Temporal Decomposition Analysis of Noncommunicable Disease Burden: The Interplay of Population Aging, Population Growth, and Low Physical Activity, 2010–2019

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Ming Lu National Center for Pediatric Cancer Surveillance, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, BJ, China

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Bin Lu Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, BJ, China

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Le Wang Department of Cancer Prevention, Zhejiang Cancer Hospital, Hangzhou, ZJ, China
Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, ZJ, China

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Background: To analyze global trends in the noncommunicable diseases (NCDs) burden attributable to low physical activity, considering the impacts of population aging and growth. Method: Based on the Global Burden Disease 2019 Study, the NCDs-related death and disability-adjusted life years attributable to low physical activity (defined as <3000 metabolic equivalent-min/wk) were obtained from 2010 to 2019. The average annual percent change was calculated using the joinpoint analysis. Decomposition analysis was applied to assess the separated contributions of 3 components (population aging, population growth, and death change due to all other factors) on the overall change in NCDs death attributed to low physical activity. Results: From 2010 to 2019, the average annual percent change of age-standardized rates of NCDs due to low physical activity was −0.09% for death and −0.06% for disability-adjusted life years. However, the global absolute number of deaths from NCDs attributable to low physical activity increased from 672,215 to 831,502, and disability-adjusted life years rose from 12,813,793 to 15,747,938. This rise was largely driven by population aging and growth, contributing to a 13.0% and 14.7% increase, respectively. The most significant impact of population aging on NCD deaths was observed in high-middle socio-demographic index countries (17.6%), whereas population growth had the greatest effect in low socio-demographic index countries (24.3%). Conclusions: The reduction in NCDs death rates attributed to low physical activity is insufficient to counteract the effects of population aging and growth. Targeted interventions for physical activity promotion should focus on the older population with special attention to diseases most sensitive to physical inactivity.

Noncommunicable diseases (NCDs), including cardiovascular diseases, diabetes, and certain cancers, pose a significant global health threat.1 According to the World Health Organization, major NCDs are largely prevented by addressing lifestyle risk factors, including low physical activity.2 Despite World Health Organization recommendations of 150 to 300 minutes of moderate-intensity aerobic activity for adults, over a quarter of the global adult population (1.4 billion) is insufficiently active.3 This lack of physical activity is estimated to contribute to 4.5% and 7.6% of global type 2 diabetes mellitus (T2DM) and cardiovascular disease deaths, respectively.4 Low physical activity has been a global concern associated with heightened risks of NCDs, premature death, and diminished life expectancy.5,6

As the world’s population ages rapidly, the number of the persons aged 65 years or over reached 727 million in 2020, comprising 9% of the total population. Projections indicate that by 2050, the total number of older adults will double (over 1.5 billion), constituting 16% of the global population.7 The question of whether increased longevity presents an opportunity or a threat to preventing NCDs is paramount.8,9 This inquiry hinges not only on increased longevity but also on the potential negative health effects of aging, including decreased physical activity and heightened vulnerability to morbidity and mortality.1012 The demographic shift toward an aging population underscores the urgency of NCDs prevention, yet there remains a scarcity of studies examining the intricate interplay among population aging, low physical activity, and NCDs.

Recognizing the evolving relationship between low physical activity and NCDs is essential for crafting targeted public health policies and promoting sustainable health behaviors. This research aims to analyze the global trends in the NCDs burden attributable to low physical activity from 2010 to 2019, examining the nuanced dynamics across demographic characteristics and regional variations. Furthermore, we explore the impact of population growth and aging on this association over the study period.

Material and Methods

Data Source

This study conducted a secondary analysis of the Global Burden of Disease (GBD) 2019 Study. Data were obtained from the Global Health Data Exchange query tool, which is directly related to and based on the GBD 2019 Study.13 The GBD is a globally collaborative study that provides modeled estimates of 369 diseases, injuries, and impairments and 87 risk factors across 204 countries and territories and produces annually updated summary measures of health using newest available epidemiological data and improved standardized methodologies.1 Data on counts, age-standardized rate, age-standardized death rate (ASDR), and age-standardized rate of disability-adjusted life years (ASDALYs) attributable to low physical activity from 2010 to 2019 were obtained. These data were further categorized by sex, age, region, and disease type. This database is maintained through ongoing international collaboration coordinated by the Institute for Health Metrics and Evaluation.

Estimation Method

Physical activity was measured in adults older than 25 years of age across leisure/recreation, work/household, and transport domains. The questionnaire captured frequency, duration, and intensity of activities performed for at least 10 minutes at a time. All reported activities were included in the analysis, regardless of the intensity level, to provide a comprehensive assessment of total physical activity. Frequency, duration, and intensity of activity were used to calculate total metabolic equivalent (MET)-minutes per week. MET is the ratio of the working metabolic rate to the resting metabolic rate. One MET is equivalent to 1 kcal·kg−1·h−1 and is equal to the energy cost of sitting quietly. One MET is also defined as the oxygen uptake in mL·kg−1·min−1 with one MET equal to the oxygen uptake of sitting quietly, around 3.5 mL·kg−1·min−1. Low physical activity was defined as <3000 MET-minutes per week based on prior studies.14,15

The dose–response meta-analysis of prospective cohort studies were used to estimate the effect sizes of the change in physical activity level on ischemic heart disease, ischemic stroke, T2DM, breast cancer, and colorectal cancer. The GBD 2019 study defined breast cancer using ICD-10 codes C50 and ICD-9 codes 174 to 175, colorectal cancer using ICD-10 codes C18 to C21 and ICD-9 codes 153 to 154, diabetes using ICD-10 codes E10 to E11, ischemic heart disease using ICD-10 codes I20 to I25 and ICD-9 codes 410 to 414, and ischemic stroke using ICD-10 codes G45 to G46 and ICD-9 codes 433 to 435. Some NCDs like chronic kidney disease were excluded due to the limited evidence for the association with physical activity in the World Cancer Research Fund criteria.16 The detailed process of attributable burden estimation can be found in the previous literature.14

Decomposition Analysis

Contributions of differences in the number of NCDs deaths were attributed to 3 factors: population growth, population aging, and disease death change due to all other reasons. In this research, the aging population was defined as the population aged 70+ years. The change in the number of deaths attributed to population aging, population growth, and change of age-specific disease death rate equals the main effects of the 3 components plus the 2-way and 3-way interactions of the 3 components, respectively. Notably, this method surpasses the limitations of existing methods, which are typically sensitive to the choice of decomposition order and the reference group selected, and may yield inconsistent results from the same data set.17 Detailed information on the model, including its formulas, is available in the published article.9

Statistical Analysis

Deaths and disability-adjusted life years (DALYs) were reported with a 95% uncertainty interval (UI) as 2.5th, and 97.5th-ranked values across all 1000 draws from the GBD modeled estimates. To assess changes in age-standardized rate over time, average annual percent change (AAPC) with a 95% CI was calculated using the joinpoint analysis in Joinpoint Trend Analysis Software (version 5.0.2, Statistical Research and Applications Branch, National Cancer Institute, Bethesda, MD). Briefly, joinpoint models identify points where linear trends change significantly in direction or magnitude. Models were fitted to trend data starting with the minimum number of joinpoints (a straight line) and testing whether additional joinpoints are statistically significant to explain variations in the trends. Each new joinpoint was added to the model, and its significance was tested using a Monte Carlo permutation method.18 The AAPC and CI boundaries determined increasing (AAPC and lower CI > 0), decreasing (AAPC and upper CI < 0), or stable trends (otherwise). Subanalyses were conducted stratifying the population by sex (male, female); territory (Asia, Africa, Europe, America, Oceania); socio-demographic index (SDI) quintiles (low, low-middle, middle, high-middle, high); age groups (25–49, 50–69, 70+ years); and disease types (ischemic heart disease, ischemic stroke, T2DM, breast cancer, and colorectal cancer). Temporal trends were analyzed for each of these stratifications using joinpoint regression. All statistical analyses and graphics were performed using R (version 4.2.1). P value < .05 was considered to be statistically significant.

Results

NCDs Burden Trends and Impact of Low Physical Activity (2010−2019)

The number of NCDs deaths attributed to low physical activity rose from 672,215 (95% UI: 349,658–1,205,803) in 2010 to 831,502 (95% UI: 427,075–1,470,299) in 2019, reflecting a 23.7% increase over the 10-year study period. This upward trend was consistently observed in both gender subgroups, with a 21.7% increase in females and a 26.5% increase in males. Following geographical and SDI grouping, the growth rates varied significantly, ranging from 9.9% in the Americas to 33.7% in Oceania, and from 8.3% in high SDI countries to 36.4% in low-middle SDI countries. For each disease, the highest increase in death was associated with diabetes mellitus (31.0%), followed by colorectal cancer (27.5%), ischemic stroke (22.9%), breast cancer (22.5%), and ischemic heart disease (21.8%; Table 1 and Supplementary Figure S1–S3 [available online]). The overall trends in the numbers of DALYs attributed to low physical activity, increasing from 12,813,793 (95% UI: 6,863,367–22,947,471) to 15,747,938 (95% UI: 8,515,094–28,617,801), were similar to those observed for deaths. The most significant increase in DALYs occurred in Oceania (35.9%) and the middle SDI countries (32.1%), whereas the lowest increase was observed in Europe (10.5%) and the high SDI countries (13.5%; Table 2 and Supplementary Figure S1–S3 [available online]).

Table 1

Deaths and ASDR Per 100,000 Population of NonCommunicable Diseases Attributable to Low Physical Activity in 2010 and 2019 Overall and by Sex, Territory, Country, and Disease Type

 Numbers of deaths (95% UI)ASDR per 100,000 population (95% CI)
20102019Growth, %20102019AAPC, %Trend
Overall672,215 (349,658 to 1,205,803)831,502 (427,075 to 1,470,299)23.712.19 (6.36 to 21.26)11.10 (5.66 to 19.51)−0.09 (−0.14 to −0.04)Decreasing
By sex
 Female389,890 (215,933 to 645,265)474,359 (259,445 to 776,467)21.712.00 (6.63 to 19.75)10.79 (5.90 to 17.66)−0.10 (−0.16 to −0.55)Decreasing
 Male282,325 (132,182 to 547,226)357,143 (169,396 to 689,789)26.512.19 (5.82 to 23.29)11.30 (5.49 to 21.31)−0.07 (−0.13 to −0.02)Decreasing
By territory
 Asia312,567 (152,975 to 590,441)416,944 (208,296 to 771,306)33.411.57 (5.72 to 21.42)10.58 (5.30 to 19.36)−0.09 (−0.15 to −0.02)Decreasing
 Africa64,626 (36,968 to 109,731)82,788 (46,434 to 139,648)28.118.93 (10.94 to 31.39)18.43 (10.65 to 30.48)−0.03 (−0.09 to 0.05)Stable
 America110,124 (60,401 to 185,173)121,015 (67,769 to 207,842)9.910.71 (5.90 to 18.06)9.18 (5.16 to 15.76)−0.14 (−0.19 to −0.09)Decreasing
 Europe183,211 (93,894 to 319,722)208,810 (108,970 to 355,450)14.012.61 (6.49 to 22.17)11.51 (5.99 to 19.85)−0.09 (−0.14 to −0.02)Decreasing
 Oceania906 (448 to 1672)1211 (584 to 2273)33.723.79 (12.12 to 42.29)24.26 (12.32 to 43.74)0.02 (−0.18 to 0.13)Stable
By country
 Low SDI29,185 (14,785 to 56,848)37,085 (18,670 to 71,177)27.110.84 (5.70 to 20.29)10.07 (5.26 to 18.76)−0.07 (−0.13 to −0.01)Decreasing
 Low-middle SDI98,504 (53,806 to 179,085)134,393 (72,961 to 243,267)36.413.26 (7.43 to 23.21)12.87 (7.02 to 22.47)−0.03 (−0.10 to 0.04)Stable
 Middle SDI188,386 (96,605 to 350,169)253,036 (130,438 to 465,025)34.313.86 (7.11 to 25.11)13.08 (6.71 to 23.45)−0.06 (−0.12 to 0.01)Stable
 High-middle SDI207,436 (108,102 to 370,601)245,873 (127,841 to 424,379)18.514.61 (7.58 to 25.88)12.72 (6.59 to 21.93)−0.13 (−0.18 to −0.77)Decreasing
 High SDI148,151 (76,936 to 254,350)160,418 (81,741 to 278,205)8.38.34 (4.36 to 14.43)7.11 (3.64 to 12.33)−0.15 (−0.20 to −0.10)Decreasing
By disease type
 Ischemic heart disease399,699 (145,040 to 824,347)486,780 (175,734 to 1,003,278)21.87.29 (2.65 to 14.78)6.52 (2.36 to 13.31)−0.11 (−0.16 to −0.06)Decreasing
 Ischemic stroke124,044 (24,669 to 317,439)152,395 (30,078 to 391,946)22.92.33 (5.92 to 0.46)2.08 (0.41 to 5.33)−0.11 (−0.17 to −0.05)Decreasing
 T2DM95,533 (48,671 to 157,409)125,195 (62,096 to 208,347)31.01.65 (0.84 to 2.71)1.62 (0.81 to 2.68)−0.02 (−0.07 to 0.03)Stable
 Breast cancer6918 (3260 to 11,641)8475 (4078 to 14,304)22.50.12 (0.05 to 0.19)0.11 (0.05 to 0.18)−0.07 (−0.13 to −0.01)Decreasing
 Colorectal cancer46,020 (12,688 to 87,092)58,657 (16,866 to 112,146)27.50.81 (0.23 to 1.51)0.77 (0.22 to 1.47)−0.05 (−0.10 to 0.01)Decreasing

Abbreviations: AAPC, average annual percentage change; ASDR, age-standardized death rate; SDI, Socio-Demographic Index; T2DM, type 2 diabetes mellitus; UI, uncertainty intervals.

Table 2

DALY and ASDALY per 100,000 Population of NonCommunicable Diseases Attributable to Low Physical Activity in 2010 and 2019 Overall and by Sex, Territory, Country, and Disease Type

 Numbers of DALY (95% UI)ASDALY per 100,000 population (95% CI)
20102019Growth, %20102019AAPC, %Trend
Overall12,813,793 (6,863,367 to 22,947,471)15,747,938 (8,515,094 to 28,617,801)22.9211.58 (114.90 to 378.69)198.42 (108.16 to 360.32)−0.06 (−0.11 to −0.01)Decreasing
By sex
 Female6,923,778 (3,986,200 to 11,678,757)8,354,899 (4,779,724 to 14,197,377)20.7206.40 (118.70 to 348.02)190.92 (109.35 to 324.61)−0.07 (−0.13 to −0.02)Decreasing
 Male5,890,015 (2,880,390 to 11,201,250)7,393,039 (3,670,579 to 14,081,417)25.5215.58 (105.54 to 405.83)205.53 (102.86 to 388.82)−0.05 (−0.10 to 0.01)Stable
By territory
 Asia6,247,697 (3,199,899 to 11,629,692)8,073,933 (4,158,040 to 15,094,441)29.2194.84 (101.53 to 359.99)181.91 (93.88 to 339.75)−0.07 (−0.13 to 0.00)Stable
 Africa1,461,599 (813,316 to 2,590,848)1,908,097 (1,039,763 to 3,298,994)30.5343.68 (197.39 to 597.81)337.10 (189.40 to 566.53)−0.02 (−0.09 to 0.06)Stable
 America2,210,434 (1,276,814 to 3,744,058)2,567,878 (1,477,053 to 4,380,884)16.2220.12 (127.25 to 373.20)202.56 (116.25 to 345.58)−0.08 (−0.14 to −0.02)Decreasing
 Europe2,857,760 (1,539,888 to 5,243,996)3,156,537 (1,740,216 to 5,616,312)10.5202.98 (110.01 to 370.03)191.31 (105.35 to 340.30)−0.06 (−0.13 to 0.02)Stable
 Oceania25,411 (11,996 to 47,796)34,526 (16,010 to 66,541)35.9505.67 (254.10 to 917.14)512.13 (255.75 to 933.30)0.01 (−0.09 to 0.12)Stable
By country
 Low SDI690,352 (342,288 to 1,324,710)868,376 (433,708 to 1,685,312)25.8200.89 (101.97 to 376.88)186.90 (96.32 to 352.25)−0.07 (−0.13 to −0.01)Decreasing
 Low-middle SDI2,177,933 (1,179,543 to 3,957,470)2,812,297 (1,509,475 to 5,217,208)29.1236.47 (130.54 to 425.85)227.48 (126.31 to 413.91)−0.04 (−0.10 to 0.02)Decreasing
 Middle SDI3,872,316 (2,011,349 to 6,962,492)5,116,312 (2,649,084 to 9,308,525)32.1235.39 (124.25 to 430.17)226.37 (118.77 to 412.39)0.04 (−0.10 to 0.03)Stable
 High-middle SDI3,574,733 (1,953,232 to 6,413,434)4,113,288 (2,253,717 to 7,387,863)15.1231.38 (126.26 to 414.83)205.66 (113.48 to 369.84)−0.11 (−0.17 to −0.05)Decreasing
 High SDI2,486,990 (1,361,068 to 4,362,487)2,822,705 (1,550,644 to 4,956,505)13.5155.14 (85.16 to 273.94)148.99 (82.29 to 265.06)−0.04 (−0.10 to 0.02)Stable
By disease type
 Ischemic heart disease6,456,035 (2,240,383 to 14,177,068)7,586,665 (2,613,511 to 16,747,205)17.5107.54 (37.28 to 233.48)96.36 (33.45 to 210.82)−0.10 (−0.16 to −0.06)Decreasing
 Ischemic stroke1,984,903 (369,125 to 5,273,386)2,409,414 (432,866 to 6,377,618)21.434.12 (6.46 to 89.87)31.16 (5.69 to 82.02)−0.09 (−0.15 to −0.03)Decreasing
 T2DM3,407,177 (1,661,297 to 5,939,104)4,549,207 (2,188,516 to 7,969,495)33.554.29 (26.71 to 93.88)55.92 (27.16 to 97.60)0.03 (−0.02 to 0.08)Stable
 Breast cancer164,302 (81,321 to 282,780)197,797 (97,517 to 345,136)20.42.54 (1.25 to 4.34)2.41 (1.18 to 4.18)−0.04 (−0.11 to 0.02)Stable
 Colorectal cancer801,375 (214,212 to 1,551,044)1,004,853 (262,148 to 1,943,581)25.413.10 (3.53 to 25.30)12.57 (3.36 to 24.19)−0.04 (−0.10 to 0.02)Stable

Abbreviations: AAPC, average annual percentage change; ASDALY, age-standardized rate of disability-adjusted life years; DALY, disability-adjusted life years; SDI, socio-demographic index; T2DM, type 2 diabetes mellitus; UI, uncertainty intervals.

Age-Standardized Rate Trends of NCDs Burden Due to Low Physical Activity (2010 − 2019)

The ASDR of NCDs due to low physical activity decreased from 12.19 (95% CI, 6.36 to 21.26) per 100,000 population in 2010 to 11.10 (95% CI, 5.66 to 19.51) per 100,000 population in 2019. A similar trend was observed in both females and males, with slight sex differences in the annual average of 0.09% for females and of 0.07% for males. In terms of territory variances, the ASDR trend associated with low physical activity exhibited a modest decrease in Asia, America, and Europe, with an annual average decline ranging from 0.09% to 0.14%, whereas it remained relatively stable in Africa and Oceania from 2010 to 2019. Regarding differences in SDI countries, the ASDR trend declined slightly in low, high-middle, and high SDI countries, showing an annual average decrease ranging from 0.07% to 0.15%. By contrast, the low-middle and middle SDI countries kept stable from 2010 to 2019. In 2019, the heaviest ASDR of specific diseases caused by low physical activity was ischemic heart disease (6.52 per 100,000 population), followed by stroke (2.08 per 100,000 population) and T2DM (1.62 per 100,000 population). From 2010 to 2019, the trends of ASDR associated with low physical activity remained stable for T2DM, while witnessing declines, notably in ischemic heart disease (−0.11% per year) and stroke (−0.11% per year; Table 1 and Supplementary Figure S1–S3 [available online]). The overall ASDALYs per 100,000 population showed a decreasing trend, with an AAPC of − 0.06%. However, the overall decreasing trend in ASDALYs is driven by significant reductions in specific subgroups, particularly among females, in the Americas, and in countries with lower SDI. Conversely, certain sex and territory subgroups exhibit stable trends, highlighting variations in health outcomes across different populations and conditions (Table 2 and Supplementary Figure S1–S3 [available online]).

Factors Influencing NCDs Death Attributable to Low Physical Activity: Population Aging and Growth (2010−2019)

The absolute number of deaths and DALYs attributed to low physical activity increased among population aged 50−69 years and 70+ years. Conversely, the age-standardized rate for both death and DALYs decreased within these age groups between 2010 and 2019 (Supplementary Figure S4 [available online]). As shown in Table 3, this rise in the number of deaths was largely driven by population aging and growth, contributing to a 13.0% and 14.7% increase, respectively. After excluding these factors, an 8.6% reduction in death numbers was observed. No substantial differences were observed in the effects of both population aging and growth on the total death change between females and males. The most pronounced impact of population aging on the change in NCDs deaths was observed in Asia (17.9%) and in the high-middle SDI countries (17.6%). The most substantial effect of population growth was witnessed in Africa (23.6%) and the low SDI countries (24.3%; Figure 1). The diseases most affected by aging was ischemic stroke (14.4%), caused by low physical activity. There were slight variations in the contributions of population growth to the specific disease deaths associated with low physical activity ranging from 14.3% to 14.8%. The most notable decrease in death rate change was observed in stroke (−10.6%), whereas the smallest decrease was noted in T2DM (−1.8%), attributed to low physical activity (Table 2 and Figure 2).

Table 3

Proportion of NonCommunicable Diseases Deaths Attributed to Low Physical Activity Associated With Population Aging, Population Growth, and Death Change Globally Between 2010 and 2019 (%) Overall and by Sex, Territory, Country, and Disease Type

SubgroupPopulation agingPopulation growthDeath changea
Overall13.014.7−8.6
By sex
 Female12.615.0−9.8
 Male13.514.4−7.0
By territory
 Asia17.915.3−8.2
 Africa1.523.6−3.2
 America9.814.3−15.1
 Europe16.54.5−8.8
 Oceania2.322.70.2
By country
 Low SDI4.524.3−7.6
 Low-middle SDI12.717.8−3.8
 Middle SDI16.514.6−5.6
 High-middle SDI17.611.3−13.3
 High SDI15.09.3−16.6
By disease type
 Ischemic heart disease13.314.8−10.3
 Ischemic stroke14.414.8−10.6
 T2DM11.214.3−1.8
 Breast cancer9.814.8−6.2
 Colorectal cancer11.514.5−4.5

Abbreviations: SDI, socio-demographic index; T2DM, type 2 diabetes mellitus.

aDeath change refers to the absolute contribution of factors, excluding population aging and growth, to the overall difference in the total number of noncommunicable diseases deaths attributed to low physical activity.

Figure 1
Figure 1

Change in proportion of NCDs deaths attributed to low physical activity associated with population aging, population growth, and death change globally and by country between 2010 and 2019. (A) Global. (B) High SDI countries. (C) High-middle SDI countries. (D) Middle SDI countries. (E) Low-middle SDI countries. (F) Low SDI countries. NCDs indicates noncommunicable diseases; SDI, socio-demographic index.

Citation: Journal of Physical Activity and Health 2025; 10.1123/jpah.2024-0201

Figure 2
Figure 2

Change in proportion of NCDs deaths attributed to low physical activity associated with population aging, population growth, and death change globally and by disease between 2010 and 2019. (A) Ischemic heart disease. (B) Ischemic stroke. (C) T2DM. (D) Breast cancer. (E) Colorectal cancer. NCDs indicates noncommunicable diseases; T2DM, type 2 diabetes mellitus.

Citation: Journal of Physical Activity and Health 2025; 10.1123/jpah.2024-0201

Demographic Shifts and Low Physical Activity Trends (2010−2019)

The percentage of the population aged 70 years and above witnessed an increase from 2010 to 2019. This demographic trend exhibited a consistent, albeit slight, growth among both females and males. Variations emerged across regions, with a rapid increase observed in the high SDI countries and Europe, contrasted by a slower rise in the low SDI countries and Africa (Supplementary Figure S5 [available online]). The prevalence of low physical activity remained relatively consistent from 2010 to 2019 across various age groups, showing an AAPC ranging from −0.01% for individuals aged 70 and above to 0.01% for those between 25 and 49 years old. However, the occurrence of low physical activity increased in tandem with age, affecting both genders (Supplementary Figure S6 [available online]). Across various diseases, death associated with low physical activity rose with age (Supplementary Figure S7 [available online]).

Discussion

This study provided a comprehensive analysis of the global trends in the burden of NCDs attributable to low physical activity from 2010 to 2019, highlighting the critical role of population aging and growth. Although the overall ASDR and ASDALY of NCDs attributable to low physical activity slightly decreased by 0.09% and 0.06%, respectively, the absolute number of deaths and DALYs saw a substantial rise. The decomposition analysis revealed that population aging contributed 13.0% and population growth contributed 14.7% to the increase in NCDs-related deaths attributable to low physical activity. Despite some reduction in the overall burden due to improvements in physical activity levels and advancements in NCDs management, these efforts were insufficient to counterbalance the adverse effects of aging and population expansion.

The rising global burden of NCDs due to low physical activity remains a significant public health concern globally. This research found a considerable increase in the absolute number of NCD deaths attributed to low physical activity, which grew by 23.7% over the study period. The findings align with existing literature, emphasizing the unignorable health impacts of physical inactivity and its contribution to the rising mortality of NCDs. Our findings also revealed that the largest increase in proportion of deaths attributable to low physical activity occurred in low-middle SDI countries and Asia. This trend is likely driven by societal shifts in lifestyle, work, and urbanization, particularly in countries like China, where rapid economic development has reduced physical activity and increased sedentary behaviors.1921 Similar changes in Latin America have contributed to a rise in obesity and NCDs.22 Despite substantial research, a significant gap remains between evidence and policy action, highlighting the urgent need for political recognition and investment to control NCDs globally.23 Deaths of ischemic heart disease attributed to low physical activity was heavy in the older adults, with the risk increasing as physical activity declines and genetic abnormalities become more prevalent with age.24,25 By contrast, deaths of T2DM attributed to low physical activity showed a stable trend, with meta-analyses highlighting an inverse association between physical activity and diabetes risk.26,27 Several biological mechanisms could explain this inverse association. Regular exercise helps patients improve insulin sensitivity and glucose levels and reduces adiposity, yet many adults with diabetes do not meet recommended activity levels.2832 This underscores the need for diabetic education to prioritize promoting physical activity by addressing barriers systematically.33

According to the results of decomposition analysis, the key driver of the increasing NCDs burden attributed to low physical activity is the growth and aging of the population. The dual pressures of an expanding older population, particularly in Asia and high-middle SDI countries, necessitate targeted interventions to mitigate the effects of these demographic shifts.34 This trend is concerning as physical inactivity is more prevalent among older adults, exacerbating risks such as fractures from falls, which hinder regular exercise.35 Future public health policies and strategies must prioritize older adults, who are disproportionately affected by NCDs and more likely to experience low levels of physical activity. By focusing on this demographic and encouraging healthier lifestyles, particularly in rapidly aging populations, there is potential to alleviate some of the burden associated with physical inactivity. Encouraging even small increases in physical activity among older adults, especially those previously inactive or with chronic illnesses, can help integrate more movement into their daily routines.36

Despite the overall increase in the absolute number of NCD deaths associated with low physical activity, this study found a slight decrease in AAPC for ASDR. The contrasting trends between death numbers and ASDR are likely driven by demographic shifts (population aging and growth). After excluding these factors, we observed an 8.6% reduction in death numbers, which suggested an improvement in the control of NCD deaths related to low physical activity. The improvement might be due to several factors, including improved awareness of physical activity, access to fitness programs, public health initiatives, and medical advancements in NCD diagnosis, treatment, and management.37 However, the limitations of the decomposition analysis method prevent further disaggregation of the factors contributing to this decline, particularly the precise role of improved physical activity and healthcare interventions. Longitudinal studies can help clarify the pathways through which increased physical activity and improved healthcare reduce NCDs-related mortality, thereby strengthening the evidence base for future policy development.

Although previous studies have examined global trends in disease burden attributable to low physical activity,38 our study provides several unique contributions. We employ a novel decomposition analysis to separate the contributions of population aging, population growth, and changes in disease rates. In addition, we provide a more comprehensive analysis of trends across different SDI quintiles and geographic regions, offering insights into disparities in the NCDs burden attributable to low physical activity. These findings have important implications for public health policy and practice. First, they emphasize the need for targeted interventions to promote physical activity among older adults, particularly in high-middle SDI countries and Asia. Second, they suggest that current efforts to reduce the NCDs burden through physical activity promotion need to be intensified to counteract the effects of population aging and growth. Finally, they highlight the importance of tailoring interventions to address the diseases most sensitive to physical inactivity, such as ischemic heart disease and stroke. This study also has several limitations. First, it relies on GBD 2019 modeling, which may introduce biases, especially in countries with limited data and underdeveloped health systems. This could lead to underreporting or underdiagnosis. Second, the definition of low physical activity as <3000 MET-minutes per week has limitations. This threshold does not align with current physical activity guidelines and may miss the initial steep part of the physical activity-death dose–response curve. Due to limitations in the granularity of the GBD data, it was not possible to conduct sensitivity analyses with different thresholds for specific populations. Future studies should consider using thresholds that better reflect current evidence and recommendations. Third, the decomposition approach, though improved, still relies on modeling assumptions and oversimplifies the complex interactions in demographic and epidemiological changes. Attributing changes in NCDs death to specific drivers simplifies the intricate global health transitions. Finally, the analysis is limited by the absence of longitudinal data and strict causal inference methods. More robust long-term analyses could better clarify the impacts of population growth, aging, and risk factors. Nonetheless, this study offers a valuable overview of global patterns using the best available data.

Conclusions

Although ASDRs for NCDs attributable to low physical activity are decreasing, this reduction is insufficient to offset the impacts of population aging and growth. These findings underscore the urgent need for targeted public health strategies to promote physical activity, particularly among aging populations and in regions experiencing rapid demographic transitions.

Acknowledgments

Authors’ Contributions: Conceived and designed the project: Wang. Analyzed and cleaned the data: M. Lu and B. Lu. Interpreted the results and wrote the manuscript: M. Lu. Critically reviewed and revised the manuscript: B. Lu. All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by Zhejiang Provincial Natural Science Foundation of China (LTGY23H260004).

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    Cui Z, Hardy LL, Dibley MJ, Bauman A. Temporal trends and recent correlates in sedentary behaviours in Chinese children. Int J Behav Nutr Phys Act. 2011;8(1):93. doi:

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    • Search Google Scholar
    • Export Citation
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    Monda KL, Gordon-Larsen P, Stevens J, Popkin BM. China’s transition: the effect of rapid urbanization on adult occupational physical activity. Soc Sci Med. 2007;64(4):858870. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
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    Ng SW, Norton EC, Popkin BM. Why have physical activity levels declined among Chinese adults? Findings from the 1991–2006 China health and nutrition surveys. Soc Sci Med. 2009;68(7):13051314. doi:

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    Rivera JA, Barquera S, González-Cossío T, Olaiz G, Sepúlveda J. Nutrition transition in Mexico and in other Latin American countries. Nutr Rev. 2004;62(7 Pt 2):S149S157. doi:

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    Heath GW, Parra DC, Sarmiento OL, et al. Evidence-based intervention in physical activity: lessons from around the world. Lancet. 2012;380(9838):272281. doi:

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    • Export Citation
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    Dai X, Wiernek S, Evans JP, Runge MS. Genetics of coronary artery disease and myocardial infarction. World J Cardiol. 2016;8(1):123. doi:

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    Sun F, Norman IJ, While AE. Physical activity in older people: a systematic review. BMC Public Health. 2013;13(1):449. doi:

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    Aune D, Norat T, Leitzmann M, Tonstad S, Vatten LJ. Physical activity and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis. Eur J Epidemiol. 2015;30(7):529542. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 27.

    Jeon CY, Lokken RP, Hu FB, van Dam RM. Physical activity of moderate intensity and risk of type 2 diabetes: a systematic review. Diabetes Care. 2007;30(3):744752. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
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    Crossman EL. Physical activity in patients with type 2 diabetes mellitus: updated consensus statement from the ACSM. Am Fam Physician. 2023;107(1):103104.

    • Search Google Scholar
    • Export Citation
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    Dai J, Dai W, Li WQ. Trends in physical activity and sedentary time among U.S. adults with diabetes: 2007–2020. Diabetes Metab Syndr. 2023;17(10):102874. doi:

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    • Search Google Scholar
    • Export Citation
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    Enyew A, Nigussie K, Mihrete T, et al. Prevalence and associated factors of physical inactivity among adult diabetes mellitus patients in Felege Hiwot Referral Hospital, Bahir Dar, Northwest Ethiopia. Sci Rep. 2023;13(1):118. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 31.

    Kanaley JA, Colberg SR, Corcoran MH, et al. Exercise/physical activity in individuals with type 2 diabetes: a consensus statement from the American college of sports medicine. Med Sci Sports Exerc. 2022;54(2):353368. doi:

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    • Search Google Scholar
    • Export Citation
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    Teich T, Zaharieva DP, Riddell MC. Advances in exercise, physical activity, and diabetes mellitus. Diabetes Technol Ther. 2019;21(suppl 1):S112S122. doi:

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  • 33.

    Gray KE, Hoerster KD, Taylor L, Krieger J, Nelson KM. Improvements in physical activity and some dietary behaviors in a community health worker-led diabetes self-management intervention for adults with low incomes: results from a randomized controlled trial. Transl Behav Med. 2021;11(12):21442154. doi:

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    • Crossref
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The global absolute number of deaths increased significantly, driven by a 13.0% rise in population aging and a 14.7% rise in population growth.

These findings underscore the urgent need for targeted public health strategies to promote physical activity, particularly among aging populations and in regions experiencing rapid demographic transitions.

  • Collapse
  • Expand
  • Figure 1

    Change in proportion of NCDs deaths attributed to low physical activity associated with population aging, population growth, and death change globally and by country between 2010 and 2019. (A) Global. (B) High SDI countries. (C) High-middle SDI countries. (D) Middle SDI countries. (E) Low-middle SDI countries. (F) Low SDI countries. NCDs indicates noncommunicable diseases; SDI, socio-demographic index.

  • Figure 2

    Change in proportion of NCDs deaths attributed to low physical activity associated with population aging, population growth, and death change globally and by disease between 2010 and 2019. (A) Ischemic heart disease. (B) Ischemic stroke. (C) T2DM. (D) Breast cancer. (E) Colorectal cancer. NCDs indicates noncommunicable diseases; T2DM, type 2 diabetes mellitus.

  • 1.

    GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258), 12041222. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 2.

    World Health Organization. Noncommunicable diseases. 2023. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases. Accessed December 23, 2023

    • Search Google Scholar
    • Export Citation
  • 3.

    World Health Organization. Physical activity. 2022. https://www.who.int/news-room/fact-sheets/detail/physical-activity. Accessed December 23, 2023

    • Search Google Scholar
    • Export Citation
  • 4.

    Katzmarzyk PT, Friedenreich C, Shiroma EJ, Lee IM. Physical inactivity and non-communicable disease burden in low-income, middle-income and high-income countries. Br J Sports Med. 2022;56(2):101106. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 5.

    Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Health. 2018;6(10):e1077e1086. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6.

    Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380(9838):219229. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    United Nations Department of Economic and Social Affairs. World population ageing 2020 highlights: Living arrangements of older persons. United Nations; 2020. https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/undesa_pd-2020_world_population_ageing_highlights.pdf. Accessed December 23, 2023

    • Search Google Scholar
    • Export Citation
  • 8.

    Beard JR, Officer A, de Carvalho IA, et al. The World report on ageing and health: a policy framework for healthy ageing. Lancet. 2016;387(10033):21452154. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9.

    Cheng X, Yang Y, Schwebel DC, et al. Population ageing and mortality during 1990-2017: a global decomposition analysis. PLoS Med. 2020;17(6):e1003138. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10.

    Chodzko-Zajko WJ, Proctor DN, Fiatarone Singh MA, et al. American college of sports medicine position stand. exercise and physical activity for older adults. Med Sci Sports Exerc. 2009;41(7):15101530. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 11.

    Gobbo S, Bergamin M, Sieverdes JC, Ermolao A, Zaccaria M. Effects of exercise on dual-task ability and balance in older adults: a systematic review. Arch Gerontol Geriatr. 2014;58(2):177187. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 12.

    López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):11941217. doi:

  • 13.

    Institute for Health Metrics and Evaluation (IHME). GBD results. 2020. https://vizhub.healthdata.org/gbd-results/. Accessed December 23, 2023

    • Search Google Scholar
    • Export Citation
  • 14.

    GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):12231249. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15.

    Kyu HH, Bachman VF, Alexander LT, et al. Physical activity and risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events: systematic review and dose-response meta-analysis for the Global Burden of Disease Study 2013. BMJ. 2016;354:i3857. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16.

    Wiseman, M. The second world cancer research fund/American institute for cancer research expert report. food, nutrition, physical activity, and the prevention of cancer: a global perspective. Proc Nutr Soc. 2008;67(3):253256. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 17.

    Bashir S, Estève J. Analysing the difference due to risk and demographic factors for incidence or mortality. Int J Epidemiol. 2000;29(5):878884. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 18.

    Kim HJ, Fay MP, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med. 2000;19(3):335351. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 19.

    Cui Z, Hardy LL, Dibley MJ, Bauman A. Temporal trends and recent correlates in sedentary behaviours in Chinese children. Int J Behav Nutr Phys Act. 2011;8(1):93. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20.

    Monda KL, Gordon-Larsen P, Stevens J, Popkin BM. China’s transition: the effect of rapid urbanization on adult occupational physical activity. Soc Sci Med. 2007;64(4):858870. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21.

    Ng SW, Norton EC, Popkin BM. Why have physical activity levels declined among Chinese adults? Findings from the 1991–2006 China health and nutrition surveys. Soc Sci Med. 2009;68(7):13051314. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 22.

    Rivera JA, Barquera S, González-Cossío T, Olaiz G, Sepúlveda J. Nutrition transition in Mexico and in other Latin American countries. Nutr Rev. 2004;62(7 Pt 2):S149S157. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 23.

    Heath GW, Parra DC, Sarmiento OL, et al. Evidence-based intervention in physical activity: lessons from around the world. Lancet. 2012;380(9838):272281. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 24.

    Dai X, Wiernek S, Evans JP, Runge MS. Genetics of coronary artery disease and myocardial infarction. World J Cardiol. 2016;8(1):123. doi:

  • 25.

    Sun F, Norman IJ, While AE. Physical activity in older people: a systematic review. BMC Public Health. 2013;13(1):449. doi:

  • 26.

    Aune D, Norat T, Leitzmann M, Tonstad S, Vatten LJ. Physical activity and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis. Eur J Epidemiol. 2015;30(7):529542. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 27.

    Jeon CY, Lokken RP, Hu FB, van Dam RM. Physical activity of moderate intensity and risk of type 2 diabetes: a systematic review. Diabetes Care. 2007;30(3):744752. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 28.

    Crossman EL. Physical activity in patients with type 2 diabetes mellitus: updated consensus statement from the ACSM. Am Fam Physician. 2023;107(1):103104.

    • Search Google Scholar
    • Export Citation
  • 29.

    Dai J, Dai W, Li WQ. Trends in physical activity and sedentary time among U.S. adults with diabetes: 2007–2020. Diabetes Metab Syndr. 2023;17(10):102874. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 30.

    Enyew A, Nigussie K, Mihrete T, et al. Prevalence and associated factors of physical inactivity among adult diabetes mellitus patients in Felege Hiwot Referral Hospital, Bahir Dar, Northwest Ethiopia. Sci Rep. 2023;13(1):118. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 31.

    Kanaley JA, Colberg SR, Corcoran MH, et al. Exercise/physical activity in individuals with type 2 diabetes: a consensus statement from the American college of sports medicine. Med Sci Sports Exerc. 2022;54(2):353368. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 32.

    Teich T, Zaharieva DP, Riddell MC. Advances in exercise, physical activity, and diabetes mellitus. Diabetes Technol Ther. 2019;21(suppl 1):S112S122. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 33.

    Gray KE, Hoerster KD, Taylor L, Krieger J, Nelson KM. Improvements in physical activity and some dietary behaviors in a community health worker-led diabetes self-management intervention for adults with low incomes: results from a randomized controlled trial. Transl Behav Med. 2021;11(12):21442154. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 34.

    Pew Research Center. Attitudes about aging: a global perspective in a rapidly graying world, Japanese are worried, Americans aren’t. 2014. https://www.hidropolitikakademi.org/uploads/wp/2015/01/Attitudes-about-Aging-A-Global-Perspective.pdf

    • Search Google Scholar
    • Export Citation
  • 35.

    Vieira ER, Palmer RC, Chaves PH. Prevention of falls in older people living in the community. BMJ. 2016;353:i1419. doi:

  • 36.

    Hupin D, Roche F, Gremeaux V, et al. Even a low-dose of moderate-to-vigorous physical activity reduces mortality by 22% in adults aged ≥60years: a systematic review and meta-analysis. Br J Sports Med. 2015;49(19):12621267. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 37.

    NCD Countdown 2030 Collaborators. NCD Countdown 2030: efficient pathways and strategic investments to accelerate progress towards the sustainable development goal target 3.4 in low-income and middle-income countries. Lancet. 2022;399(10331):12661278. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 38.

    Xu Y, Xie J, Yin H, et al. The global burden of disease attributable to low physical activity and its trends from 1990 to 2019: an analysis of the global burden of disease study. Front Public Health. 2024;10:1018866. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
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