Influence of physical activity measurement on the association between Life's Essential 8 and incident cardiovascular disease in older women.

Objective: The American Heart Association's Life's Essential 8 (LE8) metric includes self-reported physical activity as one of the metrics for assessing cardiovascular health. Self-reported physical activity is prone to misclassification, whereas accelerometer measures are less biased. We examined associations of LE8 and incident cardiovascular disease (CVD) using self-reported and accelerometer-measured physical activity. Methods: Participants in the Women's Health Initiative (WHI) Objective Physical Activity and Cardiovascular Health Study (n = 4,243; mean age = 79 ± 7 years) with no CVD history completed the WHI physical activity questionnaire and the Community Healthy Activities Model Program for Seniors (CHAMPS) questionnaire prior to wearing a hip-worn accelerometer for up to seven days in 2012-2014. LE8 components (physical activity, diet, sleep, body mass index, smoking, blood pressure, non-HDL cholesterol, and glucose) were scored according to guidelines. Scores were created using five physical activity measures: WHI questionnaire (LE8WHI), CHAMPS (LE8CHAMPS), accelerometer-measured physical activity (LE8A), and sample quantiles of accelerometer-measured physical activity (LE8AQ) and daily steps (LE8STEPS). Hazard ratios (HR) for physician-adjudicated CVD were estimated using Cox regression. Results: 707 incident CVD events occurred over an average 7.5 years. Multivariable HRs (95 % CI) comparing women in the highest vs. lowest quartiles of LE8 scores were: LE8WHI = 0.53 (0.43-0.67), LE8CHAMPS = 0.47 (0.38-0.60), LE8A = 0.44 (0.36-0.56), LE8AQ = 0.44 (0.35-0.55), and LE8STEPS = 0.45 (0.35-0.57). Conclusions: The LE8-incident CVD association varies by physical activity measurement, however all methods showed reduced risk. Device-measures of physical activity may be more accurate in the LE8, but when impractical to implement, also support use of self-reported measures.

Multilevel Longitudinal Functional Principal Component Model.

Sensor devices, such as accelerometers, are widely used for measuring physical activity (PA). These devices provide outputs at fine granularity (e.g., 10-100 Hz or minute-level), which while providing rich data on activity patterns, also pose computational challenges with multilevel densely sampled data, resulting in PA records that are measured continuously across multiple days and visits. On the other hand, a scalar health outcome (e.g., BMI) is usually observed only at the individual or visit level. This leads to a discrepancy in numbers of nested levels between the predictors (PA) and outcomes, raising analytic challenges. To address this issue, we proposed a multilevel longitudinal functional principal component analysis (mLFPCA) model to directly model multilevel functional PA inputs in a longitudinal study, and then implemented a longitudinal functional principal component regression (FPCR) to explore the association between PA and obesity-related health outcomes. Additionally, we conducted a comprehensive simulation study to examine the impact of imbalanced multilevel data on both mLFPCA and FPCR performance and offer guidelines for selecting optimal methods.

Cook and Move for Your Life, an eHealth intervention for women with breast cancer.

We tested the feasibility and preliminary efficacy of an online diet and physical activity program for women with early-stage breast cancer who had completed surgery, chemotherapy, and radiation therapy (ongoing endocrine therapy allowed). Participants with low fruit and vegetable (F/V) consumption and/or low moderate-to-vigorous physical activity (MVPA) levels were randomized to one of two doses - low (one Zoom group session) or high (12 Zoom group sessions) - of an online lifestyle program with the goal of improving F/V intake and MVPA. All participants received eHealth communications (text messages, study website access), a Fitbit, and a WiFi-enabled scale. Primary objectives evaluated feasibility. Secondary objectives compared the 6-month change in F/V intake and MVPA between the two dose groups. Seventy-four women (mean age = 58.4 years; 87% non-Hispanic White; mean time since diagnosis = 4.6 years) were accrued. Among women in the low dose group, 94% attended the single session; among women in the high dose group, 84% attended at least 8 of the 12 sessions. Retention at 6 months was 93%. High relative to low dose participants consumed 1.5 more servings/day of F/V at 6 months (P = 0.007) but MVPA levels did not differ between groups. We successfully implemented an online lifestyle program for early-stage breast cancer survivors. The high dose intervention demonstrated preliminary efficacy in improving F/V consumption in early-stage breast cancer survivors. Future trials can test the intervention in a larger and more diverse population of breast cancer survivors.

Associations of steps per day and step intensity with the risk of cancer: Findings from the Women's Health Accelerometry Collaboration cohort.

OBJECTIVE: Accumulating more steps/day is associated with a lower risk of cancer mortality and composite cancer outcomes. However, less is known about the relationship of steps/day with the risk of multiple site-specific cancers. METHODS: This study included >22,000 women from the Women's Health Accelerometry Collaboration Cohort (2011-2022), comprised of women from the Women's Health Study and Women's Health Initiative Objective Physical Activity and Cardiovascular Health Study. Steps/day and step intensity were collected with accelerometry. Incident cancer cases and deaths were adjudicated. Stratified Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of the associations of steps/day and step intensity with incident breast, colon, endometrial, lung, and ovarian cancers, a composite of 13 physical activity-related cancers, total invasive cancer, and fatal cancer. RESULTS: On average, women were 73.4 years old, accumulated 4993 steps/day, and had 7.9 years of follow-up. There were small nonsignificant inverse associations with the risks of colon cancer (HR = 0.94, 95% CI: 0.83, 1.05), endometrial cancer (HR = 0.91, 95% CI: 0.82, 1.01), and fatal cancer (HR = 0.95 95% CI: 0.90, 1.00) per 1000 steps/day. More minutes at ≥40 steps/min and a faster peak 10- and 30-min step cadence were associated with a lower risk of endometrial cancer, but findings were attenuated after adjustment for body mass index and steps/day. CONCLUSIONS: Among women 62-97 years, there were small nonsignificant inverse associations of colon, endometrial, and fatal cancer with more steps/day. Epidemiologic studies with longer follow-up and updated assessments are needed to further explore these associations.

Accelerometry-assessed physical activity and sedentary behavior patterns using single- and multi-component latent class analysis among postmenopausal women.

BACKGROUND: Patterns of physical activity and sedentary behavior among postmenopausal women are not well characterized. OBJECTIVES: To describe the patterns of accelerometer-assessed physical activity and sedentary behavior among postmenopausal women. DESIGN: Cross-sectional study. METHODS: Women 63-97 years (n = 6126) wore an ActiGraph GT3X + accelerometer on their hip for 1 week. Latent class analysis was used to classify women by patterns of percent of wake time in physical activity and sedentary behavior over the week. RESULTS: On average, participants spent two-thirds of their day in sedentary behavior (62.3%), 21.1% in light low, 11.0% in light high, and 5.6% in moderate-to-vigorous physical activity. Five classes emerged for each single-component model for sedentary behavior and light low, light high, and moderate-to-vigorous physical activity. Six classes emerged for the multi-component model that simultaneously considered the four behaviors together. CONCLUSION: Unique profiles were identified in both single- and multi-component models that can provide new insights into habitual patterns of physical activity and sedentary behavior among postmenopausal women. IMPLICATIONS: The multi-component approach can contribute to refining public health guidelines that integrate recommendations for both enhancing age-appropriate physical activity levels and reducing time spent in sedentary behavior.

Associations of Relative Intensity of Physical Activity With Incident Cardiovascular Events and All-Cause Mortality.

BACKGROUND: The relative intensity of physical activity (PA) can be estimated as the percent of one's maximal effort required. METHODS: We compared associations of relative and absolute intensity PA with incident major cardiovascular disease (CVD) and all-cause mortality in 5 633 women from the Objective Physical Activity and Cardiovascular Health Study (mean age 78.5 ±â€…6.7). Absolute intensity was measured by accelerometry. Relative intensity was estimated by dividing accelerometer-estimated metabolic equivalents (METs) by maximal MET capacity. Both were aggregated into mean daily hours of light intensity PA (LPA) and moderate-to-vigorous PA (MVPA). Cox proportional hazard models estimated hazard ratios (HRs) for 1-hour higher amounts of PA on outcomes. RESULTS: During follow-up (median = 7.4 years), there were 748 incident CVD events and 1 312 deaths. Greater LPA and MVPA, on either scale, were associated with reduced risk of both outcomes. HRs for a 1-hour increment of absolute LPA were 0.88 (95% CI: 0.83-0.93) and 0.88 (95% CI: 0.84-0.92) for incident CVD and mortality, respectively. HRs for a 1-hour increment of absolute MPVA were 0.73 (95% CI: 0.61-0.87) and 0.55 (95% CI: 0.48-0.64) for the same outcomes. HRs for a 1-hour increment of relative LPA were 0.70 (95% CI: 0.59-0.84) and 0.78 (95% CI: 0.68-0.89) for incident CVD and mortality, respectively. HRs for a 1-hour increment of relative MPVA were 0.89 (95% CI: 0.83-0.96) and 0.82 (95% CI: 0.77-0.87) for the same outcomes. On the relative scale, LPA was more strongly, and inversely associated with both outcomes than relative MVPA. Absolute MVPA was more strongly inversely associated with the outcomes than relative MVPA. CONCLUSIONS: Findings support the continued shift in the PA intensity paradigm toward recommendation of more movement, regardless of intensity. Relative LPA--a modifiable, more easily achieved behavioral target, particularly among ambulatory older adults--was associated with reduced risk of incident major CVD and death.

Using functional principal component analysis (FPCA) to quantify sitting patterns derived from wearable sensors.

BACKGROUND: Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers in SB research. The former measures body movement and the latter measures body posture. The goal of the current study is to quantify the pattern and variation of movement (by ActiGraph activity counts) during activPAL-identified sitting events, and examine associations between patterns and health-related outcomes, such as systolic and diastolic blood pressure (SBP and DBP). METHODS: The current study included 314 overweight postmenopausal women, who were instructed to wear an activPAL (at thigh) and ActiGraph (at waist) simultaneously for 24 hours a day for a week under free-living conditions. ActiGraph and activPAL data were processed to obtain minute-level time-series outputs. Multilevel functional principal component analysis (MFPCA) was applied to minute-level ActiGraph activity counts within activPAL-identified sitting bouts to investigate variation in movement while sitting across subjects and days. The multilevel approach accounted for the nesting of days within subjects. RESULTS: At least 90% of the overall variation of activity counts was explained by two subject-level principal components (PC) and six day-level PCs, hence dramatically reducing the dimensions from the original minute-level scale. The first subject-level PC captured patterns of fluctuation in movement during sitting, whereas the second subject-level PC delineated variation in movement during different lengths of sitting bouts: shorter (< 30 minutes), medium (30 -39 minutes) or longer (> 39 minute). The first subject-level PC scores showed positive association with DBP (standardized ß ^ : 2.041, standard error: 0.607, adjusted p = 0.007), which implied that lower activity counts (during sitting) were associated with higher DBP. CONCLUSION: In this work we implemented MFPCA to identify variation in movement patterns during sitting bouts, and showed that these patterns were associated with cardiovascular health. Unlike existing methods, MFPCA does not require pre-specified cut-points to define activity intensity, and thus offers a novel powerful statistical tool to elucidate variation in SB patterns and health. TRIAL REGISTRATION: ClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145 ; International Registered Report Identifier (IRRID): DERR1-10.2196/28684.

Correlates of physical activity and sedentary behavior among cancer survivors and cancer-free women: The Women's Health Accelerometry Collaboration.

BACKGROUND: Describing correlates of physical activity (PA) and sedentary behavior (SB) among postmenopausal cancer survivors can help identify risk profiles and can be used to support development of targeted interventions to improve PA and reduce SB in this population. OBJECTIVE: To describe PA/SB and identify correlates of PA/SB among cancer and cancer-free post-menopausal women. METHODS: Women from the Women's Health Study (N = 16,629) and Women's Health Initiative/Objective Physical Activity and Cardiovascular Health Study (N = 6,079) were asked to wear an accelerometer on the hip for 7 days. Multiple mixed-effects linear regression models were used to identify sociodemographic-, health-, and chronic condition-related correlates (independent variables) associated with PA and SB (dependent variables) among women with (n = 2,554) and without (n = 20,154) a history of cancer. All correlates were mutually adjusted for each other. RESULTS: In unadjusted analyses, women with a history of cancer took fewer mean daily steps (4,572 (standard deviation 2557) vs 5,029 (2679) steps/day) and had lower mean moderate-to-vigorous PA (74.9 (45.0) vs. 81.6 (46.7) minutes/day) than cancer-free women. In adjusted analyses, for cancer and cancer-free women, age, diabetes, overweight, and obesity were inversely associated with all metrics of PA (average vector magnitude, time in moderate-to-vigorous PA, step volume, time at ≥40 steps/minutes, and peak 30-minute step cadence). In unadjusted analyses, mean SB was similar for those with and without cancer (529.7 (98.1) vs. 521.7 (101.2) minutes/day). In adjusted analyses, for cancer and cancer-free women, age, diabetes, cardiovascular disease, current smoking, overweight, and obesity were positive correlates of SB, while Black or Hispanic race/ethnicity, weekly/daily alcohol intake, and excellent/very good/good self-rated health were inverse correlates of SB. CONCLUSION: Several sociodemographic, health, and chronic conditions were correlates of PA/SB for postmenopausal women with and without cancer. Future studies should examine longitudinal relationships to gain insight into potential determinants of PA/SB.

Prospective Associations of Accelerometer-Measured Machine-Learned Sedentary Behavior With Death Among Older Women: The OPACH Study.

BACKGROUND: Sedentary behavior is a recognized mortality risk factor. The novel and validated convolutional neural network hip accelerometer posture algorithm highly accurately classifies sitting and postural changes compared with accelerometer count cut points. We examined the prospective associations of convolutional neural network hip accelerometer posture-classified total sitting time and mean sitting bout duration with all-cause and cardiovascular disease (CVD) death. METHODS AND RESULTS: Women (n=5856; mean±SD age, 79±7 years; 33% Black women, 17% Hispanic or Latina women, 50% White women) in the Women's Health Initiative Objective Physical Activity and Cardiovascular Health (OPACH) Study wore the ActiGraph GT3X+ for ~7 days from May 2012 to April 2014 and were followed through February 19, 2022 for all-cause and CVD death. The convolutional neural network hip accelerometer posture algorithm classified total sitting time and mean sitting bout duration from GT3X+ output. Over follow-up (median, 8.4 years; range, 0.1-9.9), there were 1733 deaths (632 from CVD). Adjusted Cox regression hazard ratios (HRs) comparing women in the highest total sitting time quartile (>696 min/d) to those in the lowest (<556.0 min/d) were 1.57 (95% CI; 1.35-1.83; P-trend<0.001) for all-cause death and 1.78 (95% CI; 1.36-2.31; P-trend<0.001) for CVD death. HRs comparing women in the longest mean sitting bout duration quartile (>15 minutes) to the shortest (<9.3 minutes) were 1.43 (95% CI; 1.23-1.66; P-trend<0.001) for all-cause death and 1.52 (95% CI; 1.18-1.96; P-trend<0.001) for CVD death. Apparent nonlinear associations for total sitting time suggested higher all-cause death (P nonlinear=0.009) and CVD death (P nonlinear=0.008) risk after ~660 to 700 min/d. CONCLUSIONS: Higher total sitting time and longer mean sitting bout duration are associated with higher all-cause and CVD mortality risk among older women. These data support interventions aimed at reducing both total sitting time and interrupting prolonged sitting.

Accelerometer-Measured Physical Activity, Sedentary Time, and Heart Failure Risk in Women Aged 63 to 99 Years.

Importance: Heart failure (HF) prevention is paramount to public health in the 21st century. Objective: To examine incident HF and its subtypes with preserved ejection fraction (HFpEF) and reduced EF (HFrEF) according to accelerometer-measured physical activity (PA) and sedentary time. Design, Setting, and Participants: This was a prospective cohort study, the Objective Physical Activity and Cardiovascular Health (OPACH) in Older Women study, conducted from March 2012 to April 2014. Included in the analysis were women aged 63 to 99 years without known HF, who completed hip-worn triaxial accelerometry for 7 consecutive days. Follow-up for incident HF occurred through February 2022. Data were analyzed from March to December 2023. Exposure: Daily PA (total, light, moderate to vigorous PA [MVPA], steps) and sedentary (total, mean bout duration) behavior. Main Outcomes and Measures: Adjudicated incident HF, HFpEF, and HFrEF. Results: A total of 5951 women (mean [SD] age, 78.6 [6.8] years) without known HF were included in this analysis. Women self-identified with the following race and ethnicity categories: 2004 non-Hispanic Black (33.7%), 1022 Hispanic (17.2%), and 2925 non-Hispanic White (49.2%). There were 407 HF cases (257 HFpEF; 110 HFrEF) identified through a mean (SD) of 7.5 (2.6) years (range, 0.01-9.9 years) of follow-up. Fully adjusted hazard ratios (HRs) for overall HF, HFpEF, and HFrEF associated with a 1-SD increment were 0.85 (95% CI, 0.75-0.95), 0.78 (95% CI, 0.67-0.91), and 1.02 (95% CI, 0.81-1.28) for minutes per day total PA; 0.74 (95% CI, 0.63-0.88), 0.71 (95% CI, 0.57-0.88), and 0.83 (95% CI, 0.62-1.12) for steps per day; and 1.17 (95% CI, 1.04-1.33), 1.29 (95% CI, 1.10-1.51), and 0.94 (95% CI, 0.75-1.18) for minutes per day total sedentary. Cubic spline curves for overall HF and HFpEF were significant inverse for total PA and steps per day and positive for total sedentary. Light PA and MVPA were inversely associated with overall HF (HR per 1 SD: 0.88; 95% CI, 0.78-0.98 and 0.84; 95% CI, 0.73-0.97) and HFpEF (0.80; 95% CI, 0.70-0.93 and 0.85; 95% CI, 0.72-1.01) but not HFrEF. Associations did not meaningfully differ when stratified by age, race and ethnicity, body mass index, physical function, or comorbidity score. Results for sedentary bout duration were inconsistent. Conclusions and Relevance: Higher accelerometer-measured PA (MVPA, light PA, steps per day) was associated with lower risk (and greater total sedentary time with higher risk) of overall HF and HFpEF in a racially and ethnically diverse cohort of older women. Increasing PA and reducing sedentary time for primary HFpEF prevention may have relevant implications for cardiovascular resilience and healthy aging in later life.

A systematic scoping review of latent class analysis applied to accelerometry-assessed physical activity and sedentary behavior.

BACKGROUND: Latent class analysis (LCA) identifies distinct groups within a heterogeneous population, but its application to accelerometry-assessed physical activity and sedentary behavior has not been systematically explored. We conducted a systematic scoping review to describe the application of LCA to accelerometry. METHODS: Comprehensive searches in PubMed, Web of Science, CINHAL, SPORTDiscus, and Embase identified studies published through December 31, 2021. Using Covidence, two researchers independently evaluated inclusion criteria and discrepancies were resolved by consensus. Studies with LCA applied to accelerometry or combined accelerometry/self-reported measures were selected. Data extracted included study characteristics and both accelerometry and LCA methods. RESULTS: Of 2555 papers found, 66 full-text papers were screened, and 12 papers (11 cross-sectional, 1 cohort) from 8 unique studies were included. Study sample sizes ranged from 217-7931 (mean 2249, standard deviation 2780). Across 8 unique studies, latent class variables included measures of physical activity (100%) and sedentary behavior (75%). About two-thirds (63%) of the studies used accelerometry only and 38% combined accelerometry and self-report to derive latent classes. The accelerometer-based variables in the LCA model included measures by day of the week (38%), weekday vs. weekend (13%), weekly average (13%), dichotomized minutes/day (13%), sex specific z-scores (13%), and hour-by-hour (13%). The criteria to guide the selection of the final number of classes and model fit varied across studies, including Bayesian Information Criterion (63%), substantive knowledge (63%), entropy (50%), Akaike information criterion (50%), sample size (50%), Bootstrap likelihood ratio test (38%), and visual inspection (38%). The studies explored up to 5 (25%), 6 (38%), or 7+ (38%) classes, ending with 3 (50%), 4 (13%), or 5 (38%) final classes. CONCLUSIONS: This review explored the application of LCA to physical activity and sedentary behavior and identified areas of improvement for future studies leveraging LCA. LCA was used to identify unique groupings as a data reduction tool, to combine self-report and accelerometry, and to combine different physical activity intensities and sedentary behavior in one LCA model or separate models.

Calibrating Physical Activity and Sedentary Behavior for Hip-Worn Accelerometry in Older Women With Two Epoch Lengths: The Women's Health Initiative Objective Physical Activity and Cardiovascular Health Calibration Study.

Purpose: The purpose of this study was to develop 60-second epoch accelerometer intensity cutpoints for vertical axis count and vector magnitude (VM) output from hip-worn tri-axial accelerometers among women 60-91 years. We also compared these cutpoints against cutpoints derived by multiplying 15-second epoch cutpoints by four. Methods: Two hundred apparently healthy women wore an ActiGraph GT3X+ accelerometer on their hip while performing a variety of laboratory-based activities that were sedentary (watching television, assembling a puzzle), low light (washing/drying dishes), high light (laundry, dust mopping), or MVPA (400-meter walk) intensity. Oxygen uptake was measured using an Oxycon™ portable calorimeter. Sedentary behavior and physical activity intensity cutpoints for vertical axis and VM counts were derived for 60-second epochs from receiver operating characteristic (ROC) and by multiplying the 15-second cutpoints by four); both were compared to oxygen uptake. Results: The median age was 74.5 years (interquartile range 70-83). The 60-second epoch cutpoints for vertical counts were 0 sedentary, 1-73 low light, 74-578 high light, and >=579 MVPA. The 60-second epoch cutpoints for VM were 0-88 sedentary, 89-663 low light, 664-1730 high light, and >=1731 MVPA. For both sets of cutpoints, the ROC approach yielded more accurate estimates than the multiplication approach. Conclusion: The derived 60-second epoch cutpoints for vertical counts and VM can be applied to epidemiologic studies to define sedentary behavior and physical activity intensities in older adult populations.

Longitudinal Associations Between Timing of Physical Activity Accumulation and Health: Application of Functional Data Methods.

Accelerometers are widely used for tracking human movement and provide minute-level (or even 30 Hz level) physical activity (PA) records for detailed analysis. Instead of using day-level summary statistics to assess these densely sampled inputs, we implement functional principal component analysis (FPCA) approaches to study the temporal patterns of PA data from 245 overweight/obese women at three visits over a 1-year period. We apply longitudinal FPCA to decompose PA inputs, incorporating subject-specific variability, and then test the association between these patterns and obesity-related health outcomes by multiple mixed effect regression models. With the proposed methods, the longitudinal patterns in both densely sampled inputs and scalar outcomes are investigated and connected. The results show that the health outcomes are strongly associated with PA variation, in both subject and visit-level. In addition, we reveal that timing of PA during the day can impact changes in outcomes, a finding that would not be possible with day-level PA summaries. Thus, our findings imply that the use of longitudinal FPCA can elucidate temporal patterns of multiple levels of PA inputs. Furthermore, the exploration of the relationship between PA patterns and health outcomes can be useful for establishing weight-loss guidelines.

Accelerometer-measured physical activity and postmenopausal breast cancer incidence in the Women's Health Accelerometry Collaboration.

BACKGROUND: Few studies have examined accelerometer-measured physical activity and incident breast cancer (BC). Thus, this study examined associations between accelerometer-measured vector magnitude counts per 15 seconds (VM/15s) and average daily minutes of light physical activity (LPA), moderate-to-vigorous PA (MVPA), and total PA (TPA) and BC risk among women in the Women's Health Accelerometry Collaboration (WHAC). METHODS: The WHAC comprised 21,089 postmenopausal women (15,375 from the Women's Health Study [WHS]; 5714 from the Women's Health Initiative Objective Physical Activity and Cardiovascular Health Study [OPACH]). Women wore an ActiGraph GT3X+ on the hip for ≥4 days and were followed for 7.4 average years to identify physician-adjudicated in situ (n = 94) or invasive (n = 546) BCs. Multivariable stratified Cox regression estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for tertiles of physical activity measures in association with incident BC overall and by cohort. Effect measure modification was examined by age, race/ethnicity, and body mass index (BMI). RESULTS: In covariate-adjusted models, the highest (vs. lowest) tertiles of VM/15s, TPA, LPA, and MVPA were associated with BC HRs of 0.80 (95% CI, 0.64-0.99), 0.84 (95% CI, 0.69-1.02), 0.89 (95% CI, 0.73-1.08), and 0.81 (95% CI, 0.64-1.01), respectively. Further adjustment for BMI or physical function attenuated these associations. Associations were more pronounced among OPACH than WHS women for VM/15s, MVPA, and TPA; younger than older women for MVPA; and women with BMI ≥30 than <30 kg/m2 for LPA. CONCLUSION: Greater levels of accelerometer-assessed PA were associated with lower BC risk. Associations varied by age and obesity and were not independent of BMI or physical function.

Accelerometer-measured physical activity and sitting with incident mild cognitive impairment or probable dementia among older women.

INTRODUCTION: Physical activity (PA) is prospectively inversely associated with dementia risk, but few studies examined accelerometer measures of PA and sitting with rigorously-adjudicated mild cognitive impairment (MCI) and dementia risk. METHODS: We examined the associations of accelerometer measures (PA and sitting) with incident MCI/probable dementia in the Women's Health Initiative (n = 1277; mean age = 82 ± 6 years) RESULTS: Over a median follow-up of 4.2 years, 267 MCI/probable dementia cases were identified. Adjusted Cox regression HRs (95% CI) across moderate-to-vigorous PA (MVPA) min/d quartiles were 1.00 (reference), 1.28 (0.90 to 1.81), 0.79 (0.53 to 1.17), and 0.69 (0.45 to 1.06); P-trend = 0.01. Adjusted HRs (95% CI) across steps/d quartiles were 1.00 (reference), 0.73 (0.51 to 1.03), 0.64 (0.43 to 0.94), and 0.38 (0.23 to 0.61); P-trend < 0.001. The HR (95% CI) for each 1-SD increment in MVPA (31 min/d) and steps/d (1865) were 0.79 (0.67 to 0.94) and 0.67 (0.54 to 0.82), respectively. Sitting was not associated with MCI/probable dementia. DISCUSSION: Findings suggest ≥ moderate intensity PA, particularly stepping, associates with lower MCI and dementia risk. HIGHLIGHTS: Few studies have examined accelerometer-measured physical activity, including steps, and sitting with incident ADRD. Moderate-to-vigorous physical activity and steps, but not light physical activity or sitting, were inversely associated with lower ADRD risk. Among older women, at least moderate intensity physical activity may be needed to reduce ADRD risk.

Validation, Recalibration, and Predictive Accuracy of Published V̇O 2max Prediction Equations for Adults Ages 50-96 Yr.

PURPOSE: Maximal oxygen uptake ( ) is the criterion measure of cardiorespiratory fitness. Lower cardiorespiratory fitness is a strong predictor of poor health outcomes, including all-cause mortality. Because testing is resource intensive, several non-exercise-based V˙O 2max prediction equations have been published. We assess these equations' ability to predict measured V˙O 2max , recalibrate these equations, and quantify the association of measured and predicted V˙O 2max with all-cause mortality. METHODS: Baltimore Longitudinal Study of Aging participants with valid V˙O 2max tests were included ( n = 1080). Using published V˙O 2max prediction equations, we calculated predicted V˙O 2max and present performance metrics before and after recalibration (deriving new regression estimates by regressing measured V˙O 2max on Baltimore Longitudinal Study of Aging covariates). Cox proportional hazards models were fit to quantify associations of measured, predicted, and recalibration-predicted values of V˙O 2max with mortality. RESULTS: Mean age and V˙O 2max were 69.0 ± 10.4 yr and 21.6 ± 5.9 mL·kg -1 ·min -1 , respectively. The prediction equations yielded root mean square error values ranging from 4.2 to 20.4 mL·kg -1 ·min -1 . After recalibration, these values decreased to 3.9-4.2 mL·kg -1 ·min -1 . Adjusting for all covariates, all-cause mortality risk was 66% lower for the highest quartile of measured V˙O 2max relative to the lowest. Predicted V˙O 2max variables yielded similar estimates in unadjusted models but were not robust to adjustment. CONCLUSIONS: Measured V˙O 2max is an extremely strong predictor of all-cause mortality. Several published V˙O 2max prediction equations yielded the following: 1) reasonable performance metrics relative to measured V˙O 2max especially when recalibrated, and 2) all-cause mortality hazard ratios similar to those of measured V˙O 2max , especially when recalibrated, yet 3) were not robust to adjustment for basic demographic covariates likely because these were used in the equation for predicted V˙O 2max .

Robust functional principal component analysis via a functional pairwise spatial sign operator.

Functional principal component analysis (FPCA) has been widely used to capture major modes of variation and reduce dimensions in functional data analysis. However, standard FPCA based on the sample covariance estimator does not work well if the data exhibits heavy-tailedness or outliers. To address this challenge, a new robust FPCA approach based on a functional pairwise spatial sign (PASS) operator, termed PASS FPCA, is introduced. We propose robust estimation procedures for eigenfunctions and eigenvalues. Theoretical properties of the PASS operator are established, showing that it adopts the same eigenfunctions as the standard covariance operator and also allows recovering ratios between eigenvalues. We also extend the proposed procedure to handle functional data measured with noise. Compared to existing robust FPCA approaches, the proposed PASS FPCA requires weaker distributional assumptions to conserve the eigenspace of the covariance function. Specifically, existing work are often built upon a class of functional elliptical distributions, which requires inherently symmetry. In contrast, we introduce a class of distributions called the weakly functional coordinate symmetry (weakly FCS), which allows for severe asymmetry and is much more flexible than the functional elliptical distribution family. The robustness of the PASS FPCA is demonstrated via extensive simulation studies, especially its advantages in scenarios with nonelliptical distributions. The proposed method was motivated by and applied to analysis of accelerometry data from the Objective Physical Activity and Cardiovascular Health Study, a large-scale epidemiological study to investigate the relationship between objectively measured physical activity and cardiovascular health among older women.

A RIEMANN MANIFOLD MODEL FRAMEWORK FOR LONGITUDINAL CHANGES IN PHYSICAL ACTIVITY PATTERNS.

Physical activity (PA) is significantly associated with many health outcomes. The wide usage of wearable accelerometer-based activity trackers in recent years has provided a unique opportunity for in-depth research on PA and its relations with health outcomes and interventions. Past analysis of activity tracker data relies heavily on aggregating minute-level PA records into day-level summary statistics in which important information of PA temporal/diurnal patterns is lost. In this paper we propose a novel functional data analysis approach based on Riemann manifolds for modeling PA and its longitudinal changes. We model smoothed minute-level PA of a day as one-dimensional Riemann manifolds and longitudinal changes in PA in different visits as deformations between manifolds. The variability in changes of PA among a cohort of subjects is characterized via variability in the deformation. Functional principal component analysis is further adopted to model the deformations, and PC scores are used as a proxy in modeling the relation between changes in PA and health outcomes and/or interventions. We conduct comprehensive analyses on data from two clinical trials: Reach for Health (RfH) and Metabolism, Exercise and Nutrition at UCSD (MENU), focusing on the effect of interventions on longitudinal changes in PA patterns and how different modes of changes in PA influence weight loss, respectively. The proposed approach reveals unique modes of changes, including overall enhanced PA, boosted morning PA, and shifts of active hours specific to each study cohort. The results bring new insights into the study of longitudinal changes in PA and health and have the potential to facilitate designing of effective health interventions and guidelines.

Calibration of an Accelerometer Activity Index among Older Women and Its Association with Cardiometabolic Risk Factors.

Purpose: Traditional summary metrics provided by accelerometer device manufacturers, known as counts, are proprietary and manufacturer specific, making them difficult to compare studies using different devices. Alternative summary metrics based on raw accelerometry data have been introduced in recent years. However, they were often not calibrated on ground truth measures of activity-related energy expenditure for direct translation into continuous activity intensity levels. Our purpose is to calibrate, derive, and validate thresholds among women 60 years and older based on a recently proposed transparent raw data based accelerometer activity index (AAI), and to demonstrate its application in association with cardiometabolic risk factors. Methods: We first built calibration equations for estimating metabolic equivalents (METs) continuously using AAI and personal characteristics using internal calibration data (n=199). We then derived AAI cutpoints to classify epochs into sedentary behavior and intensity categories. The AAI cutpoints were applied to 4,655 data units in the main study. We then utilized linear models to investigate associations of AAI sedentary behavior and physical activity intensity with cardiometabolic risk factors. Results: We found that AAI demonstrated great predictive accuracy for METs (R2=0.74). AAI-based physical activity measures were associated in the expected directions with body mass index (BMI), blood glucose, and high density lipoprotein (HDL) cholesterol. Conclusion: The calibration framework for AAI and the cutpoints derived for women older than 60 years can be applied to ongoing epidemiologic studies to more accurately define sedentary behavior and physical activity intensity exposures which could improve accuracy of estimated associations with health outcomes.

A digital health intervention to improve nutrition and physical activity in breast cancer survivors: Rationale and design of the Cook and Move for Your Life pilot and feasibility randomized controlled trial.

BACKGROUND: The design of a randomized pilot trial evaluating the feasibility of two doses of a digital health intervention promoting changes in nutrition and physical activity in breast cancer (BC) survivors is described. METHODS: Eligible women were adults with history of early-stage breast cancer and > 60 days post-treatment, consumed <5 servings/day of fruits/vegetables and/or engaged in <150 min/week of aerobic moderate-to-vigorous physical activity, and had internet access. Participants were randomized to 6 months of either a "low" (1 session) or "high" (12 sessions) dose digital health intervention. Zoom-delivered sessions focused on improving diet and physical activity through didactic and experiential classes delivered by a registered dietitian, chef, exercise physiologist, and culinary educator. All study participants received weekly motivational texts, a Fitbit, and study website access. Diet, accelerometry, anthropometric, psychosocial, and biospecimen data were collected remotely at baseline and six months. Primary outcome was feasibility measured via accrual rate, adherence, retention, and acceptability. RESULTS: Recruitment began in December 2019, was suspended in March 2020 due to the COVID-19 pandemic, resumed September 2020, and concluded in January 2021. Women were identified from the local BC registry and flyers posted in the oncology clinic. Of 929 women recruited, 321 completed the screening assessment, and of these, 138 were eligible. A total of 74 women were enrolled and randomized to the study. CONCLUSION: BC survivors were successfully enrolled in a digital health nutrition and physical activity intervention. If feasible, this intervention will be tested in larger and more diverse populations of cancer survivors. TRIAL REGISTRATION: ClinicalTrials.govNCT04200482.