Sow serenity: automatic long-term measurement of lying behavior in crates and free-farrowing pens using 3D accelerometers.

Accelerometers are useful in analyzing lying behavior in farm animals. The effect of the farrowing system on sow lying behavior has been studied around parturition, but not long-term. In a natural environment, sows increase activity 14 d post parturition, which we expected to be also evident in housed sows when they can move freely. The objective of this study was (1) to validate the methodology to automatically measure sow lying bouts and duration with accelerometers and (2) to apply it to crated and free-farrowing sows 24-h pre-parturition until weaning. We used videos with manual behavior coding as the gold standard for validation and calculated the agreement with an intraclass correlation coefficient (ICC), which was 0.30 (95% CI: -0.10 to 0.64) for the number of lying bouts. When transitional sitting bouts were excluded from the video dataset, the ICC for lying bouts increased to 0.86 (95% CI: 0.40 to 0.95). For lying duration, the ICC was 0.93 (95% CI: 0.26 to 0.98). We evaluated the effects of housing, day relative to parturition, and time of day on lying using the accelerometer data and linear mixed models. In crated sows, the number of lying bouts increased toward parturition, peaking at about five bouts per 6 h, and decreased to almost zero bouts after parturition. Then, it increased again (P = 0.001). In free-farrowing sows, the number of lying bouts gradually decreased from a high level towards parturition and was lowest after parturition. It remained constant, as in the crated sows, until day 15, when the number of bouts increased to eight bouts on day 20 (P = 0.001). Sows in both systems were lying almost all of the time between 18:00 and 00:00 hours and on all days (P = 0.001). The crated sows showed a very similar pattern in the other three-quarters of the day with a reduced lying time before parturition, a peak after parturition, reduced lying time from days 5 to 20, and an increase again towards weaning (P = 0.001). Free-farrowing sows had a similar pattern to the crated sows from 00:00 to 06:00 hours, but without the reduction in lying time from days 5 to 20. They showed an increase in lying time toward parturition, which remained constant with a final decrease toward weaning, especially during the day (P = 0.001). This study proves the accuracy of accelerometer-based sow lying behavior classification and shows that free-farrowing systems benefit lactating sows around parturition but also towards weaning in the nest-leaving phase by facilitating activity.

We analyzed lying behavior of sows using sensors, focusing on crated versus free-farrowing sows from pre-parturition to weaning. Lying behavior varies in this time following the needs of the sow and her litter. In a natural environment, sows increase activity 14 d post parturition, which we expected to be also evident in housed sows when they are allowed to move freely. Validation with video data showed excellent agreement for duration and frequency of lying. In crated sows, the number of lying bouts peaked around parturition, decreased after parturition, and then gradually increased. In free-farrowing sows, lying down occurred less often before parturition, but increased by day 20 compared to crated sows. Both housing systems showed prolonged lying periods from 18:00 to 00:00 hours. Crated sows had reduced lying times before parturition and lied longest post-parturition, which decreased until day 5 and then increased toward weaning. Free-farrowing sows had similar nocturnal patterns but persistent lying times that increased prior to parturition and decreased prior to weaning. Overall, the study highlighted the accuracy of accelerometer-based lying behavior classification and showed that free-farrowing systems benefit lactating sows not only around parturition but also toward weaning, facilitating activity during the nest-leaving phase.

Reliability and validity of rapid assessment tools for measuring 24-hour movement behaviours in children aged 0-5 years: the Movement Behaviour Questionnaire Baby (MBQ-B) and child (MBQ-C).

BACKGROUND: The development of validated "fit-for-purpose" rapid assessment tools to measure 24-hour movement behaviours in children aged 0-5 years is a research priority. This study evaluated the test-retest reliability and concurrent validity of the open-ended and closed-ended versions of the Movement Behaviour Questionnaire for baby (MBQ-B) and child (MBQ-C). METHODS: 300 parent-child dyads completed the 10-day study protocol (MBQ-B: N = 85; MBQ-C: N = 215). To assess validity, children wore an accelerometer on the non-dominant wrist (ActiGraph GT3X+) for 7 days and parents completed 2 × 24-hour time use diaries (TUDs) recording screen time and sleep on two separate days. For babies (i.e., not yet walking), parents completed 2 × 24-hour TUDs recording tummy time, active play, restrained time, screen time, and sleep on days 2 and 5 of the 7-day monitoring period. To assess test-retest reliability, parents were randomised to complete either the open- or closed-ended versions of the MBQ on day 7 and on day 10. Test-retest intraclass correlation coefficients (ICC's) were calculated using generalized linear mixed models and validity was assessed via Spearman correlations. RESULTS: Test-retest reliability for the MBQ-B was good to excellent with ICC's ranging from 0.80 to 0.94 and 0.71-0.93 for the open- and closed-ended versions, respectively. For both versions, significant positive correlations were observed between 24-hour diary and MBQ-B reported tummy time, active play, restrained time, screen time, and sleep (rho = 0.39-0.87). Test-retest reliability for the MBQ-C was moderate to excellent with ICC's ranging from 0.68 to 0.98 and 0.44-0.97 for the open- and closed-ended versions, respectively. For both the open- and closed-ended versions, significant positive correlations were observed between 24-hour diary and MBQ-C reported screen time and sleep (rho = 0.44-0.86); and between MBQ-C reported and device-measured time in total activity and energetic play (rho = 0.27-0.42). CONCLUSIONS: The MBQ-B and MBQ-C are valid and reliable rapid assessment tools for assessing 24-hour movement behaviours in infants, toddlers, and pre-schoolers. Both the open- and closed-ended versions of the MBQ are suitable for research conducted for policy and practice purposes, including the evaluation of scaled-up early obesity prevention programs.

Unsupervised Noise Reductions for Gravitational Reference Sensors or Accelerometers Based on the Noise2Noise Method.

Onboard electrostatic suspension inertial sensors are important applications for gravity satellites and space gravitational-wave detection missions, and it is important to suppress noise in the measurement signal. Due to the complex coupling between the working space environment and the satellite platform, the process of noise generation is extremely complex, and traditional noise modeling and subtraction methods have certain limitations. With the development of deep learning, applying it to high-precision inertial sensors to improve the signal-to-noise ratio is a practically meaningful task. Since there is a single noise sample and unknown true value in the measured data in orbit, odd-even sub-samplers and periodic sub-samplers are designed to process general signals and periodic signals, and adds reconstruction layers consisting of fully connected layers to the model. Experimental analysis and comparison are conducted based on simulation data, GRACE-FO acceleration data, and Taiji-1 acceleration data. The results show that the deep learning method is superior to traditional data smoothing processing solutions.

Topographic design in wearable MXene sensors with in-sensor machine learning for full-body avatar reconstruction.

Wearable strain sensors that detect joint/muscle strain changes become prevalent at human-machine interfaces for full-body motion monitoring. However, most wearable devices cannot offer customizable opportunities to match the sensor characteristics with specific deformation ranges of joints/muscles, resulting in suboptimal performance. Adequate wearable strain sensor design is highly required to achieve user-designated working windows without sacrificing high sensitivity, accompanied with real-time data processing. Herein, wearable Ti3C2Tx MXene sensor modules are fabricated with in-sensor machine learning (ML) models, either functioning via wireless streaming or edge computing, for full-body motion classifications and avatar reconstruction. Through topographic design on piezoresistive nanolayers, the wearable strain sensor modules exhibited ultrahigh sensitivities within the working windows that meet all joint deformation ranges. By integrating the wearable sensors with a ML chip, an edge sensor module is fabricated, enabling in-sensor reconstruction of high-precision avatar animations that mimic continuous full-body motions with an average avatar determination error of 3.5 cm, without additional computing devices.

Determining jumping performance from a single body-worn accelerometer using machine learning.

External peak power in the countermovement jump is frequently used to monitor athlete training. The gold standard method uses force platforms, but they are unsuitable for field-based testing. However, alternatives based on jump flight time or Newtonian methods applied to inertial sensor data have not been sufficiently accurate for athlete monitoring. Instead, we developed a machine learning model based on characteristic features (functional principal components) extracted from a single body-worn accelerometer. Data were collected from 69 male and female athletes at recreational, club or national levels, who performed 696 jumps in total. We considered vertical countermovement jumps (with and without arm swing), sensor anatomical locations, machine learning models and whether to use resultant or triaxial signals. Using a novel surrogate model optimisation procedure, we obtained the lowest errors with a support vector machine when using the resultant signal from a lower back sensor in jumps without arm swing. This model had a peak power RMSE of 2.3 W·kg-1 (5.1% of the mean), estimated using nested cross validation and supported by an independent holdout test (2.0 W·kg-1). This error is lower than in previous studies, although it is not yet sufficiently accurate for a field-based method. Our results demonstrate that functional data representations work well in machine learning by reducing model complexity in applications where signals are aligned in time. Our optimisation procedure also was shown to be robust can be used in wider applications with low-cost, noisy objective functions.

Measurement of excitation-contraction coupling time in critical illness myopathy.

OBJECTIVE: This study aimed to develop a simple and reliable technique to assess excitation-contraction (E-C) coupling for early diagnosis of critical illness myopathy (CIM). METHODS: We prospectively performed clinical and electrophysiological examinations on patients admitted to intensive care unit (ICU). In addition to full neurological examinations and routine nerve conduction study, motor related potential (MRP) was recorded using an accelerometer attached to the base of hallux after tibial nerve stimulation, and E-C coupling time (ECCT) was measured from the latency difference between soleus compound muscle action potential (CMAP) and MRP. RESULTS: Of 41 patients evaluated, 25 met the criteria for ICU-acquired weakness, 23 of whom had CIM. The time to the first electrophysiological examination (time to first test) correlated negatively with CMAP and with MRP. Conversely, a positive correlation was observed between the time to first test and ECCT. E-C coupling impairment occurred in most of our patients with CIM by the third day of ICU admission, and prolonged ECCT could be the earliest detectable abnormality. CONCLUSIONS: The ECCT measurement is an easy and reliable technique to detect reduced muscle membrane excitability in the early stage of CIM. SIGNIFICANCE: The ECCT measured by our method using an accelerometer may be a parameter that predicts the development of CIM.

Comparison between ActiGraph GT3X and ActivPAL to assess sedentary behavior during the school period

Abstract Aims: This study aimed to compare the sedentary time measured using the ActiGraph GT3X accelerometer with the measurement of sitting and standing time obtained by ActivPAL inclinometers. Methods: This was a cross-sectional study conducted with a sample of 60 schoolchildren (34 males) of one elementary public school in Brazil. The students used both an ActiGraph GT3X accelerometer and an ActivPAL inclinometer, concurrently, positioned at the beginning and removed at the end of the school shift, for four days. For analysis, paired Student's t-tests, Pearson's correlation coefficients, intraclass correlation coefficients, and Bland-Altman plots were used. Results: When comparing sedentary time with sitting time, although correlated (r = 0.53; p < 0.001), the mean minutes were different (134.2 min/day in ActiGraph GT3X vs 120.3 min/day in ActivPAL; p < 0.001), with a bias of 13.9 min/day. When comparing the measurement of sedentary time with the sum of the sitting time plus standing time, different mean minutes were also observed (134.2 min/day in ActiGraph GT3X vs 177.0 min/day in ActivPAL; p < 0.001), and although the correlation was stronger (r = 0.75; p < 0.001), the bias was higher (−42.8 min/day). Conclusion: Sedentary time derived from the ActiGraph GT3X device should be used with caution to evaluate sedentary behavior in a school setting and may be interpreted only as non-moving activities (stationary behavior).

Association between leisure-time physical activity and the built environment in China: Empirical evidence from an accelerometer and GPS-based fitness app.

To reexamine the relationship between leisure-time physical activity (LTPA) and the built environment (BE), this paper takes advantage of the massive amount of data collected by an accelerometer and GPS-based fitness mobile app. Massive LTPA data from more than 3 million users were recorded by Codoon in 500m by 500m grid cells and aggregated to 742 natural cities in mainland China. Six BE indicators were quantified using GIS at the city scale. Robust regression analysis was used to estimate the correlation between LTPA and BE. Five of six BE indicators-connectivity, road density, land use mix, points of interest density, and density of parks and squares-were significantly, positively, independently, and linearly related to LTPA in the regression analysis. The study obtains findings that are consistent with the previous literature but also provides novel insights into the important role of POI density in encouraging LTPA, as well as how the relationship between LTPA and BE varies by time of day. The study also sheds light on the embrace of new technology and new data in public health and urban studies.

Measurement of various intensities of physical activities and categorization of "Locomotive" and "Household" activities provide a subject-specific detailed assessment.

This study aimed to compare the physical activity (PA) measured by a wearable sensor device (WSD) and the step count measurement, and to investigate the association between PAs and lifestyle. Data of 301 participants were collected from March 2019 to March 2021. Step counts, sedentary behavior, performance time of light/moderate/vigorous PA, METs × hour of "Locomotive" and "Household" categorized activities, and energy expenditure (EE) were measured by the WSD, respectively. Furthermore, the participants were classified into student, standing worker, and sitting worker groups. Data were analyzed using the Steel-Dwass and Pearson correlation coefficient tests. The correlation between the performance time of each PA and step count was weak, except for moderate PA. "Household" EE and step count also had a weak correlation. In the comparison of lifestyle, there was a significant difference in the mean performance time of each type of PA between the groups. Additionally, the standing worker and sitting worker groups had a significant difference in METs × hour of "Household" activities, indicating that the difference between the occupations is reflected in "Household" activities. The WSD measurement can be used to evaluate detailed individual PA, whereas the step count measurement showed weakness in the PA estimation.

Head Impact Sensor Triggering Bias Introduced by Linear Acceleration Thresholding.

Contact sports players frequently sustain head impacts, most of which are mild impacts exhibiting 10-30 g peak head center-of-gravity (CG) linear acceleration. Wearable head impact sensors are commonly used to measure exposure and typically detect impacts using a linear acceleration threshold. However, linear acceleration across the head can substantially vary during 6-degree-of-freedom motion, leading to triggering biases that depend on sensor location and impact condition. We conducted an analytical investigation with impact characteristics extracted from on-field American football and soccer data. We assumed typical mouthguard sensor locations and evaluated whether simulated multi-directional impacts would trigger recording based on per-axis or resultant acceleration thresholding. Across 1387 impact directions, a 10g peak CG linear acceleration impact would trigger at only 24.7% and 31.8% of directions based on a 10 g per-axis and resultant acceleration threshold, respectively. Anterior impact locations had lower trigger rates and even a 30 g impact would not trigger recording in some directions. Such triggering biases also varied by sensor location and linear-rotational head kinematics coupling. Our results show that linear acceleration-based impact triggering could lead to considerable bias in head impact exposure measurements. We propose a set of recommendations to consider for sensor manufacturers and researchers to mitigate this potential exposure measurement bias.

Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses: A cross-sectional study of 89,205 participants from the UK Biobank.

BACKGROUND: Sleep problems are both symptoms of and modifiable risk factors for many psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at scale. Here, we aimed to examine the association of accelerometer-derived sleep measures with psychiatric diagnoses and polygenic risk scores in a large community-based cohort. METHODS AND FINDINGS: In this post hoc cross-sectional analysis of the UK Biobank cohort, 10 interpretable sleep measures-bedtime, wake-up time, sleep duration, wake after sleep onset, sleep efficiency, number of awakenings, duration of longest sleep bout, number of naps, and variability in bedtime and sleep duration-were derived from 7-day accelerometry recordings across 89,205 participants (aged 43 to 79, 56% female, 97% self-reported white) taken between 2013 and 2015. These measures were examined for association with lifetime inpatient diagnoses of major depressive disorder, anxiety disorders, bipolar disorder/mania, and schizophrenia spectrum disorders from any time before the date of accelerometry, as well as polygenic risk scores for major depression, bipolar disorder, and schizophrenia. Covariates consisted of age and season at the time of the accelerometry recording, sex, Townsend deprivation index (an indicator of socioeconomic status), and the top 10 genotype principal components. We found that sleep pattern differences were ubiquitous across diagnoses: each diagnosis was associated with a median of 8.5 of the 10 accelerometer-derived sleep measures, with measures of sleep quality (for instance, sleep efficiency) generally more affected than mere sleep duration. Effect sizes were generally small: for instance, the largest magnitude effect size across the 4 diagnoses was ß = -0.11 (95% confidence interval -0.13 to -0.10, p = 3 × 10-56, FDR = 6 × 10-55) for the association between lifetime inpatient major depressive disorder diagnosis and sleep efficiency. Associations largely replicated across ancestries and sexes, and accelerometry-derived measures were concordant with self-reported sleep properties. Limitations include the use of accelerometer-based sleep measurement and the time lag between psychiatric diagnoses and accelerometry. CONCLUSIONS: In this study, we observed that sleep pattern differences are a transdiagnostic feature of individuals with lifetime mental illness, suggesting that they should be considered regardless of diagnosis. Accelerometry provides a scalable way to objectively measure sleep properties in psychiatric clinical research and practice, even across tens of thousands of individuals.

Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson's disease.

Freezing of gait (FOG) is an intermittent walking disturbance experienced by people with Parkinson's disease (PD). Wearable FOG identification systems can improve gait and reduce the risk of falling due to FOG by detecting FOG in real-time and providing a cue to reduce freeze duration. However, FOG prediction and prevention is desirable. Datasets used to train machine learning models often generate ground truth FOG labels based on visual observation of specific lower limb movements (event-based definition) or an overall inability to walk effectively (period of gait disruption based definition). FOG definition ambiguity may affect model performance, especially with respect to multiple FOG in rapid succession. This research examined whether merging multiple freezes that occurred in rapid succession could improve FOG detection and prediction model performance. Plantar pressure and lower limb acceleration data were used to extract a feature set and train decision tree ensembles. FOG was labeled using an event-based definition. Additional datasets were then produced by merging FOG that occurred in rapid succession. A merging threshold was introduced where FOG that were separated by less than the merging threshold were merged into one episode. FOG detection and prediction models were trained for merging thresholds of 0, 1, 2, and 3 s. Merging slightly improved FOG detection model performance; however, for the prediction model, merging resulted in slightly later FOG identification and lower precision. FOG prediction models may benefit from using event-based FOG definitions and avoiding merging multiple FOG in rapid succession.

Step detection and energy expenditure at different speeds by three accelerometers in a controlled environment.

Physical activity (PA) is one of the most efficient ways to prevent obesity and its associated diseases worldwide. In the USA, less than 10% of the adult population were able to meet the PA recommendations when accelerometers were used to assess PA habituation. Accelerometers significantly differ from each other in step recognition and do not reveal raw data. The aim of our study was to compare a novel accelerometer, Sartorio Xelometer, which enables to gather raw data, with existing accelerometers ActiGraph GT3X+ and activPAL in terms of step detection and energy expenditure estimation accuracy. 53 healthy subjects were divided into 2 cohorts (cohort 1 optimization; cohort 2 validation) and wore 3 accelerometers and performed an exercise routine consisting of the following speeds: 1.5, 3, 4.5, 9 and 10.5 km/h (6 km/h for 2nd cohort included). Data from optimization cohort was used to optimize Sartorio step detection algorithm. Actual taken steps were recorded with a video camera and energy expenditure (EE) was measured. To observe the similarity between video and accelerometer step counts, paired samples t test and intraclass correlation were used separately for step counts in different speeds and for total counts as well as EE estimations. In speeds of 1.5, 3, 4.5, 6, 9 and 10.5 km/h mean absolute percentage error (MAPE) % were 8.1, 3.5, 4.3, 4.2, 3.1 and 7.8 for the Xelometer, respectively (after optimization). For ActiGraph GT3X+ the MAPE-% were 96.93 (87.4), 34.69 (23.1), 2.13 (2.3), 1.96 (2.6) and 2.99 (3.8), respectively and for activPAL 6.55 (5.6), 1.59 (0.6), 0.81 (1.1), 10.60 (10.3) and 15.76 (13.8), respectively. Significant intraclass correlations were observed with Xelometer estimates and actual steps in all speeds. Xelometer estimated the EE with a MAPE-% of 30.3, activPAL and ActiGraph GT3X+ with MAPE percentages of 20.5 and 24.3, respectively. The Xelometer is a valid device for assessing step counts at different gait speeds. MAPE is different at different speeds, which is of importance when assessing the PA in obese subjects and elderly. EE estimates of all three devices were found to be inaccurate when compared with indirect calorimetry.

A comparison of head movements tests in force plate and accelerometer based posturography in patients with balance problems due to vestibular dysfunction.

This study compares HS posturography on inertial sensors (MediPost) with force platform posturography in patients with unilateral vestibular dysfunction. The study group included 38 patients (age 50.6; SD 11.6) with unilateral vestibular weakness (UV) and 65 healthy volunteers (48.7; SD 11.5). HS tests were performed simultaneously on the force plate and with MediPost sensor attached at L4. Four conditions applied: eyes open/closed, firm/foam. The tests were performed twice, with the head moving at the frequency of 0.3 Hz (HS 0.3) and 0.6 Hz (HS 0.6). Mean sway velocity was significantly lower for MediPost than force plate in 4th condition both in UV and healthy group. For HS 0.3 the differences between devices were marginal; the highest sensitivity (87%) and specificity (95%) were in 4th condition. For HS 0.6 MediPost revealed lower sensitivity than force plate although the surface parameter improved results. MediPost IMU device and force platform posturography revealed a similar ability to differentiate between patients with balance problems in course of vestibular pathology and healthy participants, despite the differences observed between measuring methods. In some tests surface parameter may be more appropriate than sway velocity in improving MediPost sensitivity.

Lack of Acceptance of Digital Healthcare in the Medical Market: Addressing Old Problems Raised by Various Clinical Professionals and Developing Possible Solutions.

Various digital healthcare devices and apps, such as blood glucose meters, blood pressure monitors, and step-trackers are commonly used by patients; however, digital healthcare devices have not been widely accepted in the medical market as of yet. Despite the various legal and privacy issues involved in their use, the main reason for its poor acceptance is that users do not use such devices voluntarily and continuously. Digital healthcare devices generally do not provide valuable information to users except for tracking self-checked glucose or walking. To increase the use of these devices, users must first understand the health data produced in the context of their personal health, and the devices must be easy to use and integrated into everyday life. Thus, users need to know how to manage their own data. Medical staff must teach and encourage users to analyze and manage their patient-generated healthcare data, and users should be able to find medical values from these digital devices. Eventually, a single customized service that can comprehensively analyze various medical data to provide valuable customized services to users, and which can be linked to various heterogeneous digital healthcare devices based on the integration of various health data should be developed. Digital healthcare professionals should have detailed knowledge about a variety of digital healthcare devices and fully understand the advantages and disadvantages of digital healthcare to help patients understand and embrace the use of such devices.

How physical activity, fitness, and motor skills contribute to math performance: Working memory as a mediating factor.

PURPOSE: The purpose of this study was to examine whether physical activity, fitness, and motor skills have an indirect association with math performance via cognitive outcomes and if so, through which aspects of cognition? METHODS: This study comprised 311 6th-9th grade adolescents (12-17 years [M age = 14.0 years], 59% girls) from seven schools throughout Finland in 2015. Math performance was measured via a teacher-rated math achievement and the Basic Arithmetic test. Cognitive functions were measured by broad cognitive test battery. Physical activity was assessed with a self-reported questionnaire and a hip-worn accelerometer. Aerobic fitness was estimated using a maximal 20-m shuttle run test, muscular fitness with curl-up and push-up tests, and motor skills with a 5-leaps test and a throwing-catching combination test. Structural equation modeling was applied to examine the associations. RESULTS: In both boys and girls, motor skills had a positive indirect association with math outcomes through visuospatial working memory. Among girls, muscular fitness had a positive indirect association with math outcomes through visuospatial working memory. Aerobic fitness was positively associated with math achievement, but the indirect path via cognition was not observed. Self-reported physical activity had a borderline indirect positive association with math outcomes through visuospatial working memory. Accelerometer-based physical activity did not correlate with math performance. CONCLUSION: Motor skills and muscular fitness had indirect positive associations with math performance, mediated by visuospatial working memory. The results give support to the viewpoints that the connections between motor skills, fitness, and physical activity to academic skills are mediated via specific cognitive skills.

Real-world gait speed estimation, frailty and handgrip strength: a cohort-based study.

Gait speed is a reliable outcome measure across multiple diagnoses, recognized as the 6th vital sign. The focus of the present study was on assessment of gait speed in long-term real-life settings with the aim to: (1) demonstrate feasibility in large cohort studies, using data recorded with a wrist-worn accelerometer device; (2) investigate whether the walking speed assessed in the real-world is consistent with expected trends, and associated with clinical scores such as frailty/handgrip strength. This cross-sectional study included n = 2809 participants (1508 women, 1301 men, [45-75] years old), monitored with a wrist-worn device for 13 consecutive days. Validated algorithms were used to detect the gait bouts and estimate speed. A set of metrics were derived from the statistical distribution of speed of gait bouts categorized by duration (short, medium, long). The estimated usual gait speed (1-1.6 m/s) appears consistent with normative values and expected trends with age, gender, BMI and physical activity levels. Speed metrics significantly improved detection of frailty: AUC increase from 0.763 (no speed metrics) to 0.798, 0.800 and 0.793 for the 95th percentile of individual's gait speed for bout durations < 30, 30-120 and > 120 s, respectively (all p < 0.001). Similarly, speed metrics also improved the prediction of handgrip strength: AUC increase from 0.669 (no speed metrics) to 0.696, 0.696 and 0.691 for the 95th percentile of individual's gait speed for bout durations < 30, 30-120 and > 120 s, respectively (all p < 0.001). Forward stepwise regression showed that the 95th percentile speed of gait bouts with medium duration (30-120 s) to be the best predictor for both conditions. The study provides evidence that real-world gait speed can be estimated using a wrist-worn wearable system, and can be used as reliable indicator of age-related functional decline.

Count- versus MAD-based accelerometry-assessed movement behaviors and associations with child adiposity and fitness.

Estimations of time spent sedentary and in various physical activity intensities may vary according to data reduction methods applied. This study compared associations between children's accelerometer data and adiposity and fitness markers using open source (mean amplitude deviation, MAD) and proprietary (counts) data reduction methods. Complete-case accelerometer, adiposity (Body Mass Index z-score, waist circumference), and fitness (cardiorespiratory, musculoskeletal) data from 118 children (10.4 ± 0.6 years, 49% girls) were analyzed. Estimates of sedentary behavior, light-, moderate-, vigorous- (VPA), and moderate- to vigorous-intensity (MVPA) physical activity were calculated using count- and MAD-based data reduction methods. Linear regression models between time in movement behaviours and fitness and adiposity markers were conducted. Significant differences in estimates of time spent in all intensities were observed between MAD-based and count-based methods. Both methods produced evidence to suggest that sedentary behavior was detrimentally, and physical activity (any intensity) was beneficially, associated with waist circumference. MVPA and VPA were beneficially associated with fitness markers using both data reduction measures. Overall, findings suggest that estimates of sedentary time and physical activity were not comparable. However, the strength and direction of the associations obtained between the different data reduction methods and adiposity and fitness outcomes were fairly comparable, with both methods finding stronger associations for VPA compared to MVPA. This suggests that future studies may be able to pool data using different data reduction approaches when examining associations between activity and health risk factors, albeit with caution.