Associations between anxiety, depression, and personal mastery in community-dwelling older adults: a network-based analysis.

BACKGROUND: In China, about 18.70% of the population aged 60 years and older are at risk of low personal mastery as well as anxiety and depression for a variety of reasons. The purpose of this study was to construct a symptom network model of the relationship between anxiety, depression, and personal mastery in community-dwelling older adults and to identify central and bridge symptoms in this network. METHODS: Depression, anxiety, and personal mastery were measured using the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder Scale (GAD-7), and Personal Mastery Scale (PMS), respectively. A total of 501 older adults in 16 communities in Changzhou and Zhenjiang, Jiangsu Province, China, were surveyed by using a combination of stratified sampling and convenience sampling methods. The R language was used to construct the network. RESULTS: (1) The network structure of anxiety-depression-personal mastery was stable, with "Nervousness" (node GAD1, strength = 1.38), "Sad mood" (node PHQ2, strength = 1.22), " Inability to change" (node PMS2, strength = 1.01) and "Involuntarily" (node PMS3, strength = 0.95) as the central symptoms. (2) "Irritability" (node GAD6, bridge strength = 0.743), "Sad mood" (node PHQ2, bridge strength = 0.655), and "Trouble relaxing" (node GAD4, bridge strength = 0.550) were the bridge symptoms connecting anxiety, depressive symptoms, and personal mastery. (3) In the network comparison test (NCT), residence, somatic chronic comorbidity and gender had no significant effect on network structure. CONCLUSIONS: The construction of the anxiety-depression-personal mastery network structure opens up new possibilities for mechanisms of action and intervention formulation for psychological disorders in community-dwelling older adults. The identification of central symptoms (e.g., nervousness, sad mood, inability to change, involuntarily) and bridge symptoms (e.g., irritability, sad mood, trouble relaxing) in community-dwelling older adults with anxiety, depression, and low sense of mastery can provide a scientific basis for the development of precise interventions.

Core and bridge symptoms of demoralization in Chinese female cancer patients: a network analysis.

Objective: In this study, we explore the core and bridge symptoms of demoralization in female cancer patients in China, and provide a basis for precise psychological intervention among female cancer patients. Methods: This study used a cross-sectional survey. Participants were recruited from three third-class hospitals in Jiangsu Province from June 2022 to June 2023 using the convenience sampling method. The severity of each symptom of demoralization was investigated in female cancer patients using the Demoralization Scale (DS). Network analysis was performed using the R language to identify core and bridge symptoms in the network and further explore some characteristic edge connections in the network. Results: The network structure model of demoralization had strong accuracy and stability. In the network, the symptoms with the highest strength centrality were "Discouragement" (C3, strength=2.19), "No self-worth" (A3, strength=1.21), "Don't want to live" (A5, strength=1.20), "Hopeless" (D4, strength=0.81), and "Vulnerability" (B3, strength=0.74), respectively. The bridge strength analysis identified "Hopeless" (D4, bridge strength=0.92), "Discouragement" (C3, bridge strength=0.85), "No self-worth" (A3, bridge strength=0.75), "Poor spirits" (E2, bridge strength=0.71), and "Vulnerability" (B3, bridge strength=0.69) as the bridge symptoms. The strongest edge connections of all dimensions were "No self-worth" and "Worthless" (A3-E6, edge weighting=0.27), "Poor spirits" and "Loss of emotional control" (E2-D1, edge weighting=0.22), "Discouragement" and "Vulnerability" (C3-B3, edge weighting=0.14), and "Hopeless" and "No meaning of survival" (D4-A4, edge weighting=0.12). Conclusion: "Discouragement (C3)", "No self-worth (A3)", "Hopeless (D4)", and "Vulnerability (B3)" are both core symptoms and bridge symptoms. These symptoms can not only trigger a patient's demoralization but also stimulate more severe symptom clusters through interactions. The early recognition of and intervention regarding these symptoms could be important for the prevention and treatment of demoralization among female cancer patients.

Exploring the dynamics of perioperative symptom networks in colorectal cancer patients: a cross-lagged panel network analysis.

BACKGROUND: Colorectal cancer incidence is on the rise, necessitating precise symptom management. However, causal relationships among symptoms have been challenging to establish due to reliance on cross-sectional data. Cross-lagged panel network (CLPN) analysis offers a solution, leveraging longitudinal data for insight. OBJECTIVE: We employed CLPN analysis to construct symptom networks in colorectal cancer patients at three perioperative time points, aiming to identify predictive relationships and intervention opportunities. METHODS: We evaluated the prevalence and severity of symptoms throughout the perioperative period, encompassing T1 the first day of admission, T2 2-3 days postoperatively, and T3 discharge, utilizing the M. D. Anderson Symptom Inventory Gastrointestinal Cancer Module (MDASI-GI). To identify crucial nodes in the network and explore predictive and interactive effects among symptoms, CLPNs were constructed from longitudinal data in R. RESULTS: The analysis revealed a stable network, with disturbed sleep exhibiting the highest out-EI (outgoing expected influence) during T1. Distress had a sustained impact throughout the perioperative. Disturbed sleep at T1 predicted T2 bloating, fatigue, distress, and pain. T1 distress predicted T2 sadness severity. T2 distress primarily predicted T3 fatigue, disturbed sleep, changes in taste, and bloating. T2 shortness of breath predicted T3 changes in taste and loss of appetite. Furthermore, biochemical markers like RBC and ALB had notable influence on symptom clusters during T1→T2 and T2→T3, respectively. CONCLUSION: Prioritizing disturbed sleep during T1 and addressing distress throughout the perioperative phase is recommended. Effective symptom management not only breaks the chain of symptom progression, enhancing healthcare impact, but also eases patient symptom burdens.