Background/purpose: For prevention of sarcopenia and functionaldecline in community-dwelling older adults, a higher daily proteinintake is needed. A new e-health strategy for dietary counselling wasused with the aim to increase total daily protein intake to optimallevels (minimal 1.2 g/kg/day, optimal 1.5 g/kg/day) through use ofregular food products.Methods: The VITAMIN (VITal Amsterdam older adults IN the city)RCT included 245 community-dwelling older adults (age ≥ 55y):control, exercise, and exercise plus dietary counselling (protein)group. The dietary counselling intervention was based on behaviourchange and personalization. Dietary intake was measured by a 3ddietary record at baseline, after 6-month intervention and 12-monthfollow-up. The primary outcome was average daily protein intake(g/kg/day). Sub-group analysis and secondary outcomes includeddaily protein distribution, sources, product groups. A Linear MixedModels (LMM) of repeated measures was performed with STATAv13.Results: Mean age of the 224 subjects was 72.0(6.5) years, a BMI of26.0(4.2). The LMM showed a significant effect of time and time*group(p<0.001). The dietary counselling group showed higher protein intakethan either control (1.41 vs 1.13 g/kg/day; β +0.32; p<0.001) or exercisegroup (1.41 vs 1.11 g/kg/day; β +0.33; p<0.001) after 6-month interventionand 12-month follow-up.Conclusions and implications: This study shows digitally supporteddietary counselling improves protein intake sufficiently in communitydwellingolder adults with use of regular food products. Protein intakeincrease by personalised counselling with e-health is a promising strategyfor dieticians.
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Digitally supported dietary counselling may be helpful in increasing the protein intake in combined exercise and nutritional interventions in community-dwelling older adults. To study the effect of this approach, 212 older adults (72.2 ± 6.3 years) were randomised in three groups: control, exercise, or exercise plus dietary counselling. The dietary counselling during the 6-month intervention was a blended approach of face-to-face contacts and videoconferencing, and it was discontinued for a 6-month follow-up. Dietary protein intake, sources, product groups, resulting amino acid intake, and intake per eating occasion were assessed by a 3-day dietary record. The dietary counselling group was able to increase the protein intake by 32% at 6 months, and the intake remained 16% increased at 12 months. Protein intake mainly consisted of animal protein sources: dairy products, followed by fish and meat. This resulted in significantly more intake of essential amino acids, including leucine. The protein intake was distributed evenly over the day, resulting in more meals that reached the protein and leucine targets. Digitally supported dietary counselling was effective in increasing protein intake both per meal and per day in a lifestyle intervention in community-dwelling older adults. This was predominantly achieved by consuming more animal protein sources, particularly dairy products, and especially during breakfast and lunch.
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Objective: To understand inactivity and relapse from PA, and to develop theory-based behaviour change strategies to stimulate and support maintenance of PA.Methods: We conducted a literature search to explore barriers to PA. Social cognitive theories and empirical evidence were evaluated and guided the process developing a theoretical framework and counselling strategies.Results: A theoretical framework is presented to understand why people do not engage in PA and often relapse once they started PA. A distinction is made between three related types of BBs. In PA counselling these three beliefs are addressed using four different BB behaviour change strategies.Conclusion: BB counselling aims to develop an individual pattern of PA for the long term that is adapted to the (often limited) motivation of the client, thereby preventing the occurrence of BBs. The client will learn to cope with factors that may inhibit PA in the future.Practice implications: The BBs approach composes a way of counselling around the central construct of barrier-beliefs to stimulate engagement in PA independently, in the long term.
DOCUMENT
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).
