Basic Study + OPACH80?Name?

Basic Study + OPACH80?Name?

Study Population N~10,173 (3923 WHIMS, 6250 non WHIMS MRC) Lowest age = 77 (79.8% are 80+) Expect completed data collection on 8,000 Expect successful blood draw on 7,600

Consent Consent Groups based on zip code SW, SE, NE, NW, Central Initial consent packet (Day 0) Thank you/reminder card (Day 7) Second consent packet (Day 21) Consent telephone calls (Day 42) Telephone consent confirmed at visit

Data Collection Organization Examination Management Services, Inc (EMSI) 242 branch offices across the US Centralized Operations and Call Center Recommended by two large epi studies Sister Study (NIEHS) REGARDS (UAB) Examiners experienced with in home measurements and phlebotomy

Examination Data Blood Pressure Pulse Height Weight Waist Circumference Short Physical Performance Battery (SPPB) Balance, Gait speed, Chair Stand Grip Strength

Vial & Sample EDTA tube (lavender) 10 ml, Plasma, DNA, red cells EDTA separator tube (pearl) 8.5 ml, Plasma Serum separator tube (red/gray) 8.5 ml, Serum EDTA tube (lavender) 2 ml, whole blood PAXgene tube 2.5 ml Stabilized RNA Blood Purpose Wide range of current and future DNA based and protein assays, e.g., proteins, products of metabolism, genotyping, sequencing, telomere length Proteomics, metabolomics; Separator tube increases the protection against changes with delayed separation that might affect certain assays. Wide range of clinical chemistry and future assays; Separator tube increases the protection against changes with delayed separation that might affect certain assays Immediate hematological assays Extracted total RNA (>18 nucleotides, including microrna) for gene expression analyses, such as real time TR PCR and microarray analysis

Blood Assays Complete Blood Count RBC, WBC, Platelets, Hgb, Hct, Auto Differential, RBC parameters, Platelet parameters Biomarkers (if funds permit) Glucose Insulin CRP Creatinine Triglycerides Total Cholesterol, HDL, LDL

Training EMSI Schedulers and Examiners EMSI standard training/certification Project specific Web based training/certification Modeled after successful training for similar projects Re certification if no WHI case assignments for 1 month Collaborative Data Services (telephone consent) Training session with CCC Database/Data Ops

Quality Control Responsible: CCC Project Manager Reports (provided monthly to SC) Consent Scheduling/Appointment Completion Training Blood Collection Quality/Quantity Form Data Entry Quality/Quantity Site visits One Site per month; 2 in person visits per Site

Andrea Z. LaCroix, PhD Women s Health Initiative Investigator Fred Hutchinson Cancer Research Center Seattle, WA

Title: Nutrition and Physical Activity Research to Promote Cardiovascular and Pulmonary Health Sponsor: National Heart, Lung, and Blood Institute Need: Studies on the types, dose, duration, and intensity of activity needed for optimum CVD health, and studies on the time course of acquisition of CVD health benefits resulting from increases in habitual activity

Most cohort studies have measured physical activity by self report Accelerometry collects more precise and complete data on physical activity Low correlation between accelerometry and selfreport (R = 0.27 0.42) (Pruitt) ~40% of adults age 65+ meet PA guidelines per selfreport (CDC); only 2.4% of adults age 60+ meet PA guidelines per accelerometry (Troiano) Pruitt LA et al. J Aging Phys Activity. 2008;16:416-434;Physical Activity Guidelines Advisory Committee (PAGAC) Report 2008; Troiano R et al. Med Sci Spor;40:181-188. 2007;40:181-188.2007;40:181-188

Manson, JE, Greenland P, LaCroix AZ. N Engl J Med 2002;347:716-725.

In a cohort of 8000 WHI participants >80 years: 1) Determine the association of total volume of physical activity (light, moderate, and vigorous) as measured by accelerometry with risks of incident CVD and total mortality. 2) Determine the associations of moderate to vigorousintensity physical activity (MVPA) and sedentary time to risks of incident CVD and total mortality. 2.1) Conduct a calibration study designed to determine accelerometry thresholds for women aged 80 and older that distinguish sedentary from light activity and light activity from moderate to vigorous activity. This calibration study makes Aim 2 directly relevant to older women.

3) Compare the magnitude and independence of associations between accelerometer measures of physical activity and sedentary time, and self reported measures of MVPA and sedentary time, with risks of incident CVD and total mortality. 4) Determine the associations of total volume of physical activity, MVPA and sedentary time with risks of incident falls, injurious falls and overall risk for any injury.

In 200 women from two WHI sites who are able to walk safely on a treadmill, we will collect: Body height and weight CHAMPS physical activity questionnaire and WHI Rating of perceived exercise capacity 400 meter walk Accelerometer and step counts during the 400 meter walk, measured using the Actigraph GT3X during the walk with the step counter function turned on. Accelerometer counts/min and oxygen consumption during standardized tasks.

Event Unadjusted Adjusted* CVD 0.85 0.68 CHD 0.81 0.65 Stroke 0.81 0.65 Mortality 0.89 0.71 *Estimated attenuation from Manson, JE, Greenland P, LaCroix AZ. N Engl J Med 2002;347:716-725

Event Unadjusted Adjusted* Unintentional All Injury Causes Unintentional Falls 0.89 0.71 0.88 0.70 *Estimated attenuation from Manson, JE, Greenland P, LaCroix AZ. N Engl J Med 2002;347:716-725

Study Year Study Activity 1 2 3 4 Surveillance of incident CVD events by WHI Contract Finalize study protocols and prepare for field activities Face-to-Face visits and Calibration Study clinic visits Falls surveillance: postcards, reminders, post-fall phone interviews Preparations for data analysis Calibration Study analysis-aim 2.1 Accelerometry data analysis and manuscript preparation

1) Descriptive study of accelerometry data 2) Comparisons between self reported and objective physical activity: Agreement between measures Pattern of disparities in physical activity and the percent of older women who meet physical activity guidelines 3) Associations between accelerometry data with assessments of health status and functional limitations

4) Associations between objective physical activity and intensity of physical activity in relation to incident cardiovascular events 5) Associations between objective physical activity and incidence of falls and injury outcomes

Investigator Affiliation Andrea LaCroix WHI-CCC David Buchner University of Illinois Beth Lewis University of Alabama- Birmingham Marcia Stefanick Stanford University William Haskell Stanford University Kelly Evenson UNC-Chapel Hill Amy Herring UNC-Chapel Hill Steve Marshall UNC-Chapel Hill Michael LaMonte University of Buffalo I-Min Lee Harvard University Jeannette Beasley WHI-CCC