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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

. 2025 Mar ; 55 (3) : 641-654. [epub] 20241221

Language English Country New Zealand Media print-electronic

Document type Journal Article, Meta-Analysis

Grant support
PMPTA22/00107 European Commission
PLEC2022-009352 European Commission
RG/16/11/32334 British Heart Foundation - United Kingdom
RHYTHM IN DEMENTIA European Commission
S011676 Medical Research Council - United Kingdom
K013351 Medical Research Council - United Kingdom
FIS 22/01111 Instituto de Salud Carlos III
Wellcome Trust - United Kingdom
EMERGIA 2020/00158 Government of Andalusia
ORG 0114-1015 University of Otago
1618 National Heart Foundation of New Zealand
RF1AG062553 NIA NIH HHS - United States
MCIN/AEI/10.13039/501100011033 State Secretary of R+D+I, and the Pluri-regional program Spain
R01AG056477 NIA NIH HHS - United States
ANR-19-CE36-0004-01 French National Research Agency
RF1 AG062553 NIA NIH HHS - United States
18-09188S Grantová Agentura České Republiky
DEP2015-63988-R Ministerio de Economía y Competitividad
221854/Z/20/Z Wellcome Trust - United Kingdom
PAIDI P20_1181 Government of Andalusia
FIS PI19/01919 Instituto de Salud Carlos III
101043884 European Commission
PI23/00663 Instituto de Salud Carlos III
PMP22/00002 Instituto de Salud Carlos III
APP1120518 National Health and Medical Research Council
APP1080186 National Health and Medical Research Council
R024227 Medical Research Council - United Kingdom
22-02392S Grantová Agentura České Republiky

Links

PubMed 39708280
PubMed Central PMC11985689
DOI 10.1007/s40279-024-02148-4
PII: 10.1007/s40279-024-02148-4
Knihovny.cz E-resources

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.

Australian Catholic University Banyo QLD Australia

Australian Catholic University North Sydney NSW Australia

Australian Catholic University Strathfield NSW Australia

Centre for Active Living and Learning College of Human and Social Futures University of Newcastle Callaghan NSW Australia

CIBER of Epidemiology and Public Health Madrid Spain

Department of Kinesiology and Physical Education McGill University Montreal QC Canada

Department of Preventive Medicine and Public Health School of Medicine Universidad Autonoma de Madrid Madrid Spain

Edge Hill University Ormskirk UK

Faculty of Medicine Health and Sports Department of Sport Sciences Universidad Europea de Madrid Villaviciosa de Odón Madrid Spain

Faculty of Physical Culture Palacký University Olomouc tř Míru 117 771 11 Olomouc Czech Republic

Faculty of Science Palacký University Olomouc 17 listopadu 12 779 00 Olomouc Czech Republic

Faculty of Sport and Health Sciences University of Jyväskylä Jyväskylä Finland

Faculty of Sport Sciences University Isabel 1 09003 Burgos Spain

Hunter Medical Research Institute New Lambton Heights NSW Australia

IMDEA Food Institute CEI UAM CSIC Madrid Spain

La Inmaculada Teacher Training Centre University of Granada 18013 Granada Spain

La Trobe University Melbourne VIC Australia

Navarrabiomed Hospital Universitario de Navarra IdiSNA Navarra Spain

The University of Newcastle Callaghan NSW Australia

The University of Queensland St Lucia QLD Australia

Universidad de Castilla La Mancha Cuenca Spain

Universidad de Sevilla Seville Spain

Universidade Federal de Santa Catarina Florianopolis Brazil

Université Paris Cité Inserm Paris France

Université Paris Cité Inserm U1153 EpiAgeing Paris France

University of Granada Granada Spain

University of Otago Christchurch New Zealand

University of Otago Dunedin New Zealand

University of South Australia Adelaide SA Australia

University of Wollongong Wollongong NSW Australia

See more in PubMed

Koliaki C, Dalamaga M, Liatis S. Update on the obesity epidemic: after the sudden rise, is the upward trajectory beginning to flatten? Curr Obes Rep. 2023;12:514–27. 10.1007/s13679-023-00527-y. PubMed PMC

World Obesity Federation. World obesity atlas 2023. London: World Obesity Federation; 2023. Available from: https://data.worldobesity.org/publications/?cat=19. [Accessed 27 Nov 2024].

Okunogbe A, Nugent R, Spencer G, Powis J, Ralston J, Wilding J. Economic impacts of overweight and obesity: current and future estimates for 161 countries. BMJ Glob Health. 2022;7: e009773. 10.1136/bmjgh-2022-009773. PubMed PMC

Swinburn BA, Sacks G, Hall KD, et al. The global obesity pandemic: shaped by global drivers and local environments. Lancet. 2011;378:804–14. 10.1016/S0140-6736(11)60813-1. PubMed

Loos RJF, Yeo GSH. The genetics of obesity: from discovery to biology. Nat Rev Genet. 2022;23:120–33. 10.1038/s41576-021-00414-z. PubMed PMC

Bray GA, Frühbeck G, Ryan DH, Wilding JPH. Management of obesity. Lancet. 2016;387:1947–56. 10.1016/S0140-6736(16)00271-3. PubMed

Wu T, Gao X, Chen M, Van Dam RM. Long-term effectiveness of diet-plus-exercise interventions vs. diet-only interventions for weight loss: a meta-analysis. Obes Rev. 2009;10:313–23. 10.1111/j.1467-789X.2008.00547.x. PubMed

Jensen SBK, Blond MB, Sandsdal RM, et al. Healthy weight loss maintenance with exercise, GLP-1 receptor agonist, or both combined followed by one year without treatment: a post-treatment analysis of a randomised placebo-controlled trial. eClinicalMedicine. 2024;69: 102475. 10.1016/j.eclinm.2024.102475. PubMed PMC

Brown T, Moore THM, Hooper L, et al. Interventions for preventing obesity in children. Cochrane Database Syst Rev. 2019. 10.1002/14651858.CD001871.pub4. PubMed PMC

Spiteri K, Broom D, Bekhet AH, de Caro JX, Laventure B, Grafton K. Barriers and motivators of physical activity participation in middle-aged and older-adults: a systematic review. J Aging Phys Act. 2019;27:929–44. 10.1123/japa.2018-0343. PubMed

Dogra S, Copeland JL, Altenburg TM, Heyland DK, Owen N, Dunstan DW. Start with reducing sedentary behavior: a stepwise approach to physical activity counseling in clinical practice. Patient Educ Couns. 2022;105:1353–61. 10.1016/j.pec.2021.09.019. PubMed

Cappuccio FP, Taggart FM, Kandala N-B, et al. Meta-analysis of short sleep duration and obesity in children and adults. Sleep. 2008;31:619–26. 10.1093/sleep/31.5.619. PubMed PMC

Morrissey B, Taveras E, Allender S, Strugnell C. Sleep and obesity among children: a systematic review of multiple sleep dimensions. Pediatr Obes. 2020;15: e12619. 10.1111/ijpo.12619. PubMed PMC

Chaput JP, Saunders TJ, Carson V. Interactions between sleep, movement and other non-movement behaviours in the pathogenesis of childhood obesity. Obes Rev. 2017;18:7–14. 10.1111/obr.12508. PubMed

Rollo S, Antsygina O, Tremblay MS. The whole day matters: understanding 24-hour movement-guideline adherence and relationships with health indicators across the lifespan. J Sport Health Sci. 2020;9:493–510. 10.1016/j.jshs.2020.07.004. PubMed PMC

López-Gil JF, Tapia-Serrano MA, Sevil-Serrano J, Sánchez-Miguel PA, García-Hermoso A. Are 24-hour movement recommendations associated with obesity-related indicators in the young population? A meta-analysis Obesity. 2023;31:2727–39. 10.1002/oby.23848. PubMed

Miatke A, Olds T, Maher C, et al. The association between reallocations of time and health using compositional data analysis: a systematic scoping review with an interactive data exploration interface. Int J Behav Nutr Phys Act. 2023;20:127. 10.1186/s12966-023-01526-x. PubMed PMC

Johnson DB, Gerstein DE, Evans AE, Woodward-Lopez G. Preventing obesity: a life cycle perspective. J Am Diet Assoc. 2006;106:97–102. 10.1016/j.jada.2005.09.048. PubMed

Dumuid D, Pedišić Ž, Stanford TE, et al. The compositional isotemporal substitution model: a method for estimating changes in a health outcome for reallocation of time between sleep, physical activity and sedentary behaviour. Stat Methods Med Res. 2018;28:846–57. 10.1177/0962280217737805. PubMed

Hartwig T, Sanders T, Parker P, et al. The relationship between sleep and physical activity across the lifespan. 2022. Available from: https://www.osf.io/gzj9w. (Accessed 27 Nov 2024).

Stewart LA, Clarke M, Rovers M, et al. Preferred reporting items for a systematic review and meta-analysis of individual participant data: the PRISMA-IPD statement. JAMA. 2015;313:1657–65. 10.1001/jama.2015.3656. PubMed

Sanders T. SleepIPD. 2022. Available from: https://motivation-and-behaviour.github.io/sleepIPD. (Accessed 27 Nov 2024).

Migueles JH, Rowlands AV, Huber F, Sabia S, van Hees VT. GGIR: a research community-driven open source R Package for generating physical activity and sleep outcomes from multi-day raw accelerometer data. J Meas Phys Behav. 2019;2:188. 10.1123/jmpb.2018-0063.

van Hees VT, Sabia S, Jones SE, et al. Estimating sleep parameters using an accelerometer without sleep diary. Sci Rep. 2018;8:12975. 10.1038/s41598-018-31266-z. PubMed PMC

Hurter L, Fairclough SJ, Knowles ZR, Porcellato LA, Cooper-Ryan AM, Boddy LM. Establishing raw acceleration thresholds to classify sedentary and stationary behaviour in children. Children. 2018;5:172. 10.3390/children5120172. PubMed PMC

Hildebrand M, Hansen BH, van Hees VT, Ekelund U. Evaluation of raw acceleration sedentary thresholds in children and adults. Scand J Med Sci Sports. 2017;27:1814–23. 10.1111/sms.12795. PubMed

Hildebrand M, van Hees VT, Hansen BH, Ekelund U. Age group comparability of raw accelerometer output from wrist- and hip-worn monitors. Med Sci Sports Exerc. 2014;46:1816–24. 10.1249/mss.0000000000000289. PubMed

García-Hermoso A, Hormazábal-Aguayo I, Fernández-Vergara O, et al. A before-school physical activity intervention to improve cognitive parameters in children: the Active-Start study. Scand J Med Sci Sports. 2020;30:108–16. 10.1111/sms.13537. PubMed

Edney S, Plotnikoff R, Vandelanotte C, et al. “Active Team” a social and gamified app-based physical activity intervention: randomised controlled trial study protocol. BMC Public Health. 2017;17:859. 10.1186/s12889-017-4882-7. PubMed PMC

Lubans DR, Smith JJ, Eather N, et al. Time-efficient intervention to improve older adolescents’ cardiorespiratory fitness: findings from the ‘Burn 2 Learn’ cluster randomised controlled trial. Br J Sports Med. 2020;55:751–8. 10.1136/bjsports-2020-103277. PubMed PMC

Lonsdale C, Sanders T, Cohen KE, et al. Scaling-up an efficacious school-based physical activity intervention: study protocol for the ‘Internet-based Professional Learning to help teachers support Activity in Youth’ (iPLAY) cluster randomized controlled trial and scale-up implementation evaluation. BMC Public Health. 2016;16:873. 10.1186/s12889-016-3243-2. PubMed PMC

Sánchez-López M, Ruiz-Hermosa A, Redondo-Tébar A, et al. Rationale and methods of the MOVI-da10! Study: a cluster-randomized controlled trial of the impact of classroom-based physical activity programs on children’s adiposity, cognition and motor competence. BMC Public Health. 2019;19:417. 10.1186/s12889-019-6742-0. PubMed PMC

Harrex HAL, Skeaff SA, Black KE, et al. Sleep timing is associated with diet and physical activity levels in 9–11-year-old children from Dunedin, New Zealand: the PEDALS study. J Sleep Res. 2018;27: e12634. 10.1111/jsr.12634. PubMed

Tercedor P, Villa-González E, Ávila-García M, et al. A school-based physical activity promotion intervention in children: rationale and study protocol for the PREVIENE Project. BMC Public Health. 2017;17:748. 10.1186/s12889-017-4788-4. PubMed PMC

Cabanas-Sánchez V, Esteban-Cornejo I, Migueles JH, et al. Twenty four-hour activity cycle in older adults using wrist-worn accelerometers: the Seniors-ENRICA-2 study. Scand J Med Sci Sports. 2020;30:700–8. 10.1111/sms.13612. PubMed

da Costa BGG, Chaput J-P, Lopes MVV, Malheiros LEA, Tremblay MS, Silva KS. Prevalence and sociodemographic factors associated with meeting the 24-hour movement guidelines in a sample of Brazilian adolescents. PLoS One. 2020;15: e0239833. 10.1371/journal.pone.0239833. PubMed PMC

Rubín L, Gába A, Dygrýn J, Jakubec L, Materová E, Vencálek O. Prevalence and correlates of adherence to the combined movement guidelines among Czech children and adolescents. BMC Public Health. 2020;20:1692. 10.1186/s12889-020-09802-2. PubMed PMC

Menai M, van Hees VT, Elbaz A, Kivimaki M, Singh-Manoux A, Sabia S. Accelerometer assessed moderate-to-vigorous physical activity and successful ageing: results from the Whitehall II study. Sci Rep. 2017;7:45772. 10.1038/srep45772. PubMed PMC

Katzmarzyk PT, Shen W, Baxter-Jones A, et al. Adiposity in children and adolescents: correlates and clinical consequences of fat stored in specific body depots. Pediatr Obes. 2012;7:e42-61. 10.1111/j.2047-6310.2012.00073.x. PubMed

Steene-Johannessen J, Hansen BH, Dalene KE, et al. Variations in accelerometry measured physical activity and sedentary time across Europe: harmonized analyses of 47,497 children and adolescents. Int J Behav Nutr Phys Act. 2020;17:38. 10.1186/s12966-020-00930-x. PubMed PMC

Skjåkødegård HF, Danielsen YS, Frisk B, et al. Beyond sleep duration: sleep timing as a risk factor for childhood obesity. Pediatr Obes. 2021;16: e12698. 10.1111/ijpo.12698. PubMed PMC

Gába A, Dygrýn J, Štefelová N, et al. How do short sleepers use extra waking hours? A compositional analysis of 24-h time-use patterns among children and adolescents. Int J Behav Nutr Phys Act. 2020;17:104. 10.1186/s12966-020-01004-8. PubMed PMC

Morrison S, Haszard JJ, Galland BC, et al. Where does the time go when children don’t sleep? A randomized crossover study. Obesity. 2023;31:625–34. 10.1002/oby.23615. PubMed

del Pozo-Cruz J, García-Hermoso A, Alfonso-Rosa RM, et al. Replacing sedentary time: meta-analysis of objective-assessment studies. Am J Prev Med. 2018;55:395–402. 10.1016/j.amepre.2018.04.042. PubMed

García-Hermoso A, Saavedra JM, Ramírez-Vélez R, Ekelund U, del Pozo-Cruz B. Reallocating sedentary time to moderate-to-vigorous physical activity but not to light-intensity physical activity is effective to reduce adiposity among youths: a systematic review and meta-analysis. Obes Rev. 2017;18:1088–95. 10.1111/obr.12552. PubMed

Ross R, Janssen I, Tremblay MS. Public health importance of light intensity physical activity. J Sport Health Sci. 2024. 10.1016/j.jshs.2024.01.010. (Epub ahead of print). PubMed PMC

Powell C, Browne LD, Carson BP, et al. Use of compositional data analysis to show estimated changes in cardiometabolic health by reallocating time to light-intensity physical activity in older adults. Sports Med. 2020;50:205–17. 10.1007/s40279-019-01153-2. PubMed

Rasmussen CL, Gába A, Stanford T, et al. The Goldilocks Day for healthy adiposity measures among children and adolescents. Front Public Health. 2023;11:1–9. 10.3389/fpubh.2023.1158634. PubMed PMC

Holtermann A, Rasmussen CL, Hallman DM, Ding D, Dumuid D, Gupta N. 24-Hour physical behavior balance for better health for all: “the sweet-spot hypothesis.” Sports Med Open. 2021;7:98. 10.1186/s40798-021-00394-8. PubMed PMC

Tarp J, Child A, White T, et al. Physical activity intensity, bout-duration, and cardiometabolic risk markers in children and adolescents. Int J Obes. 2018;42:1639–50. 10.1038/s41366-018-0152-8. PubMed PMC

Jefferis BJ, Parsons TJ, Sartini C, et al. Does duration of physical activity bouts matter for adiposity and metabolic syndrome? A cross-sectional study of older British men. Int J Behav Nutr Phys Act. 2016;13:36. 10.1186/s12966-016-0361-2. PubMed PMC

Trost SG, Brookes DSK, Ahmadi MN. Evaluation of wrist accelerometer cut-points for classifying physical activity intensity in youth. Front Digit Health. 2022;4: 884307. 10.3389/fdgth.2022.884307. PubMed PMC

Weber KS, Godkin FE, Cornish BF, McIlroy WE, Van Ooteghem K. Wrist accelerometer estimates of physical activity intensity during walking in older adults and people living with complex health conditions: retrospective observational data analysis study. JMIR Form Res. 2023;7: e41685. 10.2196/41685. PubMed PMC

Stamatakis E, Ahmadi MN, Gill JMR, et al. Association of wearable device-measured vigorous intermittent lifestyle physical activity with mortality. Nat Med. 2022;28:2521–9. 10.1038/s41591-022-02100-x. PubMed PMC

Grgic J, Dumuid D, Bengoechea EG, et al. Health outcomes associated with reallocations of time between sleep, sedentary behaviour, and physical activity: a systematic scoping review of isotemporal substitution studies. Int J Behav Nutr Phys Act. 2018;15:69. 10.1186/s12966-018-0691-3. PubMed PMC

Stevens ML, Gupta N, Inan Eroglu E, et al. Thigh-worn accelerometry for measuring movement and posture across the 24-hour cycle: a scoping review and expert statement. BMJ Open Sport Exerc Med. 2020;6: e000874. 10.1136/bmjsem-2020-000874. PubMed PMC

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