Abstract
Purpose:
To examine the relationship of self-reported television (TV) viewing time with accelerometer-derived total sedentary time, and to determine whether it differs by subgroup.
Methods:
Using data for adults (≥ 20 years) from the 2003-2004 and 2005-2006 nationally-representative US National Health and Nutrition Examination Surveys (NHANES; n=5738), linear regression models examined the associations of categories of self-reported TV viewing time (<1, 1, 2, 3, 4, and 5+ hrs/day) with accelerometer-derived sedentary time (<100 counts/min; hrs/day). Spearman’s rho assessed the correlation between participants’ rankings on the two measures. Analyses were stratified by gender, age, race/ethnicity, and, in the 2003-2004 NHANES cycle, by work status among working-aged adults (20-65years, n=2069).
Results:
TV viewing time was significantly associated with sedentary time, with positive associations for all gender, age, race/ethnicity groups, and for those not working or working part-time, but not for those in full-time work. However, correlations between rankings of the measures were only ‘fair’ overall (rho=0.22) and were similar for all gender and racial/ethnic groups, and for those of mid and older-age, but not for those of younger age (20-39 yrs, rho=0.05). In the working-aged subgroup, there was also a ‘fair’ correlation between the measures for those not working (rho=0.22), but no significant correlation for those in part-time (rho=0.14) or full-time work (rho=0.03).
Conclusions:
Associations of TV viewing time with accelerometer-derived total sedentary time were statistically significant, but correlations were of only fair magnitude and the strength of the relationship was not consistent across all population subgroups. These findings suggest TV viewing time has an influence on overall sedentary time at a population level; however, measurement of sedentary time in other domains is also important.
Keywords: sedentary behavior, measurement, gender, age, race/ethnicity, work status
INTRODUCTION
There is a growing body of evidence on the detrimental associations of sedentary behavior (prolonged sitting time) with cardio-metabolic risk biomarkers and health outcomes (28). Much of this research has focussed on television (TV) viewing time: a recent review of measures of non-occupational sedentary behavior found that all relevant papers (n=60) had assessed TV viewing time by self-report, and in 39 papers it was the sole sedentary behavior measured (9). TV viewing is a highly prevalent leisure-time behavior (4, 12, 36) and has been shown to have strong and consistent detrimental associations with cardio-metabolic risk biomarkers and health outcomes, including premature mortality (13–15, 19–22). It is also a specific behavior that may be recalled relatively accurately (9, 26), thus has an advantage over using more-imprecise overall measures (11). However, TV viewing time is one of several sedentary behaviors in which adults engage and thus may or may not be representative of overall sedentary time. In this context, Pate and colleagues (29) highlight inconsistencies in studies on sedentary behavior that report findings solely on TV viewing time, yet discuss these findings in terms of overall sedentary behavior. Evidence is needed to clarify whether TV viewing time can be representative of overall sedentary time.
The prevalence of high TV viewing time has been seen to differ by gender, age, race/ethnicity, and work status, with higher levels of TV viewing time observed in men, older adults, those of African American race/ethnicity, and among those not in paid employment (3, 10, 35). Interestingly, those population subgroups who report watching the most TV (3, 10, 35) are not always those who are identified objectively as being the most sedentary overall (25). Thus, the extent to which measures of TV viewing time could be indicative of overall sedentary time may differ between population subgroups. Such variations may provide insights into the differential associations of TV viewing time with health outcomes that have previously been observed across gender and ethnicity groups (2, 15, 16, 18, 19, 22, 35). To date, no studies have examined this issue, although TV viewing time has been shown to be a marker for self-reported total leisure-time sedentary behavior in women (36).
The concurrent assessment of self-reported TV viewing time and accelerometer-measured physical activity in the 2003-2006 population-representative US National Health and Nutrition Examination Surveys (NHANES) provides a unique opportunity to address the relationship of TV viewing time with accelerometer-derived sedentary time in a large, diverse, population-based sample. We examined this relationship in the overall adult population, as well as stratified by gender, age, race/ethnicity (non-Hispanic white, non-Hispanic black and Mexican American) and work status. Relationships were also examined in terms of relative agreement, to explore the extent to which TV viewing time may or may not be representative of total sedentary time across the day.
METHODS
Study Population and Design
The study sample was drawn from the 2003-2004 and 2005-2006 cycles of NHANES: a continuous, cross-sectional, observational study conducted by the Centers for Disease Control and Prevention (7, 25, 37). NHANES used a stratified, complex multistage probability design to obtain a nationally-representative sample of non-institutionalized civilian U.S. citizens aged six years and older (7, 25, 37). Certain populations were oversampled, including Mexican and non-Hispanic black Americans. The National Center for Health Studies Ethics Review Board approved the survey protocols, and informed consent was obtained for all participants. Data collection involved an interviewer-administered questionnaire conducted in participants’ homes by trained interviewers; and, health measurements were carried out in specially-designed and equipped mobile examination centers. In the 2003-2004 and 2005-2006 cycles, ambulatory participants were asked to wear an accelerometer (Actigraph model 7164; Actigraph, LLC, Ft. Walton Beach, FL) on an elasticized belt over the right hip during waking hours for the seven consecutive days after their examination in one of the mobile examination centers. Details of the accelerometer protocol have previously been reported (5, 25, 37).
For the purposes of the following analyses results were included from participants who: were adults (aged 20 years or older; n=10,020); wore the accelerometer for 10 or more hours per day on at least four days (and including at least one weekend day; n=5,742); and completed the TV viewing item of the questionnaire (n=5,738). A smaller sample of participants aged 20-65 years (nominal working age) was taken from the 2003-2004 survey to examine possible variations by work status (n=2,069). At the time of the current analyses, data on work status were not available for the 2005-2006 survey.
Measures
Sociodemographics:
Gender, age, race/ethnicity, education and work status were self-reported in the interviewer-administered questionnaire. Age categories were established based on age in whole years at time of interview as follows: 20-39 years, 40-59 years and 60 years and over. Racial/ethnic groups were categorized into non-Hispanic white, non-Hispanic black, Mexican-American and other race (not included in the previous categories) according to NHANES analytic guidelines (6). Work-status categories (non-working, part-time, and full-time work) were derived from participants’ self-reported work status in the previous week and the number of hours worked in that week (or hours usually worked for workers who reported less than 35 hours in the previous week). The minimum requirement for full-time work was 35 hours worked in the previous week or usually worked; less than 35 hours in the previous week was classified as part-time in those that reported working. Education was dichotomized as less than 12 years of schooling or 12 years and over, or equivalent, including post high-school training. Waist circumference was measured by trained staff during the physical examination, with measurements taken at the upper border of the right iliac crest (7).
TV viewing time:
Amount of time spent watching TV or videos was self-reported in the household interview questionnaire as total time on a typical day over the past 30 days, with the following response options: <1 hour, 1 hour, 2 hours, 3 hours, 4 hours, ≥5 hours, or, do not watch TV or videos. The category of ‘do not watch TV or videos’ was combined with ‘<1 hour’, due to low numbers reporting these categories.
Accelerometer-derived overall sedentary time:
Accelerometer-derived sedentary time was calculated as time spent in <100 counts per minute (cpm). This cut point has previously been shown to approximate sitting time (25), however, as accelerometers are not able to discriminate between sitting and standing very still, time recorded as sedentary will not strictly represent sitting time. An automated program (SAS 9.1, 25) was adapted and used to derive wear time and summarize sedentary time data. Non-wear time was defined as bouts of 60 minutes or more of consecutive zero counts with interruptions of up to two counts of ≤50 cpm (41). Sedentary time (hours per day) was corrected for monitor wear time using the residuals method (39). Non-wear time includes time spent asleep, for showering or bathing and for water-based activities.
Statistical Analyses
Analyses were conducted using Stata Statistical Software Release 11.0 (College Station, TX, Stata Corporation). To account for the complex survey design used in NHANES (6), the STATA survey commands with sampling weight, stratification, and clustering (primary sampling unit) variables were used for most analyses. Four-year examination weights (2003-2006), reweighted to correct for non-response to the accelerometry component of NHANES were used. Analyses that included occupational data, which were only available for the earlier NHANES cycle, used two-year (2003-2004) examination weights with reweighting to correct for non-response to the accelerometry component. Weights were not used when testing interactions (1), or in calculating the Spearman’s rank order correlations. Strata were collapsed when required due to low numbers in some sub-groups. Significance was set at < 0.05 for main effects and < 0.1 for interactions. Characteristics of the sample (weighted) were described as % (n) or mean (SD).
Simple linear regression analyses (with linearized variance estimation and weighting) were used to examine the bivariate association between TV viewing time and sedentary time, with data reported as population weighted mean accelerometer-derived sedentary time across categories (<1 hour, 1 hour, 2 hours, 3 hours, 4 hours, ≥5 hours) of self-reported TV viewing time. To examine whether these associations differed across population sub-groups of interest (gender, age, race/ethnicity and work status), stratified analyses were performed and interactions were tested. Interactions were examined unadjusted, then adjusted for age (in completed years), gender, waist circumference, race/ethnicity, and educational level in order to ensure that any difference in the degree of relationship was not due to imbalance of these other characteristics. The “Other Race” category was excluded from the racial/ethnic comparisons due to the diverse ethnic backgrounds within the category (n=379). In adjusted models, participants with missing data for co-variates were dropped (missing data for co-variates: total sample = 166; non-Hispanic white = 92; non-Hispanic black = 44; Mexican Americans = 26; full-time workers = 29; non-working = 17).
Spearman’s rank order correlations were used to examine the relative agreement between the participants’ rankings of TV viewing time and accelerometer-derived sedentary time overall and separately for each sub-group of interest. Magnitude of relative agreement was described using the scale reported by Landis and Koch (23) as follows: Poor = below 0, Slight = 0.00-0.20, Fair = 0.21-0.40, Moderate = 0.41-0.60, Substantial = 0.61-0.80, Almost Perfect = 0.81-1.00. This scale was developed for the Kappa statistic, however, the range of values and concept of relative agreement examined in this paper using Spearman’s rank order correlation is similar. Assessment of agreement between quintiles of TV viewing time (only approximate due to the categorical nature of the data) and sedentary time using the weighted Kappa statistic, revealed similar results to the Spearman’s correlation and are therefore not reported in this paper.
RESULTS
Attributes of the 5,738 participants included in the study are presented in Table 1. Over half the sample reported their race/ethnicity status as non-Hispanic white. Those reporting the highest category of TV viewing time (≥5 hours/day) were more likely to be in the older-age than the younger-age category (p<0.001), of non-Hispanic black than non-Hispanic white race/ethnicity (p<0.001) and working full-time than not working (among the working-aged sub-group, p<0.001). Mexican-Americans were less likely to report this high level of TV viewing time than non-Hispanic whites (p=0.02).
Table 1:
Attributes of the total study sample and sub-groups
Characteristic | N | Age (years) | More than high school education, % (n) | Waist Circumference (cm) | TV viewing reported 5+ hrs/day | Sedentary Time (hrs/day) |
---|---|---|---|---|---|---|
Total Sample | 5738 | 46.4 (16.8) | 59.5% (2811) | 97.0 (15.3) | 10.8% (800) | 8.19 (1.71) |
Gender | ||||||
Women | 2914 | 47.0 (17.1) | 60.6% (1468) | 93.8 (15.6) | 10.9% (396) | 8.25 (1.56) |
Men | 2824 | 45.6 (16.4) | 58.3% (1343) | 100.4 (14.2) | 10.7% (404) | 8.12 (1.86) |
Age Group | ||||||
20-39 years | 1650 | 29.7 (5.7) | 62.0% (915) | 93.2 (15.6) | 8.5% (156) | 7.79 (1.62) |
40-59 years | 1785 | 48.8 (5.5) | 64.9% (1013) | 98.8 (14.8) | 8.9% (195) | 7.99 (1.58) |
60+ years | 2303 | 70.9 (7.6) | 46.1% (883) | 100.3 (14.2) | 17.9% (449) | 9.23 (1.65) |
Race/Ethnicity | ||||||
Non-Hispanic white | 3121 | 48.1 (17.0) | 64.0% (1811) | 97.5 (15.4) | 9.9% (387) | 8.30 (1.68) |
Non-Hispanic black | 1068 | 43.6 (16.0) | 53.0% (517) | 98.0 (16.2) | 21.3% (251) | 8.18 (1.73) |
Mexican-American | 1170 | 38.8 (14.2) | 27.9% (279) | 95.9 (13.0) | 6.7% (120) | 7.35 (1.76) |
Working-age population | ||||||
Full-time work | 1218 | 40.1 (11.0) | 64.7% (715) | 96.3 (14.5) | 5.6% (82) | 7.72 (1.59) |
Part-time work | 271 | 39.9 (11.1) | 65.5% (140) | 96.4 (12.3) | 7.8% (32) | 8.02 (1.40) |
Non-working | 580 | 44.5 (11.9) | 49.8% (236) | 97.2 (15.1) | 22.4% (139) | 8.57 (1.27) |
Data are population weighted unadjusted means (SD) unless otherwise indicated. Total group includes ‘other race’. Participants in working-age population are aged 20-65 years and drawn from 2003-4 cycle only. Sedentary time is corrected for wear time using the residuals method.
Linear regression findings: associations of TV viewing time with total sedentary time
Overall associations:
Linear regression models revealed a positive association between self-reported TV viewing time and total accelerometer-derived sedentary time (B: 0.22, 95%CI: 0.19, 0.26, p<0.001). However, the R2 (an indication of the proportion of variance in sedentary time that is explained by the model) was low (R2 = 0.039).
Associations by gender:
TV viewing time was associated with total sedentary time in both women and men (Figure 1). There was no significant interaction between gender and TV viewing time (F(df: 5, 26)=1.52, p=0.22 without adjustment, F(df 5, 26)=2.03, p=0.11 with adjustment) and the linear trend was similar for women and men (Figure 1, also see Table, SDC 1, for population weighted percentages of participants in self-reported TV viewing time categories).
Figure 1-.
Mean (95%CI) accelerometer-derived sedentary time (hrs/day) by categories of self-reported TV viewing time (hrs/day), and linear trend (B (95%CI), p), for women and men (A), age groups (B), racial/ethnic groups (C) and work status (D). *p<0.05 compared to <1hr TV viewing per day. Population weighted percentages (n) of participants in self-reported TV viewing time categories stratified by gender, age, ethnicity and work status are included in the Table in supplemental digital content 1.
Associations by age:
There was an association between TV viewing time and sedentary time in all age groups (Figure 1). The linear trend was least evident in the younger aged (20-39yrs) adults, among whom sedentary time was only significantly different in those reporting the highest category (≥5hrs/day) versus the lowest category (<1hr/day) of TV viewing time (Figure 1). The age interaction was significant, with the association between TV viewing time and sedentary time stronger at mid-age (40-59yrs) and older age (60+yrs) than at a younger age (20-39yrs) (F(df: 10,21)=3.52, p=0.01 without adjustment, F(df: 10,21) =2.65, p=0.03 with adjustment).
Associations by race/ethnicity:
Stratified linear regression analyses revealed a positive association between TV viewing time and total sedentary time within all racial/ethnic groups (Figure 1). Interaction terms showed no significant differences in this association by race/ethnicity (F (df: 10, 21) =1.27, p=0.31 without adjustment, F (df: 10, 21) =0.75, p=0.67 with adjustment). However, for those of Non-Hispanic black and Mexican American race/ethnicity, the association between TV viewing time categories and mean sedentary time was only significant for those reporting five hours or more of TV viewing per day compared to less than one hour (Figure 1). In contrast, the relationship between the two variables was more linear for Non-Hispanic whites.
Associations by work status:
In working-aged adults (20-65 years, NHANES 2003-04), the association between TV viewing time and total sedentary time was present in the non-working sub-group and marginally significant for those in part-time work but not for those in full-time work (Figure 1). For full and part-time workers, an association between TV viewing time categories and mean sedentary time was only significantly different in those reporting the highest category (≥5hrs/day) versus the lowest category (<1hr/day) of TV viewing time, possibly more indicative of a threshold shift at this level than an overall relationship. Despite the apparent differences, the association did not differ significantly by work status (F (df: 5, 11) =2.19, p=0.13 without adjustment, F (df: 5, 11) =1.14, p=0.40 with adjustment).
Relative agreement between TV and total sedentary time
Table 2 presents correlations between participants’ self-reported TV viewing time and total accelerometer-derived sedentary time (Spearman’s rho). The measures were correlated more than would be expected by chance (p<0.001) for the overall population although the strength of the correlation was only fair.
Table 2:
Rank order correlations (Spearman’s rho) for self-reported TV viewing time with accelerometer-derived sedentary time (hrs/day), stratified by gender, age, ethnicity and work status.
|
|||
---|---|---|---|
rho | 95%CI | p value | |
Total group | 0.22 | 0.20, 0.25 | <0.001 |
Gender | |||
Women | 0.23 | 0.20, 0.27 | <0.001 |
Men | 0.21 | 0.17, 0.25 | <0.001 |
Age | |||
20-39 years | 0.05 | 0.00, 0.10 | 0.04 |
40-59 years | 0.17 | 0.12, 0.21 | <0.001 |
60+ years | 0.23 | 0.19, 0.27 | <0.001 |
Race/Ethnicity | |||
Non-Hispanic white | 0.25 | 0.21, 0.28 | <0.001 |
Non-Hispanic black | 0.20 | 0.14, 0.25 | <0.001 |
Mexican-American | 0.20 | 0.14, 0.25 | <0.001 |
Working-age population | |||
Full-time work | 0.03 | −0.02, 0.09 | 0.26 |
Part-time work | 0.14 | −0.02, 0.29 | 0.09 |
Non-working | 0.22 | 0.15, 0.30 | <0.001 |
Total group includes ‘other race’. Participants in working-age population are aged 20-65 years from 2003-4 cycle only.
Except for full-time workers and young adults (20-39 years), the relative agreement between the TV viewing and sedentary time was either fair or at the upper end of slight classification for all population subgroups. As expected from the linear regressions, relative agreement was lower among the younger age category (20-39 years) than other ages, lower (and not significant) for full-time and part-time workers than non-working adults of working age, and slightly higher among non-Hispanic whites than the other two racial/ethnic groups. Across the board, relative agreement was much below the ‘substantial’ or ‘almost perfect’ relationships that would be expected if TV viewing time was considered to be representative of total sedentary time.
DISCUSSION
Our findings show a relationship of TV viewing time with total accelerometer-derived sedentary time in the overall study population, evidenced by a significant association and rank-order correlation between the two measures. However, the strength of the agreement was only fair, and adults that reported less than one hour per day of TV viewing still spent nearly eight hours per day in sedentary time. Thus, self-report TV viewing time may not be truly representative of accelerometer-measured overall sedentary time. To capture a more complete picture of daily sedentary time, measurement of sitting time across other domains (work and travel), as well as other leisure-time behaviors (e.g. reading, computer use) may also be important.
The correlation we observed between our measures was similar or slightly lower than findings from criterion-validity studies of total sitting time questionnaires with accelerometer data as the criterion (11, 30) indicating that error associated with self-report may be at issue here. However, despite concerns regarding comparing a self-report to an objective instrument, the impact of TV viewing time on sedentary time cannot be ignored, given that it is a highly prevalent leisure-time sedentary behavior (4, 36). A recent time use study showed TV viewing was the most common leisure-time activity; over 80% of US adults reported watching TV on the surveyed day and for those who reported watching TV, viewing time averaged close to 3 ½ hrs (4). We found an association between TV viewing time and sedentary time for all groups, except those in full-time work. Therefore, those reporting higher levels of TV viewing time had higher sedentary time on average than those who reported lower levels of TV viewing time. TV viewing time may to some extent reflect a broader pattern of sedentary behavior in the population as a whole, which is important to consider in light of the findings regarding its detrimental associations with risk biomarkers and health outcomes (13–15, 19–22, 40).
No gender or race/ethnicity differences were observed in the relationship of TV viewing time with accelerometer-derived sedentary time, suggesting that TV viewing time is a useful measure across these groups. However, there was a non-linear relationship between the two measures for Mexican American and non-Hispanic blacks with a stronger association at high (5+hrs/day) levels of TV viewing time. Where possible, continuous measures of TV viewing time should be used to enable selection of cut points suitable for ethnically diverse populations.
TV viewing time was more strongly related to total sedentary time for those in the mid- (40-59yrs) and older-aged groups (60yrs+), compared to the younger age group (20-39yrs) indicating that TV viewing may better reflect total sedentary time in older than younger adults. Other media-based behaviors, such as social networking on computers and mobile ‘phones, may be contributing to sedentary time in younger adults. A recent study from Canada showed that the proportion of screen time reported as computer use (as distinct from TV viewing time) was significantly higher in younger compared to older adults (34), therefore measuring these other screen-based behaviors may be important in research involving younger adults. The age difference may also in part be due to the employment status of the participants, particularly in the oldest age group, many of whom could have retired. In the working-age population, the relationship of TV viewing time with sedentary time was present in the non-working sub-group but not those in full-time work. Previous research has shown those in full-time work are less likely to watch high levels of TV (10). Full-time workers may have less leisure time available for TV viewing, compared to those who do not work, with other sedentary behaviors comprising more of their total sedentary time. Measuring sedentary time in the working population requires development of measures of sedentary behavior in the workplace. The development of such self-report measures has been recommended in a recent review of papers reporting the association between workplace sitting and detrimental health outcomes (38).
The key strength of this study is the use of data from a large, multi-ethnic, population representative survey, with concurrent objective and self-report measures. Accelerometer-derived sedentary time, being an objective measure, has the advantage of being unaffected by recall error or self-report biases. There are some limitations, however, in the use of accelerometers as a criterion measure for true sedentary time. First, as accelerometers do not detect body position, the measure is indirect. Therefore, periods of low movement (<100 cpm) may include some time spent standing still, resulting in overestimation of sedentary time. In a small criterion validity study, the correlation between accelerometer-derived sedentary time using the <100 cpm cut point and a more direct measure of body posture (sitting, reclining and lying, 42) was only r=0.59 (25). Second, the sedentary time estimate is affected by monitor wear time, which appears to have been less than the intended coverage of “all waking hours” (average wear time=14.6 hours, range 10.3 - 24.0 hours). Times that are not captured are likely to be early mornings and late evenings, which are possibly more sedentary periods of the day, thereby resulting in underestimation of sedentary time.
Another limitation of the study is the measure of self-report TV viewing time available in NHANES. Validity of the measure has not been reported, and it has low test-retest reliability (ICC: 0.32, 95%CI: 0.14-0.48) (17) relative to other self-report measures of TV viewing time whose reliability (ICC) typically ranges from 0.7-0.9 (27, 33, 43). We cannot be certain as to whether a different level of relative agreement overall or different patterning across population sub-groups might have been seen with other TV viewing time measures. These variations in self-report measures will remain a limitation in the assessment of TV viewing time, until objective measures of this behavior suitable for use in epidemiological and health-behavior studies have been developed. Furthermore, the relationship between the two measures may have been underestimated as the measurement period for the TV viewing time question differed from the period of accelerometer wear from which sedentary time was derived. Respondents were asked to recall their average hours of daily TV viewing time over the past 30 days, while accelerometer data collection covered seven days, which has been recommended to obtain a representative measure of habitual physical inactivity (24). As our minimum number of valid days for accelerometer wear was only four, we may not have captured a typical pattern of sedentary time, however, close to half of our sample (48.2%) provided seven valid days of monitor wear. The comparison of a categorical measure to a continuous measure also presented difficulties including limiting the analyses that could be performed, and the highest option for TV viewing time (5+hrs/day) was lower than the mean sedentary time.
Conclusions
A better understanding of commonly-used indices of sedentary behaviors is a fundamental element for the interpretation of the findings of epidemiological and health-behavior studies, and to guide the development of evidence-based public health interventions (31). Objective measurement of sedentary behavior, such as by accelerometer, is expensive and often not feasible in large-scale studies. Furthermore, objective measures cannot provide domain specific data. Therefore, self-report measures of sedentary behavior, typically TV viewing time, have been widely used (8, 15, 20, 22, 32). Our findings showed that there was an association between self-report TV viewing time and accelerometer-derived sedentary time, indicating that this behavior has an important influence on sedentary time at a population level and therefore may be useful in epidemiological surveys. However, the relative agreement between the two measures was only fair. Therefore, self-reported TV viewing time did not appear to provide a good reflection of total sedentary time captured using accelerometers at the individual level. Importantly, the relationship between TV viewing time and total sedentary time was consistent for subgroups of gender and race/ethnicity, but not age and work status. Future research should take into account these differences in the performance of self-report TV viewing time measures and consider measurement of other sedentary behaviors during leisure-time and the domains of travel and work.
Supplementary Material
ACKNOWLEGEMENTS
All data used in this study were collected by the National Center for Health Statistics, Centers for Disease Control and Prevention.
Clark is supported by an Australian Post Graduate Award Scholarship and Queensland Health funding. Healy is supported by a NHMRC (#569861) / National Heart Foundation of Australia (PH 08B 3905) Postdoctoral Fellowship. Dunstan is supported by a Victorian Health Promotion Foundation Public Health Research Fellowship. Winkler, Sugiyama and Owen are supported by a Queensland Health Core Research Infrastructure grant and by NHMRC Program Grant funding (#301200). Gardiner is supported by a National Heart Foundation of Australia (#PP 06B 2889) Postgraduate Scholarship.
The results of this study do not constitute an endorsement by the American College of Sports Medicine.
Footnotes
CONFLICT OF INTEREST
The authors have no conflict of interest to declare.
References
- 1.Aitken M, Francis B, Hinde J, and Darnell R. Statistical Modelling in R. Oxford: Oxford Statistical Science, 2009. [Google Scholar]
- 2.Bertrais S, Beyeme-Ondoua J-P, Czernichow S, Galan P, Hercberg S, and Oppert J-M. Sedentary behaviors, physical activity, and metabolic syndrome in middle-aged French subjects. Obesity Research. 2005;13(5):936–44. [DOI] [PubMed] [Google Scholar]
- 3.Bowman SA. Television-viewing characteristics of adults: correlations to eating practices and overweight and health status. Preventing Chronic Disease. 2006;3(2):1–11. [PMC free article] [PubMed] [Google Scholar]
- 4.Bureau Labor Statistics. American time use survey 2009 results. Department of Labor US Government, 2009. [Google Scholar]
- 5.CDC/National Center for Health Statisitics. Documentation, codebook and frequencies: physical activity monitor NHANES 2005-2006 [Internet]. Hyattsville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention; [cited 2009 May 30] Available from: http://www.cdc.gov/nchs/data/nhanes/nhanes_05_06/paxraw_d.pdf [Google Scholar]
- 6.CDC/National Center for Health Statistics. National Health and Nutrition Examination Survey: analytic guidelines [Internet]. Hyattsville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention; [cited 2009 June 18] Available from: http://www.cdc.gov/nchs/data/nhanes/nhanes_03_04/nhanes_analytic_guidelines_dec_2005.pdf [Google Scholar]
- 7.CDC/National Center for Health Statistics. NHANES 2003-2004 and 2005-2006 data files: data, docs, codebooks, SAS code [Internet]. Hyattsville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention; [cited 2009 June 18] Available from: http://www.cdc.gov/nchs/nhanes.htm. [Google Scholar]
- 8.Ching PL, Willett WC, Rimm EB, Colditz GA, Gortmaker SL, and Stampfer MJ. Activity level and risk of overweight in male health professionals. American Journal of Public Health. 1996;86(1):25–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Clark BK, Sugiyama T, Healy GN, Salmon J, Dunstan DW, and Owen N. Validity and reliability of measures of television viewing time and other non-occupational sedentary behavior of adults: a review. Obesity Reviews. 2009;10:7–16. [DOI] [PubMed] [Google Scholar]
- 10.Clark BK, Sugiyama T, Healy GN, et al. Socio-demographic correlates of prolonged television viewing time in Australian men and women: the AusDiab study. Journal of Physical Activity and Health. 2010;7(5):595–601. [DOI] [PubMed] [Google Scholar]
- 11.Craig CL, Marshall AL, Sjostrom M, et al. International physical activity questionnaire: 12 country reliability and validity. Medicine & Science in Sports & Exercise. 2003;35:1381–95. [DOI] [PubMed] [Google Scholar]
- 12.Dong L, Block G, and Mandel S. Activities contributing to total energy expenditure in the United States: results from NHAPS study. Int J Behav Nutr Phys Act. 2004;1(4). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Dunstan DW, Barr ELM, Healy GN, et al. Television viewing time and mortality: the Australian Diabetes, Obesity and Lifestyle Study (AusDiab). Circulation. 2010;121:384–391. [DOI] [PubMed] [Google Scholar]
- 14.Dunstan DW, Salmon J, Healy GN, et al. Association of television viewing with fasting and 2-h postchallenge plasma glucose levels in adults without diagnosed diabetes. Diabetes Care. 2007;30(3):516–522. [DOI] [PubMed] [Google Scholar]
- 15.Dunstan DW, Salmon J, Owen N, et al. Associations of TV viewing and physical activity with the metabolic syndrome in Australian adults. Diabetologia. 2005;48(11):2254–2261. [DOI] [PubMed] [Google Scholar]
- 16.Dunstan DW, Salmon J, Owen N, et al. Physical activity and television viewing in relation to risk of undiagnosed abnormal glucose metabolism in adults. Diabetes Care. 2004;27(11):2603–9. [DOI] [PubMed] [Google Scholar]
- 17.Evenson KR, and McGinn AP. Test-retest reliability of adult surveillance measures for physical activity and inactivity. American Journal of Preventive Medicine. 2005;28(5):470–478. [DOI] [PubMed] [Google Scholar]
- 18.Ford ES, Kohl HW III, Mokdad AH, and Ajani UA. Sedentary behavior, physical activity, and the metabolic syndrome among U.S. adults. Obesity Research. 2005;13(3):608–14. [DOI] [PubMed] [Google Scholar]
- 19.Healy GN, Dunstan DW, Salmon J, Shaw JE, Zimmet PZ, and Owen N. Television time and continuous metabolic risk in physically active adults. Medicine and Science in Sports and Exercise. 2008;40(4):639–645. [DOI] [PubMed] [Google Scholar]
- 20.Hu FB, Leitzmann MF, Stampfer MJ, Colditz GA, Willett WC, and Rimm EB. Physical activity and television watching in relation to risk for type 2 diabetes mellitus in men. Archives of Internal Medicine. 2001;161(12):1542–8. [DOI] [PubMed] [Google Scholar]
- 21.Hu FB, Li TY, Colditz GA, Willett WC, and Manson JE. Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. Journal of American Medical Association. 2003;289:1785–91. [DOI] [PubMed] [Google Scholar]
- 22.Jacoby E, Goldstein J, Lopez A, Nunez E, and Lopez T. Social class, family, and life-style factors associated with overweight and obesity among adults in Peruvian cities. Preventive Medicine. 2003;37(5):396–405. [DOI] [PubMed] [Google Scholar]
- 23.Landis JR, and Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159–74. [PubMed] [Google Scholar]
- 24.Matthews CE, Ainsworth BE, Thompson RW, and Bassett DR Jr., Sources of variance in daily physical activity levels as measured by an accelerometer. Med Sci Sports Exerc. 2002;34(8):1376–81. [DOI] [PubMed] [Google Scholar]
- 25.Matthews CE, Chen KY, Freedson PS, et al. Amount of time spent in sedentary behaviors in the United States, 2003-2004. American Journal of Epidemiology. 2008;167(7):875–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Matton L, Wijndaele K, Duvigneaud N, et al. Reliability and validity of the Flemish Physical Activity Computerized Questionnaire in adults. Research Quarterly for Exercise and Sport. 2007;78(4):293–306. [DOI] [PubMed] [Google Scholar]
- 27.McCormack G, Giles-Corti B, and Milligan R. The test-retest reliability of habitual incidental physical activity. Australian and New Zealand Journal of Public Health. 2003;27(4):428–433. [DOI] [PubMed] [Google Scholar]
- 28.Owen N, Healy GN, Matthews CE, and Dunstan DW. Too much sitting: the population health science of sedentary behavior. Exerc Sport Sci Rev. 2010;38(3):105–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Pate RR, O’Neill JR, and Lobelo F. The evolving definition of “sedentary”. Exercise Sport Science Reviews. 2008;36(4):173–8. [DOI] [PubMed] [Google Scholar]
- 30.Rosenberg DE, Bull FC, Marshall AL, Sallis JF, and Bauman AE. Assessment of sedentary behavior with the International Physical Activity Questionnaire. Journal of Physical Activity and Health. 2008;5(Supplement 1):s30–s44. [DOI] [PubMed] [Google Scholar]
- 31.Sallis JF, Owen N, and Fotheringham MJ. Behavioral epidemiology: a systematic framework to classify phases of research on health promotion and disease prevention. Annals of Behavioral Medicine. 2000;22(4):294–8. [DOI] [PubMed] [Google Scholar]
- 32.Salmon J, Bauman A, Crawford D, Timperio A, and Owen N. The association between television viewing and overweight among Australian adults participating in varying levels of leisure-time physical activity. International Journal of Obesity & Related Metabolic Disorders. 2000;24(5):600–6. [DOI] [PubMed] [Google Scholar]
- 33.Salmon J, Owen N, Crawford D, Bauman A, and Sallis JF. Physical activity and sedentary behavior: a population-based study of barriers, enjoyment, and preference. Health Psychology. 2003;22(2):178–188. [DOI] [PubMed] [Google Scholar]
- 34.Shields M, and Tremblay MS. Screen time among Canadian adults: a profile. Health Reports. 2008;19(2):31–43. [PubMed] [Google Scholar]
- 35.Sidney S, Sternfeld B, Haskell WL, Jacobs DR Jr., Chesney MA, and Hulley SB. Television viewing and cardiovascular risk factors in young adults: the CARDIA study. Annals of Epidemiology. 1996;6(2):154–9. [DOI] [PubMed] [Google Scholar]
- 36.Sugiyama T, Healy GN, Dunstan DW, Salmon J, and Owen N. Is television viewing time a marker of a broader pattern of sedentary behavior? Annals of Behavioral Medicine. 2008;35:245–250. [DOI] [PubMed] [Google Scholar]
- 37.Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, and McDowell M. Physical activity in the United States measured by accelerometer. Medicine & Science in Sports & Exercise. 2008;40(1):181–8. [DOI] [PubMed] [Google Scholar]
- 38.van Uffelen JGZ, Wong J, Chau JY, et al. Occupational sitting and health risks: a systematic review. American Journal of Preventive Medicine. 2010;39(4):379–388. [DOI] [PubMed] [Google Scholar]
- 39.Willett W, and Stamfler M. Total energy intake: implications for epidemiologic analyses. American Journal of Epidemiology. 1986;124(1):17–27. [DOI] [PubMed] [Google Scholar]
- 40.Williams DW, Raynor HA, and Ciccolo JT. A review of TV viewing and its association with health outcomes in adults. American Journal of Lifestyle Medicine. 2008;2:250–259. [Google Scholar]
- 41.Winkler AE, Gardiner PG, Healy GN, et al. Distinguishing true sedentary from accelerometer non-wearing time: accuracy of two automated wear-time estimations. Medicine & Science in Sports & Exercise. 2009;41(5):S127–128. [Google Scholar]
- 42.Zhang K, Werner P, Sun M, F P-S, and Boozer C. Measurement of human daily physical activity. Obesity Research. 2003;11(1):33–40. [DOI] [PubMed] [Google Scholar]
- 43.Zhang M, Xie X, Lee AH, and Binns CW. Sedentary behaviors and epithelial cancer risk. Cancer Causes & Control. 2004;15:83–89. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.