Objectives: To evaluate the feasibility, facilitators of and barriers to delivering Namaste Care by volunteers and family carers to community-dwelling people with dementia, and to map family carers... Show moreObjectives: To evaluate the feasibility, facilitators of and barriers to delivering Namaste Care by volunteers and family carers to community-dwelling people with dementia, and to map family carers and volunteers' experiences with the programme. Design: Qualitative interview study with two phases: (1) preparation phase; (2) pilot phase. Setting: Private residences of community-dwelling people with dementia in the UK and the Netherlands. Participants: Family carers and volunteers of community-dwelling people with dementia (phase 1: 36 Dutch interviews, phase 2: 9 Dutch and 16 UK interviews). Intervention: Namaste Care is a multicomponent psychosocial programme, originally developed for people with dementia residing in long-term care facilities. Meaningful activities were offered by carers and volunteers. Each person with dementia was offered 10 one-hour sessions. Results: Phase 1: Namaste Care was deemed feasible for community-dwelling people with dementia and no major adaptations to the programme were considered necessary. Phase 2: perceived effects of Namaste Care on people with dementia included improved mood and increased interaction. The programme appeared enriching for both family carers and volunteers, providing joy, respite from care and new insights for coping with challenging behaviour. A flexible attitude of the Namaste provider facilitated its delivery. High caregiver burden and a strained relationship between the family carer and person with dementia were considered barriers. Experiences of family carers and volunteers with Namaste Care were very positive (mean satisfaction rating: 8.7 out of 10, SD=0.9, range 7-10). Conclusion: We recommend offering Namaste Care delivered by volunteers, preferably multiple sessions per week of 1.5-2 hours to optimise quality of life of community-dwelling people with dementia. Working with well-matched, flexible Namaste providers is pivotal. Family involvement should be encouraged, although the extent should be adapted depending on preference, caregiver burden and the relationship between the family carer and the person with dementia. Show less
Psychotherapy is an effective treatment for many common mental health problems, but the mechanisms of action and processes of change are unclear, perhaps driven by the focus on a single diagnosis... Show morePsychotherapy is an effective treatment for many common mental health problems, but the mechanisms of action and processes of change are unclear, perhaps driven by the focus on a single diagnosis which does not reflect the heterogeneous symptom experiences of many patients. The objective of this study was to better understand therapeutic change, by illustrating how symptoms evolve and interact during psychotherapy. Data from 113,608 patients from psychological therapy services who completed depression and anxiety symptom measures across three to six therapy sessions were analysed. A panel graphical vector-autoregression model was estimated in a model development sample (N = 68,165) and generalizability was tested in a confirmatory model, fitted to a separate (hold-out) sample of patients (N = 45,443). The model displayed an excellent fit and replicated in the confirmatory holdout sample. First, we found that nearly all symptoms were statistically related to each other (i.e. dense connectivity), indicating that no one symptom or association drives change. Second, the structure of symptom interrelations which emerged did not change across sessions. These findings provide a dynamic view of the process of symptom change during psychotherapy and give rise to several causal hypotheses relating to structure, mechanism, and process. Show less
Background: Depression is commonly perceived as a single underlying disease with a number of potential treatment options. However, patients with major depression differ dramatically in their... Show moreBackground: Depression is commonly perceived as a single underlying disease with a number of potential treatment options. However, patients with major depression differ dramatically in their symptom presentation and comorbidities, e.g. with anxiety disorders. There are also large variations in treatment outcomes and associations of some anxiety comorbidities with poorer prognoses, but limited understanding as to why, and little information to inform the clinical management of depression. There is a need to improve our understanding of depression, incorporating anxiety comorbidity, and consider the association of a wide range of symptoms with treatment outcomes. Method: Individual patient data from six RCTs of depressed patients (total n = 2858) were used to estimate the differential impact symptoms have on outcomes at three post intervention time points using individual items and sum scores. Symptom networks (graphical Gaussian model) were estimated to explore the functional relations among symptoms of depression and anxiety and compare networks for treatment remitters and those with persistent symptoms to identify potential prognostic indicators. Results: Item-level prediction performed similarly to sum scores when predicting outcomes at 3 to 4 months and 6 to 8 months, but outperformed sum scores for 9 to 12 months. Pessimism emerged as the most important predictive symptom (relative to all other symptoms), across these time points. In the network structure at study entry, symptoms clustered into physical symptoms, cognitive symptoms, and anxiety symptoms. Sadness, pessimism, and indecision acted as bridges between communities, with sadness and failure/worthlessness being the most central (i.e. interconnected) symptoms. Connectivity of networks at study entry did not differ for future remitters vs. those with persistent symptoms. Conclusion: The relative importance of specific symptoms in association with outcomes and the interactions within the network highlight the value of transdiagnostic assessment and formulation of symptoms to both treatment and prognosis. We discuss the potential for complementary statistical approaches to improve our understanding of psychopathology. Show less
Background This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data. Methods Individual... Show moreBackground This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data. Methods Individual patient data from all six eligible randomised controlled trials were used to develop (k = 3, n = 1722) and test (k = 3, n = 918) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum scores were developed using coefficient weights derived from network centrality statistics (models 1-3) and factor loadings from a confirmatory factor analysis (model 4). Unweighted sum score models were tested using elastic net regularised (ENR) and ordinary least squares (OLS) regression (models 5 and 6). Individual items were then included in ENR and OLS (models 7 and 8). All models were compared to one another and to a null model (mean post-baseline Beck Depression Inventory Second Edition (BDI-II) score in the training data: model 9). Primary outcome: BDI-II scores at 3-4 months. Results Models 1-7 all outperformed the null model and model 8. Model performance was very similar across models 1-6, meaning that differential weights applied to the baseline sum scores had little impact. Conclusions Any of the modelling techniques (models 1-7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression. Show less
