ABSTRACT
BACKGROUND: Public mental health (PMH) is a global challenge and a UK priority area for action. However, to progress, practitioners require a stronger evidence base regarding the effectiveness of approaches, particularly regarding promotion and prevention through community-centred interventions. In addition, policy-makers need to understand what is being delivered, particularly in areas of high need, to identify promising practices or gaps in PMH provision. Finally, and importantly, the public need better information regarding what approaches and services are available to them. We report a protocol designed to (1) identify the types of community-centred interventions used in purposively selected diverse geographical areas of England to improve PMH outcomes and (2) describe the type, target population, content and outcome measures of each intervention. METHODS AND ANALYSIS: Five local authority areas of England were selected based on either high social deprivation or differing ethnic population statistics and geographical locations. Community-centred interventions in each area will be identified through: (1) desk-based data capture from standardised searches of publicly-available information (eg, policy, strategy and intervention advertising), (2) established professional networks and service contacts, (3) chain-referral sampling of individuals involved in local mental health promotion and prevention and (4) peer researchers, who will use their personal experience and local knowledge to help identify potentially relevant organisations. Data on the key features of the interventions will be extracted from individuals either by structured interviews or by electronic questionnaires with information regarding the intervention(s) of which they have knowledge. Initial data analysis will involve tabulating descriptive information and grouping interventions according to intervention type, target population, risk/protective factor and intended primary outcome. A descriptive comparison will be made between selected geographical areas. ETHICS AND DISSEMINATION: Ethical approval was obtained from Durham University's Department of Sport and Exercise Sciences Research Ethics Committee. We plan to disseminate our findings at relevant conferences, meetings and through peer-reviewed journals. We also plan to disseminate to the public and intervention providers through social media and/or newsletters.
Subject(s)
Mental Health , Sports , England , Exercise , Health Promotion , HumansABSTRACT
BACKGROUND: Fatigue is a common and distressing consequence of stroke, and the aetiology of post-stroke fatigue (PSF) is poorly understood. It is unclear whether chronic brain changes [cerebral atrophy and white matter lesions (WML)], stroke lesion location or certain clinical features are related to its development. The aim of this study was to identify, in patients with acute stroke, whether features in different brain regions on routine CT imaging or routinely collected clinical features predicted PSF at 1 month. METHODS: In total, 107 patients (62% male) with acute ischaemic or haemorrhagic stroke were assessed for fatigue (Fatigue Assessment Scale), anxiety and depression (Hospital Anxiety and Depression Scale) at 1 month. Admission brain CT was rated using a structured scoring system for (i) severity of atrophy and (ii) severity of WML in different regions of the brain, and (iii) site of acute and previous vascular lesions. RESULTS: Cerebral atrophy of mild or greater severity was present in 84 patients (77.5%) and WML of mild or greater severity was present in 54 patients (50.5%) in at least one of the evaluated brain regions. There was no association between PSF and severity of atrophy or WML, or presence of acute or previous vascular lesions. We used the Oxfordshire Community Stroke Project (OCSP) classification to explore the possible influence of lesion location because a minority of the patients (37.4%) had visible acute lesions. Fatigue scores were higher in patients with clinically diagnosed posterior strokes (p = 0.046), in females (p = 0.05) and in those with higher depression and anxiety scores (ρ = 0.52; p < 0.001 and ρ = 0.49; p < 0.001, respectively). Structural CT variables were not significant predictors of fatigue (log FAS) in a linear regression which controlled for age, sex, pre-stroke fatigue, OCSP classification, depression and anxiety. The significant predictors of fatigue were depression (ß = 0.30; p = 0.007) and anxiety (ß = 0.28; p = 0.013; adjusted R(2) = 0.254). Stroke subtype (according to the OCSP classification) was marginally predictive (ß = 0.17; p = 0.05) and sex was not statistically significant (ß = 0.15; p = 0.08). CONCLUSIONS: Features on routine post-stroke CT do not appear to associate with fatigue at 1 month. However, clinically diagnosed posterior strokes as well as female gender, anxiety and depression may be linked with fatigue. Therefore, clinical vigilance rather than CT features should be used to predict fatigue early after stroke. Further research is needed in this area to establish whether biological mechanisms underlie the development of PSF.
