Association between physical activity and all‐cause mortality: A 15‐year follow‐up using a compositional data analysis

Abstract

The association between the composition of movement behaviors and mortality risk, acknowledging the composition nature of daily time data, is limited explored. The aim was to investigate how the composition of time spent in sedentary behaviors (SB), light intensity physical activity (LIPA), and moderate‐to‐vigorous physical activity (MVPA) is associated with all‐cause mortality, in a cohort with 15 years follow‐up time, using compositional data analysis. Eight hundred fifty‐one participants (56% women, mean age 53 years) provided objectively assessed physical activity data using an ActiGraph accelerometer and were followed for 15 years. Association of daily time composition of movement behaviors with risk of mortality was explored using compositional data analysis and hazard ratios (HR) of mortality were estimated based on a cox regression model. A significant (P < .001) positive association between time spent in SB relative to time in other behaviors and a significant (P = .018) negative association between time spent in LIPA relative to time in other behaviors, with all‐cause mortality, were found. Substituting time spent in LIPA or MVPA with time in SB increased the hazard for all‐cause mortality, with greater effect found for MVPA (20 minutes replacement; HR 1.26, 95% CI 1.04‐1.52) than for LIPA (20 minutes replacement; HR 1.06, 95% CI 0.65‐1.73). In a public health perspective, it is recommended to substitute SB with either LIPA or MVPA, but for individuals with little time spent in MVPA, the most important message may be to try to maintain that behavior.

Latent profile analysis of physical activity and sedentary behavior with mortality risk: A 15‐year follow‐up

“…This study adds to the existing literature by showing that multiple activity variables differ between physical activity profiles associating with different mortality risks. It also demonstrates considerably larger effect sizes than in previous reports on the same population 6,14 . Even if this study cannot for sure identify which physical characteristics are most important for decreasing all‐cause mortality, the correlation between the characteristics suggest a complex interplay.…”

“…Previous findings from the same cohort as in this study have shown an inverse relationship between MVPA and mortality, and replacing SB with time spent in LIPA and MVPA has been found to have beneficial effect for all-cause mortality. 6,12 This study adds to the existing literature by showing that multiple activity variables differ between physical activity profiles associating with different mortality risks. It also demonstrates considerably larger effect sizes than in previous reports on the same population.…”

“…It also demonstrates considerably larger effect sizes than in previous reports on the same population. 6,14 Even if this study cannot for sure identify which physical characteristics are most important for decreasing all-cause mortality, the correlation between the characteristics suggest a complex interplay. For example, the correlation between z-scores of total time of sedentary bouts and time spent in LIPA, and between time spent in MVPA and relative time spent in SB, was −0.66 and −0.70, respectively, indicating that multiple physical characteristics are closely related.…”

A compositional analysis of time spent in sleep, sedentary behaviour and physical activity with all-cause mortality risk

“…As more minutes are added to LIPA, it is estimated that the associated HR will increase if this time is taken from MVPA, but decrease if this time is taken from sleep or SED. Substitutions were not made beyond the range of the 2.5th to 97.5th percentile of minutes per day spent in each movement behaviour (e.g., no more than 75.4 min per day were added to the mean 16.8 min per day spent in MVPA) composition and all-cause mortality but did not include sleep data [41]. Lacking information on sleep limited the ability of these authors to consider all co-dependent parts of the 24-h movement behaviour composition within a CoDA paradigm.…”

The Design of University Physical Education Management Framework Based on Edge Computing and Data Analysis

“…To achieve informatization and standardization of physical education management in colleges and universities, it must develop a physical education management system. Through the development of the student physical education management system, the physical education management system of colleges and universities realizes management Informa ionization and standardization [11,12]. By improving the efficiency of sports information management, this will not only greatly improve the efficiency of student sports management information in colleges and universities but also further improve the physical fitness of students [13,14].…”