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Height and weight are commonly used metrics in epidemiologic studies to calculate body mass index. Large cohort studies generally assess height and weight by self-report rather than by measurement. The aim of this study was to assess the validity of self-reported height and weight in the Cancer Prevention Study-3 (CPS-3), a large, nationwide cohort recruited by the American Cancer Society between 2006-2013. In a subset of CPS-3 participants (n = 2,643), weight and height were assessed at the same time via self-report and in-person measurement. BMI was calculated and classified underweight (<18.5 kg/m2), normal (18.5-<25 kg/m2), overweight (25-<30 kg/m2), or obese (≥30 kg/m2). Self-reported and measured height, weight, and BMI were compared using mean differences and Bland-Altman plots and examined by sex, race/ethnicity, education, marital status, age group, and BMI category. Men and women slightly overreported height and underreported weight. BMI calculated from self-reported data was lower than for measured data for men and women. In analyses stratified by race/ethnicity, age, education, and marital status, older women and women with less than a college degree overreported height. Approximately 13% of men and 7% of women were misclassified into a lower self-reported BMI category, with misclassification of BMI being greatest in obese men and women. Overall, height, weight, and BMI were well-reported, and this study further suggests that BMI computed from self-reported weight and height is a valid measure in men and women across different socio-demographic groups.

The American Cancer Society (ACS) publishes the Diet and Physical Activity Guideline to serve as a foundation for its communication, policy, and commu-nity strategies and, ultimately, to affect dietary and physical activity patterns among Americans. This guideline is developed by a national panel of experts in cancer re-search, prevention, epidemiology, public health, and policy, and reflects the most current scientific evidence related to dietary and activity patterns and cancer risk. The ACS guideline focuses on recommendations for individual choices regarding diet and physical activity patterns, but those choices occur within a community context that either facilitates or creates barriers to healthy behaviors. Therefore, this com-mittee presents recommendations for community action to accompany the 4 recom-mendations for individual choices to reduce cancer risk. These recommendations for community action recognize that a supportive social and physical environment is indispensable if individuals at all levels of society are to have genuine opportunities to choose healthy behaviors. This 2020 ACS guideline is consistent with guidelines from the American Heart Association and the American Diabetes Association for the prevention of coronary heart disease and diabetes as well as for general health promotion, as defined by the 2015 to 2020 Dietary Guidelines for Americans and the 2018 Physical Activity Guidelines for Americans.

Multiple organizations around the world have issued evidence‐based exercise guidance for patients with cancer and cancer survivors. Recently, the American College of Sports Medicine has updated its exercise guidance for cancer prevention as well as for the prevention and treatment of a variety of cancer health‐related outcomes (eg, fatigue, anxiety, depression, function, and quality of life). Despite these guidelines, the majority of people living with and beyond cancer are not regularly physically active. Among the reasons for this is a lack of clarity on the part of those who work in oncology clinical settings of their role in assessing, advising, and referring patients to exercise. The authors propose using the American College of Sports Medicine's Exercise Is Medicine initiative to address this practice gap. The simple proposal is for clinicians to assess, advise, and refer patients to either home‐based or community‐based exercise or for further evaluation and intervention in outpatient rehabilitation. To do this will require care coordination with appropriate professionals as well as change in the behaviors of clinicians, patients, and those who deliver the rehabilitation and exercise programming. Behavior change is one of many challenges to enacting the proposed practice changes. Other implementation challenges include capacity for triage and referral, the need for a program registry, costs and compensation, and workforce development. In conclusion, there is a call to action for key stakeholders to create the infrastructure and cultural adaptations needed so that all people living with and beyond cancer can be as active as is possible for them.

Leisure time physical activity reduces the risk of premature mortality, but the years of life expectancy gained at different levels remains unclear. Our objective was to determine the years of life gained after age 40 associated with various levels of physical activity, both overall and according to body mass index (BMI) groups, in a large pooled analysis. We examined the association of leisure time physical activity with mortality during follow-up in pooled data from six prospective cohort studies in the National Cancer Institute Cohort Consortium, comprising 654,827 individuals, 21-90 y of age. Physical activity was categorized by metabolic equivalent hours per week (MET-h/wk). Life expectancies and years of life gained/lost were calculated using direct adjusted survival curves (for participants 40+ years of age), with 95% confidence intervals (CIs) derived by bootstrap. The study includes a median 10 y of follow-up and 82,465 deaths. A physical activity level of 0.1-3.74 MET-h/wk, equivalent to brisk walking for up to 75 min/wk, was associated with a gain of 1.8 (95% CI: 1.6-2.0) y in life expectancy relative to no leisure time activity (0 MET-h/wk). Higher levels of physical activity were associated with greater gains in life expectancy, with a gain of 4.5 (95% CI: 4.3-4.7) y at the highest level (22.5+ MET-h/wk, equivalent to brisk walking for 450+ min/wk). Substantial gains were also observed in each BMI group. In joint analyses, being active (7.5+ MET-h/wk) and normal weight (BMI 18.5-24.9) was associated with a gain of 7.2 (95% CI: 6.5-7.9) y of life compared to being inactive (0 MET-h/wk) and obese (BMI 35.0+). A limitation was that physical activity and BMI were ascertained by self report. More leisure time physical activity was associated with longer life expectancy across a range of activity levels and BMI groups. Please see later in the article for the Editors' Summary.

Traditional measures of muscular strength require in-person visits, making administration in large epidemiologic cohorts difficult. This has left gaps in the literature regarding relationships between strength and long-term health outcomes. The aim of this study was to test the feasibility and validity of a video-led, self-administered 30-second sit-to-stand (STS) test in a sub-cohort of the U.S.-based Cancer Prevention Study-3. A video was created to guide participants through the STS test. Participants submitted self-reported scores (n = 1851), and optional video recordings of tests (n = 134). Two reviewers scored all video tests. Means and standard deviations (SD) were calculated for self-reported and video-observed scores. Mean differences (95% confidence intervals (CI)) and Spearman correlation coefficients between self-reported and observed scores were calculated, stratifying by demographic characteristics. Participants who uploaded a video reported 14.1 (SD = 3.5) stands, which was not significantly different from the number of stands achieved by the full cohort (13.9 (SD = 4.2), P-difference = 0.39). Self-reported and video-observed scores were highly correlated (ρ = 0.97, mean difference = 0.3, 95% CI = 0.1-0.5). There were no significant differences in correlations by sociodemographic factors (all P-differences ≥0.42). This study suggests that the self-administered, video-guided STS test may be appropriate for participants of varying ages, body sizes, and activity levels, and is feasible for implementation within large, longitudinal studies. This video-guided test would also be useful for remote adaptation of the STS test during the COVID-19 pandemic.

We examined the one-year test re-test reliability and validity criterion of survey-assessed sleep duration collected from two separate questions. The Activity Validation Sub Study included 751 participants of the Cancer Prevention Study-3 study to further investigate rest/activity cycles. Sleep duration was collected using three methods: survey, Daysimeter device, and sleep diary. Survey-assessed sleep duration was collected using 2 different questions, each with different response options (categorical and continuous). Selected participants (n = 170) were asked to wear a Daysimeter device for seven consecutive days for two non-consecutive quarters. Participants were excluded from the current study due to incomplete/implausible survey or device data or reported working night shift. We calculated reliability of pre- and post-survey sleep duration for both survey question using Spearman correlation. We used the method of triads to estimate the validity coefficient (VC) between the three sleep duration measurements in the present study and the \"true\" latent sleep duration measure, and bootstrapping methods to calculate the 95% confidence intervals (95%CI). Of 119 participants included in the study (52.10% male), test-retest correlation showed strong and moderate correlations for sleep duration collected continuously and categorically, respectively. The VC for survey-assessed continuous sleep duration was 0.82 (95%CI 0.71, 0.90) for weekday and 0.68 (95%CI 0.46, 0.83) for weekend. Performance of the VC was slightly weaker for survey-assessed categorical sleep duration (weekday VC = 0.57 95%CI 0.42, 0.71; weekend VC = 0.47 95%CI 0.29, 0.62). The two survey-assessed sleep duration questions used in the AVSS and CPS-3 cohorts are valid approximations of sleep duration.

Diabetes is a major predictor of death from heart disease and stroke; its impact on nonvascular mortality, including specific cancers, is less understood. We examined the association of diabetes with cause-specific mortality, including deaths from specific cancers. A prospective cohort of 1,053,831 U.S. adults, without cancer at baseline, enrolled in the Cancer Prevention Study-II in 1982 and was followed for mortality until December 2008. At baseline, participants completed a self-administered questionnaire that included information on diabetes, smoking, physical activity, height, and weight. Multivariable-adjusted relative risks (RRs) (95% CI) were estimated using Cox proportional hazards regression. During 26 years of follow-up, 243,051 men and 222,109 women died. In multivariable models that controlled for age, BMI, and other variables, diabetes was associated with higher risk of all-cause mortality (women RR 1.90 [95% CI 1.87-1.93]; men 1.73 [1.70-1.75]). Among women, diabetes was associated with higher risk of death from cancers of the liver (1.40 [1.05-1.86]), pancreas (1.31 [1.14-1.51]), endometrium (1.33 [1.08-1.65]), colon (1.18 [1.04-1.33]), and breast (1.16 [1.03-1.29]). Among men, diabetes was associated with risk of death from cancers of the breast (4.20 [2.20-8.04]), liver (2.26 [1.89-2.70]), oral cavity and pharynx (1.44 [1.07-1.94]), pancreas (1.40 [1.23-1.59]), bladder (1.22 [1.01-1.47]), colon (1.15 [1.03-1.29]), and (inversely) prostate (0.88 [0.79-0.97]). Diabetes was also associated with higher risks of death involving the circulatory system, respiratory system, digestive system, genitourinary system, and external causes/accidental deaths. Diabetes is associated with higher risk of death for many diseases, including several specific forms of cancer.

Excess body fatness is an established risk factor for various types of chronic disease and all-cause mortality. Most previous studies are based on body mass index (BMI) as a general measure of adiposity, but whether measures of central adiposity that better represent metabolically active visceral fat, such as waist-to-height ratio (WtHR), may be better at predicting disease and mortality risks is less known. Data from a large, prospective cohort in the U.S. including 50,618 women and 43,783 men (mean age of 67.3 years, predominantly non-Hispanic White), among whom 21,565 women and 26,758 men died during follow-up (1997-2018), were used to calculate multivariable-adjusted hazard rate ratios and 95% CIs for WtHR, BMI, and waist circumference in relation to total and cause-specific mortality. WtHR was strongly correlated with BMI (r = 0.81). After adjustment for BMI and other covariates, WtHR (≥0.55 vs. < 0.50) was positively associated with all-cause mortality risk in women (RR = 1.23, 95% CI 1.17-1.29) and men (RR = 1.11, 95% CI 1.06-1.17). BMI and WC were also independently, positively associated with subsequent mortality risk at a similar magnitude to WtHR. Associations persisted for all grouped causes of death in women and men, with the exception of cancer and Alzheimer's disease mortality in men. Mortality associations with WtHR were generally stronger among individuals younger than age 70 years compared to older individuals. WtHR was associated with all-cause, cardiovascular disease, cancer, respiratory disease, and Alzheimer's disease mortality in women and men at a magnitude similar to BMI or WC. Excess adiposity is an established major risk factor for premature death, but different measures may better predict mortality in different populations defined by age or other factors.

Remaining controversies on the association between body mass index (BMI) and mortality include the effects of smoking and prevalent disease on the association, whether overweight is associated with higher mortality rates, differences in associations by race and the optimal age at which BMI predicts mortality. To assess the relative risk (RR) of mortality by BMI in Whites and Blacks among subgroups defined by smoking, prevalent disease, and age, 891,572 White and 38,119 Black men and women provided height, weight and other information when enrolled in the Cancer Prevention Study II in 1982. Over 28 years of follow-up, there were 434,400 deaths in Whites and 18,702 deaths in Blacks. Cox proportional-hazards regression was used to estimate multivariable-adjusted relative risks (RR) and 95% confidence intervals (CI). Smoking and prevalent disease status significantly modified the BMI-mortality relationship in Whites and Blacks; higher BMI was most strongly associated with higher risk of mortality among never smokers without prevalent disease. All levels of overweight and obesity were associated with a statistically significantly higher risk of mortality compared to the reference category (BMI 22.5-24.9 kg/m2), except among Black women where risk was elevated but not statistically significant in the lower end of overweight. Although absolute mortality rates were higher in Blacks than Whites within each BMI category, relative risks (RRs) were similar between race groups for both men and women (p-heterogeneity by race  = 0.20 for men and 0.23 for women). BMI was most strongly associated with mortality when reported before age 70 years. Results from this study demonstrate for the first time that the BMI-mortality relationship differs for men and women who smoke or have prevalent disease compared to healthy never-smokers. These findings further support recommendations for maintaining a BMI between 20-25 kg/m2 for optimal health and longevity.

Background Obesity-related cancers account for 40% of US cancer cases, and their global burden continues to rise. Cancer prevention guidelines recommend 150–300 min of moderate or 75–150 min of vigorous-intensity activity per week (7.5–15 MET-hrs/wk). However, the long-term causal effect of sustained leisure-time moderate-to-vigorous intensity physical activity (MVPA) on obesity-related cancer risk has not been quantified. Methods We emulated a target trial using data from 60,958 cancer-free adults in the Cancer Prevention Study-II Nutrition Cohort (2001–2013) to estimate 11-year risks of obesity-related cancers under four sustained MVPA strategies: (1) no intervention (observed MVPA); (2) below recommendations (> 0– < 7.5 MET-hrs/wk); (3) meeting recommendations (7.5–15 MET-hrs/wk); and (4) exceeding recommendations (> 15 MET-hrs/wk). MVPA was self-reported every 2 years. The parametric g-formula was used to estimate cancer risk under each strategy among all eligible participants and stratified by pre-intervention MVPA (meeting vs. not meeting recommendations 2 years prior to intervention). Results Over a median follow-up of 11.4 years (IQR 6.9–11.8), 4344 obesity-related cancers were diagnosed. Under no intervention, median baseline MVPA was 12.8 MET-hrs/wk (IQR 4.5–24.5) overall, 20.5 (IQR 15.2–30.8) among those meeting ( n  = 38,558), and 4.3 (IQR 1.5–6.2) among those not meeting recommendations pre-intervention ( n  = 22,400). The estimated 11-year cancer risk under no intervention was 8.2% overall, 8.1% among those meeting, and 8.7% among those not meeting recommendations pre-intervention. Compared to no intervention, risk differences were 0.18% (95% CI: 0.05% to 0.37%) for below-recommendation MVPA, 0.08% (95% CI: − 0.05% to 0.19%) for meeting, and − 0.18% (95% CI: − 0.44% to 0.01%) for exceeding recommendations. Among those meeting recommendations pre-intervention, risk differences were 0.34% (95% CI: 0.11% to 0.65%), 0.09% (95% CI: − 0.06% to 0.26%), and − 0.21% (95% CI: − 0.45% to − 0.05%), respectively. Among those not meeting recommendations, corresponding risk differences were − 0.02% (95% CI: − 0.31% to 0.27%), − 0.04% (95% CI: − 0.21% to 0.15%), and − 0.10% (95% CI: − 0.38% to 0.14%). Conclusions We estimated that, compared to no intervention, sustaining MVPA volumes below recommendations may modestly increase obesity-related cancer risk over 11 years, whereas exceeding recommendations may modestly reduce risk, particularly among participants already meeting the recommendations prior to intervention.