Sedentary behavior after breast cancer: motivational, demographic, disease, and health status correlates of sitting time in breast cancer survivors.

PURPOSE: Sedentary behavior is associated with poor health outcomes including obesity, lower quality of life, and mortality in breast cancer survivors. This study sought to identify motivational, demographic, and disease characteristics of breast cancer survivors who engage in greater amounts of sedentary behavior. METHODS: Multivariate linear regression models estimated associations between demographic, disease, and health characteristics with reported sitting in breast cancer survivors (n = 279; Mage = 60.7 (± 9.7) years). Regression models estimated associations between motivational factors and reported sitting adjusted for demographic and disease and health covariates. RESULTS: Working at least part-time and marital status were associated various sitting domains including weekday and non-leisure sitting. Higher BMI was associated with more average daily, weekend, and weekday sitting. High income was additionally associated with less non-leisure sitting. The belief that sedentary behavior is bad for health, physical function, and self-evaluative OE, and lifestyle self-efficacy were associated with multiple sitting domains in both univariate and covariate-adjusted models. CONCLUSIONS: Future work should examine the relationships between motivational, demographic, and disease predictors and objectively measured sedentary behavior over time and across different sedentary behavior domains. Understanding activity changes during and after treatment is needed to identify intervention targets and develop effective interventions.

Measurement of Active and Sedentary Behavior in Context of Large Epidemiologic Studies.

INTRODUCTION/PURPOSE: To assess the utility of measurement methods that may be more accurate and precise than traditional questionnaire-based estimates of habitual physical activity and sedentary behavior we compared the measurement properties of a past year questionnaire (AARP) and more comprehensive measures: an internet-based 24-h recall (ACT24), and a variety of estimates from an accelerometer (ActiGraph). METHODS: Participants were 932 adults (50-74 yr) in a 12-month study that included reference measures of energy expenditure from doubly labeled water (DLW) and active and sedentary time via activPAL. RESULTS: Accuracy at the group level (mean differences) was generally better for both ACT24 and ActiGraph than the AARP questionnaire. The AARP accuracy for energy expenditure ranged from -4% to -13% lower than DLW, but its accuracy was poorer for physical activity duration (-48%) and sedentary time (-18%) versus activPAL. In contrast, ACT24 accuracy was within 3% to 10% of DLW expenditure measures and within 1% to 3% of active and sedentary time from activPAL. For ActiGraph, accuracy for energy expenditure was best for the Crouter 2-regression method (-2% to -7%), and for active and sedentary time the 100 counts per minute cutpoint was most accurate (-1% to 2%) at the group level. One administration of the AARP questionnaire was significantly correlated with long-term average from the reference measures (ρTX = 0.16-0.34) overall, but four ACT24 recalls had higher correlations (ρTX = 0.48-0.60), as did 4 d of ActiGraph assessment (ρTX = 0.54-0.87). CONCLUSIONS: New exposure assessments suitable for use in large epidemiologic studies (ACT24, ActiGraph) were more accurate and had higher correlations than a traditional questionnaire. Use of better more comprehensive measures in future epidemiologic studies could yield new etiologic discoveries and possibly new opportunities for prevention.

Longitudinal functional additive model with continuous proportional outcomes for physical activity data.

Motivated by physical activity data obtained from the BodyMedia FIT device (www.bodymedia.com), we take a functional data approach for longitudinal studies with continuous proportional outcomes. The functional structure depends on three factors. In our three-factor model, the regression structures are specified as curves measured at various factor-points with random effects that have a correlation structure. The random curve for the continuous factor is summarized using a few important principal components. The difficulties in handling the continuous proportion variables are solved by using a quasilikelihood type approximation. We develop an efficient algorithm to fit the model, which involves the selection of the number of principal components. The method is evaluated empirically by a simulation study. This approach is applied to the BodyMedia data with 935 males and 84 consecutive days of observation, for a total of 78, 540 observations. We show that sleep efficiency increases with increasing physical activity, while its variance decreases at the same time.

Energy Expenditure for 70 Activities in Children and Adolescents.

BACKGROUND: Thirty-five percent of the activities assigned MET values in the Compendium of Energy Expenditures for Youth were obtained from direct measurement of energy expenditure (EE). The aim of this study was to provide directly measured EE for several different activities in youth. METHODS: Resting metabolic rate (RMR) of 178 youths (80 females, 98 males) was first measured. Participants then performed structured activity bouts while wearing a portable metabolic system to directly measure EE. Steady-state oxygen consumption data were used to compute activity METstandard (activity VO2/3.5) and METmeasured (activity VO2/measured RMR) for the different activities. RESULTS: Rates of EE were measured for 70 different activities and ranged from 1.9 to 12.0 METstandard and 1.5 to 10.0 METmeasured. CONCLUSION: This study provides directly measured energy cost values for 70 activities in children and adolescents. It contributes empirical data to support the expansion of the Compendium of Energy Expenditures for Youth.

Breast cancer survivors' beliefs and preferences regarding technology-supported sedentary behavior reduction interventions.

PURPOSE: Less time spent in sedentary behaviors is associated with improved health and disease outcomes in breast cancer survivors. However, little is known about survivors' interest in sedentary behavior reduction interventions and how to effectively reduce this risk behavior. The purpose of this study was to explore breast cancer survivors' interest in and preferences for technology-supported sedentary behavior reduction interventions. METHODS: Breast cancer survivors [n=279; Mage =60.7 (SD=9.7)] completed a battery of online questionnaires. Descriptive statistics were calculated for all data. To examine potential relationships between demographic, disease and behavioral factors, and survivors' interest in a technology-supported sedentary behavior reduction intervention, we conducted logistic regression analyses. These same factors were examined in relation to the perceptions of the effectiveness of such intervention using multiple regression analyses. RESULTS: On average, survivors spent 10.1 (SD=4.3) hours/day in sedentary activity. They believed prolonged periods of sedentary behavior were harmful to their health (87.0%) and that reducing sedentary behavior could improve their health (88.4%). Survivors believed they should move around after 30-60 (56.7%) or ≥60 (29.9%) minutes of sedentary behavior and indicated they were most likely to replace sedentary behaviors with walking around (97.1%) or walking in place (73.4%). The majority of survivors (79.9%) was interested in participating in a technology-supported sedentary behavior reduction intervention and indicated they would use a smartphone application (61.3%) 2-3 times/day (48.0%), 6 to 7 days/week (52.0%). Most survivors (73.5%) believed reminders would help them decrease sedentary behavior and preferred they be delivered after sitting for 60 minutes (60.5%) via vibrations on a wrist worn activity tracker (77.3%) or text messages (54.4%). CONCLUSIONS: Technology-supported sedentary behavior reduction interventions may be feasible and acceptable to breast cancer survivors. Data regarding user preferences for content, features, delivery mode and design will aid researchers in developing sedentary interventions that are potentially more relevant and effective from the outset.

Methods to assess an exercise intervention trial based on 3-level functional data.

Motivated by data recording the effects of an exercise intervention on subjects' physical activity over time, we develop a model to assess the effects of a treatment when the data are functional with 3 levels (subjects, weeks and days in our application) and possibly incomplete. We develop a model with 3-level mean structure effects, all stratified by treatment and subject random effects, including a general subject effect and nested effects for the 3 levels. The mean and random structures are specified as smooth curves measured at various time points. The association structure of the 3-level data is induced through the random curves, which are summarized using a few important principal components. We use penalized splines to model the mean curves and the principal component curves, and cast the proposed model into a mixed effects model framework for model fitting, prediction and inference. We develop an algorithm to fit the model iteratively with the Expectation/Conditional Maximization Either (ECME) version of the EM algorithm and eigenvalue decompositions. Selection of the number of principal components and handling incomplete data issues are incorporated into the algorithm. The performance of the Wald-type hypothesis test is also discussed. The method is applied to the physical activity data and evaluated empirically by a simulation study.

Validation of the Fitbit wireless activity tracker for prediction of energy expenditure.

OBJECTIVE: The purpose of this study was to examine the accuracy of the Fitbit wireless activity tracker in assessing energy expenditure (EE) for different activities. METHODS: Twenty participants (10 males, 10 females) wore the Fitbit Classic wireless activity tracker on the hip and the Oxycon Mobile portable metabolic system (criterion). Participants performed walking and running trials on a treadmill and a simulated free-living activity routine. Paired t tests were used to test for differences between estimated (Fitbit) and criterion (Oxycon) kcals for each of the activities. RESULTS: Mean bias for estimated energy expenditure for all activities was -4.5 ± 1.0 kcals/6 min (95% limits of agreement: -25.2 to 15.8 kcals/6 min). The Fitbit significantly underestimated EE for cycling, laundry, raking, treadmill (TM) 3 mph at 5% grade, ascent/descent stairs, and TM 4 mph at 5% grade, and significantly overestimated EE for carrying groceries. Energy expenditure estimated by the Fitbit was not significantly different than EE calculated from the Oxycon Mobile for 9 activities. CONCLUSION: The Fitbit worn on the hip significantly underestimates EE of activities. The variability in underestimation of EE for the different activities may be problematic for weight loss management applications since accurate EE estimates are important for tracking/monitoring energy deficit.

The independent and combined effects of exercise training and reducing sedentary behavior on cardiometabolic risk factors.

This pilot study examined if the combination of exercise training and reducing sedentary time (ST) results in greater changes to health markers than either intervention alone. Fifty-seven overweight/obese participants (19 males/39 females) (mean ± SD; age, 43.6 ± 9.9 years; body mass index (BMI), 35.1 ± 4.6 kg·m(-2)) completed the 12-week study and were randomly assigned to (i) EX: exercise 5 days·week(-1) for 40 min·session(-1) at moderate intensity; (ii) rST: reduce ST and increase nonexercise physical activity; (iii) EX-rST: combination of EX and rST; and (iv) CON: maintain behavior. Fasting lipids, blood pressure (BP), peak oxygen uptake, BMI, and 2-h oral glucose tolerance tests were completed pre- and post-intervention. EX and EX-rST increased peak oxygen uptake by ∼10% and decreased systolic BP (both p < 0.001). BMI decreased by -3.3% (95% confidence interval: -4.6% to -1.9%) for EX-rST and -2.2% (-3.5% to 0.0%) for EX. EX-rST significantly increased composite insulin-sensitivity index by 17.8% (2.8% to 32.8%) and decreased insulin area under the curve by 19.4% (-31.4% to -7.3%). No other groups improved in insulin action variables. rST group decreased ST by 7% (∼50 min·day(-1)); however, BP was the only health-related outcome that improved. EX and EX-rST improved peak oxygen uptake and BMI, providing further evidence that moderate-intensity exercise is beneficial. The within-group analysis provides preliminary evidence that exercising and reducing ST may result in improvements in metabolic biomarkers that are not seen with exercise alone, though between-group differences did not reach statistical significance. Future studies, with larger samples, should examine health-related outcomes resulting from greater reductions in ST over longer intervention periods.

Validation of a previous day recall for measuring the location and purpose of active and sedentary behaviors compared to direct observation.

PURPOSE: Gathering contextual information (i.e., location and purpose) about active and sedentary behaviors is an advantage of self-report tools such as previous day recalls (PDR). However, the validity of PDR's for measuring context has not been empirically tested. The purpose of this paper was to compare PDR estimates of location and purpose to direct observation (DO). METHODS: Fifteen adult (18-75 y) and 15 adolescent (12-17 y) participants were directly observed during at least one segment of the day (i.e., morning, afternoon or evening). Participants completed their normal daily routine while trained observers recorded the location (i.e., home, community, work/school), purpose (e.g., leisure, transportation) and whether the behavior was sedentary or active. The day following the observation, participants completed an unannounced PDR. Estimates of time in each context were compared between PDR and DO. Intra-class correlations (ICC), percent agreement and Kappa statistics were calculated. RESULTS: For adults, percent agreement was 85% or greater for each location and ICC values ranged from 0.71 to 0.96. The PDR-reported purpose of adults' behaviors were highly correlated with DO for household activities and work (ICCs of 0.84 and 0.88, respectively). Transportation was not significantly correlated with DO (ICC = -0.08). For adolescents, reported classification of activity location was 80.8% or greater. The ICCs for purpose of adolescents' behaviors ranged from 0.46 to 0.78. Participants were most accurate in classifying the location and purpose of the behaviors in which they spent the most time. CONCLUSIONS: This study suggests that adults and adolescents can accurately report where and why they spend time in behaviors using a PDR. This information on behavioral context is essential for translating the evidence for specific behavior-disease associations to health interventions and public policy.

Changes in sedentary time and physical activity in response to an exercise training and/or lifestyle intervention.

BACKGROUND: Individuals may compensate for exercise training by modifying nonexercise behavior (ie, increase sedentary time (ST) and decrease nonexercise physical activity [NEPA]). PURPOSE: To compare ST and NEPA during a 12-week exercise training and/or lifestyle intervention. METHODS: Fifty-seven overweight/obese participants (19 M/39 F) completed the study (mean ± SD; age 43.6 ± 9.9 y, BMI 35.1 ± 4.6 kg/m2). There were no between-group differences in activity levels at baseline. Four-arm quasi-experimental intervention study 1) EX: exercise 5 days per week at a moderate intensity (40% to 65% VO2peak) 2) rST: reduce ST and increase NEPA, 3) EX-rST: combination of EX and rST and 4) CON: maintain habitual behavior. RESULTS: For the EX group, ST did not decrease significantly (mean ((95% confidence interval) 0.48 (-2.2 to 3.1)% and there was no changes in NEPA at week-12 compared with baseline. The changes were variable, with approximately 50% of participants increasing ST and decreasing NEPA. The rST group decreased ST (-4.8 (0.8 to 7.9)% and increased NEPA. EX-rST significantly decreased ST (-5.1 (-2.2 to 7.9)% and increased time in NEPA at week-12 compared with baseline. The control group increased ST by 4.3 (0.8 to 7.9)%. CONCLUSIONS: Changes in nonexercise ST and NEPA are variable among participants in an exercise-training program, with nearly half decreasing NEPA compared with baseline. Interventions targeting multiple behaviors (ST and NEPA) may effectively reduce compensation and increase daily activity.

Accuracy of accelerometer regression models in predicting energy expenditure and METs in children and youth.

This study examined the validity of commonly used regression equations for the Actigraph and Actical accelerometers in predicting energy expenditure (EE) in children and adolescents. Sixty healthy (8-16 yrs) participants completed four treadmill (TM) and five self-paced activities of daily living (ADL). Four Actigraph (AG) and three Actical (AC) regression equations were used to estimate EE. Bias (± 95% CI) and root mean squared errors were used to assess the validity of the regression equations compared with indirect calorimetry. For children, the Freedson (AG) model accurately predicted EE for all activities combined and the Treuth (AG) model accurately predicted EE for TM activities. For adolescents, the Freedson model accurately predicted EE for TM activities and the Treuth model accurately predicted EE for all activities and for TM activities. No other equation accurately estimated EE. The percent agreement for the AG and AC equations were better for light and vigorous compared with moderate intensity activities. The Trost (AG) equation most accurately classified all activity intensity categories. Overall, equations yield inconsistent point estimates of EE.

Validity of two wearable monitors to estimate breaks from sedentary time.

UNLABELLED: Investigations using wearable monitors have begun to examine how sedentary time behaviors influence health. PURPOSE: The objective of this study is to demonstrate the use of a measure of sedentary behavior and to validate the activPAL (PAL Technologies Ltd., Glasgow, Scotland) and ActiGraph GT3X (Actigraph, Pensacola, FL) for estimating measures of sedentary behavior: absolute number of breaks and break rate. METHODS: Thirteen participants completed two 10-h conditions. During the baseline condition, participants performed normal daily activity, and during the treatment condition, participants were asked to reduce and break up their sedentary time. In each condition, participants wore two ActiGraph GT3X monitors and one activPAL. The ActiGraph was tested using the low-frequency extension filter (AG-LFE) and the normal filter (AG-Norm). For both ActiGraph monitors, two count cut points to estimate sedentary time were examined: 100 and 150 counts per minute. Direct observation served as the criterion measure of total sedentary time, absolute number of breaks from sedentary time, and break rate (number of breaks per sedentary hour (brk·sed-h)). RESULTS: Break rate was the only metric sensitive to changes in behavior between baseline (5.1 [3.3-6.8] brk·sed-h) and treatment conditions (7.3 [4.7-9.8] brk·sed-h) (mean (95% confidence interval)). The activPAL produced valid estimates of all sedentary behavior measures and was sensitive to changes in break rate between conditions (baseline, 5.1 [2.8-7.1] brk·sed-h; treatment, 8.0 [5.8-10.2] brk·sed-h). In general, the AG-LFE and AG-Norm were not accurate in estimating break rate or the absolute number of breaks and were not sensitive to changes between conditions. CONCLUSION: This study demonstrates the use of expressing breaks from sedentary time as a rate per sedentary hour, a metric specifically relevant to free-living behavior, and provides further evidence that the activPAL is a valid tool to measure components of sedentary behavior in free-living environments.

Physical activity and psychosocial and mental health of older caregivers and non-caregivers.

Few studies have been conducted on physical activity (PA) in older caregivers, a population at risk for mental and physical decline. To assess and compare PA, PA preferences, psychosocial determinants of PA, and mental health indicators between older non-exercising caregivers and non-caregivers. Caregivers (N = 24) and non-caregivers (N = 48) completed questionnaires and wore an accelerometer for 7 consecutive days. Few significant differences were noted in objectively measured or subjectively reported PA between caregivers and non-caregivers. Non-caregivers reported greater social support to exercise from family members. Caregivers reported significantly greater anxiety, depression, stress, and negative health symptoms. Caregivers were significantly more likely to prefer exercise in 10-min bouts. Caregivers are in need of interventions to increase PA and health. Efforts to help caregivers participate in multiple shorter bouts of exercise during the day could be more effective than recommending one continuous 30-minute bout.

Biomechanical examination of the 'plateau phenomenon' in ActiGraph vertical activity counts.

This paper determines if the leveling off ('plateau/inverted-U' phenomenon) of vertical ActiGraph activity counts during running at higher speeds is attributable to the monitor's signal filtering and acceleration detection characteristics. Ten endurance-trained male participants (mean (SD) age = 28.2 (4.7) years) walked at 3, 5 and 7 km h(-1), and ran at 8, 10, 12, 14, 16, 18 and 20 km h(-1) on a force treadmill while wearing an ActiGraph GT3X monitor at the waist. Triaxial accelerations of the body's center of mass (CoM) and frequency content of these accelerations were computed from the force treadmill data. GT3X vertical activity counts demonstrated the expected 'plateau/inverted-U' phenomenon. In contrast, vertical CoM accelerations increased with increasing speed (1.32 ± 0.26 g at 10 km h(-1) and 1.68 ± 0.24 g at 20 km h(-1)). The dominant frequency in the CoM acceleration signals increased with running speed (14.8 ± 3.2 Hz at 10 km h(-1) and 24.8 ± 3.2 Hz at 20 km h(-1)) and lay beyond the ActiGraph band-pass filter (0.25 to 2.5 Hz) limits. In conclusion, CoM acceleration magnitudes during walking and running lie within the ActiGraph monitor's dynamic acceleration detecting capability. Acceleration signals of higher frequencies that are eliminated by the ActiGraph band-pass filter may be necessary to distinguish among exercise intensity at higher running speeds.

The Feasibility of Reducing and Measuring Sedentary Time among Overweight, Non-Exercising Office Workers.

This study examined the feasibility of reducing free-living sedentary time (ST) and the convergent validity of various tools to measure ST. Twenty overweight/obese participants wore the activPAL (AP) (criterion measure) and ActiGraph (AG; 100 and 150 count/minute cut-points) for a 7-day baseline period. Next, they received a simple intervention targeting free-living ST reductions (7-day intervention period). ST was measured using two questionnaires following each period. ST significantly decreased from 67% of wear time (baseline period) to 62.7% of wear time (intervention period) according to AP (n = 14, P < 0.01). No other measurement tool detected a reduction in ST. The AG measures were more accurate (lower bias) and more precise (smaller confidence intervals) than the questionnaires. Participants reduced ST by ~5%, which is equivalent to a 48_min reduction over a 16-hour waking day. These data describe ST measurement properties from wearable monitors and self-report tools to inform sample-size estimates for future ST interventions.

Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample.

Previous work from our laboratory provided a "proof of concept" for use of artificial neural networks (nnets) to estimate metabolic equivalents (METs) and identify activity type from accelerometer data (Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P, J Appl Physiol 107: 1330-1307, 2009). The purpose of this study was to develop new nnets based on a larger, more diverse, training data set and apply these nnet prediction models to an independent sample to evaluate the robustness and flexibility of this machine-learning modeling technique. The nnet training data set (University of Massachusetts) included 277 participants who each completed 11 activities. The independent validation sample (n = 65) (University of Tennessee) completed one of three activity routines. Criterion measures were 1) measured METs assessed using open-circuit indirect calorimetry; and 2) observed activity to identify activity type. The nnet input variables included five accelerometer count distribution features and the lag-1 autocorrelation. The bias and root mean square errors for the nnet MET trained on University of Massachusetts and applied to University of Tennessee were +0.32 and 1.90 METs, respectively. Seventy-seven percent of the activities were correctly classified as sedentary/light, moderate, or vigorous intensity. For activity type, household and locomotion activities were correctly classified by the nnet activity type 98.1 and 89.5% of the time, respectively, and sport was correctly classified 23.7% of the time. Use of this machine-learning technique operates reasonably well when applied to an independent sample. We propose the creation of an open-access activity dictionary, including accelerometer data from a broad array of activities, leading to further improvements in prediction accuracy for METs, activity intensity, and activity type.

Validation of wearable monitors for assessing sedentary behavior.

PURPOSE: A primary barrier to elucidating the association between sedentary behavior (SB) and health outcomes is the lack of valid monitors to assess SB in a free-living environment. The purpose of this study was to examine the validity of commercially available monitors to assess SB. METHODS: Twenty overweight (mean ± SD: body mass index = 33.7 ± 5.7 kg·m(-2)) inactive, office workers age 46.5 ± 10.7 yr were directly observed for two 6-h periods while wearing an activPAL (AP) and an ActiGraph GT3X (AG). During the second observation, participants were instructed to reduce sitting time. We assessed the validity of the commonly used cut point of 100 counts per minute (AG100) and several additional AG cut points for defining SB. We used direct observation (DO) using focal sampling with duration coding to record either sedentary (sitting/lying) or nonsedentary behavior. The accuracy and precision of the monitors and the sensitivity of the monitors to detect reductions in sitting time were assessed using mixed-model repeated-measures analyses. RESULTS: On average, the AP and the AG100 underestimated sitting time by 2.8% and 4.9%, respectively. The correlation between the AP and DO was R2 = 0.94, and the AG100 and DO sedentary minutes was R2 = 0.39. Only the AP was able to detect reductions in sitting time. The AG 150-counts-per-minute threshold demonstrated the lowest bias (1.8%) of the AG cut points. CONCLUSIONS: The AP was more precise and more sensitive to reductions in sitting time than the AG, and thus, studies designed to assess SB should consider using the AP. When the AG monitor is used, 150 counts per minute may be the most appropriate cut point to define SB.