Depression is a highly prevalent and seriously impairing disorder. Evidence suggests that music therapy can decrease depression, though the music therapy that is offered is often not clearly described in studies. The purpose of this study was to develop an improvisational music therapy intervention based on insights from theory, evidence and clinical practice for young adults with depressive symptoms. The Intervention Mapping method was used and resulted in (1) a model to explain how emotion dysregulation may affect depressive symptoms using the Component Process Model (CPM) as a theoretical framework; (2) a model to clarify as to how improvisational music therapy may change depressive symptoms using synchronisation and emotional resonance; (3) a prototype Emotion-regulating Improvisational Music Therapy for Preventing Depressive symptoms (EIMT-PD); (4) a ten-session improvisational music therapy manual aimed at improving emotion regulation and reducing depressive symptoms; (5) a program implementation plan; and (6) a summary of a multiple baseline study protocol to evaluate the effectiveness and principles of EIMT-PD. EIMT-PD, using synchronisation and emotional resonance may be a promising music therapy to improve emotion regulation and, in line with our expectations, reduce depressive symptoms. More research is needed to assess its effectiveness and principles.
Objective: The aim of this study was to investigate age group, gender, and baseline depressive symptom severity as possible effect moderators in (1) cognitive versus behavioral based CBT-modules and (2) sequences of modules that started either with cognitive or behavioral modules in indicated depression prevention in adolescents. Method: We conducted a pragmatic cluster-randomized trial under four parallel conditions. Each condition consisted of four CBT-modules of three sessions (cognitive restructuring, problem solving, behavioral activation, relaxation), but the sequencing of modules differed. The CBT-modules and sequences were clustered into more cognitive versus more behavioral based approaches. The sample involved 282 Dutch adolescents with elevated depressive symptoms (Mage = 13.8; 55.7% girls, 92.9% Dutch). Assessments were conducted at baseline, after three sessions, at post-intervention and 6-month follow-up with self-reported depressive symptoms as the primary outcome. Results: We found no evidence for substantial moderation effects. Age group, gender, and depressive symptom severity level at baseline did not moderate the effects of cognitive versus behavioral modules after three sessions. No evidence was also found that these characteristics moderated the effectiveness of sequences of modules that started either with cognitive or behavioral modules at post-intervention and 6-month follow-up. Conclusion: Cognitive and behavioral based modules and sequences in the prevention of depression in adolescents might apply to a relatively wide range of adolescents in terms of age group, gender, and severity levels of depressive symptoms.
We collected, reviewed and edited articles submitted to Sustainability on the topic of communicative and behavioral interventions to increase sustainability.In this Special Issue, we would like to highlight research on interventions. How we can find interventions that help in encouraging (sustainable or less consumption) the use of fossil-free methods of transport, the implementation of renewable energy, etc. We invite you to submit your work to this Special Issue on “Communicative and Behavioral Interventions to Increase Sustainability” that contribute to the establishment of a sustainable future. We would like to collect a Special Issue that highlight evidence-based interventions to change behavior to establish the sustainable society.This Special Issue is positioned to bring together the best work on communicative and behavioral interventions. It can include (but is not limited to) dialogue, stakeholder engagement, educational programs, nudging, community building, corporate social responsibility (CSR), social innovations, etc. We would like you to focus on evidence-based interventions and encourage practical insights or ideas for applications of findings
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The main objective is to write a scientific paper in a peer-reviewed Open Access journal on the results of our feasibility study on increasing physical activity in home dwelling adults with chronic stroke. We feel this is important as this article aims to close a gap in the existing literature on behavioral interventions in physical therapy practice. Though our main target audience are other researchers, we feel clinical practice and current education on patients with stroke will benefit as well.
Chronische gewrichtsaandoeningen zijn veelvoorkomende aandoeningen waarmee patiënten bij de fysiotherapeut of oefentherapeut komen. Aandoeningen zoals artrose en reuma veroorzaken problemen in het dagelijks functioneren vanwege pijn en verminderde mobiliteit. Genezing is vaak niet mogelijk, maar het bevorderen van zelfmanagement kan verergering voorkomen. Oefentherapeuten en fysiotherapeuten spelen een centrale rol in het ondersteunen van zelfmanagement bij patiënten met gewrichtsaandoeningen. De inzet van online toepassingen, waaronder mobiele applicaties, en online platforms, die gericht zijn op het bevorderen van zelfmanagement (in dit voorstel gedefinieerd als Behavioral Intervention Technologies: BITs) kunnen patiënten met chronische gewrichtsaandoeningen ondersteunen. Echter, voor veel professionals is het onduidelijk hoe BITs kunnen worden ingezet om zelfmanagement te vergroten en hoe dit gecombineerd kan worden met fysieke begeleiding. Daarom onderzoeken we in dit tweejarige project de manier waarop oefen- en fysiotherapeuten coaching op zelfmanagement via BITs kunnen vormgeven. In werkpakket 1 brengen we met een review, observaties en een concept mapping in kaart welke elementen en randvoorwaarden van BITs belangrijk zijn voor het bevorderen van zelfmanagement. Zodra we inzicht hebben in deze elementen en randvoorwaarden wordt in co-creatie met stakeholders toegewerkt naar beroepsrollen en beroepscompetenties die voorwaardelijk zijn voor het gebruik van BITs. Met de input van deze onderzoeksactiviteiten ontwikkelen we samen met de doelgroep de AmSOS methodiek die professionals helpt bij het gebruik van BITs om zelfmanagement te bevorderen bij patiënten met chronische gewrichtsaandoeningen (WP2). Om te bepalen in hoeverre de methodiek bruikbaar is in de praktijk wordt in WP3 een haalbaarheidsstudie opgezet waarbij 25 eerstelijnsfysio- en/of oefentherapiepraktijken de AmSOS methodiek gaan gebruiken in de behandeling van patiënten met chronische gewrichtsaandoeningen. Omdat gewrichtsaandoeningen een substantieel onderdeel zijn van de curricula, maar tegelijkertijd weinig aandacht wordt besteed aan technologie en zelfmanagement, ontwikkelen we in WP4 een onderwijsmodule voor scholing van studenten en praktiserende oefen- en fysiotherapeuten.
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).
