The purpose of this study was to compare estimates of sedentary time on weekdays vs. weekend days in older adults and determine if these patterns vary by measurement method. Older adults (N = 230, M = 83.5, SD = 6.5 years) living in retirement communities completed a questionnaire about sedentary behavior and wore an ActiGraph accelerometer for seven days. Participants engaged in 9.4 (SD = 1.5) hr per day of accelerometer-measured sedentary time, but self-reported engaging in 11.4 (SD = 4.9) hr per day. Men and older participants had more accelerometer-measured sedentary time than their counterparts. The difference between accelerometer-measured weekday and weekend sedentary time was nonsignificant. However, participants self-reported 1.1 hr per day more sedentary time on weekdays compared with weekend days. Findings suggest self-reported but not accelerometer-measured sedentary time should be investigated separately for weekdays and weekend days, and that self-reports may overestimate sedentary time in older adults.
Simon Marshall, Jacqueline Kerr, Jordan Carlson, Lisa Cadmus-Bertram, Ruth Patterson, Kari Wasilenko, Katie Crist, Dori Rosenberg, and Loki Natarajan
Kelsie M. Full, Eileen Johnson, Michelle Takemoto, Sheri J. Hartman, Jacqueline Kerr, Loki Natarajan, Ruth E. Patterson, and Dorothy D. Sears
Background: For breast cancer survivors, moderate to vigorous physical activity (MVPA) is associated with improved survival. Less is known about the interrelationships of daytime activities (sedentary behavior [SB], light-intensity physical activity, and MVPA) and associations with survivors’ health outcomes. This study will use isotemporal substitution to explore reallocations of time spent in daytime activities and associations with cancer recurrence biomarkers. Methods: Breast cancer survivors (N = 333; mean age 63 y) wore accelerometers and provided fasting blood samples. Linear regression models estimated the associations between daytime activities and cancer recurrence biomarkers. Isotemporal substitution models estimated cross-sectional associations with biomarkers when time was reallocated from of one activity to another. Models were adjusted for wear time, demographics, lifestyle factors, and medical conditions. Results: MVPA was significantly associated with lower insulin, C-reactive protein, homeostatic model assessment of insulin resistance, and glucose, and higher sex hormone-binding globulin (all P < .05). Light-intensity physical activity and SB were associated with insulin and homeostatic model assessment of insulin resistance (both P < .05). Reallocating 18 minutes of SB to MVPA resulted in significant beneficial associations with insulin (−9.3%), homeostatic model assessment of insulin resistance (−10.8%), glucose (−1.7%), and sex hormone-binding globulin (7.7%). There were no significant associations when 79 minutes of SB were shifted to light-intensity physical activity. Conclusions: Results illuminate the possible benefits for breast cancer survivors of replacing time spent in SB with MVPA.
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.
Sheri J. Hartman, Catherine R. Marinac, Lisa Cadmus-Bertram, Jacqueline Kerr, Loki Natarajan, Suneeta Godbole, Ruth E. Patterson, Brittany Morey, and Dorothy D. Sears
Background: Sedentary behavior is associated with increased risk of poor outcomes in breast cancer survivors, but underlying mechanisms are not well understood. This pilot study explored associations between different aspects of sedentary behaviors (sitting, prolonged sitting, sit-to-stand transitions, and standing) and breast cancer risk-related biomarkers in breast cancer survivors (n = 30). Methods: Sedentary behavior variables were objectively measured with thigh-worn activPALs. Breast cancer risk-related biomarkers assessed were C-reactive protein (CRP), insulin, and homeostatic model assessment of insulin resistance (HOMA-IR) and were measured in fasting plasma samples. Linear regression models were used to investigate associations between sedentary behavior variables and biomarkers (log CRP, insulin, and HOMA-IR). Results: Sit-to-stand transitions were significantly associated with insulin resistance biomarkers (P < .05). Specifically, each 10 additional sit-to-stand transitions per day was associated with a lower fasting insulin concentration (β = −5.52; 95% CI, −9.79 to −1.24) and a lower HOMA-IR value (β = −0.22; 95% CI, −0.42 to −0.03). Sit-to-stand transitions were not significantly associated with CRP concentration (P = .08). Total sitting time, long sitting bouts, and standing time were not significantly associated with CRP, insulin, or HOMA-IR (P > .05). Conclusions: Sit-to-stand transitions may be an intervention target for reducing insulin resistance in breast cancer survivors, which may have favorable downstream effects on cancer prognosis.
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.
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.
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.