Greater than 40% of women are obese, a key risk factor for cardiometabolic, neurocognitive disease, mood disorders, and certain cancers. Obesity and unfavorable body composition can compromise physical and psychological health and well-being. Preliminary evidence demonstrates Meditative Movement (i.e., Tai Chi Easy) improves health outcomes and body composition among midlife/older women. This single-group pilot study explored relationships between well-being predictors related to body composition and associated behavioral risk factors in midlife/older women pre-to-post Tai Chi Easy intervention.

Eligible women 45–75 years old, participated in once-weekly 30-minute Tai Chi Easy classes over 8-weeks. Pre/post-intervention data included self-report surveys and on-site body composition. Multivariate linear regression models were fitted with putative predictor variables having correlations p-values of 0.20 or less with sleep quality and eating behaviors.

Participants (N = 36) (M age = 53.7) were White (80.4%) and attended ≥ 4 years of college (70.6%). Analyses resulted in one independent variable per model as a predictor of the dependent variables of sleep quality and emotional eating. Results indicated: (1) stress explained 13.4% sleep quality variance (F (2, 20) = 2.71, p = 0.09) and (2) self-compassion explained 42.1% emotional eating variance (F (2, 31) = 12.54, p < .01).

Findings suggest stress and self-compassion partially explain variance in the dependent variables of sleep quality and emotional eating, both associated behavioral risk factors of body composition. Additional research may guide interventions to test efficacy and examine mediators to improve well-being predictors, body composition, and associated behavioral risk factors among midlife/older women.

Sleep problems are common amongst children and adolescents with attention deficit hyperactivity disorder (ADHD). The purpose of this study was to investigate sleep problems in children and adolescents attending a specialist ADHD service.

This was a cross-sectional online survey combined with a retrospective chart review, conducted in the ADHD Assessment, Diagnosis, Management, initiation, Research and Education (ADMiRE) service, the first public specialist ADHD service for young people in Ireland. Participants were caregivers of children and adolescents with ADHD attending ADMiRE. Sleep was assessed using The Children’s Sleep Habits Questionnaire (CSHQ) and ADHD symptoms were assessed using an abbreviated version of the Swanson, Nolan and Pelham Teacher and Parent Rating Scale (SNAP-IV). Details regarding patient demographics, co-morbidities and medication were collected from patient records.

Eighty-four percent of young people scored above the clinical cut-off for a sleep disorder. The most frequently reported sleep problems were related to sleep onset and sleep duration, and 64% of respondents met the criteria for two or more sleep problems. ADHD severity was associated with greater sleep problems. Co-morbid physical, neurodevelopmental, and mental health disorders as well as stimulant use were not associated with greater sleep problems.

Sleep problems are very common amongst children and adolescents with ADHD. This study has demonstrated an association between more sleep problems and ADHD severity. These findings highlight the need for both effective ADHD treatment to ensure optional sleep in young people as well as effective interventions for sleep problems to prevent worsening of ADHD symptoms.

This study examined adherence to dietary guidelines and symptoms of sleep problems (e.g. taking a long time to fall sleep or waking up early) and their associations in a sample of older Australian women (68–73 years of age).

This was a population-based cross-sectional study. Adherence to the dietary guidelines was measured using a validated FFQ and reported as a diet quality score. Symptoms of sleep problems were measured using five questions and a total score was derived. Multivariate linear regression was used to investigate the association between these outcomes, adjusted for the potential confounding influence of demographic (i.e. age and marital status) and lifestyle (i.e. physical activity, stress, alcohol intake, sleep medication use) variables.

Respondents from the 1946–1951 cohort of the Australian Longitudinal Study on Women’s Health who completed Survey 9 were included.

Data from n 7956 older women (mean age ± sd: 70·8 ± 1·5) were included.

70·2 % reported having at least one symptom and 20·5 % had between 3 and 5 symptoms of sleep problems (mean score ± sd: 1·4 ± 1·4, range 0–5). Adherence to dietary guidelines was poor with an average diet quality score of 56·9 ± 10·7 (range 0–100). Better adherence to dietary guidelines was associated with fewer sleep problem symptoms (β: –0·065, 95 % CI: –0·012, –0·005) and remained significant after adjusting for confounding influences.

These findings support the evidence that adherence to dietary guidelines is associated with symptoms of sleep problems in older women.

Behavioural patterns are important in understanding the synergistic effect of multiple health behaviours on childhood adiposity. Most previous evidence assessing associations between patterns and adiposity were cross-sectional and investigated two or three behaviour domains within patterns. This study aimed to identify behavioural patterns comprising four behaviour domains and investigate associations with adiposity risk in children.

Parent-report and accelerometry data were used to capture daily dietary, physical activity, sedentary behaviour and sleep data. Variables were standardised and included in the latent profile analysis to derive behavioural patterns. Trained researchers measured children’s height, weight and waist circumference using standardised protocols. Associations of patterns and adiposity measures were tested using multiple linear regression.

Melbourne, Australia.

A total of 337 children followed up at 6–8 years (T2) and 9–11 years (T3).

Three patterns derived at 6–8 years were broadly identified to be healthy, unhealthy and mixed patterns. Patterns at 9–11 years were dissimilar except for the unhealthy pattern. Individual behaviours characterising the patterns varied over time. No significant cross-sectional or prospective associations were observed with adiposity at both time points; however, children displaying the unhealthy pattern had higher adiposity measures than other patterns.

Three non-identical patterns were identified at 6–8 and 9–11 years. The individual behaviours that characterised patterns (dominant behaviours) at both ages are possible drivers of the patterns obtained and could explain the lack of associations with adiposity. Identifying individual behaviour pattern drivers and strategic intervention are key to maintain and prevent the decline of healthy patterns.

Psychotic disorders develop gradually along a continuum of severity. Understanding factors associated with psychosis development, such as sleep, could aid in identification of individuals at elevated risk. This study aimed to assess (1) the dynamic relationship between psychotic experiences (PEs) and sleep quality and quantity, and (2) whether this relationship differed between different clinical stages along the psychosis continuum.

We used daily diary data (90 days) of individuals (N = 96) at early stages (i.e. before a first diagnosis of psychosis) along the psychosis continuum. Multilevel models were constructed with sleep quality and sleep quantity as predictors of PEs and vice versa. Post-hoc, we constructed a multilevel model with both sleep quality and quantity as predictors of PEs. In addition, we tested whether associations differed between clinical stages.

Within persons, poorer sleep predicted next day PEs (B = −0.02, p = 0.01), but not vice versa. Between persons, shorter sleep over the 90-day period predicted more PEs (B = −0.04, p = 0.002). Experiencing more PEs over 90-days predicted poorer (B = −0.02, p = 0.02) and shorter (B = −1.06, p = 0.008) sleep. We did not find any significant moderation effects for clinical stage.

We found a bidirectional relationship between sleep and PEs with daily fluctuations in sleep predicting next day PEs and general patterns of more PEs predicting poorer and shorter sleep. Our results highlight the importance of assessing sleep as a risk marker in the early clinical stages for psychosis.

Insomnia was associated with cerebral structural changes and Alzheimer’s disease. However, associations among cerebral perfusion, insomnia with cerebral small vessel disease (CSVD), and cognitive performance were little investigated.

This cross-sectional study included 89 patients with CSVDs and white matter hyperintensities (WMHs). They were dichotomized into the normal sleep and poor sleep group, according to Pittsburgh sleep quality index (PSQI). Baseline characteristics, cognitive performance, and cerebral blood flow (CBF) were measured and compared between the two groups. The association or correlation between cerebral perfusion, cognition, and insomnia was analyzed using binary logistic regression.

Our study found that declined MoCA score (P = .0317) was more prevalent in those with poor sleep. There was a statistical difference in the recall (P = .0342) of MMSE, the delayed recall (P = .0289) of MoCA between the two groups. Logistic regression analysis showed educational background (P < .001) and insomnia severity index (ISI) score (P = .039) were independently correlated with MoCA scores. Arterial spin labeling demonstrated that left hippocampal gray matter perfusion was significantly reduced (P = .0384) in the group with poor sleep. And, negative correlation was found between left hippocampal perfusion and PSQI scores.

In the patients with CSVDs, insomnia severity was associated with cognitive decline. Left hippocampal gray matter perfusion was correlated with PSQI scores in CSVDs.