Associations of self-reported and objectively measured sleep disturbances with depression among primary caregivers of children with disabilities

Difficulties Fulfilling Self-Care Needs Among Family Caregivers: An Observational Study

IMPORTANCE

Assuming the care of a family member with a disability or chronic illness constitutes a health risk factor for caregivers, who frequently overlook their own self-care.

OBJECTIVE

To analyze the self-care activities (SCAs) among caregivers of a family member with a disability or chronic illness and assess the impact on their satisfaction and quality of life (QoL).

DESIGN

Descriptive, cross-sectional, analytic study.

SETTING

Community.

PARTICIPANTS

Five hundred caregivers of family members with a disability or chronic illness in the city of Zaragoza, Spain.

OUTCOMES AND MEASURES

The family caregivers' occupational performance and satisfaction were assessed with the Canadian Occupational Performance Measure, and their QoL was assessed with the World Health Organization-Quality of Life.

RESULTS

In total, 32.8% of family caregivers had difficulty in all activities related to self-care, 46.6% had difficulty sleeping and resting, 31.6% had difficulty receiving health-related treatments, and 31.2% had difficulty with physical exercise. Women and younger family caregivers showed greater impairment in self-care. Occupational performance, satisfaction, and QoL worsened as the number of affected activities increased.

CONCLUSIONS AND RELEVANCE

Caring for a family member with a disability or chronic illness has a negative impact on the SCAs of caregivers, especially among female caregivers and those of younger age. Caregiving is also associated with lower occupational performance, satisfaction, and QoL. Plain-Language Summary: Caring for a family member with a disability or chronic illness can become a health risk for caregivers, who frequently ignore their own self-care. The study results found that women and younger family caregivers showed a greater decline in self-care. This study provides information to help occupational therapists to work with family caregivers to prevent a decline in their self-care and improve their quality of life.

Network science approach elucidates integrative genomic-metabolomic signature of antidepressant response and lifetime history of attempted suicide in adults with major depressive disorder

Background: Individuals with major depressive disorder (MDD) and a lifetime history of attempted suicide demonstrate lower antidepressant response rates than those without a prior suicide attempt. Identifying biomarkers of antidepressant response and lifetime history of attempted suicide may help augment pharmacotherapy selection and improve the objectivity of suicide risk assessments. Towards this goal, this study sought to use network science approaches to establish a multi-omics (genomic and metabolomic) signature of antidepressant response and lifetime history of attempted suicide in adults with MDD. Methods: Single nucleotide variants (SNVs) which associated with suicide attempt(s) in the literature were identified and then integrated with a) p180-assayed metabolites collected prior to antidepressant pharmacotherapy and b) a binary measure of antidepressant response at 8 weeks of treatment using penalized regression-based networks in 245 'Pharmacogenomics Research Network Antidepressant Medication Study (PGRN-AMPS)' and 103 'Combining Medications to Enhance Depression Outcomes (CO-MED)' patients with major depressive disorder. This approach enabled characterization and comparison of biological profiles and associated antidepressant treatment outcomes of those with (N = 46) and without (N = 302) a self-reported lifetime history of suicide attempt. Results: 351 SNVs were associated with suicide attempt(s) in the literature. Intronic SNVs in the circadian genes CLOCK and ARNTL (encoding the CLOCK:BMAL1 heterodimer) were amongst the top network analysis features to differentiate patients with and without a prior suicide attempt. CLOCK and ARNTL differed in their correlations with plasma phosphatidylcholines, kynurenine, amino acids, and carnitines between groups. CLOCK and ARNTL-associated phosphatidylcholines showed a positive correlation with antidepressant response in individuals without a prior suicide attempt which was not observed in the group with a prior suicide attempt. Conclusion: Results provide evidence for a disturbance between CLOCK:BMAL1 circadian processes and circulating phosphatidylcholines, kynurenine, amino acids, and carnitines in individuals with MDD who have attempted suicide. This disturbance may provide mechanistic insights for differential antidepressant pharmacotherapy outcomes between patients with MDD with versus without a lifetime history of attempted suicide. Future investigations of CLOCK:BMAL1 metabolic regulation in the context of suicide attempts may help move towards biologically-augmented pharmacotherapy selection and stratification of suicide risk for subgroups of patients with MDD and a lifetime history of attempted suicide.

Effects of Indoor Plants on Human Functions: A Systematic Review with Meta-Analyses

The influences of indoor plants on people have been examined by only three systematic reviews and no meta-analyses. The objective of this study was therefore to investigate the effects of indoor plants on individuals’ physiological, cognitive, health-related, and behavioral functions by conducting a systematic review with meta-analyses to fill the research gap. The eligibility criteria of this study were (1) any type of participants, (2) any type of indoor plants, (3) comparators without any plants or with other elements, (4) any type of objective human function outcomes, (5) any type of study design, and (6) publications in either English or Chinese. Records were extracted from the Web of Science (1990–), Scopus (1970–), WANFANG DATA (1980–), and Taiwan Periodical Literature (1970–). Therefore, at least two databases were searched in English and in Chinese—two of the most common languages in the world. The last search date of all four databases was on 18 February 2021. We used a quality appraisal system to evaluate the included records. A total of 42 records was included for the systematic review, which concluded that indoor plants affect participants’ functions positively, particularly those of relaxed physiology and enhanced cognition. Separate meta-analyses were then conducted for the effects of the absence or presence of indoor plants on human functions. The meta-analyses comprised only 16 records. The evidence synthesis showed that indoor plants can significantly benefit participants’ diastolic blood pressure (−2.526, 95% CI −4.142, −0.909) and academic achievement (0.534, 95% CI 0.167, 0.901), whereas indoor plants also affected participants’ electroencephalography (EEG) α and β waves, attention, and response time, though not significantly. The major limitations of this study were that we did not include the grey literature and used only two or three records for the meta-analysis of each function. In brief, to achieve the healthy city for people’s health and effective functioning, not only are green spaces needed in cities, but also plants are needed in buildings.

Poor sleep Quality and Obstructive Sleep Apnea are Associated with Maternal Mood, and Anxiety Disorders in Pregnancy

BACKGROUND

Previous studies suggest sleep quality and obstructive sleep apnea (OSA) may be associated with psychiatric symptoms, including depression, anxiety, and posttraumatic stress disorder (PTSD). However, few studies have examined the relationship between sleep quality and OSA with maternal psychiatric symptoms during pregnancy, a state of vulnerability to these disorders.

OBJECTIVE

The objective of our study is to examine the association between poor sleep quality and sleep apnea with antepartum depression, anxiety, and PTSD among pregnant women.

METHODS

A cross-sectional study was conducted among women seeking prenatal care in Lima, Peru. Sleep quality was measured using the Pittsburgh Sleep Quality Index, and the Berlin questionnaire was used to identify women at high risk for OSA. Depression, generalized anxiety, and PTSD symptoms were measured using the Patient Health Questionnaire-9, Generalized Anxiety Disorder Assessment, and PTSD Checklist - Civilian Version. Multivariate logistic regression procedures were used to estimate adjusted odds ratios (aOR) and 95% confidence intervals (95% CI).

RESULTS

Approximately 29.0X% of women had poor sleep quality, and 6.2% were at high risk for OSA. The prevalence of psychiatric symptoms was high in this cohort with 25.1%, 32.5%, and 30.9% of women reporting symptoms of antepartum depression, antepartum anxiety, and PTSD, respectively. Women with poor sleep quality had higher odds of antepartum depression (aOR = 3.28; 95%CI: 2.64-4.07), generalized anxiety (aOR = 1.94; 95%CI: 1.58-2.38), and PTSD symptoms (aOR = 2.81; 95% CI: 2.28-3.46) as compared with women who reported good sleep quality. Women with a high risk of OSA had higher odds of antepartum depression (aOR = 2.36; 95% CI: 1.57-3.56), generalized anxiety (aOR = 2.02, 95% CI: 1.36-3.00), and PTSD symptoms (aOR = 2.14; 95%CI: 1.43-3.21) as compared with those with a low risk of sleep apnea.

CONCLUSIONS

Poor sleep quality and high risk of OSA are associated with antepartum depression, generalized anxiety, and PTSD symptoms among pregnant women. Further characterizations of the associations of these prevalent sleep, mood, and anxiety conditions among pregnant women could aid in evaluating and delivering optimal perinatal care to women with these comorbidities.

How Do Sleep Disturbances Relate to Daytime Functions, Care-related Quality of Life, and Parenting Interactions in Mothers of Children with Autism Spectrum Disorder?

Sleep disturbance is prevalent in caregivers and associated with negative physical, mental, and functional outcomes. This study examined the effects of sleep disturbance on daytime functions (sleepiness, fatigue, mood, cognitive alertness), care-related quality of life, and daily parenting interactions in 20 mothers of children with autism spectrum disorder for 10 consecutive days using ecological momentary assessment design. Participants responded about daytime functions four times and care-related quality of life and daily parenting interactions once using an online survey. Sleep disturbance was significantly related to all daytime functions and frustration with childcare. Therefore, development of sleep hygiene interventions is warranted to effectively reduce the impact of sleep disturbance and enable these mothers to better tackle daily physical and mental challenges.

Sleep and eating disorders among adults enrolled in a commercial weight loss program: associations with self-report and objective sleep measures

PURPOSE

Some research suggests that eating disorders are related to poor sleep health. To increase knowledge on the relation between sleep and eating disorders, we used a multi-method approach to examine associations between sleep, chronotype, and eating disorder psychopathology.

METHODS

We investigated associations between ED psychopathology, both diagnostic categories (ascertained through self-report data) and dimensional measures, and self-report and ambulatory measures of sleep. Adults currently enrolled in a commercial weight loss program completed self-report measures as well as 1 week of ambulatory sleep monitoring and sleep diaries.

RESULTS

Participants with full- or sub-threshold bulimia nervosa and binge eating disorder reported significantly lower subjective sleep health and greater eveningness. Additionally, greater severity of eating disorder psychopathology was associated with lower subjective sleep health and greater eveningness. Eating disorder psychopathology was generally not related to objective sleep measures. Regarding diary measures, global eating disorder psychopathology was negatively correlated with subjective reports of feeling rested.

CONCLUSION

Eating disorder psychopathology is associated with participants' subjective sense of sleep quality, but appears to have little relation to objective sleep characteristics. Level of evidence Level V, descriptive study.

Personalized Sleep Parameters Estimation from Actigraphy: A Machine Learning Approach

BACKGROUND

The current gold standard for measuring sleep is polysomnography (PSG), but it can be obtrusive and costly. Actigraphy is a relatively low-cost and unobtrusive alternative to PSG. Of particular interest in measuring sleep from actigraphy is prediction of sleep-wake states. Current literature on prediction of sleep-wake states from actigraphy consists of methods that use population data, which we call generalized models. However, accounting for variability of sleep patterns across individuals calls for personalized models of sleep-wake states prediction that could be potentially better suited to individual-level data and yield more accurate estimation of sleep.

PURPOSE

To investigate the validity of developing personalized machine learning models, trained and tested on individual-level actigraphy data, for improved prediction of sleep-wake states and reliable estimation of nightly sleep parameters.

PARTICIPANTS AND METHODS

We used a dataset including 54 participants and systematically trained and tested 5 different personalized machine learning models as well as their generalized counterparts. We evaluated model performance compared to concurrent PSG through extensive machine learning experiments and statistical analyses.

RESULTS

Our experiments show the superiority of personalized models over their generalized counterparts in estimating PSG-derived sleep parameters. Personalized models of regularized logistic regression, random forest, adaptive boosting, and extreme gradient boosting achieve estimates of total sleep time, wake after sleep onset, sleep efficiency, and number of awakenings that are closer to those obtained by PSG, in absolute difference, than the same estimates from their generalized counterparts. We further show that the difference between estimates of sleep parameters obtained by personalized models and those of PSG is statistically non-significant.

CONCLUSION

Personalized machine learning models of sleep-wake states outperform their generalized counterparts in terms of estimating sleep parameters and are indistinguishable from PSG labeled sleep-wake states. Personalized machine learning models can be used in actigraphy studies of sleep health and potentially screening for some sleep disorders.

Is aging a problem?: Dependency, long-term care, and public policies in Chile

Chile is facing changes associated with the demographic transition. So far, these challenges have been framed as age related. This has contributed to approaching a broad set of issues from a narrow perspective. The focus on aging has fragmented a multidisciplinary problem into different parts-particularly health and social policies-with different actors dealing with it from the perspective of their own sector. This process has generated an incomplete picture, preventing the definition and measurement of dependency and long-term care, and hindering a coordinated response to these issues. This article advocates for a change in the definition of the problem, by adopting the concept of long-term care, in order to gain a deeper and more comprehensive perspective on the issues arising from the situation that Chile is experiencing.