Classifying physical activity levels using Mean Amplitude Deviation in adults using a chest worn accelerometer: validation of the Vivalink ECG Patch.

BACKGROUND: The development of readily available wearable accelerometers has enabled clinicians to objectively monitor physical activity (PA) remotely in the community, a superior alternative to patient self-reporting measures. Critical to the value of these monitors is the ability to reliably detect when patients are undergoing ambulatory activity. Previous studies have highlighted the strength of using mean amplitude deviation (MAD) as a universal measure for analysing raw accelerometery data and defining cut-points between sedentary and ambulatory activities. Currently however there is little evidence surrounding the use of chest-worn accelerometers which can provide simultaneous monitoring of other physiological parameters such as heart rate (HR), RR intervals, and Respiratory Rate alongside accelerometery data. We aimed to calibrate the accelerometery function within the VivaLink ECG patch to determine the cut-point MAD value for differentiating sedentary and ambulatory activities. METHODS: We recruited healthy volunteers to undergo a randomised series of 9 activities that simulate typical free-living behaviours, while wearing a VivaLink ECG Patch (Campbell, California). MAD values were applied to a Generalised Linear Mixed Model to determine cut-points between sedentary and ambulatory activities. We constructed a Receiver Operating Characteristic (ROC) curve to analyse the sensitivity and specificity of the cut-off MAD value. RESULTS: Eighteen healthy adults volunteered to the study and mean MAD values were collected for each activity. The optimal MAD cut-point between sedentary and ambulatory activities was 47.73mG. ROC curve analysis revealed an area under the curve of 0.99 (p < 0.001) for this value with a sensitivity and specificity of 98% and 100% respectively. CONCLUSION: In conclusion, the MAD cut-point determined in our study is very effective at categorising sedentary and ambulatory activities among healthy adults and may be of use in monitoring PA in the community with minimal burden. It will also be useful for future studies aiming to simultaneously monitor PA with other physiological parameters via chest worn accelerometers.

Physical activity and sedentary behavior among ambulatory children with cerebral palsy using accelerometer: a cross-sectional study.

Background and objective: Physical activity (PA) is paramount for childhood development and growth. However, children diagnosed with Cerebral Palsy (CP) were often considered sedentary, and their physical inactivity was associated with adverse health conditions and complications. Therefore, this study aimed to objectively describe and compare the PA levels and SB levels of children with and without CP of the same age group. It also studied the factors correlating with PA, SB, and step count per day in children with CP. Subjects and methods: A cross-sectional study using a wrist-worn accelerometer was conducted. PA and SB were measured over seven consecutive days. Results: Eighty-five children aged 6-12 years, consisting of 41 children with CP and 44 TD children, participated in this study with a mean age of 9.18 ± 1.95 and 8.45 ± 1.78 years, respectively. According to the gross functional measures, 53.6% of children with CP were classified as first classification. A significant amount of time was spent in SB and Light PA (LPA) by children with CP compared to TD children, and no significant differences were observed in moderate PA (MPA) or step count. Gender mainly affected MPA as girls spent more time in MPA than boys. The age, height, and weight of children with CP correlate significantly with SB. As children's age, height, and weight increase, SB increases. Additionally, children with higher weights have lower step counts per day. Conclusion: This study showed that children with CP spend more time in LPA and SB than typically developed children. Therefore, concerted efforts are needed to encourage physical activity and reduce the sedentary lifestyle, to take into account the gender and anthropometric measures of children to enhance the quality of life among children with CP, and to consider gender and anthropometric measures of the children.

Influence of physical activity on the epigenetic clock: evidence from a Japanese cross-sectional study.

BACKGROUND: Biological age, especially epigenetic age derived from the epigenetic clock, is a significant measure of aging, considering the differences in aging rates among individuals. The epigenetic clock, a machine learning-based algorithm, uses DNA methylation states to estimate biological age. Previous studies have reported inconsistent associations between physical activity (PA) and the epigenetic clock, especially second-generation clocks such as PhenoAge and GrimAge. This study aimed to clarify this relationship using cross-sectional data from Japanese participants aged 40-69. METHODS: We used two datasets from the Saga J-MICC study, of which 867 samples were available for analysis. DNA methylation data from peripheral blood samples were used to calculate the epigenetic age using the epigenetic clocks PhenoAge and GrimAge. PA and sedentary time were measured using a single-axis accelerometer, while self-reported PA, sedentary time, and covariates were assessed using a self-administered questionnaire. The association between PA or sedentary time and epigenetic age acceleration was assessed using multiple linear regression. RESULTS: Pearson's correlation coefficients between accelerometer-based and self-reported PA variables ranged from 0.09 to 0.20. Multivariable regression analysis showed that accelerometer-based PA and sedentary time were associated with epigenetic age decelerations and accelerations, respectively. However, self-reported PA was not associated with the epigenetic age accelerations. CONCLUSIONS: These results indicate that reducing sedentary time and increasing PA were associated with slowing both PhenoAge and GrimAge, even in East Asian populations with different exercise habits, body shapes, and lifestyles. This study highlights the potential of objective second-generation epigenetic age acceleration as an outcome index for healthcare interventions and clinical applications.

Player and match characteristics associated with head acceleration events in elite-level men's and women's rugby union matches.

Objective: To examine the likelihood of head acceleration events (HAEs) as a function of previously identified risk factors: match time, player status (starter or substitute) and pitch location in elite-level men's and women's rugby union matches. Methods: Instrumented mouthguard data were collected from 179 and 107 players in the men's and women's games and synchronised to video-coded match footage. Head peak resultant linear acceleration (PLA) and peak resultant angular acceleration were extracted from each HAE. Field location was determined for HAEs linked to a tackle, carry or ruck. HAE incidence was calculated per player hour across PLA recording thresholds with 95% CIs estimated. Propensity was calculated as the percentage of contact events that caused HAEs across PLA recording thresholds, with a 95% CI estimated. Significance was assessed by non-overlapping 95% CIs. Results: 29 099 and 6277 HAEs were collected from 1214 and 577 player-matches in the men's and women's games. No significant differences in match quarter HAE incidence or propensity were found. Substitutes had higher HAE incidence than starters at lower PLA recording thresholds for men but similar HAE propensity. HAEs were more likely to occur in field locations with high contact event occurrence. Conclusion: Strategies to reduce HAE incidence need not consider match time or status as a substitute or starter as HAE rates are similar throughout matches, without differences in propensity between starters and substitutes. HAE incidence is proportional to contact frequency, and strategies that reduce either frequency or propensity for contact to cause head contact may be explored.

Evaluation of indoor activities for moderate-to-vigorous physical activity in preschoolers.

The study objectives were 1) to determine the feasibility and acceptability of indoor activities to achieve moderate-to-vigorous physical activity (MVPA) in preschoolers, and 2) compare MVPA estimates between direct observation (DO) and various accelerometry placements. In this cross-sectional study, 35 preschoolers (51% female, 54% 3-year-olds) performed six, 6-minute activities (dancing to video, balloon/bubbles, stationary exergame cycling, circuits, running-in-place, and cleaning up) in sequential order, facilitated by a trained staff member. Triaxial accelerometers (Actigraph Gt3×BT) at the ankle, waist, and wrist measured MVPA using age-specific cut-points. Total activity and MVPA time were quantified via DO of video recordings. Feasibility and acceptability were assessed via parent and child report. Preschoolers contributed 4339, 15-second epochs of accelerometry and DO data (~31.0 minutes/preschooler). Preschoolers achieved MVPA ≥ 50% of the time while engaging in balloon/bubbles, cycling, and circuits; but not while dancing to video (15%), running-in-place (48.5%), or cleaning up (8%). There were no differences in MVPA by age, sex, or between screen and non-screen activities. Parents and preschoolers reported most activities were feasible (≥4.0/5.0). Waist and ankle accelerometry had strong agreement with DO (ICCs range: 0.70-0.84) while wrist had fair to low agreement (ICCs: 0.22-0.58). Multiple indoor activities show promise to increase preschoolers' MVPA.

Detecting play behaviour in weaned dairy calves using accelerometer data.

This research paper describes a validation study evaluating the ability of IceTag accelerometers (Peacock Technology, UK) to detect play behaviour in weaned dairy calves. Play behaviour is commonly observed in young animals and is regarded as an indicator of positive welfare states. Eight Holstein Friesian calves aged three to five months old were monitored using leg-mounted accelerometers for 48 h. Data generated by accelerometers to quantify calf activity included step count, lying times and a proprietary measure of overall activity termed 'motion index' (MI). Calf behaviour was filmed continuously over the same 48-h period using closed circuit television cameras and analysed using one-zero sampling to identify the presence (1) or absence (0) of play within each 15-min time period. A positive correlation between MI and visually recorded play was found. Visual observations were compared with accelerometer-generated data and analysed using 2 × 2 contingency tables and classification and regression tree analysis. A MI value of ≥69 was established as the optimum threshold to detect play behaviour (sensitivity = 94.4%; specificity = 93.6%; balanced accuracy = 94.0%). The results of this study suggest that accelerometer-generated MI data have the potential to detect play behaviour in weaned dairy calves in a more time efficient manner than traditional visual observations.

Development of an accelerometer-based wearable sensor approach for alcohol consumption detection.

BACKGROUND: Alcohol is a commonly used substance associated with significant public health consequences. Treatment is often stigmatized and limited with regard to both access and affordability, demonstrating the need for innovations in alcohol treatment. Accelerometer sensors can detect drinking without user input and are widely incorporated into wearable devices, increasing accessibility and affordability. METHODS: We compared a distributional and random forest classification approach to detect and evaluate sensor-based drinking data. Data were collected at a local state fair (n = 194), where participants drank water at specified intervals interspersed with confounding behaviors (e.g., touching nose, rubbing forehead, or yawning) while wearing an Android-based smartwatch for 10 min. Participants were randomized to receive one of three drinking container shapes: pint, martini, or wine. RESULTS: The random forest model achieved an overall testing accuracy of 93% (sensitivity = 0.32; specificity = 0.99; positive predictive value = 0.74). The distributional algorithm achieved an overall accuracy of 95% (sensitivity = 0.76; specificity = 0.97; positive predictive value = 0.72). The distributional algorithm had a significantly greater accuracy (t(193) = 7.73, p < 0.001, d = 0.56) and sensitivity (t(193) = 24.5, p < 0.001, d = 1.76). Equivalency testing demonstrated significant equivalency to the ground truth for sip duration (tlower(193) = 16.92, p < 0.001; tupper(193) = -9.85, p < 0.001) and between-sip interval (tlower(193) = 1.72, p = 0.044; thigher(193) = -3.96, p < 0.001). However, the random forest did not have significant equivalency to the ground truth for between-sip interval (tlower(193) = 1.98, p = 0.025; thigher(193) = 0.160, p = 0.564). CONCLUSIONS: Overall, the results indicated that consumer-grade smartwatches can be utilized to detect and measure alcohol use behavior using machine learning and distributional algorithms. This work provides the methodological foundation for future research to analyze the behavioral pharmacology of alcohol use and develop accessible just-in-time clinical interventions.

Application of Sensor Technology in Wheelchair Sports for Real-Time Data Collection during Training and Competition and for Assessment of Performance Parameters: A Systematic Review and Future Directions.

This review reports on the use of sensors in wheelchair sports to monitor and analyze performance during match and training time. With rapid advancements in electronics and related technologies, understanding performance metrics in wheelchair sports is essential. We reviewed nine studies using various sensor types, including electric motors, inertial measurement units, miniaturized data loggers with magnetic reed switches, and smartphones with inbuilt accelerometers and gyroscopes, operating at frequencies from 8 Hz to 1200 Hz. These studies measured parameters such as angular and translational velocities, distance, number of starts/pushes, and other performance indicators in sports such as basketball, rugby, tennis, and racing. Despite differences in sport types and methodologies, most studies found sensor-derived data effective for assessment of performance. Future developments and research in this field should focus on multi-sensor systems that could provide real-time match analysis and deeper insights into performance metrics. Overall, sensor technologies show significant potential for improving wheelchair sport performance diagnostics, contributing to better athlete training and future wheelchair design, and enhancing competitive outcomes. This review emphasizes the need for continued innovation and standardization in applying sensor technologies in wheelchair sports.

Data Poisoning Attack against Neural Network-Based On-Device Learning Anomaly Detector by Physical Attacks on Sensors.

In this paper, we introduce a security approach for on-device learning Edge AIs designed to detect abnormal conditions in factory machines. Since Edge AIs are easily accessible by an attacker physically, there are security risks due to physical attacks. In particular, there is a concern that the attacker may tamper with the training data of the on-device learning Edge AIs to degrade the task accuracy. Few risk assessments have been reported. It is important to understand these security risks before considering countermeasures. In this paper, we demonstrate a data poisoning attack against an on-device learning Edge AI. Our attack target is an on-device learning anomaly detection system. The system adopts MEMS accelerometers to measure the vibration of factory machines and detect anomalies. The anomaly detector also adopts a concept drift detection algorithm and multiple models to accommodate multiple normal patterns. For the attack, we used a method in which measurements are tampered with by exposing the MEMS accelerometer to acoustic waves of a specific frequency. The acceleration data falsified by this method were trained on an anomaly detector, and the result was that the abnormal state could not be detected.

Development and Field Test of Integrated Electronics Piezoelectric Accelerometer Based on Lead-Free Piezoelectric Ceramic for Centrifugal Pump Monitoring.

In this study, an Integrated Electronics Piezoelectric (IEPE)-type accelerometer based on an environmentally friendly lead-free piezoceramic was fabricated, and its field applicability was verified using a cooling pump owned by the Korea Atomic Energy Research Institute (KAERI). As an environmentally friendly piezoelectric material, 0.96(K,Na)NbO3-0.03(Bi,Na,K,Li)ZrO3-0.01BiScO3 (0.96KNN-0.03BNKLZ-0.01BS) piezoceramic with an optimized piezoelectric charge constant (d33) was introduced. It was manufactured in a ring shape using a solid-state reaction method for application to a compression mode accelerometer. The fabricated ceramic ring has a high piezoelectric constant d33 of ~373 pC/N and a Curie temperature TC of ~330 °C. It was found that the electrical and physical characteristics of the 0.96KNN-0.03BNKLZ-0.01BS piezoceramic were comparable to those of a Pb(Zr,Ti)O3 (PZT) ring ceramic. As a result of a vibration test of the IEPE accelerometer fabricated using the lead-free piezoelectric ceramic, the resonant frequency fr = 20.0 kHz and voltage sensitivity Sv = 101.1 mV/g were confirmed. The fabricated IEPE accelerometer sensor showed an excellent performance equivalent to or superior to that of a commercial IEPE accelerometer sensor based on PZT for general industrial use. A field test was carried out to verify the applicability of the fabricated sensor in an actual industrial environment. The test was conducted by simultaneously installing the developed sensor and a commercial PZT-based sensor in the ball bearing housing location of a centrifugal pump. The centrifugal pump was operated at 1180 RPM, and the generated vibration signals were collected and analyzed. The test results confirmed that the developed eco-friendly lead-free sensor has comparable vibration measurement capability to that of commercial PZT-based sensors.

The Detection of Physiological Changes Using a Triaxial Accelerometer and Temperature Sensor-Equipped Bolus-Type Biosensor in Calves.

In this study, a newly developed small wireless bolus biosensor, equipped with a triaxial accelerometer and temperature sensors, was applied to assess physiological changes in calves. The biosensor was orally implanted in calves, and its retention rate and location in the forestomach were examined. Data transmitted at 10 min intervals were analyzed to determine the characteristics of the calves at 10 and 100 days of age. Additionally, the daily averages of the vector magnitude (DV), changes in V over time (DV1), and reticular temperature (DRT) were analyzed during the experimental period. The biosensor was orally administered to twelve calves (six beef and six dairy) within 22 days of birth. Except for two regurgitated devices, the sensors transmitted data normally in a wireless manner for 15 weeks, recording physiological changes in the calves. The location of the biosensors was confirmed to be the reticulum. The analysis revealed that the V and V1 values were influenced by the physical characteristics of the biosensor's location. During weaning, DV and DV1 values first increased and then decreased compared to pre-weaning, while the DRT increased post-weaning and remained elevated. These findings suggest that these types of biosensors can be used for monitoring calf health; however, further research is needed to determine their ability to detect pathological states.

Sensor-Based and Visual Behavioral Profiling of Dry Holstein Cows Presenting Distinct Median Core Body Temperatures.

The consequences of heat stress during the dry period can extend into the postpartum period, affecting health and productivity in the subsequent lactation. We hypothesized that cows with distinct core body temperatures (CBTs) would exhibit disparate behaviors associated with different degrees of heat generation or dissipation. The primary objective was to investigate behavioral differences of dry Holstein cows (n = 50) classified as high-temperature (HT) or low-temperature (LT), based on median CBT during the summer months using visual observations and accelerometer technology. A secondary objective was to investigate the transcriptome of white blood cells (WBCs) collected from a subgroup of HT and LT cows (n = 5; per group). Minor behavior differences were observed during the visual observations (performed for a total of 16h/cow). Based on automated monitoring system (AMS) data, collected 24/7 over a period of 42 days per cow, HT cows displayed higher periods of high activity and lower periods of inactivity prepartum and diminished rumination time postpartum than LT cows. There were 16 differently expressed genes (DEGs) in WBCs of HT compared to LT cows. Several of the identified DEGs have been previously associated with heat stress. The observed trends in the AMS data indicate that CBT and patterns of activity prepartum may serve as valuable predictors for identifying dairy cows with distinct tolerance to heat stress.

Small Ruminant Parturition Detection Based on Inertial Sensors-A Review.

The birth process in animals, much like in humans, can encounter complications that pose significant risks to both offspring and mothers. Monitoring these events can provide essential nursing support, but human monitoring is expensive. Although there are commercial monitoring systems for large ruminants, there are no effective solutions for small ruminants, despite various attempts documented in the literature. Inertial sensors are very convenient given their low cost, low impact on animal life, and their flexibility for monitoring animal behavior. This study offers a systematic review of the literature on detecting parturition in small ruminants using inertial sensors. The review analyzed the specifics of published research, including data management and monitoring processes, behaviors indicative of parturition, processing techniques, detection algorithms, and the main results achieved in each study. The results indicated that some methods for detecting birth concentrate on classifying unique animal behaviors, employing diverse processing techniques, and developing detection algorithms. Furthermore, this study emphasized that employing techniques that include analyzing animal activity peaks, specifically recurrent lying down and getting up occurrences, could result in improved detection precision. Although none of the studies provided a completely valid detection algorithm, most results were promising, showing significant behavioral changes in the hours preceding delivery.

Clustering Functional Data With Measurement Errors: A Simulation-Based Approach.

Clustering analysis of functional data, which comprises observations that evolve continuously over time or space, has gained increasing attention across various scientific disciplines. Practical applications often involve functional data that are contaminated with measurement errors arising from imprecise instruments, sampling errors, or other sources. These errors can significantly distort the inherent data structure, resulting in erroneous clustering outcomes. In this article, we propose a simulation-based approach designed to mitigate the impact of measurement errors. Our proposed method estimates the distribution of functional measurement errors through repeated measurements. Subsequently, the clustering algorithm is applied to simulated data generated from the conditional distribution of the unobserved true functional data given the observed contaminated functional data, accounting for the adjustments made to rectify measurement errors. We illustrate through simulations show that the proposed method has improved numerical performance than the naive methods that neglect such errors. Our proposed method was applied to a childhood obesity study, giving more reliable clustering results.

Genetic parameters of behavior traits of beef cattle classified using wearable devices.

With the development of wearable devices, it is now possible to monitor livestock behavior 24 h a day. In this study, we estimated the genetic parameters of the daily duration of six behaviors (feeding, moving, lying, standing, ruminating while lying, and ruminating while standing) in beef cattle, automatically classified using wearable devices. The devices were attached to 332 Japanese beef cattle at two stations for approximately 5 months. We compared repeatability, Poisson regression, and random regression models using the deviance information criterion. Poisson regression models were selected for all traits at each station, probably because of the non-normal distribution of the phenotypes. The heritability estimates by the Poisson regression models were moderate at each station: 0.67 and 0.68 for feeding, 0.68 and 0.53 for moving, 0.47 and 0.55 for lying, 0.45 and 0.40 for standing, 0.51 and 0.59 for ruminating while lying, and 0.37 and 0.45 for ruminating while standing. The genetic correlations between these traits were all negative at both stations, whereas the residual correlations showed different directions depending on the station. Although validation studies with larger populations are needed to confirm these findings, this study provides fundamental knowledge of the genetic basis of daily behavior in beef cattle.

Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review.

BACKGROUND: Monitoring free-living physical activity (PA) through wearable devices enables the real-time assessment of activity features associated with health outcomes and provision of treatment recommendations and adjustments. The conclusions of studies on PA and health depend crucially on reliable statistical analyses of digital data. Data analytics, however, are challenging due to the various metrics adopted for measuring PA, different aims of studies, and complex temporal variations within variables. The application, interpretation, and appropriateness of these analytical tools have yet to be summarized. OBJECTIVE: This research aimed to review studies that used analytical methods for analyzing PA monitored by accelerometers. Specifically, this review addressed three questions: (1) What metrics are used to describe an individual's free-living daily PA? (2) What are the current analytical tools for analyzing PA data, particularly under the aims of classification, association with health outcomes, and prediction of health events? and (3) What challenges exist in the analyses, and what recommendations for future research are suggested regarding the use of statistical methods in various research tasks? METHODS: This scoping review was conducted following an existing framework to map research studies by exploring the information about PA. Three databases, PubMed, IEEE Xplore, and the ACM Digital Library, were searched in February 2024 to identify related publications. Eligible articles were classification, association, or prediction studies involving human PA monitored through wearable accelerometers. RESULTS: After screening 1312 articles, 428 (32.62%) eligible studies were identified and categorized into at least 1 of the following 3 thematic categories: classification (75/428, 17.5%), association (342/428, 79.9%), and prediction (32/428, 7.5%). Most articles (414/428, 96.7%) derived PA variables from 3D acceleration, rather than 1D acceleration. All eligible articles (428/428, 100%) considered PA metrics represented in the time domain, while a small fraction (16/428, 3.7%) also considered PA metrics in the frequency domain. The number of studies evaluating the influence of PA on health conditions has increased greatly. Among the studies in our review, regression-type models were the most prevalent (373/428, 87.1%). The machine learning approach for classification research is also gaining popularity (32/75, 43%). In addition to summary statistics of PA, several recent studies used tools to incorporate PA trajectories and account for temporal patterns, including longitudinal data analysis with repeated PA measurements and functional data analysis with PA as a continuum for time-varying association (68/428, 15.9%). CONCLUSIONS: Summary metrics can quickly provide descriptions of the strength, frequency, and duration of individuals' overall PA. When the distribution and profile of PA need to be evaluated or detected, considering PA metrics as longitudinal or functional data can provide detailed information and improve the understanding of the role PA plays in health. Depending on the research goal, appropriate analytical tools can ensure the reliability of the scientific findings.

Do personality profiles contribute to patterns of physical activity and sedentary behavior in adulthood? A prospective cohort study.

BACKGROUND: Despite the observed associations of personality traits with levels of moderate-to-vigorous physical activity (MVPA) and sedentary behavior (SB), studies exploring whether the personality profiles differ in terms of the pattern of accumulation of physical behavior are lacking. The aim of this study was to identify adults' personality profiles and to characterize and investigate how these profiles differ in physical behavior. METHODS: The study utilized the longitudinal data of the participants of the Jyväskylä Longitudinal Study of Personality and Social Development (n = 141-307). Information on the five-factor model of personality, including the traits of neuroticism, extraversion, conscientiousness, openness, and agreeableness, was collected at ages 33, 42, 50, and 61 years, and used to create latent personality profiles. Physical behavior, operationalized as the amount and accumulation of MVPA and SB bouts, was captured using a triaxial accelerometer worn during waking hours at age 61 years. The differences in the behavior between the personality profiles were analyzed using the Kruskal-Wallis test. RESULTS: Five personality profiles were identified: resilient (20.2%), brittle (14.0%), overcontrolled (9.8%), undercontrolled (15.3%), and ordinary (40.7%). Although there were no statistically significant differences between the personality profiles in the time spent in MVPA relative to SB (MVPA per hour of daily SB), individuals with resilient (low in neuroticism and high in other traits) and ordinary (average in each trait) profiles had MVPA-to-SB ratios of 0.12 (7 min) and those with a brittle (high in neuroticism and low in extraversion) profile had a ratio of 0.09 (5.5 min). The individuals in the resilient group exhibited a longer usual MVPA bout duration than those in the overcontrolled (low in extraversion, openness, and agreeableness) (8 min vs. 2 min) and undercontrolled (high in openness and low in conscientiousness) groups (8 min vs. 3 min). They also exhibited a longer usual SB bout duration than those in the ordinary group (29 min vs. 23 min). CONCLUSIONS: The resilient group displayed the most prolonged MVPA and SB bout patterns. The results suggest that personality characteristics may contribute to how MVPA and SB are accumulated.

Validity of the PROMIS® Early Childhood Physical Activity Scale among toddlers.

BACKGROUND: The PROMIS® Early Childhood Physical Activity (PROMIS EC PA) scale is a recently developed PROMIS Early Childhood measure to assess PA among children aged 1-5 years. The purpose of this study was to examine test-retest reliability and convergent validity of the PROMIS EC PA scale among toddlers. METHOD: An ancillary study was conducted in the toddler-mother dyad sample of the Child and Mother Physical Activity Study. Mothers completed the 7-item PROMIS EC PA scale twice: during a study visit (test) and on the last day when their child's wore an ActiGraph accelerometer on the hip for 7 days (retest). The PROMIS EC PA summed score was calculated by totaling scores from items 1-5. Test-retest reliability was assessed using intraclass correlation coefficient (ICC) for test and retest PROMIS EC PA. Convergent validity was assessed using rank correlation coefficients (rho) between PROMIS EC PA scores and accelerometer-measured moderate- and vigorous-intensity PA (MVPA). RESULTS: Among 74 participants (56% female; 19 ± 4 months of mean age with range of 12-30 months), average accelerometer-measured MVPA was 76 ± 24 min/day. The median number of days between PROMIS EC PA test and retest was 8 days (IQR = 6 to 8), with an average PROMIS EC PA summed score of 11.0 ± 3.5 at test and 10.5 ± 3.4 at retest. ICC for the test-retest PROMIS EC PA summed scores was 0.72 (95% CI = 0.59-0.82). The rank correlation between the PROMIS EC PA summed score and accelerometer-measured MVPA was 0.13 (95% CI=-0.10 to 0.35; p = 0.28). CONCLUSION: In a sample of children aged 12-30 months, test-retest reliability for the PROMIS EC PA scale was moderate and its convergent validity against accelerometer-measured MVPA was poor. Prior to a widespread use of the PROMIS EC PA scale in large-scale research and clinical practice, the tool should be further refined and validated to elucidate how young children's lived PA experience as measured in the PROMIS EC PA scale is relevant to their health and wellbeing outcomes.

From lab to field: validity and reliability of inertial measurement unit-derived gait parameters during a standardised run.

The aim was to assess concurrent validity and test-retest reliability of spatiotemporal gait parameters from a thoracic-placed inertial measurement unit (IMU) in lab- (Phase One) and field-based (Phase Two) conditions. Spatiotemporal gait parameters were compared (target speeds 3, 5 and 7.5 m·s-1) between a 100 Hz IMU and an optical measurement system (OptoJump Next, 1000 hz) in 14 trained individuals (Phase One). Additionally, 29 English Premier League football players performed weekly 3 × 60 m runs (5 m·s-1; observations = 1227; Phase Two). Mixed effects modelling assessed the effect of speed on agreement between systems (Phase One) and test-retest reliability (Phase Two). IMU step time showed strong agreement (<0.3%) regardless of individual or running speed. Direction of mean biases up to 40 ms for contact and flight time depended on the running speed and individual. Step time, length and frequency were most reliable (coefficient of variation = 1.3-1.4%) but confounded by running speed. Step time, length and frequency derived from a thoracic-placed IMU can be used confidently. Contact time could be used if bias is corrected for each individual. To optimise test-retest reliability, a minimum running distance of 40 m is needed to ensure 10 constant-speed steps is gathered.

A novel computational signal processing framework towards multimodal vital signs extraction using neck-worn wearable devices.

Pulse rate (PR) and respiratory rate (RR) are two of the most important vital signs. Monitoring them would benefit from easy-to-use technologies. Hence, wearable devices would, in principle, be ideal candidates for such systems. The neck, although highly susceptible to artifacts, presents an attractive location for a diverse pool of physiological biomarkers monitoring purposes such as airflow sensing in a non-obstructive manner. This paper presents a methodology for PR and RR estimation using photoplethysmography (PPG) and accelerometry (Acc) sensors placed on the neck. Neck PPG and Acc signals were recorded from 22 healthy participants for RR estimation, where the resting subjects performed guided breathing following a visual metronome. Neck PPG signals were obtained from 16 healthy participants who breathed through an altitude generator machine in order to acquire a wider range of PR readings while at rest. The proposed methodology was able to provide rate estimates via a combination of recursive FFT-based dominance scoring coupled with an exponentially weighted moving average (EWMA)-driven aggregation scheme. The recursion aimed at bypassing sudden intra-window amplitude deviations caused by momentary artifacts, while the EWMA-based aggregation was utilized for handling inter-window artifact-induced deviations. To further improve estimation stability and confidence, estimates were calculated in the form of rate bands taking into account the relevant clinically acceptable error margins, and results when considering rate values and rate bands are presented and discussed. The framework was able to achieve an overall pulse rate value accuracy of 93.67 ± 7.64 % within the clinically acceptable ± 5 BPM with reference to the gold-standard reference devices while providing an overall respiratory rate value accuracy within the clinically appropriate ± 3 BrPM of 94.94 ± 3.56 % with reference to the guiding visual metronome, and 88.4 ± 7.63 % with respect to the gold-standard reference device. The proposed methodology achieves acceptable PR and RR estimation capabilities, even when signals are acquired from an unusual location such as the neck. This work introduces novel ideas that can lead to the development of medical device outputs for PR and RR monitoring, especially capitalizing on the advantages of the neck as a multi-modal physiological monitoring location.