Validity, reliability, and calibration of the physical activity unit 7 item screener (PAU-7S) at population scale.

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 (ß - 0.162; p < 0.077) and waist to height ratio (ß - 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 .

Ipsilateral and Simultaneous Comparison of Responses from Acceleromyography- and Electromyography-based Neuromuscular Monitors.

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.

Individual versus Group Calibration of Machine Learning Models for Physical Activity Assessment Using Body-Worn Accelerometers.

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.

Reliability of GENEActiv accelerometers to estimate sleep, physical activity, and sedentary time in children.

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.

Comprehensive comparison of Apple Watch and Fitbit monitors in a free-living setting.

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.

Comparing subjective and objective response to medications in Parkinson's disease patients using the Personal KinetiGraph™.

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.

Error Analysis of Accelerometer- and Magnetometer-Based Stationary Alignment.

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.

Inter-unit reliability of IMU Step metrics using IMeasureU Blue Trident inertial measurement units for running-based team sport tasks.

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.

Improving Accelerometry-Based Measurement of Functional Use of the Upper Extremity After Stroke: Machine Learning Versus Counts Threshold Method.

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.

Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals.

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.

The occupational sitting and physical activity questionnaire (OSPAQ): a validation study with accelerometer-assessed measures.

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.

Reproducibility of objectively measured physical activity: Reconsideration needed.

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.

Validity of the Use of a Triaxial Accelerometer and a Physical Activity Questionnaire for Estimating Total Energy Expenditure and Physical Activity Level among Elderly Patients with Type 2 Diabetes Mellitus: CLEVER-DM Study.

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.

Accuracy of modified 30-s chair-stand test for predicting falls in older adults.

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.

How well do activity monitors estimate energy expenditure? A systematic review and meta-analysis of the validity of current technologies.

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 (I2 >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.

Reliability and validity of Turkish version of pregnancy physical activity questionnaire (PPAQ) in patients with gestational diabetes mellitus.

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 statementWhat is already known 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.

A quantitative bias analysis to estimate measurement error-related attenuation of the association between self-reported physical activity and colorectal cancer risk.

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.

Standardising the measurement of physical activity in people receiving haemodialysis: considerations for research and practice.

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.

Heart Rate Measures From Wrist-Worn Activity Trackers in a Laboratory and Free-Living Setting: Validation Study.

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.

Calibration and Validation of the Youth Activity Profile as a Physical Activity and Sedentary Behaviour Surveillance Tool for English Youth.

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.