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.

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.

In-Home Cannabis Smoking Among a Cannabis-Using Convenience Sample from the Global Drug Survey: With Weighted Estimates for U.S. Respondents

Introduction: Secondhand and thirdhand tobacco smoke exposure most often occur at home, but little is known about occurrences of in-home cannabis smoking. We ascertained in-home cannabis smoking reported by all cannabis-using (i.e., used in the last 12 months) respondents to the Global Drug Survey (GDS; international-GDS sample), and among U.S. cannabis-using respondents (US-GDS sample). Materials and Methods: We used data collected November 2019-January 2020 for the 2020 GDS, an annual anonymous, cross-sectional survey; respondents were 16+ years old, from 191 countries. We estimated any and daily in-home cannabis smoking in the last 30 days among international-GDS respondents (n=63,797), using mixed effects logistic regression. US-GDS respondents (n=6,580) were weighted to the covariate distribution of the nationally representative 2018 National Survey on Drug Use and Health cannabis-using sample, using inverse odds probability weighting, to make estimates more generalizable to the U.S. cannabis-using population. Results: For the international-GDS cannabis-using respondents, any in-home cannabis smoking was reported by 63.9% of men, 61.9% of women, and 68.6% of nonbinary people; and by age (<25 years old=62.7%, 25-34 years old=65.0%, and 35+ years old=62.8%). Daily in-home cannabis smoking was highest among nonbinary (28.7%) and respondents 35+ years of age (28.0%). For the weighted US-GDS cannabis-using respondents, any in-home cannabis smoking was reported by 49.8% of males and 61.2% of females; and by age (<25 years old=62.6%, 25-34 years old=41.8%, 35+ years old=57.9%). Weighted daily in-home smoking was 23.2% among males and 37.1% among females; by age (<25 years old=34.8%, 25-34 years old=27.8%, and 35+ years old=21.6%). Conclusions: There was high daily cannabis smoking in homes of international-GDS and US-GDS respondents who used cannabis in the last 12 months. In part, due to cannabis legalization, the number of users worldwide has increased over the past decade. Criminal stigma historically associated with cannabis continues to drive those users indoors. In this context, our findings support further investigation of cannabis use behavior to understand how often people are exposed to secondhand and thirdhand cannabis smoke and the consequences of that exposure.

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.

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.

Abstract 30: Associations of Accelerometer-Measured Machine-Learning Classified Sitting With All-Cause and Cardiovascular Disease Mortality Among Older Women: The Opach Study

Introduction: Sedentary behavior (SB) is a recognized mortality and CVD risk factor. Most studies with accelerometry classified SB using cut-points, which do not capture postural transitions as accurately as thigh-worn devices. The recently published convolutional neural network hip accelerometer posture (CHAP) algorithm more accurately classifies sitting than cut-point methods.

Hypothesis: Higher amounts of CHAP-classified sitting time (ST) and mean sitting bout duration (MSBD) are associated with higher all-cause (ACM) and CVD mortality risk.

Methods: Older women (n=6,056; mean age=79±7; 34% Black, 17% Hispanic) in the Objective Physical Activity and Cardiovascular Health (OPACH) study without prior MI or stroke wore accelerometers for 7 days in May 2012-April 2014 and were followed through February 19, 2022 for mortality. The CHAP algorithm has been shown to have higher sensitivity (97.1% vs 88.2%) and specificity (88.6% vs 59.7%) for classifying sitting compared to the 100 counts/minute cut-point. Cox models estimated hazard ratios (HR) and 95% confidence intervals (CI) for ACM and CVD mortality adjusting for age, race/ethnicity, education, alcohol, smoking, multimorbidity, self-rated health, physical functioning, HDL, triglycerides, SBP, and log hs-CRP.

Results: There were 1,808 deaths and 651 CVD deaths over a median follow-up of 8.4 years. The HR (95% CI; P-trend) comparing women in the highest ST quartile (>11.6 hr/day) to those in the lowest (<9.2 hr/day) was 1.43 (1.23-1.66; <0.001) for ACM and 1.64 (1.27-2.14; <0.001) for CVD mortality. The HR (95% CI; P-trend) comparing women in the highest MSBD quartile (>15 minutes) to those in the lowest (<9.3 minutes) was 1.33 (1.15-1.55; <0.001) for ACM and 1.50 (1.16-1.94; <0.001) for CVD mortality. The HR (95% CI) for women with the highest ST and MSBD was 1.63 (1.38-1.93) for ACM and 1.95 (1.47-2.60) for CVD mortality.

Conclusions: ST and MSBD were positively associated with ACM and CVD mortality risk, supporting interventions aimed at reducing both ST and MSBD.

In-home cannabis smoking more prevalent than in-home tobacco smoking among 2019 Global Drug Survey respondents

PURPOSE OF THE RESEARCH

Early evidence from studies of home smoking policies suggests that in-home cannabis smoking is more often allowed than in-home tobacco smoking, but there are not yet data on whether cannabis is more often smoked in the home compared to tobacco, or whether in-home cannabis and tobacco smoking differs by usage status. Using cross-sectional data from over 100,000 sentinel drug users from 17 countries, we compared cannabis and tobacco smoking in the homes of Global Drug Survey 2019 respondents who currently used cannabis only, tobacco only, both tobacco and cannabis, or neither.

PRINCIPAL RESULTS

Complete data on cannabis and tobacco use and in-home smoking were available for 107,272 adults (average age = 30 ± 12, 34% women, countries with the three highest response rates; Germany = 32%, USA = 10%, New Zealand = 9%). In total, 53.6% and 50.6% of respondents reported past-year cannabis and tobacco smoking in their home respectively. Stratifying respondents into current cannabis-only users, tobacco-only users, dual users, and non-users, past-year in-home cannabis smoking was more prevalent (78.8%) among cannabis-only users than was in-home tobacco smoking (67.9%) among tobacco-only users. Among dual users, past-year in-home cannabis smoking (82.8%; 95%CI = 80%-86%) was higher than in-home tobacco smoking (75.9%; 95%CI = 69%-81%; p < 0.001). In-home cannabis and tobacco smoking was similar among non-users (20.2% and 20.5%, respectively).

MAJOR CONCLUSIONS

Past-year in-home cannabis smoking was more prevalent than tobacco smoking in the homes of participants who used cannabis and/or tobacco, supporting our speculation that in-home cannabis smoking might be more socially acceptable than in-home tobacco smoking.

Accelerometer-Measured Patterns of Sedentary Behavior in Older Women: The OPACH Study

Excessive sedentary behavior (SB) is related to deleterious health outcomes. Understanding the patterns and contexts in which SB accumulates can promote healthy aging. Daily sitting time and mean sitting bout duration (MBD) were measured by triaxial accelerometers. Participants self-reported how much time they spent sitting while: watching TV, reading, using the computer, driving, working, or taking phone calls. Data were compared across aging-related characteristics. Age-adjusted sitting time (minutes/day) for 5,838 diverse (33.2% Black, 16.9% Hispanic), older women (mean age 78.7±6.7) were 577.2 for Hispanic women, 630.3 for Black women, and 632.0 for White women. Those in the lowest vs. highest physical function category had the longest MBD (16.1 vs. 11.7 minutes/bout). Watching television was the most common self-reported sedentary activity. The highest vs. lowest quartile of MBD spent, on average, 30.6 and 22.3 minutes/day watching television, respectively. This presentation will illuminate critical factors associated with sitting patterns in older adults.