Differential Co-Occurring Patterns Between Depressive Symptomatology and Sleep-Wake-Related Disturbances in Mood Disorders

Headache Because of Problems with Teeth, Mouth, Jaws, or Dentures in Chronic Temporomandibular Disorder Patients: A Case–Control Study

This study aimed to characterize self-reported headaches because of problems with the teeth, mouth, jaws, or dentures (HATMJD) in chronic patients with temporomandibular disorders (TMDs) in order to compare their results with those of TMD patients without such headaches and to investigate the associations of HATMJD with depression, anxiety, physical symptoms, oral behaviors, and sleep quality. We conducted a case–control study on consecutive chronic TMD patients referred to the University Medical Center of Ljubljana, Slovenia. A self-reported HATMJD was extracted from item #12 in the 49-item version of the Oral Health Impact Profile questionnaire. Axis II instruments of the Diagnostic Criteria for TMD (i.e., for screening of depression, anxiety, specific comorbid functional disorders, and oral behaviors) and the Pittsburgh Sleep Quality Index were used in this study. In total, 177 TMD patients (77.4% women; mean age: 36.3 years) participated in this study; 109 (61.6%) patients were classified as TMD patients with HATMJD. TMD patients with at least mild depressive and anxiety symptoms, with at least low somatic symptom severity, and a high number of parafunctional behaviors had more HATMJD. Parafunctional behavior and sleep quality were the most prominent predictive factors of the occurrence of HATMJD. TMD patients with HATMJD have more psychosocial dysfunction, a higher frequency of oral behaviors, and poorer sleep quality than TMD patients without such headaches.

Stress Susceptibility Moderates the Relationship Between Eveningness Preference and Poor Sleep Quality in Non-Acute Mood Disorder Patients and Healthy Controls

OBJECTIVE

This study aimed to investigate the relationship between eveningness preference and poor sleep quality and eventually examine the moderation effect of stress susceptibility.

METHODS

Individuals with non-acute major depressive disorder or bipolar affective disorder and healthy participants were recruited. The Composite Scale of Morningness (CSM) and the Pittsburgh Sleep Quality Index (PSQI) were used to evaluate chronotype and sleep quality, respectively. Eysenck Personality Questionnaire, Tridimensional Personality Questionnaire, Perceived Stress Scale, and Beck Anxiety Inventory were used to formulate stress susceptibility and as indicator variables for empirical clustering by latent class analysis (LCA). Linear regression models were used to examine the relationship between chronotype preference and sleep quality. The interaction terms of CSM and stress susceptibility were examined for the moderation effect.

RESULTS

A total of 887 individuals were enrolled in this study, with 68.2% female and 44.1% healthy participants. Three subgroups were derived from LCA and designated as low stresssusceptibility (40.2%), moderate stress susceptibility (40.9%), and high stress susceptibility (18.8%) groups. After controlling for covariates, the CSM scores inversely correlated with PSQI scores [b (se)=-0.02 (0.01), p=0.01], suggesting that individuals with eveningness preferences tend to have poor sleep quality. Moreover, stress susceptibility moderated the relationship between CSM and PSQI scores (p for interaction term = 0.04). Specifically, the inverse association between CSM and PSQI was more robust in the high stress susceptibility group than that in the low stress susceptibility group.

CONCLUSION

Eveningness preference was associated with poor sleep quality, and this relationship was moderated by stress susceptibility.

Sociodemographic and cultural determinants of mood disorders

PURPOSE OF REVIEW

The present review aims to examine, summarize and update information on the sociodemographic and cultural determinants of mood disorders.

RECENT FINDINGS

Known sociodemographic and cultural determinants continue to be good predictors of the risk of developing a mood disorder over the lifetime. Polygenic risk scores do not appear to offer any advantages over these determinants at present. There is also new and emerging understanding of the role of lifestyle and environmental factors in mediating vulnerability to mood disorder. The influence of ethnicity and migration, on the other hand, is far more complex than initially envisaged.

SUMMARY

Recent evidence on sociodemographic determinants of mood disorders confirms associations derived from existing literature. There is also new and emerging evidence on how quality of sleep, diet and the environment influence risk of mood disorders. Culture and ethnicity, depending on context, may contribute to both vulnerability and resilience. Socioeconomic deprivation may be the final common pathway through which several sociodemographic and cultural determinants of mood disorders act.

The Development of a Rapid Classification Scale for Sleep Quality in Community-Dwelling Older Adults - The Yilan Study, Taiwan

OBJECTIVE

Poor sleep quality is prevailing, deleterious, but heterogeneous in older adults. This study aimed to develop a simplified instrument to screen and classify poor sleep quality in community-dwelling older adults, by which stepped care with needs-based interventions could be implemented.

METHODS

Cohorts of adults aged 65 years and older were used to develop the Rapid Classification Scale for Sleep Quality (RCSSQ). Poor sleep quality was defined with the Pittsburgh Sleep Quality Index (PSQI). Established subgroups of poor sleep quality in the development dataset (n = 2622) were used as the criterion standard. Two independent validation datasets (n = 964 and 193, respectively) were used to examine the external validity. Questions in the PSQI were examined by the stepwise multinomial logistic regressions to determine the optimal numbers of items in the RCSSQ. On the premise of item parsimony and instrument validity, the optimal combination of reduced items was determined.

RESULTS

In the development dataset, the 4-item RCSSQ (RCSSQ-4) was the optimal predictive model. In terms of internal validity, the accuracy rates to identify PSQI-defined poor sleep quality and its subgroups in the developmental dataset by the RCSSQ-4 were 89.0% and 79.9%, respectively. Meanwhile, the RCSSQ-4 also had good external validity in the validation datasets to detect PSQI-defined poor sleep quality (accuracy rates: 89.1-90.7%). Furthermore, the profiles of PSQI component scores and comorbid conditions for the predicted subgroups in the validation dataset were comparable with the criterion standard.

CONCLUSION

The RCSSQ-4 is a valid instrument for screening and subgrouping poor sleep quality in community-dwelling older adults. The RCSSQ-4 may help guide tailored interventions under the context of stepped care in the community.