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Worldwide Effect of COVID-19 on Physical Activity: A Descriptive Study

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Geoffrey H. Tison
Geoffrey H. Tison
Geoffrey H. Tison
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Robert Avram at Montreal Heart Institute
Robert Avram
Peter Kuhar
Peter Kuhar
Peter Kuhar
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Sean Abreau at UCSF University of California, San Francisco
Sean Abreau
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OBSERVATION:BRIEF RESEARCH REPORT
Worldwide Effect of COVID-19 on Physical Activity:
A Descriptive Study
Background: On 11 March 2020, the World Health Orga-
nization declared coronavirus disease 2019 (COVID-19) to be
a global pandemic (1). To curb the spread of the disease,
various regional and national governments advocated for so-
cial distancing measures with varying degrees of enforce-
ment, ranging from unenforced recommendations to quaran-
tine and business closures. Physical activity is an important
determinant of health (2) and is likely affected by social dis-
tancing measures. Daily step count, a proxy for physical activ-
ity, has been associated with all-cause mortality (3). Beyond
physical activity, regional step count trends may also provide
a proxy for adherence to social distancing, providing real-time
insights to inform public policy decisions. Because prolonged
social distancing is considered to contain infection, it will be
important to gauge adherence to these measures and their
effect on other aspects of health, such as physical activity.
Objective: To examine worldwide changes in step count
before and after the announcement of COVID-19 as a global
pandemic.
Methods and Findings: In this descriptive study, we used
deidentified, individual-level data from 19 January to 1 June
2020 that were collected from a convenience sample of users
of the free, popular health and wellness smartphone app Ar-
gus (Azumio). Daily step counts were determined using smart-
phone accelerometers and Apple or Android algorithms for
step counting (4). User location was determined by smart-
phone IP address. The COVID-19 pandemic declaration date
used was 11 March 2020. Regional mean steps were calcu-
lated daily, and percentage of change in steps was calculated
daily as a percentage of the regional mean from 19 January to
11 March 2020. Displayed figure regions were selected to
achieve half less-affected and half more-affected regions with
regard to both COVID-19 and social distancing and greater
than 1000 and 700 users at the country and city levels, respec-
tively. This study was exempted by the University of California,
San Francisco Institutional Review Board.
A total of 19 144 639 daily step count measurements
were provided by 455 404 unique users from 187 unique
countries during the study period; 92% of smartphones were
Apple, and 8% were Android. Worldwide, within 10 days of
the pandemic declaration, there was a 5.5% decrease in mean
steps (287 steps), and within 30 days, there was a 27.3% de-
crease in mean steps (1432 steps). There was wide regional
variation in average step count change and in the timing and
rate of that change (Figures 1 and 2). For example, Italy de-
clared a nationwide lockdown on 9 March 2020 and exhibited
a 48.7% maximal decrease, whereas Sweden, to date, has pri-
marily advocated for social distancing and limitations on gath-
erings and showed a 6.9% maximal decrease. Samples from
countries such as Italy and Iran, which had earlier regional
COVID-19 outbreaks, exhibited earlier step count decreases
from their relative baselines. Samples from different countries
varied widely in the number of days after pandemic declara-
tion that a 15% step count decrease was seen: Italy (5 days),
Spain (9 days), France (12 days), India (14 days), the United
States (15 days), the United Kingdom (17 days), Australia (19
days), and Japan (24 days). Step count trends in samples from
U.S. cities exhibited similarities, although there was wide in-
ternational variability (Figure 2).
Discussion: Step counts decreased worldwide in the pe-
riod after COVID-19 was declared a global pandemic. Differ-
This article was published at Annals.org on 29 June 2020.
Figure 1.
Mean daily steps and percentage of change
from step count at baseline, by country.
7000
6000
5000
4000
Mean Daily Steps, n
3000
2000
11 Feb 2020
25 Feb 2020
10 Mar 2020
24 Mar 2020
7 Apr 2020
21 Apr 2020
5 May 2020
19 May 2020
1 Jun 2020
Country
Brazil (n= 3067)
France (n= 4114)
Iran (n= 1302)
Italy (n= 6403)
Japan (n= 4074)
South Korea (n= 1212)
Sweden (n= 2417)
Taiwan (n=2199)
United Kingdom (n= 36 284)
United States (n= 239 543)
Initiation of regional orders
Liftin
g
of re
g
ional orders
10
0
–10
–20
–30
Change From Baseline Steps, %*
–40
–50
11 Feb 2020
25 Feb 2020
10 Mar 2020
24 Mar 2020
7 Apr 2020
21 Apr 2020
5 May 2020
19 May 2020
1 Jun 2020
Top. Mean daily steps, by country. Bottom. Percentage of change in
steps from the prepandemic baseline, by country.
* Prepandemic baseline steps by country were calculated as the mean
daily steps from 19 January to 11 March 2020 for that country. All
values are plotted by region over a rolling 10-d average window for
smoothness. Region sample sizes show total number of users who
contributed data during the study period. Diamonds denote initiation
dates and squares denote lifting dates of regional social distancing
orders, if available. Specific regional orders were assembled from
publicly available sources as accurately as possible. Brazil, South Ko-
rea, Sweden, Taiwan, and the United States: no national orders.
France: stay-at-home orders, only essential businesses open (17
March to 10 May 2020). Iran: lockdown orders, only essential busi-
nesses open (14 March to 20 April 2020). Italy: lockdown orders, only
essential businesses open (9 March to 18 May 2020). Japan: state of
emergency for all prefectures and nonmandatory business closure re-
quest (16 April to 25 May 2020). United Kingdom: ongoing stay-at-
home orders, only essential businesses open (23 March 2020 to
present).
Annals of Internal Medicine LETTERS
© 2020 American College of Physicians 1
ences were seen between regions, likely reflecting regional
variation in COVID-19 timing, regional enforcement, and be-
havior change. Countries that, to date, have had relatively low
COVID-19 infection rates and have therefore not instituted
lockdowns, such as South Korea, Taiwan, and Japan, have still
exhibited decreases in overall step count. Within-region step
count trends likely reflect a combination of changes to physi-
cal activity (for example, walking and exercising) and activities
of daily living (for example, commuting and shopping) due to
social distancing efforts. Assuming no regulatory changes that
affect engaging in physical activity within a region, we suspect
that sustained population-level trends over time may reflect
changes to social distancing adherence (for example, many
regions showed increases from their regional step count nadir
before orders were lifted). Observed variation in step counts
is also likely influenced by socioeconomic inequalities among
regions and disparities in the ability to engage in or access to
recreational physical activity within a region (4).
Figure 2.
Mean daily steps and percentage of change from step count at baseline, by city.
City
Chicago (n= 5470)
Dallas (n= 7617)
Houston (n= 4626)
Los Angeles (n= 6198)
New York (n=10 288)
Philadelphia (n= 1523)
Phoenix (n= 1264)
San Antonio (n= 1250)
San Diego (n= 1468)
San Jose (n= 826)
Initiation of regional orders
Lifting of regional orders
6000
5500
5000
4000
4500
Mean Daily Steps, n
3500
3000
11 Feb 2020
25 Feb 2020
10 Mar 2020
24 Mar 2020
7 Apr 2020
21 Apr 2020
5 May 2020
19 May 2020
1 Jun 2020
A
10
0
–10
–20
Change From Baseline Steps, %*
–30
–40
11 Feb 2020
25 Feb 2020
10 Mar 2020
24 Mar 2020
7 Apr 2020
21 Apr 2020
5 May 2020
19 May 2020
1 Jun 2020
B
8000
7000
6000
4000
5000
Mean Daily Steps, n
3000
2000
11 Feb 2020
25 Feb 2020
10 Mar 2020
24 Mar 2020
7 Apr 2020
21 Apr 2020
5 May 2020
19 May 2020
1 Jun 2020
C
City
Ho Chi Minh City (n= 2312)
London (n= 9510)
New York (n=10 288)
Paris (n= 1708)
Rome (n= 873)
São Paulo (n= 790)
Seoul (n= 814)
Singapore (n= 2137)
Stockholm (n= 1128)
Tokyo (n=2051)
Initiation of regional orders
Lifting of regional orders
10
0
–10
–30
–20
Change From Baseline Steps, %*
–40
–50
11 Feb 2020
25 Feb 2020
10 Mar 2020
24 Mar 2020
7 Apr 2020
21 Apr 2020
5 May 2020
19 May 2020
1 Jun 2020
D
U.S. Cities Worldwide
A. Mean daily steps, by U.S. city. B. Percentage of change in steps from the prepandemic baseline, by U.S. city. C. Mean daily steps in a sample of
cities worldwide. D. Percentage of change in steps from the prepandemic baseline in a sample of cities worldwide.
* Prepandemic baseline steps by city were calculated as the mean daily steps from 19 January to 11 March 2020 for that city. All values are plotted
by region over a rolling 10-d average window for smoothness. Region sample sizes show the total number of users who contributed data during the
study period. Diamonds denote initiation dates and squares denote lifting dates of regional social distancing orders, if available. Specific regional
orders were assembled from publicly available sources as accurately as possible. Chicago: stay-at-home order, only essential businesses open (21
March to 3 June 2020). Dallas: shelter-in-place order, only essential businesses open (24 March to 30 April 2020). Houston: stay-at-home order, only
essential businesses open (24 March to 30 April 2020). Los Angeles: ongoing stay-at-home order, only essential businesses open (19 March 2020
to present). New York City: ongoing shelter-in-place order, only essential businesses open (22 March 2020 to present). Philadelphia: stay-at-home
order, only essential businesses open (23 March to 5 June 2020). Phoenix: stay-at-home order, phased reopening (31 March to 15 May 2020). San
Antonio: stay-at-home order, only essential businesses open (24 March to 30 April 2020). San Diego: ongoing stay-at-home order, only essential
businesses open (19 March 2020 to present). San Jose: ongoing stay-at-home order, only essential businesses open (17 March 2020 to present). Ho
Chi Minh City: nationwide isolation, only essential activities allowed (1 April to 22 April 2020). London: ongoing stay-at-home orders, only essential
businesses open (23 March 2020 to present). New York City: ongoing shelter-in-place order, only essential businesses open (22 March 2020 to
present). Paris: stay-at-home order, only essential businesses open (17 March to 10 May 2020). Rome: lockdown orders, only essential businesses
open (9 March to 17 May 2020). Sao Paulo: ongoing statewide quarantine, only essential businesses open (24 March 2020 to present). Seoul: no
regional orders, citizens asked to remain indoors for 2 weeks starting 29 February 2020. Singapore: stay-at-home order, limits on social gatherings
(7 April to 1 June 2020). Stockholm: no regional orders. Tokyo: state of emergency for Tokyo, nonmandatory business closure request (7 April to
25 May 2020).
LETTERS
2 Annals of Internal Medicine Annals.org
Limitations of this study include sampling bias due to the
reliance on smartphone and app ownership, measurement
error from smartphone-measured step counts, variability in
smartphone carry and use habits, no assessment of activity
intensity, and inability to capture nonstepping exercise (5).
Our data set is a nonrepresentative convenience sample with
a variable number of contributing daily users. It also lacks par-
ticipant characteristics beyond IP address, limiting compari-
sons among regions.
Rapid worldwide step count decreases have been seen
during the COVID-19 pandemic, with regional variability.
Within-region step count trends may reflect social distancing
measures and changes to social distancing adherence; how-
ever, more formal analytic studies are required. The effect of
social distancing measures on overall physical activity, an im-
portant determinant of health, should be considered, particu-
larly if prolonged social distancing is required.
Geoffrey H. Tison, MD, MPH
University of California, San Francisco, and Bakar Computa-
tional Health Sciences Institute
San Francisco, California
Robert Avram, MD, MSc
University of California, San Francisco
San Francisco, California
Peter Kuhar, BS
Azumio
Redwood City, California
Sean Abreau, MSc
Greg M. Marcus, MD, MAS
Mark J. Pletcher, MD, MPH
Jeffrey E. Olgin, MD
University of California, San Francisco
San Francisco, California
Financial Support: Dr. Tison received support from the National In-
stitutes of Health (NHLBI K23HL135274). Azumio provided no financial
support for this study and only provided access to the step count data.
They had no role in the decision to publish the manuscript. Data
analysis and interpretation were done independently from Azumio.
The funders had no role in study design, data collection, and anal-
ysis; preparation of the manuscript; or the decision to publish the
manuscript.
Disclosures: Disclosures can be viewed at www.acponline.org
/authors/icmje/ConflictOfInterestForms.do?msNum=M20-2665.
Reproducible Research Statement: Study protocol and statistical
code: Correspondence about methodological issues or statistical
code should be directed to Dr. Tison (e-mail, geoff.tison@ucsf.edu).
Data set: Data are used under a research license from Azumio. Inqui-
ries for collaboration can be addressed to Dr. Tison (e-mail, geoff.tison
@ucsf.edu).
Corresponding Author: Geoffrey H. Tison, MD, MPH, University of
California, San Francisco, 555 Mission Bay Boulevard, South Box 3120,
San Francisco, CA 94158; e-mail, geoff.tison@ucsf.edu.
doi:10.7326/M20-2665
References
1. World Health Organization. WHO Director-General's opening remarks at
the media briefing on COVID-19—11 March 2020. Accessed at www.who.int
/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media
-briefing-on-covid-19—11-march-2020 on 1 June 2020.
2. Lee IM, Shiroma EJ, Lobelo F, et al; Lancet Physical Activity Series Working
Group. Effect of physical inactivity on major non-communicable diseases
worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;
380:219-29. [PMID: 22818936]
3. Saint-Maurice PF, Troiano RP, Bassett DR Jr, et al. Association of daily step
count and step intensity with mortality among US adults. JAMA. 2020;323:
1151-1160. [PMID: 32207799]
4. Althoff T, Sosic R, Hicks JL, et al. Large-scale physical activity data reveal
worldwide activity inequality. Nature. 2017;547:336-339. [PMID: 28693034]
5. Case MA, Burwick HA, Volpp KG, et al. Accuracy of smartphone applications
and wearable devices for tracking physical activity data. JAMA. 2015;313:
625-6. [PMID: 25668268]
LETTERS
Annals.org Annals of Internal Medicine 3
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Full-text available
  • Sep 2024
Objective To estimate the effect of (a) the COVID-19 pandemic and (b) COVID-19 restriction stringency on daily minutes of device-measured moderate-to-vigorous physical activity (MVPA). Design Physical activity data were collected from the INTerventions, Equity, Research and Action in Cities Team (INTERACT) cohorts in Montreal, Saskatoon and Vancouver before (May 2018 to February 2019, ‘phase 1’) and during the pandemic (October 2020 to February 2021, ‘phase 2’). We estimated the effect of the two exposures by comparing daily MVPA measured (a) before vs during the pandemic (phase 1 vs phase 2) and (b) at different levels of COVID-19 restriction stringency during phase 2. Separate mixed effects negative binomial regression models were used to estimate the association between each exposure and daily MVPA, with and without controlling for confounders. Analyses were conducted on person-days with at least 600 min of wear time. Effect modification by gender, age, income, employment status, education, children in the home and city was assessed via stratification. Setting Montreal (Quebec), Saskatoon (Saskatchewan) and Vancouver (British Columbia), Canada. Main outcome measure Daily minutes of MVPA, as measured using SenseDoc, a research-grade accelerometer device. Results Daily minutes of MVPA were 21% lower in phase 2 (October 2020 to February 2021) compared with phase 1 (May 2018 to February 2019), controlling for gender, age, employment status, household income, education, city, weather and wear time (rate ratio=0.79, 95% CI 0.69, 0.92). This did not appear to be driven by changes in the sample or timing of data collection between phases. The results suggested effect modification by employment, household income and education. Restriction stringency was not associated with daily MVPA between October 2020 and February 2021 (adjusted rate ratio=0.99, 95% CI 0.96, 1.03). Conclusions Between October 2020 and February 2021, daily minutes of MVPA were significantly lower than 2 years prior, but were not associated with daily COVID-19 restriction stringency.
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Sex differences in reduction of trunk muscle mass related to falls and cognitive function during the COVID-19 pandemic in older adults
Article
Full-text available
  • Aug 2024
Aim This study aimed to examine the influence of the COVID‐19 pandemic on physical components and activity, and its relationship to physical performance in older adults. Methods Sixty‐seven participants aged 75 and older (81 ± 2 years, female: 66%), who underwent medical checkups continuously from 2018 to 2022 in one clinic, were enrolled. Muscle mass was assessed by the biometrical impedance analysis method, which was adjusted by height squared. Physical, oral, and cognitive performance data were obtained from Japanese standard questionnaires at medical checkups. Values obtained in 2018 and 2019 were defined as pre‐pandemic, and those in 2021 and 2022 were defined as during the pandemic. Results Body weight, grip strength, and skeletal mass index did not change from 2018 to 2022, but trunk muscle mass index decreased significantly. A difference in the trunk muscle mass index (TMI) was observed between 2022 and 2018/2019 in men; however, a significant difference was found between 2021 and 2022 in women. Compared with the pre‐pandemic period, TMI during the pandemic decreased only in men. The difference in TMI between the pre‐pandemic period and during the pandemic (ΔTMI) was significantly decreased in participants with low physical activity and poor oral performance before the pandemic, and in those with falls and poor cognitive function during the pandemic only in men. Conclusion Reduction of trunk muscle mass was related to falls and poor cognitive function during the COVID‐19 pandemic in older male adults. These data suggest that the difference in response to the COVID‐19 pandemic between men and women necessitates different types of support for older adults. Geriatr Gerontol Int 2024; ••: ••–•• .
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Association of Daily Step Count and Step Intensity With Mortality Among US Adults
Article
  • Mar 2020
Importance It is unclear whether the number of steps per day and the intensity of stepping are associated with lower mortality. Objective Describe the dose-response relationship between step count and intensity and mortality. Design, Setting, and Participants Representative sample of US adults aged at least 40 years in the National Health and Nutrition Examination Survey who wore an accelerometer for up to 7 days ( from 2003-2006). Mortality was ascertained through December 2015. Exposures Accelerometer-measured number of steps per day and 3 step intensity measures (extended bout cadence, peak 30-minute cadence, and peak 1-minute cadence [steps/min]). Accelerometer data were based on measurements obtained during a 7-day period at baseline. Main Outcomes and Measures The primary outcome was all-cause mortality. Secondary outcomes were cardiovascular disease (CVD) and cancer mortality. Hazard ratios (HRs), mortality rates, and 95% CIs were estimated using cubic splines and quartile classifications adjusting for age; sex; race/ethnicity; education; diet; smoking status; body mass index; self-reported health; mobility limitations; and diagnoses of diabetes, stroke, heart disease, heart failure, cancer, chronic bronchitis, and emphysema. Results A total of 4840 participants (mean age, 56.8 years; 2435 [54%] women; 1732 [36%] individuals with obesity) wore accelerometers for a mean of 5.7 days for a mean of 14.4 hours per day. The mean number of steps per day was 9124. There were 1165 deaths over a mean 10.1 years of follow-up, including 406 CVD and 283 cancer deaths. The unadjusted incidence density for all-cause mortality was 76.7 per 1000 person-years (419 deaths) for the 655 individuals who took less than 4000 steps per day; 21.4 per 1000 person-years (488 deaths) for the 1727 individuals who took 4000 to 7999 steps per day; 6.9 per 1000 person-years (176 deaths) for the 1539 individuals who took 8000 to 11 999 steps per day; and 4.8 per 1000 person-years (82 deaths) for the 919 individuals who took at least 12 000 steps per day. Compared with taking 4000 steps per day, taking 8000 steps per day was associated with significantly lower all-cause mortality (HR, 0.49 [95% CI, 0.44-0.55]), as was taking 12 000 steps per day (HR, 0.35 [95% CI, 0.28-0.45]). Unadjusted incidence density for all-cause mortality by peak 30 cadence was 32.9 per 1000 person-years (406 deaths) for the 1080 individuals who took 18.5 to 56.0 steps per minute; 12.6 per 1000 person-years (207 deaths) for the 1153 individuals who took 56.1 to 69.2 steps per minute; 6.8 per 1000 person-years (124 deaths) for the 1074 individuals who took 69.3 to 82.8 steps per minute; and 5.3 per 1000 person-years (108 deaths) for the 1037 individuals who took 82.9 to 149.5 steps per minute. Greater step intensity was not significantly associated with lower mortality after adjustment for total steps per day (eg, highest vs lowest quartile of peak 30 cadence: HR, 0.90 [95% CI, 0.65-1.27]; P value for trend = .34). Conclusions and Relevance Based on a representative sample of US adults, a greater number of daily steps was significantly associated with lower all-cause mortality. There was no significant association between step intensity and mortality after adjusting for total steps per day.
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Large-scale physical activity data reveal worldwide activity inequality
Article
  • Jul 2017
  • NATURE
To be able to curb the global pandemic of physical inactivity and the associated 5.3 million deaths per year, we need to understand the basic principles that govern physical activity. However, there is a lack of large-scale measurements of physical activity patterns across free-living populations worldwide. Here we leverage the wide usage of smartphones with built-in accelerometry to measure physical activity at the global scale. We study a dataset consisting of 68 million days of physical activity for 717,527 people, giving us a window into activity in 111 countries across the globe. We find inequality in how activity is distributed within countries and that this inequality is a better predictor of obesity prevalence in the population than average activity volume. Reduced activity in females contributes to a large portion of the observed activity inequality. Aspects of the built environment, such as the walkability of a city, are associated with a smaller gender gap in activity and lower activity inequality. In more walkable cities, activity is greater throughout the day and throughout the week, across age, gender, and body mass index (BMI) groups, with the greatest increases in activity found for females. Our findings have implications for global public health policy and urban planning and highlight the role of activity inequality and the built environment in improving physical activity and health.
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Accuracy of Smartphone Applications and Wearable Devices for Tracking Physical Activity Data
Article
  • Feb 2015
Corresponding Author: Mitesh S. Patel, MD, MBA, MS, University of Pennsylvania, 13th Floor Blockley Hall, 423 Guardian Dr, Philadelphia, PA 19104 (mpatel@upenn.edu). Author Contributions: Ms Case had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: All authors. Acquisition, analysis, or interpretation of data: Case, Patel. Drafting of the manuscript: All authors. Critical revision of the manuscript for important intellectual content: Case, Patel. Statistical analysis: Case, Patel. Administrative, technical, or material support: Case, Burwick, Patel. Study supervision: Volpp, Patel. Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Volpp reported receiving research funding from Humana, Merck, Discovery, Weight Watchers, and CVS; consulting income from CVS and VALhealth; and being a principal at VALhealth. No other disclosures were reported. Funding/Support: This study was funded in part through grant RC4 AG039114-01 from the National Institute on Aging. Dr Patel was supported by the US Department of Veteran Affairs and the Robert Wood Johnson Foundation. Role of the Funder/Sponsor: The National Institute on Aging, the US Department of Veteran Affairs, and the Robert Wood Johnson Foundation had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT Lancet Physical Activity Series Working GroupEffect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet 380: 219-229
Article
  • Jul 2012
  • LANCET
Strong evidence shows that physical inactivity increases the risk of many adverse health conditions, including major non-communicable diseases such as coronary heart disease, type 2 diabetes, and breast and colon cancers, and shortens life expectancy. Because much of the world's population is inactive, this link presents a major public health issue. We aimed to quantify the eff ect of physical inactivity on these major non-communicable diseases by estimating how much disease could be averted if inactive people were to become active and to estimate gain in life expectancy at the population level. For our analysis of burden of disease, we calculated population attributable fractions (PAFs) associated with physical inactivity using conservative assumptions for each of the major non-communicable diseases, by country, to estimate how much disease could be averted if physical inactivity were eliminated. We used life-table analysis to estimate gains in life expectancy of the population. Worldwide, we estimate that physical inactivity causes 6% (ranging from 3·2% in southeast Asia to 7·8% in the eastern Mediterranean region) of the burden of disease from coronary heart disease, 7% (3·9-9·6) of type 2 diabetes, 10% (5·6-14·1) of breast cancer, and 10% (5·7-13·8) of colon cancer. Inactivity causes 9% (range 5·1-12·5) of premature mortality, or more than 5·3 million of the 57 million deaths that occurred worldwide in 2008. If inactivity were not eliminated, but decreased instead by 10% or 25%, more than 533 000 and more than 1·3 million deaths, respectively, could be averted every year. We estimated that elimination of physical inactivity would increase the life expectancy of the world's population by 0·68 (range 0·41-0·95) years. Physical inactivity has a major health eff ect worldwide. Decrease in or removal of this unhealthy behaviour could improve health substantially. None.
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Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy
  • Jan 2012
  • 219-229
  • I M Lee
  • E J Shiroma
  • F Lobelo
Lee IM, Shiroma EJ, Lobelo F, et al; Lancet Physical Activity Series Working Group. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012; 380:219-29. [PMID: 22818936]