Health risk factors in Australian Stroke Survivors: A latent class analysis

Abstract

Issues AddressedTo (i) determine the prevalence of health risk factors (physical activity, diet, alcohol, smoking, blood pressure medication use and mental health) in community‐dwelling stroke survivors; and (ii) examine how these health risk factors cluster, and identify associations with physical functioning, independent living, or sociodemographic factors.MethodsA secondary analysis of data obtained during a national randomised controlled trial. Participants had experienced stroke and completed a baseline telephone survey on demographic and stroke characteristics, health risk factors, physical functioning and independence in activities of daily living. A latent class analysis was performed to determine health risk profiles. Univariate logistic regressions were performed to identify if participant characteristics were associated with resulting classes.ResultsData analysed from 399 participants. Two classes of health risk factors were identified: Low Mood, Food & Moves Risk (16% of participants) and Alcohol Use Risk (84% of participants). The Low Mood, Food & Moves Risk group had poorer diet quality, lower physical activity levels and higher levels of depression and anxiety. Lower levels of independence and physical functioning were predictor variables for this group. In contrast, the Alcohol Use Risk group had better physical activity and diet scores, significantly lower probability of depression and anxiety, but a higher probability of risky drinking.ConclusionsWe identified two distinct health risk factor groups in our population.So What?Future interventions may benefit from targeting the specific needs and requirements of people who have experienced stroke based on their distinct risk group. Alcohol consumption in poststroke populations requires further attention.

Latent class analysis of the sleep quality of night shift nurses and impact of shift‐related factors on the occupational stress and anxiety

“…This method maximizes differences between categories while minimizing differences within categories (Mori et al., 2020). LCA is widely applied in fields such as identifying psychological health issues like anxiety and depression in populations, as well as chronic diseases like stroke and heart failure (Blum et al., 2023; Clancy et al., 2023; Liu, Liu, et al., 2021).…”