Perspectives on Movement and Eating Behaviours in Brazilian Elderly: An Analysis of Clusters Associated with Disease Outcomes

Thuany, Mabliny; Vieira, D.A.G.; Santos, Anderson Santana; Malchrowicz-Mośko, Ewa; Gomes, Thayse Natacha

doi:10.14336/ad.2022.0131

Cited by 1 publication

(1 citation statement)

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“…Comparing this directly with other data-driven clustering studies is complicated since the limited number of papers that has focused on health behaviors in older adults assessed these health behaviors differently (e.g., TV time instead of sitting time as a measure of sedentary behavior; fruit and vegetable consumption rather than an overall food score) [43]. Despite this, the current study seems to contrast the results from a self-reported study in an elderly cohort (mean age = 71 years) [44], who found that nutrition was the most distinct health behavior, and that movement behaviors were similar. On the other hand, our findings are in line with a study in ~ 40-70-year-olds [45] that found that movement behaviors (prolonged sitting and being physically inactive) clustered within classes, but alcohol intake was similar.…”

Section: Discussionmentioning

confidence: 71%

How are different clusters of physical activity, sedentary, sleep, smoking, alcohol, and dietary behaviors associated with cardiometabolic health in older adults? A cross-sectional latent class analysis

Verswijveren,

Dingle,

Donnelly

et al. 2023

JASSB

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Background Studies to date that investigate combined impacts of health behaviors, have rarely examined device-based movement behaviors alongside other health behaviors, such as smoking, alcohol, and sleep, on cardiometabolic health markers. The aim of this study was to identify distinct classes based on device-assessed movement behaviors (prolonged sitting, standing, stepping, and sleeping) and self-reported health behaviors (diet quality, alcohol consumption, and smoking status), and assess associations with cardiometabolic health markers in older adults. Methods The present study is a cross-sectional secondary analysis of data from the Mitchelstown Cohort Rescreen (MCR) Study (2015–2017). In total, 1,378 older adults (aged 55–74 years) participated in the study, of whom 355 with valid activPAL3 Micro data were included in the analytical sample. Seven health behaviors (prolonged sitting, standing, stepping, sleep, diet quality, alcohol consumption, and smoking status) were included in a latent class analysis to identify groups of participants based on their distinct health behaviors. One-class through to six-class solutions were obtained and the best fit solution (i.e., optimal number of classes) was identified using a combination of best fit statistics (e.g., log likelihood, Akaike’s information criteria) and interpretability of classes. Linear regression models were used to test associations of the derived classes with cardiometabolic health markers, including body mass index, body fat, fat mass, fat-free mass, glycated hemoglobin, fasting glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, very-low-density lipoprotein cholesterol, systolic and diastolic blood pressure. Results In total, 355 participants (89% of participants who were given the activPAL3 Micro) were included in the latent class analysis. Mean participant ages was 64.7 years and 45% were female. Two distinct classes were identified: “Healthy time-users” and “Unhealthy time-users”. These groups differed in their movement behaviors, including physical activity, prolonged sitting, and sleep. However, smoking, nutrition, and alcohol intake habits among both groups were similar. Overall, no clear associations were observed between the derived classes and cardiometabolic risk markers. Discussion Despite having similar cardiometabolic health, two distinct clusters were identified, with differences in key behaviors such as prolonged sitting, stepping, and sleeping. This is suggestive of a complex interplay between many lifestyle behaviors, whereby one specific behavior alone cannot determine an individual’s health status. Improving the identification of the relation of multiple risk factors with health is imperative, so that effective and targeted interventions for improving health in older adults can be designed and implemented.

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Section: Discussionmentioning

confidence: 71%