- Email: [email protected]
Non-Occupational Sedentary Behaviors
Non-Occupational Sedentary Behaviors Population Changes in the Netherlands, 1975–2005 Hidde P. van der Ploeg, PhD, Kamalesh Venugopal, PhD, Josephine Y. Chau, PhD, Mireille N.M. van Poppel, PhD, Koen Breedveld, PhD, Dafna Merom, PhD, Adrian E. Bauman, PhD Background: Evidence is accumulating that sedentary behaviors have detrimental health effects. Comprehensive data on population changes in various sedentary behaviors over time are scarce.
Purpose: This study aimed to determine changes in non-occupational sedentary behaviors in the Dutch adult population between 1975 and 2005. Methods: The National Time Use Survey of the Netherlands was used, which has been collected in 5-year intervals since 1975 (seven time points, n range⫽1017–2845). Adult participants completed a 7-day time-use diary in which they recorded their primary activity in 15-minute intervals throughout or at the end of the day. A validated method was used to determine time spent in various nonoccupational sedentary behaviors. Population-weighted analyses determining changes over time in various sedentary behaviors were carried out in 2011 and 2012.
Results: Between 1975 and 2005, the proportion of non-occupational time spent sedentary remained relatively constant at ⬃60%. However, absolute time decreased, because of a 4.7-hour/week increase in occupational time. Sedentary occupational time could not be studied but has likely increased over these 3 decades. Most non-occupational sedentary behavior was during leisure, and the proportion of sedentary leisure time that comes from screen time increased from 26% in 1975 to 43% in 2005. Between 1975 and 2005, sedentary transport increased by 2 hours/week. Conclusions: The nature and distribution of sedentary behaviors in the Dutch adult population changed substantially over 3 decades. Screen-based activities are playing an increasingly dominant role. (Am J Prev Med 2013;44(4):382–387) © 2013 American Journal of Preventive Medicine
Background
A
n increasing body of evidence suggests that sedentary behaviors can have a detrimental effect on health.1– 4 Sedentary behavior is defıned as activities that incur 1.5 or fewer METs, such as sitting and reclining, and is distinct from inactivity, which refers to the lack of moderate- to vigorous-intensity physical activity.5 Prospective studies have suggested that all-cause and From the Sydney School of Public Health (van der Ploeg, Venugopal, Chau, Bauman), University of Sydney; the School of Biomedical and Health Services (Merom), University of Western Sydney, Sydney, Australia; Department of Public and Occupational Health (van der Ploeg, van Poppel), EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam; and the Mulier Institute (Breedveld), Utrecht, The Netherlands Address correspondence to: Hidde P. van der Ploeg, PhD, Department of Public and Occupational Health, VU University Medical Center, van der Boechorststraat 7, 1081BT, Amsterdam, the Netherlands. E-mail: [email protected]. 0749-3797/$36.00 http://dx.doi.org/10.1016/j.amepre.2012.11.034
382 Am J Prev Med 2013;44(4):382–387
cardiovascular mortality is adversely associated with total sitting time independently of physical activity and BMI.6 – 8 Although prospective data have suggested that sitting in a car,9 and during people’s main activities (e.g., work, school, housework),4,10 is associated with mortality from all causes and from cardiovascular disease, most of the evidence has been accumulated on the detrimental effect of leisure-time sedentary behavior.11 In particular, prolonged TV viewing,7,9,12,13 and recreational screen time in general14 have been studied more extensively, mostly because more prospective data are available on these sedentary behaviors. A meta-analysis showed TV viewing was associated with higher risks of type 2 diabetes, cardiovascular disease, and all-cause mortality.1 Reviews suggest that high volumes of sedentary behaviors might increase the risk of obesity, cardiovascular disease, diabetes, and cancer2– 4 but also note that sedentary behavior and health research is in its infancy with evidence still accumulating, and more high-quality studies with better measures of sedentary behaviors are needed.
© 2013 American Journal of Preventive Medicine • Published by Elsevier Inc.
van der Ploeg et al / Am J Prev Med 2013;44(4):382–387
Recently, the observed cardiometabolic risks of sedentary behavior have resulted in the emergence of pilot-type studies that specifıcally aim to reduce sedentary behavior.15–18 These studies generally show promising results with regard to feasibility and reductions in sitting time, mostly of sit–stand workstations in work or school settings. It has also been stressed that future controlled trials on the feasibility and effıcacy of interventions to reduce and break up sedentary behaviors among adults in domestic, workplace, and transportation environments are particularly required.19 However, there is a large gap in knowledge with regard to determinants of sedentary behaviors that needs to be addressed in order to better inform the development of successful interventions.20 Few studies have focused on changes over time in population levels of various sedentary behaviors. Data from the U.S. have shown increases in sedentary occupations between 1960 and 2010,21 and increases in TV viewing between 1950 and 2000.22 However, besides type of occupation and TV viewing, national health surveys have only more recently been collecting data on other sedentary behaviors, which makes it hard to study trends over time. Non-health-focused population surveys such as time use surveys might help bridge this gap in knowledge. Time use surveys, which have their origin in sociology, collect data on what people do throughout the whole day or even week at a detailed level,23 and have been increasingly utilized in public health research.24 –30 The authors recently showed that estimating participation in nonoccupational sedentary, light-, and moderate-intensity activities from time use surveys was more accurate than traditional physical activity surveillance systems.31 This has opened the door for using time use surveys to study population trends, which have been collected in many countries in population-representative samples since as early as the 1960s. Unfortunately, changes over the years in the methodology of collecting the time use surveys, such as in the American Time Use Survey, can compromise the study of population trends over time.32 The national time use survey of the Netherlands has been collected every 5 years between 1975 and 2005 in population-representative samples and has maintained almost the same methodology over time. This makes it an excellent set of surveys to study time trends in sedentary behavior at a detailed level. Hence, the aim was to study changes in a range of sedentary behaviors in the Dutch adult population over a 30-year period.
Methods Study Design The Time Use Survey of the Netherlands has been collected in 5-year intervals between 1975 and 2005 (seven consecutive April 2013
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surveys). The survey was commissioned by the Netherlands Institute for Social Research, a government institution within the ministry of Health, Wellbeing, and Sports. Fieldwork was undertaken by Intomart GfK, which conducts fıeldwork according to the ICC/ESOMAR Code on Market and Social Research and is a member of the Dutch Association for Market Research MOA. Over this 30-year period, fıeldwork was carried out by the same agency, and basic design and instruments were kept consistent. Data were collected in the fırst 2 weeks of October of each survey year. Access to the time use survey data sets was provided by the Data Archiving and Networked Services (DANS) from the Netherlands (www. dans.knaw.nl/en).
Participants For each survey, a representative national sample was drawn by randomly selecting households in which one person aged ⱖ12 years was randomly selected for participation. For the purpose of the present study, the focus was on participants who were aged ⱖ20 years. The number of participants in the various survey years ranged from 1017 to 2845. Potential participants were presented with written information on the study, and participants provided verbal informed consent. More details on recruitment and sampling of participants can be found elsewhere.33,34
Measurement The assessments consisted of two personal home interviews and a self-completed time use diary. The fırst home interview aimed to collect personal and household characteristics and explained the time use diary. The second home interview was used to collect additional personal characteristics and to correct inconsistencies in the time use diary. In 2005 (only), this included self-reported body height and weight, which was used to calculate BMI. The time use diary was completed for 7 consecutive days by the participant between the fırst and second home interview. The time use diary is completed during or at the end of each day and records the participant’s primary activity, secondary activity and whether they were at home or not every 15 minutes. The primary activity was the main activity that was done for the majority of each 15-minute period in the diary (e.g., walking for leisure) and any secondary activities were those done at the same time (e.g., listening to music while walking). Secondary activities were always of a lower physical intensity than the primary activity. Activities were recorded as ⬃200 preset activity codes. All original ⬃200 time use activity codes were recoded into seven domains: sleep, occupation, leisure, transport, household, education, and voluntary work; and also into three intensity categories: sedentary (ⱕ1.5 METs); light (1.5–3 METs); and moderate- to vigorous-intensity activity (ⱖ3 METs). Activities were assigned to the three intensity categories based on the Compendium of Physical Activities,35 and the intensity coding system used in the American Time Use Survey.36 This method to estimate participation in sedentary, light-intensity, and moderate- to vigorousintensity activities has been previously shown to have acceptable reliability and validity.31 Occupational activities were not recoded into the three intensity categories, because the original codes provided insuffıcient detail. Total non-occupational time was the sum
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40 35 Hours/week
30 25 20 15 10 5 0 1975
1980
1985
1990
1995
2000
2005
Year Leisure time
Household time
Occupational time
Transport time
Figure 1. Time use among Dutch adults by survey year Note: Time use is weighted and age-standardized.
of all activities in the leisure, transport, household, education, and voluntary work domains.
Data Analysis Results were presented as weighted means with SDs. Results were age-standardized to the age distribution in 2000 where appropriate. In order to form a representative sample of the Dutch adult population, participants were weighted for the number of people aged ⱖ12 years in the household, place in the household, degree of urbanization of city, age, gender, and main daily activity. In 1975 and 1980, participants were not weighted for age, gender, and main daily activity. Time trends from 1975 to 2005 were calculated with linear regression models and were adjusted for age, gender, and education level. For 2005, estimated marginal means were calculated for sedentary behaviors stratifıed by gender and BMI category using univariate general linear models and adjusted for age. All analyses were performed in SPSS, version 19, between 2011 and 2012.
Results Appendix A (available online at www.ajpmonline.org) presents the response rates and personal characteristics of participants in all seven time use surveys. The response rate was around 60% for most of the surveys, but dropped in 1995 and somewhat recovered again in the last two surveys. In line with societal trends, the participating populations over time became older and better educated, and car, TV, and computer ownership increased. Workforce participation and retirement also became more prevalent, in line with increasing gender equality and the aging of the population. Between 1975 and 2005, Dutch adults increased their time spent at work and in transit by 4.7 and 3 hours/week, respectively, while leisure and household time decreased by 4.7 and 3.6 hours/week, respectively (Figure 1; also see
Appendix B, available online at www.ajpmonline.org). Non-occupational sedentary time (excluding sleep) decreased over this 30-year period by 4.5 hours/week, but remained relatively constant at approximately 60% of total non-occupational time. The decrease in nonoccupational sedentary time was mostly accounted for by a decrease in sedentary leisure time, which decreased from 90% of leisure time in 1975 to 84% in 2005. Between 1975 and 2005, inactive forms of transportation increased by 2 hours/week (Figure 2). Figure 3 provides a closer look at sedentary leisure time by breaking it down into specifıc activities (see also Appendix C, available online at www.ajpmonline.org). The proportion of sedentary leisure time that comes from screen time (TV and computer use) increased from 26% in 1975 to 43% in 2005. Home computer use doubled every survey since it was fırst measured in 1985 and makes up an increasing proportion of total screen time. All other sedentary leisure behaviors decreased over the 30-year period, with the exception of sedentary leisure transport, which increased slightly. Appendix D (available online at www.ajpmonline. org) presents total non-occupational sedentary time (excluding sleep) and sedentary leisure-time behaviors compared between normal-weight and overweight and obese people stratifıed by gender in 2005. Sedentary leisure time and screen time were signifıcantly higher in overweight and obese men compared to normal-weight men. Screen time and TV viewing were signifıcantly higher in overweight and obese women compared to normal-weight women. No other sedentary behaviors differed between the two BMI groups. 45 40 35 30 25 20 15 10 5 0 Leisure Household Transport Leisure Household Transport Leisure Household Transport Leisure Household Transport Leisure Household Transport Leisure Household Transport Leisure Household Transport
45
Hours/week
384
1975
1980
1985
1990
1995
2000
2005
Activity/year Sedentary
Light + MVPA
Figure 2. Time spent in sedentary and active leisure, household activities, and transport activities among Dutch adults by survey year Note: Time use is weighted and age-standardized. MVPA, moderate- to vigorous-intensity physical activity
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van der Ploeg et al / Am J Prev Med 2013;44(4):382–387 16 14
Hours/week
12 10 8 6 4 2 0 1975
1980
1985
1990
1995
2000
2005
Year Screen time (TV + computer) Computer Socializing Sedentary leisure transport Other
Watching TV Reading Needle work Listening to radio/music
Figure 3. Sedentary leisure time among Dutch adults by survey year Note: Time use is weighted and age-standardized.
Discussion Between 1975 and 2005, the amount of time Dutch adults spent at work and in transit increased substantially and was accompanied by decreasing leisure time and time spent on household tasks. The increase in occupational time logically resulted in a decrease in non-occupational time and an absolute but not relative decrease in nonoccupational sedentary time. This decrease in nonoccupational sedentary time was mostly accounted for by a decrease in sedentary leisure time, whereas sedentary transport time increased over the 30-year period. However, in 2005 Dutch adults still spent 84% of leisure time being sedentary, and 59% of non-occupational sedentary time was attributed to leisure time. Hence, given the detrimental health effects of sedentary behavior, it seems that leisure time should be a primary target in intervention studies aimed at reducing sedentary behavior. The results also showed a dramatic shift within sedentary leisure time toward screen-based activities (TV and computer use) and away from most other sedentary leisure time activities. As most of the evidence on the detrimental effects of sedentary behaviors has been centered around screen-based behavior,1,9,12–14 the reported increase in screen time might not be a good development for public health. However, screen time has received the most research attention because it was included in earlier studies, whereas other sedentary behaviors were less often assessed and still need to be further researched to help build evidence. April 2013
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Although the reported higher rate of screen time for overweight and obese people compared to normalweight people seems to suggest that screen time might be a bigger health concern than other sedentary behaviors, these data were cross-sectional and made it impossible to determine causality. The lack of prospective studies that were able to attribute health risks to various types of sedentary behaviors, and especially the role of screen time in comparison to non-screen-based activities, makes it diffıcult to draw conclusions about the importance of the observed increase in screen time for public health. However, screen time is the main contributor to sedentary leisure time, which is a primary candidate to be targeted in interventions aimed at reducing sedentary behaviors. Hence, strategies to reduce screen time or make screenbased activities less sedentary could be an important component of such interventions. An important question that cannot be answered by the Dutch time use surveys is what happened to total sedentary time over these 3 decades. The intensity of activities during occupational time cannot be determined in the Dutch time use survey data because of insuffıcient detail in the original coding. However, total occupational time increased over the survey period. Data from the U.S. have suggested that jobs have become increasingly sedentary between 1960 and 2010,21 which is a trend that is likely to translate to the Dutch workforce. Hence, the observed decrease in non-occupational sedentary time could be offset or even surpassed by the likely increase in occupational sedentary time, which would imply a stable or increased health burden of sedentary behavior over time in the Netherlands. The current fındings are in line with the increases in TV viewing observed in the U.S. between 1950 and 2000, which showed a similar steady increase but higher absolute rates.22 These lower absolute rates in the Netherlands are partly due to the fact that the current study only captured TV viewing as a primary activity and not as a secondary activity. For example, watching TV during dinner would be coded as a sedentary (as both activities are sedentary) household activity and not as watching TV or sedentary leisure time. In 2005, TV viewing seemed to decline in Dutch adults, and it seems that TV viewing might have been substituted to some extent with leisure computer use, with total screen time still on the increase. Such a change in the composition of screen time is likely to have continued since 2005 with the growing popularity of game consoles, the emergence of new technologies such as computer tablets and smart phones, and the increasing availability of audiovisual content on the Internet. Leisure-time screen-based activities seem to be evolving so quickly that it might be necessary to further separate kinds of screen
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time, especially computer-based activities, when determining their influence on health outcomes. Motionbased computer games, which are unlikely to qualify as sedentary activity, are an obvious example of this. However, motion-based game consoles (such as the Nintendo Wii) were only introduced on a large scale in 2006, well after the completion of the last Dutch time use survey.
Limitations The Dutch time use surveys present a rather unique opportunity to study changes in a range of sedentary behaviors over 3 decades. A limitation of utilizing the Dutch time use surveys for this purpose was the 15-minute interval in which the diaries recorded activities. This might have resulted in under- or over-estimation of activities of relatively short duration, especially those that were also infrequent. This makes the interpretation of the changes in the moderate- to vigorous-intensity activity data diffıcult as the time use diaries might not have had suffıcient sensitivity to detect true changes over time. The measurement study that previously showed acceptable validity for moderate- to vigorous-intensity activities when compared against an activity monitor used a 5-minute interval to record activities,31 which is what most other countries have used in their time use surveys. Hence, caution should be used in interpreting results based on infrequent activities of short duration. Another limitation was the decline in the response rate in 1995, 2000, and 2005, which could have resulted in some selection bias. However, participants were weighted in order to have a representative sample of the Dutch adult population. Further, the data collection in the same 2 weeks in October made the surveys somewhat vulnerable to weather variations. For example, the data collection period in 1980 and 2000 had substantially higher volumes of rain, which seems to be reflected somewhat in lower estimates of moderate- to vigorous-intensity activity in those two surveys. However, as discussed above, results relating to moderate- to vigorous-intensity activities should be interpreted with caution. Finally, although the methodology of the Dutch time use surveys was kept relatively constant over 3 decades, there were some small changes. Most notable is the partly different weighting methodology used in 1975 and 1980. However, the age standardization would have corrected the lack of weighting for age in these 2 years. The general patterns that were identifıed in the Netherlands over 3 decades are probably generalizable to many developed countries, as illustrated by the similar prevalence of sedentary behaviors in the Australian Bureau of Statistics 2006 time use survey.37 However, there will be some differences between developing countries, such as the higher prevalence of active travel in the Netherlands compared to more car-focused countries like the
U.S. and Australia, where the increase in sedentary forms of transport that was observed might be even higher. Although many other countries have national time use surveys that could, to some extent, be utilized to study changes in sedentary behaviors over time, surveillance systems specifıcally dedicated to capturing changes in sedentary behavior at the population level are needed. Especially, good-quality data on occupational sedentary behavior and total sedentary time are urgently needed to capture the complete sedentary behavior picture. A combination of an objective measure and a self-report measure that assesses various domains of sedentary behavior is recommended.
Conclusion Over the past 30 years, the proportion of nonoccupational time that is spent in sedentary behavior has hardly changed in the Netherlands. However, there have been substantial changes in the types of sedentary behaviors that make up the total volume of non-occupational sedentary time, with increasing contributions from inactive transport and screen-based activities. The public health consequences of the changes in the types of sedentary behaviors remain unclear and should be the subject of high-quality research in the future. Time at work has increased over the past 3 decades in the Netherlands, and work from the U.S. suggests that the proportion of occupational time spent being sedentary has also increased,21 which might offset or surpass the absolute decrease in non-occupational sedentary time. Leisure time is the greatest contributor to sedentary time; 84% of leisure time is spent sedentary, and leisure screen time is the greatest contributor to this. This could have substantial health consequences depending on sedentary levels throughout the rest of the day. Leisure time is when people generally can make their own decisions on how to spend their time and have the fewest obligations; hence, it is arguably the easiest time for people to change. Thus, targeting sedentary leisure time and especially screen time in public health interventions aimed at decreasing sedentary behaviors seems to be a strategy with great potential public health gains. Efforts to reduce occupational sedentary time and sedentary forms of transport are also important38,39 and have the potential for additional benefıcial effects to important problems such as productivity loss and absenteeism, global warming, natural resource utilization, and traffıc congestion. This work was supported by the National Health and Medical Research Council of Australia (program grant #301200). No fınancial disclosures were reported by the authors of this paper.
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References 1. Grontved A, Hu FB. Television viewing and risk of type 2 diabetes, cardiovascular disease, and all-cause mortality. JAMA 2011;305: 2448 –55. 2. Proper KI, Singh AS, van Mechelen W, et al. Sedentary behaviors and health outcomes among adults. Am J Prev Med 2011;40:174 – 82. 3. Thorp AA, Owen N, Neuhaus M, et al. Sedentary behaviors and subsequent health outcomes in adults. Am J Prev Med 2011;41:207–15. 4. van Uffelen JGZ, Wong J, Chau JY, et al. Occupational sitting and health risks: a systematic review. Am J Prev Med 2010;39:379 – 88. 5. Pate RR, O’Neill JR, Lobelo F. The evolving defınition of “sedentary.” Exerc Sport Sci Rev 2008;36:173– 8. 6. Inoue M, Iso H, Yamamoto S, et al. Daily total physical activity level and premature death in men and women: results from a large-scale population-based cohort study in Japan (JPHC Study). Ann Epidemiol 2008;18:522–30. 7. Matthews CE, George SM, Moore SC, et al. Amount of time spent in sedentary behaviors and cause-specifıc mortality in U.S. adults. Am J Clin Nutr 2012;95:437– 45. 8. van der Ploeg HP, Chey T, Korda RJ, et al. Sitting time and all-cause mortality risk in 222,497 Australian adults. Arch Intern Med 2012;172:494 –500. 9. Warren TY, Barry V, Hooker SP, et al. Sedentary behaviors increase risk of cardiovascular disease mortality in men. Med Sci Sports Exerc 2010;42:879 – 85. 10. Katzmarzyk PT, Church TS, Craig CL, et al. Sitting time and mortality from all causes, cardiovascular disease, and cancer. Med Sci Sports Exerc 2009;41:998 –1005. 11. Patel AV, Bernstein L, Deka A, et al. Leisure time spent sitting in relation to total mortality in a prospective cohort of U.S. adults. Am J Epidemiol 2010;172:419 –29. 12. 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 –91. 13. Wijndaele K, Brage S, Besson H, et al. Television viewing time independently predicts all-cause and cardiovascular mortality: the EPIC Norfolk Study. Int J Epidemiol 2011;40:150 –9. 14. Stamatakis E, Hamer M, Dunstan DW. Screen-based entertainment time, all cause mortality, and cardiovascular events. J Am Coll Cardiol 2011;57:292–9. 15. Alkhajah TA, Reeves MM, Eakin EG, et al. Sit-stand workstations: a pilot intervention to reduce offıce sitting time. Am J Prev Med 2012;43:298 –303. 16. Evans RE, Fawole HO, Sheriff SA, et al. Point-of-choice prompts to reduce sitting time at work: a randomized trial. Am J Prev Med 2012;43(3):293–7. 17. Hinckson EA, Aminian S, Ikeda E, et al. Acceptability of standing workstations in elementary schools: a pilot study. Prev Med 2013;56(1):82–5. 18. Pronk NP, Katz AS, Lowry M, Payfer JR. Reducing occupational sitting time and improving worker health: the Take-a-Stand Project, 2011. Prev Chronic Dis 2012;9:E154. 19. Owen N, Sugiyama T, Eakin EE, et al. Adults’ sedentary behavior determinants and interventions. Am J Prev Med 2011;41:189 –96. 20. Rhodes RE, Mark RS, Temmel CP. Adult sedentary behaviour: a systematic review. Am J Prev Med 2012;42(3):e3– e28. 21. Church TS, Thomas DM, Tudor-Locke C, et al. Trends over 5 decades in U.S. occupation-related physical activity and their associations with obesity. PLoS One 2011;6(5):e19657.
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22. Brownson RC, Boehmer TK, Luke DA. Declining rates of physical activity in the U.S.: what are the contributors? Annu Rev Public Health 2005;26:421– 43. 23. Szalai A. The use of time: daily activities of urban and suburban populations in twelve countries. Paris, France: Mounton, 1972. 24. Adams J. Prevalence and socio-demographic correlates of “active transport” in the UK: analysis of the UK time use survey 2005. Prev Med 2010;50:199 –203. 25. Dunton GF, Berrigan D, Ballard-Barabash R, et al. Environmental influences on exercise intensity and duration in a U.S. time use study. Med Sci Sports Exerc 2009;41:1698 –705. 26. Dunton GF, Berrigan D, Ballard-Barabash R, et al. Joint associations of physical activity and sedentary behaviors with body mass index: results from a time use survey of U.S. adults. Int J Obes 2009;33:1427–36. 27. Spinney JEL, Millward H, Scott DM. Measuring active living in Canada: a time-use perspective. Soc Sci Res 2011;40:685–94. 28. Tudor-Lock C, Bittman M, Merom D, et al. Patterns of walking for transport and exercise: a novel application of the time use data. Int J Behav Nutr Phys Act 2005;2:5. 29. Tudor-Locke C, Ham SA. Walking behaviors reported in the American Time Use Survey 2003-2005. J Phys Act Health 2008;5:633– 47. 30. Tudor-Locke C, Leonardi C, Johnson WD, et al. Time spent in physical activity and sedentary behaviors on the working day. J Occup Environ Med 2011;53:1382–7. 31. van der Ploeg HP, Merom D, Chau JY, et al. Advances in population surveillance for physical activity and sedentary behavior: reliability and validity of time use surveys. Am J Epidemiol 2010;172:1199 –206. 32. Tudor-Locke C, van der Ploeg HP, Bowles HR, et al. Walking behaviours from the 1965-2003 American Heritage Time Use Study (AHTUS). Int J Behav Nutr Phys Act 2007;4:45. 33. Breedveld K, van den Broek A, de Haan J, et al. Time as a mirror: How the Dutch spend their time (Dutch). The Hague, the Netherlands: Sociaal Cultureel Plan Bureau, 2006. 34. van Ingen E, Stoop I, Breedveld K. Non-response in the Dutch time use surveys: strategies for response enhancement and bias reduction. Field Methods 2009;21:69 –90. 35. Ainsworth BE, Haskell WL, Whitt MC, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc 2000;32:498 –504. 36. Tudor-Locke C, Washington TL, Ainsworth BE, et al. Linking the American Time Use Survey (ATUS) and the Compendium of Physical Activities: methods and rationale. J Phys Act Health 2009;6:347–53. 37. Chau JY, Merom D, Grunseit A, et al. Temporal trends in nonoccupational sedentary behaviours from Australian Time Use Surveys 1992, 1997 and 2006. Int J Behav Nutr Phys Act 2012;9:76. 38. Chau JY, van der Ploeg HP, van Uffelen J, et al. Are workplace interventions to reduce sitting effective? A systematic review. Prev Med 2010;51:352– 6. 39. Dora C. A different route to health: implications of transport policies. Br Med J 1999;318:1686 –9.
Appendix Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.amepre.2012.11.034.
Purpose: This study aimed to determine changes in non-occupational sedentary behaviors in the Dutch adult population between 1975 and 2005. Methods: The National Time Use Survey of the Netherlands was used, which has been collected in 5-year intervals since 1975 (seven time points, n range⫽1017–2845). Adult participants completed a 7-day time-use diary in which they recorded their primary activity in 15-minute intervals throughout or at the end of the day. A validated method was used to determine time spent in various nonoccupational sedentary behaviors. Population-weighted analyses determining changes over time in various sedentary behaviors were carried out in 2011 and 2012.
Results: Between 1975 and 2005, the proportion of non-occupational time spent sedentary remained relatively constant at ⬃60%. However, absolute time decreased, because of a 4.7-hour/week increase in occupational time. Sedentary occupational time could not be studied but has likely increased over these 3 decades. Most non-occupational sedentary behavior was during leisure, and the proportion of sedentary leisure time that comes from screen time increased from 26% in 1975 to 43% in 2005. Between 1975 and 2005, sedentary transport increased by 2 hours/week. Conclusions: The nature and distribution of sedentary behaviors in the Dutch adult population changed substantially over 3 decades. Screen-based activities are playing an increasingly dominant role. (Am J Prev Med 2013;44(4):382–387) © 2013 American Journal of Preventive Medicine
Background
A
n increasing body of evidence suggests that sedentary behaviors can have a detrimental effect on health.1– 4 Sedentary behavior is defıned as activities that incur 1.5 or fewer METs, such as sitting and reclining, and is distinct from inactivity, which refers to the lack of moderate- to vigorous-intensity physical activity.5 Prospective studies have suggested that all-cause and From the Sydney School of Public Health (van der Ploeg, Venugopal, Chau, Bauman), University of Sydney; the School of Biomedical and Health Services (Merom), University of Western Sydney, Sydney, Australia; Department of Public and Occupational Health (van der Ploeg, van Poppel), EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam; and the Mulier Institute (Breedveld), Utrecht, The Netherlands Address correspondence to: Hidde P. van der Ploeg, PhD, Department of Public and Occupational Health, VU University Medical Center, van der Boechorststraat 7, 1081BT, Amsterdam, the Netherlands. E-mail: [email protected]. 0749-3797/$36.00 http://dx.doi.org/10.1016/j.amepre.2012.11.034
382 Am J Prev Med 2013;44(4):382–387
cardiovascular mortality is adversely associated with total sitting time independently of physical activity and BMI.6 – 8 Although prospective data have suggested that sitting in a car,9 and during people’s main activities (e.g., work, school, housework),4,10 is associated with mortality from all causes and from cardiovascular disease, most of the evidence has been accumulated on the detrimental effect of leisure-time sedentary behavior.11 In particular, prolonged TV viewing,7,9,12,13 and recreational screen time in general14 have been studied more extensively, mostly because more prospective data are available on these sedentary behaviors. A meta-analysis showed TV viewing was associated with higher risks of type 2 diabetes, cardiovascular disease, and all-cause mortality.1 Reviews suggest that high volumes of sedentary behaviors might increase the risk of obesity, cardiovascular disease, diabetes, and cancer2– 4 but also note that sedentary behavior and health research is in its infancy with evidence still accumulating, and more high-quality studies with better measures of sedentary behaviors are needed.
© 2013 American Journal of Preventive Medicine • Published by Elsevier Inc.
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Recently, the observed cardiometabolic risks of sedentary behavior have resulted in the emergence of pilot-type studies that specifıcally aim to reduce sedentary behavior.15–18 These studies generally show promising results with regard to feasibility and reductions in sitting time, mostly of sit–stand workstations in work or school settings. It has also been stressed that future controlled trials on the feasibility and effıcacy of interventions to reduce and break up sedentary behaviors among adults in domestic, workplace, and transportation environments are particularly required.19 However, there is a large gap in knowledge with regard to determinants of sedentary behaviors that needs to be addressed in order to better inform the development of successful interventions.20 Few studies have focused on changes over time in population levels of various sedentary behaviors. Data from the U.S. have shown increases in sedentary occupations between 1960 and 2010,21 and increases in TV viewing between 1950 and 2000.22 However, besides type of occupation and TV viewing, national health surveys have only more recently been collecting data on other sedentary behaviors, which makes it hard to study trends over time. Non-health-focused population surveys such as time use surveys might help bridge this gap in knowledge. Time use surveys, which have their origin in sociology, collect data on what people do throughout the whole day or even week at a detailed level,23 and have been increasingly utilized in public health research.24 –30 The authors recently showed that estimating participation in nonoccupational sedentary, light-, and moderate-intensity activities from time use surveys was more accurate than traditional physical activity surveillance systems.31 This has opened the door for using time use surveys to study population trends, which have been collected in many countries in population-representative samples since as early as the 1960s. Unfortunately, changes over the years in the methodology of collecting the time use surveys, such as in the American Time Use Survey, can compromise the study of population trends over time.32 The national time use survey of the Netherlands has been collected every 5 years between 1975 and 2005 in population-representative samples and has maintained almost the same methodology over time. This makes it an excellent set of surveys to study time trends in sedentary behavior at a detailed level. Hence, the aim was to study changes in a range of sedentary behaviors in the Dutch adult population over a 30-year period.
Methods Study Design The Time Use Survey of the Netherlands has been collected in 5-year intervals between 1975 and 2005 (seven consecutive April 2013
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surveys). The survey was commissioned by the Netherlands Institute for Social Research, a government institution within the ministry of Health, Wellbeing, and Sports. Fieldwork was undertaken by Intomart GfK, which conducts fıeldwork according to the ICC/ESOMAR Code on Market and Social Research and is a member of the Dutch Association for Market Research MOA. Over this 30-year period, fıeldwork was carried out by the same agency, and basic design and instruments were kept consistent. Data were collected in the fırst 2 weeks of October of each survey year. Access to the time use survey data sets was provided by the Data Archiving and Networked Services (DANS) from the Netherlands (www. dans.knaw.nl/en).
Participants For each survey, a representative national sample was drawn by randomly selecting households in which one person aged ⱖ12 years was randomly selected for participation. For the purpose of the present study, the focus was on participants who were aged ⱖ20 years. The number of participants in the various survey years ranged from 1017 to 2845. Potential participants were presented with written information on the study, and participants provided verbal informed consent. More details on recruitment and sampling of participants can be found elsewhere.33,34
Measurement The assessments consisted of two personal home interviews and a self-completed time use diary. The fırst home interview aimed to collect personal and household characteristics and explained the time use diary. The second home interview was used to collect additional personal characteristics and to correct inconsistencies in the time use diary. In 2005 (only), this included self-reported body height and weight, which was used to calculate BMI. The time use diary was completed for 7 consecutive days by the participant between the fırst and second home interview. The time use diary is completed during or at the end of each day and records the participant’s primary activity, secondary activity and whether they were at home or not every 15 minutes. The primary activity was the main activity that was done for the majority of each 15-minute period in the diary (e.g., walking for leisure) and any secondary activities were those done at the same time (e.g., listening to music while walking). Secondary activities were always of a lower physical intensity than the primary activity. Activities were recorded as ⬃200 preset activity codes. All original ⬃200 time use activity codes were recoded into seven domains: sleep, occupation, leisure, transport, household, education, and voluntary work; and also into three intensity categories: sedentary (ⱕ1.5 METs); light (1.5–3 METs); and moderate- to vigorous-intensity activity (ⱖ3 METs). Activities were assigned to the three intensity categories based on the Compendium of Physical Activities,35 and the intensity coding system used in the American Time Use Survey.36 This method to estimate participation in sedentary, light-intensity, and moderate- to vigorousintensity activities has been previously shown to have acceptable reliability and validity.31 Occupational activities were not recoded into the three intensity categories, because the original codes provided insuffıcient detail. Total non-occupational time was the sum
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40 35 Hours/week
30 25 20 15 10 5 0 1975
1980
1985
1990
1995
2000
2005
Year Leisure time
Household time
Occupational time
Transport time
Figure 1. Time use among Dutch adults by survey year Note: Time use is weighted and age-standardized.
of all activities in the leisure, transport, household, education, and voluntary work domains.
Data Analysis Results were presented as weighted means with SDs. Results were age-standardized to the age distribution in 2000 where appropriate. In order to form a representative sample of the Dutch adult population, participants were weighted for the number of people aged ⱖ12 years in the household, place in the household, degree of urbanization of city, age, gender, and main daily activity. In 1975 and 1980, participants were not weighted for age, gender, and main daily activity. Time trends from 1975 to 2005 were calculated with linear regression models and were adjusted for age, gender, and education level. For 2005, estimated marginal means were calculated for sedentary behaviors stratifıed by gender and BMI category using univariate general linear models and adjusted for age. All analyses were performed in SPSS, version 19, between 2011 and 2012.
Results Appendix A (available online at www.ajpmonline.org) presents the response rates and personal characteristics of participants in all seven time use surveys. The response rate was around 60% for most of the surveys, but dropped in 1995 and somewhat recovered again in the last two surveys. In line with societal trends, the participating populations over time became older and better educated, and car, TV, and computer ownership increased. Workforce participation and retirement also became more prevalent, in line with increasing gender equality and the aging of the population. Between 1975 and 2005, Dutch adults increased their time spent at work and in transit by 4.7 and 3 hours/week, respectively, while leisure and household time decreased by 4.7 and 3.6 hours/week, respectively (Figure 1; also see
Appendix B, available online at www.ajpmonline.org). Non-occupational sedentary time (excluding sleep) decreased over this 30-year period by 4.5 hours/week, but remained relatively constant at approximately 60% of total non-occupational time. The decrease in nonoccupational sedentary time was mostly accounted for by a decrease in sedentary leisure time, which decreased from 90% of leisure time in 1975 to 84% in 2005. Between 1975 and 2005, inactive forms of transportation increased by 2 hours/week (Figure 2). Figure 3 provides a closer look at sedentary leisure time by breaking it down into specifıc activities (see also Appendix C, available online at www.ajpmonline.org). The proportion of sedentary leisure time that comes from screen time (TV and computer use) increased from 26% in 1975 to 43% in 2005. Home computer use doubled every survey since it was fırst measured in 1985 and makes up an increasing proportion of total screen time. All other sedentary leisure behaviors decreased over the 30-year period, with the exception of sedentary leisure transport, which increased slightly. Appendix D (available online at www.ajpmonline. org) presents total non-occupational sedentary time (excluding sleep) and sedentary leisure-time behaviors compared between normal-weight and overweight and obese people stratifıed by gender in 2005. Sedentary leisure time and screen time were signifıcantly higher in overweight and obese men compared to normal-weight men. Screen time and TV viewing were signifıcantly higher in overweight and obese women compared to normal-weight women. No other sedentary behaviors differed between the two BMI groups. 45 40 35 30 25 20 15 10 5 0 Leisure Household Transport Leisure Household Transport Leisure Household Transport Leisure Household Transport Leisure Household Transport Leisure Household Transport Leisure Household Transport
45
Hours/week
384
1975
1980
1985
1990
1995
2000
2005
Activity/year Sedentary
Light + MVPA
Figure 2. Time spent in sedentary and active leisure, household activities, and transport activities among Dutch adults by survey year Note: Time use is weighted and age-standardized. MVPA, moderate- to vigorous-intensity physical activity
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Hours/week
12 10 8 6 4 2 0 1975
1980
1985
1990
1995
2000
2005
Year Screen time (TV + computer) Computer Socializing Sedentary leisure transport Other
Watching TV Reading Needle work Listening to radio/music
Figure 3. Sedentary leisure time among Dutch adults by survey year Note: Time use is weighted and age-standardized.
Discussion Between 1975 and 2005, the amount of time Dutch adults spent at work and in transit increased substantially and was accompanied by decreasing leisure time and time spent on household tasks. The increase in occupational time logically resulted in a decrease in non-occupational time and an absolute but not relative decrease in nonoccupational sedentary time. This decrease in nonoccupational sedentary time was mostly accounted for by a decrease in sedentary leisure time, whereas sedentary transport time increased over the 30-year period. However, in 2005 Dutch adults still spent 84% of leisure time being sedentary, and 59% of non-occupational sedentary time was attributed to leisure time. Hence, given the detrimental health effects of sedentary behavior, it seems that leisure time should be a primary target in intervention studies aimed at reducing sedentary behavior. The results also showed a dramatic shift within sedentary leisure time toward screen-based activities (TV and computer use) and away from most other sedentary leisure time activities. As most of the evidence on the detrimental effects of sedentary behaviors has been centered around screen-based behavior,1,9,12–14 the reported increase in screen time might not be a good development for public health. However, screen time has received the most research attention because it was included in earlier studies, whereas other sedentary behaviors were less often assessed and still need to be further researched to help build evidence. April 2013
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Although the reported higher rate of screen time for overweight and obese people compared to normalweight people seems to suggest that screen time might be a bigger health concern than other sedentary behaviors, these data were cross-sectional and made it impossible to determine causality. The lack of prospective studies that were able to attribute health risks to various types of sedentary behaviors, and especially the role of screen time in comparison to non-screen-based activities, makes it diffıcult to draw conclusions about the importance of the observed increase in screen time for public health. However, screen time is the main contributor to sedentary leisure time, which is a primary candidate to be targeted in interventions aimed at reducing sedentary behaviors. Hence, strategies to reduce screen time or make screenbased activities less sedentary could be an important component of such interventions. An important question that cannot be answered by the Dutch time use surveys is what happened to total sedentary time over these 3 decades. The intensity of activities during occupational time cannot be determined in the Dutch time use survey data because of insuffıcient detail in the original coding. However, total occupational time increased over the survey period. Data from the U.S. have suggested that jobs have become increasingly sedentary between 1960 and 2010,21 which is a trend that is likely to translate to the Dutch workforce. Hence, the observed decrease in non-occupational sedentary time could be offset or even surpassed by the likely increase in occupational sedentary time, which would imply a stable or increased health burden of sedentary behavior over time in the Netherlands. The current fındings are in line with the increases in TV viewing observed in the U.S. between 1950 and 2000, which showed a similar steady increase but higher absolute rates.22 These lower absolute rates in the Netherlands are partly due to the fact that the current study only captured TV viewing as a primary activity and not as a secondary activity. For example, watching TV during dinner would be coded as a sedentary (as both activities are sedentary) household activity and not as watching TV or sedentary leisure time. In 2005, TV viewing seemed to decline in Dutch adults, and it seems that TV viewing might have been substituted to some extent with leisure computer use, with total screen time still on the increase. Such a change in the composition of screen time is likely to have continued since 2005 with the growing popularity of game consoles, the emergence of new technologies such as computer tablets and smart phones, and the increasing availability of audiovisual content on the Internet. Leisure-time screen-based activities seem to be evolving so quickly that it might be necessary to further separate kinds of screen
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time, especially computer-based activities, when determining their influence on health outcomes. Motionbased computer games, which are unlikely to qualify as sedentary activity, are an obvious example of this. However, motion-based game consoles (such as the Nintendo Wii) were only introduced on a large scale in 2006, well after the completion of the last Dutch time use survey.
Limitations The Dutch time use surveys present a rather unique opportunity to study changes in a range of sedentary behaviors over 3 decades. A limitation of utilizing the Dutch time use surveys for this purpose was the 15-minute interval in which the diaries recorded activities. This might have resulted in under- or over-estimation of activities of relatively short duration, especially those that were also infrequent. This makes the interpretation of the changes in the moderate- to vigorous-intensity activity data diffıcult as the time use diaries might not have had suffıcient sensitivity to detect true changes over time. The measurement study that previously showed acceptable validity for moderate- to vigorous-intensity activities when compared against an activity monitor used a 5-minute interval to record activities,31 which is what most other countries have used in their time use surveys. Hence, caution should be used in interpreting results based on infrequent activities of short duration. Another limitation was the decline in the response rate in 1995, 2000, and 2005, which could have resulted in some selection bias. However, participants were weighted in order to have a representative sample of the Dutch adult population. Further, the data collection in the same 2 weeks in October made the surveys somewhat vulnerable to weather variations. For example, the data collection period in 1980 and 2000 had substantially higher volumes of rain, which seems to be reflected somewhat in lower estimates of moderate- to vigorous-intensity activity in those two surveys. However, as discussed above, results relating to moderate- to vigorous-intensity activities should be interpreted with caution. Finally, although the methodology of the Dutch time use surveys was kept relatively constant over 3 decades, there were some small changes. Most notable is the partly different weighting methodology used in 1975 and 1980. However, the age standardization would have corrected the lack of weighting for age in these 2 years. The general patterns that were identifıed in the Netherlands over 3 decades are probably generalizable to many developed countries, as illustrated by the similar prevalence of sedentary behaviors in the Australian Bureau of Statistics 2006 time use survey.37 However, there will be some differences between developing countries, such as the higher prevalence of active travel in the Netherlands compared to more car-focused countries like the
U.S. and Australia, where the increase in sedentary forms of transport that was observed might be even higher. Although many other countries have national time use surveys that could, to some extent, be utilized to study changes in sedentary behaviors over time, surveillance systems specifıcally dedicated to capturing changes in sedentary behavior at the population level are needed. Especially, good-quality data on occupational sedentary behavior and total sedentary time are urgently needed to capture the complete sedentary behavior picture. A combination of an objective measure and a self-report measure that assesses various domains of sedentary behavior is recommended.
Conclusion Over the past 30 years, the proportion of nonoccupational time that is spent in sedentary behavior has hardly changed in the Netherlands. However, there have been substantial changes in the types of sedentary behaviors that make up the total volume of non-occupational sedentary time, with increasing contributions from inactive transport and screen-based activities. The public health consequences of the changes in the types of sedentary behaviors remain unclear and should be the subject of high-quality research in the future. Time at work has increased over the past 3 decades in the Netherlands, and work from the U.S. suggests that the proportion of occupational time spent being sedentary has also increased,21 which might offset or surpass the absolute decrease in non-occupational sedentary time. Leisure time is the greatest contributor to sedentary time; 84% of leisure time is spent sedentary, and leisure screen time is the greatest contributor to this. This could have substantial health consequences depending on sedentary levels throughout the rest of the day. Leisure time is when people generally can make their own decisions on how to spend their time and have the fewest obligations; hence, it is arguably the easiest time for people to change. Thus, targeting sedentary leisure time and especially screen time in public health interventions aimed at decreasing sedentary behaviors seems to be a strategy with great potential public health gains. Efforts to reduce occupational sedentary time and sedentary forms of transport are also important38,39 and have the potential for additional benefıcial effects to important problems such as productivity loss and absenteeism, global warming, natural resource utilization, and traffıc congestion. This work was supported by the National Health and Medical Research Council of Australia (program grant #301200). No fınancial disclosures were reported by the authors of this paper.
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Appendix Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.amepre.2012.11.034.