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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Obes Rev. Author manuscript; available in PMC Feb 1, 2013.
Published in final edited form as:
PMCID: PMC3401184
NIHMSID: NIHMS389131

Time Use and Physical Activity: A Shift Away from Movement across the Globe

Abstract

Technology linked with reduced physical activity (PA) in occupational work, home/domestic work, and travel and increased sedentary activities, especially television viewing, dominates the globe. Using detailed historical data on time allocation, occupational distributions, energy expenditures data by activity, and time-varying measures of metabolic equivalents of task (MET) for activities when available, we measure historical and current MET by four major PA domains (occupation, home production, travel, and active leisure) and sedentary time among adults (> 18 years). Trends by domain for the United States (1965–2009), the United Kingdom (1961–2005), Brazil (2002–2007), China (1991–2009), and India (2000–2005) are presented. We also project changes in energy expenditure by domain and sedentary time (excluding sleep and personal care) to 2020 and 2030 for each of these countries. The use of previously unexplored detailed time allocation and energy expenditures and other datasets represents a useful addition to our ability to document activity and inactivity globally. Given the potential impact on weight gain and other cardiometabolic health risks, the differential declines in MET of activity and increases in sedentary time across the globe represents a major threat to global health.

Keywords: physical activity, sedentary, time use, United States, United Kingdom, Brazil, China, India

I. INTRODUCTION

Patterns and trends in energy imbalance have been adversely affected by shifts in stages of eating, drinking, and activity1, 2. These shifts have been occurring since Paleolithic time, but they appear to have accelerated to varying degrees in different regions of the world in the past century. A major component of this transition has been the shift in all domains of activity and inactivity patterns and energy expenditure. This has been operationalized in the SLOTH model, which incorporates the time and activity domains of sleep, leisure, occupation, transportation, and home-based activities3. This paper reviews data sources for fully documenting SLOTH patterns and trends and uses case studies for the United States (US), the United Kingdom (UK), Brazil, China, and India to provide an in-depth sense of patterns, trends, and future projections in each domain of activity and hours of sedentary behavior.

The health and functional benefits of being active are clear4, 5 and extend to all segments of the population 6. On the flip side, being inactive or sedentary has been shown to be a distinct risk factor independent of physical activity (PA), particularly weight gain from childhood to adulthood and mortality7, 8. Beyond structured leisure activity, however, transportation activity, such as walking and bicycling, can be equally beneficial9. Consistent walking over the transition from young to middle adulthood can reduce weight gain with clear dose effects10. Active transit is similarly associated with more favorable body mass index, waist circumference, and fitness, and transit incorporating cycling is related to lower lifetime cardiovascular disease (CVD) risk classification11. A systematic review 12 found dose-dependent reductions in CVD risk with higher walking duration, distance, energy expenditure, and pace. Studies in China show that activity related to transport, home production, and occupation activities1316 are negatively related to poor health outcomes and that overall shifts in PA are a significant cause of long-term increases in weight and obesity17.

While it is clear that there are significant health consequences associated with PA and inactivity, measuring and monitoring the levels of activity at the population level across the broad spectrum of daily living domains have been limited. To date monitoring and recommendations have focused on leisure-time activities, including walking, biking, jogging, and sports;5, 18 sedentariness, particularly television viewing and related behaviors (e.g., snacking while watching television);19, 20 or total PA levels. Consequently, the key domains of occupational and domestic work have largely been ignored, with few exceptions16, 21, 22.

Globally, monitoring PA levels tends to be limited to benchmarks in national or international PA recommendations or the International Physical Activity Questionnaire (IPAQ)23. The IPAQ-short has been used in surveys globally, including the World Health Organization (WHO) World Health Surveys,24 and has been validated for a number of populations2527. While useful in providing aggregate measures of the proportion of populations engaging in vigorous or moderate walking and sitting activities, the IPAQ-short does not provide good estimates of energy expenditure across domains. In fact one evaluation of the use of the IPAQ-short form in Colombia and Brazil found for leisure-time and transport activities the IPAQ-long needed to replace the short version28. The IPAQ-long provides more detailed information, distinguishing among work, home, travel, and leisure activities23, but is rarely used28. As part of the STEPwise approach to chronic disease risk factor surveillance (STEPS), the WHO developed the Global Physical Activity Questionnaire (GPAQ) in 2003 to provide guidance to participating countries’ health agencies monitoring chronic disease risk factors at a population level. The GPAQ, much like the IPAQ-long, asks about time spent in vigorous, moderate, light, and sitting movement in work, travel, and leisure times. Metabolic equivalents of task (MET), defined as the ratio of a person’s working metabolic rate relative to his or her resting (basal) metabolic rate,29 are then assigned. However, the IPAQ-long and the GPAQ only provide instructions for applying a standard MET value for each level of intensity within each domain for all countries,30, 31 which may not be appropriate due to vast differences in technological advancements across countries.

Consequently, most PA measurements are limited to those captured using IPAQ, GPAQ, or national or international PA recommendations. Other data sources, especially the time allocation literature that measures activities across the key domains among all respondents over a significant period of time, provide unique options. Most surveys, however, specialize in particular domains or types of activity, and each survey collects data in a slightly different way, which makes analysis complex and piecemeal. The existing measures of PA are problematic on a number of counts. First, the variation in methods across surveys makes it difficult to combine data at the domain-specific level and across all domains, preventing researchers from understanding potential trade-offs and patterns of activity across domains over time. Second, although health effects are mainly tied to total activity (or inactivity) levels, interventions are highly specific to domains, and understanding the factors driving activity (or inactivity) levels in each domain can help identify promising interventions. Third, MET databases relate mainly to modern levels of technology32, do not provide data on MET for many occupations for earlier periods of reduced access to time-saving technologies, and do not provide MET for labor-intensive occupations in rural and urban sectors in many lower-income regions.

This lack of historical and current data has meant that the long-term shifts and the relative speed of change in activity and inactivity across the globe have rarely been addressed in a rigorous manner33. For the US, the most complete study of PA is the review by Brownson et al.34 that examines current patterns and long-term trends related to activity, employment, travel behavior, and TV viewing using an array of data sources relevant within each domain. Others have looked at domain-specific patterns and trends over time, such as in occupational energy expenditure using occupational codes22, 35. For China, research has been limited to the China Health and Nutrition Survey (CHNS), one of the few longitudinal surveys that include information on various domains of activity15, 16, 36.

We attempt to improve on the limited existing work by estimating average energy expenditure in four specific domains of activity (occupation, domestic production, travel, and active leisure) and sedentary time among adults in the US, the UK, Brazil, China, and India over time. We use an array of longitudinal and cross-sectional datasets from these countries, selecting when possible those that are nationally representative. We use time use data to describe trends in energy expenditure in the four domains and sedentary time for male and female adults for the US, the UK, and China. For Brazil and India, we have only limited data on average time spent across various occupations, with additional measures of active leisure activity from one area of Brazil. Based on these trends, we project changes in energy expenditure from each of the four activity domains and sedentary time (excluding sleep) by 2020 and 2030 if nothing alleviates the situation.

II. DATA

In the appendix we outline the data sources available for estimating PA and inactivity over time for these five countries. Because there is a dearth of data that include comprehensive measures of PA across the various domains of daily living for much of the world, we turn to cross-country measures of time use along with estimates of the average energy expenditure for various activities of daily living.

A. Domain-specific time use measures

Multinational Time Use Study

The Multinational Time Use Study (MTUS), first developed in the early 1980s, harmonizes time use datasets collected in the early 1960s through the mid-1980s into a single dataset with common series of background variables and total time spent per day in 41 activities. The original MTUS allowed comparison of British time use data with the 1965 Szalai Multinational Time Budget Study and data from Canada and Denmark. The MTUS since has grown to encompass over 60 datasets from 22 countries, including the US and the UK.31 The MTUS contains harmonized time use data for the US and the UK from the last five decades.

American Time Use Survey

We also used the American Time Use Survey (ATUS) collected by the US Bureau of Labor Statistics since 200337.

China Health and Nutrition Survey

The CHNS is not designed as a time use study, but it provides data on time use and level of effort and strenuousness of reported occupations and time spent on other domains of daily living. It is a nationwide survey following approximately 20,000 individuals from 228 communities in 9 provinces of China (Guangxi, Guizhou, Heilongjiang, Henan, Hubei, Hunan, Jiangsu, Liaoning, Shandong).38 We used the 1991, 1993, 1997, 2000, 2004, 2006, and 2009 CHNS data for this study.

United Nations International Labour Organization

The United Nations (UN) International Labour Organization (ILO) compiles statistics produced by accredited national statistical institutions on labor issues. We obtained data on the hours spent per week by occupation code or economic activity for adults (total and by gender) and the proportion of adults in the various occupation codes or economic activities. The years with complete data that had a consistent methodology over time for Brazil were 2002–2007 and for India 2000–2005.

B. Sources of data on domain-specific energy requirements/intensity

Compendium of Physical Activities

The Compendium of Physical Activities was developed for use in epidemiological studies to standardize the assignment of MET intensities in PA questionnaires with updates over time (from 1993 to the 2011 version).39, 40 The values in the compendium do not estimate the energy cost of PA in individuals in ways that account for differences in body mass, adiposity, age, sex, and efficiency of movement or geographic and environmental conditions in which the activities are performed. Thus individual differences in energy expenditure for the same activity can be large, and the true energy cost for an individual may or may not be close to the stated mean MET level. Rather, it is useful for providing a classification system that standardizes the MET intensities of PA used in survey research. Moreover, energy costs of certain activities (particularly occupational and some domestic work activities) are unlikely to be static over time with the introduction and popularity of new labor-saving technologies and machinery. Unfortunately, there are no earlier (pre-1990s) versions of the compendium. For this study, when we used the compendium, we applied the 1993 values for data from 1999 or earlier, and we applied the 2000 values for data from 2000 or later. Since none of our data is from 2011 onward, we did not use the 2011 compendium values. From a historical perspective it is important to note that the Food and Agricultural Organization of the United Nations (FAO-UN) led an earlier initiative to measure daily physical activity levels (PALs) as an important dimension of research to create energy intake requirements41, 42. This work represented the global state of the art for many decades.

Literature on energy expenditure

There are important complexities and limitations to use of the Compendium of Physical Activities for developing countries such as India, where the rural sector and parts of the urban sector engage in much more labor-intensive energy-expending activities than those sectors in other countries. Consequently, in the case of India, we turned to studies that estimated energy expenditures of men and women in rural and slum settings and compared developing and industrialized countries4347 across various occupational types, and we applied these to the India ILO data. In addition, for active leisure activity in Brazil (Sao Paolo), because the IPAQ-short questionnaire was used, we applied the recommended MET according to the IPAQ-short guidelines30 rather than use the compendium. This approach does not allow us to create aggregate weighted averages of different components of active leisure for Brazil or India, and there are noted limitations to the IPAQ-short as discussed earlier28. Furthermore, energy expenditure varies with body weight; however we do not have a basis to use this to adjust the MET calculations to handle this shift over time.

C. Auxiliary data

World Bank World Development Indicators

The World Development Indicators (WDI) is the primary World Bank database for development data from officially recognized international sources.48 We obtained data on the per capita gross domestic product (GDP) adjusted for purchasing power parity (PPP) for all five countries.

United Nations World Population Prospects

From the most recent release (May 2011) of the UN World Population Prospects we obtained data on the estimated (for 2010 and earlier) and projected (for 2011 and later) population sex ratio for the five countries.

III. METHODS

A. Estimating average energy expenditure and sedentary behavior across domains

China, United States, and United Kingdom

For China, the US, and the UK, we used measures of time spent in the various domains as one of the components that affect the energy expended in each of these domains, applied the appropriate estimated MET intensity values using the Compendium of Physical Activities based on the lowest level of detail available for each time use survey, and aggregated the various subdomain activities to get each individual’s MET hours/week in each domain. In other words,

(DomainMEThrsperweek)a,i=s=1STimes,iMETs,i,and(TotalMEThrsperweek)i=a=1ADomainMEThrsperweeka,i,

where i denotes an individual, a denotes PA domains (occupational, domestic, travel, or active leisure), and s denotes subdomains (e.g., farming, food preparation, driving, playing basketball). In the case of sedentary behaviors (e.g., TV viewing, playing computer games), because there is substantial literature that shows strong associations between inactive time and negative health outcomes7, 8, we looked at weekly sedentary time (excluding sleep and personal care, such as changing clothes and showering) but did not convert this into energy expenditure.

Brazil

We calculated average occupational PA by multiplying the hours spent per week for each occupation code or economic activity reported in the ILO statistics with the appropriate estimated MET intensity values using the Compendium of Physical Activities, weighted by the proportion of adults in the various occupation codes or economic activities. For active leisure activity, we used the 2002 and 2008 Sao Paolo Physical Activity Survey and applied the MET intensity values according to the IPAQ guidelines. For domestic and travel PA and sedentary time, we used the gender-specific average activity measures found in the US and China from periods of similar economic development (based on per capita GDP PPP) for each country (2002 Brazil to 1975 US and 2006 China; 2007 Brazil to 1985 US and 2009 China), weighted by the sex ratios for Brazil in 2002–2007, to derive these time-use measures.

India

We calculated average occupational PA by multiplying the hours spent per week for each occupation code or economic activity reported in the ILO statistics with the appropriate estimated MET intensity values found in the literature43, 45, 46, weighted by the proportion of adults in the various occupation codes or economic activities. For domestic, travel, and active leisure PA and sedentary time, we used the gender-specific average activity measures found in China from periods of similar economic development (based on per capita GDP PPP) for each country (2000 India to 1995 China; 2005 India to 2000 China), weighted by the sex ratios for India in 2000–2005, to derive these time-use measures

B. Forecasting into 2020 and 2030

We were interested in estimating what the levels of PA for each domain and sedentary time would look like in the future (in 2020 and 2030) if current trends regarding modernization and time use continue. An overarching assumption is that the trends over time are linear. For countries with a limited range of years of observed data (Brazil and India), we were limited to using the slope (or rate of change) in each domain of activity and sedentary time between the first and last year for forecasting into 2020 and 2030. For the countries with data that spanned more than five years, a number of slopes were possible, including:

  1. using the last two waves of data only (2008 and 2009 for the US, 2000 and 2005 for the UK, 2006 and 2009 for China),
  2. using the middle range of data only (2003 and 2009 for the US, 1995 and 2005 for the UK, 2004 and 2009 for China),
  3. using first and last years of data (1965 and 2009 for the US, 1961 and 2005 for the UK, 1991 and 2009 for China),
  4. using the annualized three-year moving averages.

We calculated forecasts using the various possible slopes as sensitivity analyses, keeping in mind that there is a lower threshold for total PA levels. Specifically, a person who sleeps all the time will still expend 151.2 MET hours/week, but since we have ignored sleep (which has stayed consistent over time at around 50 hours/week or 47 MET hours/week)49 in our calculations, the lower threshold we are considering is around 104 MET hours/week. This provided a range of feasible forecasted PA levels and sedentary time for the US, the UK, and China.

IV. RESULTS

Table 1 summarizes our calculations for the rate of change for each of the five countries over time based on available data and their forecasted change in each domain of PA, total PA, and sedentary time for 2020 and 2030. While data were initially obtained by gender, weighted averages are used to present the average adult data in this table. We find that across all countries studied here, overall PA levels from the four domains of activity combined will continue on a downward trend and sedentary time will increase if lifestyle behaviors do not change. We report the estimates using different slopes in the appendix (appendix table 2). In general, the forecasts for total PA were robust across the various methods used for these countries.

Table 1
Observed and forecasted changes in Occupational, Domestic, Travel and Active Leisure activity, and time being sedentary

United States

Figure 1 shows that in the US total PA from the four domains in 1965 was already somewhat low at 235 MET hours/week for adults, with occupational PA constituting the majority. Total PA actually rose slightly between 1987 and 1995 driven by occupational PA. Subsequently, it fell to 160 MET hours/week in 2009, and it is forecasted to be around 142 MET hours/week by 2020 and 126 MET hours/week by 2030 (see figure 1) due to declines in occupational, domestic, and travel PA. Our forecast shows that active leisure PA will have slight increases during this period. However, time spent in sedentary behaviors will continue to increase to nearly 42 hours/week by 2030.

Figure 1
US Adults MET-hours Per Week of All Physical Activity, and Hours/Week of Time in Sedentary Behavior: Measured for 1965–2009, Forecasted for 2010–2030

United Kingdom

Total PA in the UK in 1961 was even lower than in the US in 1965, 216 MET hours/week for adults, again with occupational PA constituting the majority. Total PA fell to 173 MET hours/week (20% decline) by 2005. Our forecasting shows that by 2020 total PA will sum to 153 MET hours/week and by 2030 to 140 MET hours/week. We anticipate slight increases in travel and active leisure PA but not enough to countervail the continued declines in occupational and domestic PA. Meanwhile, our forecast shows that sedentary leisure time will increase to over 51 hours/week by 2030.

Brazil

Our estimates for Brazil in figure 3 show that total PA was 229 MET hours/week in 2002, declined to 214 in 2008, and will continue to decrease to 180 and 151 MET hours/week in 2020 and 2030, respectively. The largest absolute decline is in occupational PA, but the largest relative decline is in domestic PA. Active leisure PA is expected to continue rising over time, but that increase based on trends to date will be insufficient to make up for declines in PA elsewhere. Sedentary time is projected to rise from 24 hours/week to 29 and 33 hours/week by 2020 and 2030, respectively.

Figure 3
Brazilian Adults MET-hours Per Week of All Physical Activity, and Hours/Week of Time in Sedentary Behavior: Measured for 2002–2007, Forecasted for 2009–2030

China

Figure 4 shows that in China total PA from the four domains in 1991 was around 399 MET hours/week for adults, with occupational PA constituting the majority. Total PA fell to 213 MET hours/week by 2009 largely due to declines in occupational, domestic, and travel PA. Our forecast shows that total PA will be 200 MET hours/week by 2020 and 188 MET hours/week by 2030. We anticipate that declines in occupational PA will continue, albeit at a slower rate, along with declines in domestic PA, little change in travel PA, and slight absolute growth in active leisure PA. Time spent in sedentary behaviors will increase from about 20 hours/week in 2009 to 23 hours/week in 2020 and 25 hours/week in 2030.

Figure 4
Chinese Adults MET-hours Per Week of All Physical Activity, and Hours/Week of Time in Sedentary Behavior: Measured for 1991–2009, Forecasted for 2010–2030

India

Figure 5 shows that India is the most resistant to declines in PA among the five countries studied here, but even so there is a noticeable decrease, particularly in occupational PA, projected into 2030. At the same time sedentary time is expected to rise from 18.6 hours/week in 2000 to 20 hours/week by 2030.

Figure 5
Indian Adults MET-hours Per Week of All Physical Activity, and Hours/Week of Time in Sedentary Behavior: Measured for 2000–2005, Forecasted for 2006–2030

V. CONCLUSIONS

A. Overview of results

Based on observed trends, PA is declining rapidly across the globe. It is particularly the case in China and Brazil, which have the two highest absolute and relative rates of decline in total PA and some of the higher increases in sedentary time. For these two countries, the declines in activity have been largely driven by reductions in movement at work, at home, and to a lesser degree in travel. This is not surprising given that in the past few decades the Chinese and Brazilians have been shifting away from agriculture into the manufacturing and service/tertiary sectors, have increased their use of machines and labor-saving technology in the workplace, and have greater access to home technologies (e.g., electrification, piped water, appliances) and vehicles. Our forecasts are bleak. For instance, by 2020 the average American adult will only expend 142 MET hours/week while awake. The British are only slightly better but will reach that level by 2030. The Chinese and Brazilians continue on their steeper downward trend, reaching the US and UK total PA levels by 2030. The situation in India appears less severe, but this average masks the stark socioeconomic dichotomy that will likely continue in India, with wealthier Indians leading lifestyles more like the average British (with possibly even lower domestic PA due to the prevalence of domestic maids among this subpopulation of India).

Our findings on PA in the US and the UK are consistent with earlier work by others who looked at specific domains34, 4951. In the US and the UK second car ownership has increased, the distance walked per year has declined, and the vehicle miles traveled have increased5155. The trend is less severe in the UK, where active transport is promoted and urban design is more conducive to walking and bicycling46. The increased sedentary time in the US, the UK, Brazil, and China could very well be because of the growth in media technologies (e.g., televisions, cable, computers, the Internet) as our results fit market research on television viewing56.

India has yet to exhibit these trends, especially in the rural sector. While India’s rising middle class has attained significant access to modern technology and the ability to hire domestic help, other segments of urban areas (including slums/squatter areas) and rural areas are barely touched by modern technology in most domains of daily living4346. The Indian National Rural Employment Guarantee Act57, which provides partial employment to unemployed adults, mainly involves manual labor (laying roads, digging wells, etc.) and will likely have an impact on the PA profile in rural areas.

Our time use data for the US, the UK, and China (see appendix figures 1–3) indicate distinct differences in the activity patterns of men and women, consistent with findings elsewhere36, particularly in developing countries, where women typically hold the triple burden of child care/household production, reproduction, and occupational work58. For US women, time spent on domestic activities fell by one-third from 1965 and to 1995, from about 40 to 27 hours/week59. The opposite occurred for men overall, but there was a net reduction in time devoted to household and family care, with the decline in housework being the dominant explanatory factor. One of the more interesting time use shifts is the drop across the globe in food preparation time, very much related to the growth of processed food availability, which has resulted in a shift from about two hours/week to half an hour/week60, 61.

B. Limitations

This review provides a descriptive look at the PA levels across the various domains of daily living over time in five countries to highlight the severity of the problem many nations will face in the near future. Our work here is limited by the data available, which were incomplete for India and Brazil. Only for the US, the UK, and China were we able to unify time use and MET data, and China was the only source based on longitudinal data. To estimate the changes in certain PA domains for India and Brazil we used trends from China and the US. In addition, the data used on the intensity of activities across domains are from relatively recent periods and so most likely have resulted in conservative estimates of the PA declines (particularly for occupational and domestic PA prior to the 1990s). Our forecasting also assumes linear trends to predict PA levels into the future and may not adequately account for potential demographic changes, future rates of economic development, and technological advancements.

A major limitation of all work on physical activity is the simplification of a very complex, heterogeneous set of activities by the use of METs and also aggregate measures 28. Region, season, year, occupation and types of technology available play such important roles in explaining the metabolic effects of each activity 41, 42, 47, 62. By compacting time allocation into broad groupings and by having a compendium with so few activities, it is important to understand the limitations of these trends in METs. This is particularly the case for another reason, the shift in technology and environments from the 1960s that represent major changes in energy expended at any task in the home or workplace (e.g., from churning butter or making chappatis or bread to toasting or heating the same item today and then applying store-bought butter). There is no central depository or collection of the thousands of studies on energy expenditure by task as there is for food composition tables—the equivalent of providing calories per 100 gram for food as METs provide measures of energy expenditures per minute or hour.

This paper does not discuss the major cultural, social, physical, and economic barriers that need to be addressed if behavior change is to be promoted in favor of increased PA or to discourage sedentary behavior63. Similarly, we have not described or discussed the social gradients that exist among different income or education groups within a country as they relate to PA or inactivity. While we discuss the role of technological advancements, urbanization, or globalization on the PA trends here, we have not implicitly modeled these relationships due to the lack of data across these countries. Lastly, we do not deal with all the important changes in other stages of the life cycle.

C. Implications

It is clear that there exist major gaps in measuring PA, sedentary behavior, and energy expenditure. Research on country- or context-specific measurement of MET for a vast array of activities exists, such as that we used for India46, 64, 65, but it has never been pulled together in a complete reference volume. It would be enormously valuable to organize a searchable database of all the results of studies on energy expenditures across the globe to allow scholars to search and create context-specific MET values for an array of activities, most of which are measured in these time use surveys.

There is also a need for improved collaboration across national and international agencies to coordinate data collection on PA to represent both the various domains of daily living and measures of total activity and inactivity using objective measures (via pedometers or accelerometers). There have been some investments in this regard with the National Diet and Nutrition Survey (UK) and the National Health and Nutrition Examination Survey (US) now using objective assessments alongside self-reported PA measures. Continued investments to enhance the use of these complementary forms of measurements are needed if we are to better monitor the patterns of human movement and create policies or interventions that can be effective66.

The policy side of changing PA patterns, particularly related to transportation, will have important benefits for pollution control and climate change concerns as well. Our work here shows slight increases in travel PA for the UK, but this rate of change needs to occur faster than currently projected toward the examples of other European countries, such as the Netherlands and Denmark67. In contrast, in the US there was no change in travel PA in the last decade. This needs to improve and requires integrated policies that include different but complementary interventions, infrastructure provision, supportive land use planning, and restrictions on car use55, 68, 69. Regulatory and taxation options for improving active travel exist. These range from congestion charging schemes to reduce car use, with a resultant increase in cycling and walking for transport and other positive outcomes, such as better air quality, lower noise pollution, and lower congestion70, to a growing array of transportation options. However, without disincentives to car ownership and use along with improved mass transit, these changes will not occur. Brazil, China, and India are moving rapidly toward reduced travel PA along with growing vehicle ownership71. Slowing this down or turning it around will require a commitment to consider long-term health outcomes and environmental factors along with short-term economic growth.

Active leisure is a much more complex target. The literature on active leisure has been dominated by research from the US, the UK, Australia, and Brazil but is minimal for many other countries. The Agita Mundo initiative is exemplary for addressing movement in leisure and other modes7275. However, to date the slight upward trends in active leisure in the UK, the US, and Brazil have been small in comparison to the large declines in PA from the other domains despite significant efforts and investments to encourage active leisure. These countries increasingly recognize that truly effective efforts to encourage active leisure will require taking safety, economic, personal, psychological, cultural, and social barriers into account63. Similarly, for the rest of the world, we need to identify culturally relevant approaches to active leisure activity across countries (e.g., traditional dancing, martial arts, soccer).

These forecasted declines in PA and increases in sedentary behavior will have significant implications for the health outcomes, health care costs, and overall functional well-being of societies across the globe. By focusing on these five countries that represent over 3 billion individuals (or almost 50% of the world’s population), this study indicates what is expected if inaction continues in the face of rapid declines in PA and increases in sedentary behavior. It is our hope that the growing number of global initiatives and advocacy efforts in all regions of the world that are building momentum to study and intervene in all PA domains18, 76 will be effective and that our estimates will never become reality.

Figure 2
UK Adults MET-hours Per Week of All Physical Activity, and Hours/Week of Time in Sedentary Behavior: Measured for 1961–2005, Forecasted for 2006–2030

Supplementary Material

Suppl. Data

Acknowledgments

The work presented in this paper was supported by funds from Nike, Inc., Global Access to Sport Initiative. We thank Drs. Victor Matsudo, K. Srinath Reddy, James F. Sallis, Nick Wareham, and Fei Xu for their helpful review and comments in earlier versions of this manuscript. We thank Drs. Rosangela Pereira and Rosely Sichieri for assistance in providing supporting data, Jim Terry and Dan Blanchette for exceptional assistance with data management and programming, Ms. Frances L. Dancy for administrative assistance, Mr. Tom Swasey for graphics support, and Nithya Gopu and Anne Cotleur, our liaisons with the Nike, Inc., Global Access to Sport Initiative team.

ACRONYMS AND ABBREVIATIONS

PA
physical activity
SLOTH
sleep, leisure, occupation, transportation, home-based activities
US
United States of America
UK
United Kingdom
CVD
cardiovascular disease
IPAQ
International Physical Activity Questionnaire
WHO
World Health Organization
STEPS
STEPwise approach to surveillance
GPAQ
Global Physical Activity Questionnaire
MET
metabolic equivalents of task
CHNS
China Health and Nutrition Survey
MTUS
Multinational Time Use Study
ATUS
American Time Use Survey
UN
United Nations
ILO
International Labour Organization
WDI
World Development Indicators
GDP
gross domestic product
PPP
purchasing power parity

Footnotes

Conflicts of interest: Nike, Inc., Global Access to Sport Initiative funded this work. The authors completed the manuscript independently with assistance of reviewers. SWN and BMP conceptualized and wrote the manuscript, and SWN conducted all analyses. Neither author has conflicts of interest with respect to this manuscript.

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