Phyllis A. Richey

BACKGROUND Wearable accelerometers can record minute-level physical activity (PA) repeatedly over time. Longitudinal change is usually summarized as a change in daily activity volume, which discards when during the day activity is gained or lost. A representation of within-person change in the full daily activity curve could provide more informative measures of functional aging. OBJECTIVE This study aimed to characterize within-person changes in the timing and magnitude of daily PA in older women and to examine whether features of these changes were associated with physical function (PF). METHODS We analyzed hip-worn accelerometry from the Objective Physical Activity and Cardiovascular Health (OPACH) baseline assessment and two Women’s Health Initiative Strong and Healthy (WHISH) follow-up assessments in 1,730 women with at least two consecutive valid assessments. Change between smoothed diurnal PA curves (6:00 a.m. to midnight) was modeled as a Riemannian deformation, whose initial momenta represent timing- and magnitude-related changes. Multivariate functional principal component analysis (MFPCA) summarized these changes as participant-level principal component (PC) scores. Linear mixed-effects models related PC scores and deformation energy, a nondirectional measure of pattern change, to RAND-36 PF, adjusting for baseline PF and covariates. A second model replaced PC scores with the change in net area under the activity curve (Δnet-AUC ), a volume summary. RESULTS On average, activity declined across the day from baseline to WHISH wave 1 (baseline–W1) and from WHISH wave 1 to wave 2 (W1–W2), with morning activity shifted later, and activity after about 10:20 a.m. shifted earlier. Fifteen PCs captured at least 90% of total variance in each transition. Of the variance captured by these 15 PCs, the first PC (PC1) accounted for 22.4% in baseline–W1 and 20.8% in W1–W2. Higher PC1 scores reflected residual upward shifts in activity, especially between 10 a.m. and 7 p.m., and was highly correlated with Δnet-AUC (r=0.9). Among 1,157 participants with complete data, PC1 scores were positively associated with PF scores after adjustment for baseline PF and covariates (P<.001). An interquartile increase in PC1 corresponded to a 1.3-point higher PF score (SE = 0.33). Deformation energy was more strongly associated with PF in W1–W2 than in baseline–W1 (interaction β = 387.4, SE = 129.84, P=.003), and this interaction persisted in the model with Δnet-AUC (P=.005). CONCLUSIONS Riemannian deformation analysis of repeated wearable accelerometry characterized changes in when and how much older women were active. The dominant mode of change largely tracked activity volume and was associated with PF. Jointly modeling changes in activity timing and magnitude provides a framework for studying functional aging using longitudinal wearable data.

Objective: To examine the association between Life Essential 8 (LE8) and incident T2D in the Women’s Health Initiative (WHI), and to assess whether associations varied by race and ethnicity. Research Design and Methods: Prospective cohort study of 19,403 postmenopausal women enrolled in the WHI without T2D at baseline. Data were analyzed from 1993 through 2024. The LE8 score (range, 0–100), comprising blood glucose, blood lipids, blood pressure, smoking, physical activity, diet, sleep, and body mass index (BMI), categorized as high (80–100), moderate (50–79), and low (0–49) according to AHA definitions. Incident treated T2D was self-reported during follow-up. Cox proportional hazards models estimated hazard ratios (HRs) and 95% CIs for LE8 categories and continuous scores. Results: During a mean follow-up of 16.3 years, 3921 women developed T2D. Compared with the lowest category, women in the highest LE8 category had a 57% lower risk of T2D (HR, 0.43; 95% CI, 0.38–0.49). A 20-point increase in LE8 score was associated with a 43% lower risk (HR, 0.57; 95% CI, 0.54–0.60). Among individual domains, BMI and glucose were most strongly associated with T2D. Subgroup analyses by 20-point increase in LE8 showed greater risk reduction among Hispanic/Latina women (HR, 0.46; 95% CI, 0.41–0.53) compared with non-Hispanic women (HR, 0.58; 95% CI, 0.55–0.62), but no significant association with race was observed. Conclusions: Higher LE8 scores are associated with a reduced risk of T2D in postmenopausal women, supporting LE8 as a useful framework for lifestyle-based diabetes prevention strategies.

Background:Prolonged sitting and physical inactivity are associated with higher circulating levels of estrogens. It is unknown whether these risk factors are associated with circulating androgens/androgen metabolites, another set of hormones implicated in the etiology of cancers in postmenopausal women.Methods:We conducted a cross-sectional analysis of 1,782 postmenopausal women in the Women's Health Initiative Observational Study. Serum concentrations of 12 androgens/androgen metabolites were quantified using liquid chromatography-tandem mass spectrometry. Physical activity and sitting time were self-reported at baseline. We performed linear regression to estimate geometric means (GM) of androgen/androgen metabolite concentrations (pmol/L) according to physical activity and sitting time, adjusting for potential confounders and stratified by menopausal hormone therapy (MHT) use.Results:Physical activity (≥15 vs. 0 MET-h/wk) was inversely associated with estrogen-to-androgen ratios among never/former MHT users (adj-GM = 37.5 vs. 49.6 unconjugated estrone:androstenedione; 20.2 vs. 30.3 unconjugated estradiol:testosterone; all Ptrend ≤ 0.03) but was not associated among current MHT users. Prolonged sitting (≥10 vs. ≤5 h/d) was positively associated with these ratios among both never/former (adj-GM = 44.2 vs. 38.3, Ptrend = 0.10; adj-GM = 23.4 vs. 20.2, Ptrend = 0.17; respectively) and current MHT users (adj-GM = 197 vs. 147; 105 vs. 75.5; respectively; all Ptrend ≤0.02), but the associations were statistically significant among current MHT users only. The associations persisted after adjustment for BMI. After adjustment for adrenal androgens, physical activity and sitting were not associated with androgen metabolites.Conclusions:Physical activity and sitting were associated with serum estrogen-to-androgen ratios but not androgen metabolites.Impact:This study contributes to our understanding of the link between physical activity, sitting, and cancer risk in postmenopausal women.

Supplementary Table 1 shows population characteristics among current MHT users; Supplementary Table 2 shows relative proportions of adrenal androgens and estrogens; Supplementary Table 3-6 show the associations with adjustment for waist-to-hip ratio; Supplementary Table 7-10 show the associations stratified by current BMI

Background: Prolonged sitting and physical inactivity are associated with higher circulating levels of estrogens. It is unknown whether these risk factors are associated with circulating androgens/androgen metabolites, another set of hormones implicated in the etiology of cancers in postmenopausal women. Methods: We conducted a cross-sectional analysis of 1,782 postmenopausal women in the Women's Health Initiative Observational Study. Serum concentrations of 12 androgens/androgen metabolites were quantified using liquid chromatography-tandem mass spectrometry. Physical activity and sitting time were self-reported at baseline. We performed linear regression to estimate geometric means (GM) of androgen/androgen metabolite concentrations (pmol/L) according to physical activity and sitting time, adjusting for potential confounders and stratified by menopausal hormone therapy (MHT) use. Results: Physical activity (≥15 vs. 0 MET-h/wk) was inversely associated with estrogen-to-androgen ratios among never/former MHT users (adj-GM = 37.5 vs. 49.6 unconjugated estrone:androstenedione; 20.2 vs. 30.3 unconjugated estradiol:testosterone; all Ptrend ≤ 0.03) but was not associated among current MHT users. Prolonged sitting (≥10 vs. ≤5 h/d) was positively associated with these ratios among both never/former (adj-GM = 44.2 vs. 38.3, Ptrend = 0.10; adj-GM = 23.4 vs. 20.2, Ptrend = 0.17; respectively) and current MHT users (adj-GM = 197 vs. 147; 105 vs. 75.5; respectively; all Ptrend ≤0.02), but the associations were statistically significant among current MHT users only. The associations persisted after adjustment for BMI. After adjustment for adrenal androgens, physical activity and sitting were not associated with androgen metabolites. Conclusions: Physical activity and sitting were associated with serum estrogen-to-androgen ratios but not androgen metabolites. Impact: This study contributes to our understanding of the link between physical activity, sitting, and cancer risk in postmenopausal women.

Background: Electronic self-monitoring technology has the potential to provide unique insights into important behaviors for inducing weight loss. Objective: The aim of this study is to investigate the effects of electronic self-monitoring behavior (using the commercial Lose It! app) and weight loss interventions (with differing amounts of counselor feedback and support) on 4- and 12-month weight loss. Methods: In this secondary analysis of the Fit Blue study, we compared the results of two interventions of a randomized controlled trial. Counselor-initiated participants received consistent support from the interventionists, and self-paced participants received assistance upon request. The participants (N=191), who were active duty military personnel, were encouraged to self-monitor their diet and exercise with the Lose It! app or website. We examined the associations between intervention assignment and self-monitoring behaviors. We conducted a mediation analysis of the intervention assignment for weight loss through multiple mediators—app use (calculated from the first principal component [PC] of electronically collected variables), number of weigh-ins, and 4-month weight change. We used linear regression to predict weight loss at 4 and 12 months, and the accuracy was measured using cross-validation. Results: On average, the counselor-initiated–treatment participants used the app more frequently than the self-paced–treatment participants. The first PC represented app use frequencies, the second represented calories recorded, and the third represented reported exercise frequency and exercise caloric expenditure. We found that 4-month weight loss was partially mediated through app use (ie, the first PC; 60.3%) and the number of weigh-ins (55.8%). However, the 12-month weight loss was almost fully mediated by 4-month weight loss (94.8%). Linear regression using app data from the first 8 weeks, the number of self–weigh-ins at 8 weeks, and baseline data explained approximately 30% of the variance in 4-month weight loss. App use frequency (first PC; P=.001), self-monitored caloric intake (second PC; P=.001), and the frequency of self-weighing at 8 weeks (P=.008) were important predictors of 4-month weight loss. Predictions for 12-month weight with the same variables produced an R2 value of 5%; only the number of self–weigh-ins was a significant predictor of 12-month weight loss. The R2 value using 4-month weight loss as a predictor was 31%. Self-reported exercise did not contribute to either model (4 months: P=.77; 12 months: P=.15). Conclusions: We found that app use and daily reported caloric intake had a substantial impact on weight loss prediction at 4 months. Our analysis did not find evidence of an association between participant self-monitoring exercise information and weight loss. As 12-month weight loss was completely mediated by 4-month weight loss, intervention targets should focus on promoting early and frequent dietary intake self-monitoring and self-weighing to promote early weight loss, which leads to long-term success. Trial Registration: ClinicalTrials.gov NCT02063178; https://clinicaltrials.gov/ct2/show/NCT02063178

BACKGROUND Electronic self-monitoring technology has the potential to provide unique insights into important behaviors for inducing weight loss. OBJECTIVE The aim of this study is to investigate the effects of electronic self-monitoring behavior (using the commercial Lose It! app) and weight loss interventions (with differing amounts of counselor feedback and support) on 4- and 12-month weight loss. METHODS In this secondary analysis of the Fit Blue study, we compared the results of two interventions of a randomized controlled trial. Counselor-initiated participants received consistent support from the interventionists, and self-paced participants received assistance upon request. The participants (N=191), who were active duty military personnel, were encouraged to self-monitor their diet and exercise with the Lose It! app or website. We examined the associations between intervention assignment and self-monitoring behaviors. We conducted a mediation analysis of the intervention assignment for weight loss through multiple mediators—app use (calculated from the first principal component [PC] of electronically collected variables), number of weigh-ins, and 4-month weight change. We used linear regression to predict weight loss at 4 and 12 months, and the accuracy was measured using cross-validation. RESULTS On average, the counselor-initiated–treatment participants used the app more frequently than the self-paced–treatment participants. The first PC represented app use frequencies, the second represented calories recorded, and the third represented reported exercise frequency and exercise caloric expenditure. We found that 4-month weight loss was partially mediated through app use (ie, the first PC; 60.3%) and the number of weigh-ins (55.8%). However, the 12-month weight loss was almost fully mediated by 4-month weight loss (94.8%). Linear regression using app data from the first 8 weeks, the number of self–weigh-ins at 8 weeks, and baseline data explained approximately 30% of the variance in 4-month weight loss. App use frequency (first PC; P=.001), self-monitored caloric intake (second PC; P=.001), and the frequency of self-weighing at 8 weeks (P=.008) were important predictors of 4-month weight loss. Predictions for 12-month weight with the same variables produced an R2 value of 5%; only the number of self–weigh-ins was a significant predictor of 12-month weight loss. The R2 value using 4-month weight loss as a predictor was 31%. Self-reported exercise did not contribute to either model (4 months: P=.77; 12 months: P=.15). CONCLUSIONS We found that app use and daily reported caloric intake had a substantial impact on weight loss prediction at 4 months. Our analysis did not find evidence of an association between participant self-monitoring exercise information and weight loss. As 12-month weight loss was completely mediated by 4-month weight loss, intervention targets should focus on promoting early and frequent dietary intake self-monitoring and self-weighing to promote early weight loss, which leads to long-term success. CLINICALTRIAL ClinicalTrials.gov NCT02063178; https://clinicaltrials.gov/ct2/show/NCT02063178

Objective To determine whether there is an association between body mass index (BMI) and body esteem in young overweight and obese urban children, and to test peer relationship difficulties and perceived physical health as mediators of this relationship. Methods Child self‐reported body esteem, and parent‐reported child peer relationship difficulties (being bullied by peers and peer rejection) and physical health perceptions were obtained from 218 overweight and obese children aged 5–7 years (81% racial/ethnic minority, M BMI = 25.3) and their primary caregivers. Results Higher BMI was associated with lower body esteem for both girls and boys. This relation was mediated by poor physical health for boys but not for girls. Peer relationship difficulties did not mediate the observed association between BMI and body esteem in either group; however, girls with higher BMI experienced more bullying and being bullied by peers was associated with lower body esteem in girls. Conclusions Intervening with perceptions of physical health may buffer overweight and obese boys from developing low body esteem in early childhood.