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Objective: To identify associated (non-)motor profiles of Parkinson's disease (PD) patients with hyperhidrosis as a dominant problem. Methods: This is a cross-sectional, exploratory, analysis of participants enrolled in the Non-motor Longitudinal International Study (NILS; UKCRN No: 10084) at the Parkinson's Centre at King's College Hospital (London, UK). Hyperhidrosis scores (yes/no) on question 28 of the Non-Motor Symptom Questionnaire were used to classify patients with normal sweat function (n = 172) and excessive sweating (n = 56) (Analysis 1; n = 228). NMS scale (NMSS) question 30 scores were used to stratify participants based on hyperhidrosis severity (Analysis 2; n = 352) using an arbitrary severity grading: absent score 0 (n = 267), mild 1–4 (n = 49), moderate 5–8 (n = 17), and severe 9–12 (n = 19). NMS burden, as well as PD sleep scale (PDSS) scores were then analysed along with other correlates. Results: No differences were observed in baseline demographics between groups in either analysis. Patients with hyperhidrosis exhibited significantly higher total NMSS burden compared to those without (p < 0.001). Secondary analyses revealed higher dyskinesia scores, worse quality of life and PDSS scores, and higher anxiety and depression levels in hyperhidrosis patients (p < 0.001). Tertiary analyses revealed higher NMSS item scores for fatigue, sleep initiation, restless legs, urinary urgency, and unexplained pain (p < 0.001). Conclusions: Chronic hyperhidrosis appears to be associated with a dysautonomia dominant subtype in PD patients, which is also associated with sleep disorders and a higher rate of dyskinesia (fluctuation-related hyperhidrosis). These data should prompt the concept of hyperhidrosis being used as a simple clinical screening tool to identify PD patients with autonomic symptoms. [ABSTRACT FROM AUTHOR]

• This study examines the relationships between perceived landscape aesthetic quality and landscape physical characteristics. • These were explored using a multiscale GWR to identify the spatial scales of these relationships. • The results show that some factors have a globally consistent relationship (Remoteness). • Others exhibited highly localised relationships (Absence of human artefacts, Naturalness, Ruggedness). • The study shows how spatially aware approaches such as MGWR better inform landscape decision making. This study explores how formal measures of landscape wildness (i.e. absence of human artefacts, perceived naturalness of land cover, remoteness from mechanised access, and ruggedness of the terrain) correlate with crowdsourced measures of landscape aesthetic quality as captured in Scenic-Or-Not data for Great Britain. It evaluates multiple linear regression (MLR) and two spatially varying coefficients models: geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The MLR provided a baseline model in an analysis of national data, exhibiting the presence of spatially autocorrelated residuals and suggesting that geographically weighted models may be appropriate. A standard GWR was found to exacerbate local collinearity between covariates, both overfitting and underfitting the model with highly varied and localised results. This was due to its single one-size-fits-all bandwidth and the assumption that all relationships between the target and predictor variables operate over the same spatial scale. MGWR relaxes this assumption by determining parameter-specific bandwidths, mitigating the local collinearity issues found in a standard GWR and resulting in more spatially stable and consistent coefficient estimates. The findings also indicated that the relationship between some covariates (such as remoteness) and perceived landscape quality varied little spatially, while clear gradients were found for other covariates. For example, naturalness was stronger in the north and west, ruggedness was stronger in the south and east, and the absence of human artefacts was weaker in Scotland and the north than in England and the south. Overall, the study showed that MGWR is more sensitive than GWR to the spatial heterogeneity in the statistical relationships between landscape factors and public perceptions. These findings provide nuanced understandings of how these relationships vary spatially, underscoring the value of such approaches in landscape scale analyses to support policy and planning. The discussion section of this paper considers the MGWR as the default geographically weighted model, assessing the potential for the use of crowdsourced data in landscape studies. In so doing, it illustrates how such approaches could be used to explore both subjective and objective landscape evaluations. [ABSTRACT FROM AUTHOR]

Introduction: Visual hallucinations (VH) have a significant impact on quality of life for people with Parkinson's disease (PD). A major reason for this is the well-established link with cognitive impairment, but there is still a need for more longitudinal studies examining the specific cognitive domains which may be affected. The aim of this study was to profile decline in cognition associated with VH in a cohort of 69 individuals with PD over 1 year., Method: Visual hallucinations assessments were carried out every 3 months. Executive function and episodic memory were assessed at baseline and 1 year. All evaluations were performed via phone interviews. The presence or absence of VH was categorized based on the entirety of the year's data (i.e., no episodes and >0 episodes). We also defined a persistent VH group who had VH present at more than one time point and compared these with a no-VH group and a group with transient VH (i.e., only one episode)., Results: Linear mixed-effect models showed that VH were associated with more rapid overall cognitive decline (-0.26, t = -2.39, p = .02), which was driven by executive function (-0.28, t = -2.48, p = .02). Persistent VH were associated with decline in executive function (-0.33, t = -2.4, p = .02), while no relationship was found for non-persistent VH, suggesting that persistent VH be the major driver of this relationship., Conclusion: This finding brings greater clarity to the relationship between cognitive decline and VH in PD. Future research should examine the robustness of this phenotype for biomarkers studies and treatment interventions., (© 2018 The British Psychological Society.)