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Tonge KL, Mavilidi M, Jones RA. An updated systematic review of correlates of children's physical activity and sedentary time in early childhood education services. Child Care Health Dev 2024; 50:e13265. [PMID: 38657131 DOI: 10.1111/cch.13265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 02/06/2024] [Accepted: 03/24/2024] [Indexed: 04/26/2024]
Abstract
BACKGROUND Early childhood education services (ECE) continue to be a key setting to promote physical activity and limit sedentary behaviour. Thus, the aim of this study was to (1) provide an updated systematic review of correlates of physical activity and sedentary behaviour among children in ECE settings and (2) discuss changes in physical activity and sedentary behaviour correlates among children in ECEC settings over time. METHODS A systematic search of eight databases identified 40 studies published between 2015 and 2023 that met the inclusion criteria. The variables were categorized into four domains (child, educator, physical environmental and organizational). Fifty-eight variables were identified. RESULTS For data from 2015 to 2023, strong associations were identified in all domains (child, educator, physical environmental and organizational) for physical activity, yet no strong associations for sedentary behaviour were identified. Aggregated data (i.e., combining data from previous review and this review) showed strong associations with children's physical activity and age, motor coordination and sex (child), educator behaviour and presence (educator), presence and size of outdoor environments (physical environmental) and active opportunities and service quality (organizational). For sedentary behaviour, sex, outdoor environments and active opportunities were strongly associated in the combined data. CONCLUSION The correlates of physical activity and sedentary behaviour in ECE settings continue to be multi-dimensional and span different domains. Variables such as educator behaviours and intentionality, provision of active opportunities, use of outdoor space and service quality should be the key focus area for improving physical activity and sedentary behaviour levels of young children.
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Affiliation(s)
- Karen L Tonge
- Early Start, Faculty of the Arts, Humanities and Social Sciences, University of Wollongong, Wollongong, New South Wales, Australia
- School of Education, Faculty of The Arts, Humanities and Social Sciences, University of Wollongong, Wollongong, New South Wales, Australia
| | - Myrto Mavilidi
- Early Start, Faculty of the Arts, Humanities and Social Sciences, University of Wollongong, Wollongong, New South Wales, Australia
- School of Education, Faculty of The Arts, Humanities and Social Sciences, University of Wollongong, Wollongong, New South Wales, Australia
| | - Rachel A Jones
- Early Start, Faculty of the Arts, Humanities and Social Sciences, University of Wollongong, Wollongong, New South Wales, Australia
- School of Education, Faculty of The Arts, Humanities and Social Sciences, University of Wollongong, Wollongong, New South Wales, Australia
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Lv W, Fu J, Zhao G, He Z, Sun S, Huang T, Wang R, Chen D, Chen R. A cohort study of factors influencing the physical fitness of preschool children: a decision tree analysis. Front Public Health 2023; 11:1184756. [PMID: 38074715 PMCID: PMC10701283 DOI: 10.3389/fpubh.2023.1184756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 11/08/2023] [Indexed: 12/18/2023] Open
Abstract
Objective Based on the decision tree model, to explore the key influencing factors of children's physical fitness, rank the key influencing factors, and explain the complex interaction between the influencing factors. Methods A cohort study design was adopted. 1,276 children (ages 3-6) from 23 kindergartens in Nanchang, China, were chosen for the study to measure the children's physical fitness at baseline and a year later and to compare the physical fitness scores at the two stages. The study was conducted following the Chinese National Physical Fitness Testing Standard (Children Part); To identify the primary influencing factors of changes in physical fitness, a decision tree model was developed, and a questionnaire survey on birth information, feeding patterns, SB, PA, dietary nutrition, sleep, parental factors, and other relevant information was conducted. Results The levels of physical fitness indicators among preschool children showed a significant increase after 1 year. The accuracy of the CHAID model is 84.17%. It showed that 7 variables were strongly correlated with the physical changes of children's fitness, the order of importance of each variable was weekend PA, weekend MVPA, mother's BMI, mother's sports frequency, father's education, mother's education, and school day PA. Three factors are related to PA. Four factors are related to parental circumstances. In addition to the seven important variables mentioned, variables such as breakfast frequency on school day, puffed food, frequency of outing, school day MVPA, parental feeling of sports, father's occupation, and weekend breakfast frequency are all statistically significant leaf node variables. Conclusion PA, especially weekend PA, is the most critical factor in children's physical fitness improvement and the weekend MVPA should be increased to more than 30 min/d based on the improvement of weekend PA. In addition, parental factors and school day PA are also important in making decisions about changes in fitness for children. The mother's efforts to maintain a healthy BMI and engage in regular physical activity are crucial for enhancing the physical fitness of children. Additionally, other parental factors, such as the parents' educational levels and the father's occupation, can indirectly impact the level of physical fitness in children.
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Affiliation(s)
- Wendi Lv
- College of Physical and Health, Jiangxi University of Chinese Medicine, Nanchang, China
- School of Physical Education, Nanchang University, Nanchang, China
| | - Jinmei Fu
- Jiangxi Sports Science and Medicine Center, Nanchang, China
| | - Guanggao Zhao
- School of Physical Education, Nanchang University, Nanchang, China
| | - Zihao He
- School of Sport Science, Beijing Sport University, Beijing, China
| | - Shunli Sun
- Jiangxi Sports Science and Medicine Center, Nanchang, China
| | - Ting Huang
- School of Physical Education and Sport Science, Fujian Normal University, Fuzhou, China
| | - Runze Wang
- PLA Army Academy of Artillery and Air Defense, Nanjing, China
| | - Delong Chen
- School of Physical Education, Nanchang University, Nanchang, China
| | - Ruiming Chen
- School of Physical Education, Nanchang University, Nanchang, China
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3
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Yoong SL, Lum M, Wolfenden L, Jackson J, Barnes C, Hall AE, McCrabb S, Pearson N, Lane C, Jones JZ, Nolan E, Dinour L, McDonnell T, Booth D, Grady A. Healthy eating interventions delivered in early childhood education and care settings for improving the diet of children aged six months to six years. Cochrane Database Syst Rev 2023; 8:CD013862. [PMID: 37606067 PMCID: PMC10443896 DOI: 10.1002/14651858.cd013862.pub3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
Abstract
BACKGROUND Dietary intake during early childhood can have implications on child health and developmental trajectories. Early childhood education and care (ECEC) services are recommended settings to deliver healthy eating interventions as they provide access to many children during this important period. Healthy eating interventions delivered in ECEC settings can include strategies targeting the curriculum (e.g. nutrition education), ethos and environment (e.g. menu modification) and partnerships (e.g. workshops for families). Despite guidelines supporting the delivery of healthy eating interventions in this setting, little is known about their impact on child health. OBJECTIVES To assess the effectiveness of healthy eating interventions delivered in ECEC settings for improving dietary intake in children aged six months to six years, relative to usual care, no intervention or an alternative, non-dietary intervention. Secondary objectives were to assess the impact of ECEC-based healthy eating interventions on physical outcomes (e.g. child body mass index (BMI), weight, waist circumference), language and cognitive outcomes, social/emotional and quality-of-life outcomes. We also report on cost and adverse consequences of ECEC-based healthy eating interventions. SEARCH METHODS We searched eight electronic databases including CENTRAL, MEDLINE, Embase, CINAHL, PsycINFO, ERIC, Scopus and SportDiscus on 24 February 2022. We searched reference lists of included studies, reference lists of relevant systematic reviews, the World Health Organization International Clinical Trials Registry Platform, ClinicalTrials.gov and Google Scholar, and contacted authors of relevant papers. SELECTION CRITERIA We included randomised controlled trials (RCTs), including cluster-RCTs, stepped-wedge RCTs, factorial RCTs, multiple baseline RCTs and randomised cross-over trials, of healthy eating interventions targeting children aged six months to six years that were conducted within the ECEC setting. ECEC settings included preschools, nurseries, kindergartens, long day care and family day care. To be included, studies had to include at least one intervention component targeting child diet within the ECEC setting and measure child dietary or physical outcomes, or both. DATA COLLECTION AND ANALYSIS Pairs of review authors independently screened titles and abstracts and extracted study data. We assessed risk of bias for all studies against 12 criteria within RoB 1, which allows for consideration of how selection, performance, attrition, publication and reporting biases impact outcomes. We resolved discrepancies via consensus or by consulting a third review author. Where we identified studies with suitable data and homogeneity, we performed meta-analyses using a random-effects model; otherwise, we described findings using vote-counting approaches and via harvest plots. For measures with similar metrics, we calculated mean differences (MDs) for continuous outcomes and risk ratios (RRs) for dichotomous outcomes. We calculated standardised mean differences (SMDs) for primary and secondary outcomes where studies used different measures. We applied GRADE to assess certainty of evidence for dietary, cost and adverse outcomes. MAIN RESULTS We included 52 studies that investigated 58 interventions (described across 96 articles). All studies were cluster-RCTs. Twenty-nine studies were large (≥ 400 participants) and 23 were small (< 400 participants). Of the 58 interventions, 43 targeted curriculum, 56 targeted ethos and environment, and 50 targeted partnerships. Thirty-eight interventions incorporated all three components. For the primary outcomes (dietary outcomes), we assessed 19 studies as overall high risk of bias, with performance and detection bias being most commonly judged as high risk of bias. ECEC-based healthy eating interventions versus usual practice or no intervention may have a positive effect on child diet quality (SMD 0.34, 95% confidence interval (CI) 0.04 to 0.65; P = 0.03, I2 = 91%; 6 studies, 1973 children) but the evidence is very uncertain. There is moderate-certainty evidence that ECEC-based healthy eating interventions likely increase children's consumption of fruit (SMD 0.11, 95% CI 0.04 to 0.18; P < 0.01, I2 = 0%; 11 studies, 2901 children). The evidence is very uncertain about the effect of ECEC-based healthy eating interventions on children's consumption of vegetables (SMD 0.12, 95% CI -0.01 to 0.25; P =0.08, I2 = 70%; 13 studies, 3335 children). There is moderate-certainty evidence that ECEC-based healthy eating interventions likely result in little to no difference in children's consumption of non-core (i.e. less healthy/discretionary) foods (SMD -0.05, 95% CI -0.17 to 0.08; P = 0.48, I2 = 16%; 7 studies, 1369 children) or consumption of sugar-sweetened beverages (SMD -0.10, 95% CI -0.34 to 0.14; P = 0.41, I2 = 45%; 3 studies, 522 children). Thirty-six studies measured BMI, BMI z-score, weight, overweight and obesity, or waist circumference, or a combination of some or all of these. ECEC-based healthy eating interventions may result in little to no difference in child BMI (MD -0.08, 95% CI -0.23 to 0.07; P = 0.30, I2 = 65%; 15 studies, 3932 children) or in child BMI z-score (MD -0.03, 95% CI -0.09 to 0.03; P = 0.36, I2 = 0%; 17 studies; 4766 children). ECEC-based healthy eating interventions may decrease child weight (MD -0.23, 95% CI -0.49 to 0.03; P = 0.09, I2 = 0%; 9 studies, 2071 children) and risk of overweight and obesity (RR 0.81, 95% CI 0.65 to 1.01; P = 0.07, I2 = 0%; 5 studies, 1070 children). ECEC-based healthy eating interventions may be cost-effective but the evidence is very uncertain (6 studies). ECEC-based healthy eating interventions may have little to no effect on adverse consequences but the evidence is very uncertain (3 studies). Few studies measured language and cognitive skills (n = 2), social/emotional outcomes (n = 2) and quality of life (n = 3). AUTHORS' CONCLUSIONS ECEC-based healthy eating interventions may improve child diet quality slightly, but the evidence is very uncertain, and likely increase child fruit consumption slightly. There is uncertainty about the effect of ECEC-based healthy eating interventions on vegetable consumption. ECEC-based healthy eating interventions may result in little to no difference in child consumption of non-core foods and sugar-sweetened beverages. Healthy eating interventions could have favourable effects on child weight and risk of overweight and obesity, although there was little to no difference in BMI and BMI z-scores. Future studies exploring the impact of specific intervention components, and describing cost-effectiveness and adverse outcomes are needed to better understand how to maximise the impact of ECEC-based healthy eating interventions.
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Affiliation(s)
- Sze Lin Yoong
- Global Centre for Preventive Health and Nutrition, Institute for Health Transformation, School of Health and Social Development, Faculty of Health, Deakin University, Victoria, Australia
- Hunter New England Population Health, Hunter New England Local Health District, Wallsend, Australia
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Melanie Lum
- Hunter New England Population Health, Hunter New England Local Health District, Wallsend, Australia
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Luke Wolfenden
- Hunter New England Population Health, Hunter New England Local Health District, Wallsend, Australia
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Jacklyn Jackson
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Courtney Barnes
- Hunter New England Population Health, Hunter New England Local Health District, Wallsend, Australia
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Alix E Hall
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Sam McCrabb
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Nicole Pearson
- Hunter New England Population Health, Hunter New England Local Health District, Wallsend, Australia
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Cassandra Lane
- Hunter New England Population Health, Hunter New England Local Health District, Wallsend, Australia
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Jannah Z Jones
- Hunter New England Population Health, Hunter New England Local Health District, Wallsend, Australia
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Erin Nolan
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Lauren Dinour
- College of Education and Human Services, Montclair State University, Montclair, New Jersey, USA
| | - Therese McDonnell
- School of Nursing, Midwifery and Health Systems, University College Dublin, Dublin, Ireland
| | - Debbie Booth
- Auchmuty Library, University of Newcastle, Callaghan, Australia
| | - Alice Grady
- Hunter New England Population Health, Hunter New England Local Health District, Wallsend, Australia
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
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Schock S, Hakim A. The Physiological and Molecular Links Between Physical Activity and Brain Health: A Review. Neurosci Insights 2023; 18:26331055231191523. [PMID: 37600456 PMCID: PMC10436988 DOI: 10.1177/26331055231191523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 07/17/2023] [Indexed: 08/22/2023] Open
Abstract
There is currently an epidemic of sedentary behavior throughout the world, leading to negative impacts on physical health and contributing to both mortality and burden of disease. The consequences of this also impact the brain, where increased levels of cognitive decline are observed in individuals who are more sedentary. This review explores the physiological and molecular responses to our sedentary propensity, its contribution to several medical conditions and cognitive deficits, and the benefits of moderate levels of physical activity and exercise. Also presented is the recommended level of activity for overall physical health improvement.
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Affiliation(s)
- Sarah Schock
- Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Antoine Hakim
- Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
- Division of Neurology, University of Ottawa, Ottawa, ON, Canada
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5
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Yoong SL, Lum M, Wolfenden L, Jackson J, Barnes C, Hall AE, McCrabb S, Pearson N, Lane C, Jones JZ, Dinour L, McDonnell T, Booth D, Grady A. Healthy eating interventions delivered in early childhood education and care settings for improving the diet of children aged six months to six years. Cochrane Database Syst Rev 2023; 6:CD013862. [PMID: 37306513 PMCID: PMC10259732 DOI: 10.1002/14651858.cd013862.pub2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
BACKGROUND Dietary intake during early childhood can have implications on child health and developmental trajectories. Early childhood education and care (ECEC) services are recommended settings to deliver healthy eating interventions as they provide access to many children during this important period. Healthy eating interventions delivered in ECEC settings can include strategies targeting the curriculum (e.g. nutrition education), ethos and environment (e.g. menu modification) and partnerships (e.g. workshops for families). Despite guidelines supporting the delivery of healthy eating interventions in this setting, little is known about their impact on child health. OBJECTIVES To assess the effectiveness of healthy eating interventions delivered in ECEC settings for improving dietary intake in children aged six months to six years, relative to usual care, no intervention or an alternative, non-dietary intervention. Secondary objectives were to assess the impact of ECEC-based healthy eating interventions on physical outcomes (e.g. child body mass index (BMI), weight, waist circumference), language and cognitive outcomes, social/emotional and quality-of-life outcomes. We also report on cost and adverse consequences of ECEC-based healthy eating interventions. SEARCH METHODS We searched eight electronic databases including CENTRAL, MEDLINE, Embase, CINAHL, PsycINFO, ERIC, Scopus and SportDiscus on 24 February 2022. We searched reference lists of included studies, reference lists of relevant systematic reviews, the World Health Organization International Clinical Trials Registry Platform, ClinicalTrials.gov and Google Scholar, and contacted authors of relevant papers. SELECTION CRITERIA We included randomised controlled trials (RCTs), including cluster-RCTs, stepped-wedge RCTs, factorial RCTs, multiple baseline RCTs and randomised cross-over trials, of healthy eating interventions targeting children aged six months to six years that were conducted within the ECEC setting. ECEC settings included preschools, nurseries, kindergartens, long day care and family day care. To be included, studies had to include at least one intervention component targeting child diet within the ECEC setting and measure child dietary or physical outcomes, or both. DATA COLLECTION AND ANALYSIS Pairs of review authors independently screened titles and abstracts and extracted study data. We assessed risk of bias for all studies against 12 criteria within RoB 1, which allows for consideration of how selection, performance, attrition, publication and reporting biases impact outcomes. We resolved discrepancies via consensus or by consulting a third review author. Where we identified studies with suitable data and homogeneity, we performed meta-analyses using a random-effects model; otherwise, we described findings using vote-counting approaches and via harvest plots. For measures with similar metrics, we calculated mean differences (MDs) for continuous outcomes and risk ratios (RRs) for dichotomous outcomes. We calculated standardised mean differences (SMDs) for primary and secondary outcomes where studies used different measures. We applied GRADE to assess certainty of evidence for dietary, cost and adverse outcomes. MAIN RESULTS: We included 52 studies that investigated 58 interventions (described across 96 articles). All studies were cluster-RCTs. Twenty-nine studies were large (≥ 400 participants) and 23 were small (< 400 participants). Of the 58 interventions, 43 targeted curriculum, 56 targeted ethos and environment, and 50 targeted partnerships. Thirty-eight interventions incorporated all three components. For the primary outcomes (dietary outcomes), we assessed 19 studies as overall high risk of bias, with performance and detection bias being most commonly judged as high risk of bias. ECEC-based healthy eating interventions versus usual practice or no intervention may have a positive effect on child diet quality (SMD 0.34, 95% confidence interval (CI) 0.04 to 0.65; P = 0.03, I2 = 91%; 6 studies, 1973 children) but the evidence is very uncertain. There is moderate-certainty evidence that ECEC-based healthy eating interventions likely increase children's consumption of fruit (SMD 0.11, 95% CI 0.04 to 0.18; P < 0.01, I2 = 0%; 11 studies, 2901 children). The evidence is very uncertain about the effect of ECEC-based healthy eating interventions on children's consumption of vegetables (SMD 0.12, 95% CI -0.01 to 0.25; P =0.08, I2 = 70%; 13 studies, 3335 children). There is moderate-certainty evidence that ECEC-based healthy eating interventions likely result in little to no difference in children's consumption of non-core (i.e. less healthy/discretionary) foods (SMD -0.05, 95% CI -0.17 to 0.08; P = 0.48, I2 = 16%; 7 studies, 1369 children) or consumption of sugar-sweetened beverages (SMD -0.10, 95% CI -0.34 to 0.14; P = 0.41, I2 = 45%; 3 studies, 522 children). Thirty-six studies measured BMI, BMI z-score, weight, overweight and obesity, or waist circumference, or a combination of some or all of these. ECEC-based healthy eating interventions may result in little to no difference in child BMI (MD -0.08, 95% CI -0.23 to 0.07; P = 0.30, I2 = 65%; 15 studies, 3932 children) or in child BMI z-score (MD -0.03, 95% CI -0.09 to 0.03; P = 0.36, I2 = 0%; 17 studies; 4766 children). ECEC-based healthy eating interventions may decrease child weight (MD -0.23, 95% CI -0.49 to 0.03; P = 0.09, I2 = 0%; 9 studies, 2071 children) and risk of overweight and obesity (RR 0.81, 95% CI 0.65 to 1.01; P = 0.07, I2 = 0%; 5 studies, 1070 children). ECEC-based healthy eating interventions may be cost-effective but the evidence is very uncertain (6 studies). ECEC-based healthy eating interventions may have little to no effect on adverse consequences but the evidence is very uncertain (3 studies). Few studies measured language and cognitive skills (n = 2), social/emotional outcomes (n = 2) and quality of life (n = 3). AUTHORS' CONCLUSIONS ECEC-based healthy eating interventions may improve child diet quality slightly, but the evidence is very uncertain, and likely increase child fruit consumption slightly. There is uncertainty about the effect of ECEC-based healthy eating interventions on vegetable consumption. ECEC-based healthy eating interventions may result in little to no difference in child consumption of non-core foods and sugar-sweetened beverages. Healthy eating interventions could have favourable effects on child weight and risk of overweight and obesity, although there was little to no difference in BMI and BMI z-scores. Future studies exploring the impact of specific intervention components, and describing cost-effectiveness and adverse outcomes are needed to better understand how to maximise the impact of ECEC-based healthy eating interventions.
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Affiliation(s)
- Sze Lin Yoong
- Global Centre for Preventive Health and Nutrition, Institute for Health Transformation, School of Health and Social Development, Faculty of Health, Deakin University, Victoria, Australia
- Hunter New England Population Health, Hunter New England Local Health District, Wallsend, Australia
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Melanie Lum
- Hunter New England Population Health, Hunter New England Local Health District, Wallsend, Australia
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Luke Wolfenden
- Hunter New England Population Health, Hunter New England Local Health District, Wallsend, Australia
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Jacklyn Jackson
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Courtney Barnes
- Hunter New England Population Health, Hunter New England Local Health District, Wallsend, Australia
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Alix E Hall
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Sam McCrabb
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Nicole Pearson
- Hunter New England Population Health, Hunter New England Local Health District, Wallsend, Australia
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Cassandra Lane
- Hunter New England Population Health, Hunter New England Local Health District, Wallsend, Australia
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Jannah Z Jones
- Hunter New England Population Health, Hunter New England Local Health District, Wallsend, Australia
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
| | - Lauren Dinour
- College of Education and Human Services, Montclair State University, Montclair, New Jersey, USA
| | - Therese McDonnell
- School of Nursing, Midwifery and Health Systems, University College Dublin, Dublin, Ireland
| | - Debbie Booth
- Auchmuty Library, University of Newcastle, Callaghan, Australia
| | - Alice Grady
- Hunter New England Population Health, Hunter New England Local Health District, Wallsend, Australia
- School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton, Australia
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6
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Pulsford RM, Brocklebank L, Fenton SAM, Bakker E, Mielke GI, Tsai LT, Atkin AJ, Harvey DL, Blodgett JM, Ahmadi M, Wei L, Rowlands A, Doherty A, Rangul V, Koster A, Sherar LB, Holtermann A, Hamer M, Stamatakis E. The impact of selected methodological factors on data collection outcomes in observational studies of device-measured physical behaviour in adults: A systematic review. Int J Behav Nutr Phys Act 2023; 20:26. [PMID: 36890553 PMCID: PMC9993720 DOI: 10.1186/s12966-022-01388-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 11/25/2022] [Indexed: 03/10/2023] Open
Abstract
BACKGROUND Accelerometer measures of physical behaviours (physical activity, sedentary behaviour and sleep) in observational studies offer detailed insight into associations with health and disease. Maximising recruitment and accelerometer wear, and minimising data loss remain key challenges. How varying methods used to collect accelerometer data influence data collection outcomes is poorly understood. We examined the influence of accelerometer placement and other methodological factors on participant recruitment, adherence and data loss in observational studies of adult physical behaviours. METHODS The review was in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). Observational studies of adults including accelerometer measurement of physical behaviours were identified using database (MEDLINE (Ovid), Embase, PsychINFO, Health Management Information Consortium, Web of Science, SPORTDiscus and Cumulative Index to Nursing & Allied Health Literature) and supplementary searches to May 2022. Information regarding study design, accelerometer data collection methods and outcomes were extracted for each accelerometer measurement (study wave). Random effects meta-analyses and narrative syntheses were used to examine associations of methodological factors with participant recruitment, adherence and data loss. RESULTS 123 accelerometer data collection waves were identified from 95 studies (92.5% from high-income countries). In-person distribution of accelerometers was associated with a greater proportion of invited participants consenting to wear an accelerometer (+ 30% [95% CI 18%, 42%] compared to postal distribution), and adhering to minimum wear criteria (+ 15% [4%, 25%]). The proportion of participants meeting minimum wear criteria was higher when accelerometers were worn at the wrist (+ 14% [ 5%, 23%]) compared to waist. Daily wear-time tended to be higher in studies using wrist-worn accelerometers compared to other wear locations. Reporting of information regarding data collection was inconsistent. CONCLUSION Methodological decisions including accelerometer wear-location and method of distribution may influence important data collection outcomes including recruitment and accelerometer wear-time. Consistent and comprehensive reporting of accelerometer data collection methods and outcomes is needed to support development of future studies and international consortia. Review supported by the British Heart Foundation (SP/F/20/150002) and registered (Prospero CRD42020213465).
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Affiliation(s)
- Richard M Pulsford
- Faculty of Health and Life Sciences, University of Exeter, St Lukes Campus. EX12LU, Exeter, UK
| | - Laura Brocklebank
- Department of Behavioural Science and Health, University College London, London, WC1E 7HB, UK
| | - Sally A M Fenton
- School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Esmée Bakker
- Radboud University Medical Centre, 6500 HB, Nijmegen, The Netherlands
| | - Gregore I Mielke
- School of Public Health, The University of Queensland, ST Lucia qld, Australia
| | - Li-Tang Tsai
- Center On Aging and Mobility, University Hospital Zurich, Zurich City Hospital - Waid and University of Zurich, Zurich , Switzerland.,Department of Aging Medicine and Aging Research, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrew J Atkin
- Norwich Epidemiology Centre, University of East Anglia, Norwich, UK.,School of Health Sciences, Faculty of Medicine and Health Sciences, University of East Anglia, Norwich, NR47TJ, UK
| | - Danielle L Harvey
- School of Health Sciences, Faculty of Medicine and Health Sciences, University of East Anglia, Norwich, NR47TJ, UK
| | - Joanna M Blodgett
- Institute of Sport Exercise and Health, Division of Surgery and Interventional Science, University College London, London, W1T 7HA, UK
| | - Matthew Ahmadi
- Charles Perkins Centre, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Le Wei
- Charles Perkins Centre, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Alex Rowlands
- Diabetes Research Centre, University of Leicester, Leicester General Hospital, Gwendolen Road, Leicester, LE5 4PW, UK.,NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK.,Alliance for Research in Exercise, Nutrition and Activity (ARENA), Division of Health Sciences, Sansom Institute for Health Research, University of South Australia, Adelaide, Australia
| | - Aiden Doherty
- Big Data Institute, Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
| | - Vegar Rangul
- Department of Public Health and Nursing, HUNT Research Centre, Norwegian University of Science and Technology, Levanger, Norway
| | - Annemarie Koster
- Department of Social Medicine, CAPHRI, Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands
| | - Lauren B Sherar
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, LE113TU, UK
| | - Andreas Holtermann
- National Research Centre for the Working Environment, Copenhagen, Denmark
| | - Mark Hamer
- Institute of Sport Exercise and Health, Division of Surgery and Interventional Science, University College London, London, W1T 7HA, UK.
| | - Emmanuel Stamatakis
- Charles Perkins Centre, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
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