Associations between Suboptimal Sleep and Smoking, Poor Nutrition, Harmful Alcohol Consumption and Inadequate Physical Activity ('SNAP Risks'): A Comparison of People with and without a Mental Health Condition in an Australian Community Survey

Network analysis of smoking-related sleep characteristics in Chinese adults

The associations between multiple sleep characteristics and smoking behavior are inconsistent, and it is unclear which sleep characteristics are most crucial for tobacco prevention. This study aimed to explore the associations between smoking status/intensity and multiple sleep characteristics and to identify the potential core domain of smoking-related sleep using network analysis. Data were obtained from a survey of cancer-related risk factors among Chinese adults. Logistic regression models were used to quantify the associations between sleep characteristics and smoking status/intensity. Network analyses were employed to identify the core sleep characteristics. A total of 5,228 participants with a median age of 44 years old were included in the study. Current smoking was significantly positively associated with long nap time, difficulty falling asleep, late bedtime, getting up after 7 am, and waking up earlier than expected. There was significant positive association between current smoking and short sleep duration in young adults under 45 years old. Late bedtime and getting up after 7 am were only associated with current heavy smoking, but not current light smoking. Network analyses showed that multiple smoking-related sleep characteristics were interconnected, with difficulty falling asleep and late bedtime as central characteristics in the network. The study found that the associations between sleep characteristics and smoking varied by age and smoking intensity and highlights the potential benefits of sleep health promotion in smoking cessation, with a particular focus on difficulty falling asleep and late bedtime.

Cardiovascular Implications of Sleep Disorders Beyond Sleep Apnea

Purpose of Review

Sleep is crucial for human health and life. There is still limited attention to the association between sleep disorders beyond sleep apnea and cardiovascular (CV) health. We investigated the current evidence between non-respiratory sleep disorders and CV health.

Recent Findings

Current evidence suggests an important association between sleep duration, circadian rhythm, insomnia, disorders of hypersomnolence and CV health. Sleep-related movement disorders exhibit a moderate association with CV health. Further research is needed to explore the effects of each sleep disorder on CV health.

Summary

Given the close association between non-respiratory sleep disorders and CV health, it is crucial to recognize and address sleep disorders in patients with a high CV risk.

The Interactions between Smoking and Sleep

Smoking a cigarette before bed or first thing in the morning is a common habit. In this review, the relationship between smoking and sleep is investigated based on the existing literature. Out of 6504 unique items that were identified via a PubMed search related to smoking and sleep, 151 were included in this review. Tobacco smoking disrupts sleep architecture by reducing slow wave and rapid eye movement (REM) sleep and undermining sleep quality. Furthermore, smoking affects sleep-related co-morbidities, such as obstructive sleep apnea-hypopnea syndrome (OSAHS), insomnia, parasomnias, arousals, bruxism, and restless legs, as well as non-sleep-related conditions such as cardiovascular, metabolic, respiratory, neurologic, psychiatric, inflammatory, gynecologic and pediatric issues, while poor sleep quality also seems to worsen the chances of successful smoking cessation. In conclusion, the existing literature suggests that there is a wicked relation between smoking and sleep.

People Living With Mental Illness Perceptions of Physical Health, Mental Health and Well-Being

Understanding the perspectives of regional people living with mental illness is crucial to adapting services, improving holistic care and meeting individual needs. This study explored people living with mental illness perceptions of physical health, mental health and well-being. A descriptive qualitative study design underpinned by empowerment theory was conducted. Qualitative data were collected verbally via semi-structured interviews, with demographic details provided verbally at the end of the interview. Thematic analysis was utilised to identify themes. The COREQ checklist was used for reporting. Fourteen participants admitted to regional mental health inpatient units aged between 25 and 84 years old were interviewed. Participants felt their overall well-being was good despite feeling their physical health or mental health was suboptimal, suggesting that their perceived well-being is influenced by factors beyond their physical and mental health. Most participants reported looking after their physical health, mental health and well-being and identified various behavioural lifestyle strategies they found helpful. Thematic analysis identified three themes: functioning well, feeling in control and meeting basic needs. Mental health services and clinicians play an important role in empowering people with mental illness to improve their physical health, mental health and well-being while admitted to inpatient services; however, it is acknowledged resources can be limited. Mental health services may consider referring people with mental illness to social prescribing programmes to meet their individualised needs on discharge.

Impact of health risk factors on healthcare resource utilization, work-related outcomes and health-related quality of life of Australians: a population-based longitudinal data analysis

Background

Health risk factors, including smoking, excessive alcohol consumption, overweight, obesity, and insufficient physical activity, are major contributors to many poor health conditions. This study aimed to assess the impact of health risk factors on healthcare resource utilization, work-related outcomes and health-related quality of life (HRQoL) in Australia.

Methods

We used two waves of the nationally representative Household, Income, and Labor Dynamics in Australia (HILDA) Survey from 2013 and 2017 for the analysis. Healthcare resource utilization included outpatient visits, hospitalisations, and prescribed medication use. Work-related outcomes were assessed through employment status and sick leave. HRQoL was assessed using the SF-6D scores. Generalized estimating equation (GEE) with logit or log link function and random-effects regression models were used to analyse the longitudinal data on the relationship between health risk factors and the outcomes. The models were adjusted for age, sex, marital status, education background, employment status, equilibrium household income, residential area, country of birth, indigenous status, and socio-economic status.

Results

After adjusting for all other health risk factors covariates, physical inactivity had the greatest impact on healthcare resource utilization, work-related outcomes, and HRQoL. Physical inactivity increased the likelihood of outpatient visits (AOR = 1.60, 95% CI = 1.45, 1.76 p < 0.001), hospitalization (AOR = 1.83, 95% CI = 1.66-2.01, p < 0.001), and the probability of taking sick leave (AOR = 1.31, 95% CI = 1.21-1.41, p < 0.001), and decreased the odds of having an above population median HRQoL (AOR = 0.48, 95% CI = 0.45-0.51, p < 0.001) after adjusting for all other health risk factors and covariates. Obesity had the greatest impact on medication use (AOR = 2.02, 95% CI = 1.97-2.29, p < 0.001) after adjusting for all other health risk factors and covariates.

Conclusion

Our study contributed to the growing body of literature on the relative impact of health risk factors for healthcare resource utilization, work-related outcomes and HRQoL. Our results suggested that public health interventions aim at improving these risk factors, particularly physical inactivity and obesity, can offer substantial benefits, not only for healthcare resource utilization but also for productivity.

Association Between Comorbid Anxiety and Depression and Health Risk Behaviors Among Chinese Adolescents: Cross-Sectional Questionnaire Study

BACKGROUND

Comorbidity of psychiatric disorders such as depression and anxiety is very common among children and adolescents. Few studies have examined how comorbid anxiety and depression are associated with health risk behaviors (HRBs) in adolescents, which could inform preventative approaches for mental health.

OBJECTIVE

We evaluated the association between HRBs and comorbid anxiety and depression in a large adolescent cohort.

METHODS

We used data from 22,868 adolescents in the National Youth Cohort (China). Anxiety and depression symptoms were assessed using the 9-item Patient Health Questionnaire scale and the 7-item Generalized Anxiety Disorder scale, respectively. Comorbidity was determined by the coexistence of anxiety and depression. HRBs including poor diet, smoking, physical inactivity, and poor sleep, as well as the above HRB scores, were added to obtain the total HRB score (HRB risk index). Based on single and total HRB scores, we divided participants into low-, medium-, and high-risk groups. Potential confounders included gender, presence of siblings, regional economic level, educational status, self-rated health, parental education level, self-reported family income, number of friends, learning burden, and family history of psychosis. Correlation analysis was used to explore associations between single risk behaviors. Binary logistic regression estimated the association between HRBs and anxiety-depression comorbidity before and after adjusting for potential confounders.

RESULTS

The comorbidity rate of anxiety and depression among Chinese adolescents was 31.6% (7236/22,868). There was a statistically significant association between each HRB (P<.05), and HRBs were positively associated with comorbid anxiety and depression in the above population. For single HRBs, adolescents with poor diet, smoking, and poor sleep (medium-risk) were more prone to anxiety-depression comorbidity after adjusting for confounders compared to low-risk adolescents. However, adolescents with all high-risk HRBs were more likely to have comorbid anxiety and depression after adjusting for confounders (poor diet odds ratio [OR] 1.50, 95% CI 1.39-1.62; smoking OR 2.17, 95% CI 1.67-2.81; physical inactivity OR 1.16, 95% CI 1.06-1.28; poor sleep OR 1.84, 95% CI 1.70-2.01). Moreover, in both unadjusted (medium risk OR 1.79, 95% CI 1.56-2.05; high risk OR 3.09, 95% CI 2.72-3.52) and adjusted (medium risk OR 1.57, 95% CI 1.37-1.80; high risk OR 2.33, 95% CI 2.03-2.68) models, HRB risk index, like clustered HRBs, was positively associated with anxiety-depression comorbidity, and the strength of the association was stronger than for any single HRB. In addition, we found that compared to girls, the association between clustered HRBs and anxiety-depression comorbidity was stronger in boys after adjustment.

CONCLUSIONS

We provide evidence that HRBs are related to comorbid anxiety and depression. Interventions that decrease HRBs may support mental health development in adolescence, with the potential to improve health and well-being through to adulthood.

Smoking-Induced Disturbed Sleep. A Distinct Sleep-Related Disorder Pattern?

The relationship between smoking and sleep disorders has not been investigated sufficiently yet. Many aspects, especially regarding non-obstructive sleep apnea−hypopnea (OSA)-related disorders, are still to be addressed. All adult patients who visited a tertiary sleep clinic and provided information about their smoking history were included in this cross-sectional study. In total, 4347 patients were divided into current, former and never smokers, while current and former smokers were also grouped, forming a group of ever smokers. Sleep-related characteristics, derived from questionnaires and sleep studies, were compared between those groups. Ever smokers presented with significantly greater body mass index (BMI), neck and waist circumference and with increased frequency of metabolic and cardiovascular co-morbidities compared to never smokers. They also presented significantly higher apnea−hypopnea index (AHI) compared to never smokers (34.4 ± 24.6 events/h vs. 31.7 ± 23.6 events/h, p < 0.001) and were diagnosed more frequently with severe and moderate OSA (50.3% vs. 46.9% and 26.2% vs. 24.8% respectively). Epworth sleepiness scale (ESS) (p = 0.13) did not differ between groups. Ever smokers, compared to never smokers, presented more frequent episodes of sleep talking (30.8% vs. 26.6%, p = 0.004), abnormal movements (31.1% vs. 27.7%, p = 0.021), restless sleep (59.1% vs. 51.6%, p < 0.001) and leg movements (p = 0.002) during sleep. Those were more evident in current smokers and correlated significantly with increasing AHI. These significant findings suggest the existence of a smoking-induced disturbed sleep pattern.

Go to bed! A systematic review and meta-analysis of bedtime procrastination correlates and sleep outcomes

Bedtime procrastination is defined as the volitional delay of going to bed, without any external circumstances causing the delay, and is associated with inadequate sleep. Alleviating bedtime procrastination is an important target for interventions promoting adequate sleep, yet the correlates of bedtime procrastination are poorly understood. This study examined (1) correlates of bedtime procrastination, and (2) strength and direction of the association between bedtime procrastination and sleep outcomes. Six databases (CINAHL, EMBASE, PsychINFO, PubMed, Scopus, Web of Science) were searched from inception to September 2021 against pre-determined eligibility criteria. Forty-three studies were included (GRADE = low). Meta-analysis revealed that bedtime procrastination had a moderate negative association with self-control (z = -0.39; CI: -0.45, -0.29) and a moderate positive association with evening chronotype (z = 0.43; CI: 0.32, 0.48). Furthermore, bedtime procrastination was moderately negatively associated with sleep duration (z = -0.31; CI: -0.37, -0.24), sleep quality (z = -0.35; CI: -0.42, -0.27) and moderately positively associated with daytime fatigue (z = 0.32; CI: 0.25, 0.38). Further high-quality studies are needed to identify causal relationships between bedtime procrastination and correlates, as well as bedtime procrastination and sleep. Future work will guide the development of interventions targeting bedtime procrastination for improved sleep outcomes. STUDY REGISTRATION: PROSPERO registration number CRD42021248891.

The association between electronic cigarettes, sleep duration, and the adverse cardiovascular outcomes: Findings from behavioral risk factor surveillance system, 2020

The joint effect of electronic cigarette smoking and insufficient sleep duration on cardiovascular disease (CVD) was unclear. This cross-sectional study aimed to evaluate the association between electronic cigarettes, sleep duration, and risk of CVD among American adults. The participants who completed the survey from the behavioral risk factor surveillance system in 2020 were included in this study. The status of electronic cigarette smoking was divided into never, former, and current use. The duration of sleep was categorized into insufficient (<6 h), appropriate (6–9 h), and excessive (>9 h) groups. The CVD group was defined as a patient having any of the following conditions: heart attack, coronary heart disease, or stroke according to self-report. The multivariate logistic regression model was adopted to determine the association between electronic cigarettes, sleep duration, and the risk of CVD. Sensitivity analyses were performed to assess the joint effects on the risk of CVD subtypes, including heart attack, coronary heart disease, and strokes, respectively. Subgroup analyses were performed to estimate the joint effects within the stratum of the age group. The total number of participants included in the present study was 253,561. Of which, 22,908 patients had CVD. In total, 61,293 participants had previously or currently used electronic cigarettes and 37,429 participants had inappropriate sleep duration. Former electronic cigarette users had a 10.8% increased risk of having CVD (OR = 1.108, 95% CI: 1.001–1.227) compared to users who never had electronic cigarettes. Insufficient and excessive sleep durations are associated with increased risks of CVD (OR = 1.592, 95% CI: 1.460–1.735; OR = 1.523, 95% CI: 1.320–1.758). The participants with current vaping status and lack of sleep had a 159.6% increased risk of CVD (OR = 2.596, 95% CI: 1.810–3.723). Sensitivity analyses found similar joint effects of current vaping and insufficient sleep on the risk of heart attack, coronary heart attack, and stroke. The subgroup analyses across each age stratum found that the middle-aged group is most vulnerable to the joint effect of current vaping and insufficient sleep. This study found that both current vaping and inappropriate sleep duration were associated with CVD. Additionally, there was a significant joint effect of current vaping and insufficient sleep on the risk of CVD, especially for middle-aged participants.

Shared brain and genetic architectures between mental health and physical activity

Physical activity is correlated with, and effectively treats various forms of psychopathology. However, whether biological correlates of physical activity and psychopathology are shared remains unclear. Here, we examined the extent to which the neural and genetic architecture of physical activity and mental health are shared. Using data from the UK Biobank (N = 6389), we applied canonical correlation analysis to estimate associations between the amplitude and connectivity strength of subnetworks of three major neurocognitive networks (default mode, DMN; salience, SN; central executive networks, CEN) with accelerometer-derived measures of physical activity and self-reported mental health measures (primarily of depression, anxiety disorders, neuroticism, subjective well-being, and risk-taking behaviors). We estimated the genetic correlation between mental health and physical activity measures, as well as putative causal relationships by applying linkage disequilibrium score regression, genomic structural equational modeling, and latent causal variable analysis to genome-wide association summary statistics (GWAS N = 91,105-500,199). Physical activity and mental health were associated with connectivity strength and amplitude of the DMN, SN, and CEN (r's ≥ 0.12, p's < 0.048). These neural correlates exhibited highly similar loading patterns across mental health and physical activity models even when accounting for their shared variance. This suggests a largely shared brain network architecture between mental health and physical activity. Mental health and physical activity (including sleep) were also genetically correlated (|rg| = 0.085-0.121), but we found no evidence for causal relationships between them. Collectively, our findings provide empirical evidence that mental health and physical activity have shared brain and genetic architectures and suggest potential candidate subnetworks for future studies on brain mechanisms underlying beneficial effects of physical activity on mental health.