The authors examined whether the associations between physical activity (PA) levels and fatigue vary by body mass index and physical performance, and whether substituting sedentary time (ST) with low light PA, high light PA, and moderate to vigorous PA (MVPA) was associated with better mean fatigue scores. In total, 6,111 participants (aged 65 years and older) were from the Women’s Health Initiative Objective Physical Activity and Cardiovascular Health Study. PA levels were from a hip-worn GT3X accelerometer. Overall fatigue, energy, and weariness subdomains were from the RAND-36 Vitality subscale. Isotemporal substitution models examined the time-substitution effects. Interactions were observed between MVPA and short physical performance battery performance measure (p < .05). Substituting ST with 34.3 min of MVPA was associated on average with a 1.63-point improvement in fatigue score. Substituting ST with 50.2 min of low light PA and 34.3 min of MVPA was associated on average with an energy score improvement of 1.18 and 2.06 points respectively. Substituting ST with 34.3 min of MVPA was associated on average with a 1.08-point improvement in weariness score (p < .05 for all).
Oleg Zaslavsky, Yan Su, Eileen Rillamas-Sun, Inthira Roopsawang, and Andrea Z. LaCroix
I-Min Lee, Eric J. Shiroma, Kelly R. Evenson, Masamitsu Kamada, Andrea Z. LaCroix, and Julie E. Buring
In recent years, it has become feasible to use devices for assessing physical activity and sedentary behavior among large numbers of participants in epidemiologic studies, allowing for more precise assessments of these behaviors and quantification of their associations with health outcomes. Between 2011–2015, the Women’s Health Study (WHS) used the Actigraph GT3X+ device to measure physical activity and sedentary behavior over seven days, during waking hours, among 17,708 women (M age, 72 years) living throughout the United States. Devices were sent to and returned by participants via mail. We describe here the methods used to collect and process the accelerometer data for epidemiologic data analyses. We also provide metrics that describe the quality of the accelerometer data collected, as well as expanded findings regarding previously published associations of physical activity or sedentary behavior with all-cause mortality during an average follow-up of 2.3 years (207 deaths). The WHS is one of the earliest “next generation” epidemiologic studies of physical activity, utilizing wearable devices, in which long-term follow-up of participants for various health outcomes is anticipated. It therefore serves as a useful case study in which to discuss unique challenges and issues faced.
Alexander Ivan B. Posis, John Bellettiere, Rany M. Salem, Michael J. LaMonte, JoAnn E. Manson, Ramon Casanova, Andrea Z. LaCroix, and Aladdin H. Shadyab
The goal of this study was to examine associations between accelerometer-measured physical activity (PA) and sedentary time (ST) with mortality by a genetic risk score (GRS) for longevity. Among 5,446 women, (mean [SD]: age, 78.2 [6.6] years), 1,022 deaths were observed during 33,350 person-years of follow-up. Using multivariable Cox proportional hazards models, higher light PA and moderate to vigorous PA were associated with lower mortality across all GRS for longevity categories (low/medium/high; all p trend < .001). Higher ST was associated with higher mortality (p trend across all GRS categories < .001). Interaction tests for PA and ST with the GRS were not statistically significant. Findings support the importance of higher PA and lower ST for reducing mortality risk in older women, regardless of genetic predisposition for longevity.
Mariana Wingood, Levi Bonnell, Andrea Z. LaCroix, Dori Rosenberg, Rod Walker, John Bellettiere, Mikael Anne Greenwood-Hickman, David Wing, and Nancy Gell
Though it is known that most older adults do not meet the recommended physical activity (PA) guidelines, little is known regarding their participation in balance activities or the full guidelines. Therefore, we sought to describe PA patterns among 1,352 community-dwelling older adult participants of the Adult Changes in Thought study, a longitudinal cohort study exploring dementia-related risk factors. We used a modified version of the Community Healthy Activities Model Program for Seniors questionnaire to explore PA performed and classify participants as meeting or not meeting the full guidelines or any component of the guidelines. Logistic regression was used to identify factors associated with meeting PA guidelines. Despite performing 10 hr of weekly PA, only 11% of participants met the full guidelines. Older age, greater body mass index, needing assistance with instrumental daily activities, and heart disease were associated with decreased odds of meeting PA guidelines. These results can guide interventions that address PA among older adults.
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.
John Bellettiere, Fatima Tuz-Zahra, Jordan A. Carlson, Nicola D. Ridgers, Sandy Liles, Mikael Anne Greenwood-Hickman, Rod L. Walker, Andrea Z. LaCroix, Marta M. Jankowska, Dori E. Rosenberg, and Loki Natarajan
Little is known about how sedentary behavior (SB) metrics derived from hip- and thigh-worn accelerometers agree for older adults. Thigh-worn activPAL (AP) micro monitors were concurrently worn with hip-worn ActiGraph (AG) GT3X+ accelerometers (with SB measured using the 100 counts per minute [cpm] cut point; AG100cpm) by 953 older adults (age 77 ± 6.6, 54% women) for 4–7 days. Device agreement for sedentary time and five SB pattern metrics was assessed using mean error and correlations. Logistic regression tested associations with four health outcomes using standardized (i.e., z scores) and unstandardized SB metrics. Mean errors (AP − AG100cpm) and 95% limits of agreement were: sedentary time −54.7 [−223.4, 113.9] min/day; time in 30+ min bouts 77.6 [−74.8, 230.1] min/day; mean bout duration 5.9 [0.5, 11.4] min; usual bout duration 15.2 [0.4, 30] min; breaks in sedentary time −35.4 [−63.1, −7.6] breaks/day; and alpha −.5 [−.6, −.4]. Respective Pearson correlations were: .66, .78, .73, .79, .51, and .40. Concordance correlations were: .57, .67, .40, .50, .14, and .02. The statistical significance and direction of associations were identical for AG100cpm and AP metrics in 46 of 48 tests, though significant differences in the magnitude of odds ratios were observed among 13 of 24 tests for unstandardized and five of 24 for standardized SB metrics. Caution is needed when interpreting SB metrics and associations with health from AG100cpm due to the tendency for it to overestimate breaks in sedentary time relative to AP. However, high correlations between AP and AG100cpm measures and similar standardized associations with health outcomes suggest that studies using AG100cpm are useful, though not ideal, for studying SB in older adults.
Supun Nakandala, Marta M. Jankowska, Fatima Tuz-Zahra, John Bellettiere, Jordan A. Carlson, Andrea Z. LaCroix, Sheri J. Hartman, Dori E. Rosenberg, Jingjing Zou, Arun Kumar, and Loki Natarajan
Background: Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior “in the wild.” Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms. Method: Twenty-eight free-living women wore an ActiGraph GT3X+ accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task. Results: The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering. Conclusion: Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model’s ability to deal with the complexity of free-living data and its potential transferability to new populations.
Jordan A. Carlson, Fatima Tuz-Zahra, John Bellettiere, Nicola D. Ridgers, Chelsea Steel, Carolina Bejarano, Andrea Z. LaCroix, Dori E. Rosenberg, Mikael Anne Greenwood-Hickman, Marta M. Jankowska, and Loki Natarajan
Background: The authors assessed agreement between participant diaries and two automated algorithms applied to activPAL (PAL Technologies Ltd, Glasgow, United Kingdom) data for classifying awake wear time in three age groups. Methods: Study 1 involved 20 youth and 23 adults who, by protocol, removed the activPAL occasionally to create nonwear periods. Study 2 involved 744 older adults who wore the activPAL continuously. Both studies involved multiple assessment days. In-bed, out-of-bed, and nonwear times were recorded in the participant diaries. The CREA (in PAL processing suite) and ProcessingPAL (secondary application) algorithms estimated out-of-bed wear time. Second- and day-level agreement between the algorithms and diary was investigated, as were associations of sedentary variables with self-rated health. Results: The overall accuracy for classifying out-of-bed wear time as compared with the diary was 89.7% (Study 1) to 95% (Study 2) for CREA and 89.4% (Study 1) to 93% (Study 2) for ProcessingPAL. Over 90% of the nonwear time occurring in nonwear periods >165 min was detected by both algorithms, while <11% occurring in periods ≤165 min was detected. For the daily variables, the mean absolute errors for each algorithm were generally within 0–15% of the diary mean. Most Spearman correlations were very large (≥.81). The mean absolute errors and correlations were less favorable for days on which any nonwear time had occurred. The associations between sedentary variables and self-rated health were similar across processing methods. Conclusion: The automated awake wear-time classification algorithms performed similarly to the diary information on days without short (≤2.5–2.75 hr) nonwear periods. Because both diary and algorithm data can have inaccuracies, best practices likely involve integrating diary and algorithm output.
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.
Mikael Anne Greenwood-Hickman, Rod Walker, John Bellettiere, Andrea Z. LaCroix, Boeun Kim, David Wing, KatieRose Richmire, Paul K. Crane, Eric B. Larson, and Dori E. Rosenberg
Neighborhood walkability has been associated with self-reported sedentary behavior (SB) and self-reported and objective physical activity. However, self-reported measures of SB are inaccurate and can lead to biased estimates, and few studies have examined how associations differ by gender and age. The authors examined the relationships between perceived neighborhood walkability measured with the Physical Activity Neighborhood Environment Scale (scored 1.0–4.0) and device-based SB and physical activity in a cohort of community-dwelling older adults (N = 1,077). The authors fit linear regression models adjusting for device wear time, demographics, self-rated health, and accounting for probability of participation. The Higher Physical Activity Neighborhood Environment Scale was associated with higher steps (+676 steps/point on the Physical Activity Neighborhood Environment Scale, p = .001) and sit-to-stand transitions (+2.4 transitions/point, p = .018). Though not statistically significant, stratified analyses suggest an attenuation of effect for those aged 85 years and older and for women. Consistent with previous literature, neighborhood walkability was associated with more steps, though not with physical activity time. The neighborhood environment may also influence SB.