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Background: Caregiving is commonly undertaken by older women. Research is mixed, however, about the impact of prolonged caregiving on their health, well‐being, and mortality risk. Using a prospective study design, we examined the association of caregiving with mortality in a cohort of older women. Methods: Participants were 158,987 postmenopausal women aged 50–79 years at enrollment into the Women's Health Initiative (WHI) who provided information on current caregiving status and caregiving frequency at baseline (1993–1998) and follow‐up (2004–2005). Mortality was ascertained from baseline through March of 2019. Cox regression with caregiving status defined as a time‐varying exposure was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for mortality, adjusting for sociodemographic factors, smoking, and history of diabetes, hypertension, cardiovascular disease (CVD), and cancer. Stratified analyses explored whether age, race‐ethnicity, depressive symptoms, frequency of caregiving, optimism, and living status modified the association between caregiver status and mortality. Results: At baseline, 40.7% of women (mean age 63.3 years) self‐identified as caregivers. During a mean 17.5‐year follow‐up, all‐cause mortality (50,526 deaths) was 9% lower (multivariable‐adjusted HR = 0.91, 95% CI: 0.89–0.93) in caregivers compared to non‐caregivers. The inverse association between caregiving and all‐cause mortality did not differ according to caregiving frequency or when stratified by age, race‐ethnicity, depressive symptoms, optimism, or living status (interaction p > 0.05, all). Caregiving was inversely associated with CVD and cancer mortality. Conclusion: Among postmenopausal women residing across the United States, caregiving was associated with lower mortality. Studies detailing the type and amount of caregiving are needed to further determine its impact on older women. See related editorial by Dillon et al in this issue. [ABSTRACT FROM AUTHOR]

Background Epigenetic age acceleration (EAA), a measure of accelerated biological aging, has been associated with an increased risk of several age-related chronic conditions. This is the first study to prospectively examine the relationship between EAA and both multimorbidity count and a weighted multimorbidity score among long-lived postmenopausal women. Methods We included 1 951 women from the Women's Health Initiative who could have survived to age 90. EAA was estimated using the Horvath pan-tissue, Hannum, PhenoAge, and GrimAge "clocks." Twelve chronic conditions were included in the multimorbidity count. The multimorbidity score was weighted for each morbidity's relationship with mortality in the study population. Using mixed-effects Poisson and linear regression models that included baseline covariates associated with both EAA and multimorbidity, we estimated relative risks (RRs) and 95% confidence intervals (CIs) for the relationships between each EAA measure at the study baseline with both multimorbidity count and weighted multimorbidity score at age 90, respectively. Results For every one standard deviation increase in AgeAccelPheno, the rate of multimorbidity accumulation increased 6% (RR = 1.06; 95% CI = 1.01–1.12; p  = .025) and the multimorbidity score by 7% (RR = 1.07; 95% CI = 1.01–1.13; p  = .014) for women who survived to age 90. The results for a one standard deviation increase in AgeAccelHorvath, AgeAccelHannum, and AgeAccelGrim with multimorbidity accumulation and score were weaker compared to AgeAccelPheno, and the latter 2 did not reach statistical significance. Conclusion AgeAccelPheno and AgeAccelHannum may predict multimorbidity count and score at age 90 in older women and, thus, may be useful as a biomarker predictor of multimorbidity burden in the last decades of life. [ABSTRACT FROM AUTHOR]

The relationship between average daily step counts and age, body mass index (BMI), self-reported physical activity (PA) level, and perceived health was determined in 85 middle-aged and older adults who wore a pedometer for 7 consecutive days. Average daily steps were significantly (p less than 0.05) correlated with BMI (r = -0.26), age (r = -0.44) and perceived health (r = 0.53) but not with self-reported PA level (r = 0.19). The adjusted percentage of participants classified as meeting the PA recommendation in less than or equal to 5,000, 5,001-9,999, and greater than or equal to 10,000 steps/day categories ranged from 53 to 61%. These findings support previous evidence in younger populations suggesting that the recommended minimal level of health-related PA may be achieved despite not accumulating 10,000 steps/day. (Contains 2 tables.)

The purpose of this study was to develop a regression equation to predict maximal oxygen uptake (VO[subscript 2]max) based on nonexercise (N-EX) data. All participants (N = 100), ages 18-65 years, successfully completed a maximal graded exercise test (GXT) to assess VO[subscript 2]max (M = 39.96 mL[middle dot]kg[superscript -1][middle dot]min[superscript -1], SD = 9.54). The N-EX data collected just before the maximal GXT included the participant's age; gender; body mass index (BMI); perceived functional ability (PFA) to walk, jog, or run given distances; and current physical activity (PA-R) level. Multiple linear regression generated the following N-EX prediction equation (R = 0.93, SEE = 3.45 mL[middle dot]kg[superscript -1][middle dot]min[superscript -1], % SEE = 8.62): VO[subscript 2]max (mL[middle dot]kg[superscript -1][middle dot]min[superscript -1]) = 48.0730 + (6.1779 x gender; women = 0, men = 1) - (0.2463 x age) - (0.6186 x BMI) + (0.7115 x PFA) + (0.6709 x PA-R). Cross validation using PRESS (predicted residual sum of squares) statistics revealed minimal shrinkage (R[subscript p] = 0.91 and SEE[subscript p] = 3.63 mL[middle dot]kg[superscript -1][middle dot]min[superscript -1]); thus, this model should yield acceptable accuracy when applied to an independent sample of adults (ages 18-65 years) with a similar cardiorespiratory fitness level. Based on standardized Beta-weights, the PFA variable (0.41) was the most effective at predicting VO[subscript 2]max followed by age (-0.34), gender (0.33), BMI (-0.27), and PA-R (0.16). This study provides a N-EX regression model that yields relatively accurate results and is a convenient way to predict VO[subscript 2]max in adult men and women. (Contains 3 tables and 2 figures.)