Physical Activity 2016: Progress and Challenges

Transcription

16tl0662
Series
RMH
THELANCET-D-16-00662R2
S0140-6736(16)30581-5
Embargo: July 27, 2016—23:30 (BST)
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Physical Activity 2016: Progress and Challenges
Progress in physical activity over the Olympic quadrennium
James F Sallis, Fiona Bull, Regina Guthold, Gregory W Heath, Shigeru Inoue, Paul Kelly, Adewale L Oyeyemi, Lilian G Perez, Justin Richards,
Pedro C Hallal, for the Lancet Physical Activity Series 2 Executive Committee*
On the eve of the 2012 summer Olympic Games, the first Lancet Series on physical activity established that physical
inactivity was a global pandemic, and global public health action was urgently needed. The present paper summarises
progress on the topics covered in the first Series. In the past 4 years, more countries have been monitoring the
prevalence of physical inactivity, although evidence of any improvements in prevalence is still scarce. According to
emerging evidence on brain health, physical inactivity accounts for about 3·8% of cases of dementia worldwide. An
increase in research on the correlates of physical activity in low-income and middle-income countries (LMICs) is
providing a better evidence base for development of context-relevant interventions. A finding specific to LMICs was
that physical inactivity was higher in urban (vs rural) residents, which is a cause for concern because of the global
trends toward urbanisation. A small but increasing number of intervention studies from LMICs provide initial
evidence that community-based interventions can be effective. Although about 80% of countries reported having
national physical activity policies or plans, such policies were operational in only about 56% of countries. There are
important barriers to policy implementation that must be overcome before progress in increasing physical activity
can be expected. Despite signs of progress, efforts to improve physical activity surveillance, research, capacity for
intervention, and policy implementation are needed, especially among LMICs.
Published Online
July 27, 2016
http://dx.doi.org/10.1016/
S0140-6736(16)30581-5
Introduction
See Online/Articles
S0140-6736(16)30370-1, and
S0140-6736(16)30383-X
Every 4 years, the summer Olympic Games divert much
of the world’s attention from the conflicts and tragedy
that regularly dominate the news. The sight of talented
athletes pushing their bodies to the limits inspires some
viewers to greater achievements in sport and life. Health
professionals hope that 2 weeks of exposure to images
and stories of athletics will lead viewers to make increased
efforts to be physically active in their own lives, even if at
a much lower level than the athletes. Although no
evidence has shown that the Olympics impact physical
activity in the host country or elsewhere,1 the Olympic
Games aim a powerful media spotlight on human
movement.
As the London Olympic Games were poised to open in
July, 2012, the first Lancet Series on physical activity
identified physical inactivity as a global pandemic and
urgent public health priority. A wide variety of
interventions have been shown to be effective, but they
have not been widely implemented, so public health
agencies were called upon to collaborate with sectors
such as transportation, health care, and sport to mount a
stronger response to this health challenge.2 The
2012 Series was widely covered in media worldwide, and
the Series papers have been heavily cited. With the
imminent inauguration of the 2016 summer Olympic
Games in Rio de Janeiro, we ask how much progress has
been made during the Olympic quadrennium in
research, practice, and policy regarding physical activity.
This first paper in this second Lancet physical activity
Series provides a progress report on the topics covered in
the 2012 Series. Different approaches to identifying
progress were taken that were deemed appropriate to
each topic. The progress reports on physical activity
surveillance and national policies to promote physical
activity have strong continuity with papers in the first
physical activity Series. Rather than provide an update on
deaths from physical inactivity-related non-communicable
diseases (NCDs), the present section on health effects
summarises new evidence on the link between physical
activity and dementia. To complement the papers in the
first Series, the sections on correlates of physical activity
and intervention studies focus specifically on progress in
low-income and middle-income countries (LMICs).
Authors of each section used different methods because
of the diverse nature of the topics.
Progress on surveillance of physical inactivity worldwide
We used comparable country estimates for physical
inactivity from WHO to analyse the evolution of physical
activity surveillance over the Olympic quadrennium
(panel 1). In 2012, we obtained adult physical inactivity
surveillance data from 122 countries representing 88·9%
of the world’s population.11 For the present analyses, data
were available for 146 countries, representing 93·3% of
the world’s population (figure 1). The increased global
population coverage was mainly due to the addition of
populous nations such as Nigeria, Egypt, and Tanzania.
Data were available from 82% (40 of 49) of high-income
countries (HICs), 75% (41 of 55) of upper-middle-income
countries (U-MICs), 69% (38 of 55) of lower-middle-income
countries (L-MICs), and 77% (27 of 35) of low-income
countries (LICs). The proportion of countries contributing
surveillance data among adult populations increased in all
regions, except southeast Asia: Africa (72–87%), Americas
(43–57%), eastern Mediterranean (43–57%), Europe
(68–75%), southeast Asia (82%, no change), and western
Pacific (70–89%).
www.thelancet.com Published online July 27, 2016 http://dx.doi.org/10.1016/S0140-6736(16)30581-5
This paper forms part of the
Physical Activity 2016 Series
*Members listed at end of the
report
See Online/Comment
S0140-6736(16)31070-4,
S0140-6736(16)30960-6,
S0140-6736(16)30929-1, and
S0140-6736(16)30881-9
See Online/Series
S0140-6736(16)30728-0
Department of Family
Medicine and Public Health,
University of California,
San Diego, CA, USA
(Prof J F Sallis PhD); Center for
Built Environment and Health,
The University of Western
Australia, Perth, WA, Australia
(F Bull PhD); Prevention of
Noncommunicable Diseases
Department, World Health
Organization, Geneva,
Switzerland (R Guthold PhD);
Department of Health &
Human Performance and
Medicine, University of
Tennessee, Chattanooga, TN,
USA (G W Heath DHSc);
Department of Preventive
Medicine and Public Health,
Tokyo Medical University,
Tokyo, Japan (S Inoue MD);
Physical Activity for Health
Research Centre, University of
Edinburgh, Edinburgh, UK
(P Kelly PhD); Department of
Physiotherapy, College of
Medical Sciences, University of
Maiduguri, Maiduguri, Nigeria
(A L Oyeyemi PhD); Physical
Activity, Sport and Recreation
Research Focus Area, Faculty of
Health Sciences, North-West
University, Potchefstroom,
1
Series
South Africa (A L Oyeyemi);
UCSD/SDSU Joint Doctoral
Program in Public Health
(Global Health), San Diego, CA,
USA (L G Perez MPH); School of
Public Health & Charles Perkins
Centre, University of Sydney,
Sydney, NSW, Australia
(J Richards DPhil); and Federal
University of Pelotas, Pelotas,
Brazil (P C Hallal PhD)
Correspondence to:
Prof James F Sallis, Department
of Family Medicine and Public
Health, University of California
San Diego, 3900 Fifth Avenue,
Suite 310, San Diego, CA 92103,
USA
[email protected]
Key messages
• In the 4 years since the 2012 Lancet Series that identified
physical inactivity as a global pandemic, progress has been
made in the breadth of national surveillance, evidence
about physical activity as a protective factor for dementia,
adoption of national policies and action plans, and research
on correlates and interventions in low-income and middleincome countries. However, progress in the implementation
of national actions to address one of the biggest health
challenges of the 21st century has been insufficient.
• Most countries have done population surveys of physical
activity, with an extra 24 countries providing adult data and
15 countries providing adolescent data since 2012. The
global prevalence of physical inactivity was about 23% for
adults and about 80% for school-going adolescents,
although self-report data have limitations. Few countries
provided trend data for adults, and trend data for
adolescents showed an increase in proportion of people
who were physically inactive in most countries.
• In addition to the major impact of physical inactivity on the
global burden of non-communicable diseases documented
4 years ago, evidence now shows that almost 300 000 cases
of dementia could be avoided annually if all people were
adequately active, and this figure is increasing as the global
population ages.
• Research examining reasons why people are and are not
physically active has increased substantially in
middle-income countries, but not in low-income countries.
Unlike evidence from high-income countries, urban (vs
rural) residence emerged as an inverse correlate of physical
activity in low-income and middle-income countries
(LMICs), which is a concern given global trends toward
urbanisation. These results can be used to design
interventions informed by local data.
• Research and evaluation of physical activity interventions
has increased in LMICs. Although several examples of
effective interventions have been reported, the evidence is
still scarce. An important next step is to build capacity for
intervention research in LMICs so interventions can be
developed or adapted for local conditions, then rigorously
assessed.
• Due largely to the inclusion of physical activity in the WHO
Global Action Plan on NCDs and the establishment of a
global target to reduce inactivity by 10% by 2025, many
countries have now adopted national policies or action
plans to increase physical activity. However,
implementation appears to be weak. Meaningful action will
require increasing the infrastructure and resources for
physical activity, including providing capacity-building,
country technical assistance, creating effective multisector
coalitions, and reaching consensus on a few highest-priority
actions for each country.
• Overall, physical activity surveillance, research, and policy
adoption worldwide improved. However, policy
implementation appears to be poor, and evidence of an
increasing trend in global physical activity was absent.
Thus, the global pandemic of physical inactivity remains,
and the capacity for nations to respond is improving too
slowly.
Panel 1: WHO Global Health Observatory physical inactivity estimates
Adult estimates
The WHO Global Health Observatory displays comparable
country prevalence estimates for physical inactivity among
adults aged 18 years or older that are based on the global
recommendations on physical activity for health.3,4
The recommendations state that adults should do at least
150 min of moderate-intensity, or 75 min of
vigorous-intensity aerobic physical activity per week,
or an equivalent combination of the two.
Inclusion criteria were that data be from national or
subnational cross-sectional population-based surveys
undertaken with random sampling, reporting prevalence of
inactivity based on the current3 or former recommendations,5
and including all domains of activity (work, household,
transport, leisure). Through statistical regression modelling,
when necessary, adjustments were made for the reported
prevalence in case it was based on the former
recommendations, known over-reporting of the International
Physical Activity Questionnaire (IPAQ),6–9 survey coverage if a
survey only covered urban areas, and age coverage if the
2
survey age range was narrower than 18 years or older.
For comparison purposes, final estimates were adjusted to
the WHO standard population.10
School-going adolescent estimates
The adolescent estimates used here reflect data from the
WHO Global Health Observatory for school-going adolescents
aged 11–17 years, based on the global recommendations on
physical activity for health that indicate that youth should
engage in at least 60 min of moderate-intensity to
vigorous-intensity physical activity daily.3,4
Data were included if they came from national or subnational
cross-sectional school surveys covering at least 3 years of the
adolescent ages, reporting prevalence for the definition
above, or for doing at least 60 min of physical activity on at
least 5 days per week. Through statistical regression
modelling, when necessary, adjustments were made to
harmonise the definition to reflect the current physical
activity recommendations, and for survey coverage if only
urban areas were included.
Series
Both adult and adolescent data available
Adult data available
Adolescent data available
No data available
Not applicable
0
850 1700
3400
km
Figure 1: Physical activity data availability for school-going adolescents (aged 11–17 years) and adults (aged ≥18 years)
Data are from WHO Global Health Observatory, 2015.
Notably, the algorithm used to estimate physical
inactivity among adult populations has changed from
that presented in the 2012 Lancet Series11 to align with the
new standards used by the WHO Global Health
Observatory.4 In 2012, inactivity was defined as not
achieving 5 days of 30 min of moderate-intensity activity,
or 3 days of 20 min of vigorous-intensity activity, per
week, or an equivalent combination, according to the
recommendations at that time.5 Reflecting scientific
evidence12 and following updated physical activity
recommendations,3 inactivity was defined for the present
analyses as not achieving 150 min of moderate-intensity
activity or 75 min of vigorous-intensity activity per week,
or an equivalent combination, regardless of the weekly
frequency. This recommendation is easier to achieve.
Thus, the estimated prevalence of inactivity among adult
populations worldwide changed from 31·1% in 2012 to
23·3% in 2016, a reduction that primarily reflects
changes in the recommendations rather than a real
increase in physical activity. The lack of substantial
change is confirmed by findings from the 12 countries
with trend data that included domains of leisure,
transportation, and occupation. Six countries (Argentina,
Belgium, Iran, Kuwait, Mongolia, and Singapore)
reported a numerical increase in the prevalence of
inactivity, and six countries (Maldives, New Zealand,
South Korea, Seychelles, South Africa, and USA) reported
a decrease (for references for trends see appendix p 1).
Notable disparities remain in the prevalence of physical
inactivity between men and women, with 137 of the
146 countries showing higher inactivity among women.4,13
Older age groups continue to be at higher risk for
inactivity, with the oldest age category showing more
than double the prevalence of the youngest (aged 80 years
or older, 55·3% vs aged 18–29 years, 19·4%).
Improvements in global surveillance coverage of physical
activity were also documented for school-going adolescents.
In the 2012 publication11 we analysed data of adolescents
aged 13–15 years from 105 countries. For the present
analyses, estimates were available for adolescents aged
11–17 years from 120 countries4 (figure 1), with data mainly
from the Global School-based Student Health Survey14 and
the Health Behaviour in School-aged Children Study.15 The
population coverage of adolescent surveillance increased
from 68·0% in 2012 to 76·3% in 2016. Availability of selfreport data for adolescents was 81·6% in HICs (40 of 49),
70·9% in U-MICs (39 of 55), 60·0% in L-MICs (33 of 55),
and 20·0% in LICs (seven of 35). The proportion of
countries contributing surveillance data from adolescents
increased in all world regions, except Africa and southeast
Asia: Africa (30%, no change), Americas (57–77%), eastern
Mediterranean (57–76%), Europe (64–68%), southeast Asia
(55%, no change), and western Pacific (33–78%). We
identified 50 countries that reported comparable trend
data for adolescents. For 32 of the 50 countries, the
prevalence of inactivity numerically increased, whereas for
the other 18, prevalence of inactivity decreased.
Consistent with the 2012 Series, adolescent inactivity
prevalence was defined as not achieving at least 60 min of
moderate to vigorous physical activity daily.3,11 Inactivity
prevalence continued to be extremely high, with a global
average of 78·4% for boys and 84·4% for girls. In the vast
majority of countries (115 of 120 countries with data), more
than a quarter of school-going adolescents did not reach
the recommended level of activity.4,13 The apparent higher
inactivity prevalence for adolescents than adults was partly
a result of the higher recommended level for youth.
However, prevalence cannot be compared directly across
age groups because the questionnaires differed greatly.
Given known limitations of self-reports, the use of
objective physical activity measures, such as
accelerometers, to estimate national prevalence is
growing. A 2015 review16 of accelerometer studies in
adults found 76 studies across 36 countries that had
See Online for appendix
3
Series
used devices in at least 400 participants, with 13 identified
as national population-based cohorts. From this review,
eight studies from seven HICs met our definition of
reporting national prevalence.17–25 Prevalence estimates
varied from 1% to 52% for meeting physical activity
recommendations. However, estimates were not
comparable across countries as a result of large
variations in data collection methods, data processing,
and scoring. Experts agree that standardised
accelerometer methods are needed,16 and prevalence
estimates from accelerometers should not be compared
with self-report data.25
In children and adolescents (aged 5–19 years) we found
accelerometer-based population prevalence estimates of
engaging in 60 min or more of physical activity daily in six
studies from five HICs.17,21,23,26–28 Once again, prevalence esti
mates were not comparable and reflected methodological
inconsistencies. The International Children’s Accelero
metry Database (ICAD) had accelerometry data from
20 studies worldwide, and allowed comparisons because of
standardised methods,29 but most samples were not
nationally representative. ICAD data also showed large
between-country physical activity prevalence variations
ranging between 15% and 28%.29
Panel 2: Formulae for calculation of population
attributable fraction (PAF)
Formula 1 (unadjusted PAF)
Formula 1 provides an estimate for the PAF assuming no
confounding exists between physical inactivity and
dementia. It requires prevalence data for physical inactivity in
the population (Pe) and an unadjusted relative risk (RRunadj);
PAF (%)=
Pe(RRunadj – 1)
Pe(RRunadj –1) + 1
× 100
Formula 1 provides a crude estimate for the PAF, but
calculating an adjusted PAF is indicated because several
confounding factors for physical inactivity and dementia
have been previously identified (eg, genetic markers).
Formula 2 (adjusted PAF)
Formula 2 provides an estimate for the PAF assuming that
confounding exists between physical inactivity and dementia.
It requires prevalence data for physical inactivity in people
eventually developing dementia (Pd) and the adjusted relative
risk (RRadj);
PAF (%)=
Pd(RRadj – 1)
× 100
RRadj
Formula 2 provides a conservative estimate for the PAF
because some of the confounders included in the calculation
of the RRadj are exacerbated by physical inactivity (eg, physical
function).
4
Comment
More countries are collecting physical activity
surveillance data, although reporting on adolescents in
LICs has not improved much. About a quarter of adults
and 80% of adolescents were not meeting guidelines
according to self-report data. Though trend data were
scarce, no evidence has shown that physical inactivity
declined globally. More countries are using objective
measures for surveillance, demonstrating feasibility. To
promote wide use of objective measures for surveillance,
methods should be standardised, and data collection in
LICs should be supported.
Health consequences of physical inactivity:
focus on dementia
In the 2012 Lancet Series, Lee and colleagues30 reported
large global population attributable fractions (PAFs) of
physical inactivity for coronary heart disease (6%), type 2
diabetes (7%), breast cancer (10%), colon cancer (10%),
and all-cause mortality (9%). These estimates have
probably changed little, but understanding of other
health consequences of physical inactivity has progressed.
The most notable of these might be the association
between physical activity and cognition.
Growing evidence supports the role of physical activity
in developing and maintaining cognitive capacity
throughout life. Previous work has focused heavily on
biophysiological plausibility derived from animal studies
and findings from neuroanatomy.31 Methodological
advances have enabled studies that show the impact
of physical activity on neurogenesis, neuroelectric
potentiation, and neurochemical factors in the
hippocampus and areas of the brain responsible for
higher levels of executive control during childhood.32
These findings are consistent with substantial evidence
of improved cognitive function and scholastic
achievement in physically active children.31–33
In adult populations, a large body of observational
data34 suggests that physical activity can contribute to
preventing dementia, and some experimental evidence
has shown neurobiological changes in response to
visuomotor training,35 which supports the plausibility of
a causal relationship. This relationship is of increasing
importance in an ageing population globally. WHO
estimates that 47·5 million people are living with
dementia.36 Approximately 7·7 million new diagnoses are
made each year worldwide, and 58% of existing cases are
from LMICs.36,37 60–70% of dementia cases are thought to
be caused by Alzheimer’s disease, and previous estimates
suggest that 12·7% of cases could be avoided worldwide
if physical inactivity was eliminated.36,38 Although this
calculation was made using an adjusted relative risk
(RR), Norton and colleagues38 applied a formula for
unadjusted estimates of PAF. Consequently, assessment
of an appropriately adjusted PAF is indicated, and we
focused on the broader diagnosis of dementia, which has
not been assessed previously.
Series
Overall
Prevalence of inactivity Prevalence of inactivity in
in population
people eventually
developing dementia
Population attributable
fraction with unadjusted
relative risk
Population attributable
fraction with adjusted
relative risk
23·8% (4·1–65·0)
12·3% (2·4–27·7)
3·8% (0·7–10·5)
3·4% (0·9–7·6)
27·9% (4·8–76·2)
WHO region
Africa
20·8% (5·8–46·9)
24·4% (6·8–55·0)
10·9% (3·3–21·7)
Eastern Mediterranean
38·2% (15·6–61·0)
44·8% (18·3–71·5)
18·4% (8·4–26·5)
6·2% (2·5–9·9)
Europe
22·8% (9·5–42.9)
26.7% (11·1–50·3)
11·8% (5·3–20·2)
3·7% (1·5–6·9)
Latin America and Caribbean*
31·1% (13·3–63·6)
36·4% (15·6–74·5)
15·5% (7·3–27·3)
5·0% (2·1–10·3)
North America
27·8% (23·2–32·4)
32·6% (27·2–38·0)
14·0% (12·0–16·0)
4·5% (3·7–5·2)
Southeast Asia
14·8% (4·1–30·7)
17·3% (4·8–36·0)
8·0% (2·4–15·3)
2·4% (0·7–5·0)
Western Pacific
24·0% (5·6–65·0)
28·1% (6·6–76·2)
12·4% (3·2–27·7)
3·9% (0·9–10·5)
High
28.7% (9.5–61.0)
33·6% (11.1–71.5)
14·5% (5·3–26·5)
4·6% (1·5–9·9)
Upper middle
27·9% (14·8–65·0)
32·6% (17·3–76·2)
14·1% (8·0–27·7)
4·5% (2·4–10·5)
Lower middle
20·6% (5.6–45.1)
24·1% (6.6–52.8)
10·8% (3·2–21·0)
3·3% (0·9–7·3)
Low
14·8% (4·1–27·5)
17·3% (4·8–32·2)
8·0% (2·4–14·0)
2·4% (0·7–4·4)
World Bank income classification
Data are median (range of median for all relevant countries); details of country-specific values are provided in appendix p 7. Physical inactivity was defined as insufficient
physical activity to meet current recommendations. *WHO region of the Americas split into Latin America and Caribbean, and North America to ensure consistency with
previously published paper.30
Table 1: Summary of estimates of prevalence of physical inactivity and population attributable fractions for dementia associated with physical inactivity
We applied similar methods to those described for the
analysis of disease burden in the 2012 Lancet Series to
calculate both adjusted and unadjusted PAFs (panel 2).30
We searched MEDLINE and Embase databases using
keywords related to physical activity (“physical activity”,
“motor activity”, “energy expenditure”, “walking”,
“exercise”) and dementia (“dementia”, “cognitive decline”,
“cognitive impairment”, “cognition”, “Alzheimer’s
disease”) as of April 1, 2015. We screened 9396 titles to
identify the most recent peer-reviewed meta-analysis
reporting an adjusted RR.34 The unadjusted RR was
calculated using crude data, and age-adjusted data from
the papers was included in this meta-analysis
(appendix p 4).
During our literature review we also identified relevant
cohort studies to estimate the prevalence of physical
inactivity in people who eventually developed dementia.
This identification involved calculating an adjustment
factor for each study by taking a ratio of baseline physical
inactivity for subsequent dementia cases to baseline
physical inactivity for the entire study population (appendix
p 5).30 The average adjustment factor across studies was
1·17 (SE 0·07). This adjustment factor was applied to the
most recent WHO data to estimate the prevalence of
physical inactivity in subsequent dementia cases.13
The results are summarised in table 1. Blondell and
colleagues34 pooled maximally adjusted RRs for the
association between physical activity and dementia. The
pooled RR was 0·86 (95% CI 0·76–0·97). Taking the
inverse to obtain the adjusted RR for inactivity, we
calculated an RR of 1·16 (95% CI 1·03–1·32). Our
calculation of the pooled unadjusted RR was 1·59
(95% CI 1·35–1·82). The adjusted PAF of physical
inactivity for dementia ranged from 0·7% (Nepal) to
10·5% (Cook Islands), with an overall median of 3·8%.
This finding suggests that 292 600 new dementia cases
could be avoided globally each year if all people were
active. If physical activity does not improve, this number
is likely to increase substantially as the proportion of the
global population who are older adults (aged 65 years and
older) continues to grow. Table 1 also summarises
differences in PAF patterns according to WHO regions
and the 2014 World Bank classifications of income status.
When considering WHO regions, the median PAF was
lowest in southeast Asia (2·4%) and highest in the
eastern Mediterranean (6·2%). The median PAF was
lowest in LICs (2·4%) and highest in HICs (4·6%). We
also calculated PAFs by country and applied 10 000 Monte
Carlo simulations to estimate 95% CI (appendix p 7).
The adjusted PAF of physical inactivity for dementia
appears to be modest compared with the disease
outcomes reported in the 2012 Lancet Series, which
ranged from about 6% to 10%.30 However, the previous
calculations were based on a higher prevalence of
physical inactivity globally, primarily because of the
revised 2010 physical activity recommendations3 (see
preceding section on surveillance of physical inactivity).
When using the same physical activity data and
recommendations as applied by Lee and colleagues,30 we
calculated a median PAF of 5·7% for dementia.
In reporting the PAFs we focused on the adjusted
results and consequently might have overadjusted for
factors on the causal pathway. The use of underestimated
physical inactivity prevalence could have also contributed
to conservative PAF estimates. Finally, despite using low
versus high physical activity for calculating the RRs, the
5
Series
comparison group was not truly inactive, and our results
might still be conservative relative to estimates for other
risk factors (eg, reference group for calculating a PAF for
smoking is non-smokers).
Comment
Dementia is growing as a global health priority because of
the rapidly increasing numbers of older adults. Evidence
about the role of physical inactivity in dementia makes it
a timely topic for analysis of global health impact. The
PAF of physical inactivity for dementia was 3·8%, which
is substantial but lower than PAFs for other NCDs.
Progress in research on correlates and
determinants of physical activity in LMICs
Understanding physical activity correlates (crosssectional) and determinants (prospective) is crucial to
designing effective interventions that target evidencebased mechanisms of change. Among recommendations
to use objective physical activity measures, apply
prospective designs, and target understudied popu
lations,39 research in LMICs is especially urgent, because
almost three-quarters of NCD deaths (28 million) occur
in these countries, indicating a large potential for
preventive interventions.4,13 To determine progress since
the 2012 Lancet Series39 we systematically searched articles
on physical activity correlates in LMICs using similar
methods to previous reviews39,40 (appendix pp 10–13).
We screened 1383 articles and identified 197 relevant
papers (appendix pp 13–30). The number of publications
from LMICs increased from 7·2 publications per year in
1999–2011 to 32·8 publications per year between 2012 and
February, 2015, while the number of countries in which
studies were done was stable at 22–23 countries. Most
studies were from U-MICs, especially Brazil and China.
Improvements to methods included measure
ments of
multiple physical activity domains (eg, transport,
recreation) and use of accelerometers, but 94·2% of
studies were cross-sectional rather than prospective.
The significance and direction of physical activity
correlates reported in five or more studies are
summarised in table 2. Studies of adults (aged
18–64 years) and older adults (aged 65 years and older)
had mixed evidence of positive associations of younger
age and male sex with higher physical activity, with a few
studies showing inverse associations. Differences in
sociocultural roles for older adults and women in LMICs
might explain these different results. Regarding
psychological and social factors, most of the directions of
association were similar to those from HICs. For physical
environmental factors, proximity to destinations,
neighbourhood aesthetics, and access to open space were
consistent correlates of higher physical activity, similar to
results from HICs.
Some inconsistent results with HICs had important
implications. In particular, high socioeconomic status
and urban (vs rural) residence were related to lower
6
physical activity among adults and youth. Rapid
urbanisation, access to motorisation, and increases in
sedentary work could be potential drivers of inactive
lifestyles in LMICs.41–43 Considering the increasing
urbanisation worldwide,44 activity-friendly urban design
could be an effective strategy to mitigate the impacts of
urbanisation on physical activity in LMICs.
In studies of children and adolescents, male sex, higher
self-efficacy, participating in school sports, higher social
support, proximity to destinations, and access to open
space were consistent positive correlates. As was the case
with adults, high socioeconomic status and urban
residence emerged as inverse correlates of physical
activity.
Comment
Publications on physical activity correlates from LMICs
increased substantially since 2012. However, most
studies were from a few U-MICs. The continuing dearth
of studies from LICs highlights the gap between where
research is done and where the largest public health
impacts of physical inactivity are located.45 Consistent
correlates were found at individual, social, and environ
mental levels of influence, and most of the directions of
association were similar to those from HICs. Implications
of these results are that interventions should be
developed that operate at multiple levels of influence and
are informed by correlates of research from LMICs.
Progress in research on physical activity
interventions in LMICs
The 2012 Lancet Series paper on physical activity
interventions identified a paucity of studies in LMICs.46
Therefore, this update identified intervention studies
done in LMICs. We searched the English, Spanish, and
Portuguese 2010–15 literature using the same search
methods as in our 2012 paper.46 We identified 147 potential
papers using multiple search engines and completed full
reviews of 64 relevant papers. The table in the appendix
p 31, summarises study characteristics and results for the
most relevant and highest-quality 15 papers.
Intervention strategies to increase physical activity in
whole populations have been categorised as communitywide, informational, behavioural, social, policy, and built
environmental approaches.39,47,48 Intervention strategies
were classified in a manner consistent with our 2012
Lancet Series paper.46,49,50 Multilevel approaches that operate
across personal (eg, biological, psychological), social (eg,
family, co-workers), and built environmental (eg,
neighbourhoods designed so that homes are near shops
and services, access to parks, bicycle facilities) levels of
influence could be more successful in increasing physical
activity than those targeting only one level.12 In this section
we highlight some of the best LMIC interventions in each
category. Case studies describing characteristics of
several exemplary interventions from LMICs are in the
appendix p 35.
Series
Community-wide campaigns
Community-wide campaigns often use multicomponent
(eg, media, behavioural, social, policy, and environ
mental), multisector (eg, public health, transportation,
recreation, health care), and multisite (eg, work, school,
community organisation) interventions.46 Campaigns
usually represent large-scale, high-intensity program
ming and often use multiple communication media to
raise programme awareness and disseminate health
messages. Community-wide inter
ventions among
Examined direction
Adults and elderly (n=124)
Children and adolescents (n=73)
Number of examined
papers
Directions of
associations
Number of
examined papers
Directions of
associations
78
+
37
+
9
00
··
··
Demographic and biological factors
Age
Younger
Occupation or parent occupation
Manual or blue collar
Education
High education
68
+
··
··
Gender
Male
57
+
37
++
Cardiovascular risks
High risk
12
0
··
··
Family income and socioeconomic status
High income or socioeconomic status
52
–
34
–
Marital status
Married
43
0
··
··
Overweight and obesity
Overweight or obese
30
–
21
0
Race and ethnicity
Non-white
18
00
Parental education
High parental education
6
00
29
0
Psychological, cognitive, and emotional factors
Perceived barriers
Barrier present
7
0
··
··
Perceived benefits
Benefits present
5
++
··
··
Perceived health and fitness
Healthy or high fitness
20
++
··
··
Psychological health and distress
Good psychological health
7
0
··
··
Self-efficacy
High self-efficacy
7
++
8
++
··
Behavioural attributes and skills
Alcohol
More drinking
12
0
··
Dietary habits
High quality of diet
5
0
··
··
School sports, physical education, and supervised physical activity
Good education
··
··
13
++
Smoking status
Smoker
17
0
6
0
Sedentary behaviour (TV time, screen time, and sedentary time)
Highly sedentary
··
··
19
–
Social and cultural factors
Exercise and physical activity role models
Role model present
··
··
5
+
Social support from friends and peers
Support present
7
++
8
++
Social support from spouse and family
Support present
5
0
9
++
Physical environment factors
Access to destinations (land use mix)*: objective measurement
Good access
7
++
··
··
Access to destinations (land use mix): perceived measurement
Good access
12
++
9
++
Lighting
Adequate lighting
··
··
··
··
Enjoyable scenery and aesthetics
Good scenery
7
+
··
··
Traffic
Safe level of traffic
16
00
5
0
Neighbourhood safety from crime
Safe neighbourhood
21
0
7
0
Sidewalks, cycle lanes, and paths
Present
14
00
5
0
Urban location of residence
Urban living
17
–
12
–
Access to open space (eg, parks, trails, green space)
Good access
14
+
5
++
Walkability (composite)
Walkable
··
··
··
··
Street connectivity
High connectivity
6
–
··
··
Residential density
High density
10
0
··
··
Safety of facilities (eg, park safety, trail safety)
Safe facilities
··
··
··
··
Correlates or determinants for which less than five papers were reported were excluded from the table. ++=repeatedly documented positive association with physical activity. +=weak or mixed evidence of
positive association with physical activity. 00=repeatedly documented absence of association with physical activity. 0=weak or mixed evidence of no association with physical activity. – –=repeatedly documented
negative association with physical activity. –=weak or mixed evidence of negative association with physical activity. ··=not enough data available. *Land use mix refers to homes being near shops, services, and
jobs, thus providing destinations within walking distance.
Table 2: Directions and strength of relationship of physical activity correlates or determinants in low-income and middle-income countries
7
Series
LMICs that used multisector collaborations were
reported from Iran,51 China,52 South Africa,53 Vietnam,54
and India and Indonesia.55 Some campaigns targeted
only physical activity, and others targeted multiple risk
factors (appendix p 31). These studies, mostly using
quasi-experimental designs, showed that evidence for
community-wide campaigns has grown in number and
quality among LMICs since our 2012 review.46 Because
of the diversity of approaches, contexts, and assessment
methods, we could not identify principles of effective
strategies used in these community-wide interventions.
Social support interventions in community settings
Strategies to increase social support for physical activity
include buddy systems, behavioural contracting, and
walking groups.46 Promising interventions in LMICs that
represent this approach were found with rural com
munities in India,56 health-care workers in South Africa,57
and women civil servants in Vanuatu, a South Pacific
island.58
Physical activity classes in community settings
Providing physical activity classes in public settings was
shown to be a promising strategy.46 Parra and colleagues59
showed the effectiveness of this strategy in Recife, Brazil.
These results were supported by studies in Aracaju,
Brazil,60 and Santiago, Chile.61
School-based interventions
School-based interventions can increase physical activity
among children during and after school.46 Investigations in
LMICs showed mixed results, with a study of classroom
physical activity in Beijing, China, showing effectiveness
over 2 years,62 and a physical education intervention with
favorable effects at one year63 but a controlled study of girls
in Karachi, Pakistan, showing no effects.64 Further studies
of school-based strategies in LMICs are encouraged to
assess co-benefits for cognitive function and school
performance given the positive findings for these
outcomes in HICs31–33,65 and their importance for school
officials.
Community-wide policies and programmes
Community-wide policies and planning to improve
built environments, combined with efforts to promote
physical activity, have shown promise in Latin
America.46 This intervention strategy not only uses
information to motivate individual behaviour change,
but also provides built and social environmental
support to sustain physical activity.46 A study in Bogotá,
Colombia, reported modest effectiveness among survey
respondents who reported regularly using Ciclovia
(streets closed to cars but open to cyclists and
pedestrians) and Cicloruta (protected bicycle facilities)
compared with irregular users.66 The use of sport-fordevelopment programmes is an emerging strategy in
sub-Saharan Africa, where sport is used to promote
8
physical activity and community cohesiveness, as well
as to enhance human capital.67
Comment
15 studies of physical activity interventions in LMICs
were identified, representing an increase from these
resource-constrained contexts. Multiple types of inter
ventions were assessed, and many of the studies reported
increased physical activity. Quality of programme
assessment was variable, so investigators are encouraged
to apply a standard yet flexible approach to programme
assessment.68 These studies provided promising evidence
that population-wide physical activity interventions can
be effective in LMICs, especially those in which inter
sectoral collaboration exists. However, documentation of
the development, adaptation, and assessment of physical
activity interventions among LMICs needs to be
improved. Greater implementation of evidence-based
interventions could help control NCDs in LMICs.
Progress on national physical activity policies
Increasing physical activity requires multiple strategies,
including policies in multiple sectors that lay out the
problem, solutions, stakeholders, timelines, and desired
outcomes. Without adequate national public policy,
public health responses tend to be restricted in scope and
strength, uncoordinated, underfunded, and shortterm.
Since the 1990s, there has been a call for national physical
activity policies and implementation (or action) plans,69,70
but response was poor. The first global policy outlining
national actions to address physical inactivity was not
launched by WHO until 2004. The Global Strategy for
Diet, Physical Activity and Health71 laid out the epi
demiological rationale for systematic national policy and
action to increase physical activity. This call was
reinforced in the UN Declaration on NCDs in 201172 and
further defined in the Global Action Plan (GAP) for the
Prevention and Control of NCDs, 2013–20. GAP
positioned physical inactivity as one of the key NCD risk
factors and set for all countries the target of achieving a
10% decrease in inactivity by 2025 (relative to each
country’s baseline).73 Given these notable developments
in global policy, it is timely to ask what progress has been
made in the adoption and implementation of national
physical activity policy in the decade since the WHO
global strategy recommendations were made.
Collecting data on physical activity policy is difficult
because of publication in different languages, definitional
differences, relevance of multiple government ministries,
accessibility of government reports, and challenges in
verifying content. The development of physical activity
policy audit tools74 allows a more systematic approach,
and several initiatives have commenced to track national
policy and action initiatives.75-–77 In 2000, WHO initiated
an assessment of NCD policy development and country
capacity, and since 2013 this survey has formed part of
the Global NCD Monitoring and Evaluation framework.78
Series
A
2010
2013
2015
100
90
Proportion of countries (%)
80
70
60
50
40
30
20
10
0
World
Africa
Americas
Eastern
Mediterranean
Europe
Southeast
Asia
Western
Pacific
Region
B
100
90
Policy exists
Policy is operational†
80
Proportion of countries (%)
Figure 2A shows the status of national policy on
physical activity in 2010, 2013, and 2015 globally and by
regions as assessed by WHO. By 2015, 91% of the
160 countries responding at all three timepoints reported
having a national physical activity policy. This proportion
has increased since 2010 (75%), which might reflect the
increased global focus on NCDs and GAP. Notable
progress was seen in countries in the African region
(from 45% in 2010 to 100% in 2015), such that by 2015 all
but the Americas region had over 90% of countries
reporting the presence of physical activity in national
policy. A decade ago only 29% of the 133 responding
countries reported having a physical activity policy
(figure 2B). However, having a policy and implementing
the policy are distinctly different. Monitoring surveys
done in 2010, 2013, and 2015 (figure 2B) included
questions on the status of each policy, and results reveal a
notable gap in policy implementation. In 2010, 75% of
countries reported having a physical activity policy
but only 44% reported their countries’ policy to be
operational—ie, both active and funded (figure 2B). The
implementation gap remains clearly visible in 2013 and
2015, albeit narrowing (57% and 71% reported operational
plans, respectively). Although this trend is positive, 2015
data reveal that globally approximately a quarter of
national policies on physical activity are not being put
into practice. Without policy implementation, substantial
improvements in population physical activity are unlikely.
Despite good progress in developing national physical
activity policy, the substantial implementation gap
indicates countries are having difficulties in translating
policy intent into action. Many local contextual challenges
could occur, but three common barriers to policy action
are highlighted here. The first barrier is an insufficient
workforce to implement physical activity policies. WHO
2013 data show that 94% of countries now have an NCD
unit within their ministry of health, an increase from
89% of countries in 2010 and 61% in 2000.79 However,
virtually no data are available on dedicated resources
for implementation of physical activity strategies.
Insufficient numbers of trained workers with knowledge
and skills to develop, implement, and assess programmes
and to build intersectoral partnerships will hinder a
country’s ambitions to increase physical activity.
Experience in training a professional workforce and
strengthening research capacity on physical activity has
been accumulating, especially in the Americas. More
than 50 physical activity and public health training
courses coordinated by the US Centers for Disease
Control and Prevention have produced over
3500 graduates who can develop and deliver national,
state, and local actions.80
A second barrier to progress is the need to form and
sustain effective multisector partnerships, deemed
necessary because the policies that hinder physical
activity are within transport, education, sport, recreation,
and urban planning sectors.81 Countries are showing
70
60
50
40
30
20
10
0
2005*
2010
2013
2015
Year
Figure 2: Progress on national physical activity policies
(A) Presence of national policy, strategy, or action plan (ie, physical activity plan or have physical activity
incorporated in an integrated non-communicable disease plan) on physical activity in 160 countries by WHO
region. Data were provided by WHO from Country Capacity Surveys done in 2010, 2013, and 2015; analysis
includes only 160 countries with responses at all three timepoints. (B) Global progress and implementation of
national policy and action plans on physical activity. Data are from WHO Country Capacity Survey Reports 2005,
2010, and 2013; unpublished data for 2015 were provided by WHO; n=160 countries included except in 2005 in
which n=133 countries. *2005 survey item not identical to later years. †Operational refers to reporting the plan is
being implemented and funded.
signs of establishing multisector collaborations to
address NCDs. In 2013, 94% of countries reported such a
mechanism, an increase from 61% in 2010.78 However,
only 33% of these countries reported that the committees
remained operational in 2013, which is further evidence
of the implementation gap and that securing crosssectoral engagement in physical activity policies is a
common challenge.
The third barrier to policy progress is the absence of
clarity on the actions most likely to be effective and
feasible in a given context. Until the global action plan on
NCDs in 2013,73 most of the national policies on physical
activity came from Europe, North America, and
Australasia. These policies drew on extensive scientific
evidence, largely from the same regions. A frequent
request from other regions is for support to develop the
9
Series
evidence and select, adapt, and implement solutions that
fit local cultural, religious, geographical, and economic
contexts. Although the preceding section of this paper
reports progress in physical activity intervention studies
in LMICs, a stronger emphasis on physical activity
interventions is needed, linked with national policies, to
accelerate implementation of effective and promising
strategies on a large scale. A clear consensus on effective
interventions will support national policy making, and
practical resources and toolkits can support imple
mentation, particularly in LMICs. Civil society developed
a consensus document of the seven best investments for
increasing physical activity,82 and a toolkit to guide
implementation that is tailored to national contexts is
warranted. The rapid adoption of national physical activity
policies creates an opportunity and the need to create
tools and resources to support improved implementation
in each country, with a special focus on LMICs.
Comment
Almost all ministries of health now have NCD units, and
most countries have a physical activity policy or action
plan. However, implementation of physical activity
policies appears to be scarce, probably because of an
insufficient workforce with relevant skills, multisector
partnerships, and clarity on the most effective
interventions. Training programmes in physical activity
and public health are available but need to be expanded.
Conclusion
In the 4 years since the 2012 Lancet Series on physical
activity,2 global progress on the topics covered in the
present paper has been modest, yet each sign of progress
indicates the shortcomings of current actions. More
countries are collecting physical activity surveillance data
than in previous years, but physical activity is not
increasing worldwide. Although many studies show
physical activity enhances brain health, this new
knowledge has not yet been translated into action.
Evidence on correlates of physical activity is increasing in
LMICs, but few studies have been done in LICs. Although
it is encouraging that effective interventions are being
assessed in LMICs, strong assessment methods and
tailoring to local contexts are needed. National physical
activity policies and plans have been adopted by almost all
countries, yet major challenges with implementation
remain.
Progress on physical activity has been far from
proportionate to the documented burden of disease
from physical inactivity in countries of all income
levels.13,30 The most progress might have been made in
putting physical activity on the health agenda of LMICs.
LMICs are laying the groundwork for effective public
health action on physical activity, but it is not clear
where the resources will be found to scale up effective
interventions, build a physical activity workforce in
public health, expand research in LMICs, and take bold
10
initiatives to alter policies that will increase physical
activity in all countries.
Contributors
All authors drafted sections and edited the manuscript. JFS and PCH
conceptualised the paper, and JFS coordinated the writing process.
RG managed data collection. RG, JR, and PCH did the analyses. FB, JR,
PCH, GWH, SI, PK, ALO, and LGP conducted searches.
Declaration of interests
JFS has received grants and personal fees from the Robert Wood Johnson
Foundation outside of this article, grants and non-financial support from
Nike outside of this article, and is a consultant and receiver of royalties
from Sportime/SPARK of School Specialty Inc. RG is a staff member of
the World Health Organization. All other authors declare no competing
interests. The authors alone are responsible for the views expressed in
this publication and they do not necessarily represent the decisions,
policy, or views of the World Health Organization.
Lancet Physical Activity Series 2 Executive Committee
Adrian E Bauman, Ding Ding, Ulf Ekelund, Pedro C Hallal,
Gregory W Heath, Harold W Kohl 3rd, I-Min Lee, Kenneth E Powell,
Michael Pratt, Rodrigo S Reis, James F Sallis.
Acknowledgments
We thank Shiho Amagasa (Department of Preventive Medicine and Public
Health, Tokyo Medical University, Japan), Noritoshi Fukushima
(Department of Preventive Medicine and Public Health, Tokyo Medical
University, Japan), Andrea Ramirez Varela (PhD student, Post Graduate
Program in Epidemiology, Universidade Federal de Pelotas, Brazil),
Noriko Takeda (Division of Liberal Arts, Kogakuin University, Japan),
Debra Rubio (Department of Family Medicine and Public Health,
University of California, San Diego, CA, USA), Ding Ding (Sydney School
of Public Health, University of Sydney, Australia), and I-Min Lee (Harvard
Medical School and Harvard T H Chan School of Public Health, Boston,
MA, USA). We thank reviewers Loretta DiPietro (Department of Exercise
and Nutrition Sciences, Milken Institute School of Public Health,
The George Washington University, Washington, DC, USA) and
Lars Bo Andersen (Department of Sports Science and Clinical
Biomechanics, University of Southern Denmark, Odense, Denmark) for
their valuable critiques.
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Appendix 1: For Surveillance Section
Reference list for physical activity trend data
Argentina
Ministerio de Salud de la Nación. Encuesta nacional de factores de riesgo 2005. Buenos Aires, Argentina:
Ministerio de Salud de la Nación, 2006.
Ministerio de Salud de la Nación. Segunda encuesta nacional de factores de riesgo para enfermedades no
transmisibles. Buenos Aires, Argentina: Ministerio de Salud de la Nación, 2011.
Ministerio de Salud de la Nación. 3o Encuesta nacional de factores de riesgo para enfermedades no transmisibles.
Buenos Aires, Argentina: Ministerio de Salud de la Nación, 2014.
Ferrante D, Virgolini M. National Risk Factor Survey 2005: Main Results. Prevalence of Cardiovascular Risk
Factors in Argentina. Rev Argent Cardiol 2007;75:20-9.
Belgium
Scientific Institute of Public Health. Highlights of the Belgian Health Interview Survey 2008. Brussels, Belgium:
Scientific Institute of Public Health, 2011.
Tafforeau J. Enquête de santé par interview, Belgique 2008. La pratique d’activités physiques. Bruxelles, Belgique:
Institut Scientifique de Santé Publique.
Drieskens S, Charafeddine R, Demarest S, Gisle L, Tafforeau J. Van der Heyden J.
Health Interview Survey, Belgium, 1997 - 2001 - 2004 - 2008 - 2013: Health Interview Survey Interactive Analysis.
Brussels, Belgium: WIV-ISP. https://hisia.wiv-isp.be/ (accessed Sept 1, 2015).
Iran
Koohpayehzadeh J, Etemad K, Abbasi M, et al. Gender-specific changes in physical activity pattern in Iran: national
surveillance of risk factors of non-communicable diseases (2007-2011). Int J Public Health 2014;59:231-41.
Asgari F, Mirzazadeh A, Miri HH. Iran Non-communicable Diseases Risk Factors Surveillance Data Book for 2007.
http://www.who.int/chp/steps/iran/en/ (accessed Aug 10, 2015).
World Health Organization. WHO STEPS. Chronic Disease Risk Factor Surveillance Data book I. R. Iran 2008.
World Health Organization. WHO STEPS. Chronic Disease Risk Factor Surveillance Data book I. R. Iran 2009.
1
Kuwait
Ministry of Health of the State of Kuwait. STEPS report Kuwait 2006. Kuwait, Kuwait: Ministry of the State of
Kuwait, 2008. http://www.who.int/chp/steps/STEPS_Report_Kuwait.pdf (accessed Oct 14, 2015).
World Health Organization. Kuwait STEPS Survey 2014. Fact Sheet. http://www.who.int/chp/steps/kuwait/en/
(accessed Oct 14, 2015).
Maldives
World Health Organization. Survey on Non Communicable Disease Risk Factors Maldives, 2004.
http://www.who.int/chp/steps/maldives/en/ (accessed June 20, 2015). Personal communication with authors.
World Health Organization. WHO STEPS survey on risk factors for noncommunicable diseases, Maldives, 2011.
http://www.who.int/chp/steps/maldives/en/ (accessed June 20, 2015). Personal communication with authors.
Mongolia
World Health Organization Western Pacific Region. Mongolian STEPS Survey on the Prevalence of
Noncommunicable Disease Risk Factors 2006. http://www.who.int/chp/steps/mongolia/en/ (accessed July 23, 2015).
World Health Organization Western Pacific Region. Mongolian STEPS Survey on the Prevalence of
Noncommunicable Disease Risk Factors 2009. http://www.who.int/chp/steps/mongolia/en/ (accessed July 23, 2015).
Personal communication with authors.
New Zealand
Ministry of Health. New Zealand Health Survey: Annual update of key findings 2012/13. Wellington, New Zealand:
Ministry of Health, 2013.
Ministry of Health. New Zealand Health Survey Methodology Report 2012/13. Wellington, New Zealand: Ministry
of Health, 2013.
Ministry of Health. New Zealand Health Survey: Adult data tables: Health Status, health behaviours and risk factors.
http://www.health.govt.nz/publication/new-zealand-health-survey-annual-update-key-findings-2012-13 (accessed
July 29, 2015).
Sport and Recreation New Zealand (SPARC). The New Zealand Physical Activity Questionnaires. Report on the
validation and the use of the NZPAQ-LF and the NZPAQ-SF self-report physical activity survey instruments.
Wellington, New Zealand: Sport and Recreation New Zealand, 2004.
2
Ministry of Health. A Portrait of Health. Key Results of the 2006/07 New Zealand Health Survey. Wellington, New
Zealand. Ministry of Health, 2008.
Republic of Korea
Ministry of Health and Welfare and Korea Centers for Disease Control and Prevention. Korea National Health and
Nutrition Examination Survey. Major Results. https://knhanes.cdc.go.kr/knhanes/eng/sub01/sub01_05.do (accessed
Oct 13, 2015). Personal communication with authors.
Seychelles
Bovet P, William F, Viswanathan B, et al. The Seychelles Heart Study 2004: methods and main findings. Victoria,
Republic of Seychelles: Ministry of Health and Social Development, 2007.
http://www.who.int/chp/steps/seychelles/en/ (accessed Oct 5, 2015).
Bovet P, Viswanathan B, Louange M, Gedeon J. National Survey of Noncommunicable Diseases in Seychelles
2013-2014 (Seychelles Heart Study IV): methods and main findings. Victoria, Republic of Seychelles: Ministry of
Health and Social Development, 2015. http://www.who.int/chp/steps/seychelles/en/ (accessed Oct 5, 2015).
Singapore
Ministry of Health Singapore. National Health Surveillance Survey 2007. Singapore, Republic of Singapore:
Ministry of Health Singapore. National Health Surveillance Survey 2010. Singapore, Republic of Singapore:
South Africa
Department of Health, Medical Research Council, OrcMacro. South Africa Demographic and Health Survey 2003.
Pretoria, South Africa: Department of Health, 2007.
Phaswana-Mafuya N, Peltzer K, Schneider M, et al. Study on global AGEing and adult health (SAGE), South Africa
2007-2008. Geneva, Switzerland: World Health Organization 2012.
United States of America
Centers for Disease Control and Prevention. National Health and Nutrition Examination Survey. Questionnaires,
Datasets, and Related Documentation. http://www.cdc.gov/nchs/nhanes/nhanes_questionnaires.htm
3
Appendix 2: For Health Consequences Section
Meta-analysis to obtain the unadjusted relative risk (RR) of dementia associated with physical inactivity
The original meta-analysis by Blondell et al34 calculated the adjusted RR by comparing high vs. low physical
activity levels in 26 studies. We obtained the primary papers and calculated the crude RR from 18 of these studies.
An additional 4 of these studies did not provide adequate crude data, but provided an age-adjusted RR. The
remaining 4 studies did not provide data from which crude or age-adjusted RRs could be obtained. We pooled the
crude and age-adjusted RRs and called this the pooled unadjusted RR.
We conducted a simple random effects meta-regression to account for heterogeneity across studies. The pooled
unadjusted relative risk associated with physical activity was 0·63 (95% CI: 0·55–0·74). Taking the inverse to
obtain the unadjusted RR for inactivity, we calculated a RR of 1·59 (95% CI: 1·35–1·82). This magnitude of risk
increase was similar to that pooling only the crude RRs, which yielded 1·56 (95% CI: 1·35–1·82). Consequently, we
used the unadjusted RR to include a larger number of studies and ensure a closer parallel between studies used to
calculate the pooled unadjusted and adjusted RRs.30 The studies included in these calculations varied in the number
and type of included adjustment factors, but those most commonly modelled were: age, sex, education, baseline
cognition / mental health, physical functional capacity, genetic markers (e.g. apolipoprotein E ɛ4 allele), history of
comorbidities (i.e. heart disease, stroke, diabetes, hypertension, hypercholesterolemia) and related behavioural risk
factors (i.e. smoking, alcohol).
For complete references to the studies shown, please refer to Blondell et al34.
4
Prevalence of physical inactivity (%) overall and among cases of dementia
Study Name
Location
Canadian Study of Health and Aging (1991-92)a
Prevalence of physical
inactivity at baseline (%)
Overall
Cases
Adjustment
Factor
Canada
42.02
48.42
1.15
Finland
37.48
27.27
0.73
Finland
34.72
29.55
0.85
Finland
58.83
71.70
1.22
Iceland
95.49
96.20
1.01
Italy
38.72
51.16
1.32
The Rotterdam Study (1997-1999) - leisure
Netherlands
49.98
54.55
1.09
Ibadan Study of Aging (2003-04)
Nigeria
30.78
42.35
1.38
International Dementia Research Program in
Developing Countries (2001-03)
South Korea
27.61
53.33
1.93
Caerphilly Prospective Study (1979-1983) - vocation
UK
40.30
37.50
0.93
Caerphilly Prospective Study (1979-1983) - leisure
UK
29.22
34.72
1.19
Honolulu Heart Program (1965-68)
USA
60.66
70.89
1.17
Cardiovascular Health Cognition Study (1992)a
USA
49.99
55.32
1.11
Aging, Demographics & Memory Study (2001-03)a
USA
67.45
77.98
1.16
Adult Changes in Thought (1994-96)
USA
23.98
32.91
1.37
Washington Heights-Inwood Columbia Aging Project
(1992, 1999)
USA
62.23
71.28
1.15
Cardiovascular risk factors, Aging and Incidence of
Dementia (1972, 77, 82, 87) - vocation
Dementia (1972, 77, 82, 87) - transport
Dementia (1972, 77, 82, 87)
Age Gene/Environment Susceptability - Reykjavik Study
(1967)
Conselice Study of Brain Aging (1999-2000)
a
a
MEAN ADJUSTMENT FACTOR (SE) =
1.17 (0.07)
NOTE: Physical inactivity was defined by the author of each included study.
a
Data was dichotomised using cut-off values that closely approximated meeting the current global physical activity recommendations.
We initially only included studies that applied physical activity cut-offs closely approximating current global
recommendations to calculate an adjustment factor of 1·18 (SE=0·04). However, this limited the geographical
representation of studies to Canada, Italy and USA. Subsequent calculations applying physical activity level
5
classifications defined by the author of each study broadened the global representativeness of the data and yielded a
more conservative adjustment factor of 1·17 (SE=0·07). Consequently, we have included all of these cohorts in our
final analyses.
6
Estimated prevalence of physical inactivity and PAF's for dementia associated with physical inactivity, by
country
World
Bank
Country
income
(sorted by WHO region)
class.
(2014)
Africa:
Benin
Burkina Faso
Central African Republic
Chad
Comoros
Congo, Dem. Rep.
Eritrea
Ethiopia
Gambia, The
Guinea
Kenya
Liberia
Madagascar
Malawi
Mali
Mozambique
Niger
Rwanda
Sierra Leone
Tanzania
Togo
Zimbabwe
Cameroon
Cape Verde
Congo, Rep.
Cote d'Ivoire
Ghana
Lesotho
Mauritania
Nigeria
Sao Tome and Principe
Senegal
Swaziland
Zambia
Algeria
Botswana
Gabon
Mauritius
Namibia
Seychelles
South Africa
Eastern Mediterranean
Egypt, Arab Rep.
Pakistan
Iran, Islamic Rep.
Prevalence of physical inactivity
Population
a
(5.4 - 8.4)
(15.9 - 20.9)
(1.0 - 23.1)
(-1.7 - 50.8)
(13.1 - 15.4)
(20.4 - 31.7)
(9.1 - 12.2)
(-3.6 - 41.5)
(16.3 - 26.7)
(0.3 - 19.5)
(-3.4 - 41.8)
(23.5 - 31.5)
(14.9 - 21.0)
(6.6 - 8.5)
(-2.1 - 49.4)
(3.9 - 7.8)
(20.6 - 29.6)
(14.0 - 16.6)
(11.0 - 17.4)
(5.9 - 7.8)
(8.9 - 11.9)
(-2.2 - 47.0)
(5.4 - 56.1)
(12.6 - 26.6)
(-0.9 - 51.7)
(-2.6 - 47.7)
(13.1 - 18.1)
(5.9 - 8.5)
(12.7 - 77.4)
(-6.6 - 51.2)
(13.7 - 17.5)
(-1.5 - 51.5)
(5.5 - 68.1)
(-3.2 - 44.2)
(30.4 - 38.5)
(19.0 - 35.4)
(6.6 - 45.5)
(-1.3 - 51.7)
(1.9 - 61.6)
(18.5 - 23.1)
(42.7 - 51.1)
People eventually
developing
dementiaa
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
LM
LM
LM
LM
LM
LM
LM
LM
LM
LM
LM
LM
UM
UM
UM
UM
UM
UM
UM
6.9
18.4
12.0
24.6
14.2
26.0
10.7
18.9
21.5
9.9
19.2
27.5
17.9
7.5
23.7
5.8
25.1
15.3
14.2
6.9
10.4
22.4
30.7
19.6
25.4
22.6
15.6
7.2
45.1
22.3
15.6
25.0
36.8
20.5
34.4
27.2
26.0
25.2
31.8
20.8
46.9
8.1 (4.1 - 12.2)
21.6 (11.5 - 31.6)
14.1 (-10.1 - 38.3)
28.8 (-14.3 - 71.7)
16.6 (9.2 - 24.2)
30.5 (15.2 - 45.7)
12.5 (6.6 - 18.3)
22.1 (-12.7 - 57.0)
25.2 (12.2 - 38.0)
11.6 (-8.6 - 31.7)
22.5 (-14.6 - 59.5)
32.2 (17.1 - 47.3)
21.0 (11.0 - 31.0)
8.8 (4.7 - 12.8)
27.8 (-12.4 - 67.7)
6.8 (2.9 - 10.7)
29.4 (15.2 - 43.6)
17.9 (9.8 - 26.0)
16.6 (8.3 - 25.0)
8.1 (4.3 - 11.8)
12.2 (6.5 - 17.9)
26.2 (-13.5 - 65.9)
36.0 (-10.3 - 82.2)
23.0 (9.5 - 36.4)
29.8 (-13.3 - 72.7)
26.5 (-13.6 - 66.4)
18.3 (9.6 - 26.9)
8.4 (4.4 - 12.5)
52.8 (-1.8 - 107.2)
26.1 (-9.3 - 61.4)
18.3 (9.9 - 26.7)
29.3 (-12.1 - 70.7)
43.1 (-8.6 - 94.7)
24.0 (-13.7 - 61.7)
40.3 (21.8 - 58.8)
31.9 (14.3 - 49.3)
30.5 (-16.0 - 76.9)
29.5 (-12.7 - 71.7)
37.3 (-10.8 - 85.2)
24.4 (13.2 - 35.5)
55.0 (30.0 - 79.7)
LM
LM
UM
32.3 (27.8 - 36.8) 37.8 (20.1 - 55.4)
26.0 (-1.4 - 53.3) 30.5 (-13.2 - 73.9)
33.5 (32.1 - 34.9) 39.3 (21.7 - 56.6)
7
Population Attributable Fraction
Unadjusted
3.9
9.8
6.6
12.7
7.1
13.3
5.9
10.0
11.3
5.5
10.2
14.0
9.6
4.2
12.3
3.3
12.9
8.3
7.7
3.9
5.8
11.7
15.3
10.4
13.0
11.8
8.4
4.1
21.0
11.6
8.4
12.9
17.8
10.8
16.9
13.8
13.3
12.9
15.8
10.9
21.7
b
(2.5 - 5.6)
(6.5 - 13.5)
(1.4 - 12.2)
(1.4 - 23.1)
(5.2 - 10.5)
(8.8 - 18.4)
(3.9 - 8.2)
(0.2 - 19.7)
(7.3 - 15.6)
(1.0 - 10.4)
(0.3 - 19.7)
(9.4 - 18.7)
(6.2 - 13.1)
(2.8 - 5.9)
(1.1 - 22.5)
(2.0 - 5.0)
(8.6 - 17.6)
(5.5 - 11.3)
(5.0 - 10.9)
(2.6 - 5.5)
(3.8 - 8.0)
(0.8 - 21.7)
(4.7 - 25.5)
(6.2 - 15.0)
(1.8 - 23.4)
(0.8 - 22.1)
(5.6 - 11.6)
(2.6 - 5.7)
(8.8 - 32.3)
(-1.2 - 23.0)
(5.7 - 11.5)
(1.2 - 23.2)
(5.2 - 29.2)
(0.1 - 20.5)
(11.5 - 22.3)
(8.7 - 19.4)
(5.0 - 21.9)
(1.5 - 23.6)
(3.5 - 27.0)
(7.4 - 14.8)
(15.3 - 28.0)
16.0 (11.0 - 21.4)
13.3 (2.1 - 24.3)
16.5 (11.4 - 21.7)
Adjusted
1.1
3.0
1.9
4.0
2.3
4.2
1.7
3.1
3.5
1.6
3.1
4.4
2.9
1.2
3.8
0.9
4.1
2.5
2.3
1.1
1.7
3.6
5.0
3.2
4.1
3.7
2.5
1.2
7.3
3.6
2.5
4.0
5.9
3.3
5.6
4.4
4.2
4.1
5.1
3.4
7.6
b
(0.3 - 2.0)
(0.9 - 5.4)
(-0.8 - 5.5)
(-0.9 - 10.4)
(0.7 - 4.1)
(1.2 - 7.5)
(0.5 - 3.1)
(-1.0 - 8.2)
(1.1 - 6.3)
(-0.8 - 4.6)
(-1.1 - 8.7)
(1.4 - 8.0)
(0.8 - 5.2)
(0.4 - 2.2)
(-0.7 - 9.8)
(0.3 - 1.8)
(1.2 - 7.3)
(0.8 - 4.4)
(0.7 - 4.2)
(0.3 - 2.0)
(0.5 - 3.0)
(-0.9 - 9.5)
(-0.5 - 12.0)
(0.9 - 5.9)
(-0.8 - 10.6)
(-0.9 - 9.5)
(0.8 - 4.5)
(0.4 - 2.1)
(0.4 - 16.0)
(-0.5 - 9.1)
(0.8 - 4.5)
(-0.7 - 10.4)
(-0.2 - 13.9)
(-1.0 - 8.8)
(1.7 - 10.0)
(1.2 - 8.1)
(-1.2 - 11.0)
(-0.7 - 10.3)
(-0.5 - 12.6)
(1.0 - 6.0)
(2.2 - 13.5)
5.2 (1.6 - 9.2)
4.2 (-0.9 - 10.9)
5.4 (1.6 - 9.6)
Iraq
UM
Jordan
UM
Lebanon
UM
Libya
UM
Tunisia
UM
Kuwait
H
Qatar
H
Saudi Arabia
H
United Arab Emirates
H
Europe:
Georgia
LM
Kyrgyz Republic
LM
Moldova
LM
Ukraine
LM
Uzbekistan
LM
Bosnia and Herzegovina
UM
Bulgaria
UM
Hungary
UM
Kazakhstan
UM
Romania
UM
Serbia
UM
Turkey
UM
Andorra
H
Austria
H
Belgium
H
Croatia
H
Cyprus
H
Czech Republic
H
Denmark
H
Estonia
H
Finland
H
France
H
Germany
H
Greece
H
Ireland
H
Italy
H
Latvia
H
Lithuania
H
Luxembourg
H
Malta
H
Netherlands
H
Norway
H
Poland
H
Portugal
H
Russian Federation
H
Slovak Republic
H
Slovenia
H
Spain
H
Sweden
H
United Kingdom
H
Latin America and Caribbean:
Guatemala
LM
Paraguay
LM
Argentina
UM
Brazil
UM
49.3
15.6
38.8
38.0
23.5
56.6
41.6
61.0
38.4
(45.4 - 53.2)
(14.3 - 17.0)
(33.0 - 44.6)
(34.7 - 41.4)
(-2.0 - 49.0)
(54.3 - 58.8)
(38.0 - 45.1)
(57.2 - 64.8)
(7.1 - 69.7)
57.8 (31.7 - 83.7)
18.3 (10.0 - 26.5)
45.5 (24.0 - 66.8)
44.5 (24.4 - 64.6)
27.5 (-12.7 - 67.7)
66.3 (36.7 - 95.6)
48.7 (26.7 - 70.6)
71.5 (39.4 - 103.3)
45.0 (-8.7 - 98.5)
22.5
8.4
18.6
18.3
12.2
25.0
19.7
26.5
18.5
(15.9 - 28.8)
(5.7 - 11.5)
(12.7 - 24.6)
(12.8 - 23.9)
(1.4 - 22.5)
(18.1 - 31.7)
(13.9 - 25.8)
(19.2 - 33.6)
(6.3 - 29.5)
8.0
2.5
6.3
6.1
3.8
9.1
6.7
9.9
6.2
(2.5 - 14.0)
(0.8 - 4.5)
(1.9 - 11.0)
(1.9 - 10.8)
(-0.8 - 9.8)
(2.8 - 16.2)
(2.0 - 12.0)
(2.9 - 17.5)
(-0.2 - 14.8)
20.6
13.3
12.3
12.2
19.2
18.1
21.0
18.1
20.6
25.3
38.7
32.8
26.1
23.8
33.2
16.2
34.7
23.8
24.3
11.9
23.5
23.8
21.1
12.9
35.1
33.2
22.0
18.4
28.5
42.9
15.5
25.8
18.7
34.9
9.5
17.8
21.3
30.5
28.7
37.3
(18.9 - 22.4)
(11.6 - 15.0)
(10.9 - 13.6)
(-4.0 - 28.4)
(16.9 - 21.5)
(-3.7 - 40.0)
(-3.6 - 45.5)
(-4.0 - 40.1)
(-3.4 - 44.6)
(-2.1 - 52.8)
(7.4 - 69.9)
(2.9 - 62.7)
(-1.4 - 53.5)
(-2.1 - 49.7)
(3.3 - 63.2)
(-3.4 - 35.8)
(4.1 - 65.3)
(18.2 - 29.3)
(-2.0 - 50.6)
(-4.3 - 28.1)
(-2.2 - 49.3)
(19.8 - 27.8)
(-3.5 - 45.7)
(-3.9 - 29.7)
(4.4 - 65.8)
(2.9 - 63.4)
(-3.5 - 47.5)
(11.7 - 25.0)
(0.0 - 57.0)
(10.6 - 75.2)
(-3.8 - 34.8)
(-1.1 - 52.8)
(-4.1 - 41.6)
(4.3 - 65.6)
(6.5 - 12.5)
(-4.5 - 40.2)
(-2.8 - 45.3)
(1.0 - 59.9)
(0.2 - 57.2)
(35.9 - 38.8)
24.1 (13.2 - 35.0)
15.6 (8.3 - 22.7)
14.4 (7.8 - 20.9)
14.3 (-11.5 - 40.0)
22.5 (12.1 - 32.9)
21.2 (-12.2 - 54.5)
24.6 (-14.8 - 63.9)
21.2 (-13.7 - 56.0)
24.1 (-14.0 - 62.1)
29.6 (-14.2 - 73.5)
45.3 (-6.0 - 96.5)
38.4 (-9.8 - 86.6)
30.6 (-4.1 - 65.1)
27.9 (-13.8 - 69.5)
38.9 (-9.4 - 87.2)
19.0 (-12.8 - 50.7)
40.7 (-9.9 - 91.1)
27.9 (13.7 - 42.0)
28.5 (-14.2 - 71.0)
13.9 (-11 - 38.9)
27.5 (-14.2 - 69.2)
27.9 (14.5 - 41.1)
24.7 (-14.8 - 64.2)
15.1 (-11.5 - 41.8)
41.1 (-9.7 - 91.8)
38.9 (-11 - 88.6)
25.8 (-14.9 - 66.3)
21.6 (8.7 - 34.3)
33.4 (-13.2 - 79.9)
50.3 (-4.3 - 104.7)
18.2 (-13.1 - 49.4)
30.2 (-13.4 - 73.8)
21.9 (-13.8 - 57.6)
40.9 (-9.9 - 91.6)
11.1 (4.9 - 17.4)
20.9 (-12.0 - 53.7)
25.0 (-15.1 - 64.9)
35.7 (-11.7 - 83.0)
33.6 (-12.9 - 80.1)
43.7 (24.3 - 63.1)
10.8
7.3
6.8
6.7
10.2
9.6
11.0
9.6
10.8
13.0
18.6
16.2
13.3
12.3
16.4
8.7
17.0
12.3
12.5
6.6
12.2
12.3
11.1
7.1
17.2
16.4
11.5
9.8
14.4
20.2
8.4
13.2
9.9
17.1
5.3
9.5
11.2
15.3
14.5
18.0
(7.3 - 14.6)
(4.8 - 10.0)
(4.5 - 9.3)
(-0.6 - 14.2)
(6.8 - 13.9)
(-0.2 - 19.0)
(-0.1 - 21.2)
(-0.4 - 19.1)
(0.4 - 20.8)
(1.4 - 23.9)
(6.3 - 29.7)
(4.0 - 27.3)
(1.7 - 24.0)
(0.8 - 22.9)
(4.3 - 27.3)
(-0.2 - 17.4)
(4.8 - 28.4)
(7.9 - 17.0)
(1.3 - 23.1)
(-1.0 - 14.2)
(1.0 - 22.3)
(8.2 - 16.7)
(0.2 - 21.1)
(-0.6 - 14.5)
(4.8 - 28.2)
(4.1 - 27.2)
(0.1 - 21.8)
(5.7 - 14.3)
(2.8 - 25.4)
(7.9 - 31.6)
(-0.5 - 17.1)
(1.8 - 23.7)
(-0.4 - 19.6)
(5.2 - 28.4)
(3.2 - 7.8)
(-0.7 - 18.8)
(0.6 - 21.2)
(3.0 - 26.5)
(2.3 - 25.5)
(12.6 - 23.4)
3.3
2.1
2.0
2.0
3.1
2.9
3.4
2.9
3.3
4.1
6.3
5.3
4.2
3.8
5.4
2.6
5.6
3.8
3.9
1.9
3.8
3.8
3.4
2.1
5.7
5.4
3.6
3.0
4.6
6.9
2.5
4.2
3.0
5.6
1.5
2.9
3.4
4.9
4.6
6.0
(1.0 - 5.9)
(0.7 - 3.8)
(0.6 - 3.5)
(-1.0 - 5.6)
(1.0 - 5.5)
(-0.9 - 7.8)
(-1.0 - 9.1)
(-1.1 - 7.9)
(-1.0 - 9.1)
(-0.8 - 10.8)
(0.1 - 14.3)
(-0.4 - 12.7)
(0.0 - 9.8)
(-1.0 - 10.1)
(-0.3 - 12.9)
(-1.0 - 7.2)
(-0.3 - 13.6)
(1.1 - 7.1)
(-0.9 - 10.4)
(-0.9 - 5.6)
(-1.0 - 10.0)
(1.1 - 6.9)
(-1.0 - 9.2)
(-0.9 - 6.0)
(-0.2 - 13.4)
(-0.4 - 12.9)
(-1.1 - 9.6)
(0.8 - 5.6)
(-0.8 - 11.5)
(0.3 - 15.8)
(-1.1 - 6.9)
(-0.8 - 10.6)
(-1.1 - 8.2)
(-0.4 - 13.7)
(0.4 - 2.9)
(-0.9 - 7.8)
(-1.1 - 9.4)
(-0.6 - 12.4)
(-0.6 - 11.7)
(1.8 - 10.7)
7.3
12.7
18.8
14.4
(-0.7 - 15.5)
(1.4 - 23.2)
(6.6 - 30.2)
(2.4 - 24.9)
2.1
4.0
6.3
4.5
(-1.0 - 6.2)
(-0.9 - 10.3)
(0.0 - 14.6)
(-0.6 - 11.2)
13.3 (-4.7 - 31.2) 15.6 (-13.2 - 44.3)
24.6 (-1.9 - 51.1) 28.8 (-13.7 - 71.2)
39.2 (7.6 - 70.8) 45.9 (-5.9 - 97.7)
27.8 (-0.2 - 55.7) 32.6 (-12.2 - 77.2)
8
Colombia
Dominica
Dominican Republic
Ecuador
Grenada
Jamaica
Mexico
Saint Lucia
Bahamas, The
Barbados
Chile
Saint Kitts and Nevis
Trinidad and Tobago
Uruguay
North America:
Canada
United States
South-East Asia:
Bangladesh
Myanmar
Nepal
Bhutan
India
Indonesia
Sri Lanka
Maldives
Thailand
Western Pacific:
Cambodia
Kiribati
Lao PDR
Micronesia, Fed. Sts.
Mongolia
c
Nauru
c
Niue
Papua New Guinea
Philippines
Samoa
Solomon Islands
Vanuatu
Vietnam
China
c
Cook Islands
Fiji
Malaysia
Marshall Islands
Tonga
Australia
Japan
New Zealand
Republic of Korea
Singapore
UM
UM
UM
UM
UM
UM
UM
UM
H
H
H
H
H
H
H
H
63.6
21.8
35.9
25.2
30.5
27.9
26.0
41.2
43.0
37.6
21.3
32.4
41.5
31.7
(58.4 - 68.8)
(18.8 - 24.7)
(5.0 - 66.8)
(-1.6 - 52.0)
(26.3 - 34.8)
(-0.5 - 56.3)
(20.2 - 31.9)
(36.0 - 46.3)
(36.4 - 49.5)
(33.5 - 41.7)
(18.3 - 24.3)
(25.8 - 39.2)
(38.6 - 44.4)
(26.0 - 37.5)
74.5 (40.9 - 108.0)
25.5 (13.6 - 37.3)
42.1 (-8.5 - 92.5)
29.5 (-13.6 - 72.6)
35.7 (19.0 - 52.4)
32.7 (-12.9 - 78.2)
30.5 (15.1 - 45.8)
48.3 (25.9 - 70.5)
50.4 (26.5 - 74.0)
44.1 (23.9 - 64.1)
25.0 (13.3 - 36.6)
38.0 (19.1 - 56.7)
48.6 (26.8 - 70.4)
37.1 (19.2 - 55.1)
27.3
11.4
17.5
12.9
15.3
14.1
13.3
19.6
20.2
18.2
11.2
16.0
19.7
15.8
(19.7 - 34.6) 10.3 (3.2 - 18.1)
(7.6 - 15.4) 3.5 (1.0 - 6.3)
(5.3 - 28.7) 5.8 (-0.3 - 13.6)
(1.4 - 23.5) 4.1 (-0.9 - 10.5)
(10.3 - 20.3) 4.9 (1.5 - 8.8)
(2.5 - 25.1) 4.5 (-0.7 - 11.5)
(8.7 - 18.1) 4.2 (1.2 - 7.5)
(13.7 - 25.6) 6.7 (1.5 - 9.3)
(13.8 - 26.5) 6.9 (2.0 - 12.6)
(12.5 - 23.7) 6.1 (1.8 - 10.8)
(7.4 - 15.1) 3.4 (1.1 - 6.1)
(10.7 - 21.7) 5.2 (2.0 - 12.1)
(13.8 - 25.6) 6.7 (2.1 - 11.8)
(10.7 - 21.2) 5.1 (1.5 - 9.2)
23.2 (-2.2 - 48.7) 27.2 (-13.8 - 68.2)
32.4 (29.8 - 34.9) 38.0 (20.8 - 54.9)
12.0 (0.8 - 22.4)
16.0 (11.0 - 21.2)
3.7 (-0.8 - 9.8)
5.2 (1.5 - 9.3)
L
L
L
LM
LM
LM
LM
UM
UM
26.8
9.9
4.1
8.7
13.4
23.7
23.8
30.7
14.8
(25.8 - 27.7)
(8.2 - 11.7)
(3.7 - 4.6)
(7.4 - 10.1)
(12.1 - 14.7)
(18.7 - 28.8)
(21.8 - 25.8)
(6.6 - 54.7)
(13.5 - 16.1)
31.4 (17.4 - 45.3)
11.6 (6.0 - 17.2)
4.8 (2.6 - 7.1)
10.2 (5.4 - 15.0)
15.7 (8.6 - 22.8)
27.8 (13.9 - 41.6)
27.9 (15.3 - 40.4)
36.0 (-15.0 - 86.7)
17.3 (9.5 - 25.1)
13.7
5.5
2.4
4.9
7.3
12.3
12.3
15.3
8.0
4.3
1.6
0.7
1.4
2.2
3.8
3.8
5.0
2.4
L
LM
LM
LM
LM
LM
LM
LM
LM
LM
LM
LM
LM
UM
UM
UM
UM
UM
UM
H
H
H
H
H
10.3
41.1
10.3
36.0
21.4
40.7
5.6
14.7
15.8
16.2
35.1
8.4
23.9
24.1
65.0
17.0
52.3
44.5
21.6
23.8
33.8
39.8
33.4
33.1
(9.3 - 11.4)
(34.4 - 47.8)
(8.6 - 12.0)
(32.9 - 39.1)
(18.4 - 24.4)
(38.2 - 43.2)
(4.0 - 7.3)
(13.0 - 16.4)
(-4.4 - 36.1)
(13.9 - 18.5)
(30.2 - 39.9)
(7.4 - 9.3)
(15.8 - 32.1)
(21.7 - 26.5)
(60.5 - 69.5)
(14.6 - 19.3)
(47.1 - 57.6)
(39.4 - 49.7)
(19.3 - 23.9)
(-2.4 - 50.0)
(3.5 - 64.1)
(37.5 - 42.0)
(3.0 - 63.8)
(30.7 - 35.5)
12.1 (6.6 - 17.6)
48.2 (25.2 - 71.0)
12.1 (6.3 - 17.8)
42.2 (23.1 - 61.2)
25.1 (13.3 - 36.7)
47.7 (26.3 - 68.9)
6.6 (3.0 - 10.2)
17.2 (9.3 - 25.0)
18.5 (-13.6 - 50.7)
19.0 (10.1 - 27.9)
41.1 (21.9 - 60.2)
9.8 (5.3 - 14.3)
28.0 (11.8 - 44.2)
28.2 (15.3 - 41.0)
76.2 (41.9 - 110.2)
19.9 (10.6 - 29.1)
61.3 (33.3 - 89.1)
52.1 (28.1 - 76.0)
25.3 (13.8 - 36.9)
27.9 (-13.6 - 69.3)
39.6 (-8.6 - 87.8)
46.6 (25.7 - 67.3)
39.1 (-10.6 - 88.7)
38.8 (21.3 - 56.1)
5.7
19.5
5.7
17.5
11.2
19.4
3.2
8.0
8.5
8.7
17.2
4.7
12.4
12.5
27.7
9.1
23.6
20.8
11.3
12.3
16.6
19.0
16.5
16.3
(9.3 - 18.0)
(3.6 - 7.7)
(1.5 - 3.3)
(3.2 - 6.8)
(4.8 - 10.0)
(8.0 - 17.0)
(8.4 - 16.6)
(5.2 - 24.9)
(5.3 - 11.0)
(3.8 - 7.9)
1.7 (0.5 - 2.9)
(13.3 - 25.8) 6.6 (2.0 - 12.0)
(3.8 - 8.0)
1.7 (0.5 - 3.0)
(12.2 - 23.1) 5.8 (1.7 - 10.3)
(7.5 - 15.2) 3.5 (1.0 - 6.2)
(13.5 - 25.2) 6.6 (2.0 - 11.7)
(1.9 - 4.8)
0.9 (0.3 - 1.7)
(5.3 - 10.9) 2.4 (0.7 - 4.2)
(-0.9 - 17.3) 2.6 (-1.1 - 7.1)
(5.8 - 12.1) 2.6 (0.8 - 4.7)
(11.8 - 22.7) 5.7 (1.7 - 10.3)
(3.1 - 6.6)
1.4 (0.4 - 2.4)
(7.6 - 17.6) 3.9 (1.0 - 7.2)
(8.5 - 16.7) 3.9 (1.2 - 7.0)
(19.9 - 34.9) 10.5 (3.2 - 18.4)
(6.1 - 12.4) 2.7 (0.8 - 4.9)
(16.6 - 30.2) 8.5 (2.6 - 14.9)
(14.5 - 27.1) 7.2 (2.2 - 12.8)
(7.7 - 15.2) 3.5 (1.1 - 6.2)
(1.1 - 23.1) 3.8 (-0.9 - 10.0)
(4.1 - 27.9) 5.5 (-0.2 - 12.9)
(13.2 - 24.8) 6.4 (2.0 - 11.4)
(4.2 - 27.6) 5.4 (-0.3 - 13.2)
(11.3 - 21.5) 5.3 (1.6 - 9.5)
NOTE: Physical inactivity was defined as insuffient physical activity to meet present recommendations
L = low income. LM = lower-middle income. UM = upper-middle income. H = high income.
a
c
Prevalence (95% confidence interval).
b
Population attributable fraction (95% confidence interval).
Income classification not provided by World Bank (classified according to GDP relative to other countries on World Bank list)
9
(1.3 - 7.7)
(0.5 - 2.9)
(0.2 - 1.2)
(0.4 - 2.5)
(0.7 - 3.9)
(1.1 - 7.0)
(1.2 - 6.9)
(-0.8 - 12.7)
(0.7 - 4.2)
Appendix 5: Search Formula for Correlates/Determinants Section
Search Formula: <A by title > and <B by title/abstract > and <C by title/abstract >
•
A: Physical activity key words
–
•
B: Correlates/determinants key words
–
•
physical activity OR physically active OR physical inactivity OR physically inactive OR exercis*
OR sport* OR walk OR walking OR sedentary OR sitting OR television OR TV OR active
transport* OR commut* OR bicycle OR bicycling OR bike OR biking OR active living
correlate OR correlates OR determinant OR determinants OR attribute OR attributes OR factor
OR factors OR psychological OR psychosocial OR self-efficacy OR social support OR attitude
OR attitudes OR barrier OR barriers OR motivation* OR enjoy* OR walkability OR stage of
change OR transtheoretical OR planned behaviour OR planned behavior OR learning theory OR
built environment OR built environmental OR perceived environment OR perceived environments
OR environmental perception OR environmental perceptions OR physical environment OR
physical environments OR objective environment OR objective environments OR neighbourhood
environment OR neighbourhood environments OR neighborhood environment OR neighborhood
environments OR community environment OR community environments OR residential
environment OR residential environments OR exercise facility OR exercise facilities OR sports
facility OR sports facilities OR physical activity facility OR physical activity facilities OR sports
club OR sports clubs OR park OR parks OR trail OR trails OR open space OR open spaces OR
work environment OR work environments OR working environment OR working environments
OR worksite environment OR worksite environments OR occupational environment OR
occupational environments OR school environment OR school environments OR socio-economic
status OR social class OR ses OR income OR social capital
C: Low to middle income countries key words
–
Angola OR Albania OR Algeria OR Samoa OR Argentina OR Azerbaijan OR Belarus OR Belize
OR Bosnia OR Herzegovina OR Botswana OR Brazil OR Bulgaria OR China OR Colombia OR
Rica OR Cuba OR Dominica OR Dominican OR Ecuador OR Fiji OR Gabon OR Grenada OR
Hungary OR Iran OR Iraq OR Jamaica OR Jordan OR Kazakhstan OR Lebanon OR Libya OR
Macedonia OR Malaysia OR Maldives OR Marshall OR Mauritius OR Mexico OR Montenegro
OR Namibia OR Palau OR Panama OR Peru OR Romania OR Serbia OR Seychelles OR South
Africa OR Lucia OR Vincent OR Grenadines OR Suriname OR Thailand OR Tonga OR Tunisia
OR Turkey OR Turkmenistan OR Tuvalu OR Venezuela OR Armenia OR Bhutan OR Bolivia OR
Cameroon OR Cabo OR Congo OR Côte OR d'Ivoire OR Djibouti OR Egypt OR Salvador OR
Georgia OR Ghana OR Guatemala OR Guyana OR Honduras OR Indonesia OR India OR Kiribati
OR Kosovo OR Kyrgyz OR Lao OR Lesotho OR Mauritania OR Micronesia OR Moldova OR
Mongolia OR Morocco OR Nicaragua OR Nigeria OR Pakistan OR Papua OR Guinea OR
Paraguay OR Philippines OR Samoa OR Tomé OR Senegal OR Solomon OR Sudan OR Lanka
OR Sudan OR Swaziland OR Syrian OR Timor OR Leste OR Ukraine OR Uzbekistan OR
Vanuatu OR Vietnam OR Gaza OR Yemen OR Zambia OR Afghanistan OR Bangladesh OR
Benin OR Burkina OR Burundi OR Cambodia OR Central African OR Chad OR Comoros OR
Congo OR Eritrea OR Ethiopia OR Gambia OR Guinea OR Guinea OR Bissau OR Haiti OR
10
Kenya OR Korea OR Liberia OR Madagascar OR Malawi OR Mali OR Mozambique OR
Myanmar OR Nepal OR Niger OR Rwanda OR Sierra OR Leone OR Somalia OR Tajikistan OR
Tanzania OR Togo OR Uganda OR Zimbabwe OR Angolan OR Angolese OR Albanian OR
Skipetar OR Algerian OR Algerine OR American Samoan OR Argentine OR Argentinian OR
Argentino OR Azerbaijani OR Belarusian OR Belarussian OR Belizean OR Bosnian,
Herzegovinian OR Botswanian OR Motswana OR Batswana OR Brazilian OR Bulgar OR
Bulgarian OR Chinese OR Colombian OR Costa Rican OR Cuban OR Dominican OR Dominican
OR Ecuadorian OR Fijian OR Gabonese OR Grenadian OR Hungarian OR Iranian OR Persian
OR Irani OR Iraqi OR Jamaican OR Jordanian OR Kazakhstani OR Lebanese OR Libyan OR
Macedonian OR Malaysian OR Maldivian OR Marshallese OR Mauritian OR Mexican OR
Montenegrin OR Namibian OR Palauan OR Panaman OR Panamanian OR Peruvian OR
Romanian OR Serb OR Serbian OR Seychellois OR South African OR Saint Lucian OR
Vincentian OR Saint Vincentian OR Surinamer OR Surinamese OR Thai OR Tongan OR
Tunisian OR Turkish OR Turk OR Turkmen OR Tuvaluan OR Venezuelan OR Armenian OR
Bhutanese OR Bolivian OR Cameroonian OR Cabo Verdean OR Congolese OR Ivorian OR
Djiboutian OR Egyptian OR Salvadoran OR Georgian OR Ghanaian OR Guatemalan OR
Guyanese OR Honduran OR Indonesian OR Indian OR I-Kiribati OR Kosovar OR Kyrgyz OR
Kyrgyzstani OR Lao OR Laotian OR Mosotho OR Basotho OR Mauritanian OR Micronesian OR
Moldovan OR Mongolian OR Moroccan OR Nicaraguan OR Nigerian OR Pakistani OR Papua
New Guinean OR Paraguayan OR Filipino OR Samoan OR Sao Tomean OR Senegalese OR
Solomon Islander OR South Sudanese OR Sri Lankan OR Sudanese OR Swazi OR Syrian OR
Timorese OR Ukrainian OR Uzbekistani OR Ni-Vanuatu OR Vietnamese OR Yemeni OR
Zambian OR Afghan OR Bangladeshi OR Beninese OR Burkinabe OR Burundian OR Cambodian
OR Central African OR Chadian OR Comoran OR Congolese OR Eritrean OR Ethiopian OR
Gambian OR Guinean OR Bissau-Guinean OR Haitian OR Kenyan OR North Korean OR
Liberian OR Malagasy OR Malawian OR Malian OR Mozambican OR Burmese OR Nepali OR
Nigerien OR Rwandan OR Sierra Leonean OR Somali OR Tajikistani OR Tanzanian OR
Togolese OR Ugandan OR Zimbabwean
Appendix 6: Inclusion Criteria for Correlate/Determinants Section
1.
Publication after January of 1999 to March of 2015
2.
Publication from LMICs. Studies for which the sample include LMICs but not have been analyzed country
specific results were not included. Classification of income level of countries was based on the World Bank
classification
3.
Studies which main focus was on physical activity (including sedentary behavior)
4.
Physical activity was supposed to be outcome, dependent variables. We did not include paper which
examined correlates/determinants of physical fitness.
5.
Correlates/determinants means sociodemographic, biological, psychological, behavioral, social, and
environmental factors those were examined as independent variables We did not include studies which
examined the association of PA as independent variables with obesity and cardiovascular risk factors as
dependent variables.
11
6.
Intervention studies which analyzed intervention effects on mediators and the relation of mediator change
to PA change.
7.
We did not include qualitative research.
8.
No language limitation (currently)
9.
All age groups (children:5-12, adolescents:12-18, adults:18-64, older adults:65-)
10. Publications which examined healthy people, we did not include patients. We included papers using obese
population.
Appendix 7: Flow of Systematic Search for Correlates/Determinants Section
1383 articles identified through systematic search
937 articles excluded through title screening
446 abstracts for screening
230 articles excluded through abstract screening
216 of text articles assessed for eligibility
18 of full text articles excluded,
not correlates/determinants studies
not low to middle income countries
198 text for reading
Appendix 8: Criteria for Judgment of Directions of Association of Correlates/Determinants
with Physical Activity
1.
Categories are ++, repeatedly documented positive association with physical activity; +, weak or mixed
evidence of positive association with physical activity; 00, repeatedly documented lack of association with
physical activity; 0, weak or mixed evidence of no association with physical activity; --, repeatedly
documented negative association with physical activity; -, weak or mixed evidence of negative association
with physical activity.
2.
If the dominant categories occupy two thirds or more, judgements are “++”, ”00”, or ”--”.
3.
If the dominant categories occupy less than two thirds, judgements are “+”, ”0”, or ”-”.
12
4.
If there are two first categories (same in scores), judgement will be conservative, that is “0”.
5.
Blank spaces indicate not enough data available (less than 5 papers examined the correlates/determinants).
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15
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18
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19
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109. Suzuki CS, Moraes SA, Freitas IC. Physical activity and correlates among adults living in Ribeirao Preto,
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110. Siti Affira K, Mohd Nasir MT, Hazizi AS, Kandiah M. Socio-demographic and psychosocial factors associated
with physical activity of working woman in Petaling Jaya, Malaysia. Malaysian journal of nutrition 2011;
17(3): 315-24.
111. Silva SG, Silva MC, Nahas MV, Viana SL. [Variables associated with leisure-time physical inactivity and main
barriers to exercise among industrial workers in Southern Brazil]. Cadernos de saude publica 2011; 27(2): 24959.
112. Silva KS, Vasques DG, Martins Cde O, Williams LA, Lopes AS. Active commuting: prevalence, barriers, and
associated variables. Journal of physical activity & health 2011; 8(6): 750-7.
113. Silva KS, Nahas MV, Borgatto AF, Oliveira ES, Del Duca GF, Lopes AS. Factors associated with active
commuting to school and to work among Brazilian adolescents. Journal of physical activity & health 2011;
8(7): 926-33.
114. Siegel SR, Malina RM, Reyes ME, Barahona EE, Cumming SP. Correlates of physical activity and inactivity in
urban Mexican youth. American journal of human biology : the official journal of the Human Biology Council
2011; 23(5): 686-92.
115. Samir N, Mahmud S, Khuwaja AK. Prevalence of physical inactivity and barriers to physical activity among
obese attendants at a community health-care center in Karachi, Pakistan. BMC research notes 2011; 4: 174.
116. Sa Silva SP, Sandre-Pereira G, Salles-Costa R. [Socio-demographic factors and leisure-time physical activity
among men and women of Duque de Caxias / RJ]. Ciencia & saude coletiva 2011; 16(11): 4491-501.
117. Ozmert EN, Ince T, Pektas A, Ozdemir R, Uckardes Y. Behavioral correlates of television viewing in young
adolescents in Turkey. Indian pediatrics 2011; 48(3): 229-31.
118. Oyeyemi AL, Adegoke BO, Oyeyemi AY, Sallis JF. Perceived environmental correlates of physical activity and
walking in African young adults. American journal of health promotion : AJHP 2011; 25(5): e10-9.
20
119. Oliveira AJ, Lopes CS, de Leon AC, et al. Social support and leisure-time physical activity: longitudinal
evidence from the Brazilian Pro-Saude cohort study. The international journal of behavioral nutrition and
physical activity 2011; 8: 77.
120. Najdi A, El Achhab Y, Nejjari C, Norat T, Zidouh A, El Rhazi K. Correlates of physical activity in Morocco.
Preventive medicine 2011; 52(5): 355-7.
121. Mushtaq MU, Gull S, Mushtaq K, Shahid U, Shad MA, Akram J. Dietary behaviors, physical activity and
sedentary lifestyle associated with overweight and obesity, and their socio-demographic correlates, among
Pakistani primary school children. The international journal of behavioral nutrition and physical activity 2011;
8: 130.
122. Momenan AA, Delshad M, Mirmiran P, Ghanbarian A, Azizi F. Leisure Time Physical Activity and Its
Determinants among Adults in Tehran: Tehran Lipid and Glucose Study. International journal of preventive
medicine 2011; 2(4): 243-51.
123. Hino AA, Reis RS, Sarmiento OL, Parra DC, Brownson RC. The built environment and recreational physical
activity among adults in Curitiba, Brazil. Preventive medicine 2011; 52(6): 419-22.
124. Hino AA, Reis RS, Sarmiento OL, Parra DC, Brownson RC. The built environment and recreational physical
activity among adults in Curitiba, Brazil. Preventive medicine 2011; 52(6): 419-22.
125. Florindo AA, Salvador EP, Reis RS, Guimaraes VV. Perception of the environment and practice of physical
activity by adults in a low socioeconomic area. Revista de saude publica 2011; 45(2): 302-10.
126. El-Gilany AH, Badawi K, El-Khawaga G, Awadalla N. Physical activity profile of students in Mansoura
University, Egypt. Eastern Mediterranean health journal = La revue de sante de la Mediterranee orientale =
al-Majallah al-sihhiyah li-sharq al-mutawassit 2011; 17(8): 694-702.
127. Ding D, Sallis JF, Hovell MF, et al. Physical activity and sedentary behaviours among rural adults in Suixi,
China: a cross-sectional study. The international journal of behavioral nutrition and physical activity 2011; 8:
37.
128. de Farias Junior JC, Lopes Ada S, Mota J, Santos MP, Ribeiro JC, Hallal PC. Perception of the social and built
environment and physical activity among Northeastern Brazil adolescents. Preventive medicine 2011; 52(2):
114-9.
129. Dan SP, Mohd NM, Zalilah MS. Determination of factors associated with physical activity levels among
adolescents attending school in Kuantan, Malaysia. Malaysian journal of nutrition 2011; 17(2): 175-87.
130. Cui Z, Hardy LL, Dibley MJ, Bauman A. Temporal trends and recent correlates in sedentary behaviours in
Chinese children. The international journal of behavioral nutrition and physical activity 2011; 8: 93.
131. Corseuil MW, Schneider IJ, Silva DA, et al. Perception of environmental obstacles to commuting physical
activity in Brazilian elderly. Preventive medicine 2011; 53(4-5): 289-92.
132. Churangsarit S, Chongsuvivatwong V. Spatial and social factors associated with transportation and recreational
physical activity among adults in Hat Yai City, Songkhla,Thailand. Journal of physical activity & health 2011;
8(6): 758-65.
133. Cheah YK. Influence of socio-demographic factors on physical activity participation in a sample of adults in
Penang, Malaysia. Malaysian journal of nutrition 2011; 17(3): 385-91.
21
134. Bauman A, Ma G, Cuevas F, et al. Cross-national comparisons of socioeconomic differences in the prevalence
of leisure-time and occupational physical activity, and active commuting in six Asia-Pacific countries. Journal
of epidemiology and community health 2011; 65(1): 35-43.
135. Bauman A, Ma G, Cuevas F, et al. Cross-national comparisons of socioeconomic differences in the prevalence
of leisure-time and occupational physical activity, and active commuting in six Asia-Pacific countries. Journal
of epidemiology and community health 2011; 65(1): 35-43.
136. Alves JG, Figueiroa JN, Alves LV. Prevalence and predictors of physical inactivity in a slum in Brazil. Journal
of urban health : bulletin of the New York Academy of Medicine 2011; 88(1): 168-75.
137. Adegoke BO, Oyeyemi AL. Physical inactivity in Nigerian young adults: prevalence and socio-demographic
correlates. Journal of physical activity & health 2011; 8(8): 1135-42.
138. Zanchetta LM, Barros MB, Cesar CL, Carandina L, Goldbaum M, Alves MC. [Physical inactivity and
associated factors in adults, Sao Paulo, Brazil]. Revista brasileira de epidemiologia = Brazilian journal of
epidemiology 2010; 13(3): 387-99.
139. Zaitune MP, Barros MB, Cesar CL, Carandina L, Goldbaum M, Alves MC. [Factors associated with global and
leisure-time physical activity in the elderly: a health survey in Sao Paulo (ISA-SP), Brazil]. Cadernos de saude
publica 2010; 26(8): 1606-18.
140. Xu F, Li J, Liang Y, et al. Associations of residential density with adolescents' physical activity in a rapidly
urbanizing area of Mainland China. Journal of urban health : bulletin of the New York Academy of Medicine
2010; 87(1): 44-53.
141. Suzuki CS, de Moraes SA, de Freitas IC. [Sitting-time means and correlates in adults living in Ribeirao PretoSP, Brazil, in 2006: OBEDIARP project]. Revista brasileira de epidemiologia = Brazilian journal of
epidemiology 2010; 13(4): 699-712.
142. Salvador EP, Reis RS, Florindo AA. Practice of walking and its association with perceived environment among
elderly Brazilians living in a region of low socioeconomic level. The international journal of behavioral
nutrition and physical activity 2010; 7: 67.
143. Salehi L, Eftekhar H, Mohammad K, Taghdisi MH, Shojaeizadeh D. Physical activity among a sample of
Iranians aged over 60 years: an application of the transtheoretical model. Archives of Iranian medicine 2010;
13(6): 528-36.
144. Peters TM, Moore SC, Xiang YB, et al. Accelerometer-measured physical activity in Chinese adults. American
journal of preventive medicine 2010; 38(6): 583-91.
145. Peltzer K. Leisure time physical activity and sedentary behavior and substance use among in-school adolescents
in eight African countries. International journal of behavioral medicine 2010; 17(4): 271-8.
146. Ortiz-Hernandez L, Ramos-Ibanez N. Sociodemographic factors associated with physical activity in Mexican
adults. Public health nutrition 2010; 13(7): 1131-8.
147. Oliveira TC, Silva AA, Santos Cde J, Silva JS, Conceicao SI. Physical activity and sedentary lifestyle among
children from private and public schools in Northern Brazil. Revista de saude publica 2010; 44(6): 996-1004.
148. Morris C, Bourne PA, Eldemire-Shearer D, McGrowder DA. Social determinants of physical exercise in older
men in Jamaica. North American journal of medical sciences 2010; 2(2): 87-96.
22
149. Lopes JA, Longo GZ, Peres KG, Boing AF, de Arruda MP. [Factors associated with insufficient physical
activity: a population-based study in southern Brazil]. Revista brasileira de epidemiologia = Brazilian journal
of epidemiology 2010; 13(4): 689-98.
150. Huang SJ, Hung WC, Sharpe PA, Wai JP. Neighborhood environment and physical activity among urban and
rural schoolchildren in Taiwan. Health & place 2010; 16(3): 470-6.
151. Herazo-Beltran Y, Dominguez-Anaya R. [Perception of the environment and physical activity levels in adults
from a neighbourhood in Cartagena]. Revista de salud publica (Bogota, Colombia) 2010; 12(5): 744-53.
152. Hallal PC, Reis RS, Parra DC, Hoehner C, Brownson RC, Simoes EJ. Association between perceived
environmental attributes and physical activity among adults in Recife, Brazil. Journal of physical activity &
health 2010; 7 Suppl 2: S213-22.
153. Gomez LF, Sarmiento OL, Parra DC, et al. Characteristics of the built environment associated with leisure-time
physical activity among adults in Bogota, Colombia: a multilevel study. Journal of physical activity & health
2010; 7 Suppl 2: S196-203.
154. Ferreira MT, Matsudo SM, Ribeiro MC, Ramos LR. Health-related factors correlate with behavior trends in
physical activity level in old age: longitudinal results from a population in Sao Paulo, Brazil. BMC public health
2010; 10: 690.
155. Fermino RC, Rech CR, Hino AA, Rodriguez Anez CR, Reis RS. Physical activity and associated factors in
high-school adolescents in Southern Brazil. Revista de saude publica 2010; 44(6): 986-95.
156. Dumith SC, Hallal PC, Menezes AM, Araujo CL. Sedentary behavior in adolescents: the 11-year follow-up of
the 1993 Pelotas (Brazil) birth cohort study. Cadernos de saude publica 2010; 26(10): 1928-36.
157. Dumith SC, Domingues MR, Gigante DP, Hallal PC, Menezes AM, Kohl HW. Prevalence and correlates of
physical activity among adolescents from Southern Brazil. Revista de saude publica 2010; 44(3): 457-67.
158. Bicalho PG, Hallal PC, Gazzinelli A, Knuth AG, Velasquez-Melendez G. Adult physical activity levels and
associated factors in rural communities of Minas Gerais State, Brazil. Revista de saude publica 2010; 44(5):
884-93.
159. Amorim TC, Azevedo MR, Hallal PC. Physical activity levels according to physical and social environmental
factors in a sample of adults living in South Brazil. Journal of physical activity & health 2010; 7 Suppl 2:
S204-12.
160. Alves JG, Siqueira FV, Figueiroa JN, et al. [Physical inactivity among adults and elderly living in areas covered
by primary healthcare units with and without the Family Health Program in Pernambuco State, Brazil].
Cadernos de saude publica 2010; 26(3): 543-56.
161. Adeniyi AF, Chedi H. Levels and predictors of physical activity in a sample of pre-retirement and retired civil
servants in Nigeria. East African journal of public health 2010; 7(2): 140-3.
162. Trang NH, Hong TK, Dibley MJ, Sibbritt DW. Factors associated with physical inactivity in adolescents in Ho
Chi Minh City, Vietnam. Medicine and science in sports and exercise 2009; 41(7): 1374-83.
163. Salvador EP, Florindo AA, Reis RS, Costa EF. Perception of the environment and leisure-time physical activity
in the elderly. Revista de saude publica 2009; 43(6): 972-80.
23
164. Reis RS, Hino AA, Florindo AA, Anez CR, Domingues MR. Association between physical activity in parks and
perceived environment: a study with adolescents. Journal of physical activity & health 2009; 6(4): 503-9.
165. Ng N, Hakimi M, Van Minh H, et al. Prevalence of physical inactivity in nine rural INDEPTH Health and
Demographic Surveillance Systems in five Asian countries. Global health action 2009; 2.
166. Martins TG, Assis MA, Nahas MV, Gauche H, Moura EC. Leisure-time physical inactivity in adults and factors
associated. Revista de saude publica 2009; 43(5): 814-24.
167. Florindo AA, Guimaraes VV, Cesar CL, Barros MB, Alves MC, Goldbaum M. Epidemiology of leisure,
transportation, occupational, and household physical activity: prevalence and associated factors. Journal of
physical activity & health 2009; 6(5): 625-32.
168. da Silva KS, Nahas MV, Peres KG, Lopes Ada S. [Factors associated with physical activity, sedentary behavior,
and participation in physical education among high school students in Santa Catarina State, Brazil]. Cadernos
de saude publica 2009; 25(10): 2187-200.
169. Ceschini FL, Andrade DR, Oliveira LC, Araujo Junior JF, Matsudo VK. Prevalence of physical inactivity and
associated factors among high school students from state's public schools. Jornal de pediatria 2009; 85(4): 3016.
170. Al-Tannir M, Kobrosly S, Itani T, El-Rajab M, Tannir S. Prevalence of physical activity among Lebanese
adults: a cross-sectional study. Journal of physical activity & health 2009; 6(3): 315-20.
171. Trinh OT, Nguyen ND, Dibley MJ, Phongsavan P, Bauman AE. The prevalence and correlates of physical
inactivity among adults in Ho Chi Minh City. BMC public health 2008; 8: 204.
172. Siqueira FV, Facchini LA, Piccini RX, et al. [Physical activity in young adults and the elderly in areas covered
by primary health care units in municipalities in the South and Northeast of Brazil]. Cadernos de saude publica
2008; 24(1): 39-54.
173. Savio KE, Costa TH, Schmitz Bde A, Silva EF. [Sex, income and level of education associated with physical
activity level among workers]. Revista de saude publica 2008; 42(3): 457-63.
174. Piko BF, Keresztes N. Sociodemographic and socioeconomic variations in leisure time physical activity in a
sample of Hungarian youth. International journal of public health 2008; 53(6): 306-10.
175. Laosupap K, Sota C, Laopaiboon M. Factors affecting physical activity of rural Thai midlife women. Journal of
the Medical Association of Thailand = Chotmaihet thangphaet 2008; 91(8): 1269-75.
176. Bastos JP, Araujo CL, Hallal PC. Prevalence of insufficient physical activity and associated factors in Brazilian
adolescents. Journal of physical activity & health 2008; 5(6): 777-94.
177. Azevedo MR, Horta BL, Gigante DP, Victora CG, Barros FC. [Factors associated to leisure-time sedentary
lifestyle in adults of 1982 birth cohort, Pelotas, Southern Brazil]. Revista de saude publica 2008; 42 Suppl 2:
70-7.
178. Zaitune MP, Barros MB, Cesar CL, Carandina L, Goldbaum M. [Variables associated with sedentary leisure
time in the elderly in Campinas, Sao Paulo State, Brazil]. Cadernos de saude publica 2007; 23(6): 1329-38.
179. Reichert FF, Barros AJ, Domingues MR, Hallal PC. The role of perceived personal barriers to engagement in
leisure-time physical activity. American journal of public health 2007; 97(3): 515-9.
24
180. Lee SA, Xu WH, Zheng W, et al. Physical activity patterns and their correlates among Chinese men in
Shanghai. Medicine and science in sports and exercise 2007; 39(10): 1700-7.
181. Jurj AL, Wen W, Gao YT, et al. Patterns and correlates of physical activity: a cross-sectional study in urban
Chinese women. BMC public health 2007; 7: 213.
182. Baretta E, Baretta M, Peres KG. [Physical activity and associated factors among adults in Joacaba, Santa
Catarina, Brazil]. Cadernos de saude publica 2007; 23(7): 1595-602.
183. Ammouri AA, Neuberger G, Nashwan AJ, Al-Haj AM. Determinants of self-reported physical activity among
Jordanian adults. Journal of nursing scholarship : an official publication of Sigma Theta Tau International
Honor Society of Nursing / Sigma Theta Tau 2007; 39(4): 342-8.
184. Shi Z, Lien N, Kumar BN, Holmboe-Ottesen G. Physical activity and associated socio-demographic factors
among school adolescents in Jiangsu Province, China. Preventive medicine 2006; 43(3): 218-21.
185. Li M, Dibley MJ, Sibbritt D, Yan H. Factors associated with adolescents' physical inactivity in Xi'an City,
China. Medicine and science in sports and exercise 2006; 38(12): 2075-85.
186. Hallal PC, Wells JC, Reichert FF, Anselmi L, Victora CG. Early determinants of physical activity in
adolescence: prospective birth cohort study. BMJ (Clinical research ed) 2006; 332(7548): 1002-7.
187. Bracco MM, Colugnati FA, Pratt M, Taddei JA. Multivariate hierarchical model for physical inactivity among
public school children. Jornal de pediatria 2006; 82(4): 302-7.
188. Pitanga FJ, Lessa I. [Prevalence and variables associated with leisure-time sedentary lifestyle in adults].
Cadernos de saude publica 2005; 21(3): 870-7.
189. Masson CR, Dias-da-Costa JS, Olinto MT, et al. [Prevalence of physical inactivity in adult women in Sao
Leopoldo, Rio Grande do Sul, Brazil]. Cadernos de saude publica 2005; 21(6): 1685-95.
190. Masson CR, Dias-da-Costa JS, Olinto MT, et al. [Prevalence of physical inactivity in adult women in Sao
Leopoldo, Rio Grande do Sul, Brazil]. Cadernos de saude publica 2005; 21(6): 1685-95.
191. Hallal PC, Azevedo MR, Reichert FF, Siqueira FV, Araujo CL, Victora CG. Who, when, and how much?
Epidemiology of walking in a middle-income country. American journal of preventive medicine 2005; 28(2):
156-61.
192. Gomez LF, Duperly J, Lucumi DI, Gamez R, Venegas AS. [Physical activity levels in adults living in Bogota
(Colombia): prevalence and associated factors]. Gaceta sanitaria / SESPAS 2005; 19(3): 206-13.
193. Shapo L, Pomerleau J, McKee M. Physical inactivity in a country in transition: a population-based survey in
Tirana City, Albania. Scandinavian journal of public health 2004; 32(1): 60-7.
194. Gomez LF, Mateus JC, Cabrera G. Leisure-time physical activity among women in a neighbourhood in Bogota,
Colombia: prevalence and socio-demographic correlates. Cadernos de saude publica 2004; 20(4): 1103-9.
195. Seclen-Palacin JA, Jacoby ER. [Sociodemographic and environmental factors associated with sports physical
activity in the urban population of Peru]. Revista panamericana de salud publica = Pan American journal of
public health 2003; 14(4): 255-64.
25
196. Hernandez B, de Haene J, Barquera S, et al. [Factors associated with physical activity among Mexican women
of childbearing age]. Revista panamericana de salud publica = Pan American journal of public health 2003;
14(4): 235-45.
197. Monge-Rojas R, Nunez HP, Garita C, Chen-Mok M. Psychosocial aspects of Costa Rican adolescents' eating
and physical activity patterns. The Journal of adolescent health : official publication of the Society for
Adolescent Medicine 2002; 31(2): 212-9.
26
Appendix 10: Characteristics of Articles on Physical Activity Correlates/Determinants from 1999 to 2015 February
Total (1999-2015 Feb)
No
%
Number of publications
Number of publications per year
Number of countries*
1999-2011
No
%
2012-2015 Feb
No
197
93
104
12·2
7·2
32·8
31
22
23
%
Country of publication
·
Brazil(102)
51·8
Brazil(47)
50·5
Brazil(55)
52·9
China(17)
8·6
China(8)
8·6
China(9)
8·7
Malaysia(10)
5·1
Colombia(4)
4·3
Malaysia(7)
6·7
Iran(7)
3·6
Mexico(4)
4·3
Iran(5)
4·8
Colombia(6)
3·0
Malaysia(3)
3·2
Nigeria(3)
2·9
Mexico(6)
3·0
Colombia(2)
1·9
3·0
Nigeria(3)
Vietnam(3)
3·2
Nigeria(6)
3·2
India(2)
1·9
Vietnam(5)
2·5
Iran(2)
2·2
Mexico(2)
1·9
Thailand(4)
2·0
Pakistan(2)
2·2
Nepal(2)
1·9
Costa Rica(2)
1·0
Thailand(2)
2·2
Thailand(2)
1·9
Egypt(2)
1·0
Albania(1)
1·1
Vietnam(2)
1·9
Ghana(2)
1·0
Chinese Taipei(1)
1·1
South-Africa(2)
1·9
India(2)
1·0
Costa Rica(1)
1·1
Argentina(1)
1·0
Lebanon(2)
1·0
Egypt(1)
1·1
Costa Rica(1)
1·0
Nepal(2)
1·0
Ghana(1)
1·1
Egypt(1)
1·0
Pakistan(2)
1·0
Hungary(1)
1·1
Ghana(1)
1·0
Peru(2)
1·0
Jamaica(1)
1·1
Kenya(1)
1·0
South Africa(2)
1·0
Jordan(1)
1·1
Lebanon(1)
1·0
Albania(1)
0·5
Lebanon(1)
1·1
Libya(1)
1·0
Argentina(1)
0·5
Morocco(1)
1·1
Mozambique(1)
1·0
Chinese Taipei(1)
0·5
Peru(1)
1·1
Peru(1)
1·0
Hungary(1)
0·5
Turkey(1)
1·1
Sri Lanka(1)
1·0
Jamaica(1)
0·5
Collaborative
studies(3)
3·2
Ukraine(1)
1·0
Jordan(1)
0·5
Kenya(1)
0·5
27
Libya(1)
0·5
Morocco(1)
0·5
Mozambique(1)
0·5
Sri Lanka(1)
0·5
Turkey(1)
0·5
Ukraine(1)
0·5
Collaborative
studies(3)
1·5
Upper middle income
168
85·3
79
84·9
89
85·6
Lower middle income
22
11·2
11
11·8
11
10·6
Low income
4
2·0
0
0·0
4
3·8
Collaborative studies
3
1·5
3
3·2
-
-
Income level of countries**
Age group
Children/Adolescents
73
37·1
29
31·2
44
42·3
124
62·9
64
68·8
60
57·7
Male
4
2·0
2
2·2
2
1·9
Female
9
4·6
6
6·5
3
2·9
184
93·4
85
91·4
99
95·2
26
13·2
8
8·6
18
17·3
Adults/Elderly
Gender
Both
Population （multiple choice）
National
Local governmental
117
59·4
57
61·3
60
57·7
Community
13
6·6
8
8·6
5
4·8
Worksite
10
5·1
5
5·4
5
4·8
School
50
25·4
16
17·2
34
32·7
Others
5
2·5
3
3·2
2
1·9
148
75·1
74
79·6
74
71·2
49
24·9
19
20·4
30
28·8
Sampling strategy
Random
Nonrandom/Unclear
28
Response rate
Available
Not available/Unclear
117
59·4
61
65·6
56
53·8
80
40·6
32
34·4
48
46·2
Study design
Cross-sectional
189
95·9
91
97·8
98
94·2
Longitudinal
8
4·1
2
2·2
6
5·8
Intervention
0
0·0
0
0·0
0
0·0
PA （multiple choice）
Total PA
111
56·3
51
54·8
60
57·7
Leisure PA/Exercise
79
40·1
37
39·8
42
40·4
Work-related PA
12
6·1
5
5·4
7
6·7
Transport PA (walking/cycling)
45
22·8
19
20·4
26
25·0
Domestic/household PA
11
5·6
5
5·4
6
5·8
Others (e·g sedentray)
32
16·2
18
19·4
14
13·5
Self-report/Interview
PA measure
183
92·9
90
96·8
93
89·4
Objective method
8
4·1
1
1·1
7
6·7
Both
5
2·5
2
2·2
3
2·9
129
65·5
55
59·1
74
71·2
68
34·5
38
40·9
30
28·8
Dem & Biol
160
81·2
77
82·8
83
79·8
Psych & cog
55
27·9
23
24·7
32
30·8
Behav
67
34·0
36
38·7
31
29·8
Social
40
20·3
15
16·1
25
24·0
Enviorn
87
44·2
39
41·9
48
46·2
Reliability and/or validity of PA measure
Examined/cited
Unexamined and Original
Correlates/determinants （multiple choice）
Reliability and/or validity of correlates measure
29
Examined/cited
76
38·6
33
35·5
43
41·3
Unexamined and Original
121
61·4
60
64·5
61
58·7
Multiple variate
187
94·9
91
97·8
96
92·3
Univariate only
10
5·1
2
2·2
8
7·7
Analyses
*: Countries which conducted only collaborative studies were not included
**: Classification of income level of countries was based on the World Bank classification
30
Appendix 11: Summary of Physical Activity Intervention Studies Reviewed
Summary of Studies Included by Intervention Domain/Strategy
Campaigns and Informational
Behavioral and Social
Policy and
Approaches/Community-wide
Environmental/Community-wide
Campaigns
Policies and Planning
5
9
1
Iran, China, South Africa, India,
Vanatu, India, South Africa, Chile,
Colombia
Indonesia, Vietnam
Brazil, China, Pakistan
80 (4/5)
56 (5/9)
100 (1/1)
Number of studies included
Countries represented
% (#) of studies with some
evidence
Characteristics of Studies Included by Intervention Domain/Strategy
Stud
Study
Intervention
Sample
Author
Study
y
Country strategy
population/setting
Effect
size
(year)
period desi
description
measure
gn
Campaigns and Informational Approaches/Community-wide Campaigns
Rabiei
2000–
QE1
Iran
Education –
Adult residents of 3
Baseline
∆ LTPA
(2010)51
2006
public media and communities in
N=6000
and
campaigns for
central Iran
(Interventi Transport
entire population (intervention areas:
on/
ation PA
and specific
1 urban, 1 rural;
reference
target groups;
reference area: 1
n not
Environment –
urban/rural)
reported)
urban
environment
modifications to
reduce personal
vehicle use and
promote active
transportation
(e.g., cycling)
Policy – adding
exercise time in
the afternoon
shift of schools
Lv (2014)52
2008–
2011
QE1
China
Community
mobilization,
structural
change, health
education, social
marketing
adjacent districts of
Hangzhou, China (2
intervention; 1
reference area)
Baseline
I= 1016
R= 1000
∆ reported
total PA
(walking,
moderate
PA, and
vigorous
PA)
Krishnan
(2011)55
2003–
2008
CS3
India and
Indonesi
a
Media;
environmental
change; health
messages
selected sites: 1 in
India and 1in
Indonesia
Baseline
India N =
5143
Indonesia
N = 1806
Work,
leisuretime, and
transporta
tion PA
31
Results
Value used in
summary
Followup time
∆ LTPA (METmin/d), 2001 vs.
2000
Women
I = +13·4*; R =
+6·5*
Men
I = +7·3***; R =
-10·9
2 years
∆ Transportation
PA (METmin/d), 2001 vs.
2000
Women
I = -3·3; R = 30·9*
Men
I = -12·4; R = 46·7*
∆ PA (METmin/wk)
I – sig ∆ in low
levels of PA, no
∆ in R sites
Sig ∆ in mod PA
and vig PA in I
and decrease in
R sites
Total PA net ∆ of
+5% in I sites vs.
R sites
∆ Inactivity (%)
India
Men: -3·0;
Women:
+18·3***
Indonesia
Men: -3·9;
Women: 19·2***
2 years
3 years
QE1
Viet
Nam
Media; health
messages;
community
support
Adult residents of
two rural
communes (1
intervention; 1
reference)
I = 2,298
R = 2352
Work,
leisuretime, and
transporta
tion PA
∆ Physical
inactivity (%)
I vs. R +6·1 net
effect – (I site
underwent rapid
urban change vs.
R community)
3 years
Behavioral and Social
Siefken
2011
(2015)58
CS3
Vanatu
Health
messages;
walking
campaign
Female civil
servants at one
worksite
N = 207
Pedomete
r
measured
steps
over 12
weeks
12 weeks
Kain (2014)
RT4
Chile
Classroom
nutrition
education;
increase physical
education (PE)
class time;
increase time in
moderate
activity during
PE classes
6-8 y/o low-income
students attending
primary school in
Santiago – exposed
or not exposed to
intervention
I = 651
C = 823
∆ PE
class
time
Net increase in
steps – pre-post =
+26% among all
subjects
Net steps low risk
(n = 101) +82%;
high risk (n =
24), 228%
∆ PE class time
(min)
I – +9·1
C – +3·4
Nguyen
(2012)54
2006–
2009
2011–
2012
Balagopal
(2012)56
2007–
2008
CS3
India
Community
Health Worker
(CHW)delivered health
education; social
support
Adult residents of
selected rural
community; low
and high SES
N = 1638
Skaal (2012)57
2008
QE1
South
Africa
Social support;
media; PA
challenge
Hospital employees
(medical and
nonmedical)
Medical n
= 100;
Nonmedical n
= 100
Vio (2011)61
2006
QE1
Chile
PA classes led
by trained
instructors
3x/week for 6
months
N = 331
GrA = 82
GrB = 80
GrC = 84
GrD = 85
Parra (2010)59
2007
CS3
Brazil
PA classes
Low SES women
residing in selected
community
4 groups – GrAPA; GrB - Diet
only; GrC- PA;
GrD- Control
Community
32
32,974
∆ class
time in
MVPA
as
measured
by
pedomete
r
Work
PA;
househol
d chores;
brisk
walking;
vigorous
or
manual
PA;
transport
ation PA;
leisuretime PA
Selfreport
stage of
physical
activity
based on
Transtheoretica
l Model
(TTM)
PA class
adherenc
e
PA
1 year
∆ Class time in
MVPA (%)
I – -1·1
C – -8·3*
∆ MVPA (%):
+11·6** among
normal fasting
blood glucose
participants;
+14·2** among
glucose
intolerant;
+4·2*** among
T2DM
participants
6 months
TTM mean stages
Pre-test = 2·64
Post-test =
3·74***
6 months
Mean PA class
adherence: GrA:
49·8%; GrC37·5%
6 months
People using
5 years
(Academia da
Cidade - ACP)
in public parks
led by PA
instructors
residents of Recife
who visited both
ACP and Non-ACP
Parks
Mendonca
(2010)60
2008
CS3
Brazil
PA classes
(Academia da
Cidade - ACP)
in public parks
led by PA
instructors
Adult residents of
selected community
Li (2011)62
2009
QE1
China
Classroom-based
PA sessions:
2 daily 10-min
PA sessions in
between class
breaks (Happy10
program)
8-11 y/o students
attending primary
schools in Beijingexposed or not
exposed to
Happy10
School-based PE
program 30 min
sessions, 4x/wk
9-11 y/o school
girls from 4 schools
(2 Intervention; 2
Reference)
Almas
(2013)63
2008
QE1
Pakistan
people
(adults/chi
ldren/yout
h)
observed
during
5589
observatio
n visits to
ACP and
non-ACP
parks
N = 2267
Observed
SOPARC
among
people
visiting
both
ACP and
non-ACP
Parks
ACP parks more
likely to engage
in MVPA (64%
vs. 49%)
Meeting
PA
guideline
s based
on
leisuretime
MVPA
N = 4700
Selfreport PA
– 7 day
recall;
BMI z
scores
Meeting
guidelines was
associated with
having ever heard
about ACP [OR
1·8 (95% CI: 1·4,
2·2)]; having
seen a ACP class
[OR = 1·6 (95%
CI: 1·1, 2·3)];
being a current
ACP participant
[OR = 14·3 (95%
CI: 12·3,16·4)];
and being a past
ACP participant
[OR =4·0 (95%
CI: 1·4, 11·3)]
Mean ∆ in BMI z
scores between I
schools vs. R
schools = -0·15
kg/m2
I = 131
R = 146
Adherenc
e to PE
classes;
Reported
PA
More women
(45% vs. 42%)
and older adults
(14·7% vs. 5·7%)
in ACP vs. nonACP sites
Post 1** and
2*** year = I
maintained BMI
z vs. R schools
I adherence =
80%;
R adherence =
78·5%
4 years
2 years
20 weeks
No net difference
in reported PA
Jemmont
(2014)53
2007–
2010
RT4
South
Africa
Small group
activities, games,
brainstorming,
videos,
discussions
Men reporting
coitus in the
previous 3 months
and living in one of
the selected
neighborhoods
randomized to
condition (PA vs.
33
I = 572
C = 609
Adheren
ce to PA
guidelin
es by
selfreport
% Meeting PA
guidelines
Total PA OR – I
vs. C = 1·28 (1·05,
1·57); PA vig –
1·30 (1·06, 1·57);
PA mod – OR
1·25(1·02, 1·52)
1 year
attention-control)
Policy and Environmental/Community-wide Policies and Planning
Torres
2009
CS3
Colombi
Community
Adult residents of
65
(2013)
a
planning and
selected community
policies –
Ciclovia (public
streets) and
Cicloruta (bike
paths)
Ciclovia N
=1000
Cicloruta
N=1000
Meeting
PA
guidelin
es
Meeting PA
guidelines among
regular
participants vs.
infrequent
participants of
Ciclovia – OR =
1·7 (1·1, 2·4)
Meeting PA
guidelines among
regular users vs.
infrequent users of
Cicloruta – OR =
10·2 (6·1, 16·8)
1
Quasi-experimental – intervention and referent communities/sites
Cohort – pre-intervention and post-intervention measures
3
Cross Sectional – pre – post intervention measures
4
Randomized prospective study – intervention vs. control group
5
Other
*= p<0·0001
**= p<0·01
***= p< 0·05
2
34
N/A
Appendix 12: Exemplary Interventions from Low- and Middle-Income Countries
Author/Year of Publication: Rabiei, et al., 2010 51
Intervention strategy: Community-wide campaign
Country: Iran
Study design: Longitudinal – quasi-experimental
Study Period: 6 years – 2000–2002
A Non-communicable disease (NCD) intervention project was carried out in a major Iranian urban center with a
population of ~1·8M. A comparison urban center of ~ 670,000 served as the referent community. NCD risk factor
assessments (surveys) were carried out at baseline in year 2000, with a survey sample size of 6126 in the
Intervention community and 6293 in the referent community. Subsequent surveys following 1 year of intervention
were 2937 in the intervention community and 2887 in the referent community, with the final year of follow-up
yielding sample sizes of 2364 and 2381 respectively for the intervention and referent sites. The intervention
strategies included educational interventions, environmental, and legislative efforts to promote physical activity as
the primary NCD prevention strategy. This included provider-based assessment and counseling for physical activity
across the lifecycle. These efforts were complemented by neighborhood-based physical activity community groups
and population-specific media targeting with physical activity messages. Environmental interventions including
improved infrastructure for active transport by bicycle and pedestrian activities, increased park space, and increased
physical activity programming in these public spaces was also carried out. The referent community did not have any
of the aforementioned intervention strategies, were isolated from media messaging which targeted the intervention
community, and were only provided with routine medical care and public health services. An assessment of the
physical activity outcomes were carried out using the Baecke Questionnaire for the assessment of leisure –time
physical activity (LTPA). This measure was initially assessed among all respondents to the community surveys at
baseline and then subsequently at the end of year 1 and year 2. Results indicated a 13·4% and 6·5% increase in the
levels of measured LTPA among women in the intervention and referent communities after year 1, respectively.
These differences provided a significant (p<0·0001) net intervention effect favoring the intervention community.
During this same period, LTPA increased among intervention community men by 7.3%, while LTPA levels fell by a
35
-10·87% among the referent community men (p<0·05, net intervention effect). Examining changes in active
transport, the intervention women registered a -3·3% drop in active transport compared to a -30·93% drop among
referent community women (p<0·0001), while intervention men dropped -12·4% and referent men -46·7% in active
transport (p<0·0001). An explanation for these findings can partially be explained by the significant increase in
motorized transport infrastructure placed in both communities during the course of the study period, despite this, the
intervention community demonstrated a lesser impact on active transport compared with the referent community.
Author: Parra, et al., 2010 59
Country: Brazil
Intervention strategy: Social and Behavioral Approaches
Study Design: Cross sectional observations of physical activity in intervention parks and non-intervention parks
Study Period: 2002–2007
Residents of Recife, Brazil were exposed to an Intervention strategy that consisted of physical activity promotion
through physical activity classes at no cost in public parks. The physical activity classes were delivered 3x per week
by qualified physical activity instructors in local “public spaces.” The intervention period started in 2002 with cross
sectional observations being completed by the close of 2007. Children, youth, adults, and older adults were observed
either in parks that offered Academia da Cidade Programs (ACP), the free leader led group physical activity
programs in parks or Non-ACP parks. Physical activity of adults, children, youth, and older adults was measured by
using the System for Observing Play and Recreation in Communities (SOPARC) in 128 targeted areas in 10 park
sites (5 ACP sites, 5 non-ACP sites) to obtain data on the number of users and their physical activity levels and
estimated age. Each area was assessed 4 times a day for 11 days over a 4-week period. Observed physical activity
included moderate-to-vigorous and vigorous physical activity. A total of 32,974 people were observed during 5589
observation visits to the targeted park areas. The investigators found that people using ACP parks were at greater
odds of being observed engaging in moderate-to-vigorous (64% vs 49%) and vigorous (25% vs 10%) physical
activity compared with observations of people frequenting non-ACP parks. More participants in ACP sites
36
compared with participants at non-ACP sites were females (45% vs 42% of park users) and older adults (14·7% vs
5·7%) among park users).
Author: Torres et al., 2013 65
Country: Bogota, Colombia
Intervention: Community planning and policies
Study design: Cross-sectional population-based surveys
Intervention duration: Ciclovia and Cicloruta activities, 2004–2010
Physical activity interventions were implemented starting in 2004 with a community event, “Ciclovia,” a community
– wide physical activity opportunity provided every week (Sunday afternoons) through streets which are closed to
motorized vehicular traffic. Implemented in Bogota, the largest city in Colombia, SA. A second, concurrent
community-wide intervention, ‘Cicloruta’ consists of construction of bicycle infrastructure comprised of dedicated
bikeways which is shielded from motor traffic accompanied by a community-wide informational campaign
promoting active transport and recreation. Two population-based surveys among community respondents were
conducted to assess both exposure to these intervention strategies and the impact of such exposure on physical
activity behaviors. The study population consisted of urban residents of Bogota, Colombia who were adults 18 and
older who either had used the Cicloruta or had been a participant in Civlovia. The sample size consisted of 1000
respondents for each of the surveys. The survey instrument used to assess physical activity was the International
Physical Activity Questionnaire (IPAQ) long version. The outcome of interest was the odds of participants meeting
the national and World Health Organization physical activity guidelines. Among respondents who were regular
participants in the Ciclovia compared to participants who were infrequent participants, the regular participants were
over 1·7 (95% CI, 1·1, 2·4) the odds of meeting physical activity guidelines. Similarly, participants who were
regular users of the Cicloruta compared with infrequent users were 10·2 (95% CI, 6·1, 16·8) times the odds of
meeting physical activity guidelines than their infrequent counterparts.
37

Physical Activity 2016: Progress and Challenges

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