Abstract
Purpose:
Physical activity and sedentary behavior among diverse United States’ (US) Hispanic/Latino youth is not well documented. The aim of this study was to describe physical activity and sedentary behavior among a representative sample of Hispanic/Latino youth from four US communities using accelerometry and self-reported measures.
Methods:
From 2012–2014, 1466 Hispanic/Latino youth ages 8 to 16 years, children of participants in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), enrolled in the SOL Youth. Physical activity and sedentary behavior was assessed by interview. Following this, youth wore an Actical accelerometer for one week. All statistical analyses accounted for the complex survey design and used sampling weights.
Results:
The accelerometer wear time adjusted mean minutes/day was: 604.6 sedentary, 178.9 light, 25.4 moderate, and 10.2 vigorous. Generally, higher levels of moderate and vigorous activity occurred among males, Mexican backgrounds, and youth 8–10 years old compared to older age groups. Higher levels of sedentary behavior occurred among youth 15–16 years old compared to younger age groups. The most common activities (reported >=1/month) were of lower intensity, including listening to music (91.9%), homework (87.0%), riding in car/bus (84.3%), and hanging out with friends (83.4%). Common active pursuits included travel by walking (74.6%), physical education class (71.7%), running (71.4%), and recess (71.3%).
Conclusion:
Time, intensity, and type of physical activity and sedentary behavior varied among Hispanic/Latino youth. These findings can inform efforts to increase physical activity and reduce sedentary behavior among US Hispanic/Latino youth.
Keywords: accelerometry, activity type, adherence, Hispanic, questionnaire, self-report
Introduction
The Physical Activity Guidelines for Americans, second edition (1), developed from a recent scientific review (2), recommended that youth ages 6 to 17 years should engage in at least 60 minutes 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 57.5 million Hispanics/Latinos in the US, 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 is even more limited than for Hispanics/Latinos overall.
In the US, 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 (YRBSS), the Health Behavior in School-aged Children quadrennial surveys, and the National Health and Nutrition Examination Survey (NHANES). The biennial YRBSS 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 four years and includes 11-, 13-, and 15-year olds. The NHANES includes a wider range of youth, for example from age 6 to 17 years for accelerometry measures collected in 2003–2006 and 2011–2014, 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 years, 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 18 to 74 years 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 paper described the HCHS/SOL sample design, cohort selection, and participation rates among the adults (11).
The SOL Youth enrolled children ages 8 to 16 years 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 one week. 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-seconds, and was initialized to start recording at midnight of the day following the clinic visit. We included time from 5:00am to midnight for all 7 days. Non-wear was defined as consecutive zero counts for at least 90 minutes (window 1), allowing for short time intervals with nonzero counts lasting up to 2 minutes if no counts were detected during both the 30 minutes (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 hours/day of wear time for >=3 days.
Cutpoints were guided by a calibration study using the Actical among 10 to 15 year old youth (15). The following cutpoints were used: sedentary at 0–17 counts/15-seconds, light at 18–440 counts/15-seconds, moderate at 441–872 counts/15-seconds, and vigorous at >=873 counts/15-seconds. MVPA was defined as >=441 counts/15-seconds. The number of minutes/day in each intensity were summed and averaged across adherent days, and then multiplied by 7 to obtain minutes/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 (Supplemental Table 1, SDC 1, items from the questionnaire). The response options included never, 1–2 times/month, 1–2 times/week, 3–4 times/week, 5–6 times/week, and daily. These options were converted to create a number of times/month as follows:
Never → 0 times/month
1–2 times/month → 1.5 times/month
1–2 times/week → 4–8 times/month → 6 times/month
3–4 times/week → 12–16 times/month → 14 times/month
5–6 times/week → 20–24 times/month → 22 times/month
Daily → 30 times/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 (non-active), and talking on the phone or text messaging. These activities were summed to create total time (in hours/day and minutes/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 kilograms 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 (17), with the 2016 update to better accommodate extremely high values using this program (https://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas.htm). BMI percentiles were grouped as follows: underweight (<5th percentile), normal weight (5th-84th percentile), overweight (85th-94th percentile), obese (>=95th percentile, BMI <35 kg/m2 and 120% of 95th percentile), and severely obese (>=95th percentile, BMI >=35 kg/m2 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 non-response 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 non-trimmed sample weights. These were then calibrated using 18 categories (9 age groups for each year (ages 8 to 16) by sex) in the target population from the 2010 US Census. Finally, these calibrated, trimmed, non-response 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 chi-square general test of association with the Wald chi-square 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 non-adherence, we used multiple imputation, specifically a multivariate imputation by fully-conditional specification, to impute accelerometer missing values for sedentary, light, moderate, and vigorous minutes/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 non-sports, 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 (Supplemental Table 2, SDC 2, comparison of those who wore and returned the Actical to those who did not).
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 hours of wear for at least 3 of the 7 days. Accelerometer adherence was lower among Puerto Rican and South American backgrounds, and was progressively lower with each successively older age group (Supplemental Table 3, SDC 3, comparison of those who provided adherent accelerometry data to those who did not). 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 hours/day, and differed by site (range 12.6–15.0 hours/day) and Hispanic/Latino background (13.1–15.1 hours/day) (Table 2).
Table 1.
Unweighted percentage of participants by number of adherent accelerometer days (>=8 hours of wear), overall and by age group and sex; SOL Youth Study, 2012–2014
| N | Number of Adherent Days | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 3 or more | |||
| Full Sample | Overall | 1238 | 2.7 | 3.3 | 4.9 | 6.6 | 11.3 | 14.5 | 26.4 | 30.3 | 89.2 |
| Male | 607 | 3.1 | 2.6 | 5.4 | 7.3 | 11.7 | 12.9 | 26.9 | 30.2 | 88.8 | |
| Female | 631 | 2.2 | 4.0 | 4.3 | 6.0 | 10.9 | 16.2 | 26.0 | 30.4 | 89.5 | |
| 8–10 years | Overall | 410 | 1.5 | 1.7 | 3.9 | 6.8 | 9.8 | 14.2 | 28.3 | 33.9 | 92.9 |
| Male | 202 | 1.5 | 0.5 | 5.0 | 6.9 | 13.9 | 11.9 | 26.2 | 34.2 | 93.1 | |
| Female | 208 | 1.4 | 2.9 | 2.9 | 6.7 | 5.8 | 16.4 | 30.3 | 33.7 | 92.8 | |
| 11–12 years | Overall | 300 | 0.7 | 3.3 | 2.7 | 6.7 | 13.3 | 13.3 | 27.0 | 33.0 | 93.3 |
| Male | 155 | 1.3 | 3.2 | 3.2 | 7.7 | 9.7 | 12.3 | 31.6 | 31.0 | 92.3 | |
| Female | 145 | 0.0 | 3.5 | 2.1 | 5.5 | 17.2 | 14.5 | 22.1 | 35.2 | 94.5 | |
| 13–14 years | Overall | 310 | 5.2 | 2.9 | 5.8 | 6.8 | 11.9 | 18.4 | 22.9 | 26.1 | 86.1 |
| Male | 149 | 5.4 | 3.4 | 6.0 | 7.4 | 9.4 | 17.5 | 25.5 | 25.5 | 85.2 | |
| Female | 161 | 5.0 | 2.5 | 5.6 | 6.2 | 14.3 | 19.3 | 20.5 | 26.7 | 87.0 | |
| 15–16 years | Overall | 218 | 4.1 | 6.9 | 8.3 | 6.0 | 10.6 | 11.5 | 27.1 | 25.7 | 80.7 |
| Male | 101 | 5.9 | 5.0 | 8.9 | 6.9 | 13.9 | 8.9 | 22.8 | 27.7 | 80.2 | |
| Female | 117 | 2.6 | 8.6 | 7.7 | 5.1 | 7.7 | 13.7 | 30.8 | 23.9 | 81.2 | |
Table 2.
Description of sample and adjusted mean (SE) min/day of accelerometry-assessed physical activity and sedentary behavior, overall and by sociodemographic and BMI categories; SOL Youth Study, 2012–2014
| N | Weighted Percent |
Wear Time | Sedentary | Light | Moderate | Vigorous | Moderate to Vigorous | |
|---|---|---|---|---|---|---|---|---|
| Mean | Mean (SE) | Mean (SE) | Mean (SE) | Mean (SE) | Mean (SE) | |||
| Overall | 1466 | 100 | 13.7 | 604.6 (4.4) | 178.9 (2.5) | 25.4 (0.6) | 10.2 (0.5) | 35.7 (1.0) |
| Site: | ||||||||
| Bronx | 422 | 35.9 | 15.0 | 613.7 (6.7) | 171.1 (4.5) | 24.9 (1.0) | 9.5 (0.8) | 34.4 (1.6) |
| Chicago | 372 | 16.2 | 13.6 | 606.6 (7.5) | 179.0 (5.8) | 24.6 (1.0) | 9.0a (1.2) | 33.6 (2.0) |
| Miami | 263 | 13.6 | 12.8 | 605.3 (5.9) | 182.8 (4.4) | 23.4 (1.0) | 7.8b (0.6) | 31.1 (1.4) |
| San Diego | 409 | 34.3 | 12.6 | 593.9 (5.5) | 185.5 (3.9) | 27.3 (1.1) | 12.5a,b (1.0) | 39.8 (1.9) |
| Hispanic/Latino background: | ||||||||
| Central American | 112 | 6.0 | 13.7 | 620.9 (8.3) | 168.7 (7.1) | 22.3 (1.6) | 7.3 (1.2) | 29.6a (2.5) |
| Cuban | 103 | 5.4 | 13.2 | 616.5 (6.6) | 175.7 (5.6) | 20.6 (1.6) | 6.5 (1.0) | 27.1b (2.4) |
| Dominican | 167 | 12.8 | 15.1 | 617.6 (8.2) | 171.5 (6.3) | 22.7 (1.6) | 7.4 (1.1) | 30.1c (2.4) |
| Mexican | 648 | 46.4 | 13.1 | 599.3 (5.2) | 181.6 (3.5) | 26.5 (0.8) | 11.8 (0.8) | 38.3a,b,c (1.5) |
| Puerto Rican | 128 | 9.5 | 14.5 | 607.2 (11.1) | 175.8 (8.2) | 27.2 (2.0) | 9.1 (1.3) | 36.3 (3.0) |
| South American | 68 | 4.0 | 13.4 | 608.8 (17.3) | 174.6 (12.7) | 24.7 (2.6) | 11.2 (3.0) | 35.8 (5.2) |
| Mixed | 135 | 9.6 | 14.0 | 612.6 (8.2) | 170.6 (7.0) | 25.9 (1.8) | 10.1 (1.4) | 36.0 (2.8) |
| Other/missing | 105 | 6.4 | 13.6 | 573.5 (12.2) | 206.3 (9.2) | 27.6 (2.0) | 11.7 (1.4) | 39.4 (3.1) |
| Sex: | ||||||||
| Male | 728 | 51.2 | 13.8 | 597.5 (5.4) | 181.8 (3.9) | 27.9a (0.8) | 12.0a (0.7) | 39.9a (1.3) |
| Female | 738 | 48.8 | 13.5 | 612.1 (5.2) | 175.9 (2.9) | 22.9a (0.8) | 8.4a (0.6) | 31.2a (1.2) |
| Age: | ||||||||
| 8–10 years | 494 | 32.4 | 13.7 | 560.9a,b,c (5.1) | 215.6a,b,c (2.9) | 29.4a,b,c (0.8) | 13.3a,b,c (0.8) | 42.7a,b,c (1.4) |
| 11–12 years | 350 | 21.8 | 13.7 | 599.3a,d,e (5.9) | 186.3a,d,e (4.5) | 24.1a (1.1) | 9.6a (0.8) | 33.7a (1.7) |
| 13–14 years | 371 | 22.3 | 13.7 | 627.0b,d,f (6.1) | 160.4b,d,f (4.7) | 23.3b (1.2) | 8.6b (0.9) | 31.9b (2.0) |
| 15–16 years | 251 | 23.6 | 13.5 | 648.4c,e,f (7.6) | 139.4c,e,f (5.0) | 23.3c (1.3) | 8.2c (1.2) | 31.5c (2.2) |
| BMI percentile group: | ||||||||
| Underweight | 37 | 2.8 | 14.1 | 599.0 (17.5) | 180.3 (13.9) | 27.1 (2.9) | 12.8 (2.7) | 39.9 (5.2) |
| Normal weight | 712 | 50.4 | 13.7 | 601.6 (5.1) | 179.7 (3.6) | 26.4 (0.8) | 11.5 (0.7) | 37.8 (1.4) |
| Overweight | 305 | 20.1 | 13.6 | 604.3 (7.3) | 179.3 (4.7) | 25.3 (1.3) | 10.3 (1.1) | 35.6 (2.2) |
| Obese | 262 | 16.8 | 13.7 | 606.1 (6.8) | 180.4 (5.5) | 24.6 (1.1) | 8.1 (1.1) | 32.6 (2.0) |
| Severely obese | 150 | 9.9 | 13.5 | 619.3 (10.1) | 171.2 (7.2) | 22.0 (1.7) | 6.7 (1.3) | 28.7 (2.7) |
| Generational status: | ||||||||
| Foreign born (1st generation) |
305 | 20.5 | 13.4 | 618.9 (8.0) | 167.7 (5.7) | 23.4 (1.2) | 9.2 (1.2) | 32.6 (2.2) |
| US born 2nd generation | 913 | 63.1 | 13.5 | 602.0 (4.8) | 181.1 (3.1) | 25.6 (0.7) | 10.6 (0.6) | 36.2 (1.2) |
| US born 3rd+ generation | 208 | 16.4 | 14.5 | 597.0 (7.3) | 184.7 (5.4) | 27.4 (1.4) | 10.2 (1.0) | 37.6 (2.2) |
| Language preference: | ||||||||
| Spanish | 287 | 20.5 | 12.9 | 600.0 (6.9) | 181.9 (5.0) | 25.8 (1.2) | 11.3 (1.2) | 37.0 (2.2) |
| English | 1175 | 79.5 | 13.8 | 605.7 (4.6) | 177.9 (2.6) | 25.3 (0.6) | 9.9 (0.5) | 35.3 (1.0) |
Abbreviations: BMI, body mass index; SE, standard error
Note: Accelerometry means were adjusted for wear time. All statistics accounted for the complex design and are weighted to account for probability of selection and non-response.
Means sharing the same superscript are significantly different (pairwise comparison p-value ≤ 0.01) and highlighted in bold, except when the comparison includes background mixed or other/missing which is not meaningful. Pairwise comparisons were only conducted when the overall test was significant at an alpha level of 0.0001 given the large number of comparisons performed. Wear time differences were not statistically explored.
Accelerometry-Assessed Physical Activity and Sedentary Behavior
Overall, the accelerometry wear time adjusted mean physical activity in minutes/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 8–10 years old.
Overall, the wear time adjusted mean sedentary behavior was 604.6 minutes/day (Table 2). Higher levels of sedentary behavior occurred among those 15–16 years old compared to 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 (Supplemental Table 4, SDC 4, common activities by sex/age group) and by Hispanic/Latino background (Table 3) (Supplemental Table 5, SDC 5, common activities by background) to reveal preferences, which generally had similar rankings.
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
| Activities | Overall | Central American |
Cuban | Dominican | Mexican | Puerto Rican |
South American |
Mixed | Other/ Missing |
|---|---|---|---|---|---|---|---|---|---|
| Listening to music | 91.9 | 88.0 | 94.3 | 91.1 | 91.2 | 93.8 | 77.0 | 85.9 | 77.4 |
| Homework | 87.0 | 74.5 | 88.5 | 93.1 | 89.0 | 84.7 | 86.7 | 90.7 | 86.0 |
| Riding in car/bus | 84.3 | 93.5 | 94.2 | 72.1 | 82.2 | 84.6 | 82.0 | 80.9 | 74.6 |
| Hang with friends | 83.4 | 86.0 | 97.6 | 80.4 | 90.4 | 87.2 | 82.4 | 88.6 | 64.8 |
| Travel by walking | 74.6 | 61.7 | 64.4 | 89.2 | 74.8 | 70.8 | 66.7 | 80.2 | 52.9 |
| Physical education class | 71.7 | 66.9 | 86.1 | 76.5 | 71.2 | 72.3 | 67.2 | 72.5 | 78.5 |
| Running/jogging | 71.4 | 60.9 | 76.5 | 70.1 | 79.4 | 68.8 | 52.6 | 70.8 | 70.8 |
| Recess/exercise period | 71.3 | 63.5 | 64.6 | 70.5 | 76.4 | 76.7 | 76.2 | 58.0 | 87.5 |
| Calisthenics | 69.8 | 69.2 | 66.9 | 64.8 | 77.7 | 73.2 | 59.7 | 79.1 | 55.6 |
| Indoor chores | 68.8 | 60.7 | 43.5 | 65.5 | 69.6 | 58.7 | 64.4 | 63.1 | 49.3 |
| Play catch | 62.2 | 49.7 | 67.0 | 72.9 | 65.2 | 71.7 | 70.0 | 77.9 | 80.4 |
| Shopping | 61.8 | 57.5 | 59.8 | 58.5 | 61.4 | 53.8 | 52.5 | 58.4 | 61.8 |
| Play with younger children | 60.2 | 43.4 | 39.9 | 62.3 | 55.3 | 43.8 | 44.5 | 63.0 | 55.5 |
| Walking for exercise | 58.3 | 67.1 | 28.5 | 50.1 | 54.5 | 57.1 | 43.1 | 71.5 | 49.4 |
| Reading for fun | 56.5 | 48.8 | 44.8 | 54.6 | 47.6 | 50.2 | 46.9 | 61.4 | 62.1 |
| Play with pets | 55.2 | 50.0 | 43.7 | 54.9 | 53.1 | 71.3 | 49.2 | 56.5 | 53.7 |
| Video games | 53.9 | 69.7 | 66.6 | 68.7 | 55.8 | 64.7 | 67.5 | 66.3 | 71.9 |
| Play dodge ball, kickball | 52.8 | 53.8 | 73.6 | 58.5 | 58.0 | 53.2 | 63.8 | 53.7 | 74.9 |
| Basketball | 49.7 | 76.6 | 66.8 | 74.9 | 50.5 | 67.2 | 73.8 | 64.9 | 58.0 |
| Church | 49.0 | 55.0 | 37.5 | 49.4 | 50.5 | 42.1 | 56.7 | 41.4 | 49.3 |
Abbreviations: SE, standard error
Note: The mean number of times/month are for those who reported having done the activity at least once/week (no zero counts included). All statistics accounted for the complex design and are weighted to account for probability of selection and non-response.
Based on self-report, on average youth spent 475.5 minutes/day in screen time activities including television or video watching, computer or internet, video/computer games (non-active), 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.
Table 4:
Mean (SE) number of minutes/day in screen time, overall and by sociodemographic and BMI categories; SOL Youth Study, 2012–2014
| Total Screen Time | TV / Video Watching |
Computer / Internet |
Video / Computer Games |
Phone / Text Messaging |
||
|---|---|---|---|---|---|---|
| N | Mean (SE) | Mean (SE) | Mean (SE) | Mean (SE) | Mean (SE) | |
| Overall | 1466 | 475.5 (12.4) | 167.1 (5.4) | 113.4 (4.5) | 82.5 (4.4) | 114.2 (6.4) |
| Site: | ||||||
| Bronx | 422 | 554.6a,b (22.8) | 207.2a,b,c (10.7) | 123.2 (8.1) | 102.4 (8.7) | 121.8 (12.4) |
| Chicago | 372 | 432.3a (18.3) | 154.0a (8.2) | 109.3 (10.3) | 71.9 (8.8) | 101.8 (8.5) |
| Miami | 263 | 492.7 (26.4) | 142.2b (9.3) | 120.2 (9.8) | 86.4 (9.3) | 149.2 (15.0) |
| San Diego | 409 | 410.1b (21.1) | 142.2c (8.3) | 102.8 (7.4) | 65.9 (6.8) | 99.2 (11.3) |
| Hispanic/Latino background: | ||||||
| Central American | 112 | 535.2 (40.1) | 177.0 (25.7) | 126.1 (16.5) | 91.1 (16.0) | 141.8 (21.6) |
| Cuban | 103 | 528.1 (38.6) | 152.5 (15.8) | 126.0 (10.6) | 76.0 (11.9) | 178.4 (24.6) |
| Dominican | 167 | 589.3a,b (35.5) | 197.7 (13.9) | 131.3 (11.6) | 107.3 (15.7) | 153.1 (22.6) |
| Mexican | 648 | 430.9a,c (16.4) | 155.3 (7.8) | 108.9 (6.5) | 71.0 (6.3) | 97.2 (8.7) |
| Puerto Rican | 128 | 557.5c,d (44.3) | 215.6 (21.0) | 119.3 (13.6) | 102.6 (14.1) | 121.8 (21.4) |
| South American | 68 | 383.8b,d (43.6) | 113.6 (14.8) | 111.7 (17.0) | 78.1 (15.2) | 80.4 (18.6) |
| Mixed | 135 | 491.2 (41.6) | 162.2 (17.7) | 108.4 (12.8) | 92.4 (18.1) | 129.2 (21.6) |
| Other/missing | 105 | 391.7 (36.7) | 166.5 (17.7) | 87.3 (13.5) | 73.3 (14.8) | 69.8 (20.3) |
| Sex: | ||||||
| Male | 728 | 483.0 (16.3) | 175.6 (8.1) | 107.0 (6.0) | 118.4a (7.4) | 83.7a (6.7) |
| Female | 738 | 467.7 (17.2) | 158.2 (6.4) | 120.0 (6.1) | 45.0a (3.6) | 146.2a (10.9) |
| Age: | ||||||
| 8–10 years | 494 | 337.0a,b,c (14.6) | 165.5 (8.6) | 72.4a,b,c (5.7) | 75.7 (6.2) | 25.6a,b,c (3.2) |
| 11–12 years | 350 | 408.8a,d,e (22.2) | 163.6 (9.5) | 102.3a,d,e (6.8) | 87.9 (8.5) | 55.0a,d,e (8.5) |
| 13–14 years | 371 | 550.1b,d,f (22.9) | 161.5 (8.1) | 146.4b,d (9.3) | 87.0 (8.9) | 158.3b,d,f (12.0) |
| 15–16 years | 251 | 672.3c,e,f (24.7) | 178.3 (13.5) | 151.7c,e (10.5) | 82.6 (9.8) | 260.3c,e,f (18.2) |
| BMI percentile group: | ||||||
| Underweight | 37 | 482.9 (68.0) | 198.3 (46.2) | 99.3 (18.3) | 111.0 (25.4) | 79.5 (31.3) |
| Normal weight | 712 | 469.1 (17.4) | 157.6 (6.7) | 106.8 (5.5) | 80.2 (6.3) | 125.6 (8.8) |
| Overweight | 305 | 476.5 (24.6) | 176.5 (13.4) | 120.4 (9.9) | 77.8 (10.1) | 103.3 (16.4) |
| Obese | 262 | 472.4 (28.3) | 164.7 (11.3) | 108.9 (10.2) | 85.4 (10.7) | 116.8 (15.6) |
| Severely obese | 150 | 509.8 (36.3) | 191.5 (18.1) | 144.4 (18.1) | 90.3 (11.2) | 83.9 (11.7) |
| Generational status: | ||||||
| Foreign born (1st generation) |
305 | 502.7 (24.2) | 169.9 (12.6) | 123.7 (8.6) | 74.9 (8.9) | 135.7 (11.5) |
| US born 2nd generation | 913 | 467.1 (14.5) | 164.2 (6.6) | 112.2 (5.5) | 81.0 (6.2) | 111.2 (8.6) |
| US born 3rd+ generation | 208 | 462.9 (30.2) | 170.6 (11.6) | 101.1 (8.8) | 92.7 (11.5) | 98.9 (15.4) |
| Language preference: | ||||||
| Spanish | 287 | 425.7 (26.1) | 158.2 (11.0) | 96.9 (8.2) | 78.2 (10.2) | 93.8 (13.8) |
| English | 1175 | 487.9 (13.6) | 168.7 (6.2) | 117.7 (5.0) | 82.9 (4.9) | 120.4 (7.5) |
Abbreviations: BMI, body mass index; SE, standard error
Note: The results from this table come only from the four screen-time activities reported in minutes/day. All statistics accounted for the complex design and are weighted to account for probability of selection and non-response.
Means sharing the same superscript are significantly different (pairwise comparison p-value ≤ 0.01) and highlighted in bold, except when the comparison includes background mixed or other/missing which is not meaningful. Pairwise comparisons were only conducted when the overall test was significant at an alpha level of 0.0001 given the large number of comparisons performed.
Discussion
The present study provides novel information about accelerometry-assessed and self-reported physical activity and sedentary behavior among US youth age 8–16 years 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 years engaged in a mean of 25 minutes/day of moderate (~3% wearing time) and 10 minutes/day 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 2003–2006 indicate that Mexican American youth engaged in a mean of 85, 37, and 28 minutes/day of MVPA across ages 6–11, 12–15, and 16–19 years, respectively (21), with no appreciable differences between 2003–2004 and 2005–2006 (22). In SOL Youth, conducted 6 to 11 years later, the amount of mean time spent in MVPA was remarkably similar among older ages compared to NHANES Mexican American youth (43, 34, 32, and 32 minutes/day for ages 8–10, 11–12, 13–14, and 15–16 years 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. While 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 to males, and a markedly lower MVPA comparing 8–10 year olds to 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).
MVPA 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. While 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 2003–2006 found that Mexican American youth on average engaged in sedentary behavior for 5.9, 7.9, and 7.9 hours/day across ages 6–11, 12–15, and 16–19 years, respectively (21). This is quite a bit lower than the time spent in sedentary behavior found in this study, where youth 8 to 16 years recorded on average 10 hours 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 non-school related activities for 3 or more hours/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/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 hour, 1 hour, 2 hours, 3 hours, 4 hours, and 5 hours or more. Among 12–15 year olds, the percent reporting 2 hours/day or more of screen time was 77% for males and 73% for females. In the SOL Youth Study, the percent of 8–16 year olds reporting at least 2 hours/day 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. While 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 towards 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 to younger ages. Lower participation or adherence has been reported among older youth compared to younger youth in studies of 11 to 14 year 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 hours/day among adherent days, in line with studies of high school youth (12.1 hours/day) (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, that would not be differentiated. Second, while 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 10–15 year 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 non-wear 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.
Conclusion
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 to 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 life-long health.
Supplementary Material
—Items from the questionnaire
—Comparison of those who wore and returned the Actical to those who did not
—Comparison of those who provided adherent accelerometry data to those who did not
—Common activities by sex/age group
—Common activities by background
Acknowledgment:
The authors thank the participants and staff of the HCSH/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 Hispanic Community Health Study (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. [Google Scholar]
- 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. [Google Scholar]
- 3.Tremblay MS, Leblanc AG, Janssen I, et al. Canadian sedentary behaviour guidelines for children and youth. Appl Physiol Nutr Metab. 2011;36(1):59–71. [DOI] [PubMed] [Google Scholar]
- 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(Suppl 5):869. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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):811–829. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 J, Pate R. Physical activity assessment in children and adolescents. Sport Med. 2001;31(6):439–54. [DOI] [PubMed] [Google Scholar]
- 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. Accessed at https://www.cdc.gov/nchs/data/series/sr_02/sr02_163.pdf. [PubMed] [Google Scholar]
- 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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. [DOI] [PubMed] [Google Scholar]
- 16.Centers for Disease Control and Prevention. 2000 CDC Growth Charts: United States. In: National Center for Health Statistics and the National Center for Chronic Disease Prevention and Health Promotion; 2000. [Google Scholar]
- 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. [Google Scholar]
- 18.LaVange LM, Koch GG, Schwartz TA. Applying sample survey methods to clinical trials data. Stat Med. 2001;20(17–18):2609–23. [DOI] [PubMed] [Google Scholar]
- 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):143–149. [Google Scholar]
- 20.Rubin D Multiple Imputation for Nonresponse in Surveys. New York: John Wiley and Sons; 2004. [Google Scholar]
- 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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. [DOI] [PubMed] [Google Scholar]
- 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. [DOI] [PubMed] [Google Scholar]
- 24.Kann L, McManus T, Harris WA, et al. Youth Risk Behavior Surveillance - United States, 2015. MMWR Surveill Summ. 2016;65(6):1–174. [DOI] [PubMed] [Google Scholar]
- 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):506–512. [DOI] [PubMed] [Google Scholar]
- 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Sirard JR, Slater ME. Compliance with wearing physical activity accelerometers in high school students. J Phys Act Health. 2009;6 Suppl 1:S148–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 Suppl 1:S98–111. [DOI] [PubMed] [Google Scholar]
- 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). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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. [DOI] [PubMed] [Google Scholar]
- 32.Trost S, McIver K, Pate R. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc. 2005;37(11 Suppl):S531–S543. [DOI] [PubMed] [Google Scholar]
- 33.Belton S, O’Brien W, Wickel EE, Issartel J. Patterns of non-compliance in adolescent field based accelerometer research. J Phys Act Health. 2013;10(8):1181–1185. [DOI] [PubMed] [Google Scholar]
- 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. [DOI] [PubMed] [Google Scholar]
- 35.Pate RR, McIver K, Dowda M, Schenkelberg MA, Beets M, DiStefano C. EASY-An Instrument for Surveillance of Physical Activity in Youth. Med Sci Sports Exerc. 2018;50(6):1216–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Evenson K, Catellier D, Gill K, Ondrak K, McMurray R. Calibration of two objective measures of physical activity for children. J Sport Sci. 2008;26(14):1557–1565. [DOI] [PubMed] [Google Scholar]
- 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. [DOI] [PubMed] [Google Scholar]
- 38.Evenson KR, Sotres-Alvarez D, Deng Y, et al. Accelerometer adherence and performance in a cohort study of US Hispanic adults. Med Sci Sports Exerc. 2017;47(4):725–734. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Sedentary Behaviour Research Network. Standardized use of the terms “sedentary” and “sedentary behaviours”. Appl Physiol Nutr Metab. 2012;37:540–542. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
—Items from the questionnaire
—Comparison of those who wore and returned the Actical to those who did not
—Comparison of those who provided adherent accelerometry data to those who did not
—Common activities by sex/age group
—Common activities by background