In studies that compare physical activity between groups of individuals, it is common for physical activity to be quantified by step count, which is measured by accelerometers or other wearable devices. Missing step count data often arise in these settings and can lead to bias or imprecision in the estimated effect if handled inappropriately. Replacing each missing value in accelerometer data with a single value using the Expectation–Maximization (EM) algorithm has been advocated in the literature, but it can lead to underestimation of variances and could seriously compromise study conclusions. We compare the performance in terms of bias and variance of two missing data methods, the EM algorithm and Multiple Imputation (MI), through a simulation study where data are generated from a parametric model to reflect characteristics of a trial on physical activity. We also conduct a reanalysis of the 2019 MOVE-IT trial. The EM algorithm leads to an underestimate of the variance of effects of interest, in both the simulation study and the reanalysis of the MOVE-IT trial. MI should be the preferred approach to handling missing data in accelerometer, which provides valid point and variance estimates.
Mia S. Tackney, Daniel Stahl, Elizabeth Williamson, and James Carpenter
Linda Yin-king Lee, Rebecca Cho-kwan Pang, and Mimi Mei-ha Tiu
The aim of this study was to estimate older adults’ physical activity level in all types and categories of physical activities and calculate their total physical activity level. This cross-sectional descriptive study estimated the physical activity level of older adults on a quota sample of 500 physically independent older adults living in a densely populated city (in this case, Hong Kong). It used the Physical Activity Questionnaire (Hong Kong version) to assess participants’ physical activity level. Based on the frequency, duration, and intensity of each type of physical activity being performed by the participants, their physical activity level in terms of energy expenditure (in kilocalories per day) for all types and categories of physical activities and the total physical activity level were calculated. Independent t test or analysis of variance, whatever appropriate, was used to examine the difference in the total physical activity level between participants with different individual characteristics. Linear regression analysis was conducted to determine the contribution of individual characteristics to the total physical activity level (p < .05). Results indicated that the participants mostly engaged in leisurely sitting, watching television, listening to radio, and leisurely walking. They spent the greatest amount of energy on the category of “leisure activity” (710.77 kcal/day). Their total physical activity level was 1,727.09 kcal/day, which was much less than previously reported. Linear regression indicates that age accounted for 3.1% of the variance of the total physical activity level (p = .001) with senior older adults warranting additional support. Future research is suggested to confirm the role of specific neighborhood-level factors on the physical activity performance of older adults.
Yiyan Wang, Hengjing Wu, Jie Sun, Minqian Wei, Jiaqi Wang, Husheng Li, Xubo Wu, and Jing Wu
Background: Carotid intima–media thickness (cIMT) is a validated surrogate marker of atherosclerosis that is independently associated with the risk for cardiovascular disease. Recent studies on the effect of exercise on cIMT have yielded conflicting results. Methods: Studies that were available up until October 30, 2021 from the PubMed, Cochrane Library, Embase, and Web of Science databases were included in the analysis. Subgroup analyses were performed to determine the effects of the type, intensity, and duration of exercise on cIMT. Results: This review included 26 studies with 1370 participants. Compared with control participants, those who engaged in exercise showed a decline in cIMT (weighted mean difference [WMD] −0.02; 95% confidence interval [CI], −0.03 to −0.01; I 2 = 90.1%). Participants who engaged in aerobic (WMD −0.02; 95% CI, −0.04 to −0.01; I 2 = 52.7%) or resistance (WMD −0.01; 95% CI, −0.02 to −0.00; I 2 = 38.5%) exercise showed lower cIMT compared with control participants. An exercise duration of >6 months was associated with a 0.02 mm reduction in cIMT. In participants with low cIMT at baseline (<0.7 mm), exercise alone was not associated with a change in cIMT (WMD −0.01; 95% CI, −0.03 to 0.00; I 2 = 93.9%). Conclusions: Exercise was associated with reduced cIMT in adults. Aerobic exercise is associated with a greater decline in cIMT than other forms of exercise. Large, multicenter, randomized controlled trials are required to establish optimal exercise protocols for improving the pathological process of atherosclerosis.
Mark Urtel, NiCole Keith, and Rafael E. Bahamonde
This article documents the highlights achieved by the Department of Kinesiology at Indiana University Purdue University Indianapolis over the span of 25 years that culminated with their being awarded the Inclusive Excellence award as sponsored by the American Kinesiology Association. Furthermore, this journey was presented using the special issue focus on leadership. Presented experiences occurred within the typical faculty understanding of teaching, research, and service. Recognition was given to the university and campus that hosts this department as it related to the overall diversity and inclusion culture developed on the broader scale, as this is important to acknowledge. This journey could inform or inspire other similar units as they strive to enhance diversity and inclusive excellence in their respective institutions.
Jeffrey T. Fairbrother and Jared Russell
Paul R. Hibbing, Seth A. Creasy, and Jordan A. Carlson
Physical behaviors (e.g., sleep, sedentary behavior, and physical activity) often occur in sustained bouts that are punctuated with brief interruptions. To detect and classify these interrupted bouts, researchers commonly use wearable devices and specialized algorithms. Most algorithms examine the data in chronological order, initiating and terminating bouts whenever specific criteria are met. Consequently, the bouts may encapsulate or overlap with later periods that also meet the activation and termination criteria (i.e., alternative bout solutions). In some cases, it is desirable to compare these alternative bout solutions before making a final classification. Thus, comparison-focused algorithms are needed, which can be used in isolation or in concert with their chronology-focused counterparts. In this technical note, we present a comparison-focused algorithm called CRIB (Clustered Recognition of Interrupted Bouts). It uses agglomerative hierarchical clustering to facilitate the comparison of different bout solutions, with the final classification being made in favor of the smallest number of bouts that comply with user-specified criteria (i.e., limits on the number, individual duration, and cumulative duration of interruptions). For demonstration, we use CRIB to assess bouts of moderate to vigorous physical activity in accelerometer data from the National Health and Nutrition Examination Survey, and we include a comparison against results from two established chronology-focused algorithms. Our discussion explores strengths and limitations of CRIB, as well as potential considerations and applications for using it in future studies. An online vignette (https://github.com/paulhibbing/PBpatterns/blob/main/vignettes/CRIB.pdf) is available to assist users with implementing CRIB in R.
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.