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Meta-Analysis
. 2025 Mar;55(3):641-654.
doi: 10.1007/s40279-024-02148-4. Epub 2024 Dec 21.

Reallocating Time Between 24-h Movement Behaviors for Obesity Management Across the Lifespan: A Pooled Data Meta-Analysis of More Than 9800 Participants from Seven Countries

Aleš Gába  1 , Timothy B Hartwig  2 , Paulína Jašková  3 , Taren Sanders  4 , Jan Dygrýn  3 , Ondřej Vencálek  5 , Devan Antczak  6 , James Conigrave  7 , Phillip Parker  4 , Borja Del Pozo Cruz  8 , Stuart J Fairclough  9 , Shona Halson  10 , Karel Hron  5 , Michael Noetel  11 , Manuel Ávila-García  12   13 , Veronica Cabanas-Sánchez  14   15   16 , Iván Cavero-Redondo  17 , Rachel G Curtis  18 , Bruno G G da Costa  19 , Jesus Del Pozo-Cruz  20 , Antonio García-Hermoso  21 , Angus A Leahy  22 , David R Lubans  23   24   25 , Carol A Maher  18 , David Martínez-Gómez  14   15   16 , Kim Meredith-Jones  26 , Andrés Redondo-Tébar  17 , Séverine Sabia  27 , Kelly S Silva  28 , Paula Skidmore  29 , Emilio Villa-González  30 , Manasa S Yerramalla  31 , Chris Lonsdale  4
Affiliations
Meta-Analysis

Reallocating Time Between 24-h Movement Behaviors for Obesity Management Across the Lifespan: A Pooled Data Meta-Analysis of More Than 9800 Participants from Seven Countries

Aleš Gába et al. Sports Med. 2025 Mar.

Abstract

Background: The distribution of time across physical activity, sedentary behaviors, and sleep appears to be essential for the management of obesity. However, the impact of reallocating time among these behaviors, collectively known as 24-h movement behaviors, remains underexplored.

Objective: This study examines the theoretical effects of reallocating time between 24-h movement behaviors on obesity indicators across different age groups.

Methods: We performed a pooled data meta-analysis of 9818 participants from 11 observational and experimental studies. To estimate the time spent in movement behaviors, we reprocessed and harmonized individual-level raw accelerometer-derived data. Isotemporal substitution models estimated theoretical changes in body mass index (BMI) and waist circumference (WC) associated with time reallocation between movement behaviors. We performed the analysis separately for children, adolescents, adults, and older adults.

Results: Even minor reallocations of 10 min led to significant changes in obesity indicators, with pronounced effects observed when 30 min were reallocated. The most substantial adverse effects on BMI and WC occurred when moderate-to-vigorous physical activity (MVPA) was reallocated to other movement behaviors. For 30-min reallocations, the largest increase in BMI (or BMI z-score for children) occurred when MVPA was reallocated to light-intensity physical activity (LPA) in children (0.26 units, 95% confidence interval [CI] 0.15, 0.37) and to sedentary behavior (SB) in adults (0.72 kg/m2, 95% CI 0.47, 0.96) and older adults (0.73 kg/m2, 95% CI 0.59, 0.87). The largest increase in WC was observed when MVPA was substituted with LPA in adults (2.66 cm, 95% CI 1.42, 3.90) and with SB in older adults (2.43 cm, 95% CI 2.07, 2.79). Conversely, the highest magnitude of the decrease in obesity indicators was observed when SB was substituted with MVPA. Specifically, substituting 30 min of SB with MVPA was associated with a decrease in BMI z-score by - 0.15 units (95% CI - 0.21, - 0.10) in children and lower BMI by - 0.56 kg/m2 (95% CI - 0.74, - 0.39) in adults and by - 0.52 kg/m2 (95% CI - 0.61, - 0.43) in older adults. Reallocating time away from sleep and LPA showed several significant changes but lacked a consistent pattern. While the predicted changes in obesity indicators were generally consistent across age groups, inconsistent findings were observed in adolescents, particularly for reallocations between MVPA and other behaviors.

Conclusions: This investigation emphasizes the crucial role of MVPA in mitigating obesity risk across the lifespan, and the benefit of substituting SB with low-intensity movement behaviors. The distinct patterns observed in adolescents suggest a need for age-specific lifestyle interventions to effectively address obesity. Emphasizing manageable shifts, such as 10-min reallocations, could have significant public health implications, promoting sustainable lifestyle changes that accommodate individuals with diverse needs, including those with severe obesity.

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Conflict of interest statement

Declarations. Funding: This study was supported by grants from the Czech Science Foundation (18-09188S and 22-02392S). ART and ICR are supported by the Ministry of Economy and Competitiveness-Carlos III Health Institute (FIS PI19/01919). BPC was supported by the Government of Andalusia (Spain), Research Talent Recruitment Program (EMERGIA 2020/00158). CM is funded by the National Health and Medical Research Council Australia, project grant (APP1080186). DL is funded by the National Health and Medical Research Council Australia (APP1120518) and the New South Wales Department of Education School Sport Unit. EVG and MAG are supported by the Spanish Ministry of Economy and Competitiveness (DEP2015-63988-R, MINECO-FEDER). JPC is supported by the Development and Innovation Programme (PAIDI P20_1181) of the Government of Andalusia and Ministry of Universities (22330) and the European Union—NextGenerationEU program for the requalification of university teaching staff. KMJ and PS are supported by the University of Otago (Grant number ORG 0114–1015) and National Heart Foundation of New Zealand (Grant number 1618). SS is supported by the French National Research Agency (ANR-19-CE36-0004-01) and by the European Union (ERC, RHYTHM IN DEMENTIA, 101043884). The Whitehall II study has been supported by grants from the National Institute on Aging, National Institutes of Health (R01AG056477, RF1AG062553), UK Medical Research Council (R024227, S011676, K013351), the British Heart Foundation (RG/16/11/32334), and the Wellcome Trust (221854/Z/20/Z). The funding of the Seniors-ENRICA-2 study (DMG and VCS) was obtained from grants FIS 22/01111, PI23/00663, and PMP22/00002 from the Carlos III Health Institute (ISCIII), the State Secretary of R + D + I, and the Pluri-regional program Spain ERDF 2021–2027, the MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU/PRTR” (PLEC2022-009352 grant), and the ISCIII-CDTI and the European Union “NextGenerationEU/PRTR” (PMPTA22/00107 grant). Conflicts of Interest/Competing Interests: The authors have no conflicts of interest that are directly relevant to the content of this article. Shona Halson is an Editorial Board member of Sports Medicine but was not involved in the selection of peer reviewers for this manuscript or any of the subsequent editorial decisions. Ethics Approval: Not applicable. Consent to Participate: Not applicable. Consent for Publication: Not applicable. Availability of Data and Material: The study protocol and analytic code are available on request from the corresponding author. Individual participant data used in this article were obtained, with permission, from original data custodians. These de-identified data may be available to researchers who provide a methodologically sound proposal. Requests for access to individual participant data can be e-mailed to Dr. Hartwig (timothy.hartwig@acu.edu.au). Data share agreements may need to be signed. Code Availability: Not applicable. Authors’ Contributions: AG, BPC, and TH conceptualized the study. The data included in this study were collected by AG, AGH, AL, ART, BC, CL, CM, DL, DMG, EVG, ICR, JD, JPC, KMJ, KS, MAG, MSY, PS, RC, SS, TS, and VCS. TS had full access to all the data in the study and developed the sleepIPD R package to process accelerometer data. CL, JD, PP, and TS were responsible for maintaining research data. AG, OV, and PJ carried out the data analysis, contributed to the data interpretation, and were responsible for data presentation. BPC and HK oversaw the analysis plan. AG drafted the manuscript for publication. All co-authors approved the final version of the manuscript and are accountable for all aspects of the work.

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