Search Results

You are looking at 1 - 5 of 5 items for

  • Author: Chongzhi Di x
  • Refine by Access: All Content x
Clear All Modify Search
Restricted access

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

Kelly R. Evenson, Fang Wen, Christopher C. Moore, Michael J. LaMonte, I-Min Lee, Andrea Z. LaCroix, and Chongzhi Di

Purpose: The purpose of this study was to develop 60-s epoch accelerometer intensity cut points for vertical axis count and vector magnitude (VM) output from hip-worn triaxial accelerometers among women 60–91 years old. We also compared these cut points against cut points derived by multiplying 15-s epoch cut points 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 and assembling a puzzle), low light (washing/drying dishes), high light (laundry and dust mopping), or moderate-to-vigorous physical activity (400-m walk) intensity. Oxygen uptake was measured using an Oxycon portable calorimeter. Sedentary behavior and physical activity intensity cut points for vertical axis and VM counts were derived for 60-s epochs from receiver operating characteristic and by multiplying the 15-s cut points by four; both were compared with oxygen uptake. Results: The median age was 74.5 years (interquartile range 70–83). The 60-s epoch cut points for vertical counts were 0 sedentary, 1–73 low light, 74–578 high light, and ≥579 moderate-to-vigorous physical activity. The 60-s epoch cut points for VM were 0–88 sedentary, 89–663 low light, 664–1,730 high light, and ≥1,731 moderate-to-vigorous physical activity. For both sets of cut points, the receiver operating characteristic approach yielded more accurate estimates than the multiplication approach. Conclusion: The derived 60-s epoch cut points for vertical counts and VM can be applied to epidemiologic studies to define sedentary behavior and physical activity intensities in older adult populations.

Restricted access

Calibration of an Accelerometer Activity Index Among Older Women and Its Association With Cardiometabolic Risk Factors

Guangxing Wang, Sixuan Wu, Kelly R. Evenson, Ilsuk Kang, Michael J. LaMonte, John Bellettiere, I-Min Lee, Annie Green Howard, Andrea Z. LaCroix, and Chongzhi Di

Purpose: Traditional summary metrics provided by accelerometer device manufacturers, known as counts, are proprietary and manufacturer specific, making it 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 continuously using AAI and personal characteristics using internal calibration data (N = 199). We then derived AAI cutpoints to classify epochs into sedentary behavior and physical activity 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 estimating metabolic equivalents (R 2 = .74). AAI-Based physical activity measures were associated in the expected directions with body mass index, blood glucose, and high-density lipoprotein 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.

Restricted access

Comparison of Questionnaire and Device Measures of Physical Activity and Sedentary Behavior in a Multi-Ethnic Cohort of Older Women

Michael J. LaMonte, I-Min Lee, Eileen Rillamas-Sun, John Bellettiere, Kelly R. Evenson, David M. Buchner, Chongzhi Di, Cora E. Lewis, Dori E. Rosenberg, Marcia L. Stefanick, and Andrea Z. LaCroix

Background: Limited data are available regarding the correlation between questionnaire and device-measured physical activity (PA) and sedentary behavior (SB) in older women. Methods: We evaluated these correlations in 5,992 women, aged 63 and older, who completed the Women’s Health Initiative (WHI) and Community Healthy Activities Model Program for Seniors (CHAMPS) PA questionnaires and the CARDIA SB questionnaire prior to wearing a hip-worn accelerometer for 7 consecutive days. Accelerometer-measured total, light, and moderate-to-vigorous PA (MVPA), and total SB time were defined according to cutpoints established in a calibration study. Spearman coefficients were used to evaluate correlations between questionnaire and device measures. Results: Mean time spent in PA and SB was lower for questionnaire than accelerometer measures, with variation in means according to age, race/ethnicity, body mass index, and functional status. Overall, correlations between questionnaires and accelerometer measures were moderate for total PA, MVPA, and SB (r ≈ 0.20–0.40). Light intensity PA correlated weakly for WHI (r ≈ 0.01–0.06) and was variable for CHAMPS (r ≈ 0.07–0.22). Conclusion: Questionnaire and accelerometer estimates of total PA, MVPA, and SB have at best moderate correlations in older women and should not be assumed to be measuring the same behaviors or quantity of behavior. Light intensity PA is poorly measured by questionnaire. Because light intensity activities account for the largest proportion of daily activity time in older adults, and likely contribute to its health benefits, further research should investigate how to improve measurement of light intensity PA by questionnaires.

Restricted access

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

Benjamin T. Schumacher, John Bellettiere, Michael J. LaMonte, Kelly R. Evenson, Chongzhi Di, I-Min Lee, David A. Sleet, Charles B. Eaton, Cora E. Lewis, Karen L. Margolis, Lesley F. Tinker, and Andrea Z. LaCroix

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]; p trend 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]; p trend = .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.

Open access

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

John Bellettiere, Supun Nakandala, Fatima Tuz-Zahra, Elisabeth A.H. Winkler, Paul R. Hibbing, Genevieve N. Healy, David W. Dunstan, Neville Owen, Mikael Anne Greenwood-Hickman, Dori E. Rosenberg, Jingjing Zou, Jordan A. Carlson, Chongzhi Di, Lindsay W. Dillon, Marta M. Jankowska, Andrea Z. LaCroix, Nicola D. Ridgers, Rong Zablocki, Arun Kumar, and Loki Natarajan

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.