Sedentary behaviour and risk of all-cause, cardiovascular and cancer mortality, and incident type 2 diabetes: a systematic review and dose response meta-analysis
European Journal of Epidemiology
Sedentary behaviour and risk of all-cause, cardiovascular and cancer mortality, and incident type 2 diabetes: a systematic review and dose response meta-analysis
Richard Patterson 0
Eoin McNamara 0
Marko Tainio 0
Thiago He´ rick de Sa´ 0
Andrea D. Smith 0
Stephen J. Sharp 0
Phil Edwards 0
James Woodcock 0
Søren Brage 0
Katrien Wijndaele 0
0 & Richard Patterson
Purpose: To estimate the strength and shape of the dose-response relationship between sedentary behaviour and all-cause, cardiovascular disease (CVD) and cancer mortality, and incident type 2 diabetes (T2D), adjusted for physical activity (PA). Data Sources: Pubmed, Web of Knowledge, Medline, Embase, Cochrane Library and Google Scholar (through September-2016); reference lists. Study Selection: Prospective studies reporting associations between total daily sedentary time or TV viewing time, and C one outcome of interest. Data Extraction: Two independent reviewers extracted data, study quality was assessed; corresponding authors were approached where needed. Data Synthesis: Thirty-four studies (1,331,468 unique participants; good study quality) covering 8 exposure-outcome combinations were included. For total sedentary behaviour, the PA-adjusted relationship was non-linear for all-cause mortality (RR per 1 h/day: were 1.01 (1.00-1.01) B 8 h/day; 1.04 (1.03-1.05) [ 8 h/day of exposure), and for CVD mortality (1.01 (0.99-1.02) B 6 h/day; 1.04 (1.03-1.04) [ 6 h/day). The association was linear (1.01 (1.00-1.01)) with T2D and non-significant with cancer mortality. Stronger PA-adjusted associations were found for TV viewing (h/day); non-linear for all-cause mortality (1.03 (1.01-1.04) B 3.5 h/day; 1.06 (1.05-1.08) [ 3.5 h/day) and for CVD mortality (1.02 (0.99-1.04) B 4 h/day; 1.08 (1.05-1.12) [ 4 h/day). Associations with cancer mortality (1.03 (1.02-1.04)) and T2D were linear (1.09 (1.07-1.12)). Conclusions: Independent of PA, total sitting and TV viewing time are associated with greater risk for several major chronic disease outcomes. For all-cause and CVD mortality, a threshold of 6-8 h/day of total sitting and 3-4 h/day of TV viewing was identified, above which the risk is increased.
Richard Patterson: Corresponding author and guarantor.
Eoin McNamara and Marko Tainio: Authors listed as joint second
Electronic supplementary material The online version of this article
(https://doi.org/10.1007/s10654-018-0380-1) contains supplementary
material, which is available to authorized users.
Public Health Policy Evaluation Unit, School of Public
Health, Imperial College London, London W6 8RP, UK
MRC Epidemiology Unit, School of Clinical Medicine,
University of Cambridge, Cambridge CB2 0QQ, UK
Since the mid-twentieth century people have spent an
increasingly greater amount of their time sedentary [
Sedentary behaviours are defined as any waking time
activity during which one is in seated, reclined or lying
posture, expending low levels of energy [
spend 55% of their waking time, or 7.7 h/day, sedentary
]. Europeans are estimated to spend on average 40% of
Centre for Epidemiological Research in Nutrition and Health,
University of Sa˜o Paulo, Sa˜o Paulo, Brazil
Research Department of Behavioural Science and Health,
University College London, London WC1E 6BT, UK
Faculty of Epidemiology and Population Health, London
School of Hygiene and Tropical Medicine,
London WC1E 7HT, UK
their leisure time watching TV [
], equal to 2.8 h/day in the
UK, which is not declining [
]. Accumulation of sedentary
time is independent from lack of accumulation of
moderate-to-vigorous physical activity (MVPA), e.g. sufficient
levels in MVPA do not preclude relatively high levels of
sedentary time and vice versa [
]. Moreover, the health
effects of sedentary behaviours tend to persist, with some
attenuation, after accounting for MVPA [
recent meta-analysis including over 1 million adults
documented that high levels of sitting time increased
premature mortality risk in all but the most physically active
individuals who accumulate C 1 h/day of
moderate-intensity activity [
Previous meta-analyses have attempted to estimate the
potential impact of sedentary behaviour on specific health
]. However, they were not without
considerable limitations, such as inclusion of
nonprospective studies [
] and use of an ambiguous
sedentary behaviour exposure, defined by different
exposure types across studies (i.e. a mix of total sitting, TV
viewing or total leisure sitting time, which show different
health associations [
]), and/or different exposure
units or categories [
10, 11, 16, 17
]. Most importantly, few
meta-analyses have examined dose–response associations,
to determine whether there is a marked increase in risk of
incident disease or mortality at a specific level on the
sedentary time continuum [
]. This information is
essential to determine whether recommendations, currently
only providing guidance to ‘‘sit less’’, need further
quantification. For all-cause mortality, spending [ 3 or [ 4
h/day of TV viewing and [ 7 h/day in any sitting activity
have been suggested as detrimental [
]. It is currently
unknown whether these thresholds (if any) are the same for
cardiovascular disease (CVD) and cancer mortality. For
type 2 diabetes (T2D), existence of such threshold has only
been examined in relation to TV viewing time (based on 3
studies only) , which is not reflective of total sitting
time. Recent studies reported 3.8–5.9% of all deaths are
due to daily sitting time [
]. So far this is unknown for
TV viewing time, which shows stronger health associations
 and may be one of the most amenable types of
sedentary behaviour [
We therefore aimed to examine the dose–response
association between separate types of sedentary behaviour
and all-cause, CVD and cancer mortality, as well as
incident T2D and CVD, using the current prospective
evidence. As PA is known to attenuate sedentary behaviour
], we also aimed to map this attenuating
effect across the whole continuous sedentary behaviour
dose-spectrum, by comparing dose–response curves with
and without adjustment for PA. Finally, in order to
demonstrate the population impact of the established dose–
response relationships, we calculated the population
attributable fraction (PAF) due to TV viewing for these
health outcomes in England.
Data sources and searches
Studies had to have assessed the association between total
daily sitting/sedentary, TV viewing or leisure sitting time,
and at least one of the outcomes of interest: all-cause, CVD
or cancer mortality, incident (fatal and non-fatal) CVD and
incident T2D. Time spent sitting/sedentary could be
selfreported or objectively measured. Only primary research
studies with a prospective design, with at least an abstract
in English and investigating non-diseased adults
(C 18 years) in the general population were included.
1. Electronic literature databases: Pubmed, Web of
Knowledge, Medline, Embase, Cochrane Library and
Google Scholar from 1st August 2014 to 30th
September 2016. Search terms are listed in Online
Appendix Table 1.
2. Reference lists of existing systematic reviews
9–12, 14–17, 20, 21
], examining associations between
sedentary behaviours and health outcomes, which
together cover up to October 2015.
3. Authors’ personal literature databases up to 30th
4. Reference lists of included articles.
Titles and abstracts were screened by one author (RP)
using the inclusion criteria, full reports were assessed
where these were met or where there was uncertainty,
allowing a final decision on eligibility. Where different
articles formed part of the same cohort study, only data
from the most recent publication for any given
exposureoutcome combination was used. A minimum of 4 different
eligible cohorts were required in order to carry out an
analysis. Authors were contacted for additional information
Data extraction and quality assessment
Using a pre-designed data extraction spreadsheet, two
authors carried out independent extractions and
disagreement was resolved through discussion (RP and EM).
The quality of each study was assessed using these
criteria: size of cohort, length of follow-up, description of
inclusion criteria and sampling strategy and sample
representativeness, based on the Newcastle–Ottawa scale
]. No overall quality score was assigned for use in
analyses, to prevent the scale itself becoming a source of
Data synthesis and analysis
Extracted data were harmonized, converting each measure
into one of: total sedentary, TV viewing or leisure
sedentary time, quantified in h/day. Categories of sedentary time
were assigned a dose, either the mid-point, or, in case of
open-ended categories, half the width of the adjacent
interval from the boundary (Online Appendix Tables 2 and
3). Where the lowest exposure was not the referent
category, hazards were recalculated [
Estimates of the linear association for each contributing
study were calculated using Generalized Least-Squares
]. These were used to perform a random
effects meta-analysis within each exposure-outcome
combination, for both the most adjusted model without
adjustment for PA and the least adjusted model with
adjustment for PA . These provided the summary RR
per additional hour/day of exposure. Statistical
heterogeneity of contributing studies was assessed with the I2
statistic, which was considered low if \ 25%, and high if
[ 75% [
]. To examine publication bias and small study
effect, Funnel plots were used, Egger’s tests were derived
for each exposure-outcome combination with C 5
contributing studies [
Following the estimation of a linear association, a
restricted cubic spline transformation was carried out to
investigate the shape of the dose–response relationship.
Knots were placed at the 10th, 50th and 90th percentiles
]. A random effects meta-analysis was then carried out
to estimate the non-linear relationship between sedentary
time and the respective health outcome. Where a change in
strength of association was seen at a certain level on the
exposure continuum, the RR (per h/day increment in
exposure) on either side of this exposure level was
calculated as the difference in log(RR) divided by difference in
hours of exposure. Results were presented back on the
original scale and were based on PA-adjusted analyses.
Sensitivity analyses were carried out to investigate the
influence of study characteristics which might lead to risk
of bias. Where sufficient data were available these were
carried out on the linear PA adjusted associations. Factors
investigated include: adiposity adjustment, sex, length of
follow-up, age of cohort at baseline and representativeness
Population attributable fraction (PAF)
PAF estimates were calculated for TV viewing, indicating
the proportional reduction in incidence of the respective
outcome if prevalent TV viewing levels were reduced to
zero, assuming causality. As we did not have access to TV
viewing data worldwide, TV viewing data from the 2012
Health Survey for England (HSE), a nationally
representative sample of the English population, were used to carry
out a Monte-Carlo micro-simulation as an illustration of
the potential magnitude of the impact. Each 17? year old
participant in HSE was probabilistically assigned an RR
based on the RR and associated uncertainty from the
PAadjusted dose–response analysis which corresponded to
their TV viewing category (0, 0 to \ 2, 2 to \ 4, 4 to \ 6
and 6? h/day of TV viewing). These assigned RRs, along
with the proportional contribution of each individual,
according to HSE survey weights, were used to calculate an
attributable fraction for each participant, according to the
formula below [
Equation 1—Population attributable fraction
PAF ¼ i¼1 PPiR Rin¼i 1 PiR Ri¼i 1 P0iRRi ð1Þ
Pi = proportion of population at exposure level i,
Pi= proportion of population at counterfactual exposure
level, i.e. zero exposure, RR = the relative risk at exposure
level i. n = the number of exposure levels.
This procedure was then repeated 5000 times and the
final PAF estimate was the median value across the 5000
simulations. A 95% CI was calculated using the 2.5th and
97.5th percentiles of the simulated distribution.
Stata version 14.2, StataCorp, USA was used for the
meta-analysis. The PAF calculations were carried out using
Analytica Free 101 edition, Lumina Decision Systems Inc.,
Role of the funding source
This work was supported by the British Heart Foundation,
the Medical Research Council, Cancer Research UK,
Economic and Social Research Council, National Institute
for Health Research, and the Wellcome Trust. The funders
had no role in study design, conduct, or reporting of the
The literature search provided 2201 potential articles.
Following screening of titles and abstracts, full text was
retrieved for 124 publications, rendering 39 studies for
which inclusion criteria were met (Fig. 1). For 5 studies
there was an insufficient number of comparator cohorts
within the same exposure-outcome combination [
leaving 34 studies, across 8 exposure-outcome
combinations, to be included in the analysis [
insufficient number of cohorts was identified to allow
investigation of associations between leisure sedentary
time with any outcome. There were also insufficient studies
investigating incident CVD with any exposure. We were
therefore unable to carry out the planned analysis on these
exposures and outcomes. Additional data were successfully
obtained from 11 studies [
Data from a total of 1,331,468 unique participants was
included. Table 1 and Online Appendix Table 4
summarize the characteristics of the 34 included studies (with
additional data from publications describing cohort
]). The size of included studies ranged
from 208  to 240,819 [
] participants with a mean of
39,161. Follow-up was on average 8.9 years and varied
from 2 [
] to 31 [
] years. The numbers of cases and
participants by outcome are shown in Table 2. Of the 34
studies, 17 were from North America, 9 from Europe, 4
from Australia and 4 from Asia. The dates of publication
spanned 2001–2016, although 65% were published in 2013
Study quality and methods of measurement
The quality of the included studies was generally good (see
Table 1 and Online Appendix Table 4). Three of the five
smallest cohorts (presenting 32 cases/208 participants [
], and 409/2918 [
]) measured sedentary
time objectively. Of 34 studies, 22 had C 10,000
39–41, 45, 48, 50, 52–62, 64, 65, 68, 70, 71
were 3 all-male studies [
42, 47, 52
] and 5 all-female studies
53, 58, 59, 63, 68
] (providing data from 4 cohorts). Some
articles presented results for multiple exposures and/or
outcomes, with 34 publications presenting 57 analyses; the
numbers of contributing articles for each
exposure/outcome combination are presented in Table 2.
Most studies assessed sedentary behaviour by
questionnaire (31 out of 34) with three studies measuring
sedentary time objectively using accelerometers worn on
the participant’s hip, waist or lower back for up to 7 days
(Online Appendix Table 4) [
42, 51, 67
Outcomes were assessed objectively in 27 of the 34
studies (Online Appendix Table 4). This represents those
studies with a mortality outcome assessed using death
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registries, in addition to four studies which used an
objective measure to define T2D status [
38, 44, 50, 65
remainder being self-reported T2D.
All included studies reported adjusted effect estimates,
with adjustment for PA present in all but four studies
44, 46, 51, 70
]. One study presented results for some
outcomes with PA adjustment and some without .
Adjustment for PA varied in detail; from simply meeting
the PA guideline or not, to calculating weight-adjusted
energy expenditure across multiple domains of PA (Online
Appendix Table 4).
The association between total sedentary behaviour and
allcause mortality appeared to be non-linear, both with and
without adjustment for PA (Fig. 2). Testing for
non-linearity supported this finding. At lower levels of exposure,
there were small increases in risk associated with
increasing sedentary behaviour; above approximately 8 h/day of
sedentary behaviour, the risk increased more rapidly. In PA
adjusted analyses this resulted in an estimated RR of 1.01
(1.00–1.01) for each additional hour of exposure below
8 h/day and 1.04 (1.03–1.05) for each hour above 8 h/day
(Online Appendix Table 5).
For TV viewing, the association also appeared to be
non-linear (Fig. 2 and Table 2) with a change in gradient at
approximately 3.5 h/day in the PA adjusted analyses
(Fig. 2). Below this level the RR was 1.03 (1.01–1.04) per
hour/day, and greater increases in risk were seen above this
level (RR = 1.06 (1.05–1.08) per h/day; Online Appendix
Table 5). Due to insufficient availability of data,
investigation of the associations between leisure-time SB and
mortality could not be undertaken.
For total sedentary behaviour, non-linearity was seen again
for both the non-PA adjusted and PA adjusted models
(Fig. 2 and Table 2). In the PA adjusted analysis, the
threshold was in the region of 6 h/day of exposure, below
which each additional hour was associated with an
estimated RR of 1.01 (0.99–1.02) and above which each
additional hour was associated with an RR of 1.04
(1.03–1.04) (Online Appendix Table 5).
The PA adjusted non-linear association between CVD
mortality and TV viewing showed greater risk increases
with every hour above a threshold of approximately
4 h/day (Fig. 2). The estimated RR for each additional
hour of TV viewing was 1.02 (0.99–1.04) below 4 h and
1.08 (1.05–1.12) above.
The linear association between total sedentary behaviour
and cancer mortality was marginally non-significant and
unaffected by PA adjustment; the PA adjusted estimate was
1.01 (1.00–1.02). There was no evidence for non-linearity
The linear association between TV viewing and cancer
mortality was estimated to be 1.03 (1.02–1.04) in non-PA
adjusted models and 1.02 (1.01–1.03) when adjusted for
PA. There was no evidence for non-linearity for either PA
adjusted or unadjusted associations (Fig. 2 and Table 2).
Type 2 diabetes
The linear association between total sedentary behaviour
and T2D was estimated to be 1.01 (1.00, 1.01), which could
only be estimated with PA adjustment as there were
insufficient studies without PA adjustment (Table 2 and
PA adjusted analysis of the association between TV
viewing and T2D shows some deviation from linearity
(Fig. 2) although statistical evidence was equivocal
(p = 0.066; Table 2). The PA adjusted linear association
was estimated to be 1.09 (1.07, 1.12) However, larger
increases in risk were seen with increasing TV viewing
below approximately 4 h (1.12 (1.08–1.15) in PA adjusted
analysis), above this level increasing TV viewing was
associated with lower increases in risk (1.05 (1.03–1.07)).
In non PA-adjusted analyses the association appeared
linear with an estimated RR of 1.12 (1.08, 1.16) associated
with each additional hour of TV viewing.
Across all combinations, PA adjustment appeared to
attenuate the effect estimates. The difference between the
effect sizes with and without PA adjustment was relatively
small when total sedentary time was the exposure, but
somewhat greater when TV viewing was examined.
Substantial heterogeneity was observed for the pooled effect
estimates, ranging from I2 values of 0% for both total
sedentary behaviour and CVD, and total sedentary
behaviour and cancer mortality, to I2 of 90.1% for TV viewing
and CVD mortality. Funnel plots and Egger’s tests, showed
no definitive evidence for publication bias (Online
Appendix Fig. 5). However, the low numbers of
contributing studies for some associations made it difficult to
rule out these biases.
Sensitivity analyses are presented in Online Appendix
Table 6. The RR of CVD mortality associated with each
additional hour of TV viewing was greater when studies of
younger participants were excluded, 1.08 (1.06–1.10)
compared with 1.04 (1.01–1.08) in the main analysis. All
other sensitivity analyses showed no substantive change
from the main findings.
Population attributable fractions for TV viewing
For all-cause mortality, 8% (6–10%) was associated with
TV-viewing in the English population, when using the PAF
method. This estimate was 5% (1–8%) for CVD and 5%
(2–7%) for cancer mortality. For T2D 29% (26–32%) of
incidence was estimated to be related to TV-viewing.
This meta-analysis, incorporating data of 1,331,468
participants, shows an increased risk for all-cause and CVD
mortality and incidence of T2D with higher levels of total
sitting as well as TV viewing time, independent of PA. For
all outcomes, associations with TV viewing were stronger,
and the strongest association overall was found between
TV viewing and incident of T2D. There was also evidence
of an independent association between sedentary behaviour
and cancer mortality, although only for a specific type of
sedentary behaviour, i.e. TV viewing time.
Most importantly, investigation of the shape of the
associations indicated that the increased risk of all-cause
and CVD mortality was strongest for sitting time volumes
greater than 8 and 6 h/day, respectively, in PA adjusted
analyses. For TV viewing time, an increased risk for
allcause and CVD mortality was strongest above levels of
about 3–4 h/day. The associations between TV viewing
with T2D and cancer mortality appeared to be more linear.
In general, PA adjustment resulted in some attenuation of
the estimated linear and non-linear associations, which was
somewhat stronger for TV viewing compared to total
sitting time. Furthermore, we estimated that a sizeable
fraction of mortality and incidence of all examined outcomes
were associated with TV viewing, ranging from 5% for
CVD and cancer mortality, to a substantial 29% for T2D.
Biological mechanisms have been suggested to explain the
independent associations of sedentary behaviour, in
particular for cardio-metabolic diseases, through independent
effects of prolonged sitting on lipid and glucose
metabolism in the large skeletal muscles involved in posture (legs
and core) and on hemodynamic vascular signalling
potentially causing atherogenesis [
]. Associations for TV
viewing were generally stronger than those for total sitting
time with the same outcome which could be explained by
several factors. Firstly, TV viewing has been linked to
higher intakes of energy and macronutrients along with
greater energy from snacks . Poor diet quality and
increased total calorie intake have been associated with
increased risk of mortality and is a strong determinant of
T2D, suggesting an important mediating role for dietary
intake which is likely less relevant for total sitting time
]. Second, a potentially different confounding
structure for TV viewing may be more difficult to fully
account for. Third, criterion validity of self-reported TV
viewing estimates tends to be stronger than those for total
sitting time estimates [
]. Lastly, the typical timing of
TV viewing, i.e. in the evening following the main meal of
the day [
], may exacerbate the repetitive cardiovascular
effects of postprandial glucose and lipid excursions
following this meal, especially if TV viewing is
predominantly accumulated in prolonged bouts of sitting [
Limitations of the available evidence
The methods used to measure exposure varied;
measurement of sedentary behaviour is still primarily reliant on
self-report questionnaires. Heterogeneity in question
phrasing, the time period considered and whether a
question is single or multipart can all influence validity [
Misclassification of sedentary exposure would potentially
dilute the association in our analysis, resulting in possible
underestimation of effect size. The use of accelerometer
measured sedentary time addresses some of the limitations
of questionnaires, however this data has its own
limitations. For example, some accelerometer methods cannot
detect cycling or swimming, or fail to distinguish between
sitting/lying and standing still [
]. This substantial
heterogeneity in exposure measurement contributed to the
high heterogeneity indices (I2) for the pooled estimates
which may have influenced our overall findings. It is
possible that only some of the constituents of total sitting are
detrimental to health, for example sitting while reading is
potentially advantageous [
]. That we were unable to
investigate leisure sitting time in this meta-analysis due to
insufficient studies would indicate that more research is
required on the effect of different domains of sedentary
behaviour. In addition to the exposure measure, the quality
of the measurement of important covariates, such as PA,
diet and socio-economic position, varied greatly between
studies, if included at all, potentially leading to residual
confounding. The low number of studies for some
combinations meant that investigation of leisure time sitting
could not be carried out. It also led to a lack of statistical
power for subgroup or sensitivity analysis and bias
assessment. This also meant that meta-regression
techniques to investigate the impact of the potential sources of
heterogeneity were precluded.
Strengths and limitations of the meta-analysis
This meta-analysis considered total sedentary behaviour
and TV viewing time as separate exposures. This is
important as they have different associated
socio-demographic and/or behavioural patterns (e.g. dietary intake)
and therefore different confounding/mediating patterns
]. Inclusion of emerging research using objectively
measured sedentary time is another strength. In addition,
we investigated the shape of the dose–response curves, to
identify where the greatest risk/benefits lie along the
spectrum of exposure for all exposure—outcome
combinations. Moreover, to our knowledge, this is the first study
to calculate PAF estimates for TV viewing time and all
considered health outcomes based on meta-analytical risk
estimates when potential non-linearity of associations were
taken into account.
However, our meta-analysis also has certain limitations.
The use of summary data means heterogeneity of used
statistical methods may influence comparability of
included studies [
]. In order to investigate the effect of PA
adjustment we had to select models which were as similar
as possible except for adjustment for PA. However, in
some studies additional differences in covariates were seen
between these models and this may have resulted in
residual confounding of the considered study-specific risk
estimates. It was also necessary to make several
assumptions during the dose-assignment procedure. Whilst these
assumptions may have been crude in studies reporting little
detail on the exposure, this approach allowed us to consider
the totality of the currently published evidence. Treating
the many and heterogeneous conditions that make up
cancer as one outcome may have contributed to our mixed
findings with these analyses. Investigating separate cancer
types may be more informative, where there is enough data
]. Attempting to reduce reverse causality by only
including prospective studies may not have been entirely
effective, especially in the case of T2D. An estimated 27%
of those with the condition have no formal diagnosis,
therefore having the condition may have preceded
ascertainment of exposure data [
]. Finally, the calculation of
PAFs rests on the assumption of causality, and the use of
unbiased estimates with no measurement error.
Public health impact
To calculate the PAF estimates we have used the exposure
profile representative of the population of England. These
might not be representative for other countries. However,
average US TV viewing levels, for example, are similar,
2.6 h on a weekday and 3.3 h on a weekend day compared
with 2.7 h and 3.1 h/day respectively in England [
The estimated 29% of T2D in England in 2012 that could
be prevented or postponed by eliminating TV viewing,
assuming causality, reflects the high RRs seen for this
association. The linear association and relatively high RR
even at lower exposure levels are important contributors, as
a large proportion of HSE participants report lower TV
viewing levels (75% of participants report \ 4 h/day), but
only a small proportion reports no TV viewing (3%). The
PAF estimates for all-cause mortality (8%) and CVD and
cancer mortality (both 5%) also suggest a potentially
important burden of preventable disease from current
population levels of TV viewing.
The differing nature of the relationships between TV
viewing and different outcomes may complicate any
prevention strategy. The prevention of T2D would perhaps be
best served by reducing TV viewing among the whole
population, however, to prevent other outcomes targeting
those with highest exposure levels may be more
appropriate as these are the individuals for whom any reduction
would confer the greatest benefit. Furthermore, the effect
of any behaviour change will also be influenced by the
nature of the replacement activity [
]. For example,
greater reductions in risk may occur when replacing
sedentary time with strenuous exercise compared with
walking for pleasure. Replacing some sedentary behaviours
may confer greater benefits than others, e.g. replacing TV
viewing may be more beneficial than replacing general
screen time [
Another potentially important determinant of the health
effects of sedentary behaviour is the extent to which breaks
are taken in extended periods of sitting. None of the studies
included in this meta-analysis took into account
accumulation pattern of sitting and therefore this falls outwith the
scope of this study.
This study demonstrates an increasing risk of disease and
mortality with increasing total sitting time and TV viewing
time. It also revealed a threshold of 6–8 h/day of total
sitting and 3–4 h/day of TV viewing, above which risk for
several important health outcomes increased more rapidly.
This suggests that sedentary behaviour guidelines may
need further quantification of sitting time volumes that
should be avoided, although for some outcomes such as
T2D, any sitting time reductions would be beneficial. With
8% of all mortality and 29% of T2D in the English
population associated with certain sedentary behaviours, there
is great potential for substantial public health benefits.
Improvements in the measurement of sedentary time and a
better understanding of its confounding structure are
therefore essential to improving future public health and
Acknowledgements This work was supported by the British Heart
Foundation (Intermediate Basic Science Research Fellowship Grant
No. FS/12/58/29709 to KW) and the Medical Research Council (Unit
Programme No. MC_UU_12015/3 for SB and KW and
MC_UU_12015/1 for SJS). JW’s MT’s contributions were undertaken
under the auspices of the Centre for Diet and Activity Research
(CEDAR), a UKCRC Public Health Research Centre of Excellence
which is funded by the British Heart Foundation, Cancer Research
UK, Economic and Social Research Council, Medical Research
Council, the National Institute for Health Research, and the Wellcome
Trust. RP is funded via a NIHR Research Professorship award to
Christopher Millett. AS is supported by a Medical Research Council
Doctoral Training Studentship. This manuscript does not reflect the
opinions of any of these funding bodies.
Author contributions JW, SB and KW conceived this study. PE, SS
and MT contributed to the design of the study. RP and EMc
conducted data extraction with help from TdS. RP conducted the analysis
with help from AS, SS and MT. RP and KW wrote the initial draft of
the manuscript, all authors contributed to revisions. All authors
approved the final manuscript.
Compliance with ethical standards
Conflict of interest All authors have completed the ICMJE uniform
disclosure form at www.icmje.org/coi_disclosure.pdf and declare: no
support from any organisation for the submitted work; no financial
relationships with any organisations that might have an interest in the
submitted work in the previous three years; no other relationships or
activities that could appear to have influenced the submitted work.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://creative
commons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
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