Classification of high-risk depressed mood groups in cancer patients based on Health Ecology Model

The Interplay of Chronic Stress and Cancer: Pathophysiology and Implications for Integrated Care

BACKGROUND

Cancer-associated depression is a multifaceted condition that arises from the interplay of biological, psychological, and social factors in individuals diagnosed with cancer. Understanding this condition involves exploring how cancer and its treatments can precipitate depressive symptoms and the mechanisms behind this association. Chronic stress, inflammation, and immunological responses play a crucial role in the development of both cancer and depression. The objective of this review is to describe and synthesize information on the complex interactions between chronic stress, inflammation, immunological responses, and cancer development. Additionally, it aims to review existing evidence regarding mechanisms such as neurotransmitter imbalances, structural brain changes, and genetic predispositions as key contributors to depression in cancer patients.

RECENT FINDINGS

A comprehensive literature search on Cancer-associated Depression was conducted in electronic databases, including APA PsycINFO, Medline, Google Scholar, Embase, PubMed, Scopus, and Web of Science. The research focused on understanding the potential relationship between stress-induced depression and cancer by examining neurochemical, anatomical, immunological, genetic, and psychological changes. The findings revealed a compilation of both quantitative and qualitative studies on depression in cancer patients. Evidence suggested a potential link between cancer-induced stress and depression, with increased levels of proinflammatory cytokines (such as IL-6) and dysregulation of neurotransmitters, including serotonin, contributing to the onset of depression. Furthermore, studies indicated that antidepressants, along with psychological interventions, were effective in managing depression among cancer patients.

CONCLUSION

This narrative review provides insights into the importance of integrating oncology and mental health services to address the psychosocial needs of cancer patients. Future research should focus on the bidirectional interactions between stress and cancer, aiming to improve cancer care by incorporating mental health support. Addressing the mental health aspects of cancer treatment can significantly enhance patient outcomes and overall quality of life.

Fear of cancer recurrence among adolescent and young adult cancer survivors: a mixed-methods systematic review

PURPOSE

Fear of cancer recurrence (FCR) is common and burdensome in adolescent and young adult (AYA) cancer survivors. This systematic review examines FCR assessment, prevalence, severity, progression, related factors, consequences, and interventions in AYA cancer survivors.

METHODS

Electronic databases including PubMed, CINAHL, PsycINFO, Cochrane, and Embase were searched from their inception to October 2024. Two reviewers identified eligible peer-reviewed empirical studies, including both qualitative and quantitative ones on FCR in AYA survivors. The risk of bias was assessed using the Mixed-Methods Appraisal Tool. Quantitative studies were synthesized narratively, while qualitative studies underwent thematic synthesis.

RESULTS

Among the 5340 studies identified, 34 studies were included finally. 21.4 to 93.3% of AYA cancer survivors experienced FCR. About one-third of survivors with moderate or high FCR improved over time. Female survivors, those with advanced cancer stages, and individuals with negative illness perceptions or higher anxiety reported higher FCR. Persistent FCR was associated with increased psychological problems and lower quality of life. Two studies on behavioral interventions, particularly internet-based cognitive-behavioral therapy (CBT), were acceptable but had limited evidence of efficacy.

CONCLUSION

FCR is prevalent among AYA cancer survivors, particularly in female survivors, those with advanced cancer stages, and those with negative perceptions or higher anxiety. A significant proportion experience persistent, but variability in current evidence limits a comprehensive understanding of its severity, progression, and intervention efficacy. To enhance FCR management, the use of validated assessment tools is essential in both research and clinical contexts. Addressing the physical and psychological aspects of survivorship through comprehensive care is crucial for alleviating FCR and improving overall well-being.

IMPLICATIONS FOR CANCER SURVIVORS

Identifying the key factors of FCR will stimulate the research and the development of targeted interventions for AYA cancer survivors.

A study on the identification of factors related to depression in a population with an increasing number of chronic diseases in the short term in China based on a health ecology model

BACKGROUND

The rapid increase in the number of patients with chronic diseases and depression, as well as the rapid spread of their effects, have led to these two health problems gradually developing into major public health issues in China and around the world. Currently, many individuals with chronic diseases are experiencing depressive symptoms one after another. Therefore, it is imperative to conduct research on how to prevent depression in this growing population of individuals with chronic diseases in a timely manner.

METHODS

Based on the data of the 2015 and 2018 national follow-up surveys of the China Health and Retirement Longitudinal Study, a total of 7641 patients with short-term increase in the number of chronic diseases were selected as the study objects, and a binary logistic regression model was constructed according to the five dimensions of the health ecology model. The neural network model was used to explore the main (first two) factors affecting the increase in the number of chronic diseases in China in the short term, and the random forest and extreme value gradient lifting algorithm were used to verify them, and effective suggestions were put forward.

RESULTS

The detection rate of depression in the population with increasing number of chronic diseases from 2015 to 2018 was 42.13 %. The model was established based on five dimensions of the health ecology model: Model 1 (Personal trait layer), Model 2 (Personal trait layer plus Behavioral feature layer), Model 3 (Personal trait layer plus Behavioral feature layer plus Living and working conditions layer), Model 4 (Personal trait layer plus Behavioral feature layer plus Living and working conditions layer plus Networking layer) and Model 5 (Personal trait layer plus Behavioral feature layer plus Living and working conditions layer plus Networking layer plus Policy environment layer).The prediction accuracy of the five models was 66.4 %, 68.3 %, 70.7 %, 71.6 % and 71.6 %, respectively, and Model 5 showed that the P values of gender, self-rated health, night's sleep time (h), disability, life satisfaction, child satisfaction, place of residence and highest level of education were all <0.05, life satisfaction and self-rated health importance were 0.249 (100 %) and 0.226 (90.8 %).

CONCLUSION

Gender, self-rated health, night sleep duration, disability, satisfaction with life, satisfaction with children, place of residence and highest level of education were the main influencing factors for the increase of depressive symptoms in the population with chronic diseases in the short term, among which life satisfaction and self-rated health have the greatest impact on depressive symptoms, and there is an interaction between the two.

An explainable predictive model for anxiety symptoms risk among Chinese older adults with abdominal obesity using a machine learning and SHapley Additive exPlanations approach

Background

Early detection of anxiety symptoms can support early intervention and may help reduce the burden of disease in later life in the elderly with abdominal obesity, thereby increasing the chances of healthy aging. The objective of this research is to formulate and validate a predictive model that forecasts the probability of developing anxiety symptoms in elderly Chinese individuals with abdominal obesity.

Method

This research's model development and internal validation encompassed 2,427 participants from the 2017-2018 Study of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Forty-six variables were defined based on the Health Ecology Model (HEM) theoretical framework. Key variables were screened using LASSO regression, and the XGBoost (Extreme Gradient Boosting) model was further introduced to forecast the risk of developing anxiety symptoms in the elderly with abdominal obesity. SHapley Additive exPlanations (SHAP) was adopted to further interpret and show how the eigenvalues contributed to the model predictions.

Results

A total of 240 participants (9.89%) with anxiety symptoms out of 2,427 participants were included. LASSO regression identified nine key variables: looking on the bright side, self-reported economic status, self-reported quality of life, self-reported health status, watching TV or listening to the radio, feeling energetic, feeling ashamed/regretful/guilty, feeling angry, and fresh fruits. All the evaluation indicators of the XGBoost model showed good predictive efficacy. Based on the significance of the features identified by SHAP (Model Interpretation Methodology), the feature 'looking on the bright side' was the most important, and the feature 'self-reported quality of life' was the least important. The SHAP beeswarm plot illustrated the impacts of features affected by XGBoost.

Conclusion

Utilizing machine learning techniques, our predictive model can precisely evaluate the risk of anxiety symptoms among elderly individuals with abdominal obesity, facilitating the timely adoption of targeted intervention measures. The integration of XGBoost and SHAP offers transparent interpretations for customized risk forecasts.

Relationship between social participation and depressive symptoms in patients with multimorbidity: the chained mediating role of cognitive function and activities of daily living

OBJECTIVE

The potential mechanisms linking social participation and depressive symptoms in Chinese individuals with multimorbidity are not yet fully understood. This study aims to explore how cognitive function and activities of daily living (ADLs) mediate the relationship between social participation and depressive symptoms in individuals with multimorbidity.

METHODS

We selected 3782 participants with multimorbidity from the 2018 China Health and Retirement Longitudinal Study. Data related to social participation, cognitive function, ADLs, and depressive symptoms were extracted. Regression and Bootstrap analyses were used to explore the sequential mediating effects of social participation, cognitive function, ADLs, and depressive symptoms.

RESULTS

(1) There was a significant correlation between social participation, cognitive function, activities of daily living, and depressive symptoms (p < 0.01). (2) Social participation directly affected depressive symptoms (β = -0.205, p < 0.05). (3) Cognitive function (β = -0.070, p < 0.01) and activities of daily living (β = -0.058, p < 0.01) played separate mediating roles in the effect of social participation on depressive symptoms. (4) Cognitive function and activities of daily living had a chain-mediated role in the relationship between social participation and depressive symptoms in patients with multimorbidity (β = -0.020, p < 0.01).

CONCLUSION

A chained mediating effect was found between cognitive function, ADLs, social participation, and depressive symptoms in patients with multimorbidity. Social participation was found to improve the cognitive function of patients with multimorbidity, which in turn enhanced their daily life activities and ultimately alleviated their depressive symptoms.

Sleep quality and related factors among healthcare workers in designated quarantine hospital site in post-pandemic based on the health ecological model: a cross-sectional study in Nanjing, China

AIMS

This study aims to assess the status and related factors among healthcare workers (HCWs) in designated quarantine-hospital-site (DQHS) based on the model of health ecology.

METHODS

The cross-sectional study was conducted from April to May, 2022, which included 351 valid samples. We measured sleep quality using the Pittsburgh Sleep Quality Index, which encompasses seven dimensions: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. Each dimension is scored individually, contributing to an overall sleep quality score. Factors associated with the sleep quality of HCWs in DQHS were divided into individual, behavioral, interpersonal and social dimensions. Hierarchical linear regressions were conducted to identify the potential factors associated with sleep quality among HCWs in DQHS.

RESULTS

HCWs in DQHS had a statistically higher sleep quality than the Chinese national norm. HCWs who were female, afraid of Coronavirus disease, had more negative emotions, frequently worked overtime, were married, and had a higher income were more likely to experience worse sleep quality (p < 0.05), while those who worked between 51 and 70 h weekly, treated over 10 patients daily, and engaged in more health behaviors may have better sleep quality (p < 0.05).

CONCLUSION

This study revealed a worrying level of sleep quality among HCWs in DQHS. The government, hospital managers, and families should collaborate to ensure the sleep quality of HCWs in DQHS.