A Novel Approach for Improving Gait Speed Estimation Using a Single Inertial Measurement Unit Embedded in a Smartphone: Validity and Reliability Study.

Background: Gait speed is a valuable biomarker for mobility and overall health assessment. Existing methods to measure gait speed require expensive equipment or personnel assistance, limiting their use in unsupervised, daily-life conditions. The availability of smartphones equipped with a single inertial measurement unit (IMU) presents a viable and convenient method for measuring gait speed outside of laboratory and clinical settings. Previous works have used the inverted pendulum model to estimate gait speed using a non-smartphone-based IMU attached to the trunk. However, it is unclear whether and how this approach can estimate gait speed using the IMU embedded in a smartphone while being carried in a pants pocket during walking, especially under various walking conditions. Objective: This study aimed to validate and test the reliability of a smartphone IMU-based gait speed measurement placed in the user's front pants pocket in both healthy young and older adults while walking quietly (ie, normal walking) and walking while conducting a cognitive task (ie, dual-task walking). Methods: A custom-developed smartphone application (app) was used to record gait data from 12 young adults and 12 older adults during normal and dual-task walking. The validity and reliability of gait speed and step length estimations from the smartphone were compared with the gold standard GAITRite mat. A coefficient-based adjustment based upon a coefficient relative to the original estimation of step length was applied to improve the accuracy of gait speed estimation. The magnitude of error (ie, bias and limits of agreement) between the gait data from the smartphone and the GAITRite mat was calculated for each stride. The Passing-Bablok orthogonal regression model was used to provide agreement (ie, slopes and intercepts) between the smartphone and the GAITRite mat. Results: The gait speed measured by the smartphone was valid when compared to the GAITRite mat. The original limits of agreement were 0.50 m/s (an ideal value of 0 m/s), and the orthogonal regression analysis indicated a slope of 1.68 (an ideal value of 1) and an intercept of -0.70 (an ideal value of 0). After adjustment, the accuracy of the smartphone-derived gait speed estimation improved, with limits of agreement reduced to 0.34 m/s. The adjusted slope improved to 1.00, with an intercept of 0.03. The test-retest reliability of smartphone-derived gait speed was good to excellent within supervised laboratory settings and unsupervised home conditions. The adjustment coefficients were applicable to a wide range of step lengths and gait speeds. Conclusions: The inverted pendulum approach is a valid and reliable method for estimating gait speed from a smartphone IMU placed in the pockets of younger and older adults. Adjusting step length by a coefficient derived from the original estimation of step length successfully removed bias and improved the accuracy of gait speed estimation. This novel method has potential applications in various settings and populations, though fine-tuning may be necessary for specific data sets.

School-level intra-cluster correlation coefficients and autocorrelations for children's accelerometer-measured physical activity in England by age and gender.

BACKGROUND: Randomised, cluster-based study designs in schools are commonly used to evaluate children's physical activity interventions. Sample size estimation relies on accurate estimation of the intra-cluster correlation coefficient (ICC), but published estimates, especially using accelerometry-measured physical activity, are few and vary depending on physical activity outcome and participant age. Less commonly-used cluster-based designs, such as stepped wedge designs, also need to account for correlations over time, e.g. cluster autocorrelation (CAC) and individual autocorrelation (IAC), but no estimates are currently available. This paper estimates the school-level ICC, CAC and IAC for England children's accelerometer-measured physical activity outcomes by age group and gender, to inform the design of future school-based cluster trials. METHODS: Data were pooled from seven large English datasets of accelerometer-measured physical activity data between 2002-18 (> 13,500 pupils, 540 primary and secondary schools). Linear mixed effect models estimated ICCs for weekday and whole week for minutes spent in moderate-to-vigorous physical activity (MVPA) and being sedentary for different age groups, stratified by gender. The CAC (1,252 schools) and IAC (34,923 pupils) were estimated by length of follow-up from pooled longitudinal data. RESULTS: School-level ICCs for weekday MVPA were higher in primary schools (from 0.07 (95% CI: 0.05, 0.10) to 0.08 (95% CI: 0.06, 0.11)) compared to secondary (from 0.04 (95% CI: 0.03, 0.07) to (95% CI: 0.04, 0.10)). Girls' ICCs were similar for primary and secondary schools, but boys' were lower in secondary. For all ages, combined the CAC was 0.60 (95% CI: 0.44-0.72), and the IAC was 0.46 (95% CI: 0.42-0.49), irrespective of follow-up time. Estimates were higher for MVPA vs sedentary time, and for weekdays vs the whole week. CONCLUSIONS: Adequately powered studies are important to evidence effective physical activity strategies. Our estimates of the ICC, CAC and IAC may be used to plan future school-based physical activity evaluations and were fairly consistent across a range of ages and settings, suggesting that results may be applied to other high income countries with similar school physical activity provision. It is important to use estimates appropriate to the study design, and that match the intended study population as closely as possible.

Smartphone IMU Sensors for Human Identification through Hip Joint Angle Analysis.

Gait monitoring using hip joint angles offers a promising approach for person identification, leveraging the capabilities of smartphone inertial measurement units (IMUs). This study investigates the use of smartphone IMUs to extract hip joint angles for distinguishing individuals based on their gait patterns. The data were collected from 10 healthy subjects (8 males, 2 females) walking on a treadmill at 4 km/h for 10 min. A sensor fusion technique that combined accelerometer, gyroscope, and magnetometer data was used to derive meaningful hip joint angles. We employed various machine learning algorithms within the WEKA environment to classify subjects based on their hip joint pattern and achieved a classification accuracy of 88.9%. Our findings demonstrate the feasibility of using hip joint angles for person identification, providing a baseline for future research in gait analysis for biometric applications. This work underscores the potential of smartphone-based gait analysis in personal identification systems.

Capturing How the Accelerometer Measured Physical Activity Profile Differs in People with Diabetic Foot Ulceration.

Diabetic Foot Ulcers (DFUs) are a major complication of diabetes, with treatment requiring offloading. This study aimed to capture how the accelerometer-assessed physical activity profile differs in those with DFUs compared to those with diabetes but without ulceration (non-DFU). Participants were requested to wear an accelerometer on their non-dominant wrist for up to 8days. Physical activity outcomes included average acceleration (volume), intensity gradient (intensity distribution), the intensity of the most active sustained (continuous) 5-120 min of activity (MXCONT), and accumulated 5-120 min of activity (MXACC). A total of 595 participants (non-DFU = 561, DFU = 34) were included in the analysis. Average acceleration was lower in DFU participants compared to non-DFU participants (21.9 mg [95%CI:21.2, 22.7] vs. 16.9 mg [15.3, 18.8], p < 0.001). DFU participants also had a lower intensity gradient, indicating proportionally less time spent in higher-intensity activities. The relative difference between DFU and non-DFU participants was greater for sustained activity (MXCONT) than for accumulated (MXACC) activity. In conclusion, physical activity, particularly the intensity of sustained activity, is lower in those with DFUs compared to non-DFUs. This highlights the need for safe, offloaded modes of activity that contribute to an active lifestyle for people with DFUs.

Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease.

Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.

Deep Learning System for User Identification Using Sensors on Doorknobs.

Door access control systems are important to protect the security and integrity of physical spaces. Accuracy and speed are important factors that govern their performance. In this paper, we investigate a novel approach to identify users by measuring patterns of their interactions with a doorknob via an embedded accelerometer and gyroscope and by applying deep-learning-based algorithms to these measurements. Our identification results obtained from 47 users show an accuracy of 90.2%. When the sex of the user is used as an input feature, the accuracy is 89.8% in the case of male individuals and 97.0% in the case of female individuals. We study how the accuracy is affected by the sample duration, finding that is its possible to identify users using a sample of 0.5 s with an accuracy of 68.5%. Our results demonstrate the feasibility of using patterns of motor activity to provide access control, thus extending with it the set of alternatives to be considered for behavioral biometrics.

Probabilistic Estimation of Cadence and Walking Speed From Floor Vibrations.

OBJECTIVE: This research aims to extract human gait parameters from floor vibrations. The proposed approach provides an innovative methodology on occupant activity, contributing to a broader understanding of how human movements interact within their built environment. METHODS AND PROCEDURES: A multilevel probabilistic model was developed to estimate cadence and walking speed through the analysis of floor vibrations induced by walking. The model addresses challenges related to missing or incomplete information in the floor acceleration signals. Following the Bayesian Analysis Reporting Guidelines (BARG) for reproducibility, the model was evaluated through twenty-seven walking experiments, capturing floor vibration and data from Ambulatory Parkinson's Disease Monitoring (APDM) wearable sensors. The model was tested in a real-time implementation where ten individuals were recorded walking at their own selected pace. RESULTS: Using a rigorous combined decision criteria of 95% high posterior density (HPD) and the Range of Practical Equivalence (ROPE) following BARG, the results demonstrate satisfactory alignment between estimations and target values for practical purposes. Notably, with over 90% of the 95% HPD falling within the region of practical equivalence, there is a solid basis for accepting the estimations as probabilistically aligned with the estimations using the APDM sensors and video recordings. CONCLUSION: This research validates the probabilistic multilevel model in estimating cadence and walking speed by analyzing floor vibrations, demonstrating its satisfactory comparability with established technologies such as APDM sensors and video recordings. The close alignment between the estimations and target values emphasizes the approach's efficacy. The proposed model effectively tackles prevalent challenges associated with missing or incomplete data in real-world scenarios, enhancing the accuracy of gait parameter estimations derived from floor vibrations. CLINICAL IMPACT: Extracting gait parameters from floor vibrations could provide a non-intrusive and continuous means of monitoring an individual's gait, offering valuable insights into mobility and potential indicators of neurological conditions. The implications of this research extend to the development of advanced gait analysis tools, offering new perspectives on assessing and understanding walking patterns for improved diagnostics and personalized healthcare.Clinical and Translational Impact Statement: This manuscript introduces an innovative approach for unattended gait assessments with potentially significant implications for clinical decision-making. By utilizing floor vibrations to estimate cadence and walking speed, the technology can provide clinicians with valuable insights into their patients' mobility and functional abilities in real-life settings. The strategic installation of accelerometers beneath the flooring of homes or care facilities allows for uninterrupted daily activities during these assessments, reducing the reliance on specialized clinical environments. This technology enables continuous monitoring of gait patterns over time and has the potential for integration into healthcare platforms. Such integration can enhance remote monitoring, leading to timely interventions and personalized care plans, ultimately improving clinical outcomes. The probabilistic nature of our model enables uncertainty quantification in the estimated parameters, providing clinicians with a nuanced understanding of data reliability.

Measuring 24-h use of time in people with a diabetes-related foot ulcer: A feasibility study.

BACKGROUND: Physical activity (PA), sleep and sedentary time are now recognised as mutually exclusive and exhaustive parts of the 24-h day-if PA decreases, time spent sleeping, being sedentary or both must increase so that all components equate to 24 h. Recent advances in time-use epidemiology suggest that we should not consider time-use domains (PA, sleep and sedentary time) in isolation from each other, but in terms of a composition-the mix of time-use domains across the 24-h day. While interrelated daily activities are known to be important in the management of diabetes mellitus, few studies have investigated the interrelated daily activities in people with an active diabetes-related foot ulcer (DFU) and their impact on important outcomes such as wound severity, blood glucose control and health-related quality of life (HRQoL). This feasibility study aims to determine the acceptability and practicality of measuring 24-h use of time data in people with a DFU and its associations on important outcome measures for this population. METHODS: Participants wore a wrist-worn accelerometer for two weeks and completed demographic and HRQoL questionnaires. Outcomes were participant engagement, reported levels of study burden and value and compositional data analysis as a methodological approach for evaluating 24-h use of time data. RESULTS: Twenty-six participants reported low levels of study burden and rated the study value highly. The protocol appears feasible in terms of recruitment (81%) and retention rate (86%). On average, participants were relatively sedentary spending 747, 172 and 18 min in sedentary time, light physical activity and moderate-to-vigorous activity, respectively. Sleep appeared adequate with participants obtaining an average of 485 min, but quality of sleep was notably poor with average sleep efficiency of 75%. Compositional data analysis was able to quantify the integrated associations of 24-h use of time with HRQoL. CONCLUSION: The protocol provides an acceptable method to collect 24-h use of time data in people with a DFU. Efforts to consider and analyse PA as part of a 24-h activity composition may provide holistic and realistic understandings of PA in this clinical population.

Validity of predictive equations for total energy expenditure against doubly labeled water.

Variations in physical activity energy expenditure can make accurate prediction of total energy expenditure (TEE) challenging. The purpose of the present study was to determine the accuracy of available equations to predict TEE in individuals varying in physical activity (PA) levels. TEE was measured by DLW in 56 adults varying in PA levels which were monitored by accelerometry. Ten different models were used to predict TEE and their accuracy and precision were evaluated, considering the effect of sex and PA. The models generally underestimated the TEE in this population. An equation published by Plucker was the most accurate in predicting the TEE in our entire sample. The Pontzer and Vinken models were the most accurate for those with lower PA levels. Despite the levels of accuracy of some equations, there were sizable errors (low precision) at an individual level. Future studies are needed to develop and validate these equations.

Effects of metronome walking on long-term attractor divergence and correlation structure of gait: a validation study in older people.

This study investigates the effects of metronome walking on gait dynamics in older adults, focusing on long-range correlation structures and long-range attractor divergence (assessed by maximum Lyapunov exponents). Sixty older adults participated in indoor walking tests with and without metronome cues. Gait parameters were recorded using two triaxial accelerometers attached to the lumbar region and to the foot. We analyzed logarithmic divergence of lumbar acceleration using Rosenstein's algorithm and scaling exponents for stride intervals from foot accelerometers using detrended fluctuation analysis (DFA). Results indicated a concomitant reduction in long-term divergence exponents and scaling exponents during metronome walking, while short-term divergence remained largely unchanged. Furthermore, long-term divergence exponents and scaling exponents were significantly correlated. Reliability analysis revealed moderate intrasession consistency for long-term divergence exponents, but poor reliability for scaling exponents. Our results suggest that long-term divergence exponents could effectively replace scaling exponents for unsupervised gait quality assessment in older adults. This approach may improve the assessment of attentional involvement in gait control and enhance fall risk assessment.

Accelerometer Thresholds for Estimating Physical Activity Intensity Levels in Infants: A Preliminary Study.

Lack of physical activity (PA) at a young age can result in health issues. Thus, monitoring PA is important. Wearable accelerometers are the preferred tool to monitor PA in children. Validated thresholds are used to classify activity intensity levels, e.g., sedentary, light, and moderate-to-vigorous, in ambulatory children. No previous work has developed accelerometer thresholds for infancy (pre-ambulatory children). Therefore, this work aims to develop accelerometer thresholds for PA intensity levels in pre-ambulatory infants. Infants (n = 10) were placed in a supine position and allowed free movement. Their movements were synchronously captured using video cameras and accelerometers worn on each ankle. The video data were labeled by activity intensity level (sedentary, light, and moderate-to-vigorous) in two-second epochs using observational rating (gold standard). Accelerometer thresholds were developed for acceleration and jerk using two optimization approaches. Four sets of thresholds were developed for dual (two ankles) and for single-worn (one ankle) accelerometers. Of these, for a typical use case, we recommend using acceleration-based thresholds of 1.00 m/s to distinguish sedentary and light activity and 2.60 m/s to distinguish light and moderate-to-vigorous activity. Acceleration and jerk are both suitable for measuring PA.

Toward Concurrent Identification of Human Activities with a Single Unifying Neural Network Classification: First Step.

The characterization of human behavior in real-world contexts is critical for developing a comprehensive model of human health. Recent technological advancements have enabled wearables and sensors to passively and unobtrusively record and presumably quantify human behavior. Better understanding human activities in unobtrusive and passive ways is an indispensable tool in understanding the relationship between behavioral determinants of health and diseases. Adult individuals (N = 60) emulated the behaviors of smoking, exercising, eating, and medication (pill) taking in a laboratory setting while equipped with smartwatches that captured accelerometer data. The collected data underwent expert annotation and was used to train a deep neural network integrating convolutional and long short-term memory architectures to effectively segment time series into discrete activities. An average macro-F1 score of at least 85.1 resulted from a rigorous leave-one-subject-out cross-validation procedure conducted across participants. The score indicates the method's high performance and potential for real-world applications, such as identifying health behaviors and informing strategies to influence health. Collectively, we demonstrated the potential of AI and its contributing role to healthcare during the early phases of diagnosis, prognosis, and/or intervention. From predictive analytics to personalized treatment plans, AI has the potential to assist healthcare professionals in making informed decisions, leading to more efficient and tailored patient care.

Influence of Gestational Age on Pelvic Floor Muscle Activity, Plantar Contact, and Functional Mobility in High-Risk Pregnant Women: A Cross-Sectional Study.

During pregnancy, biomechanical changes are observed due to hormonal and physical modifications, which can lead to alterations in the curvature of the spine, balance, gait patterns, and functionality of the pelvic floor muscles. This study aimed to investigate the progressive impact of biomechanical changes that occur during gestational weeks on the myoelectric activity of the pelvic floor muscles, plantar contact area, and functional mobility of high-risk pregnant women. METHODS: This was a cross-sectional observational study carried out from November 2022 to March 2023. A total of 62 pregnant women of different gestational ages with high-risk pregnancies were analyzed using surface electromyography to assess the functionality of the pelvic floor muscles, plantigraphy (Staheli index and plantar contact area), and an accelerometer and gyroscope using the timed up and go test via an inertial sensor on a smartphone. Descriptive statistics and multivariate linear regression analyses were carried out to test the predictive value of the signature. RESULTS: Increasing weeks of gestation resulted in a decrease in the RMS value (ß = -0.306; t = -2.284; p = 0.026) according to the surface electromyography analyses. However, there was no association with plantar contact (F (4.50) = 0.697; p = 0.598; R2 = 0.53). With regard to functional mobility, increasing weeks of gestation resulted in a decrease in time to standing (ß = -0.613; t = -2.495; p = 0.016), time to go (ß = -0.513; t = -2.264; p = 0.028), and first gyrus peak (ß = -0.290; t = -2.168; p = 0.035). However, there was an increase in the time to come back (ß = 0.453; t = 2.321; p = 0.025) as the number of gestational weeks increased. CONCLUSIONS: Increased gestational age is associated with a reduction in pelvic floor myoelectric activity. The plantar contact area did not change over the weeks. Advancing gestation was accompanied by a decrease in time to standing, time to go, and first gyrus peak, as well as an increase in time to come back.

Apple Watch 6 vs. Galaxy Watch 4: A Validity Study of Step-Count Estimation in Daily Activities.

The purpose of this study was to examine the validity of two wearable smartwatches (the Apple Watch 6 (AW) and the Galaxy Watch 4 (GW)) and smartphone applications (Apple Health for iPhone mobiles and Samsung Health for Android mobiles) for estimating step counts in daily life. A total of 104 healthy adults (36 AW, 25 GW, and 43 smartphone application users) were engaged in daily activities for 24 h while wearing an ActivPAL accelerometer on the thigh and a smartwatch on the wrist. The validities of the smartwatch and smartphone estimates of step counts were evaluated relative to criterion values obtained from an ActivPAL accelerometer. The strongest relationship between the ActivPAL accelerometer and the devices was found for the AW (r = 0.99, p < 0.001), followed by the GW (r = 0.82, p < 0.001), and the smartphone applications (r = 0.93, p < 0.001). For overall group comparisons, the MAPE (Mean Absolute Percentage Error) values (computed as the average absolute value of the group-level errors) were 6.4%, 10.5%, and 29.6% for the AW, GW, and smartphone applications, respectively. The results of the present study indicate that the AW and GW showed strong validity in measuring steps, while the smartphone applications did not provide reliable step counts in free-living conditions.

Physical Functioning, Physical Activity, and Variability in Gait Performance during the Six-Minute Walk Test.

Instrumenting the six-minute walk test (6MWT) adds information about gait quality and insight into fall risk. Being physically active and preserving multi-directional stepping abilities are also important for fall risk reduction. This analysis investigated the relationship of gait quality during the 6MWT with physical functioning and physical activity. Twenty-one veterans (62.2 ± 6.4 years) completed the four square step test (FSST) multi-directional stepping assessment, a gait speed assessment, health questionnaires, and the accelerometer-instrumented 6MWT. An activity monitor worn at home captured free-living physical activity. Gait measures were not significantly different between minutes of the 6MWT. However, participants with greater increases in stride time (ρ = -0.594, p < 0.01) and stance time (ρ = -0.679, p < 0.01) during the 6MWT reported lower physical functioning. Neither physical activity nor sedentary time were related to 6MWT gait quality. Participants exploring a larger range in stride time variability (ρ = 0.614, p < 0.01) and stance time variability (ρ = 0.498, p < 0.05) during the 6MWT required more time to complete the FSST. Participants needing at least 15 s to complete the FSST meaningfully differed from those completing the FSST more quickly on all gait measures studied. Instrumenting the 6MWT helps detect ranges of gait performance and provides insight into functional limitations missed with uninstrumented administration. Established FSST cut points identify aging adults with poorer gait quality.

Automated Detection of In-Home Activities with Ultra-Wideband Sensors.

As Canada's population of older adults rises, the need for aging-in-place solutions is growing due to the declining quality of long-term-care homes and long wait times. While the current standards include questionnaire-based assessments for monitoring activities of daily living (ADLs), there is an urgent need for advanced indoor localization technologies that ensure privacy. This study explores the use of Ultra-Wideband (UWB) technology for activity recognition in a mock condo in the Glenrose Rehabilitation Hospital. UWB systems with built-in Inertial Measurement Unit (IMU) sensors were tested, using anchors set up across the condo and a tag worn by patients. We tested various UWB setups, changed the number of anchors, and varied the tag placement (on the wrist or chest). Wrist-worn tags consistently outperformed chest-worn tags, and the nine-anchor configuration yielded the highest accuracy. Machine learning models were developed to classify activities based on UWB and IMU data. Models that included positional data significantly outperformed those that did not. The Random Forest model with a 4 s data window achieved an accuracy of 94%, compared to 79.2% when positional data were excluded. These findings demonstrate that incorporating positional data with IMU sensors is a promising method for effective remote patient monitoring.

Research on Monitoring Assistive Devices for Rehabilitation of Movement Disorders through Multi-Sensor Analysis Combined with Deep Learning.

This study aims to integrate a convolutional neural network (CNN) and the Random Forest Model into a rehabilitation assessment device to provide a comprehensive gait analysis in the evaluation of movement disorders to help physicians evaluate rehabilitation progress by distinguishing gait characteristics under different walking modes. Equipped with accelerometers and six-axis force sensors, the device monitors body symmetry and upper limb strength during rehabilitation. Data were collected from normal and abnormal walking groups. A knee joint limiter was applied to subjects to simulate different levels of movement disorders. Features were extracted from the collected data and analyzed using a CNN. The overall performance was scored with Random Forest Model weights. Significant differences in average acceleration values between the moderately abnormal (MA) and severely abnormal (SA) groups (without vehicle assistance) were observed (p < 0.05), whereas no significant differences were found between the MA with vehicle assistance (MA-V) and SA with vehicle assistance (SA-V) groups (p > 0.05). Force sensor data showed good concentration in the normal walking group and more scatter in the SA-V group. The CNN and Random Forest Model accurately recognized gait conditions, achieving average accuracies of 88.4% and 92.3%, respectively, proving that the method mentioned above provides more accurate gait evaluations for patients with movement disorders.

Relationship between quantitative physical activity and deterioration of locomotive function: a cross-sectional study using baseline data from a cohort.

BACKGROUND: In aged society, health policies aimed at extending healthy life expectancy are critical. Maintaining physical activity is essential to prevent the deterioration of body functions. Therefore, it is important to understand the physical activity levels of the target age group and to know the content and intensity of the required physical activity quantitatively. Especially we focused the role of non-exercise activity thermogenesis and sedentary time, which are emphasized more than the introduction of exercise in cases of obesity or diabetes. METHODS: A total of 193 patients from 25 institutions were included. Participants underwent a locomotive syndrome risk test (stand-up test, 2-step test, and Geriatric Locomotive Function Scale-25 questionnaire) and were classified into three stages. Physical activity was quantitatively monitored for one week with 3-axial accelerometer. Physical activity was classified into three categories; (1) Sedentary behavior (0 ∼ ≤ 1.5 metabolic equivalents (METs)), (2) Light physical activity (LPA:1.6 ∼ 2.9 METs), and (3) Moderate to vigorous physical activity (MVPA: ≥3 METs). We investigated the relationship between physical activity, including the number of steps, and the stages after gender- and age- adjustment. We also investigated the relationship between social isolation using Lubben's Social Network Scale (LSNS), as social isolation would lead to fewer opportunities to go out and less outdoor walking. RESULTS: Comparison among the three stages showed significant difference for age (p = 0.007) and Body Mass Index (p < 0.001). After gender-and age-adjustment, there was a significant relation with a decrease in the number of steps (p = 0.002) and with MVPA. However, no relation was observed in sedentary time and LPA. LSNS did not show any statistically significant difference. Moderate to high-intensity physical activity and the number of steps is required for musculoskeletal disorders. The walking, not sedentary time, was associated to the locomotive stages, and this finding indicated the importance of lower extremity exercise. CONCLUSIONS: Adjusting for age and gender, the number of steps and moderate to vigorous activity levels were necessary to prevent worsening, and there was no effect of sedentary behavior. Merely reducing sedentary time may be inadequate for locomotive disorders. It is necessary to engage in work or exercise that moves lower extremities more actively.

Comparing national device-based physical activity surveillance systems: a systematic review.

BACKGROUND: Physical activity surveillance systems are important for public health monitoring but rely mostly on self-report measurement of physical activity. Integration of device-based measurements in such systems can improve population estimates, however this is still relatively uncommon in existing surveillance systems. This systematic review aims to create an overview of the methodology used in existing device-based national PA surveillance systems. METHODS: Four literature databases (PubMed, Embase.com, SPORTDiscus and Web of Science) were searched, supplemented with backward tracking. Articles were included if they reported on population-based (inter)national surveillance systems measuring PA, sedentary time and/or adherence to PA guidelines. When available and in English, the methodological reports of the identified surveillance studies were also included for data extraction. RESULTS: This systematic literature search followed the PRISMA guidelines and yielded 34 articles and an additional 18 methodological reports, reporting on 28 studies, which in turn reported on one or multiple waves of 15 different national and 1 international surveillance system. The included studies showed substantial variation between (waves of) systems in number of participants, response rates, population representativeness and recruitment. In contrast, the methods were similar on data reduction definitions (e.g. minimal number of valid days, non-wear time and necessary wear time for a valid day). CONCLUSIONS: The results of this review indicate that few countries use device-based PA measurement in their surveillance system. The employed methodology is diverse, which hampers comparability between countries and calls for more standardized methods as well as standardized reporting on these methods. The results from this review can help inform the integration of device-based PA measurement in (inter)national surveillance systems.