasions of grocery shopping and three collective eating occasions in the neighbourhood. A thematic approach was used to analyse the qualitative data. Results When we asked the older adults unprompted why they eat what they eat, the influence of interpersonal determinants did not appear directly; respondents rather mentioned individual (e.g. habits) and environmental factors (e.g. food accessibility). Key findings regarding interpersonal factors were: 1) Behaviours are shaped by someone’s context; 2) Living alone influences (determinants of) eating behaviour via multiple ways; 3) There is a salient norm that people do not interfere with others’ eating behaviour; 4) Older adults make limited use of social support (both formal and informal) for grocery shopping and cooking, except for organised eating activities in the neighbourhood. In this particular neighbourhood, many facilities (e.g. shops at walking distance) are present, and events (e.g. dinners) are organised with and for the target group, which likely impact (determinants of) their behaviours. Conclusions The study showed that older adults do not directly think of interpersonal factors influencing their eating behaviour, but rather of individual or environmental factors. However, multiple interpersonal factors did appear in the interviews and observations. Moreover, neighbourhood-specific factors seem to play a role, which underlines the need to understand the specific (social) setting when developing and implementing intervention programmes. Insights from this study can assist in developing health-promoting strategies for older adults, taking into account the context of the specific neighbourhood.
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Objective: To study the effects of a comprehensive secondary prevention programme on weight loss and to identify determinants of weight change in patients with coronary artery disease (CAD). Methods: We performed a secondary analysis focusing on the subgroup of overweight CAD patients (BMI ≥27 kg/m2) in the Randomised Evaluation of Secondary Prevention by Outpatient Nurse SpEcialists-2 (RESPONSE-2) multicentre randomised trial. We evaluated weight change from baseline to 12-month follow-up; multivariable logistic regression with backward elimination was used to identify determinants of weight change. Results: Intervention patients (n=280) lost significantly more weight than control patients (n=257) (-2.4±7.1 kg vs -0.2±4.6 kg; p<0.001). Individual weight change varied widely, with weight gain (≥1.0 kg) occurring in 36% of interventions versus 41% controls (p=0.21). In the intervention group, weight loss of ≥5% was associated with higher age (OR 2.94), lower educational level (OR 1.91), non-smoking status (OR 2.92), motivation to start with weight loss directly after the baseline visit (OR 2.31) and weight loss programme participation (OR 3.33), whereas weight gain (≥1 kg) was associated with smoking cessation ≤6 months before or during hospitalisation (OR 3.21), non-Caucasian ethnicity (OR 2.77), smoking at baseline (OR 2.70), lower age (<65 years) (OR 1.47) and weight loss programme participation (OR 0.59). Conclusion: The comprehensive secondary prevention programme was, on average, effective in achieving weight loss. However, wide variation was observed. As weight gain was observed in over one in three participants in both groups, prevention of weight gain may be as important as attempts to lose weight.
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In service design projects, collaboration between design consultant and service provider can be problematic. The nature of these projects requires a high level of shared understanding and commitment, which providers may not be used to. We studied designer-provider collaboration in multiple real-life cases, in order to uncover determinants for successful collaboration. The case studies involved six service innovation projects, performed by Dutch design agencies. Independent researchers closely monitored the projects. Additional interviews with designers and providers gave insights in how both parties experienced their collaboration in the innovation projects. During data analysis, a coding scheme was created inductively. The scheme supported us in formulating 12 themes for designer-provider collaboration, amongst them four contextual determinants of shared understanding and stakeholder commitment in SD-projects. The insights from this study were then grounded in literature. Knowledge gaps were identified on themes about agreements of responsibilities, the open-endedness of an SD-process, an opportunitysearching approach, and organizational change that is required for the successful implementation of innovative service concepts.
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.
