Skip to main content
NCBI home page
Lippincott Open Access logoLink to Lippincott Open Access
. 2022 Oct 21;55(3):507–516. doi: 10.1249/MSS.0000000000003068

Descriptive Epidemiology of Cardiorespiratory Fitness in UK Adults: The Fenland Study

TOMAS I GONZALES 1, KATE WESTGATE 1, STEFANIE HOLLIDGE 1, TIM LINDSAY 1, KATRIEN WIJNDAELE 1, NITA G FOROUHI 1, SIMON GRIFFIN 1, NICK WAREHAM 1, SOREN BRAGE 1
PMCID: PMC9924962  PMID: 36730941

ABSTRACT

Introduction

Cardiorespiratory fitness (CRF) is rarely measured in population studies. Most studies of CRF do not examine differences by population subgroups or seasonal trends. We examined how estimated CRF levels vary by anthropometric, sociodemographic, and behavioral characteristics in a population-based cohort of UK adults (the Fenland Study).

Methods

We used a validated submaximal exercise test to obtain CRF estimates (CRFestimated) in 5976 women and 5316 men, residing in the East of England. CRFestimated was defined as estimated maximal oxygen consumption per kilogram total body mass (V̇O2maxtbm) and fat-free mass (V̇O2maxffm). Descriptive statistics were computed across anthropometric and sociodemographic characteristics, and across the year. Progressive multivariable analyses were performed to examine associations with physical activity energy expenditure (PAEE) and body mass index (BMI).

Results

Mean ± SD V̇O2maxtbm was lower in women (35.2 ± 7.5 mL·min−1·kg−1) than men (41.7 ± 7.3 mL·min−1·kg−1) but V̇O2maxffm was similar (women: 59.2 ± 11.6 mL·min−1·kg−1; men: 62.0 ± 10.3 mL·min−1·kg−1). CRFestimated was inversely associated with age but not after adjustment for PAEE. People in more physically demanding jobs were fitter compared with those in sedentary jobs, but this association was attenuated in women and reversed in men after adjustment for total PAEE. Physical activity energy expenditure and BMI were positively associated with CRFestimated at all levels of adjustment when expressed relative to fat-free mass. CRFestimated was 4% higher in summer than in winter among women, but did not differ by season among men.

Conclusions

CRFestimated was inversely associated with age but less steeply than anticipated, suggesting older generations are comparatively fitter than younger generations. Physical activity energy expenditure and BMI were stronger determinants of the variance in CRFestimated than other characteristic including age. This emphasizes the importance of modifiable physical activity behaviors in public health interventions.

Key Words: CARDIORESPIRATORY FITNESS, SOCIODEMOGRAPHIC, PHYSICAL ACTIVITY, ENERGY EXPENDITURE, AGING

Cardiorespiratory fitness (CRF) is inversely related to mortality and cardiometabolic disease risk (1–5) but is not widely recognized as a clinical vital sign in the United Kingdom. Most UK primary care providers do not routinely measure CRF, and only a few epidemiological studies have documented CRF levels in UK population subgroups. The Welsh Heart Health Survey (6) and Tuxworth et al. (7) are the earliest epidemiological studies of CRF in UK adults. The Allied-Dunbar National Fitness Survey was the first to establish normative CRF data for the UK population (8). These data were extended by the Northern Ireland Health and Activity Survey (9) and the 2008 Health Survey for England (10). These studies have several strengths: they use dynamic exercise testing to measure differences in CRF levels by anthropometric characteristics, relate CRF to other health behaviors, such as physical activity, and describe differences in CRF by cardiometabolic risk factors. Exercise test selection bias limits generalizability of their findings to the UK population, however, and data on relationships between CRF, sociodemographic characteristics, and clinical characteristics are scarce. It is also unclear how these relationships may be mediated through physical activity. These limitations impede public health action for improved CRF in the population. Here we use a validated submaximal exercise test to obtain CRF estimates (CRFestimated) in a population-based cohort of UK adults (the Fenland Study) and examine how CRFestimated levels vary by anthropometric, sociodemographic, and behavioral characteristics.

METHODS

Study population

The Fenland Study included 12,435 participants born between 1950 and 1975 and recruited from general practice lists around Cambridgeshire, UK from January 2005 until April 2015, as described in more detail elsewhere (11). Exclusion criteria for participation in the Fenland study were prevalent diabetes, pregnancy or lactation, inability to walk unaided for at least 10 min, psychosis, or terminal illness. Participant eligibility for exercise testing was assessed using a 12-lead resting ECG (Seca CT6i), excluding those presenting with unstable angina. In addition, participants who self-reported having a heart condition were examined by a study nurse to determine whether treadmill testing could be conducted safely. The present analysis included 5976 women and 5316 men with available data on CRFestimated. The Health Research Authority NRES Committee East of England-Cambridge Central approved the study in accordance with the Declaration of Helsinki. All participants gave written informed consent. The Fenland Study has a dedicated Patient and Public Involvement panel, who provided input on the acceptability of the study protocols and participant data confidentiality. This study complied with the items listed in the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.

Anthropometric, sociodemographic, clinical characteristics, and physical activity

Participants arrived at a clinical testing facility to complete baseline assessments after an overnight fast and after abstaining from smoking and vigorous physical activity the morning of their visit. During the clinic visit, height was measured with a rigid stadiometer (SECA 240; Seca, Birmingham, UK), total body mass was measured in light clothing with calibrated scales (TANITA model BC-418 MA; Tanita, Tokyo, Japan), and fat-free mass was measured using dual-energy x-ray absorptiometry (DEXA; Lunar-DPX) (12). Self-report questionnaires were used to determine sociodemographic characteristics, including participant sex, age, ethnicity (White, South Asian, Black, East Asian, other or unknown), education level (basic–compulsory schooling, further–A level (Advanced level subject-based qualification)/apprenticeship/subdegree level, higher-degree level or above), work type (sedentary, standing, manual, retired, unemployed, unknown), annual household income level (<£20,000, £20,000–£40,000, >£40,000), marital status (single, married/living as married, widowed/separated/divorced), smoking status (never, ex-smoker, current), and testing location (Cambridge, Ely, Wisbech). Resting heart rate was measured while supine using a 12-lead ECG (Seca CT6i). After the clinic visit, objective physical activity was assessed using a combined heart rate and uniaxial movement sensor (Actiheart, CamNtech, Cambridge, UK), worn continuously for at least 72 h and at most 6 d. Heart rate was individually calibrated (13) and total physical activity energy expenditure (PAEE) was computed for the wear period as described and validated elsewhere (11,14).

Cardiorespiratory fitness assessment

We used an incremental, multistage, and submaximal treadmill test to quantify CRFestimated, where CRFestimated was defined as estimated maximal oxygen consumption (V̇O2max). A diagram and description of the testing procedure is provided in Supplemental Figure 1 (see Supplemental Digital Content, http://links.lww.com/MSS/C731). Heart rate was monitored and recorded during testing using the combined heart rate and movement sensor (Actiheart; CamNtech) (15). The test ended if one of the following criteria were satisfied: 1) leveling-off of heart rate (<3 bpm per min) despite an increase in work rate; 2) reaching 90% of the participant’s age-predicted maximal heart rate (16); 3) exercising above 80% of age-predicted maximal heart rate for over 2 min; 4) reaching a respiratory exchange ratio of 1.1; 5) the participant wanted to stop; 6) participant indication of angina, light-headedness, or nausea; or 7) failure of the testing equipment. For participants on beta blockers (n = 36), the test was terminated after 5 min.

To estimate V̇O2max per kg total-body mass (V̇O2maxtbm) from exercise test performance, we extrapolated the linear relationship between heart rate and work rate (17) to age-predicted maximal heart rate (16), which was reduced by 7 bpm in smokers (18,19), added an estimate of resting energy expenditure (20), and then converted the resultant work rate value to net V̇O2 using a caloric equivalent for oxygen of 20.35 J·ml O2−1 (21). The metabolic cost of the treadmill protocol was estimated in an early validation study (13) using the Weir equation to convert oxygen consumption and carbon dioxide production from respiratory gas analysis to energy expenditure, and expressed above measured resting metabolic rate. This approach accounts for anaerobic metabolism and the resulting estimation of activity metabolism for each stage of the treadmill protocol was found to vary minimally between individuals. A separate substudy was conducted to validate the V̇O2max estimation approach described above against directly measured V̇O2max, demonstrating acceptable agreement (see Supplemental Methods and Results, Supplemental Digital Content, http://links.lww.com/MSS/C731). V̇O2max per kg fat-free mass (V̇O2maxffm) was estimated by multiplying V̇O2maxtbm values by total body mass and dividing by fat-free mass.

Statistical analyses

Descriptive statistics were computed across BMI groups and sociodemographic characteristics by sex- and age-stratified groups. Cuzick’s test (22) was performed to test for trend across participant characteristics. Differences in CRFestimated by age, BMI groups (<25, 25 to 30, and >30 kg·m−2) and PAEE groups (<40, 40 to 60, and >60 kJ·d−1·kg−1) were visualized using boxplots. Univariate associations of CRFestimated with age, BMI, and PAEE were computed as Pearson’s r; bivariate relationships were investigated using linear regression.

We used sex-stratified and sequentially-adjusted multivariable linear regression to evaluate associations between CRFestimated and sociodemographic characteristics (model 1) with additional adjustment for PAEE (model 2) and BMI (model 3). The season of the year when CRFestimated values were obtained was considered in these analyses by including two orthogonal sine functions in the regression model: “Winter” reaching a value of “1” on January 1 and “−1” on July 1st, and “Spring” reaching a value of “1” on April 1 and “−1” on October 1. Seasonal trends were further described on a monthly basis using a binned regression procedure, controlling for seasonal variation in the measurement of sociodemographic characteristics (23). All analyses were performed in Stata/SE 16.1 (StataCorp, College Station, TX). Statistical significance was set at P < 0.05.

RESULTS

In women, mean ± SD estimated V̇O2maxtbm was 35.2 ± 7.5 mL O2·min−1·kg−1 and estimated V̇O2maxffm was 59.2 ± 11.6 mL O2·min−1·kg−1. In men, estimated V̇O2maxtbm was 41.7 ± 7.3 mL O2·min−1·kg−1 and estimated V̇O2maxffm was 62.0 ± 10.3 mL O2·min−1·kg−1. Per 5 yr age difference, estimated V̇O2maxtbm was lower on average by 0.2 mL O2·min−1·kg−1 in women and by 0.3 mL O2·kg·min−1 in men. Estimated V̇O2maxffm was not associated with age in women, however in men estimated V̇O2maxffm was on average 0.05 mL O2·min−1·kg−1 lower per 5 yr. Trends for other characteristics are reported in Table 1.

TABLE 1.

Participant characteristics by sex-specific age groups

Sex Women
Age Group (yr) Pooled 29–34 35–39 40–44 45–49 50–54 55–59 60–64
N 5976 184 736 1210 1322 1269 972 283 P
Height (cm) 164 ± 6 165 ± 6 165 ± 7 165 ± 6 165 ± 6 164 ± 6 163 ± 6 163 ± 6 <0.01
Total body mass (kg) 70.6 ± 13.9 67.0 ± 12.3 70.0 ± 15.2 71.0 ± 14.7 71.0 ± 13.8 70.8 ± 13.3 70.4 ± 13.1 69.7 ± 12.8 <0.01
Body mass index (kg·m−2) 26.2 ± 5.0 24.6 ± 4.2 25.7 ± 5.3 26.2 ± 5.2 26.2 ± 5.0 26.4 ± 4.8 26.4 ± 4.7 26.3 ± 4.6 <0.01
Fat-free mass (kg) 41.4 ± 5.2 40.9 ± 4.5 42.0 ± 5.5 42.1 ± 5.5 41.9 ± 5.2 41.0 ± 5.0 40.3 ± 5.0 39.9 ± 4.9 <0.01
Resting heart rate (bpm) 64 ± 8 64 ± 8 64 ± 9 64 ± 89 65 ± 9 64 ± 8 64 ± 8 63 ± 8 0.08
V̇O2max per kg bodyweight (mL O2·min−1·kg−1) 35.2 ± 7.5 35.9 ± 6.2 36.0 ± 7.0 35.5 ± 7.1 35.3 ± 7.5 34.8 ± 7.6 34.6 ± 7.9 34.6 ± 8.3 <0.01
V̇O2max per kg fat-free mass (mL O2·min−1·kg−1) 59.2 ± 11.6 58.1 ± 8.8 58.8 ± 10.1 58.8 ± 10.3 59.0 ± 11.4 59.5 ± 12.4 59.8 ± 13.0 59.7 ± 13.8 0.12
PAEE (kJ·d−1·kg−1) 51 ± 20 59 ± 21 57 ± 21 53 ± 20 51 ± 20 48 ± 19 46 ± 19 43 ± 16 <0.01
Sex Men
Age Group (yr) Pooled 29–34 35–39 40–44 45–49 50–54 55–59 60–64
N 5316 161 735 1010 1135 1072 929 274 P
Height (cm) 178 ± 7 179 ± 7 178 ± 7 179 ± 7 178 ± 7 177 ± 7 176 ± 7 177 ± 7 <0.01
Bodyweight (kg) 85.9 ± 13.9 84.6 ± 13.6 84.7 ± 14.6 86.2 ± 13.9 86.1 ± 13.9 86.4 ± 13.3 85.8 ± 13.9 85.4 ± 14.2 0.03
Body mass index (kg·m−2) 27.2 ± 4.0 26.4 ± 3.8 26.7 ± 4.2 27.0 ± 3.9 27.2 ± 4.0 27.5 ± 4.0 27.5 ± 4.0 27.3 ± 3.9 <0.01
Fat-free mass (kg) 57.3 ± 6.8 57.6 ± 6.8 57.4 ± 7.0 58.1 ± 6.7 57.6 ± 6.7 57.3 ± 6.7 56.3 ± 6.7 56.3 ± 7.0 <0.01
Resting heart rate (bpm) 61 ± 9 62 ± 9 60 ± 9 61 ± 9 61 ± 9 61 ± 9 62 ± 9 62 ± 9 <0.01
V̇O2max per kg bodyweight (mL O2·min−1·kg−1) 41.7 ± 7.3 42.0 ± 6.0 42.9 ± 6.9 42.6 ± 6.9 42.0 ± 7.3 41.0 ± 7.0 40.9 ± 8.0 40.4 ± 8.9 <0.01
V̇O2max per kg fat-free mass (mL O2·min−1·kg−1) 62.0 ± 10.3 61.0 ± 8.4 62.5 ± 9.5 62.6 ± 9.3 62.4 ± 10.4 61.4 ± 10.1 61.6 ± 11.3 60.8 ± 13.0 <0.01
PAEE (kJ·d−1·kg−1) 59 ± 23 65 ± 25 66 ± 24 64 ± 24 60 ± 23 57 ± 22 52 ± 21 47 ± 21 <0.01
Open in a new tab

The Fenland study 2005 to 2015.

Values are mean ± SD, unless otherwise indicated. P value computed from Cuzick’s test for trend.

Figure 1 shows differences in estimated V̇O2maxtbm and V̇O2maxffm by age, BMI groups, and PAEE groups. The association of CRFestimated with age, BMI, and PAEE was strongest when expressed as V̇O2maxtbm compared with V̇O2maxffm. We investigated this further by conducting univariate and bivariate analyses of estimated V̇O2maxtbm and V̇O2maxffm with BMI, PAEE, and age (Supplemental Figs. 2 and 3, Supplemental Digital Content, http://links.lww.com/MSS/C731). The association of estimated V̇O2maxtbm with PAEE (Pearson’s r for women: 0.38; men: r: 0.38) was higher than associations with BMI (Pearson’s r for women: −0.34; men: −0.24) and age (Pearson’s r for women: −0.06; men: −0.11). The combination of BMI and PAEE explained more variance in estimated V̇O2maxtbm (20% for women, 17% for men) than bivariate combinations with age. For estimated V̇O2maxffm, univariate and bivariate analyses had weaker associations and less explained variance than analogous results for estimated V̇O2maxtbm.

FIGURE 1.

FIGURE 1

Open in a new tab

Sex-stratified estimated maximal oxygen consumption per kilogram total body mass (V̇O2maxtbm; top panel) and per kilogram fat-free mass (V̇O2maxffm; bottom panel) by age, BMI, and PAEE. Box plots represent medians, interquartile ranges, and minimum–maximum ranges without outliers. The Fenland Study 2005 to 2015.

Unadjusted mean V̇O2maxtbm estimates, stratified by sex and sociodemographic characteristics, are provided in Table 2. Estimated V̇O2maxtbm was generally higher in participants with higher educational attainment (Women: higher education: 36.0 ± 7.2 mL O2·min−1·kg−1; no education: 33.0 ± 6.9 mL O2·min−1·kg−1; men: higher education: 42.0 ± 7.1 mL O2·min−1·kg−1; no education: 40.3 ± 6.7 mL O2·min−1·kg−1). Workers in more physically demanding jobs were fitter than those in sedentary jobs (female manual workers: 35.9 ± 8.0 mL O2·min−1·kg−1; female sedentary workers: 34.9 ± 7.0 mL O2·min−1·kg−1; male manual workers: 42.4 ± 7.8 mL O2·min−1·kg−1 male sedentary workers: 41.7 ± 6.9 mL O2·min−1·kg−1). Current smokers had similar V̇O2maxtbm estimates compared with nonsmokers (female smokers: 35.7 ± 8.0 mL O2·min−1·kg−1; female nonsmokers: 35.0 ± 7.4 mL O2·min−1·kg−1; male smokers: 41.7 ± 7.7 mL O2·min−1·kg−1; male nonsmokers: 41.8 ± 7.2 mL O2·min−1·kg−1). Estimated V̇O2maxtbm differed by ethnicity; however, sample sizes were disproportionate between Whites and other racial and ethnic groups. Differences in estimated V̇O2maxtbm were not apparent between income levels, marital status, and testing sites. Analogous results for estimated V̇O2maxffm are presented in Table 3.

TABLE 2.

Sequentially adjusted multivariable analysis of estimated V̇O2max per kg total body mass (mL O2·min−1·kg−1) by sociodemographic characteristics.

Sex (N) Women (5784) Men (5209)
Model Count (%) Unadjusted Model 1 Model 2 Model 3 Count (%) Unadjusted Model 1 Model 2 Model 3
Reference mean 35.6* (34.3; 37.0) 35.2* (34.0; 36.5) 34.7* (33.5; 36.0) 42.3* (40.9; 43.7) 42.2* (40.9; 43.6) 41.8* (40.5; 43.1)
Age group (yr)
 29–34 171 (3.0%) 35.9 ± 6.2 Reference Reference Reference 154 (3.0%) 42.0 ± 6.2 Reference Reference Reference
 35–39 711 (12.3%) 36.0 ± 7.0 −0.6 (−1.9; 0.6) −0.4 (−1.6; 0.7) −0.1 (−1.2; 1.0) 714 (13.7%) 43.0 ± 6.9 −0.0 (−1.3; 1.3) −0.1 (−1.3; 1.1) −0.0 (−1.2; 1.1)
 40–44 1168 (20.2%) 35.5 ± 7.1 −1.1 (−2.3; 0.1) −0.3 (−1.5; 0.8) 0.0 (−1.1; 1.1) 994 (19.1%) 42.7 ± 6.9 −0.5 (−1.8; 0.7) −0.2 (−1.4; 0.9) −0.1 (−1.3; 1.0)
 45–49 1278 (22.1%) 35.3 ± 7.5 −1.3** (−2.5; −0.1) −0.2 (−1.4; 0.9) 0.0 (−1.1; 1.1) 1107 (21.3%) 42.2 ± 7.2 −1.2 (−2.4; 0.1) −0.3 (−1.5; 0.8) −0.2 (−1.4; 0.9)
 50–54 1229 (21.2%) 34.8 ± 7.6 −1.8* (−3.0; −0.6) −0.3 (−1.5; 0.8) 0.0 (−1.1; 1.0) 1052 (20.2%) 41.1 ± 7.0 −2.1* (−3.4; −0.9) −0.8 (−2.0; 0.3) −0.7 (−1.8; 0.5)
 55–59 946 (16.4%) 34.6 ± 7.9 −2.2* (−3.4; −0.9) −0.4 (−1.5; 0.8) −0.2 (−1.3; 1.0) 917 (17.6%) 40.8 ± 7.9 −2.5* (−3.7; −1.2) −0.6 (−1.8; 0.6) −0.5 (−1.7; 0.7)
 60–64 281 (4.9%) 34.6 ± 8.3 −2.4* (−3.9; −0.9) −0.2 (−1.6; 1.2) −0.1 (−1.5; 1.3) 271 (5.2%) 40.4 ± 8.8 −3.1* (−4.6; −1.6) −0.4 (−1.8; 1.0) −0.4 (−1.8; 0.9)
Ethnicity
 White 5357 (92.6%) 35.3 ± 7.4 Reference Reference Reference 4816 (92.5%) 42.0 ± 7.3 Reference Reference Reference
 South Asian 63 (1.1%) 34.7 ± 10.5 −1.1 (−3.0; 0.7) −0.1 (−1.8; 1.6) −0.2 (−1.8; 1.5) 70 (1.3%) 40.3 ± 7.3 −1.4 (−3.1; 0.3) −0.5 (−2.1; 1.1) −0.9 (−2.5; 0.6)
 Black 21 (0.4%) 34.8 ± 8.0 −1.4 (−4.5; 1.8) −1.4 (−4.4; 1.5) −0.8 (−3.6; 2.1) 28 (0.5%) 38.6 ± 8.2 −3.2** (−5.8; −0.5) −2.1 (−4.6; 0.4) −1.9 (−4.4; 0.5)
 East Asian 38 (0.7%) 38.7 ± 9.8 2.9** (0.5; 5.3) 3.7* (1.5; 5.9) 2.8** (0.7; 4.9) 19 (0.4%) 43.5 ± 8.7 1.8 (−1.5; 5.1) 2.6 (−0.5; 5.6) 1.8 (−1.2; 4.8)
 Others or unknown 305 (5.3%) 32.5 ± 6.6 −2.1* (−3.1; −1.2) −1.6* (−2.4; −0.7) −1.9* (−2.7; −1.1) 276 (5.3%) 39.5 ± 6.0 −2.2* (−3.2; −1.2) −1.7* (−2.6; −0.7) −1.7* (−2.6; −0.9)
Education level
 None 31 (0.5%) 33.0 ± 6.9 −1.6 (−4.3; 1.0) −1.4 (−3.9; 1.0) −1.5 (−3.9; 0.9) 21 (0.4%) 40.3 ± 6.7 −0.3 (−3.4; 2.9) 0.3 (−2.6; 3.2) 0.5 (−2.4; 3.4)
 Basic 1191 (20.6%) 34.7 ± 8.0 Reference Reference Reference 859 (16.5%) 42.0 ± 7.8 Reference Reference Reference
 Further 2617 (45.2%) 34.8 ± 7.4 0.1 (−0.4; 0.6) −0.1 (−0.6; 0.4) −0.2 (−0.7; 0.3) 2426 (46.6%) 41.6 ± 7.3 −0.2 (−0.8; 0.3) −0.1 (−0.6; 0.4) −0.1 (−0.6; 0.4)
 Higher 1945 (33.6%) 36.0 ± 7.2 1.1* (0.5; 1.7) 0.7** (0.2; 1.3) 0.2 (−0.3; 0.7) 1903 (36.5%) 42.0 ± 7.1 0.4 (−0.3; 1.1) 0.5 (−0.1; 1.1) 0.1 (−0.5; 0.8)
Work type
 Sedentary 2762 (47.8%) 34.9 ± 7.0 Reference Reference Reference 2721 (52.2%) 41.7 ± 6.9 Reference Reference Reference
 Standing 1852 (32.0%) 35.5 ± 7.7 0.9* (0.4; 1.3) −0.1 (−0.5; 0.3) −0.0 (−0.4; 0.4) 703 (13.5%) 42.0 ± 7.6 0.9* (0.2; 1.5) −0.3 (−0.8; 0.3) −0.1 (−0.7; 0.4)
 Manual 437 (7.6%) 35.9 ± 8.0 1.6* (0.8; 2.4) −0.4 (−1.1; 0.4) −0.0 (−0.7; 0.7) 1517 (29.1%) 42.4 ± 7.8 1.2* (0.6; 1.7) −1.0* (−1.5; −0.5) −0.9* (−1.4; −0.4)
 Retired 160 (2.8%) 34.3 ± 7.5 0.3 (−1.0; 1.6) −0.3 (−1.5; 0.9) −0.2 (−1.3; 1.0) 112 (2.2%) 39.8 ± 7.2 −0.5 (−2.0; 0.9) −1.5** (−2.8; −0.2) −1.4** (−2.7; −0.1)
 Unemployed 61 (1.1%) 34.6 ± 7.8 −0.7 (−2.5; 1.2) −0.6 (−2.3; 1.1) 0.1 (−1.6; 1.8) 64 (1.2%) 39.3 ± 6.8 −1.8 (−3.6; 0.1) −2.2** (−3.9; −0.5) −1.9** (−3.6; −0.3)
 Unknown 512 (8.9%) 35.4 ± 8.2 0.7** (0.0; 1.4) −0.1 (−0.7; 0.6) −0.1 (−0.8; 0.5) 92 (1.8%) 40.7 ± 9.2 0.3 (−1.3; 1.8) 0.6 (−0.9; 2.0) 0.8 (−0.6; 2.2)
Income
 < £20,000 892 (15.4%) 34.8 ± 7.8 −0.2 (−0.8; 0.5) 0.2 (−0.3; 0.8) 0.2 (−0.3; 0.8) 472 (9.1%) 41.4 ± 7.9 0.2 (−0.6; 0.9) 0.8** (0.0; 1.5) 0.6 (−0.1; 1.3)
 £20,000–£40,000 2038 (35.2%) 34.9 ± 7.5 Reference Reference Reference 1719 (33.0%) 41.5 ± 7.8 Reference Reference Reference
 > £40,000 2691 (46.5%) 35.5 ± 7.2 0.4 (−0.0; 0.9) 0.1 (−0.3; 0.5) 0.1 (−0.3; 0.5) 2928 (56.2%) 42.0 ± 6.9 0.5** (0.1; 1.0) 0.3 (−0.1; 0.8) 0.4 (−0.0; 0.8)
 Unknown 163 (2.8%) 35.5 ± 9.9 1.1 (−0.1; 2.3) 0.9 (−0.2; 2.0) 0.9 (−0.2; 2.0) 90 (1.7%) 42.4 ± 7.6 0.7 (−0.8; 2.3) 1.5** (0.0; 2.9) 1.3 (−0.1; 2.8)
Marital status
 Single 380 (6.6%) 35.9 ± 7.1 0.3 (−0.6; 1.1) 0.2 (−0.6; 0.9) 0.3 (−0.5; 1.0) 389 (7.5%) 42.2 ± 7.8 −0.1 (−0.9; 0.7) 0.2 (−0.5; 1.0) 0.0 (−0.7; 0.7)
 Married/living as married 3547 (61.3%) 35.5 ± 7.5 Reference Reference Reference 3350 (64.3%) 42.0 ± 7.4 Reference Reference Reference
 Widowed/separated/divorced 489 (8.5%) 35.7 ± 7.7 0.5 (−0.2; 1.3) 0.4 (−0.3; 1.0) 0.4 (−0.3; 1.0) 277 (5.3%) 42.3 ± 7.2 0.6 (−0.3; 1.5) 0.4 (−0.4; 1.2) 0.4 (−0.5; 1.2)
 Unknown 1368 (23.7%) 33.9 ± 7.1 −1.4* (−1.9; −0.9) −1.4* (−1.9; −0.9) −1.3* (−1.8; −0.9) 1193 (22.9%) 40.8 ± 6.7 −1.4* (−2.0; −0.8) −1.3* (−1.8; −0.8) −1.4* (−1.9; −0.8)
Smoker status
 Never smoked 3246 (56.1%) 35.0 ± 7.4 Reference Reference Reference 2718 (52.2%) 41.8 ± 7.2 Reference Reference Reference
 Ex-smoker 1858 (32.1%) 35.3 ± 7.4 0.4 (−0.0; 0.8) 0.1 (−0.3; 0.4) 0.3 (−0.1; 0.7) 1760 (33.8%) 41.9 ± 7.4 0.3 (−0.2; 0.7) 0.1 (−0.4; 0.5) 0.2 (−0.2; 0.6)
 Current smoker 615 (10.6%) 35.7 ± 8.0 1.0* (0.4; 1.7) −0.4 (−1.0; 0.3) −0.1 (−0.7; 0.5) 680 (13.1%) 41.7 ± 7.7 −0.1 (−0.7; 0.6) −1.3* (−1.9; −0.7) −1.3* (−1.9; −0.7)
 Unknown 65 (1.1%) 34.7 ± 6.3 −0.1 (−1.9; 1.7) −0.8 (−2.5; 0.9) −0.6 (−2.2; 1.0) 51 (1.0%) 40.8 ± 7.2 −0.9 (−2.9; 1.1) −1.4 (−3.2; 0.5) −1.1 (−2.9; 0.8)
Test site
 Cambridge 2060 (35.6%) 35.6 ± 7.5 Reference Reference Reference 1995 (38.3%) 41.7 ± 7.5 Reference Reference Reference
 Ely 2201 (38.1%) 35.1 ± 7.4 0.3 (−0.2; 0.8) 0.6** (0.1; 1.0) 0.8* (0.4; 1.2) 1841 (35.3%) 41.9 ± 7.2 0.8* (0.3; 1.3) 0.8* (0.4; 1.3) 1.2* (0.7; 1.6)
 Wisbech 1523 (26.3%) 34.7 ± 7.4 −0.3 (−0.8; 0.3) −0.0 (−0.5; 0.5) 0.4 (−0.1; 0.9) 1373 (26.4%) 41.9 ± 7.3 0.4 (−0.2; 1.0) 0.3 (−0.2; 0.8) 0.8* (0.3; 1.4)
Seasonality
 Spring 0.0 (−0.3; 0.3) 0.1 (−0.2; 0.3) 0.1 (−0.1; 0.3) −0.2 (−0.4; 0.1) −0.1 (−0.3; 0.2) −0.1 (−0.4; 0.2)
 Winter −0.7* (−1.0; −0.4) −0.4* (−0.7; −0.2) −0.5* (−0.7; −0.2) −0.4* (−0.7; −0.1) 0.0 (−0.3; 0.2) 0.0 (−0.3; 0.3)
PAEE (kJ·d−1·kg−1) 0.1* (0.1; 0.2) 0.1* (0.1; 0.1) 0.1* (0.1; 0.1) 0.1* (0.1; 0.1)
BMI (kg·m−2) −0.4* (−0.4; −0.3) −0.3* (−0.4; −0.3)
Open in a new tab

The Fenland study 2005 to 2015.

Unadjusted values represent mean ± SD V̇O2max estimates computed within each substratum. Model values are mean (95% CI). Model 1 is mutually adjusted for sociodemographic characteristics. Model 2 is additionally adjusted for PAEE (kJ·d−1·kg−1). Model 3 is additionally adjusted for BMI (kg·m−2). Mean ± SD PAEE for women: 49 ± 20 kJ·d−1·kg−1; for men: 59 ± 23 kJ·d−1·kg−1. Mean ± SD BMI for women: 26.5 ± 5.3 kg·m−2; for men: 27.3 ± 4.1 kg·m−2.

*P < 0.01.

**P < 0.05.

TABLE 3.

Sequentially adjusted multivariable analysis of estimated maximal oxygen consumption (V̇O2max) per kg fat free mass (mL O2·min−1·kg−1) by sociodemographic characteristics.

Sex (N) Women (5530) Men (4980)
Model Count (%) Unadjusted Model 1 Model 2 Model 3 Count (%) Unadjusted Model 1 Model 2 Model 3
Reference mean 59.3* (57.1; 61.5) 59.1* (56.9; 61.3) 59.5* (57.4; 61.7) 61.7* (59.5; 63.8) 61.7* (59.6; 63.7) 62.3* (60.2; 64.3)
Age group (yr)
 29–34 157 (2.8%) 58.1 ± 8.8 Reference Reference Reference 136 (2.7%) 61.0 ± 8.4 Reference Reference Reference
 35–39 664 (12.0%) 58.8 ± 10.1 −0.7 (−2.8; 1.3) −0.6 (−2.6; 1.4) −0.9 (−2.8; 1.1) 673 (13.5%) 62.5 ± 9.5 0.3 (−1.6; 2.3) 0.2 (−1.6; 2.1) 0.1 (−1.7; 1.9)
 40–44 1104 (20.0%) 58.8 ± 10.3 −0.9 (−2.9; 1.1) −0.4 (−2.3; 1.6) −0.7 (−2.6; 1.2) 950 (19.1%) 62.6 ± 9.3 0.1 (−1.8; 2.0) 0.3 (−1.5; 2.2) 0.2 (−1.6; 2.0)
 45–49 1224 (22.1%) 59.0 ± 11.4 −0.7 (−2.7; 1.3) 0.0 (−1.9; 2.0) −0.3 (−2.2; 1.7) 1053 (21.1%) 62.4 ± 10.4 −0.2 (−2.1; 1.7) 0.4 (−1.4; 2.3) 0.3 (−1.5; 2.1)
 50–54 1179 (21.3%) 59.5 ± 12.4 −0.2 (−2.2; 1.8) 0.8 (−1.2; 2.7) 0.5 (−1.4; 2.4) 1006 (20.2%) 61.4 ± 10.1 −1.2 (−3.0; 0.7) −0.1 (−1.9; 1.8) −0.3 (−2.1; 1.5)
 55–59 921 (16.7%) 59.8 ± 13.0 −0.3 (−2.4; 1.7) 0.9 (−1.1; 3.0) 0.7 (−1.2; 2.7) 892 (17.9%) 61.6 ± 11.3 −1.3 (−3.3; 0.6) 0.2 (−1.7; 2.1) 0.0 (−1.8; 1.9)
 60–64 281 (5.1%) 59.7 ± 13.8 −0.7 (−3.2; 1.7) 0.8 (−1.6; 3.2) 0.7 (−1.7; 3.0) 270 (5.4%) 60.8 ± 13.0 −2.1 (−4.3; 0.1) 0.1 (−2.1; 2.3) 0.1 (−2.1; 2.2)
Ethnicity
 White 5144 (93.0%) 59.4 ± 11.6 Reference Reference Reference 4634 (93.1%) 62.2 ± 10.3 Reference Reference Reference
 South Asian 61 (1.1%) 60.2 ± 15.6 1.4 (−1.6; 4.3) 2.1 (−0.8; 5.0) 2.1 (−0.8; 5.0) 70 (1.4%) 61.8 ± 11.6 0.2 (−2.2; 2.7) 1.0 (−1.4; 3.3) 1.5 (−0.8; 3.8)
 Black 21 (0.4%) 56.4 ± 13.1 −2.9 (−7.9; 2.0) −2.9 (−7.8; 1.9) −3.6 (−8.4; 1.3) 26 (0.5%) 54.5 ± 11.7 −7.5* (−11.5; −3.6) −6.8* (−10.7; −3.0) −7.1* (−10.9; −3.3)
 East Asian 38 (0.7%) 61.8 ± 14.4 3.0 (−0.7; 6.7) 3.5 (−0.1; 7.2) 4.4** (0.8; 8.0) 19 (0.4%) 62.3 ± 12.5 1.3 (−3.4; 5.9) 1.9 (−2.6; 6.4) 2.9 (−1.5; 7.3)
 Others or unknown 266 (4.8%) 54.2 ± 10.0 −3.5* (−5.1; −2.0) −3.2* (−4.8; −1.7) −2.9* (−4.5; −1.4) 231 (4.6%) 58.7 ± 8.2 −2.4* (−3.9; −0.9) −1.9** (−3.4; −0.4) −1.9** (−3.3; −0.4)
Education level
 None 28 (0.5%) 57.6 ± 13.7 −2.7 (−7.0; 1.7) −2.8 (−7.0; 1.5) −2.9 (−7.1; 1.4) 18 (0.4%) 62.6 ± 11.0 −0.2 (−5.0; 4.6) 0.8 (−3.9; 5.4) 0.4 (−4.2; 5.0)
 Basic 1113 (20.1%) 59.9 ± 13.0 Reference Reference Reference 790 (15.9%) 62.9 ± 10.8 Reference Reference Reference
 Further 2486 (45.0%) 59.1 ± 11.6 −0.5 (−1.3; 0.4) −0.6 (−1.4; 0.2) −0.5 (−1.3; 0.3) 2295 (46.1%) 62.2 ± 10.5 −0.4 (−1.3; 0.4) −0.4 (−1.2; 0.4) −0.4 (−1.3; 0.4)
 Higher 1903 (34.4%) 58.9 ± 10.8 −0.5 (−1.5; 0.4) −0.9 (−1.8; 0.1) −0.4 (−1.3; 0.6) 1877 (37.7%) 61.4 ± 9.8 −0.8 (−1.8; 0.2) −0.7 (−1.7; 0.2) −0.3 (−1.3; 0.6)
Work type
 Sedentary 2665 (48.2%) 58.7 ± 10.8 Reference Reference Reference 2639 (53.0%) 61.7 ± 9.7 Reference Reference Reference
 Standing 1763 (31.9%) 59.7 ± 12.1 0.8** (0.1; 1.5) 0.1 (−0.6; 0.8) −0.0 (−0.7; 0.7) 665 (13.4%) 62.7 ± 10.9 0.9 (−0.0; 1.8) −0.1 (−0.9; 0.8) −0.2 (−1.1; 0.6)
 Manual 407 (7.4%) 60.4 ± 13.5 1.6* (0.4; 2.9) 0.3 (−0.9; 1.5) −0.0 (−1.2; 1.2) 1419 (28.5%) 62.5 ± 10.8 0.3 (−0.4; 1.1) −1.6* (−2.3; −0.8) −1.7* (−2.5; −1.0)
 Retired 159 (2.9%) 59.8 ± 13.2 0.7 (−1.3; 2.7) 0.2 (−1.8; 2.2) 0.0 (−1.9; 2.0) 111 (2.2%) 59.6 ± 10.4 −1.6 (−3.6; 0.4) −2.4** (−4.4; −0.4) −2.5** (−4.4; −0.5)
 Unemployed 58 (1.0%) 59.8 ± 12.0 0.6 (−2.4; 3.6) 0.6 (−2.4; 3.6) −0.0 (−3.0; 2.9) 60 (1.2%) 59.3 ± 9.3 −2.9** (−5.6; −0.3) −3.1** (−5.7; −0.5) −3.7* (−6.2; −1.1)
 Unknown 478 (8.6%) 58.9 ± 11.8 0.1 (−1.0; 1.3) −0.5 (−1.6; 0.7) −0.4 (−1.5; 0.7) 86 (1.7%) 61.4 ± 14.4 −0.3 (−2.6; 2.0) 0.1 (−2.1; 2.3) −0.2 (−2.4; 1.9)
Income
 < £20,000 838 (15.2%) 59.7 ± 12.6 0.2 (−0.7; 1.2) 0.5 (−0.4; 1.5) 0.6 (−0.4; 1.5) 430 (8.6%) 62.0 ± 11.8 0.6 (−0.5; 1.8) 1.1** (0.0; 2.2) 1.3** (0.2; 2.4)
 £20,000–£40,000 1924 (34.8%) 59.2 ± 11.7 Reference Reference Reference 1620 (32.5%) 61.7 ± 10.9 Reference Reference Reference
 > £40,000 2617 (47.3%) 58.9 ± 10.9 0.1 (−0.6; 0.8) −0.1 (−0.8; 0.6) −0.0 (−0.8; 0.7) 2850 (57.2%) 62.1 ± 9.6 0.8** (0.1; 1.5) 0.6 (−0.1; 1.3) 0.5 (−0.2; 1.2)
 Unknown 151 (2.7%) 60.6 ± 15.9 1.4 (−0.5; 3.3) 1.2 (−0.7; 3.1) 1.2 (−0.7; 3.1) 80 (1.6%) 63.3 ± 11.9 1.6 (−0.7; 4.0) 2.4** (0.1; 4.7) 2.6** (0.3; 4.8)
Marital status
 Single 377 (6.8%) 60.2 ± 11.5 0.7 (−0.6; 2.0) 0.6 (−0.7; 1.9) 0.5 (−0.7; 1.8) 383 (7.7%) 61.7 ± 11.4 −0.4 (−1.5; 0.8) −0.2 (−1.3; 0.9) 0.2 (−0.9; 1.3)
 Married/living as married 3519 (63.6%) 59.6 ± 11.8 Reference Reference Reference 3328 (66.8%) 62.4 ± 10.4 Reference Reference Reference
 Widowed/separated/divorced 486 (8.8%) 60.1 ± 12.0 0.5 (−0.7; 1.6) 0.3 (−0.8; 1.5) 0.3 (−0.9; 1.4) 275 (5.5%) 62.6 ± 10.4 0.4 (−0.9; 1.7) 0.2 (−1.0; 1.4) 0.2 (−1.0; 1.5)
 Unknown 1148 (20.8%) 57.1 ± 10.8 −2.1* (−2.9; −1.2) −2.1* (−2.9; −1.2) −2.1* (−3.0; −1.3) 994 (20.0%) 60.7 ± 9.4 −1.6* (−2.5; −0.8) −1.6* (−2.4; −0.8) −1.5* (−2.3; −0.7)
Smoker status
 Never smoked 3126 (56.5%) 58.8 ± 11.5 Reference Reference Reference 2608 (52.4%) 61.7 ± 10.0 Reference Reference Reference
 Ex-smoker 1770 (32.0%) 59.6 ± 11.7 0.7** (0.1; 1.4) 0.5 (−0.2; 1.2) 0.3 (−0.4; 0.9) 1695 (34.0%) 62.8 ± 10.5 1.2* (0.5; 1.8) 1.0* (0.4; 1.6) 0.7** (0.1; 1.3)
 Current smoker 573 (10.4%) 59.7 ± 12.2 0.7 (−0.3; 1.8) −0.3 (−1.3; 0.7) −0.5 (−1.5; 0.5) 628 (12.6%) 61.3 ± 10.9 −0.4 (−1.4; 0.5) −1.5* (−2.4; −0.6) −1.5* (−2.4; −0.6)
 Unknown 61 (1.1%) 58.5 ± 11.0 −0.1 (−3.0; 2.8) −0.6 (−3.5; 2.3) −1.0 (−3.8; 1.9) 49 (1.0%) 60.3 ± 9.2 −1.0 (−3.9; 1.9) −1.6 (−4.4; 1.2) −1.8 (−4.5; 1.0)
Test site
 Cambridge 2044 (37.0%) 59.4 ± 11.6 Reference Reference Reference 1982 (39.8%) 61.7 ± 10.6 Reference Reference Reference
 Ely 2190 (39.6%) 59.1 ± 12.0 0.5 (−0.2; 1.3) 0.7 (−0.0; 1.5) 0.5 (−0.2; 1.3) 1833 (36.8%) 62.4 ± 10.0 1.1* (0.4; 1.8) 1.2* (0.5; 1.9) 0.7** (0.0; 1.4)
 Wisbech 1296 (23.4%) 60.7 ± 11.9 1.0** (0.1; 1.9) 1.2* (0.3; 2.1) 0.8 (−0.0; 1.7) 1165 (23.4%) 64.0 ± 10.8 2.0* (1.2; 2.8) 1.9* (1.1; 2.7) 1.2* (0.4; 2.0)
Seasonality
 Spring −0.1 (−0.5; 0.3) −0.1 (−0.5; 0.3) −0.1 (−0.5; 0.3) −0.4 (−0.8; 0.0) −0.3 (−0.7; 0.1) −0.3 (−0.7; 0.1)
 Winter −1.4* (−1.8; −1.0) −1.2* (−1.7; −0.8) −1.2* (−1.6; −0.8) −0.4 (−0.8; 0.0) −0.1 (−0.5; 0.3) −0.1 (−0.5; 0.3)
PAEE (kJ·d−1·kg−1) 0.1* (0.1; 0.1) 0.1* (0.1; 0.1) 0.1* (0.1; 0.1) 0.1* (0.1; 0.1)
BMI (kg·m−2) 0.4* (0.3; 0.4) 0.4* (0.4; 0.5)
Open in a new tab

The Fenland study 2005 to 2015.

Unadjusted values represent mean ± SD V̇O2max estimates computed within each substratum. Model values are mean (95% CI). Model 1 is mutually adjusted for sociodemographic characteristics. Model 2 is additionally adjusted for PAEE (kJ·d−1·kg−1). Model 3 is additionally adjusted for BMI (kg·m−2). Mean ± SD PAEE for women: 49 ± 20 kJ·d−1·kg−1; for men: 59 ± 23 kJ·d−1·kg−1. Mean ± SD BMI for women: 26.5 ± 5.3 kg·m−2; for men: 27.3 ± 4.1 kg·m−2.

*P < 0.01.

**P < 0.05.

Sequentially adjusted multivariable analyses of associations between estimated V̇O2maxtbm and sociodemographic characteristics are reported in Table 2. Age was still inversely associated with estimated V̇O2maxtbm but was attenuated to the null with adjustment for PAEE. Among women, occupation type and smoker status were not associated with differences in estimated V̇O2maxtbm after adjustment. Among men, estimated V̇O2maxtbm was higher in manual workers compared with sedentary workers when associations were adjusted for sociodemographic characteristics only. When additionally adjusted for PAEE and BMI, estimated V̇O2maxtbm was lower in manual workers (−0.9 mL O2−1·min·kg−1; 95% confidence interval [CI], −1.4 to −0.4), retirees (−1.4 mL O2·min−1·kg−1; 95% CI, −2.7 to −0.1), and the unemployed (−1.9 mL O2·min−1·kg−1; 95% CI, −3.6 to −0.3) relative to sedentary workers. Current male smokers had lower estimated V̇O2maxtbm relative to nonsmokers (−1.3 mL O2·min−1·kg−1; 95% CI, −1.9 to −0.7). In both women and men, estimated V̇O2maxtbm did not differ by education level, income, and marital status after adjustment. Table 3 presents analogous results for estimated V̇O2maxffm. The direction and magnitude of differences in estimated V̇O2maxffm across sociodemographic factors were similar to those found for estimated V̇O2maxtbm; however, Black men had lower V̇O2maxffm (−7.1 mL O2·min−1·kg−1, 95% CI, −10.9 to −3.3) relative to White men.

Women tested in the winter had lower estimated V̇O2maxtbm and V̇O2maxffm than women tested at other times of the year. Seasonal variation in estimated V̇O2maxtbm and V̇O2maxffm was not observed in men after adjustment for PAEE. To investigate this further, we analyzed seasonal variation in CRFestimated when stratified by higher (≥50 kJ·d−1·kg−1) and lower (<50 kJ·d−1·kg−1) PAEE levels (Fig. 2). Seasonal variation in CRFestimated persisted in men and women with higher PAEE levels. Women with lower PAEE levels also demonstrated seasonal variation, however CRFestimated did not differ by season in men with lower PAEE.

FIGURE 2.

FIGURE 2

Open in a new tab

Seasonal variation in estimated maximal oxygen consumption per kilogram total body mass (V̇O2maxtbm) and per kilogram fat-free mass (V̇O2maxffm) stratified by sex and PAEE levels. Dots and bars represent point estimates and 95% CIs from a binned regression procedure, adjusted for age, ethnicity, education level, work type, income, marital status, smoker status, and fitness testing site. Superimposed curves represent seasonal fitness values derived from an unadjusted cosinor model. Vertical dashed lines represent seasonal peaks in fitness values where seasonal variation is observed. The Fenland Study 2005 to 2015.

DISCUSSION

Here we have examined how CRF estimated from a submaximal exercise test (CRFestimated) varies by anthropometric, sociodemographic, and behavioral characteristics in a population-based sample of UK adults (The Fenland Study). The direct relationship between physical activity and CRFestimated was stronger than the inverse relationship between age and CRFestimated. Although CRF and CRF trainability are known to have a strong genetic component (24), our findings reinforce the importance of maintaining an active lifestyle in adulthood to counteract declines in CRF with age. This extends previous reports of CRF in the United Kingdom and provides direction for future population-based studies of CRF.

CRFestimated was lower in older versus younger UK adults, however the magnitude of difference per 5 yr (approximately 0.3 mL O2·kg−1·min−1) was less than values published in some CRF registries from different countries. Cardiorespiratory fitness declined by approximately 2 mL O2·kg−1·min−1 per 5 yr in the US-based FRIEND registry (25), Brazil-based Fleury study (26), and Norway-based HUNT study (27). Similarly, the age-gradient in the Prevention First Registry (28) was 1.5 mL O2·kg·min−1 but 1 mL O2·kg−1·min−1 in the SHIP study (29), both from Germany, whereas the Danish Health Examination Survey (30) and Korean-based KISS FitS study (31) reported a difference of 1.3 mL O2·kg−1·min−1 per 5 yr. Caution should be used when making direct comparisons across these studies because of differences in CRF measurement approaches, sampling of study populations, and different eras in which these studies were conducted.

Unadjusted CRFestimated reported in the present study from the East of England were generally greater than values reported by previous UK-based population studies of CRF. In a sample of male factory workers assessed from 1979, Tuxworth et al. (7) found mean V̇O2maxtbm to be 29.9 mL O2·min−1·kg−1. In the Welsh Heart Health Survey from 1985 (6), mean V̇O2maxtbm among women 30 yr or older was 28.4 mL O2·min−1·kg−1 and among men was 33.7 mL O2·min·kg−1. In the Allied-Dunbar National Fitness Survey from 1990 (8), mean V̇O2maxtbm was 29.9 mL O2·min−1·kg−1 in women and 38.9 mL O2·min−1·kg−1 men. More recent studies of CRF in the UK (9,10) report values greater than these. It is possible that CRF in UK adults has improved since these earlier surveys due to the adoption of improved health behaviors in the general population. However, the difference in mean CRF values between these historical studies and the present study may be due to differences in study design. The Fenland cohort may have also recruited a higher proportion of healthy and enthusiastic people compared with the general population (11). Thus, it is possible that higher CRFestimated values reported here reflect regional differences in health across the United Kingdom.

The association between CRFestimated and physical activity was stronger than that for age; a one SD higher PAEE equates to the same difference in CRF (~2 mL O2·kg−1·min−1 higher) as being 25 yr younger. Similarly, a one SD higher BMI was associated with lower CRF (1.2 to 1.6 mL O2·kg−1·min−1), adjusted for age, physical activity and other sociodemographic characteristics. Thus, physical activity and BMI were stronger determinants of CRF than age and other factors that were examined. Physical activity is known to improve and explain a majority of the variance in CRF among adults (32–34), but age-related decline in CRF is not wholly due to physical inactivity (35,36). Reduced cardiac output and impaired skeletal muscle oxidative capacity with age are also contributing factors, particularly after the seventh decade of life (37–39). We did not directly measure cardiac output or skeletal muscle function, and therefore cannot investigate whether the preservation of CRF with age is related to maintenance of these factors. We also did not obtain CRFestimated in adults 70 yr and older, when the impact of higher physical activity on improved CRF may wane. Nevertheless, our data suggest that higher physical activity can alter the trajectory of CRF decline with age in generally healthy adults. It is unclear whether this finding is reflective of health promotion strategies to increase physical activity and CRF within the Cambridgeshire region (40). If so, future work could seek how these strategies may be adapted to UK regions with high cardiometabolic disease prevalence.

People in more physically demanding occupations were fitter than those in sedentary occupations. When accounting for PAEE and BMI, however, CRFestimated did not differ by occupation in women; in men, CRFestimated was statistically lower in manual, retired, and unemployed workers compared to sedentary workers. Other studies report that manual workers may have greater muscle strength but lower CRF than the general population (41,42). This may be due to sedentary workers participating in leisure-time physical activity, which may have more CRF-enhancing effects than workplace physical activity. Although it is not immediately clear the mechanism by which manual work would lower CRF in men, it is likely occupation specific and could be related to diminished lung function from exposure to occupational respiratory health hazards (43). Previously, we reported that manual workers in the Fenland cohort had greater physical activity levels than other occupation types (11). It is therefore reassuring to observe that the negative effect of manual work on CRF—whatever the mechanism—is partly ameliorated by higher physical activity among male workers. Alternatively, the observed association between low CRF and manual work could be due to residual confounding for socioeconomic status. Previous research suggest that lower CRF in retired and unemployed male workers is related to advanced age and cardiovascular deconditioning after long-lasting physical inactivity (44).

We present CRFestimated scaled by both total body mass (V̇O2maxtbm) and fat-free mass (V̇O2maxffm). In multivariable analysis, estimated V̇O2maxtbm was negatively associated with BMI, however estimated V̇O2maxffm was positively associated. Other studies demonstrate that CRF is independent of adiposity when scaled by fat-free mass and suggest V̇O2maxffm can be considered an indirect measure of musculoskeletal tissue metabolic quality (45,46). More direct measurements of muscle oxidative capacity, such as tissue biopsy or imaging (47), could be used to elucidate whether this is preserved in otherwise overweight and obese participants with higher V̇O2maxffm values. Indeed, ectopic fat infiltration of skeletal muscle may be more related to impaired muscle oxidative capacity and reduced force production than overall adiposity (48,49).

CRFestimated values were generally higher in the summer compared to the spring and winter. Physical activity behaviors displayed a similar pattern as reported in previous analyses (11). PAEE adjustment negated seasonal variation of CRF in men, however in women seasonal variation persisted. Given the cross-sectional analysis used in this study, we recognize that the results regarding CRF seasonality should be interpreted cautiously and a repeated measures design would be more appropriate for quantifying seasonal variation. Such a design would be less feasible for a population-based study of CRF, however, since increased test frequency would be costly to scale and could increase lost-to-follow-up rates. Future work could elucidate whether seasonal variation in CRF among women is related to seasonal variation in endogenous factors, such as circannual hormonal rhythms (50).

Our study has strengths and limitations. We objectively assessed CRFestimated in a large participant sample, enabling the investigation of differences by sociodemographic characteristics in the UK. We also quantified and compared the influence of objectively measured PAEE and BMI on associations between CRFestimated and these characteristics, which allow judgment of their relative importance. A limitation of our study includes using heart rate response to a submaximal exercise test, rather than directly measured maximal oxygen consumption. We show in the validation substudy that the CRFestimated from this method agree with direct measurements of CRF, which provides reassurance of our findings. Other sources of CRF estimation error do still exist, however, including the conversion of the energetic cost of the treadmill test to oxygen cost and the estimation of resting energy expenditure. This could impact the classification of participants into CRF categories in clinical settings (51). Another potential limitation is the non-representativeness of the Fenland cohort compared to the random population sampling frame. Compared to non-responders, participants were slightly older, leaner, less likely to smoke, more likely to drink alcohol, and less likely to live in deprived neighborhoods. These differences were small, however, and observed physical activity levels—the strongest determinant of CRF—were similar to those observed in the general UK population (11,52), suggesting findings may generalize more widely.

CONCLUSIONS

We have described variation in CRFestimated within a UK adult population by sociodemographic factors and lifestyle behaviors. CRFestimated was inversely associated with age but less steeply than anticipated, suggesting older generations are comparatively fitter than younger generations. Physical activity and body size were stronger determinants of the variance in CRFestimated than any other factor including age. A one SD difference in physical activity had the same impact on CRF as being 25 yr younger. This emphasizes the importance of maintaining physical activity across adulthood.

Acknowledgments

The authors are grateful to the Fenland Study participants for their willingness and time to take part in this study. We thank all members of the following teams responsible for practical aspects of the study; Study Coordination, Field Epidemiology, Anthropometry Team, Physical Activity Technical Team, IT, Data Management, and Statistics.

Conflict of Interest and Funding Source: The Fenland study was funded by the Medical Research Council and the Wellcome Trust. The current work was supported by the Medical Research Council (T. I. G., S. B., K. Wi., S. H., grant MC_UU_12015/3), (S. G., grant MC_UU_12015/4), (N. W., grant MC_UU_12015/1), (N. G. F., grant MC_UU_12015/5); the National Institute of Health Research Cambridge (NIHR) Biomedical Research Centre (K. We., S. B., N. G. F., and N. W., grant IS-BRC-1215-20014); and the Cambridge Trust and St Catharine’s College (T.L.). The funders had no role in the design, analysis or writing of this manuscript. The authors have no relevant financial or non-financial interests to disclose. The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The results of the present study do not constitute endorsement by the American College of Sports Medicine.

Author Contributions: The authors contributed to the present manuscript as follows: Idea for analysis (T. I. G., S. B.); acquisition and analysis of cardiorespiratory fitness data (K. We., S. H., S. B.); acquisition and analysis of raw physical activity data (K. We., S. H., S. B.); analysis of epidemiological data (T. I. G., T. L.); drafting of the manuscript (T. I. G.); revising work critically for important intellectual content (all authors); approval of the final version before submission (all authors). Chief Investigator (N. W.) and Principal Investigators (N. G. F., S. G., S. B.) of the Fenland Study.

Ethics Approval: The participants in the Fenland study were recruited from general practice lists as the population-based sampling frame. The National Research Ethics Service (NRES), the body that approves the ethics of research involving NHS patients, considered and approved the study through its East of England Cambridge Central Committee in accordance with the Declaration of Helsinki.

Consent to Participate: All participants provided written informed consent.

Data Availability: The datasets generated and analyzed during the current study are available at request via the University of Cambridge MRC Epidemiology Unit website (http://www.mrc-epid.cam.ac.uk/research/data-sharing/).

Footnotes

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (www.acsm-msse.org).

Contributor Information

TOMAS I. GONZALES, Email: tomas.gonzales@mrc-epid.cam.ac.uk.

KATE WESTGATE, Email: kate.westgate@mrc-epid.cam.ac.uk.

STEFANIE HOLLIDGE, Email: Stefanie.Hollidge@mrc-epid.cam.ac.uk.

TIM LINDSAY, Email: Tim.Lindsay@mrc-epid.cam.ac.uk.

KATRIEN WIJNDAELE, Email: katrien.wijndaele@mrc-epid.cam.ac.uk.

NITA G. FOROUHI, Email: nita.forouhi@mrc-epid.cam.ac.uk.

SIMON GRIFFIN, Email: simon.griffin@mrc-epid.cam.ac.uk.

NICK WAREHAM, Email: nick.wareham@mrc-epid.cam.ac.uk.

REFERENCES

  • 1.Blair SN Kampert JB Kohl HW 3rd, et al. Influences of cardiorespiratory fitness and other precursors on cardiovascular disease and all-cause mortality in men and women. JAMA. 1996;276(3):205–10. [PubMed] [Google Scholar]
  • 2.Lee D, Sui X, Church TS, Lee I-M, Blair SN. Associations of cardiorespiratory fitness and obesity with risks of impaired fasting glucose and type 2 diabetes in men. Diabetes Care. 2009;32(2):257–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Kodama S Saito K Tanaka S, et al. Cardiorespiratory fitness as a quantitative predictor of all-cause mortality and cardiovascular events in healthy men and women: a meta-analysis. JAMA. 2009;301(19):2024–35. [DOI] [PubMed] [Google Scholar]
  • 4.Kaminsky LA Arena R Ellingsen Ø, et al. Cardiorespiratory fitness and cardiovascular disease—the past, present, and future. Prog Cardiovasc Dis. 2019;62(2):86–93. [DOI] [PubMed] [Google Scholar]
  • 5.Gonzales TI Westgate K Strain T, et al. Cardiorespiratory fitness assessment using risk-stratified exercise testing and dose–response relationships with disease outcomes. Sci Rep. 2021;11(1):15315. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Cooke C, Smith G, Hoinville G, Catford J, Tuxworth W. Two national surveys of activity, fitness and health: The Allied Dunbar National Fitness Survey and the Welsh Heart Health Survey. In: Norgan N, editor. Physical activity and health. Cambridge University Press: Cambridge, United Kingdom; 1992. [Google Scholar]
  • 7.Tuxworth W, Nevill AM, White C, Jenkins C. Health, fitness, physical activity, and morbidity of middle age male factory workers. I. Br J Ind Med. 1986;43(11):733–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Fentem P Collins M Tuxworth W, et al. Allied Dunbar National Fitness Survey: Technical report. Activity and Health Research; 1994. [Google Scholar]
  • 9.MacAuley D Mc Crum EE Stott G, et al. Levels of physical activity, physical fitness and their relationship in the Northern Ireland Health and Activity Survey. Int J Sports Med. 1998;19(7):503–11. [DOI] [PubMed] [Google Scholar]
  • 10.O’Donovan G, Hillsdon M, Ukoumunne OC, Stamatakis E, Hamer M. Objectively measured physical activity, cardiorespiratory fitness and cardiometabolic risk factors in the health survey for England. Prev Med. 2013;57(3):201–5. [DOI] [PubMed] [Google Scholar]
  • 11.Lindsay T Westgate K Wijndaele K, et al. Descriptive epidemiology of physical activity energy expenditure in UK adults (The Fenland study). Int J Behav Nutr Phys Act. 2019;16(1):126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Salamone LM Fuerst T Visser M, et al. Measurement of fat mass using DEXA: a validation study in elderly adults. J Appl Physiol (1985). 2000;89(1):345–52. [DOI] [PubMed] [Google Scholar]
  • 13.Brage S Ekelund U Brage N, et al. Hierarchy of individual calibration levels for heart rate and accelerometry to measure physical activity. J Appl Physiol (1985). 2007;103(2):682–92. [DOI] [PubMed] [Google Scholar]
  • 14.Brage S Westgate K Franks PW, et al. Estimation of free-living energy expenditure by heart rate and movement sensing: a doubly-labelled water study. PLoS One. 2015;10(9):e0137206. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Brage S, Brage N, Franks PW, Ekelund U, Wareham NJ. Reliability and validity of the combined heart rate and movement sensor Actiheart. Eur J Clin Nutr. 2005;59(4):561–70. [DOI] [PubMed] [Google Scholar]
  • 16.Tanaka H, Monahan KD, Seals DR. Age-predicted maximal heart rate revisited. J Am Coll Cardiol. 2001;37(1):153–6. [DOI] [PubMed] [Google Scholar]
  • 17.Maritz JS, Morrison JF, Peter J, Strydom NB, Wyndham CH. A practical method of estimating an individual’s maximal oxygen intake. Ergonomics. 1961;4(2):97–122. [Google Scholar]
  • 18.Papathanasiou G Georgakopoulos D Papageorgiou E, et al. Effects of smoking on heart rate at rest and during exercise, and on heart rate recovery, in young adults. Hell J Cardiol. 2013;54(3):168–77. [PubMed] [Google Scholar]
  • 19.Sandvik L Erikssen J Ellestad M, et al. Heart rate increase and maximal heart rate during exercise as predictors of cardiovascular mortality: a 16-year follow-up study of 1960 healthy men. Coron Artery Dis. 1995;6(8):667–79. [DOI] [PubMed] [Google Scholar]
  • 20.Henry C. Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutr. 2005;8(7A):1133–52. [DOI] [PubMed] [Google Scholar]
  • 21.di Prampero PE. Energetics of muscular exercise. Rev Physiol Biochem Pharmacol. 1981;89:143–222. [DOI] [PubMed] [Google Scholar]
  • 22.Cuzick J. A Wilcoxon-type test for trend. Stat Med. 1985;4(1):87–90. [DOI] [PubMed] [Google Scholar]
  • 23.Cattaneo MD, Crump RK, Farrell MH, Feng Y. Binscatter regressions. arXiv. 2021;190209615. [Google Scholar]
  • 24.Bouchard C. Genomic predictors of trainability. Exp Physiol. 2012;97(3):347–52. [DOI] [PubMed] [Google Scholar]
  • 25.Kaminsky LA Arena R Myers J, et al. Updated reference standards for cardiorespiratory fitness measured with cardiopulmonary exercise testing: data from the Fitness Registry and the Importance of Exercise National Database (FRIEND). Mayo Clin Proc. 2022;97(2):285–93. [DOI] [PubMed] [Google Scholar]
  • 26.Rossi Neto JM Tebexreni AS Alves ANF, et al. Cardiorespiratory fitness data from 18,189 participants who underwent treadmill cardiopulmonary exercise testing in a Brazilian population. PLoS One. 2019;14(1):e0209897. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Loe H, Nes BM, Wisløff U. Predicting VO2peak from submaximal- and peak exercise models: The HUNT 3 fitness study, Norway. PLoS One. 2016;11(1):e0144873. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Rapp D, Scharhag J, Wagenpfeil S, Scholl J. Reference values for peak oxygen uptake: cross-sectional analysis of cycle ergometry-based cardiopulmonary exercise tests of 10 090 adult German volunteers from the Prevention First Registry. BMJ Open. 2018;8(3):e018697. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Koch B Schäper C Ittermann T, et al. Reference values for cardiopulmonary exercise testing in healthy volunteers: the SHIP study. Eur Respir J. 2009;33(2):389–97. [DOI] [PubMed] [Google Scholar]
  • 30.Eriksen L, Grønbæk M, Helge JW, Tolstrup JS. Cardiorespiratory fitness in 16 025 adults age 18–91 years and associations with physical activity and sitting time. Scand J Med Sci Sports. 2016;26(12):1435–43. [DOI] [PubMed] [Google Scholar]
  • 31.Jang WY Kim W Kang DO, et al. Reference values for cardiorespiratory fitness in healthy Koreans. J Clin Med. 2019;8(12):2191. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Skinner JS Wilmore KM Krasnoff JB, et al. Adaptation to a standardized training program and changes in fitness in a large, heterogeneous population: the HERITAGE family study. Med Sci Sports Exerc. 2000;32(1):157–61. [DOI] [PubMed] [Google Scholar]
  • 33.O’Donovan G Owen A Bird SR, et al. Changes in cardiorespiratory fitness and coronary heart disease risk factors following 24 wk of moderate- or high-intensity exercise of equal energy cost. J Appl Physiol (1985). 2005;98(5):1619–25. [DOI] [PubMed] [Google Scholar]
  • 34.Lakoski SG, Barlow CE, Farrell SW, Berry JD, Morrow JR, Jr, Haskell WL. Impact of body mass index, physical activity, and other clinical factors on cardiorespiratory fitness (from the Cooper Center longitudinal study). Am J Cardiol. 2011;108(1):34–9. [DOI] [PubMed] [Google Scholar]
  • 35.Stratton JR, Levy WC, Cerqueira MD, Schwartz RS, Abrass IB. Cardiovascular responses to exercise. Effects of aging and exercise training in healthy men. Circulation. 1994;89(4):1648–55. [DOI] [PubMed] [Google Scholar]
  • 36.Fleg JL Morrell CH Bos AG, et al. Accelerated longitudinal decline of aerobic capacity in healthy older adults. Circulation. 2005;112(5):674–82. [DOI] [PubMed] [Google Scholar]
  • 37.Betik AC, Hepple RT. Determinants of VO2max decline with aging: an integrated perspective. Appl Physiol Nutr Metab. 2008;33(1):130–40. [DOI] [PubMed] [Google Scholar]
  • 38.Proctor DN, Joyner MJ. Skeletal muscle mass and the reduction of VO2max in trained older subjects. J Appl Physiol (1985). 1997;82(5):1411–5. [DOI] [PubMed] [Google Scholar]
  • 39.Short KR Vittone JL Bigelow ML, et al. Impact of aerobic exercise training on age-related changes in insulin sensitivity and muscle oxidative capacity. Diabetes. 2003;52(8):1888–96. [DOI] [PubMed] [Google Scholar]
  • 40.Simmons RK van Sluijs EM Hardeman W Sutton S Griffin SJ, ProActive project team . Who will increase their physical activity? Predictors of change in objectively measured physical activity over 12 months in the ProActive cohort. BMC Public Health. 2010;10:226. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Ruzic L, Heimer S, Misigoj-Durakovic M, Matkovic BR. Increased occupational physical activity does not improve physical fitness. Occup Environ Med. 2003;60(12):983–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Savinainen M, Nygård C, Ilmarinen J. A 16-year follow-up study of physical capacity in relation to perceived workload among ageing employees. Ergonomics. 2004;47(10):1087–102. [DOI] [PubMed] [Google Scholar]
  • 43.Norheim KL, Samani A, Bønløkke JH, Omland Ø, Madeleine P. Physical performances show conflicting associations in age manual workers. Sci Rep. 2020;10(1):2254. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Kreuzfeld S, Preuss M, Weippert M, Stoll R. Health effects and acceptance of a physical activity program for older long-term unemployed workers. Int Arch Occup Environ Health. 2013;86(1):99–105. [DOI] [PubMed] [Google Scholar]
  • 45.Goran M, Fields DA, Hunter GR, Herd SL, Weinsier RL. Total body fat does not influence maximal aerobic capacity. Int J Obes Relat Metab Disord. 2000;24(7):841–8. [DOI] [PubMed] [Google Scholar]
  • 46.Schnurr TM Gjesing AP Sandholt CH, et al. Genetic correlation between body fat percentage and cardiorespiratory fitness suggests common genetic etiology. PLoS One. 2016;11(11):e0166738. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Correa-de-Araujo R, Harris-Love MO, Miljkovic I, Fragala MS, Anthony BW, Manini TM. The need for standardized assessment of muscle quality in skeletal muscle function deficit and other aging-related muscle dysfunctions: a symposium report. Front Physiol. 2017;8:87. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Miljkovic I, Zmuda JM. Epidemiology of myosteatosis. Curr Opin Clin Nutr Metab Care. 2010;13(3):260–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Harris-Love MO Gonzales TI Wei Q, et al. Association between muscle strength and modeling estimates of muscle tissue heterogeneity in young and old adults. J Ultrasound Med. 2019;38(7):1757–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Tendler A Bar A Mendelsohn-Cohen N, et al. Hormone seasonality in medical records suggests circannual endocrine circuits. Proc Natl Acad Sci U S A. 2021;118(7):e2003926118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Peterman JE Harber MP Imboden MT, et al. Accuracy of exercise-based equations for estimating cardiorespiratory fitness. Med Sci Sports Exerc. 2021;53(1):74–82. [DOI] [PubMed] [Google Scholar]
  • 52.Brage S Lindsay T Venables M, et al. Descriptive epidemiology of energy expenditure in the UK: findings from the National Diet and Nutrition Survey 2008–15. Int J Epidemiol. 2020;49(3):1007–21. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Medicine and Science in Sports and Exercise are provided here courtesy of Wolters Kluwer Health

RESOURCES