Understanding resilience: Lifestyle-based behavioral predictors of mental health and well-being in community-dwelling older adults during the COVID-19 pandemic.

BACKGROUND: Changes in sleep, physical activity and mental health were observed in older adults during early stages of the COVID-19 pandemic. Here we describe effects of the COVID-19 pandemic on older adult mental health, wellbeing, and lifestyle behaviors and explore predictors of better mid-pandemic mental health and wellbeing. METHODS: Participants in the Adult Changes in Thought study completed measures of lifestyle behaviors (e.g., sleep, physical activity) and mental health and wellbeing both pre-pandemic during regular study visits and mid-pandemic via a one-time survey. We used paired t-tests to compare differences in these measures pre- vs. mid-pandemic. Using multivariate linear regression, we further explored demographic, health, and lifestyle predictors of pandemic depressive symptoms, social support, and fatigue. We additionally qualitatively coded free text data from the mid-pandemic survey for related comments. RESULTS: Participants (N = 896) reported significant changes in mental health and lifestyle behaviors at pre-pandemic vs. mid-pandemic measurements (p < 0.0001). Qualitative findings supported these behavioral and wellbeing changes. Being male, never smoking, and lower pre-pandemic computer time and sleep disturbance were significantly associated with lower pandemic depressive symptoms. Being partnered, female, never smoking, and lower pre-pandemic sleep disturbance were associated with higher pandemic social support. Pre-pandemic employment, more walking, less computer time, and less sleep disturbance were associated with less pandemic fatigue. Participant comments supported these quantitative findings, highlighting gender differences in pandemic mental health, changes in computer usage and physical activity during the pandemic, the value of spousal social support, and links between sleep disturbance and mental health and wellbeing. Qualitative findings also revealed additional factors, such as stresses from personal and family health situations and the country's concurrent political environment, that impacted mental health and wellbeing. CONCLUSIONS: Several demographic, health, and lifestyle behaviors appeared to buffer the effects of the COVID-19 pandemic and may be key sources of resilience. Interventions and public health measures targeting men and unpartnered individuals could promote social support resilience, and intervening on modifiable behaviors like sleep quality, physical activity and sedentary activities like computer time may promote resilience to fatigue and depressive symptoms during future community stressor events. Further research into these relationships is warranted.

Movement- and Posture-based Measures of Sedentary Patterns and Associations with Metabolic Syndrome in Hispanic/Latino and non-Hispanic Adults.

BACKGROUND: Sedentary behavior has been identified as a significant risk factor for Metabolic Syndrome (MetS). However, it is unclear if the sedentary pattern measurement approach (posture vs. movement) impacts observed associations or if associations differ for Hispanic/Latino communities, who have higher risk of MetS. METHODS: Participants from the Community of Mine (CoM) study (N = 602) wore hip-based accelerometers for 14 days and completed MetS-associated biomarker assessment (triglycerides, blood pressure, fasting glucose, HDL cholesterol, waist circumference). Sedentary patterns were classified using both cutpoints (movement-based) and the Convolutional Neural Network Hip Accelerometer Posture (CHAP) algorithm (posture-based). We used logistic regression to estimate associations between MetS with sedentary patterns overall and stratified by Hispanic/Latino ethnicity. RESULTS: CHAP and cutpoint sedentary patterns were consistently associated with MetS. When controlling for total sedentary time and moderate to vigorous physical activity, only CHAP-measured median sedentary bout duration (OR = 1.15, CI: 1.04, 1.28) was significant. In stratified analysis, CHAP-measured median bout duration and time spent in sedentary bouts ≥ 30 min were each associated with increased odds of MetS, but the respective associations were stronger for Hispanic/Latino ethnicity (OR = 1.71 and 1.48; CI = 1.28-2.31 and 1.12-1.98) than for non-Hispanic/Latino ethnicity (OR = 1.43 and 1.40; CI = 1.10-1.87 and 1.06-1.87). CONCLUSIONS: The way sedentary patterns are measured can impact the strength and precision of associations with MetS. These differences may be larger in Hispanic/Latino ethnic groups and warrants further research to inform sedentary behavioral interventions in these populations.

Pharmacoepidemiology evaluation of bumetanide as a potential candidate for drug repurposing for Alzheimer's disease.

INTRODUCTION: Bumetanide, a loop diuretic, was identified as a candidate drug for repurposing for Alzheimer's disease (AD) based on its effects on transcriptomic apolipoprotein E signatures. Cross-sectional analyses of electronic health records suggest that bumetanide is associated with decreased prevalence of AD; however, temporality between bumetanide exposure and AD development has not been established. METHODS: We evaluated Medicare claims data using Cox proportional hazards regression to evaluate the association between time-dependent use of bumetanide and time to first AD diagnosis while controlling for patient characteristics. Multiple sensitivity analyses were conducted to test the robustness of the findings. RESULTS: We sampled 833,561 Medicare beneficiaries, 60.8% female, with mean (standard deviation) age of 70.4 (12). Bumetanide use was not significantly associated with AD risk (hazard ratio 1.05; 95% confidence interval, 0.99-1.10). DISCUSSION: Using a nationwide dataset and a retrospective cohort study design, we were not able to identify a time-dependent effect of bumetanide lowering AD risk. HIGHLIGHTS: Bumetanide was identified as a candidate for repurposing for Alzheimer's disease (AD). We evaluated the association between bumetanide use and risk of AD. We used Medicare data and accounted for duration of bumetanide use. Bumetanide use was not significantly associated with risk of AD.

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.

How Generalizable Are Findings from a Community-Based Prospective Cohort Study? Extending Estimates from the Adult Changes in Thought Study to Its Source Population.

Background: The Adult Changes in Thought (ACT) study is a cohort of Kaiser Permanente Washington members ages 65+ that began in 1994. Objective: We wanted to know how well ACT participants represented all older adults in the region, and how well ACT findings on eye disease and its relationship with Alzheimer's disease generalized to all older adults in the Seattle Metropolitan Region. Methods: We used participation weights derived from pooling ACT and Behavioral Risk Factor Surveillance System (BRFSS) data to estimate prevalences of common eye diseases and their associations with Alzheimer's disease incidence. Cox proportional hazards models accounted for age, education, smoking, sex, and APOE genotype. Confidence intervals for weighted analyses were bootstrapped to account for error in estimating the weights. Results: ACT participants were fairly similar to older adults in the region. The largest differences were more self-reported current cholesterol medication use in BRFSS and higher proportions with low education in ACT. Incorporating the weights had little impact on prevalence estimates for age-related macular degeneration or glaucoma. Weighted estimates were slightly higher for diabetic retinopathy (weighted 5.7% (95% Confidence Interval 4.3, 7.1); unweighted 4.1% (3.6, 4.6)) and cataract history (weighted 51.8% (49.6, 54.3); unweighted 48.6% (47.3, 49.9)). The weighted hazard ratio for recent diabetic retinopathy diagnosis and Alzheimer's disease was 1.84 (0.34, 4.29), versus 1.32 (0.87, 2.00) in unweighted ACT. Conclusions: Most, but not all, associations were similar after participation weighting. Even in community-based cohorts, extending inferences to broader populations may benefit from evaluation with participation weights.

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.

Historic Cognitive Function Trajectories as Predictors of Sedentary Behavior and Physical Activity in Older Adults.

BACKGROUND: We examined whether trajectories of cognitive function over 10 years predict later-life physical activity (PA), sedentary time (ST), and sleep. METHODS: Participants were from the Adult Changes in Thought (ACT) cohort study. We included 611 ACT participants who wore accelerometers and had 3+ measures of cognition in the 10 years prior to accelerometer wear. The Cognitive Assessment Screening Instrument (CASI) measured cognition and was scored using item-response theory (IRT). activPAL and ActiGraph accelerometers worn over 7 days measured ST and PA outcomes. Self-reported time in bed and sleep quality measured sleep outcomes. Analyses used growth mixture modeling to classify CASI-IRT scores into latent groups and examine associations with PA, ST, and sleep including demographic and health covariates. RESULTS: Participants (Mean age = 80.3 (6.5) years, 90.3% White, 57.1% female, 29.3% had less than 16 years of education) fell into 3 latent trajectory groups: average stable CASI (56.1%), high stable CASI (34.0%), and declining CASI (9.8%). The declining group had 16 minutes less stepping time (95% confidence interval [95% CI]: 0.6, 31.4), 1 517 fewer steps per day (95% CI: 138, 2 896), and 16.3 minutes per day less moderate-to-vigorous PA (95% CI: 1.3, 31.3) compared to the average stable group. There were no associations between CASI trajectory and sedentary or sleep outcomes. CONCLUSIONS: Declining cognition predicted lower PA providing some evidence of a reverse relationship between PA and cognition in older adults. However, this conclusion is limited by having outcomes at only one time point, a nonrepresentative sample, self-reported sleep outcomes, and using a global cognition measure.

The Women's Health Initiative Randomized Trials and Clinical Practice: A Review.

Importance: Approximately 55 million people in the US and approximately 1.1 billion people worldwide are postmenopausal women. To inform clinical practice about the health effects of menopausal hormone therapy, calcium plus vitamin D supplementation, and a low-fat dietary pattern, the Women's Health Initiative (WHI) enrolled 161 808 postmenopausal US women (N = 68 132 in the clinical trials) aged 50 to 79 years at baseline from 1993 to 1998, and followed them up for up to 20 years. Observations: The WHI clinical trial results do not support hormone therapy with oral conjugated equine estrogens plus medroxyprogesterone acetate for postmenopausal women or conjugated equine estrogens alone for those with prior hysterectomy to prevent cardiovascular disease, dementia, or other chronic diseases. However, hormone therapy is effective for treating moderate to severe vasomotor and other menopausal symptoms. These benefits of hormone therapy in early menopause, combined with lower rates of adverse effects of hormone therapy in early compared with later menopause, support initiation of hormone therapy before age 60 years for women without contraindications to hormone therapy who have bothersome menopausal symptoms. The WHI results do not support routinely recommending calcium plus vitamin D supplementation for fracture prevention in all postmenopausal women. However, calcium and vitamin D are appropriate for women who do not meet national guidelines for recommended intakes of these nutrients through diet. A low-fat dietary pattern with increased fruit, vegetable, and grain consumption did not prevent the primary outcomes of breast or colorectal cancer but was associated with lower rates of the secondary outcome of breast cancer mortality during long-term follow-up. Conclusions and Relevance: For postmenopausal women, the WHI randomized clinical trials do not support menopausal hormone therapy to prevent cardiovascular disease or other chronic diseases. Menopausal hormone therapy is appropriate to treat bothersome vasomotor symptoms among women in early menopause, without contraindications, who are interested in taking hormone therapy. The WHI evidence does not support routine supplementation with calcium plus vitamin D for menopausal women to prevent fractures or a low-fat diet with increased fruits, vegetables, and grains to prevent breast or colorectal cancer. A potential role of a low-fat dietary pattern in reducing breast cancer mortality, a secondary outcome, warrants further study.

Racial and ethnic inequities in spatial access to pharmacies: A geographic information system analysis.

BACKGROUND: Pharmacy accessibility is crucial for equity in health care access because community pharmacists may reach individuals who do not have access to other health care providers. OBJECTIVE: The objective of this study was to determine whether spatial access to pharmacies differs among racial/ethnic groups across the rural-urban continuum. METHODS: We obtained a 30% random sample of the Research Triangle Institute synthetic population, sampled at the census block level. For each individual, we defined optimal pharmacy access as having a driving distance ≤2 miles to the closest pharmacy in urban counties, ≤5 miles in suburban counties, and ≤10 miles in rural counties. We used a logistic regression model to measure the association between race/ethnicity and pharmacy access, while controlling for racial/ethnic composition of the census tract, area deprivation index, income, age, gender, and U.S. region. The model included an interaction between race/ethnicity and urbanicity to evaluate whether racial/ethnic inequities differed across the rural-urban continuum. RESULTS: The sample included 90,749,446 individuals of whom 80.6% had optimal pharmacy access. Racial/ethnic inequities in pharmacy access differed across the rural-urban continuum (P value for interaction= <0.0001). In rural areas, Black (OR 0.87; 95% CI 0.86-0.87), Hispanic (OR 0.80; 95% CI 0.79-0.80), and indigenous (OR 0.47; 95% CI 0.47-0.48) individuals had lower odds of optimal pharmacy access, than White individuals. Hispanic (OR 0.96; 95% CI 0.96-0.97) and Indigenous individuals (OR 0.75; 95% CI 0.75-0.76) had lower odds of optimal pharmacy access compared to White individuals in suburban areas. In Western states, Asian had lower odds of optimal pharmacy access in suburban (OR 0.88; 95% CI 0.86-0.90) and rural areas (OR 0.91; 95% CI 0.87-0.95) compared to White individuals. CONCLUSIONS: Racial/ethnic inequities in spatial access to community pharmacies vary between urban and rural communities. Underrepresented racial/ethnic groups have significantly lower pharmacy access in rural and some suburban areas, but not in urban areas.

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.

Loneliness, social isolation, and cardiovascular disease among nonveteran and veteran women.

We examined the prospective associations of social isolation and loneliness with incident cardiovascular disease (CVD) among aging nonveteran and veteran women, and effect modification by veteran status. Participants with no history of myocardial infarction (MI), stroke, coronary heart disease (CHD), or coronary heart failure from the Women's Health Initiative Extension Study II self-reported social isolation, loneliness, health behaviors, health status, and veteran status. CVD and CVD subevents were physician adjudicated. Hazard ratios (HR) and 95% confidence intervals (CI) for the Interquartile Range (IQR) in social isolation (IQR = 1) and loneliness (IQR=.33) were calculated using Cox proportional hazard models adjusting for sociodemographic, health behavior, and health status characteristics. Veteran status was tested as an effect modifier. Among 52,442 women (Mean age = 79 ± 6.1; veterans n = 1023; 89.2% non-Hispanic White), 3579 major CVD events occurred over an average 5.8 follow-up years. Compared to nonveterans, veteran women reported higher levels of social isolation (p < .01) and loneliness (p < .01). The CVD HR was 1.07 (95% CI, 1.04-1.10) for the IQR in social isolation and 1.03 (95% CI, 1.10-1.06) for the IQR in loneliness. The HR for the IQR in both social isolation and loneliness was 1.10 (95% CI, 1.05-1.15). Social isolation was associated with CHD (HR = 1.12; 95% CI, 1.03-1.21). The CHD HR for the IQR in social isolation was 1.12 (95% CI, 1.03-1.21). Associations did not differ by veteran status (all p-interactions > 0.08). Findings suggest that the adverse associations of social isolation and loneliness with CVD are similar among veteran and nonveteran women.

Development, validation, and transportability of several machine-learned, non-exercise-based VO2max prediction models for older adults.

BACKGROUND: There exist few maximal oxygen uptake (VO2max) non-exercise-based prediction equations, fewer using machine learning (ML), and none specifically for older adults. Since direct measurement of VO2max is infeasible in large epidemiologic cohort studies, we sought to develop, validate, compare, and assess the transportability of several ML VO2max prediction algorithms. METHODS: The Baltimore Longitudinal Study of Aging (BLSA) participants with valid VO2max tests were included (n = 1080). Least absolute shrinkage and selection operator, linear- and tree-boosted extreme gradient boosting, random forest, and support vector machine (SVM) algorithms were trained to predict VO2max values. We developed these algorithms for: (a) the overall BLSA, (b) by sex, (c) using all BLSA variables, and (d) variables common in aging cohorts. Finally, we quantified the associations between measured and predicted VO2max and mortality. RESULTS: The age was 69.0 ± 10.4 years (mean ± SD) and the measured VO2max was 21.6 ± 5.9 mL/kg/min. Least absolute shrinkage and selection operator, linear- and tree-boosted extreme gradient boosting, random forest, and support vector machine yielded root mean squared errors of 3.4 mL/kg/min, 3.6 mL/kg/min, 3.4 mL/kg/min, 3.6 mL/kg/min, and 3.5 mL/kg/min, respectively. Incremental quartiles of measured VO2max showed an inverse gradient in mortality risk. Predicted VO2max variables yielded similar effect estimates but were not robust to adjustment. CONCLUSION: Measured VO2max is a strong predictor of mortality. Using ML can improve the accuracy of prediction as compared to simpler approaches but estimates of association with mortality remain sensitive to adjustment. Future studies should seek to reproduce these results so that VO2max, an important vital sign, can be more broadly studied as a modifiable target for promoting functional resiliency and healthy aging.

An empowerment model for managing menopause.

Menopause eventually happens to all people with typically functioning ovaries, and almost one billion women worldwide are postmenopausal. Although the biology of typical menopause is ubiquitous, the experience varies substantially. Factors contributing to the experience include not only individual factors, such as the nature and severity of symptoms, but also psychological, social, and contextual considerations, many of which are modifiable. In this first paper in the Lancet Series on menopause, we argue for a new approach that goes beyond the treatment of specific symptoms, to encompass a broad model to support women transitioning this life stage, using the model of empowerment. WHO defines empowerment as an active process of gaining knowledge, confidence, and self-determination to self-manage health and make informed decisions about care. Rather than focusing on menopause as an endocrine deficiency, we propose an empowerment model that recognises factors modifying the experience, in which the patient is an expert in their own condition and the health-care worker supports the patient to become an equal and active partner in managing their own care.

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.

The association of leukocyte telomere length with exceptional longevity among older women.

The association of leukocyte telomere length (LTL) with survival to late life with intact mobility has not been adequately studied. This prospective cohort study consisted of 1451 postmenopausal women from a Women's Health Initiative ancillary study, who were eligible, because of birth year, to survive to age 90 as of March 6, 2021. LTL was measured by Southern blot at baseline (1993-1998). Associations between LTL and survival to age 90 were evaluated using logistic regression models adjusted for socio-demographic characteristics, health factors, and lifestyle factors. Multinominal logistic regression was utilized to examine associations of LTL with survival to age 90 with or without intact mobility. Mediation analysis examined the extent to which incident coronary heart disease and stroke-mediated the association between LTL and longevity. Overall, 76.7% of women were White, and 23.3% were Black; average age at baseline was 70.4±3.5 years. Relative to death before age 90, the odds of survival to age 90 were 60% higher (OR, 1.60; 95% CI, 1.28-2.01), the odds of survival to age 90 with mobility limitation were 72% higher (OR, 1.72; 95% CI, 1.33-2.21), and the odds of survival to age 90 with intact mobility were 44% higher (OR, 1.44; 95% CI, 1.06-1.95) for every one kilobase longer LTL. Absence of CHD, stroke, or CHD/stroke mediated the association of LTL with survival to age 90 by 11.1%, 37.4%, and 31.3%, respectively; however, these findings were not significant. Longer LTL was associated with higher odds of survival to age 90 among older women.

Validating a model for predicting breast cancer and nonbreast cancer death in women aged 55 years and older.

BACKGROUND: To support mammography screening decision making, we developed a competing-risk model to estimate 5-year breast cancer risk and 10-year nonbreast cancer death for women aged 55 years and older using Nurses' Health Study data and examined model performance in the Black Women's Health Study (BWHS). Here, we examine model performance in predicting 10-year outcomes in the BWHS, Women's Health Initiative-Extension Study (WHI-ES), and Multiethnic Cohort (MEC) and compare model performance to existing breast cancer prediction models. METHODS: We used competing-risk regression and Royston and Altman methods for validating survival models to calculate our model's calibration and discrimination (C index) in BWHS (n = 17 380), WHI-ES (n = 106 894), and MEC (n = 49 668). The Nurses' Health Study development cohort (n = 48 102) regression coefficients were applied to the validation cohorts. We compared our model's performance with breast cancer risk assessment tool (Gail) and International Breast Cancer Intervention Study (IBIS) models by computing breast cancer risk estimates and C statistics. RESULTS: When predicting 10-year breast cancer risk, our model's C index was 0.569 in BWHS, 0.572 in WHI-ES, and 0.576 in MEC. The Gail model's C statistic was 0.554 in BWHS, 0.564 in WHI-ES, and 0.551 in MEC; IBIS's C statistic was 0.547 in BWHS, 0.552 in WHI-ES, and 0.562 in MEC. The Gail model underpredicted breast cancer risk in WHI-ES; IBIS underpredicted breast cancer risk in WHI-ES and in MEC but overpredicted breast cancer risk in BWHS. Our model calibrated well. Our model's C index for predicting 10-year nonbreast cancer death was 0.760 in WHI-ES and 0.763 in MEC. CONCLUSIONS: Our competing-risk model performs as well as existing breast cancer prediction models in diverse cohorts and predicts nonbreast cancer death. We are developing a website to disseminate our model.

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.