Associations Of 24-hour Compositional Behaviors With Cardiorespiratory Fitness: Study Of Muscle, Mobility And Aging (SOMMA)

Physical activity (PA) regression models do not adequately capture the time co-dependency of behaviors in a 24-hour period. For example, a regression modeling the association of higher amounts of moderate-to-vigorous PA (MVPA) with cardiorespiratory fitness (CRF) fails to account for the reallocation of time from another behavior (e.g., sleep, sedentary behavior (SB), or light intensity PA (LPA)) to attain the higher amounts of MVPA, thus simultaneously modeling the effects of higher MVPA and lower amounts of another behavior. PURPOSE: Utilize CoDA methods to address the time co-dependency and quantify the interrelation between 24-hour behaviors (sleep duration (hereinafter sleep), SB, LPA, and MVPA) and CRF. METHODS: At baseline, SOMMA participants wore an ActiGraph GT9X on their non-dominant wrist for at least three days and up to seven days. Intensities were categorized using Montoye et al. 2021 cutpoints for activity and the Cole-Kripke algorithm for sleep. CRF (VO2peak, mL/kg/min) was measured using treadmill cardiopulmonary exercise testing. Compositional isotemporal substitution models examined the predicted change in VO2peak when 30 minutes was theoretically reallocated from one behavior to another, after adjusting for age, sex, race, site, smoking status, and a multimorbidity index (0 - 11 conditions). RESULTS: Participants (N = 769; mean age 76.2 ± 4.9 years; 57.3% women) had a 24-hour compositional mean of 30.5% sleep (439 min/d), 49.9% SB (718 min/d), 7.0% LPA (102 min/d), and 12.6% MVPA (181 min/d). Mean VO2peak was 20.2 ± 4.9 mL/kg/min. Reallocating 30 minutes of SB to MVPA resulted in 0.55 mL/kg/min higher predicted VO2peak when compared to values at the mean composition (P < 0.05). Additionally, reallocating 30 minutes of LPA to MVPA resulted in the strongest observed association with 1.22 mL/kg/min higher predicted VO2peak (P < 0.05). Reallocating time from sleep to MVPA was not associated with predicted VO2peak. CONCLUSION: As VO2peak is a hallmark biomarker of aging, understanding its interrelations with 24-hour behaviors may inform targeted interventions—likely aimed at increasing MVPA at the expense of LPA—to promote the healthier, independent aging of older adults. SOMMA funded by: NIH R01 AG059416, P30 AG024827, P30 AG021332, and UL1 TR001420. BTS funded by NIA T32AG000181. SOMMA funded by: NIH R01 AG059416, P30 AG024827, P30 AG021332, and UL1 TR001420. BTS funded by NIA T32AG000181.

Authors

Institutions

Publication Details

Journal
Medicine & Science in Sports & Exercise
Published
2024-09-16
DOI
https://doi.org/10.1249/01.mss.0001060568.08826.ca
Primary Topic
Physical Activity and Health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Associations Of 24-hour Compositional Behaviors With Cardiorespiratory Fitness: Study Of Muscle, Mobility And Aging (SOMMA)

Nancy W. Glynn, Megan Hetherington‐Rauth, Melissa L. Erickson, Lauren S. Roe et al.
Medicine & Science in Sports & Exercise
Physical Activity and Health
article

Associations Of 24-hour Compositional Behaviors With Cardiorespiratory Fitness: Study Of Muscle, Mobility And Aging (SOMMA)

Nancy W. Glynn, Megan Hetherington‐Rauth, Melissa L. Erickson, Lauren S. Roe, Katie L. Stone, Peggy M. Cawthon, Benjamin T. Schumacher, Samaneh Farsijani, Yujia Qiao
article en

Abstract

Physical activity (PA) regression models do not adequately capture the time co-dependency of behaviors in a 24-hour period. For example, a regression modeling the association of higher amounts of moderate-to-vigorous PA (MVPA) with cardiorespiratory fitness (CRF) fails to account for the reallocation of time from another behavior (e.g., sleep, sedentary behavior (SB), or light intensity PA (LPA)) to attain the higher amounts of MVPA, thus simultaneously modeling the effects of higher MVPA and lower amounts of another behavior. PURPOSE: Utilize CoDA methods to address the time co-dependency and quantify the interrelation between 24-hour behaviors (sleep duration (hereinafter sleep), SB, LPA, and MVPA) and CRF. METHODS: At baseline, SOMMA participants wore an ActiGraph GT9X on their non-dominant wrist for at least three days and up to seven days. Intensities were categorized using Montoye et al. 2021 cutpoints for activity and the Cole-Kripke algorithm for sleep. CRF (VO2peak, mL/kg/min) was measured using treadmill cardiopulmonary exercise testing. Compositional isotemporal substitution models examined the predicted change in VO2peak when 30 minutes was theoretically reallocated from one behavior to another, after adjusting for age, sex, race, site, smoking status, and a multimorbidity index (0 - 11 conditions). RESULTS: Participants (N = 769; mean age 76.2 ± 4.9 years; 57.3% women) had a 24-hour compositional mean of 30.5% sleep (439 min/d), 49.9% SB (718 min/d), 7.0% LPA (102 min/d), and 12.6% MVPA (181 min/d). Mean VO2peak was 20.2 ± 4.9 mL/kg/min. Reallocating 30 minutes of SB to MVPA resulted in 0.55 mL/kg/min higher predicted VO2peak when compared to values at the mean composition (P < 0.05). Additionally, reallocating 30 minutes of LPA to MVPA resulted in the strongest observed association with 1.22 mL/kg/min higher predicted VO2peak (P < 0.05). Reallocating time from sleep to MVPA was not associated with predicted VO2peak. CONCLUSION: As VO2peak is a hallmark biomarker of aging, understanding its interrelations with 24-hour behaviors may inform targeted interventions—likely aimed at increasing MVPA at the expense of LPA—to promote the healthier, independent aging of older adults. SOMMA funded by: NIH R01 AG059416, P30 AG024827, P30 AG021332, and UL1 TR001420. BTS funded by NIA T32AG000181. SOMMA funded by: NIH R01 AG059416, P30 AG024827, P30 AG021332, and UL1 TR001420. BTS funded by NIA T32AG000181.

Medicine & Science in Sports & ExerciseVol. 56(10S)
University of Pittsburgh (US), University of California, San Francisco (US), AdventHealth Orlando (US)
Zero hunger
Openalex Percentile: Top 78%
Physical Activity and Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.