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Background: The accuracy and precision of currently available, widely used acceleromyograph and electromyograph neuromuscular blockade monitors have not been well studied. In addition, the normalization of the train-of-four ratio from acceleromyography (train-of-four ratio [T4/T1] divided by the baseline train-of-four ratio) has not been validated in comparison to mechanomyography., Methods: Enrolled patients had surgery under general anesthesia with a supraglottic airway and without any neuromuscular blocking drugs. Three acceleromyograph monitors, three electromyograph monitors, and a mechanomyograph built in the authors' laboratory were tested. Most patients had an electromyograph and the mechanomyograph on one arm and a third monitor on the contralateral arm. Train-of-four ratios were collected every 12 to 20 s for the duration of the anesthetic. At least 1,000 train-of-four ratios were recorded for each device. Gauge repeatability and reproducibility analysis was performed., Results: Twenty-eight patients were enrolled. In total, 9,498 train-of-four ratio measurements were collected. Since no neuromuscular blocking drugs were used, the expected train-of-four ratio was 1.0. All of the acceleromyograph monitors produced overshoot in the train-of-four ratio (estimated means, 1.10 to 1.13) and substantial variability (gauge SDs, 0.07 to 0.18). Normalization of the train-of-four ratio measured by acceleromyography improved the estimated mean for each device (0.97 to 1.0), but the variability was not improved (gauge SDs, 0.06 to 0.17). The electromyograph and the mechanomyograph monitors produced minimal overshoot (estimated means, 0.99 to 1.01) and substantially less variation (gauge SDs, 0.01 to 0.02). For electromyography and mechanomyography, 0.3% of all train-of-four ratios were outside of the range 0.9 to 1.1. For acceleromyography, 27 to 51% of normalized train-of-four ratios were outside the range of 0.9 to 1.1., Conclusions: Three currently available acceleromyograph monitors produced overshoot and substantial variability that could be clinically significant. Normalization corrected the overshoot in the average results but did not reduce the wide variability. Three electromyograph monitors measured the train-of-four ratio with minimal overshoot and variability, similar to a mechanomyograph., (Copyright © 2024 American Society of Anesthesiologists. All Rights Reserved.)

Purpose: We sought to determine if individually calibrated machine learning models yielded higher accuracy than a group calibration approach for physical activity intensity assessment., Methods: Participants (n = 48) wore accelerometers on the right hip and nondominant wrist while performing activities of daily living in a semistructured laboratory and/or free-living setting. Criterion measures of activity intensity (sedentary, light, moderate, vigorous) were determined using direct observation. Data were reintegrated into 30-s epochs, and eight random forest models were created to determine physical activity intensity by using all possible conditions of training data (individual vs group), protocol (laboratory vs free-living), and placement (hip vs wrist). A 2 × 2 × 2 repeated-measures analysis of variance was used to compare epoch-level accuracy statistics (% accuracy, kappa [κ]) of the models when used to determine activity intensity in an independent sample of free-living participants., Results: Main effects were significant for the type of training data (group: accuracy = 80%, κ = 0.59; individual: accuracy = 74% [P = 0.02], κ = 0.50 [P = 0.01]) and protocol (free-living: accuracy = 81%, κ = 0.63; laboratory: accuracy = 74% [P = 0.04], κ = 0.47 [P < 0.01]). Main effects were not significant for placement (hip: accuracy = 79%, κ = 0.58; wrist: accuracy = 75% [P = 0.18]; κ = 0.52 [P = 0.18]). Point estimates for mean absolute error were generally lowest for the group training, free-living protocol, and hip placement., Conclusions: Contrary to expectations, individually calibrated machine learning models yielded poorer accuracy than a traditional group approach. In addition, models should be developed in free-living settings when possible to optimize predictive accuracy., (Copyright © 2021 by the American College of Sports Medicine.)

Background: The paucity of easy-to-use, reliable objective neuromuscular monitors is an obstacle to universal adoption of routine neuromuscular monitoring. Electromyography (EMG) has been proposed as the optimal neuromuscular monitoring technology since it addresses several acceleromyography limitations. This clinical study compared simultaneous neuromuscular responses recorded from induction of neuromuscular block until recovery using the acceleromyography-based TOF-Watch SX and EMG-based TetraGraph., Methods: Fifty consenting patients participated. The acceleromyography and EMG devices analyzed simultaneous contractions (acceleromyography) and muscle action potentials (EMG) from the adductor pollicis muscle by synchronization via fiber optic cable link. Bland-Altman analysis described the agreement between devices during distinct phases of neuromuscular block. The primary endpoint was agreement of acceleromyography- and EMG-derived normalized train-of-four ratios greater than or equal to 80%. Secondary endpoints were agreement in the recovery train-of-four ratio range less than 80% and agreement of baseline train-of-four ratios between the devices., Results: Acceleromyography showed normalized train-of-four ratio greater than or equal to 80% earlier than EMG. When acceleromyography showed train-of-four ratio greater than or equal to 80% (n = 2,929), the bias was 1.3 toward acceleromyography (limits of agreement, -14.0 to 16.6). When EMG showed train-of-four ratio greater than or equal to 80% (n = 2,284), the bias was -0.5 toward EMG (-14.7 to 13.6). In the acceleromyography range train-of-four ratio less than 80% (n = 2,802), the bias was 2.1 (-16.1 to 20.2), and in the EMG range train-of-four ratio less than 80% (n = 3,447), it was 2.6 (-14.4 to 19.6). Baseline train-of-four ratios were higher and more variable with acceleromyography than with EMG., Conclusions: Bias was lower than in previous studies. Limits of agreement were wider than expected because acceleromyography readings varied more than EMG both at baseline and during recovery. The EMG-based monitor had higher precision and greater repeatability than acceleromyography. This difference between monitors was even greater when EMG data were compared to raw (nonnormalized) acceleromyography measurements. The EMG monitor is a better indicator of adequate recovery from neuromuscular block and readiness for safe tracheal extubation than the acceleromyography monitor., (Copyright © 2021, the American Society of Anesthesiologists. All Rights Reserved.)

Background: Validation of self-reported tools, such as physical activity (PA) questionnaires, is crucial. The aim of this study was to determine test-retest reliability, internal consistency, and the concurrent, construct, and predictive validity of the short semi-quantitative Physical Activity Unit 7 item Screener (PAU-7S), using accelerometry as the reference measurement. The effect of linear calibration on PAU-7S validity was tested., Methods: A randomized sample of 321 healthy children aged 8-16 years (149 boys, 172 girls) from the nationwide representative PASOS study completed the PAU-7S before and after wearing an accelerometer for at least 7 consecutive days. Weight, height, and waist circumference were measured. Cronbach alpha was calculated for internal consistency. Test-retest reliability was determined by intra-class correlation (ICC). Concurrent validity was assessed by ICC and Spearman correlation coefficient between moderate to vigorous PA (MVPA) derived by the PAU-7S and by accelerometer. Concordance between both methods was analyzed by absolute agreement, weighted kappa, and Bland-Altman statistics. Multiple linear regression models were fitted for construct validity and predictive validity was determined by leave-one-out cross-validation., Results: The PAU-7S overestimated MVPA by 18%, compared to accelerometers (106.5 ± 77.0 vs 95.2 ± 33.2 min/day, respectively). A Cronbach alpha of 0.76 showed an acceptable internal consistency of the PAU-7S. Test-retest reliability was good (ICC 0.71 p < 0.001). Spearman correlation and ICC coefficients of MVPA derived by the PAU-7S and accelerometers increased from 0.31 to 0.62 and 0.20 to 0.62, respectively, after calibration of the PAU-7S. Between-methods concordance improved from a weighted kappa of 0.24 to 0.50 after calibration. A slight reduction in ICC, from 0.62 to 0.60, yielded good predictive validity. Multiple linear regression models showed an inverse association of MVPA with standardized body mass index (β - 0.162; p < 0.077) and waist to height ratio (β - 0.010; p < 0.014). All validity dimensions were somewhat stronger in boys compared to girls., Conclusion: The PAU-7S shows a good test-retest reliability and acceptable internal consistency. All dimensions of validity increased from poor/fair to moderate/good after calibration. The PAU-7S is a valid instrument for measuring MVPA in children and adolescents., Trial Registration: Trial registration number ISRCTN34251612 ., (© 2021. The Author(s).)

The aim of this study was to determine the inter-unit reliability of IMU Step biomechanical load monitoring metrics using IMeasureU Blue Trident inertial measurement units in tasks common to running-based team sports. Knowledge of variability between units is required before researchers and practitioners can make informed decisions on "true" differences between limbs. Sixteen male college soccer players performed five running-based tasks, generating 224 trials and 17,012 steps. Data were analysed for each task and for the whole session, investigating six IMU Step metrics: step count; impact load; bone stimulus; and low, medium and high intensity steps. Inter-unit reliability was excellent (ICC ≥ 0.90) for 21 out of 26 metrics, and good (0.83 ≤ ICC ≤ 0.86) for all other metrics except for Yo-Yo impact load (ICC = 0.79) which was acceptable . These findings confirm the inter-unit reliability of IMU Step metrics using IMeasureU Blue Trident inertial measurement units for running-based team sports. Now that inter-unit variability has been quantified, researchers and practitioners can use this information when interpreting inter-limb differences for monitoring external biomechanical training load.

Background: Reliable estimates of habitual sleep, physical activity, and sedentary time are essential to investigate the associations between these behaviours and health outcomes. While the number of days needed and hours/day for estimates of physical activity and sedentary time are generally known, the criteria for sleep estimates are more uncertain. The objective of this study was to identify the number of nights needed to obtain reliable estimates of habitual sleep behaviour using the GENEActiv wrist worn accelerometer. The number of days to obtain reliable estimate of physical activity was also examined., Methods: Data was used from a two-year longitudinal study. Children wore an accelerometer for up to 8 days 24 h/day across three timepoints. The sample included 2,745 children (51 % girls) between the ages of 7-12-years-old (mean = 9.8 years, SD = 1.1 year) with valid accelerometer data from any timepoint. Reliability estimates were calculated for sleep duration, sleep efficiency, sleep onset, wake time, time in bed, light physical activity, moderate physical activity, moderate-to-vigorous physical activity, vigorous physical activity, and sedentary time., Results: Intraclass correlations and the Spearman Brown prophecy formula were used to determine the nights and days needed for reliable estimates. We found that between 3 and 5 nights were needed to achieve acceptable reliability (ICC = 0.7) in sleep outcomes, while physical activity and sedentary time outcomes required between 3 and 4 days., Conclusions: To obtain reliable estimates, researchers should consider these minimum criteria when designing their studies and prepare strategies to ensure sufficient wear time compliance.

Background: Management of motor symptoms in Parkinson's Disease(PD) relies on subjective information provided by patients, the quality of which can be affected by many factors., Rationale: Objective data collected during daily life could complement this information and improve management of motor symptoms., Objectives: To assess the usefulness of the Personal KinetiGraph (PKG) in characterizing the intensity and timing of motor symptoms in PD patients., Methods: Retrospective study of all PD patients followed at a tertiary academic movement disorders center assessed by PKG between December 1, 2016 and October 30, 2018. PKG was worn for 7 days prior to the clinical visit. We compared the information obtained from the interview and the clinical visit, and assessed the impact of the PKG on treatment decision making., Results: 170 PKG results were reviewed. PKG complemented patient input in 82.9%(141/170) and led to medication changes in 71%(100/141) of the complemented inputs. PKG contributed the least to correcting or complementing patients' input when patients self-reported as undertreated (22%) and the most when patient were unable to answer all questions regarding motor response to individual doses (100%) (Fisher, p < 0.0001). The majority of patient undergoing 3 or 4 PKG encounters did not reach a controlled state as defined by PKG until the 3rd or 4th encounter, suggesting that repeated use of the PKG might be needed to help optimize motor control as therapy changes done after one encounter might not be enough., Conclusions: PKG might be useful in supplementing patient-provided information for accurate assessment and treatment plan., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Objectives: The aim of this study was to evaluate the accuracy of three consumer-based activity monitors, Fitbit Charge 2, Fitbit Alta, and the Apple Watch 2, all worn on the wrist, in estimating step counts, moderate-to-vigorous minutes (MVPA), and heart rate in a free-living setting., Methods: Forty-eight participants (31 females, 17 males; ages 18-59) were asked to wear the three consumer-based monitors mentioned above on the wrist, concurrently with a Yamax pedometer as the criterion for step count, an ActiGraph GT3X+ (ActiGraph) for MVPA, and a Polar H7 chest strap for heart rate. Participants wore the monitors for a 24-hour free-living condition without changing their usual active routine. MVPA was calculated in bouts of ≥10 minutes. Pearson correlation, mean absolute percent error (MAPE), and equivalence testing were used to evaluate the measurement agreement., Results: The average step counts recorded for each device were as follows: 11,734 (Charge2), 11,922 (Alta), 11,550 (Apple2), and 10,906 (Yamax). The correlations in steps for the above monitors ranged from 0.84 to 0.95 and MAPE ranged from 17.1% to 35.5%. For MVPA minutes, the average were 76.3 (Charge2), 63.3 (Alta), 49.5 (Apple2), and 47.8 (ActiGraph) minutes accumulated in bouts of 10 or greater minutes. The correlation from MVPA estimation for above monitors were 0.77, 0.91, and 0.66. MAPE from MVPA estimation ranged from 44.7% to 55.4% compared to ActiGraph. For heart rate, correlation for Charge2 and Apple2 was higher for sedentary behavior and lower for MVPA. The MAPE ranged from 4% to 16%., Conclusion: All three consumer monitors estimated step counts fairly accurately, and both the Charge2 and Apple2 reported reasonable heart rate estimation. However, all monitors substantially underestimated MVPA in free-living settings., Competing Interests: The authors have declared that no competing interests exist.

This paper revisits the stationary attitude initialization problem, i.e., the stationary alignment, of Attitude and Heading Reference Systems (AHRSs). A detailed and comprehensive error analysis is proposed for four of the most representative accelerometer- and magnetometer-based stationary attitude determination methods, namely, the Three-Axis Attitude Determination (TRIAD), the QUaternion ESTimator (QUEST), the Factored Quaternion Algorithm (FQA), and the Arc-TANgent (ATAN). For the purpose of the error analysis, constant biases in the accelerometer and magnetometer measurements are considered (encompassing, hence, the effect of hard-iron magnetism), in addition to systematic errors in the local gravity and Earth magnetic field models (flux density magnitude, declination angle, and inclination angle). The contributions of this paper are novel closed-form formulae for the residual errors (normality, orthogonality, and alignment errors) developed in the computed Direction Cosine Matrices (DCM). As a consequence, analytical insight is provided into the problem, allowing us to properly compare the performance of the investigated alignment formulations (in terms of ultimate accuracy), as well as to remove some misleading conclusions reported in previous works. The adequacy of the proposed error analysis is validated through simulation and experimental results.

Background: Wrist-worn accelerometry provides objective monitoring of upper-extremity functional use, such as reaching tasks, but also detects nonfunctional movements, leading to ambiguity in monitoring results., Objective: Compare machine learning algorithms with standard methods (counts ratio) to improve accuracy in detecting functional activity., Methods: Healthy controls and individuals with stroke performed unstructured tasks in a simulated community environment (Test duration = 26 ± 8 minutes) while accelerometry and video were synchronously recorded. Human annotators scored each frame of the video as being functional or nonfunctional activity, providing ground truth. Several machine learning algorithms were developed to separate functional from nonfunctional activity in the accelerometer data. We also calculated the counts ratio, which uses a thresholding scheme to calculate the duration of activity in the paretic limb normalized by the less-affected limb., Results: The counts ratio was not significantly correlated with ground truth and had large errors ( r = 0.48; P = .16; average error = 52.7%) because of high levels of nonfunctional movement in the paretic limb. Counts did not increase with increased functional movement. The best-performing intrasubject machine learning algorithm had an accuracy of 92.6% in the paretic limb of stroke patients, and the correlation with ground truth was r = 0.99 ( P < .001; average error = 3.9%). The best intersubject model had an accuracy of 74.2% and a correlation of r =0.81 ( P = .005; average error = 5.2%) with ground truth., Conclusions: In our sample, the counts ratio did not accurately reflect functional activity. Machine learning algorithms were more accurate, and future work should focus on the development of a clinical tool.

Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous analysis of behavioral ethogram descriptions and neural activity measurements. Wireless electroencephalography (EEG) and wireless inertial measurement units (IMU) allow the realization of experimental data recording with improved ecological validity where the subjects can be carrying out natural activities while data recording is minimally invasive. Specifically, we aim to show that EEG and IMU data fusion allows improved human activity recognition in a natural setting. We have defined an experimental protocol composed of natural sitting, standing and walking activities, and we have recruited subjects in two sites: in-house ([Formula: see text]) and out-house ([Formula: see text]) populations with different demographics. Experimental protocol data capture was carried out with validated commercial systems. Classifier model training and validation were carried out with scikit-learn open source machine learning python package. EEG features consist of the amplitude of the standard EEG frequency bands. Inertial features were the instantaneous position of the body tracked points after a moving average smoothing to remove noise. We carry out three validation processes: a 10-fold cross-validation process per experimental protocol repetition, (b) the inference of the ethograms, and (c) the transfer learning from each experimental protocol repetition to the remaining repetitions. The in-house accuracy results were lower and much more variable than the out-house sessions results. In general, random forest was the best performing classifier model. Best cross-validation results, ethogram accuracy, and transfer learning were achieved from the fusion of EEG and IMUs data. Transfer learning behaved poorly compared to classification on the same protocol repetition, but it has accuracy still greater than 0.75 on average for the out-house data sessions. Transfer leaning accuracy among repetitions of the same subject was above 0.88 on average. Ethogram prediction accuracy was above 0.96 on average. Therefore, we conclude that wireless EEG and IMUs allow for the definition of natural experimental designs with high ecological validity toward human computational neuroethology research. The fusion of both EEG and IMUs signals improves activity and ethogram recognition.

Background: The Occupational Sitting and Physical Activity Questionnaire (OSPAQ) was developed as an easy-to-use instrument for self-reported assessment of percentage sitting, standing, walking, and performing heavy labour in a workplace setting. This study aimed to evaluate the concurrent validity of all dimensions of the OSPAQ compared to accelerometer-assessed measures of occupational physical activities in a mixed sample of sedentary and physically active professions., Methods: Data from the Flemish Employees' Physical Activity (FEPA) study were used, including employees from the service and production sector. All participants filled in a questionnaire, underwent clinical measurements, and wore two Axivity AX3 accelerometers for at least 2 consecutive working days. Intraclass (ICC) and Spearman rho correlations (r) were analyzed to assess concurrent validity., Results: The sample included 401 workers (16% sedentary profession) with a mean age of 39.2 (± 11) years. Concurrent validity was good and moderate for assessing percentage of sitting (ICC = 0.84; r = 0.53), and standing (ICC = 0.64; r = 0.53), respectively. The concurrent validity for walking was weak to moderate (ICC = 0.50; r = 0.49), and weak for performing heavy labour (ICC = 0.28; r = 0.35). Stronger validity scores were found in sedentary professions for occupational sitting and standing. In physically active professions, an underestimation of self-reported sitting and standing was found, and an overestimation of self-reported walking and heavy labour. No significant self-reported over- or underestimation was found for sitting and heavy labour in sedentary professions, but an underestimation of self-reported standing and an overestimation of self-reported walking was observed., Conclusions: The OSPAQ has acceptable measurement properties for assessing occupational sitting and standing. Accelerometer-assessed measures of occupational walking and heavy labour are recommended, since a poor concurrent validity was found for both.

Background: Postural stability during sit-to-stand (STS) movements depends on visual and somatosensory information. A modification of the 30-sec chair-stand test (30s-CST) with visual and somatosensory alteration (m30CST) may improve the ability to identify fall status., Objective: This study aimed to investigate the accuracy of the m30CST in predicting falls in older adults., Methods: This prospective cohort study recruited a convenience sample of 73 individuals from Kao Kilo community, Chonburi, Thailand. Eligibility criteria were age≥65 years and independent STS ability. All participants performed the 30s-CST and m30CSTs (i.e., eyes closed and a foam surface and eyes closed and a foam surface). The fall incidence during a 6-month follow-up was recorded. The area under the receiver operating characteristic curve (AUC) was calculated. Twenty participants were designated for reliability and validity analyses using the 30s-CST and the Fullerton Advanced Balance (FAB) Scale, estimating intraclass correlation coefficients (ICCs)., Results: We included 37 fallers and 36 non-fallers. All tests showed excellent accuracy in classifying fallers (AUC=0.77-0.91). The m30CST with eyes closed and a foam surface had the highest AUC (0.91), with a cutoff score of 9.25 repetitions, sensitivity 92%, and specificity 81%. The m30CSTs presented excellent inter-rater reliability (ICC=0.93-0.96) and test-retest reliability (ICC=0.90-0.96), good to excellent correlation with the 30s-CST (r=0.90-0.98), and moderate to good correlation with the FAB Scale (r=0.64-0.73)., Conclusions: The m30CST could be used as an alternative evaluation for predicting the risk of falls in community-dwelling older adults, with excellent accuracy., (Copyright © 2019 Elsevier Masson SAS. All rights reserved.)

Reliability of accelerometer-determined physical activity (PA), and thus the required length of a monitoring period, appears to depend on the analytic approach used for its calculation. We compared reliability of objectively measured PA using different resolution of data in a sample of 221 Norwegian 2-6-year-old children providing 2-3 valid 14-day periods of accelerometer monitoring (ActiGraph GT3X+) during September-October, January-February, and May-June 2015-2016. Reliability (intra-class correlation [ICC]) was measured for 1-14 days of monitoring across the measurement periods using linear mixed effect modelling. These results were compared to reliability estimated using different resolution of data using the Spearman-Brown formula. The measured reliability improved only marginally with increased monitoring length and levelled off after 5-6 days. Estimated reliability differed substantially when derived from different resolution of data: 3.9-5.4, 6.7-9.2, 13.4-26.7 and 26.3-87.7 days of monitoring was required to achieve an ICC = 0.80 using an hour-by-hour, a day-by-day, a week-by-week and a period-by-period approach, respectively. Reliability could not be correctly estimated from any single resolution of data. We conclude that reconsideration is needed with regard to how reproducibility of objectively measured PA is analysed and interpreted.

Objective: To determine the accuracy of wrist and arm-worn activity monitors' estimates of energy expenditure (EE)., Data Sources: SportDISCUS (EBSCOHost), PubMed, MEDLINE (Ovid), PsycINFO (EBSCOHost), Embase (Ovid) and CINAHL (EBSCOHost)., Design: A random effects meta-analysis was performed to evaluate the difference in EE estimates between activity monitors and criterion measurements. Moderator analyses were conducted to determine the benefit of additional sensors and to compare the accuracy of devices used for research purposes with commercially available devices., Eligibility Criteria: We included studies validating EE estimates from wrist-worn or arm-worn activity monitors against criterion measures (indirect calorimetry, room calorimeters and doubly labelled water) in healthy adult populations., Results: 60 studies (104 effect sizes) were included in the meta-analysis. Devices showed variable accuracy depending on activity type. Large and significant heterogeneity was observed for many devices (I 2 >75%). Combining heart rate or heat sensing technology with accelerometry decreased the error in most activity types. Research-grade devices were statistically more accurate for comparisons of total EE but less accurate than commercial devices during ambulatory activity and sedentary tasks., Conclusions: EE estimates from wrist and arm-worn devices differ in accuracy depending on activity type. Addition of physiological sensors improves estimates of EE, and research-grade devices are superior for total EE. These data highlight the need to improve estimates of EE from wearable devices, and one way this can be achieved is with the addition of heart rate to accelerometry., Prosperoregistration Number: CRD42018085016., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Self-reported physical activity is inaccurate, yet few investigators attempt to adjust for measurement error when estimating risks for health outcomes. We estimated what the association between self-reported physical activity and colorectal cancer risk would be if physical activity had been assessed using accelerometry instead., Methods: We conducted a validation study in which 235 Australian adults completed a telephone-administered International Physical Activity Questionnaire (IPAQ), and wore an accelerometer (Actigraph GT3X+) for 7 days. Using accelerometer-assessed physical activity as the criterion measure, we calculated validity coefficients and attenuation factors using a structural equation model adjusted for age, sex, education and body mass index. We then used a regression calibration approach to apply the attenuation factors to data from the Melbourne Collaborative Cohort Study (MCCS) to compute bias-adjusted hazard ratios (HR) and 95% confidence intervals (CI)., Results: Average daily minutes of physical activity from the short form of the International Physical Activity Questionnaire (IPAQ-short) were substantially higher than accelerometer-measured duration (55 versus 32 min). The validity coefficient (0.32; 95% CI: 0.20, 0.43) and attenuation factor (0.20; 95% CI: 0.12, 0.28) were low. The HRs for colorectal cancer risk for high (75th percentile; 411 min/week) versus low (25th percentile; 62 min/week) levels of self-reported physical activity were 0.95 (95% CI: 0.87, 1.05) before and 0.78 (95% CI: 0.47, 1.28) after bias adjustment., Conclusions: Over-estimation of physical activity by the IPAQ-short substantially attenuates the association between physical activity and colorectal cancer risk, suggesting that the protective effect of physical activity has been previously underestimated., (© The Author(s) 2019; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Gestational diabetes mellitus (GDM) is a common complication during pregnancy. Evaluation of the quantitative physical activity in diabetic pregnant women is critical. The aim of this study was to test the reliability and validity of the Pregnancy Physical Activity Questionnaire (PPAQ) in Turkish patients with GDM. A total of 120 pregnant women between the ages of 18 and 44 years with GDM were included. The reliability of the questionnaire was measured by internal consistency and analysis of 2-week test-retest reliability. Of the patients, 74 completed the test-retest procedure. Concurrent validity was examined by comparing the PPAQ with the Short Form of the International Physical Activity Questionnaire (IPAQ) in 36 patients. Test-retest intraclass correlation coefficient scores varied between 0.72 and 0.95. The Spearman rank correlation analysis showed that the PPAQ total activity values were statistically significantly correlated with the total values of IPAQ-Short Form ( r  = 0.410 and p  = .030). In conclusion, the Turkish version of the PPAQ is a valid and reliable tool for the measurement of the physical activity level of pregnant women with GDM.Impact statement What is already know n on this subject? The pregnancy physical activity questionnaire (PPAQ) developed in 2004 by Chasan-Taber et al.; is a simple and short questionnaire measuring the frequency, duration, and intensity of physical activity in pregnant women. To date, this questionnaire has been translated into many languages and has been used in a number of studies. What do the results of this study add? The aim of this study was to test the reliability and validity of the PPAQ in Turkish pregnant women with GDM. On the basis of our study results, we suggest that the Turkish version of the PPAQ is a valid and reliable tool for the measurement of the physical activity level of pregnant women with GDM. What are the implications of these findings for clinical practice and/or further research? Evaluation of the quantitative physical activity in diabetic pregnant women may contribute to gain a better understanding of the role of physical activity during treatment and may be useful to compare the results of different studies carried out in different places more effectively.

Introduction: Evaluation of total energy expenditure (TEE) and physical activity level (PAL) is important for treatment of patients with type 2 diabetes mellitus (T2DM). However, the validity of accelerometers (ACC) and physical activity questionnaires (PAQ) for estimating TEE and PAL remains unknown in elderly populations with T2DM. We evaluated the accuracy of TEE and PAL results estimated by an ACC (TEEACC and PALACC) and a PAQ (TEEPAQ and PALPAQ) in elderly patients with T2DM., Methods: Fifty-one elderly patients with T2DM (aged 61-79 years) participated in this study. TEEACC was calculated with PALACC using a triaxial ACC (Active style Pro HJA-750c) over 2 weeks and predicted basal metabolic rate (BMR) by Ganpule's equation. TEEPAQ was estimated using predicted BMR and the PALPAQ from the -Japan Public Health Center Study-Long questionnaire. We compared the results to TEEDLW measured with the doubly labeled water (DLW) method and PALDLW calculated with BMR using indirect calorimetry., Results: TEEDLW was 2,165 ± 365 kcal/day, and TEEACC was 2,014 ± 339 kcal/day; TEEACC was strongly correlated with TEEDLW (r = 0.87, p < 0.01) but significantly underestimated (-150 ± 183 kcal/day, p < 0.05). There was no significant difference in TEEPAQ and TEEDLW (-49 ± 284 kcal/day), while the range of difference seemed to be larger than TEEACC. PALDLW, PALACC, and PALPAQ were calculated to be 1.71 ± 0.17, 1.69 ± 0.16, and 1.78 ± 0.24, respectively. -PALACC was strongly correlated with PALDLW (r = 0.71, p < 0.01), and there was no significant difference between the 2 values. PALPAQ was moderately correlated with PALDLW (r = 0.43, p < 0.01) but significantly overestimated. Predicted BMR was significantly lower than the BMR -measured by indirect calorimetry (1,193 ± 186 vs. 1,262 ± 155 kcal/day, p < 0.01)., Conclusions: The present ACC and questionnaire showed acceptable correlation of TEE and PAL compared with DLW method in elderly patients with T2DM. Systematic errors in estimating TEE may be improved by the better equation for predicting BMR., (© 2020 S. Karger AG, Basel.)

Background: Physical activity (PA) is exceptionally low amongst the haemodialysis (HD) population, and physical inactivity is a powerful predictor of mortality, making it a prime focus for intervention. Objective measurement of PA using accelerometers is increasing, but standard reporting guidelines essential to effectively evaluate, compare and synthesise the effects of PA interventions are lacking. This study aims to (i) determine the measurement and processing guidance required to ensure representative PA data amongst a diverse HD population, and; (ii) to assess adherence to PA monitor wear amongst HD patients., Methods: Clinically stable HD patients from the UK and China wore a SenseWear Armband accelerometer for 7 days. Step count between days (HD, Weekday, Weekend) were compared using repeated measures ANCOVA. Intraclass correlation coefficients (ICCs) determined reliability (≥0.80 acceptable). Spearman-Brown prophecy formula, in conjunction with a priori ≥  80% sample size retention, identified the minimum number of days required for representative PA data., Results: Seventy-seven patients (64% men, mean ± SD age 56 ± 14 years, median (interquartile range) time on HD 40 (19-72) months, 40% Chinese, 60% British) participated. Participants took fewer steps on HD days compared with non-HD weekdays and weekend days (3402 [95% CI 2665-4140], 4914 [95% CI 3940-5887], 4633 [95% CI 3558-5707] steps/day, respectively, p < 0.001). PA on HD days were less variable than non-HD days, (ICC 0.723-0.839 versus 0.559-0.611) with ≥ 1 HD day and ≥  3 non-HD days required to provide representative data. Using these criteria, the most stringent wear-time retaining ≥ 80% of the sample was ≥7 h., Conclusions: At group level, a wear-time of ≥7 h on ≥1HD day and ≥ 3 non-HD days is required to provide reliable PA data whilst retaining an acceptable sample size. PA is low across both HD and non- HD days and future research should focus on interventions designed to increase physical activity in both the intra and interdialytic period.

Background: Functional capacity assessment is a core component of current perioperative cardiovascular evaluation and management guidelines for noncardiac surgery. The authors investigated the ability of standardized physical function questions to predict whether participants engaged in moderate physical activity as measured by hip accelerometers., Methods: Participant responses to physical functioning questions and whether they engaged in moderate physical activity were extracted from the National Health and Nutrition Examination Survey (2003 to 2004 and 2005 to 2006). Physical activity intensity was measured using hip accelerometers. Adult participants with at least one Revised Cardiac Risk Index condition were included in the analysis. Standardized physical function questions were evaluated using a classification and regression tree analysis. Training and test datasets were randomly generated to create and test the analysis., Results: Five hundred and twenty-two participants were asked the physical functioning questions and 378 of 522 (72.4%) had a bout of moderate-vigorous activity. Classification and regression tree analysis identified a "no difficulty" response to walking up 10 stairs and the ability to walk two to three blocks as the most sensitive questions to predict the presence of a 2-min bout of moderate activity. Participants with positive responses to both questions had a positive likelihood ratio of 3.7 and a posttest probability greater than 90% of a 2-min bout of moderate-vigorous activity. The sensitivity and specificity of positive responses to physical functioning questions in the pruned tree were 0.97 (95% CI, 0.94 to 0.98) and 0.16 (95% CI, 0.10 to 0.23) for training data, and 0.88 (95% CI, 0.75 to 0.96) and 0.10 (95% CI, 0.00 to 0.45) for the test data. Participants with at least one 2-min bout of moderate activity had a greater percentage of overall daily active time (35.4 ± 0.5 vs. 26.7 ± 1.2; P = 0.001) than those without., Conclusions: Standardized physical function questions are highly sensitive but poorly specific to identify patients who achieve moderate physical activity. Additional strategies to evaluate functional capacity should be considered.

The development of accelerometers has revolutionized measurement of physical activity, and they are used to a large extent in research and have started to be implemented into clinical settings. However, achievement of reliable outcomes requires good methodological knowledge and skills by the user. Otherwise, significant measurement errors may occur, interfering with assessment of the physical activity level in the population, group differences, associations with health parameters or effect of treatments. This paper by the Swedish Network for Objective Measurement of Movement (NORM) provides an overview of physical activity measurement including sections of data collection, processing of raw data into useful metrics and statistical analysis. It targets users of accelerometer in research, health care and national surveys.

Background: Wrist-worn activity trackers are popular, and an increasing number of these devices are equipped with heart rate (HR) measurement capabilities. However, the validity of HR data obtained from such trackers has not been thoroughly assessed outside the laboratory setting., Objective: This study aimed to investigate the validity of HR measures of a high-cost consumer-based tracker (Polar A370) and a low-cost tracker (Tempo HR) in the laboratory and free-living settings., Methods: Participants underwent a laboratory-based cycling protocol while wearing the two trackers and the chest-strapped Polar H10, which acted as criterion. Participants also wore the devices throughout the waking hours of the following day during which they were required to conduct at least one 10-min bout of moderate-to-vigorous physical activity (MVPA) to ensure variability in the HR signal. We extracted 10-second values from all devices and time-matched HR data from the trackers with those from the Polar H10. We calculated intraclass correlation coefficients (ICCs), mean absolute errors, and mean absolute percentage errors (MAPEs) between the criterion and the trackers. We constructed decile plots that compared HR data from Tempo HR and Polar A370 with criterion measures across intensity deciles. We investigated how many HR data points within the MVPA zone (≥64% of maximum HR) were detected by the trackers., Results: Of the 57 people screened, 55 joined the study (mean age 30.5 [SD 9.8] years). Tempo HR showed moderate agreement and large errors (laboratory: ICC 0.51 and MAPE 13.00%; free-living: ICC 0.71 and MAPE 10.20%). Polar A370 showed moderate-to-strong agreement and small errors (laboratory: ICC 0.73 and MAPE 6.40%; free-living: ICC 0.83 and MAPE 7.10%). Decile plots indicated increasing differences between Tempo HR and the criterion as HRs increased. Such trend was less pronounced when considering the Polar A370 HR data. Tempo HR identified 62.13% (1872/3013) and 54.27% (5717/10,535) of all MVPA time points in the laboratory phase and free-living phase, respectively. Polar A370 detected 81.09% (2273/2803) and 83.55% (9323/11,158) of all MVPA time points in the laboratory phase and free-living phase, respectively., Conclusions: HR data from the examined wrist-worn trackers were reasonably accurate in both the settings, with the Polar A370 showing stronger agreement with the Polar H10 and smaller errors. Inaccuracies increased with increasing HRs; this was pronounced for Tempo HR., (©Andre Matthias Matthias Müller, Nan Xin Wang, Jiali Yao, Chuen Seng Tan, Ivan Cherh Chiet Low, Nicole Lim, Jeremy Tan, Agnes Tan, Falk Müller-Riemenschneider. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 02.10.2019.)

Self-reported youth physical activity (PA) is typically overestimated. We aimed to calibrate and validate a self-report tool among English youth. Four-hundred-and-two participants (aged 9-16 years; 212 boys) wore SenseWear Armband Mini devices (SWA) for eight days and completed the self-report Youth Activity Profile (YAP) on the eighth day. Calibration algorithms for temporally matched segments were generated from the YAP data using quantile regression. The algorithms were applied in an independent cross-validation sample, and student- and school-level agreement were assessed. The utility of the YAP algorithms to assess compliance to PA guidelines was also examined. The school-level bias for the YAP estimates of in-school, out-of-school, and weekend moderate-to-vigorous PA (MVPA) were 17.2 (34.4), 31.6 (14.0), and -4.9 (3.6) min·week -1 , respectively. Out-of-school sedentary behaviour (SB) was over-predicted by 109.2 (11.8) min·week -1 . Predicted YAP values were within 15%-20% equivalence of the SWA estimates. The classification accuracy of the YAP MVPA estimates for compliance to 60 min·day -1 and 30 min·school-day -1 MVPA recommendations were 91%/37% and 89%/57% sensitivity/specificity, respectively. The YAP generated robust school-level estimates of MVPA and SB and has potential for surveillance to monitor compliance with PA guidelines. The accuracy of the YAP may be further improved through research with more representative UK samples to enhance the calibration process and to refine the resultant algorithms.

Background: Wearable inertial sensors have been shown to provide valid mean gait characteristics assessments, however, assessment of variability is less convincingly established., Research Question: What level of concurrent validity, and session-to-session reliability does an ankle-worn inertial measurement unit (IMU)-based gait assessment with a novel angular velocity-based gait event detection algorithm have among older adults?, Methods: Twenty seven (women N = 17) participants volunteered (age 74.4 (SD 4.3) years, body mass 74.5 (12.0) kg, height 165.9 (9.9) cm). Right leg stance, swing, and stride duration and stride length, and stride velocity were concurrently assessed with motion capture and with an IMU from a 3 min self-paced walk up and back a 14 m track repeated twice a week apart. Gait variability was assessed as the SD of all of the registered strides., Results: Significant difference was observed between methods for many of the mean stride characteristics and stride variability (all p < 0.05), fair to excellent agreement was observed for mean values of all of the five stride characteristics evaluated (intra-class correlation coefficient [ICC] from 0.43 to 1.00). However, poor agreement was observed for the SD of all of the evaluated stride characteristics (ICC from -0.25 to 0.00). Both methods indicated excellent session to session reliability for all of the five stride characteristics evaluated (ICC from 0.84 to 0.98, CV% RMS from 1.6% to 3.6%), whereas the variability characteristics exhibited poor to good reliability (ICC from 0.0 to 0.69, CV% RMS from 18.0% to 34.4%)., Significance: Excellent concurrent validity and reliability was observed for mean spatiotemporal stride characteristics, however, gait variability exhibited poor concurrent validity and reliability. Although IMUs and the presented algorithm could be used to assess mean spatiotemporal stride characteristics among older individuals, either a more reliable gait event detection algorithm or alternative analytical approaches should be used for gait variability., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: The extensive heterogeneity between patients with amyotrophic lateral sclerosis (ALS) complicates the quantification of disease progression. In this study, we determine the value of remote, accelerometer-based monitoring of physical activity in patients with ALS., Methods: This longitudinal cohort study was conducted in a home-based setting; all study materials were sent by mail. Patients wore the ActiGraph during waking hours for 7 days every 2-3 months and provided information regarding their daily functioning (ALSFRS-R). We defined four accelerometer-based endpoints that either reflect the average daily activity or quantify the patient's physical capacity., Results: A total of 42 patients participated; the total valid monitoring period was 9288 h with a 93.0% adherence rate. At baseline, patients were active 27.9% (range 11.6-52.4%) of their time; this declined by 0.64% (95% 0.43-0.86, p < 0.001) per month. Accelerometer-based endpoints were strongly associated with the ALSFRS-R (r 0.78, 95% CI 0.63-0.92, p < 0.001), but showed less variability over time than the ALSFRS-R (coefficient of variation 0.64-0.81 vs. 1.06, respectively). Accelerometer-based endpoints could reduce sample size by 30.3% for 12-month trials and 44.6% for 18-month trials; for trials lasting less than 9 months, the ALSFRS-R resulted in smaller sample sizes., Conclusion: Accelerometry is an objective method for quantifying disease progression, which could obtain real-world insights in the patient's physical functioning and may personalize the delivery of care. In addition, remote monitoring provides patients with the opportunity to participate in clinical trials from home, paving the way to a patient-centric clinical trial model.

The present study examined the efficacy of accelerometers for the assessment of free play physical activity (PA) in pre-school-aged children with consideration of epoch length and wear location. Following ethics approval, parental informed consent, and child assent, 66 pre-schoolers aged 3-4 years (30 females and 36 males) wore an accelerometer (ActiGraph GT3X; sampling at 100 Hz) on their non-dominant wrist and their right hip during 1 hour of free play. Concurrently, direct observation, using the OSRAC-P, was used to determine sedentary behavior (SB), light (LPA), or moderate-to-vigorous (MVPA) intensity PA. For the ActiGraph, vertical axis counts and summed vector magnitude (VM) for hip, and VM for wrist, were downloaded using 5-, 10-, 15-, and 30-second epoch lengths. Accelerometer counts were averaged over each 30 seconds to match the observation periods. Receiver operating curve analysis was used to evaluate the ability of the ActiGraph to predict SB, LPA, and MVPA. SB and MVPA obtained from wrist- and hip-worn accelerometers demonstrated fair agreement with direct observation (AUC => 0.7). LPA determined by accelerometer had poor agreement with observed LPA, for both the hip and wrist placement (AUC = 0.53-0.56), with weak levels of specificity (0.34-0.43), although sensitivity was fair (0.74-0.84). This study is the first to examine accelerometer validity, considering wear location and epoch in pre-schoolers during free play, and suggests that the ActiGraph is a fair measure for SB and MVPA in pre-school children. Neither placement performed predominantly better irrespective of epochs or used count data (vertical axis, VM)., (© 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Objectives: Our aim is to demonstrate how a data-driven accelerometer metric, the acceleration above which a person's most active minutes are accumulated, can (a) quantify the prevalence of meeting current physical activity guidelines for global surveillance and (b) moving forward, could inform accelerometer-driven physical activity guidelines. Unlike cut-point methods, the metric is population-independent (e.g. age) and potentially comparable across datasets., Design: Cross-sectional, secondary data analysis., Methods: Analyses were carried out on five datasets using wrist-worn accelerometers: children (N=145), adolescent girls (N=1669), office workers (N=114), pre- (N=1218) and post- (N=1316) menopausal women, and adults with type 2 diabetes (N=475). Open-source software (GGIR) was used to generate the magnitude of acceleration above which a person's most active 60, 30 and 2min are accumulated: M60 ACC ; M30 ACC and M2 ACC , respectively., Results: The proportion of participants with M60 ACC (children) and M30 ACC (adults) values higher than accelerations representative of brisk walking (i.e., moderate-to-vigorous physical activity) ranged from 17 to 68% in children and 15 to 81% in adults, tending to decline with age. The proportion of pre-and post-menopausal women with M2 ACC values meeting thresholds for bone health ranged from 6 to 13%., Conclusions: These metrics can be used for global surveillance of physical activity, including assessing prevalence of meeting current physical activity guidelines. As accelerometer and corresponding health data accumulate it will be possible to interpret the metrics relative to age- and sex- specific norms and derive evidence-based physical activity guidelines directly from accelerometer data for use in future global surveillance. This is where the potential advantages of these metrics lie., (Copyright © 2019 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)

Background: The shoulder is one of the joints with the greatest mobility within the human body and its evaluation is complex. An assessment can be conducted using questionnaires or functional tests, and goniometry can complement the information obtained in this assessment. However, there are now validated devices that can provide more information on the realization of movement, such as inertial sensors. The cost of these devices is usually high and they are not available to all clinicians, but there are also inertial sensors that are implemented in mobile phones which are cheaper and widely available. Results from the inertial sensors integrated into mobile devices can have the same reliability as those from dedicated sensors., Objective: This study aimed to validate the use of the Nexus 4 smartphone as a measuring tool for the mobility of the humerus during shoulder movement compared with a dedicated InertiaCube3 (Intersense) sensor., Methods: A total of 43 subjects, 27 affected by shoulder pathologies and 16 asymptomatic, participated in the study. Shoulder flexion, abduction, and scaption were measured using an InertiaCube3 and a Nexus 4 smartphone, which were attached to the participants to record the results simultaneously. The interclass correlation coefficient (ICC) was calculated based on the 3 movements performed., Results: The smartphone reliably recorded the velocity values and simultaneously recorded them alongside the inertial sensor. The ICCs of the 3 gestures and for each of the axes of movement were analyzed with a 95% CI. In the abduction movement, the devices demonstrated excellent interclass reliability for the abduction humeral movement axis (Cronbach alpha=.98). The axis of abduction of the humeral showed excellent reliability for the movements of flexion (Cronbach alpha=.93) and scaption (Cronbach alpha=.98)., Conclusions: Compared with the InertiaCube3, the Nexus 4 smartphone is a reliable and valid tool for recording the velocity produced in the shoulder., (©Cristina Roldán-Jiménez, Jaime Martin-Martin, Antonio I Cuesta-Vargas. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 06.09.2019.)

Purpose : The purpose of this study was to evaluate the agreement of five commercially available accelerometers in estimating energy expenditure while performing an acute bout of high-intensity functional training (HIFT). Methods : Participants ( n = 47; average age: 28.5 ± 11.6 years) consisted of recreationally active, healthy adults. Each participant completed a session of HIFT: a 15-minute workout consisting of 12 repetitions each of air-squats, sit-ups, push-ups, lunges, pull-ups, steps-ups, and high-knees; performed circuit-style by completing as many rounds as possible. During this session, each participant wore the Cosmed K4b2 portable metabolic analyzer (PMA) and five different accelerometers (ActiGraph GT3X, Nike Fuelband, Fitbit One, Fitbit Charge HR, and Jawbone UP Move). Results : Four of the five activity trackers reported lower ( p < .05) total EE values compared to the PMA during the acute bout of HIFT. The waist-mounted device (ActiGraph, 182.55 ± 37.93 kcal) was not significantly different from, and most closely estimated caloric expenditure compared to the PMA (144.99 ± 37.13 kcal) ( p = .056). A repeated-measures ANOVA showed that all activity trackers were significantly different from the reference measure (PMA) ( p < .05). Systematic relative agreement between the activity trackers was calculated, exhibiting a significant ICC = 0.426 (F [46,230] = 5.446 [ p < .05]). Conclusion : The wrist- and hip-mounted activity trackers did not accurately assess energy expenditure during HIFT exercise. With the exception of the ActiGraph GT3X, the remaining four activity trackers showed inaccurate estimates of the amount of kilocalories expended during the HIFT exercise bout compared to the PMA.

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.

Background: The accelerometer-based navigation (ABN) system is an emerging navigation system for total knee arthroplasty (TKA). This study aimed to determine whether the ABN system could improve the accuracy of mechanical alignment, component positioning, and short-term clinical outcomes for TKA when compared to conventional instruments (CON)., Methods: A total of 204 patients were selected and divided into two groups (CON: 135, ABN: 69) after applying the inclusion and exclusion criteria. Then, 1:1 propensity score matching was performed for age, gender, body mass index, hip-knee-ankle angle (HKA), Knee Society Score (KSS), Western Ontario and McMaster Universities (WOMAC) score, and follow-up times. A total of 82 consecutive patients (82 knees) underwent total knee arthroplasty using ABN (n = 41) or CON (n = 41) were enrolled in this study. The postoperative HKA, frontal femoral component (FFC) angle, frontal tibial component (FTC) angle, lateral femoral flexion (LFF) angle, and lateral tibial component (LTC) angle were compared between the two groups to evaluate mechanical alignment and component positioning. Additional clinical parameters, including haemoglobin reduction, the KSS, and the WOMAC score, were assessed at the final follow-up (the mean follow-up period was 20.9 months in the CON group and 21.2 months in the ABN group)., Results: The ABN group had a significantly improved mean absolute deviation of HKA (P = 0.033), FFC (P = 0.004), FTC (P = 0.017), LFF (P = 0.023), and LTC (P = 0.031) compared to those of the CON group. The numbers of FFCs and LTCs within 3° were significantly different (P = 0.021, P = 0.023, respectively) between the two groups. However, no differences in the numbers of FTCs within 3° (P = 0.166) and LFF within 3° (P = 0.556) were found. The ABN group had a significantly higher KS function score (P = 0.032), and the pain and stiffness scores were significantly different (P = 0.034, P = 0.020, respectively) between the two groups. Moreover, the ABN system could reduce hidden blood loss postoperatively. However, no difference was found in the KS knee score and the total WOMAC score between the two groups., Conclusion: This study demonstrates that ABN system improved TKA mechanical alignment and component positioning and decreased the hidden blood loss postoperatively compared to conventional instruments. However, no significant differences were found in short-term clinical outcomes between ABN and conventional instruments at the final follow-up. However, whether this system contributes to revision rates and long-term clinical outcomes requires further study.

Objective: To examine the validity of the GT3X ® ActiGraph accelerometer and the Google Fit ® smartphone application in estimating stepping activity in people with chronic stroke., Methods: Thirty-seven stroke survivors walked along a straight, 10 metre hallway over 5 min at their fastest speeds, wearing the GT3X ® ActiGraph accelerometer and a smartphone on their paretic lower limb. The criterion-standard measurement made was the actual number of steps, counted by a trained examiner., Results: The mean estimated steps measured by the GT3X ® ActiGraph and Google Fit ® respectively were 276.7 ± 97.6 and 481.0 ± 119.8; that calculated from the examiner's measurements was 472.0 ± 93.9. Statistically significant associations were found between the actual steps and those estimated by the GT3X ® ActiGraph (r = 0.56; p < 0.001) and Google Fit ® (r = 0.89; p < 0.001). The Google Fit ® application demonstrated the highest reliability coefficient (ICC[2,1] = 0.93; p < 0.001; p = 0.37), compared to that of the GT3X ® ActiGraph (ICC[2,1] = 0.32; p < 0.001; p < 0.001)., Conclusions: The GT3X ® ActiGraph underestimated the data and may not be the most appropriate device to estimate the stepping activity of stroke patients. The findings support the validity of a smartphone application in estimating the stepping activity of individuals with stroke, when worn on the paretic side., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Background: Although some gait parameters from inertial sensors have been shown to be associated with important clinical issues, because of controversial results, it remains uncertain which parameters for which axes are clinically valuable. Following the idea that a comprehensive score obtained by summing various gait parameters would sensitively reflect declines in gait performance, we developed a scoring method for community-dwelling older adults, the Comprehensive Gait Assessment using InerTial Sensor score (C-GAITS score). The aim of this study was to examine the internal consistency and the construct validity of this method., Methods: In this cross-sectional study, the gait performance of 378 community-dwelling older people (mean age = 71.7 ± 4.2 years, 210 women) was assessed using inertial sensors attached to the heel and lower trunk. Participants walked along a 15-m walkway, and accelerations, angular velocity, and walking time were measured. From these data, walking speed, mean stride time, coefficients of variation of stride time and swing time, and autocorrelation coefficients and harmonic ratios of acceleration in vertical, mediolateral, and anteroposterior directions at the lower trunk were calculated. Scoring was performed based on quartile by gender (i.e., scored from 0 to 3) for each of the 10 gait parameters. The C-GAITS score was the sum of these scores (range: 0-30). Lower extremity strength, balance function, fall history, and fear of falling were also assessed., Results: An exploratory factor analysis revealed that the C-GAITS score yielded four distinct factors explaining 57.1% of the variance. The Cronbach's alpha coefficient was 0.77. A single linear regression analysis showed a significant relationship between total C-GAITS score and walking speed (adjusted R 2  = 0.28). Results from bivariate comparisons using unpaired t-tests showed that the score was significantly related to age (p = 0.002), lower extremity strength (p = 0.007), balance function (p <  0.001), fall history (p = 0.04), and fear of falling (p <  0.001)., Conclusions: Good internal consistency and appropriate construct validity of the C-GAITS score were confirmed among community-dwelling older adults. The score might be useful in clinical settings because of ease of use and interpretation and capability of capturing functional decline.

Background: Activity trackers are now ubiquitous in certain populations, with potential applications for health promotion and monitoring and chronic disease management. Understanding the accuracy of this technology is critical to the appropriate and productive use of wearables in health research. Although other peer-reviewed validations have examined other features (eg, steps and heart rate), no published studies to date have addressed the accuracy of automatic activity type detection and duration accuracy in wearable trackers., Objective: The aim of this study was to examine the ability of 4 commercially available wearable activity trackers (Fitbits Flex 2, Fitbit Alta HR, Fitbit Charge 2, and Garmin Vívosmart HR), in a controlled setting, to correctly and automatically identify the type and duration of the physical activity being performed., Methods: A total of 8 activity types, including walking and running (on both a treadmill and outdoors), a run embedded in walking bouts, elliptical use, outdoor biking, and pool lap swimming, were tested by 28 to 34 healthy adult participants (69 total participants who participated in some to all activity types). Actual activity type and duration were recorded by study personnel and compared with tracker data using descriptive statistics and mean absolute percent error (MAPE)., Results: The proportion of trials in which the activity type was correctly identified was 93% to 97% (depending on the tracker) for treadmill walking, 93% to 100% for treadmill running, 36% to 62% for treadmill running when preceded and followed by a walk, 97% to 100% for outdoor walking, 100% for outdoor running, 3% to 97% for using an elliptical, 44% to 97% for biking, and 87.5% for swimming. When activities were correctly identified, the MAPE of the detected duration versus the actual activity duration was between 7% and 7.9% for treadmill walking, 8.7% and 144.8% for treadmill running, 23.6% and 28.9% for treadmill running when preceded and followed by a walk, 4.9% and 11.8% for outdoor walking, 5.6% and 9.6% for outdoor running, 9.7% and 13% for using an elliptical, 9.5% and 17.7% for biking, and was 26.9% for swimming., Conclusions: In a controlled setting, wearable activity trackers provide accurate recognition of the type of some common physical activities, especially outdoor walking and running and walking on a treadmill. The accuracy of measurement of activity duration varied considerably by activity type and tracker model and was poor for complex sets of activity, such as a run embedded within 2 walking segments., (©Diana Dorn, Jessica Gorzelitz, Ronald Gangnon, David Bell, Kelli Koltyn, Lisa Cadmus-Bertram. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 23.05.2019.)

Objectives: We aimed to quantify the agreement between self-report, standard cut-point accelerometer, and machine learning accelerometer estimates of physical activity (PA), and exam- ine how agreement changes over time among older adults in an intervention setting. Methods: Data were from a randomized weight loss trial that encouraged increased PA among 333 postmenopausal breast cancer survivors. PA was estimated using accelerometry and a validated questionnaire at baseline and 6-months. Accelerometer data were processed using standard cut-points and a validated machine learning algorithm. Agreement of PA at each time-point and change was assessed using mixed effects regression models and concordance correlation. Results: At baseline, self-report and machine learning provided similar PA estimates (mean dif- ference = 11.5 min/day) unlike self-report and standard cut-points (mean difference = 36.3 min/ day). Cut-point and machine learning methods assessed PA change over time more similarly than other comparisons. Specifically, the mean difference of PA change for the cut-point versus machine learning methods was 5.1 min/day for intervention group and 2.9 in controls, whereas it was ≥ 24.7 min/day for other comparisons. Conclusions: Intervention researchers are facing the issue of self-report measures introducing bias and accelerometer cut-points being insensi- tive. Machine learning approaches may bridge this gap.

Physical activity is an important determinant of health in children and adults. Assessment of physical activity is therefore an important factor in the promotion of health and in several childhood and adulthood pathological situations. Physical activity can be estimated by various methods: pedometry, heart rate monitoring, questionnaires, the doubly labelled water method, and accelerometry. The choice of the type of device depends on the objectives of the clinician or researcher. Accelerometry currently represents the best objective method for measuring physical activity and is widely used in clinical or epidemiological studies. The purpose of this paper is to help practitioners and researchers to make better decisions when using accelerometry as a device for measuring physical activity measurement in order to obtain the most accurate and comparable information., (Copyright © 2018 Elsevier Masson SAS. All rights reserved.)

Low-cost physical activity monitors may provide a more accurate measure of physical activity than subjective methods (e.g., self-report) while being less costly than research-grade accelerometers. The present study assessed the validity of a low-cost monitor (Movband 2) to estimate physical activity behavior/intensity. Participants (N = 23, n = 16 female, BMI = 22.9 ± 4.0 kg/m 2 , age = 21.9 ± 1.6 years) completed four, five-minute treadmill stages (2.0, 3.0, 4.0, 5.0 MPH) while wearing both the Movband and the previously-validated Actigraph monitor. Oxygen consumption (VO 2 ) was recorded during each stage (Laboratory assessment). A subset (n = 15, n = 10 female, BMI = 22.2 ± 3.2 kg/m 2 , age = 21.5 ± 0.8 years) of these participants then wore the two accelerometers for three days (Free-living assessment). During the Laboratory assessment there were strong, significant (r = 0.94, p ≤ 0.001) relationships between Movband and Actigraph counts and VO 2 . During Free-living assessment there was also a strong, significant (r = 0.97, p < 0.001) correlation between Movband and Actigraph counts. The low-cost, Movband accelerometer appears to provide a valid assessment of physical activity behavior/intensity.

Although analysis of heart rate variability is widely used for the assessment of autonomic function, its fundamental framework linking low-frequency and high-frequency components of heart rate variability with sympathetic and parasympathetic autonomic divisions has developed in the 1980s. This simplified framework is no longer able to deal with much evidence about heart rate variability accumulated over the past half-century. This review addresses the pitfalls caused by the old framework and discusses the points that need attention in autonomic assessment by heart rate variability.

Objective measurement of physical activity (PA) and sedentary behavior (SB) associated with metabolic syndrome (MetS) has gained more attention among researchers in recent years. Although almost similar trends of results have been reported worldwide, they were not always statistically significant. This systematic review summarizes the evidence of observational studies reporting the associations between objectively measured PA, SB, and MetS in adults. Registration in PROSPERO was made (CRD42017078929). Literature search was conducted in the PubMed database for observational studies associating objectively measured PA and SB with MetS among adults. Secondary manual search was also conducted to find more related studies. A total of 44 studies were included in this systematic review. The quality score of studies obtained using National Institutes of Health Quality Assessment Tool ranged between 4 and 11. For total PA, most studies showed negative association with MetS. Similar association with MetS was reported in most studies assessing moderate-to-vigorous PA (MVPA) (total, bouts, sporadic), vigorous-intensity PA (VPA) (total, bouts), moderate-intensity PA (MPA) (total, bouts), and light-intensity PA (LIPA) bouts. However, more studies assessing LIPA total and LIPA sporadic found no significant association with MetS, with only a few studies reporting negative association. Meanwhile, all studies assessing step counts reported negative association with MetS. In contrast, MetS was positively associated in all studies investigating physical inactivity. MetS was also positively associated in most studies reporting sedentary (SED) time and bouts, while SED breaks showed no significant association with MetS. Further studies are required to ascertain the interaction and independent contribution of objectively measured PA and SB to the overall risk of MetS.

In elite football, measurement of running kinetics with inertial measurement units (IMUs) may be useful as a component of periodic health examination (PHE). This study determined the reliability of, and agreement between a research orientated IMU and clinically orientated IMU system for initial peak acceleration (IPA) and IPA symmetry index (SI) measurement during running in elite footballers. On consecutive days, 16 participants performed treadmill running at 14kmph and 18kmph. Both IMUs measured IPA and IPA SI concurrently. All measurements had good or excellent within-session reliability (intraclass correlation coefficient (ICC 2,1 ) range = 0.79-0.96, IPA standard error of measurement (SEM) range = 0.19-0.62 g, IPA SI SEM range = 2.50-8.05%). Only the research orientated IMU demonstrated acceptable minimal detectable changes (MDCs) for IPA at 14kmph (range = 7.46-9.80%) and IPA SI at both speeds (range = 6.92-9.21%). Considering both systems, between-session IPA reliability ranged from fair to good (ICC 2,1 range = 0.63-0.87, SEM range = 0.51-1.10 g) and poor to fair for IPA SI (ICC 2,1 range = 0.32-0.65, SEM range = 8.07-11.18%). All MDCs were >10%. For IPA and SI, the 95% levels of agreement indicated poor between system agreement. Therefore, the use of IMUs to evaluate treadmill running kinetics cannot be recommended in this population as a PHE test to identify prognostic factors for injuries or for rehabilitation purposes., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

During single leg squats (SLS), tibial angle (TA) quantification using inertial measurement units (IMU) may offer a practical alternative to frontal plane projection angle (FPPA) measurement using 2-dimensional (2D) video analysis. This study determined: (i) the reliability of IMUs and 2D video analysis for TA measurement, and 2D video analysis for FPPA measurement; (ii) the agreement between IMU TA and both 2D video TA and FPPA measurements during single leg squats in elite footballers. 18 players were tested on consecutive days. Absolute TA (ATA) and relative TA (RTA) were measured with IMUs. ATA and FPPA were measured concurrently using 2D video analysis. Within-session reliability for all measurements varied across days (intraclass correlation coefficient (ICC) range = 0.27-0.83, standard error of measurement (SEM) range = 2.12-6.23°, minimal detectable change (MDC) range = 5.87-17.26°). Between-sessions, ATA reliability was good for both systems (ICCs = 0.70-0.74, SEMs = 1.64-7.53°, MDCs = 4.55-7.01°), while IMU RTA and 2D FPPA reliability ranged from poor to good (ICCs = 0.39-0.72, SEMs = 2.60-5.99°, MDCs = 7.20-16.61°). All limits of agreement exceeded a 5° acceptability threshold. Both systems were reliable for between-session ATA, although agreement was poor. IMU RTA and 2D video FPPA reliability was variable. For SLS assessment, IMU derived TAs are not useful surrogates for 2D video FPPA measures in this population., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Adolescents spend significant amounts of time engaged in various types of sedentary behaviour (SB). This study examined associations between adolescents' objectively measured sedentary time, sitting time, specific self-reported SBs and social connectedness. Adolescents ( n = 429, 15.5 years, 41% male) completed an online survey reporting time in seven SBs (TV/videos/DVDs, computer/video games, internet, homework, reading, car and bus travel; examined individually and summed for screen time and total SB), and social connectedness using the eight-item Social Connectedness Scale. A subsample ( n = 353) also wore an ActiGraph GT3X+ (model GT3X+, Pensacola, FL, USA) accelerometer to measure sedentary time (<100 cpm) and n = 237 wore an activ PAL (PAL Technologies Ltd., Glasgow, Scotland) inclinometer to measure sitting time. Multiple linear mixed models determined associations between each SB variable and social connectedness, adjusting for confounders. Adolescents spent on average 7.8 h/day in self-reported total SB, 4.4 h/day in screen time, 9.1 h/day in ActiGraph-measured sedentary time, and 9.5 h/day in activ PAL-measured sitting time. After adjusting for age, sex and area level socioeconomic status, total SB (-0.24, 95%CI: -0.37, -0.11), screen time (-0.23, 95%CI: -0.41, -0.05) and two individual SBs (computer/video games (-1.07, 95%CI: -1.53, -0.60), homework (-0.61, 95%CI: ⁻1.04, -0.18) were negatively associated with social connectedness. There were no associations with the objective measures. The relationships may be bi-directional; therefore, future research should involve longitudinal designs and explore other potential contributing factors., Competing Interests: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Introduction: Recent developments in smoking cessation support systems and interventions have highlighted the requirement for unobtrusive, passive ways to measure smoking behavior. A number of systems have been developed for this that either use bespoke sensing technology, or expensive combinations of wearables and smartphones. Here, we present StopWatch, a system for passive detection of cigarette smoking that runs on a low-cost smartwatch and does not require additional sensing or a connected smartphone., Methods: Our system uses motion data from the accelerometer and gyroscope in an Android smartwatch to detect the signature hand movements of cigarette smoking. It uses machine learning techniques to transform raw motion data into motion features, and in turn into individual drags and instances of smoking. These processes run on the smartwatch, and do not require a smartphone., Results: We conducted preliminary validations of the system in daily smokers (n = 13) in laboratory and free-living conditions running on an Android LG G-Watch. In free-living conditions, over a 24-h period, the system achieved precision of 86% and recall of 71%., Conclusions: StopWatch is a system for passive measurement of cigarette smoking that runs entirely on a commercially available Android smartwatch. It requires no smartphone so the cost is low, and needs no bespoke sensing equipment so participant burden is also low. Performance is currently lower than other more expensive and complex systems, though adequate for some applications. Future developments will focus on enhancing performance, validation on a range of smartwatches, and detection of electronic cigarette use., Implications: We present a low-cost, smartwatch-based system for passive detection of cigarette smoking. It uses data from the motion sensors in the watch to identify the signature hand movements of cigarette smoking. The system will provide the detailed measures of individual smoking behavior needed for context-triggered just-in-time smoking cessation support systems, and to enable just-in-time adaptive interventions. More broadly, the system will enable researchers to obtain detailed measures of individual smoking behavior in free-living conditions that are free from the recall errors and reporting biases associated with self-report of smoking.

Background Context: Lumbar spinal stenosis (LSS) and knee osteoarthritis (OA) are 2 of the leading causes of disability worldwide. In order to provide disease-specific prescriptions for physical activity, there is a clear need to better understand physical activity in daily life (performance) in these populations., Purpose: To discover performance phenotypes for LSS and OA by applying novel analytical methods to accelerometry data. Specific objectives include the following: (1) to identify characteristic features (phenotypes) of free-living physical activity unique to individuals with LSS and OA, and (2) to determine which features can best differentiate between these conditions., Study Design and Setting: Leveraging data from 3 existing cross-sectional cohorts, accelerometry signal feature characterization and selection were performed in a computational laboratory., Patient Sample: Data from a total of 4,028 individuals were analyzed from the following 3 datasets: LSS Accelerometry Database (n=75); OA Initiative (n=1950); and the 2003 to 2004 National Health and Nutrition Examination Survey (pain-free controls, n=2003)., Methods: In order to characterize the accelerometry signals, data were examined using (1) standard intervals for counts/minute from Freedson et al. and (2) the physical performance intervals for mobility-limited pain populations. From this, 42 novel accelerometry features were defined and evaluated for significance in discriminating between the groups (LSS, OA, and controls) in order to then determine which sparse set of features best differentiates between the groups. These sparse sets of features defined the performance phenotypes., Outcome Measures: Accelerometry features and their ability to differentiate between individuals with LSS, OA, and controls., Results: Given age and gender, classification rates were at least 80% accurate (pairwise) between diseased and pain-free populations (LSS vs. controls and OA vs. controls). The most important features to distinguish between disease groups corresponded to measures in the light and sedentary activity intervals. The more subtle classification between diseased populations (LSS vs. OA) was 72% accurate, with light and moderate activity providing the prominent distinguishing features., Conclusions: We describe the discovery of performance phenotypes of LSS and OA from accelerometry data, revealed through a novel set of features that characterize daily patterns of movement in people with LSS and OA. These performance phenotypes provide a new method for analyzing free-living physical activity (performance) in LSS and OA, and provide the groundwork for more personalized approaches to measuring and improving function., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Objectives: To determine the between-visit reliability of an accelerometer as a measure of lower-extremity impact acceleration at a variety of gait speeds in children., Design: Absolute reliability assessment., Methods: Ten children with no known gait pathology attended two testing sessions, three weeks apart. A tri-axial accelerometer was fixed to the child's distal tibia to measure peak positive acceleration responses while walking and running on the treadmill at three different speeds (comfortable walking, threshold walking, and jogging). Reliability of the average and standard deviation Peak Positive Acceleration (avgPPA and sdPPA, respectively) was calculated by intra-class correlation coefficients (ICC) and Minimum Detectable Change (MDC)., Results: Excellent reliability was indicated with ICC values for avgPPA of 0.90, 0.95, and 0.81 for comfortable walking, threshold walking, and jogging, respectively. Moderate reliability was found for the sdPPA measures. MDC values were calculated to be 18%, 26%, and 23% for comfortable walking, threshold walking, and jogging, respectively, indicating the amount by which an avgPPA value would need to change to ensure that the change is greater than a measurement error., Conclusions: An accelerometer attached to the distal tibia is practical for use in a clinical environment to collect lower extremity acceleration data in children. Clinicians can utilise this technique for assessing a change following an intervention, such as biofeedback gait retraining., (Copyright © 2018 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)

Background: Accelerometers and pedometers have been used to monitor the number of steps. However, the evidence on the step-counting accuracy of these devices - especially accelerometers - is limited in persons with Down syndrome (DS). This study therefore examined the accuracy of accelerometers placed on the hip or wrist and of a pedometer with a uni-axial accelerometer mechanism in measuring steps in persons with DS and whether device error is associated with walking speed, height, weight, body mass index, waist circumference, leg length, age or sex., Method: Seventeen persons with DS (eight women and nine men; age 33 ± 15 years) walked over-ground for 6 min at their preferred speed. The steps were measured with a hip-worn and a wrist-worn ActiGraph accelerometer using the manufacturer's default (DF) and low-frequency extension (LFE) filters, and with the NL-1000 New Lifestyles pedometer on the hip. Steps were also measured with hand tally which served as the criterion., Results: Absolute percent error was considerable and differed statistically between devices (P = 0.001); however, error improved for accelerometers when LFE was applied (Hip-DF: 31.6 ± 18.8%; Hip-LFE: 9.7 ± 12.8%; Wrist-DF: 32.7 ± 14.2%; Wrist-LFE: 13.6 ± 10.2%; Pedometer: 23.2 ± 22.8%). Bland-Altman plots indicated underestimation of steps for accelerometers and the pedometer. Application of LFE, however, improved the prediction of the accelerometers. The number of steps measured by the hip accelerometer with LFE and by the pedometer did not differ statistically from actual steps. Steps by the remaining methods were significantly lower than hand tally (P ≤ 0.001). Correlations between percent error for each device and walking speed, anthropometry, age or sex ranged between -0.28 and +0.48, and were non-significant, except for age., Conclusions: The results demonstrated that the pedometer and ActiGraph accelerometers have considerable error in measuring steps of persons with DS. Application of LFE, however, significantly improved the step-counting performance of the Actigraph accelerometers., (© 2018 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.)

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.

Objective: The Life Microscope is a new wristband-based life recorder system that can identify various human movements. We aimed to compare physical activity data captured using the Life Microscope with data from a commonly used accelerometer., Results: Twenty-nine participants (34.6 ± 12.5 years) wore both the Life Microscope and an Active Style Pro accelerometer for 7 days. Physical activity categories were calculated by converting daily accelerometer data output into time spent at sedentary, light, moderate, and vigorous physical activity. Correlations between the physical activity category and step count data obtained from the two accelerometers were assessed using Pearson correlations, paired t-tests, intra-class coefficients, and the Bland-Altman method. Our results showed good reliability between the physical activity patterns and daily step counts obtained using both devices. Bland-Altman analysis showed good agreement between data from both accelerometers. In conclusion, both accelerometers were comparable in their measurement of step counts and time spent in different physical activity intensities under free-living conditions, and either could be used for population studies.