Search

The purpose of this paper is to introduce a method of measuring spatiotemporal gait patterns, tibial accelerations, and heart rate that are matched with high resolution geographical terrain features using publicly available data. These methods were demonstrated using data from 218 Marines, who completed loaded outdoor ruck hikes between 5-20 km over varying terrain. Each participant was instrumented with two inertial measurement units (IMUs) and a GPS watch. Custom code synchronized accelerometer and positional data without a priori sensor synchronization, calibrated orientation of the IMUs in the tibial reference frame, detected and separated only periods of walking or running, and computed acceleration and spatiotemporal outcomes. GPS positional data were georeferenced with geographic information system (GIS) maps to extract terrain features such as slope, altitude, and surface conditions. This paper reveals the ease at which similar data can be gathered among relatively large groups of people with minimal setup and automated data processing. The methods described here can be adapted to other populations and similar ground-based activities such as skiing or trail running.

Introduction: IMU sensors (three-dimensional accelerometer, gyroscope and magnetometer) enable assessment of walking in older adults outside the laboratory. We studied whether IMUs are valid for detecting walking parameters (step events, time, length, and cadence) in a laboratory and outdoors on a level surface in older adults., Methods: This validation study is part of a larger cross-sectional study. Twenty-six participants (mean age 76 years, 65 % female) walked on a treadmill indoors and on a sport track outdoors at self-selected speed. IMUs were attached laterally on the shanks and on the lower back at the level of L3-L4. Initial contact (IC) and step lengths were also estimated using acceleration signals (vertical, antero-posterior) from the pelvic IMU. Terminal contact (TC) was determined from the shank IMU sagittal angular velocity. For step length, inverted pendulum model and participant's leg length (0.53 x height) was used. Step duration was calculated from IC to the opposite leg IC and stride duration from IC to next ipsilateral IC. Cadence was calculated as steps/min. As reference data, 3D motion capture was used in the laboratory and a high-speed video camera outdoors. Intraclass correlation coefficients (ICC), root mean squared errors (RMSE), typical errors and Bland-Altman plots were calculated and drawn., Results: When comparing IC timing between IMU and reference data, mean bias was 0.031 s in the laboratory and -0.004 s outdoors, and for TC -0.057 s and -0.070 s respectively. Step and stride duration and cadence showed ICC values >0.80 and mean bias was <0.005 s for step and stride durations and <0.05 steps/min for cadence in both environments. Step length ICC values were <0.40 in the laboratory and outdoors., Significance: IMUs can be used to monitor temporal walking variables in older adults and may be useful for rehabilitation interventions and functional capacity assessment., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Smartphones, with embedded accelerometers, may be a viable method to monitor gait variability in the free-living environment. However, measurements estimated using smartphones must first be compared to known quantities to ensure validity. This study assessed the validity and reliability of smartphone-derived gait measures compared to a gold-standard footswitch system during overground walking. Seventeen adults completed three 8-minute overground walking trials during 3 separate visits. The stride time series was calculated as the time difference between consecutive right heel contact events within the footswitch and smartphone-accelerometry signals. Linear (average stride time, stride time standard deviation, and stride time coefficient of variation) and nonlinear (fractal scaling index, approximate entropy, and sample entropy) measures were calculated for each stride time series. Bland-Altman plots with 95% limits of agreement assessed agreement between systems. Intraclass correlation coefficients assessed reliability across visits. Bland-Altman plots revealed acceptable limits of agreement for all measures. Intraclass correlation coefficients revealed good-to-excellent reliability for both systems, except for fractal scaling index, which was moderate. The smartphone system is a valid method and performs similarly to gold-standard research equipment. These findings suggest the development and implementation of an inexpensive, easy-to-use, and ubiquitous telehealth instrument that may replace traditional laboratory equipment for use in the free-living environment.

Background: Scoliosis is a multifaceted three-dimensional deformity that significantly affects patients' balance function and walking process. While existing research primarily focuses on spatial and temporal parameters of walking and trunk/pelvic kinematics asymmetry, there remains controversy regarding the symmetry and regularity of bilateral lower limb gait. This study aims to investigate the symmetry and regularity of bilateral lower limb gait and examine the balance control strategy of the head during walking in patients with idiopathic scoliosis., Methods: The study involved 17 patients with idiopathic scoliosis of Lenke 1 and Lenke 5 classifications, along with 17 healthy subjects for comparison. Three-dimensional accelerometers were attached to the head and L5 spinous process of each participant, and three-dimensional motion acceleration signals were collected during a 10-meter walking test. Analysis of the collected acceleration signals involved calculating five variables related to the symmetry and regularity of walking: root mean square (RMS) of the acceleration signal, harmonic ratio (HR), step regularity, stride regularity, and gait symmetry., Results: Our analysis reveals that, during the walking process, the three-dimensional motion acceleration signals acquired from the lumbar region of patients diagnosed with idiopathic scoliosis exhibit noteworthy disparities in the RMS of the vertical axis (RMS-VT) and the HR of the vertical axis (HR-VT) when compared to the corresponding values in the healthy control (RMS-VT: 1.6 ± 0.41 vs . 3 ± 0.47, P  < 0.05; HR-VT: 3 ± 0.72 vs . 3.9 ± 0.71, P  < 0.05). Additionally, the motion acceleration signals of the head in three-dimensional space, including the RMS in the anterior-posterior and vertical axis, the HR-VT, and the values of step regularity in both anterior-posterior and vertical axis, as well as the values of stride regularity in all three axes, are all significantly lower than those in the healthy control group ( P  < 0.05)., Conclusion: The findings of the analysis suggest that the application of three-dimensional accelerometer sensors proves efficacious and convenient for scrutinizing the symmetry and regularity of walking in individuals with idiopathic scoliosis. Distinctive irregularities in gait symmetry and regularity manifest in patients with idiopathic scoliosis, particularly within the antero-posterior and vertical direction. Moreover, the dynamic balance control strategy of the head in three-dimensional space among patients with idiopathic scoliosis exhibits a relatively conservative nature when compared to healthy individuals., Competing Interests: The authors declare there are no competing interests., (©2024 Gan et al.)

Background: Many individuals with neurodegenerative (NDD) and immune-mediated inflammatory disorders (IMID) experience debilitating fatigue. Currently, assessments of fatigue rely on patient reported outcomes (PROs), which are subjective and prone to recall biases. Wearable devices, however, provide objective and reliable estimates of gait, an essential component of health, and may present objective evidence of fatigue. This study explored the relationships between gait characteristics derived from an inertial measurement unit (IMU) and patient-reported fatigue in the IDEA-FAST feasibility study., Methods: Participants with IMIDs and NDDs (Parkinson's disease (PD), Huntington's disease (HD), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), primary Sjogren's syndrome (PSS), and inflammatory bowel disease (IBD)) wore a lower-back IMU continuously for up to 10 days at home. Concurrently, participants completed PROs (physical fatigue (PF) and mental fatigue (MF)) up to four times a day. Macro (volume, variability, pattern, and acceleration vector magnitude) and micro (pace, rhythm, variability, asymmetry, and postural control) gait characteristics were extracted from the accelerometer data. The associations of these measures with the PROs were evaluated using a generalised linear mixed-effects model (GLMM) and binary classification with machine learning., Results: Data were recorded from 72 participants: PD = 13, HD = 9, RA = 12, SLE = 9, PSS = 14, IBD = 15. For the GLMM, the variability of the non-walking bouts length (in seconds) with PF returned the highest conditional R2, 0.165, and with MF the highest marginal R2, 0.0018. For the machine learning classifiers, the highest accuracy of the current analysis was returned by the micro gait characteristics with an intrasubject cross validation method and MF as 56.90% (precision = 43.9%, recall = 51.4%). Overall, the acceleration vector magnitude, bout length variation, postural control, and gait rhythm were the most interesting characteristics for future analysis., Conclusions: Counterintuitively, the outcomes indicate that there is a weak relationship between typical gait measures and abnormal fatigue. However, factors such as the COVID-19 pandemic may have impacted gait behaviours. Therefore, further investigations with a larger cohort are required to fully understand the relationship between gait and abnormal fatigue., (© 2024. The Author(s).)

Background: The nonlinear variability present during steady-state gait may provide a signature of health and showcase one's walking adaptability. Although treadmills can capture vast amounts of walking data required for estimating variability within a small space, gait patterns may be misrepresented compared to an overground setting. Smartphones may provide a low-cost and user-friendly estimate of gait patterns among a variety of walking settings. However, no study has investigated differences in gait patterns derived from a smartphone between treadmill walking (TW) and overground walking (OW)., Research Question: This study implemented a smartphone accelerometer to compare differences in temporal gait variability and gait dynamics between TW and OW., Methods: Sixteen healthy adults (8F; 24.7 ± 3.8 years) visited the laboratory on three separate days and completed three 8-minute OW and three TW trials, at their preferred speed, during each visit. The inter-stride interval was calculated as the time difference between right heel contact events located within the vertical accelerometery signals recorded from a smartphone while placed in participants front right pant pocket during walking trials. The inter-stride interval series was used to calculate stride time standard deviation (SD) and coefficient of variation (COV), statistical persistence (fractal scaling index), and statistical regularity (sample entropy). Two-way analysis of variance compared walking condition and laboratory visits for each measure., Results: Compared to TW, OW displayed significantly (p < 0.01) greater stride time SD (0.014 s, 0.020 s), COV (1.26 %, 1.82 %), fractal scaling index (0.70, 0.79) and sample entropy (1.43, 1.63). No differences were found between visits for all measures., Significance: Smartphone-based assessment of gait provides the ability to distinguish between OW and TW conditions, similar to previously established methodologies. Furthermore, smartphones may be a low-cost and user-friendly tool to estimate gait patterns outside the laboratory to improve ecological validity, with implications for free-living monitoring of gait among various populations., Competing Interests: Declaration of Competing Interest None, (Copyright © 2024 Elsevier B.V. All rights reserved.)

The COVID-19 pandemic is causing mass disruption to our daily lives. We integrate mobility data from mobile devices and area-level data to study the walking patterns of 1.62 million anonymous users in 10 metropolitan areas in the United States. The data covers the period from mid-February 2020 (pre-lockdown) to late June 2020 (easing of lockdown restrictions). We detect when users were walking, distance walked and time of the walk, and classify each walk as recreational or utilitarian. Our results reveal dramatic declines in walking, particularly utilitarian walking, while recreational walking has recovered and even surpassed pre-pandemic levels. Our findings also demonstrate important social patterns, widening existing inequalities in walking behavior. COVID-19 response measures have a larger impact on walking behavior for those from low-income areas and high use of public transportation. Provision of equal opportunities to support walking is key to opening up our society and economy.

Purpose: This study aimed to determine and compare the accuracy of different activity monitors in assessing intermittent outdoor walking in both healthy and clinical populations through the development and validation of processing methodologies., Methods: In study 1, an automated algorithm was implemented and tested for the detection of short (≤1 min) walking and stopping bouts during prescribed walking protocols performed by healthy subjects in environments with low and high levels of obstruction. The following parameters obtained from activity monitors were tested, with different recording epochs0.1s/0.033s/1s/3s/10s and wearing locationsscapula/hip/wrist/ankle: GlobalSat DG100 (GS) and Qstarz BT-Q1000XT/-Q1000eX (QS) speed; ActiGraph wGT3X+ (AG) vector magnitude (VM) raw data, VM counts, and steps; and StepWatch3 (SW) steps. Furthermore, linear mixed models were developed to estimate walking speeds and distances from the monitors parameters. Study 2 validated the performance of the activity monitors and processing methodologies in a clinical population showing profile of intermittent walking due to functional limitations during outdoor walking sessions., Results: In study 1, GS1s, scapula, QS1s, scapula/wrist speed, and AG0.033s, hip VM raw data provided the highest bout detection rates (>96.7%) and the lowest root mean square errors in speed (≤0.4 km·h-1) and distance (<18 m) estimation. Using SW3s, ankle steps, the root mean square error for walking/stopping duration estimation reached 13.6 min using proprietary software and 0.98 min using our algorithm (total recording duration, 282 min). In study 2, using AG0.033s, hip VM raw data, the bout detection rate (95% confidence interval) reached 100% (99%-100%), and the mean (SD) absolute percentage errors in speed and distance estimation were 9% (6.6%) and 12.5% (7.9%), respectively., Conclusions: GPS receivers and AG demonstrated high performance in assessing intermittent outdoor walking in both healthy and clinical populations., (Copyright © 2021 by the American College of Sports Medicine.)

Purpose: The aims of this project were twofold: 1) to assess the concurrent validity of raw accelerometer outputs with ground reaction forces (GRF) and loading rates (LR) calculated from force plate across a range of simulated habitual PA and 2) to identify the optimal wear site among the ankle, hip, and wrist with the strongest relationships between accelerometer and force plate and/or skeletal outcomes., Methods: Thirty healthy young adults (23.0 ± 4.5 yr, 50% female) wore a triaxial accelerometer at the right ankle, hip, and wrist while performing eight trials of walking, jogging, running, low box drops, and high box drops over an in-ground force plate. Repeated-measures correlations and linear mixed models were used to assess concurrent validity of accelerometer and force plate outcomes across wear sites., Results: Strong repeated-measures associations were observed between peak hip resultant acceleration and resultant LR (rrm 1169 = 0.74, P < 0.001, 95% confidence interval = 0.718, 0.769) and peak hip resultant accelerations and resultant GRF (rrm 1169 = 0.69, P < 0.001, 95% confidence interval = 0.660, 0.720) when data were combined across activities. By contrast, small to moderate associations were seen between ankle-based outcomes and corresponding GRF and LR during walking and jogging (rrm 209 = 0.17-0.34, all P < 0.001). No significant associations were seen with wrist-based outcomes during any activity. In addition, linear mixed models suggested that 24%-50% of the variability in peak GRF and LR could be attributed to measured accelerations at the hip., Conclusion: Peak accelerations measured at the hip were identified as the strongest proxies for skeletal loading assessed via force plate., (Copyright © 2020 by the American College of Sports Medicine.)

The activPAL is a valid measure of step counts and posture, but its ability to determine physical activity intensity is unclear. This study tested the criterion validity of the activPAL using its built-in linear cadence-metabolic equivalents (METs) equation (activPAL-linear) versus an individualized height-adjusted curvilinear cadence-METs equation (activPAL-curvilinear) to estimate intensity-related physical activity. Forty adults (25±6 years, 23.3±4.1 kg/m 2 ) wore an activPAL during a 7-stage progressive treadmill walking protocol (criterion: indirect calorimetry). A sub-sample (n=32) wore the device during free-living conditions for 7-days (criterion: PiezoRxD monitor). In the laboratory, the activPAL-linear overestimated METs during slow walking (1.5-3.0 miles•hour -1 ) but underestimated METs during fast walking (3.5-4.5 miles•hour -1 ) (all, p<0.001). In the free-living condition, the activPAL-linear overestimated time in light-intensity activity and underestimated moderate-intensity activity (both, p<0.001), but did not register any vigorous-intensity activity. In contrast, the activPAL-curvilinear estimated values statistically equivalent to indirect calorimetry for treadmill stages 1-6 (1.5-4.0 miles•hour -1 ) and to the PiezoRxD determined light- and moderate-intensity activity during free-living. We present a simple, data processing technique that uses an alternative curvilinear cadence-MET equation that improves the ability of the activPAL to measure intensity-related physical activity in both laboratory and free-living settings.

To determine whether interrupting prolonged sitting with three different activity breaks has both acute and chronic effects on postprandial lipid metabolism immediately after the activity-break period and on the following day, this study is a secondary analysis of an experimental research, which included 16 sedentary healthy adults (7 men, 24 ± 3 years, BMI 22.2 ± 2.3 kg/m 2 ) who completed four 26-h laboratory trials. Participants spent 22.5 hours in a whole room calorimeter for testing energy expenditure (EE), including a 9-h activity-break period: (a) 9-h prolonged sitting (SIT); (b) 3 minutes of brisk walking (60% VO 2max ) in between every 30-min sitting bout (WALK3), (c) 5 minutes every 45-min (WALK5), and (d) 8 minutes every 60-min (WALK8). Total area under the curve (tAUC) and incremental AUC (iAUC) for 2-h postprandial serum triglyceride (TG) levels and non-esterified fatty acid (NEFA) levels were examined immediately after the 9-h trial (post-dinner) and the next morning (post-breakfast). WALK8 reduced the post-breakfast TG tAUC by 11% (P = .041) relative to SIT, and the effect was attenuated after adjustment for EE. The tAUC and iAUC indicated no significant treatment effects on post-dinner TG and NEFA, and post-breakfast NEFA in any of the activity-break trials. EE was positively associated with the post-breakfast NEFA iAUC (unstandardized β = 0.17 µmol/L/MJ [0.05-0.28], P = .006). There was a chronic effect of interrupting sitting with short bouts (8 minutes) of brisk walking every 60 minutes on postprandial lipemia the following morning after intervention, and higher activity bout-induced EE may be more effective in sedentary, healthy adults., (© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Background: When performing quantitative analysis of gait in older adults we need to strike a balance between capturing sufficient data for reliable measurement and avoiding issues such as fatigue. The optimal bout duration is that which contains sufficient gait cycles to enable a reliable and representative estimate of gait performance., Research Question: How does the number of gait cycles in a walking bout influence reliability of spatiotemporal gait parameters measured using body-worn inertial sensors in a cohort of community dwelling older adults?, Methods: One hundred and fifteen (115) community dwelling older adults executed three 30-metre walk trials in a single measurement session. Bilateral gait data were collected using two inertial sensors attached to each participant's right and left shank, and gait events detected from the medio-lateral angular velocity signal. The number of gait cycles selected from each walking trial was varied from 3 to 16. Intraclass correlation coefficients (ICC(2,k)) were calculated to evaluate the reliability of each spatiotemporal gait parameter according to the number of gait cycles included in the analysis., Results: The specified algorithm and the clipping procedure for extracting short bouts of gait data seem appropriate for assessing older adults, providing reliable spatiotemporal measures from three gait cycles (three strides per leg) and good reliability for most parameters describing gait variability and gait asymmetry after six gait cycles (six strides per leg)., Significance: A combination of using bilateral sensor data and adaptive thresholds for gait event detection enable reliable measures of spatiotemporal gait parameters over short walking bouts (minimum six gait cycles) in community dwelling older adults. This opens new possibilities in the use of wearable sensors in gait assessment based on short walking tasks. We recommend the number of gait cycles should be reported along with the calculated measures as reference values., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Previous studies translating the daily moderate-to-vigorous physical activity (MVPA) recommendation of total steps/day among adolescents are inconsistent, and those with cadence-based steps are scarce. The main purpose was to compare the accuracy of different daily steps index-based cut-points related to the daily 60 minutes of MVPA recommendation measured by a waist-worn accelerometer for adolescents. Following a cross-sectional design, 428 Spanish adolescents (final sample 351, 50.4% males), aged 13-16 years old, wore an ActiGraph GT3X/+ accelerometer (reference standard = MVPA; index tests = total steps/day, average steps/min and peak 1-min cadence) on the right hip for eight consecutive days. 32.5% of the adolescents met the daily MVPA recommendation. The multiple ROC curve comparisons showed that the accuracy of the daily total step-based recommendation (AUC = 0.97) was statistically higher than for those with the steps/min (AUC = 0.90) and peak 1-min cadence (AUC = 0.58) ( p < 0.001). The 10,000-step-per-day cut-point ( k = 0.59-0.83) showed highest accuracy values than the 12,000 steps/day ( k = 0.20-0.32). Daily total step-based recommendations are more accurate than those with steps/min and peak 1-min cadence for classifying adolescents as being physically active or inactive. A 10,000-step-per-day target is simple and accurate for both male and female adolescents.

Background: Accumulating evidence suggests that the built environment is associated with physical activity. The extent to which the built environment may support adherence to physical activity interventions is unclear. The aim of this study was to investigate whether the neighbourhood built environment constrains or facilitates adherence and steps taken during a 12-week internet-delivered pedometer-based physical activity intervention (UWALK)., Method: The study was undertaken in Calgary (Canada) between May 2016 and August 2017. Inactive adults (n = 573) completed a telephone survey measuring sociodemographic characteristics and perceived neighbourhood walkability. Following the survey, participants were mailed a pedometer and instructions for joining UWALK. Participants were asked to report their daily pedometer steps into the online program on a weekly basis for 12 weeks (84 days). Walk Score® estimated objective neighbourhood walkability and the Neighbourhood Environment Walkability Scale-Abbreviated (NEWS-A) measured participants self-reported neighbourhood walkability. Regression models estimated covariate-adjusted associations of objective and self-reported walkability with: 1) adherence to the UWALK intervention (count of days with steps reported and count of days with 10000 steps reported), and; 2) average daily pedometer steps., Results: On average, participants undertook 8565 (SD = 3030) steps per day, reported steps on 67 (SD = 22.3) of the 84 days, and achieved ≥10000 steps on 22 (SD = 20.5) of the 84 days. Adjusting for covariates, a one-unit increase in self-reported walkability was associated on average with 45.76 (95CI 14.91, 76.61) more daily pedometer steps. Walk Score® was not significantly associated with steps. Neither objective nor self-reported walkability were significantly associated with the UWALK adherence outcomes., Conclusion: The neighbourhood built environment may support pedometer-measured physical activity but may not influence adherence to pedometer interventions. Perceived walkability may be more important than objectively-measured walkability in supporting physical activity during pedometer interventions., Competing Interests: The authors have declared that no competing interests exist.

Background: It is unclear whether walking can decrease cardiovascular disease (CVD) risk or if high intensity physical activity (HIPA) is needed, and whether the association is modified by age. We investigated how sedentary behaviour, walking, and HIPA, were associated with systolic blood pressure (SBP), waist circumference (WC), and low-density lipoprotein cholesterol (LDL-C) among adults and older adults in a general population sample using compositional data analysis. Specifically, the measure of association was quantified by reallocating time between sedentary behaviour and 1) walking, and 2) HIPA., Methods: Cross-sectional data from the fifth examination of the Copenhagen City Heart Study was used. Using the software Acti4, we estimated daily time spent in physical behaviours from accelerometer data worn 24 h/day for 7 days (i.e., right frontal thigh and iliac crest; median wear time: 6 days, 23.8 h/day). SBP, WC, and LDL-C were measured during a physical examination. Inclusion criteria were ≥ 5 days with ≥16 h of accelerometer recordings per day, and no use of antihypertensives, diuretics or cholesterol lowering medicine. The 24-h physical behaviour composition consisted of sedentary behaviour, standing, moving, walking, HIPA (i.e., sum of climbing stairs, running, cycling, and rowing), and time in bed. We used fitted values from linear regression models to predict the difference in outcome given the investigated time reallocations relative to the group-specific mean composition., Results: Among 1053 eligible participants, we found an interaction between the physical behaviour composition and age. Age-stratified analyses (i.e.,

Background: Sex-related differences of plantar pressure distribution during activities should be thoroughly inspected as it can help establish treatment and prevention strategies for foot and ankle problems. In-shoe measurement systems are preferable without space and activity restrictions; however, previously reported systems are still heavy and bulky and induce unnatural movement. Therefore, a slim and light plantar pressure sensor was newly developed to detect the effect of sex difference on plantar pressure during standing and walking., Methods: One-hundred healthy adult volunteers (50 women and 50 men) were recruited. Ten plantar pressure sensors were implanted in a 1-mm thick insole, with a total weight of 29 g. Plantar pressure was recorded with 200 Hz during 3 s of standing and while walking 10 steps. The maximum loads during standing and walking were analyzed in each sensor, and the results were compared between different areas of the foot in the antero-posterior direction and the medio-lateral direction and between different time points. The movement of the center of pressure (COP) during walking was also evaluated. Analyses were adjusted for body mass index and gait speed., Results: The movement of COP was constant for both sexes. In all cases, the maximum load was observed on the medial of the foot. Women had a significantly higher peak pressure on the hallux, toes, forefoot, and medial aspect of the foot compared to men while standing and walking (p < .05)., Conclusions: A newly introduced in-shoe plantar pressure sensor demonstrated a typical loading transition pattern of the foot. Furthermore, higher plantar pressure in the forefoot was detected in healthy women as compared to men during standing and walking activities.

Background: Recent studies reported that wearable sensor devices show low validity for assessing the amount of energy expenditure in individuals after stroke., Objective: We aimed to evaluate the validity of energy expenditure calculation based on the product of energy cost and walked distance estimated by wearable devices in individuals after hemispheric stroke., Methods: We recruited individuals with hemispheric stroke sequelae who were able to walk without human assistance. The participants wore a tri-axial accelerometer (Actigraph GT3x) and a pedometer (ONStep 400) on the unaffected hip in addition to a respiratory gas exchange analyzer (METAMAX 3B) during 6min of walking at their self-selected walking speed and mode. The energy expenditure was calculated from the product of energy cost measured by the METAMAX 3B and the distance estimated by wearable devices. It was compared to the energy expenditure measured by the METAMAX 3B and the energy expenditure values recorded by the devices according to the manufacturer's algorithms. The validity was investigated by Bland-Altman analysis (mean bias [MB], root mean square error [RMSE], limits of agreement [95%LoA]), and Pearson correlation analysis (r)., Results: We included 26 participants (mean [SD] age 64.6 [14.8] years). With the pedometer, the energy expenditure calculated from the product of energy cost and walked distance showed high accuracy and agreement with METAMAX 3B values (MB=-1.6kcal; RMSE=4.1kcal; 95%LoA=-9.9; 6.6kcal; r=0.87, P<0.01) but low accuracy and agreement with Actigraph GT3x values (MB=15.7kcal; RMSE=8.7kcal; 95%LoA=-1.3; 32.6kcal; r=0.44, P=0.02) because of poorer estimation of walked distance. With the pedometer, this new method of calculation strongly increased the validity parameter values for estimating energy expenditure as compared with the manufacturer's algorithm., Conclusions: This new method based on the energy cost and distance estimated by wearable devices provided better energy expenditure estimates for the pedometer than did the manufacturer's algorithm. The validity of this method depended on the accuracy of the sensor to measure the distance walked by an individual after stroke., (Copyright © 2019. Published by Elsevier Masson SAS.)

Purpose: This study aimed to investigate whether prospectively reallocating time away from sedentary behavior (SB) into different physical activity intensities is associated with 12-month change to cardiometabolic health in a cohort at high risk of type 2 diabetes (T2DM)., Methods: Participants with known risk factors for T2DM were recruited from primary care (Leicestershire, United Kingdom) as part of the Walking Away from Type 2 Diabetes trial (n = 808). Participants were followed up at 12, 24, and 36 months. SB, light-intensity physical activity (LPA) and moderate-to-vigorous intensity physical activity (MVPA) were measured objectively by accelerometer. Postchallenge glucose, triglycerides, HDL cholesterol, systolic blood pressure, and waist circumference were analyzed individually and combined into a clustered cardiometabolic risk score (CMRS). Associations of changing SB over each consecutive 12-month period were analyzed taking account of repeated measures., Results: Reallocating 30 min from SB to LPA was associated with 0.21-cm (95% confidence interval, 0.03-0.38 cm) reduction in waist circumference, 0.09-mmol·L (0.04-0.13 mmol·L) reduction in 2-h glucose, 0.02-mmol·L (0.00-0.04 mmol·L) reduction in triglycerides, and 0.02 (0.01-0.03) reduction in CMRS. Every 30-min reallocation from SB to MVPA was associated with 1.23-cm (0.68-1.79 cm) reduction in waist circumference, 0.23-mmol·L (0.10-0.36 mmol·L) reduction in 2-h glucose, 0.04-mmol·L (0.00-0.09 mmol·L) reduction in triglycerides, and 0.07 (0.04-0.11) reduction in CMRS. Reallocating 30 min from LPA into MVPA was also associated with 1.02-cm (0.43-1.60 cm) reduction in waist circumference, 0.16-mmol·L (0.02-0.30 mmol·L) reduction in 2-h glucose, and 0.05 (0.01-0.09) reduction in CMRS., Conclusion: Over 12 months, reallocating time away from SB into LPA or MVPA was associated with improved cardiometabolic health in a population at risk of T2DM, with the greatest benefits observed for MVPA.

In pedestrian inertial navigation, multi-sensor fusion is often used to obtain accurate heading estimates. As a widely distributed signal source, the geomagnetic field is convenient to provide sufficiently accurate heading angles. Unfortunately, there is a broad presence of artificial magnetic perturbations in indoor environments, leading to difficulties in geomagnetic correction. In this paper, by analyzing the spatial distribution model of the magnetic interference field on the geomagnetic field, two quantitative features have been found to be crucial in distinguishing normal magnetic data from anomalies. By leveraging these two features and the classification and regression tree (CART) algorithm, we trained a decision tree that is capable of extracting magnetic data from distorted measurements. Furthermore, this well-trained decision tree can be used as a reject gate in a Kalman filter. By combining the decision tree and Kalman filter, a high-precision indoor pedestrian navigation system based on a magnetically assisted inertial system is proposed. This system is then validated in a real indoor environment, and the results show that our system delivers state-of-the-art positioning performance. Compared to other baseline algorithms, an improvement of over 70% in the positioning accuracy is achieved.

Purpose: The aim of this study was to analyze the effect of epoch length on intensity classification during continuous and intermittent activities., Methods: Ten active students exercised under controlled conditions on a treadmill for four 5-min bouts by combining two effort intensities (running and walking) and two physical activity (PA) patterns (continuous or intermittent). The testing session was designed to generate a known level of moderate to vigorous PA (MVPA) for each condition. These PA levels were used as criterion measures to compare with the accelerometer measures. Data obtained from the accelerometer were reintegrated into 1-sec, 10-sec, 30-sec and 60-sec epochs. Equivalence testing was used to examine measurement agreements between MVPA values obtained with the different epochs and the reference values. Mean absolute percent errors (MAPE) were also calculated to provide an indicator of overall measurement error., Results: During the intermittent conditions, only the value obtained with the 1-sec epoch was significantly equivalent to the reference value. With longer epochs the difference increased for both intermittent conditions but in an opposite way: with longer epochs, MVPA decreased during walking but increased during running. Regarding the measurement accuracy, the pattern of variations according to the epoch length selected during the intermittent conditions was identical between walking and running: MAPE increased with the increase in epoch length. MAPE remained low only for the 1-sec epoch (7.6% and 2.7% for walking and running, respectively), increased at 31.3% and 34% for the 10-sec epoch and until near 100% with the 30- and 60-sec epoch lengths., Conclusion: This study highlighted the misclassification of exercise intensity based on accelerometer measurement and described for the first time the extent and the direction of this misclassification. Moreover, we can confirm that the shorter epochs are more accurate to measure the real exercise intensity during intermittent PA whatever the intensity., Competing Interests: The authors have declared that no competing interests exist.

Objective: Tools to measure physical activity, such as pedometers, have become more prevalent and attracted popular interest in recent years. Despite this trend, research has not yet quantified pedometer-measured physical activity among Emergency Physicians. This study aims to provide the first characterization of physical activity among on-duty Emergency Physicians in terms of step count., Methods: Emergency Physicians wore Empatica E4 research-grade accelerometers while performing routine clinical care in the Emergency Department. A publicly available algorithm was used to estimate the number of steps taken., Results: Fifty-one Emergency Physicians, including thirty-four residents and seventeen attending physicians, contributed over 1500 h of accelerometer data. On average, this cohort took 577 steps per hour (SD: 72.6), totaling 4950 steps per recorded shift (SD: 737.8), which is approximately 2.6 miles (SD: 0.31). Residents walked more than attending physicians (595.9 steps per hour (SD: 99.7) vs 563.0 steps per hour (SD: 89.0), respectively; p = 0.02)., Conclusion: The average emergency physician in this cohort walked roughly half the daily recommended number of steps during their recorded shift. Residents walk significantly more than attending physicians., (Copyright © 2019 Elsevier Inc. All rights reserved.)

This study investigated whether using an inertial measurement unit (IMU) can identify different walking conditions, including level walking (LW), descent (DC) and ascent (AC) slope walking as well as downstairs (DS) and upstairs (US) walking. Thirty healthy participants performed walking under five conditions. The IMU was stabilised on the exterior of the left shoe. The data from IMU were used to establish a customised prediction model by cut point and a prediction model by using deep learning method. The accuracy of both prediction models was evaluated. The customised prediction model combining the angular velocity of dorsi-plantar flexion in the heel-strike (HS) and toe-off (TO) phases can distinctly determine real conditions during DC and AC slope, DS, and LW (accuracy: 86.7-96.7%) except for US walking (accuracy: 60.0%). The prediction model established by deep learning using the data of three-axis acceleration and three-axis gyroscopes can also distinctly identify DS, US, and LW with 90.2-90.7% accuracy and 84.8% and 82.4% accuracy for DC and AC slope walking, respectively. In conclusion, inertial measurement units can be used to identify walking patterns under different conditions such as slopes and stairs with customised prediction model and deep learning prediction model.

Objective: Measuring three-dimensional (3-D) foot trajectories with foot-worn inertial measurement units (IMUs) is essential for a variety of applications, such as gait analysis and fall risk assessment. IMU-based foot trajectory is usually reconstructed by double integrating the global coordinate acceleration, in which drifts of signals are accumulated and lead to unbounded error increase. To reduce drift errors, a smoother-based method is proposed in this paper., Methods: The smoother-based method not only corrects initial values of integrations, but also smooths integrating processes through a backward update. Both the orientation estimation and the velocity estimation are improved in this concept, which contribute to the improvement of the trajectory estimation., Results: The final results are compared with an optical motion capture system as reference. Accuracy is evaluated with ground level walking of nine adult participants, 2302 strides in total. Errors are reduced by 62% on stride length and 44% on stride width of the estimation without our method, with final errors [Formula: see text] and [Formula: see text]., Conclusion/significance: Results prove that our method can improve the accuracy of 3-D foot trajectory estimation. Furthermore, this smoother-based method can reduce drift-related errors when estimating trajectories, which will allow it to expand its applications into other IMU-based measurements.

Objectives: To assess the validity of the Polar Team Pro Sensor for measuring speed and distance indoors during continuous locomotive and change-of-direction tasks at low, medium, and high intensities., Design: Descriptive validation study., Methods: 26 recreationally-active participants (age: 32.2 ± 11.0 yr; stature: 173.3 ± 9.9 cm; body mass: 74.2 ± 16.2 kg) completed three trials of low- (walking speed), medium- (jogging speed), and high-intensity (maximal sprinting speed) continuous locomotive and change-of-direction tasks. Participants wore back- and chest-mounted sensors to determine mean speed and total distance covered. One-way analysis of variance, t-tests, Pearson's Product moment correlation, and Bland-Altman plots were utilised to compare the speed and distance measured with the back- and chest-mounted sensors to reference measures (measured distance of the court via a trundle wheel and speed derived from measured distance and electronic timing lights)., Results: Speed and distance measured using the back- and chest-mounted sensors showed wide limits of agreement, which increased at high intensities for speed. The sensors typically underestimated speed and distance by as much as 2.76 km h -1 and 32.6 m, and overestimated speed and distance by as much as 4.52 km h -1 and 59.6 m across tasks and intensities compared to reference measures (168.45 and 40.00 m)., Conclusions: There was low agreement between both back- and chest-mounted sensors and the reference devices for measuring speed and distance indoors. Practitioners should understand the limitations and potential for error when using the Polar Team Pro Sensors indoors to measure speed and distance during continuous locomotive and change-of-direction tasks., (Copyright © 2019 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)

Background: Many recent commercial devices aim at providing a practical way to measure energy expenditure. However, those devices are limited in accuracy., Objective: This study aimed to build a model of energy consumption during walking applicable to a range of sloped surfaces, used in conjunction with a simple, wearable device., Methods: We constructed a model of energy consumption during gradient walking by using arguments based in mechanics. We built a foot monitoring system that used pressure sensors on the foot insoles. We did experiments in which participants walked on a treadmill wearing the foot monitoring system, and indirect calorimetry was used for validation. We found the parameters of the model by fitting to the data., Results: When walking at 1.5 m/s, we found that the model predicted a calorie consumption rate of 5.54 kcal/min for a woman with average height and weight and 6.89 kcal/min for an average man. With the obtained parameters, the model predicted the data with a root-mean-square deviation of 0.96 kcal/min and median percent error of 12.4%., Conclusions: Our model was found to be an accurate predictor of energy consumption when walking on a range of slopes. The model uses few variables; thus, it can be used in conjunction with a convenient wearable device., (©Soon Ho Kim, Jong Won Kim, Jung-Jun Park, Myung Jun Shin, MooYoung Choi. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 23.10.2019.)

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: People with dysvascular lower limb amputation (LLA) achieve one-third of the recommended steps per day and experience severe disability. Although physical function improves with rehabilitation after dysvascular LLA, physical activity remains largely unchanged, and factors contributing to limited daily step count are unknown., Objectives: To identify factors that contribute to daily step count after dysvascular LLA., Design: Cross-sectional, secondary data analysis., Setting: Outpatient rehabilitation facilities., Participants: Fifty-eight patients with dysvascular major LLA (age: 64 ± 9 years, body mass index: 30 ± 8 kg/m 2 , male: 95%, transtibial LLA: 95%)., Methods: Data were collected by a blinded assessor after dysvascular LLA. Candidate explanatory variables included (1) demographics, (2) LLA characteristics, (3) comorbidities and health behaviors, and (4) physical function. Variables with univariate associations with log steps/day (transformed due to non-normality) were included in a multiple linear regression model using backward elimination to identify factors that explained significant variability in log steps/day., Primary Outcome Measure: The primary outcome, daily step count, was measured with accelerometer-based activity monitors worn by participants for 10 days., Results: Participants took an average (± SD) of 1450 ± 1309 steps/day. After backward elimination, the final model included four variables explaining 62% of the overall daily step count (P < .0001): 2-minute walk distance (32%), assistive device use (11%), cardiovascular disease (10%), and pre-amputation walking time (11%)., Conclusions: Average daily step count of 1450 steps/day reflects the lowest category of sedentary behavior. Physical function, cardiovascular disease, and pre-amputation walking time explain 62% of daily step count after dysvascular LLA. Although physical rehabilitation commonly focuses on improving physical function, interventions to increase daily step count after dysvascular LLA should also consider chronic disease and health behaviors that predate LLA., Level of Evidence: III., (© 2019 American Academy of Physical Medicine and Rehabilitation. This article has been contributed to by US Government employees and their work is in the public domain in the USA.)

Introduction: Although numerous activity trackers have been validated in healthy populations, validation is lacking in chronic heart failure patients who normally walk at a slower pace, making it difficult for researchers and clinicians to implement activity monitors during physical activity interventions., Methods: Six consumer-level activity monitors were validated in a 3-day field study in patients with chronic heart failure and healthy individuals under free living conditions. Furthermore, the same devices were evaluated in a lab-based study during treadmill walking at speeds of 2.4, 3.0, 3.6, and 4.2 km·h-1. Concordance correlation coefficients (CCC) were used to evaluate the agreement between the activity monitors and the criterion, and mean absolute percentage errors (MAPE) were calculated to assess differences between each device and the criterion (MAPE <10% was considered as a threshold for validity)., Results: In the field study of healthy individuals, all but one of the activity monitors showed a substantial correlation (CCC ≥0.95) with the criterion device and MAPE <10%. In patients with heart failure, the correlation of only two activity monitors (Garmin vívofit 3 and Withings Go) was classified as at least moderate (CCC ≥0.90) and none of the devices had MAPE <10%. In the lab-based study at speeds 4.2 and 3.6 km·h-1, all activity monitors showed substantial to almost perfect correlations (CCC ≥0.95) with the criterion and MAPE in the range 1%-3%. However, at slower speeds of 3.0 and 2.4 km·h-1, the accuracy of all devices substantially deteriorated: their correlation with the criterion decreased below 90% and their MAPE increased to 4-8% and 10-45%, respectively., Conclusions: Even though none of the tested activity monitors fall within arbitrary thresholds for validity, most of them perform reasonably well enough to be useful tools that clinicians can use to simply motivate chronic heart failure patients to walk more., Competing Interests: The authors have declared that no competing interests exist.

Background: The 6-min walk test (6MWT) encompasses potential and untapped information related to exercise capacity. However, this test does not yield any information about gait pattern. Recently, we used a ventilatory polygraph to reveal respiratory adaptation during the 6MWT with subjects having high or low body mass index (BMI). In this study, we aimed to determine gait parameters with the same device, which integrates an accelerometer., Methods: Using a 30-m corridor, steps and U-turns were detected with a custom-made algorithm, compared to video recordings as a reference method, and analyzed offline. From the vertical acceleration signal, we were able to determine cadence and step length, and we could calculate the total distance covered in 6 min (6MWD). We then compared these variables between subjects with low BMI ( n = 13 subjects) or high BMI ( n = 29 subjects)., Results: Steps and U-turn detection correlated with video results ( r = 0.99, P < .001 for both). The 6MWD calculation was also in line with classical measurements ( r = 0.99, P < .001). High BMI subjects had a significantly lower 6MWD, cadence, and step length than controls ( P < .001 for each). Walking speed was more closely correlated with step length ( r = 0.92) than with cadence ( r = 0.64) for both groups., Conclusion: Our results demonstrated that a ventilatory polygraph with an embedded accelerometer can be used to detect steps and U-turns, and to calculate 6MWD. This method is sufficiently sensitive to characterize significant BMI-dependent differences in gait pattern during a 6MWT and appears to be a promising tool for routine clinical use., Competing Interests: The authors have disclosed no conflicts of interest., (Copyright © 2019 by Daedalus Enterprises.)

A biomechanical understanding of gait stability is needed to reduce falling risk. As a typical parameter, the COM-COP (center of mass-center of pressure) inclination angle (IA) could provide valuable insight into postural control and balance recovery ability. In this study, an artificial neural network (ANN) model was developed to estimate COM-COP IA based on signals using an inertial sensor. Also, we evaluated how different types of ANN and the cutoff frequency of the low-pass filter applied to input signals could affect the accuracy of the model. An inertial measurement unit (IMU) including an accelerometer, gyroscope, and magnetometer sensors was fabricated as a prototype. The COM-COP IA was calculated using a 3D motion analysis system including force plates. In order to predict the COM-COP IA, a feed-forward ANN and long-short term memory (LSTM) network was developed. As a result, the feed-forward ANN showed a relative root-mean-square error (rRMSE) of 15% while the LSTM showed an improved accuracy of 9% rRMSE. Additionally, the LSTM displayed a stable accuracy regardless of the cutoff frequency of the filter applied to the input signals. This study showed that estimating the COM-COP IA was possible with a cheap inertial sensor system. Furthermore, the neural network models in this study can be implemented in systems to monitor the balancing ability of the elderly or patients with impaired balancing ability.

Inertial measurement units are an efficient tool to estimate the orientation of a rigid body with respect to a global or navigation frame. Thanks to their relatively small scale, these devices are often employed in clinical environments in form of wearable devices. A direct consequence of this large use of inertial sensors has been the development of many sensor fusion techniques for pose estimation in many practical applications. In this paper we study the feasibility of a nonlinear "Unscented" variant of the well-known Kalman Filter for gyroscope/accelerometer sensor fusion in pelvis pose estimation during treadmill walking. In addition, orientation estimation has been obtained without IMU magnetometer data, in order to propose a method suitable also for environments where magnetic disturbances could arise. Pelvis heading (yaw), bank (roll) and attitude (pitch) angles have been evaluated both using the proposed filter and a gold standard optometric system. The root mean square errors obtained using the proposed sensor fusion with respect to the gold standard are below 1 degree for each axis, showing also a significant high correlation (> 0.90). Findings of this study highlight the suitability of a magnetometer-free UKF approach for pose estimation of pelvis during human walking on treadmill, providing information useful also for further estimation of center of mass displacement in the same experimental conditions.

Background: Hospital stays are associated with high levels of sedentary behavior and physical inactivity. To objectively investigate physical behavior of hospitalized patients, these is a need for valid measurement instruments. The aim of this study was to assess the criterion validity of three accelerometers to measure lying, sitting, standing and walking., Methods: This cross-sectional study was performed in a university hospital. Participants carried out several mobility tasks according to a structured protocol while wearing three accelerometers (ActiGraph GT9X Link, Activ8 Professional and Dynaport MoveMonitor). The participants were guided through the protocol by a test leader and were recorded on video to serve as reference. Sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) were determined for the categories lying, sitting, standing and walking., Results: In total 12 subjects were included with a mean age of 49.5 (SD 21.5) years and a mean body mass index of 23.8 kg/m2 (SD 2.4). The ActiGraph GT9X Link showed an excellent sensitivity (90%) and PPV (98%) for walking, but a poor sensitivity for sitting and standing (57% and 53%), and a poor PPV (43%) for sitting. The Activ8 Professional showed an excellent sensitivity for sitting and walking (95% and 93%), excellent PPV (98%) for walking, but no sensitivity (0%) and PPV (0%) for lying. The Dynaport MoveMonitor showed an excellent sensitivity for sitting (94%), excellent PPV for lying and walking (100% and 99%), but a poor sensitivity (13%) and PPV (19%) for standing., Conclusions: The validity outcomes for the categories lying, sitting, standing and walking vary between the investigated accelerometers. All three accelerometers scored good to excellent in identifying walking. None of the accelerometers were able to identify all categories validly., Competing Interests: The authors have declared that no competing interests exist.

Objective: To examine the concurrent validity of the GT3X ® ActiGraph accelerometer and Google Fit ® smartphone application in estimating energy expenditure in people who had suffered a stroke, during fast overground walking., Methods: Thirty community-dwelling stroke individuals walked on a 10-meter hallway over 5min at their fastest speeds, wearing a Cortex Metamax 3B ® ergoespirometer, a GT3X ® ActiGraph accelerometer, and a smartphone with the Google Fit ® application. Pearson correlation coefficients were calculated to verify the associations between measures of energy expenditure, in kilocalories (kcal), estimated by both devices and those obtained with the Cortex Metamax 3B ® ergoespirometer (gold-standard measure)., Results: Fair association was found between the energy expenditure values estimated from the combined formula of the ActiGraph GT3X ® and those obtained with the gold-standard measure (r=0.37; p=0.04). No significant associations were found between the energy expenditure values estimated by the Google Fit ® application and those provided by the gold-standard measure., Conclusions: The findings demonstrated that both the GT3X ® ActiGraph accelerometer and the Google Fit ® smartphone application do not provide valid measures of energy expenditure in chronic stroke individuals during fast overground walking., (Copyright © 2018 Associação Brasileira de Pesquisa e Pós-Graduação em Fisioterapia. Publicado por Elsevier Editora Ltda. All rights reserved.)

Objective: To determine the validity of the ActiGraph GT3X accelerometer in step count quantification when compared to observed step count in hospitalised adults recovering from critical illness., Setting: Large National Health Service (NHS) Hospitals Trust., Subjects: In total, 20 hospital ward-based adults (age: mean 62.3, SD 11.5) who had required greater than 48 hours of mechanical ventilation in the intensive care unit., Main Measures: Participants walked self-selected distances and speeds as part of a semi-structured movement protocol not exceeding 3 hours. Two ActiGraph GT3X accelerometers were worn, one on the thigh and one on the ankle of the non-dominant leg. Accelerometer-recorded step counts were compared against observed step counts., Results: In total, 31 separate walking episodes were analysed. A mean (SD) of 45.87 (±19.72) steps was calculated for observed step count (range 15-90). Mean differences (95% limits of agreement) of -0.84 steps (-3.88 to 2.2) for the ankle placement and -17.7 steps (-40.63 to 5.25) for the thigh were calculated. Intraclass correlation coefficients (95% confidence intervals) of 0.99 (0.99 to 1.0) and 0.46 (-0.1 to 0.78) were determined for the ankle and thigh, respectively. Placement sites were well tolerated by 95% of participants., Conclusion: An ankle-mounted ActiGraph GT3X accelerometer demonstrates validity in quantification of step count in hospitalised adults recovering from critical illness. A thigh placement was not considered valid.

This paper addresses the significant need for improvements in device version reporting and practice across the academic and technical activity monitoring literature, and it recommends assessments for new and updated consumer sensing devices. Reproducibility and data veracity are central to good scholarship, and particularly significant in clinical and health applications. Across the literature there is an absence of device version reporting and a failure to recognize that device validity is not maintained when firmware and software updates can, and do, change device performance and parameter estimation. In this paper, we propose the use of tractable methods to assess devices at their current version and provide an example empirical approach. Experimental results for heart rate and step count acquisitions during walking and everyday living activities from Garmin Vivosmart 3 (v4.10) wristband monitors are presented and analyzed, and the reliability issues of optically-acquired heart rates, especially during periods of activity, are demonstrated and discussed. In conclusion, the paper recommends the empirical assessment of new and updated activity monitors and improvements in device version reporting across the academic and technical literature.

Background: Currently, there is an increase in the application of accelerometers to quantify physical activity level through daily steps, and therefore, it is required that the devices are valid and reliable. The current study tested the validity of the WIMU PROTM inertial device in steps detection and the inter-device reliability for steps measurement., Methods: Ten endurance-trained men volunteered to participate in this study (height: 1.78±0.05 m; body mass: 72.96±4.2 kg). Twelve trials of 40.32 m at different speeds (four trials at each speed) were performed: 1) walking (<5 km/h); 2) jogging (5-10 km/h) and running (10-15 km/h). The video analysis was used as criterion to assess validity, while, two WIMU PROTM inertial devices were compared to assess between-unit reliability., Results: A near-perfect validity between video analysis and WIMU PROTM (ICC=0.999; bias=0.03) and a very good reliability between WIMU PROTM devices (bias=-0.01; CV=0.02%) was found. The highest values were found at walking (ICC=0.999) and the lowest values were found at running (ICC=0.995)., Conclusions: WIMU PROTM is a reliable and valid device to quantify physical activity level through steps measurement.

With the rising popularity of activity tracking, there is a desire to not only count the number of steps a person takes, but also identify the type of step (e.g., walking or running) they are taking. For rehabilitation and athletic training, this difference is important to the prescribed regiment. Fourteen healthy adults walked, jogged and ran on a treadmill at three different constant speeds (1.21, 2.01, 2.68 m/s) for 90 s. An inertial measurement unit (IMU) with accelerometer and gyroscope was affixed to their left ankle. Collected acceleration and angular velocity data were partitioned into individual time-normalised strides. These data were used as features in the artificial neural network (ANN) that classified the type of stride. Several ANN models were tested: using only acceleration, only angular velocity and both. Using primarily acceleration data in the trained ANN yielded the best results ( > 94% correct stride-type identification) after cross-validation. The ANN models were able to accurately classify the gait type of each stride using a single wearable IMU. The accuracy of the method should improve further as more data is added to the ANN training.

Background: Step counts, obtained via activity monitors, provide insight into activity level in the free-living environment. Accuracy assessments of activity monitors are limited among individuals with lower-limb amputations., Objectives: (1) To evaluate the step count accuracy of both monitors during forward-linear and complex walking and (2) compare monitor step counts in the free-living environment., Study Design: Cross-sectional study., Methods: Adult prosthetic users with a unilateral transtibial amputation were equipped with StepWatch and FitBit One™. Participants completed an in-clinic evaluation to evaluate each monitor's step count accuracy during forward linear and complex walking followed by a 7-day step count evaluation in the free-living environment., Results: Both monitors showed excellent accuracy during forward, linear walking (intraclass correlation coefficients = 0.97-0.99, 95% confidence interval = 0.93-0.99; percentage error = 4.3%-6.2%). During complex walking, percentage errors were higher (13.0%-15.5%), intraclass correlation coefficients were 0.88-0.90, and 95% confidence intervals were 0.69-0.96. In the free-living environment, the absolute percentage difference between monitor counts was 25.4%, but the counts had a nearly perfect linear relationship., Conclusion: Both monitors accurately counted steps during forward linear walking. StepWatch appears to be more accurate than FitBit during complex walking but a larger sample size may confirm these findings. FitBit consistently counted fewer steps than StepWatch during free-living walking. Clinical relevance The StepWatch and FitBit are acceptable tools for assessing forward, linear walking for individuals with transtibial amputation. Given the results' consistenty in the free-living enviorment, both tools may ultimiately be able to be used to count steps in the real world, but more research is needed to confirm these findings.

Background: Physical activity among children and adolescents (collectively, youth) is important to ensure adult health. Population density is a factor that affects physical activity via various environmental factors. However, the relationship between population density and physical activity among youth is not fully understood, especially in extremely high density area. The aim of this study was to examine the relationship between population density and physical activity of youth using pedometer-determined step data., Methods: A total of 13,688 youth between 6 to 15 years of age were identified from the 2011 Tokyo Metropolitan Survey of Physical Fitness, Physical Activity and Lifestyle. Participants were divided into five subgroups according to the population density of their municipality of residence. The population density's fixed effects on in-school, out-of-school, and daily total step count adjusted for gender and school grade were estimated., Results: The lowest (< 2500 people/km 2 ) and highest (> 10,000 people/km 2 ) population density subgroups had significantly lower daily total step count and out-of-school step count than those of the reference population (5000-7500 people/km 2 ). In contrast, in-school step count did not significantly differ according to population density., Conclusions: Both low population density and also high population density were related to lower step count. Low physical activity in high density areas has not been well documented in previous research. Considering population growth in urbanized area globally, these results suggest the importance of continued research of physical activity determinants in high population density areas.

Objective: To evaluate the effect of accelerometer-based feedback on physical activity in hospitalized patients with ischemic stroke., Design: Randomized controlled trial., Setting: Acute care hospital., Subjects: A total of 55 patients with ischemic stroke who could walk without assistance were randomly assigned to the intervention group ( n = 27) or the control group ( n = 28)., Interventions: At the baseline measurement, patients did not receive accelerometer-based feedback. At follow-up, a physical therapist provided instruction on accelerometer-based feedback, discussed physical activity targets and encouraged the patients to walk more until discharge., Main Measures: The average daily number of steps taken was used as the index of daily hospitalized physical activity., Results: The study sample consisted of 48 patients, of whom 23 patients comprised the intervention group and 25 patients comprised the control group. Although there were no significant differences in physical activity values between the two groups at the baseline measurement, the values in the intervention group at follow-up were significantly higher than those in the control group (5180.5 ± 2314.9 vs. 3113.6 ± 1150.9 steps/day, P = 0.0003). The effect size of physical activity values (Cohen's d = 1.15) at follow-up was large between the two groups., Conclusion: Exercise training combined with accelerometer-based feedback effectively increased physical activity in hospitalized patients with ischemic stroke.

Background: Reliable step counting is a critical part of locomotion research. Current counting methods can be inaccurate, time consuming, expensive or encumbering to the subject. Here, we present a camera-based optical method for automatically counting steps., Methods: Fifteen healthy adults walked, jogged and ran on a treadmill at three different constant speeds (1.21, 2.01, 2.68 m/s) and once at varying speed (1.21-2.68 m/s) for 90 s. Subjects had visual marker affixed to their left foot while walking. Video was recorded synchronously at low- and high-resolution during trials. The step count found manually from the video was compared to an automated video analysis system using the two configurations of the optical system., Results: Bland-Altman plots, Intra-class correlation coefficients (ICC) and relative error comparison were used for quantitative assessment of device reliability. Reliability of optical method was high (ICC ≥0.98)., Conclusions: The method produces accurate step count results for the range of speeds tested. They use customisable open-source software and off-the-shelf hardware. The method has a low cost of implementation compared to many consumer products and grants researchers access to the raw sensor data.

This report evaluates the relationship between acculturation and assimilation with the physical activity (PA) outcomes of a 12-month walking intervention for postpartum Latinas ( n = 81, M age = 29.2 years, M BMI [body mass index] = 30.0). PA was measured by ActiGraph GT1M accelerometers. Acculturation and assimilation were measured by the Hazuda Acculturation and Assimilation Scales. Data were collected at baseline, 6 months, and 12 months. Results showed a trend for participants classified in the least acculturated groups to engage in more moderate-to-vigorous PA than participants classified in the higher acculturated/assimilated groups for two dimensions of acculturation (Adult Proficiency in English Versus Spanish, p = .002; Adult Pattern of English Versus Spanish Language Usage, p = .001) and two dimensions of assimilation (Childhood Interaction With Members of Mainstream Society, p = .028; Adult Functional Integration With Mainstream Society, p ≤ .001). No other significant effects were observed. Findings highlight the continued need to understand the context in which acculturation and assimilation influence PA.

This study presents a novel method for the detection and classification of a wide range of physical activities, including standing, sitting, lying, level walking, and walking upstairs and downstairs using a single chest-mounted accelerometer. The trunk inclination angle and variation of the gravitational component of the accelerometer recording were used for detection and classification of postural transitions and walking modalities. In addition, biomechanical features of each transition were used to reject false detections. To validate the accuracy of the presented method, two studies were performed, first in the (1) laboratory environment, where a motion capture system was the reference system (ten healthy subjects), and second (2) in the free-living environment where a handheld camera was the reference system (ten healthy subjects). The first study showed that the proposed method obtained higher accuracy, sensitivity, and specificity in detection of postural transitions and walking modalities compared to other methods in the literature when implemented on the same dataset. The second study obtained (1) the sensitivity and specificity of 100% for detection of sit-to-lie, lie-to-sit, and stand-to-sit, and 100% and 97%, respectively, for detection of sit-to-stand, and (2) the accuracy of 99%, 99%, and 95% for detection of slow, normal, and fast level walking, and 97% and 96% for detection of walking upstairs and downstairs. The proposed method enabled detection and classification of postural transitions and walking modalities with high sensitivity and specificity using only one chest-mounted accelerometer. This approach can be used for convenient and reliable assessment of physical activities in long-term., (Copyright © 2018 IPEM. Published by Elsevier Ltd. All rights reserved.)

Introduction: The purpose of this study is to identify whether weather-related factors moderate the effect of a physical activity (PA) intervention., Methods: Participants (N=204, 77% female, mean age 33 [SD=11] years, mean BMI 28.2 [SD=7.1]) from the Make Better Choices 1 trial, enrolled April 2005 to April 2008, were randomized to one of two treatment conditions: (1) increase moderate to vigorous physical activity (MVPA) treatment group, or (2) decrease sedentary behavior control group. Participants wore an accelerometer for 5 weeks: a 2-week baseline assessment followed by a 3-week intervention. Accelerometer data were used to estimate minutes/day of MVPA. Average daily temperature, day length, and precipitation were obtained from the National Climatic Data Center and combined with the accelerometer data. Linear mixed effects models were used to determine whether these weather-related factors moderated the effect of the intervention on MVPA. Separate models were fit for season, daily average temperature, and day length., Results: There was a significant moderating effect of season on MVPA such that the PA intervention, as compared with control, increased MVPA 10.4 minutes more in the summer than in the winter (95% CI=1.1, 19.6, p=0.029). There was a significant moderating effect of daily temperature such that every 10°F increase in temperature was associated with an additional 1.5 minutes/day increase in the difference in MVPA increase between the two intervention conditions (95% CI=0.1, 2.9, p=0.015). There was a significant moderating effect of day length such that every additional hour of daylight was associated with a 2.23-minute increase in the PA intervention's impact on increasing MVPA (95% CI=0.8, 3.7, p=0.002)., Conclusions: Day length and temperature had a significant moderating effect on change in MVPA during a PA intervention such that the intervention was less effective on colder days and on shorter days, independently. These results suggest that strategies to overcome environmental barriers should be considered when designing PA interventions for adults., (Copyright © 2018 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

Background: Activity monitors provide objective measurements of physical activity, however, the accuracy of these devices in people with polymyalgia rheumatica (PMR) is unknown. Therefore, this study aimed to obtain preliminary evidence of the accuracy of two activity monitors and explore if clinical and gait-related factors altered device accuracy in people with PMR., Methods: The ActiGraph with low frequency extension (+LFE) and standard (-LFE) algorithms, Fitbit-Zip (waist) and Fitbit-Zip (shirt) were concurrently tested using a two-minute walk test (2MWT) and stairs test in 27 people with PMR currently treated with prednisolone. To determine accuracy, activity monitor step-count was compared to a gold-standard step-count (GSSC; calculated from video recording) using Bland-Altman plots., Results: The Fitbit-Zip (waist) achieved closest agreement to the GSSC for the 2MWT (mean bias (95%CI): 10 (-3, 23); 95%LOA: -55, 74). The ActiGraph (+LFE) achieved closest agreement to the GSSC for the stairs test (mean bias (95%CI): 0 (-1, 1); 95%LOA: -5, 5). The ActiGraph (-LFE) performed poorly in both tests. All devices demonstrated reduced accuracy in participants with lower gait velocity, reduced stride length, longer double-limb support phase and greater self-reported functional impairment., Conclusion: Our preliminary results suggest that in controlled conditions, the Fitbit-Zip fairly accurately measures step-count during walking in people with PMR receiving treatment. However, device error was greater than data published in healthy people. The ActiGraph may not be recommended without activation of the LFE. We identified clinical and gait-related factors associated with higher levels of functional impairment that reduced device accuracy. Further work is required to evaluate the validity of the activity monitors in field conditions., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: Accurate measurement devices are required to objectively quantify physical activity. Wearable activity monitors, such as pedometers, may serve as affordable and feasible instruments for measuring physical activity levels in older adults during their normal activities of daily living. Currently few available accelerometer-based steps counting devices have been shown to be accurate at slow walking speeds, therefore there is still lacking appropriate devices tailored for slow speed ambulation, typical of older adults. This study aimed to assess the validity of step counting using the pedometer function of the ADAMO Care Watch, containing an embedded algorithm for measuring physical activity in older adults., Methods: Twenty older adults aged ≥ 65 years (mean ± SD, 75±7 years; range, 68-91) and 20 young adults (25±5 years, range 20-40), wore a care watch on each wrist and performed a number of randomly ordered tasks: walking at slow, normal and fast self-paced speeds; a Timed Up and Go test (TUG); a step test and ascending/descending stairs. The criterion measure was the actual number of steps observed, counted with a manual tally counter. Absolute percentage error scores, Intraclass Correlation Coefficients (ICC), and Bland-Altman plots were used to assess validity., Results: ADAMO Care Watch demonstrated high validity during slow and normal speeds (range 0.5-1.5 m/s) showing an absolute error from 1.3% to 1.9% in the older adult group and from 0.7% to 2.7% in the young adult group. The percentage error for the 30-metre walking tasks increased with faster pace in both young adult (17%) and older adult groups (6%). In the TUG test, there was less error in the steps recorded for older adults (1.3% to 2.2%) than the young adults (6.6% to 7.2%). For the total sample, the ICCs for the ADAMO Care Watch for the 30-metre walking tasks at each speed and for the TUG test were ranged between 0.931 to 0.985., Conclusion: These findings provide evidence that the ADAMO Care Watch demonstrated highly accurate measurements of the steps count in all activities, particularly walking at normal and slow speeds. Therefore, these data support the inclusion of the ADAMO Care Watch in clinical applications for measuring the number of steps taken by older adults at normal, slow walking speeds.

Introduction and Objectives: There are various recommendations for physical activity (PA). However agreement between all of these measures has not been established. Furthermore, given the challenges of measuring PA there is interest in evaluating whether a measure of exercise performance can be used as a surrogate measure to identify who is likely to achieve the recommendations., Methods: A total of 184 people with COPD were recruited, 128 of which had complete data for these analyses. Participants wore the SenseWear Armband for 7 consecutive days and all performed an incremental shuttle walk test (ISWT). We extracted moderate to vigorous physical activity (MVPA) in bouts of ≥10 min using a 3 metabolic equivalent (MET) threshold and an individually prescribed MET threshold (based on performance on the ISWT). Average daily step count and the physical activity level were also calculated., Results: There was poor agreement between the four PA recommendations, with agreement on all four achieved in only 30 participants. People were least likely to be active using MVPA in ≥10 min bouts using 3 MET threshold (21.1% active), and most likely to be active using MVPA in ≥10 min bouts using an individually prescribed threshold (64.9% active). It was not possible to identify a threshold on the ISWT that would reliably predict those that achieved any of the four recommendations., Conclusion: Agreement between various physical activity recommendations is poor. This should be considered when measuring and describing physical activity adherence. The ISWT cannot be used to reliably predict adherence to physical activity guidelines., (© 2016 John Wiley & Sons Ltd.)

Purpose: The aim of this study was to compare gait stability and variability between walking conditions and age groups., Methods: Twenty-six healthy younger and older females participated. Trunk acceleration in the vertical (V), medial-lateral (ML) and anteriorposterior (AP) directions during 5 minutes walking overground and 3 minutes walking on the treadmill at self-selected speed were recorded. Root mean square and standard deviations of acceleration, stride time and its variability, Lyapunov exponents (LE), multiscale entropy (MSE) and harmonic ratios (HR) were computed., Results: Both age groups showed significantly higher stride time variability and short-term LE in all directions during overground walking. For the older group, overground walking showed higher V and AP standard deviation. Significantly lower values for overground walking were observed for long-term LE (V and ML for the younger group, ML for the older group), HR (ML for the older group) and MSE (V for the older group). Significant age-related differences were found for V long-term LE for overground walking., Conclusions: The present findings suggest that both linear and advanced computational techniques for gait stability and variability assessment in older adults are sensitive to walking conditions.

In order to evaluate the ability of Parkinson's patients to walk comprehensively, a system based on MEMS to aid clinical quantification of ability in Parkinson's is established. The inertial units are respectively fixed on the back and the waist of subject to be measured. The Kalman fusion algorithm is used to extract the characteristic parameters of accelerometer and gyroscope data. SVM classifier is designed to train and test the classifier by the feature. The results show that the system possesses a high recognition rate for Parkinson's patients and normal subjects and for the classification of the walking ability of patients with Parkinson's disease. So, this system can aid doctors to give more object diagnostic conclusion.

Objectives: We estimated associations between objectively determined neighbourhood 'walkability' attributes and accelerometer-derived sedentary time (ST) by sex, city or type of day., Design: A cross-sectional study., Setting: The URBAN (Understanding the Relationship between Activity and Neighbourhoods) study was conducted in 48 neighbourhoods across four cities in New Zealand (August 2008 to October 2010)., Participants: The response rate was 41% (2029 recruited participants/5007 eligible households approached). In total, 1762 participants (aged 41.4±12.1, mean±SD) met the data inclusion criteria and were included in analyses., Primary and Secondary Outcome Measures: The exposure variables were geographical information system (GIS) measures of neighbourhood walkability (ie, street connectivity, residential density, land-use mix, retail footprint area ratio) for street network buffers of 500 m and 1000 m around residential addresses. Participants wore an accelerometer for 7 days. The outcome measure was average daily minutes of ST., Results: Data were available from 1762 participants (aged 41.4±12.1 years; 58% women). No significant main effects of GIS-based neighbourhood walkability measures were found with ST. Retail footprint area ratio was negatively associated with sedentary time in women, significant only for 500 m residential buffers. An increase of 1 decile in street connectivity was significantly associated with a decrease of over 5 min of ST per day in Christchurch residents for both residential buffers., Conclusion: Neighbourhoods with proximal retail and higher street connectivity seem to be associated with less ST. These effects were sex and city specific., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)