Pre-Diagnosis Recreational Physical Activity and Lung Cancer Mortality within the California Teachers Study

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Cauble, Mia Blanchard, Peggy Reynolds, Emma S. Spielfogel, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8138657/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Apr, 2026 Read the published version in Cancer Causes & Control → Version 1 posted 11 You are reading this latest preprint version Abstract Purpose Although physical activity (PA) levels have been linked to decreased lung cancer mortality, the magnitude of associations and delineation of biological and behavioral risk factors is often inconsistent. Our study aims to address this gap by elucidating the associations of lung cancer mortality with time-varying and exertion-varying pre-diagnosis PA levels. Methods We examined the associations between PA and lung cancer mortality among 1,768 women enrolled in the California Teachers Study cohort and diagnosed with lung cancer between 1995–2019. Pre-diagnosis lifetime and recent PA were assessed. Multivariable Cox regressions provided hazard ratio (HR) and 95% confidence interval (CI) estimates. Results Similar risks of lung cancer mortality were observed across all PA variables. Ever and/or former smokers who engaged in higher levels of moderate, lifetime PA had a lower risk of lung cancer mortality. Ever and/or current smokers who engaged in intermediate to high levels of strenuous, lifetime PA had increased risk of lung cancer mortality, while never smokers saw a protective effect on lung cancer mortality. Conclusion The results of this study suggest that smoking significantly modifies the association between PA and lung cancer mortality. Although the mechanisms underlying these findings remain unclear, we hypothesize that excessive strenuous PA among ever and/or current smokers exacerbates the inflammatory damage already induced by smoke exposure, compromising immune cell recovery and leading to reduced lung cancer survival in this group. Lung cancer Survivorship. Physical activity Risk factors Cohort study Figures Figure 1 Figure 2 Figure 5 Introduction According to the American Cancer Society, lung cancer is the 2nd most common cancer in women the United States, and leading cause of cancer death [ 1 ]. Although incidence and mortality rates have decreased over time due to advancements in treatment and early detection, the number of lung cancer-related deaths remains substantial; women have also been shown to have smaller decreases in incidence compared men (8% vs 14% decrease) [ 1 – 4 ]. In 2024, it was estimated that lung cancer will account for over 125,000 deaths in the United States [ 1 – 3 ]. The most common form of lung cancer, non-small cell lung cancer (NSCLC), is often diagnosed at an advanced stage [ 1 , 5 ]. NSCLC accounts for 80–85% of lung cancer cases, and even with treatment, has a 5-year survival rate of only 28%; specifically, women have a 31.3% 5-year survival estimate [ 6 , 7 ]. The 5-year survival rate for small cell lung cancer is even lower at 7% [ 6 ]. Successful treatment depends on disease severity, and while systemic therapy modestly prolongs survival in patients with advanced lung cancer, tumors in some patients are highly resistant to therapy [ 5 , 8 ]. This grave picture highlights the crucial necessity to investigate biological mechanisms and identify factors that may be used to develop interventions, tailor treatment regimens, and improve lung cancer prognosis. Smoking is widely recognized as a primary risk factor for lung cancer development and progression for both men and women, with a substantial body of literature reporting that smokers have an increased risk for developing and dying from lung cancer compared to non-smokers [ 9 , 10 ]. Research examining the biological mechanisms of this association has demonstrated that smokers have elevated levels of various immune markers (e.g., increased white blood cell counts and pro-inflammatory markers), as well as increased oxidative stress compared to non-smokers [ 11 ]. Smoking promotes chronic, systemic inflammation by directly impacting epithelial and immune cells within the airway (via the oral and nasal cavities), releasing various pro-inflammatory immune markers and activating additional immune cells [ 9 , 12 ]. To help prevent lung cancer development and improve prognoses, healthier lifestyle choices are often encouraged, including physical activity (PA) and smoking cessation. Although PA levels have been linked to decreased lung cancer risk and mortality in previous epidemiological studies, these studies differ greatly in the magnitude of significant associations and inconsistently delineate by biological sex at birth, smoking status, PA levels, and lifetime PA leading up to a lung cancer diagnosis [ 13 – 22 ]. Our study aims to further elucidate the associations of lung cancer mortality with time-varying and exertion-varying PA levels and to address the inconsistent associations presented in previous epidemiological studies. Methods Study Population and Data Collection The California Teacher Study (CTS) cohort was established in 1995–1996 and consists of 133,477 active and retired female teachers and administrators in California. The cohort has been previously described [ 23 ], and participants provided informed consent at baseline. This project was approved by the Institutional Review Board of Claremont Graduate University. All methods were executed in accordance with relevant institutional and national guidelines. Due to standard cohort exclusions (i.e., consenting to breast cancer research only, moving out of California before completion of the baseline survey, invalid baseline surveys due to missing data, participant death before the return of the baseline survey, person(s) other than an identified proxy completing the baseline survey, or follow-up questionnaire was completed prior to the baseline survey), the starting eligible population for this cohort study was 125,120 women. Study start date was the date that each participant completed the baseline questionnaire (late 1990’s). The following additional exclusions were applied to arrive at a final analytical cohort: participants whose start age was the same as their end age were censored (no follow-up) (N = 64), lung cancer incidence before start date (N = 188), incomplete smoke exposure data (N = 841), lung cancer was not classified as malignant (invasive) in both ICD-O-3 and ICD-O-2 (N = 6), incomplete PA data (N = 857), and incomplete alcohol consumption data (N = 6,061). A total of 1,768 participants (including 516 never smokers) who were diagnosed with lung cancer between the time of joining the cohort to the end of 2019 were deemed eligible and are included in our analyses. Of the eligible cohort, 1,043 women died of lung cancer during the study period. At the start of the study follow-up period, participants submitted a baseline questionnaire that covered extensive demographic and personal information, including PA levels (lifetime PA [from high school through age 54 years] and recent PA [in the three years before joining the cohort]), recent and past hormonal therapy use, menopausal status, smoking status/exposure, etc. The CTS cohort is linked annually with the California Cancer Registry (CCR) and the California Department of Public Health (CDPH) to ascertain cancer diagnoses and tumor information, as well as date and cause of death in cohort members, respectively. Participant covariates Covariates collected at baseline and considered for our analysis have been associated with lung cancer mortality in previous studies [ 15 , 16 , 19 ]. These covariates include age, race/ethnicity (Non-Hispanic White and Other), first-degree family history of lung cancer (parent, sibling, or child: yes, no, and adopted/not provided), body mass index (BMI) calculated from collected weight and height variables (BMI; <18 kg/m 2 , 18–24 kg/m 2 , 25–29 kg/m 2 , ≥ 30 kg/m 2 ), education level (less than high school, technical/high school diploma, associate degree/some college, and university or higher [graduated]), and alcohol consumption (none, < 20grams/day, or ≥20grams/day). Menopausal status (premenopausal, perimenopausal, and postmenopausal) was collected at baseline and derived from responses about menstrual periods; additional data were collected for duration and timing of estrogen and progestin therapy and ages at reported reproductive organ surgeries, if relevant. Participants also) provided detailed information regarding active and passive smoking history. Respondents were asked if they had ever smoked 100 or more cigarettes during their lifetime and, if so, when they started and stopped smoking. Information on smoking history was also collected, including total lifetime smoking pack years, the presence of household passive smoke exposure, and years since quitting for former smokers. A derived smoking variable was generated that incorporated smoking status and total pack years and was defined as the following levels: never smokers (no pack years), former smokers who had low pack years (≤ median pack years for former smokers), former smokers who had high pack years (> median pack years for former smokers), current smokers who had low pack years (≤ median pack years for current smokers), and current smokers who had high pack years (> median pack years for current smokers). Physical Activity Variables Participants provided detailed information on the baseline questionnaire regarding recreational PA across various periods of their lives (while in high school; between the ages of 18 and 24, 25 and 34, 35 and 44, and 45 and 54 years; as well as during the 3 years before completing the questionnaire). For each time interval, they were asked to indicate the average amount of time spent participating in all moderate activities (e.g., brisk walking, recreational tennis, volleyball, golf, softball, and cycling on level street) and in all strenuous activities (e.g., swimming laps, aerobics, calisthenics, running, jogging, cycling on hills, and racquetball). Participants reported the average number of hours per week (categories: none, 0.5, 1, 1.5, 2, 3, 4–6, 7–10, and ≥ 11 hours) and months per year (categories: 1–3, 4–6, 7–9, and 10–12 months) they engaged in moderate and strenuous PA. For each time interval, separate "hours per week" variables were created for strenuous and moderate PA by multiplying the hours spent per week by the portion of the year in which the woman engaged in the activity. Lifetime PA was calculated for each participant by multiplying the average hours per week per year (h/wk/y) of activity performed during one of the time periods by the number of years of the relevant time interval and then summing across all time periods. The cumulative measure was divided by the total number of years spent in all the time periods, to provide an average annual lifetime (beginning with high school through current age if < 55 years at baseline) or quasi-average annual lifetime (if 55 years or older at baseline) measure of PA for each woman. A woman’s PA during the three years before completing the baseline questionnaire (recent activity) was also assessed. Each PA variable (moderate and strenuous for lifetime and recent activity) was categorized as tertiles for further analysis (descriptive statistics presented in Online Resource 1). Additionally, a combined PA variable (moderate + strenuous activity, presented as tertiles) was created for both lifetime and recent activity (Online Resource 1). Statistical Analyses Descriptive analyses were conducted to characterize the study population. Multivariable-adjusted hazards ratios (HR) and 95% confidence intervals (CI) for lung cancer mortality associated with PA were obtained by fitting Cox proportional hazards regression models using age as a timescale (where subjects enter at the age they were diagnosed with lung cancer and exit at their event/censoring age). Using age as the time metric ensures that women of the same age are compared and, therefore, controls for differences in the risk of death due to age alone. The first analytical model (Model 1) included adjusting only for respective PA variables as appropriate (e.g., when assessing the mortality risk for moderate lifetime PA, the model was adjusted for age at diagnosis and strenuous lifetime PA). This approach allows for the association for the specific PA variable to be assessed without any confounding effects from the other PA variables (e.g., strenuous activity can be assessed irrespective of the impact of moderate activity). The second model (Model 2) expanded upon Model 1, with additional adjustments for menopause status with hormonal therapy use, smoking total pack-years by smoking status, and alcohol consumption. Sensitivity analyses were performed for additional covariates of interest, including BMI, education level, passive smoke exposure, smoking quit-years, and disease stage. Additionally, detailed treatment data were not available; however, since lung cancer treatment is based greatly on tumor stage, stage at diagnosis was used as a proxy in our statistical models Follow-up time was calculated as the number of days between the lung cancer diagnosis and either date of death, date participant moved out of California, or end of study date (December 31, 2019), whichever came first. Women who moved out of California, died from a cause other than lung cancer, or did not have an event before December 31, 2019, were censored and contributed person-days to the analysis only up to the date of the respective event. Stratified Cox proportional hazards analyses were conducted to further assess potential effect modification by smoking status (never, ever, former, and current smoker). Kaplan-Meier curves and log-rank tests were used to examine the differences in survival by level of PA and are based on time since diagnosis, not age at diagnosis. All statistical analyses were performed using SAS ® software, version 9.4 [ 24 ] and were performed in the secure CTS platform [ 25 ]. Results The mean follow-up period from baseline to the date of diagnosis was 15 years. The average age at diagnosis was 74.7 (± 9.9) years (Table 1 ). The majority of women were diagnosed with NSCLC (93.2%) and nearly half presented with distant metastases (49.7%). Premenopausal women were more likely to have higher levels of combined (moderate + strenuous), lifetime PA (42.9%) and lower levels of combined, recent PA (37.3%). While current smokers were more likely to have lower levels of combined, recent PA (43.3%), former smokers were more likely to have higher levels of combined, recent PA (37.2%). Women with a BMI ≥ 30 kg/m3 had lower levels of combined, recent PA (51%). Table 1 Baseline participant characteristics among 1,768 women diagnosed with lung cancer in the California Teachers Study stratified by pre-diagnosis physical activity levels (presented as tertiles). Lifetime physical activity a Recent physical activity a,b Characteristic N (%) Low Intermediate High Low Intermediate High No. invasive lung cancer cases 1768 590 591 587 598 589 581 Mean age at diagnosis ± SD 75.6 ± 9.4 74.4 ± 9.7 74.1 ± 10.5 74.7 ± 10 74.1 ± 9.8 75.2 ± 9.8 Race/ethnicity (%) Non-Hispanic White 1569 (88.7%) 33.1 33.1 33.8 32.4 33.8 33.8 Other c 199 (11.3%) 35.2 36.2 28.6 45.2 29.7 25.1 Socioeconomic status (%) Low SES 68 (3.9%) 30.9 41.2 27.9 39.7 35.3 25.0 2nd quartile 275 (15.6%) 35.6 28.7 35.6 43.3 27.3 29.5 3rd quartile 515 (29.1%) 36.3 31.8 31.8 35.3 32.2 32.4 High SES 900 (50.9%) 31.2 34.9 33.9 29.8 35.3 34.9 Menopausal status (%) Premenopausal 177 (10%) 22.0 35.0 42.9 37.3 33.9 28.8 Postmenopausal No hormone therapy use 391 (22.1%) 34.0 32.5 33.5 35.3 31.0 33.8 Current/former hormone therapy use 975 (55.2%) 34.8 33.5 31.7 32.1 33.0 34.9 All Other 225 (12.7%) 35.1 33.3 31.6 36.0 38.2 25.8 Smoking status (%) Never smoker 516 (29.2%) 36.1 31.4 32.6 32.2 36.8 31.0 Former smoker 822 (46.5%) 32.5 35.3 32.2 29.9 32.9 37.2 Current smoker 430 (24.3%) 31.9 32.3 35.8 43.3 30.0 26.7 Mean pack-years ± SD 35.8 ± 24.3 30.9 ± 21.9 32.9 ± 22.5 37.9 ± 25.6 30.8 ± 21.7 30.5 ± 20.5 Body mass index, kg/m 2 (%) <18 129 (7.3%) 42.6 30.2 27.1 37.2 34.9 27.9 18–24 963 (54.5%) 32.6 34.4 33.0 28.7 33.0 38.3 25–30 476 (26.9%) 31.7 33.6 34.7 35.9 35.1 29.0 ≥30 200 (11.3%) 35.0 30.5 34.5 51.5 29.5 19.0 Cancer Histology (%) Non-Small Cell Lung Cancer 1647 (93.2%) 33.0 33.9 33.1 33.2 33.5 33.3 Small Cell Lung Cancer 121 (6.8%) 38.0 27.3 34.7 43.0 30.6 26.5 Stage (%) Localized 455 (25.7%) 28.8 36.9 34.3 32.3 35.2 32.5 Regional extension only 123 (7%) 30.9 37.4 31.7 29.3 33.3 37.4 Regional lymph nodes only 134 (7.6%) 37.3 31.3 31.3 38.1 35.1 26.9 Regional extension and lymph nodes 87 (4.9%) 31.0 29.9 39.1 32.2 29.9 37.9 Distant 879 (49.7%) 35.4 31.4 33.2 33.9 32.9 33.2 a Lifetime and recent categories include physical activity presented as tertiles b Recent includes PA reported within 3 years prior to baseline c The “Other” category for race/ethnicity includes Hispanic, Native American, Asian/Pacific Islander, Mixed and Unknown The results (Model 1 and Model 2) for the associations between all PA variables and lung cancer mortality are presented in Table 2 . Overall, the results for lung cancer mortality were relatively unremarkable across all PA variables for lifetime and recent activity. Women who reported higher levels of combined, lifetime PA level had the highest median survival time (MST) of 2.5 years compared to 2.1 years for women who reported lower activity levels and 2.3 years for women who reported intermediate levels ( Log-rank p-value : 0.321; Fig. 1 ). In contrast, for combined, recent PA, those with intermediate levels of activity survived longer (MST = 2.5 years) compared to those with lower (MST = 2.2) and higher combined, recent PA levels (MST = 2.2 years) ( Log-rank p-value : 0.4321; Fig. 2 ). Table 2 Multivariable hazard ratios (HR) and 95% confidence intervals (CI) for the association between physical activity (measured in tertiles) and mortality among 1,768 women diagnosed with invasive lung cancer following enrollment in the California Teachers Study. N Cases N Deaths Model 1 a Model 2 b HR (95% CI) HR (95% CI) Lifetime physical activity Moderate Low 611 370 1.00 (Reference) 1.00 (Reference) Intermediate 571 331 0.96 (0.82–1.11) 0.96 (0.82–1.12) High 586 342 0.87 (0.74–1.03) 0.87 (0.74–1.03) P for trend 0.1017 0.1214 Strenuous Low 590 353 1.00 (Reference) 1.00 (Reference) Intermediate 589 343 1.01 (0.87–1.17) 1.09 (0.94–1.27) High 589 347 1.00 (0.84–1.18) 1.06 (0.90–1.26) P for trend 0.9682 0.4252 Moderate + Strenuous Low 590 360 1.00 (Reference) 1.00 (Reference) Intermediate 591 339 0.95 (0.82–1.11) 0.97 (0.83–1.12) High 587 344 0.93 (0.80–1.08) 0.97 (0.83–1.13) P for trend 0.3187 0.6882 Recent physical activity Moderate Low 630 362 1.00 (Reference) 1.00 (Reference) Intermediate 688 402 1.04 (0.9–1.2) 1.06 (0.92–1.23) High 450 279 1.08 (0.91–1.27) 1.06 (0.9–1.26) P for trend 0.3896 0.4535 Strenuous Low 896 539 1.00 (Reference) 1.00 (Reference) Intermediate 322 189 1.17 (0.99–1.39) 1.11 (0.94–1.32) High 550 315 0.93 (0.8–1.08) 0.98 (0.84–1.13) P for trend 0.4861 0.93 Moderate + Strenuous Low 598 350 1.00 (Reference) 1.00 (Reference) Intermediate 589 337 0.96 (0.83–1.12) 1.04 (0.89–1.21) High 581 356 1.03 (0.88–1.19) 1.06 (0.92–1.24) P for trend 0.7471 0.4191 a Model 1: adjusted for age at lung cancer diagnosis and the other respective PA variables b Model 2: adjusted for factors in Model 1 and menopause status with hormonal therapy use, smoking total pack-years by smoking status, and alcohol consumption Associations stratified by smoking status are presented in Table 3 . Ever smokers (HR = 0.77, 95%CI = 0.64–0.94) and former smokers (HR = 0.63, 95%CI = 0.49–0.82) who engaged in higher levels of moderate, lifetime PA had a decreased risk of lung cancer mortality. Ever smokers who engaged in intermediate (HR = 1.24, 95%CI = 1.04–1.48) and higher (HR = 1.22, 95%CI = 1.00-1.49) levels of strenuous, lifetime PA saw increased risk of lung cancer mortality. Similarly, current smokers who engaged in intermediate levels of strenuous, lifetime PA also saw increased risk of lung cancer mortality (HR = 1.39, 95%CI = 1.04–1.87). In contrast, never smokers who engaged in higher levels of strenuous, lifetime PA saw a decrease in lung cancer mortality risk (HR = 0.69, 95%CI = 0.49–0.97). Additionally, never smokers saw an increased risk of lung cancer mortality with intermediate levels of strenuous, recent PA (HR = 1.62, 95%CI = 1.12–2.34). Table 3 Multivariable hazard ratios (HR) and 95% confidence intervals (CI) for the association between physical activity and mortality among 1,768 women diagnosed with invasive lung cancer following enrollment in the California Teachers Study stratified by smoking status. Never Smokers Ever Smokers Former Smokers Current Smokers N Model 1 a Model 2 b N Model 1 a Model 2 b N Model 1 a Model 2 b N Model 1 a Model 2 b Deaths/Cases HR (95% CI) HR (95% CI) Deaths/Cases HR (95% CI) HR (95% CI) Deaths/Cases HR (95% CI) HR (95% CI) Deaths/Cases HR (95% CI) HR (95% CI) Lifetime PA Moderate Low 91/188 1.00 (Reference) 1.00 (Reference) 279/423 1.00 (Reference) 1.00 (Reference) 175/282 1.00 (Reference) 1.00 (Reference) 104/141 1.00 (Reference) 1.00 (Reference) Intermediate 85/163 1.16 (0.86–1.57) 1.15 (0.85–1.57) 246/408 0.87 (0.72–1.03) 0.88 (0.74–1.05) 162/272 0.86 (0.69–1.08) 0.84 (0.67–1.05) 84/136 0.86 (0.64–1.16) 1.03 (0.76–1.41) High 87/165 1.11 (0.80–1.54) 1.12 (0.80–1.55) 255/421 0.76 (0.62–0.92) 0.77 (0.64–0.94) 146/268 0.64 (0.50–0.82) 0.63 (0.49–0.82) 109/153 1.09 (0.78–1.54) 1.26 (0.89–1.78) P for trend 0.4805 0.482 0.0058 0.0105 0.0005 0.0004 0.6768 0.2196 Strenuous Low 99/179 1.00 (Reference) 1.00 (Reference) 254/411 1.00 (Reference) 1.00 (Reference) 155/269 1.00 (Reference) 1.00 (Reference) 99/142 1.00 (Reference) 1.00 (Reference) Intermediate 81/171 0.73 (0.54–0.98) 0.69 (0.51–0.94) 271/418 1.18 (0.99–1.41) 1.24 (1.04–1.48) 173/286 1.16 (0.93–1.46) 1.24 (0.99–1.56) 98/132 1.31 (0.98–1.75) 1.39 (1.04–1.87) High 83/166 0.71 (0.51–0.99) 0.69 (0.49–0.97) 264/423 1.17 (0.96–1.42) 1.22 (1.00-1.49) 155/267 1.15 (0.90–1.48) 1.18 (0.91–1.53) 109/156 1.16 (0.83–1.62) 1.10 (0.78–1.55) P for trend 0.0327 0.024 0.0898 0.0312 0.2434 0.1668 0.2755 0.3603 Moderate + Strenuous Low 97/186 1.00 (Reference) 1.00 (Reference) 263/404 1.00 (Reference) 1.00 (Reference) 164/267 1.00 (Reference) 1.00 (Reference) 99/137 1.00 (Reference) 1.00 (Reference) Intermediate 78/162 0.89 (0.66–1.20) 0.80 (0.58–1.09) 261/429 0.98 (0.82–1.16) 1.01 (0.85–1.20) 170/290 1.00 (0.80–1.24) 1.02 (0.82–1.27) 91/139 0.98 (0.74–1.32) 1.06 (0.78–1.43) High 88/168 0.89 (0.66–1.19) 0.87 (0.64–1.18) 256/419 0.94 (0.79–1.12) 0.98 (0.83–1.17) 149/265 0.83 (0.67–1.04) 0.83 (0.66–1.05) 107/154 1.24 (0.94–1.64) 1.27 (0.95–1.70) P for trend 0.4231 0.3792 0.4897 0.8489 0.1139 0.1217 0.1282 0.1038 Recent PA Moderate Low 81/179 1.00 (Reference) 1.00 (Reference) 281/451 1.00 (Reference) 1.00 (Reference) 152/266 1.00 (Reference) 1.00 (Reference) 129/185 1.00 (Reference) 1.00 (Reference) Intermediate 116/221 1.27 (0.94–1.70) 1.16 (0.86–1.57) 286/467 0.97 (0.82–1.15) 0.97 (0.82–1.15) 190/321 1.00 (0.80–1.25) 0.94 (0.75–1.17) 96/146 0.98 (0.75–1.29) 1.05 (0.79–1.39) High 66/116 1.43 (1.02–2.02) 1.28 (0.90–1.81) 213/334 0.93 (0.76–1.12) 0.94 (0.78–1.14) 141/235 0.89 (0.69–1.14) 0.85 (0.66–1.09) 72/99 1.09 (0.79–1.50) 1.13 (0.82–1.56) P for trend 0.0338 0.1564 0.4355 0.5457 0.3568 0.1865 0.6506 0.4713 Strenuous Low 127/253 1.00 (Reference) 1.00 (Reference) 412/643 1.00 (Reference) 1.00 (Reference) 250/416 1.00 (Reference) 1.00 (Reference) 162/227 1.00 (Reference) 1.00 (Reference) Intermediate 46/90 1.48 (1.04–2.11) 1.62 (1.12–2.34) 143/232 1.03 (0.85–1.25) 1.05 (0.87–1.28) 80/136 1.05 (0.81–1.35) 1.04 (0.81–1.34) 63/96 0.96 (0.71–1.29) 0.96 (0.71–1.31) High 90/173 1.03 (0.78–1.38) 1.08 (0.81–1.45) 225/377 0.93 (0.78–1.10) 0.94 (0.79–1.12) 153/270 0.99 (0.79–1.22) 0.97 (0.78–1.21) 72/107 0.89 (0.66–1.20) 0.94 (0.69–1.28) P for trend 0.7206 0.54 0.4619 0.5988 0.9365 0.862 0.4323 0.6581 Moderate + Strenuous Low 80/166 1.00 (Reference) 1.00 (Reference) 270/432 1.00 (Reference) 1.00 (Reference) 140/246 1.00 (Reference) 1.00 (Reference) 130/186 1.00 (Reference) 1.00 (Reference) Intermediate 90/190 0.91 (0.67–1.23) 0.87 (0.64–1.18) 247/399 1.06 (0.89–1.26) 1.06 (0.88–1.26) 161/270 1.15 (0.92–1.45) 1.11 (0.88–1.41) 86/129 1.01 (0.77–1.34) 1.07 (0.80–1.43) High 93/160 1.31 (0.97–1.78) 1.21 (0.89–1.65) 263/421 0.94 (0.79–1.11) 0.95 (0.80–1.13) 182/306 1.04 (0.83–1.30) 0.98 (0.78–1.22) 81/115 0.95 (0.71–1.26) 1.03 (0.77–1.38) P for trend 0.0862 0.2175 0.4535 0.5381 0.8168 0.7714 0.72 0.8137 a Model 1: adjusted for age at lung cancer diagnosis and the other respective PA variables b Model 2: adjusted for factors in Model 1 and BMI, education level, and alcohol consumption Sensitivity analyses were performed by adjusting for BMI, education level, disease stage (local vs non-local lung cancer, smoking quit-years (among ever and former smokers only), and passive smoke exposure (among never smokers). Inclusion of disease stage in the models attenuated the notable associations, except for the association of increased mortality risk among never smokers who engaged in strenuous, recent activity. Disease stage may act as a mediator in the relationship between PA levels and lung cancer mortality, hence was not included in the final models so that the total effect was not obscured. Additionally, we found that including smoking quit-years did not alter any model results except for ever smokers who engaged in strenuous, lifetime PA, suggesting that quit-years may act as a confounding variable for this specific relationship. Discussion We assessed associations between PA and lung cancer mortality among 1,768 women diagnosed with lung cancer and enrolled in the California Teachers Study from 1995–2019. We highlight the following notable associations from this study: 1) women who have smoked (ever and/or former) and engaged in higher levels of moderate, lifetime PA had a lower risk of lung cancer mortality, 2) ever and/or current smokers who engaged in intermediate to high levels of strenuous, lifetime PA saw an increased risk for lung cancer mortality, while never smokers saw a protective effect with higher levels of strenuous, lifetime PA, and 3) never smokers who engaged in strenuous, recent PA had increased lung CA mortality risk. While a relatively small number of studies have examined PA and lung cancer-specific mortality, fewer still have explored this association while also stratifying by smoking status, a key determinant of both incidence and mortality in lung cancer. Among those that do not stratify by smoking status, increased PA levels were consistently associated with reduced lung cancer mortality [ 14 , 20 ]. We found only three studies to date that examine PA and lung cancer mortality risk across smoke exposure groups in women [ 15 , 15 , 26 ]. However, these studies either assess recent PA only or do not account for both duration and exertion of PA, limiting the comparability of their findings to those of the present study. PA is known to support immune cell infiltration, reduce cancer cell growth and survival, improve the tumor microenvironment, reduce oxidative stress and increase DNA repair processes [ 27 ]. Despite these benefits, several studies suggest that PA’s therapeutic effects persist to an extent, while excessive strenuous PA may have an opposite and deleterious effect on lung cancer mortality [ 16 , 19 , 28 ]. Exercise immunology research indicates that high-intensive PA without adequate recovery can trigger immunosuppressive effects that temporarily increase the risk of illness [ 29 , 30 ]. Specifically, it is thought that following strenuous PA, antibody production as well as circulating levels of lymphocytes and natural killer cells significantly decrease [ 29 , 31 , 32 ]. While more research is warranted to confirm our findings, this temporary immunosuppressive state may offer some explanation for the increased lung cancer mortality risk we observed among never-smoking women who engaged in strenuous, recent PA. This explanation is also consistent with the increased mortality risks we observed in women with a history of smoking, especially in light of the chronic respiratory strain and low-grade inflammation induced by smoking. The deleterious effects of smoke exposure are known to weaken the immune system for years after cessation [ 9 ]. These effects, when coupled with the immune impairment precipitated by elevated strenuous PA, may thus account for the elevated lung cancer mortality risks we observed among ever and/or current smokers who engaged in higher levels of strenuous, lifetime PA. Notably, women in our study who were not subject to the inflammatory effects of smoke exposure (never smokers) saw protective effects for lung cancer survival with this same level of increased, lifetime strenuous PA. Moreover, former and ever smokers who engaged in high levels of moderate lifetime PA also saw protective effects for lung cancer mortality, highlighting the importance of quality and time spent in PA recovery. Moderate, lifetime PA, such as brisk walking or playing golf, may allow for cell recovery and immune repair necessary to mitigate the deleterious effects of smoking, while high levels of strenuous PA, like running or cycling uphill, may not; an observation that is supported in the literature [ 9 , 15 ]. The limited research on this topic has largely focused on recent, pre-lung cancer diagnosis PA. Interestingly, our stratified analyses revealed a stark contrast in lung cancer mortality risks between lifetime PA and recent PA groups, suggesting that PA and smoking behaviors from high school through adulthood may play a more prominent role in women’s lung cancer survival than in the several years preceding diagnosis. Further studies examining lifetime and exertion-varying PA variables and lung cancer mortality are warranted. Our findings ultimately expand on those of previous studies with the following arguments about PA and lung cancer mortality prevention: 1) PA regimens (e.g., PA intensity and duration) should be determined through a personalized approach that considers factors like disease stage, physical fitness, smoking status, and other relevant health behaviors; 2) PA interventions aimed at lung cancer mortality prevention should target at-risk individuals as early as is feasible [ 18 ], and 3) interventions and public/clinical health promotion efforts should consider that high levels of strenuous PA among those with a history of smoking may not benefit lung cancer prognosis. The strengths of this study include its prospective design, recent and length of follow-up time (up to 2019), detailed lifetime and recent PA information categorized by exertion level, and extensive data regarding potential confounding factors (e.g., total smoking pack-years and passive household smoke exposure). There are limitations to this study, including 1) PA measures were collected at baseline, with a varying number of years preceding diagnosis, 2) detailed information regarding specific types of PA performed during each time period, which would be needed to calculate MET-hours/week, were not collected, 3) the smoking variables included in this study were collected at baseline, are restricted to cigarette smoking and do not reflect smoking cessation during follow-up, and 4) our study was based in California, a state reported to have high pollution levels, which have previously been associated with lung cancer risk [ 33 , 34 ]. Our study population is restricted to women only, potentially reducing generalizability; however, postmenopausal women are seldom the focus of studies on lung cancer mortality and PA levels, despite lung cancer remaining the second most common cancer in women, secondary to breast cancer [ 1 ]. Given the potential for clinical significance if our findings are confirmed in future studies, further investigation is warranted. Declarations Conflict of interest statement: The authors declare no potential conflicts of interest Acknowledgements The authors would like to express our highest gratitude to all the women who participated in the California Teachers Study. We would like to also thank the California Teachers Study Steering Committee, researchers, analysts, and staff who have contributed to the success of this research; they are responsible for the formation and maintenance of the Study within which this research was conducted. A full list of California Teachers Study team members is available https://www.calteachersstudy.org/team. We also wish to specially acknowledge the late Dr. Leslie Bernstein for her pivotal contributions to the establishment of the California Teachers Study and the development of comprehensive physical activity measures—her work continues to shape and strengthen this research. Funding The California Teachers Study and the research reported in this publication were supported by the National Cancer Institute of the National Institutes of Health under award number U01-CA199277; P30-CA033572; P30-CA023100; UM1-CA164917; and R01-CA077398. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health. The collection of cancer incidence data used in the California Teachers Study was supported by the California Department of Public Health pursuant to California Health and Safety Code Section 103885; Centers for Disease Control and Prevention’s National Program of Cancer Registries, under cooperative agreement 5NU58DP006344; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under contract HHSN261201800032I awarded to the University of California, San Francisco, contract HHSN261201800015I awarded to the University of Southern California, and contract HHSN261201800009I awarded to the Public Health Institute. The opinions, findings, and conclusions expressed herein are those of the author(s) and do not necessarily reflect the official views of the State of California, Department of Public Health, the National Cancer Institute, the National Institutes of Health, the Centers for Disease Control and Prevention or their Contractors and Subcontractors, or the Regents of the University of California, or any of its programs. The authors declare that no funds, grants, or other support not listed here were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions Conceptualization: Jessica Clague DeHart, Peggy Reynolds; Material preparation: Emma S. Spielfogel; Data analyses: Emily L. Cauble, Jessica Clague DeHart; Original draft preparation: Emily L. Cauble, Jessica Clague DeHart, Mia Blanchard; Writing-review and editing: all authors; Supervision: Jessica Clague DeHart. All authors have read and approved the final manuscript. Data Availability The California Teachers Study data and resources are made available in accordance with the National Institute of Health’s Policy for Data Management and Sharing and the NIH Genomic Data Sharing Policy. The CTS Data Environment, which includes all CTS data, software, and documentation, is open and free of charge to anyone who agrees to and signs the CTS Confidentiality Pledge. Individuals interested in accessing CTS data can sign up for the CTS Researcher Platform and submit a project for feasibility review here: https://calteachersstudy.my.site.com/researchers/s/ The dataset generated and analyzed for the current study are not publicly available as they are housed within the CTS Data Environment but can be made available by the corresponding author on reasonable request. Ethics Approval This study was performed in line with the principles of the Declaration of Helsinki and was approved by the Institutional Review Board of Claremont Graduate University. Consent to Participate Informed consent was obtained from all participants included in this study. References American Cancer Society medical and editorial content team (2024) Lung cancer statistics: how common is lung cancer? American Cancer Society. https://www.cancer.org/cancer/types/lung-cancer/about/key-statistics.html. Accessed 24 November 2024 Lung Cancer Research Foundation (2024) Lung cancer facts. Lung Cancer Research Foundation. https://www.lungcancerresearchfoundation.org/for-patients/free-educational-materials/lung-cancer-facts/. Accessed 24 November 2024 American Lung Association (2024) Lung cancer trends brief: mortality. American Lung Association. https://www.lung.org/research/trends-in-lung-disease/lung-cancer-trends-brief. 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(2020) Insights from adopting a data commons approach for large-scale observational cohort studies: the California Teachers Study. Cancer Epidemiol Biomark Prev 29:777–786. https://doi.org/10.1158/1055-9965.EPI-19-1166 Alfano CM, Klesges RC, Murray DM, Bowen DJ, McTiernan A, Vander Weg MW, et al. (2004) Physical activity in relation to all-site and lung cancer incidence and mortality in current and former smokers. Cancer Epidemiol Biomark Prev 13:2233–2241. https://aacrjournals.org/cebp/article/13/12/2233/165189 Chen W, Liu A, Jiang Y, Lin Y, Li X, Pan C, et al. (2024) Association between strenuous sports or other exercises and lung cancer risk: a Mendelian randomization study. Transl Lung Cancer Res 13:1210–1221. https://doi.org/10.21037/tlcr-24-189 Qie R, Han M, Huang H, Sun P, Xie Y, He J, et al. (2023) Physical activity and risk of lung cancer: a systematic review and dose–response meta-analysis of cohort studies. J Natl Cancer Cent 3:48–55. https://doi.org/10.1016/j.jncc.2023.01.002 Peake JM, Neubauer O, Walsh NP, Simpson RJ (2017) Recovery of the immune system after exercise. J Appl Physiol 122:1077–1087. https://doi.org/10.1152/japplphysiol.00622.2016 Nieman DC, Miller AR, Henson DA, Warren BJ, Gusewitch G, Johnson RL, et al. (1993) Effects of high- vs moderate-intensity exercise on natural killer cell activity. Med Sci Sports Exerc 25:1126–1134. https://doi.org/10.1249/00005768-199310000-00008 Bird SR, Linden M, Hawley JA (2014) Acute changes to biomarkers as a consequence of prolonged strenuous running. Ann Clin Biochem 51:137–150. https://doi.org/10.1177/0004563213492147 Nielsen HB, Secher NH, Christensen NJ, Pedersen BK (1996) Lymphocytes and NK cell activity during repeated bouts of maximal exercise. Am J Physiol Regul Integr Comp Physiol 271:R222–R227. https://doi.org/10.1152/ajpregu.1996.271.1.R222 Wang T, Zhao B, Liou KN, Gu Y, Jiang Z, Song K, et al. (2019) Mortality burdens in California due to air pollution attributable to local and nonlocal emissions. Environ Int 133:105232. https://doi.org/10.1016/j.envint.2019.105232 Cheng I, Yang J, Tseng C, Wu J, Shariff-Marco S, Park S-Shim L, et al. (2022) Traffic-related air pollution and lung cancer incidence: the California Multiethnic Cohort Study. Am J Respir Crit Care Med 206:1008–1018. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Apr, 2026 Read the published version in Cancer Causes & Control → Version 1 posted Editorial decision: Revision requested 12 Feb, 2026 Reviews received at journal 11 Feb, 2026 Reviews received at journal 11 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviews received at journal 31 Dec, 2025 Reviewers agreed at journal 30 Dec, 2025 Reviewers invited by journal 01 Dec, 2025 Editor assigned by journal 19 Nov, 2025 Submission checks completed at journal 19 Nov, 2025 First submitted to journal 17 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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22:35:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":246810,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curve displaying the survival time of the women in the study who engaged in recent, combined physical activity\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8138657/v1/9a8b4d7877b74f421f8f14bc.png"},{"id":97391360,"identity":"c5362ea0-ae96-44e5-a21c-4ac65526d96c","added_by":"auto","created_at":"2025-12-03 22:35:20","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25002,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curve displaying the survival time of the women in the study who engaged in lifetime, combined physical activity.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8138657/v1/be4743e677c705f3d4449034.jpeg"},{"id":107929569,"identity":"8d996f19-209b-4bd6-a38f-ef7dbee7f989","added_by":"auto","created_at":"2026-04-27 16:19:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":959059,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8138657/v1/f7094e6e-4758-41f6-a4da-47c7e36324e0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pre-Diagnosis Recreational Physical Activity and Lung Cancer Mortality within the California Teachers Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the American Cancer Society, lung cancer is the 2nd most common cancer in women the United States, and leading cause of cancer death [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although incidence and mortality rates have decreased over time due to advancements in treatment and early detection, the number of lung cancer-related deaths remains substantial; women have also been shown to have smaller decreases in incidence compared men (8% vs 14% decrease) [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In 2024, it was estimated that lung cancer will account for over 125,000 deaths in the United States [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe most common form of lung cancer, non-small cell lung cancer (NSCLC), is often diagnosed at an advanced stage [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. NSCLC accounts for 80\u0026ndash;85% of lung cancer cases, and even with treatment, has a 5-year survival rate of only 28%; specifically, women have a 31.3% 5-year survival estimate [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The 5-year survival rate for small cell lung cancer is even lower at 7% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Successful treatment depends on disease severity, and while systemic therapy modestly prolongs survival in patients with advanced lung cancer, tumors in some patients are highly resistant to therapy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This grave picture highlights the crucial necessity to investigate biological mechanisms and identify factors that may be used to develop interventions, tailor treatment regimens, and improve lung cancer prognosis.\u003c/p\u003e\u003cp\u003eSmoking is widely recognized as a primary risk factor for lung cancer development and progression for both men and women, with a substantial body of literature reporting that smokers have an increased risk for developing and dying from lung cancer compared to non-smokers [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Research examining the biological mechanisms of this association has demonstrated that smokers have elevated levels of various immune markers (e.g., increased white blood cell counts and pro-inflammatory markers), as well as increased oxidative stress compared to non-smokers [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Smoking promotes chronic, systemic inflammation by directly impacting epithelial and immune cells within the airway (via the oral and nasal cavities), releasing various pro-inflammatory immune markers and activating additional immune cells [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo help prevent lung cancer development and improve prognoses, healthier lifestyle choices are often encouraged, including physical activity (PA) and smoking cessation. Although PA levels have been linked to decreased lung cancer risk and mortality in previous epidemiological studies, these studies differ greatly in the magnitude of significant associations and inconsistently delineate by biological sex at birth, smoking status, PA levels, and lifetime PA leading up to a lung cancer diagnosis [\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our study aims to further elucidate the associations of lung cancer mortality with time-varying and exertion-varying PA levels and to address the inconsistent associations presented in previous epidemiological studies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population and Data Collection\u003c/h2\u003e\u003cp\u003eThe California Teacher Study (CTS) cohort was established in 1995\u0026ndash;1996 and consists of 133,477 active and retired female teachers and administrators in California. The cohort has been previously described [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and participants provided informed consent at baseline. This project was approved by the Institutional Review Board of Claremont Graduate University. All methods were executed in accordance with relevant institutional and national guidelines. Due to standard cohort exclusions (i.e., consenting to breast cancer research only, moving out of California before completion of the baseline survey, invalid baseline surveys due to missing data, participant death before the return of the baseline survey, person(s) other than an identified proxy completing the baseline survey, or follow-up questionnaire was completed prior to the baseline survey), the starting eligible population for this cohort study was 125,120 women. Study start date was the date that each participant completed the baseline questionnaire (late 1990\u0026rsquo;s). The following additional exclusions were applied to arrive at a final analytical cohort: participants whose start age was the same as their end age were censored (no follow-up) (N\u0026thinsp;=\u0026thinsp;64), lung cancer incidence before start date (N\u0026thinsp;=\u0026thinsp;188), incomplete smoke exposure data (N\u0026thinsp;=\u0026thinsp;841), lung cancer was not classified as malignant (invasive) in both ICD-O-3 and ICD-O-2 (N\u0026thinsp;=\u0026thinsp;6), incomplete PA data (N\u0026thinsp;=\u0026thinsp;857), and incomplete alcohol consumption data (N\u0026thinsp;=\u0026thinsp;6,061). A total of 1,768 participants (including 516 never smokers) who were diagnosed with lung cancer between the time of joining the cohort to the end of 2019 were deemed eligible and are included in our analyses. Of the eligible cohort, 1,043 women died of lung cancer during the study period.\u003c/p\u003e\u003cp\u003eAt the start of the study follow-up period, participants submitted a baseline questionnaire that covered extensive demographic and personal information, including PA levels (lifetime PA [from high school through age 54 years] and recent PA [in the three years before joining the cohort]), recent and past hormonal therapy use, menopausal status, smoking status/exposure, etc. The CTS cohort is linked annually with the California Cancer Registry (CCR) and the California Department of Public Health (CDPH) to ascertain cancer diagnoses and tumor information, as well as date and cause of death in cohort members, respectively.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eParticipant covariates\u003c/h3\u003e\n\u003cp\u003eCovariates collected at baseline and considered for our analysis have been associated with lung cancer mortality in previous studies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These covariates include age, race/ethnicity (Non-Hispanic White and Other), first-degree family history of lung cancer (parent, sibling, or child: yes, no, and adopted/not provided), body mass index (BMI) calculated from collected weight and height variables (BMI; \u0026lt;18 kg/m\u003csup\u003e2\u003c/sup\u003e, 18\u0026ndash;24 kg/m\u003csup\u003e2\u003c/sup\u003e, 25\u0026ndash;29 kg/m\u003csup\u003e2\u003c/sup\u003e, \u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e), education level (less than high school, technical/high school diploma, associate degree/some college, and university or higher [graduated]), and alcohol consumption (none, \u0026lt;\u0026thinsp;20grams/day, or \u0026ge;20grams/day). Menopausal status (premenopausal, perimenopausal, and postmenopausal) was collected at baseline and derived from responses about menstrual periods; additional data were collected for duration and timing of estrogen and progestin therapy and ages at reported reproductive organ surgeries, if relevant.\u003c/p\u003e\u003cp\u003eParticipants also) provided detailed information regarding active and passive smoking history. Respondents were asked if they had ever smoked 100 or more cigarettes during their lifetime and, if so, when they started and stopped smoking. Information on smoking history was also collected, including total lifetime smoking pack years, the presence of household passive smoke exposure, and years since quitting for former smokers. A derived smoking variable was generated that incorporated smoking status and total pack years and was defined as the following levels: never smokers (no pack years), former smokers who had low pack years (\u0026le;\u0026thinsp;median pack years for former smokers), former smokers who had high pack years (\u0026gt;\u0026thinsp;median pack years for former smokers), current smokers who had low pack years (\u0026le;\u0026thinsp;median pack years for current smokers), and current smokers who had high pack years (\u0026gt;\u0026thinsp;median pack years for current smokers).\u003c/p\u003e\n\u003ch3\u003ePhysical Activity Variables\u003c/h3\u003e\n\u003cp\u003eParticipants provided detailed information on the baseline questionnaire regarding recreational PA across various periods of their lives (while in high school; between the ages of 18 and 24, 25 and 34, 35 and 44, and 45 and 54 years; as well as during the 3 years before completing the questionnaire). For each time interval, they were asked to indicate the average amount of time spent participating in all moderate activities (e.g., brisk walking, recreational tennis, volleyball, golf, softball, and cycling on level street) and in all strenuous activities (e.g., swimming laps, aerobics, calisthenics, running, jogging, cycling on hills, and racquetball). Participants reported the average number of hours per week (categories: none, 0.5, 1, 1.5, 2, 3, 4\u0026ndash;6, 7\u0026ndash;10, and \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;11 hours) and months per year (categories: 1\u0026ndash;3, 4\u0026ndash;6, 7\u0026ndash;9, and 10\u0026ndash;12 months) they engaged in moderate and strenuous PA. For each time interval, separate \"hours per week\" variables were created for strenuous and moderate PA by multiplying the hours spent per week by the portion of the year in which the woman engaged in the activity.\u003c/p\u003e\u003cp\u003eLifetime PA was calculated for each participant by multiplying the average hours per week per year (h/wk/y) of activity performed during one of the time periods by the number of years of the relevant time interval and then summing across all time periods. The cumulative measure was divided by the total number of years spent in all the time periods, to provide an average annual lifetime (beginning with high school through current age if\u0026thinsp;\u0026lt;\u0026thinsp;55 years at baseline) or quasi-average annual lifetime (if 55 years or older at baseline) measure of PA for each woman. A woman\u0026rsquo;s PA during the three years before completing the baseline questionnaire (recent activity) was also assessed. Each PA variable (moderate and strenuous for lifetime and recent activity) was categorized as tertiles for further analysis (descriptive statistics presented in Online Resource 1). Additionally, a combined PA variable (moderate\u0026thinsp;+\u0026thinsp;strenuous activity, presented as tertiles) was created for both lifetime and recent activity (Online Resource 1).\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eDescriptive analyses were conducted to characterize the study population. Multivariable-adjusted hazards ratios (HR) and 95% confidence intervals (CI) for lung cancer mortality associated with PA were obtained by fitting Cox proportional hazards regression models using age as a timescale (where subjects enter at the age they were diagnosed with lung cancer and exit at their event/censoring age). Using age as the time metric ensures that women of the same age are compared and, therefore, controls for differences in the risk of death due to age alone. The first analytical model (Model 1) included adjusting only for respective PA variables as appropriate (e.g., when assessing the mortality risk for moderate lifetime PA, the model was adjusted for age at diagnosis and strenuous lifetime PA). This approach allows for the association for the specific PA variable to be assessed without any confounding effects from the other PA variables (e.g., strenuous activity can be assessed irrespective of the impact of moderate activity). The second model (Model 2) expanded upon Model 1, with additional adjustments for menopause status with hormonal therapy use, smoking total pack-years by smoking status, and alcohol consumption. Sensitivity analyses were performed for additional covariates of interest, including BMI, education level, passive smoke exposure, smoking quit-years, and disease stage. Additionally, detailed treatment data were not available; however, since lung cancer treatment is based greatly on tumor stage, stage at diagnosis was used as a proxy in our statistical models\u003c/p\u003e\u003cp\u003eFollow-up time was calculated as the number of days between the lung cancer diagnosis and either date of death, date participant moved out of California, or end of study date (December 31, 2019), whichever came first. Women who moved out of California, died from a cause other than lung cancer, or did not have an event before December 31, 2019, were censored and contributed person-days to the analysis only up to the date of the respective event.\u003c/p\u003e\u003cp\u003eStratified Cox proportional hazards analyses were conducted to further assess potential effect modification by smoking status (never, ever, former, and current smoker). Kaplan-Meier curves and log-rank tests were used to examine the differences in survival by level of PA and are based on time since diagnosis, not age at diagnosis. All statistical analyses were performed using SAS\u003csup\u003e\u0026reg;\u003c/sup\u003e software, version 9.4 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and were performed in the secure CTS platform [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe mean follow-up period from baseline to the date of diagnosis was 15 years. The average age at diagnosis was 74.7 (\u0026plusmn;\u0026thinsp;9.9) years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The majority of women were diagnosed with NSCLC (93.2%) and nearly half presented with distant metastases (49.7%). Premenopausal women were more likely to have higher levels of combined (moderate\u0026thinsp;+\u0026thinsp;strenuous), lifetime PA (42.9%) and lower levels of combined, recent PA (37.3%). While current smokers were more likely to have lower levels of combined, recent PA (43.3%), former smokers were more likely to have higher levels of combined, recent PA (37.2%). Women with a BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m3 had lower levels of combined, recent PA (51%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline participant characteristics among 1,768 women diagnosed with lung cancer in the California Teachers Study stratified by pre-diagnosis physical activity levels (presented as tertiles).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eLifetime physical activity\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eRecent physical activity\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo. invasive lung cancer cases\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e581\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean age at diagnosis\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e74.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e74.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e75.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace/ethnicity (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1569 (88.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e33.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e199 (11.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e45.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e25.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocioeconomic status (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow SES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68 (3.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e39.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e25.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2nd quartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e275 (15.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e43.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e29.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3rd quartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e515 (29.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e32.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh SES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e900 (50.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e34.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMenopausal status (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePremenopausal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e177 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e28.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostmenopausal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo hormone therapy use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e391 (22.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e31.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e33.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent/former hormone therapy use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e975 (55.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e34.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll Other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e225 (12.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e38.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e25.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking status (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516 (29.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e36.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e31.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e822 (46.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e37.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e430 (24.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e43.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e26.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean pack-years\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.8\u0026thinsp;\u0026plusmn;\u0026thinsp;24.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.9\u0026thinsp;\u0026plusmn;\u0026thinsp;21.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;22.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.9\u0026thinsp;\u0026plusmn;\u0026thinsp;25.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30.8\u0026thinsp;\u0026plusmn;\u0026thinsp;21.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e30.5\u0026thinsp;\u0026plusmn;\u0026thinsp;20.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBody mass index, kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e129 (7.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e34.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e27.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e963 (54.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e28.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e38.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e476 (26.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e29.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e200 (11.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCancer Histology (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Small Cell Lung Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1647 (93.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e33.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmall Cell Lung Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121 (6.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e43.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e26.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStage (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocalized\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e455 (25.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e32.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegional extension only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e123 (7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e37.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegional lymph nodes only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e134 (7.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e26.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegional extension and lymph nodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e37.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e879 (49.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e33.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eLifetime and recent categories include physical activity presented as tertiles\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eRecent includes PA reported within 3 years prior to baseline\u003c/p\u003e\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eThe \u0026ldquo;Other\u0026rdquo; category for race/ethnicity includes Hispanic, Native American, Asian/Pacific Islander, Mixed and Unknown\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results (Model 1 and Model 2) for the associations between all PA variables and lung cancer mortality are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Overall, the results for lung cancer mortality were relatively unremarkable across all PA variables for lifetime and recent activity. Women who reported higher levels of combined, lifetime PA level had the highest median survival time (MST) of 2.5 years compared to 2.1 years for women who reported lower activity levels and 2.3 years for women who reported intermediate levels (\u003cem\u003eLog-rank p-value\u003c/em\u003e: 0.321; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In contrast, for combined, recent PA, those with intermediate levels of activity survived longer (MST\u0026thinsp;=\u0026thinsp;2.5 years) compared to those with lower (MST\u0026thinsp;=\u0026thinsp;2.2) and higher combined, recent PA levels (MST\u0026thinsp;=\u0026thinsp;2.2 years) (\u003cem\u003eLog-rank p-value\u003c/em\u003e: 0.4321; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable hazard ratios (HR) and 95% confidence intervals (CI) for the association between physical activity (measured in tertiles) and mortality among 1,768 women diagnosed with invasive lung cancer following enrollment in the California Teachers Study.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eN Cases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eN Deaths\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLifetime physical activity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.96 (0.82\u0026ndash;1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.96 (0.82\u0026ndash;1.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87 (0.74\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.87 (0.74\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1214\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStrenuous\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01 (0.87\u0026ndash;1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.09 (0.94\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (0.84\u0026ndash;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.06 (0.90\u0026ndash;1.26)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate\u0026thinsp;+\u0026thinsp;Strenuous\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.95 (0.82\u0026ndash;1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97 (0.83\u0026ndash;1.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.93 (0.80\u0026ndash;1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97 (0.83\u0026ndash;1.13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6882\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRecent physical activity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.04 (0.9\u0026ndash;1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.06 (0.92\u0026ndash;1.23)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.08 (0.91\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.06 (0.9\u0026ndash;1.26)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4535\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStrenuous\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.17 (0.99\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.11 (0.94\u0026ndash;1.32)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.93 (0.8\u0026ndash;1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.98 (0.84\u0026ndash;1.13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate\u0026thinsp;+\u0026thinsp;Strenuous\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.96 (0.83\u0026ndash;1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.04 (0.89\u0026ndash;1.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.03 (0.88\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.06 (0.92\u0026ndash;1.24)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4191\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eModel 1: adjusted for age at lung cancer diagnosis and the other respective PA variables\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eModel 2: adjusted for factors in Model 1 and menopause status with hormonal therapy use, smoking total pack-years by smoking status, and alcohol consumption\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAssociations stratified by smoking status are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Ever smokers (HR\u0026thinsp;=\u0026thinsp;0.77, 95%CI\u0026thinsp;=\u0026thinsp;0.64\u0026ndash;0.94) and former smokers (HR\u0026thinsp;=\u0026thinsp;0.63, 95%CI\u0026thinsp;=\u0026thinsp;0.49\u0026ndash;0.82) who engaged in higher levels of moderate, lifetime PA had a decreased risk of lung cancer mortality. Ever smokers who engaged in intermediate (HR\u0026thinsp;=\u0026thinsp;1.24, 95%CI\u0026thinsp;=\u0026thinsp;1.04\u0026ndash;1.48) and higher (HR\u0026thinsp;=\u0026thinsp;1.22, 95%CI\u0026thinsp;=\u0026thinsp;1.00-1.49) levels of strenuous, lifetime PA saw increased risk of lung cancer mortality. Similarly, current smokers who engaged in intermediate levels of strenuous, lifetime PA also saw increased risk of lung cancer mortality (HR\u0026thinsp;=\u0026thinsp;1.39, 95%CI\u0026thinsp;=\u0026thinsp;1.04\u0026ndash;1.87). In contrast, never smokers who engaged in higher levels of strenuous, lifetime PA saw a decrease in lung cancer mortality risk (HR\u0026thinsp;=\u0026thinsp;0.69, 95%CI\u0026thinsp;=\u0026thinsp;0.49\u0026ndash;0.97). Additionally, never smokers saw an increased risk of lung cancer mortality with intermediate levels of strenuous, recent PA (HR\u0026thinsp;=\u0026thinsp;1.62, 95%CI\u0026thinsp;=\u0026thinsp;1.12\u0026ndash;2.34).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable hazard ratios (HR) and 95% confidence intervals (CI) for the association between physical activity and mortality among 1,768 women diagnosed with invasive lung cancer following enrollment in the California Teachers Study stratified by smoking status.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eNever Smokers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eEver Smokers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eFormer Smokers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eCurrent Smokers\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeaths/Cases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDeaths/Cases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDeaths/Cases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eDeaths/Cases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLifetime PA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91/188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e279/423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e175/282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e104/141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85/163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.16 (0.86\u0026ndash;1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.15 (0.85\u0026ndash;1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e246/408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.87 (0.72\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.88 (0.74\u0026ndash;1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e162/272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.86 (0.69\u0026ndash;1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.84 (0.67\u0026ndash;1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e84/136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.86 (0.64\u0026ndash;1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.03 (0.76\u0026ndash;1.41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87/165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.11 (0.80\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.12 (0.80\u0026ndash;1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e255/421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.76 (0.62\u0026ndash;0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.77 (0.64\u0026ndash;0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e146/268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.64 (0.50\u0026ndash;0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.63 (0.49\u0026ndash;0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e109/153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.09 (0.78\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.26 (0.89\u0026ndash;1.78)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP for trend\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.0004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.6768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.2196\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStrenuous\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99/179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e254/411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e155/269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e99/142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81/171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.73 (0.54\u0026ndash;0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.69 (0.51\u0026ndash;0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e271/418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.18 (0.99\u0026ndash;1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.24 (1.04\u0026ndash;1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e173/286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.16 (0.93\u0026ndash;1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.24 (0.99\u0026ndash;1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e98/132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.31 (0.98\u0026ndash;1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.39 (1.04\u0026ndash;1.87)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83/166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.71 (0.51\u0026ndash;0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.69 (0.49\u0026ndash;0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e264/423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.17 (0.96\u0026ndash;1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.22 (1.00-1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e155/267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.15 (0.90\u0026ndash;1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.18 (0.91\u0026ndash;1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e109/156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.16 (0.83\u0026ndash;1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.10 (0.78\u0026ndash;1.55)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP for trend\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.2434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.1668\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.2755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.3603\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate\u0026thinsp;+\u0026thinsp;Strenuous\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97/186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e263/404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e164/267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e99/137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78/162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.89 (0.66\u0026ndash;1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.80 (0.58\u0026ndash;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e261/429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.98 (0.82\u0026ndash;1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.01 (0.85\u0026ndash;1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e170/290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00 (0.80\u0026ndash;1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.02 (0.82\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e91/139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.98 (0.74\u0026ndash;1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.06 (0.78\u0026ndash;1.43)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88/168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.89 (0.66\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87 (0.64\u0026ndash;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e256/419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.94 (0.79\u0026ndash;1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.98 (0.83\u0026ndash;1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e149/265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.83 (0.67\u0026ndash;1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.83 (0.66\u0026ndash;1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e107/154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.24 (0.94\u0026ndash;1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.27 (0.95\u0026ndash;1.70)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP for trend\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.1139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.1217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.1282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.1038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRecent PA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81/179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e281/451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e152/266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e129/185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116/221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.27 (0.94\u0026ndash;1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.16 (0.86\u0026ndash;1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e286/467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.97 (0.82\u0026ndash;1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.97 (0.82\u0026ndash;1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e190/321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00 (0.80\u0026ndash;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.94 (0.75\u0026ndash;1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e96/146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.98 (0.75\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.05 (0.79\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66/116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.43 (1.02\u0026ndash;2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.28 (0.90\u0026ndash;1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e213/334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.93 (0.76\u0026ndash;1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.94 (0.78\u0026ndash;1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e141/235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.89 (0.69\u0026ndash;1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.85 (0.66\u0026ndash;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e72/99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.09 (0.79\u0026ndash;1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.13 (0.82\u0026ndash;1.56)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP for trend\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.5457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.3568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.1865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.6506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.4713\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStrenuous\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127/253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e412/643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e250/416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e162/227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46/90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.48 (1.04\u0026ndash;2.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.62 (1.12\u0026ndash;2.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e143/232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.03 (0.85\u0026ndash;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.05 (0.87\u0026ndash;1.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e80/136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.05 (0.81\u0026ndash;1.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.04 (0.81\u0026ndash;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e63/96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.96 (0.71\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.96 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(0.79\u0026ndash;1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.97 (0.78\u0026ndash;1.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e72/107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.89 (0.66\u0026ndash;1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.94 (0.69\u0026ndash;1.28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP for trend\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.5988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.9365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.4323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.6581\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate\u0026thinsp;+\u0026thinsp;Strenuous\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80/166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e270/432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e140/246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e130/186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90/190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91 (0.67\u0026ndash;1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87 (0.64\u0026ndash;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e247/399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.06 (0.89\u0026ndash;1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.06 (0.88\u0026ndash;1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e161/270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.15 (0.92\u0026ndash;1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.11 (0.88\u0026ndash;1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e86/129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.01 (0.77\u0026ndash;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.07 (0.80\u0026ndash;1.43)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93/160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.31 (0.97\u0026ndash;1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.21 (0.89\u0026ndash;1.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e263/421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.94 (0.79\u0026ndash;1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.95 (0.80\u0026ndash;1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e182/306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.04 (0.83\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.98 (0.78\u0026ndash;1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e81/115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.95 (0.71\u0026ndash;1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.03 (0.77\u0026ndash;1.38)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP for trend\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.5381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.8168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.7714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.8137\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eModel 1: adjusted for age at lung cancer diagnosis and the other respective PA variables\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eModel 2: adjusted for factors in Model 1 and BMI, education level, and alcohol consumption\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSensitivity analyses were performed by adjusting for BMI, education level, disease stage (local vs non-local lung cancer, smoking quit-years (among ever and former smokers only), and passive smoke exposure (among never smokers). Inclusion of disease stage in the models attenuated the notable associations, except for the association of increased mortality risk among never smokers who engaged in strenuous, recent activity. Disease stage may act as a mediator in the relationship between PA levels and lung cancer mortality, hence was not included in the final models so that the total effect was not obscured. Additionally, we found that including smoking quit-years did not alter any model results except for ever smokers who engaged in strenuous, lifetime PA, suggesting that quit-years may act as a confounding variable for this specific relationship.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe assessed associations between PA and lung cancer mortality among 1,768 women diagnosed with lung cancer and enrolled in the California Teachers Study from 1995\u0026ndash;2019. We highlight the following notable associations from this study: 1) women who have smoked (ever and/or former) and engaged in higher levels of moderate, lifetime PA had a lower risk of lung cancer mortality, 2) ever and/or current smokers who engaged in intermediate to high levels of strenuous, lifetime PA saw an increased risk for lung cancer mortality, while never smokers saw a protective effect with higher levels of strenuous, lifetime PA, and 3) never smokers who engaged in strenuous, recent PA had increased lung CA mortality risk.\u003c/p\u003e\u003cp\u003eWhile a relatively small number of studies have examined PA and lung cancer-specific mortality, fewer still have explored this association while also stratifying by smoking status, a key determinant of both incidence and mortality in lung cancer. Among those that do not stratify by smoking status, increased PA levels were consistently associated with reduced lung cancer mortality [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. We found only three studies to date that examine PA and lung cancer mortality risk across smoke exposure groups in women [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, these studies either assess recent PA only or do not account for both duration and exertion of PA, limiting the comparability of their findings to those of the present study.\u003c/p\u003e\u003cp\u003ePA is known to support immune cell infiltration, reduce cancer cell growth and survival, improve the tumor microenvironment, reduce oxidative stress and increase DNA repair processes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Despite these benefits, several studies suggest that PA\u0026rsquo;s therapeutic effects persist to an extent, while excessive strenuous PA may have an opposite and deleterious effect on lung cancer mortality [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Exercise immunology research indicates that high-intensive PA without adequate recovery can trigger immunosuppressive effects that temporarily increase the risk of illness [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Specifically, it is thought that following strenuous PA, antibody production as well as circulating levels of lymphocytes and natural killer cells significantly decrease [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. While more research is warranted to confirm our findings, this temporary immunosuppressive state may offer some explanation for the increased lung cancer mortality risk we observed among never-smoking women who engaged in strenuous, recent PA. This explanation is also consistent with the increased mortality risks we observed in women with a history of smoking, especially in light of the chronic respiratory strain and low-grade inflammation induced by smoking. The deleterious effects of smoke exposure are known to weaken the immune system for years after cessation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These effects, when coupled with the immune impairment precipitated by elevated strenuous PA, may thus account for the elevated lung cancer mortality risks we observed among ever and/or current smokers who engaged in higher levels of strenuous, lifetime PA. Notably, women in our study who were not subject to the inflammatory effects of smoke exposure (never smokers) saw protective effects for lung cancer survival with this same level of increased, lifetime strenuous PA.\u003c/p\u003e\u003cp\u003eMoreover, former and ever smokers who engaged in high levels of moderate lifetime PA also saw protective effects for lung cancer mortality, highlighting the importance of quality and time spent in PA recovery. Moderate, lifetime PA, such as brisk walking or playing golf, may allow for cell recovery and immune repair necessary to mitigate the deleterious effects of smoking, while high levels of strenuous PA, like running or cycling uphill, may not; an observation that is supported in the literature [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe limited research on this topic has largely focused on recent, pre-lung cancer diagnosis PA. Interestingly, our stratified analyses revealed a stark contrast in lung cancer mortality risks between lifetime PA and recent PA groups, suggesting that PA and smoking behaviors from high school through adulthood may play a more prominent role in women\u0026rsquo;s lung cancer survival than in the several years preceding diagnosis. Further studies examining lifetime and exertion-varying PA variables and lung cancer mortality are warranted.\u003c/p\u003e\u003cp\u003eOur findings ultimately expand on those of previous studies with the following arguments about PA and lung cancer mortality prevention: 1) PA regimens (e.g., PA intensity and duration) should be determined through a personalized approach that considers factors like disease stage, physical fitness, smoking status, and other relevant health behaviors; 2) PA interventions aimed at lung cancer mortality prevention should target at-risk individuals as early as is feasible [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and 3) interventions and public/clinical health promotion efforts should consider that high levels of strenuous PA among those with a history of smoking may not benefit lung cancer prognosis.\u003c/p\u003e\u003cp\u003eThe strengths of this study include its prospective design, recent and length of follow-up time (up to 2019), detailed lifetime and recent PA information categorized by exertion level, and extensive data regarding potential confounding factors (e.g., total smoking pack-years and passive household smoke exposure). There are limitations to this study, including 1) PA measures were collected at baseline, with a varying number of years preceding diagnosis, 2) detailed information regarding specific types of PA performed during each time period, which would be needed to calculate MET-hours/week, were not collected, 3) the smoking variables included in this study were collected at baseline, are restricted to cigarette smoking and do not reflect smoking cessation during follow-up, and 4) our study was based in California, a state reported to have high pollution levels, which have previously been associated with lung cancer risk [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Our study population is restricted to women only, potentially reducing generalizability; however, postmenopausal women are seldom the focus of studies on lung cancer mortality and PA levels, despite lung cancer remaining the second most common cancer in women, secondary to breast cancer [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Given the potential for clinical significance if our findings are confirmed in future studies, further investigation is warranted.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express our highest gratitude to all the women who participated in the California Teachers Study. We would like to also thank the California Teachers Study Steering Committee, researchers, analysts, and staff who have contributed to the success of this research; they are responsible for the formation and maintenance of the Study within which this research was conducted. A full list of California Teachers Study team members is available https://www.calteachersstudy.org/team. We also wish to specially acknowledge the late Dr. Leslie Bernstein for her pivotal contributions to the establishment of the California Teachers Study and the development of comprehensive physical activity measures—her work continues to shape and strengthen this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe California Teachers Study and the research reported in this publication were supported by the National Cancer Institute of the National Institutes of Health under award number U01-CA199277; P30-CA033572; P30-CA023100; UM1-CA164917; and R01-CA077398. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health. The collection of cancer incidence data used in the California Teachers Study was supported by the California Department of Public Health pursuant to California Health and Safety Code Section 103885; Centers for Disease Control and Prevention’s National Program of Cancer Registries, under cooperative agreement 5NU58DP006344; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under contract HHSN261201800032I awarded to the University of California, San Francisco, contract HHSN261201800015I awarded to the University of Southern California, and contract HHSN261201800009I awarded to the Public Health Institute. The opinions, findings, and conclusions expressed herein are those of the author(s) and do not necessarily reflect the official views of the State of California, Department of Public Health, the National Cancer Institute, the National Institutes of Health, the Centers for Disease Control and Prevention or their Contractors and Subcontractors, or the Regents of the University of California, or any of its programs. The authors declare that no funds, grants, or other support not listed here were received during the preparation of this manuscript. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Jessica Clague DeHart, Peggy Reynolds; Material preparation: Emma S. Spielfogel; Data analyses: Emily L. Cauble, Jessica Clague DeHart; Original draft preparation: Emily L. Cauble, Jessica Clague DeHart, Mia Blanchard; Writing-review and editing: all authors; Supervision: Jessica Clague DeHart. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe California Teachers Study data and resources are made available in accordance with the National Institute of Health’s Policy for Data Management and Sharing and the NIH Genomic Data Sharing Policy. The CTS Data Environment, which includes all CTS data, software, and documentation, is open and free of charge to anyone who agrees to and signs the CTS Confidentiality Pledge. Individuals interested in accessing CTS data can sign up for the CTS Researcher Platform and submit a project for feasibility review here: https://calteachersstudy.my.site.com/researchers/s/\u003c/p\u003e\n\u003cp\u003eThe dataset generated and analyzed for the current study are not publicly available as they are housed within the CTS Data Environment but can be made available by the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki and was approved by the Institutional Review Board of Claremont Graduate University. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants included in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAmerican Cancer Society medical and editorial content team (2024) Lung cancer statistics: how common is lung cancer? 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(2002) High breast cancer incidence rates among California teachers: results from the California Teachers Study (United States). Cancer Causes Control 13:625–635. https://doi.org/10.1023/A:1019569902266\u003c/li\u003e\n \u003cli\u003eSAS Institute Inc. (2024) SAS 9.4 software overview for the customer. https://support.sas.com/software/94/\u003c/li\u003e\n \u003cli\u003eLacey JV, Chung NT, Hughes P, Benbow JL, Duffy C, Savage KE, et al. (2020) Insights from adopting a data commons approach for large-scale observational cohort studies: the California Teachers Study. Cancer Epidemiol Biomark Prev 29:777–786. https://doi.org/10.1158/1055-9965.EPI-19-1166 \u003c/li\u003e\n \u003cli\u003eAlfano CM, Klesges RC, Murray DM, Bowen DJ, McTiernan A, Vander Weg MW, et al. (2004) Physical activity in relation to all-site and lung cancer incidence and mortality in current and former smokers. Cancer Epidemiol Biomark Prev 13:2233–2241. https://aacrjournals.org/cebp/article/13/12/2233/165189\u003c/li\u003e\n \u003cli\u003eChen W, Liu A, Jiang Y, Lin Y, Li X, Pan C, et al. (2024) Association between strenuous sports or other exercises and lung cancer risk: a Mendelian randomization study. Transl Lung Cancer Res 13:1210–1221. https://doi.org/10.21037/tlcr-24-189\u003c/li\u003e\n \u003cli\u003eQie R, Han M, Huang H, Sun P, Xie Y, He J, et al. (2023) Physical activity and risk of lung cancer: a systematic review and dose–response meta-analysis of cohort studies. J Natl Cancer Cent 3:48–55. https://doi.org/10.1016/j.jncc.2023.01.002\u003c/li\u003e\n \u003cli\u003ePeake JM, Neubauer O, Walsh NP, Simpson RJ (2017) Recovery of the immune system after exercise. J Appl Physiol 122:1077–1087. https://doi.org/10.1152/japplphysiol.00622.2016\u003c/li\u003e\n \u003cli\u003eNieman DC, Miller AR, Henson DA, Warren BJ, Gusewitch G, Johnson RL, et al. (1993) Effects of high- vs moderate-intensity exercise on natural killer cell activity. Med Sci Sports Exerc 25:1126–1134. https://doi.org/10.1249/00005768-199310000-00008\u003c/li\u003e\n \u003cli\u003eBird SR, Linden M, Hawley JA (2014) Acute changes to biomarkers as a consequence of prolonged strenuous running. Ann Clin Biochem 51:137–150. https://doi.org/10.1177/0004563213492147\u003c/li\u003e\n \u003cli\u003eNielsen HB, Secher NH, Christensen NJ, Pedersen BK (1996) Lymphocytes and NK cell activity during repeated bouts of maximal exercise. Am J Physiol Regul Integr Comp Physiol 271:R222–R227. https://doi.org/10.1152/ajpregu.1996.271.1.R222\u003c/li\u003e\n \u003cli\u003eWang T, Zhao B, Liou KN, Gu Y, Jiang Z, Song K, et al. (2019) Mortality burdens in California due to air pollution attributable to local and nonlocal emissions. Environ Int 133:105232. https://doi.org/10.1016/j.envint.2019.105232\u003c/li\u003e\n \u003cli\u003eCheng I, Yang J, Tseng C, Wu J, Shariff-Marco S, Park S-Shim L, et al. (2022) Traffic-related air pollution and lung cancer incidence: the California Multiethnic Cohort Study. Am J Respir Crit Care Med 206:1008–1018.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-causes-and-control","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caco","sideBox":"Learn more about [Cancer Causes \u0026 Control](https://www.springer.com/journal/10552)","snPcode":"10552","submissionUrl":"https://submission.nature.com/new-submission/10552/3","title":"Cancer Causes \u0026 Control","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Lung cancer, Survivorship. Physical activity, Risk factors, Cohort study","lastPublishedDoi":"10.21203/rs.3.rs-8138657/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8138657/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eAlthough physical activity (PA) levels have been linked to decreased lung cancer mortality, the magnitude of associations and delineation of biological and behavioral risk factors is often inconsistent. Our study aims to address this gap by elucidating the associations of lung cancer mortality with time-varying and exertion-varying pre-diagnosis PA levels.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe examined the associations between PA and lung cancer mortality among 1,768 women enrolled in the California Teachers Study cohort and diagnosed with lung cancer between 1995\u0026ndash;2019. Pre-diagnosis lifetime and recent PA were assessed. Multivariable Cox regressions provided hazard ratio (HR) and 95% confidence interval (CI) estimates.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSimilar risks of lung cancer mortality were observed across all PA variables. Ever and/or former smokers who engaged in higher levels of moderate, lifetime PA had a lower risk of lung cancer mortality. Ever and/or current smokers who engaged in intermediate to high levels of strenuous, lifetime PA had increased risk of lung cancer mortality, while never smokers saw a protective effect on lung cancer mortality.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe results of this study suggest that smoking significantly modifies the association between PA and lung cancer mortality. Although the mechanisms underlying these findings remain unclear, we hypothesize that excessive strenuous PA among ever and/or current smokers exacerbates the inflammatory damage already induced by smoke exposure, compromising immune cell recovery and leading to reduced lung cancer survival in this group.\u003c/p\u003e","manuscriptTitle":"Pre-Diagnosis Recreational Physical Activity and Lung Cancer Mortality within the California Teachers Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 22:35:15","doi":"10.21203/rs.3.rs-8138657/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-12T15:28:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-11T22:10:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-11T20:03:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285940345085218187444868452817554505038","date":"2026-02-11T19:49:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179491597570396924624358577329744030775","date":"2026-01-07T16:04:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-31T17:38:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279706197028146179774818372315958959490","date":"2025-12-30T22:04:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T23:15:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-19T07:19:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-19T07:17:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Causes \u0026 Control","date":"2025-11-17T19:24:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-causes-and-control","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caco","sideBox":"Learn more about [Cancer Causes \u0026 Control](https://www.springer.com/journal/10552)","snPcode":"10552","submissionUrl":"https://submission.nature.com/new-submission/10552/3","title":"Cancer Causes \u0026 Control","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f23bbe4c-6aa1-4bad-a9e1-eebe8efbf152","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:18:50+00:00","versionOfRecord":{"articleIdentity":"rs-8138657","link":"https://doi.org/10.1007/s10552-026-02169-6","journal":{"identity":"cancer-causes-and-control","isVorOnly":false,"title":"Cancer Causes \u0026 Control"},"publishedOn":"2026-04-22 15:58:49","publishedOnDateReadable":"April 22nd, 2026"},"versionCreatedAt":"2025-12-03 22:35:15","video":"","vorDoi":"10.1007/s10552-026-02169-6","vorDoiUrl":"https://doi.org/10.1007/s10552-026-02169-6","workflowStages":[]},"version":"v1","identity":"rs-8138657","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8138657","identity":"rs-8138657","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}