Accurate Measurement Of Physical Activity Research Articles

Physical activity intensity classification during activities of daily living in older adults using accelerometers: Is the ear the new wrist?

Introduction
 Accurate measurement of physical activity (PA) in older adults is important, both in health research and personalized prevention. Accelerometers, used to overcome the limitations of self-reporting, were initially worn on the hips, but are increasingly worn on the non-dominant wrist. While this can improve wear compliance, the accuracy of PA intensity classification can be compromised. Given the high prevalence of mild to severe hearing loss in the older population, this study explores a novel approach: integrating an accelerometer into a hearing aid (ear sensor). We aimed to assess its accuracy and compare it to research-grade sensors worn at different locations.
 Methods 60 middle-aged to older adults (64.0 ± 8.0 years, 48% women) were included in this study. Each subject performed 12-13 different activities, which were pseudo-randomly selected from a list of 33 activities of daily living. Each activity lasted 8 min and included sedentary activities (e.g., lying, playing cards) low-intensity activities (e.g., hanging laundry), activities of changing intensity or without physical displacement (e.g., yoga, squats), indoor activities related to locomotion (e.g., walking, running), outdoor activities (e.g., walking uphill, cycling), and activities with aids (e.g., walking with a stroller). Oxygen consumption was measured via indirect calorimetry and used to classify activity intensity into sedentary behavior (SB, metabolic equivalent of task [MET] < 1.5), light intensity PA (LPA, 1.5 ≤ MET < 3.0), or moderate to vigorous intensity PA (MVPA, MET ≥ 3.0). The ear sensor was placed behind the left ear, while the research-grade sensors were placed on both wrists and ankles, on the hip, chest, and forehead. Estimation of PA intensity classes was done using mean amplitude deviations and ROC analyses. Contingency tables were used to determine classification accuracy.
 Results
 Overall accuracy of the ear sensor was 82.6%, performing better than both wrists (left 81.1%, right 76.0%) and both ankles (left 81.1%, right 81.9%), but worse than the forehead (83.6%), hip (85.6%) and the chest (85.9%). ROC analyses show that all sensors can effectively discriminate between sedentary vs. non-sedentary activities (AUC 0.97-0.98, exception ankles: AUC 0.95-0.96) and between MVPA vs. other (AUC 0.96-0.97, exception wrists: AUC 0.89-0.92).
 Discussion/Conclusion
 This study is the first to show that an accelerometer integrated into a hearing aid can accurately classify PA intensity and differentiate MVPA and sedentary behavior in older adults. It also confirms previous investigations showing that wrist-worn sensors – although increasingly being used to monitor PA – are less effective in capturing MVPA compared to sensors worn closer to the center of mass (including the head/ear in our study). Although the optimal wear site in older adults is a subject of ongoing debate, our data shows that a sensor integrated into a hearing aid offers a promising balance of classification accuracy and (possibly) user compliance. Further studies should explore integrating in-ear heart rate monitoring to enhance accuracy even further.

Person-Specific Template Matching Using a Dynamic Time Warping Step-Count Algorithm for Multiple Walking Activities.

This study aimed to develop and evaluate a new step-count algorithm, StepMatchDTWBA, for the accurate measurement of physical activity using wearable devices in both healthy and pathological populations. We conducted a study with 30 healthy volunteers wearing a wrist-worn MOX accelerometer (Maastricht Instruments, NL). The StepMatchDTWBA algorithm used dynamic time warping (DTW) barycentre averaging to create personalised templates for representative steps, accounting for individual walking variations. DTW was then used to measure the similarity between the template and accelerometer epoch. The StepMatchDTWBA algorithm had an average root-mean-square error of 2 steps for healthy gaits and 12 steps for simulated pathological gaits over a distance of about 10 m (GAITRite walkway) and one flight of stairs. It outperformed benchmark algorithms for the simulated pathological population, showcasing the potential for improved accuracy in personalised step counting for pathological populations. The StepMatchDTWBA algorithm represents a significant advancement in accurate step counting for both healthy and pathological populations. This development holds promise for creating more precise and personalised activity monitoring systems, benefiting various health and wellness applications.

Inferring Activity Patterns from Sparse Step Counts Data with Recurrent Neural Networks

As an accurate measurement of physical activity, step counts data can be collected expediently by smartphones and wearable devices. Complete and high time-resolution step counts data record the time and intensity of individuals’ physical activity in a day, and can be used to mine activity habits or to recommend customized workout plans. However, sparse step counts data are common in practice due to hardware and software limitations. Understanding the value of sparse step counts data can contribute to its application in healthcare, and also can help us design cost-effective hardware and software. In this article, we aim to infer activity patterns from sparse step counts data. We design a deep learning model based on recurrent neural networks, namely MLP-GRU, which considers bidirectional short-term dependency and long-term regularity of sparse step counts data, and implements data-driven imputation and classification. We also develop an interpretable and elastic method to obtain sparse step counts data labeled with multi-granular activity patterns to train MLP-GRU. Evaluations on real-world datasets reveal that MLP-GRU outperforms other strong baseline methods. The results also show that activity patterns can be inferred from extremely sparse step counts data with high accuracy, provided that proper granularity is used for data of different sparsity.

Statistical Learning Methods to Identify Nonwear Periods From Accelerometer Data

Background: Accelerometers are used to objectively measure movement in free-living individuals. Distinguishing nonwear from sleep and sedentary behavior is important to derive accurate measures of physical activity, sedentary behavior, and sleep. We applied statistical learning approaches to examine their promise in detecting nonwear time and compared the results with commonly used wear time (WT) algorithms. Methods: Fifteen children, aged 4–17, wore an ActiGraph wGT3X-BT monitor on their hip during overnight polysomnography. We applied Hidden Markov Models (HMM) and Gaussian Mixture Models (GMM) to classify states of nonwear and wear in triaxial acceleration data. Performance of methods was compared with WT algorithms across two conditions with differing amounts of consecutive nonwear. Clinical scoring of polysomnography served as the gold standard. Results: When the length of nonwear was less than or equal to WT algorithms’ predefined thresholds for consecutive nonwear time, GMM methods yielded improved classification error, specificity, positive predictive value, and negative predictive value over commonly used algorithms. HMM was superior to one algorithm for sensitivity and negative predictive value. When the length of nonwear was longer, results were mixed, with the commonly used algorithms performing better on some parameters but GMM with the greatest specificity. However, all approached the upper limits of performance for almost all metrics. Conclusions: GMM and HMM demonstrated robust, consistently strong performance across multiple conditions, surpassing or remaining competitive with commonly used WT algorithms which had marked inaccuracy when nonwear time periods were shorter. Of the two statistical learning algorithms, GMM was superior to HMM.

Comparability of The Netherlands Physical Activity Questionnaire with Accelerometer-Measured Physical Activity in Samoan Children: A Retrospective Analysis of Ola Tuputupua'e Data.

Accurate measurement of physical activity is critical to understand its role in cardiometabolic health and obesity development in children and to monitor trends in behavior and evaluate interventions. An ongoing mixed-longitudinal study of child growth and development in Samoa is collecting physical activity data with both accelerometers and the Netherlands Physical Activity Questionnaire (NPAQ). The aims of our analyses were to (1) describe the response frequency and correlations of individual questions in the NPAQ, (2) develop modified NPAQ scores with selected questions and (3) examine the concordance of modified NPAQ scores with accelerometer outcomes among children aged 2–4 years. We developed two modified NPAQ scores with combinations of questions and assessed concordance of the modified scores with accelerometer data using estimated marginal means adjusted for monitor wear time. Although the evenly distributed tertiles of the modified 15-point NPAQ score showed promising trends of increasing minutes of accelerometer-assessed high-intensity physical activity with increasing tertile, the estimated marginal means were imprecise with high variance, demonstrating that NPAQ score could not accurately assess physical activity levels of preschool-aged children in Samoa. Considering that questionnaires are often considered more cost-effective tools for physical activity measurement than accelerometry, further research is necessary to develop a culturally and age-appropriate physical activity questionnaire in this population.

Transient Change In Steps And MVPA Due To Activity Monitoring In Adults With Knee Osteoarthritis

Accurate measurement of physical activity (PA) is vital for clinical trials and epidemiological studies that assess important health outcomes. While objective monitoring has become a standardized approach, it is possible that people may alter their behavior due to awareness of being monitored, i.e., the Hawthorne effect. While 7 days of wear time is recommended for valid measurement, it is not known to what extent PA may be influenced by the Hawthorne effect. PURPOSE: To investigate the differences in PA, i.e., steps/day and minutes/day in Moderate to Vigorous intensity PA (MVPA) across each wear day of an Actigraph monitor. METHODS: We used data from the Osteoarthritis Initiative (OAI), a large cohort study of adults with or at risk for knee OA. An Actigraph GTM1 accelerometer was used at the 48-month follow-up visit to measure steps/day and minutes in MVPA (MVPA/day) for each day of wear. For this analysis, participants must have worn the device for 7 days and for ≥10 hours/day. To determine differences in steps/day and MVPA/day between each wear day, we used generalized estimating equations with Day 1 as our reference, adjusted for weekday vs weekend. RESULTS: 1511 participants (age = 65.2 ± 9.1 years, sex = 53.6% female, BMI = 28.2 ± 4.7 kg/m2) were included. For steps/day, participants walked 192 fewer steps on Day 4 (95% CI = [-369.6, -14.5]), 335 fewer on Day 5 [-510.7, -158.6], 275 fewer on Day 6 [-449.9, -98.9], and 238 fewer on Day 7 [-403.0, -73.1] than Day 1 (6632.1 [6438.0, 6826.2], which met statistical significance). For MVPA/day, we observed similar trends (Fig. 1). CONCLUSION: For both steps/day and MVPA/day, Days 1-3 of Actigraph wear were similar while Days 4-7 were significantly lower than Day 1. This suggests the Hawthorne effect may have influenced PA the first 3 days as participants may have increased their PA during that time. However, while statistically significant, without an MCID for PA in adults with knee OA, these results may not represent meaningful differences.

A validation study of the use of smartphones and wrist-worn ActiGraphs to measure physical activity at different levels of intensity and step rates in older people

BackgroundPhysical activity promotes healthy ageing in older people. Accurate measurement of physical activity in free-living environment is important in evaluating physical activity interventions. Research question:What is the criterion validity of 1)an ActiGraph to identify physical activity at different intensity levels and 2)an ActiGraph and a smartphone to measure step rate? MethodsCommunity-dwelling older people aged≥60 were recruited. The index tests were using ActiGraph worn in different positions (i.e.,both wrists and hip) to measure physical activity intensity and step rate and using smartphone (i.e., Samsung J2 pro and Google Fit) worn in different positions (i.e.,trousers pocket and waist pouch) to measure the step rate. The reference standards were using indirect calorimetry (i.e.,CosMedK4b 2) to measure physical activity intensity and using direct observation for step rate. Subjects were exposed in different physical activity intensity levels (i.e.,sedentary:MET < 1.5,light: MET = 1.5–2.99, moderate:MET = 3.0–6.0, vigorous:MET>6) and step rates through walking on a treadmill at different speeds (i.e.,2−8 km) for approximately 30 min. Spearman’s rho, ROC analysis, and percentage error were employed to report the criterion validity. Results:31 participants completed the tests. ActiGraphs worn in different body positions could significantly differentiate physical activity intensity at the levels of “light- or-above” (VM cut-off = 279.5–1959.1,AUC = 0.932−0.954), “moderate-or-above” (VM cut- off = 1051.0–4212.9,AUC = 0.918−0.932), and “vigorous” (VM cut-off = 3335.4–5093.0, AUC = 0.890−0.907) well with different cut-off points identified. The step rate measured by direct observation correlated significantly with ActiGraph and smartphone (rho = 0.415−0.791). Both ActiGraph and smartphone at different positions generally underestimated the step rate (%error= -20.5,-30.3). SignificanceA wrist-worn ActiGraph can accurately identify different physical activity intensity levels in older people, but lower cut-off points in older people should be adopted. To measure step rate, a hip-mounted ActiGraph is preferable than a wrist- worn one. A smartphone employing Google Fit generally underestimates step rate but it gives a relatively more accurate estimation of step rate when the older people walk at a speed of 4−8 km/h.

What Is the Recommended Dose of Physical Activity in the Treatment of Depression in Adults? A Protocol for a Systematic Review

The accurate measurement of physical activity (PA) in adults with depression is critical to identify important health consequences and determinants of this behavior. This study aimed to propose a protocol for a systematic review investigating the recommended dose of PA in the treatment of depression in adults aged 18 to 65 years. Additionally, we intend to examine the effect of PA interventions on the prevalence of depression. This protocol for a systematic review has been submitted in the Prospective International Register submitted in Register of Systematic Reviews of PROSPERO and is being prepared in accordance with the Declaration of Preferential Items for Systematic Reviews and Meta-Analysis Protocols. This protocol provides justification and planned methods for a systematic review to examine the respective dose of PA and how interventions have a beneficial impact on adults with depression.

Drudgery reduction, physical activity and energy requirements in rural livelihoods

Low and middle-income countries in Asia and Africa have been witnessing a process of rural transformation, characterised by rising agricultural productivity, commercialisation of agriculture, improved infrastructure and access to services, over several decades. However, there is little empirical evidence on how this transformation process has affected the patterns and intensity of physical activity and time use in rural livelihoods. The lack of empirical evidence can be attributed to the constraints in accurate measurement of physical activity and energy expenditure in the context of free-living populations. Using wearable accelerometry devices, we develop robust energy expenditure profiles for men and women in rural households for two case studies in India and Ghana. An innovative feature of this study is the integration of data on energy expenditure (derived from accelerometers) with data on time-use, which has hitherto not been feasible in observational studies of rural populations. Using the data on physical activity, energy expenditure and time use from the case studies, we examine the impact of drudgery reduction- the substitution of less intense for more intense activities – on energy requirements for men and women in rural households. Our results show that drudgery reduction can have large effects on human energy (calorie) requirements, with an hour of drudgery reduction reducing energy requirements by 11–22 % for men and 13–17 % for women in Ghana and India. There are significant gender differences in energy expenditure patterns and drudgery reduction effects vary by socio-demographic characteristics and endowments of households. Our results suggest that drudgery reduction can offer rural households an important route to improved nutritional status. At the same time, drudgery reduction can lead to increased incidence of overweight and obesity for some segments of the population. The design of development interventions needs to explicitly consider the effects on nutrition and well-being through the energy expenditure dimension.

Machine learning algorithms for activity recognition in ambulant children and adolescents with cerebral palsy

BackgroundCerebral palsy (CP) is the most common physical disability among children (2.5 to 3.6 cases per 1000 live births). Inadequate physical activity (PA) is a major problem effecting the health and well-being of children with CP. Practical, yet accurate measures of PA are needed to evaluate the effectiveness of surgical and therapy-based interventions to increase PA. Accelerometer-based motion sensors have become the standard for objectively measuring PA in children and adolescents; however, current methods for estimating physical activity intensity in children with CP are associated with significant error and may dramatically underestimate HPA in children with more severe mobility limitations. Machine learning (ML) models that first classify the PA type and then predict PA intensity or energy expenditure using activity specific regression equations may be more accurate than standalone regression models. However, the feasibility and validity of ML methods has not been explored in youth with CP. Therefore, the purpose of this study was to develop and test ML models for the automatic identification of PA type in ambulant children with CP.MethodsTwenty two children and adolescents (mean age: 12.8 ± 2.9 y) with CP classified at GMFCS Levels I to III completed 7 activity trials while wearing an ActiGraph GT3X+ accelerometer on the hip and wrist. Trials were categorised as sedentary (SED), standing utilitarian movements (SUM), comfortable walking (CW), and brisk walking (BW). Random forest (RF), support vector machine (SVM), and binary decision tree (BDT) classifiers were trained with features extracted from the vector magnitude (VM) of the raw acceleration signal using 10 s non-overlapping windows. Performance was evaluated using leave-one-subject out cross validation.ResultsSVM (82.0–89.0%) and RF (82.6–88.8%) provided significantly better classification accuracy than BDT (76.1–86.2%). Hip (82.7–85.5%) and wrist (76.1–82.6%) classifiers exhibited comparable prediction accuracy, while the combined hip and wrist (86.2–89.0%) classifiers achieved the best overall performance. For all classifiers, recognition accuracy was excellent for SED (94.1–97.9%), good to excellent for SUM (74.0–96.6%) and brisk walking (71.5–86.0%), and modest for comfortable walking (47.6–70.4%). When comfortable and brisk walking were combined into a single walking class, recognition accuracy ranged from 90.3 to 96.5%.ConclusionsML methods provided acceptable classification accuracy for detection of a range of activities commonly performed by ambulatory children with CP. The resultant models can help clinicians more effectively monitor bouts of brisk walking in the community. The results indicate that 2-step models that first classify PA type and then predict energy expenditure using activity specific regression equations are worthy of exploration in this patient group.

FACTOR ANALYSIS OF THE MINNESOTA LEISURE TIME ACTIVITIES QUESTIONNAIRE - BRAZILIAN PORTUGUESE

ABSTRACT Introduction: Accurate measures of physical activity to establish dose-response relationship in health outcomes are still controversial. Scales that estimate caloric expenditure are proposed with a view to categorizing the phenomenon globally. Objective: To carry out a factor analysis of the structure of the Minnesota Leisure Time Activities Questionnaire - Brazilian Portuguese, and propose a new adapted version for the Brazilian elderly community. Methods: Participants were elderly female members of the community, without any distinction in terms of ethnic group and/or social class, who frequented extension projects of two higher education institutions. Those with cognitive impairments; dependence on walking aids; musculoskeletal pain in the last two weeks and neurological diseases were excluded from the study. All participants answered the questionnaire with sociodemographic and clinical data and the Minnesota Leisure Time Activities Questionnaire - Brazilian Portuguese version. We determined whether each activity had been undertaken by the participants and the average number of times it was performed over the last two weeks, considering average time, in minutes, on each occasion. Each activity was considered a single item and energy expenditure was calculated according to the authors’ instructions. We used factor analysis with Varimax orthogonal rotation, excluding items whose variance was equal to zero. Correlation between items was determined using the Pearson correlation matrix. Internal consistency between items, before and after the factor analysis, was verified using Cronbach’s α coefficient. 5% significance level. Results: Participants were 220 women (70.8 ± 5.9 years). The factor analysis resulted in 10 components, which explained, in total, 61.87% of the variance of the total score on the scale. Each component was composed of one, two or three aggregate items. Internal consistency by Cronbach’s α coefficient was 0.30. Conclusion: The factor analysis of the structure of the Minnesota Leisure Time Activities Questionnaire - Brazilian Portuguese- has shown a new range with 10 components, which explained, in total, more than 60% of the variance of the total score on the scale, yet with low internal consistency. Level of Evidence III; Study of nonconsecutive patients; without consistently applied reference ‘‘gold’’ standard.

Using open source accelerometer analysis to assess physical activity and sedentary behaviour in overweight and obese adults

BackgroundPhysical activity and sedentary behaviour are difficult to assess in overweight and obese adults. However, the use of open-source, raw accelerometer data analysis could overcome this. This study compared raw accelerometer and questionnaire-assessed moderate-to-vigorous physical activity (MVPA), walking and sedentary behaviour in normal, overweight and obese adults, and determined the effect of using different methods to categorise overweight and obesity, namely body mass index (BMI), bioelectrical impedance analysis (BIA) and waist-to-hip ratio (WHR).MethodsOne hundred twenty adults, aged 24–60 years, wore a raw, tri-axial accelerometer (Actigraph GT3X+), for 3 days and completed a physical activity questionnaire (IPAQ-S). We used open-source accelerometer analyses to estimate MVPA, walking and sedentary behaviour from a single raw accelerometer signal. Accelerometer and questionnaire-assessed measures were compared in normal, overweight and obese adults categorised using BMI, BIA and WHR.ResultsRelationships between accelerometer and questionnaire-assessed MVPA (Rs = 0.30 to 0.48) and walking (Rs = 0.43 to 0.58) were stronger in normal and overweight groups whilst sedentary behaviour were modest (Rs = 0.22 to 0.38) in normal, overweight and obese groups. The use of WHR resulted in stronger agreement between the questionnaire and accelerometer than BMI and BIA. Finally, accelerometer data showed stronger associations with BMI, BIA and WHR (Rs = 0.40 to 0.77) than questionnaire data (Rs = 0.24 to 0.37).ConclusionsOpen-source, raw accelerometer data analysis can be used to estimate MVPA, walking and sedentary behaviour from a single acceleration signal in normal, overweight and obese adults. Our data supports the use of WHR to categorise overweight and obese adults. This evidence helps researchers obtain more accurate measures of physical activity and sedentary behaviour in overweight and obese populations.

Validation of SenseWear Armband in children, adolescents, and adults.

SenseWear Armband (SW) is a multisensor monitor to assess physical activity and energy expenditure. Its prediction algorithms have been updated periodically. The aim was to validate SW in children, adolescents, and adults. The most recent SW algorithm 5.2 (SW5.2) and the previous version 2.2 (SW2.2) were evaluated for estimation of energy expenditure during semi-structured activities in 35 children, 31 adolescents, and 36 adults with indirect calorimetry as reference. Energy expenditure estimated from waist-worn ActiGraph GT3X+ data (AG) was used for comparison. Improvements in measurement errors were demonstrated with SW5.2 compared to SW2.2, especially in children and for biking. The overall mean absolute percent error with SW5.2 was 24% in children, 23% in adolescents, and 20% in adults. The error was larger for sitting and standing (23%-32%) and for basketball and biking (19%-35%), compared to walking and running (8%-20%). The overall mean absolute error with AG was 28% in children, 22% in adolescents, and 28% in adults. The absolute percent error for biking was 32%-74% with AG. In general, SW and AG underestimated energy expenditure. However, both methods demonstrated a proportional bias, with increasing underestimation for increasing energy expenditure level, in addition to the large individual error. SW provides measures of energy expenditure level with similar accuracy in children, adolescents, and adults with the improvements in the updated algorithms. Although SW captures biking better than AG, these methods share remaining measurements errors requiring further improvements for accurate measures of physical activity and energy expenditure in clinical and epidemiological research.

Validity and responsiveness of the Global Physical Activity Questionnaire (GPAQ) in assessing physical activity during pregnancy.

The physiological and biomechanical changes that occur during pregnancy make accurate measurement of physical activity (PA) a challenge during this unique period. The Global Physical Activity Questionnaire (GPAQ) has been used extensively in low-to-middle income countries, but has never been validated in a pregnant population. In this longitudinal study, 95 pregnant women (mean age: 29.5±5.7 years; BMI: 26.9±5.0 kg/m2) completed the GPAQ and were asked to wear an accelerometer for 7 days at two time points during pregnancy (14–18 and 29–33 weeks gestation). There was a significant difference between accelerometry and GPAQ when measuring moderate-to-vigorous physical activity (MVPA) at 29–33 weeks gestation (16.6 vs 21.4 min/day; p = 0.02) as well as sedentary behaviour (SB) at both 14–18 weeks (457.0 vs 300 min/day; p < 0.01) and 29–33 weeks gestation (431.5 vs 300 min/day; p < 0.01). There was poor agreement between the GPAQ and accelerometry for both PA and SB at both time points (ICC: -0.05–0.08). Bland Altman plots indicated that the GPAQ overestimates PA by 14.8 min/day at 14–18 weeks and by 15.8 min/day at 29–33 weeks gestation. It underestimates SB by 127.5 min/day at 14–18 weeks and by 89.2 min/day at 29–33 weeks gestation. When compared to accelerometry, the GPAQ shows poor agreement and appears to overestimate PA and underestimate SB during pregnancy.

Measurement Methods for Physical Activity and Energy Expenditure: a Review.

Physical activity is defined as any bodily movement produced by skeletal muscles that results in energy expenditure. The benefits of physical activity for health maintenance have been well documented, especially in the prevention and management of chronic diseases. Therefore, accurate measurement of physical activity and energy expenditure is essential both for epidemiological studies and in the clinical context. Given the large number of available methods, it is important to have an understanding of each, especially when one needs to choose a technique to use. The purpose of this review was to discuss the components of total energy expenditure and present advantage and limitations of different methods of physical activity and energy expenditure assessment.

Physical activity and sedentary behavior in Belgium (BNFCS2014): design, methods and expected outcomes.

BackgroundThere is strong evidence to indicate that regular moderate intensity physical activity is associated with health benefits. Furthermore, sedentary behavior has been related with an increased risk for all-cause mortality. The accurate measurement of physical activity and sedentary behavior is therefore vital to evaluate their health impact and provide evidence for the development of public health recommendations. This paper describes the methodology used for assessing physical activity and sedentary behavior in the Belgian population in the context of the Belgian National Food Consumption Survey 2014 (BNFCS2014).ResultsData about physical activity and sedentary behavior were collected as part of the cross-sectional BNFCS2014 between February 2014 and May 2015. A nationally-representative sample of children (3–9 years) and adolescents (10–17 years) were asked to wear an accelerometer (Actigraph® GT3X) during their waking hours for 7 consecutive days. Data were recorded in 15-second epochs and respondents with at least 2 valid week days (i.e., 10 h of wear-time) and 1 valid week-end day (i.e., 8 h of wear-time) were retained for the analyses. The Evenson cut points were used to assess the time spent in each physical activity intensity level: sedentary, low, moderate and vigorous. Complementary, diaries were provided to register the activities performed when the accelerometer was removed; these activities were added to the measures provided by the accelerometers. In addition, age-specific self-reported questionnaires (ToyBox and FPAQ) were completed to provide contextual information about the type of activities performed. Due to financial constraints, physical activity in adults (18–64 years) was assessed and described through the self-reported International Physical Activity Questionnaire (IPAQ long version) only.ConclusionData were collected in the context of the BNFCS2014 to provide a comprehensive picture of the physical activity and sedentary behavior in the Belgian population, with a special focus on children (3–9 years) and adolescents (10–17 years). Levels of physical activity and sedentary behavior can be compared to international guidelines and analyzed according to several background variables, such as age, gender, Body Mass Index, education level and region. Such results are aimed to underpin future policies in the field of physical activity.

Criterion Validity of the activPAL Activity Monitor for Sedentary and Physical Activity Patterns in People Who Have Rheumatoid Arthritis.

Accurate measurement of physical activity and sedentary behavior is an important consideration for health care professionals. The activPAL activity monitor has not been validated against a criterion measure for people with rheumatoid arthritis (RA). The objective of this study was to determine the criterion validity of the activPAL activity monitor for measuring step counts, transition counts, and time spent in sedentary, standing, and walking behaviors in people with RA. A laboratory-based criterion validation study was conducted. Participants with a confirmed medical diagnosis of RA were recruited from 2 outpatient rheumatology clinics. The testing procedure consisted of standardized testing components and tasks related to activities of daily living. Participants wore an activPAL activity monitor and were video recorded throughout the testing procedure. Direct observation was used as the criterion measure. Data analysis consisted of validation analysis of the activPAL activity monitor data and the criterion measure data. Twenty-four people participated in the study. Data from 20 participants were included in the final analysis. The activPAL significantly underestimated step counts by 26% and transition counts by 36%. There was no significant difference between the activPAL activity monitor and the criterion measure for time spent in sedentary, standing or light activity, and walking behaviors. Validation of activities of daily living in a laboratory environment is a limitation of this study. The activPAL activity monitor underestimated step and transition counts and, therefore, is not valid for measuring these outcomes in people with RA. Relative to direct observation, the activPAL activity monitor is valid for measuring time spent in sedentary, standing, and walking behaviors in people with RA.

AN EXAMINATION OF ACCELEROMETER CUT-POINTS FOR QUANTIFYING PHYSICAL ACTIVITY IN CARDIAC POPULATIONS

Accurate measurement of physical activity (PA) is necessary to monitor trends, assess relations with health and measure adherence to PA guidelines. While accelerometers provide accurate measures of PA they rely on validated cut-points to quantify time spent in light, moderate and vigorous intensities. Cut-points have largely been established using healthy populations; however, accelerometer output can differ based on cardiorespiratory fitness, suggesting that among individuals with compromised functioning such as those with coronary artery disease (CAD); these cut-points may lead to a misclassification of intensity.

CEREBRAL OXYGENATION/PERFUSION, CARDIOPULMONARY AND HEMODYNAMIC RESPONSES DURING MAXIMAL INCREMENTAL EXERCISE IN HEART TRANSPLANT RECIPIENTS VS. HEALTHY CONTROL SUBJECTS

BACKGROUND: Accurate measurement of physical activity (PA) is necessary to monitor trends, assess relations with health and measure adherence to PA guidelines. While accelerometers provide accurate measures of PA they rely on validated cut-points to quantify time spent in light, moderate and vigorous intensities. Cut-points have largely been established using healthy populations; however, accelerometer output can differ based on cardiorespiratory fitness, suggesting that among individuals with compromised functioning such as those with coronary artery disease (CAD); these cut-points may lead to a misclassification of intensity. The objective of this study was to compare well-known and widely used cut-points (Sasaki) derived from healthy adults with those developed in a CAD population (Mark). METHODS: Two-hundred and thirty-one adults with CAD (mean SD: 63.7 9.2 years) wore an ActiGraph GT3X accelerometer for a minimum of 4 consecutive days and 10 hours/day. Daily time spent in moderate-to-vigorous PA (MVPA) was compared using two cut-point definitions: Mark (58.3 10.7 years, mean v

Validation of the Fitbit One activity monitor device during treadmill walking

In order to quantify the effects of physical activity such as walking on chronic disease, accurate measurement of physical activity is needed. The objective of this study was to determine the validity and reliability of a new activity monitor, the Fitbit One, in a population of healthy adults. Cross-sectional study. Thirty healthy adults ambulated at 5 different speeds (0.90, 1.12, 1.33, 1.54, 1.78 m/s) on a treadmill while wearing three Fitbit One activity monitors (two on the hips and one in the pocket). The order of each speed condition was randomized. Fitbit One step count output was compared to observer counts and distance output was compared to the calibrated treadmill output. Two-way repeated measures ANOVA, concordance correlation coefficients, and Bland and Altman plots were used to assess validity and intra-class correlation coefficients (ICC) were used to assess reliability. No significant differences were noted between Fitbit One step count outputs and observer counts, and concordance was substantial (0.97-1.00). Inter-device reliability of the step count was high for all walking speeds (ICC ≥ 0.95). Percent relative error was less than 1.3%. The distance output of the Fitbit One activity monitors was significantly different from the criterion values for each monitor at all speeds (P<0.001) and exhibited poor concordance (0.0-0.05). Inter-device reliability was excellent for all treadmill speeds (ICC ≥ 0.90). Percent relative error was high (up to 39.6%). The Fitbit One activity monitors are valid and reliable devices for measuring step counts in healthy young adults. The distance output of the monitors is inaccurate and should be noted with caution.