Objective and subjective measures of sedentary behavior of community-dwelling elderly people

Abstract

Introduction: Advancing age and the absence of the obligations of decades of work make older adults much more susceptible to adopting behaviors considered sedentary, defined as activities with energy expenditure of less than 1.5 metabolic equivalents (METs) in seated, reclined, or lying positions. This behavior is associated with health risks, including metabolic syndrome, poorer quality of life, cognitive decline, increased risk of cardiovascular disease, and all-cause mortality. Objective: To evaluate the time spent in objective and subjective sedentary behavior (SB) of community-dwelling older adults monitored by the Mobile Senior Health Platform. Materials and Methods: This was a cross-sectional, observational, and quantitative study conducted at the Open University for Maturity (OUM) and the Senior Citizens' Center (SCC) in Campina Grande, Paraíba, Brazil. The study population comprised community-dwelling older adults (> 60 years), functionally independent and of either sex, selected by non-probabilistic convenience sampling. Participants were monitored for seven consecutive days using a wearable device, the Fitbit Inspire II, which captured objective data on time spent in SB. At the end of this period, the Longitudinal Aging Study Amsterdam - Sedentary Behavior Questionnaire" (LASA-SBQ) was applied to obtain subjective data on SB. The extracted data were statistically analyzed using the IBM SPSS Statistics version 25.0 program, adopting a statistical significance level of p<0.05. Results: The sample findings (n=51) revealed a high prevalence of SB among the participants, with a higher incidence during nighttime periods and on weekends. The average time in CS estimated by Fitbit was 11.7 ± 2.3 hours per day, while by LASA-SBQ it was 6.7 ± 3.4 hours per day. No significant correlation was observed between the two methods (r (49) = 0.159, p = 0.265). Furthermore, analyses revealed statistically significant differences between the shifts of the day, with longer time in SB recorded in the evening compared to the afternoon (β = 45.5 min/day, 95% CI 35.4; 55.6, p<0.001) and the morning (β = 67.8 min/day, 95% CI 53.7; 82.0, p<0.001). These findings suggest the need for interventions aimed at reducing SB, especially in the evening. Conclusion: The study demonstrated the feasibility of using wearable devices for objective assessment of time spent in SB among community-dwelling older adults. The findings reinforce the importance of interventions to reduce SB and improve the health conditions of this population. The integration of the Fitbit device with the Mobile Senior Health Platform allows for stimulating behavior change and improving awareness of the determinants of health in this population.