http://orcid.org/0000-0003-3177-6576
Figures
Abstract
Background
Most population-based studies from Latin America have used questionnaires to measure physical activity (PA) and sedentary behaviors (SB). Low reliability and validity of the questionnaires has limited the capacity to examine associations between PA and health. The purpose of this study was to compare self-reported and accelerometer–measured PA and SB and their associations with body composition in Latin American countries.
Methods
Data were obtained from the Latin American Study of Nutrition and Health (aged 15–65 years), collected from September 2014 to February 2015. PA and SB were assessed using the International Physical Activity Questionnaire (long version) and the Actigraph GT3X+ accelerometer. Outcomes of interest included: body mass index (BMI), waist (WC) and neck circumference (NC). We used the Pearson and intraclass correlation coefficient, Bland-Altman plots, and multilevel linear regression models.
Results
Mean moderate-to-vigorous physical activity (MVPA) by accelerometer and IPAQ were 34.4 min/day (95% CI: 33.4 to 35.4) and 45.6 min/day (95% CI: 43.2 to 48.1), respectively. For SB (accelerometer and IPAQ) the means were 573.1 (95% CI: 568.2 to 577.9) and 231.9 min/day (95% CI: 225.5 to 238.3). MVPA, measured by the accelerometer was negatively associated with BMI (β = -1.95; 95% CI: -2.83 to -1.08), WC (β = -5.04; 95% CI: -7.18 to -2.89) and NC (β = -1.21; 95% CI: -1.79 to -0.63). The MVPA estimated through IPAQ was not significantly associated with any of the three outcome variables. SB, measured by the accelerometer, was positively associated with BMI (β = 0.26; 95% CI: 0.08 to 0.44) and WC (β = 0.48; 95% CI: 0.13 to 0.91). SB estimated through IPAQ was positively associated with NC only.
Conclusions
Low correlation coefficients were observed for accelerometer-derived and IPAQ-reported estimates of PA and SB. Caution is advised when making comparisons between accelerometer-measured and self-reported PA and SB. Further, studies examining associations between movement and health should discuss the impact of PA and SB measurement methodology on the results obtained.
Citation: Ferrari GLdM, Kovalskys I, Fisberg M, Gómez G, Rigotti A, Sanabria LYC, et al. (2020) Comparison of self-report versus accelerometer – measured physical activity and sedentary behaviors and their association with body composition in Latin American countries. PLoS ONE 15(4): e0232420. https://doi.org/10.1371/journal.pone.0232420
Editor: Cherilyn N. McLester, Kennesaw State University, UNITED STATES
Received: December 21, 2019; Accepted: April 14, 2020; Published: April 28, 2020
Copyright: © 2020 Ferrari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Due to ethical and legal restrictions of the eight institutions involved, the data underlying this study are available upon request and must be approved by the Publishing Committee of ELANS. Data are available from the ILSI Argentina Institutional Data Access for researchers who meet the criteria for access to confidential data and also approved by the Publishing Committee of ELANS. To apply for access to these data, interested researchers must submit a detailed project proposal to the above Institution. The authors confirm that the data underlying this study will be shared provided that requests are submitted through appropriate channels. Requests for the data can be made to the Executive Secretary of the Institution, Mrs Patricia Torres, ptorres@ilsi.org.ar
Funding: The ELANS field work was originally supported by a scientific grant from the Coca Cola Company (Atlanta, GA, USA) and by grants and/or support from the ILSI Argentina, InstitutoPensi/ Hospital Infantil Sabara, Universidad de Costa Rica, Pontificia Universidad Cato ´lica de Chile, Pontificia Universidad Javeriana de Colombia,Universidad Central de Venezuela/Fundacio ´n Bengoa, Universidad San Francisco de Quito, and Instituto de Investigacio ´n Nutricional de Peru ´. The initial unrestricted grant from Coca Cola Company supported the design, data collection, field work and initial data analysis. Latin AmericanILSI branches (Argentina, Brazil, Mesoamerica, South Andean and North Andean) gave their support contributing with the fees for the open access journals. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing, any of the manuscripts, and in the decision to publish the results. The main researchers of ELANS are the only ones responsible for the scientific information that is published.
Competing interests: M.F. is speaker in conferences for Abbott, Danone Research, Nestle, EMS and Sanofi; member of the Board of Novo Nordisk and ILSI Brazil; and has been awarded grants for research from Pensi Institute, ILSI Brazil, Danone Research and Danone Institute International. A.R. has received research grant support from Coca-Cola Chile y AB Chile. The ELANS field work was originally supported by a scientific grant from the Coca Cola Company (Atlanta, GA, USA). The initial unrestricted grant from Coca Cola Company supported the design, data collection, field work and initial data analysis. G.L.M.F., I.K., G.G., L.Y.C.S., M.C.Y.G., R.G.P.T., M.H.C., I.Z.Z., V.G., M.P., C.A.M.P., R.C.C., and D.S. declare that they have no potential conflicts of interest in relation to the topic of this manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products associated with this research to declare.
Introduction
There are clear signs from population-based surveys that levels of physical activity (PA) are negatively associated with cognitive diseases, cancer, chronic disease, and all-cause mortality [1, 2]. Consistent evidence also shows positive associations between sedentary behaviour (SB) and cardiometabolic health markers, including higher triglycerides, higher body composition indices such as body mass index and waist circumference and poor health outcomes, including cardiovascular disease and mortality [3–5].
Both in high-income and in low- and middle-income countries, such as most Latin American countries, the majority of published research on the relationship between PA and SB and health has used self-reported measures of PA and SB [6, 7]. Indirect measurement (questionnaires) are practical, easy to implement in population surveillance studies, and provide important contextual information about PA and SB [8, 9]. Questionnaires are limited by social desirability bias and recall difficulties [8,9]. The International Physical Activity Questionnaire (IPAQ) is a validated questionnaire and was originally developed to allow for cross-national comparisons [10]. Accelerometers are reliable and valid objective instruments for measuring PA, but more expensive and time-consuming than self-report methods [11, 12]. IPAQ performs at least as well as other self-report questionnaires and has been shown to have weak to moderately (r = 0.17–0.55) significant associations with accelerometer-measured PA among adolescents and adults [10, 13, 14].
The high financial burden and technical expertise required to implement accelerometry in epidemiological survey studies has challenged investigators in low- and middle-income countries [15]. As a result, Latin American professionals have relied on questionnaires to measure PA behaviors [16, 17]. Determining the magnitude and cause and effect relationship between PA or SB and risks of adverse health conditions in people from different countries in Latin America depends on an accurate assessment of these behaviors. Inadequate or inconsistent methods can increase the proportion of errors and mask or alter the actual associations between behaviors and outcomes [18].
PA and SB self-report questionnaires are widely applied, increasing the possibilities for comparisons between studies; however, the imprecise values from self-reported results limit their utility in comparison with objective measures such as accelerometry [19] [13]. This is especially true for understanding associations between PA and SB and health outcomes. Therefore, comparison between accelerometry and self-report and validation of self-report is essential for better understanding the strength of the relationship between self-reported PA and SB and objectively assessed with body composition variables in Latin American countries [13]. The Latin American Study of Nutrition and Health (Estudio Latinoamericano de Nutrición y Salud - ELANS) has both self-reported and accelerometer-measured PA and SB, providing an opportunity to compare how these two measures are associated with PA and SB in eight Latin America countries. This is especially relevant in Latin America since the region has very high levels of self-reported physical inactivity (> 39.1%) [6]. One of the aims of ELANS was to compare PA and SB as assessed by self-report versus accelerometer and their associations with body composition in Latin America countries.
Material and methods
Study design and participants
ELANS was conducted in eight Latin American countries: Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Peru, and Venezuela. ELANS is a household-based, multi-national, cross-sectional study of urban populations [20]. A standard harmonized protocol was used to collect data across eight countries, and all study personnel underwent comprehensive training to ensure quality and consistency in data collection. Data were collected between 2014 and 2015. Study design, methods, sampling strategy and exclusion criteria have been published in detail [20, 21].
A total sample of 9,218 (52.2% women) participants (aged 15.0–65.0 years) was included in the ELANS study. The current paper is based on a sample of 2,368 participants aged 15–65 years with valid accelerometer and IPAQ data, representing 29.6% of the total ELANS cohort. Accelerometer assessed PA and SB data were collected in a subsample representative of the total study population by sex, age and socioeconomic level [21]. Overall, the response rate for IPAQ was 99.4% and the response rate for the accelerometer measures was 86.5% of the total accelerometer sample (n = 2,737) (Fig 1).
Anthropometric data
Body weight (to the nearest 0.1kg) was measured using a portable scale Seca® (Hamburg, Germany) up to 200 kg, after all heavy clothing, pocket items, shoes, and socks were removed [22]. Height was measured with a Seca 213® portable stadiometer (Hamburg, Germany) at the end of a deep inhalation with the participant’s head in the Frankfort Plane whose measuring range was from 0–205 cm [22]. Body mass index in kg/m2 was calculated [23].
Neck circumference (in centimeters) was measured at the point just below the larynx (thyroid cartilage), perpendicular to the long axis of the neck (with the tape line in the front of the neck at the same height as the tape line at the back of the neck), using an non-elastic tape measure [24]. Waist circumference was measured (in centimeters) midway between the lowest rib and the iliac crest after normal breath according to World Health Organization (WHO) guidelines, and was conducted on the skin using an non-elastic tape [23, 25]. Two measurements of the anthropometric variables were performed, and the average was used for analysis.
Assessment of physical activity and sedentary behavior
Accelerometer.
Mean min/day of moderate-to-vigorous physical activity (MVPA) and SB were measured objectively using the model GT3X+ ActiGraph (Pensacola, FL, USA) accelerometer on an elasticized belt at the right mid-axillary line. The Actigraph GT3X provides reliable and valid estimates of PA [26, 27]. Participants were asked to wear the device while awake and to remove it only when sleeping and during water activities (e.g. showering or swimming). To further ensure protocol compliance, participants filled in an accelerometer log sheet that noted the start- and end-time of use per day. The minimal amount of accelerometer day time that was considered acceptable for inclusion in the sample was 5 days (including at least 1 weekend day) with at least 10 hours/day of wear time following the removal of sleep time [28, 29]. After exclusion of the nocturnal sleep period, waking non-wear time was defined as any sequence of ≥60 consecutive minutes of 0 activity counts [30].
On the eighth day of data collection, the research team went to each participant’s home to retrieve the accelerometers. The team downloaded the data using the latest version of Actilife software (version 6.0; ActiGraph, Pensacola, FL) available. Data were collected at a sampling rate of 30 Hz in 60 second epochs [31]. SB was defined as all activity at ≤100 activity counts/min, ≥1952–5724 activity counts/min for moderate PA, ≥5725 activity counts/min for vigorous PA, and ≥1952 activity counts/min for MVPA [32].
International physical activity questionnaire.
PA and SB were assessed using the IPAQ, long version for the last seven days, a validated self-report measurement tool for PA and SB that has been widely used in Latin America [10, 33, 34]. PA was assessed using six IPAQ items on the frequency and duration of MVPA as well as walking in leisure time and the amount of cycling undertaken for transport.
IPAQ PA data are reported as min/day of walking, moderate and vigorous PA. Total time (min/day) and time spent in total PA (i.e., transport and leisure-time) were estimated and used as analysis variables. We analyzed transport PA (walking + bicycle) and leisure PA (walking + moderate + vigorous) together.
Data were analyzed in agreement with the IPAQ scoring protocol (https://sites.google.com/site/theipaq/scoring-protocol). Although walking is a moderate intensity activity by MET value, the IPAQ questionnaire includes walking as a separate activity domain from moderate activity, explicitly excluding walking in the moderate activity questions. Thus, to provide a comparable index to the accelerometer- derived moderate activity measure for analysis, the walking and moderate activity domains from the IPAQ were combined into a single ‘moderate’ activity domain.
We considered sitting time as an indicator of SB. Sitting time was assessed from the question in the long-form IPAQ [10, 35]. Participants were asked to report time spent sitting over the past 7 days, separately for weekdays and weekends. We calculated average sitting time per day (min/day) as follows: (weekday time*5 + weekend time*2)/7 [36].
Demographic variables
Demographic characteristics including sex, age, socioeconomic level and race/ethnicity were assessed using standard questionnaires during in-person interviews. Age was categorized into the following groups: 15–19, 20–34, 35–49 and 50–65 years. While socioeconomic categorization varied across countries, socioeconomic level was grouped into three broad levels of classification (low, medium, high) for all countries. Race/ethnicity was classified as white, mixed (born of father and mother of different race/ethnicities) and other (black, asian, indigenous, mulatto, and gypsy). Complete data can be found in a previous study [20].
Ethics statement
The overarching ELANS protocol was approved by the Western Institutional Review Board (#20140605) and is registered at Clinical Trials (#NCT02226627). In addition, each country also obtained approval from their local ethical review boards of participating institutions and all participants provided written informed consent.
Statistical analysis
Descriptive statistics included means, 95% confidence intervals (95% CI) or frequencies. The Kolmogorov-Smirnov test and histograms were used to check data distribution. Accelerometer and IPAQ values were compared using paired student t-tests. The correlation and agreement between estimates obtained from the two methods were evaluated with Pearson correlation coefficient (r), intraclass correlation coefficient (ICC) and Bland-Altman plots.
Multilevel linear regression models, with country as the 2nd level, were used to study the effect of PA on BMI, on neck circumference, and on waist circumference. The models were adjusted for sex, age, socioeconomic level, and race/ethnicity. Statistical analyses were carried out with SPSS (version 24.0). Bland-Altman plots were built with the packages Bland Altman Leh [37] and ggplot2 [38] available in the software R [39]. A significance level of 5% was considered (p < 0.05).
Results
The sample included 2,368 participants (women: 51.9%; 95% CI: 50.0 to 53.9; n = 1230) aged from 15–65 years (mean: 36.5; 95% CI: 35.9 to 37.1). About half were classified as having a low socioeconomic level (51.0%; 95% CI: 48.9 to 53.0) and being of mixed race/ethnicity (51.7%: 95% CI: 49.8 to 53.5). The means of body mass index, neck and waist circumference were 26.9 kg/m2 (95% CI: 26.7 to 27.1), 35.6 cm (95% CI: 35.5 to 35.8) and 88.4 cm (95% CI: 87.8 to 88.9), respectively. Mean MVPA by accelerometer and IPAQ were 34.4 min/day (95% CI: 33.4 to 35.4) and 45.6 min/day (95% CI: 43.2 to 48.1), respectively. For SB (accelerometer and IPAQ) means were 573.1 (95% CI: 568.2 to 577.9) and 231.9 min/day (95% CI: 225.5 to 238.3), respectively (Table 1). The description of total sample profile by country is presented in S1 Table.
Mean min/day of PA (moderate, vigorous, and MVPA) estimated from IPAQ were significantly higher than those obtained by accelerometry (p<0.001). Correlation coefficients (r = 0.192 for moderate PA, r = 0.180 for vigorous PA, and r = 0.224 for MVPA), the ICC (ICC = 0.247 for moderate PA, ICC = 0.073 for vigorous PA, and ICC = 0.269 for MVPA), and the Bland-Altman plots (Fig 2) show low correlations and low agreement between the minutes of PA obtained by accelerometry and IPAQ. Similar results were found for SB (p<0.001, r = 0.184, and ICC 0.304) (Table 2). Fig 2 shows that differences are similarly distributed for moderate and vigorous PA (mainly negative values), while they were more unilateral for SB (mainly positive values).
Table 3 shows results of the multilevel regression models for the effects of MVPA (accelerometer and IPAQ) on body mass index, neck circumference and waist circumference. The coefficient (β) represents the change in the dependent variables per 100-minute change in moderate-to-vigorous PA. Results show that MVPA, measured by accelerometer, has a negative effect on the three variables (p<0.001)–an increase of 100 minutes per day of MVPA decreases the body mass index by 1.9 kg/m2 (95% CI: -2.8 to -1.1), neck circumference by 1.2 cm (95% CI: -1.8 to -0.6) and waist circumference by 5.0 cm (95% CI: -7.2 to -2.9), independent of sex, age, socioeconomic level, and ethnicity. MVPA estimated from IPAQ was not significantly associated with any of the three variables (p>0.05).
SB, measured by accelerometer was significantly associated with body mass index and waist circumference (p<0.005) - an increase of 100 minutes per day of SB increases the body mass index by 0.3 kg/m2 (95% CI: 0.1 to 0.4) and the waist circumference by 0.5 cm (95% CI: 0.1 to 0.9), independent of sex, age, socioeconomic level, and ethnicity. SB (accelerometer) was not associated with neck circumference. SB estimated from IPAQ was significantly associated with neck circumference (p<0.05) (Table 4).
Discussion
We aimed to compare self-reported versus accelerometer–measured PA and SB and their associations with body composition in eight Latin American countries. This study found large differences in and low correlations between PA and SB values for IPAQ versus accelerometer measurements. The concordance correlation coefficients between minutes of PA and SB from accelerometry and IPAQ (moderate PA, vigorous PA, MVPA, and SB) were low. MVPA, measured by accelerometer, was negatively associated with body mass index, neck circumference and waist circumference. MVPA estimated from IPAQ was not significantly associated with any of the three anthropometric variables. SB measured by accelerometer was positively associated with body mass index and waist circumference. SB estimated from IPAQ was positively associated only with neck circumference.
The subjective (IPAQ) and the objective (accelerometer) methods used in this study capture different aspects of PA and SB. Accelerometers capture acceleration and laboratory-derived intensity thresholds are used to determine how much time was spent at different intensities of movement (sedentary, light, moderate and vigorous). The questionnaires are designed to provide somewhat similar information to what is captured by accelerometry by asking people to report minutes of activity performed according to intensity as defined by "breathing faster, feeling hotter or sweating". The risk of misinterpretation of intensity coupled with difficulties in remembering the frequency and duration of many activities makes it easy to understand why estimates are so different between methods. Accelerometers capture all movements while worn but may substantially under estimate activity such as cycling that involves less acceleration of the center of gravity. The greater time of self-reported vigorous and moderate intensity PA compared to accelerometer measured MVPA in the current study has been consistently observed in previous studies [9, 40]. Several factors explain this difference. First, accelerometers do not measure activities that do not involve vertical acceleration, such as bicycle movement and upper body movement. In addition, higher-intensity activity sessions (e.g., sports, working out at the gym) are easier to recall and can easily be over-estimated given that many activities involve brief intermittent bursts of activity as well as extended periods with little motion. The observed differences between the two measures increased with higher intensities of activity, similar to previous IPAQ validation studies. Differences and low correlation in PA and SB estimates between accelerometers and questionnaires are evident in comparative studies [13, 40, 41]. The poor Pearson correlation coefficient (i.e., r ≤0.224) observed between accelerometer-measured and self-reported PA in this study is consistent with previous reports in the literature [13, 14, 27, 41].
Precise recall of volume and intensity of PA is not a simple task, as it is influenced by social desirability bias and the subject's inability to recall precise time and intensity of PA [42]. Another strategy to compare procedures that evaluate PA is to assess whether associations with health outcomes are similar. PA measured by accelerometer was significantly associated with body mass index, waist circumference and neck circumference; while PA by self-reported IPAQ was not associated with any body composition variables.
Low correlations and the large differences between mean values of PA measured by accelerometer and self-report are not surprising given that these instruments do not measure the same constructs. IPAQ has questions about time spent on PA by domain (e.g., leisure and active transportation) and intensity (e.g., moderate and vigorous). On the other hand, accelerometry measures continuous motion data or continuous rate of acceleration above defined limits. Despite the challenges and limitations associated with self-reported data on PA and SB, the reality for many large-scale health studies is that this technique is the only executable option given the high cost and logistical complexity of more precise measures such as accelerometry. Accelerometers do not evaluate energy expenditure or intensity in different modalities such as aquatic activity (e.g., swimming, water aerobics) cycling, endurance, and weighted and static exercises, probably leading to a small underestimation of total PA. In addition, data collection, processing, and analysis with accelerometers is substantially more complex than for self-reported questionnaires. PA researchers generally believe that estimates of PA obtained from subjective and objective methods are both useful and complementary, but are not directly comparable. Rather than focusing on which method is “correct”, experts suggest acknowledging that each method contributes unique and complementary information to our understanding of human movement and behavior [9].
The use of self-report remains a good method for large-scale population studies, and it is imperative that these measurements are as accurate and reliable as possible. [43]. The question about sitting time in the last week is a useful metric that has been used in many other surveys [9, 10, 14]. We showed that the correlation of sedentary time estimates obtained using IPAQ and accelerometry was low. The combination of large and relative underestimation and low precision is also likely to significantly reduce the ability to detect associations with outcomes [27, 44]. This may explain publications that report different associations with health outcomes and self-reported and objectively measured sedentary time. Our results contribute to the literature emphasizing the association of SB measured by accelerometer with body composition.
We acknowledge some limitations of this study. The cross-sectional nature of the study limits conclusions regarding of causality. The results may not extrapolate to the total population in the participating Latin American countries. An additional general limitation of accelerometers is that they do not properly capture some activities such as cycling and static exercise. SB defined as ≤100 activity counts/min is a widely accepted standard but does not account for posture and cannot separate sitting from lying down. Therefore, the ≤100 activity counts/min SB classification may either overestimate or underestimate SB and should be used with caution [45]. The major strengths of this study include a large sample size, unique pooling of the data across eight countries from Latin America, concordant assessment of PA and SB using IPAQ and accelerometry, and unique comparable data collection protocols. There are relatively few studies that have used objective measures of PA and SB in Latin American countries, since most international epidemiological investigations use self-reported questionnaires [15]. Our study is the first to compare two methods of assessing PA in different Latin American countries using a comparable methodology.
Conclusions
IPAQ-reported PA and SB were not significantly associated with health outcomes, while objectively-measured PA and SB were. Self-reported methods may underestimate the strength of some relationships between activity and body composition. The discrepancies observed between accelerometer- and questionnaire-measured PA confirms previous research from other countries. It is important to be aware that these differences between methods also exist in Latin American countries. The results of this study will help with the interpretation of surveillance data collected in the future as well as aiding in the understanding of the associations between movement and health outcomes.
Supporting information
S1 Table. Descriptive analysis (% or mean, 95% CI) of sample profiles concerning demographic, body composition and physical activity variables in participants from the ELANS study.
https://doi.org/10.1371/journal.pone.0232420.s001
(DOCX)
Acknowledgments
We would like to thank the following individuals at each of the participating sites who made substantial contributions to the ELANS: Luis A. Moreno, Beate Lloyd, Brenda Lynch, Mariela Jauregui, Alejandra Guidi, Luis Costa, and Regina Mara Fisberg.
¶ The following are members of ELANS Study Group: Chairs: Mauro Fisberg and Irina Kovalskys; Co-chair: Georgina Gómez Salas; Core Group Members: Attilio Rigotti, Lilia Yadira Cortés Sanabria, Georgina Gómez, Martha Cecilia Yépez García, Rossina Gabriella Pareja Torres and Marianella Herrera-Cuenca; Steering Committee: Berthold Koletzko, Luis A. Moreno and Michael Pratt; External Advisors: Regina Mara Fisberg and Agatha Nogueira Previdelli. Project Managers: Viviana Guajardo, y Ioná Zalcman Zimberg; International Life Sciences Institute (ILSI)-Argentina: Irina Kovalskys, Viviana Guajardo, María Paz Amigo, Ximena Janezic, y Fernando Cardini; Universidad ISALUD: Myriam Echeverry and Martin Langsman. Instituto Pensi-Hospital Infantil Sabara- Brasil: Mauro Fisberg, Ioná Zalcman Zimberg, y Natasha Aparecida Grande de França; Pontificia Universidad Católica de Chile: Attilio Rigotti, Guadalupe Echeverría, Leslie Landaeta, y Óscar Castillo; Pontificia Universidad Javeriana-Colombia: Lilia Yadira Cortés Sanabria, Luz Nayibe Vargas, Luisa Fernanda Tobar y Yuri Milena Castillo; Universidad de Costa Rica: Georgina Gómez y Anne Chinnock, Instituto Costarricense de Enseñanza e Investigación en Nutrición y Salud: Rafael Monge Rojas; Universidad San Francisco de Quito Ecuador: Martha Cecilia Yépez García, Mónica Villar Cáceres, y María Belén Ocampo; Instituto de Investigación Nutricional Perú: Rossina Pareja Torres, María Reyna Liria, Krysty Meza and Mellisa Abad; Universidad Central de Venezuela/Fundación Bengoa para la Alimentación y Nutrición: Marianella Herrera-Cuenca, Maritza Landaeta-Jiménez, Betty Méndez, Maura Vásquez, Guillermo Ramírez, Pablo Hernández, Carmen Meza, Omaira Rivas, Vanessa Morales; Accelerometry analysis: Priscila Bezerra Gonçalves and Claudia Alberico; Physical activity advisor: Gerson Luis de Moraes Ferrari.
References
- 1. Lear SA, Hu W, Rangarajan S, Gasevic D, Leong D, Iqbal R, et al. The effect of physical activity on mortality and cardiovascular disease in 130 000 people from 17 high-income, middle-income, and low-income countries: the PURE study. Lancet. 2017;390(10113):2643–54. Epub 2017/09/26. pmid:28943267.
- 2. Zhao M, Veeranki SP, Li S, Steffen LM, Xi B. Beneficial associations of low and large doses of leisure time physical activity with all-cause, cardiovascular disease and cancer mortality: a national cohort study of 88,140 US adults. Br J Sports Med. 2019. Epub 2019/03/21. pmid:30890520.
- 3. Pandey A, Salahuddin U, Garg S, Ayers C, Kulinski J, Anand V, et al. Continuous Dose-Response Association Between Sedentary Time and Risk for Cardiovascular Disease: A Meta-analysis. JAMA Cardiol. 2016;1(5):575–83. pmid:27434872.
- 4. Dempsey PC, Hadgraft NT, Winkler EAH, Clark BK, Buman MP, Gardiner PA, et al. Associations of context-specific sitting time with markers of cardiometabolic risk in Australian adults. Int J Behav Nutr Phys Act. 2018;15(1):114. Epub 2018/11/22. pmid:30458790; PubMed Central PMCID: PMC6245709.
- 5. Matthews CE, Keadle SK, Troiano RP, Kahle L, Koster A, Brychta R, et al. Accelerometer-measured dose-response for physical activity, sedentary time, and mortality in US adults. Am J Clin Nutr. 2016;104(5):1424–32. pmid:27707702; PubMed Central PMCID: PMC5081718.
- 6. Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1.9 million participants. Lancet Glob Health. 2018;6(10):e1077–e86. pmid:30193830.
- 7. Yusuf S, Joseph P, Rangarajan S, Islam S, Mente A, Hystad P, et al. Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. Lancet. 2019. Epub 2019/09/08. pmid:31492503.
- 8. Ainsworth B, Cahalin L, Buman M, Ross R. The current state of physical activity assessment tools. Prog Cardiovasc Dis. 2015;57(4):387–95. Epub 2014/12/03. pmid:25446555.
- 9. Colley RC, Butler G, Garriguet D, Prince SA, Roberts KC. Comparison of self-reported and accelerometer-measured physical activity in Canadian adults. Health Rep. 2018;29(12):3–15. Epub 2018/12/20. pmid:30566204.
- 10. Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95. pmid:12900694.
- 11. Martinez-Gomez D, Welk GJ, Calle ME, Marcos A, Veiga OL, Group AS. Preliminary evidence of physical activity levels measured by accelerometer in Spanish adolescents: the AFINOS Study. Nutr Hosp. 2009;24(2):226–32. pmid:19593496
- 12. Trost SG, McDonald S, Cohen A. Measurement of general and specific approaches to physical activity parenting: a systematic review. Child Obes. 2013;9 Suppl:S40–50. pmid:23944923; PubMed Central PMCID: PMC3746243.
- 13. Scholes S, Bridges S, Ng Fat L, Mindell JS. Comparison of the Physical Activity and Sedentary Behaviour Assessment Questionnaire and the Short-Form International Physical Activity Questionnaire: An Analysis of Health Survey for England Data. PLoS One. 2016;11(3):e0151647. pmid:26990093; PubMed Central PMCID: PMC4798726.
- 14. Garriguet D, Tremblay S, Colley RC. Comparison of Physical Activity Adult Questionnaire results with accelerometer data. Health Rep. 2015;26(7):11–7. Epub 2015/07/16. pmid:26177042.
- 15. Ferrari GLM, Kovalskys I, Fisberg M, Gomez G, Rigotti A, Sanabria LYC, et al. Socio-demographic patterning of objectively measured physical activity and sedentary behaviours in eight Latin American countries: findings from the ELANS study. Eur J Sport Sci. 2019:1–26. Epub 2019/10/12. pmid:31603392.
- 16. Ferrari GL, Araujo TL, Oliveira L, Matsudo V, Mire E, Barreira TV, et al. Association Between Television Viewing and Physical Activity in 10-Year-Old Brazilian Children. J Phys Act Health. 2015;12(10):1401–8. pmid:25831554.
- 17. Luis de Moraes Ferrari G, Kovalskys I, Fisberg M, Gomez G, Rigotti A, Sanabria LYC, et al. Original research Socio-demographic patterning of self-reported physical activity and sitting time in Latin American countries: findings from ELANS. BMC Public Health. 2019;19(1):1723. Epub 2019/12/25. pmid:31870408.
- 18. Corder K, Ekelund U, Steele RM, Wareham NJ, Brage S. Assessment of physical activity in youth. J Appl Physiol (1985). 2008;105(3):977–87. Epub 2008/07/19. pmid:18635884.
- 19. Kowalski K, Rhodes R, Naylor PJ, Tuokko H, MacDonald S. Direct and indirect measurement of physical activity in older adults: a systematic review of the literature. Int J Behav Nutr Phys Act. 2012;9:148. Epub 2012/12/19. pmid:23245612; PubMed Central PMCID: PMC3549726.
- 20. Fisberg M, Kovalskys I, Gomez G, Rigotti A, Cortes LY, Herrera-Cuenca M, et al. Latin American Study of Nutrition and Health (ELANS): rationale and study design. BMC Public Health. 2016;16(1):93. pmid:26829928; PubMed Central PMCID: PMC4736497.
- 21. Ferrari GLM, Kovalskys I, Fisberg M, Gómez G, Rigotti A, Sanabria LYC, et al. Methodological design for the assessment of physical activity and sedentary time in eight Latin American countries - the ELANS study. MethodsX. 2020;S2214-1405(19)30076-3.
- 22.
Lohman T, Roche AF, Martorell RE. Anthropometric Standardization Reference Manual. Champaign, IL: Human Kinetics; 1988.
- 23.
World Health Organization (WHO). Obesity: preventing and managing the global epidemic. WHO Technical Report Series, 894. Geneva. 1998.
- 24. Cornier MA, Despres JP, Davis N, Grossniklaus DA, Klein S, Lamarche B, et al. Assessing adiposity: a scientific statement from the American Heart Association. Circulation. 2011;124(18):1996–2019. pmid:21947291.
- 25.
World Health Organization (WHO). Obesity: preventing and managing the global epidemic: Report of a WHO Consultation. Technical Report Series. Geneva: World Health Organization; p. 894. 2000.
- 26. Dowd KP, Szeklicki R, Minetto MA, Murphy MH, Polito A, Ghigo E, et al. A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study. Int J Behav Nutr Phys Act. 2018;15(1):15. Epub 2018/02/10. pmid:29422051; PubMed Central PMCID: PMC5806271.
- 27. Celis-Morales CA, Perez-Bravo F, Ibanez L, Salas C, Bailey ME, Gill JM. Objective vs. self-reported physical activity and sedentary time: effects of measurement method on relationships with risk biomarkers. PLoS One. 2012;7(5):e36345. pmid:22590532; PubMed Central PMCID: PMC3348936.
- 28. Colley R, Connor Gorber S, Tremblay MS. Quality control and data reduction procedures for accelerometry-derived measures of physical activity. Health Rep. 2010;21(1):63–9. Epub 2010/04/30. pmid:20426228.
- 29. Trost SG, Loprinzi PD, Moore R, Pfeiffer KA. Comparison of accelerometer cut points for predicting activity intensity in youth. Med Sci Sports Exerc. 2011;43(7):1360–8. pmid:21131873.
- 30. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181–8. pmid:18091006.
- 31. Brond JC, Arvidsson D. Sampling frequency affects the processing of Actigraph raw acceleration data to activity counts. J Appl Physiol (1985). 2016;120(3):362–9. Epub 2015/12/05. pmid:26635347.
- 32. Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc. 1998;30(5):777–81. pmid:9588623.
- 33. Salvo D, Reis RS, Sarmiento OL, Pratt M. Overcoming the challenges of conducting physical activity and built environment research in Latin America: IPEN Latin America. Prev Med. 2014;69 Suppl 1:S86–92. pmid:25456800; PubMed Central PMCID: PMC4268364.
- 34. Lee PH, Macfarlane DJ, Lam TH, Stewart SM. Validity of the International Physical Activity Questionnaire Short Form (IPAQ-SF): a systematic review. Int J Behav Nutr Phys Act. 2011;8:115. pmid:22018588; PubMed Central PMCID: PMC3214824.
- 35. Network SBR. Sedentary Behaviour Research Network. Standardized use of the terms "sedentary" and "sedentary behaviours". Appl Physiol Nutr Metab 37: 540–542. 2012. pmid:22540258
- 36. Bauman A, Ainsworth BE, Sallis JF, Hagstromer M, Craig CL, Bull FC, et al. The descriptive epidemiology of sitting. A 20-country comparison using the International Physical Activity Questionnaire (IPAQ). Am J Prev Med. 2011;41(2):228–35. pmid:21767731.
- 37.
Bernhard Lehnert (2015). Bland AltmanLeh: Plots (Slightly Extended) Bland-Altman Plots. R package version 0.3.1.
- 38.
Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
- 39.
R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
- 40. Dyrstad SM, Hansen BH, Holme IM, Anderssen SA. Comparison of self-reported versus accelerometer-measured physical activity. Med Sci Sports Exerc. 2014;46(1):99–106. pmid:23793232.
- 41. Colley RC, Butler G, Garriguet D, Prince SA, Roberts KC. Comparison of self-reported and accelerometer-measured physical activity among Canadian youth. Health Rep. 2019;30(7):3–12. Epub 2019/07/18. pmid:31314124.
- 42. Ekelund U, Tomkinson G, Armstrong N. What proportion of youth are physically active? Measurement issues, levels and recent time trends. British journal of sports medicine. 2011;45(11):859–65. pmid:21836170.
- 43. Healy GN, Clark BK, Winkler EA, Gardiner PA, Brown WJ, Matthews CE. Measurement of adults' sedentary time in population-based studies. Am J Prev Med. 2011;41(2):216–27. pmid:21767730; PubMed Central PMCID: PMC3179387.
- 44. Stamatakis E, Hamer M, Tilling K, Lawlor DA. Sedentary time in relation to cardio-metabolic risk factors: differential associations for self-report vs accelerometry in working age adults. International journal of epidemiology. 2012;41(5):1328–37. pmid:22634868.
- 45. Kerr J, Marshall SJ, Godbole S, Chen J, Legge A, Doherty AR, et al. Using the SenseCam to improve classifications of sedentary behavior in free-living settings. Am J Prev Med. 2013;44(3):290–6. Epub 2013/02/19. pmid:23415127.