The longitudinal associations between mental health indicators and digital media use and physical activity during adolescence: A latent class approach

IntroductionEvidence for the relationship between movement behaviors and mental health among adolescents is inconclusive. We aimed to identify profiles of digital media use (including related bedtime delay) and leisure-time physical activity (LTPA) in adolescence, and to examine whether preadolescent mental health predicted later behavior profiles.MethodsThis study included 1285 participants assessed at 11 years of age, and followed-up four years later. Participants completed the Self-Perception Profile for Children (SPPC), Center for Epidemiological Studies Depression Scale for Children (CES-DC) and Screen for Child Anxiety-Related Emotional Disorders (SCARED) at baseline, and reported digital media use (active and passive use, gaming, and related bedtime delays) and LTPA at follow-up. A latent class approach was employed to identify behavior profiles, membership of which was then predicted with mental health and covariates, including baseline digital media use and LTPA.ResultsWe identified four behavior profiles: 1) high digital media use/moderate LTPA (20% of adolescents; 78% boys), 2) moderate digital media use/high LTPA (31%; 28%), 3) high digital media use/high LTPA (26%; 15%), 4) high passive digital media use and gaming/low LTPA (23%; 89%). After adjusting for covariates, higher LTPA and better perception of athletic competence at baseline associated with higher odds of belonging to any other profile than to the unhealthiest profile (4) at follow-up. Symptoms of depression or anxiety did not associate with later behavior profiles.ConclusionsLTPA and related self-esteem seem to be stronger predictors of future digital media use and LTPA behavior during adolescence than mental health symptoms alone.     

Associations of accelerometer-based sleep duration and self-reported sleep difficulties with cognitive function in late mid-life: the Finnish Retirement and Aging Study

ObjectivesPrior evidence suggests that sleep duration and sleep difficulties may be associated with cognitive function in old age, but little is known about the sleep–cognition association in late mid-life. Our aim was to examine the associations of accelerometer-based sleep duration as well as subjective sleep difficulties with different domains of cognitive function among aging workers.MethodsThe study population consisted of 289 participants (mean age 62.4 years, SD 1.02; 83% women) from the Finnish Retirement and Aging Study (FIREA). Sleep difficulties were measured using Jenkins Sleep Problem Scale (difficulties falling asleep, difficulties maintaining sleep, waking up too early in the morning, and nonrestorative sleep). Sleep duration was measured with wrist-worn accelerometer and self-report, and participants were divided into short (<7 h/night), mid-range (7–9 h/night) and long (≥9 h/night) sleepers. Participants underwent extensive cognitive testing covering three domains: (1) memory, (2) executive function, and (3) attention and information processing.ResultsGreater difficulties in waking up too early in the morning were associated with poorer executive function measured with Spatial Working Memory (SWM) test (p = 0.005). Additionally, nonrestorative sleep was associated with poorer executive function measured with Trail Making Test, B–A, (p = 0.036) and borderline significantly with lower SWM (p = 0.056). Compared to mid-range sleepers, long sleepers tended to have poorer cognitive function (all memory function tests and SWM), but the associations were not statistically significant due to small number of long sleepers.ConclusionsSubjective sleep difficulties may be linked to poorer executive function in a relatively healthy population of older workers in their 60 s. Thus, promoting good sleep quality may translate into better cognitive health in late mid-life.     

The association among demographic factors,health behaviors and sleep quality in youth with Autism Spectrum Disorder

BackgroundA majority of youth with Autism Spectrum Disorder (ASD) have disrupted sleep patterns, but there has been limited research examining factors associated with sleep in this population. Objective: The objective of this study was to compare demographic and lifestyle behaviors with sleep quality in youth with ASD. Methods: A total of 49 children (12.44 years; 78% male) with ASD wore the Actigraph GT9X accelerometer over seven days and nights to assess moderate to vigorous physical activity (MVPA), sedentary behavior (SB), total sleep duration, and sleep efficiency. Parents reported their child’s weekly amount of screen time and demographic information. Participants were classified according to whether they met sleep criteria for duration and efficiency (8–9 h of sleep duration and ≥85% sleep efficiency). T-tests and ANOVA were used to compare demographic and lifestyle factors between the groups. Results: Participants who meet both sleep duration and efficiency criteria had greater minutes of MVPA per day (113.65 min/day) than participants who only met sleep efficiency criteria (40.27 min/day) and participants who did not meet either sleep criteria (67.5 min/day; p < 0.0001). Additionally, participants who met both sleep criteria had fewer minutes of SB compared to those who only met sleep efficiency criteria (384.79 vs 526.05 min/day; p = 0.02). Conclusions: Youth who had indicators of good sleep quality had greater amounts of MVPA and lower amounts of SB. Studies should further examine the relationship between sleep and health behaviors in youth with ASD to determine causal mechanisms, leading to more effective sleep interventions.     

Inter-ictal assay of peripheral circulating inflammatory mediators in migraine patients under adjunctive cervical non-invasive vagus nerve stimulation (nVNS): A proof-of-concept study

Objective

To assay peripheral inter-ictal cytokine serum levels and possible relations with non-invasive vagus nerve stimulation (nVNS) responsiveness in migraineurs.

Could non-HDL-cholesterol be a better marker of atherogenic dyslipidemia in obstructive sleep apnea?

Background/objectiveObstructive sleep apnea (OSA) is independently associated with dyslipidemia, a surrogate marker of atherosclerosis. Low-density lipoprotein (LDL)-cholesterol is accepted as a major independent risk factor for cardiovascular disease. However, non-high-density lipoprotein (HDL)-cholesterol is a better marker of atherogenic dyslipidemia and recommended as a target of lipid lowering therapy. We aimed to assess the prevalence of atherogenic dyslipidemia, and relationship between OSA severity and serum LDL-cholesterol and non-HDL cholesterol levels in OSA patients.MethodsWe retrospectively evaluated treatment naïve 2361 subjects admitted to the sleep laboratory of a university hospital for polysomnography. All subjects’ lipid profile including total cholesterol, LDL-cholesterol, HDL-cholesterol, triglycerides, and non-HDL-cholesterol were measured.ResultsOut of 2361 patients (mean age 49.6 ± 11.9 years; 68.9% male, apnea-hypopnea index 36.6 ± 28.4/h), 185 (7.8%) had no OSA and 2176 (92.2%) had OSA. Atherogenic dyslipidemia prevalence was high (57–66%) in OSA patients, and especially increased in severe OSA compared to other groups (p < 0.05). Though total and LDL-cholesterol did not differ between those with and without OSA, non-HDL-cholesterol (p = 0.020), and triglycerides (p = 0.001) were higher and HDL-cholesterol levels (p = 0.018) were lower in OSA patients than non-OSA. Non-HDL-cholesterol was significantly correlated with OSA severity (p < 0.001) and hypoxia parameters (p < 0.01), whereas LDL-cholesterol showed no correlation.ConclusionsAtherogenic dyslipidemia is highly prevalent and non-HDL-cholesterol levels are significantly increased, predominantly in severe OSA patients. Non-HDL-cholesterol but not LDL-cholesterol, is significantly correlated with OSA severity and hypoxia parameters. Therefore, it could be better to use non-HDL-cholesterol, which is a guideline recommended target of lipid therapy, as a marker of atherosclerotic cardiovascular risk in OSA patients.     

刘中勇从“气血瘀滞”论治不寐的临证经验

不寐,现代医学称睡眠障碍,是一种临床常见病和多发病,本病发病原因尚不明确,通常不伴明显器质性病变,病人自觉症状重,西医尚无明确的治愈方法。祖国医学很早就认识到本病,积累了丰富的经验。刘中勇教授在古人的基础上提出从"气血瘀滞"的角度论治不寐,临床取得较好疗效,本文将从病因病机、理论探析、辨证论治三个方面介绍其相关经验,供同行参考。     

The challenges in scoring hypopneas in children: is pulse wave amplitude drop the answer?

BackgroundIdentifying electroencephalogram (EEG) cortical arousals are crucial in scoring hypopneas and respiratory efforts related arousals (RERAs) during a polysomnogram. As children have high arousal threshold, many of the flow limited breaths or hypopneas may not be associated with visual EEG arousals, hence this may lead to potential underestimation of the degree of sleep disordered breathing. Pulse wave amplitude (PWA) is a signal obtained from finger photoplethysmography which correlates directly to finger blood flow. The drop in PWA has been shown to be a sensitive marker for subcortical/autonomic and cortical arousals. Our aim was to use the drop in PWA as a surrogate for arousals to guide scoring of respiratory events in pediatric patients.MethodsTen polysomnograms for patients between the ages of 5–15 years who had obstructive apnea-hypopnea indices between 1 and 5 events/hour were identified. Patients with syndromes were excluded. A drop in PWA signal of at least 30% that lasted for 3 s was needed to identify subcortical/autonomic arousals. Arousals were rescored based on this criteria and subsequently respiratory events were rescored. Paired t-tests were employed to compare PSG indices scored with or without PWA incorporation.ResultsThe sample of 10 children included 2 females, and the average age was 9.8 ± 3.1 years. Overall, polysomnography revealed an average total sleep time of 464.1 ± 25 min, sleep efficiency of 92% +/−4.2, sleep latency of 19.6 ± 17.0 min, rapid eye movement (REM) latency 143 ± 66 min, N1 3.9% +/−2.0, N2 50.3% +/−12.0, N3 28.2% +/−9.1, REM 16.7% +/−4.0, and wakefulness after sleep onset (WASO) 18.1 ± 7.5 min. Including arousals from PWA changes, respiratory indices significantly increased including total AHI (2.3 ± 0.7 vs 5.7 ± 2.1, p < 0.001), obstructive AHI (1.45 ± 0.7 vs 4.8 ± 1.8, p < 0.001), and RDI (2.36 ± 0.7 vs 7.6 ± 2.0, p < 0.001). Likewise, total arousal index was significantly higher (8.7 ± 2.3 vs 29.4 ± 6.5, p < 0.001).ConclusionsThe drop in pulse wave amplitude signal is a useful marker to guide scoring arousals that are not otherwise easily identified in pediatric polysomnography and subsequently helped in scoring respiratory events that otherwise would not be scored. Further studies are needed to delineate if such methodology would affect clinical outcome.