James Woodcock
email: james.woodcock@lshtm.ac.uk

Oscar H Franco

Nicola Orsini

G Roberts

Ian Roberts

James Mr

Lecturer, Woodcock

Non-vigorous physical activity and all-cause mortality: systematic review and meta-analysis of cohort studies

Keywords: physical activity, walking, systematic review, meta-analysis, exercise, cohort study Physical activity, exercise, walking, mortality, systematic review, meta-analysis, cohort study, dose-response

Introduction

Non-vigorous physical activity is a central focus of health promotion. [START_REF]Evidence on the impact of physical activity and its relationship to health: A report from the Chief Medical Officer[END_REF][START_REF] Haskell | Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association[END_REF] Nevertheless, the expected benefit of different amounts of physical activity on all-cause mortality remains unclear. Many cohort studies have estimated a dose response but these have varied in their size, precision and findings, while some suggest a linear relationship with increasing activity, other suggest no additional benefit from higher doses. [START_REF] Hayasaka | Physical activity and all-cause mortality in Japan: the Jichi Medical School (JMS) Cohort Study[END_REF][START_REF] Trolle-Lagerros | Physical activity as a determinant of mortality in women[END_REF][START_REF] Villeneuve | Physical activity, physical fitness, and risk of dying[END_REF][START_REF] Orsini | Combined effects of obesity and physical activity in predicting mortality among men[END_REF] We aimed to quantify and characterise the nature of the association between non-vigorous physical activity and allcause mortality. [START_REF] Bekkering | How much of the data published in observational studies of the association between diet and prostate or bladder cancer is usable for meta-analysis?[END_REF] Active travel is frequently recommended as a way to increase physical activity; and walking is the most popular form of physical activity. [START_REF]Evidence on the impact of physical activity and its relationship to health: A report from the Chief Medical Officer[END_REF] Therefore, in addition to studies looking at multiple aspects of non-vigorous activity we looked for studies that estimated the exclusive effect of walking or cycling on all-cause mortality. Furthermore we investigated if there was a difference in effect according to different gender, age, the quality of the study and the extent of adjustment for potentially intermediary variables.

Methods

Selection criteria

Inclusion criteria were 1) prospective cohort study in a healthy/general population with more than 10,000 people at baseline; 2) measure of light or moderate physical activity (either in terms of duration, frequency, distance or a combination); 3) association with all cause mortality. We excluded studies that only measured work related activity. We only included studies of physical activity not physical fitness. We only included studies that compared more than two exposure levels.

Search strategy

We searched Medline, Embase, Cochrane (DARE), Web of Science, Global Health (in July 2008 and then an update in June 2009), for cohort studies. No time period restrictions were included. Key words used in Medline included, "physical activity", "bicycling", "walking", "exercise", "active travel", "active commuting", "active transport", in combination with "mortality", "life expectancy" and "death". See "On line Appendix: Search strategy". Mesh headings included, "Exercise", "Exercise Therapy", "Physical Fitness", and "Exertion". We 3 searched the reference lists of included studies and other systematic reviews. We also contacted authors of all studies with over 10,000 participants identified as of February 2009 for unpublished studies. All remaining references were assessed by two independent reviewers (JW and OHF) and any disagreements resolved by discussion and mutual agreement. No language restrictions were employed.

Data extraction

We used a data collection form designed before the search for studies took place. We collected information on participants and study characteristics (including age, sex, setting and follow up); measurement of exposure (including domain, restrictions on intensity or duration of activity, physical activity instrument tool and whether this used a questionnaire or interview); ascertainment of outcome; study exclusion criteria and adjustment for potential confounders (e.g. smoking, education); and estimate of treatment effect (the estimate most adjusted for potential confounders).

If a study produced multiple estimates based on different kinds of physical activity we selected the estimate most relevant to non-vigorous activities of daily living, rather than activity done as exercise. If the study also presented results for a group undertaking vigorous activity we ignored this group but included other results from the study, even if this reduced the sample size to fewer than 10,000 people. For secondary analyses we selected estimates exclusively of walking or cycling. If multiple publications were available on the same cohort we chose the most recent publication that met all other inclusion criteria. Some analyses exclude deaths that occur soon after measurement of baseline data to reduce the risk of reverse causation. If available we took the results for the full time period but in the sensitivity analysis we considered studies excluding a time period.

We extracted available data on the duration and intensity of physical activity per week, combining both intensity and duration of activity. The intensity of activities can be categorised according to subjective exertion or with a fixed measure for each activity, usually measured as METs (Metabolic Equivalent Tasks). A MET is a unit of energy expenditure adjusted for mass, with the reference category of 1 MET being the typical energy expenditure of an individual at rest, (4.18 kilojoules/kilogram/hour). [START_REF] Ainsworth | Compendium of physical activities: an update of activity codes and MET intensities[END_REF] Activities below 6 METs are generally defined as moderate and those below 3 METs as light for adults under 65 years. [START_REF] Haskell | Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association[END_REF] The intensity of selected activities were based on the compendium of physical activities from Ainsworth 2000. [START_REF] Ainsworth | Compendium of physical activities: an update of activity codes and MET intensities[END_REF] We did not include study results based on activities of greater intensity than 6 METs. Time spent in activities of different intensity over a week can be combined to give an estimate of total MET hours per week. The measure of MET hours per week incorporates both the intensity and the total time per week spent on physical activity.

Quality assessment

We assessed the quality of the studies using the Newcastle Ottawa Scale. [START_REF] Wells | The Newcastle-Ottawa Scale (NOS) for assessing the quality of non-randomised studies in metaanalyses[END_REF] In this scale studies are assessed in three areas: the selection of exposed and unexposed participants, the comparability of the groups, and the assessment of the outcome. A star is awarded for high quality in each area. The Newcastle Ottawa Scale requires selection of a confounder considered to be the most important and in this case, because of its strong association with mortality 10 , we selected smoking.

Analysis

We pooled the studies using two stage random effects dose-response meta-regression models developed by Greenland and Longnecker as implemented by Orsini and colleagues. [START_REF] Orsini | Generalized least squares for trend estimation of summarized dose-response data[END_REF] This method allows estimation of the dose response gradient of mortality risk across studies taking into account non-independent relative risks presented within a single study. For each study we assigned the midpoint of each exposure interval as the median dose corresponding to the relative risk. If the highest exposure interval was defined as greater than a given value we imputed the median exposure for that interval by assuming a linear decline in the population density function (the number of people in each exposure interval divided by the width of the exposure interval) with increasing exposure. The population density function at the start of the highest exposure interval was estimated based on the population density function for the lower exposure interval.

We converted exposure measures from each study into MET hours of activity per week using the data available in the report and by selecting the estimate from the compendium of activities [START_REF] Ainsworth | Compendium of physical activities: an update of activity codes and MET intensities[END_REF] that appeared most applicable. If an exposure interval could either represent a given duration of moderate intensity physical activity or a shorter duration of more intensive physical activity we assumed the exposure was based on the longer duration of moderate intensity physical activity. If activity was represented as a number of sessions an average duration of 0.5 hours per session was assumed. For estimates that included MET hours spent in sedentary activities we assumed a minimum level of activity in the lowest group based on the data available and calibrated all other estimates to this. If the reference category was high activity we used the method suggested by Hamling [START_REF] Hamling | Facilitating meta-analyses by deriving relative effect and precision estimates for alternative comparisons from a set of estimates presented by exposure level or disease category[END_REF] to convert the reference category to the lowest exposure category. If information on person-years of follow-up and deaths per group were not provided we decided to impute these based on the study size, length of follow up and differing mortality rates. Statistical heterogeneity among studies included in the meta-analysis was assessed using the Q statistic and I-squared. [START_REF] Higgins | Measuring inconsistency in meta-analyses[END_REF] Small study effects were assessed by the Egger's regression asymmetry test [START_REF] Egger | Bias in meta-analysis detected by a simple, graphical test[END_REF] and by visual inspection of a funnel plot.

Assessment of non-linear dose-response relationships

We investigated potential departure from linearity between physical activity and mortality by using first-degree fractional polynomials with different power transformations (0.25, 0.375, 0.50, 0.75) and a log transformation of the exposure (MET hours per week). [START_REF] Royston | Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling[END_REF] We used the Akaike's information criterion (AIC, a summary measure that combine fit and complexity) to choose the model that best (lowest AIC value) fitted the data. 17

Sensitivity and Stratified Analyses

We pre-specified five subgroup/sensitivity analyses: (i) studies that provided an estimate based on walking alone and, (ii) studies that provided an estimate on cycling alone. We looked (iii) at the extent to which the study controlled for cardio-metabolic variables (blood pressure, blood glucose, lipid levels and cholesterol) with the hypothesis that greater adjustment may be associated with a lower effect estimate of physical activity on all-cause mortality. To investigate (iv) the robustness we investigated if effects were sensitive to the exclusion of deaths that occurred soon after collection of baseline data, the exclusion of non-brisk walking and to the quality of the studies. Finally, (v) we investigated if the effect varied by age, sex, or year of study by stratifying our analyses by these variables. For each stratified variable, if individual studies reporting stratified results by that variable were identified then we meta-analysed only that subset of studies. All analyses were conducted using Stata, release 10 (StataCorp, College Station, TX, USA). All statistical tests were 2-sided.

Results

Search results

We retrieved 6210 records. Initial screening of title and abstract excluded 5725, see Figure 1. No additional reports were retrieved from searching reference lists. We identified one additional report from personal communication. [START_REF] Rmv | Combined impact of lifestyle factors on mortality: prospective cohort study in US women[END_REF] We retrieved 42 reports for further inspection. We excluded 13 reports on examination of full text, including three for not assessing the exposure and outcome in at least 10,000 participants, [START_REF] Yates | Exceptional longevity in men: modifiable factors associated with survival and function to age 90 years[END_REF][START_REF] Schnohr | Long-term physical activity in leisure time and mortality from coronary heart disease, stroke, respiratory diseases, and cancer. The Copenhagen City Heart Study[END_REF][START_REF] Fried | Risk factors for 5-year mortality in older adults: The cardiovascular health study[END_REF][START_REF] Mensink | Physical activity and its association with cardiovascular risk factors and mortality[END_REF][START_REF] Arraiz | Risk assessment of physical activity and physical fitness in the Canada health survey mortality follow-up study[END_REF] five for not providing an estimate for non-vigorous activity [START_REF] King | Turning back the clock: adopting a healthy lifestyle in middle age[END_REF][START_REF] Meyer | Body mass index and mortality: The influence of physical activity and smoking[END_REF][START_REF] Hillsdon | Can a simple measure of vigorous physical activity predict future mortality? Results from the OXCHECK study[END_REF][START_REF] Holme | Physical activity at work and at leisure in relation to coronary risk factors and social class. A 4-year mortality follow-up. The Oslo Study[END_REF][START_REF] Fraser | Ten years of life -Is it a matter of choice?[END_REF] , three for not reporting on all-cause mortality [START_REF] Wisloff | A single weekly bout of exercise may reduce cardiovascular mortality: How little pain for cardiac gain? 'The HUNT study, Norway[END_REF][START_REF] Manson | Walking compared with vigorous exercise for the prevention of cardiovascular events in women[END_REF][START_REF] Orsini | Association of physical activity with cancer incidence, mortality, and survival: a population-based study of men[END_REF] , and one for only comparing two exposure levels [START_REF] Park | Physical activity and physical fitness as predictors of all-cause mortality in Korean men[END_REF] . Contact with one author produced additional data on hazard ratios for physical activity categories. [START_REF] Orsini | Combined effects of obesity and physical activity in predicting mortality among men[END_REF] Table 1 presents the characteristics of the 22 independent studies [START_REF] Hayasaka | Physical activity and all-cause mortality in Japan: the Jichi Medical School (JMS) Cohort Study[END_REF][START_REF] Trolle-Lagerros | Physical activity as a determinant of mortality in women[END_REF][START_REF] Villeneuve | Physical activity, physical fitness, and risk of dying[END_REF][START_REF] Orsini | Combined effects of obesity and physical activity in predicting mortality among men[END_REF][START_REF] Arrieta | Effects of leisure and non-leisure physical activity on mortality in U.S. adults over two decades[END_REF][START_REF] Besson | Relationship between Subdomains of Total Physical Activity and Mortality[END_REF][START_REF] Inoue | 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)[END_REF][START_REF] Pedersen | The combined influence of leisure-time physical activity and weekly alcohol intake on fatal ischaemic heart disease and all-cause mortality[END_REF][START_REF] Ford | Self-rated health and a healthy lifestyle are the most important predictors of survival in elderly women[END_REF][START_REF] Matthews | Influence of exercise, walking, cycling, and overall nonexercise physical activity on mortality in Chinese women.[see comment[END_REF][START_REF] Leitzmann | Physical activity recommendations and decreased risk of mortality[END_REF][START_REF] Carlsson | Low physical activity and mortality in women: Baseline lifestyle and health as alternative explanations[END_REF][START_REF] Schooling | Obesity, physical activity, and mortality in a prospective chinese elderly cohort[END_REF][START_REF] Hu | The effects of physical activity and body mass index on cardiovascular, cancer and all-cause mortality among 47,212 middle-aged Finnish men and women[END_REF][START_REF] Fujita | Walking and mortality in Japan: the Miyagi Cohort Study[END_REF][START_REF] Barengo | Low physical activity as a predictor for total and cardiovascular disease mortality in middle-aged men and women in Finland[END_REF][START_REF] Batty | Physical activity and cause-specific mortality in men: Further evidence from the Whitehall study[END_REF][START_REF] Rockhill | Physical activity and mortality: A prospective study among women[END_REF][START_REF] Lee | Associations of light, moderate, and vigorous intensity physical activity with longevity. The Harvard Alumni Health Study[END_REF][START_REF] Kujala | Relationship of leisure-time physical activity and mortality: The Finnish Twin Cohort[END_REF][START_REF] Leon | Leisure time physical activity and the 16-year risks of mortality from coronary heart disease and all-causes in the Multiple Risk Factor Intervention Trial (MRFIT)[END_REF][START_REF] Kushi | Physical activity and mortality in postmenopausal women.[see comment[END_REF][START_REF] Kampert | Physical activity, physical fitness, and all-cause and cancer mortality: a prospective study of men and women[END_REF] reported in 30 reports. [START_REF] Rmv | Combined impact of lifestyle factors on mortality: prospective cohort study in US women[END_REF][START_REF] Khaw | Work and leisure time physical activity assessed using a simple, pragmatic, validated questionnaire and incident cardiovascular disease and all-cause mortality in men and women: The European Prospective Investigation into Cancer in Norfolk prospective population study[END_REF][START_REF] Schnohr | Does educational level influence the effects of smoking, alcohol, physical activity, and obesity on mortality? A prospective population study[END_REF][START_REF] Lee | Exercise intensity and longevity in men: The Harvard Alumni Health Study[END_REF][START_REF] Paffenbarger | Changes in physical activity and other lifeway patterns influencing longevity[END_REF][START_REF] Leon | Physical activity and 10.5 year mortality in the multiple risk factor intervention trial (MRFIT)[END_REF][START_REF] Leon | Leisure-time physical activity levels and risk of coronary heart disease and death. The Multiple Risk Factor Intervention Trial[END_REF][START_REF] Paffenbarger | Physical activity, all-cause mortality, and longevity of college alumni[END_REF] The studies included nearly 1 million participants (977,925), with more women (643,187) than men (334,738). The cohort studies were conducted in Europe (8 studies), North America (8 studies), East Asia (5 studies), and Australasia (1 study) with the most recent report for each study being published between 1996 and 2009. The studies covered a broad range of populations of middle and older age but younger adults were under represented, see Table 1. Mean age at baseline ranged from 38 [START_REF] Kujala | Relationship of leisure-time physical activity and mortality: The Finnish Twin Cohort[END_REF] to 72 [START_REF] Schooling | Obesity, physical activity, and mortality in a prospective chinese elderly cohort[END_REF] . Estimated exposure in the studies and the associated relative risks and CIs are presented in web only Table 2.

Quality

Out of a maximum of 9 stars, we found that the median and mean number of stars awarded to a study was 6. The highest score was 8 stars (2 studies [START_REF] Hayasaka | Physical activity and all-cause mortality in Japan: the Jichi Medical School (JMS) Cohort Study[END_REF][START_REF] Ford | Self-rated health and a healthy lifestyle are the most important predictors of survival in elderly women[END_REF] ) and the lowest score was 4 stars (3 studies [START_REF] Carlsson | Low physical activity and mortality in women: Baseline lifestyle and health as alternative explanations[END_REF][START_REF] Kushi | Physical activity and mortality in postmenopausal women.[see comment[END_REF][START_REF] Kampert | Physical activity, physical fitness, and all-cause and cancer mortality: a prospective study of men and women[END_REF] ) (Table 3). The majority of the included studies were of the general population of men or women from a geographically defined area 3-6, 33-39, 59-60 and all selected controls from the same population. Most studies excluded or adjusted on the basis of self reported health status, rather than clinical assessment. All studies controlled for smoking, using variables ranging from a simple yes or no question on current smoking status (7 studies [START_REF] Hayasaka | Physical activity and all-cause mortality in Japan: the Jichi Medical School (JMS) Cohort Study[END_REF][START_REF] Arrieta | Effects of leisure and non-leisure physical activity on mortality in U.S. adults over two decades[END_REF][START_REF] Matthews | Influence of exercise, walking, cycling, and overall nonexercise physical activity on mortality in Chinese women.[see comment[END_REF][START_REF] Carlsson | Low physical activity and mortality in women: Baseline lifestyle and health as alternative explanations[END_REF][START_REF] Schooling | Obesity, physical activity, and mortality in a prospective chinese elderly cohort[END_REF][START_REF] Kushi | Physical activity and mortality in postmenopausal women.[see comment[END_REF][START_REF] Kampert | Physical activity, physical fitness, and all-cause and cancer mortality: a prospective study of men and women[END_REF] ) to current smoking status plus pack years [START_REF] Trolle-Lagerros | Physical activity as a determinant of mortality in women[END_REF][START_REF] Orsini | Combined effects of obesity and physical activity in predicting mortality among men[END_REF][START_REF] Kujala | Relationship of leisure-time physical activity and mortality: The Finnish Twin Cohort[END_REF] . Three studies presented equivalent data suitable for inclusion both with and without exclusion of deaths in the first years. [START_REF] Arrieta | Effects of leisure and non-leisure physical activity on mortality in U.S. adults over two decades[END_REF][START_REF] Fujita | Walking and mortality in Japan: the Miyagi Cohort Study[END_REF][START_REF] Kushi | Physical activity and mortality in postmenopausal women.[see comment[END_REF] Four studies only provided suitable data after excluding deaths that occurred soon after measurement of baseline data [START_REF] Orsini | Combined effects of obesity and physical activity in predicting mortality among men[END_REF][START_REF] Carlsson | Low physical activity and mortality in women: Baseline lifestyle and health as alternative explanations[END_REF][START_REF] Rockhill | Physical activity and mortality: A prospective study among women[END_REF][START_REF] Kujala | Relationship of leisure-time physical activity and mortality: The Finnish Twin Cohort[END_REF] , four studies said excluding such deaths did not substantially alter the effect estimate but did not provide the numbers. [START_REF] Trolle-Lagerros | Physical activity as a determinant of mortality in women[END_REF][START_REF] Pedersen | The combined influence of leisure-time physical activity and weekly alcohol intake on fatal ischaemic heart disease and all-cause mortality[END_REF][START_REF] Matthews | Influence of exercise, walking, cycling, and overall nonexercise physical activity on mortality in Chinese women.[see comment[END_REF][START_REF] Leitzmann | Physical activity recommendations and decreased risk of mortality[END_REF] . Six studies adjusted for physical activity in other domains. 34-35, 38-39, 44, 47

Assessment of physical activity

The studies used a range of methods to assess and then to combine physical activity (Table 4). Four studies [START_REF] Hayasaka | Physical activity and all-cause mortality in Japan: the Jichi Medical School (JMS) Cohort Study[END_REF][START_REF] Arrieta | Effects of leisure and non-leisure physical activity on mortality in U.S. adults over two decades[END_REF][START_REF] Matthews | Influence of exercise, walking, cycling, and overall nonexercise physical activity on mortality in Chinese women.[see comment[END_REF][START_REF] Schooling | Obesity, physical activity, and mortality in a prospective chinese elderly cohort[END_REF] used an interview, while the rest used a self-completed questionnaire. Only one study included a repeat assessment of physical activity. [START_REF] Rockhill | Physical activity and mortality: A prospective study among women[END_REF] Of the included studies most looked at time or frequency of bouts of activity but three studies estimated total MET hours over 24 hrs [START_REF] Hayasaka | Physical activity and all-cause mortality in Japan: the Jichi Medical School (JMS) Cohort Study[END_REF][START_REF] Orsini | Combined effects of obesity and physical activity in predicting mortality among men[END_REF][START_REF] Carlsson | Low physical activity and mortality in women: Baseline lifestyle and health as alternative explanations[END_REF] , three presented results on walking alone [START_REF] Besson | Relationship between Subdomains of Total Physical Activity and Mortality[END_REF][START_REF] Matthews | Influence of exercise, walking, cycling, and overall nonexercise physical activity on mortality in Chinese women.[see comment[END_REF][START_REF] Lee | Associations of light, moderate, and vigorous intensity physical activity with longevity. The Harvard Alumni Health Study[END_REF] , two on active commuting [START_REF] Barengo | Low physical activity as a predictor for total and cardiovascular disease mortality in middle-aged men and women in Finland[END_REF][START_REF] Batty | Physical activity and cause-specific mortality in men: Further evidence from the Whitehall study[END_REF] , two on cycling alone [START_REF] Besson | Relationship between Subdomains of Total Physical Activity and Mortality[END_REF][START_REF] Matthews | Influence of exercise, walking, cycling, and overall nonexercise physical activity on mortality in Chinese women.[see comment[END_REF] , one on walking and cycling combined [START_REF] Besson | Relationship between Subdomains of Total Physical Activity and Mortality[END_REF] and 11 included different kinds of activities measured by duration, frequency and intensity 4-5, 33, 36-37, 40-41, 44, 47-48, 50-51, 61 . All but one [START_REF] Arrieta | Effects of leisure and non-leisure physical activity on mortality in U.S. adults over two decades[END_REF] specifically mentioned walking (recorded as either transport, work or leisure). One study excluded non-brisk

F o r R e v i e w O n l y
walking [START_REF] Leitzmann | Physical activity recommendations and decreased risk of mortality[END_REF] , one study only included 'long walks' [START_REF] Kushi | Physical activity and mortality in postmenopausal women.[see comment[END_REF] and one study only included sessions of activity of a least 30 minutes. Most studies specifically included cycling as a moderate intensity activity.

For all studies the median exposure in each category had to be estimated. In 20 studies the highest exposure category was defined as greater than a given value, with only two studies providing more information. [START_REF] Kujala | Relationship of leisure-time physical activity and mortality: The Finnish Twin Cohort[END_REF][START_REF] Leon | Leisure time physical activity and the 16-year risks of mortality from coronary heart disease and all-causes in the Multiple Risk Factor Intervention Trial (MRFIT)[END_REF] The highest exposure in our dataset was estimated to be 145 MET hours/wk (equating to 32 hours of moderate activity). [START_REF] Matthews | Influence of exercise, walking, cycling, and overall nonexercise physical activity on mortality in Chinese women.[see comment[END_REF]

Effect of non-vigorous physical activity on all-cause mortality

We first assessed possible non-linearity between non-vigorous physical activity as measured in MET-hours per week and all-cause mortality risk. We found that the power 0.25 model for physical activity, as measured in MET-hours/week, has a better fit (smaller AIC) compared with other power transformations as well as the simple linear trend (linear AIC 142.12, log AIC 40.40, power 0.75 AIC 94.86, power 0.5 AIC 49.69, power 0.375 AIC 36.45, power 0.25 AIC 35.69). The relationship is presented graphically in Figure 2.

Compared with inactive (0 MET-hours/week) individuals 11 MET hours of non-vigorous physical activity per week (about 2.5 hours per week) of moderate physical activity was associated with a 19% reduction in the mortality rate (95% CI 15 to 24%, heterogeneity Q = 196.77, I-squared= 85.8%). Compared with inactive individuals 31 MET hours per week, about 7 hours per week of light and moderate activity, was associated with 22% (95% CI 17 to 26%) and 24% (95% CI 19 to 29%) lower mortality rates respectively (see web-only Table 5).

Only three [START_REF] Schooling | Obesity, physical activity, and mortality in a prospective chinese elderly cohort[END_REF][START_REF] Barengo | Low physical activity as a predictor for total and cardiovascular disease mortality in middle-aged men and women in Finland[END_REF][START_REF] Batty | Physical activity and cause-specific mortality in men: Further evidence from the Whitehall study[END_REF] out of the 29 sets of relative risks arising from 22 studies did not find a trend towards lower mortality with increased physical activity (Figure 3). The Egger's regression asymmetry test did not detect strong evidence of publication bias or small-study effects (P=0.053), as shown in the funnel plot in Figure 4. In their most comprehensive assessment of physical activity, four studies provided estimates of exposure higher than 67.5 MET hours (equivalent to 15 h/wk of moderate intensity activity) [START_REF] Hayasaka | Physical activity and all-cause mortality in Japan: the Jichi Medical School (JMS) Cohort Study[END_REF][START_REF] Inoue | 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)[END_REF][START_REF] Matthews | Influence of exercise, walking, cycling, and overall nonexercise physical activity on mortality in Chinese women.[see comment[END_REF][START_REF] Carlsson | Low physical activity and mortality in women: Baseline lifestyle and health as alternative explanations[END_REF] and three of these studies found increasing benefit at all levels of energy expenditure.

Walking and cycling

We identified five studies that provided estimates based on walking exposure alone [START_REF] Besson | Relationship between Subdomains of Total Physical Activity and Mortality[END_REF][START_REF] Matthews | Influence of exercise, walking, cycling, and overall nonexercise physical activity on mortality in Chinese women.[see comment[END_REF][START_REF] Fujita | Walking and mortality in Japan: the Miyagi Cohort Study[END_REF][START_REF] Rockhill | Physical activity and mortality: A prospective study among women[END_REF][START_REF] Lee | Associations of light, moderate, and vigorous intensity physical activity with longevity. The Harvard Alumni Health Study[END_REF] , and one that measured walking and standing time. [START_REF] Inoue | 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)[END_REF] In the assessment of possible nonlinearity, we found that the power 0.375 model for walking as measured in MET hours per week, presented graphically in Figure 5, has a better fit compared with other power transformations as well as the simple linear trend (power 0.25 AIC -33.70, power 0.375 AIC -34.01, power 0.5 AIC -33.98, power 0.75 AIC -33.01, log AIC -33.78, linear AIC -31.05).

Compared with no walking per week, 2.5 hour of brisk walking per week (approximately 11 MET hours per week) was associated with a RR=0.89 (95% CI 0.82 to 0.96). Even among the estimates that of walking alone there remained considerable heterogeneity (Q = 15.85, Pheterogeneity = 0.003, I-squared 74.8%), see Figure 6.

The study of walking and standing found a similar effect to the studies specifically on walking (results not shown) [START_REF] Inoue | 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)[END_REF] . The two studies that provided estimates based on active commuting alone (excluding walking and cycling for other reasons) [START_REF] Barengo | Low physical activity as a predictor for total and cardiovascular disease mortality in middle-aged men and women in Finland[END_REF][START_REF] Batty | Physical activity and cause-specific mortality in men: Further evidence from the Whitehall study[END_REF] did not find evidence of an

F o r R e v i e w O n l y effect.
Of the two studies that provided point estimates specifically based on cycling [START_REF] Besson | Relationship between Subdomains of Total Physical Activity and Mortality[END_REF][START_REF] Matthews | Influence of exercise, walking, cycling, and overall nonexercise physical activity on mortality in Chinese women.[see comment[END_REF] , one suggested a substantial benefit [START_REF] Matthews | Influence of exercise, walking, cycling, and overall nonexercise physical activity on mortality in Chinese women.[see comment[END_REF] , while the other found no evidence of an effect. [START_REF] Besson | Relationship between Subdomains of Total Physical Activity and Mortality[END_REF]

Subgroup/Sensitivity analyses

In all the following subgroup and sensitivity analyses we fitted a power 0.25 transformation of the exposure variable. We found a larger effect in those studies that adjusted for more metabolic variables compared with those that adjusted for fewer metabolic variables (11 MET hours per week RR 0.79 vs RR 0.83).

We found that excluding deaths that occurred soon after measurement of baseline data randomisation had little effect on the results [START_REF] Arrieta | Effects of leisure and non-leisure physical activity on mortality in U.S. adults over two decades[END_REF][START_REF] Fujita | Walking and mortality in Japan: the Miyagi Cohort Study[END_REF][START_REF] Kushi | Physical activity and mortality in postmenopausal women.[see comment[END_REF] (11 MET hours/wk RR 0.72 excluding early deaths vs RR 0.73 not excluding early deaths). We found a larger benefit in the higher quality studies (11 MET hours p/wk RR 0.80 studies with>=6 stars vs RR 0.83 studies with <6 stars). In one large study [START_REF] Leitzmann | Physical activity recommendations and decreased risk of mortality[END_REF] a minimum threshold of moderate activity was set, excluding nonbrisk walking and other light activities. In one other study only 'long walks' were included. [START_REF] Kushi | Physical activity and mortality in postmenopausal women.[see comment[END_REF] Excluding these studies did not change the size of the effect (11 MET hours/wk RR 0.81).

We next investigated if there was evidence of a difference in effect between men and women, using the seven studies that presented separate estimates for both sexes. We found a slightly larger effect in women than in men (11 MET hours/wk RR 0.85 for men and 0.83 for women). The effect in the group of studies that presented separate results by sex was smaller than in the overall analysis. We found little difference between the older studies compared with more recent studies (mid-year of study pre 1991 RR 0.80 vs, mid-year of study post 1990 RR 0.81). We found a larger reduction in mortality from lower physical activity doses in adults aged 65 years and older (11 MET hours/wk RR 0.78 for older adults vs 0.81 for younger adults).

DISCUSSION

In the meta-analysis we found an inverse association between physical activity (measured as MET hours) and the risk for all-cause mortality. This study is the first systematic review to estimate the dose response effect of non-vigorous intensity physical activity on all cause mortality. Based on these findings, in populations with low levels of activity 2.5 hours per week of moderate intensity activity would reduce mortality by 19%. Increasing this to one hour session seven days a week (7 hours per week) of activity could increase the benefit to 24%. Evidence on the dose response function allows estimation of the marginal benefits for groups with different levels of activity. Our analysis suggests a non-linear relationship with the greatest benefit appearing in the process of changing from a sedentary lifestyle to low levels of activity and smaller additional benefits from higher levels of activity. We found that walking reduces all-cause mortality but the effect was smaller than in studies that looked across activities in different domains.

Strengths and weaknesses

This systematic review benefits from the inclusion of large recently published cohort studies, [START_REF] Hayasaka | Physical activity and all-cause mortality in Japan: the Jichi Medical School (JMS) Cohort Study[END_REF][START_REF] Orsini | Combined effects of obesity and physical activity in predicting mortality among men[END_REF][START_REF] Arrieta | Effects of leisure and non-leisure physical activity on mortality in U.S. adults over two decades[END_REF][START_REF] Besson | Relationship between Subdomains of Total Physical Activity and Mortality[END_REF][START_REF] Inoue | 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)[END_REF][START_REF] Pedersen | The combined influence of leisure-time physical activity and weekly alcohol intake on fatal ischaemic heart disease and all-cause mortality[END_REF] in total representing nearly 1 million people. We sought unbiased estimates from large studies and were not short of power for the overall analysis. The analyses included study populations from Europe, North America, East Asia and Australasia. However, the studies were highly heterogeneous in their methods and their findings.

There are a number of factors that may have led to us over or under estimating the dose response relationship. Reasons for over-estimating the effect include any remaining publication bias or outcome reporting bias and residual confounding. Although all the studies adjusted for multiple potential confounders (see Table 1) there are likely to remain potentially important confounding differences (such as in dietary factors) between people with higher and lower levels of physical activity that could substantially affect the results. Reasons for under-estimating the effect include the exclusion of people with existing disease, which might have been caused by lack of activity, and misclassification of exposure, both at baseline and over time. Previous research has found a low to moderate correlation between self reported and objectively measured physical activity, [START_REF] Prince | A comparison of direct versus selfreport measures for assessing physical activity in adults: a systematic review[END_REF] and a larger effect on allcause mortality from objectively measured physical fitness than recalled physical activity. [START_REF] Nocon | Association of physical activity with all-cause and cardiovascular mortality: a systematic review and meta-analysis[END_REF] There is also a high probability of unrecorded change in exposure over time, given the length of follow up (25 year in the longest study [START_REF] Batty | Physical activity and cause-specific mortality in men: Further evidence from the Whitehall study[END_REF] ) and the lack of repeat measures of exposure. These issues might not just affect the strength of association observed but also the shape of the dose response relationship. The small difference in the AIC result between the 0.25 power transformation and the 0.375 power exposure transformation models, further limits our confidence in the precise nature of the relationship. Surprisingly, we found a larger effect in those studies that adjusted for more cardiometabolic at baseline. This finding could indicate that the benefits of physical activity on mortality occur largely independent of the effect on metabolic variables. However, there could be confounding at the study level. We only found a slightly larger effect in women than among men. This contrasts with a previous systematic review that found a notably larger effect in women. [START_REF] Nocon | Association of physical activity with all-cause and cardiovascular mortality: a systematic review and meta-analysis[END_REF] Compared with this review our meta-analysis had the strength of only including studies that reported stratified results for men and for women, which should reduce the impact of study level confounding.

Effect estimates from previous reviews

A recent systematic review (search date 2007) and meta-analysis, including studies with more than 5000 people, found a 29% reduction in all cause mortality from self-reported physical activity, between the least and most active groups. [START_REF] Nocon | Association of physical activity with all-cause and cardiovascular mortality: a systematic review and meta-analysis[END_REF] This review did not suggest the shape of the dose response relationship. An earlier review proposed an inverse linear dose response across studies but did not undertake a meta-analysis. [START_REF] Lee | Physical activity and all-cause mortality: what is the dose-response relation?[END_REF] It suggested energy expenditure of about 4200 kilojoules per week would reduce mortality by 20 to 30%. A more recent review reporting median results suggested a curvilinear relationship. 65 It found an approximate reduction of 20% in the risk of all cause mortality from 1.5 hours per week of moderate to vigorous activity, and a further 20% reduction for 7 hours of activity. Our results suggest a smaller benefit and a more clearly non-linear relationship than that identified by these earlier reviews. It is possible these differences could be explained because the earlier reviews included smaller studies which found a larger effect. Alternatively our selection of point estimates specifically relating to non-vigorous activities of daily living may have led to a smaller effect estimate.

Walking and cycling

We found a smaller effect in those studies that included walking alone. If the people in the walking studies were active in other domains then this smaller effect would be expected with a non-linear relationship between total physical activity and all-cause mortality. However, it could be the case that even within light and moderate activities, activities of greater intensity than walking may bring additional benefit beyond the increase in MET hours. Furthermore, our confidence in the exact nature of the exposure response relationship between walking and mortality is limited by the small differences in the AIC between the different exposure transformations. One recent systematic review (search dates 2007) [START_REF] Hamer | Walking and primary prevention: a meta-analysis of prospective cohort studies[END_REF] looked at walking and all-cause mortality. It reported a 20% risk reduction for an estimated exposure approximately equivalent to 3 hours per week at 3 km per hour. This is notably larger than our observed association. However, their analysis combined results from studies comparing different walking speeds as well as different walking durations or distances.

We found limited evidence on cycling. Estimates from both the Matthews [START_REF] Matthews | Influence of exercise, walking, cycling, and overall nonexercise physical activity on mortality in Chinese women.[see comment[END_REF] study from China, which found a large effect, and the Besson study from England [START_REF] Besson | Relationship between Subdomains of Total Physical Activity and Mortality[END_REF] , which found no evidence of an effect, both came with considerable statistical uncertainty. The two studies of active commuting did not find evidence of an effect in men. [START_REF] Barengo | Low physical activity as a predictor for total and cardiovascular disease mortality in middle-aged men and women in Finland[END_REF][START_REF] Batty | Physical activity and cause-specific mortality in men: Further evidence from the Whitehall study[END_REF] In the first study, the lack of association might be partly explained by the high levels of work related activity amongst the study population. In the second study, the length of follow up, at 25 years, may have led to a high degree of misclassification of exposure over time. Cycling is usually categorised as more vigorous than walking [START_REF] Ainsworth | Compendium of physical activities: an update of activity codes and MET intensities[END_REF] and, until more robust evidence is available, it may be reasonable to assume the benefit is similar to the overall benefit from moderate intensity activity, if applied to a given baseline level of activity.

Future research

Further research should investigate how different approaches to increasing one domain (such as walking) impacts on activities in other domains (such as leisure activity) and on other health behaviours, in particular diet and smoking.

The finding of a strongly non-linear relationship means that estimates of additional benefit from increasing activity will be strongly sensitive to assumptions on baseline activity levels. Health promotion recommendations for increasing moderate physical activity are primarily targeted at populations with low levels of vigorous activity. Although many of the studies included in our meta-analysis adjusted for vigorous activity, only one study [START_REF] Villeneuve | Physical activity, physical fitness, and risk of dying[END_REF] presented stratified results for people engaging and not engaging in vigorous physical activity. We recommend that future cohort investigators provide analyses stratified by time spent in vigorous activity, in addition to providing estimates controlling for other kinds of activity.

We recommend standardising measures of physical activity, the most promising measure being the International Physical Activity Questionnaire. [START_REF] Craig | International physical activity questionnaire: 12-country reliability and validity[END_REF] We encourage cohort investigators [START_REF] Bekkering | How much of the data published in observational studies of the association between diet and prostate or bladder cancer is usable for meta-analysis?[END_REF] to report the information required for meta-analysis of the dose response; in particular reporting the median exposure dose for each exposure interval not just the range.

Conclusion

Our systematic review and meta-analysis shows that non-vigorous physical activity has a dose-response protection effect against all cause mortality. The largest benefit was found in moving from sedentary behaviour to low levels of activity, but even at high levels of activity benefits accrue from additional activity. CI indicates confidence interval. The size of each square is proportional to the study's weight (inverse of variance). Data were fitted with a random-effect model including a power transformation of 0.375 for walking MET-hours/week (Q=15.85, p-value = 0.003, I-squared=75%).

F o r R e v i e w O n l y 8 CF

 8 Written self report Outcome not present at start Cancer 0.5 self recorded, 1 if clinical assessment CVD 0.5 self recorded, 1 if clinical assessment Other serious condition 0.5 self recorded, 1 if clinical assessment Additional 1 point awarded for other relevant exclusions Comparability Smoking assessed 4 categories: A* Pack years & current smoking or excluding ever smokers B* Quit length C Never/past/current D Yes/no Other A* Study controls for important other additional factors Outcome Assessment of outcome B* Record linkage Was follow-up long enough for outcomes to occur? A* Yes (minimum 2 years) B No (less than 2 years) Adequacy of follow up of cohorts A* Complete follow up -all subjects accounted for B* Participants lost to follow up unlikely to introduce bias : > 95 % follow up, or description provided of those lost C Follow up rate < 95% and no description of those lost D No statement

  physical activity, METs-hours/week

Figure 2 .

 2 Figure 2. Association between MET hours per week of non-vigorous physical activity and relative risk for all cause mortality 29 estimates from 22 studies, 52,294 deaths, and 7,569,742 person years of follow up. Data were fitted with a random-effect model including a power transformation of 0.25 for MET-hours/week. Shaded areas represent 95% confidence intervals.

Figure 4 .

 4 Figure 4. Funnel plot with pseudo 95% confidence intervals for the association between 11 MET-hours per week of non-vigorous physical activity and all-cause mortality in each of the studies.29 estimates from 22 studies. Data were fitted with a power transformation of 0.25 for nonvigorous physical activity.

Figure 5 .

 5 Figure 5. Association between MET hours per week of non-vigorous physical activity and adjusted relative risk for all cause mortality in the studies of walking alone 5 estimates from 5 studies, 22,882 deaths, and 1,581,769 person years of follow up. Shaded area represents 95% confidence intervals. Data were fitted with a random-effect model including a power transformation of 0.375 for walking MET-hours/week.

Figure 6 .

 6 Figure 6. Adjusted relative risk of all-cause mortality for 11 vs 0 MET-hours/week of walking.

  11-

	F o r
	R
	e v
	i e
	w
	O n l
	y
	4

1 Table 1 :

 11 Summary of studies

	Leitzmann	National Institutes of Health-	Around 5	252,925	F/M 50 to 71	Sex, BMI, ethnicity, education, marital status, family history of cancer, menopausal
	2007 (39)	American Association of Retired	years				hormone therapy, aspirin use, multivitamin use, vegetable intake, fruit, red meat and
		Persons Diet and Health Study, USA Web					alcohol, vigorous activity Smoking: never, past 1-19/d, past >=20/d, current 1-19/d, current >-20/d
		Shanghai Women's Health Study,	Mean 5.7	67,143	F	40 to 70	Age, marital status, education, household income, alcohol drinking, number of
	Matthews 2007 (38) Carlsson 2006 (40) Schooling 2006 (41) Trolle-Lagerros 2006 (4) Fujita 2004	F o r R e v i e w years. pregnancies, oral contraceptive use, menopausal status, other types of physical activity, O n l y pre existing medical conditions (including diabetes & hypertension). Smoking: never, ever Swedish Mammography Cohort, China Sweden 5 years 27,734 F 51 to 83 Age, education, number of children, hormone replacement therapy, intake of fruit and vegetables, BMI, hypertension, thrombosis, angina pectoris, myocardial infarction, stroke, diabetes, asthma, cataract, fractures, arthritis, bile stones, renal calculus, high cholesterol, benign node in breast, disablement pension and cancer. Smoking: yes, no Hong Kong, China Average 4.1 years 54,088 F/M >=65 Age, sex, socioeconomic status, ever use of alcohol. Smoking: ever, never Women's Lifestyle and Health cohort, Norway and Sweden Average 11.4 years 99,099 F 30 to 49 Age, years of education, BMI, alcohol intake and country of origin and physical activity at earlier ages. Smoking: current, former, never/ mean number of cigarettes, years smoking Miyagi, Japan 11 years 41,163 F/M 40 to 64 Age, education, marital status, past history of diseases, drinking, BMI (grouped), and F o r R e v i e w O n l y
	(43)						dietary variables.
							Smoking: never, former, current 1-19/d, >20/d
	2 4

Table 2 :

 2 Strength of association in each study used in the main analysis

	Author	Study name	Exposure (METs/wk)
			RR (95% confidence interval)
	Hayasaka 2009 (3)	Jichi Medical School Cohort	Exposure	0.0	36.4	70.0	101.3
		Study, Japan	RR	1.0	0.69 (0.55-0.88)	0.59 (0.45-0.76)	0.75 (0.55-1.04)
			(men)
			RR	1.0	0.79 (0.58-1.08)	0.90 (0.66-1.22)	0.83 (0.59-1.16)
			(women)
	Arrieta 2008 (33)	NHANES I (NHEFS for follow	Adults
		up), USA	Exposure	0.0	13.5	27
			RR	1.0	0.67 (0.54-0.83)	0.57 (0.47-0.69)
			Elderly
			Exposure	0.0	10.5	21
			RR	1.0	0.72(0.58-0.89)	0.63 (0.51-0.78)
	Besson 2008 (34)	European Prospective	Exposure	0.0	1.8	5.5	13.8
		Investigation into Cancer in	RR	1.0	0.96 (0.79-1.17)	0.99 (0.82-1.21)	0.82 (0.67-1.0)
		Norfolk , UK
	Ford 2008 (37)	Australian Longitudinal Study	Exposure	1.1	6.8	14.6	25.1
		on Women's Health	RR	1.0	0.86 (0.7-1.05)	0.69 (0.55-0.86)	0.70(0.53-0.91)
	Inoue 2008 (35)	Japan Public Health Center-	Men	7.0	27.0	91.4
		based Prospective Study,	Exposure
		Japan	RR	1.0	0.90 (0.81-0.99)	0.80 (0.73-0.88)
			Women	7.0	27.0	105.7
			Exposure
			RR	1.0	0.86 (0.74-0.99)	0.64 (0.55-0.73)
	Orsini 2008 (6)	Cohort of Swedish Men,	Exposure	3.6	6.6	9.7
		Sweden	RR	1.0	0.83 (0.69-0.99)	0.63 (0.51-0.77)
	Pedersen 2008 (36)	Copenhagen City Heart study, Men	3.5	10.5	18.4

Table 3 :

 3 Quality

	Study	Selection

Table 4 :

 4 Physical activity assessment Metabolic variables include blood glucose, BMI, blood pressure and cholesterol. Score is based on 1 point for every metabolic variable adjusted for as continuous and 0.5 for every variable adjusted for as discrete, every variable excluded on and every variable stratified by.

	Study	Physical	Physical	Assessment of physical activity	Walking	Physical activity	Physical activity instrument	Questionnaire or
		activity	activity			specified /	other domains	name and validation	examination
		domain	metric			minimum
						walking
						intensity
	Hayasaka	All (24 hours)	METs	Time spent sleeping, working and	Yes/any	All included	Based on Framingham criteria	Face to face
	2009 (3)		(quartiles)	in leisure with activities grouped				questionnaire
				into 5 exertion levels.
	Arrieta	Non-leisure	Qualitativ	"In your usual day, aside from	No/ unclear No adjustment	Not specified.	Physician
	2008 (33)		e (3	recreation, are you physically				examination, lab
			bands)	very active, moderately active, or				tests, and medical
				quite inactive?"				history interview
	Besson	Walking/	METs	•	Inactive (0 MET h/wk)	Yes/any	Adjusted for activity	Study specific plus EPAQ2	Questionnaire and
	2008 (34)	cycling			Moderately inactive (0 -		at home, for sport or	"EPAQ" was validated against	clinical visit
					MET h/wk)		exercise and at work.	repeated measures of free-living
				•	Moderately active (3-8 MET			energy expenditure estimated
					h/wk)			from a 4-d individually calibrated
				•	Active (>=8 MET h/wk)			minute-by-minute heart rate
		Walking	Hours	• • •	Non-walkers Walking <= 90 m/wk Walking >90 m/wk	Yes/any	Adjusted for activity at home, for sport or exercise, at work and	monitoring throughout the year suggesting that the questionnaire is valid for ranking individuals."
							cycling.
		Cycling	Hours	•	Non-cyclists	NA	Adjusted for activity
				•	Cycling <= 30 m/wk		at home, for sport or
				•	Cycling >30 m/wk		exercise, at work and
							cycling.

*

Table 5 :

 5 Association between hours of light and moderate physical activity and RR (Relative Risk) for all cause mortality An activity of approximately 2.5 METs such as walking at 3.2 kilometres per hour ** An activity of approximately 4.5 METs such as 5.6 kilometres per hour carrying less than 11 kilograms

		Light activity*	Moderate activity**
	Hours week	RR (95% CI)	RR (95% CI)
	0	1.00 (Referent)	1.00 (Referent)
	1	0.86 (0.83-0.89)	0.84 (0.81-0.88)
	2.5	0.83 (0.79-0.87)	0.81 (0.76-0.85)
	5	0.80 (0.76-0.84)	0.77 (0.73-0.82)
	7	0.78 (0.74-0.83)	0.76 (0.71-0.81)
	10	0.77 (0.72-0.82)	0.74 (0.68-0.79)
	14	0.75 (0.70-0.80)	0.72 (0.66-0.78)

*

Author

CI indicates confidence interval. The size of each square is proportional to the study's weight (inverse of variance). Data were fitted with a random-effect model including a power transformation of 0.25 for nonvigorous physical activity (Q=196.77, p-value < 0.001, I-squared=86%).