It is well established that sleep disorders have neuropsychological consequences in otherwise healthy people. Studies of night-time sleep problems and cognition in Parkinson's disease (PD), however, paint a mixed picture, with many reporting no relationship between sleep problems and neuropsychological performance. This review aimed to meta-analyse this research and to examine the factors underlying these mixed results. A literature search was conducted of published and unpublished studies, resulting in 16 papers that met inclusion criteria. Data were analysed in the domains of: global cognitive function; memory (general, long-term verbal recognition, long-term verbal recall); and executive function (general, shifting, updating, inhibition, generativity, fluid reasoning). There was a significant effect of sleep on global cognitive function, long-term verbal recall, long-term verbal recognition, shifting, updating, generativity, and fluid reasoning. Although there are effects on memory and executive function associated with poor sleep in PD, the effects were driven by a small number of studies. Numerous methodological issues were identified. Further studies are needed reliably to determine whether disturbed sleep impacts on cognition via mechanisms of hypoxia, hypercapnia, sleep fragmentation, chronic sleep debt or decreased REM and/or slow wave sleep in PD, as this may have important clinical implications.

Aim Depression is common in Type 2 diabetes, yet rates vary. Overlap between symptoms of depression and diabetes may account for this variability in depression prevalence rates. We examined to what extent depression prevalence was a function of the proportion of depression–diabetes symptom overlap (items within symptom dimensions) and sample characteristics. Methods Electronic and hand searching of published and unpublished works identified 147 eligible papers. Of 3656 screened, 147 studies (149 samples, N = 17–229 047, mean sample age 25.4–82.8 years, with 152 prevalence estimates), using 24 validated depression questionnaires were selected. Sample size, publication type, sample type, gender, age, BMI, HbA1c, depression questionnaire and prevalence rates were extracted. Results Prevalence rates ranged from 1.8% to 88% (mean = 28.30%) and were higher in younger samples, samples with higher mean HbA1c and clinic samples. Diabetes–depression symptom overlap did not affect prevalence. A higher proportion of anhedonia, cognition, cognitive, negative affect and sleep disturbance symptoms, and a lower proportion of somatic symptoms were consistently associated with higher depression prevalence. Conclusions The lack of an overall effect of diabetes–depression symptom overlap might suggest that assessment of depression in Type 2 diabetes is generally not confounded by co‐occuring symptoms. However, questionnaires with proportionally more or fewer items measuring other symptom categories were associated with higher estimates of depression prevalence. Depression measures that focus on the cardinal symptoms of depression (e.g. negative affect and cognition), limiting symptoms associated with increasing diabetes symptomatology (e.g. sleep disturbance, cognitive) may most accurately diagnose depression.

HA native Andean children have more respiratory events when scoring relies on SpO2 desaturation due to inherent SpO2 instability. Use of American Academy of Sleep Medicine scoring criteria may yield false-positive results for obstructive sleep-disordered breathing at HA.

Objective: Intraindividual variability (IIV) -variance in an individuals' cognitive performance -may be associated with subsequent cognitive decline and/or conversion to dementia in older adults. This novel measure of cognition encompasses two main operationalisations: inconsistency (IIV-I) and dispersion (IIV-D), referring to variance within or across tasks respectively. Each operationalisation can also be measured with or without covariates. This meta-analytic study explores the association between IIV and subsequent cognitive outcomes regardless of operational definitions and measurement approaches. Method:Longitudinal studies (N = 13) that have examined IIV in association with later cognitive decline and/or conversation to MCI/dementia were analysed. The effect of IIV operationalisation was explored. Additional sub group analysis of measurement approaches could not be examined due to the limited number of appropriate studies available for inclusion. Results: Meta-analytic estimates suggest IIV is associated with subsequent cognitive decline and/or conversion to MCI/dementia (r = .20 , 95% CI [.09, .31]) with no significant difference between the two operationalisations observed (Q = 3.41, p = .065). Conclusion:Cognitive IIV, including both IIV-I and IIV-D operationalisations, appears to be associated with subsequent cognitive decline and/or dementia and may offer a novel indicator of incipient dementia in both clinical and research settings.

Many studies have sought to describe the relationship between sleep disturbance and cognition in Parkinson’s disease (PD). The Parkinson’s Disease Sleep Scale (PDSS) and its variants (the Parkinson’s disease Sleep Scale-Revised; PDSS-R, and the Parkinson’s Disease Sleep Scale-2; PDSS-2) quantify a range of symptoms impacting sleep in only 15 items. However, data from these scales may be problematic as included items have considerable conceptual breadth, and there may be overlap in the constructs assessed. Multidimensional measurement models, accounting for the tendency for items to measure multiple constructs, may be useful more accurately to model variance than traditional confirmatory factor analysis. In the present study, we tested the hypothesis that a multidimensional model (a bifactor model) is more appropriate than traditional factor analysis for data generated by these types of scales, using data collected using the PDSS-R as an exemplar. 166 participants diagnosed with idiopathic PD participated in this study. Using PDSS-R data, we compared three models: a unidimensional model; a 3-factor model consisting of sub-factors measuring insomnia, motor symptoms and obstructive sleep apnoea (OSA) and REM sleep behaviour disorder (RBD) symptoms; and, a confirmatory bifactor model with both a general factor and the same three sub-factors. Only the confirmatory bifactor model achieved satisfactory model fit, suggesting that PDSS-R data are multidimensional. There were differential associations between factor scores and patient characteristics, suggesting that some PDSS-R items, but not others, are influenced by mood and personality in addition to sleep symptoms. Multidimensional measurement models may also be a helpful tool in the PDSS and the PDSS-2 scales and may improve the sensitivity of these instruments.