Performance of digital screening mammography in a population-based cohort of black and white women

There is scarce information on whether digital screening mammography performance differs between black and white women

Analysis of the 24-Hour Activity Cycle: An illustration examining the association with cognitive function in the Adult Changes in Thought (ACT) Study

The 24-hour activity cycle (24HAC) is a new paradigm for studying activity behaviors in relation to health outcomes. This approach captures the interrelatedness of the daily time spent in physical activity (PA), sedentary behavior (SB), and sleep. We illustrate and compare the use of three popular approaches, namely isotemporal substitution model (ISM), compositional data analysis (CoDA), and latent profile analysis (LPA) for modeling outcome associations with the 24HAC. We apply these approaches to assess an association with a cognitive outcome, measured by CASI item response theory (IRT) score, in a cohort of 1034 older adults (mean [range] age = 77 [65-100]; 55.8% female; 90% White) who were part of the Adult Changes in Thought (ACT) Activity Monitoring (ACT-AM) sub-study. PA and SB were assessed with thigh-worn activPAL accelerometers for 7 days. We highlight differences in assumptions between the three approaches, discuss statistical challenges, and provide guidance on interpretation and selecting an appropriate approach. ISM is easiest to apply and interpret; however, the typical ISM model assumes a linear association. CoDA specifies a non-linear association through isometric logratio transformations that are more challenging to apply and interpret. LPA can classify individuals into groups with similar time-use patterns. Inference on associations of latent profiles with health outcomes need to account for the uncertainty of the LPA classifications which is often ignored. The selection of the most appropriate method should be guided by the scientific questions of interest and the applicability of each model's assumptions. The analytic results did not suggest that less time spent on SB and more in PA was associated with better cognitive function. Further research is needed into the health implications of the distinct 24HAC patterns identified in this cohort.Comment: 51 pages, 11 tables, 8 figure

Front Psychol

The 24-h activity cycle (24HAC) is a new paradigm for studying activity behaviors in relation to health outcomes. This approach inherently captures the interrelatedness of the daily time spent in physical activity (PA), sedentary behavior (SB), and sleep. We describe three popular approaches for modeling outcome associations with the 24HAC exposure. We apply these approaches to assess an association with a cognitive outcome in a cohort of older adults, discuss statistical challenges, and provide guidance on interpretation and selecting an appropriate approach. We compare the use of the isotemporal substitution model (ISM), compositional data analysis (CoDA), and latent profile analysis (LPA) to analyze 24HAC. We illustrate each method by exploring cross-sectional associations with cognition in 1,034 older adults (Mean age = 77; Age range = 65-100; 55.8% female; 90% White) who were part of the Adult Changes in Thought (ACT) Activity Monitoring (ACT-AM) sub-study. PA and SB were assessed with thigh-worn activPAL accelerometers for 7-days. For each method, we fit a multivariable regression model to examine the cross-sectional association between the 24HAC and Cognitive Abilities Screening Instrument item response theory (CASI-IRT) score, adjusting for baseline characteristics. We highlight differences in assumptions and the scientific questions addressable by each approach. ISM is easiest to apply and interpret; however, the typical ISM assumes a linear association. CoDA uses an isometric log-ratio transformation to directly model the compositional exposure but can be more challenging to apply and interpret. LPA can serve as an exploratory analysis tool to classify individuals into groups with similar time-use patterns. Inference on associations of latent profiles with health outcomes need to account for the uncertainty of the LPA classifications, which is often ignored. Analyses using the three methods did not suggest that less time spent on SB and more in PA was associated with better cognitive function. The three standard analytical approaches for 24HAC each have advantages and limitations, and selection of the most appropriate method should be guided by the scientific questions of interest and applicability of each model's assumptions. Further research is needed into the health implications of the distinct 24HAC patterns identified in this cohort

Physical Activity Program Participation and the Risk of Falls for Older Group Health Members

Thesis (Master's)--University of Washington, 2014Introduction: Falls are one of the biggest health concerns for aging adults. Despite evidence suggesting the importance of regular physical activity (PA) for reducing fall risk, few older adults engage in fall-prevention-oriented exercise. Regular PA through exercise programs offered as a Medicare or health-plan-covered benefit may be one method to increase PA and reduce fall risk. Here we investigate the effectiveness of participating in EnhanceFitness (EF) and Silver Sneakers (SS), two nationally-disseminated senior exercise programs, in reducing risk of falls resulting in medical care. Methods: A population-based, retrospective cohort study was conducted using data from Group Health Cooperative (GHC) members over age 65. Participants were classified as consistent users (having used EF/SS 2 or more times each year they were enrolled in GHC during the study period [2005-2011]); intermittent users (having used EF/SS two or more times in one or more years they were enrolled in GHC during the study period but not all years), or non-users of the EF/SS. A time-to-first fall requiring medical treatment (identified via ICD-9 code and E-codes in the medical record) analysis using Cox proportional hazards models was used for both programs to generate hazard ratios (HR) comparing consistent and intermittent users with non-users of either program. Hierarchical adjustment was used to address confounding by demographic characteristics and comorbidities (measured by ICD-9 codes in electronic health records). Results: In fully adjusted models, there was evidence of a dose-response relationship between EF participation and decreased fall risk compared to non-users (consistent EF user HR= 0.75, 95% CI = 0.64-0.89 and intermittent EF user HR = 0.87, 95% CI = 0.80-0.94). Participation in SS was not significantly associated with a decrease in risk for consistent users (HR= 0.97, 95% CI = 0.90-1.04), but a small significant reduction in risk was seen for intermittent users (HR= 0.93 95% CI= 0.90-0.97). Analyses evaluating effect modification showed that SS use was related to significantly lower fall risk among individuals over age 75 or with a BMI of 28 or below. Conclusion: Participation in EF provides a protective effect against falls resulting in medical care, with an indication of a dose-response relationship wherein this effect is strongest for consistent users. Results are less clear for SS participation, suggesting a small protective effect against medical falls for consistent and intermittent users that is potentially stronger for older and lower-BMI users

Reducing sitting time in obese older adults: the I-STAND randomized controlled trial

Background: The authors tested the efficacy of the 'I-STAND' intervention for reducing sitting time, a novel and potentially health-promoting approach, in older adults with obesity. Methods: The authors recruited 60 people (mean age = 68 ± 4.9 years, 68% female, 86% White; mean body mass index = 35.4). The participants were randomized to receive the I-STAND sitting reduction intervention (n = 29) or healthy living control group (n = 31) for 12 weeks. At baseline and at 12 weeks, the participants wore activPAL devices to assess sitting time (primary outcome). Secondary outcomes included fasting glucose, blood pressure, and weight. Linear regression models assessed between-group differences in the outcomes. Results: The I-STAND participants significantly reduced their sitting time compared with the controls (–58 min per day; 95% confidence interval [–100.3, –15.6]; p = .007). There were no statistically significant changes in the secondary outcomes. Conclusion: I-STAND was efficacious in reducing sitting time, but not in changing health outcomes in older adults with obesity. © 2020 Human Kinetics, Inc

The CNN Hip Accelerometer Posture (CHAP) Method for Classifying Sitting Patterns from Hip Accelerometers: A Validation Study.

IntroductionSitting patterns predict several healthy aging outcomes. These patterns can potentially be measured using hip-worn accelerometers, but current methods are limited by an inability to detect postural transitions. To overcome these limitations, we developed the Convolutional Neural Network Hip Accelerometer Posture (CHAP) classification method.MethodsCHAP was developed on 709 older adults who wore an ActiGraph GT3X+ accelerometer on the hip, with ground-truth sit/stand labels derived from concurrently worn thigh-worn activPAL inclinometers for up to 7 d. The CHAP method was compared with traditional cut-point methods of sitting pattern classification as well as a previous machine-learned algorithm (two-level behavior classification).ResultsFor minute-level sitting versus nonsitting classification, CHAP performed better (93% agreement with activPAL) than did other methods (74%-83% agreement). CHAP also outperformed other methods in its sensitivity to detecting sit-to-stand transitions: cut-point (73%), TLBC (26%), and CHAP (83%). CHAP's positive predictive value of capturing sit-to-stand transitions was also superior to other methods: cut-point (30%), TLBC (71%), and CHAP (83%). Day-level sitting pattern metrics, such as mean sitting bout duration, derived from CHAP did not differ significantly from activPAL, whereas other methods did: activPAL (15.4 min of mean sitting bout duration), CHAP (15.7 min), cut-point (9.4 min), and TLBC (49.4 min).ConclusionCHAP was the most accurate method for classifying sit-to-stand transitions and sitting patterns from free-living hip-worn accelerometer data in older adults. This promotes enhanced analysis of older adult movement data, resulting in more accurate measures of sitting patterns and opening the door for large-scale cohort studies into the effects of sitting patterns on healthy aging outcomes

The CNN Hip Accelerometer Posture (CHAP) Method for Classifying Sitting Patterns from Hip Accelerometers: A Validation Study.

IntroductionSitting patterns predict several healthy aging outcomes. These patterns can potentially be measured using hip-worn accelerometers, but current methods are limited by an inability to detect postural transitions. To overcome these limitations, we developed the Convolutional Neural Network Hip Accelerometer Posture (CHAP) classification method.MethodsCHAP was developed on 709 older adults who wore an ActiGraph GT3X+ accelerometer on the hip, with ground-truth sit/stand labels derived from concurrently worn thigh-worn activPAL inclinometers for up to 7 d. The CHAP method was compared with traditional cut-point methods of sitting pattern classification as well as a previous machine-learned algorithm (two-level behavior classification).ResultsFor minute-level sitting versus nonsitting classification, CHAP performed better (93% agreement with activPAL) than did other methods (74%-83% agreement). CHAP also outperformed other methods in its sensitivity to detecting sit-to-stand transitions: cut-point (73%), TLBC (26%), and CHAP (83%). CHAP's positive predictive value of capturing sit-to-stand transitions was also superior to other methods: cut-point (30%), TLBC (71%), and CHAP (83%). Day-level sitting pattern metrics, such as mean sitting bout duration, derived from CHAP did not differ significantly from activPAL, whereas other methods did: activPAL (15.4 min of mean sitting bout duration), CHAP (15.7 min), cut-point (9.4 min), and TLBC (49.4 min).ConclusionCHAP was the most accurate method for classifying sit-to-stand transitions and sitting patterns from free-living hip-worn accelerometer data in older adults. This promotes enhanced analysis of older adult movement data, resulting in more accurate measures of sitting patterns and opening the door for large-scale cohort studies into the effects of sitting patterns on healthy aging outcomes

Protocol for a randomized controlled trial of sitting reduction to improve cardiometabolic health in older adults

Older adults with obesity spend the majority of their waking hours sedentary. Given substantial barriers to regular physical activity in this population, approaches to reduce sedentary time could be an effective health promotion strategy. We present the protocol of a randomized controlled trial to reduce sitting time in older adults with a body mass index of 30 kg/m2 or above. Participants (N = 284) will be randomized to receive a sitting reduction intervention (termed I-STAND) or a healthy living focused attention control condition. I-STAND includes 10 contacts with a health coach (10 sessions total) and participants receive a wrist-worn prompting device and portable standing desk. The healthy living condition includes 10 sessions with a health coach to set goals around various topics relating to healthy aging. Participants receive their assigned intervention for 6 months. After 6 months, those receiving the I-STAND condition are re-randomized to receive five booster health coaching sessions by ‘phone or no further contact; healthy living participants receive no further contact and those in both conditions are followed for an additional 6 months. Measurements initially included wearing an activPAL device and completing several biometric tests (e.g., blood pressure, HbA1c), at baseline, 3 months, 6 months, and 12 months; however, during the COVID-19 pandemic we shifted to remote assessments and were unable to collect all of these measures. The primary outcomes remained activPAL-assessed sitting time and blood pressure. Recruitment is anticipated to be completed in 2022

CHAP-child: an open source method for estimating sit-to-stand transitions and sedentary bout patterns from hip accelerometers among children

BackgroundHip-worn accelerometer cut-points have poor validity for assessing children's sedentary time, which may partly explain the equivocal health associations shown in prior research. Improved processing/classification methods for these monitors would enrich the evidence base and inform the development of more effective public health guidelines. The present study aimed to develop and evaluate a novel computational method (CHAP-child) for classifying sedentary time from hip-worn accelerometer data.MethodsParticipants were 278, 8-11-year-olds recruited from nine primary schools in Melbourne, Australia with differing socioeconomic status. Participants concurrently wore a thigh-worn activPAL (ground truth) and hip-worn ActiGraph (test measure) during up to 4 seasonal assessment periods, each lasting up to 8 days. activPAL data were used to train and evaluate the CHAP-child deep learning model to classify each 10-s epoch of raw ActiGraph acceleration data as sitting or non-sitting, creating comparable information from the two monitors. CHAP-child was evaluated alongside the current practice 100 counts per minute (cpm) method for hip-worn ActiGraph monitors. Performance was tested for each 10-s epoch and for participant-season level sedentary time and bout variables (e.g., mean bout duration).ResultsAcross participant-seasons, CHAP-child correctly classified each epoch as sitting or non-sitting relative to activPAL, with mean balanced accuracy of 87.6% (SD = 5.3%). Sit-to-stand transitions were correctly classified with mean sensitivity of 76.3% (SD = 8.3). For most participant-season level variables, CHAP-child estimates were within ± 11% (mean absolute percent error [MAPE]) of activPAL, and correlations between CHAP-child and activPAL were generally very large (> 0.80). For the current practice 100 cpm method, most MAPEs were greater than ± 30% and most correlations were small or moderate (≤ 0.60) relative to activPAL.ConclusionsThere was strong support for the concurrent validity of the CHAP-child classification method, which allows researchers to derive activPAL-equivalent measures of sedentary time, sit-to-stand transitions, and sedentary bout patterns from hip-worn triaxial ActiGraph data. Applying CHAP-child to existing datasets may provide greater insights into the potential impacts and influences of sedentary time in children