The associations between illness perceptions and social rhythm stability on mood symptoms among patients with bipolar disorder

The relationship between anxiety symptoms and disturbances in biological rhythms in patients with depression

BACKGROUND

Biological rhythms denote the cyclical patterns of life activities anchored to a 24-hour cycle. Research shows that depression exhibits disturbances in biological rhythms. Yet, the relationship between these biological rhythms and concomitant anxiety symptoms is insufficiently investigated in structured clinical assessments.

METHODS

This multicenter study, carried out in four Chinese hospitals, comprehensively examined the relationship between anxiety and disruptions in biological rhythms among patients with depression. The study encompassed 218 patients diagnosed with depression and 205 matched healthy controls. The Chinese version of the Biological Rhythms Interview of Assessment in Neuropsychiatry was utilized to evaluate the participants' biological rhythms, focusing on four dimensions: sleep, activity, social, and diet.

RESULTS

In patients with depression, there is a significant positive correlation between the severity of anxiety symptoms and the disturbances in biological rhythms. The severity of anxiety and depression, along with the quality of life, are independently associated with disruptions in biological rhythms. The mediation model reveals that anxiety symptoms mediate the relationship between depressive symptoms and biological rhythms.

CONCLUSION

This research highlights the role of anxiety within the spectrum of depressive disorders and the associated disturbances in biological rhythms. Our findings shed light on potential pathways towards more targeted preventive strategies and therapeutic interventions for individuals battling depression and anxiety.

Impact of biological rhythms on perception of illness and cognitive flexibility in bipolar patients in remission

Our study aims to examine the possible mediating effects of biological rhythms on the relationship between illness perception, cognitive flexibility, and functionality in bipolar patients in remission. A total of 150 patients with bipolar disorder (BD) were enrolled. The sociodemographic data form, Biological Rhythm Interview of Assessment in Neuropsychiatry (BRIAN), Brief Illness Perception Questionnaire (BIPQ), Cognitive Flexibility Scale (CFS), Young Mania Rating Scale, Montgomery and Asberg Depression Scale, Beck Anxiety Inventory, and Short Functionality Assessment Scale were applied to the patients in the study. The mean age of the patients was 42.10 ± 12.92 (SD). The participants were 48.7% (n = 73) female and 66.6% (n = 100) BD-I. There was a negative correlation between the total BRIAN score and favorable BIPQ scores and a positive correlation between the total BRIAN score and unfavorable BIPQ scores (except timeline). Additionally, multiple regression analyses revealed that the total BRIAN score could predict favorable BIPQ (except treatment control) and unfavorable BIPQ (except timeline) scores (p < 0.05). The total CFS score also could predict favorable BIPQ (treatment control) and unfavorable BIPQ scores (except timeline). The second step mediation analysis showed that biological rhythm mediated the relationship between illness perception and cognitive flexibility. Our study found that biological rhythms played a full mediating role in the relationship between the perception of illness and cognitive flexibility. In addition, worsening in biological rhythms in bipolar patients could cause negative beliefs and attitudes towards their diseases with an unfavorable clinical course. Therefore, regularity in biological rhythms should be highly recommended for bipolar patients.

The association between illness perception and quality of life among Chinese adults with epilepsy: The mediating role of coping style

OBJECTIVE

To evaluate the associations between illness perception, quality of life (QOL), and coping style among patients with epilepsy (PWE), and to establish the behavior of coping style as a mediator of the interplay between illness perception and QOL.

METHODS

A cross-sectional study of 135 adult Chinese PWE was performed. All patients completed clinical and demographic questionnaires, the Chinese version of the Revised Illness Perception Questionnaire (CIPQ-R), the quality of life in epilepsy-31 inventory (QOLIE-31), and the Simplified Coping Style Questionnaire (SCSQ). Collected data were assessed through correlation analyses, structural equation modeling (SEM), and multiple stepwise linear regression assessments.

RESULTS

These patients exhibited a mean QOLIE-31 total score of 46.9 points, consistent with moderately low QOL. Under model III (F = 9.447, p < 0.01, R² = 0.486), all included variables were found to explain 48.6% of the observed variation in QOL, with illness perception and coping style, respectively, explaining 27.3% and 7% of such variation. SEM findings illustrated that the total influence value of illness perception on QOL was 77.5% (β = -0.775, p < 0.001). Moreover, the illness perception was found to have a direct impact on QOL (β = -0.620, p = 0.001), negative coping (β = 0.309, p < 0.001), and positive coping (β = -0.265, p = 0.014), with negative coping (β = -0.256, p = 0.003), and positive coping (β = 0.288, p = 0.006) also having a direct impact on such QOL. Positive and negative coping styles also served as mediators of an indirect relationship between illness perception and QOL (β = -0.27*0.29 + 0.31* - 0.26 = -0.159, p = 0.001), with coping style thus serving as a significant mediator of the association between QOL and illness perception. The mediating impact of coping style on QOL accounted for 20.5% (-0.159/-0.775) of the total influence.

CONCLUSION

Both coping style and illness perception were detected to be significantly correlated with the QOL of Chinese adult PWE, with coping style serving as a mediator of the association between QOL and illness perception in this patient population. As such, when seeking to control seizures, medical workers should assess illness perceptions and coping styles among PWE as quickly as possible in order to select the optimal interventions most likely to improve the QOL of these patients.

Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis

BACKGROUND

Behavioral representations obtained from mobile sensing data can be helpful for the prediction of an oncoming psychotic relapse in patients with schizophrenia and the delivery of timely interventions to mitigate such relapse.

OBJECTIVE

In this study, we aim to develop clustering models to obtain behavioral representations from continuous multimodal mobile sensing data for relapse prediction tasks. The identified clusters can represent different routine behavioral trends related to daily living of patients and atypical behavioral trends associated with impending relapse.

METHODS

We used the mobile sensing data obtained from the CrossCheck project for our analysis. Continuous data from six different mobile sensing-based modalities (ambient light, sound, conversation, acceleration, etc) obtained from 63 patients with schizophrenia, each monitored for up to a year, were used for the clustering models and relapse prediction evaluation. Two clustering models, Gaussian mixture model (GMM) and partition around medoids (PAM), were used to obtain behavioral representations from the mobile sensing data. These models have different notions of similarity between behaviors as represented by the mobile sensing data, and thus, provide different behavioral characterizations. The features obtained from the clustering models were used to train and evaluate a personalized relapse prediction model using balanced random forest. The personalization was performed by identifying optimal features for a given patient based on a personalization subset consisting of other patients of similar age.

RESULTS

The clusters identified using the GMM and PAM models were found to represent different behavioral patterns (such as clusters representing sedentary days, active days but with low communication, etc). Although GMM-based models better characterized routine behaviors by discovering dense clusters with low cluster spread, some other identified clusters had a larger cluster spread, likely indicating heterogeneous behavioral characterizations. On the other hand, PAM model-based clusters had lower variability of cluster spread, indicating more homogeneous behavioral characterization in the obtained clusters. Significant changes near the relapse periods were observed in the obtained behavioral representation features from the clustering models. The clustering model-based features, together with other features characterizing the mobile sensing data, resulted in an F2 score of 0.23 for the relapse prediction task in a leave-one-patient-out evaluation setting. The obtained F2 score was significantly higher than that of a random classification baseline with an average F2 score of 0.042.

CONCLUSIONS

Mobile sensing can capture behavioral trends using different sensing modalities. Clustering of the daily mobile sensing data may help discover routine and atypical behavioral trends. In this study, we used GMM-based and PAM-based cluster models to obtain behavioral trends in patients with schizophrenia. The features derived from the cluster models were found to be predictive for detecting an oncoming psychotic relapse. Such relapse prediction models can be helpful in enabling timely interventions.

Previous functional social and behavioral rhythms affect resilience to COVID-19-related stress among old adults

BACKGROUND

Functioning of Social Behavioral Rhythms (SBRs) may affect resilience toward stressful events across different age groups. However, the impact of SBRs on the coronavirus disease of 2019 (COVID-19) in elder people is yet to ascertain, representing the aim of the present report.

DESIGN AND METHODS

Follow-up of a peer-reviewed randomized controlled trial on exercise on old adults (³65 years), concurrent to the onset of the pandemic-related lockdown. Post-RCT evaluations occurred after further 12 and 36 weeks since the beginning of the lockdown phase. People with Major Depressive Episode (MDE) at week-48 (follow-up endpoint) were deemed as cases, people without such condition were considered controls. MDE was ascertained using the Patient Health Questionnaire-9 (PHQ-9); SBRs functioning at week 12 onward, through the Brief Symptom Rating Scale (BSRS).

RESULTS

Seventy-nine individuals (53.2%, females) entered the RCT-follow-up phase. The frequency of MDE did not significantly change before versus during lockdown (OR 2.60, CI95%=0.87-9.13). People with BSRS>1 standard deviation of the whole sample score at week-12 had an inflated risk of DE during lockdown (OR=5.6, 95%CI: 1.5-21.4) compared to those with lower BSRS scores. Such odd hold after excluding individuals with MDD at week-12. The post-hoc analysis could be potentially affected by selection bias.

CONCLUSIONS

Overall, older adults were resilient during the first phase of the pandemic when functioning of pre-lockdown was still preserved, in contrast to the subsequent evaluations when the impairment of daily rhythms was associated with impaired reliance.