Current physical activity guidelines recommend at least 150 min of moderate intensity, or 75 min high-intensity, physical activity per week for all adults (1). However, around one third of the adult population worldwide do not meet these recommendations (2). This observation highlights the difficulty many people have incorporating physical activity recommendations into their daily lives in a sustainable way (3). Lack of time is frequently cited as the primary barrier to meeting the current recommendations (4).
Walking is the most common form of physical activity that adults perform and is acceptable and accessible to almost the entire population (5). A meta-analysis of randomized controlled trials found that increased walking time led to increased fitness and decreased body weight, body mass index (BMI), percentage body fat and systolic blood pressure in adults (6), all well-known risk factors for premature mortality and morbidity (7). Previous smaller studies, mostly conducted on older adults, found an inverse association between objectively assessed walking pace and all-cause mortality (8–10). Recent analysis of UK Biobank data found that self-reported walking pace was strongly associated with both all-cause and cardiovascular disease (CVD) mortality; indeed the risk associated with walking at <3 mph, compared with ≥4 mph, was stronger than for smoking (7,11). However, these previous studies did not investigate whether the associations with walking pace vary with, and are independent of, time spent walking or whether dose response relationships are present. Furthermore, they did not investigate associations of walking pace with a wider range of health outcomes, such as respiratory disease and cancer subtypes. Therefore, the aim of this study was to investigate, in a large, prospective population-based cohort of middle age and older adults, the associations between usual walking pace and a range of cardiovascular, respiratory and cancer health outcomes. A secondary aim was to explore whether there were dose relationships or threshold effects, and whether the associations varied with, and were independent of, total time spent walking.
Between April 2007 and December 2010, UK Biobank recruited 502,628 participants (5.5% response rate), age 40 to 69 yr from the general population (12–15). However, only 318,185 with full data available were included in this study. Participants attended 1 of 22 assessment centers across England, Wales, and Scotland (12–15) where they completed a touch-screen questionnaire, had physical measurements taken and provided biological samples, as described in detail elsewhere (12–15). All-cause mortality and CVD, respiratory disease, chronic obstructive pulmonary disease (COPD) and cancer mortality and incidence were the main outcomes; and walking pace (slow, average, and brisk) was the exposure of interest. Sociodemographic factors (age, sex, ethnicity, employment status, and area-based deprivation), lifestyle factors (smoking status, self-reported discretionary screen time, total physical activity, grip strength and dietary intake), health-related parameters (systolic blood pressure, diabetes, medication for CVD, and self-reported health rating), BMI, and month of recruitment were treated as potential confounders. To minimize potential reverse causality, that is, those that are less well are not able to walk as fast, all analyses were conducted using landmark analysis excluding events occurring in the first 2 yr of follow-up. Furthermore, participants with baseline medical diagnoses of depression, COPD, chronic asthma, chronic liver diseases, alcohol problems, substance abuse, eating disorders, schizophrenia, cognitive impartment, Parkinson’s disease, dementia, chronic pain syndrome, heart diseases, diabetes, and cancer were excluded (n = 71,026). Those who reported being unable to walk (n = 1929) or those who did not answer these questions were also excluded from the study (n = 7669).
Date of death was obtained from death certificates held by the National Health Service Information Centre (England and Wales) and the National Health Service Central Register Scotland (Scotland). Date and cause of hospital admissions were identified via record linkage to Health Episode Statistics (HES) (England and Wales) and to the Scottish Morbidity Records (Scotland). Detailed information regarding the linkage procedure can be found at http://www.ic.nhs.uk/services/medical-research-information-service. At the time of analysis, mortality data were available up to January 31, 2016. Mortality analysis was therefore censored at these dates or date of death if this occurred earlier. Hospital admission data were available until March 31, 2015, resulting in disease-specific analyses being censored at this date, or the date of hospital admission or death if these occurred earlier. Follow-up information on cancer was obtained via linkage to three routine administrative databases, death certificates, hospital admissions and cancer registrations, with complete follow-up available until March 31, 2015. Incident CVD was defined as a hospital admission or death with ICD10 code I60, I61, I63, I64, I21, I21.4 or I21.9; respiratory disease was defined as ICD10 code J09-J98 or I26-I27 and COPD was defined as ICD10 code J44. All-cause cancer was defined as an ICD code of C0.0-C9.9, D3.7-9 or D4.0-8. Cause-specific cancers were defined using the following ICD10 codes: breast cancer (C50), prostate cancer (C61), lung cancer (C34), and colorectal cancer (C18, C19, and C20).
Walking pace was self-reported using a touch-screen questionnaire completed at the baseline visit. The participants who indicated they were able to walk were asked “How would you describe your usual walking pace?” and they could choose one of the following: slow pace defined as <3 mph; average pace defined as 3 to 4 mph; and brisk pace defined as >4 mph. Physical activity was based on self-report, using the IPAQ short form, and total physical activity was computed as the sum of walking, moderate and vigorous activity, measured as metabolic equivalents (MET·h·wk−1) (16). A proxy measure of total discretionary time spent in screen-related behaviors (TV-viewing and PC-screen) was calculated. Participants were asked “In a typical day, how many hours do you spend watching TV, doing PC screening or driving during your leisure time?,” and this combined figure was used as a proxy for discretionary sedentary measure (expressed as hours per week). Age- and sex-specific walking categories were derived from total walking minutes (from IPAQ) per day (see Table, Supplemental Digital Content 1, age- and sex-specific cutoffs, https://links.lww.com/MSS/B407). Grip strength was assessed using a Jamar J00105 hydraulic hand dynamometer and the mean of the three measurements for each hand were used, grip strength was expressed as kg (17). Fitness was measured in a subset of the cohort (n = 67,700) using a previously validated 6-min incremental ramp cycle ergometer test, as described previously (18).
Dietary information was collected via the Oxford WebQ; a Web-based 24-h recall questionnaire which was developed specifically for use in large population studies (19,20). Area-based socioeconomic status was derived from postcode of residence, using the Townsend score (21). Age was calculated from dates of birth and baseline assessment. Smoking status was self-reported as never, former or current smoking. Employment status, self-health rating (excellent, good, average and bad) was self-reported. Medical history (physician diagnosis of illness) was collected using the self-completed, baseline questionnaire. Height and body weight were measured by trained nurses during the initial assessment center visit. Body mass index was calculated as (weight/height2) and the WHO criteria applied to classify BMI into: underweight, <18.5; normal weight, 18.5 to 24.9; overweight, 25.0 to 29.9; and obese, ≥30.0 kg·m−2 (22). Waist circumference was used to derive central obesity: ≥88 cm for women and ≥102 cm for men (22). Body composition (body fat and fat free mass) were measured using bio-impedance (Tanita BC418MA) by trained nurses. Further details of these measurements can be found in the UK Biobank online protocol (http://www.ukbiobank.ac.uk).
The associations between walking pace and health outcomes were investigated using separate Cox-proportional hazard models using slow walking pace as the reference group. Results are reported as hazard ratios (HR), together with 95% confidence intervals (CI). An HR for trend was estimated by fitting walking pace as ordinal variable into the model (0, slow pace; 1, average pace; and 3, brisk pace), the trend HR indicate the hazard equivalent to moving one category up in walking pace. The models for disease-specific outcomes were conducted excluding participants with the relevant disease at baseline (as mentioned earlier). Moreover, we excluded from all analysis individuals who reported comorbidities which could affect walking pace and time spent walking or those with missing data and those who reported being unable to walk were excluded from the analyses.
For each outcome, we ran three models included an increasing number of covariates: model 1 included month of recruitment and sociodemographic covariates (age, sex, ethnicity, deprivation index and employment status); model 2 was also adjusted for systolic blood pressure, medication for CVD, self-health rating and BMI categories; and model 3 was also adjusted for smoking, discretionary screen time, dietary intake (alcohol, red meat, processed meat, oily fish, processed meat and fruit and vegetables), handgrip strength, and total physical activity (this variable was replaced by moderate-to-vigorous physical activity when the interaction between walking pace and walking time tertiles was investigated).
Tertiles of time spent walking daily were derived for age, sex strata (see Table, Supplemental Digital Content 1, age- and sex-specific cutoffs, https://links.lww.com/MSS/B407). To investigate whether the association between walking pace and health outcomes differed by time spent walking, a multiplicative interaction term between walking pace and walking tertiles was fitted into our models for each outcome.
The proportional hazards assumption was checked by tests based on Schoenfeld residuals. All analyses were performed using STATA 14 statistical software (StataCorp LP).
The UK Biobank study was approved by the North West Multi-Centre Research Ethics Committee and all participants provided written informed consent to participate in the UK Biobank study.
The 2-yr landmark analyses and exclusion of individuals with major comorbidities at baseline meant 318,185 participants were included in the analyses. The mean follow-up period for all-cause and cause-specific mortality was 5.0 yr (ranging from 3.3 to 7.8) and 4.1 yr (ranging from 2.4 to 7.0) for cause-specific incidence. Over the follow-up period, 18,568 (5.8%) participants developed CVD, 5430 (1.7%) respiratory disease and 19,234 (6.0%) cancer, and 5890 (1.9%) participants died (1761 (0.6%) from CVD, 878 (0.3%) from respiratory disease and 3687 (1.2%) from cancer.
The characteristics of participants by walking pace category are summarized in Table 1. Compared with individuals who walked slowly, brisk walkers were less deprived and had a lower prevalence of smoking and obesity. They had lower BMI, waist circumference and percentage body fat, and lower intake of processed and red meat, but higher intake of alcohol, oily fish, fruit and vegetables. They also had higher levels of physical activity, fitness and muscle strength, and lower levels of discretionary screen time (Table 1). The cohort characteristics stratified by sex are presented (see Table, Supplemental Digital Content 2, baseline characteristics by walking pace in women, https://links.lww.com/MSS/B408 and Table, Supplemental Digital Content 3, baseline characteristics by walking pace in men, https://links.lww.com/MSS/B409).
Overall, our analyses taking detailed account of the above confounders, suggest that both average and brisk walking pace were associated with a lower hazard for all-cause, CVD, respiratory disease and COPD mortality in both men and women, compared to slow walking pace (Fig. 1). The HR for health outcomes mortality, both minimally and fully adjusted, are presented (see Table, Supplemental Digital Content 4, walking pace and mortality in women, https://links.lww.com/MSS/B410, and Table, Supplemental Digital Content 5, walking pace and mortality in men, https://links.lww.com/MSS/B411). In summary, the fully adjusted hazard per one category increment in walking pace was 0.91 (95% CI, 0.85–0.98) and 0.90 (95% CI, 0.85–0.95), for all-cause mortality in women and men, respectively. The magnitude of the association was stronger for CVD women, 0.71 (95% CI, 0.61–0.83); men, 0.81 (95% CI, 0.73–0.90), respiratory (women, 0.72; 95% CI, 0.58–0.89; men, 0.76; 95% CI, 0.66–0.88), and COPD (women, 0.19; 95% CI, 0.08–0.45; men, 0.49; 95% CI, 0.33–0.73), compared with all-cause mortality per one category increment in walking pace, as shown in Figure 1).
Similar results were observed for cause-specific incidence, except for prostate cancer where a higher hazard was observed in those reporting either average or brisk walking pace (Fig. 2). The HR for health outcomes incidence, both minimally and fully adjusted, are presented (see Table, Supplemental Digital Content 6, walking pace and incidence in women, https://links.lww.com/MSS/B412, and Table, Supplemental Digital Content 7, walking pace and incidence in men, https://links.lww.com/MSS/B413). In our fully adjusted model, the hazards per one category increment in walking pace were 0.92 (95% CI, 0.88–0.96) and 0.94 (95% CI: 0.91–0.97), for CVD incidence in women and men, respectively. Similar trends were observed for all-respiratory disease incidence (women, 0.84; 95% CI, 0.78–0.90; men, 0.84; 95% CI, 0.79–0.89), and COPD (women, 0.54; 95% CI, 0.42–0.70; men, 0.60; 95% CI, 0.48–0.74) per one category increment in walking pace, as shown in Figure 2. However, the hazard for prostate cancer risk was 1.10 (95% CI, 1.02–1.19) per one category increment in walking pace.
When the associations between walking pace and health outcomes were stratified by walking time tertiles (expressed as minutes per day), significant interactions were found for all-cause mortality but not for CVD, respiratory disease, and all-cancer incidence and mortality (Figs. 3 and 4 and see Table, Supplemental Digital Content 8, walking pace tertiles and mortality in women, https://links.lww.com/MSS/B414, and Table, Supplemental Digital Content 9, walking pace tertiles and mortality in men, https://links.lww.com/MSS/B415)). Our findings show that, compared to those reporting brisk walking pace and who were classified in the higher walking time category, individuals who reported slow walking pace were at higher risk for all-cause mortality if they were classified in the middle or lower walking time categories. However, for CVD and respiratory incidence and mortality, those who reported a slow pace were at higher risk for these health outcomes regardless if they were classified in the higher, middle or lower walking time categories. Interestingly, those who were in the middle or lower walking time tertiles were not at higher risk for these outcomes if they reported brisk walking pace (Figs. 3 and 4). These trends were not observed for cancer mortality or incidence. All these findings were independent of major confounding factors.
Usual walking pace was associated with a range of health outcomes that extended beyond CVD and all-cause mortality, to all-respiratory diseases and COPD in both men and women. The associations demonstrated were independent of measured confounders; most notably total physical activity. Our findings also show that those reporting normally walking at a slow pace had higher hazard for all-cause mortality and CVD and respiratory incident and mortality regardless the time spent walking. Future research, with appropriately designed randomized-controlled trials, is needed to determine if the current observations reflect a causal association and if so these findings could have import implications for physical activity recommendations.
Comparison with previous studies
The current finding of an inverse association between self-reported usual walking pace and all-cause and CVD mortality is consistent with previous studies (8,23–31). The majority of these previous studies have, however, been carried out in older people and looked at maximal walking pace (23,24,31–33) making the findings less relevant for the general population. Previous studies carried out over a wider age range have generally been small or modest in size (1255–38,981 participants) (9,25,26,30) and have involved studies of recreational walkers (25) and CVD patients (26,30). Two recent studies by Yates et al. (11), and Ganna and Ingelsson (7), using UK Biobank data found that walking pace, that can be simply obtained by verbal interviews without physical examination, was a strong predictor of all-cause and CVD. The current study, in a well characterized and large cohort, confirms that a slow walking pace is associated with an increased all-cause and CVD mortality risk and extends these findings with novel data to demonstrate that an average (3–4 mph) or brisk walking pace (>4 mph) is associated with a lower risk of COPD and all-cancer mortality while a brisk walking pace is associated with a lower risk of lung cancer mortality and incidence. Surprisingly an average and brisk walking pace were also associated with an increased risk of prostate cancer incidence, but not mortality. Previously positive associations between prostate cancer incidence and physical activity (or fitness a surrogate of total physical activity) has been reported in some (34,35) but not all (36) studies. The exact reasons behind these observations are unknown, but differences in health-seeking behaviors, may be a contributing factor (34). For example, it has been postulated that health-conscious men (who are more likely to walk briskly), may be more likely to attend screening or report symptoms leading to increased detection of early cancers and improved prognosis (34).
We have extended the findings that slow walking pace is associated with increased risk of poor health outcomes by also demonstrating, that this association is present regardless of the amount of total time spent walking. Not only were those reporting a slow walking pace at higher risk for all-cause, CVD and respiratory incidence and mortality compared to those reporting brisk walking pace but the incidence and mortality risk was the same in those reporting brisk walking across all three walking time categories. Taken together, this indicates that the pace at which walking is habitually carried out maybe more important for health outcomes than the total amount of time spent walking, although it is worth pointing out that whilst we have carried out robust statistical adjustment here we cannot rule out reverse causality (i.e., those that are less well are not able to walk as fast) even though we tried to minimize this in our landmark analyses and by taking out all individuals with long standing illness. These findings are, however, similar to those reported in the Caerphilly study where only leisure activity classified as heavy or vigorous was associated with a reduced risk of CVD mortality (37), although this study was confined to middle-age men and included all forms of activity, with the current study focused on walking only. Appropriately designed randomized controlled trials are needed to determine if these findings are causal.
Implications of findings
Walking is frequently recommended as a tool to increase physical activity levels as it is free and generally accessible to all, but currently, the primary focus has been to increase the time spent or the number of steps walked (38,39) with the pace of walking often receiving less focus. If future trials confirm the findings of the current study this may indicate that although strategies to increase total walking time, which is currently the primary focus, will be of benefit it may be prudent to also ensure promotion of a brisk walking pace, where the individual is capable, to further enhance the benefits of walking. On the other hand, if these findings are shown not to be causal, the data indicates that self-reported walking pace may be a useful tool to indicate subclinical illness which may progress to poorer health outcomes.
STRENGTHS AND LIMITATIONS
The UK Biobank is reasonably representative of the general population in terms of age, sex, ethnicity, and socioeconomic status but is unrepresentative in terms of lifestyle (40). Therefore, caution is needed in generalizing summary statistics to the general population, but estimates of the magnitude of the associations are, nevertheless, generalizable. Participants were more likely to be older, to be women, and to live in less socioeconomically deprived areas; were less likely to be obese, to smoke, or to drink alcohol on a daily basis; and had fewer self-reported health outcomes. Rates of all-cause mortality and incidence of cancer were also lower (41,42). This does not detract from the ability to generalize estimates of the magnitude of associations. Our study benefited from a very large number of participants, recruited from the general population, across the whole of the United Kingdom. We had sufficient power to undertake subgroup analyses by sex, which overcomes limitations from previous evidence. Reverse causality is possible in any observational study; however, when participants with existing disease diagnosed at baseline were removed from the analysis, the associations remained significant. Moreover, our results were broadly similar after a landmark analysis was conducted removing all events that occurred within the first 2 yr of follow up. Walking pace was self-reported and, to our knowledge, the question used has not previously been validated. Although prevalent disease and comorbidities at baseline were self-reported, these self-reports were of physician diagnosed disease.
In conclusion, the current data have demonstrated that, irrespective of total walking time, a faster walking pace is associated with lower risk of a wide range of health outcomes. The findings require determination of causality in appropriately designed trials, but could have important implications for physical activity recommendations. They tentatively imply guidelines should encourage people to increase their walking pace (if low to begin with), rather than simply focusing on total time spent walking. As lack of time is the most commonly cited barrier to increasing activity levels; brisk walking of shorter duration may be easier to accommodate into busy schedules and the benefits may be greatest among those failing to meet the current recommendations.
The authors are grateful to UK Biobank participants. This research has been conducted using the UK Biobank Resource under Application Number 7155. The UK Biobank was supported by the Wellcome Trust, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government and the British Heart Foundation. The research was designed, conducted, analyzed and interpreted by the authors entirely independently of the funding sources. The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation, and statement that results of the present study do not constitute endorsement by ACSM.
C. C. M., S. G., F. P., J. P. P., N. S., J. M. R. G. contributed to the conception and design of the study, advised on all statistical aspects and interpreted the data. C. C. M., S. G., and F. P. performed the statistical analyses. C. C. M., S. G., F. P., J. M. R. G., J. P. P., and N. S. drafted the article. All authors reviewed the article and approved the final version to be published. C. C. M., S. G., J. P. P., N. S., and J. M. R. G. had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
The authors declare that they have no conflict of interest.
1. WHO. Global recommendations on physical activity for health. World Health Organization
. 2010. Available: http://www.who.int/dietphysicalactivity/publications/9789241599979/en/
2. Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet
3. Darker CD, French DP, Eves FF, Sniehotta FF. An intervention to promote walking amongst the general population based on an ‘extended’ theory of planned behaviour: a waiting list randomised controlled trial. Psychol Health
4. Trost SG, Owen N, Bauman AE, Sallis JF, Brown W. Correlates of adults’ participation in physical activity: review and update. Med Sci Sports Exerc
5. Morris JN, Hardman AE. Walking to health. Sports Med
6. Murphy MH, Nevill AM, Murtagh EM, Holder RL. The effect of walking on fitness, fatness and resting blood pressure: a meta-analysis of randomised, controlled trials. Prev Med
7. Ganna A, Ingelsson E. 5 year mortality predictors in 498 103 UK Biobank
participants: a prospective population-based study. Lancet
8. Liu B, Hu XH, Zhang Q, et al. Usual walking speed and all-cause mortality risk in older people: a systematic review and meta-analysis. Gait Posture
9. Elbaz A, Sabia S, Brunner E, et al. Association of walking speed in late midlife with mortality: results from the Whitehall II cohort study. Age
10. Cooper R, Kuh D, Hardy R, Mortality Review Group; FALCon and HALCyon Study Teams. Objectively measured physical capability levels and mortality: systematic review and meta-analysis. BMJ
11. Yates T, Zaccardi F, Dhalwani NN, et al. Association of walking pace and handgrip strength with all-cause, cardiovascular, and cancer mortality: a UK Biobank
observational study. Eur Heart J
12. Thompson SG, Willeit P. UK Biobank
comes of age. Lancet
13. Watts G. UK Biobank
opens its data vaults to researchers. Br Med J
14. Palmer LJ. UK Biobank
: bank on it. Lancet
15. Ollier W, Sprosen T, Peakman T. UK Biobank
: from concept to reality. Pharmacogenomics
16. IPAQ. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ)—short Form, Version 2.0: IPAQ; 2004. Available: www.ipaq.ki.se
17. Celis-Morales CA, Welsh P, Lyall DM, et al. Associations of grip strength with cardiovascular, respiratory, and cancer outcomes and all cause mortality: prospective cohort study of half a million UK Biobank
18. Celis-Morales CA, Lyall DM, Steell L, et al. Associations of discretionary screen time with mortality, cardiovascular disease and cancer are attenuated by strength, fitness and physical activity: findings from the UK Biobank
study. BMC Med
19. Liu B, Young H, Crowe FL, et al. Development and evaluation of the Oxford WebQ, a low-cost, web-based method for assessment of previous 24 h dietary intakes in large-scale prospective studies. Public Health Nutr
20. Galante J, Adamska L, Young A, et al. The acceptability of repeat internet-based hybrid diet assessment of previous 24-h dietary intake: administration of the Oxford WebQ in UK Biobank
. Br J Nutr
21. Townsend P, Phillimore M, Beattie A. Health and Deprivation: Inequality and the North
. London: Croom Helm Ltd; 1988.
22. WHO. Obesity: Preventing and Managing the Global Epidemic Report of a WHO Consultation
. 2000; 0512-3054. Available: http://apps.who.int/iris/bitstream/10665/42330/1/WHO_TRS_894.pdf
23. Dumurgier J, Elbaz A, Ducimetiere P, Tavernier B, Alperovitch A, Tzourio C. Slow walking speed and cardiovascular death in well functioning older adults: prospective cohort study. Br Med J
24. Newman AB, Simonsick EM, Naydeck BL, et al. Association of long-distance corridor walk performance with mortality, cardiovascular disease, mobility limitation, and disability. JAMA
25. Williams PT, Thompson PD. The relationship of walking intensity to total and cause-specific mortality. Results from the National Walkers’ Health Study. Plos One
26. Williams PT. Reduced total and cause-specific mortality from walking and running in diabetes. Med Sci Sports Exerc
27. Saevereid HAS, Schnohr PS, Prescott EP. Speed and duration of walking and other leisure time physical activity and the risk of heart failure: the Copenhagen City Heart study. Eur Heart J
28. Hamer M, Chida Y. Walking and primary prevention: a meta-analysis of prospective cohort studies. Br J Sports Med
29. Hakim AA, Petrovitch H, Burchfiel CM, et al. Effects of walking on mortality among nonsmoking retired men. N Engl J Med
30. Chiaranda G, Bernardi E, Codeca L, et al. Treadmill walking speed and survival prediction in men with cardiovascular disease: a 10-year follow-up study. BMJ Open
31. Chen PJ, Lin MH, Peng LN, et al. Predicting cause-specific mortality of older men living in the veterans home by handgrip strength and walking speed: a 3-year, prospective cohort study in Taiwan. J Am Med Dir Assoc
32. Cesari M, Pahor M, Marzetti E, et al. Self-assessed health status, walking speed and mortality in older Mexican-Americans. Gerontology
33. Rolland Y, Lauwers-Cances V, Cesari M, Vellas B, Pahor M, Grandjean H. Physical performance measures as predictors of mortality in a cohort of community-dwelling older French women. Eur J Epidemiol
34. Lakoski SG, Willis BL, Barlow CE, et al. Midlife cardiorespiratory fitness, incident cancer, and survival after cancer in men: the cooper center longitudinal study. JAMA Oncol
35. Byun W, Sui X, Hébert JR, et al. Cardiorespiratory fitness and risk of prostate cancer: findings from the Aerobics Center Longitudinal study. Cancer Epidemiol
36. Oliveria SA, Kohl HW, Trichopoulos D, Blair SN. The association between cardiorespiratory fitness and prostate cancer. Med Sci Sports Exerc
37. Yu S, Yarnell JW, Sweetnam PM, Murray L. What level of physical activity protects against premature cardiovascular death? The Caerphilly study. Heart
38. Tudor-Locke C, Craig CL, Brown WJ, et al. How many steps/day are enough? For adults. Int J Behav Nutr Phys Act
39. Baker G, Mutrie N, Lowry R. Using pedometers as motivational tools: are goals set in steps more effective than goals set in minutes for increasing walking? Int J Health Promot Educ
. 2008; 46(1):21–6.
40. Swanson JM. The UK Biobank
and selection bias. Lancet
41. Collins R. What makes UK Biobank
42. Manolio TA, Collins R. Enhancing the feasibility of large cohort studies. JAMA