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Introduction Despite impressive declines in age-standardized coronary heart disease (CHD) mortality rates since the 1960s, cardiovascular disease (CVD) remains the leading cause of death in the United States and globally.1 More than half a million older American individuals die of CVD annually.2 From 1979 to 2011, declines in CHD mortality among Americans aged 65 to 84 years were faster in older men than women.3 While prevalence of myocardial infarction (MI) remains higher among men than women across the adult age spectrum, incidence rates of MI or fatal CHD are higher among women 85 years and older (120 per 1000) than men (85 per 1000).4 Yet, the prevention of CHD in older women is understudied.

ster in older men than women.3 While prevalence of myocardial infarction (MI) remains higher among men than women across the adult age spectrum, incidence rates of MI or fatal CHD are higher among women 85 years and older (120 per 1000) than men (85 per 1000).4 Yet, the prevention of CHD in older women is understudied. Physical activity (PA) is a key candidate for reducing CHD risk in older women. The long-standing, prevailing paradigm in PA research is that moderate to vigorous PA (MVPA) for at least 150 minutes per week is needed to prevent CVD in adults. However, a meta-analysis5 of 9 epidemiologic studies found reduced risks of CHD associated with levels of self-reported MVPA (≥3 metabolic equivalent tasks [METs]) that were lower than the recommended guidelines. Light PA at intensity levels of 1.5 to 3.0 METs is poorly measured by self-reported questionnaires because they fail to capture light movements performed habitually throughout the day.6,7 Recent reports reveal that light PA measured by accelerometry is associated with reduced risks of total8,9 and CVD9 mortality, as well as favorable levels of CVD risk factors.10 To our knowledge, no studies have yet evaluated whether light PA is associated with reduced risks of incident CHD and CVD in adults overall or in older women specifically. The objectives of this prospective cohort study were to investigate whether device-measured light PA was associated with reduced risk of CHD or CVD in a large and diverse cohort of older women followed from the OPACH baseline (March 2012 to April 2014) and whether any associations varied by baseline levels of MVPA, estimated CVD risk, or physical functioning.

study were to investigate whether device-measured light PA was associated with reduced risk of CHD or CVD in a large and diverse cohort of older women followed from the OPACH baseline (March 2012 to April 2014) and whether any associations varied by baseline levels of MVPA, estimated CVD risk, or physical functioning. Methods Study Participants The Objectively Measured Physical Activity and Cardiovascular Health (OPACH) study is an ancillary study to the Women’s Health Initiative (WHI) that began in the early 1990s to rectify the widespread lack of data on postmenopausal women and chronic disease. Postmenopausal women aged 50 to 79 years were enrolled in the WHI clinical trials or the observational study from 40 clinical sites throughout the United States from 1993 to 1998. Between 2012 and 2014, a total of 7058 ambulatory community-dwelling women 63 years and older from the WHI were enrolled in the OPACH. Details on the WHI and OPACH have been published previously.11,12,13

HI clinical trials or the observational study from 40 clinical sites throughout the United States from 1993 to 1998. Between 2012 and 2014, a total of 7058 ambulatory community-dwelling women 63 years and older from the WHI were enrolled in the OPACH. Details on the WHI and OPACH have been published previously.11,12,13 Briefly, participants were distributed accelerometers (GT3X+; ActiGraph, LLC) to wear 24 hours per day on an elastic band over their right hip for a requested 7 days. Participants self-reported in-bed and out-of-bed times using sleep logs on days when the accelerometer was worn. Of the 6489 women who wore accelerometers, 6381 had at least 1 day with 10 or more waking hours of accelerometer wear. Women with an MI or stroke before the OPACH baseline (n = 520) were excluded, leaving 5861 women (96.1% with ≥4 days with 10 awake hours of accelerometer wear time) in the analytic study population. The protocol for this study was approved by the Fred Hutchinson Cancer Research Center Institutional Review Board, and all women provided written informed consent or telephone informed consent using an institutional review board–approved script. This report followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for cohort studies.

r Research Center Institutional Review Board, and all women provided written informed consent or telephone informed consent using an institutional review board–approved script. This report followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for cohort studies. CHD and CVD Outcome Ascertainment From the OPACH baseline (March 2012 to April 2014) through February 28, 2017, medical updates were collected annually by mail or phone. In this report, CHD and CVD events were investigated as separate outcomes, with CHD identified as the primary end point because of its historically stronger associations with self-reported PA. Reports of incident CHD events (MI or coronary death) or incident CVD events (CHD, revascularization, carotid artery disease, hospitalized angina, congestive heart failure, stroke, or death from other CVD) were ascertained, and first events of any type were adjudicated by physician review of medical records (except angina).14 Defining criteria for each outcome are detailed elsewhere.13 There was excellent agreement among the WHI physicians on adjudication of CVD outcomes, with values ranging between 0.67 and 0.94 for κ statistics.15 Because older women with a prevalent CVD condition (eg, angina, heart failure, or revascularization) remain at risk for other incident manifestations of CVD, we examined incident CVD events in the same population at risk as for the CHD end point (women with no history of MI or stroke), without additional exclusion of other prevalent CVD conditions at baseline. Sensitivity analyses were conducted to determine if findings were consistent when the baseline population excluded women with the symptomatic conditions of angina and heart failure at the OPACH baseline.

(women with no history of MI or stroke), without additional exclusion of other prevalent CVD conditions at baseline. Sensitivity analyses were conducted to determine if findings were consistent when the baseline population excluded women with the symptomatic conditions of angina and heart failure at the OPACH baseline. PA Measures Accelerometer data, originally collected at 30 Hz, were aggregated to 15-second epochs using the normal frequency filter within ActiLife version 6 software (ActiGraph, LLC). Accelerometer nonwear periods were identified and removed using the Choi algorithm as previously described.13,16 Sleep time was removed using reported in-bed and out-of-bed times from sleep logs. Missing bed times were imputed using participant-specific mean times or, if all data were missing, the OPACH population mean (10:45 pm for in-bed time and 7:22 am for out-of-bed time).

ing the Choi algorithm as previously described.13,16 Sleep time was removed using reported in-bed and out-of-bed times from sleep logs. Missing bed times were imputed using participant-specific mean times or, if all data were missing, the OPACH population mean (10:45 pm for in-bed time and 7:22 am for out-of-bed time). Time spent in light PA and MVPA was computed from accelerometer data using activity intensity thresholds determined in the OPACH Calibration Study.17 Light PA, movements with energy expenditure measured by indirect calorimetry between 1.6 and 2.9 METs, was computed as the mean minutes per day of 15-second epochs having vector magnitude (VM) counts between 19 and 518 per day.17 The MVPA (METs ≥3.0) was computed as the mean minutes per day of 15-second epochs with VM counts of at least 519 per day.17 The PA measures were averaged over all days with awake wear time of at least 10 hours, and all such days were included in this analysis. Light PA and MVPA were adjusted for awake wear time using the residuals method to account for any systematic variations in in-bed or nonwear times.18

counts of at least 519 per day.17 The PA measures were averaged over all days with awake wear time of at least 10 hours, and all such days were included in this analysis. Light PA and MVPA were adjusted for awake wear time using the residuals method to account for any systematic variations in in-bed or nonwear times.18 Covariates Potential confounders were selected based on previous literature and included age, self-identified race/ethnicity from questionnaire categories (white, black, or Hispanic/Latina), body mass index (BMI), highest education (high school or less, some college, or college graduate), current smoking (yes or no), alcohol consumption (nondrinker, <1 drink per week, ≥1 drink per week, or unknown), physical functioning (using a 10-item subscale from the RAND 36-Item Health Survey 1.0 (RAND-36),19 ranging from 0 [low] to 100 [high]), number of non-CVD chronic conditions (none, 1-2, or ≥3 from the sum of cancer, chronic obstructive pulmonary disease, cognitive impairment, depression, diabetes, and osteoarthritis), and systolic blood pressure, as well as self-rated health (excellent or very good; good; fair or poor). Race/ethnicity was assessed in the WHI to allow investigation of disparities. Fasting serum glucose, insulin, total cholesterol, high-density lipoprotein cholesterol (HDL-C), and high-sensitivity C-reactive protein (hsCRP) assays were conducted at the University of Minnesota Fairview Advanced Research and Diagnostic Laboratory, Minneapolis, using standardized Clinical Laboratory Improvement Act–approved methods. The Reynolds Risk Score, a strong predictor of CVD risk in the WHI cohort,20 was computed as previously described but without glycated hemoglobin level in women with diabetes, which was not available.

search and Diagnostic Laboratory, Minneapolis, using standardized Clinical Laboratory Improvement Act–approved methods. The Reynolds Risk Score, a strong predictor of CVD risk in the WHI cohort,20 was computed as previously described but without glycated hemoglobin level in women with diabetes, which was not available. Statistical Analysis Participant characteristics were summarized across quartiles of light PA using means and standard deviations for continuous variables and percentages for categorical variables. F tests and Pearson χ2 tests assessed differences across quartiles for continuous and categorical variables, respectively.

Statistical Analysis Participant characteristics were summarized across quartiles of light PA using means and standard deviations for continuous variables and percentages for categorical variables. F tests and Pearson χ2 tests assessed differences across quartiles for continuous and categorical variables, respectively. Hazard ratios (HRs) for new CHD and CVD events were estimated for quartiles of light PA and MVPA (with quartile 1 as reference) using Cox proportional hazards regression. Time to event was computed as the number of days from the OPACH baseline to the date of first occurrence of a CHD or CVD event, death, or the last medical update. Regression models were progressively adjusted as follows: model 1 (n = 5861) included age and race/ethnicity; model 2 (n = 5822) added highest education, current smoking, and alcohol consumption; model 3 (n = 5750) added physical functioning, comorbidity, and self-rated health; and model 4 (n = 5861) added CVD risk factors (BMI, systolic blood pressure, hsCRP, total cholesterol, and HDL-C) thought to be in the causal pathway between PA and CVD. Biomarker data were missing from 1226 participants for whom no blood specimens were available. Therefore, models with biomarkers used data that were imputed by multivariable chained equations using 100 iterations and including CHD, CVD, both times to event, light PA, MVPA, and all covariates in the process.21 Model 4 results using complete case analysis are listed in eTable 1 in the Supplement. P values for linear trend tests were computed from Cox proportional hazards regression models that contained the continuous functional form of light PA and MVPA. Tests based on Schoenfeld residuals22 were used to check the proportional hazards assumptions. No violations were observed.

ted in eTable 1 in the Supplement. P values for linear trend tests were computed from Cox proportional hazards regression models that contained the continuous functional form of light PA and MVPA. Tests based on Schoenfeld residuals22 were used to check the proportional hazards assumptions. No violations were observed. To examine the dose-response association of light PA (continuous variable) with CHD and CVD, restricted cubic spline functions23 were added to Cox proportional hazards regression model 3 with knots placed at the recommended 5th, 50th, and 95th percentiles (results were insensitive to whether 3 or 4 knots were used [eTable 2 in the Supplement]).24 Linearity of the dose-response association was checked using Wald tests. Dose-response trajectories were then plotted using the 10th percentile of the light PA distribution (3.3 hours per day) as the referent category.8 To test whether associations of light PA with CHD and CVD events were independent of MVPA, spline analyses were repeated adjusted for MVPA.

ion was checked using Wald tests. Dose-response trajectories were then plotted using the 10th percentile of the light PA distribution (3.3 hours per day) as the referent category.8 To test whether associations of light PA with CHD and CVD events were independent of MVPA, spline analyses were repeated adjusted for MVPA. Stratified analyses were conducted to evaluate the consistency of associations across high and low levels of baseline estimated CVD risk based on the Reynolds Risk Score (median, 9.2), MVPA (median, 44.3 minutes per day), and RAND-36 physical function score (median, 75.0) defined using median splits for each variable. Hazard ratios were computed from model 3 for light PA and MVPA comparing the 75th and 25th percentiles of light PA (difference of 1.60 hours per day) and MVPA (difference of 42 minutes per day) within each strata. The statistical significance of possible effect modification was tested by adding a cross product interaction term to model 3. All variables were first mean centered to reduce multicollinearity. All data analyses were conducted using statistical software (R, version 3.3.2; R Foundation for Statistical Computing) with the survival and rms packages. Statistical tests were all 2 sided, with the level of significance set to .05.

Stratified analyses were conducted to evaluate the consistency of associations across high and low levels of baseline estimated CVD risk based on the Reynolds Risk Score (median, 9.2), MVPA (median, 44.3 minutes per day), and RAND-36 physical function score (median, 75.0) defined using median splits for each variable. Hazard ratios were computed from model 3 for light PA and MVPA comparing the 75th and 25th percentiles of light PA (difference of 1.60 hours per day) and MVPA (difference of 42 minutes per day) within each strata. The statistical significance of possible effect modification was tested by adding a cross product interaction term to model 3. All variables were first mean centered to reduce multicollinearity. All data analyses were conducted using statistical software (R, version 3.3.2; R Foundation for Statistical Computing) with the survival and rms packages. Statistical tests were all 2 sided, with the level of significance set to .05. Sensitivity Analyses Models were also further adjusted for use of lipid-lowering and antihypertensive medications and for the Healthy Eating Index.25 Because symptoms preceding new CVD events could lead women to engage in less PA, all models were repeated after excluding CHD and CVD cases that occurred within the first 6 months of follow-up. To test whether the symptomatic conditions (angina and heart failure) were altering associations between light PA and CVD, we (1) repeated model 3 after excluding women with a history of hospitalized angina or heart failure at the OPACH baseline and (2) repeated model 3 after excluding hospitalized angina and heart failure from the CVD end point.

tic conditions (angina and heart failure) were altering associations between light PA and CVD, we (1) repeated model 3 after excluding women with a history of hospitalized angina or heart failure at the OPACH baseline and (2) repeated model 3 after excluding hospitalized angina and heart failure from the CVD end point. Results The mean (SD) age of 5861 OPACH participants was 78.5 (6.7) years (range, 63-99 years) (Table 1). One-third (33.5%) of the OPACH women were black, 17.6% were Hispanic, and 48.8% were of white race/ethnicity. The mean daily time spent in light PA ranged from 0.6 to 10.3 hours per day, with women in the lowest quartile having less than 3.9 hours per day and women in the highest quartile engaging in more than 5.6 hours per day. Greater proportions of younger women and black and Hispanic/Latina women were seen in the higher quartiles of light PA, but there were no differences by educational attainment. Women with more light PA had lower mean BMI, higher RAND-36 physical function scores, and lower levels of comorbidity (Table 1). As reported previously,10 the Reynolds Risk Score and levels of blood pressure and CVD biomarkers (glucose, insulin, and lipid levels) were more favorable among women with higher levels of light PA.

ith more light PA had lower mean BMI, higher RAND-36 physical function scores, and lower levels of comorbidity (Table 1). As reported previously,10 the Reynolds Risk Score and levels of blood pressure and CVD biomarkers (glucose, insulin, and lipid levels) were more favorable among women with higher levels of light PA. Table 1. Baseline Characteristics by Quartile of Time Spent in Light PA Among 5861 Women Characteristic No./Total No. (%)a P Value Q1 (Low) Q2 Q3 Q4 (High) No. 1466 1465 1465 1465 Age, mean (SD), y 79.9 (6.7) 78.7 (6.7) 78.1 (6.6) 77.4 (6.5) <.001 Race/ethnicity White 895 (61.1) 742 (50.6) 655 (44.7) 571 (39.0) <.001 Black 399 (27.2) 490 (33.4) 524 (35.8) 553 (37.7) Hispanic/Latina 172 (11.7) 233 (15.9) 286 (19.5) 341 (23.3) BMI, mean (SD) 30.2 (6.2) 28.6 (5.5) 27.5 (5.3) 26.3 (5.1) <.001 Highest education High school or less 277/1454 (19.1) 287/1455 (19.7) 287/1449 (19.8) 325/1464 (22.2) .26 Some college 581/1454 (40.0) 580/1455 (39.9) 551/1449 (38.0) 535/1464 (36.5) College graduate 596/1454 (41.0) 588/1455 (40.4) 611/1449 (42.2) 604/1464 (41.3) Current smoker 48 (3.3) 38 (2.6) 28 (1.9) 27 (1.8) .04 Alcohol consumption Nondrinker 544 (37.1) 474 (32.4) 480 (32.8) 467 (31.9) <.001 <1 Drink/wk 471 (32.1) 476 (32.5) 465 (31.7) 433 (29.6) ≥1 Drinks/wk 301 (20.5) 383 (26.1) 404 (27.6) 442 (30.2) Unknown 150 (10.2) 132 (9.0) 116 (7.9) 123 (8.4) RAND-36 physical function score, mean (SD) 60.5 (27.7) 68.2 (25.9) 73.7 (23.3) 76.4 (22.4) <.001 No.

drinker 544 (37.1) 474 (32.4) 480 (32.8) 467 (31.9) <.001 <1 Drink/wk 471 (32.1) 476 (32.5) 465 (31.7) 433 (29.6) ≥1 Drinks/wk 301 (20.5) 383 (26.1) 404 (27.6) 442 (30.2) Unknown 150 (10.2) 132 (9.0) 116 (7.9) 123 (8.4) RAND-36 physical function score, mean (SD) 60.5 (27.7) 68.2 (25.9) 73.7 (23.3) 76.4 (22.4) <.001 No. of chronic conditionsb None 340 (23.2) 356/1464 (24.3) 376 (25.7) 410 (28.0) <.001 1-2 951 (64.9) 972/1464 (66.4) 963 (65.7) 945 (64.5) ≥3 175 (11.9) 136/1464 (9.3) 126 (8.6) 110 (7.5) Self-rated health Excellent or very good 690/1461 (47.2) 727/1458 (49.9) 793/1460 (54.3) 818/1458 (56.1) <.001 Good 596/1461 (40.8) 603/1458 (41.4) 543/1460 (37.2) 536/1458 (36.8) Fair or poor 175/1461 (12.0) 128/1458 (8.8) 124/1460 (8.5) 104/1458 (7.1) Uses antihypertensive medication 985 (67.2) 924 (63.1) 908 (62.0) 873 (59.6) <.001 Uses antilipidemic medication 620 (42.3) 641 (43.8) 549 (37.5) 513 (35.0) <.001 Light PA, mean (SD), min/d 196.0 (32.2) 262.2 (14.2) 309.6 (14.0) 379.6 (38.8) <.001 Reynolds Risk Score, mean (SD) 16.2 (13.2) 12.7 (10.3) 11.5 (9.5) 9.6 (8.2) <.001 MVPA, mean (SD), min/d 34.2 (25.8) 47.2 (29.8) 56.5 (33.7) 66.2 (35.7) <.001 Blood pressure, mean (SD), mm Hg Systolic 127.6 (15.2) 126.0 (13.9) 124.8 (13.8) 124.1 (13.7) <.001 Diastolic 73.5 (9.4) 73.0 (8.5) 72.5 (8.4) 71.6 (8.4) <.001 hsCRP, mean (SD), mg/Lc 0.8 (1.1) 0.7 (1.0) 0.6 (1.0) 0.4 (1.0) <.001 Cholesterol, mean (SD), mg/dL Total 195.4 (40.0) 198.1 (39.6) 199.9 (39.4) 202.5 (38.1) <.001 HDL 56.6 (13.8) 59.8 (14.1) 62.1 (15.3) 64.1 (15.2) <.001 Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); HDL, high-density lipoprotein; hsCRP, high-sensitivity C-reactive protein; MVPA, moderate to vigorous PA; PA, physical activity; Q, quartile.

38.1) <.001 HDL 56.6 (13.8) 59.8 (14.1) 62.1 (15.3) 64.1 (15.2) <.001 Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); HDL, high-density lipoprotein; hsCRP, high-sensitivity C-reactive protein; MVPA, moderate to vigorous PA; PA, physical activity; Q, quartile. SI conversion factors: To convert cholesterol level to millimoles per liter, multiply by 0.0259; to convert hsCRP level to nanomoles per liter, multiply by 9.524. a Adjusted for awake wear time using the residuals method. Quartile cut points are 36 to 236 min/d for Q1, 237 to 285 min/d for Q2, 286 to 333 min/d for Q3, and 334 to 617 min/d for Q4. For some variables in the table, totals are less than the column headings because of missing data. b Cancer, chronic obstructive pulmonary disease, cognitive impairment, depression, diabetes, and osteoarthritis. c Natural log transformed. A total of 143 incident cases of CHD and 570 incident cases of CVD occurred during 20 718 person-years of follow-up (mean, 3.53 years; range, 0.01-4.91 years) (Table 2). The CHD HR comparing the highest vs lowest quartiles adjusted for age and race/ethnicity (model 1) was 0.42 (95% CI, 0.25-0.70; P for trend <.001). The HR after model 3 adjustments was 0.58 (95% CI, 0.34-0.99; P for trend = .004). The HR after further model 4 adjustment for CVD risk factors, including BMI, systolic blood pressure, hsCRP, total cholesterol, and HDL-C, was 0.68 (95% CI, 0.39-1.18; P for trend = .03; 42% lower risk of CHD).

-0.70; P for trend <.001). The HR after model 3 adjustments was 0.58 (95% CI, 0.34-0.99; P for trend = .004). The HR after further model 4 adjustment for CVD risk factors, including BMI, systolic blood pressure, hsCRP, total cholesterol, and HDL-C, was 0.68 (95% CI, 0.39-1.18; P for trend = .03; 42% lower risk of CHD). Table 2. Associations of Incident CHD and CVD With Light Physical Activity and MVPA in the Objectively Measured Physical Activity and Cardiovascular Health (OPACH) Cohort (2012-2017) Outcome and Modela HR (95% CI)b P Value for Trendc Q1 (Low) Q2 Q3 Q4 (High) Light PA Incident CHD events (crude incidence rate per 1000 person-years) 59 (11.8) 36 (7.0) 28 (5.4) 20 (3.8) NA Model 1 1 [Reference] 0.67 (0.44-1.01) 0.55 (0.35-0.87) 0.42 (0.25-0.70) <.001 Model 2 1 [Reference] 0.71 (0.47-1.08) 0.60 (0.38-0.96) 0.46 (0.28-0.78) <.001 Model 3 1 [Reference] 0.79 (0.51-1.20) 0.72 (0.45-1.15) 0.58 (0.34-0.99) .004 Model 4 1 [Reference] 0.82 (0.54-1.26) 0.79 (0.49-1.27) 0.68 (0.39-1.18) .03 Incident CVD events (crude incidence rate per 1000 person-years) 183 (37.9) 161 (32.3) 124 (24.3) 102 (19.7) NA Model 1 1 [Reference] 0.93 (0.75-1.15) 0.73 (0.58-0.92) 0.63 (0.49-0.81) <.001 Model 2 1 [Reference] 0.96 (0.78-1.19) 0.77 (0.61-0.97) 0.66 (0.52-0.85) <.001 Model 3 1 [Reference] 1.02 (0.82-1.27) 0.88 (0.69-1.11) 0.78 (0.60-1.00) .004 Model 4 1 [Reference] 1.05 (0.84-1.30) 0.90 (0.71-1.14) 0.82 (0.63-1.07) .02 MVPA Incident CHD events (crude incidence rate per 1000 person-years) 77 (15.6) 25 (4.9) 24 (4.6) 17 (3.2) NA Model 1 1 [Reference] 0.38 (0.24-0.61) 0.42 (0.26-0.68) 0.34 (0.19-0.59) <.001 Model 2 1 [Reference] 0.40 (0.25-0.63) 0.44 (0.27-0.71) 0.38 (0.22-0.67) <.001 Model 3 1 [Reference] 0.46 (0.29-0.72) 0.55 (0.34-0.90) 0.54 (0.30-0.96) .001 Model 4 1 [Reference] 0.45 (0.28-0.72) 0.58 (0.36-0.95) 0.58 (0.32-1.04) .003 Incident CVD events (crude incidence rate per 1000 person-years) 229 (48.7) 143 (28.7) 106 (20.6) 92 (17.5) NA Model 1 1 [Reference] 0.68 (0.55-0.84) 0.54 (0.42-0.68) 0.50 (0.39-0.65) <.001 Model 2 1 [Reference] 0.69 (0.56-0.86) 0.55 (0.44-0.71) 0.53 (0.41-0.69) <.001 Model 3 1 [Reference] 0.77 (0.62-0.96) 0.65 (0.51-0.84) 0.69 (0.53-0.91) .009 Model 4 1 [Reference] 0.75 (0.61-0.93) 0.66 (0.52-0.84) 0.71 (0.54-0.93) .02 Abbreviations: CHD, coronary heart disease; CVD, cardiovascular disease; HR, hazard ratio; MVPA, moderate to vigorous physical activity; NA, not applicable; PA, physical activity; Q, quartile.

(0.62-0.96) 0.65 (0.51-0.84) 0.69 (0.53-0.91) .009 Model 4 1 [Reference] 0.75 (0.61-0.93) 0.66 (0.52-0.84) 0.71 (0.54-0.93) .02 Abbreviations: CHD, coronary heart disease; CVD, cardiovascular disease; HR, hazard ratio; MVPA, moderate to vigorous physical activity; NA, not applicable; PA, physical activity; Q, quartile. a Data used for model 4 were imputed because biomarker data were missing from 1226 women. Results from complete case analysis are listed in eTable 1 in the Supplement. Regression models were progressively adjusted as follows: model 1 (n = 5861) included age and race/ethnicity; model 2 (n = 5822) added highest education, current smoking, and alcohol consumption; model 3 (n = 5750) added physical functioning, comorbidity, and self-rated health; and model 4 (n = 5861) added CVD risk factors (body mass index, systolic blood pressure, high-sensitivity C-reactive protein, total cholesterol, and high-density lipoprotein cholesterol) thought to be in the causal pathway between PA and CVD. b Adjusted for awake wear time using the residuals method. Quartile cut points for light PA are 36 to 236 min/d for Q1, 237 to 285 min/d for Q2, 286 to 333 min/d for Q3, and 334 to 617 min/d for Q4. Quartile cut points for MVPA less than 26 min/d for Q1, 27 to 44 min/d for Q2, 45 to 68 min/d for Q3, and 69 to 350 min/d for Q4. c P values from Cox proportional hazards regression models that include light PA as a continuous variable.

b Adjusted for awake wear time using the residuals method. Quartile cut points for light PA are 36 to 236 min/d for Q1, 237 to 285 min/d for Q2, 286 to 333 min/d for Q3, and 334 to 617 min/d for Q4. Quartile cut points for MVPA less than 26 min/d for Q1, 27 to 44 min/d for Q2, 45 to 68 min/d for Q3, and 69 to 350 min/d for Q4. c P values from Cox proportional hazards regression models that include light PA as a continuous variable. Associations between light PA and incident CVD events followed a similar pattern (Table 2). The CVD HRs comparing the highest vs lowest quartiles were 0.63 (95% CI, 0.49-0.81; P for trend <.001) after minimal adjustment (model 1), 0.78 (95% CI, 0.60-1.00; P for trend = .004) after adjustment for confounders (model 3), and 0.82 (95% CI, 0.63-1.07; P for trend = .02; 18% lower risk of CVD) after inclusion of CVD risk factors likely to be in the causal pathway (model 4). For MVPA, HRs indicated statistically significant risk reductions for both CHD and CVD beginning at quartile 2, which corresponds to 27 minutes or more of MVPA daily, agreeing well with PA guidelines (Table 2). The HRs comparing the women in the highest vs lowest MVPA quartiles after adjusting for model 3 confounders were 0.54 (95% CI, 0.30-0.96; P for trend = .001; 46% lower risk of CHD) for CHD and 0.69 (95% CI, 0.53-0.91; P for trend = .009; 31% lower risk of CVD) for CVD.

daily, agreeing well with PA guidelines (Table 2). The HRs comparing the women in the highest vs lowest MVPA quartiles after adjusting for model 3 confounders were 0.54 (95% CI, 0.30-0.96; P for trend = .001; 46% lower risk of CHD) for CHD and 0.69 (95% CI, 0.53-0.91; P for trend = .009; 31% lower risk of CVD) for CVD. Analyzing light PA as a continuous variable, the risk for incident CHD and CVD events decreased in a linear dose-dependent manner over increasing light PA levels (eTable 2 in the Supplement). Hazard ratios adjusting for potential confounders (model 3) for each 1-hour increment in light PA were 0.80 (95% CI, 0.69-0.93; P for trend = .004) for CHD (Figure 1A) and 0.90 (95% CI, 0.83-0.97; P for trend = .004) for CVD (Figure 1B). Adjustment for MVPA (model 4) slightly attenuated associations, with HRs for 1 hour of light PA changing to 0.86 (95% CI, 0.73-1.00; P for trend = .05) for incident CHD events and to 0.92 (95% CI, 0.85-0.99; P for trend = .03) for incident CVD events.

nd 0.90 (95% CI, 0.83-0.97; P for trend = .004) for CVD (Figure 1B). Adjustment for MVPA (model 4) slightly attenuated associations, with HRs for 1 hour of light PA changing to 0.86 (95% CI, 0.73-1.00; P for trend = .05) for incident CHD events and to 0.92 (95% CI, 0.85-0.99; P for trend = .03) for incident CVD events. Figure 1. Continuous Dose-Response Association of Light Physical Activity (PA) With Coronary Heart Disease (CHD) and Cardiovascular Disease (CVD) Events A, Association with incident CHD events. B, Association with incident CVD events. C, Distribution of daily light PA for the Objectively Measured Physical Activity and Cardiovascular Health (OPACH) cohort. All associations were estimated using multivariable linear Cox proportional hazards regression models adjusted for age, race/ethnicity, highest education, current smoking, alcohol consumption, physical functioning, comorbidity, and self-rated health (blue lines). Orange lines show results after additional adjustment for moderate to vigorous PA (MVPA). The reference category was set to the 10th percentile of light PA (3.3 hours per day). Respective hazard ratios (HRs) and 95% CIs for 4, 5, and 6 hours per day of light PA (compared with the reference) were for CHD: not adjusted for MVPA 0.84 (0.75-0.95), 0.68 (0.52-0.88), 0.54 (0.36-0.82); adjusted for MVPA 0.89 (0.79-1.00), 0.76 (0.58-1.00), 0.65 (0.42-1.01). For CVD: not adjusted for MVPA 0.92 (0.87-0.97), 0.83 (0.73-0.94), 0.74 (0.61-0.91); adjusted for MVPA 0.94 (0.88-1.00), 0.86 (0.75-0.99), 0.79 (0.64-0.98). Results were trimmed at the 1st and 99th percentiles.

, 0.68 (0.52-0.88), 0.54 (0.36-0.82); adjusted for MVPA 0.89 (0.79-1.00), 0.76 (0.58-1.00), 0.65 (0.42-1.01). For CVD: not adjusted for MVPA 0.92 (0.87-0.97), 0.83 (0.73-0.94), 0.74 (0.61-0.91); adjusted for MVPA 0.94 (0.88-1.00), 0.86 (0.75-0.99), 0.79 (0.64-0.98). Results were trimmed at the 1st and 99th percentiles. As shown in Figure 2 comparing women in the 75th vs 25th percentiles of light PA, reduced risks of incident CHD events were observed across high and low levels of Reynolds Risk Score and RAND-36 physical function score. Hazard ratios for light PA appeared stronger among women with low MVPA, but the interaction was not statistically significant. Similar results were observed in stratified analyses for incident CVD events. For MVPA, HRs were somewhat stronger than for light PA for CHD overall (HR for the 75th vs 25th percentiles, 0.59; 95% CI, 0.42-0.81) and in some strata. No statistically significant interactions were observed between MVPA and any of the stratifying factors (Figure 2). No statistically significant interactions were observed between light PA and race/ethnicity for either the CHD or CVD outcomes.

r the 75th vs 25th percentiles, 0.59; 95% CI, 0.42-0.81) and in some strata. No statistically significant interactions were observed between MVPA and any of the stratifying factors (Figure 2). No statistically significant interactions were observed between light PA and race/ethnicity for either the CHD or CVD outcomes. Figure 2. Associations of Physical Activity (PA) With Coronary Heart Disease (CHD) and Cardiovascular Disease (CVD) Events, by Selected Participant Characteristics A, Associations comparing the 75th vs 25th percentiles of light PA (difference of 1.6 hours per day) with incident CHD and CVD events. B, Associations comparing the 75th vs 25th quartiles of moderate to vigorous PA (MVPA) (difference of 42 minutes per day) with incident CHD and CVD events. Hazard ratios (HR) were adjusted for age, race/ethnicity, highest education, current smoking, alcohol consumption, physical functioning, comorbidity, and self-rated health (where appropriate). Reynolds Risk Score, MVPA, physical functioning, and light PA were split at the median. Hazard ratios below 1 indicate favorable associations (ie, lower risk), whereas those above 1 indicate harmful associations (ie, higher risk). NA indicates not applicable; error bars, 95% CIs. The n values for subanalyses stratified by Reynolds Risk Score do not sum to 5750 because of missing biomarker data.

he median. Hazard ratios below 1 indicate favorable associations (ie, lower risk), whereas those above 1 indicate harmful associations (ie, higher risk). NA indicates not applicable; error bars, 95% CIs. The n values for subanalyses stratified by Reynolds Risk Score do not sum to 5750 because of missing biomarker data. Sensitivity analyses indicated that results were unchanged when CHD or CVD events that occurred during the first 6 months of follow-up were excluded or when additional adjustments were made for use of lipid-lowering medication, antihypertensive medication, and the Healthy Eating Index. Results were also unchanged when women with angina and heart failure at the OPACH baseline were excluded from the analytic sample and when angina and heart failure were excluded from the CVD end point.

adjustments were made for use of lipid-lowering medication, antihypertensive medication, and the Healthy Eating Index. Results were also unchanged when women with angina and heart failure at the OPACH baseline were excluded from the analytic sample and when angina and heart failure were excluded from the CVD end point. Discussion In this prospective cohort study of older women, light PA measured by accelerometry was associated with a dose-responsive, independent reduced risk of incident CHD and CVD events. The highest quartile of light PA was associated with a 42% reduced risk of MI or coronary death and a 22% reduced risk of incident CVD events compared with the lowest quartile of light PA. These reduced risks persisted after multivariable adjustment that included physical functioning and other measures of health status, even though some covariates may themselves be altered by PA and thus dilute the associations. The reduced risks of CHD and CVD were also statistically significant after simultaneous adjustment for MVPA. In this study, intensity of PA was classified using a triaxial accelerometer VM count cut point specifically calibrated in a clinic-based study17 for older women. To our knowledge, this is the first study to investigate accelerometer-measured light PA in relation to incident CHD, including nonfatal and fatal events in older women.

y, intensity of PA was classified using a triaxial accelerometer VM count cut point specifically calibrated in a clinic-based study17 for older women. To our knowledge, this is the first study to investigate accelerometer-measured light PA in relation to incident CHD, including nonfatal and fatal events in older women. The majority of active time in older adults is spent in light PA, which contributes about equally to daily PA energy expenditure as MVPA in older people.26 Yet, little is known about the cardiovascular consequences of light PA. Previous studies5 on the dose response between PA and CHD risk have focused on amounts of self-reported MVPA, not on the entire range of PA intensity that could be associated with benefit. A major barrier has been that self-reported questionnaires measuring leisure-time PA do not adequately capture light PA that is acquired throughout the day in activities of daily living. In the OPACH cohort, there was essentially no correlation between light PA measured by the WHI physical activity questionnaire27,28 and by accelerometry (r = 0.03).29 In a recent analysis of National Health and Nutrition Examination Survey data,8 US adults 40 years and older who spent 5 or more hours per day in accelerometer-measured light PA had a 23% lower risk of mortality compared with those who spent less than 3 hours per day in light PA (HR, 0.77; 95% CI, 0.60-1.00). However, other reports found no association of accelerometer-measured light PA with total or CVD mortality.30,31 By contrast, the OPACH women in the highest vs lowest tertiles of low light PA (19-225 VM counts per 15 seconds) had a 36% reduction in risk of CVD mortality (95% CI, 0.41-0.99) after adjustments similar to those of the present study, and women in the highest tertile of high light PA (226-518 VM counts per 15 seconds) had a 70% reduced risk of CVD mortality (95% CI, 0.17-0.51).9 The inconsistencies among these previous results may be due to differences in study populations, length of follow-up, cut points used to classify PA intensity, approaches to adjustment, and statistical power, as well as whether or not early events and deaths were excluded to account for reverse causality. The strong, independent associations of light PA with reduced risks of incident CHD and CVD in the present study add notably to this growing evidence base because both fatal and nonfatal incident CVD events were studied.

, as well as whether or not early events and deaths were excluded to account for reverse causality. The strong, independent associations of light PA with reduced risks of incident CHD and CVD in the present study add notably to this growing evidence base because both fatal and nonfatal incident CVD events were studied. Associations of light PA with incident CHD are biologically plausible. In the OPACH cohort, women who engaged in greater light PA had more favorable baseline levels of HDL-C and low-density lipoprotein cholesterol, triglycerides, glucose, CRP, BMI, and Reynolds Risk Score.20 Adjustment for CVD risk factors attenuated associations between light PA and first CHD or CVD events, supporting the possibility that light PA alters CHD risk partially, but not completely, through its association with these risk factors. Accelerometer-measured light PA has also been associated with lower levels of subclinical atherosclerosis, including carotid femoral pulse wave velocity and carotid intima media thickness in older men.32

bility that light PA alters CHD risk partially, but not completely, through its association with these risk factors. Accelerometer-measured light PA has also been associated with lower levels of subclinical atherosclerosis, including carotid femoral pulse wave velocity and carotid intima media thickness in older men.32 The present findings are consistent with a large body of evidence showing that self-reported MVPA reduces risk of CHD and CVD in the United States and worldwide.5,33 In the OPACH cohort, women in the highest quartile of MVPA had a 46% reduced risk of incident CHD and a 31% reduced risk of CVD events compared with their less active peers in the lowest quartile (Table 2). For light PA, the same comparisons yielded risk reductions of 42% and 22% for CHD and CVD, respectively. The magnitude of these associations for light PA and their consistency across strata of CVD risk, physical functioning, and MVPA suggest that light PA could have much to offer older women in the prevention of CVD whether or not they can or choose to engage in MVPA.

ductions of 42% and 22% for CHD and CVD, respectively. The magnitude of these associations for light PA and their consistency across strata of CVD risk, physical functioning, and MVPA suggest that light PA could have much to offer older women in the prevention of CVD whether or not they can or choose to engage in MVPA. Strengths and Limitations This prospective study had numerous strengths, including the large, diverse cohort of women. Substantial representation of women older than 80 years provides evidence in an understudied but increasingly numerous segment of the US population. Inclusion of fatal and nonfatal physician-adjudicated CHD and CVD end points is a major strength. Use of accelerometers with calibrated age-appropriate cut points for distinguishing light PA from MVPA is a major and unique strength of this study. Resting metabolic rate declines with age,34 and the energy costs of activity increase with age.35,36 The OPACH Calibration Study17 showed that the typically used National Health and Nutrition Examination Survey cut points37 result in underestimation of both MVPA and light PA. We were not able to examine relative intensity, which requires individual calibration with maximal exercise testing. For some women, a MET value of 1.5 to 3.0 could fall within the moderate range of PA intensity. The study had up to 5 years of follow-up and was conducted only among older women. However, our results appear generalizable to men given a recent study38 of 1181 older British men that reported an HR for light PA of 0.74 (95% CI, 0.41-1.34) for incident CVD events, which is remarkably similar to the HR of 0.78 (95% CI, 0.60-1.00) in the present study. Longer-term prospective studies with inclusion of both sexes are needed to increase the strength of the evidence base on light PA in relation to CVD prevention.

an HR for light PA of 0.74 (95% CI, 0.41-1.34) for incident CVD events, which is remarkably similar to the HR of 0.78 (95% CI, 0.60-1.00) in the present study. Longer-term prospective studies with inclusion of both sexes are needed to increase the strength of the evidence base on light PA in relation to CVD prevention. Conclusions In 2016, an estimated 25% of US women 75 years and older met federal PA guidelines for aerobic activity,39 which require 75 minutes of vigorous activity or 150 minutes of moderate activity per day. These guidelines may have discouraged PA when perceived to be unattainable by large segments of the population. The present findings support the newly released 2018 Physical Activity Guidelines Advisory Committee Scientific Report, which states that “[f]or individuals who perform no or little moderate-to-vigorous physical activity, replacing sedentary behavior with light-intensity physical activity reduces the risk of all-cause mortality, cardiovascular disease incidence and mortality”40(pA-4) and suggests that “all movement counts” when it comes to CHD and CVD prevention in older women. Large randomized trials, such as the ongoing Women’s Health Initiative Strong and Healthy Study (WHISH41), are needed to conclusively determine whether pragmatic interventions can increase light PA among older women and whether doing so reduces the occurrence of CVD. Given the low risks of light PA and the abundance of light movements that are part of everyday life, even in the absence of definitive trial data, it may be prudent to encourage older women to increase light PA to improve their CVD health and reduce the occurrence of CVD events.

doing so reduces the occurrence of CVD. Given the low risks of light PA and the abundance of light movements that are part of everyday life, even in the absence of definitive trial data, it may be prudent to encourage older women to increase light PA to improve their CVD health and reduce the occurrence of CVD events. Supplement. eTable 1. Hazard Ratios and 95% CIs From Multivariable Cox PH Models With and Without Multiple Imputation eTable 2. P Values From Restricted Cubic Spline Cox Regression Models and From Linear Cox Regression Models Click here for additional data file.

Introduction Approximately 1.5 million fractures occur annually in women who live in the United States, accounting for $12.7 billion in health care costs.1 Approximately 14% of fractures occur in the hip1; mortality after hip fracture is as high as 20%.2 Fracture has been associated with low bone mineral density (BMD), propensity to fall, and declines in muscle strength, balance, mobility, and physical functioning.3,4,5 The 2008 Physical Activity Guidelines Advisory Committee evaluated quality and quantity of evidence from 21 studies and concluded that people with higher total physical activity (PA) levels have 36% to 68% lower risk of hip fracture.6 The Advisory Committee Report for the 2018 revision of the PA guidelines did not include an explicit update on fracture outcomes but did indicate that evidence supports the conclusion that higher amounts of total PA are associated with lower risk of falls and fall-related injuries, including bone fracture.7 In both the 2008 and 2018 PA guidelines, consensus was lacking regarding fracture risk at sites beside the hip. The majority of published studies assessed PA as a composite measure; thus, the role of PA types and intensities in fracture is unclear. Sedentary behavior (eg, sitting time) is becoming an established modifiable risk factor for major forms of morbidity and mortality, independent of PA habits7; however, its contribution to fracture has not been systematically evaluated.6,7

osite measure; thus, the role of PA types and intensities in fracture is unclear. Sedentary behavior (eg, sitting time) is becoming an established modifiable risk factor for major forms of morbidity and mortality, independent of PA habits7; however, its contribution to fracture has not been systematically evaluated.6,7 The Women’s Health Initiative (WHI) is a prospective cohort study among postmenopausal women with ongoing assessment of fractures. We examined recreational PA, household activities, walking, and sedentary behavior in association with incident fracture and the extent to which age, race/ethnicity, or fall frequency modified this association in older, community-dwelling, ambulatory women. Methods Study Population The WHI observational study design has been published.8 Recruitment of participants was conducted at 40 US clinic centers from October 1993 through December 1998, enrolling 93 676 postmenopausal women aged 50 to 79 years.9 Women with predicted survival of less than 3 years or with conditions that might compromise retention were ineligible. Study protocols were approved by institutional review boards at participating institutions. Written informed consent was obtained from participants. The initial WHI observational study concluded in 2005. Additional follow-up was obtained from women who consented to participate in 2 WHI Extension Studies (2005-2010 and 2010-2015). This study conformed to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

ticipants. The initial WHI observational study concluded in 2005. Additional follow-up was obtained from women who consented to participate in 2 WHI Extension Studies (2005-2010 and 2010-2015). This study conformed to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. Assessment of PA and Sedentary Behavior Participants completed baseline self-administered questionnaires asking frequency (days per week) and duration (minutes) of usual mild, moderate, and strenuous recreational PA.10 Strenuous PA was defined as exercise resulting in sweating and a fast heartbeat, such as aerobics, aerobics dancing, jogging, tennis, or swimming laps; moderate PA, less exhausting activities, such as biking outdoors, using an exercise machine, calisthenics, easy swimming, or popular or folk dancing; and mild PA, slow dancing, bowling, or golf. Walking was assessed separately from these activities with the following questions: “how often do you walk outside the home for more than 10 minutes without stopping,” “when you walk outside the home for more than 10 minutes without stopping, for how many minutes do you usually walk,” and “what is your usual speed.” Nonrecreational activities, including the time (hours per week) spent on heavy indoor household chores (ie, scrubbing floors, sweeping, or vacuuming) and yard work (ie, mowing, raking, gardening, or shoveling snow) were queried using questions specific to these constructs.

usually walk,” and “what is your usual speed.” Nonrecreational activities, including the time (hours per week) spent on heavy indoor household chores (ie, scrubbing floors, sweeping, or vacuuming) and yard work (ie, mowing, raking, gardening, or shoveling snow) were queried using questions specific to these constructs. Physical activity was summarized as energy expenditure, calculated as the product of metabolic equivalent (MET) intensity values for each activity multiplied by the hours per week of reported participation (MET hours per week). Standard MET values were assigned to mild (3.0 METs), moderate (4.5 METs), and strenuous (7.0 METs) activity, 4 walking speeds (ie, slow, <2 mph, 2.0 METs; average or normal, approximately 2-3 mph, 3.0 METs; fairly fast, approximately 3-4 mph, 4.0 METs; and fast, >4 mph, 5.0 METs), heavy chores (3.5 METs), and yard work (4.0 METs).11 Sedentary behavior was assessed by self-reported usual time spent sitting (hours per day and night) including at work, at the table eating, driving or riding in a car or bus, watching television, or talking; and usual time spent lying down (hours per day and night), and resting but not asleep or watching television.12 Self-reported intensity-specific PA, walking, total PA, yard work, chores, and inactivity assessed by WHI questionnaires have demonstrated reproducibility (intraclass correlation coefficients, 0.51-0.77)10,13 and validity (Spearman ρ, 0.45-0.52 with accelerometer).14

and resting but not asleep or watching television.12 Self-reported intensity-specific PA, walking, total PA, yard work, chores, and inactivity assessed by WHI questionnaires have demonstrated reproducibility (intraclass correlation coefficients, 0.51-0.77)10,13 and validity (Spearman ρ, 0.45-0.52 with accelerometer).14 Ascertainment of Incident Fracture Participants were observed from enrollment through September 2015 using annual mailed health questionnaires. As hip fracture was a primary outcome in the WHI, all hip fractures were adjudicated in the main study (1994-2005) and Extension Study I (2005-2010). Trained physicians reviewed radiology reports, with hospital discharge summaries, operative reports, and clinic and progress notes as additional sources. Self-reported fractures at sites other than the hip were not adjudicated. From 2010 to 2015, self-reported fractures at all sites including hip were not adjudicated. Self-reported fracture in WHI has good agreement (a mean of 76%) with criterion medical records.15 In the present study, we evaluated total PA in association with total and site-specific fracture end points. We then conducted additional analysis on specific PA types in association with a reduced set of fracture end points (ie, hip, wrist or forearm, and clinical vertebral), which are designated major osteoporotic fracture events in the WHI.16 This approach was used to reduce the total number of statistical comparisons involving secondary exposures and in subgroups where power may be limited for site-specific end points.

cture end points (ie, hip, wrist or forearm, and clinical vertebral), which are designated major osteoporotic fracture events in the WHI.16 This approach was used to reduce the total number of statistical comparisons involving secondary exposures and in subgroups where power may be limited for site-specific end points. Assessment of Other Variables At enrollment, participants completed questionnaires regarding sociodemographic characteristics including self-reporting of race/ethnicity, family and personal medical history, medication and supplement use, and lifestyle factors. Physical function was assessed using the RAND-36.10 Weight and height were measured using a calibrated clinical scale and stadiometer.

nnaires regarding sociodemographic characteristics including self-reporting of race/ethnicity, family and personal medical history, medication and supplement use, and lifestyle factors. Physical function was assessed using the RAND-36.10 Weight and height were measured using a calibrated clinical scale and stadiometer. Statistical Analysis Participant characteristics were assessed across total PA categories using analysis of variance and χ2 tests. Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% CIs for associations with incident fracture. The primary exposure variable was total PA categorized as none (0 MET h/wk; referent group) and the following tertiles: more than 0 to 7.5 MET h/wk, more than 7.5 to 17.7 MET h/wk, and more than 17.7 MET h/week. Additional activity exposures were defined as follows: (1) yard work and chores, categorized as none and median of active participants; (2) walking, categorized as none and tertiles of more than 0 to 3.5 MET h/wk, more than 3.5 to 7.5 MET h/wk, and more than 7.5 MET h/wk; (3) mild activity, categorized as none, more than 0 to 3.5 MET h/wk, and more than 3.5 MET h/wk; and (4) moderate to vigorous activity, categorized as less than 9 MET h/wk and at least 9 MET h/wk, to be comparable with guideline-recommended levels. Time to first fracture was analyzed, with censoring at death, loss to follow-up, or the end of follow-up on September 30, 2015. Death was not an event in the models. Owing to differences in hip fracture ascertainment between WHI Extension Study II (ie, self-reported) and the rest of the study (ie, adjudicated), models evaluating hip and total fracture outcomes included a time-dependent stratification by hip ascertainment type within the Cox models.

h was not an event in the models. Owing to differences in hip fracture ascertainment between WHI Extension Study II (ie, self-reported) and the rest of the study (ie, adjudicated), models evaluating hip and total fracture outcomes included a time-dependent stratification by hip ascertainment type within the Cox models. Multivariable-adjusted models included the following covariates: age, race/ethnicity, education, smoking status, alcohol use, height, weight, history of fracture after age 55 years, bone drug use, corticosteroid use, calcium intake, vitamin D intake, lifetime hormone therapy use, falls in the past year, physical function, thiazide use, diabetes, age at menopause, and osteoporosis history. Additional models controlled mutually for sedentary time or PA to assess their independent association with fracture risk. The joint association of total PA and sedentary time with risk of total fracture was also examined. Multiplicative interactions for total PA with enrollment age, race/ethnicity, and fall frequency were explored using cross-product terms. Sensitivity analysis was conducted limiting follow-up to the first 10 years. Robustness of results from primary analyses using baseline PA and sedentary exposures were evaluated using time-varying exposure analysis based on updated information collected 3 and 6 years after baseline. P values were for 2-sided hypothesis tests conducted at a statistical significance of .05. Analyses were conducted from June 2017 to August 2019 using SAS version 9.4 (SAS Institute).

ntary exposures were evaluated using time-varying exposure analysis based on updated information collected 3 and 6 years after baseline. P values were for 2-sided hypothesis tests conducted at a statistical significance of .05. Analyses were conducted from June 2017 to August 2019 using SAS version 9.4 (SAS Institute). Results After exclusions (n = 3165) and missing covariates (n = 13 305) (eTable 8 in the Supplement), 77 206 women were included in this study. Baseline characteristics of the cohort appear in Table 1. Participants had a mean (SD) age of 63.4 (7.3) years at enrollment, were mainly white (66 072 [85.6%]), with more than a high school education (61 688 [79.9%]) and a low prevalence of current smoking (4663 [6.0%]), osteoporosis (6485 [8.4%]), and bone drug use (2986 [3.9%]). Slightly less than one-third (24 813 [32.1%]) reported at least 1 fall in the past year. Because of the large cohort size, most baseline characteristics were significantly associated with total PA, although, in some instances, absolute differences were modest (Table 1). Table 1. Baseline Characteristics by Categories of Total Recreational Physical Activity Characteristic No. (%) P Valuea All Participants (N = 77 206) Total Physical Activity, MET h/wk 0 (n = 10 127) >0 to 7.5 (n = 22 904) >7.5 to 17.7 (n = 21 705) >17.7 (n = 22 470) Age, y Mean (SD) 63.4 (7.3) 63.1 (7.5) 63.6 (7.4) 63.5 (7.3) 63.2 (7.2) <.001 No.

stics by Categories of Total Recreational Physical Activity Characteristic No. (%) P Valuea All Participants (N = 77 206) Total Physical Activity, MET h/wk 0 (n = 10 127) >0 to 7.5 (n = 22 904) >7.5 to 17.7 (n = 21 705) >17.7 (n = 22 470) Age, y Mean (SD) 63.4 (7.3) 63.1 (7.5) 63.6 (7.4) 63.5 (7.3) 63.2 (7.2) <.001 No. (%) 50-59 25 309 (32.8) 3531 (34.9) 7376 (32.2) 6949 (32.0) 7453 (33.2) <.001 60-69 34 120 (44.2) 4278 (42.3) 9915 (43.3) 9717 (44.7) 10 210 (45.4) 70-79 17 777 (23.0) 2318 (22.9) 5613 (24.5) 5039 (23.2) 4807 (21.4) Race/ethnicity White 66 072 (85.6) 8141 (8.4) 19 169 (83.6) 18 916 (87.1) 19 846 (88.3) <.001 Black 5214 (6.8) 1068 (1.6) 1810 (7.9) 1271 (5.9) 1065 (4.7) Hispanic 2389 (3.1) 445 (4.4) 830 (3.6) 568 (2.6) 546 (2.4) American Indian 287 (0.4) 42 (0.4) 101 (0.4) 66 (0.3) 78 (0.3) Asian/Pacific Islander 2233 (2.9) 288 (2.8) 683 (3.0) 612 (2.8) 650 (2.9) Unknown 1011 (1.3) 143 (1.4) 311 (1.4) 272 (1.3) 285 (1.3) Education ≤High school 15 518 (20.1) 3048 (3.1) 5497 (24.0) 3798 (17.5) 3191 (14.2) <.001 >High school 28 103 (36.4) 3950 (39.0) 8726 (38.1) 7749 (35.7) 7685 (34.2) ≥College degree 33 585 (43.5) 3129 (3.9) 8681 (37.9) 10 158 (46.8) 11 594 (51.6) Smoking Never 38 897 (50.4) 5063 (5.0) 12 030 (52.5) 10 989 (50.7) 10 815 (48.1) <.001 Past 33 646 (43.6) 4050 (4.0) 9145 (39.9) 9662 (44.5) 10 789 (48.0) Current 4663 (6.0) 1014 (1.0) 1729 (7.6) 1054 (4.9) 866 (3.9) Falls in past year 0 52 393 (67.9) 6784 (67.0) 15377 (67.1) 14 780 (68.1) 15 452 (68.8) <.001 1 15 397 (20.0) 2029 (2.1) 4618 (20.2) 4400 (20.3) 4350 (19.4) 2 6271 (8.1) 814 (8.0) 1952 (8.5) 1751 (8.1) 1754 (7.8) ≥3 3145 (4.1) 500 (4.9) 957 (4.2) 774 (3.6) 914 (4.1) Alcohol use and frequency Never 8046 (10.4) 1458 (14.4) 2793 (12.2) 2176 (10.0) 1619 (7.2) <.001 Past 13 923 (18.0) 2457 (24.2) 4620 (20.2) 3510 (16.2) 3336 (14.8) <1 Drink/mo 9010 (11.7) 1529 (15.1) 2872 (12.5) 2525 (11.6) 2084 (9.3) 1 Drink/mo to <1 drink/wk 15 564 (20.2) 1938 (19.1) 4651 (20.3) 4449 (20.5) 4526 (20.1) 1 Drink/wk to <7 drinks/wk 20 474 (26.5) 1747 (17.3) 5467 (23.9) 6061 (27.9) 7199 (32.1) ≥7 Drinks/wk 10 189 (13.2) 998 (9.8) 2501 (10.9) 2984 (13.7) 3706 (16.5) Weight, kg ≤58.9 15 621 (20.2) 1388 (13.7) 3869 (16.9) 4538 (20.9) 5826 (25.9) <.001 59.0-65.2 15 704 (20.3) 1479 (14.6) 4020 (17.5) 4643 (21.4) 5562 (24.8) 65.3-72.2 15 749 (20.4) 1776 (17.5) 4509 (19.7) 4650 (21.4) 4814 (21.4) 72.3-82.5 15 419 (20.0) 2146 (21.2) 5051 (22.1) 4365 (20.1) 3857 (17.2) >82.5 14 713 (19.1) 3338 (33.0) 5455 (23.8) 3509 (16.2) 2

3.7) 3869 (16.9) 4538 (20.9) 5826 (25.9) <.001 59.0-65.2 15 704 (20.3) 1479 (14.6) 4020 (17.5) 4643 (21.4) 5562 (24.8) 65.3-72.2 15 749 (20.4) 1776 (17.5) 4509 (19.7) 4650 (21.4) 4814 (21.4) 72.3-82.5 15 419 (20.0) 2146 (21.2) 5051 (22.1) 4365 (20.1) 3857 (17.2) >82.5 14 713 (19.1) 3338 (33.0) 5455 (23.8) 3509 (16.2) 2 411 (10.7) Height, cm ≤156.4 14 834 (19.2) 2151 (21.2) 4624 (20.2) 3997 (18.4) 4065 (18.1) <.001 156.5-160.0 15 293 (19.8) 1951 (19.3) 4646 (20.3) 4292 (19.8) 4409 (19.6) 160.1-163.4 15 822 (20.5) 2116 (20.9) 4615 (20.1) 4488 (20.7) 4604 (20.5) 163.5-167.0 15 502 (20.1) 1996 (19.7) 4464 (19.5) 4455 (20.5) 4600 (20.5) >167.0 15 755 (20.4) 1913 (18.9) 4555 (19.9) 4483 (20.7) 4807 (21.4) History of diabetes No 73 191 (94.8) 9337 (92.2) 21 415 (93.5) 20 511 (95.4) 21 728 (96.7) <.001 Yes 4015 (5.2) 790 (7.8) 1489 (6.5) 998 (4.6) 742 (3.3) Age at menopause, mean (SD), y 48.2 (6.4) 47.5 (6.7) 47.9 (6.5) 48.4 (6.2) 48.7 (6.1) <.001 History of fracture after age 55 y No 68 455 (88.7) 9009 (89.0) 20 281 (88.5) 19 250 (88.7) 19 915 (88.6) .72 Yes 8751 (11.3) 1118 (11.0) 2623 (11.5) 2455 (11.3) 2555 (11.4) History of any fracture No 48 322 (62.6) 6447 (63.6) 14 423 (63.0) 13 603 (62.7) 13 849 (61.6) .001 Yes 28 884 (37.4) 3680 (36.4) 8481 (37.0) 8102 (37.3) 8621 (38.4) History of osteoporosis No 70 721 (91.6) 9236 (91.2) 20 934 (91.4) 19 860 (91.5) 20 650 (91.9) .10 Yes 6485 (8.4) 891 (8.8) 1970 (8.6) 1845 (8.5) 1820 (8.1) Lifetime hormone therapy use, y 0 28 980 (37.5) 4134 (40.8) 9164 (40.0) 7925 (36.5) 7757 (34.5) <.001 0.1-5.0 19 518 (25.3) 2574 (25.4) 5603 (24.5) 5476 (25.3) 5865 (26.1) 5.1-10.0 11 387 (14.7) 1307 (12.9) 3223 (14.1) 3280 (15.1) 3577 (15.9) >10.0 17 321 (22.4) 2112 (2.9) 4914 (21.4) 5024 (23.1) 5271 (23.5) Bone drug use No 74 220 (96.1) 9801 (96.8) 22 087 (96.4) 20 821 (95.9) 21 511 (95.7) <.001 Yes 2986 (3.9) 326 (3.2) 817 (3.6) 884 (4.1) 959 (4.3) Corticosteroid use No 76 194 (98.7) 9907 (97.8) 22 533 (98.4) 21 451 (98.8) 22 303 (99.3) <.001 Yes 1012 (1.3) 220 (2.2) 371 (1.6) 254 (1.2) 167 (0.7) Thiazide use No 73 114 (94.7) 9469 (93.5) 21 553 (94.1) 20 598 (94.9) 21 526 (95.8) <.001 Yes 4092 (5.3) 658 (6.5) 1351 (5.9) 1107 (5.1) 944 (4.2) Calcium intake, mg ≤618.3 14 815 (19.2) 2872 (28.3) 5165 (22.5) 3619 (16.7) 3178 (14.1) <.001 618.4-930.7 15 330 (19.9) 2241 (22.1) 4962 (21.6) 4269 (19.6) 3878 (17.2) 930.8-1276.9 15 604 (20.2) 1997 (19.7) 4624 (20.2) 4512 (20.8) 4486 (20.0) 1277.0-1751.5 15 630 (20.2) 1604 (15.8) 4286 (18.7) 4636 (21.3) 5112 (22.

.2) Calcium intake, mg ≤618.3 14 815 (19.2) 2872 (28.3) 5165 (22.5) 3619 (16.7) 3178 (14.1) <.001 618.4-930.7 15 330 (19.9) 2241 (22.1) 4962 (21.6) 4269 (19.6) 3878 (17.2) 930.8-1276.9 15 604 (20.2) 1997 (19.7) 4624 (20.2) 4512 (20.8) 4486 (20.0) 1277.0-1751.5 15 630 (20.2) 1604 (15.8) 4286 (18.7) 4636 (21.3) 5112 (22. 7) >1751.5 15 827 (20.5) 1429 (14.1) 3896 (17.0) 4696 (21.6) 5831 (25.9) Vitamin D intake, IU ≤121.0 15 014 (19.4) 2571 (25.4) 4903 (21.4) 3765 (17.3) 3775 (16.8) <.001 121.1-234.3 15 307 (19.8) 2233 (22.1) 4848 (21.2) 4206 (19.4) 4020 (17.9) 234.4-471.0 15 410 (20.0) 1933 (19.1) 4518 (19.7) 4419 (20.4) 4540 (20.2) 471.1-609.4 15 634 (20.2) 1757 (17.4) 4473 (19.5) 4582 (21.1) 4822 (21.5) >609.4 15 841 (20.5) 1633 (16.1) 4162 (18.2) 4733 (21.8) 5313 (23.6) Physical function score >90 No 47 069 (61.0) 7785 (76.9) 16 237 (70.9) 12 994 (59.9) 10 053 (44.7) <.001 Yes 30 137 (39.0) 2342 (23.1) 6667 (29.1) 8711 (40.1) 12 417 (55.3) Abbreviation: MET, metabolic equivalent. a Based on t tests for continuous variables and χ2 tests for categorical variables.

7) >1751.5 15 827 (20.5) 1429 (14.1) 3896 (17.0) 4696 (21.6) 5831 (25.9) Vitamin D intake, IU ≤121.0 15 014 (19.4) 2571 (25.4) 4903 (21.4) 3765 (17.3) 3775 (16.8) <.001 121.1-234.3 15 307 (19.8) 2233 (22.1) 4848 (21.2) 4206 (19.4) 4020 (17.9) 234.4-471.0 15 410 (20.0) 1933 (19.1) 4518 (19.7) 4419 (20.4) 4540 (20.2) 471.1-609.4 15 634 (20.2) 1757 (17.4) 4473 (19.5) 4582 (21.1) 4822 (21.5) >609.4 15 841 (20.5) 1633 (16.1) 4162 (18.2) 4733 (21.8) 5313 (23.6) Physical function score >90 No 47 069 (61.0) 7785 (76.9) 16 237 (70.9) 12 994 (59.9) 10 053 (44.7) <.001 Yes 30 137 (39.0) 2342 (23.1) 6667 (29.1) 8711 (40.1) 12 417 (55.3) Abbreviation: MET, metabolic equivalent. a Based on t tests for continuous variables and χ2 tests for categorical variables. During a mean (SD) follow-up period of 14.0 (5.2) years, 25 516 women (33.1%) reported experiencing at least 1 fracture. Table 2 presents site-specific fracture event rates and HRs according to categories of total PA. Compared with inactive women (ie, 0 MET h/wk) and adjusted for covariates and sedentary time, the HRs for total fracture were 0.94 (95% CI, 0.90-0.98) for women with more than 0 to 7.5 MET h/wk, 0.95 (95% CI, 0.91-0.99) for women with more than 7.5 to 17.7 MET h/wk, and 0.94 (95% CI, 0.90-0.98) for women with more than 17.7 MET h/wk (P for trend = .16). Women in the highest total PA tertile had an 18% lower risk of hip fracture (HR, 0.82; 95% CI, 0.72-0.95; P for trend < .001). Knee fracture was positively associated with total PA (highest tertile vs inactive: HR, 1.26; 95% CI, 1.05-1.50; P for trend = .08). Elbow fracture was positively associated with PA (highest tertile vs inactive: HR, 1.11; 95% CI, 0.91-1.35; P for trend = .02). Sensitivity analysis limiting follow-up time to the first 10 years showed a stronger inverse association for total PA with hip fracture (highest tertile vs inactive: HR, 0.62; 95% CI, 0.51-0.77; P for trend < .001) and an inverse association with total fracture risk (HR, 0.93; 95% CI, 0.88-0.99; P for trend = .40); however, the associations with knee (HR, 1.09; 95% CI, 0.85-1.40; P for trend = .78) and elbow fracture (HR, 1.12; 95% CI, 0.86-1.47; P for trend = .05) were no longer evident (eTable 1 in the Supplement).

rend < .001) and an inverse association with total fracture risk (HR, 0.93; 95% CI, 0.88-0.99; P for trend = .40); however, the associations with knee (HR, 1.09; 95% CI, 0.85-1.40; P for trend = .78) and elbow fracture (HR, 1.12; 95% CI, 0.86-1.47; P for trend = .05) were no longer evident (eTable 1 in the Supplement). Table 2. Associations of Total Recreational Physical Activity With Total and Site-Specific Fractures Model Adjusted HR (95% CI) P Valuea 0 MET h/wk (n = 10 127) >0 to 7.5 MET h/wk (n = 22 904) >7.5 to 17.7 MET h/wk (n = 21 705) >17.7 MET h/wk (n = 22 470) MET h/wk, median (range) 0 (0-0) 3.8 (0.5-7.5) 12.5 (7.6-17.7) 27.3 (17.8-142.3) NA Total Fracture: 25 355 Events Events, No. (annualized %) 3164 (2.86) 7278 (2.79) 7315 (2.82) 7598 (2.75) NA Age 1 [Reference] 0.94 (0.91-0.98) 0.94 (0.90-0.98) 0.92 (0.88-0.96) <.001 Multivariableb 1 [Reference] 0.94 (0.90-0.98) 0.95 (0.91-0.99) 0.94 (0.90-0.98) .16 Hip Fracture: 2673 Events Events, No. (annualized %) 320 (0.24) 847 (0.28) 784 (0.26) 722 (0.22) NA Age 1 [Reference] 1.03 (0.90-1.17) 0.93 (0.82-1.06) 0.81 (0.71-0.93) <.001 Multivariableb 1 [Reference] 1.01 (0.89-1.15) 0.92 (0.80-1.05) 0.82 (0.72-0.95) <.001 Wrist or Forearm Fracture: 5473 Events Events, No. (annualized %) 643 (0.50) 1481 (0.49) 1595 (0.53) 1754 (0.55) NA Age 1 [Reference] 0.96 (0.87-1.05) 1.03 (0.94-1.13) 1.07 (0.97-1.17) .005 Multivariableb 1 [Reference] 0.93 (0.85-1.02) 0.98 (0.89-1.08) 1.00 (0.92-1.11) .11 Clinical Vertebral Fracture: 4056 Events Events, No. (annualized %) 504 (0.39) 1201 (0.39) 1165 (0.38) 1186 (0.37) NA Age 1 [Reference] 0.95 (0.86-1.06) 0.90 (0.81-1.00) 0.86 (0.78-0.96) .002 Multivariableb 1 [Reference] 0.96 (0.86-1.07) 0.92 (0.83-1.02) 0.91 (0.82-1.02) .10 Elbow Fracture: 1207 Events Events, No. (annualized %) 146 (0.11) 311 (0.10) 337 (0.11) 413 (0.13) NA Age 1 [Reference] 0.89 (0.73-1.09) 0.96 (0.79-1.16) 1.10 (0.91-1.32) .02 Multivariableb 1 [Reference] 0.90 (0.74-1.09) 0.96 (0.79-1.18) 1.11 (0.91-1.35) .02 Foot Fracture: 3859 Events Events, No. (annualized %) 503 (0.39) 1094 (0.36) 1104 (0.37) 1158 (0.36) NA Age 1 [Reference] 0.93 (0.83-1.03) 0.93 (0.84-1.03) 0.91 (0.82-1.01) .18 Multivariableb 1 [Reference] 0.93 (0.84-1.03) 0.94 (0.84-1.05) 0.94 (0.84-1.05) .66 Hand Fracture: 947 Events Events, No.

1.11 (0.91-1.35) .02 Foot Fracture: 3859 Events Events, No. (annualized %) 503 (0.39) 1094 (0.36) 1104 (0.37) 1158 (0.36) NA Age 1 [Reference] 0.93 (0.83-1.03) 0.93 (0.84-1.03) 0.91 (0.82-1.01) .18 Multivariableb 1 [Reference] 0.93 (0.84-1.03) 0.94 (0.84-1.05) 0.94 (0.84-1.05) .66 Hand Fracture: 947 Events Events, No. (annualized %) 111 (0.08) 295 (0.10) 246 (0.08) 295 (0.09) NA Age 1 [Reference] 1.10 (0.89-1.37) 0.90 (0.72-1.13) 1.01 (0.81-1.25) .51 Multivariableb 1 [Reference] 1.11 (0.89-1.39) 0.93 (0.74-1.17) 1.06 (0.84-1.34) .97 Knee Fracture: 1664 Events Events, No. (annualized %) 179 (0.14) 506 (0.16) 452 (0.15) 527 (0.16) NA Age 1 [Reference] 1.17 (0.99-1.39) 1.02 (0.86-1.22) 1.12 (0.94-1.32) .83 Multivariableb 1 [Reference] 1.20 (1.01-1.43) 1.11 (0.93-1.32) 1.26 (1.05-1.50) .08 Lower Leg Fracture: 4140 Events Events, No. (annualized %) 559 (0.44) 1252 (0.42) 1134 (0.38) 1195 (0.37) NA Age 1 [Reference] 0.95 (0.86-1.05) 0.86 (0.77-0.95) 0.84 (0.76-0.93) <.001 Multivariableb 1 [Reference] 0.97 (0.88-1.07) 0.90 (0.81-1.00) 0.92 (0.82-1.02) .10 Pelvis Fracture: 1664 Events Events, No. (annualized %) 182 (0.14) 449 (0.15) 501 (0.16) 532 (0.16) NA Age 1 [Reference] 0.97 (0.82-1.16) 1.06 (0.90-1.26) 1.06 (0.90-1.26) .19 Multivariableb 1 [Reference] 0.90 (0.76-1.08) 0.93 (0.78-1.11) 0.91 (0.76-1.09) .66 Tailbone Fracture: 546 Events Events, No. (annualized %) 74 (0.06) 166 (0.05) 162 (0.05) 144 (0.04) NA Age 1 [Reference] 0.90 (0.69-1.19) 0.86 (0.65-1.13) 0.71 (0.54-0.94) .009 Multivariableb 1 [Reference] 0.96 (0.73-1.27) 0.97 (0.73-1.29) 0.87 (0.65-1.18) .03 Upper Arm Fracture: 2964 Events Events, No. (annualized %) 373 (0.29) 877 (0.29) 823 (0.27) 891 (0.27) NA Age 1 [Reference] 0.95 (0.84-1.08) 0.88 (0.78-0.99) 0.89 (0.79-1.01) .06 Multivariableb 1 [Reference] 1.00 (0.88-1.13) 0.97 (0.86-1.10) 1.03 (0.91-1.18) .45 Upper Leg Fracture: 1147 Events Events, No. (annualized %) 136 (0.10) 354 (0.11) 321 (0.10) 336 (0.10) NA Age 1 [Reference] 1.02 (0.84-1.25) 0.90 (0.73-1.09) 0.88 (0.72-1.07) .05 Multivariableb 1 [Reference] 1.01 (0.83-1.23) 0.88 (0.72-1.08) 0.88 (0.71-1.08) .08 Other Fracture: 8288 Events Events, No. (annualized %) 1019 (0.80) 2304 (0.77) 2428 (0.82) 2537 (0.80) NA Age 1 [Reference] 0.93 (0.86-1.00) 0.95 (0.88-1.02) 0.92 (0.86-0.99) .19 Multivariableb 1 [Reference] 0.92 (0.86-1.00) 0.96 (0.89-1.03) 0.95 (0.88-1.02) .88 Abbreviations: HR, hazard ratio; MET, metabolic equivalent; NA, not applicable.

vents Events, No. (annualized %) 1019 (0.80) 2304 (0.77) 2428 (0.82) 2537 (0.80) NA Age 1 [Reference] 0.93 (0.86-1.00) 0.95 (0.88-1.02) 0.92 (0.86-0.99) .19 Multivariableb 1 [Reference] 0.92 (0.86-1.00) 0.96 (0.89-1.03) 0.95 (0.88-1.02) .88 Abbreviations: HR, hazard ratio; MET, metabolic equivalent; NA, not applicable. a Derived from a separate survival model with the outcome of interest as a function of linear trend across group medians. b Adjusted for age, race/ethnicity, education, smoking status, alcohol use, height, weight, history of fracture after age 55 years, bone drug use, corticosteroid use, calcium intake, vitamin D intake, lifetime hormone therapy use (years), falls in the past year, physical function construct, thiazide use, diabetes, age at menopause, history of osteoporosis, and sedentary time. Associations between walking amount and a subset of major osteoporotic fractures appear in eTable 2 in the Supplement. Adjusting for covariates, other types of PA, and sedentary time, hip fracture was inversely associated with walking categories (>0 to 3.5 MET h/wk: HR, 0.99; 95% CI, 0.89-1.11; >3.5 to 7.5 MET h/wk: HR, 0.92; 95% CI, 0.83-1.02; >7.5 MET h/wk: HR, 0.88; 95% CI, 0.78-0.98; P for trend = .01). There was no association between walking amount and clinical vertebral fracture. Walking was not clearly associated with total or wrist and forearm fracture.

0 to 3.5 MET h/wk: HR, 0.99; 95% CI, 0.89-1.11; >3.5 to 7.5 MET h/wk: HR, 0.92; 95% CI, 0.83-1.02; >7.5 MET h/wk: HR, 0.88; 95% CI, 0.78-0.98; P for trend = .01). There was no association between walking amount and clinical vertebral fracture. Walking was not clearly associated with total or wrist and forearm fracture. Risk of fracture according to PA intensity appears in eTable 3 and eTable 4 in the Supplement. Following adjustment for covariates, moderate, strenuous, and walking activities and sedentary time, inverse associations were observed for mild activity with hip fracture (HR, 0.82; 95% CI, 0.73-0.93; P for trend = .003), clinical vertebral fracture (HR, 0.87; 95% CI, 0.78-0.96; P for trend = .006), and total fracture (HR, 0.91; 95% CI, 0.87-0.94; P for trend < .001) among women with more than 3.5 MET h/wk compared with women with no mild PA (eTable 3 in the Supplement). When combining moderate and strenuous activity and walking 2 mph or faster into moderate to vigorous PA (MVPA), women with MVPA comparable with guideline recommendations (ie, ≥9 MET h/wk) had lower hip fracture risk (HR, 0.88; 95% CI, 0.81-0.96; P for trend = .002) but higher risk of wrist or forearm fracture (HR, 1.09; 95% CI: 1.03-1.15; P for trend = .004) than women with less than 9 MET h/wk MVPA (eTable 4 in the Supplement). Moderate to vigorous physical activity was not associated with risks of total fracture or clinical vertebral fracture.

CI, 0.81-0.96; P for trend = .002) but higher risk of wrist or forearm fracture (HR, 1.09; 95% CI: 1.03-1.15; P for trend = .004) than women with less than 9 MET h/wk MVPA (eTable 4 in the Supplement). Moderate to vigorous physical activity was not associated with risks of total fracture or clinical vertebral fracture. We next examined nonrecreational activity, including yard work and heavy household chores. In models adjusted for total recreational PA and sedentary time, more than 6 MET h/wk of yard work was associated with lower risk of total fracture (HR, 0.95; 95% CI, 0.92-0.98; P for trend = .002) and hip fracture (HR, 0.90; 95% CI, 0.82-0.99; P for trend = .04) compared with no yard work (Table 3). Yard work was not associated with risks of clinical vertebral or wrist and forearm fractures. Energy expenditure from heavy chores was not associated with total or site-specific fractures (eTable 5 in the Supplement).

nd hip fracture (HR, 0.90; 95% CI, 0.82-0.99; P for trend = .04) compared with no yard work (Table 3). Yard work was not associated with risks of clinical vertebral or wrist and forearm fractures. Energy expenditure from heavy chores was not associated with total or site-specific fractures (eTable 5 in the Supplement). Table 3. Associations of Yard Work With Hip, Wrist or Forearm, Clinical Vertebral, and Total Fractures Model Adjusted HR (95% CI) P Valuea 0 MET h/wk (n = 38 538) >0 to 6 MET h/wk (n = 21 301) >6 MET h/wk (n = 17 367) MET h/wk, median (range) 0 (0-0) 3.3 (1.3-5.3) 13.3 (7.3-44.0) NA Total Fracture: 25 355 Events Events, No. (annualized %) 12 436 (2.83) 7037 (2.74) 5882 (2.79) NA Age 1 [Reference] 0.97 (0.94-1.00) 0.96 (0.93-0.99) .03 Multivariableb 1 [Reference] 0.97 (0.94-1.00) 0.95 (0.92-0.98) .002 Hip Fracture: 2673 Events Events, No. (annualized %) 1348 (0.26) 720 (0.24) 605 (0.24) NA Age 1 [Reference] 0.96 (0.88-1.05) 0.91 (0.82-1.00) .05 Multivariableb 1 [Reference] 0.96 (0.87-1.05) 0.90 (0.82-0.99) .04 Wrist or Forearm Fracture: 5473 Events Events, No. (annualized %) 2582 (0.51) 1546 (0.52) 1345 (0.55) NA Age 1 [Reference] 1.04 (0.97-1.10) 1.08 (1.01-1.15) .03 Multivariableb 1 [Reference] 1.02 (0.96-1.08) 1.03 (0.96-1.10) .47 Clinical Vertebral Fracture: 4056 Events Events, No. (annualized %) 2020 (0.39) 1074 (0.36) 962 (0.39) NA Age 1 [Reference] 0.92 (0.85-0.99) 0.96 (0.89-1.04) .38 Multivariableb 1 [Reference] 0.94 (0.87-1.01) 0.97 (0.89-1.05) .49 Abbreviations: HR, hazard ratio; MET, metabolic equivalent; NA, not applicable.

6-1.10) .47 Clinical Vertebral Fracture: 4056 Events Events, No. (annualized %) 2020 (0.39) 1074 (0.36) 962 (0.39) NA Age 1 [Reference] 0.92 (0.85-0.99) 0.96 (0.89-1.04) .38 Multivariableb 1 [Reference] 0.94 (0.87-1.01) 0.97 (0.89-1.05) .49 Abbreviations: HR, hazard ratio; MET, metabolic equivalent; NA, not applicable. a Derived from a separate survival model with the outcome of interest as a function of linear trend across group medians. b Adjusted for age, race/ethnicity, education, smoking status, alcohol use, height, weight, history of fracture after age 55 years, bone drug use, corticosteroid use, calcium intake, vitamin D intake, lifetime hormone therapy use (years), falls in the past year, physical function construct, thiazide use, diabetes, age at menopause, history of osteoporosis, recreational physical activity, and sedentary time.

, history of fracture after age 55 years, bone drug use, corticosteroid use, calcium intake, vitamin D intake, lifetime hormone therapy use (years), falls in the past year, physical function construct, thiazide use, diabetes, age at menopause, history of osteoporosis, recreational physical activity, and sedentary time. Table 4 presents associations between sedentary behavior and fracture risks. In age-adjusted models, 9.5 hours of daily sitting or lying down was associated with higher risks of hip fracture (HR, 1.11; 95% CI, 1.01-1.21; P for trend = .03), clinical vertebral fracture (HR, 1.09; 95% CI, 1.01-1.17; P for trend = .02), wrist or forearm fracture (HR, 1.07; 95% CI, 1.01-1.14; P for trend = .11), and total fracture (HR, 1.10; 95% CI, 1.07-1.13; P for trend < .001). Associations were attenuated and not statistically significant in the multivariable-adjusted models, with the exception of total fracture risk, which was attenuated but remained statistically significant, including when further adjusted for total PA (HR, 1.04; 95% CI, 1.01-1.07; P for trend = .01).

P for trend < .001). Associations were attenuated and not statistically significant in the multivariable-adjusted models, with the exception of total fracture risk, which was attenuated but remained statistically significant, including when further adjusted for total PA (HR, 1.04; 95% CI, 1.01-1.07; P for trend = .01). Table 4. Associations of Sedentary Behavior With Hip, Wrist or Forearm, Clinical Vertebral, and Total Fractures Model Adjusted HR (95% CI) P Valuea <6.5 h/d (n = 25 506) ≥6.5 to 9.5 h/d (n = 22 523) >9.5 h/d (n = 29 177) Sedentary h/d, median (range) 5.0 (0-6.5) 8.0 (7.0-9.5) 12.0 (10.0-24.0) NA Total Fracture: 25 355 Events Events, No. (annualized %) 8002 (2.71) 7478 (2.82) 9875 (2.85) NA Age 1 [Reference] 1.04 (1.00-1.07) 1.10 (1.07-1.13) <.001 Multivariableb 1 [Reference] 1.00 (0.97-1.03) 1.04 (1.01-1.07) .01 Hip Fracture: 2673 Events Events, No. (annualized %) 866 (0.25) 828 (0.26) 979 (0.24) NA Age 1 [Reference] 1.04 (0.95-1.14) 1.11 (1.01-1.21) .03 Multivariableb 1 [Reference] 0.98 (0.89-1.08) 1.02 (0.93-1.12) .57 Wrist or Forearm Fracture: 5473 Events Events, No. (annualized %) 1753 (0.52) 1575 (0.52) 2145 (0.53) NA Age 1 [Reference] 0.99 (0.92-1.06) 1.07 (1.01-1.14) .02 Multivariableb 1 [Reference] 0.97 (0.90-1.03) 1.05 (0.98-1.12) .11 Clinical Vertebral Fracture: 4056 Events Events, No. (annualized %) 1297 (0.38) 1214 (0.39) 1545 (0.38) NA Age 1 [Reference] 1.02 (0.95-1.11) 1.09 (1.01-1.17) .02 Multivariableb 1 [Reference] 0.98 (0.90-1.06) 1.02 (0.94-1.10) .60 Abbreviations: HR, hazard ratio; NA, not applicable.

0.97 (0.90-1.03) 1.05 (0.98-1.12) .11 Clinical Vertebral Fracture: 4056 Events Events, No. (annualized %) 1297 (0.38) 1214 (0.39) 1545 (0.38) NA Age 1 [Reference] 1.02 (0.95-1.11) 1.09 (1.01-1.17) .02 Multivariableb 1 [Reference] 0.98 (0.90-1.06) 1.02 (0.94-1.10) .60 Abbreviations: HR, hazard ratio; NA, not applicable. a Derived from a separate survival model with the outcome of interest as a function of linear trend across group medians. b Adjusted for age, race/ethnicity, education, smoking status, alcohol use, height, weight, history of fracture after age 55 years, bone drug use, corticosteroid use, calcium intake, vitamin D intake, lifetime hormone therapy use (years), falls in the past year, physical function construct, thiazide use, diabetes, age at menopause, history of osteoporosis, and total recreational physical activity. We next evaluated risk of total fracture according to jointly classified total PA and sedentary time exposures (Figure). Fracture was inversely associated with total PA, regardless of time spent sedentary.

b Adjusted for age, race/ethnicity, education, smoking status, alcohol use, height, weight, history of fracture after age 55 years, bone drug use, corticosteroid use, calcium intake, vitamin D intake, lifetime hormone therapy use (years), falls in the past year, physical function construct, thiazide use, diabetes, age at menopause, history of osteoporosis, and total recreational physical activity. We next evaluated risk of total fracture according to jointly classified total PA and sedentary time exposures (Figure). Fracture was inversely associated with total PA, regardless of time spent sedentary. Figure. Risk of Total Fracture According to Jointly Classified Sedentary Behavior and Total Physical Activity Exposures Models are adjusted for age, race/ethnicity, education, smoking status, alcohol use, height, weight, history of fracture after age 55 years, bone drug use, corticosteroid use, calcium intake, vitamin D intake, lifetime hormone therapy use, falls in the past year, physical function construct, thiazide use, diabetes, age at menopause, and history of osteoporosis. The reference category was sedentary for more than 9.5 h/d and 0 metabolic equivalent (MET) h/wk physical activity. Whiskers represent 95% CIs. We examined whether the associations of total PA with hip, wrist or forearm, clinical vertebral, and total fracture differed after stratifying on categories of age, race/ethnicity, and fall frequency history at baseline. There were no significant interactions observed (data not shown).

Figure. Risk of Total Fracture According to Jointly Classified Sedentary Behavior and Total Physical Activity Exposures Models are adjusted for age, race/ethnicity, education, smoking status, alcohol use, height, weight, history of fracture after age 55 years, bone drug use, corticosteroid use, calcium intake, vitamin D intake, lifetime hormone therapy use, falls in the past year, physical function construct, thiazide use, diabetes, age at menopause, and history of osteoporosis. The reference category was sedentary for more than 9.5 h/d and 0 metabolic equivalent (MET) h/wk physical activity. Whiskers represent 95% CIs. We examined whether the associations of total PA with hip, wrist or forearm, clinical vertebral, and total fracture differed after stratifying on categories of age, race/ethnicity, and fall frequency history at baseline. There were no significant interactions observed (data not shown). Finally, to evaluate the robustness of results from our primary analysis that used baseline PA and sedentary exposures, we repeated the analysis using time-varying exposures based on updated information collected after baseline (eTable 6 and eTable 7 in the Supplement). Adjusting for covariates and sedentary time, total PA was significantly inversely associated with total fracture (>0 to 7.5 MET hr/wk: HR, 0.97; 95% CI, 0.93-1.005; >7.5 to 17.7 MET h/wk: HR, 0.96; 95% CI, 0.92-1.003; >17.7 MET h/wk: HR, 0.94; 95% CI, 0.90-0.98; P for trend = .007), hip fracture (>0 to 7.5 MET hr/wk: HR, 0.95; 95% CI, 0.84-1.06; >7.5 to 17.7 MET h/wk: HR, 0.80; 95% CI, 0.71-0.90; >17.7 MET h/wk: HR, 0.69; 95% CI, 0.61-0.79; P for trend < .001), clinical vertebral fracture (>0 to 7.5 MET hr/wk: HR, 0.97; 95% CI, 0.88-1.07; >7.5 to 17.7 MET h/wk: HR, 0.99; 95% CI, 0.89-1.09; >17.7 MET h/wk: HR, 0.88; 95% CI, 0.80-0.98; P for trend = .01), and upper leg fracture (>0 to 7.5 MET hr/wk: HR, 0.73; 95% CI, 0.61-0.88; >7.5 to 17.7 MET h/wk: HR, 0.85; 95% CI, 0.71-1.02; >17.7 MET h/wk: HR, 0.71; 95% CI, 0.59-0.86; P for trend = .03). Total PA was positively associated with wrist or forearm fracture (>0 to 7.5 MET hr/wk: HR, 0.97; 95% CI, 0.88-1.06; >7.5 to 17.7 MET h/wk: HR, 1.05; 95% CI, 0.96-1.15; >17.7 MET h/wk: HR, 1.09; 95% CI, 0.99-1.19; P for trend = .003) (eTable 6 in the Supplement). Controlling for covariates and total PA, time-varying sedentary time was positively associated only with total fracture (≥6.5 to 9.5 h/d: HR, 1.00; 95% CI, 0.97-1.03; >9.5 h/d: HR, 1.06; 95% CI, 1.03-1.10; P for trend < .001) (eTable 7 in the Supplement).

CI, 0.99-1.19; P for trend = .003) (eTable 6 in the Supplement). Controlling for covariates and total PA, time-varying sedentary time was positively associated only with total fracture (≥6.5 to 9.5 h/d: HR, 1.00; 95% CI, 0.97-1.03; >9.5 h/d: HR, 1.06; 95% CI, 1.03-1.10; P for trend < .001) (eTable 7 in the Supplement). Discussion This large cohort study among older, community-dwelling, ambulatory women found that recreational and nonrecreational PA was inversely associated with risks of hip, clinical vertebral, and total fractures. Total PA was positively associated with knee and elbow fracture. Mild-intensity PA was associated with lower risks of hip, vertebral, and total fracture, and MVPA was associated with lower risk of hip fracture but higher risk of wrist or forearm fracture. Yard work was inversely associated with hip and total fractures. Results of time-varying exposure analysis were materially the same for sedentary time but were somewhat stronger for PA compared with those for baseline exposures. Taken with results of sensitivity analyses showing somewhat stronger results for PA when restricting follow-up to the initial 10 years, it could be that PA in the recent term is most relevant to fracture. To our knowledge, this is the most comprehensive evaluation of PA and fracture incidence in older women.

exposures. Taken with results of sensitivity analyses showing somewhat stronger results for PA when restricting follow-up to the initial 10 years, it could be that PA in the recent term is most relevant to fracture. To our knowledge, this is the most comprehensive evaluation of PA and fracture incidence in older women. Of the more than 50 available studies on PA and fracture, relatively few are prospective studies in older women that have assessed various PA types and fracture risks at multiple body sites.17,18,19,20,21,22,23,24,25,26,27 Our study supports the consistent evidence of an inverse association of PA with hip fracture. We extended understanding by demonstrating associations of PA with fractures at other sites previously studied infrequently or not at all. Vertebral fracture has poor long-term morbidity and mortality28; thus, its prevention is imperative. Moderate to vigorous PA has been inversely associated with radiographic vertebral fractures in older women.20 In a 2-year randomized exercise trial,29 incidence of vertebral fracture was higher in the control group than in the intervention group. Conversely, 2 other studies30,31 did not find an association between PA and vertebral fracture. Our study suggests that risk of vertebral fracture was not increased with greater amount or intensity of PA and that mild intensity PA is associated with reduced risk of clinical vertebral fractures in later life. Additional research is needed on a potential role for PA, even at lighter intensities, in the prevention of this fracture type that carries poor prognosis in older adults.

sed with greater amount or intensity of PA and that mild intensity PA is associated with reduced risk of clinical vertebral fractures in later life. Additional research is needed on a potential role for PA, even at lighter intensities, in the prevention of this fracture type that carries poor prognosis in older adults. The inverse association of PA with hip and clinical vertebral fractures is biologically plausible. Physical activity could attenuate the age-related reduction in spine and hip BMD.6,32,33,34 Regular PA can help improve balance, range of motion, and muscle strength,35 thereby reducing falls,36 a major risk factor for fracture.37 Because hip and vertebral fractures occur frequently in older women, even a modest protective association with PA could account for a meaningful number of averted fracture cases and related complications within the population. While ambulatory PA can mitigate age-related hip and spine bone loss, it may exert less stress on the wrist and forearm and minimally influence BMD at this site. The greater demands of MVPA might be assumed to increase the risk of falling. However, recent results in a WHI substudy38 on women aged 63 to 99 years whose PA was directly measured using accelerometers showed that fall rates were elevated in women engaging in low but not moderate or higher levels of MVPA. Women capable of doing MVPA may be more functional and more likely to break a fall with outstretched hands, which could account for the higher prevalence of wrist and forearm fractures associated with MVPA in the present study.

fall rates were elevated in women engaging in low but not moderate or higher levels of MVPA. Women capable of doing MVPA may be more functional and more likely to break a fall with outstretched hands, which could account for the higher prevalence of wrist and forearm fractures associated with MVPA in the present study. The 2018 revised PA guidelines39 recommend regular participation in MVPA to maintain and promote health in adults. It has been suggested that even mild (ie, light) activity could be beneficial for older adults.40 Our study found that mild PA and walking were associated with lower risk of hip fracture in older women. This is an important and relatively novel finding. To date, there has been insufficient evidence available to support recommending lighter intensity activities as part of public health guidelines.6,7,40 If other studies confirm our results showing that light-intensity activity is associated with fracture benefit, there could be basis for a future guideline recommendation. Mild PA and walking account for the majority of daily activity time in WHI participants.41 Lower-intensity activities are more easily adopted by older individuals and should be recommended when such activity is not contraindicated.

iated with fracture benefit, there could be basis for a future guideline recommendation. Mild PA and walking account for the majority of daily activity time in WHI participants.41 Lower-intensity activities are more easily adopted by older individuals and should be recommended when such activity is not contraindicated. A positive association between sedentary behavior and total fracture risk was observed in the present study, even after controlling for fracture risk factors and total recreational PA. Women reporting more than 9.5 h/d of sedentary time experienced a 4% higher risk of all fractures combined compared with women with the least amount of sedentary time. When jointly classified with total PA, fracture risk associated with sedentary behavior was no longer present. To our knowledge, few studies have been published on the association of sedentary time with fracture in adults.42,43,44,45 Results have been mixed in men. A 2014 study42 reported a 38% lower multivariable (including total PA) adjusted relative risk of hip fracture; another45 reported a more than 2-fold higher risk of hip fracture and a 68% higher risk of total fracture when comparing inactive men with their most active counterparts. Results in women are also inconsistent, with 1 study43 showing no association between weekly sitting time and hip fracture in younger postmenopausal women and another44 showing a 35% higher multivariable relative risk of hip fracture in women who are sedentary compared with highly active women, aged 20 to 93 years. Prolonged time spent in sedentary behaviors is associated with reduced physical functioning12 and leg blood flow,46 which could predispose individuals to falls,47 reduced bone quality,19 and fracture.48 On the other hand, lack of movement because of more time spent sedentary would theoretically reduce the opportunity for a fracture event while standing or during ambulation. Inconsistency of study results may partly reflect the difficulty of assessing sedentary behavior using questionnaires, particularly in older adults and women. A recent accelerometer substudy in WHI suggested time spent sedentary is considerably more than what is self-reported in women aged 63 to 99 years.49 Evaluation of fracture risks associated with sedentary behavior measured objectively is needed.

ary behavior using questionnaires, particularly in older adults and women. A recent accelerometer substudy in WHI suggested time spent sedentary is considerably more than what is self-reported in women aged 63 to 99 years.49 Evaluation of fracture risks associated with sedentary behavior measured objectively is needed. Strengths and Limitations Strengths of our study include its prospective design, large sample size, and long follow-up period with low loss to follow-up. We were able to distinguish between various types and intensities of PA; sedentary behavior was also assessed. Mutual adjustment for PA and sedentary behavior when analyzing each exposure provides an approach for teasing out these interrelated factors of fracture incidence. Repeated assessments of PA and sedentary time permitted time-varying exposure analysis, the results of which were similar to those for baseline sedentary exposure and somewhat stronger than those for baseline PA.

lyzing each exposure provides an approach for teasing out these interrelated factors of fracture incidence. Repeated assessments of PA and sedentary time permitted time-varying exposure analysis, the results of which were similar to those for baseline sedentary exposure and somewhat stronger than those for baseline PA. Limitations include the use of self-report questionnaires to assess PA; misclassification on exposure is inevitable.49 Because the PA (and sedentary behavior) assessment preceded fracture occurrence, exposure misclassification might be expected to be nondifferential and any resulting bias of associations toward the null,50 but this may not be the case in all such circumstance.51 The questionnaire reliability,10,13 similarity of results for baseline and time-varying exposure analyses, and previously observed associations with major disease end points affecting older women12,52,53 enhances confidence in the study results. Fracture outcome ascertainment was based only on self-report after 2010 in the WHI. High validity for self-reported hip (78%) and wrist or forearm (81%) fractures, but lower validity (51%) for vertebral fractures has been reported in WHI.15 Physical activity can be influenced by multilevel sociocultural and ecological forces,54 which are challenging to quantify and account for statistically in studies such as ours. Further, we conducted many analyses, and some associations could be owing to chance alone; results should be interpreted accordingly.

n WHI.15 Physical activity can be influenced by multilevel sociocultural and ecological forces,54 which are challenging to quantify and account for statistically in studies such as ours. Further, we conducted many analyses, and some associations could be owing to chance alone; results should be interpreted accordingly. Conclusions In this cohort study, greater amounts of total PA were associated with lower risk of total fracture, but associations varied by fracture site. Greater MVPA was associated with lower risk of hip fracture but higher risk of wrist or forearm fracture. Mild activity was inversely associated with risks of hip, clinical vertebral, and total fracture, independent of other PA and sedentary behavior. The current results suggest that lower-intensity activities, including walking and nonrecreational activities, could have benefit on fracture risk at older ages. If confirmed, future recommendations on fracture prevention in postmenopausal women should promote light PA, especially in those who are frail and unable to safely engage in more intense activities. Sedentary behavior as an independent factor predisposing individuals to fracture requires further investigation. Supplement. eTable 1. Associations Between Total Recreational Physical Activity and Incidence of Total and Site-Specific Fractures Limited to First 10 Years of Follow-up eTable 2. Associations Between Walking and Incidence of Hip, Wrist or Forearm, Clinical Vertebral, and Total Fractures

Conclusions In this cohort study, greater amounts of total PA were associated with lower risk of total fracture, but associations varied by fracture site. Greater MVPA was associated with lower risk of hip fracture but higher risk of wrist or forearm fracture. Mild activity was inversely associated with risks of hip, clinical vertebral, and total fracture, independent of other PA and sedentary behavior. The current results suggest that lower-intensity activities, including walking and nonrecreational activities, could have benefit on fracture risk at older ages. If confirmed, future recommendations on fracture prevention in postmenopausal women should promote light PA, especially in those who are frail and unable to safely engage in more intense activities. Sedentary behavior as an independent factor predisposing individuals to fracture requires further investigation. Supplement. eTable 1. Associations Between Total Recreational Physical Activity and Incidence of Total and Site-Specific Fractures Limited to First 10 Years of Follow-up eTable 2. Associations Between Walking and Incidence of Hip, Wrist or Forearm, Clinical Vertebral, and Total Fractures eTable 3. Associations Between Mild Physical Activity and Incidence of Hip, Wrist or Forearm, Clinical Vertebral, and Total Fractures eTable 4. Associations Between Moderate to Vigorous Physical Activity and Incidence of Hip, Wrist or Forearm, Clinical Vertebral, and Total Fractures eTable 5. Associations Between Heavy Chores and Incidence of Hip, Wrist or Forearm, Clinical Vertebral, and Total Fractures

eTable 3. Associations Between Mild Physical Activity and Incidence of Hip, Wrist or Forearm, Clinical Vertebral, and Total Fractures eTable 4. Associations Between Moderate to Vigorous Physical Activity and Incidence of Hip, Wrist or Forearm, Clinical Vertebral, and Total Fractures eTable 5. Associations Between Heavy Chores and Incidence of Hip, Wrist or Forearm, Clinical Vertebral, and Total Fractures eTable 6. Associations Between Time-Dependent Total Recreational Physical Activity and Total and Site-Specific Fractures eTable 7. Associations Between Time-Dependent Sedentary Behavior and Hip, Wrist or Forearm, Clinical Vertebral, and Total Fractures eTable 8. Tabulation of Exclusions and Missing Covariate Information Click here for additional data file.