Association between stigma and sleep quality in patients with breast cancer: A latent profile and mediation analysis

The interplay between sleep and cancer-related fatigue in breast cancer: A casual and computer-simulated network analysis

Objective

Sleep problems and cancer-related fatigue are common symptoms in women for breast cancer, during and after treatment. Identifying key intervention targets for this symptom cluster may improve patient reported outcomes. This study aimed to explore the relationship between sleep and cancer-related fatigue to identify optimal intervention targets.

Methods

In the "Be Resilient to Breast Cancer" program, self report data were collected on sleep and cancer-related fatigue the Multidimensional Fatigue Symptom Inventory-Short Form and the Pittsburgh Sleep Quality Index. Gaussian network analysis was employed to identify central symptoms and nodes, while a Bayesian network explored their causal relationships. Computer-simulated interventions were used to identify core symptoms as targets for intervention.

Results

General fatigue (Str ​= ​0.95, Bet ​= ​7, Clo ​= ​0.007) was considered the node with the strongest centrality. The daytime dysfunction item on the Pittsburgh sleep quality index had the strongest bridge strength. Core symptoms were identified as targets for intervention by the computer-simulated analysis.

Conclusions

Sleep quality is the strongest predictor of cancer-related fatigue from a casual networking perspective. Sleep latency and daytime dysfunction should be targeted to break the chained symptom interaction between sleep and cancer-related fatigue.

Association between fear of cancer recurrence and emotional distress in breast cancer: a latent profile and moderation analysis

BACKGROUND

Breast cancer patients often experience significant psychological challenges, particularly fear of cancer recurrence (FCR), which is a prevalent and distressing concern following diagnosis. FCR can lead to heightened emotional distress, including anxiety and depression. Resilience, the ability to adapt positively to adversity, may play a crucial role in mitigating these negative emotional outcomes. This study aims to explore the heterogeneity of FCR among breast cancer patients and examine the moderating effect of resilience on the relationship between FCR and emotional distress.

MATERIALS AND METHODS

A cohort of 398 breast cancer patients participated in the Be Resilient to Breast Cancer (BRBC) program between May and December 2023. Surveys were administered to assess FCR, resilience, and emotional distress levels. Data were analyzed using two approaches: latent profile analysis (LPA) to identify distinct FCR profiles and moderation analysis to evaluate the role of resilience.

RESULTS

Three distinct FCR profiles were identified: low (27.5%), middle (53%), and high (19.5%). Resilience significantly moderated the association between FCR and anxiety (B = 0.115, SE = 0.046, P = 0.014), but no significant moderating effect was observed for depression.

DISCUSSION

The findings highlight significant heterogeneity in FCR among breast cancer patients, with a substantial proportion experiencing moderate to high levels of FCR. Resilience was found to buffer the impact of FCR on anxiety, suggesting that interventions aimed at enhancing resilience could alleviate anxiety related to FCR in this population. These results underscore the importance of incorporating resilience-focused strategies into psychological therapies for breast cancer patients.

Demoralization and sleep in breast cancer: A casual and computer-simulated network analysis

PURPOSE

There is evidence in studies with patients diagnosed with breast cancer that suggests demoralization, which is described as a sense of hopelessness and inability to cope, is associated with a decline in sleep. However, there are limited studies that fully explain the association. The purpose of this study was to further explore demoralization and sleep in patients.

METHODS

In the "Be Resilient to Breast Cancer" study, self-report data were collected on sleep and demoralization. Sleep was measured with the Pittsburgh Sleep Quality Index for a total sleep quality score and 7 sleep domains. The Demoralization Scale provides 16 items. Gaussian network analysis was employed to identify core symptoms and bridge symptoms. A Bayesian network was used to examine how these symptoms are causally related to each other. Computer-simulated interventions were used to identify targets for intervention.

RESULTS

The distress item of the Demoralization Scale was the core symptom (Str = 1.17, Bet = 52, Clo = 0.003), while the daytime dysfunction domain of sleep was considered the bridge symptom. Sleep quality was the key parent node. Computer-simulated intervention suggests targeting distress and loss of emotional control.

CONCLUSIONS

Sleep may contribute to demoralization from the findings of casual networking analysis. Distress and loss of emotional control should be targeted to decrease the adverse interaction with sleep.

The longitudinal association between resilience and sleep quality in breast cancer

PURPOSE

To estimate the longitudinal association between resilience and sleep quality in patients with newly diagnosed breast cancer within the first 6 months.

METHOD

Between July 2023 and September 2023, 155 newly diagnosed BC patients were recruited to participate in the Be Resilience to Breast Cancer program (Abbreviated as BRBC). They completed the 10-item Connor-Davidson Resilience scale and Pittsburgh Sleep Quality Index Scale. The following three timepoints were set to collect the data, including 1 month after initial diagnosis (T0), 3 months (T1), and 6 months (T2). Data were analyzed using Cross-lagged Panel Model (CLPM), and Parallel Latent Growth Model (PLGM).

RESULTS

Excluded questionnaires with a large number of missing items and finally 125 patients were included, with the response rate of 83.3%. CLPM indicated that resilience at T1 predicted PSQI at T2 (r = -0.168, P < 0.001), and PSQI at T1 predicted resilience at T2 (r = -0.112, P< 0.001). PLGM demonstrated that changes in resilience was significantly associated with changes in PSQI (r = -0.874, P< 0.001).

CONCLUSION

A longitudinal association between resilience and sleep quality was confirmed in patients with newly diagnosed breast cancer. Resilience was a protective factor in the development of sleep quality.

Social support, fear of cancer recurrence and sleep quality in breast cancer: A moderated network analysis

PURPOSE

Fear of cancer recurrence (FCR) contributes to sleep problems and social support is a buffering factor in the literature. However, the moderating effect of social support between FCR and sleep quality is unclear.

METHODS

The moderating role of social support was examined in a cohort of 460 breast cancer patients from the 2024 Be Resilient to Breast Cancer (BRBC) program from a microscopic perspective using moderated network analysis, and then assessed macroscopically by Johnson-Neyman and response surface analysis. The Fear of Progression Questionnaire-Short Form, Perceived Social Support Scale and Pittsburgh Sleep Quality Index scale were employed in this study.

RESULTS

Social support significantly moderated the relationship between general anxiety and sleep efficiency. General anxiety was positively correlated with sleep efficiency at high levels of social support (t = 3.774, P < 0.001). Patients with high social support and low FCR experienced better sleep (F = 6.166, P < 0.01).

CONCLUSION

Our study deepens the understanding of the association between FCR, social support, and sleep quality, and emphasizes social support as a positive strategy for cancer patients to improve their physical and mental health.

Navigating specific targets of breast cancer symptoms: An innovative computer-simulated intervention analysis

PURPOSE

To pinpoint optimal interventions by dissecting the complex symptom interactions, encompassing both their static and temporal dimensions.

METHODS

The study incorporated a cross-sectional survey utilizing the MD Anderson Symptom Inventory. Participants with breast cancer undergoing chemotherapy were recruited from the "Be Resilient to Breast Cancer" from April 2023 to June 2024. Static symptom interrelationships were elucidated using undirected and Bayesian network models, complemented by an exploration of their dynamic counterparts through computer-simulated interventions.

RESULTS

The study included 602 patients with breast cancer. Both undirected networks and computer-simulated interventions concurred on the symptoms of distress and fatigue as optimal alleviation targets. The Bayesian network and computer-simulated interventions both emphasized "shortness of breath" as preventive care. Notably, Distress appeared to be the most effective target for interventions, and compared to fatigue (decreasing score = 1.84-2.20, decreasing prevalence = 14.2-16.7%). Conversely, disturbed sleep, despite its high position in Bayesian network, had no propelling effects on increasing the network's overall symptom activity levels (increasing score<1).

CONCLUSIONS

Computer-simulated intervention integrating with traditional network analysis can improve intervention precision and efficacy by prioritizing individual symptom impacts, both statically and dynamically.

The Complex Interaction Between Sleep-Related Information, Misinformation, and Sleep Health: Call for Comprehensive Research on Sleep Infodemiology and Infoveillance

The complex interplay between sleep-related information-both accurate and misleading-and its impact on clinical public health is an emerging area of concern. Lack of awareness of the importance of sleep, and inadequate information related to sleep, combined with misinformation about sleep, disseminated through social media, nonexpert advice, commercial interests, and other sources, can distort individuals' understanding of healthy sleep practices. Such misinformation can lead to the adoption of unhealthy sleep behaviors, reducing sleep quality and exacerbating sleep disorders. Simultaneously, poor sleep itself impairs critical cognitive functions, such as memory consolidation, emotional regulation, and decision-making. These impairments can heighten individuals' vulnerability to misinformation, creating a vicious cycle that further entrenches poor sleep habits and unhealthy behaviors. Sleep deprivation is known to reduce the ability to critically evaluate information, increase suggestibility, and enhance emotional reactivity, making individuals more prone to accepting persuasive but inaccurate information. This cycle of misinformation and poor sleep creates a clinical public health issue that goes beyond individual well-being, influencing occupational performance, societal productivity, and even broader clinical public health decision-making. The effects are felt across various sectors, from health care systems burdened by sleep-related issues to workplaces impacted by decreased productivity due to sleep deficiencies. The need for comprehensive clinical public health initiatives to combat this cycle is critical. These efforts must promote sleep literacy, increase awareness of sleep's role in cognitive resilience, and correct widespread sleep myths. Digital tools and technologies, such as sleep-tracking devices and artificial intelligence-powered apps, can play a role in educating the public and enhancing the accessibility of accurate, evidence-based sleep information. However, these tools must be carefully designed to avoid the spread of misinformation through algorithmic biases. Furthermore, research into the cognitive impacts of sleep deprivation should be leveraged to develop strategies that enhance societal resilience against misinformation. Sleep infodemiology and infoveillance, which involve tracking and analyzing the distribution of sleep-related information across digital platforms, offer valuable methodologies for identifying and addressing the spread of misinformation in real time. Addressing this issue requires a multidisciplinary approach, involving collaboration between sleep scientists, health care providers, educators, policy makers, and digital platform regulators. By promoting healthy sleep practices and debunking myths, it is possible to disrupt the feedback loop between poor sleep and misinformation, leading to improved individual health, better decision-making, and stronger societal outcomes.

Integrating the symptom experience and coping in patients with stage I-III breast cancer in China: A qualitative study

PURPOSE

To develop an in-depth understanding of the meaning of symptoms in the context of how women with stage I-III breast cancer in China cope with the effects of primary and adjuvant therapies for breast cancer.

METHOD

A qualitative descriptive approach was used. A purposive sample of women diagnosed with stage I-III breast cancer were recruited from the "Be Resilient to Breast Cancer" study between November 2023 and March 2024. Data was collected from in person interviews using a semi-structured interview guide. Interviews were audio-recorded and transcribed verbatim. The framework analysis method was used to generate codes and themes.

RESULTS

A sample of 17 women with breast cancer agreed to participate. The average age was 50.1 years (SD = 8.45), and the majority (65%) had stage III. The overarching theme was Confronting Physical and Psychological Symptoms. The four themes explaining the experience were Changed Identity, Uncertainty, Finding Meaning and Seeking Support and Solace. Changed Identity and Uncertainty reflected the challenges of coping with multiple symptoms from the treatment. The themes of Finding Meaning and Seeking Support and Solace captured how women adapted a positive perspective to cope with the experience.

CONCLUSIONS

This study contributed to the evidence of the integration of the symptom experience in coping with breast cancer treatment in the context of a collectivist Chinese culture. It enhanced the understanding of the physical and psychological symptom experience of curative intent breast cancer therapy and offered insight into how women from China cope in early survivorship.

Body image, self-efficacy, and sleep quality among patients with breast cancer: A latent profile and mediation analysis

PURPOSE

As a sign of femininity, impaired breast after surgery causes particularly confusion for patients with breast cancer resulting in increased body image distress, which has negative impacts on sleep quality. And self-efficacy enables patients to use positive and effective coping strategies to maintain a favorable night's sleep. Therefore, our study is to explore the heterogeneity in body image experienced by patients with breast cancer and to examine the mediation effects of self-efficacy between body image and sleep quality.

METHOD

Between July 2023 and October 2023, 251 patients with breast cancer were recruited for the Be Resilient to Breast Cancer program. They responded to the General Perceived Self-Efficacy Scale, Body Image Scale, and Pittsburgh Sleep Quality Index Scale. Data were analyzed using a latent profile analysis (LPA) and mediation analysis.

RESULTS

Results of the LPA indicated that body image could be classified into three subgroups as follows: low (43.0%), moderate (45.5%), and high (11.5%). Furthermore, the mediation analysis demonstrated two partially mediated effects upon comparing the low and moderate (standard error, SE = 0.548, 95% confidence interval, CI = 0.009, 0.366) and the high and low (SE = 0.848, 95% CI = 0.570, 3.909) body image groups.

CONCLUSION

Heterogeneity exists in body image, and self-efficacy mediates the relationship between body image and sleep quality. Hence, promoting self-efficacy can buffer the negative impacts of body image on sleep quality in patients with breast cancer, and self-efficacy-orientated interventions should also receive more attention in clinic.

Sentinel and networked symptoms in patients with breast cancer undergoing chemotherapy

PURPOSE

It was designed to identify the symptom clusters and sentinel symptoms among patients with breast cancer receiving chemotherapy at the community level, and to explore core and bridge symptoms at the global level.

METHODS

A cross-sectional survey was conducted using the MD Anderson Symptom Inventory. Patients with breast cancer receiving chemotherapy, recruited from the "Be Resilient to Breast Cancer" project between January 2023 and December 2023, were included in the study. Symptom clusters and their sentinel symptoms were identified using exploratory factor analysis and Apriori algorithm. Core and bridge symptoms were identified using network analysis.

RESULTS

A total of 468 patients with breast cancer participated in the current study. At the community level, three symptom clusters and their corresponding sentinel symptoms were identified: a gastrointestinal symptom cluster (with nausea as the sentinel symptom), a psycho-sleep-related symptom cluster (with distress as the sentinel symptom), and a neurocognition symptom cluster (with dry mouth as the sentinel symptom). At the global level, fatigue emerged as the core symptom, while disturbed sleep and lack of appetite as bridge symptoms.

CONCLUSION

Addressing nausea, distress, and dry mouth are imperative for alleviating specific symptom clusters at the community level. Furthermore, targeting fatigue, disturbed sleep, and lack of appetite are crucial to break the interactions among diverse symptoms at the global level.

Sleep Disturbance as a Mediator of Lung Cancer Stigma on Psychological Distress and Physical Symptom Burden

OBJECTIVE

This study tested sleep disturbance as a mediator through which stigma and discrimination predict psychological distress and physical symptom burden in adults with lung cancer.

METHODS

Lung cancer patients on active oncological treatment ( N = 108; 74.1% stage IV) completed questionnaires on lung cancer stigma, sleep, distress, and physical symptoms at study entry and at 6- and 12-week follow-up. Mediation analyses were conducted to investigate whether stigma and discrimination predicted distress and physical symptoms at study entry and across 12 weeks through disrupted sleep.

RESULTS

Higher discrimination ( b = 5.52, 95% confidence interval [CI] = 2.10-8.94) and constrained disclosure ( b = 0.45, 95% CI = 0.05-0.85) were associated significantly with higher sleep disruption at study entry. Sleep disruption, in turn, was associated with higher distress ( b = 0.19, 95% CI = 0.09-0.29) and physical symptoms ( b = 0.28, 95% CI = 0.17-0.40) at study entry. Sleep disruption significantly mediated relationships between higher discrimination and the outcomes of distress (indirect effect = 1.04, 95% CI = 0.13-1.96) and physical symptoms (indirect effect = 1.58, 95% CI = 0.37-2.79) at study entry. Sleep disruption also mediated relationships between constrained disclosure and the outcomes of distress (indirect effect = 0.85, 95% CI = < 0.01-0.17) and physical symptoms (indirect effect = 0.13, 95% CI = 0.01-0.25).

CONCLUSIONS

Lung cancer patients evidenced pronounced sleep disruption, which mediated relationships between indicators of lung cancer stigma and distress and physical symptoms at study entry. Research is needed to test additional mechanisms through which lung cancer stigma predicts these outcomes longitudinally.

The interplay between stigma and sleep quality in breast cancer: A cross-sectional network analysis

PURPOSE

Stigma, a subjective internal shame, arises from the association of cancer with death. Sleep quality can be considered a product of stigma. However, the extent of overlap or difference between the two remains unclear.

METHODS

In total, 512 survivors with breast cancer were recruited from the "Be Resilient to Breast Cancer" project between May and August 2023. This study estimated the stigma, sleep quality, and their relationship by conducting a cross-sectional network analysis. The social impact scale and Pittsburgh Sleep Quality Index scale were employed in this study.

RESULTS

The core symptom for stigma from the network analysis was alienation by people (Strength = 1.213, Betweenness = 13, Closeness = 0.00211). The core symptom for sleep quality were the sleep quality (Str = 1.114, Bet = 17, Clo = 0.01586). Regarding the combination network, results showed that self-isolation and daytime dysfunction were the bridge nodes and that daytime dysfunction was positively associated with feeling less capable than before (according to self) (r = 0.15).

CONCLUSION

Our study demonstrates the core symptoms in different symptomatic networks, which can be targeted for treatment personalization and aid in the improvement of sleep quality and stigma in breast cancer patients.

Brain connectomics improve prediction of 1-year decreased quality of life in breast cancer: A multi-voxel pattern analysis

PURPOSE

Whether brain connectomics can predict 1-year decreased Quality of Life (QoL) in patients with breast cancer are unclear. A longitudinal study was utilized to explore their prediction abilities with a multi-center sample.

METHODS

232 breast cancer patients were consecutively enrolled and 214 completed the 1-year QoL assessment (92.2%). Resting state functional magnetic resonance imaging was collected before the treatment and a multivoxel pattern analysis (MVPA) was performed to differentiate whole-brain resting-state connectivity patterns. Net Reclassification Improvement (NRI) as well as Integrated Discrimination Improvement (IDI) were calculated to estimate the incremental value of brain connectomics over conventional risk factors.

RESULTS

Paracingulate Gyrus, Superior Frontal Gyrus and Frontal Pole were three significant brain areas. Brain connectomics yielded 7.8-17.2% of AUC improvement in predicting 1-year decreased QoL. The NRI and IDI ranged from 20.27 to 54.05%, 13.21-33.34% respectively.

CONCLUSION

Brain connectomics contribute to a more accurate prediction of 1-year decreased QoL in breast cancer. Significant brain areas in the prefrontal lobe could be used as potential intervention targets (i.e., Cognitive Behavioral Group Therapy) to improve long-term QoL outcomes in breast cancer.