Protocols for evaluating equivalency of accelerometry-based activity monitors

Unravelling upright events: a descriptive epidemiology of the behavioural composition and temporal distribution of upright events in participants from the 1970 British Cohort Study

BACKGROUND

Continued proliferation of accelerometers in physical activity research has opened new avenues for understanding activity behaviours beyond simple aggregate measures of frequency and duration. This study explores the standing and stepping composition, and the temporal distribution, of upright events, and investigates their associations with sociodemographic and health factors.

METHODS

Participants from the 1970 British Cohort Study wore activPAL3 accelerometers for seven days. Event-based analysis was used to extract a time series of upright, standing, and stepping events. Derived metrics included daily number of upright and stepping events, total upright and stepping time, the burstiness of upright events and burstiness of sedentary events (burstiness refers to the pattern of how physical activity and sedentary behaviour are distributed throughout a given time period), within-event stepping proportion, within-event step count, and stepping cadence. Generalized linear regression models, adjusted for total step count, were employed to explore associations between derived metrics and sociodemographic and health-related factors.

RESULTS

A total of 4527 participants, provided 30992 valid days (≥ 10 h of waking wear) and 1.64 million upright events. Upright event composition and temporal distribution varied across a range of sociodemographic and health-related factors. Females had more upright events than males (4.39 [3.41,5.38] n), spent more time upright, and exhibited burstier patterns of upright events (0.05 [0.04,0.05] Bn). Individuals with higher BMI had fewer upright events and a lower daily step count, but their temporal distribution of upright events was less bursty (overweight -0.02 [-0.02,-0.01] Bn; obese -0.03 [-0.04,-0.02] Bn), and upright events had a higher step count. People in active occupations were upright for longer, displayed burstier patterns of upright events (standing 0.04 [0.03,0.05] Bn; physical work 0.05 [0.04,0.05] Bn; heavy manual 0.06 [0.04,0.07] Bn), with more variable durations and shorter, slower paced stepping events compared with sedentary occupations.

CONCLUSIONS

This study has revealed novel phenotypes of standing and sitting that go beyond simple aggregate measures of total steps, step event duration or time between events. People with the same volume of stepping and frequency of gaps between upright events can accumulate their steps in very different ways. These differences and associations with population sub-groups, which persisted after adjustment for total stepping volume, may have important relations with functional and health outcomes. The findings lay the groundwork for future studies to investigate how different sitting and standing phenotypes can add to our understanding of the relationship between physical activity and health.

The Analytical and Clinical Validity of the pfSTEP Digital Biomarker of the Susceptibility/Risk of Declining Physical Function in Community-Dwelling Older Adults

Measures of stepping volume and rate are common outputs from wearable devices, such as accelerometers. It has been proposed that biomedical technologies, including accelerometers and their algorithms, should undergo rigorous verification as well as analytical and clinical validation to demonstrate that they are fit for purpose. The aim of this study was to use the V3 framework to assess the analytical and clinical validity of a wrist-worn measurement system of stepping volume and rate, formed by the GENEActiv accelerometer and GENEAcount step counting algorithm. The analytical validity was assessed by measuring the level of agreement between the wrist-worn system and a thigh-worn system (activPAL), the reference measure. The clinical validity was assessed by establishing the prospective association between the changes in stepping volume and rate with changes in physical function (SPPB score). The agreement of the thigh-worn reference system and the wrist-worn system was excellent for total daily steps (CCC = 0.88, 95% CI 0.83-0.91) and moderate for walking steps and faster-paced walking steps (CCC = 0.61, 95% CI 0.53-0.68 and 0.55, 95% CI 0.46-0.64, respectively). A higher number of total steps and faster paced-walking steps was consistently associated with better physical function. After 24 months, an increase of 1000 daily faster-paced walking steps was associated with a clinically meaningful increase in physical function (0.53 SPPB score, 95% CI 0.32-0.74). We have validated a digital susceptibility/risk biomarker-pfSTEP-that identifies an associated risk of low physical function in community-dwelling older adults using a wrist-worn accelerometer and its accompanying open-source step counting algorithm.

Development and Evaluation of Sedentary Time Cut-Points for the activPAL in Adults Using the GGIR R-Package

The purpose of this study was to develop sedentary cut-points for the activPAL and evaluate their performance against a criterion measure (i.e., activPAL processed by PALbatch). Part 1: Thirty-five adults (23.4 ± 3.6 years) completed 12 laboratory activities (6 sedentary and 6 non-sedentary activities). Receiver operator characteristic (ROC) curves proposed optimal Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) cut-points of 26.4 mg (ENMO) and 30.1 mg (MAD). Part 2: Thirty-eight adults (22.6 ± 4.1 years) wore an activPAL during free-living. Estimates from PALbatch and MAD revealed a mean percent error (MPE) of 2.2%, mean absolute percent error (MAPE) of 6.5%, limits of agreement (LoA) of 19% with absolute and relative equivalence zones of 5% and 0.3 SD. Estimates from PALbatch and ENMO revealed an MPE of -10.6%, MAPE of 14.4%, LoA of 31% and 16% and 1 SD equivalence zones. After standing was isolated from sedentary behaviours, ROC analysis proposed an optimal cut-off of 21.9 mg (herein ENMOs). Estimates from PALbatch and ENMOs revealed an MPE of 3.1%, MAPE of 7.5%, LoA of 25% and 9% and 0.5 SD equivalence zones. The MAD and ENMOs cut-points performed best in discriminating between sedentary and non-sedentary activity during free-living.

Comparing estimates of physical activity in children across different cut-points and the associations with weight status

This study aimed to compare sedentary time (SED) and intensity-specific physical activity (PA) estimates and the associations of SED and PA with body mass index (BMI) and waist circumference (WC) using three different sets of cut-points in preschool-aged children. A total of 751 children (4.7 ± 0.9 years, boys 52.7%) wore an ActiGraph GT3X+BT accelerometer on their hip for 7 days (24 h). Euclidean norm -1 G with negative values rounded to zero (ENMO) and activity counts from vertical axis (VACounts) and vector magnitude (VMCounts) were derived. Estimates of SED and light, moderate, vigorous, and moderate-to-vigorous PA (MVPA) were calculated for commonly used cut-points by Hildebrand et al., Butte et al., and Evenson et al. Furthermore, the prevalence of meeting the PA recommendation, 180 min/day of which at least 60 min/day being MVPA, were assessed for the cut-points. Multilevel mixed analysis was used to examine associations of SED and PA with BMI and WC. In accordance with the results, SED and PA intensity estimates differed largely across cut-points (i.e., SED = 22-341 min/day; light PA = 52-257 min/day; moderate PA = 5-18 min/day; vigorous PA = 7-17 min/day; MVPA = 13-35 min/day), and the prevalence of children meeting the PA recommendation varied from 4% to 70%. Associations of SED and PA with BMI or WC varied between the cut-points. Our results indicate that SED and PA estimates in preschool-aged children between studies using these cut-points are poorly comparable. Methods facilitating accelerometer-derived PA estimate comparison between studies are highly warranted.

The Cut-Off Value for Classifying Active Italian Children Using the Corresponding National Version of the Physical Activity Questionnaire

The present study aimed to determine a cut-off value following the filling in of a questionnaire (PAQ-C-It) to identify active Italian children. One-hundred-twenty-nine primary school children (5 Piedmont schools; 47.3% female; mean age = 10 ± 1 years) wore an accelerometer (Actigraph wGT3X-BT) to objectively quantify individual moderate-to-vigorous physical activity during one week. Afterwards, the PAQ-C-It was filled in by participants. A ROC curve procedure was applied to obtain an active/non-active cut-off point. Spearman’s correlation coefficient was also applied to establish the relationship between the two parameters. According to the ROC analysis, the PAQ-C-It cut-off point value is identifiable at >2.75 to indicate active children (area under the curve = 0.62; standard error = 0.05; p = 0.025; coefficient intervals = 0.518−0.716; sensitivity = 0.592, specificity = 0.382), determining that 65 participants (55%) were non-active (mean PAQ-C-It value = 2.3 ± 0.4; active mean PAQ-C-It value = 3.3 ± 0.4). Spearman’s correlation coefficient results were significant but with a small effect size (rho = 0.214; p = 0.008). In conclusion, the present results suggest that the PAQ-C-It can be cautiously used as tool to practically classify active Italian children because of a non-solid relationship between respective accelerometer data and MVPA daily data.

Recommendations for Determining the Validity of Consumer Wearables and Smartphones for the Estimation of Energy Expenditure: Expert Statement and Checklist of the INTERLIVE Network

BACKGROUND

Consumer wearables and smartphone devices commonly offer an estimate of energy expenditure (EE) to assist in the objective monitoring of physical activity to the general population. Alongside consumers, healthcare professionals and researchers are seeking to utilise these devices for the monitoring of training and improving human health. However, the methods of validation and reporting of EE estimation in these devices lacks rigour, negatively impacting on the ability to make comparisons between devices and provide transparent accuracy.

OBJECTIVES

The Towards Intelligent Health and Well-Being Network of Physical Activity Assessment (INTERLIVE) is a joint European initiative of six universities and one industrial partner. The network was founded in 2019 and strives towards developing best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice validation protocol for consumer wearables and smartphones in the estimation of EE.

METHODS

The recommendations were developed through (1) a systematic literature review; (2) an unstructured review of the wider literature discussing the potential factors that may introduce bias during validation studies; and (3) evidence-informed expert opinions from members of the INTERLIVE network.

RESULTS

The systematic literature review process identified 1645 potential articles, of which 62 were deemed eligible for the final dataset. Based on these studies and the wider literature search, a validation framework is proposed encompassing six key domains for validation: the target population, criterion measure, index measure, testing conditions, data processing and the statistical analysis.

CONCLUSIONS

The INTERLIVE network recommends that the proposed protocol, and checklists provided, are used to standardise the testing and reporting of the validation of any consumer wearable or smartphone device to estimate EE. This in turn will maximise the potential utility of these technologies for clinicians, researchers, consumers, and manufacturers/developers, while ensuring transparency, comparability, and replicability in validation.

TRIAL REGISTRATION

PROSPERO ID: CRD42021223508.

Personalised Accelerometer Cut-point Prediction for Older Adults' Movement Behaviours using a Machine Learning approach

BACKGROUND AND OBJECTIVES

Body-worn accelerometers are the most popular method for objectively assessing physical activity in older adults. Many studies have developed generic accelerometer cut-points for defining activity intensity in metabolic equivalents for older adults. However, methodological diversity in current studies has led to a great deal of variation in the resulting cut-points, even when using data from the same accelerometer. In addition, the generic cut-point approach assumes that 'one size fits all' which is rarely the case in real life. This study proposes a machine learning method for personalising activity intensity cut-points for older adults.

METHODS

Firstly, raw accelerometry data was collected from 33 older adults who performed set activities whilst wearing two accelerometer devices: GENEActive (wrist worn) and ActiGraph (hip worn). ROC analysis was applied to generate personalised cut-point for each data sample based on a device. Four cut-points have been considered: Sensitivity optimised Sedentary Behaviour; Specificity optimised Moderate to Vigorous Physical Activity; Youden optimised Sedentary Behaviour; and Youden optimised Moderate to Vigorous Physical Activity. Then, an additive regression algorithm trained on biodata features, that concern the individual characteristics of participants, was used to predict the cut-points. As the model output is a numeric cut-point value (and not discrete), evaluation was based on two error metrics, Mean Absolute Error and Root Mean Square Error. Standard Error of estimation was also calculated to measure the accuracy of prediction (goodness of fit) and this was used for performance comparison between our approach and the state-of-the-art. Hold-out and 10-Fold cross validation methods were used for performance validation and comparison.

RESULTS

The results show that our personalised approach performed consistently better than the state-of-the-art with 10-Fold cross validation on all four cut-points considered for both devices. For the ActiGraph device, the Standard Error of estimation from our approach was lower by 0.33 (Youden optimised Sedentary Behaviour), 9.50 (Sensitivity optimised Sedentary Behaviour), 0.64 (Youden optimised Moderate to Vigorous Physical Activity) and 22.11 (Specificity optimised Moderate to Vigorous Physical Activity). Likewise, the Standard Error of estimation from our approach was lower for the GENEActiv device by 2.29 (Youden optimised Sedentary Behaviour), 41.65 (Sensitivity optimised Sedentary Behaviour), 4.31 (Youden optimised Moderate to Vigorous Physical Activity) and 347.15 (Specificity optimised Moderate to Vigorous Physical Activity).

CONCLUSIONS

personalised cut-point can be predicted without prior knowledge of accelerometry data. The results are very promising especially when we consider that our method predicts cut-points without prior knowledge of accelerometry data, unlike the state-of-the-art. More data is required to expand the scope of the experiments presented in this paper.

An exploratory study of discrepancies between objective and subjective measurement of the physical activity level in female patients with chronic fatigue syndrome

OBJECTIVE

To explore the ability of a self-report activity diary to measure the physical activity level (PAL) in female patients with chronic fatigue syndrome (CFS) and whether illness-related complaints, health-related quality of life domains (HRQOL) or demographic factors are associated with discrepancies between self-reported and objectively measured PAL.

METHODS

Sixty-six patients with CFS, recruited from the chronic fatigue clinic of a university hospital, and twenty matched healthy controls wore an accelerometer (Actical) for six consecutive days and registered their activities in an activity diary in the same period. Participants' demographic data was collected and all subjects completed the CFS Symptom List (illness-related complaints) daily and Short-Form-36 (HRQOL domains) during the first and second appointment.

RESULTS

A significant, but weak association between the activity diary and Actical was present in patients with CFS (r_s = 0.376 and r_s = 0.352; p < 0.001) and a moderately strong association in healthy controls (r_s = 0.605; and r_s = 0.644; p < 0.001) between week and weekend days, respectively. A linear mixed model identified a negative association between age and the discrepancy between the self-reported and objective measure of PA in both patients with CFS and healthy controls.

CONCLUSION

The activity diary showed limited ability to register the PAL in female patients with CFS. The discrepancy between measures was not explained by illness-related complaints, HRQOL domains or demographic factors. The activity diary cannot replace objective activity monitoring measured with an accelerometer, but may provide additional information about the perceived activity.

Recommendations for determining the validity of consumer wearable heart rate devices: expert statement and checklist of the INTERLIVE Network

Assessing vital signs such as heart rate (HR) by wearable devices in a lifestyle-related environment provides widespread opportunities for public health related research and applications. Commonly, consumer wearable devices assessing HR are based on photoplethysmography (PPG), where HR is determined by absorption and reflection of emitted light by the blood. However, methodological differences and shortcomings in the validation process hamper the comparability of the validity of various wearable devices assessing HR. Towards Intelligent Health and Well-Being: Network of Physical Activity Assessment (INTERLIVE) is a joint European initiative of six universities and one industrial partner. The consortium was founded in 2019 and strives towards developing best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice validation protocol for consumer wearables assessing HR by PPG. The recommendations were developed through the following multi-stage process: (1) a systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, (2) an unstructured review of the wider literature pertaining to factors that may introduce bias during the validation of these devices and (3) evidence-informed expert opinions of the INTERLIVE Network. A total of 44 articles were deemed eligible and retrieved through our systematic literature review. Based on these studies, a wider literature review and our evidence-informed expert opinions, we propose a validation framework with standardised recommendations using six domains: considerations for the target population, criterion measure, index measure, testing conditions, data processing and the statistical analysis. As such, this paper presents recommendations to standardise the validity testing and reporting of PPG-based HR wearables used by consumers. Moreover, checklists are provided to guide the validation protocol development and reporting. This will ensure that manufacturers, consumers, healthcare providers and researchers use wearables safely and to its full potential.

Recommendations for determining the validity of consumer wearable and smartphone step count: expert statement and checklist of the INTERLIVE network

Consumer wearable and smartphone devices provide an accessible means to objectively measure physical activity (PA) through step counts. With the increasing proliferation of this technology, consumers, practitioners and researchers are interested in leveraging these devices as a means to track and facilitate PA behavioural change. However, while the acceptance of these devices is increasing, the validity of many consumer devices have not been rigorously and transparently evaluated. The Towards Intelligent Health and Well-Being Network of Physical Activity Assessment (INTERLIVE) is a joint European initiative of six universities and one industrial partner. The consortium was founded in 2019 and strives to develop best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice consumer wearable and smartphone step counter validation protocol. A two-step process was used to aggregate data and form a scientific foundation for the development of an optimal and feasible validation protocol: (1) a systematic literature review and (2) additional searches of the wider literature pertaining to factors that may introduce bias during the validation of these devices. The systematic literature review process identified 2897 potential articles, with 85 articles deemed eligible for the final dataset. From the synthesised data, we identified a set of six key domains to be considered during design and reporting of validation studies: target population, criterion measure, index measure, validation conditions, data processing and statistical analysis. Based on these six domains, a set of key variables of interest were identified and a 'basic' and 'advanced' multistage protocol for the validation of consumer wearable and smartphone step counters was developed. The INTERLIVE consortium recommends that the proposed protocol is used when considering the validation of any consumer wearable or smartphone step counter. Checklists have been provided to guide validation protocol development and reporting. The network also provide guidance for future research activities, highlighting the imminent need for the development of feasible alternative 'gold-standard' criterion measures for free-living validation. Adherence to these validation and reporting standards will help ensure methodological and reporting consistency, facilitating comparison between consumer devices. Ultimately, this will ensure that as these devices are integrated into standard medical care, consumers, practitioners, industry and researchers can use this technology safely and to its full potential.

Effect of handling breaks on estimation of heart rate responses to bouts of physical activity among young women: An accelerometer research issue

BACKGROUND

One of the research interests with regard to accelerometry is the evaluation of physical activity bout under free-living conditions.

RESEARCH QUESTION

This study aimed to examine the effect of treatment interruption on the estimation of heart rate (HR) response to moderate-to-vigorous intensity physical activity (MVPA) bout.

METHODS

Twenty-five young women performed at least 30 min of MVPA during 24 h while wearing a uniaxial accelerometer on their waist and a portable HR monitor on their chest simultaneously. Based on the time series data, MVPA bout was defined as physical activity that was maintained at no less than 3 metabolic equivalents (METs) for >10 min without or with interruption, i.e., a 1-, 2-, or 3-min interruption.

RESULTS

The frequency and duration of MVPA bout significantly increased with interruption (p < 0.01). The HR and estimated MET value for an MVPA bout with interruption were significantly lower than those without interruption (p < 0.01), and the difference gradually increased depending on the duration of the interruptions (p < 0.01).

SIGNIFICANCE

These findings indicate that treatment interruptions could influence the estimation of cardiorespiratory response to MVPA bout under free-living conditions, and interruptions could result in an overestimation of the actual intensity of an MVPA bout.

Quantification of Postures for Low-Height Object Manipulation Conducted by Manual Material Handlers in a Retail Environment

Occupational Applications Manual material handlers performing stocking tasks spent substantial amounts of time in bent postures but used traditional stoops and squats infrequently. Instead, they often used split-legged stoops and squats, where one foot is further forward than the other, and one-legged ("golfer's") lifts. During object manipulation, the distance workers reached away from their body, and the height at which they manipulated objects, were correlated with the posture used by the worker. Workers also stayed in different postures for different lengths of time. It is likely that certain postures are more comfortable for the workers to remain in, provide additional mobility or operational radius, or require less energy to use. Understanding these factors in more detail could lead to improved worker training programs, where the postures taught not only have low injury risk but are comfortable so are actually adopted and used by the workers.

PHYSICAL ACTIVITY IN DAILY LIFE, EXERCISE CAPACITY AND QUALITY OF LIFE IN PATIENTS WITH CROHN'S DISEASE ON INFLIXIMAB-INDUCED REMISSION: A PRELIMINARY STUDY

BACKGROUND

Physical activity in daily life and exercise capacity have not been assessed in patients with Crohn's disease to date.

OBJECTIVE

To evaluate the physical activity in daily life, exercise capacity, quality of life, and prevalence of mood disorders in patients with moderate-to-severe Crohn's disease on infliximab-induced remission and the possible associations among variables.

METHODS

A cross-sectional preliminary study was conducted. Twenty-six patients with Crohn's disease and 20 controls were selected. Participants underwent evaluation of physical activity in daily life (triaxial accelerometer), exercise capacity (shuttle walk test), handgrip strength, quality of life, and presence of mood disorders.

RESULTS

The number of steps taken (7446±3081 vs 7898±2487), active time (80.6±42 vs 89.7±24.3min), shuttle walk test distance [665 (405) vs 710 (409) m] and handgrip strength [31 (15) vs 29 (20) kgf did not show any difference between the patients with Crohn's disease and the controls. The time spent lying down [95.8 (68.8) vs 60.9 (74.7) min] was greater and some domains of the quality of life were superior in the patients with Crohn's disease. No correlation was observed between the physical activity in daily life and quality of life or presence of mood disorders in patients with Crohn's disease.

CONCLUSION

Patients with Crohn's disease on infliximab-induced remission, despite to more time spent lying down, they have the same level of physical activity in daily life and exercise capacity min compared with the controls.

Standardizing Analytic Methods and Reporting in Activity Monitor Validation Studies

INTRODUCTION

A lack of standardization with accelerometry-based monitors has made it hard to advance applications for both research and practice. Resolving these challenges is essential for developing methods for consistent, agnostic reporting of physical activity outcomes from wearable monitors in clinical applications.

METHODS

This article reviewed the literature on the methods used to evaluate the validity of contemporary consumer activity monitors. A rationale for focusing on energy expenditure as a key outcome measure in validation studies was provided followed by a summary of the strengths and limitations of different analytical methods. The primary review included 23 recent validation studies that collectively reported energy expenditure estimates from 58 monitors relative to values from appropriate criterion measures.

RESULTS

The majority of studies reported weak indicators such as correlation coefficients (87%), but only half (52%) reported the recommended summary statistic of mean absolute percent error needed to evaluate actual individual error. Fewer used appropriate tests of agreement such as equivalence testing (22%).

CONCLUSIONS

The use of inappropriate analytic methods and incomplete reporting of outcomes is a major limitation for systematically advancing research with both research grade and consumer-grade activity monitors. Guidelines are provided to standardize analytic methods and reporting in these types of studies to enhance the utility of the devices for clinical mHealth applications.

Validity of six consumer-level activity monitors for measuring steps in patients with chronic heart failure

INTRODUCTION

Although numerous activity trackers have been validated in healthy populations, validation is lacking in chronic heart failure patients who normally walk at a slower pace, making it difficult for researchers and clinicians to implement activity monitors during physical activity interventions.

METHODS

Six consumer-level activity monitors were validated in a 3-day field study in patients with chronic heart failure and healthy individuals under free living conditions. Furthermore, the same devices were evaluated in a lab-based study during treadmill walking at speeds of 2.4, 3.0, 3.6, and 4.2 km·h-1. Concordance correlation coefficients (CCC) were used to evaluate the agreement between the activity monitors and the criterion, and mean absolute percentage errors (MAPE) were calculated to assess differences between each device and the criterion (MAPE <10% was considered as a threshold for validity).

RESULTS

In the field study of healthy individuals, all but one of the activity monitors showed a substantial correlation (CCC ≥0.95) with the criterion device and MAPE <10%. In patients with heart failure, the correlation of only two activity monitors (Garmin vívofit 3 and Withings Go) was classified as at least moderate (CCC ≥0.90) and none of the devices had MAPE <10%. In the lab-based study at speeds 4.2 and 3.6 km·h-1, all activity monitors showed substantial to almost perfect correlations (CCC ≥0.95) with the criterion and MAPE in the range 1%-3%. However, at slower speeds of 3.0 and 2.4 km·h-1, the accuracy of all devices substantially deteriorated: their correlation with the criterion decreased below 90% and their MAPE increased to 4-8% and 10-45%, respectively.

CONCLUSIONS

Even though none of the tested activity monitors fall within arbitrary thresholds for validity, most of them perform reasonably well enough to be useful tools that clinicians can use to simply motivate chronic heart failure patients to walk more.

A monitoring system to provide feedback on student physical activity during physical education lessons

School-based physical education (PE) provides opportunities to accumulate moderate-to-vigorous physical activity (MVPA), but many students are insufficiently active during PE lessons. Providing teachers with feedback regarding their students' physical activity may increase the effectiveness of PE for achieving MVPA goals, but existing physical activity monitoring technologies have limitations in class environments. Therefore, the purpose of this study was to develop and validate a system capable of providing feedback on PE lesson MVPA. Equations for translating step counts to %MVPA were derived from measures in 492 students who concurrently wore an ActiGraph GT3X+ (ActiGraph) and Yamax pedometer (Yamax) during a PE lesson. To enhance feedback availability during PE lessons, we then developed a bespoke monitoring system using wireless tri-axial pedometers (HMM) and a smart device app. After developing and testing the monitoring system, we assessed its validity and reliability in 100 students during a PE lesson. There was a strong correlation of 0.896 between step counts and accelerometer-determined %MVPA and quantile regression equations showed good validity for translating step counts to %MVPA with a mean absolute difference of 5.3 (95% CI, 4.4-6.2). The physical activity monitoring system was effective at providing %MVPA during PE lessons with a mean difference of 1.6 ± 7.1 compared with accelerometer-determined %MVPA (7% difference between the two measurement methods). Teachers and students can use a smart device app and wireless pedometers to conveniently obtain feedback during PE lessons. Future studies should determine whether such technologies help teachers to increase physical activity during PE lessons.

Influence of Accelerometer Calibration Approach on Moderate-Vigorous Physical Activity Estimates for Adults

PURPOSE

To compare estimates of moderate-vigorous physical activity (MVPA) duration derived from accelerometers calibrated only to walking and running activities to estimates from calibrations based on a broader range of lifestyle and ambulatory activities.

METHODS

In a study of 932 older (50-74 yr) adults we compared MVPA estimates from accelerometer counts based on three ambulatory calibration methods (Freedson 1952 counts per minute; Sasaki 2690 counts per minute; activPAL 3+ METs) to estimates based on calibrations from lifestyle and ambulatory activities combined (Matthews 760 counts per minute; Crouter 3+ METs; Sojourn3x 3+ METs). We also examined data from up to 6 previous-day recalls describing the MVPA in this population.

RESULTS

The MVPA duration values derived from ambulatory calibration methods were significantly lower than methods designed to capture a broader range of both lifestyle and ambulatory activities (P < 0.05). The MVPA (h·d) estimates in all participants were: Freedson (median, 0.35; interquartile range, 0.17-0.58); Sasaki (median, 0.91; interquartile range, 0.59-1.32); and activPAL (median, 0.97; interquartile range, 0.71-1.26) compared with Matthews (median, 1.82; interquartile range, 1.37-2.34); Crouter (2.28 [1.72-2.82]); and Sojourn3x (median, 1.85; interquartile range, 1.42-2.34). Recall-based estimates in all participants were comparable (median, 1.61; interquartile range, 0.89-2.57) and indicated participation in a broad range of lifestyle and ambulatory MVPA.

CONCLUSIONS

Accelerometer calibration studies that employ only ambulatory activities may produce MVPA duration estimates that are substantially lower than methods calibrated to a broader range of activities. These findings highlight the potential to reduce potentially large differences among device-based measures of MVPA due to variation in calibration study design by including a variety of lifestyle and ambulatory activities.

Wrist Acceleration Cut Points for Moderate-to-Vigorous Physical Activity in Youth

PURPOSE

This study aimed to examine the validity of wrist acceleration cut points for classifying moderate (MPA), vigorous (VPA), and moderate-to-vigorous (MVPA) physical activity.

METHODS

Fifty-seven children (5-12 yr) completed 15 semistructured activities. Three sets of wrist cut points (>192 mg, >250 mg, and >314 mg), previously developed using Euclidian norm minus one (ENMO192+), GENEActiv software (GENEA250+), and band-pass filter followed by Euclidian norm (BFEN314+), were evaluated against indirect calorimetry. Analyses included classification accuracy, equivalence testing, and Bland-Altman procedures.

RESULTS

All cut points classified MPA, VPA, and MVPA with substantial accuracy (ENMO192+: κ = 0.72 [95% confidence interval = 0.72-0.73], MVPA: area under the receiver operating characteristic curve (ROC-AUC) = 0.85 [0.85-0.86]; GENEA250+: κ = 0.75 [0.74-0.76], MVPA: ROC-AUC = 0.85 [0.85-0.86]; BFEN314+: κ = 0.73 [0.72-0.74], MVPA: ROC-AUC = 0.86 [0.86-0.87]). BFEN314+ misclassified 19.7% non-MVPA epochs as MPA, whereas ENMO192+ and GENEA250+ misclassified 32.6% and 26.5% of MPA epochs as non-MVPA, respectively. Group estimates of MPA time were equivalent (P < 0.01) to indirect calorimetry for the BFEN314+ MPA cut point (mean bias = -1.5%, limits of agreement [LoA] = -57.5% to 60.6%), whereas estimates of MVPA time were equivalent (P < 0.01) to indirect calorimetry for the ENMO192+ (mean bias = -1.1%, LoA = -53.7% to 55.9%) and GENEA250+ (mean bias = 2.2%, LoA = -56.5% to 52.2%) cut points. Individual variability (LoA) was large for MPA (min: BFEN314+, -60.6% to 57.5%; max: GENEA250+, -42.0% to 104.1%), VPA (min: BFEN314+, -238.9% to 54.6%; max: ENMO192+, -244.5% to 127.4%), and MVPA (min: ENMO192+, -53.7% to 55.0%; max: BFEN314+, -83.9% to 25.3%).

CONCLUSION

Wrist acceleration cut points misclassified a considerable proportion of non-MVPA and MVPA. Group-level estimates of MVPA were acceptable; however, error for individual-level prediction was larger.

Moving Forward With Accelerometer-Assessed Physical Activity: Two Strategies to Ensure Meaningful, Interpretable, and Comparable Measures

Significant advances have been made in the measurement of physical activity in youth over the past decade. Monitors and protocols promote very high compliance, both night and day, and raw measures are available rather than "black box" counts. Consequently, many surveys and studies worldwide now assess children's physical behaviors (physical activity, sedentary behavior, and sleep) objectively 24 hours a day, 7 days a week using accelerometers. The availability of raw acceleration data in many of these studies is both an opportunity and a challenge. The richness of the data lends itself to the continued development of innovative metrics, whereas the removal of proprietary outcomes offers considerable potential for comparability between data sets and harmonizing data. Using comparable physical activity outcomes could lead to improved precision and generalizability of recommendations for children's present and future health. The author will discuss 2 strategies that he believes may help ensure comparability between studies and maximize the potential for data harmonization, thereby helping to capitalize on the growing body of accelerometer data describing children's physical behaviors.

Using accelerometers to characterize recovery after surgery in children

BACKGROUND

Assessment of recovery after surgery in children remains highly subjective. However, advances in wearable technology present an opportunity for clinicians to have an objective assessment of postoperative recovery. The aims of this pilot study are to: (1) evaluate acceptability of accelerometer use in pediatric surgical patients, (2) use accelerometer data to characterize the recovery trajectory of physical activity, and (3) determine if postoperative adverse events are associated with a decrease in physical activity.

STUDY DESIGN

Children aged 3-18-years-old undergoing elective inpatient and outpatient surgical procedures were invited to participate. Physical activity was measured using an Actigraph GT3X wristworn accelerometer for ≥2days preoperatively and 5-14days postoperatively. Time spent performing light (LPA) and moderate-to-vigorous physical activity (MVPA) was expressed in minutes/day. Physical activity for each postoperative day was calculated as a percentage of preoperative activity, and recovery trajectories were produced. Adverse events were reported and mapped against recovery trajectories.

RESULTS

Of 60 patients enrolled, 25 (10 inpatients, 15 outpatients) completed the study procedures and were included in the analysis. For outpatient procedures, LPA recovered to preoperative level on postoperative day (POD) 7 and MVPA peaked at 90% on POD 8. For inpatient procedures, LPA peaked at 70% on POD 11, and MVPA peaked at 53% on POD 10. Adverse events in 2 patients were associated with a decline in activity.

CONCLUSIONS

This study demonstrates that objective monitoring of postoperative physical activity using accelerometers is feasible in the pediatric surgical population. Recovery trajectories for inpatient and outpatient procedures differ. Accelerometer technology presents clinicians with a new potential tool for assessing and managing surgical recovery, and for determining if children are not recovering as expected.

TYPE OF STUDY

Diagnostic Study.

LEVEL OF EVIDENCE

III.

Individual Differences in the Competence for Physical-Activity-Related Affect Regulation Moderate the Activity-Affect Association in Real-Life Situations

Physical activity (PA) is positively associated with affective well-being in adults. This study investigated the moderating role of the competence for PA-related affect regulation in the PA-affect association in real-life situations. A total of 37 women and 27 men completed an ecological momentary assessment study in which the authors used accelerometers to record PA and e-diaries to collect data on affective well-being on 4 study days. They applied multilevel analyses to estimate the within-person effects of PA on affective well-being and cross-level interactions between PA (within person) and PA-related affect regulation (between persons). Results revealed cross-level interaction effects between within-person PA variations and competence for PA-related affect regulation on the affect dimensions of calmness (p < .01) and valence (p = .04). Thus, the competence for PA-related affect regulation moderates the PA-affect association in real-life situations. Therefore, individual-based PA promotion should consider these individual differences to develop tailored interventions.

Convergent validity of ActiGraph and Actical accelerometers for estimating physical activity in adults

PURPOSE

The aim of the present study was to examine the convergent validity of two commonly-used accelerometers for estimating time spent in various physical activity intensities in adults.

METHODS

The sample comprised 37 adults (26 males) with a mean (SD) age of 37.6 (12.2) years from San Diego, USA. Participants wore ActiGraph GT3X+ and Actical accelerometers for three consecutive days. Percent agreement was used to compare time spent within four physical activity intensity categories under three counts per minute (CPM) threshold protocols: (1) using thresholds developed specifically for each accelerometer, (2) applying ActiGraph thresholds to regression-rectified Actical CPM data, and (3) developing new 'optimal' Actical thresholds.

RESULTS

Using Protocol 1, the Actical estimated significantly less time spent in light (-16.3%), moderate (-2.8%), and vigorous (-0.4%) activity than the ActiGraph, but greater time spent sedentary (+20.5%). Differences were slightly more pronounced when the low frequency extension filter on the ActiGraph was enabled. The two adjustment methods (Protocols 2 and 3) improved agreement in this sample.

CONCLUSIONS

Our findings show that ActiGraph and Actical accelerometers provide significantly different estimates of time spent in various physical activity intensities. Regression and threshold adjustment were able to reduce these differences, although some level of non-agreement persisted. Researchers should be aware of the inherent limitations of count-based physical activity assessment when reporting and interpreting study findings.

Converting between estimates of moderate-to-vigorous physical activity derived from raw accelerations measured at the wrist and from ActiGraph counts measured at the hip: the Rosetta Stone

The ability to compare published group-level estimates of objectively measured moderate-to-vigorous physical activity (MVPA) across studies continues to increase in difficulty. The objective of this study was to develop conversion equations and demonstrate their utility to compare estimates of MVPA derived from the wrist and hip. Three studies of youth (N = 232, 9-12yrs, 50% boys) concurrently wore a hip-worn ActiGraph and a wrist-worn GENEActiv for 7-days. ActiGraph hip count data were reduced using four established cutpoints. Wrist accelerations were reduced using the Hildebrand MVPA 200 mg threshold. Conversion equations were developed on a randomly selected subsample of 132 youth. Equations were cross-validated and absolute error, absolute percent error, and modified Bland-Altman plots were evaluated for conversion accuracy. Across equations R²_adj was 0.51-0.56 with individual-level absolute error in minutes ranging from 7 (wrist-to-hip Puyau) to 14.5 minutes (wrist-to-hip Freedson 3MET) and absolute percent differences ranging from 13.9%-24.5%. Group-level cross-validation to convert hip-to-wrist MVPA resulted in average absolute percent errors ranging from 3.1%-4.9%. Conversion of wrist-to-hip MVPA resulted in average absolute percent errors ranging from 3.0%-10.0%. We recommend the use of these equations to compare published estimates of MVPA between the wear-site cut-point combinations presented.

Recommendations for Assessment of the Reliability, Sensitivity, and Validity of Data Provided by Wearable Sensors Designed for Monitoring Physical Activity

Although it is becoming increasingly popular to monitor parameters related to training, recovery, and health with wearable sensor technology (wearables), scientific evaluation of the reliability, sensitivity, and validity of such data is limited and, where available, has involved a wide variety of approaches. To improve the trustworthiness of data collected by wearables and facilitate comparisons, we have outlined recommendations for standardized evaluation. We discuss the wearable devices themselves, as well as experimental and statistical considerations. Adherence to these recommendations should be beneficial not only for the individual, but also for regulatory organizations and insurance companies.

Comparability of children's sedentary time estimates derived from wrist worn GENEActiv and hip worn ActiGraph accelerometer thresholds

OBJECTIVES

To examine the comparability of children's free-living sedentary time (ST) derived from raw acceleration thresholds for wrist mounted GENEActiv accelerometer data, with ST estimated using the waist mounted ActiGraph 100count·min^-1 threshold.

DESIGN

Secondary data analysis.

METHOD

108 10-11-year-old children (n=43 boys) from Liverpool, UK wore one ActiGraph GT3X+ and one GENEActiv accelerometer on their right hip and left wrist, respectively for seven days. Signal vector magnitude (SVM; mg) was calculated using the ENMO approach for GENEActiv data. ST was estimated from hip-worn ActiGraph data, applying the widely used 100count·min^-1 threshold. ROC analysis using 10-fold hold-out cross-validation was conducted to establish a wrist-worn GENEActiv threshold comparable to the hip ActiGraph 100count·min^-1 threshold. GENEActiv data were also classified using three empirical wrist thresholds and equivalence testing was completed.

RESULTS

Analysis indicated that a GENEActiv SVM value of 51mg demonstrated fair to moderate agreement (Kappa: 0.32-0.41) with the 100count·min^-1 threshold. However, the generated and empirical thresholds for GENEActiv devices were not significantly equivalent to ActiGraph 100count·min^-1. GENEActiv data classified using the 35.6mg threshold intended for ActiGraph devices generated significantly equivalent ST estimates as the ActiGraph 100count·min^-1.

CONCLUSIONS

The newly generated and empirical GENEActiv wrist thresholds do not provide equivalent estimates of ST to the ActiGraph 100count·min^-1 approach. More investigation is required to assess the validity of applying ActiGraph cutpoints to GENEActiv data. Future studies are needed to examine the backward compatibility of ST data and to produce a robust method of classifying SVM-derived ST.

Effect of the Shaping Healthy Choices Program, a Multicomponent, School-Based Nutrition Intervention, on Physical Activity Intensity

OBJECTIVE

Physical activity has been shown to have a wide range of beneficial health effects, yet few youth meet the United States physical activity recommendation of 60 minutes of moderate-to-vigorous physical activity (MVPA) everyday. The objective of this study was to determine whether physical activity patterns improved in a subsample of fourth-graders participating in the multicomponent intervention, the Shaping Healthy Choices Program (SHCP).

METHODS

At pre- and post-intervention assessments, youth at the control and intervention schools wore a Polar Active monitor on their nondominant wrist 24 h/d for at least 2 consecutive days. Multiple linear regression was used to evaluate change in physical activity by adjusting for covariates and other potential confounders, including ethnicity/race, household income, and sex. Statistical significance was set at p < 0.05.

RESULTS

Mean minutes of MVPA significantly increased at the intervention school (22.3 + 37.8; p = 0.01) and at the control school (29.1 + 49.5; p = 0.01). There were no significant differences in the change in MVPA between the schools. Youth at the intervention school significantly decreased mean minutes in sedentary activity compared to the controls (p = 0.02).

CONCLUSIONS

Youth who participated in the SHCP decreased time spent in sedentary activity and increased very vigorous physical activity from pre- to post-intervention, while these changes were not observed at the control school. The overall small physical activity intensity pattern shift supports that physical activity is an important area to target within a multicomponent nutrition intervention aimed at preventing childhood obesity.

Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults

Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers. Three cut-off point algorithms and a Random Forest machine learning model were developed and cross-validated using the collected data. Detailed analyses were performed to check algorithm robustness, and examine and benchmark both overall and participant-specific balanced accuracies. This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity. Nevertheless, the machine learning algorithm outperformed the cut-off point models by being robust for all individual's physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities. Therefore, we propose that Random Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry.

Accuracy of Posture Allocation Algorithms for Thigh- and Waist-Worn Accelerometers

PURPOSE

The objective of this study is to compare the accuracy of the activPAL and ActiGraph GT3X+ (waist and thigh) proprietary postural allocation algorithms and an open-source postural allocation algorithm applied to GENEActiv (thigh) and ActiGraph GT3X+ (thigh) data.

METHODS

Thirty-four adults (≥18 yr) wore the activPAL3, GENEActiv, and ActiGraph GT3X+ on the right thigh and an ActiGraph on the right hip while performing four lying, seven sitting, and five upright activities in the laboratory. Lying and sitting tasks incorporated a range of leg angles (e.g., lying with legs bent and sitting with legs crossed). Each activity was performed for 5 min while being directly observed. The percentage of the time the posture was correctly classified was calculated.

RESULTS

Participants consisted of 14 males and 20 females (mean age, 27.2 ± 5.9 yr; mean body mass index, 23.8 ± 3.7 kg·m). All postural allocation algorithms applied to monitors worn on the thigh correctly classified ≥93% of the time lying, ≥91% of the time sitting, and ≥93% of the time upright. The ActiGraph waist proprietary algorithm correctly classified 72% of the time lying, 58% of the time sitting, and 74% of the time upright. Both the activPAL and ActiGraph thigh proprietary algorithms misclassified sitting on a chair with legs stretched out (58% and 5% classified incorrectly, respectively). The ActiGraph thigh proprietary and open-source algorithm applied to the thigh-worn ActiGraph misclassified participants lying on their back with their legs bent 27% and 9% of the time, respectively.

CONCLUSION

All postural allocation algorithms when applied to devices worn on the thigh were highly accurate in identifying lying, sitting, and upright postures. Given the poor accuracy of the waist algorithm for detecting sitting, caution should be taken if inferring sitting time from a waist-worn device.

Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study

Background

The use of embedded smartphone sensors offers opportunities to measure physical activity (PA) and human movement. Big data—which includes billions of digital traces—offers scientists a new lens to examine PA in fine-grained detail and allows us to track people’s geocoded movement patterns to determine their interaction with the environment.

Objective

The objective of this study was to examine the validity of the Movn smartphone app (Moving Analytics) for collecting PA and human movement data.

Methods

The criterion and convergent validity of the Movn smartphone app for estimating energy expenditure (EE) were assessed in both laboratory and free-living settings, compared with indirect calorimetry (criterion reference) and a stand-alone accelerometer that is commonly used in PA research (GT1m, ActiGraph Corp, convergent reference). A supporting cross-validation study assessed the consistency of activity data when collected across different smartphone devices. Global positioning system (GPS) and accelerometer data were integrated with geographical information software to demonstrate the feasibility of geospatial analysis of human movement.

Results

A total of 21 participants contributed to linear regression analysis to estimate EE from Movn activity counts (standard error of estimation [SEE]=1.94 kcal/min). The equation was cross-validated in an independent sample (N=42, SEE=1.10 kcal/min). During laboratory-based treadmill exercise, EE from Movn was comparable to calorimetry (bias=0.36 [−0.07 to 0.78] kcal/min, t₈₂=1.66, P=.10) but overestimated as compared with the ActiGraph accelerometer (bias=0.93 [0.58-1.29] kcal/min, t₈₉=5.27, P<.001). The absolute magnitude of criterion biases increased as a function of locomotive speed (F_1,4=7.54, P<.001) but was relatively consistent for the convergent comparison (F_1,4=1.26, P<.29). Furthermore, 95% limits of agreement were consistent for criterion and convergent biases, and EE from Movn was strongly correlated with both reference measures (criterion r=.91, convergent r=.92, both P<.001). Movn overestimated EE during free-living activities (bias=1.00 [0.98-1.02] kcal/min, t₆₁₂₃=101.49, P<.001), and biases were larger during high-intensity activities (F_3,6120=1550.51, P<.001). In addition, 95% limits of agreement for convergent biases were heterogeneous across free-living activity intensity levels, but Movn and ActiGraph measures were strongly correlated (r=.87, P<.001). Integration of GPS and accelerometer data within a geographic information system (GIS) enabled creation of individual temporospatial maps.

Conclusions

The Movn smartphone app can provide valid passive measurement of EE and can enrich these data with contextualizing temporospatial information. Although enhanced understanding of geographic and temporal variation in human movement patterns could inform intervention development, it also presents challenges for data processing and analytics.

Telerehabilitation of Patients with Injuries of the Lower Extremities

INTRODUCTION

Timely access to orthopedic rehabilitation is an important problem for the Ukrainian healthcare system. The international orthopedic community aims to achieve the best possible outcome for patient care by modifying rehabilitation methods and using telemedicine technology. The theme of this article is to discuss the integration of telemedicine technology in the rehabilitation of patients with injuries of the lower extremities.

MATERIALS AND METHODS

Consecutive patients were recruited over a 2-year period. A total of 74 subjects with lower extremity injuries were enrolled in the study and monitored during a 3-month period. Twenty-six patients from the control group underwent traditional rehabilitation procedures for a 3-month period after injury. A total of 48 subjects were enrolled in the telerehabilitation group for a 3-month study period and were trained with a set of exercises for home use. Home remote monitoring for the 48 test subjects included use of a smartphone with gyroscope, G-sensor, magnetometer, and barometer, which was fixed to the injured limb. Software for the smartphone was developed at the I. Horbachevsky Ternopil Medical University, Ukraine. Software permits the monitoring of exercise time, the frequency of active movements of the injured limb, the number of steps per hour, and the walking speed.

RESULTS

The 3-month rehabilitation period started with the power load of 10% of body weight, following 3-5 steps five times per day with a speed no >1 km/h. The orthopedic surgeon, during telerehabilitation, took significantly less time to consult patients (2.3 min ± 0.4) than the traditional rehabilitation (12.6 min ± 2.9). Patient satisfaction was higher for the telerehabilitation (83.1% ± 14.2) than for the orthopedic surgeon's traditional rehabilitation (33.1% ± 8.9).

CONCLUSIONS

Telerehabilitation is a good alternative for providing access to specialty care for the growing number of patients with traumatic injuries.

The Association of Daily Activity Levels and Estimated Kidney Function in Men and Women With Predialysis Chronic Kidney Disease

Introduction

Chronic kidney disease (CKD) is often accompanied by complications including poor physical activity level. However, only a few studies have objectively characterized physical activity levels in predialysis CKD. Our study sought to measure daily activity levels by accelerometry in individuals with CKD (stages III−V) and to determine the association between daily activity and kidney function.

Methods

We determined kidney function by means of the estimated glomerular filtration rate (eGFR) using the Modification of Diet and Renal Disease (MDRD) equation. Participants wore an accelerometer for 7 consecutive days, and we measured multiple physical activity outcomes including total daily activity, sedentary, light, and moderate−vigorous activity. Average durations and intensity of activity were determined according to stage of CKD. The association between kidney function and activity level was determined by regression analysis.

Results

We analyzed data from 110 individuals (60% men and 40% women) with stages III to V CKD. The mean age of our participants was 64 years, mean body mass index was 27.5 kg/m², and mean eGFR was 23.7 ± 1.2 ml/min/1.73 m². Our participants were primarily sedentary (mean duration of inactivity = 1152 ± 100 minutes per day; 79% of day). Light activity was performed 280 ± 99 minutes per day, and individuals participated in only 6 ± 9 minutes per day of moderate−vigorous activity. The eGFR did not predict physical activity level (P > 0.05 for all).

Discussion

Individuals with stages III to V CKD are sedentary, and do not meet the national recommendations of 150 minutes of moderate−vigorous activity per week. Further study is required to determine whether interventions to increase activity levels in patients with CKD are associated with improved health outcomes.

Assessment of laboratory and daily energy expenditure estimates from consumer multi-sensor physical activity monitors

Wearable physical activity monitors are growing in popularity and provide the opportunity for large numbers of the public to self-monitor physical activity behaviours. The latest generation of these devices feature multiple sensors, ostensibly similar or even superior to advanced research instruments. However, little is known about the accuracy of their energy expenditure estimates. Here, we assessed their performance against criterion measurements in both controlled laboratory conditions (simulated activities of daily living and structured exercise) and over a 24 hour period in free-living conditions. Thirty men (n = 15) and women (n = 15) wore three multi-sensor consumer monitors (Microsoft Band, Apple Watch and Fitbit Charge HR), an accelerometry-only device as a comparison (Jawbone UP24) and validated research-grade multi-sensor devices (BodyMedia Core and individually calibrated Actiheart™). During discrete laboratory activities when compared against indirect calorimetry, the Apple Watch performed similarly to criterion measures. The Fitbit Charge HR was less consistent at measurement of discrete activities, but produced similar free-living estimates to the Apple Watch. Both these devices underestimated free-living energy expenditure (-394 kcal/d and -405 kcal/d, respectively; P<0.01). The multi-sensor Microsoft Band and accelerometry-only Jawbone UP24 devices underestimated most laboratory activities and substantially underestimated free-living expenditure (-1128 kcal/d and -998 kcal/d, respectively; P<0.01). None of the consumer devices were deemed equivalent to the reference method for daily energy expenditure. For all devices, there was a tendency for negative bias with greater daily energy expenditure. No consumer monitors performed as well as the research-grade devices although in some (but not all) cases, estimates were close to criterion measurements. Thus, whilst industry-led innovation has improved the accuracy of consumer monitors, these devices are not yet equivalent to the best research-grade devices or indeed equivalent to each other. We propose independent quality standards and/or accuracy ratings for consumer devices are required.

Comparing accelerometer, pedometer and a questionnaire for measuring physical activity in bronchiectasis: a validity and feasibility study?

Background

There are challenges for researchers and clinicians to select the most appropriate physical activity tool, and a balance between precision and feasibility is needed. Currently it is unclear which physical activity tool should be used to assess physical activity in Bronchiectasis. The aim of this research is to compare assessment methods (pedometer and IPAQ) to our criterion method (ActiGraph) for the measurement of physical activity dimensions in Bronchiectasis (BE), and to assess their feasibility and acceptability.

Methods

Patients in this analysis were enrolled in a cross-sectional study. The ActiGraph and pedometer were worn for seven consecutive days and the IPAQ was completed for the same period. Statistical analyses were performed using SPSS 20 (IBM). Descriptive statistics were used; the percentage agreement between ActiGraph and the other measures were calculated using limits of agreement. Feedback about the feasibility of the activity monitors and the IPAQ was obtained.

Results

There were 55 (22 male) data sets available. For step count there was no significant difference between the ActiGraph and Pedometer, however, total physical activity time (mins) as recorded by the ActiGraph was significantly higher than the pedometer (mean ± SD, 232 (75) vs. 63 (32)). Levels of agreement between the two devices was very good for step count (97% agreement); and variation in the levels of agreement were within accepted limits of ±2 standard deviations from the mean value.

IPAQ reported more bouted- moderate - vigorous physical activity (MVPA) [mean, SD; 167(170) vs 6(9) mins/day], and significantly less sedentary time than ActiGraph [mean, SD; 362(115) vs 634(76) vmins/day]. There were low levels of agreement between the two tools (57% sedentary behaviour; 0% MVPA₁₀₊), with IPAQ under-reporting sedentary behaviour and over-reporting MVPA₁₀₊ compared to ActiGraph. The monitors were found to be feasible and acceptable by participants and researchers; while the IPAQ was accepta ble to use, most patients required assistance to complete it.

Conclusions

Accurate measurement of physical activity is feasible in BE and will be valuable for future trials of therapeutic interventions. ActiGraph or pedometer could be used to measure simple daily step counts, but ActiGraph was superior as it measured intensity of physical activity and was a more precise measure of time spent walking. The IPAQ does not appear to represent an accurate measure of physical activity in this population.

Trial registration

Clinical Trials Registration Number NCT01569009: Physical Activity in Bronchiectasis.

Subjective and objective assessment of physical activity in multiple sclerosis and their relation to health-related quality of life

Background

Physical activity (PA) is frequently restricted in people with multiple sclerosis (PwMS) and aiming to enhance PA is considered beneficial in this population. We here aimed to explore two standard methods (subjective plus objective) to assess PA reduction in PwMS and to describe the relation of PA to health-related quality of life (hrQoL).

Methods

PA was objectively measured over a 7-day period in 26 PwMS (EDSS 1.5–6.0) and 30 matched healthy controls (HC) using SenseWear mini® armband (SWAmini) and reported as step count, mean total and activity related energy expenditure (EE) as well as time spent in PA of different intensities. Measures of EE were also derived from self-assessment with IPAQ (International Physical Activity Questionnaire) long version, which additionally yielded information on the context of PA and a classification into subjects’ PA levels. To explore the convergence between both types of assessment, IPAQ categories (low, moderate, high) were related to selected PA parameters from objective assessment using ANOVA. Group differences and associated effect sizes for all PA parameters as well as their relation to clinical and hrQoL measures were determined.

Results

Both, SWAmini and IPAQ assessment, captured differences in PA between PwMS and HC. IPAQ categories fit well with common cut-offs for step count (p = 0.002) and mean METs (p = 0.004) to determine PA levels with objective devices. Correlations between specifically matched pairs of IPAQ and SWAmini parameters ranged between r .288 and r .507. Concerning hrQoL, the lower limb mobility subscore was related to four PA measures, while a relation with patients’ report of general contentment was only seen for one.

Conclusions

Both methods of assessment seem applicable in PwMS and able to describe reductions in daily PA at group level. Whether they can be used to track individual effects of interventions to enhance PA levels needs further exploration. The relation of PA measures with hrQoL seen with lower limb mobility suggests lower limb function not only as a major target for intervention to increase PA but also as a possible surrogate for PA changes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12883-016-0783-0) contains supplementary material, which is available to authorized users.

Measuring Physical Activity in Free-Living Conditions-Comparison of Three Accelerometry-Based Methods

We examined the agreement in time spent on different physical activity (PA) levels using (1) mean amplitude deviation (MAD) of raw acceleration from the hip, (2) wrist-worn Polar Active, and (3) hip-worn Actigraph counts using Freedson's cut-points among adults under free-living conditions. PA was measured in 41 volunteers (mean age 47.6 years) for 14 days. Two MET-based threshold sets were used for MAD and Polar Active for sedentary time (ST) and time spent in light (LPA), moderate (MPA), and vigorous (VPA) PA. Actigraph counts were divided into PA classes, ≤100 counts/min for ST and Freedson's cut-points for LPA, MPA, and VPA. Analysis criteria were simultaneous use of devices for at least 4 days of >500 min/d. The between-method differences were analyzed using a repeated measures analysis of variance test. Bland-Altman plots and ROC graphs were also employed. Valid data were available from 27 participants. Polar Active produced the highest amount of VPA with both thresholds (≥5 and ≥6 MET; mean difference 17.9-30.9 min/d, P < 0.001). With the threshold 3-6 MET for MPA, Polar Active indicated 19.2 min/d more than MAD (95% CI 5.8-32.6) and 51.0 min/d more than Actigraph (95% CI 36.7-65.2). The results did not differ with 3.5-5 MET for MPA [F_{(1.44, 37.43)} = 1.92, P = 0.170]. MAD and Actigraph were closest to each other for ST with the threshold < 1.5 MET (mean difference 22.2 min/d, 95% CI 7.1-37.3). With the threshold <2 MET, Polar Active and Actigraph provided similar results (mean difference 7.0 min/d, 95% CI -17.8-31.7). Moderate to high agreement (area under the ROC curve 0.806-0.963) was found between the methods for the fulfillment of the recommendation for daily moderate-to-vigorous PA of 60 min. In free-living conditions the agreement between MAD, Polar Active, and Actigraph for measuring time spent on different activity levels in adults was dependent on the activity thresholds used and PA intensity. ROC analyses showed moderate to high agreement for the fulfillment of the recommendation for daily MVPA. Without additional statistical adjustment, these methods cannot be used interchangeably when measuring daily PA, but any of the methods can be used to identify persons with insufficient daily amount of MVPA.

Wrist-Worn Accelerometer-Brand Independent Posture Classification

INTRODUCTION

Access to raw acceleration data should facilitate comparisons between accelerometer outputs regardless of monitor brand.

PURPOSE

To evaluate the accuracy of posture classification using the Sedentary Sphere in data from two widely used wrist-worn triaxial accelerometers.

METHODS

Laboratory: Thirty-four adults wore a GENEActiv and an ActiGraph GT3X+ on their nondominant wrist while performing four lying, seven sitting, and five upright activities. Free-living: The same participants wore both accelerometers on their nondominant wrist and an activPAL3 on their right thigh during waking hours for 2 d.

RESULTS

Laboratory: Using the Sedentary Sphere with 15-s epoch GENEActiv data, sedentary and upright postures were correctly identified 74% and 91% of the time, respectively. Corresponding values for the ActiGraph data were 75% and 90%. Free-living: Total sedentary time was estimated at 534 ± 144, 523 ± 143, and 528 ± 137 min by the activPAL, the Sedentary Sphere with GENEActiv data and with ActiGraph data, respectively. The mean bias, relative to the activPAL, was small with moderate limits of agreement (LoA) for both the GENEActiv (mean bias = -12.5 min, LoA = -117 to 92 min) and ActiGraph (mean bias = -8 min, LoA = -103 to 88 min). Strong intraclass correlations (ICC) were evident for the activPAL with the GENEActiv (0.93, 0.84-0.97 (95% confidence interval) and the ActiGraph (0.94, 0.86-0.97). Agreement between the GENEActiv and ActiGraph posture classifications was very high (ICC = 0.98 (0.94-0.99), mean bias = +3 min, LoA = -58 to 63 min).

CONCLUSIONS

These data support the efficacy of the Sedentary Sphere for classification of posture from a wrist-worn accelerometer in adults. The approach is equally valid with data from both the GENEActiv and ActiGraph accelerometers.

Wear Compliance and Activity in Children Wearing Wrist- and Hip-Mounted Accelerometers

PURPOSE

This study aimed to 1) explore children's compliance to wearing wrist- and hip-mounted accelerometers, 2) compare children's physical activity (PA) derived from raw accelerations of wrist and hip, and 3) examine differences in raw and counts PA measured by hip-worn accelerometry.

METHODS

One hundred and twenty-nine 9- to 10-yr-old children wore a wrist-mounted GENEActiv accelerometer (GAwrist) and a hip-mounted ActiGraph GT3X+ accelerometer (AGhip) for 7 d. Both devices measured raw accelerations, and the AGhip also provided count-based data.

RESULTS

More children wore the GAwrist than those from the AGhip regardless of wear time criteria applied (P < 0.001-0.035). Raw data signal vector magnitude (r = 0.68), moderate PA (MPA) (r = 0.81), vigorous PA (VPA) (r = 0.85), and moderate-to-vigorous PA (MVPA) (r = 0.83) were strongly associated between devices (P < 0.001). GAwrist signal vector magnitude (P = 0.001), MPA (P = 0.037), VPA (P = 0.002), and MVPA (P = 0.016) were significantly greater than those from the AGhip. According to GAwrist raw data, 86.9% of children engaged in at least 60 min · d(-1) of MVPA, compared with 19% for AGhip. ActiGraph MPA (raw) was 42.00 ± 1.61 min · d(-1) compared with 35.05 ± 0.99 min · d(-1) (counts) (P = 0.02). ActiGraph VPA was 7.59 ± 0.46 min · d(-1) (raw) and 37.06 ± 1.85 min · d(-1) (counts; P = 0.19).

CONCLUSIONS

In children, accelerometer wrist placement promotes superior compliance than the hip. Raw accelerations were significantly higher for GAwrist compared with those for AGhip possibly because of placement location and technical differences between devices. AGhip PA calculated from raw accelerations and counts differed substantially, demonstrating that PA outcomes derived from cut points for raw output and counts cannot be directly compared.

Reliability and Validity of the PAQ-C Questionnaire to Assess Physical Activity in Children

BACKGROUND

Physical activity (PA) assessment by questionnaire is a cornerstone in the field of sport epidemiology studies. The Physical Activity Questionnaire for Children (PAQ-C) has been used widely to assess PA in healthy school populations. The aim of this study was to evaluate the reliability and validity of the PAQ-C questionnaire in Spanish children using triaxial accelerometry as criterion.

METHODS

Eighty-three (N = 46 boys, N = 37 girls) healthy children (age 10.98 ± 1.17 years, body mass index 19.48 ± 3.51 kg/m(2) ) were volunteers and completed the PAQ-C twice and wore an accelerometer for 8 consecutive days. Reliability was analyzed by the intraclass correlation coefficient (ICC) and the internal consistency by the Cronbach's α coefficient. The PAQ-C was compared against total PA and moderate to vigorous PA (MVPA) obtained by accelerometry.

RESULTS

Test-retest reliability showed an ICC = 0.96 for the final score of PAQ-C. Small differences between first and second questionnaire administration were detected. Few and low correlations (rho = 0.228-0.278, all ps < .05) were observed between PAQ-C and accelerometry. The highest correlation was observed for item 9 (rho = 0.311, p < .01).

CONCLUSIONS

PAQ-C had a high reliability but a questionable validity for assessing total PA and MVPA in Spanish children. Therefore, PA measurement in children should not be limited only to self-report measurements.

Validation of the SenseWear Mini activity monitor in 5-12-year-old children

OBJECTIVES

This study aimed to validate SenseWear Mini software algorithm versions 2.2 (SW2.2) and 5.2 (SW5.2) for estimating energy expenditure (EE) in children.

DESIGN

Laboratory-based validation study.

METHODS

57 children aged 5-12 y completed a protocol involving 15 semi-structured sedentary (SED), light-intensity (LPA), and moderate- to vigorous-intensity (MVPA) physical activities. EE was estimated using portable indirect calorimetry (IC). The accuracy of EE estimates (kcal·min^-1) from SW2.2 and SW5.2 were examined at the group level and individual level using the mean absolute percentage error (MAPE), Bland-Altman plots and equivalence testing.

RESULTS

MAPE values were lower for SW5.2 (30.1±10.7%) than for SW2.2 (44.0±6.2%). Although mean differences for SW5.2 were smaller than for SW2.2 during SED (-0.23±0.22 vs. -0.61±0.20kcal·min^-1), LPA (-0.69±0.76 vs. -1.07±0.46kcal·min^-1) and MVPA (-2.22±1.15 vs. -2.57±1.15kcal·min^-1), limits of agreement did not decrease for the updated algorithms. For all activities, SW2.2 and SW5.2 were not equivalent to IC (p>0.05). Errors increased with increasing intensity.

CONCLUSION

The current SenseWear Mini algorithms SW5.2 underestimated EE. The overall improved accuracy for SW5.2 was not accompanied with improved accuracy at the individual level and EE estimates were not equivalent to IC.

Working postures and physical activity among registered nurses

Nurses report a high prevalence of musculoskeletal discomfort, particularly of the low back and neck/shoulder. This study characterized the full-shift upper arm and trunk postures and movement velocities of registered nurses using inertial measurement units (IMUs). Intensity of occupational physical activity (PA) was also ascertained using a waist-worn PA monitor and using the raw acceleration data from each IMU. Results indicated that nurses spent a relatively small proportion of their work time with the arms or trunk in extreme postures, but had few opportunities for rest and recovery in comparison to several other occupational groups. Comparisons between nurses in different PA groups suggested that using a combination of accelerometers secured to several body locations may provide more representative estimates of physical demands than a single, waist-worn PA monitor. The findings indicate a need for continued field-based research with larger sample sizes to facilitate the development of maximally effective intervention strategies.

Evaluation of four sensor locations for physical activity assessment

Direct measurements of physical activity (PA) obtained with inertial measurement units (IMUs) secured to the upper arms and trunk of 36 registered nurses working a full shift were compared to measurements obtained with a commercially-available PA monitor (ActiGraph wGT3X-BT) worn at the waist. Raw accelerations from each device were summarized into PA counts/min and metabolic equivalent (METs) categories using standard definitions. Differences between measurements were examined using repeated measures one-way analyses of variance (ANOVA) and agreement was assessed using Bland-Altman plots. Statistically significant differences were observed between all sensor locations for all PA summary metrics except for between the left and right arm for percentages of work time in the light and moderate counts/min categories. Bland-Altman plots suggested limited agreement between measurements obtained with the IMUs and measurements obtained with the wGT3X-BT waist-worn PA monitor. Results indicate that PA measurements vary substantially based on sensor location.

Prediction of activity-related energy expenditure using accelerometer-derived physical activity under free-living conditions: a systematic review

BACKGROUND/OBJECTIVES

Activity-related energy expenditure (AEE) might be an important factor in the etiology of chronic diseases. However, measurement of free-living AEE is usually not feasible in large-scale epidemiological studies but instead has traditionally been estimated based on self-reported physical activity. Recently, accelerometry has been proposed for objective assessment of physical activity, but it is unclear to what extent this methods explains the variance in AEE.

SUBJECTS/METHODS

We conducted a systematic review searching MEDLINE database (until 2014) on studies that estimated AEE based on accelerometry-assessed physical activity in adults under free-living conditions (using doubly labeled water method). Extracted study characteristics were sample size, accelerometer (type (uniaxial, triaxial), metrics (for example, activity counts, steps, acceleration), recording period, body position, wear time), explained variance of AEE (R(2)) and number of additional predictors. The relation of univariate and multivariate R(2) with study characteristics was analyzed using nonparametric tests.

RESULTS

Nineteen articles were identified. Examination of various accelerometers or subpopulations in one article was treated separately, resulting in 28 studies. Sample sizes ranged from 10 to 149. In most studies the accelerometer was triaxial, worn at the trunk, during waking hours and reported activity counts as output metric. Recording periods ranged from 5 to 15 days. The variance of AEE explained by accelerometer-assessed physical activity ranged from 4 to 80% (median crude R(2)=26%). Sample size was inversely related to the explained variance. Inclusion of 1 to 3 other predictors in addition to accelerometer output significantly increased the explained variance to a range of 12.5-86% (median total R(2)=41%). The increase did not depend on the number of added predictors.

CONCLUSIONS

We conclude that there is large heterogeneity across studies in the explained variance of AEE when estimated based on accelerometry. Thus, data on predicted AEE based on accelerometry-assessed physical activity need to be interpreted cautiously.

Systematic review of the validity and reliability of consumer-wearable activity trackers

BACKGROUND

Consumer-wearable activity trackers are electronic devices used for monitoring fitness- and other health-related metrics. The purpose of this systematic review was to summarize the evidence for validity and reliability of popular consumer-wearable activity trackers (Fitbit and Jawbone) and their ability to estimate steps, distance, physical activity, energy expenditure, and sleep.

METHODS

Searches included only full-length English language studies published in PubMed, Embase, SPORTDiscus, and Google Scholar through July 31, 2015. Two people reviewed and abstracted each included study.

RESULTS

In total, 22 studies were included in the review (20 on adults, 2 on youth). For laboratory-based studies using step counting or accelerometer steps, the correlation with tracker-assessed steps was high for both Fitbit and Jawbone (Pearson or intraclass correlation coefficients (CC) > =0.80). Only one study assessed distance for the Fitbit, finding an over-estimate at slower speeds and under-estimate at faster speeds. Two field-based studies compared accelerometry-assessed physical activity to the trackers, with one study finding higher correlation (Spearman CC 0.86, Fitbit) while another study found a wide range in correlation (intraclass CC 0.36-0.70, Fitbit and Jawbone). Using several different comparison measures (indirect and direct calorimetry, accelerometry, self-report), energy expenditure was more often under-estimated by either tracker. Total sleep time and sleep efficiency were over-estimated and wake after sleep onset was under-estimated comparing metrics from polysomnography to either tracker using a normal mode setting. No studies of intradevice reliability were found. Interdevice reliability was reported on seven studies using the Fitbit, but none for the Jawbone. Walking- and running-based Fitbit trials indicated consistently high interdevice reliability for steps (Pearson and intraclass CC 0.76-1.00), distance (intraclass CC 0.90-0.99), and energy expenditure (Pearson and intraclass CC 0.71-0.97). When wearing two Fitbits while sleeping, consistency between the devices was high.

CONCLUSION

This systematic review indicated higher validity of steps, few studies on distance and physical activity, and lower validity for energy expenditure and sleep. The evidence reviewed indicated high interdevice reliability for steps, distance, energy expenditure, and sleep for certain Fitbit models. As new activity trackers and features are introduced to the market, documentation of the measurement properties can guide their use in research settings.