Title : Longitudinal relationships between depression and frailty symptoms in middle-aged and older adults: A cross-lagged panel network analysis
Abstract:
Background: Several studies have examined cross-sectional relationships between depression and frailty. However, these studies cannot address the directionality of the temporal relationships. Therefore, based on the perspective of network analysis, we conducted cross-lagged panel network (CLPN) analysis aiming to explore the longitudinal network association of depression and frailty in middle-aged and older adults.
Methods: A total of 42719 participants (female: 50.9%, mean age at T1: 55.15 years, mean age at T2: 64.25 years) from the UK Biobank were included in this study. Frailty (Frailty Phenotype) and depression (PHQ-4) symptoms were assessed at baseline (T1) and the first follow-up (T2). Their longitudinal relationships were examined by a cross-lagged panel network analysis.
Results: Depression and frailty symptoms were closely related in middle-aged and old adults. CLPN analysis shows that more depressive symptoms predict frailty symptoms. “Walking pace” exhibits the highest out-expected influence (out-EI) in the network, while “weight change” and “walking pace” display the lowest in-expected influence (in-EI). Moreover, “tiredness/lethargy” is the central role in both types of symptoms.
Conclusions: The study suggests that interventions targeting depression may help reduce frailty symptoms in middle-aged and older adults. Specifically, intervening to improve “walking pace” may enhance overall mental well-being.
Audience Take Away:
- The audience will learn about temporal dynamics between depression and frailty in middle-aged and older adults, and understand that depressive symptoms can predict the development of frailty symptoms, and recognizing which symptoms have the highest out-EI and in-EI in this network.
- They will gain insights into the central symptoms, particularly focusing on improving “walking pace” to enhance overall mental and physical well-being. With these insights, they can design more effective treatment plans that address both mental and physical health, thereby providing holistic care to middle-aged and older adults.
- Researchers can apply cross-lagged panel network analysis to other longitudinal datasets to uncover similar relationships between different health conditions, expanding the scope of this methodological approach.
- The findings can inform policy development aimed at integrated care strategies for aging populations, emphasizing the importance of mental health in maintaining physical health and vice versa.
