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EPIDEMIOLOGY

Physical Activity and Sedentary Behavior among US Hispanic/Latino Youth: The SOL Youth Study

EVENSON, KELLY R.1; ARREDONDO, ELVA M.2; CARNETHON, MERCEDES R.3; DELAMATER, ALAN M.4; GALLO, LINDA C.5; ISASI, CARMEN R.6; PERREIRA, KRISTA M.7; FOTI, SAMANTHA A.8; VAN HORN, LINDA9; VIDOT, DENISE C.10; SOTRES-ALVAREZ, DANIELA11

Author Information
Medicine & Science in Sports & Exercise: May 2019 - Volume 51 - Issue 5 - p 891-899
doi: 10.1249/MSS.0000000000001871

Abstract

The Physical Activity Guidelines for Americans, second edition (1), developed from a recent scientific review (2), recommended that youth ages 6 to 17 yr should engage in at least 60 min of moderate to vigorous physical activity (MVPA) daily. The guidelines recommended three types of physical activities: aerobic, muscle strengthening, and bone strengthening activities. Other countries, including Canada (3) and Australia (4), provide further guidance on sedentary behavior, such as to limit screen, sitting, and indoor time.

National surveillance of physical activity is an essential public health priority, used to monitor population trends and guide interventions (5). Surveillance of sedentary behavior is an important emerging priority due to its deleterious association with cardiovascular and cancer risk factors and other health outcomes (6). Surveillance systems assessing physical activity and sedentary behavior would ideally provide estimates for the largest race/ethnic groups in the population, particularly since health disparities exist across race/ethnicity. In mid-2016, Hispanics/Latinos comprised 17.8% or 57.5 million people in the US population (7); yet, only limited information is available on their physical activity and sedentary behavior. Among the Hispanics/Latinos in the United States, their diverse Hispanic/Latino background includes 63.2% Mexican, 9.5% Puerto Rican, 3.9% Cuban, 3.8% Salvadoran, 3.3% Dominican, and 2.5% Guatemalan (7). Physical activity and sedentary behavior surveillance data for specific Hispanic/Latino backgrounds are even more limited than for Hispanics/Latinos overall.

In the United States, surveillance systems for youth physical activity and sedentary behavior traditionally relies on self-report, but is known for greater inaccuracies among younger ages (8). Surveillance systems include the Youth Risk Behavior Surveillance System, the Health Behavior in School-aged Children quadrennial surveys, and the National Health and Nutrition Examination Survey (NHANES). The biennial Youth Risk Behavior Surveillance System relies exclusively on self-report and focuses on 9th through 12th grades, with optional coverage among middle school grades. The Health Behavior in School-aged Children occurs every 4 yr and includes 11-, 13-, and 15-yr-olds. The NHANES includes a wider range of youth, for example from age 6 to 17 yr for accelerometry measures collected in 2003 to 2006 and 2011 to 2014, but is unable to provide estimates beyond Mexican American background due to sampling and sample sizes. In 2012, the NHANES National Youth Fitness Survey assessed physical activity and fitness levels among youth age 3 to 15 yr, but the total sample size of 1576 was not large enough to explore findings by Hispanic/Latino backgrounds (9).

Other studies need to fill the gap in the US surveillance of physical activity and sedentary behavior among Hispanic/Latino youth, by describing population levels of these behaviors overall and by Hispanic/Latino background. In support of this, the 2014 American Heart Association position statement emphasized the importance of studying race/ethnic diversity and prevalence of cardiovascular risk factors among Hispanics/Latinos (10). The main aim of this study was to describe physical activity and sedentary behavior among US youth from diverse Hispanic/Latino backgrounds using both accelerometry and self-report methods. This included quantifying the most common types of physical activities and sedentary behaviors in which youth engaged. Secondarily, this study identified sociodemographic, health, and cultural characteristics associated with physical activity, sedentary behavior, accelerometer participation, and accelerometer wear.

METHODS

Study population

The Hispanic Community Health Study/Study of Latinos (HCHS/SOL) is a population-based cohort study designed to examine diabetes, pulmonary, and cardiovascular disease risk factors, morbidity, and mortality among adults (11). From 2008 to 2011, 16,415 self-identified Hispanic/Latino men and women age 18 to 74 yr were recruited using a two-stage probability sampling approach from randomly selected households in four US communities (Bronx, NY; Chicago, IL; Miami, FL; San Diego, CA). A previous article described the HCHS/SOL sample design, cohort selection, and participation rates among the adults (11).

The SOL youth enrolled children ages 8 to 16 yr who lived with a HCHS/SOL adult participant and were free from serious health issues (12,13). All eligible youth in a family were invited to participate between 2012 and 2014. Of 1777 eligible youth identified through screening, 1466 (82.5%) participated. The institutional review boards at all institutions involved approved the study (e.g., SOL youth centers, central laboratory, nutrition coordinating center, and coordinating center), and informed consent was obtained from all adult participants. Parents provided consent for their children to participate, and children provided either written consent or assent per the guidelines at their institution.

Accelerometry measurement

During the clinic visit, participants were asked to wear an Actical accelerometer (version B-1; model 198-0200-03) for 1 wk. All accelerometers were calibrated at Philips Respironics (Murrysville, PA) before study deployment. Participants were fitted with a belt and left the clinic visit wearing the accelerometer. They were instructed to continue to wear it above the iliac crest on the right side, the location most sensitive to vertical movement consistent with ambulation. Participants were asked to undertake their usual activities for the following week while wearing the accelerometer, and to remove it only for swimming, showering, and sleeping.

For the Actical, a microprocessor converted accelerations to a unit called “counts” over a given epoch or time period. The Actical captured accelerations in counts every 15 s, and was initialized to start recording at midnight of the day following the clinic visit. We included time from 5:00 am to midnight for all 7 d. Nonwear was defined as consecutive zero counts for at least 90 min (window 1), allowing for short time intervals with nonzero counts lasting up to 2 min if no counts were detected during both the 30 min (window 2) upstream and downstream from that interval; any nonzero counts except the allowed short intervals were considered as wear time (14). Accelerometer “participation” was defined as returning the Actical and having any recorded wear time. “Adherence” was defined as ≥8 h·d−1 of wear time for ≥3 d.

Cutpoints were guided by a calibration study using the Actical among 10- to 15-yr-old youth (15). The following cutpoints were used: sedentary at 0 to 17 counts per 15 s, light at 18 to 440 counts per 15 s, moderate at 441 to 872 counts per 15 s, and vigorous at ≥873 counts per 15 s. MVPA was defined as ≥441 counts per 15 s. The number of minutes per day in each intensity were summed and averaged across adherent days, and then multiplied by 7 to obtain minutes per week for each category.

Self-reported physical activity and sedentary behavior

The SOL youth designed the physical activity questionnaire specifically for this study to complement the accelerometry by providing type of activity. The questionnaire is available for download elsewhere (https://biolincc.nhlbi.nih.gov/studies/hchssol/). The interviewer asked youth to estimate how often they participated in each of 68 activities in the past month or week (Table, Supplemental Digital Content 1, items from the questionnaire, https://links.lww.com/MSS/B479). The response options included never, one to two times per month, one to two times per week, three to four times per week, five to six times per week, and daily. These options were converted to create a number of times/month as follows:

  • Never → 0 times per month
  • 1–2 times per month → 1.5 times per month
  • 1–2 times per week → 4–8 times per month → 6 times per month
  • 3–4 times per week → 12–16 times per month → 14 times per month
  • 5–6 times per week → 20–24 times per month → 22 times per month
  • Daily → 30 times per month

Additionally, youth were asked how much time per day (in hours and minutes) they spent in the following: television or video watching, computer or internet, video/computer games (nonactive), and talking on the phone or text messaging. These activities were summed to create total time (in hours per day and minutes per day) spent in screen time.

Other measures

Participants self-reported their Hispanic/Latino background, nativity (born in US mainland or not), immigrant generational status, and their preferred language (Spanish or English). Puerto Ricans were classified as foreign born for these analyses. The “other” category included those who reported more than one background.

Using standardized protocols, weight was measured to the nearest 0.1 kg on a digital scale (Tanita Body Composition Analyzer; TBF 300, Japan) and height was measured to the nearest centimeter using a wall-mounted stadiometer (SECA 222, Germany). Body mass index (BMI) was calculated and standardized to BMI percentile groups using the 2000 Centers for Disease and Prevention Growth Charts (16), with the 2016 update to better accommodate extremely high values using this program (https://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas.htm). The BMI percentiles were grouped as follows: underweight (<5th percentile), normal weight (5th–84th percentile), overweight (85th–94th percentile), obese (≥95th percentile, BMI <35 kg·m−2 and 120% of 95th percentile), and severely obese (≥ 95th percentile, BMI ≥ 35 kg·m−2 and 120% of 95th percentile).

Statistical analysis

All statistical analyses accounted for the complex survey design (stratified and clustered) and used sampling weights. Similar to the HCHS/SOL (adult study), the SOL youth sampling weights were first adjusted by household-level and person-level nonresponse in HCHS/SOL and then in SOL youth. The nonresponse adjusted sample weights were then trimmed using the 95th percentile within each field center to reduce their variability and impact of extremely large weights, and the trimmed difference was evenly distributed among all of the nontrimmed sample weights. These were then calibrated using 18 categories (9 age groups for each year [ages 8 to 16 yr] by sex) in the target population from the 2010 US Census. Finally, these calibrated, trimmed, nonresponse adjusted sample weights were normalized using the entire youth sample. The lower level of clustering (children within a household) was accounted for in the analyses by specifying sampling with replacement for variance computations and the top-level sampling unit in SOL youth census block (17,18).

Differences in accelerometer participation and adherence were explored by sociodemographic and BMI categories using the Cochran–Mantel–Haenszel χ2 general test of association with the Wald χ2 statistic for nominal variables (site, Hispanic/Latino background, sex, generational status, language preference) and the Cochran–Mantel–Haenszel trend test for ordinal variables (age groups, BMI percentile groups). Significance was set at P < 0.05.

We estimated the mean time in accelerometer-assessed physical activity and sedentary behavior by sociodemographic and BMI categories using linear regression adjusting for accelerometer wear time. We conducted pairwise comparisons of mean values within characteristics using a Bonferroni correction for the multiple comparisons. First, we conducted an overall test for all adjusted means being equal (e.g., all adjusted means by site being equal). We used an alpha level of 0.0001 for significance given the large number of comparisons performed (i.e., seven sociodemographic and BMI characteristics for each of five accelerometer intensities, five self-reported physical activity types, and five self-reported screen time types). Only if the overall test was significant, then pairwise comparisons using an alpha level of 0.01 was performed.

Given missing accelerometer data mostly due to nonadherence, we used multiple imputation, specifically a multivariate imputation by fully conditional specification, to impute accelerometer missing values for sedentary, light, moderate, and vigorous minutes per day and an indicator for whether an adherent weekend day was included to summarize the data. We created 10 imputed complete datasets and the imputation model included sex, age group, BMI, Hispanic/Latino background, generational status, self-reported physical activity (transportation, sports, leisure but nonsports, school, household), self-reported screen time, and the strata and sampling weights to account for the complex survey design (19). After the imputation, we calculated wear time as the sum of minutes in sedentary, light, moderate, and vigorous activities. We combined the results of 10 separate analyses using Rubin’s rule to account for the uncertainty in the imputation (20).

Descriptive statistics are reported for both the accelerometry and self-reported physical activity and sedentary behavior. All analyses were performed using SAS 9.3 software (SAS Institute, Cary, NC) and SUDAAN software release 11 (RTI International, Research Triangle Park, NC) was used to account for the complex survey design and sampling weights except where indicated.

RESULTS

Accelerometry participation and adherence

Among the 1466 youth, 1238 returned the accelerometer with at least some wear time. Any accelerometer participation (e.g., returning the accelerometer with some wear) differed by site, but was not different by Hispanic/Latino background, sex, age group, BMI percentile group, generational status, or language preference (Table, Supplemental Digital Content 2, comparison of those who wore and returned the Actical to those who did not, https://links.lww.com/MSS/B480).

The range of adherent accelerometer wear overall and by age and sex for each adherent day is provided in Table 1. Among the 1238 that provided at least some accelerometer wear, 1104 met the adherent criteria of ≥8 h of wear for at least 3 of the 7 d. Accelerometer adherence was lower among Puerto Rican and South American backgrounds, and was progressively lower with each successively older age group (Table, Supplemental Digital Content 3, comparison of those who provided adherent accelerometry data to those who did not, https://links.lww.com/MSS/B481). Adherence did not differ by site, sex, BMI percentile group, generational status, or language preference. The average accelerometer wear time among the adherent group was 13.7 h·d−1, and differed by site (range, 12.6–15.0 h·d−1) and Hispanic/Latino background (range, 13.1–15.1 h·d−1) (Table 2).

T1
TABLE 1:
Unweighted percentage of participants by number of adherent accelerometer days (≥ 8 h of wear), overall and by age group and sex; SOL Youth Study, 2012–2014.
T2
TABLE 2:
Description of sample and adjusted mean (SE) minutes per day of accelerometry-assessed physical activity and sedentary behavior, overall and by sociodemographic, and BMI categories; SOL Youth Study, 2012–2014.

Accelerometry-assessed physical activity and sedentary behavior

Overall, the accelerometry wear time adjusted mean physical activity in minutes per day by intensity was: 178.9, light; 25.4, moderate; and 10.2 vigorous (Table 2). Higher levels of MVPA occurred among males, Mexican backgrounds, and youth ages 8 to 10 yr.

Overall, the wear time adjusted mean sedentary behavior was 604.6 min·d−1 (Table 2). Higher levels of sedentary behavior occurred among those 15 to 16 yr in age compared with younger age groups.

Self-reported physical activity and sedentary behavior

The most commonly self-reported activities based on reporting at least once per month were of lower intensity, including listening to music, homework, riding in car/bus, and hanging out with friends (Table 3). Common active pursuits included travel by walking, physical education class, running, and recess. These common activities were further stratified by sex/age group (Table, Supplemental Digital Content 4, common activities by sex/age group, https://links.lww.com/MSS/B482) and by Hispanic/Latino background (Table 3) (Table, Supplemental Digital Content 5, common activities by background, https://links.lww.com/MSS/B483) to reveal preferences, which generally had similar rankings.

T3
TABLE 3:
Percent of physical activities and sedentary behaviors youth reported at least once per month on the questionnaire, overall and by Hispanic/Latino background; SOL Youth Study, 2012–2014.

Based on self-report, on average youth spent 475.5 min·d−1 in screen time activities including television or video watching, computer or internet, video/computer games (nonactive), and talking on the phone or text messaging (Table 4). Higher levels of screen time occurred among those from the Bronx, Dominican and Puerto Rican backgrounds, and older age groups.

T4
TABLE 4:
Mean (SE) number of minutes per day in screen time, overall and by sociodemographic and BMI categories; SOL Youth Study, 2012–2014.

DISCUSSION

The present study provides novel information about accelerometry-assessed and self-reported physical activity and sedentary behavior among US youth age 8 to 16 yr from various Hispanic/Latino backgrounds living in four US communities. This level of detail by Hispanic/Latino background cannot be currently ascertained from the US surveillance systems.

Physical activity

We found Hispanic/Latino youth 8 to 16 yr engaged in a mean of 25 min·d−1 of moderate (~3% wearing time) and 10 min·d−1 of vigorous (~1% wearing time) activity. The time spent in MVPA is well below the national guidelines of an hour or more each day (1). For context, national accelerometry data from 2003 to 2006 indicate that Mexican American youth engaged in a mean of 85, 37, and 28 min·d−1 of MVPA across ages 6 to 11 yr, 12 to 15 yr, and 16 to 19 yr, respectively (21), with no appreciable differences between 2003–2004 and 2005–2006 (22). In SOL youth, conducted 6 to 11 yr later, the amount of mean time spent in MVPA was remarkably similar among older ages compared with NHANES Mexican American youth (43, 34, 32, and 32 min·d−1 for ages 8–10 yr, 11–12 yr, 13–14 yr, and 15–16 yr from Table 2). It is important to note that the type of accelerometer, and therefore the cutpoints used to define MVPA, differed between the two studies. Although the goal is to have comparable metrics for MVPA, the differences in accelerometer type still may contribute some error.

Both NHANES Mexican Americans (21) and this study reveal lower accelerometer-assessed MVPA among females compared with males, and a markedly lower MVPA comparing 8- to 10-yr-olds with older age groups. These age-related findings indicate a need for interventions for Hispanic/Latino youth that help maintain and promote physical activity, particularly elementary to middle school transitions (22).

Moderate to vigorous physical activity was lowest among youth of Central American, Cuban, and Dominican background, and highest among youth of Mexican and Puerto Rican background. One implication of these findings is that relying predominately on Mexican youth in US surveillance studies will miss important differences occurring among those with differing Hispanic/Latino backgrounds. If these differences hold true nationwide, then the use of Mexican youth only will overestimate the prevalence of MVPA among Latinos/Hispanics. Although our study did not detect significant differences, another accelerometry-based study of Latino/Hispanic adolescents identified Spanish language preference and non–US-born associated with higher MVPA (23). It is hypothesized that as acculturation occurs, Hispanic/Latino youth lifestyle patterns, including physical activity, become more like their US-born peers (10).

This study utilized the SOL Youth physical activity questionnaire to complement accelerometry findings by describing the most common physical activities undertaken by this ethnically diverse Hispanic/Latino population. Engaging youth in activities they potentially enjoy can help inform program development and allocation of resources directly to the activities that are engaged in and adopt. For example, the most common moderate to vigorous leisure activities reported included recess, running/jogging, and calisthenics, and sporting activities included playing catch, dodge ball, kickball, and basketball. Sixty percent of youth played with younger children, indicating the importance of family-based activities. The distribution of the types of activities, as well as the intensity, indicates the diversity of physical activity choices among this population.

Sedentary

National accelerometry data, from 2003 to 2006, found that Mexican American youth on average engaged in sedentary behavior for 5.9, 7.9, and 7.9 h·d−1 across ages 6 to 11 yr, 12 to 15 yr, and 16 to 19 yr, respectively (21). This is quite a bit lower than the time spent in sedentary behavior found in this study, where youth 8 to 16 yr recorded on average 10 h of sedentary behavior (~74% wearing time). However, the dissimilarity between the accelerometers and cutpoints used, as well as the study dates, differing proportions of immigrants, and amount of wearing time could contribute to some of these differences.

The SOL youth accelerometer findings are supported by national surveillance of high school students in 2015 that found 42% reported playing video games or used a computer for nonschool-related activities for three or more hours per day on an average school day (24). The 2012 NHANES National Youth Fitness Survey collected self-reported past month screen time from two questions on the number of hours per day spent (i) sitting and watching TV or videos and (ii) using a computer or playing games outside of school (25). Response options included less than 1, 1, 2, 3, 4, and 5 h or longer. Among 12- to 15-yr-olds, the percent reporting 2 h·d−1 or more of screen time was 77% for males and 73% for females. In the SOL youth study, the percent of 8- to 16 yr-olds reporting at least 2 h·d−1 of total screen time was even higher (92%, data not shown). However, the dissimilarities may be due to the differing ascertainment methods, particularly because the screen time behaviors could co-occur.

Adherence

A secondary aim of this study was to explore accelerometer participation and wear. Although the use of accelerometry among youth provides data free of recall bias, these benefits are attained only when the accelerometer is adequately worn. Identifying characteristics associated with accelerometry participation and wear can help account for the potential differential missing accelerometry data, for example, by using these characteristics to create inverse probability weights which allow correction for the bias of the estimates obtained by a complete-case analysis. Identifying these characteristics can also help future studies target efforts toward improving wear time. In this study, returning the accelerometer with some wear (i.e., participation) differed only by site. However, we found adherence to a minimum accelerometry wearing protocol was lower among youth of Puerto Rican and South American background, and among teenagers compared with younger ages. Lower participation or adherence has been reported among older youth compared to younger youth in studies of 11- to 14-yr-olds (26) and 9th to 12th graders (27). Past studies find adherence to be lower among participants who were male (28–30), overweight (26,29,30), or not overweight (31), but we did not identify meaningful differences by sex or weight status. Other studies also find lower adherence among youth living in neighborhoods with high deprivation or disadvantage (28,30), with lower educated mothers (29,30), with an illness or disability limiting daily activity (30), who exercised only once a week or less (30), and who reported higher weekday outdoor play (28).

We defined accelerometer adherence as wearing the accelerometer as directed by study staff according to the research protocol. The mean accelerometer wear time was 13.7 h·d−1 among adherent days, in line with studies of high school youth (12.1 h·d−1) (27). Methods exist to attempt to increase adherence with accelerometer wearing among youth, such as by providing incentives (27,32), a text message or phone reminder (32,33), showing example output of nonwear (32), completing a monitoring log (32). This study required the accelerometer to be worn at the hip. As newer studies move to the wrist or ankle, adherence should be improved (34).

Limitations

This study is subject to several limitations. First, the SOL youth physical activity and sedentary behavior questionnaire lacked psychometric testing, specifically test–retest reliability and validity. However, a recently developed surveillance questionnaire that asked about participation in similar activities in the past week had acceptable test–retest reliability and validity (35). A further limitation of the screen time questions is that sedentary behaviors could be co-occurring, such as watching television and spending time on the phone, which would not be differentiated. Second, although these data address limitations of national US surveillance, they still come from just four major metropolitan communities. However, these study sites included diverse youth from various Hispanic/Latino backgrounds recruited in a population-based sampling frame. Third, there may be unmeasured characteristics associated with participation or adherence to the accelerometer assessment that we did not measure. Moreover, our description of correlates related to physical activity and sedentary behavior was not comprehensive, and certainly omitted important intrapersonal, interpersonal, environmental, and policy factors.

Fourth, the accelerometry cutpoints assigned to define intensity levels is an estimate based on a calibration study of 10- to 15-yr-old youth. It is reassuring that other calibration studies on the Actical found similar results (36,37). Fifth, the cleaning program used to determine accelerometry nonwear for this study was developed with youth wearing the ActiGraph accelerometer (14), and it is not known whether it performs as well for the Actical. Further work is needed to define the most accurate algorithms for defining Actical wear-time. It is likely that the ideal algorithm for proprietary counts will differ between the Actical and ActiGraph, since the performance of counts varies between these accelerometers (38,39). Finally, also related to accelerometry, the use of proprietary counts does not distinguish sitting from lying posture. The Sedentary Behavior Research Network defines sedentary behavior as “any waking behavior characterized by an energy expenditure ≤1.5 METs while in a sitting or reclining posture” (40). Newer-generation accelerometers should be able to distinguish standing from sitting or reclining to better define sedentary behavior.

CONCLUSIONS

Levels of MVPA well below recommended amounts and excessive sedentary behavior characterized this population-based cohort of Hispanic/Latino youth from four US communities, particularly among those who were older compared with younger ages. A large proportion of the day was spent in screen time behaviors. Accelerometer-assessed MVPA differed by Hispanic/Latino background. Given our findings, coupled with national data (21,22), successful and long-lasting changes at the individual, interpersonal, environmental, and policy levels are needed to reverse the persistent drop-off of MVPA during school transitions. Interventions designed to encourage Hispanic/Latino families to continue to be physically activity and minimize sedentary behavior are needed to promote positive and lifelong health.

The authors thank the participants and staff of the HCHS/SOL and HCHS/SOL youth study for their important contributions. A complete list of staff and investigators is available on the study website (http://www.cscc.unc.edu/hchs/). The authors also thank the anonymous reviewers. The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation, and do not constitute endorsement by the American College of Sports Medicine.

Funding: The Study of Latinos (SOL) Youth was supported by NHLBI R01 HL102130. The HCHS/SOL was carried out as a collaborative study supported by contracts from the National Institutes of Health (NIH), National Heart, Lung, and Blood Institute (NHLBI) to the University of North Carolina (N01-HC65233), University of Miami (N01-HC65234), Albert Einstein College of Medicine (N01-HC65235), Northwestern University (N01-HC65236), and San Diego State University (N01-HC65237). The following Institutes/ Centers/ Offices contributed to the HCHS/SOL through a transfer of funds to the NHLBI: National Institute on Minority Health and Health Disparities, National Institute on Deafness and Other Communication Disorders, National Institute of Dental and Craniofacial Research, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke, and NIH Institution-Office of Dietary Supplements. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

REFERENCES

1. US Department of Health and Human Services. Physical Activity Guidelines for Americans. 2nd edition. Washington, DC: US Department of Health and Human Services; 2018.
2. 2018 Physical Activity Guidelines Advisory Committee. 2018 Physical Activity Guidelines Advisory Committee Scientific Report. Accessed at https://health.gov/paguidelines/second-edition/report.aspx. Washington, DC: Department of Health and Human Services; 2018.
3. Tremblay MS, Leblanc AG, Janssen I, et al. Canadian sedentary behaviour guidelines for children and youth. Appl Physiol Nutr Metab. 2011;36(1):59–64.
4. Okely AD, Ghersi D, Hesketh KD, et al. A collaborative approach to adopting/adapting guidelines—the Australian 24-hour movement guidelines for the early years (birth to 5 years): an integration of physical activity, sedentary behavior, and sleep. BMC Public Health. 2017;17(5 Suppl):869.
5. Fulton JE, Carlson SA, Ainsworth BE, et al. Strategic priorities for physical activity surveillance in the United States. Med Sci Sports Exerc. 2016;48(10):2057–69.
6. Patterson R, McNamara E, Tainio M, et al. Sedentary behaviour and risk of all-cause, cardiovascular and cancer mortality, and incident type 2 diabetes: a systematic review and dose response meta-analysis. Eur J Epidemiol. 2018;33(9):811–29.
7. U.S. Bureau of the Census. Profile America facts for features: CB17-FF.17. Hispanic Heritage Month 2017. Washington, D.C.; 2017. Accessed at https://www.census.gov/newsroom/facts-for-features/2017/hispanic-heritage.html.
8. Sirard JR, Pate RR. Physical activity assessment in children and adolescents. Sports Med. 2001;31(6):439–54.
9. Borrud L, Chiappa MM, Burt VL, et al. National Health and nutrition examination survey: national youth fitness survey plan, operations, and analysis, 2012. Vital Health Stat 2. 2014;(163):1–24.
10. Rodriguez CJ, Allison M, Daviglus ML, et al. Status of cardiovascular disease and stroke in Hispanics/Latinos in the United States: a science advisory from the American Heart Association. Circulation. 2014;130(7):593–625.
11. Lavange LM, Kalsbeek WD, Sorlie PD, et al. Sample design and cohort selection in the Hispanic Community Health Study/Study of Latinos. Ann Epidemiol. 2010;20(8):642–9.
12. Ayala GX, Carnethon M, Arredondo E, et al. Theoretical foundations of the Study of Latino (SOL) youth: implications for obesity and cardiometabolic risk. Ann Epidemiol. 2014;24(1):36–43.
13. Isasi CR, Carnethon MR, Ayala GX, et al. The Hispanic Community Children’s Health Study/Study of Latino youth (SOL youth): design, objectives, and procedures. Ann Epidemiol. 2014;24(1):29–35.
14. Choi L, Liu Z, Matthews CE, Buchowski MS. Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc. 2011;43(2):357–64.
15. Romanzini M, Petroski EL, Ohara D, Dourado AC, Reichert FF. Calibration of ActiGraph GT3X, Actical and RT3 accelerometers in adolescents. Eur J Sport Sci. 2014;14(1):91–9.
16. Centers for Disease Control and Prevention. National Center for Health Statistics and the National Center for Chronic Disease Prevention and Health Promotion; 2000. United States: CDC Growth Charts; 2000.
17. Angeles G, Guilkey D, Mroz T. The impact of community level variables on individual level outcomes: theoretical results and applications. Sociol Methods Res. 2005;34(2005):76–121.
18. LaVange LM, Koch GG, Schwartz TA. Applying sample survey methods to clinical trials data. Stat Med. 2001;20(17–18):2609–23.
19. Reiter J, Raghunathan T, Kinney S. The importance of modeling the sampling design in multiple imputation for missing data. Survey Methodology. 2006;32(2):143–9.
20. Rubin D. Multiple Imputation for Nonresponse in Surveys. New York: John Wiley and Sons; 2004.
21. Belcher BR, Berrigan D, Dodd KW, Emken BA, Chou CP, Spruijt-Metz D. Physical activity in US youth: effect of race/ethnicity, age, gender, and weight status. Med Sci Sports Exerc. 2010;42(12):2211–21.
22. Gortmaker SL, Lee R, Cradock AL, Sobol AM, Duncan DT, Wang YC. Disparities in youth physical activity in the United States: 2003-2006. Med Sci Sports Exerc. 2012;44(5):888–93.
23. Lee H, Cardinal BJ, Loprinzi PD. Effects of socioeconomic status and acculturation on accelerometer-measured moderate-to-vigorous physical activity among Mexican American adolescents: findings from NHANES 2003–2004. J Phys Act Health. 2012;9(8):1155–62.
24. Kann L, McManus T, Harris WA, et al. Youth risk behavior surveillance - United States, 2015. MMWR Surveill Summ. 2016;65(6):1–174.
25. Porter AK, Matthews KJ, Salvo D, Kohl HW 3rd. Associations of physical activity, sedentary time, and screen time with cardiovascular fitness in United States adolescents: results from the NHANES National Youth Fitness Survey. J Phys Act Health. 2017;14(7):506–12.
26. Toftager M, Kristensen PL, Oliver M, et al. Accelerometer data reduction in adolescents: effects on sample retention and bias. Int J Behav Nutr Phys Act. 2013;10(1):140.
27. Sirard JR, Slater ME. Compliance with wearing physical activity accelerometers in high school students. J Phys Act Health. 2009;6(1 Suppl):S148–55.
28. Wells SL, Kipping RR, Jago R, et al. Characteristics associated with requested and required accelerometer wear in children. BMJ Open. 2013;3(8):e003402.
29. Mattocks C, Ness A, Leary S, et al. Use of accelerometers in a large field-based study of children: protocols, design issues, and effects on precision. J Phys Act Health. 2008;5(1 Suppl):S98–111.
30. Rich C, Cortina-Borja M, Dezateux C, et al. Predictors of non-response in a UK-wide cohort study of children’s accelerometer-determined physical activity using postal methods. BMJ Open. 2013;3(3).
31. Van Coevering P, Harnack L, Schmitz K, Fulton JE, Galuska DA, Gao S. Feasibility of using accelerometers to measure physical activity in young adolescents. Med Sci Sports Exerc. 2005;37(5):867–71.
32. Trost SG, McIver KL, Pate RR. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc. 2005;37(11 Suppl):S531–43.
33. Belton S, O’Brien W, Wickel EE, Issartel J. Patterns of noncompliance in adolescent field-based accelerometer research. J Phys Act Health. 2013;10(8):1181–5.
34. Fairclough SJ, Noonan R, Rowlands AV, Van Hees V, Knowles Z, Boddy LM. Wear compliance and activity in children wearing wrist- and hip-mounted accelerometers. Med Sci Sports Exerc. 2016;48(2):245–53.
35. Pate RR, McIver KL, Dowda M, Schenkelberg MA, Beets MV, DiStefano C. EASY—an instrument for surveillance of physical activity in youth. Med Sci Sports Exerc. 2018;50(6):1216–23.
36. Evenson KR, Catellier DJ, Gill K, Ondrak KS, McMurray RG. Calibration of two objective measures of physical activity for children. J Sports Sci. 2008;26(14):1557–65.
37. 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.
38. Evenson KR, Sotres-Alvarez D, Deng YU, et al. Accelerometer adherence and performance in a cohort study of US Hispanic adults. Med Sci Sports Exerc. 2015;47(4):725–34.
39. Evenson KR, Wen F. Performance of the ActiGraph accelerometer using a national population-based sample of youth and adults. BMC Res Notes. 2015;8:7.
40. Sedentary Behaviour Research Network. Letter to the editor: standardized use of the terms “sedentary” and “sedentary behaviours.” Appl Physiol Nutr Metab. 2012;37:540–2.
Keywords:

ACCELEROMETRY; ACTIVITY TYPE; ADHERENCE; HISPANIC; QUESTIONNAIRE; SELF-REPORT

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