Validation of accelerometer wear and nonwear time classification algorithm

Daily physical activity patterns from hip- and wrist-worn accelerometers

Accelerometer wear location may influence physical activity estimates. This study investigates this relationship through the examination of activity patterns throughout the day. Participants from the aging research evaluating accelerometry (AREA) study (n men  =  37, n women  =  47, mean age (SD)  =  78.9 (5.5) years) were asked to wear accelerometers in a free-living environment for 7 d at three different wear locations; one on each wrist and one on the right hip. During waking hours, wrist-worn accelerometers consistently produced higher median activity counts, about 5  ×  higher, as well as wider variability compared to hip-worn monitors. However, the shape of the accrual pattern curve over the course of the day for the hip and wrist are similar; there is a spike in activity in the morning, with a prolonged tapering of activity level as the day progresses. The similar patterns of hip and wrist activity accrual provide support that each location is capable of estimating total physical activity volume. The examination of activity patterns over time may provide a more detailed way to examine differences in wear location and different subpopulations.

Validation study of Polar V800 accelerometer

BACKGROUND

The correct quantification of physical activity (PA) and energy expenditure (EE) in daily life is an important target for researchers and professionals. The objective of this paper is to study the validity of the Polar V800 for the quantification of PA and the estimation of EE against the ActiGraph (ActiTrainer) in healthy young adults.

METHODS

Eighteen Caucasian active people (50% women) aged between 19-23 years wore an ActiTrainer on the right hip and a Polar V800 on the preferred wrist during 7 days. Paired samples t-tests were used to analyze differences in outcomes between devices, and Pearson's correlation coefficients to examine the correlation between outcomes. The agreement was studied using the Bland-Altman method. Also, the association between the difference and the magnitude of the measurement (heteroscedasticity) was examined. Sensitivity, specificity and area under the receiver operating characteristic curve (ROC-AUC value) were calculated to evaluate the ability of the devices to accurately define a person who fulfills the recommendation of 10,000 daily steps.

RESULTS

The devices significantly differed from each other on all outcomes (P<0.05), except for Polar V800's alerts vs. ActiTrainer's 1 hour sedentary bouts (P=0.595) and Polar V800's walking time vs. ActiTrainer's lifestyle time (P=0.484). Heteroscedasticity analyses were significant for all outcomes, except for Kcal and sitting time. The ROC-AUC value was fair (0.781±0.048) and the sensitivity and specificity was 98% and 58%, respectively.

CONCLUSIONS

The Polar V800 accelerometer has a comparable validity to the accelerometer in free-living conditions, regarding "1 hour sedentary bouts" and "V800's walking time vs. ActiTrainer's lifestyle time" in young adults.

Visual Inspection for Determining Days When Accelerometer Is Worn: Is This Valid?

PURPOSE

Logs have been traditionally used for ascertaining accelerometer wear days in mail study designs, but not all participants complete logs. Visual inspection of accelerometer output may supplement missing logs; however, no data on the validity of this method are available.

METHODS

We compared visual inspection with participant logs in 197 women (mean age, 71.0 yr). Women were mailed an accelerometer to be worn during waking hours for 7 d, marking each wear day on a log before returning the accelerometer by mail. For every participant, we created a series of graphs of accelerometer counts by time of day (one chart for each day with accelerometer output, including mail days). Two raters, masked to log wear status, independently inspected these graphs and scored each day as "worn" or "not worn."

RESULTS

The median (interquartile range) number of valid wear days using either visual inspection or log was 7 (7-7). For rater 1, the sensitivity and specificity of visual inspection was 99.7% (95% confidence interval, 99.2%-99.9%) and 97.2% (95.2%-98.6%), respectively; for rater 2, the sensitivity and specificity of visual inspection was 99.7% (99.2%-99.9%) and 97.0% (94.9%-98.4%), respectively. Interrater agreement was 99.5%.

CONCLUSIONS

Visual inspection of accelerometer data is a valid alternative to missing participant wear logs when determining wear days in mail study designs.

An Activity Index for Raw Accelerometry Data and Its Comparison with Other Activity Metrics

Accelerometers have been widely deployed in public health studies in recent years. While they collect high-resolution acceleration signals (e.g., 10-100 Hz), research has mainly focused on summarized metrics provided by accelerometers manufactures, such as the activity count (AC) by ActiGraph or Actical. Such measures do not have a publicly available formula, lack a straightforward interpretation, and can vary by software implementation or hardware type. To address these problems, we propose the physical activity index (AI), a new metric for summarizing raw tri-axial accelerometry data. We compared this metric with the AC and another recently proposed metric for raw data, Euclidean Norm Minus One (ENMO), against energy expenditure. The comparison was conducted using data from the Objective Physical Activity and Cardiovascular Health Study, in which 194 women 60-91 years performed 9 lifestyle activities in the laboratory, wearing a tri-axial accelerometer (ActiGraph GT3X+) on the hip set to 30 Hz and an Oxycon portable calorimeter, to record both tri-axial acceleration time series (converted into AI, AC, and ENMO) and oxygen uptake during each activity (converted into metabolic equivalents (METs)) at the same time. Receiver operating characteristic analyses indicated that both AI and ENMO were more sensitive to moderate and vigorous physical activities than AC, while AI was more sensitive to sedentary and light activities than ENMO. AI had the highest coefficients of determination for METs (0.72) and was a better classifier of physical activity intensity than both AC (for all intensity levels) and ENMO (for sedentary and light intensity). The proposed AI provides a novel and transparent way to summarize densely sampled raw accelerometry data, and may serve as an alternative to AC. The AI's largely improved sensitivity on sedentary and light activities over AC and ENMO further demonstrate its advantage in studies with older adults.

Multiple imputation of completely missing repeated measures data within person from a complex sample: application to accelerometer data in the National Health and Nutrition Examination Survey

The Physical Activity Monitor component was introduced into the 2003-2004 National Health and Nutrition Examination Survey (NHANES) to collect objective information on physical activity including both movement intensity counts and ambulatory steps. Because of an error in the accelerometer device initialization process, the steps data were missing for all participants in several primary sampling units, typically a single county or group of contiguous counties, who had intensity count data from their accelerometers. To avoid potential bias and loss in efficiency in estimation and inference involving the steps data, we considered methods to accurately impute the missing values for steps collected in the 2003-2004 NHANES. The objective was to come up with an efficient imputation method that minimized model-based assumptions. We adopted a multiple imputation approach based on additive regression, bootstrapping and predictive mean matching methods. This method fits alternative conditional expectation (ace) models, which use an automated procedure to estimate optimal transformations for both the predictor and response variables. This paper describes the approaches used in this imputation and evaluates the methods by comparing the distributions of the original and the imputed data. A simulation study using the observed data is also conducted as part of the model diagnostics. Finally, some real data analyses are performed to compare the before and after imputation results. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Assessing Daily Physical Activity in Older Adults: Unraveling the Complexity of Monitors, Measures, and Methods

At the 67th Gerontological Society of America Annual Meeting, a preconference workshop was convened to discuss the challenges of accurately assessing physical activity in older populations. The advent of wearable technology (eg, accelerometers) to monitor physical activity has created unprecedented opportunities to observe, quantify, and define physical activity in the real-world setting. These devices enable researchers to better understand the associations of physical activity with aging, and subsequent health outcomes. However, a consensus on proper methodological use of these devices in older populations has not been established. To date, much of the validation research regarding device type, placement, and data interpretation has been performed in younger, healthier populations, and translation of these methods to older populations remains problematic. A better understanding of these devices, their measurement properties, and the data generated is imperative to furthering our understanding of daily physical activity, its effects on the aging process, and vice versa. The purpose of this article is to provide an overview of the highlights of the preconference workshop, including properties of the different types of accelerometers, the methodological challenges of employing accelerometers in older study populations, a brief summary of ongoing aging-related research projects that utilize different types of accelerometers, and recommendations for future research directions.

The effect of a school-centered multicomponent intervention on daily physical activity and sedentary behavior in primary school children: The Active Living study

The aim of the current study was to examine the effectiveness of a school-centered multicomponent PA intervention, called 'Active Living', on children's daily PA levels. A quasi-experimental design was used including 9 intervention schools and 9 matched control schools located in the Netherlands. The baseline measurement took place between March-June 2013, and follow-up measurements were conducted 12months afterwards. Accelerometer (ActiGraph, GT3X+) data of 520 children aged 8-11years were collected and supplemented with demographics and weather conditions data. Implementation magnitude of the interventions was measured by keeping logbooks on the number of implemented physical environmental interventions (PEIs) and social environmental interventions (SEIs). Multilevel multivariate linear regression analyses were used to study changes in sedentary behavior (SB), light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) between baseline and follow-up. Finally, effect sizes (ESs) were calculated using Cohen's d. No pooled effects on PA and SB were found between children exposed and not exposed to Active Living after 12months. However, children attending Active Living schools that implemented larger numbers of both PEIs and SEIs engaged in 15 more minutes of LPA per weekday at follow-up than children in the control condition (ES=0.41; p<.05). Moreover, children attending these schools spent less time in SB at follow-up (ES=0.33), although this effect was non-significant. No significant effects were found on MVPA. A school-centered multicomponent PA intervention holds the potential to activate children, but a comprehensive set of intervention elements with a sufficient magnitude is necessary to achieve at least moderate effect sizes.

Pain Response after Maximal Aerobic Exercise in Adolescents across Weight Status

INTRODUCTION

Pain reports are greater with increasing weight status, and exercise can reduce pain perception. It is unknown, however, whether exercise can relieve pain in adolescents of varying weight status. The purpose of this study was to determine whether adolescents across weight status report pain relief after high-intensity aerobic exercise (exercise-induced hypoalgesia (EIH)).

METHODS

Sixty-two adolescents (15.1 ± 1.8 yr, 29 males) participated in the following three sessions: 1) pressure pain thresholds (PPT) before and after quiet rest, clinical pain (McGill Pain Questionnaire), and physical activity levels (self-report and ActiSleep Plus Monitors) were measured, 2) PPT were measured with a computerized algometer at the fourth finger's nailbed, middle deltoid muscle, and quadriceps muscle before and after maximal oxygen uptake test (V˙O2max Bruce Treadmill Protocol), and 3) body composition was measured with dual-energy x-ray absorptiometry.

RESULTS

All adolescents met criteria for V˙O2max. On the basis of body mass index z-score, adolescents were categorized as having normal weight (n = 33) or being overweight/obese (n = 29). PPT increased after exercise (EIH) and were unchanged with quiet rest (trial × session, P = 0.02). EIH was similar across the three sites and between normal-weight and overweight/obese adolescents. Physical activity and clinical pain were not correlated with EIH. Overweight/obese adolescents had similar absolute V˙O2max (L·min(-1)) but lower relative V˙O2max (mL·kg(-1)·min(-1)) compared with normal-weight adolescents. When adolescents were categorized using FitnessGram standards as unfit (n = 15) and fit (n = 46), the EIH response was similar between fitness levels.

CONCLUSIONS

This study is the first to establish that both overweight and normal-weight adolescents experience EIH. EIH after high-intensity aerobic exercise was robust in adolescents regardless of weight status and not influenced by physical fitness.

Statistical approaches to account for missing values in accelerometer data: Applications to modeling physical activity

Physical inactivity is a recognized risk factor for many chronic diseases. Accelerometers are increasingly used as an objective means to measure daily physical activity. One challenge in using these devices is missing data due to device nonwear. We used a well-characterized cohort of 333 overweight postmenopausal breast cancer survivors to examine missing data patterns of accelerometer outputs over the day. Based on these observed missingness patterns, we created psuedo-simulated datasets with realistic missing data patterns. We developed statistical methods to design imputation and variance weighting algorithms to account for missing data effects when fitting regression models. Bias and precision of each method were evaluated and compared. Our results indicated that not accounting for missing data in the analysis yielded unstable estimates in the regression analysis. Incorporating variance weights and/or subject-level imputation improved precision by >50%, compared to ignoring missing data. We recommend that these simple easy-to-implement statistical tools be used to improve analysis of accelerometer data.

Evaluation of a Physical Activity Intervention for Adults With Brain Impairment

Background. Individuals with brain impairment (BI) are less active than the general population and have increased risk of chronic disease. Objective. This controlled trial evaluated the efficacy of a physical activity (PA) intervention for community-dwelling adults with BI. Methods. A total of 43 adults with BI (27 male, 16 female; age 38.1 ± 11.9 years; stage of change 1-3) who walked as their primary means of locomotion were allocated to an intervention (n = 23) or control (n = 20) condition. The intervention comprised 10 face-to-face home visits over 12 weeks, including a tailored combination of stage-matched behavior change activities, exercise prescription, community access facilitation, and relapse prevention strategies. The control group received 10 face-to-face visits over 12 weeks to promote sun safety, healthy sleep, and oral health. Primary outcomes were daily activity counts and minutes of moderate-to-vigorous-intensity PA (MVPA) measured with the ActiGraph GT1M at baseline (0 weeks), postintervention (12 weeks) and follow-up (24 weeks). Between-group differences were evaluated for statistical significance using repeated-measures ANOVA. Results. MVPA for the intervention group increased significantly from baseline to 12 weeks (20.8 ± 3.1 to 31.2 ± 3.1 min/d; P = .01), but differences between baseline and 24 weeks were nonsignificant (20.8 ± 3.1 to 25.3 ± 3.2 min/d; P = .28). MVPA changes for the control group were negligible and nonsignificant. Between-group differences for change in MVPA were significant at 12 weeks ( P = .03) but not at 24 weeks ( P = .49). Conclusion. The 12-week intervention effectively increased adoption of PA in a sample of community-dwelling adults with BI immediately after the intervention but not at follow-up. Future studies should explore strategies to foster maintenance of PA participation.

Action control in dyads: A randomized controlled trial to promote physical activity in everyday life

RATIONALE

Engaging in regular physical activity requires substantial self-regulatory effort such as action control (e.g., continuously monitoring and evaluating an ongoing behavior with regard to one's standards).

OBJECTIVE

The present study examined the effectiveness of an ecological momentary action control intervention for promoting daily physical activity. Also, we tested whether a dyadic compared to an individual intervention displayed an additional benefit.

METHODS

121 overweight and obese individuals and their partners were randomly allocated to an intervention (n = 60; information + action control text messages) or a control group (n = 61; information only). The intervention was delivered in a dyadic vs. individual version of action control. Allocation ratio was 1:1:2 for the dyadic, individual, and control groups, respectively. Daily physical activity was assessed with triaxial accelerometers during a 14-day intervention phase and a 14-day follow-up phase.

RESULTS

Participants in the intervention group showed a higher probability (36.5%) to achieve the recommended daily activity levels (≥30 min of moderate-to-vigorous physical activity per day performed in bouts of at least 10 min) during the intervention and follow-up phase compared to those in the control group (23.0%). The intervention and control group did not differ in terms of daily moderate-to-vigorous physical activity (40.7 vs. 38.6 min per day, p = 0.623).

CONCLUSION

Interventions facilitating action control via text messages seem to be an effective tool for increasing adherence to physical activity guidelines in everyday life. The comparable effects for the dyadic and individual intervention suggest that automated text messages may be just as effective as personalized messages from the romantic partner. Further investigation is needed to examine the usefulness of a dyadic conceptualizing of action control. (controlled-trials.com ISRCTN15705531).

Calibration of context-specific survey items to assess youth physical activity behaviour

This study tests calibration models to re-scale context-specific physical activity (PA) items to accelerometer-derived PA. A total of 195 4th-12th grades children wore an Actigraph monitor and completed the Physical Activity Questionnaire (PAQ) one week later. The relative time spent in moderate-to-vigorous PA (MVPA_%) obtained from the Actigraph at recess, PE, lunch, after-school, evening and weekend was matched with a respective item score obtained from the PAQ's. Item scores from 145 participants were calibrated against objective MVPA_% using multiple linear regression with age, and sex as additional predictors. Predicted minutes of MVPA for school, out-of-school and total week were tested in the remaining sample (n = 50) using equivalence testing. The results showed that PAQ β-weights ranged from 0.06 (lunch) to 4.94 (PE) MVPA_% (P < 0.05) and models root mean square error ranged from 4.2% (evening) to 20.2% (recess). When applied to an independent sample, differences between PAQ and accelerometer MVPA at school and out-of-school ranged from -15.6 to +3.8 min and the PAQ was within 10-15% of accelerometer measured activity. This study demonstrated that context-specific items can be calibrated to predict minutes of MVPA in groups of youth during in- and out-of-school periods.

The Effect of Light Rail Transit on Physical Activity: Design and Methods of the Travel-Related Activity in Neighborhoods Study

Background

Use of mass transit has been proposed as a way to incorporate regular physical activity into daily life because transit use typically requires additional travel to access and depart the stop or station. If this additional travel is active, a small but potentially important amount of physical activity can be achieved daily. Although prior research has shown that transit use is associated with physical activity, important questions remain unanswered. Utilizing a major expansion of the Houston, TX, USA light-rail system as a natural experiment, the Houston Travel-Related Activity in Neighborhoods (TRAIN) Study was developed to address these unanswered questions.

Purpose

The purpose of the TRAIN Study is to determine if the development of light-rail lines in Houston, TX, USA will prospectively affect both transit use and physical activity over 4 years. We also aim to understand how contextual effects (i.e., moderators or interaction effects), such as the neighborhood built environment and socioeconomic factors, affect the primary relations under study.

Methods

The TRAIN Study is a longitudinal cohort design, in which participants are recruited at baseline from a 3-mile buffer around each of the three new lines and measured annually four times. Recruitment is accomplished via telephone contact, ads in newspapers and advertising circulars, and targeted community outreach. Data are collected via mail and include questionnaire-assessed factors, such as perceived neighborhood characteristics, attitudes about transportation, demographics, and reported physical activity; a travel diary; and accelerometry. Additionally, field-based neighborhood audits are conducted to capture micro-scale environmental features. To assess macro-scale environmental characteristics, we utilize GIS mapping and spatial analyses. Statistical analyses will be conducted using latent growth curve modeling and discrete choice models, with a focus on identifying moderating factors (i.e., statistical interaction effects). Selection bias will be controlled via propensity score analysis.

Conclusion

The TRAIN study is a unique opportunity to study how a multi-billion dollar investment in mass transit can simultaneously affect transportation needs and physical activity behavior. This comprehensive evaluation will provide needed evidence for policy makers, and can inform health impact assessments of future transportation projects around the world.

Electronic physiologic and subjective data acquisition in home-dwelling heart failure patients: An assessment of patient use and perception of usability

BACKGROUND

The current approach to the outpatient management of heart failure involves patients recollecting what has happened to them since their last clinic visit. But patients' recollection of their symptoms may not be sufficiently accurate to optimally manage their disease. Most of what is known about heart failure is related to patients' diurnal symptoms and activities. Some mobile electronic technologies can operate continuously to collect data from the time patients go to bed until they get up in the morning. We were therefore interested to evaluate if patients would use a system of selected patient-facing devices to collect physiologic and subjective state data in and around the patients' period of sleep, and if there were differences in device use and perceptions of usability at the device level

METHODS

This descriptive observational study of home-dwelling patients with heart failure, between 21 and 90 years of age, enrolled in an outpatient heart failure clinic was conducted between December 2014 and June 2015. Patients received five devices, namely, body weight scale, blood pressure device, an iPad-based subjective states assessment, pulse oximeter, and actigraph, to collect their physiologic (body weight, blood pressure, heart rate, blood oxygen saturation, and physical activity) and subjective state data (symptoms and subjective states) at home for the next six consecutive nights. Use was defined as the ratio of observed use over expected use, where 1.0 is observed equals expected. Usability was determined by the overall System Usability Scale score.

RESULTS

Participants were 39 clinical heart failure patients, mean age 68.1 (SD, 12.3), 72% male, 62% African American. The ratio of observed over expected use for the body weight scale, blood pressure device, iPad application, pulse oximeter and actigraph was 0.8, 1.0, 1.1, 0.9, and 1.9, respectively. The mean overall System Usability Scale score for each device were 84.5, 89.7, 85.7, 87.6, and 85.2, respectively.

CONCLUSIONS

Patients were able to use all of the devices and they rated the usability of all the devices higher than expected. Our study provides support for at-home patient-collected physiologic and subjective state data. To our knowledge, this is the first study to assess the use and usability of electronic objective and subjective data collection devices in heart failure patients' homes overnight.

Movement prediction using accelerometers in a human population

We introduce statistical methods for predicting the types of human activity at sub-second resolution using triaxial accelerometry data. The major innovation is that we use labeled activity data from some subjects to predict the activity labels of other subjects. To achieve this, we normalize the data across subjects by matching the standing up and lying down portions of triaxial accelerometry data. This is necessary to account for differences between the variability in the position of the device relative to gravity, which are induced by body shape and size as well as by the ambiguous definition of device placement. We also normalize the data at the device level to ensure that the magnitude of the signal at rest is similar across devices. After normalization we use overlapping movelets (segments of triaxial accelerometry time series) extracted from some of the subjects to predict the movement type of the other subjects. The problem was motivated by and is applied to a laboratory study of 20 older participants who performed different activities while wearing accelerometers at the hip. Prediction results based on other people's labeled dictionaries of activity performed almost as well as those obtained using their own labeled dictionaries. These findings indicate that prediction of activity types for data collected during natural activities of daily living may actually be possible.

Role of heart rate in the relation between regional body fat and subendocardial viability ratio in women

Subendocardial viability ratio (SEVR) is a measure of left ventricular function, specifically; it is an index of myocardial perfusion relative to left ventricular workload. Women have lower SEVR than men, partly due to a faster resting heart rate that reduces diastolic time (i.e., time for myocardial perfusion). It is unclear if body fat relates to SEVR, thus the purpose of this study was to examine the relation between body fat and SEVR in women. Twenty-eight middle-aged (31-45 years) and 31 older (60-80 years) women were examined. Radial artery applanation tonometry was used to calculate SEVR from a synthesized central aortic pressure wave. Dual-energy X-ray absorptiometry was used to assess body composition including fat in the trunk, legs, android and gynoid regions. Body fat was not related (P>.05) with SEVR in older women. In middle-aged women, all measures of regional fat were correlated with heart rate (range, r=.49-.59, P≤.01) and SEVR (range, r=.43-.53, P≤.01). Android-to-gynoid ratio was identified as the strongest predictor (r(2) =-.26, P<.01) of SEVR among measures of regional fat. Middle-aged women with lower android-to-gynoid fat ratio had higher SEVR (1.96±0.33 vs 1.66±0.20, P=.009) than women with higher fat ratio, even after adjusting for age, height, daily physical activity, and aortic mean pressure (P=.02). Adjusting for heart rate or diastolic time abolished the difference in SEVR between groups (1.80±0.09 vs 1.82±0.09, P=.56). These results suggest that middle-aged women with a greater distribution of fat in the abdomen have poorer left ventricular function that is dependent on the negative influence of heart rate on diastolic time.

What Is the Effect on Obesity Indicators from Replacing Prolonged Sedentary Time with Brief Sedentary Bouts, Standing and Different Types of Physical Activity during Working Days? A Cross-Sectional Accelerometer-Based Study among Blue-Collar Workers

Introduction

The aim of the study was to investigate if (a) substituting total sedentary time or long sedentary bouts with standing or various types of physical activity and (b) substituting long sedentary bouts with brief sedentary bouts; is associated with obesity indicators using a cross sectional isotemporal substitution approach among blue-collar workers.

Methods

A total of 692 workers from transportation, manufacturing and cleaning sectors wore an Actigraph GT3X+ accelerometer on the thigh for 1–4 working days. The sedentary (sit and lie), standing, walking, and moderate to vigorous physical activity (MVPA) time on working days was computed using validated Acti4 software. The total sedentary time and uninterrupted sedentary time spent in brief (≤5 mins), moderate (>5 and ≤30 mins), and long (>30mins) bouts, were determined for the whole day and during work and non-work time separately. The obesity indicators, BMI (kg/m²), waist circumference (cm) and fat percentage were objectively measured. Isotemporal substitution modelling was utilized to determine the linear association with obesity indicators of replacing 30 min of total sedentary time or long sedentary bouts with standing, walking or MVPA and separately replacing 30 min of long sedentary bouts with brief sedentary bouts.

Results

Workers [mean (standard deviation, SD); age = 45.1 (9.9) years, BMI = 27.5 (4.9) kg/m², %BF = 29.6 (9.5), waist circumference = 94.4 (13.0) cm] sat for 2.4 hours (~32% of the measured time, SD = 1.8 hours) across the day during work period and 5.5 hours (~62% of the measured time, SD = 1.5 hours) during non-work period. Most of the sedentary time was accrued in moderate bouts [work = 1.40 (SD = 1.09) hours] during work and in long bouts during non-work [2.7 (SD = 1.4) hours], while least in long sedentary bouts during work [work = 0.5 (SD = 0.9)] and in brief sedentary bouts [0.5 hours (SD = 0.3)] during non-work. Significant associations with all obesity indicators were found when 30 min of total sedentary time or long sedentary bouts were replaced with standing time (~1–2% lower) or MVPA (~4–9% lower) during whole day, work, and non-work periods. The exception was that a statistically significant association was not observed with any obesity indicator when replacing total sedentary time or long sedentary bouts with standing time during the work period. Significant beneficial associations were found when replacing the long sedentary bouts with brief sedentary bouts (~3–5% lower) during all domains.

Conclusion

Replacing total sedentary time and long sedentary bouts, respectively, not only with MVPA but also standing time appears to be beneficially associated with obesity indicators among blue-collar workers. Additionally, replacing long sedentary bouts with brief sedentary bouts was also beneficially associated with obesity indicators. Studies using prospective design are needed to confirm the findings.

Affect and Subsequent Physical Activity: An Ambulatory Assessment Study Examining the Affect-Activity Association in a Real-Life Context

Traditionally, cognitive, motivational, and volitional determinants have been used to explain and predict health behaviors such as physical activity. Recently, the role of affect in influencing and regulating health behaviors received more attention. Affects as internal cues may automatically activate unconscious processes of behavior regulation. The aim of our study was to examine the association between affect and physical activity in daily life. In addition, we studied the influence of the habit of being physically active on this relationship. An ambulatory assessment study in 89 persons (33.7% male, 25 to 65 years, M = 45.2, SD = 8.1) was conducted. Affect was assessed in the afternoon on 5 weekdays using smartphones. Physical activity was measured continuously objectively using accelerometers and subjectively using smartphones in the evening. Habit strength was assessed at the beginning of the diary period. The outcomes were objectively and subjectively measured moderate-to-vigorous physical activity (MVPA) performed after work. Multilevel regression models were used to analyze the association between affect and after work MVPA. In addition, the cross-level interaction of habit strength and affect on after work MVPA was tested. Positive affect was positively related to objectively measured and self-reported after work MVPA: the greater the positive affect the more time persons subsequently spent on MVPA. An inverse relationship was found for negative affect: the greater the negative affect the less time persons spent on MVPA. The cross-level interaction effect was significant only for objectively measured MVPA. A strong habit seems to strengthen both the positive influence of positive affect and the negative influence of negative affect. The results of this study confirm previous results and indicate that affect plays an important role for the regulation of physical activity behavior in daily life. The results for positive affect were consistent. However, in contrast to previous reports of no or an inverse association, negative affect decreased subsequent MVPA. These inconsistencies may be—in part—explained by the different measurements of affect in our and other studies. Therefore, further research is warranted to gain more insight into the association between affect and physical activity.

National youth sedentary behavior and physical activity daily patterns using latent class analysis applied to accelerometry

Background

Applying latent class analysis (LCA) to accelerometry can help elucidated underlying patterns. This study described the patterns of accelerometer-determined sedentary behavior and physical activity among youth by applying LCA to a nationally representative United States (US) sample.

Methods

Using 2003–2006 National Health and Nutrition Examination Survey data, 3998 youths 6–17 years wore an ActiGraph 7164 accelerometer for one week, providing > =3 days of wear for > =8 h/day from 6:00 am-midnight. Cutpoints defined sedentary behavior (<100 counts/minute), light activity (100–2295 counts/minute), moderate to vigorous physical activity (MVPA; > = 2296 counts/minute), and vigorous activity (> = 4012 counts/minute). To account for wear time differences, outcomes were expressed as percent of day in a given intensity. LCA was used to classify daily (Monday through Sunday) patterns of average counts/minute, sedentary behavior, light activity, MVPA, and vigorous activity separately. The latent classes were explored overall and by age (6–11, 12–14, 15–17 years), gender, and whether or not youth attended school during measurement. Estimates were weighted to account for the sampling frame.

Results

For average counts/minute/day, four classes emerged from least to most active: 40.9 % of population (mean 323.5 counts/minute/day), 40.3 % (559.6 counts/minute/day), 16.5 % (810.0 counts/minute/day), and 2.3 % (1132.9 counts/minute/day). For percent of sedentary behavior, four classes emerged: 13.5 % of population (mean 544.6 min/day), 30.1 % (455.1 min/day), 38.5 % (357.7 min/day), and 18.0 % (259.2 min/day). For percent of light activity, four classes emerged: 12.3 % of population (mean 222.6 min/day), 29.3 % (301.7 min/day), 41.8 % (384.0 min/day), and 16.6 % (455.5 min/day). For percent of MVPA, four classes emerged: 59.9 % of population (mean 25.0 min/day), 33.3 % (60.9 min/day), 3.1 % (89.0 min/day), and 3.6 % (109.3 min/day). For percent of vigorous activity, three classes emerged: 76.8 % of population (mean 7.1 min/day), 18.5 % (23.9 min/day), and 4.7 % (47.4 min/day). Classes were developed by age, gender, and school attendance since some patterns differed when stratifying by these factors.

Conclusion

The models supported patterns for average intensity, sedentary behavior, light activity, MVPA, and vigorous activity. These latent class derived patterns can be used in other youth studies to explore correlates or outcomes and to target sedentary behavior or physical activity interventions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12966-016-0382-x) contains supplementary material, which is available to authorized users.

Motivational Interviewing to Prevent Childhood Obesity: A Cluster RCT

OBJECTIVE

The objective was to evaluate a manualized theory-driven primary preventive intervention aimed at early childhood obesity. The intervention was embedded in Swedish child health services, starting when eligible children were 9 to 10 months of age and continuing until the children reached age 4.

METHODS

Child health care centers in 8 Swedish counties were randomized into intervention and control units and included 1355 families with 1369 infants. Over ∼39 months, families in the intervention group participated in 1 group session and 8 individual sessions with a nurse trained in motivational interviewing, focusing on healthy food habits and physical activity. Families in the control group received care as usual. Primary outcomes were children's BMI, overweight prevalence, and waist circumference at age 4. Secondary outcomes were children's and mothers' food and physical activity habits and mothers' anthropometrics. Effects were assessed in linear and log-binominal regression models using generalized estimating equations.

RESULTS

There were no statistically significant differences in children's BMI (β = -0.11, 95% confidence interval [CI]: -0.31 to 0.08), waist circumference (β = -0.48, 95% CI: -0.99 to 0.04), and prevalence of overweight (relative risk = 0.95, 95% CI: 0.69 to 1.32). No significant intervention effects were observed in mothers' anthropometric data or regarding mothers' and children's physical activity habits. There was a small intervention effect in terms of healthier food habits among children and mothers.

CONCLUSIONS

There were no significant group differences in children's and mothers' anthropometric data and physical activity habits. There was, however, some evidence suggesting healthier food habits, but this should be interpreted with caution.

CANPLAY study: Secular trends in steps/day amongst 5-19year-old Canadians between 2005 and 2014

INTRODUCTION

The Canadian Physical Activity Levels Among Youth (CANPLAY) study collected pedometer data from eight surveys between 2005 and 2014, making it a unique database of objective population physical activity surveillance. The purpose of this study was to describe secular physical activity trends for 5-19year olds.

METHODS

Canadian children from nationally representative samples (10,000 recruited, n≅5500 per survey) were mailed a pedometer kit, asked to wear the pedometer for 7 consecutive days, log steps daily, then return the log by mail. Weighted medians and prevalence estimates were calculated. Trends were tested by χ(2) test of independence.

RESULTS

An overall median of 10,935 steps/day was taken by Canadian children 5-19years of age (n=43,806) across the eight surveys. Steps/day increased between 2005-06 and 2007-08, then decreased in 2012-14. The prevalence of taking sufficient steps/day (defined as ≥10,000 steps/day for 5year olds, ≥13,000 steps/day for 6-11year-old boys; ≥11,000 steps/day for 6-11year-old girls; and ≥10,000 steps/day for 12-19year olds;) also increased then decreased over time, whereas the prevalence of accumulating <7000 steps/day generally increased over time. Trends were significant for boys, girls and each age group.

DISCUSSION

The CANPLAY surveillance system provided comparable data at multiple time points over 9years. An overall shift in the distribution of steps/day towards a less active lifestyle occurred between 2005-06 and 2012-14 for boys, girls and each age group. This provides evidence that the national policy goal to increase children's steps/day by 2015 has not been met.

Hip and Wrist Accelerometer Algorithms for Free-Living Behavior Classification

PURPOSE

Accelerometers are a valuable tool for objective measurement of physical activity (PA). Wrist-worn devices may improve compliance over standard hip placement, but more research is needed to evaluate their validity for measuring PA in free-living settings. Traditional cut-point methods for accelerometers can be inaccurate and need testing in free living with wrist-worn devices. In this study, we developed and tested the performance of machine learning (ML) algorithms for classifying PA types from both hip and wrist accelerometer data.

METHODS

Forty overweight or obese women (mean age = 55.2 ± 15.3 yr; BMI = 32.0 ± 3.7) wore two ActiGraph GT3X+ accelerometers (right hip, nondominant wrist; ActiGraph, Pensacola, FL) for seven free-living days. Wearable cameras captured ground truth activity labels. A classifier consisting of a random forest and hidden Markov model classified the accelerometer data into four activities (sitting, standing, walking/running, and riding in a vehicle). Free-living wrist and hip ML classifiers were compared with each other, with traditional accelerometer cut points, and with an algorithm developed in a laboratory setting.

RESULTS

The ML classifier obtained average values of 89.4% and 84.6% balanced accuracy over the four activities using the hip and wrist accelerometer, respectively. In our data set with average values of 28.4 min of walking or running per day, the ML classifier predicted average values of 28.5 and 24.5 min of walking or running using the hip and wrist accelerometer, respectively. Intensity-based cut points and the laboratory algorithm significantly underestimated walking minutes.

CONCLUSIONS

Our results demonstrate the superior performance of our PA-type classification algorithm, particularly in comparison with traditional cut points. Although the hip algorithm performed better, additional compliance achieved with wrist devices might justify using a slightly lower performing algorithm.

Evidence for data missing at random in youth physical activity monitoring research

This study examined whether or not activity monitor data collected as part of a typical 7-day physical activity (PA) measurement protocol can be expected to be missing at random. A total of 315 participants (9-18 years) each wore a SenseWear Armband monitor for 7 consecutive days. Participants were classified as "compliant" (86 boys and 124 girls) if they had recorded accelerometer data during 70% or more of the predefined awake time (7 AM-10 PM) on four different days; and "non-compliant" (44 boys and 51 girls) when not meeting these criteria. Linear mixed models were used to examine differences in energy expenditure (EE) levels by compliance across 10 different time periods. The results indicated that non-compliant girls were older (13.4 ± 2.9 vs. 12.2 ± 2.5) and taller (156.8 ± 10.3 vs. 152.8 ± 11.3) than their same gender compliant peers (P < .05). Comparisons of EE rates at segmented portions of the day revealed no differences between compliant and non-compliant groups (P ≥ .05). Differences in EE ranged from -0.32 kcal · kg^-1 · h^-1 (before school time) to 0.62 kcal · kg^-1 · h^-1 (physical education class) in boys and -0.39 kcal · kg^-1 · h^-1 (transportation from school) to 0.37 kcal · kg^-1 · hour^-1 (recess) in girls. The results showed that compliant and non-compliant individuals differed in a few demographic characteristics but exhibited similar activity patterns. This suggests that data were considered to be missing at random, but additional work is needed to confirm this observation in a representative sample of children using other types of activity monitors and protocols.

Physical activity attenuates the effect of the FTO genotype on obesity traits in European adults: The Food4Me study

OBJECTIVE

To examine whether the effect of FTO loci on obesity-related traits could be modified by physical activity (PA) levels in European adults.

METHODS

Of 1,607 Food4Me participants randomized, 1,280 were genotyped for FTO (rs9939609) and had available PA data. PA was measured objectively using accelerometers (TracmorD, Philips), whereas anthropometric measures [BMI and waist circumference (WC)] were self-reported via the Internet.

RESULTS

FTO genotype was associated with a higher body weight [β: 1.09 kg per risk allele, (95% CI: 0.14-2.04), P = 0.024], BMI [β: 0.54 kg m(-2) , (0.23-0.83), P < 0.0001], and WC [β: 1.07 cm, (0.24-1.90), P = 0.011]. Moderate-equivalent PA attenuated the effect of FTO on BMI (P[interaction]  = 0.020). Among inactive individuals, FTO increased BMI by 1.06 kg m(-2) per allele (P = 0.024), whereas the increase in BMI was substantially attenuated among active individuals (0.16 kg m(-2) , P = 0.388). We observed similar effects for WC (P[interaction]  = 0.005): the FTO risk allele increased WC by 2.72 cm per allele among inactive individuals but by only 0.49 cm in active individuals.

CONCLUSIONS

PA attenuates the effect of FTO genotype on BMI and WC. This may have important public health implications because genetic susceptibility to obesity in the presence of FTO variants may be reduced by adopting a physically active lifestyle.

Physical Activity Levels in U.S. Latino/Hispanic Adults: Results From the Hispanic Community Health Study/Study of Latinos

INTRODUCTION

Physical activity (PA) prevalence among U.S. Latino/Hispanic adults of diverse backgrounds is not well known. This study describes PA among a representative sample of U.S. Latino/Hispanic adults.

METHODS

A population-based cohort of Hispanic/Latino adults (aged 18-74 years) participating in the Hispanic Community Health Study/Study of Latinos from March 2008 to June 2011 (N=16,415) was recruited in four urban areas from Miami, the Bronx, Chicago, and San Diego. Participants wore an Actical hip accelerometer for 1 week (n=12,253) and completed the Global Physical Activity Questionnaire (n=15,741). Data were analyzed in 2015.

RESULTS

Based on accelerometry, Hispanics/Latinos engaged in 23.8 minutes/day (10.3 minutes/day when only considering minutes from sustained 10-minute bouts) of moderate to vigorous PA (MVPA). Individuals of Puerto Rican and Dominican background had the most minutes/day of MVPA (32.1 and 29.1, respectively), whereas those of Cuban background had the fewest (15.3). Based on the Global Physical Activity Questionnaire, 65% of Hispanic/Latinos met the aerobic component of 2008 Physical Activity Guidelines for Americans. Men and individuals of Puerto Rican background had the most minutes/day of leisure-time MVPA (30.3 and 30.2, respectively). Individuals of Puerto Rican and Dominican background had the most minutes/day of transportation-related PA (48.7 and 39.7, respectively). Individuals of Mexican and Central American background had the most minutes/day of work-related MVPA (90.7 and 93.2, respectively).

CONCLUSIONS

Among Hispanics/Latinos, self-reported data provided information on the type of PA and helped explain variability identified from accelerometer-assessed PA. These findings highlight variability in PA among Hispanics from diverse ethnic backgrounds.

Correlates of US adult physical activity and sedentary behavior patterns

OBJECTIVES

Physical activity and sedentary behavior patterns may be differentially associated with socio-demographic and health measures. We explored correlates of day-to-day patterns over a week in accelerometer measured physical activity and sedentary behavior to inform intervention development.

DESIGN

Cross-sectional study.

METHODS

National Health and Nutrition Examination Survey (NHANES) adult participants (≥20 years) in 2003-2006 wore an accelerometer for 1 week. Accelerometer data from 7236 participants were used to derive latent classes describing day-to-day patterns over a week of physical activity and sedentary behavior. Correlates of each pattern were identified using multinomial logistic regression from 21 potential variables grouped into four domains: socio-demographic, acculturation, cardiovascular, and health history.

RESULTS

Older age, female sex, higher body mass index, and history of chronic disease were consistently associated with lower odds of being in a more active compared to the least active class. In contrast, being employed, speaking Spanish at home, and having better self-rated health were associated with higher odds of being in a more active compared to the least active class.

CONCLUSIONS

Correlates of physical activity and sedentary behavior patterns were identified from all domains (socio-demographic, acculturation, cardiovascular, and health history). Most correlates that were positively associated with physical activity were negatively associated with sedentary behavior. Better understanding of the correlates of physical activity and sedentary behavior patterns can inform interventions to promote physical activity and reduce sedentary behavior.

Objectively Measured Physical Activity in European Adults: Cross-Sectional Findings from the Food4Me Study

Background

Comparisons of objectively measured physical activity (PA) between residents of European countries measured concurrently with the same protocol are lacking. We aimed to compare PA between the seven European countries involved in the Food4Me Study, using accelerometer data collected remotely via the Internet.

Methods

Of the 1607 participants recruited, 1287 (539 men and 748 women) provided at least 3 weekdays and 2 weekend days of valid accelerometer data (TracmorD) at baseline and were included in the present analyses.

Results

Men were significantly more active than women (physical activity level = 1.74 vs. 1.70, p < 0.001). Time spent in light PA and moderate PA differed significantly between countries but only for women. Adherence to the World Health Organization recommendation to accumulate at least 150 min of moderate-equivalent PA weekly was similar between countries for men (range: 54–65%) but differed significantly between countries for women (range: 26–49%). Prevalence estimates decreased substantially for men and women in all seven countries when PA guidelines were defined as achieving 30 min of moderate and vigorous PA per day.

Conclusions

We were able to obtain valid accelerometer data in real time via the Internet from 80% of participants. Although our estimates are higher compared with data from Sweden, Norway, Portugal and the US, there is room for improvement in PA for all countries involved in the Food4Me Study.

Missing value imputation for physical activity data measured by accelerometer

An accelerometer, a wearable motion sensor on the hip or wrist, is becoming a popular tool in clinical and epidemiological studies for measuring the physical activity. Such data provide a series of activity counts at every minute or even more often and displays a person's activity pattern throughout a day. Unfortunately, the collected data can include irregular missing intervals because of noncompliance of participants and therefore make the statistical analysis more challenging. The purpose of this study is to develop a novel imputation method to handle the multivariate count data, motivated by the accelerometer data structure. We specify the predictive distribution of the missing data with a mixture of zero-inflated Poisson and Log-normal distribution, which is shown to be effective to deal with the minute-by-minute autocorrelation as well as under- and over-dispersion of count data. The imputation is performed at the minute level and follows the principles of multiple imputation using a fully conditional specification with the chained algorithm. To facilitate the practical use of this method, we provide an R package accelmissing. Our method is demonstrated using 2003-2004 National Health and Nutrition Examination Survey data.

Effects of Varying Epoch Lengths, Wear Time Algorithms, and Activity Cut-Points on Estimates of Child Sedentary Behavior and Physical Activity from Accelerometer Data

Objective

To examine the effects of accelerometer epoch lengths, wear time (WT) algorithms, and activity cut-points on estimates of WT, sedentary behavior (SB), and physical activity (PA).

Methods

268 7–11 year-olds with BMI ≥ 85^th percentile for age and sex wore accelerometers on their right hips for 4–7 days. Data were processed and analyzed at epoch lengths of 1-, 5-, 10-, 15-, 30-, and 60-seconds. For each epoch length, WT minutes/day was determined using three common WT algorithms, and minutes/day and percent time spent in SB, light (LPA), moderate (MPA), and vigorous (VPA) PA were determined using five common activity cut-points. ANOVA tested differences in WT, SB, LPA, MPA, VPA, and MVPA when using the different epoch lengths, WT algorithms, and activity cut-points.

Results

WT minutes/day varied significantly by epoch length when using the NHANES WT algorithm (p < .0001), but did not vary significantly by epoch length when using the ≥ 20 minute consecutive zero or Choi WT algorithms. Minutes/day and percent time spent in SB, LPA, MPA, VPA, and MVPA varied significantly by epoch length for all sets of activity cut-points tested with all three WT algorithms (all p < .0001). Across all epoch lengths, minutes/day and percent time spent in SB, LPA, MPA, VPA, and MVPA also varied significantly across all sets of activity cut-points with all three WT algorithms (all p < .0001).

Conclusions

The common practice of converting WT algorithms and activity cut-point definitions to match different epoch lengths may introduce significant errors. Estimates of SB and PA from studies that process and analyze data using different epoch lengths, WT algorithms, and/or activity cut-points are not comparable, potentially leading to very different results, interpretations, and conclusions, misleading research and public policy.

Impact of activity outcome and measurement instrument on estimates of youth compliance with physical activity guidelines: a cross-sectional study

BACKGROUND

The national physical activity guidelines (PAG) in many countries recommend that youth accumulate 60 min or more of moderate-to-vigorous physical activity (MVPA) daily (PAG-MVPA). A daily target of ≥ 11,500 steps/day has been proposed as a step count alternative to this guideline (PAG-Steps). Contemporary activity monitors are capable of estimating both MVPA and steps, but it is not clear how these units compare when used to evaluate compliance with the national PAG. The purpose of this study was to compare prevalence estimates of meeting the PAG-MVPA and PAG-Steps using two commonly used monitors, the ActiGraph (AG) and SenseWear Armband (SWA).

METHODS

A sample of 69 children (25 girls and 44 boys) aged 9-16 years each wore a wrist-mounted AG and a SWA over a one-week period. Days with ≥10 h of wear time for both monitors were included in the analysis. Estimates of time spent in MVPA were obtained using the Crouter equation for the AG and from proprietary algorithms for the SWA. Step counts for the AG and SWA were directly obtained from the respective software. The prevalence of meeting the PAG-MVPA and PAG-Steps was compared within each monitor, using Cohen's kappa (κ) statistic. Agreement was similarly assessed between monitors using each guideline individually.

RESULTS

When assessed with the AG, the prevalence of meeting PAG was substantially higher for the PAG-MVPA (87.2 %) than for the PAG-Steps (54.2 %), with fair classification agreement (κ = 0.30) between the two guidelines. Higher prevalence rates were also observed for the PAG-MVPA (83.6 %) than for the PAG-Steps (33.8 %) when assessed using the SWA, but the prevalence rates and classification agreement (κ = 0.18) were lower than the values from the AG. Classification agreement between AG and SWA was lower for the PAG-MVPA (κ = 0.42) than for the PAG-Steps (κ = 0.55).

CONCLUSIONS

The results show differential patterns of compliance with the PAG-MVPA and PAG-Steps, as assessed by the AG and SWA. Additional research is needed to directly evaluate and compare findings from public health research based on different guidelines and measurement methods.

Fatigue, patient reported outcomes, and objective measurement of physical activity in systemic lupus erythematosus

OBJECTIVE

Fatigue is a common symptom in systemic lupus erythematosus (SLE), and engaging in physical activity may reduce fatigue. We aimed to characterize relationships between fatigue, other health status measures assessed with the Patient Reported Outcomes Measurement Information System (PROMIS) instruments, and accelerometer-based physical activity measurements in patients with SLE. The internal consistency of each PROMIS measure in our SLE sample was also evaluated.

METHODS

This cross-sectional study analyzed 123 adults with SLE. The primary fatigue outcome was Fatigue Severity Scale score. Secondary outcomes were PROMIS standardized T-scores in seven health status domains. Accelerometers were worn for seven days, and mean daily minutes of light, moderate/vigorous, and bouted (10 minutes) moderate/vigorous physical activity were estimated. Cronbach's alpha was determined for each PROMIS measure to assess internal consistency. Relationships between Fatigue Severity Scale, PROMIS, and physical activity were summarized with Spearman partial correlation coefficients (r), adjusted for average daily accelerometer wear time.

RESULTS

Mean Fatigue Severity Scale score (4.3, SD 1.6) was consistent with clinically relevant levels of fatigue. Greater daily and bouted moderate/vigorous physical activity minutes correlated with lower Mean Fatigue Severity Scale score (r = -0.20, p = 0.03 and r = -0.30, p = 0.0007, respectively). For PROMIS, bouted moderate/vigorous physical activity minutes correlated with less fatigue (r = -0.20, p = 0.03). PROMIS internal consistency was excellent, with Cronbach's alpha > 0.90 for each domain. Mean PROMIS T-scores for fatigue, pain interference, anxiety, sleep disturbance, sleep-related impairment, and physical function were worse than reported for the general US population. More moderate/vigorous physical activity minutes were associated with less pain interference (r = -0.22, p = 0.01). Both light physical activity and moderate/vigorous physical activity minutes correlated with better physical function (r = 0.19, p = 0.04 and r = 0.25, p = 0.006, respectively).

CONCLUSION

More time spent in moderate/vigorous physical activity was associated with less fatigue (Fatigue Severity Scale and PROMIS), less pain interference, and better physical function (PROMIS). PROMIS had excellent internal consistency in our SLE sample, and six of seven PROMIS measures indicated poorer average health status in SLE patients compared with the general US population.

Changes in Physical Activity Following a Genetic-Based Internet-Delivered Personalized Intervention: Randomized Controlled Trial (Food4Me)

Background

There is evidence that physical activity (PA) can attenuate the influence of the fat mass- and obesity-associated (FTO) genotype on the risk to develop obesity. However, whether providing personalized information on FTO genotype leads to changes in PA is unknown.

Objective

The purpose of this study was to determine if disclosing FTO risk had an impact on change in PA following a 6-month intervention.

Methods

The single nucleotide polymorphism (SNP) rs9939609 in the FTO gene was genotyped in 1279 participants of the Food4Me study, a four-arm, Web-based randomized controlled trial (RCT) in 7 European countries on the effects of personalized advice on nutrition and PA. PA was measured objectively using a TracmorD accelerometer and was self-reported using the Baecke questionnaire at baseline and 6 months. Differences in baseline PA variables between risk (AA and AT genotypes) and nonrisk (TT genotype) carriers were tested using multiple linear regression. Impact of FTO risk disclosure on PA change at 6 months was assessed among participants with inadequate PA, by including an interaction term in the model: disclosure (yes/no) × FTO risk (yes/no).

Results

At baseline, data on PA were available for 874 and 405 participants with the risk and nonrisk FTO genotypes, respectively. There were no significant differences in objectively measured or self-reported baseline PA between risk and nonrisk carriers. A total of 807 (72.05%) of the participants out of 1120 in the personalized groups were encouraged to increase PA at baseline. Knowledge of FTO risk had no impact on PA in either risk or nonrisk carriers after the 6-month intervention. Attrition was higher in nonrisk participants for whom genotype was disclosed (P=.01) compared with their at-risk counterparts.

Conclusions

No association between baseline PA and FTO risk genotype was observed. There was no added benefit of disclosing FTO risk on changes in PA in this personalized intervention. Further RCT studies are warranted to confirm whether disclosure of nonrisk genetic test results has adverse effects on engagement in behavior change.

Trial Registration

ClinicalTrials.gov NCT01530139; http://clinicaltrials.gov/show/NCT01530139 (Archived by WebCite at: http://www.webcitation.org/6XII1QwHz)

Comparison of physical activity assessed using hip- and wrist-worn accelerometers

OBJECTIVES

It is unclear how physical activity estimates differ when assessed using hip- vs wrist-worn accelerometers. The objective of this study was to compare physical activity assessed by hip- and wrist-worn accelerometers in free-living older women.

DESIGN

A cross-sectional study collecting data in free-living environment.

METHODS

Participants were from the Women's Health Study, in which an ancillary study is objectively measuring physical activity using accelerometers (ActiGraph GT3X+). We analyzed data from 94 women (mean (SD) age=71.9 (6.0) years) who wore a hip-worn and wrist-worn accelerometers simultaneously for 7 days.

RESULTS

Using triaxial data (vector magnitude, VM), total activity volume (counts per day) between the two locations was moderately correlated (Spearman's r=0.73). Hip and wrist monitors wear locations identically classified 71% individuals who were at the highest 40% or lowest 40% of their respective distributions. Similar patterns and slightly stronger agreements were observed when examining steps instead of VM counts.

CONCLUSIONS

Accelerometer-assessed physical activity using hip- vs wrist-worn devices was moderately correlated in older, free-living women. However, further research needs to be conducted to examine comparisons of specific activities or physical activity intensity levels.

A Randomized Study of the Effects of Additional Fruit and Nuts Consumption on Hepatic Fat Content, Cardiovascular Risk Factors and Basal Metabolic Rate

Background

Fruit has since long been advocated as a healthy source of many nutrients, however, the high content of sugars in fruit might be a concern.

Objectives

To study effects of an increased fruit intake compared with similar amount of extra calories from nuts in humans.

Methods

Thirty healthy non-obese participants were randomized to either supplement the diet with fruits or nuts, each at +7 kcal/kg bodyweight/day for two months. Major endpoints were change of hepatic fat content (HFC, by magnetic resonance imaging, MRI), basal metabolic rate (BMR, with indirect calorimetry) and cardiovascular risk markers.

Results

Weight gain was numerically similar in both groups although only statistically significant in the group randomized to nuts (fruit: from 22.15±1.61 kg/m² to 22.30±1.7 kg/m², p = 0.24 nuts: from 22.54±2.26 kg/m² to 22.73±2.28 kg/m², p = 0.045). On the other hand BMR increased in the nut group only (p = 0.028). Only the nut group reported a net increase of calories (from 2519±721 kcal/day to 2763±595 kcal/day, p = 0.035) according to 3-day food registrations. Despite an almost three-fold reported increased fructose-intake in the fruit group (from 9.1±6.0 gram/day to 25.6±9.6 gram/day, p<0.0001, nuts: from 12.4±5.7 gram/day to 6.5±5.3 gram/day, p = 0.007) there was no change of HFC. The numerical increase in fasting insulin was statistical significant only in the fruit group (from 7.73±3.1 pmol/l to 8.81±2.9 pmol/l, p = 0.018, nuts: from 7.29±2.9 pmol/l to 8.62±3.0 pmol/l, p = 0.14). Levels of vitamin C increased in both groups while α-tocopherol/cholesterol-ratio increased only in the fruit group.

Conclusions

Although BMR increased in the nut-group only this was not linked with differences in weight gain between groups which potentially could be explained by the lack of reported net caloric increase in the fruit group. In healthy non-obese individuals an increased fruit intake seems safe from cardiovascular risk perspective, including measurement of HFC by MRI.

Trial Registration

ClinicalTrials.gov NCT02227511

Physical activity using wrist-worn accelerometers: comparison of dominant and non-dominant wrist

The purpose of this study was to determine whether there is a difference in physical activity assessment between a wrist-worn accelerometer at the dominant or non-dominant arm. The secondary purpose was to assess the concurrent validity of measures of physical activity from the wrist-worn accelerometer and the waist-worn accelerometer. Forty adults wore three accelerometers simultaneously, one on the waist and one each on the non-dominant wrist and dominant wrist, respectively, for 24 consecutive hours of free-living conditions. Data were uploaded from the monitor to a computer following a 1-day test period. There were no significant differences in physical activity when comparing the dominant versus the non-dominant wrist, regardless of axis (P>0·05). Mean daily accelerometer output data from both wrists were strongly correlated with average counts per minute from the ActiGraph worn around the waist (r = 0·88, P<0·001). Findings suggest that the choice to wear the accelerometer on the non-dominant or dominant wrist has no impact on results. Data from this study contribute to the knowledge of how to best assess physical activity habits.

Effect of wearable sensor dynamics on physical activity estimates: A comparison between SCI vs. healthy individuals

Accuracy of physical activity estimates predicted by activity monitoring technologies may be affected by device location, analysis algorithms, type of technology (i.e. wearable/stickable) and population demographics (disability) being studied. Consequently, the main purpose of this investigation was to study such sensor dynamics (i.e. effect of device location, type and population demographics on energy expenditure estimates) of two commercial activity monitors. It was hypothesized that device location, population studied (disability), choice of proprietary algorithm and type of technology used will significantly impact the accuracy of the predicted physical activity metrics. 10 healthy controls and eight individuals with spinal cord injury (SCI) performed structured activities in a laboratory environment. All participants wore, (i) three ActiGraph-G3TX's one each on their wrist, waist & ankle, (ii) a stickable activity monitor (Metria-IH1) on their upper-arm and (3) a Cosmed-K4B² metabolic unit, while performing sedentary (lying), low intensity (walk 50 steps at self-speed) and vigorous activity (a 6 minute walk test). To validate the hypothesis, the energy expenditures (EE) predicted by ActiGraph-GT3X and Metria-IH1 were benchmarked with estimated EE per Cosmed K4B² metabolic unit. To verify the step count accuracy predicted by ActiGraph-GT3X's and Metria-IH1, the manually calculated step count during the low intensity activity were compared to estimates from both devices. Results suggest that Metria-IH1 out-performed ActiGraph-GT3X in estimating EE during sedentary activity in both groups. The device location and population demographics, significantly affected the accuracy of predicted estimates. In conclusion, selecting activity monitor locations, analysis algorithm and choice of technology plays based on the movement threshold of population being studied can pave a better way for reliable healthcare decisions and data analytics in population with SCI.

Sedentary Behavior and Light Physical Activity Are Associated with Brachial and Central Blood Pressure in Hypertensive Patients

Background

Physical activity is recommended as a part of a comprehensive lifestyle approach in the treatment of hypertension, but there is a lack of data about the relationship between different intensities of physical activity and cardiovascular parameters in hypertensive patients. The purpose of this study was to investigate the association between the time spent in physical activities of different intensities and blood pressure levels, arterial stiffness and autonomic modulation in hypertensive patients.

Methods

In this cross-sectional study, 87 hypertensive patients (57.5 ± 9.9 years of age) had their physical activity assessed over a 7 day period using an accelerometer and the time spent in sedentary activities, light physical activities, moderate physical activities and moderate-to-vigorous physical activities was obtained. The primary outcomes were brachial and central blood pressure. Arterial stiffness parameters (augmentation index and pulse wave velocity) and cardiac autonomic modulation (sympathetic and parasympathetic modulation in the heart) were also obtained as secondary outcomes.

Results

Sedentary activities and light physical activities were positively and inversely associated, respectively, with brachial systolic (r = 0.56; P < 0.01), central systolic (r = 0.51; P < 0.05), brachial diastolic (r = 0.45; P < 0.01) and central diastolic (r = 0.42; P < 0.05) blood pressures, after adjustment for sex, age, trunk fat, number of antihypertensive drugs, accelerometer wear time and moderate-to-vigorous physical activities. Arterial stiffness parameters and cardiac autonomic modulation were not associated with the time spent in sedentary activities and in light physical activities (P > 0.05).

Conclusion

Lower time spent in sedentary activities and higher time spent in light physical activities are associated with lower blood pressure, without affecting arterial stiffness and cardiac autonomic modulation in hypertensive patients.

Comparison of Self-Reported and Accelerometer-Assessed Physical Activity in Older Women

Background

Self-reported physical activity measures continue to be validated against accelerometers; however, the absence of standardized, accelerometer moderate-to-vigorous physical activity (MVPA) definitions has made comparisons across studies difficult. Furthermore, recent accelerometer models assess accelerations in three axes, instead of only the vertical axis, but validation studies have yet to take incorporate triaxial data.

Methods

Participants (n = 10 115) from the Women’s Health Study wore a hip-worn accelerometer (ActiGraph GT3X+) for seven days during waking hours (2011–2014). Women then completed a physical activity questionnaire. We compared self-reported with accelerometer-assessed MVPA, using four established cutpoints for MVPA: three using only vertical axis data (760, 1041 and 1952 counts per minute (cpm)) and one using triaxial data (2690 cpm).

Results

According to self-reported physical activity, 66.6% of women met the US federal physical activity guidelines, engaging in ≥150 minutes per week of MVPA. The percent of women who met guidelines varied widely depending on the accelerometer MVPA definition (760 cpm: 50.0%, 1041 cpm: 33.0%, 1952 cpm: 13.4%, and 2690 cpm: 19.3%).

Conclusions

Triaxial count data do not substantially reduce the difference between self-reported and accelerometer-assessed MVPA.

Recruitment strategies, design, and participant characteristics in a trial of weight-loss and metformin in breast cancer survivors

Weight loss and metformin are hypothesized to improve breast cancer outcomes; however the joint impacts of these treatments have not been investigated. Reach for Health is a randomized trial using a 2 × 2 factorial design to investigate the effects of weight loss and metformin on biomarkers associated with breast cancer prognosis among overweight/obese postmenopausal breast cancer survivors. This paper describes the trial recruitment strategies, design, and baseline sample characteristics. Participants were randomized in equal numbers to (1) placebo, (2) metformin, (3) weight loss intervention and placebo, or (4) weight-loss intervention and metformin. The lifestyle intervention was a personalized, telephone-based program targeting a 7% weight-loss in the intervention arm. The metformin dose was 1500 mg/day. The duration of the intervention was 6 months. Main outcomes were biomarkers representing 3 metabolic systems putatively related to breast cancer mortality: glucoregulation, inflammation, and sex hormones. Between August 2011 and May 2015, we randomized 333 breast cancer survivors. Mass mailings from the California Cancer Registry were the most successful recruitment strategy with over 25,000 letters sent at a cost of $191 per randomized participant. At baseline, higher levels of obesity were significantly associated with worse sleep disturbance and impairment scores, lower levels of physical activity and higher levels of sedentary behavior, hypertension, hypercholesterolemia, and lower quality of life (p<0.05 for all). These results illustrate the health burden of obesity. Results of this trial will provide mechanistic data on biological pathways and circulating biomarkers associated with lifestyle and pharmacologic interventions to improve breast cancer prognosis.

Deviation between self-reported and measured occupational physical activity levels in office employees: effects of age and body composition

OBJECTIVES

Whether occupational physical activity (PA) will be assessed via questionnaires or accelerometry depends on available resources. Although self-reported data collection seems feasible and inexpensive, obtained information could be biased by demographic determinants. Thus, we aimed at comparing self-reported and objectively measured occupational sitting, standing, and walking times adjusted for socio-demographic variables.

METHODS

Thirty-eight office employees (eight males, 30 females, age 40.8 ± 11.4 years, BMI 23.9 ± 4.2 kg/m(2)) supplied with height-adjustable working desks were asked to report sitting, standing, and walking times using the Occupational Sitting and Physical Activity Questionnaire during one working week. The ActiGraph wGT3X-BT was used to objectively measure occupational PA during the same week. Subjectively and objectively measured data were compared computing the intra-class correlation coefficients, paired t tests and Bland-Altman plots. Furthermore, repeated-measurement ANOVAs for measurement (subjective vs. objective) and socio-demographic variables were calculated.

RESULTS

Self-reported data yielded a significant underestimation of standing time (13.3 vs. 17.9%) and an overestimation of walking time (12.7 vs. 5.0%). Significant interaction effects of age and measurement of standing time (F = 6.0, p = .02, ηp(2) = .14) and BMI group and measurement of walking time were found (F = 3.7, p = .04, ηp(2) = .17). Older employees (>39 years) underestimated their standing time, while underweight workers (BMI < 20 kg/m(2)) overestimated their walking time.

CONCLUSIONS

Self-reported PA data differ from objective data. Demographic variables (age, BMI) affect the amount of self-reported misjudging of PA. In order to improve the validity of self-reported data, a correction formula for the economic assessment of PA by subjective measures is needed, considering age and BMI.

Accelerometer-measured sedentary time among Hispanic adults: Results from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Excessive sedentary behavior is associated with negative health outcomes independent of physical activity. Objective estimates of time spent in sedentary behaviors are lacking among adults from diverse Hispanic/Latino backgrounds. The objective of this study was to describe accelerometer-assessed sedentary time in a large, representative sample of Hispanic/Latino adults living in the United States, and compare sedentary estimates by Hispanic/Latino background, sociodemographic characteristics and weight categories. This study utilized baseline data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) that included adults aged 18-74 years from four metropolitan areas (N = 16,415). Measured with the Actical accelerometer over 6 days, 76.9% (n = 12,631) of participants had > 10 h/day and > 3 days of data. Participants spent 11.9 h/day (SD 3.0), or 74% of their monitored time in sedentary behaviors. Adjusting for differences in wear time, adults of Mexican background were the least (11.6 h/day), whereas adults of Dominican background were the most (12.3 h/day), sedentary. Women were more sedentary than men, and older adults were more sedentary than younger adults. Household income was positively associated, whereas employment was negatively associated, with sedentary time. There were no differences in sedentary time by weight categories, marital status, or proxies of acculturation. To reduce sedentariness among these populations, future research should examine how the accumulation of various sedentary behaviors differs by background and region, and which sedentary behaviors are amenable to intervention.

Practical considerations in using accelerometers to assess physical activity, sedentary behavior, and sleep

Increasingly, behavioral and epidemiological research uses activity-based measurements (accelerometry) to provide objective estimates of physical activity, sedentary behavior, and sleep in a variety of study designs. As interest in concurrently assessing these domains grows, there are key methodological considerations that influence the choice of monitoring instrument, analysis algorithm, and protocol for measuring these behaviors. The purpose of this review is to summarize evidence-guided information for 7 areas that are of importance in the design and interpretation of studies using actigraphy: (1) choice of cut-points; (2) impact of epoch length; (3) accelerometer placement; (4) duration of monitoring; (5) approaches for distinguishing sleep, nonwear times, and sedentary behavior; (6) role for a sleep and activity diary; and (7) epidemiological applications. Recommendations for future research are outlined and are intended to enhance the appropriate use of accelerometry for assessing physical activity, sedentary behavior, and sleep behaviors in research studies.

Predicting human movement with multiple accelerometers using movelets

PURPOSE

The study aims were 1) to develop transparent algorithms that use short segments of training data for predicting activity types and 2) to compare the prediction performance of the proposed algorithms using single accelerometers and multiple accelerometers.

METHODS

Sixteen participants (age, 80.6 yr (4.8 yr); body mass index, 26.1 kg·m (2.5 kg·m)) performed 15 lifestyle activities in the laboratory, each wearing three accelerometers at the right hip and left and right wrists. Triaxial accelerometry data were collected at 80 Hz using ActiGraph GT3X+. Prediction algorithms were developed, which, instead of extracting features, build activity-specific dictionaries composed of short signal segments called movelets. Three alternative approaches were proposed to integrate the information from the multiple accelerometers.

RESULTS

With at most several seconds of training data per activity, the prediction accuracy at the second-level temporal resolution was very high for lying, standing, normal/fast walking, and standing up from a chair (the median prediction accuracy ranged from 88.2% to 99.9% on the basis of the single-accelerometer movelet approach). For these activities, wrist-worn accelerometers performed almost as well as hip-worn accelerometers (the median difference in accuracy between wrist and hip ranged from -2.7% to 5.8%). Modest improvements in prediction accuracy were achieved by integrating information from multiple accelerometers.

DISCUSSION AND CONCLUSIONS

It is possible to achieve high prediction accuracy at the second-level temporal resolution with very limited training data. To increase prediction accuracy from the simultaneous use of multiple accelerometers, a careful selection of integrative approaches is required.