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

BACKGROUND: The relative intensity of a 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±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 hazards models estimated hazard ratios (HRs) for one-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, was associated with reduced risk of both outcomes. HRs for a one-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 one-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 one-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 one-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, inversely associated with both outcomes than relative MVPA. Absolute MVPA was more strongly inversely associated with the outcomes than relative MVPA. CONCLUSION: Findings support the continued shift in the PA intensity paradigm towards 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.

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.

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.

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.

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 .

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.

Smoking Cessation Smartphone App Use Over Time: Predicting 12-Month Cessation Outcomes in a 2-Arm Randomized Trial.

BACKGROUND: Little is known about how individuals engage over time with smartphone app interventions and whether this engagement predicts health outcomes. OBJECTIVE: In the context of a randomized trial comparing 2 smartphone apps for smoking cessation, this study aimed to determine distinct groups of smartphone app log-in trajectories over a 6-month period, their association with smoking cessation outcomes at 12 months, and baseline user characteristics that predict data-driven trajectory group membership. METHODS: Functional clustering of 182 consecutive days of smoothed log-in data from both arms of a large (N=2415) randomized trial of 2 smartphone apps for smoking cessation (iCanQuit and QuitGuide) was used to identify distinct trajectory groups. Logistic regression was used to determine the association of group membership with the primary outcome of 30-day point prevalence of smoking abstinence at 12 months. Finally, the baseline characteristics associated with group membership were examined using logistic and multinomial logistic regression. The analyses were conducted separately for each app. RESULTS: For iCanQuit, participants were clustered into 3 groups: "1-week users" (610/1069, 57.06%), "4-week users" (303/1069, 28.34%), and "26-week users" (156/1069, 14.59%). For smoking cessation rates at the 12-month follow-up, compared with 1-week users, 4-week users had 50% higher odds of cessation (30% vs 23%; odds ratio [OR] 1.50, 95% CI 1.05-2.14; P=.03), whereas 26-week users had 397% higher odds (56% vs 23%; OR 4.97, 95% CI 3.31-7.52; P<.001). For QuitGuide, participants were clustered into 2 groups: "1-week users" (695/1064, 65.32%) and "3-week users" (369/1064, 34.68%). The difference in the odds of being abstinent at 12 months for 3-week users versus 1-week users was minimal (23% vs 21%; OR 1.16, 95% CI 0.84-1.62; P=.37). Different baseline characteristics predicted the trajectory group membership for each app. CONCLUSIONS: Patterns of 1-, 3-, and 4-week smartphone app use for smoking cessation may be common in how people engage in digital health interventions. There were significantly higher odds of quitting smoking among 4-week users and especially among 26-week users of the iCanQuit app. To improve study outcomes, strategies for detecting users who disengage early from these interventions (1-week users) and proactively offering them a more intensive intervention could be fruitful.

Accelerometer-Derived Daily Life Movement Classified by Machine Learning and Incidence of Cardiovascular Disease in Older Women: The OPACH Study.

Background Current physical activity guidelines focus on volume and intensity for CVD prevention rather than common behaviors responsible for movement, including those for daily living activities. We examined the associations of a machine-learned, accelerometer-measured behavior termed daily life movement (DLM) with incident CVD. Methods and Results Older women (n=5416; mean age, 79±7 years; 33% Black, 17% Hispanic) in the Women's Health Initiative OPACH (Objective Physical Activity and Cardiovascular Health) study without prior CVD wore ActiGraph GT3X+ accelerometers for up to 7 days from May 2012 to April 2014 and were followed for physician-adjudicated incident CVD through February 28th, 2020 (n=616 events). DLM was defined as standing and moving in a confined space such as performing housework or gardening. Cox models estimated hazard ratios (HR) and 95% CI, adjusting for age, race and ethnicity, education, alcohol use, smoking, multimorbidity, self-rated health, and physical function. Restricted cubic splines examined the linearity of the DLM-CVD dose-response association. We examined effect modification by age, body mass index, Reynolds Risk Score, and race and ethnicity. Adjusted HR (95% CIs) across DLM quartiles were: 1.00 (reference), 0.68 (0.55-0.84), 0.70 (0.56-0.87), and 0.57 (0.45-0.74); p-trend<0.001. The HR (95% CI) for each 1-hour increment in DLM was 0.86 (0.80-0.92) with evidence of a linear dose-response association (p non-linear>0.09). There was no evidence of effect modification by age, body mass index, Reynolds Risk Score, or race and ethnicity. Conclusions Higher DLM was independently associated with a lower risk of CVD in older women. Describing the beneficial associations of physical activity in terms of common behaviors could help older adults accumulate physical activity.

Associations of Daily Steps and Step Intensity With Incident Diabetes in a Prospective Cohort Study of Older Women: The OPACH Study.

OBJECTIVE: The primary aim was to assess associations between total steps per day and incident diabetes, whereas the secondary aim was to assess whether the intensity and/or cadence of steps is associated with incident diabetes. RESEARCH DESIGN AND METHODS: Women without physician-diagnosed diabetes (n = 4,838; mean [SD] age 78.9 [6.7] years) were followed up to 6.9 years; 395 developed diabetes. Hip-worn ActiGraph GT3X+ accelerometers worn for 1 week enabled measures of total, light-intensity, and moderate- to vigorous-intensity (MV-intensity) steps per day. Using Cox proportional hazards analysis we modeled adjusted change in the hazard rate for incident diabetes associated with total, light-intensity, and MV-intensity steps per day. We further estimated the proportion of the steps-diabetes association mediated by BMI. RESULTS: On average, participants took 3,729 (SD 2,114) steps/day, of which 1,875 (791) were light-intensity steps and 1,854 ± 1,762 were MV-intensity. More steps per day were associated with a lower hazard rate for incident diabetes. Confounder-adjusted models for a 2,000 steps/day increment yielded hazard ratio (HR) 0.88 (95% CI 0.78-1.00; P = 0.046). After further adjustment for BMI, HR was 0.90 (95% CI 0.80-1.02; P = 0.11). BMI did not significantly mediate the steps-diabetes association (proportion mediated = 17.7% [95% CI -55.0 to 142.0]; P = 0.09]). The relationship between MV-intensity steps per day (HR 0.86 [95% CI 0.74-1.00]; P = 0.04) and incident diabetes was stronger than for light-intensity steps per day (HR 0.97 [95% CI 0.73-1.29]; P = 0.84). CONCLUSIONS: These findings suggest that for older adults, more steps per day are associated with lower incident diabetes and MV-intensity steps are most strongly associated with a lower hazard of diabetes. This evidence supports that regular stepping is an important risk factor for type 2 diabetes prevention in older adults.

Accelerometer-Measured Daily Steps, Physical Function, and Subsequent Fall Risk in Older Women: The Objective Physical Activity and Cardiovascular Disease in Older Women Study.

Steps per day were measured by accelerometer for 7 days among 5,545 women aged 63-97 years between 2012 and 2014. Incident falls were ascertained from daily fall calendars for 13 months. Median steps per day were 3,216. There were 5,473 falls recorded over 61,564 fall calendar-months. The adjusted incidence rate ratio comparing women in the highest versus lowest step quartiles was 0.71 (95% confidence interval [0.54, 0.95]; ptrend across quartiles = .01). After further adjustment for physical function using the Short Physical Performance Battery, the incidence rate ratio was 0.86 ([0.64, 1.16]; ptrend = .27). Mediation analysis estimated that 63.7% of the association may be mediated by physical function (p = .03). In conclusion, higher steps per day were related to lower incident falls primarily through their beneficial association with physical functioning. Interventions that improve physical function, including those that involve stepping, could reduce falls in older adults.

CHAP-Adult: A Reliable and Valid Algorithm to Classify Sitting and Measure Sitting Patterns Using Data From Hip-Worn Accelerometers in Adults Aged 35.

Background: Hip-worn accelerometers are commonly used, but data processed using the 100 counts per minute cut point do not accurately measure sitting patterns. We developed and validated a model to accurately classify sitting and sitting patterns using hip-worn accelerometer data from a wide age range of older adults. Methods: Deep learning models were trained with 30-Hz triaxial hip-worn accelerometer data as inputs and activPAL sitting/nonsitting events as ground truth. Data from 981 adults aged 35-99 years from cohorts in two continents were used to train the model, which we call CHAP-Adult (Convolutional Neural Network Hip Accelerometer Posture-Adult). Validation was conducted among 419 randomly selected adults not included in model training. Results: Mean errors (activPAL - CHAP-Adult) and 95% limits of agreement were: sedentary time -10.5 (-63.0, 42.0) min/day, breaks in sedentary time 1.9 (-9.2, 12.9) breaks/day, mean bout duration -0.6 (-4.0, 2.7) min, usual bout duration -1.4 (-8.3, 5.4) min, alpha .00 (-.04, .04), and time in ≥30-min bouts -15.1 (-84.3, 54.1) min/day. Respective mean (and absolute) percent errors were: -2.0% (4.0%), -4.7% (12.2%), 4.1% (11.6%), -4.4% (9.6%), 0.0% (1.4%), and 5.4% (9.6%). Pearson's correlations were: .96, .92, .86, .92, .78, and .96. Error was generally consistent across age, gender, and body mass index groups with the largest deviations observed for those with body mass index ≥30 kg/m2. Conclusions: Overall, these strong validation results indicate CHAP-Adult represents a significant advancement in the ambulatory measurement of sitting and sitting patterns using hip-worn accelerometers. Pending external validation, it could be widely applied to data from around the world to extend understanding of the epidemiology and health consequences of sitting.

Cohort profile: the Women's Health Accelerometry Collaboration.

PURPOSE: This paper describes the Women's Health Accelerometry Collaboration, a consortium of two prospective cohort studies of women age 62 years or older, harmonised to explore the association of accelerometer-assessed physical activity and sedentary behaviour with cancer incidence and mortality. PARTICIPANTS: A total of 23 443 women (age mean 73.4, SD 6.8) living in the USA and participating in an observational study were included; 17 061 from the Women's Health Study (WHS) and 6382 from the Women's Health Initiative Objective Physical Activity and Cardiovascular Health (WHI/OPACH) Study. FINDINGS TO DATE: Accelerometry, cancer outcomes and covariate harmonisation was conducted to align the two cohort studies. Physical activity and sedentary behaviour were measured using similar procedures with an ActiGraph GT3X+ accelerometer, worn at the hip for 1 week, during 2011-2014 for WHS and 2012-2014 for WHI/OPACH. Cancer outcomes were ascertained via ongoing surveillance using physician adjudicated cancer diagnosis. Relevant covariates were measured using questionnaire or physical assessments. Among 23 443 women who wore the accelerometer for at least 10 hours on a single day, 22 868 women wore the accelerometer at least 10 hours/day on ≥4 of 7 days. The analytical sample (n=22 852) averaged 4976 (SD 2669) steps/day and engaged in an average of 80.8 (SD 46.5) min/day of moderate-to-vigorous, 105.5 (SD 33.3) min/day of light high and 182.1 (SD 46.1) min/day of light low physical activity. A mean of 8.7 (SD 1.7) hours/day were spent in sedentary behaviour. Overall, 11.8% of the cohort had a cancer diagnosis (other than non-melanoma skin cancer) at the time of accelerometry measurement. During an average of 5.9 (SD 1.6) years of follow-up, 1378 cancer events among which 414 were fatal have occurred. FUTURE PLANS: Using the harmonised cohort, we will access ongoing cancer surveillance to quantify the associations of physical activity and sedentary behaviour with cancer incidence and mortality.