Prevalence and Correlates of Insufficient Physical Activity Among Adults Aged 18–69 Years in India: Findings From the National Noncommunicable Disease Monitoring Survey

in Journal of Physical Activity and Health

Background: Sufficient physical activity (PA) significantly contributes to the prevention and control of noncommunicable diseases. This study aims to determine the prevalence of insufficient PA and associated sociodemographic and lifestyle factors among adults aged 18–69 years in India. Methods: A national population-based, cross-sectional survey was conducted during 2017–2018 among 12,000 adults that adapted globally standard data collection tools. The data were weighted and analyzed using complex samples analysis. Logistic regression analysis was performed to identify the sociodemographic and lifestyle factors associated with insufficient PA. Results: Age standardized prevalence of insufficient PA among adults in India was 41.4%. A higher proportion of women (52.4%) and urban adults (51.7%) were not doing sufficient PA. Men (118.8 min) spent more time in PA per day than women (55.3 min). Higher odds of insufficient PA were significantly associated with unemployment (adjusted odds ratio [aOR] = 6.45), highest wealth quintile (aOR = 1.86), presence of central obesity (aOR = 1.24), and raised blood pressure (aOR = 1.22). Conclusion: This study provides the baseline prevalence of insufficient PA to monitor the set PA targets for India by 2025. The identified associated factors can guide policy makers to plan tailored interventions targeting high-risk groups and a multisectoral approach to promote PA.

Noncommunicable diseases (NCDs) including cancer, cardiovascular diseases, diabetes, and stroke account for 71% of global deaths each year (41 million people approximately).1 The risk of death due to NCDs increases with increased tobacco use, alcohol consumption, physical inactivity, and unhealthy diet.1 Among them, physical inactivity has contributed to more than 5 million deaths per year and those insufficiently active were at 20% to 30% increased risk of death.2 The World Health Organization (WHO) recognizes physical inactivity as a global public health concern with more than one-fourth of adults failing to meet the global recommended levels of physical activity (PA).2 Regular and consistent PA can prevent and control NCDs as well as related metabolic risk factors like raised blood pressure, raised blood glucose, and obesity.3

To combat the increasing burden of NCDs and promote PA, the WHO, through its global monitoring framework for NCDs, recommends its country members to achieve the target of 10% reduction in physical inactivity at the national level by 2025.4 A recent 16-year trend analysis found that the existing 3.5% reduction in physical inactivity is too slow to achieve this target.5 Furthermore, in 2018, the WHO global action plan on physical activity fixed a target of 15% relative reduction in physical inactivity among adults by 2030 from 2016 as the baseline.6 Many countries are making efforts by enhancing surveillance, conducting research on correlates, adopting policies, and implementing action plans to achieve this target.7 Globally, studies are being increasingly carried out to find the levels of PA and factors associated with it.811 However, inadequate implementation strategies, lack of awareness, shortage of trained workforce, and insufficient multisectoral collaborations hinders the process of target achievement.7

In India, the estimated proportion of disability-adjusted life years attributable to NCDs has doubled (29% in 1990 to 58% in 2019), indicating a rapid epidemiological transition.12 Physical inactivity is one of the important NCD risk factors. India, in its National NCD Monitoring Framework and Action Plan, has proposed to reduce physical inactivity by 10% by 2025.13 In view of this, research assessing the prevalence of physical inactivity among adults has been carried out in a few states of India across different time periods.1418 However, factors associated with insufficient PA among Indian adults have been inadequately investigated.19 National level, periodical PA estimates and associated risk factors are limited but highly essential for policymakers to frame locally relevant policies and evidence-based public health interventions. Therefore, this study aims to determine the prevalence at the national level and to identify the factors associated with PA in India among adults aged 18–69 years using population-based data from the National Noncommunicable Disease Monitoring Survey (NNMS).

Methods

The NNMS was a national, community-based, cross-sectional survey conducted during 2017–2018 in India.20 The survey aimed to generate national estimates of key NCD indicators identified in the National NCD Monitoring Framework among adults and adolescents aged 15–69 years. The NNMS was coordinated and implemented by the Indian Council of Medical Research (ICMR)–National Centre for Disease Informatics and Research, Bengaluru, in collaboration with 10 reputed institutes across the country.

Sampling Design and Study Tools

The survey adopted a multistage cluster sampling design that covered a nationally representative sample of 12,000 adults aged 18–69 years selected from 600 primary sampling units (300 wards and 300 villages) across the country. The primary sampling units (PSUs) were selected using probability proportional to size method. Detailed methodology of the survey has been published elsewhere.2022

The tools used for the study were adapted from the WHO–STEPwise approach to NCD risk factor surveillance (WHO–STEPS),23 Integrated Disease Surveillance Project–NCD risk factor survey,24 and WHO–Global Adult Tobacco Survey.25 Questions on PA were derived from the WHO–Global Physical Activity Questionnaire.26 All study tools were developed in English and later translated into 11 regional languages of India. Data were collected using the Open Data Kit, an Android-based offline application.27

Ethical Approval and Consent

The Institutional Ethics Committee at the ICMR–National Centre for Disease Informatics and Research approved the study (approval no: NCDIR/IEC/2017/4 dated February 03, 2017). All 10 implementing agencies—All India Institute of Medical Sciences (AIIMS)–New Delhi,  AIIMS–Jodhpur, AIIMS–Bhopal, AIIMS–Bhubaneshwar, National Centre for Disease Control–New Delhi, Assam Medical College, Dibrugarh, BJG Medical College and Sassoon General Hospitals–Pune, National Institute of Nutrition (ICMR)–Hyderabad, Sree Chitra Tirunal Institute for Medical Sciences and Technology–Trivandrum, National Institute of Epidemiology (ICMR)–Chennai—received ethical clearance from their respective Institutional Ethics Committees. Study overview and purpose was briefed to the selected participants, and a written informed consent was obtained from all those who voluntarily agreed to participate in the survey. All participants were provided with a pamphlet in their local language on aspects of health promotion relevant to NCDs. Those who reported inadequate PA were counseled to take steps to increase it.

Measures

The PA was assessed using the Global Physical Activity Questionnaire,26 which covered self-reported physical activities in the following 3 domains in a typical week: home/work place, travel-related, and leisure time (including sports, fitness, and recreational activities). Under each domain, the activities were recorded based on the level of intensity—vigorous- and moderate-intensity levels of activity. Each domain included questions corresponding to the time spent in PA by level of intensity that included the number of days in a typical week and number of hours or minutes in a typical day.

The total time spent in PA was calculated as the sum of time spent across all the 3 domains. Adults who spent <150 minutes of moderate-intensity PA per week or <75 minutes of vigorous-intensity PA per week or an equivalent combination of moderate- and vigorous-intensity PA accumulating <600 metabolic equivalent of task (MET) minutes per week were defined as adults with insufficient PA.4 Along with the 3 domains, the questions also included time spent being sedentary excluding time spent sleeping in a typical day. Sedentary time included time spent sitting or reclining, time spent watching television or working on a computer or playing games on a mobile/tablet or talking to friends, or doing other sitting activities like knitting, embroidery, etc.

In addition, survey information collected on sociodemographic characteristics—behavioral risk factors (smoked tobacco use, alcohol consumption) and metabolic risk factors (overweight/obesity, central obesity, raised blood pressure, raised fasting blood glucose, and raised cholesterol)—were included for this analysis. Estimates on behavioral and metabolic indicators were derived using standard definitions (Supplementary Table S1 [available online]).4,20 Ten-year cardiovascular risk estimates for adults aged 40–69 years were determined using the WHO–International Society of Hypertension Cardiovascular Disease Risk Prediction Charts (2007) for South East Asia region according to age, sex, systolic blood pressure, current smoked tobacco use, and diabetes (previously diagnosed/fasting blood glucose concentration ≥ 126 mg/dL).28 Wealth index was derived using the principal component analysis method, which is a composite index of assets and housing characteristics classified into quintiles.29

Statistical Analysis

Sampling weight for individual adults was calculated considering the sampling design adjusted for nonresponse to account for unequal probabilities of selection and scale the PSUs to national level. Detailed methodology is published elsewhere.20 Cleaned and weighted data were analyzed using SPSS statistical software (version 27.0) by complex samples analysis module considering the sampling weight, stratification, and clustering. Descriptive analysis was presented as proportions/means with 95% confidence interval (CI). Age-standardized prevalence estimates of PA were weighted using the WHO World Standard Population (2000–2025).30 Independent t test was applied to find the significant differences in average time spent in PA across sex and place of residence strata. Univariate logistic regression analysis was performed to identify the factors significantly associated with PA, and odds ratio (OR) with 95% CI was calculated. Multivariate logistic regression analysis was further performed, taking into account those factors significant in univariate analysis, and adjusted odds ratio (aOR) was reported. A P value <.05 was considered statistically significant.

Results

Out of 12,000 households selected, 10,659 adults completed the survey with an overall response rate of 96.3%. The present study included 10,554 adults for the analysis and excluded 105 adults who reported total number of hours of PA exceeding 18 hours per day (Supplementary Figure S1 [available online]). After adding the weights standardized for the total sample size, the results represent a sample of 10,560 adults.

Figure 1 presents the prevalence (crude and age standardized estimate) of insufficient PA among the respondents. Four out of 10 respondents (crude prevalence [CP] = 41.3%; 95% CI, 39.4–43.3) did not meet the WHO recommendations for a minimum of 600 MET per week. More than half (CP = 52.4%; 95% CI, 50.0–54.7) of women reported doing insufficient PA in comparison with one-third (CP = 30.9%; 95% CI, 28.3–33.7) of men. Similarly, a higher proportion of urban residents (CP = 51.7%; 95% CI, 48.6–54.8) were doing insufficient PA compared with rural (CP = 36.1%, 95% CI, 33.9–38.3) residents. Age-standardized insufficient PA prevalence estimates were almost similar (0.1% higher among both sex and rural residents, 0.2% lower among urban residents) compared with crude estimates.

Figure 1
Figure 1

—Prevalence of insufficient PA among adults (18–69 y) in India. PA indicates physical activity.

Citation: Journal of Physical Activity and Health 19, 3; 10.1123/jpah.2021-0688

Table 1 gives the general characteristics of the respondents and risk factors according to their levels of PA. A total of 69.6% of the respondents were in the age group of 18–44 years, 79.7% were married, 71.2% had school or higher education, and 56.3% were employed. Almost half (48.8%; 95% CI, 45.6–52.0) of homemakers reported insufficient PA. One-third of adults with insufficient PA were overweight (31.7%; 95% CI, 29.3–34.3) and had raised blood pressure (31.4%; 95% CI, 29.4–33.4).

Table 1

General Characteristics of Adults (18–69 Years) in India According to Levels of PA

Levels of PA
SufficientInsufficientOverall
Variablesn% (95% CI)n% (95% CI)n% (95% CI)
Sex
 Men376460.7 (58.8–62.6)168738.7 (36.1–41.3)545151.6 (50.2–53.0)
 Women243439.3 (37.4–41.2)267561.3 (58.7–63.9)510948.4 (47.0–49.8)
Age groups, y
 18–44441771.3 (69.3–73.1)293167.2 (64.9–69.5)734869.6 (67.9–71.3)
 45–69178128.7 (26.9–30.7)143132.8 (30.5–35.1)321230.4 (28.7–32.1)
Place of residence
 Rural449072.4 (68.5–76.0)253358.1 (53.0–63.0)702366.5 (62.4–70.4)
 Urban170827.6 (24.0–31.5)182941.9 (37.0–47.0)353733.5 (29.6–37.6)
Marital status
 Never married86614.0 (12.6–15.4)52111.9 (10.6–13.4)138713.1 (12.1–14.2)
 Divorced/ widowed/separated3896.3 (5.4–7.3)3708.5 (7.5–9.6)7597.2 (6.5–7.9)
 Married494379.7 (78.1–81.3)347179.6 (77.9–81.1)841479.7 (78.5–80.8)
Education status
 No education183029.5 (27.2–32.0)121127.8 (24.8–31.0)304128.8 (26.8–30.9)
 School education374460.4 (58.0–62.7)246156.4 (53.2–59.6)620558.8 (56.7–60.8)
 Degree or higher62410.1 (8.7–11.7)69015.8 (13.8–18.1)131412.4 (11.1–13.9)
Occupation statusa
 Employed436070.4 (68.1–72.5)157836.2 (33.2–39.4)593856.3 (54.2–58.3)
 Student2694.3 (3.6–5.2)2084.8 (4.0–5.8)4774.5 (3.9–5.2)
 Unemployed2293.7 (3.0–4.5)44210.2 (8.9–11.6)6716.4 (5.6–7.2)
 Homemaker133921.6 (19.7–23.6)212548.8 (45.6–52.0)346432.8 (30.9–34.9)
Wealth index
 First quintile189230.5 (27.0–34.3)91020.9 (17.4–24.8)280226.5 (23.3–30.0)
 Second quintile155625.1 (23.0–27.3)86219.8 (17.7–22.0)241823.0 (21.1–24.7)
 Third quintile117018.9 (17.0–20.9)88320.2 (18.2–22.4)205319.4 (17.8–21.2)
 Fourth quintile91614.8 (13.1–16.6)83819.2 (17.3–21.3)175416.6 (15.1–18.2)
 Fifth quintile66510.7 (8.9–12.8)86819.9 (17.0–23.2)153314.5 (12.5–16.8)
Current smoked tobacco use
 Yes73811.9 (10.3–13.7)2876.6 (5.4–8.0)10259.7 (8.5–11.1)
 No546088.1 (86.3–89.7)407593.4 (92.0–94.6)953590.3 (89.0–91.5)
Current alcohol use
 Yes121219.6 (17.3–22.0)46010.6 (9.1–12.2)167215.8 (14.2–17.6)
 No498680.4 (78.0–82.7)390289.4 (87.8–90.9)888884.2 (82.3–85.8)
BMIa
 Underweight/normal469377.6 (75.4–79.7)288568.3 (65.7–70.8)757973.8 (71.8–75.7)
 Overweight135422.4 (20.3–24.7)133931.7 (29.3–34.3)269326.2 (24.3–28.2)
Central obesitya
 Yes161226.6 (24.1–29.2)172440.7 (38.0–43.4)333632.4 (30.1–34.7)
 No444673.4 (70.8–75.9)251659.3 (56.6–62.0)696267.6 (65.3–69.9)
Raised blood pressurea
 Yes162226.4 (24.4–28.5)136531.4 (29.4–33.4)298728.5 (26.9–30.1)
 No452473.6 (71.5–75.6)298468.6 (66.6–70.6)750871.5 (69.9–73.1)
Raised fasting blood glucosea
 Yes4387.8 (6.6–9.1)46311.6 (10.1–13.2)9019.4 (8.3–10.5)
 No518792.2 (90.9–93.4)353988.4 (86.8–89.9)872690.6 (89.5–91.7)
Reported raised cholesterol
 Yes601.0 (0.7–1.4)811.9 (1.3–2.7)1411.3 (1.0–1.8)
 No613899.0 (98.6–99.3)428198.1 (97.3–98.7)1041998.7 (98.2–99.0)

Abbreviations: BMI, body mass index; CI, confidence intervals; MET, metabolic equivalent of task; PA, physical activity; WHO, World Health Organization. Note: Levels of PA = insufficient PA was defined as <600 MET minutes per week and sufficient activity as ≥600 MET minutes per week. Current smoked tobacco use = adults used smoke tobacco products, such as bidis, cigarettes, cigars, chillum, pipes, hookah, or any other local smoked tobacco products in the last 12 months preceding the survey. Current alcohol use = adults who consumed alcohol in the last 12 months preceding the survey. BMI = (as per WHO classification)—underweight/normal: ≤24.9 kg/m2; overweight: ≥25.0 kg/m2. Raised blood pressure = adults whose systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg including those on medication for raised blood pressure. Raised fasting blood glucose = adults whose fasting blood glucose were ≥126 mg/dL including those on medication for raised blood glucose. Raised cholesterol = adults those who reported being diagnosed as having raised blood cholesterol either by a doctor or health worker. Central obesity = adults whose waist circumference ≥90 cm for men and ≥80 cm for women (as per South Asia Pacific Guidelines).

aNo of participants excludes “no response.”

Table 2 presents the average time spent in PA per day by place of residence and sex. Rural respondents spent an average of 101.1 minutes per day (95% CI, 93.5–108.7) doing PA, which was significantly higher than those reported by urban respondents (62.2 min/d; 95% CI, 54.2–70.2). Rural residents reported higher levels of vigorous- and moderate-intensity activity; whereas, leisure-time activity and sedentary behaviors were higher in urban residents. The time spent in PA at home/workplace by rural adults (65.1 min/d; 95% CI, 59.2–71.0) was almost twice that by adults from urban areas. A similar pattern was observed for travel-related activity. The time spent in PA by men was consistently higher than by women for all types of activities and across place of residence. Home/workplace activity contributed majorly to PA among women across place of residence (Figure 2).

Table 2

Time (in Minutes) Spent in PA per Day by Adults (18–69 Years) in India

UrbanRuralUrbanRural
MenWomenMenWomen
VariablesMean (95% CI)P valueMean (95% CI)P valueMean (95% CI)P value
Total minutes of PA per day80.5 (69.1–91.8)41.7 (34.9–48.6)<.001*138.9 (128.1–149.7)61.9 (53.7–70.0)<.001*62.2 (54.2–70.2)101.1 (93.5–108.7)<.001*
PA by level of intensity  
 Vigorous-intensity activity9.4 (7.1–11.7)1.1 (0.7–1.6)<.001*17.5 (15.3–19.7)4.0 (3.2–4.9)<.001*5.5 (4.2–6.8)10.9 (9.6–12.2)<.001*
 Moderate-intensity activity71.1 (60.7–81.4)40.6 (34.0–47.3)<.001*121.3 (111.3–131.4)57.8 (50.3–65.4)<.001*56.7 (49.2–64.2)90.2 (83.2–97.2)<.001*
PA by domain 
 Leisure-time activity28.2 (22.3–34.0)5.5 (3.2–7.8)<.001*20.4 (16.3–24.6)2.4 (0.9–3.9)<.001*17.5 (13.9–21.0)11.6 (9.4–13.8)<.001*
 Home/workplace-related activity37.0 (29.0–45.0)30.0 (24.4–35.6)<.003*83.3 (75.2–91.4)46.3 (39.9–52.7)<.001*33.7 (27.6–39.9)65.1 (59.2–71.0)<.001*
 Travel-related activity15.3 (12.7–17.9)6.3 (4.8–7.7)<.001*35.1 (30.7–39.6)13.2 (10.8–15.6)<.001*11.0 (9.4–12.7)24.4 (21.7–27.1)<.001*
 Sedentary activity314.8 (292.5–337.0)335.2 (315.6–354.7)<.002*277.7 (260.4–295.3)325.2 (306.8–343.7)<.001*324.4 (304.8–343.9)301.1 (285.3–316.9)<.001*

Abbreviations: CI, confidence interval; PA, physical activity. Note: Vigorous-intensity activity included activities which took hard physical effort and made adults breathe much harder than normal. Moderate-intensity activity included activities which took moderate physical effort and made adults breathe somewhat harder than normal. Leisure-time activity was defined as combination of vigorous (sports, fitness related or recreational) and moderate (swimming, cycling, volleyball etc) level activities done during recreation time. Total minutes of PA per day included sum of vigorous- and moderate-intensity activity or sum of leisure-time activity, home/workplace-related activity, and travel-related activity. Sedentary activity included activities like sitting, reclining and watching television, working on a computer, playing games in mobile/tablet, talking with friends, or doing other sitting activities like knitting, embroidery etc, including time spent in school/college/office and excluding time spent sleeping.

*Significant at 5% alpha level using independent t test.

Figure 2
Figure 2

—Composition of time spent in PA by domain among adults (18–69 y) in India. PA indicates physical activity.

Citation: Journal of Physical Activity and Health 19, 3; 10.1123/jpah.2021-0688

Table 3 gives the factors associated with insufficient PA in adults. Multivariate logistic regression analysis showed that residential status, marital status, education status, occupation status, economic status, central obesity, and raised blood pressure were significantly associated with insufficient PA. Insufficient PA was more among adults who were divorced/widowed/separated (aOR = 1.76; 95% CI, 1.24–2.49; P = .002), with a degree or higher education (aOR = 1.73; 95% CI, 1.25–2.40; P < .001), and unemployed (aOR = 6.45; 95% CI, 4.98–8.34; P < .001) compared with never married, uneducated, and employed adults, respectively. Adults in the highest wealth quintile reported insufficient PA twice higher than those in the lowest quintile. Presence of raised blood pressure and central obesity were found to be significantly associated with increased odds of insufficient PA. Factors, namely current use of smoked tobacco, current alcohol use, BMI, raised fasting blood glucose, and reported raised cholesterol, which showed association in univariate analysis, became insignificant predictors of insufficient PA in multivariate analysis.

Table 3

Factors Associated With PA (Insufficient Versus Sufficient) Among Adults (18–69 Years) in India

Total minutes of PA per dayUnivariate analysisMultivariate analysis
VariablesMean (95% CI)OR (95% CI)P valueaOR (95% CI)P value
Sex
 Men118.8 (109.9–127.7)11
 Women55.3 (49.4–61.2)2.45 (2.11–2.85)<.001*1.15 (0.89–1.48).285
Age groups, y
 18–4491.3 (84.6–98.0)11
 45–6980.8 (73.3–88.2)1.21 (1.08–1.35).001*1.01 (0.86–1.19).862
Place of residence
 Rural101.1 (93.5–108.7)11
 Urban62.2 (54.2–70.2)1.90 (1.62–2.22)<.001*1.53 (1.21–1.93)<.001*
Marital status
 Never married102.8 (89.7–116.0)1
 Divorced/ widowed/separated83.0 (70.5–95.6)1.58 (1.23–2.05)<.001*1.76 (1.24–2.49).002*
 Married86.1 (79.9–92.3)1.17 (0.99–1.38).0681.42 (1.09–1.85).010*
Educational status
 No education98.4 (88.3–108.6)11
 School education88.5 (81.4–95.6)0.99 (0.83–1.19).9421.10 (0.88–1.37).414
 Degree or higher62.2 (51.9–72.4)1.67 (1.31–2.13)<.001*1.73 (1.25–2.40).001*
Occupation status
 Employed132.0 (122.8–141.3)1
 Student91.2 (73.6–108.7)2.14 (1.63–2.82)<.001*2.42 (1.65–3.54)<.001*
 Unemployed35.4 (26.2–44.7)5.32 (4.17–6.78)<.001*6.45 (4.98–8.34)<.001*
 Homemaker22.7 (21.3–24.1)4.39 (3.70–5.20)<.001*3.98 (3.08–5.15)<.001*
Wealth index
 First quintile109.5 (91.1–121.8)1
 Second quintile104.1 (93.9–114.3)1.15 (0.95–1.40).1431.09 (0.91–1.31).343
 Third quintile84.6 (74.0–95.2)1.57 (1.27–1.94)<.001*1.38 (1.10–1.73).005*
 Fourth quintile69.5 (61.7–77.3)1.91 (1.53–2.37)<.001*1.43 (1.08–1.88).012*
 Fifth quintile49.5 (42.0–57.1)2.72 (2.11–3.49)<.001*1.86 (1.32–2.60)<.001*
Current smoked tobacco use
 No134.4 (116.4–152.5)11
 Yes83.1 (77.1–89.0)0.52 (0.41–0.66)<.001*0.96 (0.75–1.22).719
Current alcohol use
 No131.0 (116.5–145.6)11
 Yes80.0 (74.2–85.8)0.49 (0.41–0.58)<.001*0.86 (0.69–1.06).151
BMI
 Underweight/normal98.7 (91.7–105.7)11
 Overweight61.7 (54.8–68.6)1.61 (1.40–1.85)<.001*0.91 (0.74–1.12).394
Central obesity
 No104.2 (96.9–111.5)11
 Yes56.9 (50.5–63.4)1.89 (1.66–2.15)<.001*1.24 (1.01–1.53).036*
Raised blood pressure
 No92.1 (85.5–98.8)11
 Yes77.5 (69.9–85.1)1.28 (1.12–1.45)<.001*1.22 (1.05–1.42).011*
Raised fasting blood glucose
 No90.7 (84.4–97.0)11
 Yes64.6 (53.3–75.9)1.55 (1.27–1.90)<.001*1.08 (0.85–1.37).539
Reported raised cholesterol
 No88.5 (82.5–94.6)11
 Yes53.4 (33.8–73.0)1.91 (1.18–3.11).009*0.97 (0.53–1.78).932

Abbreviations: aOR, adjusted OR; BMI, body mass index; CI, confidence interval; OR, odds ratio; PA, physical activity; WHO, World Health Organization. Note: Current smoked tobacco use = adults used smoke tobacco products, such as bidis, cigarettes, cigars, chillum, pipes, hookah, or any other local smoked tobacco products in the last 12 months preceding the survey. Current alcohol use = adults who consumed alcohol in the last 12 months preceding the survey. BMI = (as per WHO classification)—underweight/normal: ≤24.9 kg/m2; overweight: ≥25.0 kg/m2. Raised blood pressure = adults whose systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg including those on medication for raised blood pressure. Raised fasting blood glucose = adults whose fasting blood glucose were ≥126 mg/dL including those on medication for raised blood glucose. Raised cholesterol = adults those who reported being diagnosed as having raised blood cholesterol either by a doctor or health worker. Central obesity = adults whose waist circumference ≥90 cm for men and ≥80 cm for women (as per South Asia Pacific Guidelines).

*Significant at 5% alpha level.

Table 4 presents the 10-year CVD risk for adults aged 40–69 years in India according to levels of PA. Positive association was found between insufficient PA and 10-year CVD risk, with higher odds of insufficient PA (OR = 2.05; 95% CI, 1.36−3.08; P = .001) among adults with >30% 10-year CVD risk. However, the association was not significant when adjusted for sociodemographic and lifestyle factors.

Table 4

The 10-Year CVD Risk for Adults (40–69 Years) in India by Levels of PA

Levels of PA
SufficientInsufficientUnivariate analysisMultivariate analysis
10-y CVD riskn% (95% CI)n% (95% CI)P valueOR (95% CI)P valueaOR# (95% CI)P value
10%116779.1 (76.3–81.6)847 69.6 (66.2–72.8)11
20%15110.2 (8.5–12.3)158 12.9 (11.0–15.2).012*1.44 (1.08–1.90).012*1.09 (0.78–1.52).207
30%906.1 (4.7–7.7)1119.2 (7.5–11.2).003*1.71 (1.21–2.43).003*1.20 (0.77–1.87).346
>30%684.6 (3.5–6.0)1008.3 (6.5–10.5).001*2.05 (1.36–3.08).001*1.39 (0.83–2.31).637

Abbreviations: aOR, adjusted OR; CI, confidence interval; CVD, cardiovascular disease; OR, odds ratio; PA, physical activity.

*Significant at 5% alpha level. #Adjusted for all variables provided in Table 3.

Discussion

This article provides a comprehensive assessment of PA and its correlates from a nationally representative sample of adult men and women residing in urban and rural areas of India. The present study found that 41.3% of adults in India were doing insufficient PA. This was found to differ in various national level studies: 34.0%, pooled analysis of population-based survey5; 54.4%, ICMR–INDIAB study (phase 1) (2008–2010)14; 35% among older adults aged ≥45 years, Longitudinal Aging Study in India Wave 1 (2017–2018) compared with 45% of adults aged between 45 and 69 years in the present study31; and 20.0%, multicenter PAN India study (2017).32 These differences could be attributed to the population covered, study design, sample size, study tool used, and study period. Although similar findings were reported in various studies conducted in parts of India and South Asian countries, estimates on national prevalence of insufficient PA among adults aged 18–69 years were inadequate.3335

Available studies on PA in India differ in either sample national representativeness, or use of standard methodology tools and indicator definitions or a combination of these to be considered as a baseline for monitoring the global and national NCD monitoring framework targets including PA.5,14,31,32 The current study was specifically designed to meet the requirements of the monitoring framework and thus addresses all the limitations of the available studies. Future assessments on the progress made toward the NCD targets including PA can be measured by adapting the current survey methods and tools. Hence, the prevalence found in this study can be considered as baseline for NCD global monitoring framework PA target assessment for India.

The sociodemographic characteristics of the respondents were comparable with demographic data from Census of India 2011, except for the minimal difference (<3%) in literate proportion. This difference could be due to growing population and difference in data collection time periods.36

Men in India were found to be more physically active that women. Lower levels of PA among women could be attributed to their perception, cultural norms, inadequate time, and motivation, especially among urban women residents.37 Also, men spent more time in all types of PA (vigorous, moderate, and leisure-time activity at home/workplace as well as during travel). These results align with the studies published across various parts of India33,38,39 as well as in other countries.34,4043

The present study found that insufficient PA was significantly higher (aOR = 1.53; 95% CI, 1.21–1.93) among urban respondents (51.7%) than rural respondents (36.1%). Economic growth, rapid urbanization, and improper planning in the urban areas has led to environmental problems, disturbed lifestyle, and reduced PA.44 Hence, appropriate urban planning that includes structured residences, work places, educational institutes, green tree canopies on the sides of the road, pedestrian-centric roads, and increased use of public transport should be promoted.45 This would profoundly influence the PA levels of urban population, positively impacting mental and physical well-being of community.

The present study found that rural respondents spent more time in PA at workplace/home and during travel than their urban counterparts. This was similar to the findings reported by studies undertaken in the neighboring countries like Bangladesh46 and Nepal47 as well in the multicountry comparison study across the world.48 Agriculture is the major source of employment in rural areas of India and requires labor-intensive physical work, thus explaining the study findings. In contrast, urban adults spent more time in leisure–ime PA, owing to the sedentary time–bound nature of work and enabling environment. A similar pattern was observed in studies from parts of India.19 However, it was different than those reported by studies from neighboring countries like Nepal where, irrespective of the place of residence, leisure-time PA was the least contributor to PA.47

Several factors were found to be associated with insufficient PA in the present study. The PA level among Indian adults was significantly associated with marital and employment status, with sufficient PA levels higher in never-married and employed adults. The majority in these categories were men and rural respondents. Never-married adults also showed lower prevalence of other behavioral and metabolic risk factors like consumption of alcohol (14.7%), use of smoked tobacco (12.2%), raised blood pressure (15.6%), and raised fasting blood glucose (2.6%); thus, indicating healthy behaviors in this group of adults. However, employed adults had higher prevalence of risk factors—alcohol use (24.6%), smoked tobacco use (19.6%), raised blood pressure (29.7%), and raised fasting blood glucose (8.7%). Home/workplace-related activity accounted for 65.2% of total PA for employed adults, hence, leading to the higher levels of sufficient PA among them. These findings are also comparable with the risk-factor survey results of the Nepalese population.49

A significantly higher proportion of adults doing insufficient PA (aOR range = 1.38–1.86) belonged to the high socioeconomic group. Several studies conducted across the globe irrespective of being developed or developing countries showed similar results.46,47,50 Those in the high socioeconomic group derived their METs from leisure-time activities, indicating the need for domain-specific interventions. Home/workplace-related activity contributed to higher levels of PA among lower socioeconomic groups. With economic growth and rapid urbanization, lifestyle changes like walking and cycling to work, engaging in leisure-time activities at the individual level, and an enabling environment at the workplace at the community level should be ensured to improve PA. Raised blood pressure and central obesity were significantly associated with insufficient levels of PA. Bidirectional causality is also plausible between insufficient PA and metabolic risk factors of NCDs. Educational status, BMI, current smoked tobacco use, current alcohol use, raised fasting blood glucose, and raised cholesterol were not significantly associated with insufficient PA. However, studies from other countries reported association with educational status, diabetes, and obesity.51 A recent study demonstrated that an increase in moderate-intensity PA would reduce the incidence of CVD.52 Present study findings demonstrate that insufficient PA contributed to a greater 10-year CVD risk in Indians. Evidence suggests that sufficient levels of PA reduce the possible risk of development of NCDs.53 Strong public health initiatives in promoting regular and sufficient PA through multisectoral coordination can mitigate the burden of NCDs.

The main strengths of the survey can be attributed to rigorous methodology, which includes a nationally representative sample, multistage sampling design, and a well-structured, reliable study tool that supports global comparison of NCD indicators.21,22 Considering the limitations of other existing national and subnational level estimates, the prevalence of insufficient PA estimated in this study can be considered as baseline for target assessment of sustainable development goals. Use of the Global Physical Activity Questionnaire study tool enabled the analysis of PA by domain (home/workplace-related, travel-related, and leisure time) and by level of intensity (moderate and vigorous PA). However, detailed assessment of sedentary activity could be explored in future studies which would provide more insights on the impact of technology-driven leisurely activities. The study excluded time spent sleeping which limited the evaluation of 24-hour activity. Furthermore, estimation of MET values from self-reported data to Indian population could be supplemented using objective measurements like accelerometers to subsamples of the study, thus improving the reliability of the results.

Conclusion

The national level NCD monitoring survey provides the baseline prevalence of insufficient PA to monitor the set PA targets for India by 2025. Prevalence of insufficient PA was found to be high among Indian adults and necessitates promotion of leisure-time activity with special attention to women and travel/work-related activity among urban residents. The study determined that residential status, marital status, occupation status, economic status, raised blood pressure, and central obesity were significantly associated with PA. The identified associated factors can guide policy makers to develop tailored intervention strategies targeting high-risk populations and a multisectoral approach to promote PA among adults. It is time for the country to have an evidence-driven policy on PA for promoting the health of its citizens, thus achieving the targeted reduction in physical inactivity.

Acknowledgments

The authors acknowledge the support and facilitation provided by the Ministry of Health and Family Welfare, Government of India, ICMR, WHO, expert panel of the national technical working group of the survey and all the ICMR–NNMS investigators and collaborators. We also thank all participants for providing the required information for this study. The NNMS was funded by the Ministry of Health and Family Welfare, Government of India (Dy. No.C-707, dated July 06, 2015). The funders had no role in the study planning, implementation, and preparation of this manuscript.

References

  • 1.

    World Health Organisation. Noncommunicable Diseases. 2021. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases. Accessed December 14, 2021.

    • Search Google Scholar
    • Export Citation
  • 2.

    World Health Organisation. Physical Activity. 2020. https://www.who.int/news-room/fact-sheets/detail/physical-activity. Accessed July 27, 2021.

    • Search Google Scholar
    • Export Citation
  • 3.

    World Health Organisation. Noncommunicable Diseases. 2021. https://www.who.int/westernpacific/health-topics/noncommunicable-diseases. Accessed August 17, 2021.

    • Search Google Scholar
    • Export Citation
  • 4.

    World Health Organization. Noncommunicable Diseases Global Monitoring Framework: Indicator Definitions and Specifications. 2014. https://www.who.int/nmh/ncd-tools/indicators/GMF_Indicator_Definitions_Version_NOV2014.pdf. Accessed May 16, 2021.

    • 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 [published correction appears in Lancet Glob Health. 2019 Jan;7(1):e36]. Lancet Glob Health. 2018;6(10):e1077e1086. PubMed ID: 30193830 doi:10.1016/S2214-109X(18)30357-7

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

    World Health Organization. Global action plan on physical activity 2018–2030: more active people for a healthier world. 2018. https://apps.who.int/iris/bitstream/handle/10665/272721/WHO-NMH-PND-18.5-eng.pdf. Accessed July 27, 2021.

    • Search Google Scholar
    • Export Citation
  • 7.

    Sallis JF, Bull F, Guthold R, et al. Progress in physical activity over the Olympic quadrennium. Lancet. 2016;388(10051):13251336. PubMed ID: 27475270 doi:10.1016/S0140-6736(16)30581-5

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Rodrigues DN, Mussi RFF, Almeida CB, Nascimento JRA Jr, Moreira SR, Carvalho FO. Sociodemographic determinants associated with physical activity level of quilombolas in the Brazilian state of Bahia: 2016 survey. Determinantes sociodemográficos associados ao nível de atividade física de quilombolas baianos, inquérito de 2016. Epidemiol Serv Saude. 2020;29(3):e2018511. PubMed ID: 32667457 doi:10.5123/s1679-49742020000300019

    • Search Google Scholar
    • Export Citation
  • 9.

    Baretta E, Baretta M, Peres KG. Nível de atividade física e fatores associados em adultos no Município de Joaçaba, Santa Catarina, Brasil [Physical activity and associated factors among adults in Joaçaba, Santa Catarina, Brazil]. Cad Saude Publica. 2007;23(7):15951602. PubMed ID: 17572808 doi:10.1590/S0102-311X2007000700010

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

    Chikafu H, Chimbari MJ. Levels and correlates of physical activity in rural Ingwavuma Community, uMkhanyakude District, KwaZulu-Natal, South Africa. Int J Environ Res Public Health. 2020;17(18):6739. doi:10.3390/ijerph17186739

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

    Werneck AO, Baldew SS, Miranda JJ, et al. Physical activity and sedentary behavior patterns and sociodemographic correlates in 116,982 adults from six South American countries: the South American physical activity and sedentary behavior network (SAPASEN). Int J Behav Nutr Phys Act. 2019;16(1):68. PubMed ID: 31429772 doi:10.1186/s12966-019-0839-9

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

    Institute for Health Metrics and Evaluation (IHME). GBD Compare Data Visualization. 2021. http://vizhub.healthdata.org/gbd-compare. Accessed August 18, 2021.

    • Search Google Scholar
    • Export Citation
  • 13.

    Ministry of Health & Family Welfare Government of India. National Action Plan and Monitoring  Framework for Prevention and Control of Noncommunicable Diseases (NCDs) in India. 2013. https://www.iccp-portal.org/system/files/plans/India%20-%20National_Action_Plan_and_Monitoring_Framework_Prevention_NCD_2013.pdf. Accessed July 04, 2021.

    • Search Google Scholar
    • Export Citation
  • 14.

    Anjana RM, Pradeepa R, Das AK, et al. Physical activity and inactivity patterns in India - results from the ICMR-INDIAB study (Phase-1) [ICMR-INDIAB-5]. Int J Behav Nutr Phys Act. 2014;11(1):26. doi:10.1186/1479-5868-11-26

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

    Adlakha D, Parra DC. Mind the gap: gender differences in walkability, transportation and physical activity in urban India. J Transp Health. 2020;18:100875. doi:10.1016/j.jth.2020.100875

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

    Aslesh OP, Mayamol P, Suma RK, Usha K, Sheeba G, Jayasree AK. Level of physical activity in population aged 16 to 65 years in rural Kerala, India. Asia Pac J Public Health. 2016;28(suppl 1):53S61S. doi:10.1177/1010539515598835

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

    Devi KS, Nilupher, Gupta U, Dhall M, Kapoor S. Incidence of obesity, adiposity and physical activity pattern as risk factor in adults of Delhi, India. Clin Epidemiol Glob Health. 2020;8(1):812. doi:10.1016/j.cegh.2019.03.008

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

    Singh H, Singh S, Singh A, Baker JS. Physical activity levels among the adults of Majha region of Punjab, India: a cross-sectional study [published online ahead of print, 2020 Nov 10]. Am J Hum Biol. 2020;33(6):e23533. PubMed ID: 33174286 doi:10.1002/ajhb.23533

    • Search Google Scholar
    • Export Citation
  • 19.

    Newtonraj A, Murugan N, Singh Z, Chauhan RC, Velavan A, Mani M. Factors associated with physical inactivity among adult urban population of Puducherry, India: a population based cross-sectional study. J Clin Diagn Res. 2017;11(5):LC15LC17. PubMed ID: 28658812 doi:10.7860/jcdr/2017/24028.9853

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

    Indian Council of Medical Research - National Centre for Disease Informatics and Research. National Noncommunicable disease monitoring Survey (NNMS) 2017-18. 2020. https://www.ncdirindia.org/nnms/. Accessed May 14, 2021.

    • Search Google Scholar
    • Export Citation
  • 21.

    Mathur P, Kulothungan V, Leburu S, et al. National noncommunicable disease monitoring survey (NNMS) in India: estimating risk factor prevalence in adult population. PLoS One. 2021;16(3):e0246712. PubMed ID: 33651825 doi:10.1371/journal.pone.0246712

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

    Mathur P, Kulothungan V, Leburu S, et al. Baseline risk factor prevalence among adolescents aged 15-17 years old: findings from national non-communicable disease monitoring survey (NNMS) of India. BMJ Open. 2021;11(6):e044066. PubMed ID: 34187814 doi:10.1136/bmjopen-2020-044066

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

    World Health Organization. STEPwise Approach to NCD Risk Factor Surveillance (STEPS). 2003. https://www.who.int/teams/noncommunicable-diseases/surveillance/systems-tools/steps. Accessed July 04, 2021.

    • Search Google Scholar
    • Export Citation
  • 24.

    Indian Council of Medical Research–National Institute of Medical Statistics. IDSP Non-communicable Disease Risk Factors Survey, Phase-I States of India, 2007–08. 2009. https://www.who.int/ncds/surveillance/steps/2007_STEPS_Report_India_7States.pdf. Accessed May 14, 2021.

    • Search Google Scholar
    • Export Citation
  • 25.

    Global Adult Tobacco Survey Collaborative Group. Global Adult Tobacco Survey (GATS): Core Questionnaire with Optional Questions, Version 2.0. Atlanta, GA: Centers for Disease Control and Prevention, 2010. https://www.who.int/tobacco/surveillance/en_tfi_gats_corequestionnairewithoptionalquestions_v2_FINAL_03Nov2010.pdf. Accessed July 04, 2021.

    • Search Google Scholar
    • Export Citation
  • 26.

    World Health Organization. Global Physical Activity Questionnaire (GPAQ) Analysis Guide. 2006. http://www.who.int/chp/steps/resources/GPAQ_Analysis_Guide.pdf. Accessed July 04, 2021.

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

    ODK Open Data Kit. 2016. https://opendatakit.org/. Accessed July 04, 2021.

  • 28.

    World Health Organization/ISH Risk prediction charts for 14 WHO epidemiological sub-regions [internet]. 2007. https://www.who.int/ncds/management/WHO_ISH_Risk_Prediction_Charts.pdf?ua=1. Accessed July 04, 2021.

    • Search Google Scholar
    • Export Citation
  • 29.

    Vyas S, Kumaranayake L. Constructing socio-economic status indices: how to use principal components analysis. Health Policy Plan. 2006;21(6):459468. PubMed ID: 17030551 doi:10.1093/heapol/czl029

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

    Ahmad OB, Pinto CB, Lopez AD, Murray CJL, Lozano R, Inoue M. Age Standardization of Rates: A New WHO Standard. GPE Discussion Paper Series: No 31, World Health Organization. 2001. ···https://www·who·int/healthinfo/paper31·pdf. Accessed July 14, 2021.

    • Search Google Scholar
    • Export Citation
  • 31.

    International Institute for Population Sciences (IIPS), National Programme for Health Care of Elderly (NPHCE), MoHFW, Harvard T. H. Chan School of Public Health (HSPH) and the University of Southern California (USC) 2020. Longitudinal Ageing Study in India (LASI) Wave 1, 2017-18, India Report. 2020. https://www.iipsindia.ac.in/sites/default/files/LASI_India_Report_2020_compressed.pdf. Accessed August 7, 2021.

    • Search Google Scholar
    • Export Citation
  • 32.

    Podder V, Nagarathna R, Anand A, Patil SS, Singh AK, Nagendra HR. Physical activity patterns in India stratified by zones, age, region, BMI and implications for COVID-19: a nationwide study. Ann Neurosci. 2020;27(3–4):193203. PubMed ID: 34556960 doi:10.1177/0972753121998507

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

    Sivanantham P, Sahoo J, Lakshminarayanan S, Bobby Z, Kar SS. Profile of risk factors for non-communicable diseases (NCDs) in a highly urbanized district of India: findings from Puducherry district-wide STEPS Survey, 2019–20. PLoS One. 2021;16(1):e0245254. doi:10.1371/journal.pone.0245254

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

    Ranasinghe CD, Ranasinghe P, Jayawardena R, Misra A. Physical activity patterns among South-Asian adults: a systematic review. Int J Behav Nutr Phys Act. 2013;10(1):116. PubMed ID: 24119682 doi:10.1186/1479-5868-10-116

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35.

    Devamani CS, Oommen AM, Mini G K, Abraham VJ, George K. Levels of physical inactivity in rural and urban Tamil Nadu, India: a cross-sectional study. J Clin Prev Cardiol. 2019;8:13–7. doi:10.4103/JCPC.JCPC_32_18.

    • Search Google Scholar
    • Export Citation
  • 36.

    Office of the Registrar General & Census Commissioner, India. Census Info. 2011. https://censusindia.gov.in/2011-Common/CensusInfo.html. Accessed December 22, 2021.

    • Search Google Scholar
    • Export Citation
  • 37.

    Mathews E, Lakshmi JK, Ravindran TK, Pratt M, Thankappan KR. Perceptions of barriers and facilitators in physical activity participation among women in Thiruvananthapuram City, India. Glob Health Promot. 2016;23(4):2736. PubMed ID: 25829405 doi:10.1177/1757975915573878

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

    Tripathy JP, Thakur JS, Jeet G, Chawla S, Jain S, Prasad R. Urban rural differences in diet, physical activity and obesity in India: are we witnessing the great Indian equalisation? Results from a cross-sectional STEPS survey. BMC Public Health. 2016;16(1):816. PubMed ID: 27538686 doi:10.1186/s12889-016-3489-8

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 39.

    Krishnan A, Shah B, Lal V, Shukla DK, Paul E, Kapoor SK. Prevalence of risk factors for non-communicable disease in a rural area of Faridabad district of Haryana. Indian J Public Health. 2008;52(3):117124. PubMed ID: 19189832

    • Search Google Scholar
    • Export Citation
  • 40.

    Khuwaja AK, Kadir MM. Gender differences and clustering pattern of behavioural risk factors for chronic non-communicable diseases: community-based study from a developing country. Chronic Illn. 2010;6(3):163170. PubMed ID: 20444764 doi:10.1177/1742395309352255

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41.

    Kolahi AA, Moghisi A, Kousha A, Soleiman-Ekhtiari Y. Physical activity levels and related sociodemographic factors among Iranian adults: results from a population-based national STEPS survey. Med J Islam Repub Iran. 2021;34:172. doi:10.47176/mjiri.34.172

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Fan M, Su M, Tan Y, et al. Gender, age, and education level modify the association between body mass index and physical activity: a cross-sectional study in Hangzhou, China. PLoS One. 2015;10(5):e0125534. doi:10.1371/journal.pone.0125534

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 43.

    Mitáš J, Cerin E, Reis RS, et al. Do associations of sex, age and education with transport and leisure-time physical activity differ across 17 cities in 12 countries? Int J Behav Nutr Phys Act. 2019;16(1):121. PubMed ID: 31796070 doi:10.1186/s12966-019-0894-2

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    United Nation. Department of Economic and Social Affairs, Population Division (2019). World Urbanization Prospects: The 2018 Revision (DT.ESA/SER.A/420). New York: United Nations. https://www.un-ilibrary.org/content/books/9789210043144/read. Accessed May 20, 2021.

    • Search Google Scholar
    • Export Citation
  • 45.

    Devarajan R, Prabhakaran D, Goenka S. Built environment for physical activity-an urban barometer, surveillance, and monitoring. Obes Rev. 2020;21(1):e12938. doi:10.1111/obr.12938

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 46.

    World Health Organization. National STEPS Survey for Non-Communicable Diseases Risk Factors in Bangladesh. https://apps.who.int/iris/handle/10665/332886. Accessed May 16, 2021.

    • Search Google Scholar
    • Export Citation
  • 47.

    World Health Organization. Noncommunicable Disease Risk Factors: STEPS Survey Nepal. 2019. https://www.who.int/docs/default-source/nepal-documents/ncds/ncd-steps-survey-2019-compressed.pdf?sfvrsn=807bc4c6_2. Accessed May 16, 2021.

    • Search Google Scholar
    • Export Citation
  • 48.

    Strain T, Wijndaele K, Garcia L, et al. Levels of domain-specific physical activity at work, in the household, for travel and for leisure among 327 789 adults from 104 countries. Br J Sports Med. 2020;54(24):14881497. PubMed ID: 33239355 doi:10.1136/bjsports-2020-102601

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 49.

    Pedisic Z, Shrestha N, Loprinzi PD, Mehata S, Mishra SR. Prevalence, patterns, and correlates of physical activity in Nepal: findings from a nationally representative study using the global physical activity questionnaire (GPAQ). BMC Public Health. 2019;19(1):864. PubMed ID: 31269984 doi:10.1186/s12889-019-7215-1

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 50.

    Gaskin CJ, Orellana L. Factors associated with physical activity and sedentary behavior in older adults from six low- and middle-income countries. Int J Environ Res Public Health. 2018;15(5):908. doi:10.3390/ijerph15050908

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 51.

    Katulanda P, Jayawardena R, Ranasinghe P, Rezvi Sheriff MH, Matthews DR. Physical activity patterns and correlates among adults from a developing country: the Sri Lanka diabetes and cardiovascular study [published correction appears in Public Health Nutr. 2013 Sep;16(9):1719. Jayawardana, Ranil [corrected to Jayawardena, Ranil]]. Public Health Nutr. 2013;16(9):16841692. PubMed ID: 22995708 doi:10.1017/S1368980012003990

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 52.

    Yerramalla MS, McGregor DE, van Hees VT, et al. Association of daily composition of physical activity and sedentary behaviour with incidence of cardiovascular disease in older adults. Int J Behav Nutr Phys Act. 2021;18(1):83. PubMed ID: 34247647 doi:10.1186/s12966-021-01157-0

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 53.

    Bassuk SS, Manson JE. Epidemiological evidence for the role of physical activity in reducing risk of type 2 diabetes and cardiovascular disease. J Appl Physiol. 2005;99(3):11931204. doi:10.1152/japplphysiol.00160.2005

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation

The authors are with the Indian Council of Medical Research–National Centre for Disease Informatics and Research, Bengaluru, India.

Mathur (director-ncdir@icmr.gov.in) is corresponding author.
  • View in gallery

    —Prevalence of insufficient PA among adults (18–69 y) in India. PA indicates physical activity.

  • View in gallery

    —Composition of time spent in PA by domain among adults (18–69 y) in India. PA indicates physical activity.

  • 1.

    World Health Organisation. Noncommunicable Diseases. 2021. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases. Accessed December 14, 2021.

    • Search Google Scholar
    • Export Citation
  • 2.

    World Health Organisation. Physical Activity. 2020. https://www.who.int/news-room/fact-sheets/detail/physical-activity. Accessed July 27, 2021.

    • Search Google Scholar
    • Export Citation
  • 3.

    World Health Organisation. Noncommunicable Diseases. 2021. https://www.who.int/westernpacific/health-topics/noncommunicable-diseases. Accessed August 17, 2021.

    • Search Google Scholar
    • Export Citation
  • 4.

    World Health Organization. Noncommunicable Diseases Global Monitoring Framework: Indicator Definitions and Specifications. 2014. https://www.who.int/nmh/ncd-tools/indicators/GMF_Indicator_Definitions_Version_NOV2014.pdf. Accessed May 16, 2021.

    • 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 [published correction appears in Lancet Glob Health. 2019 Jan;7(1):e36]. Lancet Glob Health. 2018;6(10):e1077e1086. PubMed ID: 30193830 doi:10.1016/S2214-109X(18)30357-7

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

    World Health Organization. Global action plan on physical activity 2018–2030: more active people for a healthier world. 2018. https://apps.who.int/iris/bitstream/handle/10665/272721/WHO-NMH-PND-18.5-eng.pdf. Accessed July 27, 2021.

    • Search Google Scholar
    • Export Citation
  • 7.

    Sallis JF, Bull F, Guthold R, et al. Progress in physical activity over the Olympic quadrennium. Lancet. 2016;388(10051):13251336. PubMed ID: 27475270 doi:10.1016/S0140-6736(16)30581-5

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Rodrigues DN, Mussi RFF, Almeida CB, Nascimento JRA Jr, Moreira SR, Carvalho FO. Sociodemographic determinants associated with physical activity level of quilombolas in the Brazilian state of Bahia: 2016 survey. Determinantes sociodemográficos associados ao nível de atividade física de quilombolas baianos, inquérito de 2016. Epidemiol Serv Saude. 2020;29(3):e2018511. PubMed ID: 32667457 doi:10.5123/s1679-49742020000300019

    • Search Google Scholar
    • Export Citation
  • 9.

    Baretta E, Baretta M, Peres KG. Nível de atividade física e fatores associados em adultos no Município de Joaçaba, Santa Catarina, Brasil [Physical activity and associated factors among adults in Joaçaba, Santa Catarina, Brazil]. Cad Saude Publica. 2007;23(7):15951602. PubMed ID: 17572808 doi:10.1590/S0102-311X2007000700010

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

    Chikafu H, Chimbari MJ. Levels and correlates of physical activity in rural Ingwavuma Community, uMkhanyakude District, KwaZulu-Natal, South Africa. Int J Environ Res Public Health. 2020;17(18):6739. doi:10.3390/ijerph17186739

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

    Werneck AO, Baldew SS, Miranda JJ, et al. Physical activity and sedentary behavior patterns and sociodemographic correlates in 116,982 adults from six South American countries: the South American physical activity and sedentary behavior network (SAPASEN). Int J Behav Nutr Phys Act. 2019;16(1):68. PubMed ID: 31429772 doi:10.1186/s12966-019-0839-9

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

    Institute for Health Metrics and Evaluation (IHME). GBD Compare Data Visualization. 2021. http://vizhub.healthdata.org/gbd-compare. Accessed August 18, 2021.

    • Search Google Scholar
    • Export Citation
  • 13.

    Ministry of Health & Family Welfare Government of India. National Action Plan and Monitoring  Framework for Prevention and Control of Noncommunicable Diseases (NCDs) in India. 2013. https://www.iccp-portal.org/system/files/plans/India%20-%20National_Action_Plan_and_Monitoring_Framework_Prevention_NCD_2013.pdf. Accessed July 04, 2021.

    • Search Google Scholar
    • Export Citation
  • 14.

    Anjana RM, Pradeepa R, Das AK, et al. Physical activity and inactivity patterns in India - results from the ICMR-INDIAB study (Phase-1) [ICMR-INDIAB-5]. Int J Behav Nutr Phys Act. 2014;11(1):26. doi:10.1186/1479-5868-11-26

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

    Adlakha D, Parra DC. Mind the gap: gender differences in walkability, transportation and physical activity in urban India. J Transp Health. 2020;18:100875. doi:10.1016/j.jth.2020.100875

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

    Aslesh OP, Mayamol P, Suma RK, Usha K, Sheeba G, Jayasree AK. Level of physical activity in population aged 16 to 65 years in rural Kerala, India. Asia Pac J Public Health. 2016;28(suppl 1):53S61S. doi:10.1177/1010539515598835

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

    Devi KS, Nilupher, Gupta U, Dhall M, Kapoor S. Incidence of obesity, adiposity and physical activity pattern as risk factor in adults of Delhi, India. Clin Epidemiol Glob Health. 2020;8(1):812. doi:10.1016/j.cegh.2019.03.008

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

    Singh H, Singh S, Singh A, Baker JS. Physical activity levels among the adults of Majha region of Punjab, India: a cross-sectional study [published online ahead of print, 2020 Nov 10]. Am J Hum Biol. 2020;33(6):e23533. PubMed ID: 33174286 doi:10.1002/ajhb.23533

    • Search Google Scholar
    • Export Citation
  • 19.

    Newtonraj A, Murugan N, Singh Z, Chauhan RC, Velavan A, Mani M. Factors associated with physical inactivity among adult urban population of Puducherry, India: a population based cross-sectional study. J Clin Diagn Res. 2017;11(5):LC15LC17. PubMed ID: 28658812 doi:10.7860/jcdr/2017/24028.9853

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

    Indian Council of Medical Research - National Centre for Disease Informatics and Research. National Noncommunicable disease monitoring Survey (NNMS) 2017-18. 2020. https://www.ncdirindia.org/nnms/. Accessed May 14, 2021.

    • Search Google Scholar
    • Export Citation
  • 21.

    Mathur P, Kulothungan V, Leburu S, et al. National noncommunicable disease monitoring survey (NNMS) in India: estimating risk factor prevalence in adult population. PLoS One. 2021;16(3):e0246712. PubMed ID: 33651825 doi:10.1371/journal.pone.0246712

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

    Mathur P, Kulothungan V, Leburu S, et al. Baseline risk factor prevalence among adolescents aged 15-17 years old: findings from national non-communicable disease monitoring survey (NNMS) of India. BMJ Open. 2021;11(6):e044066. PubMed ID: 34187814 doi:10.1136/bmjopen-2020-044066

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

    World Health Organization. STEPwise Approach to NCD Risk Factor Surveillance (STEPS). 2003. https://www.who.int/teams/noncommunicable-diseases/surveillance/systems-tools/steps. Accessed July 04, 2021.

    • Search Google Scholar
    • Export Citation
  • 24.

    Indian Council of Medical Research–National Institute of Medical Statistics. IDSP Non-communicable Disease Risk Factors Survey, Phase-I States of India, 2007–08. 2009. https://www.who.int/ncds/surveillance/steps/2007_STEPS_Report_India_7States.pdf. Accessed May 14, 2021.

    • Search Google Scholar
    • Export Citation
  • 25.

    Global Adult Tobacco Survey Collaborative Group. Global Adult Tobacco Survey (GATS): Core Questionnaire with Optional Questions, Version 2.0. Atlanta, GA: Centers for Disease Control and Prevention, 2010. https://www.who.int/tobacco/surveillance/en_tfi_gats_corequestionnairewithoptionalquestions_v2_FINAL_03Nov2010.pdf. Accessed July 04, 2021.

    • Search Google Scholar
    • Export Citation
  • 26.

    World Health Organization. Global Physical Activity Questionnaire (GPAQ) Analysis Guide. 2006. http://www.who.int/chp/steps/resources/GPAQ_Analysis_Guide.pdf. Accessed July 04, 2021.

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

    ODK Open Data Kit. 2016. https://opendatakit.org/. Accessed July 04, 2021.

  • 28.

    World Health Organization/ISH Risk prediction charts for 14 WHO epidemiological sub-regions [internet]. 2007. https://www.who.int/ncds/management/WHO_ISH_Risk_Prediction_Charts.pdf?ua=1. Accessed July 04, 2021.

    • Search Google Scholar
    • Export Citation
  • 29.

    Vyas S, Kumaranayake L. Constructing socio-economic status indices: how to use principal components analysis. Health Policy Plan. 2006;21(6):459468. PubMed ID: 17030551 doi:10.1093/heapol/czl029

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

    Ahmad OB, Pinto CB, Lopez AD, Murray CJL, Lozano R, Inoue M. Age Standardization of Rates: A New WHO Standard. GPE Discussion Paper Series: No 31, World Health Organization. 2001. ···https://www·who·int/healthinfo/paper31·pdf. Accessed July 14, 2021.

    • Search Google Scholar
    • Export Citation
  • 31.

    International Institute for Population Sciences (IIPS), National Programme for Health Care of Elderly (NPHCE), MoHFW, Harvard T. H. Chan School of Public Health (HSPH) and the University of Southern California (USC) 2020. Longitudinal Ageing Study in India (LASI) Wave 1, 2017-18, India Report. 2020. https://www.iipsindia.ac.in/sites/default/files/LASI_India_Report_2020_compressed.pdf. Accessed August 7, 2021.

    • Search Google Scholar
    • Export Citation
  • 32.

    Podder V, Nagarathna R, Anand A, Patil SS, Singh AK, Nagendra HR. Physical activity patterns in India stratified by zones, age, region, BMI and implications for COVID-19: a nationwide study. Ann Neurosci. 2020;27(3–4):193203. PubMed ID: 34556960 doi:10.1177/0972753121998507

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

    Sivanantham P, Sahoo J, Lakshminarayanan S, Bobby Z, Kar SS. Profile of risk factors for non-communicable diseases (NCDs) in a highly urbanized district of India: findings from Puducherry district-wide STEPS Survey, 2019–20. PLoS One. 2021;16(1):e0245254. doi:10.1371/journal.pone.0245254

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

    Ranasinghe CD, Ranasinghe P, Jayawardena R, Misra A. Physical activity patterns among South-Asian adults: a systematic review. Int J Behav Nutr Phys Act. 2013;10(1):116. PubMed ID: 24119682 doi:10.1186/1479-5868-10-116

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35.

    Devamani CS, Oommen AM, Mini G K, Abraham VJ, George K. Levels of physical inactivity in rural and urban Tamil Nadu, India: a cross-sectional study. J Clin Prev Cardiol. 2019;8:13–7. doi:10.4103/JCPC.JCPC_32_18.

    • Search Google Scholar
    • Export Citation
  • 36.

    Office of the Registrar General & Census Commissioner, India. Census Info. 2011. https://censusindia.gov.in/2011-Common/CensusInfo.html. Accessed December 22, 2021.

    • Search Google Scholar
    • Export Citation
  • 37.

    Mathews E, Lakshmi JK, Ravindran TK, Pratt M, Thankappan KR. Perceptions of barriers and facilitators in physical activity participation among women in Thiruvananthapuram City, India. Glob Health Promot. 2016;23(4):2736. PubMed ID: 25829405 doi:10.1177/1757975915573878

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

    Tripathy JP, Thakur JS, Jeet G, Chawla S, Jain S, Prasad R. Urban rural differences in diet, physical activity and obesity in India: are we witnessing the great Indian equalisation? Results from a cross-sectional STEPS survey. BMC Public Health. 2016;16(1):816. PubMed ID: 27538686 doi:10.1186/s12889-016-3489-8

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 39.

    Krishnan A, Shah B, Lal V, Shukla DK, Paul E, Kapoor SK. Prevalence of risk factors for non-communicable disease in a rural area of Faridabad district of Haryana. Indian J Public Health. 2008;52(3):117124. PubMed ID: 19189832

    • Search Google Scholar
    • Export Citation
  • 40.

    Khuwaja AK, Kadir MM. Gender differences and clustering pattern of behavioural risk factors for chronic non-communicable diseases: community-based study from a developing country. Chronic Illn. 2010;6(3):163170. PubMed ID: 20444764 doi:10.1177/1742395309352255

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41.

    Kolahi AA, Moghisi A, Kousha A, Soleiman-Ekhtiari Y. Physical activity levels and related sociodemographic factors among Iranian adults: results from a population-based national STEPS survey. Med J Islam Repub Iran. 2021;34:172. doi:10.47176/mjiri.34.172

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Fan M, Su M, Tan Y, et al. Gender, age, and education level modify the association between body mass index and physical activity: a cross-sectional study in Hangzhou, China. PLoS One. 2015;10(5):e0125534. doi:10.1371/journal.pone.0125534

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 43.

    Mitáš J, Cerin E, Reis RS, et al. Do associations of sex, age and education with transport and leisure-time physical activity differ across 17 cities in 12 countries? Int J Behav Nutr Phys Act. 2019;16(1):121. PubMed ID: 31796070 doi:10.1186/s12966-019-0894-2

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    United Nation. Department of Economic and Social Affairs, Population Division (2019). World Urbanization Prospects: The 2018 Revision (DT.ESA/SER.A/420). New York: United Nations. https://www.un-ilibrary.org/content/books/9789210043144/read. Accessed May 20, 2021.

    • Search Google Scholar
    • Export Citation
  • 45.

    Devarajan R, Prabhakaran D, Goenka S. Built environment for physical activity-an urban barometer, surveillance, and monitoring. Obes Rev. 2020;21(1):e12938. doi:10.1111/obr.12938

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 46.

    World Health Organization. National STEPS Survey for Non-Communicable Diseases Risk Factors in Bangladesh. https://apps.who.int/iris/handle/10665/332886. Accessed May 16, 2021.

    • Search Google Scholar
    • Export Citation
  • 47.

    World Health Organization. Noncommunicable Disease Risk Factors: STEPS Survey Nepal. 2019. https://www.who.int/docs/default-source/nepal-documents/ncds/ncd-steps-survey-2019-compressed.pdf?sfvrsn=807bc4c6_2. Accessed May 16, 2021.

    • Search Google Scholar
    • Export Citation
  • 48.

    Strain T, Wijndaele K, Garcia L, et al. Levels of domain-specific physical activity at work, in the household, for travel and for leisure among 327 789 adults from 104 countries. Br J Sports Med. 2020;54(24):14881497. PubMed ID: 33239355 doi:10.1136/bjsports-2020-102601

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 49.

    Pedisic Z, Shrestha N, Loprinzi PD, Mehata S, Mishra SR. Prevalence, patterns, and correlates of physical activity in Nepal: findings from a nationally representative study using the global physical activity questionnaire (GPAQ). BMC Public Health. 2019;19(1):864. PubMed ID: 31269984 doi:10.1186/s12889-019-7215-1

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 50.

    Gaskin CJ, Orellana L. Factors associated with physical activity and sedentary behavior in older adults from six low- and middle-income countries. Int J Environ Res Public Health. 2018;15(5):908. doi:10.3390/ijerph15050908

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 51.

    Katulanda P, Jayawardena R, Ranasinghe P, Rezvi Sheriff MH, Matthews DR. Physical activity patterns and correlates among adults from a developing country: the Sri Lanka diabetes and cardiovascular study [published correction appears in Public Health Nutr. 2013 Sep;16(9):1719. Jayawardana, Ranil [corrected to Jayawardena, Ranil]]. Public Health Nutr. 2013;16(9):16841692. PubMed ID: 22995708 doi:10.1017/S1368980012003990

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 52.

    Yerramalla MS, McGregor DE, van Hees VT, et al. Association of daily composition of physical activity and sedentary behaviour with incidence of cardiovascular disease in older adults. Int J Behav Nutr Phys Act. 2021;18(1):83. PubMed ID: 34247647 doi:10.1186/s12966-021-01157-0

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 53.

    Bassuk SS, Manson JE. Epidemiological evidence for the role of physical activity in reducing risk of type 2 diabetes and cardiovascular disease. J Appl Physiol. 2005;99(3):11931204. doi:10.1152/japplphysiol.00160.2005

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1858 1858 397
PDF Downloads 637 637 60