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.
This study aimed to evaluate outcomes and support use in 12- to 25-year-old visitors of the @ease mental health walk-in centres, a Dutch initiative offering free counselling by trained and supervised peers.
MULTIFILE
BACKGROUND: Higher levels of physical activity (PA) after treatment are associated with beneficial effects on physical and psychosocial functioning of cancer survivors. However, survivors often do not meet the recommended levels of PA. In order to promote PA, we developed a closed internet-based program. The aim of the study is to evaluate the (cost-)effectiveness of an internet-based PA-promotion program, alone or combined with physiotherapy counselling, compared to usual care, on PA-levels of breast or prostate cancer survivors. In this multicenter randomised controlled trial (RCT), breast or prostate cancer survivors who completed their primary treatment 3-12 months earlier, will be randomised to either 6-months access to a fully-automated internet-based intervention alone, an internet-based intervention plus remote support by a physiotherapist, or a control group. The intervention is based on the Transtheoretical Model and includes personalized feedback, information, video's and assignments. Additionally, in a second arm, physiotherapy counselling is provided through monthly scheduled and on-demand telephone calls. The control group will receive usual care and a leaflet with PA guidelines.METHODS: At baseline, 6 and 12 months, the primary outcome (PA) will be measured during 7 consecutive days by accelerometers. Secondary outcomes are self-reported PA, fatigue, mood, health-related quality of life, and costs. The group differences for primary and secondary outcomes will be analyzed using linear mixed models.DISCUSSION: If proven to be (cost)effective, this internet-based intervention, either alone or in combination with telephone support, will be a welcome addition to previous RCT's.TRIAL REGISTRATION: Netherlands trial register (NTR6911), Date of trial registration: December 21, 2017.
MULTIFILE
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).
