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Previous research has explored the physical activity habits of people with dementia and their family carers separately, with little consideration of how physical habits are associated within dyads. In this observational study, we sought to explore the relationship between people with dementia and their carers' physical activity, at a group level and at a dyadic level. Twenty-six participant dyads (persons with dementia and their carer spouses) were asked to wear an accelerometer for 30 days continuously. Comparisons were made at a group level and a dyadic level. People with dementia did not participate in significantly more moderate to vigorous physical activity (M = 15.44 min/day; SD = 14.40) compared with carers (M = 17.95 min/day; SD = 17.01). Within dyads, there were moderately strong associations between daily moderate to vigorous physical activity (r =.48–.54), but not with overall activity levels (r =.24). Despite physical activity habits remaining relatively low within people with dementia and carers, respectively, moderate to vigorous physical activity levels appear to be correlated within dyads. Understanding mutual influence on physical activity levels within dyads is an important pathway to promote an active lifestyle. [ABSTRACT FROM AUTHOR]

The aim of this study was to quantify physical activity and sedentary behavior in older adults recovering from hip fracture and to identify groups based on movement patterns. In this cross-sectional cohort study, older adults (≥70 years) were included 3 months after surgery for proximal femoral fracture. Patients received an accelerometer for 7 days. Demographics and outcomes related to physical function, mobility, cognitive functions, quality of life, and hip fracture were assessed. In total, 43 patients with sufficient accelerometer wear time were included. Across all groups, participants engaged in very low levels of physical activity, spending an average of 11 hr/day in prolonged sedentary behavior. Based on the extracted components from a principal component analysis, three groups with substantial differences in levels of physical activity and sedentary behavior could be distinguished. [ABSTRACT FROM AUTHOR]

Purpose: This scoping review aims to identify the primary racket and arm-mounted technologies based on inertial measurement units that enable the quantification of hitting load in racket sports. Methods: A comprehensive search of several databases (PubMed, SPORTDiscus, Web of Science, and IEEE Xplore) and Google search engines was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews guidelines. Included records primarily focused on monitoring hitting load in racket sports using commercialized racket or arm-mounted inertial sensors through noncompetitive and competitive racket-sports players. Results: A total of 484 records were identified, and 19 finally met the inclusion criteria. The largest number of systems found were compatible with tennis (n = 11), followed by badminton (n = 4), table tennis (n = 2), padel (n = 1), and squash (n = 1). Four sensor locations were identified: grip-attached (n = 8), grip-embedded (n = 6), wrist (n = 3), and dampener sensors (n = 2). Among the tennis sensors, only 4 out of the 11 (36.4%) demonstrated excellent reliability (>.85) in monitoring the number of shots hit either during analytic drills or during simulated matches. None of the other racket-sports sensors have undergone successful, reliable validation for hitting-volume quantification. Conclusions: Despite recent advancements in this field, the quantification of hitting volume in racket sports remains a challenge, with only a limited number of tennis devices demonstrating reliable results. Thus, further progress in technology and research is essential to develop comprehensive solutions that adequately address these specific requirements. [ABSTRACT FROM AUTHOR]

Wearable activity sensors typically count movement quantity, such as the number of steps taken or the number of upper extremity (UE) counts achieved. However, for some applications, such as neurologic rehabilitation, it may be of interest to quantify the quality of the movement experience (QOME), defined, for example, as how diverse or how complex movement epochs are. We previously found that individuals with UE impairment after stroke exhibited differences in their distributions of forearm postures across the day and that these differences could be quantified with kurtosis-an established statistical measure of the peakedness of distributions. In this paper, we describe further progress toward the goal of providing real-time feedback to try to help people learn to modulate their movement diversity. We first asked the following: to what extent do different movement activities induce different values of kurtosis? We recruited seven unimpaired individuals and evaluated a set of 12 therapeutic activities for their forearm postural diversity using kurtosis. We found that the different activities produced a wide range of kurtosis values, with conventional rehabilitation therapy exercises creating the most spread-out distribution and cup stacking the most peaked. Thus, asking people to attempt different activities can vary movement diversity, as measured with kurtosis. Next, since kurtosis is a computationally expensive calculation, we derived a novel recursive algorithm that enables the real-time calculation of kurtosis. We show that the algorithm reduces computation time by a factor of 200 compared to an optimized kurtosis calculation available in SciPy, across window sizes. Finally, we embedded the kurtosis algorithm on a commercial smartwatch and validated its accuracy using a robotic simulator that wore the smartwatch, emulating movement activities with known kurtosis. This work verifies that different movement tasks produce different values of kurtosis and provides a validated algorithm for the real-time calculation of kurtosis on a smartwatch. These are needed steps toward testing QOME-focused, wearable rehabilitation.

Objective assessment of activity via accelerometry can provide valuable insights into dog health and welfare. Common activity metrics involve using acceleration cut-points to group data into intensity categories and reporting the time spent in each category. Lack of consistency and transparency in cut-point derivation makes it difficult to compare findings between studies. We present an alternative metric for use in dogs: the acceleration threshold (as a fraction of standard gravity, 1 g = 9.81 m/s2) above which the animals X most active minutes are accumulated (MXACC) over a 24-hour period. We report M2ACC, M30ACC and M60ACC data from a colony of healthy beagles (n = 6) aged 3-13 months. To ensure that reference values are applicable across a wider dog population, we incorporated labelled data from beagles and volunteer pet dogs (n = 16) of a variety of ages and breeds. The dogs normal activity patterns were recorded at 200 Hz for 24 hours using collar-based Axivity-AX3 accelerometers. We calculated acceleration vector magnitude and MXACC metrics. Using labelled data from both beagles and pet dogs, we characterize the range of acceleration outputs exhibited enabling meaningful interpretation of MXACC. These metrics will help standardize measurement of canine activity and serve as outcome measures for veterinary and translational research.

Purpose: There are important methodological considerations for translating wearable-based gait-monitoring data to field settings. This study investigated different devices' sampling rates, signal lengths, and testing frequencies for athlete monitoring using dynamical systems variables. Methods: Secondary analysis of previous wearables data (N = 10 runners) from a 5-week intensive training intervention investigated impacts of sampling rate (100–2000 Hz) and signal length (100–300 strides) on detection of gait changes caused by intensive training. Primary analysis of data from 13 separate runners during 1 week of field-based testing determined day-to-day stability of outcomes using single-session data and mean data from 2 sessions. Stride-interval long-range correlation coefficient α from detrended fluctuation analysis was the gait outcome variable. Results: Stride-interval α reduced at 100- and 200- versus 300- to 2000-Hz sampling rates (mean difference: −.02 to −.08; P ≤.045) and at 100- compared to 200- to 300-stride signal lengths (mean difference: −.05 to −.07; P <.010). Effects of intensive training were detected at 100, 200, and 400 to 2000 Hz (P ≤.043) but not 300 Hz (P =.069). Within-athlete α variability was lower using 2-session mean versus single-session data (smallest detectable change:.13 and.22, respectively). Conclusions: Detecting altered gait following intensive training was possible using 200 to 300 strides and a 100-Hz sampling rate, although 100 and 200 Hz underestimated α compared to higher rates. Using 2-session mean data lowers smallest detectable change values by nearly half compared to single-session data. Coaches, runners, and researchers can use these findings to integrate wearable-device gait monitoring into practice using dynamic systems variables. [ABSTRACT FROM AUTHOR]

Purpose: School recess provides a valuable opportunity for children's daily moderate- to vigorous-intensity physical activity (MVPA). This study aimed to quantify MVPA during school recess in a representative sample of Scottish children and examine whether recess MVPA varied by gender, socioeconomic status, season, urban/rural residency, and recess length. Method: Five-day accelerometry MVPA data were analyzed from 773 children (53.9% girls, 46.1% boys, 10- to 11-y-olds) from 471 schools. Binary logistic regression explored associations between meeting/not meeting the recommendation to spend 40% of recess time in MVPA and the aforementioned risk factors. Descriptive recess data were also analyzed. Results: Participants spent an average of 3.2 minutes (SD 2.1) in MVPA during recess. Girls engaged in 2.5 minutes (SD 1.7) of MVPA compared with 4.0 minutes (SD 2.2) for boys. Only 6% of children met the recess MVPA recommendation. The odds of girls (odds ratio 0.09; 95% confidence interval, 0.04–0.25) meeting the recommendation was lower (P <.001) compared with boys. No statistically significant differences were observed in meeting the recommendation for the other risk factors. Conclusion: Levels of MVPA during school recess are very low in Scottish children, and interventions aimed at increasing MVPA during recess are needed. [ABSTRACT FROM AUTHOR]

BACKGROUND: Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers in SB research. The former measures body movement and the latter measures body posture. The goal of the current study is to quantify the pattern and variation of movement (by ActiGraph activity counts) during activPAL-identified sitting events, and examine associations between patterns and health-related outcomes, such as systolic and diastolic blood pressure (SBP and DBP). METHODS: The current study included 314 overweight postmenopausal women, who were instructed to wear an activPAL (at thigh) and ActiGraph (at waist) simultaneously for 24 hours a day for a week under free-living conditions. ActiGraph and activPAL data were processed to obtain minute-level time-series outputs. Multilevel functional principal component analysis (MFPCA) was applied to minute-level ActiGraph activity counts within activPAL-identified sitting bouts to investigate variation in movement while sitting across subjects and days. The multilevel approach accounted for the nesting of days within subjects. RESULTS: At least 90% of the overall variation of activity counts was explained by two subject-level principal components (PC) and six day-level PCs, hence dramatically reducing the dimensions from the original minute-level scale. The first subject-level PC captured patterns of fluctuation in movement during sitting, whereas the second subject-level PC delineated variation in movement during different lengths of sitting bouts: shorter (< 30 minutes), medium (30 -39 minutes) or longer (> 39 minute). The first subject-level PC scores showed positive association with DBP (standardized β ^ : 2.041, standard error: 0.607, adjusted p = 0.007), which implied that lower activity counts (during sitting) were associated with higher DBP. CONCLUSION: In this work we implemented MFPCA to identify variation in movement patterns during sitting bouts, and showed that these patterns were associated with cardiovascular health. Unlike existing methods, MFPCA does not require pre-specified cut-points to define activity intensity, and thus offers a novel powerful statistical tool to elucidate variation in SB patterns and health. TRIAL REGISTRATION: ClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145 ; International Registered Report Identifier (IRRID): DERR1-10.2196/28684.

Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities. This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participants level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the bodys center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearsons r = -0.9929 to 0.9986, p < 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individuals stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers.

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

IntroductionAttention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder where hyperactivity often manifests as fidgeting, a non-goal-directed motoric action. Many studies demonstrate fidgeting varies under different conditions as a self-regulating mechanism for attention and alertness during cognitively demanding tasks. Fidgeting has also been associated with reaction time variability. However, a lack of standard variables to define and quantify fidgeting can lead to discrepancies in data and interpretability issues across studies. Furthermore, little is known about fidgeting in adults with ADHD compared to youth. This study aims to design a framework to quantify meaningful fidgeting variables and to apply them to test the relation between fidgeting and performance on a cognitive task, the Flanker, in adults with ADHD.MethodOur study included 70 adult participants diagnosed with ADHD, aged 18-50 years (30.5 ± 7.2 years). Screening included a structured clinical interview, childhood, current self and current observer ratings of ADHD symptoms. Actigraphy devices were attached to the left wrist and right ankle during completion of a cognitive control, attention task (the Flanker). Laboratory testing was subsequently completed on a single day. The relation between task performance, reaction time variability and fidgeting was examined.Results and discussionOur analysis revealed increased fidgeting during correct trials as defined by our new variables, consistent with previous observations. Furthermore, differences in fidgeting were observed between early and later trials while the percentage of correct trials were not significantly different. This suggests a relation between the role of fidgeting and sustaining attention. Participants with low reaction time variability, that is, those with more consistent reaction times, fidgeted more during later trials. This observation supports the theory that fidgeting aids arousal and improves sustained attention. Finally, a correlation analysis using ADHD-symptom rating scales validated the relevance of the fidget variables in relation to ADHD symptom severity. These findings suggest fidgeting may be a compensatory mechanism that aids in sustained attention for those with ADHD, although alternative explanations exist.ConclusionOur study suggests that fidgeting may aid in sustained attention during the attention-demanding, cognitive control processes for adults with ADHD, with more fidgeting observed during correct trials and among participants with lower reaction time variability. Furthermore, the newly defined fidget variables were validated through a significant correlation with ADHD rating scales. By sharing our implementation of fidget variables, we hope to standardize and encourage further quantitative research into the role of fidgeting in ADHD.

Background: The liver effects of concentrated vs. more evenly distributed moderate-to-vigorous physical activity (MVPA) patterns remain unclear. We aimed to examine the association of accelerometer-measured MVPA and different MVPA patterns with liver outcomes. Methods: Eighty-eight thousand six hundred fifty-six participants without prior liver diseases from UK Biobank were included. MVPA was measured by a wrist-worn accelerometer. Based on the guideline-based threshold (≥ 150 min/week), MVPA patterns were defined as inactive (< 150 min/week), active weekend warrior (WW; ≥ 150 min/week with ≥ 50% of total MVPA achieved within 1–2 days), and regularly active (≥ 150 min/week but not active WW) patterns. The primary outcome was incident nonalcoholic fatty liver disease (NAFLD). Results: During a median follow-up of 6.8 years, 562 participants developed NAFLD. Overall, there was a nonlinear inverse association of total MVPA with incident NAFLD (P for nonlinearity = 0.009): the risk of NAFLD rapidly decreased with the increment of MVPA (per 100 min/week increment: HR = 0.68; 95%CI, 0.57–0.81) when MVPA < 208 min/week, while moderately declined (HR = 0.91; 95%CI, 0.84–0.99) when MVPA ≥ 208 min/week. For MVPA patterns, compared with inactive group, both active WW (HR = 0.55, 95%CI, 0.44–0.67) and active regular (HR = 0.49, 95%CI, 0.38–0.63) group were associated with a similar lower risk of NAFLD. Similar results were observed for each secondary outcome, including incident severe liver diseases, incident liver cirrhosis, and liver magnetic resonance imaging-based liver steatosis and fibrosis. Conclusions: Regardless of whether MVPA was concentrated within 1 to 2 days or spread over most days of the week, more MVPA was associated with a lower risk of incident liver outcomes, including NAFLD, liver cirrhosis, liver steatosis, and fibrosis, to MVPA more evenly distributed. [ABSTRACT FROM AUTHOR]

Background: The World Health Organisation (WHO) has called for more evidence on 24-hour movement behaviours from low- and middle-income countries. We examined the proportion of Ethiopian children aged 3.0-4.9 years who met the WHO guidelines on physical activity, screen time and sleep for children under the age of five, and the associations between meeting the guidelines and socio-demographic factors. Methods: A cross-sectional study was conducted in Adama and Lume district, Ethiopia. Children were recruited through kindergartens in Adama city and rural villages of Lume district, Ethiopia. Physical activity and sleep were measured using ActiGraph accelerometer. Screen time and restrained sitting were parent-reported. Multivariable logistic regression models tested associations between meeting the individual and combined WHO guidelines and socio-demographic factors. Results: A total of 430 children participated in the study (mean age 4.2 ± 0.6 years). More than half the children (58.0%) met all the WHO guidelines. A higher proportion met the physical activity (96.1%) and sleep guidelines (91.9%) compared to the screen time guideline (63.5%). Children who lived in rural areas were more likely to meet the screen time (84.6% vs. 38.2%, AOR = 7.31; 95%CI: 3.93, 14.02), sleep (98.6% vs. 83.8%, AOR = 8.60; 95%CI: 3.55, 23.73) and combined (81.3% vs. 30.1%, AOR = 7.41; 95%CI: 4.04, 13.97) guidelines than those who lived in urban areas. Conclusions: Children from rural Ethiopia were more compliant with the WHO guidelines than their urban counterparts. Strategies to reduce screen time and promote healthy movement behaviours in urban areas are needed. Further studies with a larger representative sample might provide better insight across the regions within the country. [ABSTRACT FROM AUTHOR]

Background: Accurately measuring energy expenditure during physical activity outside of the laboratory is challenging, especially on a large scale. Thigh-worn accelerometers have gained popularity due to the possibility to accurately detect physical activity types. The use of machine learning techniques for activity classification and energy expenditure prediction may improve accuracy over current methods. Here, we developed a novel composite energy expenditure estimation model by combining an activity classification model with a stride specific energy expenditure model for walking, running, and cycling. Methods: We first trained a supervised deep learning activity classification model using pooled data from available adult accelerometer datasets. The composite energy expenditure model was then developed and validated using additional data based on a sample of 69 healthy adult participants (49% female; age = 25.2 ± 5.8 years) who completed a standardised activity protocol with indirect calorimetry as the reference measure. Results: The activity classification model showed an overall accuracy of 99.7% across all five activity types during validation. The composite model for estimating energy expenditure achieved a mean absolute percentage error of 10.9%. For running, walking, and cycling, the composite model achieved a mean absolute percentage error of 6.6%, 7.9% and 16.1%, respectively. Conclusions: The integration of thigh-worn accelerometers with machine learning models provides a highly accurate method for classifying physical activity types and estimating energy expenditure. Our novel composite model approach improves the accuracy of energy expenditure measurements and supports better monitoring and assessment methods in non-laboratory settings. [ABSTRACT FROM AUTHOR]

Progressive gait impairment is common among aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data. Validation involved two diverse cohorts, including over 1000 participants without gait labels, as well as 83 participants with labeled data: older adults with Parkinson's disease, proximal femoral fracture, chronic obstructive pulmonary disease, congestive heart failure, and healthy adults. ElderNet presented high accuracy (96.43 ± 2.27), specificity (98.87 ± 2.15), recall (82.32 ± 11.37), precision (86.69 ± 17.61), and F1 score (82.92 ± 13.39). The suggested method yielded superior performance compared to two state-of-the-art gait detection algorithms, with improved accuracy and F1 score (p < 0.05). In an initial evaluation of construct validity, ElderNet identified differences in estimated daily walking durations across cohorts with different clinical characteristics, such as mobility disability (p < 0.001) and parkinsonism (p < 0.001). The proposed self-supervised method has the potential to serve as a valuable tool for remote phenotyping of gait function during daily living in aging adults, even among those with gait impairments. [ABSTRACT FROM AUTHOR]

There is limited validation for portable methods in evaluating high-speed running biomechanics, with inertial measurement unit (IMU) systems commonly used as wearables for this purpose. This study aimed to evaluate the validity of an IMU system in high-speed running compared to a 3D motion analysis system (MAS). One runner performed incremental treadmill running, from 12 to 18 km/h, on two separate days. Sagittal angles for the shank, knee, hip and pelvis were measured simultaneously with three IMUs and the MAS at the point of contact (POC), the timing when the foot initially hits the ground, as identified by IMU system acceleration, and compared to the POC identified via force plate. Agreement between the systems was evaluated using intra-class correlation coefficients, Pearson's r, Bland–Altman limits of agreements, root mean square error and paired t-tests. The IMU system reliably determined POC (which subsequently was used to calculate stride time) and measured hip flexion angle and anterior pelvic tilt accurately and consistently at POC. However, it displayed inaccuracy and inconsistency in measuring knee flexion and shank angles at POC. This information provides confidence that a portable IMU system can aid in establishing baseline running biomechanics for performance optimisation, and/or inform injury prevention programs. [ABSTRACT FROM AUTHOR]

Overnight sleep staging is an important part of the diagnosis of various sleep disorders. Polysomnography is the gold standard for sleep staging, but less-obtrusive sensing modalities are of emerging interest. Here, we developed and validated an algorithm to perform "proxy" sleep staging using cardiac and respiratory signals derived from a chest-worn accelerometer. We collected data in two sleep centers, using a chest-worn accelerometer in combination with full PSG. A total of 323 participants were analyzed, aged 13–83 years, with BMI 18–47 kg/m2. We derived cardiac and respiratory features from the accelerometer and then applied a previously developed method for automatic cardio-respiratory sleep staging. We compared the estimated sleep stages against those derived from PSG and determined performance. Epoch-by-epoch agreement with four-class scoring (Wake, REM, N1+N2, N3) reached a Cohen's kappa coefficient of agreement of 0.68 and an accuracy of 80.8%. For Wake vs. Sleep classification, an accuracy of 93.3% was obtained, with a sensitivity of 78.7% and a specificity of 96.6%. We showed that cardiorespiratory signals obtained from a chest-worn accelerometer can be used to estimate sleep stages among a population that is diverse in age, BMI, and prevalence of sleep disorders. This opens up the path towards various clinical applications in sleep medicine. [ABSTRACT FROM AUTHOR]

Rumination behavior in cattle can provide valuable information for monitoring health status and animal welfare, but continuous monitoring is essential to detect changes in rumination behavior. In a previous study validating the use of a respiration rate sensor equipped with a triaxial accelerometer, the regurgitation process was also clearly visible in the pressure and accelerometer data. The aim of the present study, therefore, was to measure the individual lengths of rumination cycles and to validate whether the sensor data showed the same number of regurgitations as those counted visually (video or direct observation). For this purpose, 19 Holstein Friesian cows equipped with a respiration rate sensor were observed for two years, with a focus on rumination behavior. The results showed a mean duration of 59.27 ± 9.01 s (mean ± SD) per rumination cycle and good agreement (sensitivity: 99.1–100%, specificity: 87.8–95%) between the two methods (sensor and visual observations). However, the frequency of data streaming (continuously or every 30 s) from the sensor to the data storage system strongly influenced the classification performance. In the future, an algorithm and a data cache will be integrated into the sensor to provide rumination time as an additional output. [ABSTRACT FROM AUTHOR]

In this study, a method for the monitoring of internal combustion engine operation by vibration signals is proposed. The work falls within the context of the increasingly stringent standards relating to the environmental impact of engines and the development of monitoring and control techniques to ensure increased engine performance as well as fuel saving and reduction of pollutant emissions. Experimentation was performed on a turbocharged light-duty compression ignition direct-injection engine. Two monoaxial accelerometers were installed on the engine compressor case, the speed of which has been demonstrated to be closely related to the engine operation. Vibration measurements of the engine compressor case have been processed by combining the Empirical Mode Decomposition technique with Independent Component Analysis and Short Time Fourier Transform to indirectly estimate the turbocharger speed. The obtained traces have been compared to the direct turbocharger velocity measures during the stationary running of the engine (speed and load conditions varied in the complete engine's range of operation). The results point out the potentiality of the methodology in algorithms devoted to identifying modifications of the combustion development regarding regular operation via indirect turbocharger speed monitoring. [ABSTRACT FROM AUTHOR]

Wearable sensors provide the ability to assess ambulatory activity in the community after hip preservation surgery (HPS). In combination with gait analysis and patient reported outcomes, more perspective on post-operative function is gained. The purpose of this study was to assess the relationship between self-reported function/activity, temporo-spatial parameters and walking kinematics to objectively measured ambulatory activity. Forty-nine participants (38 Females; age range 16–38 years) who were five years or more post-surgery and the following diagnoses were included: Acetabular Dysplasia (n=34), Femoroacetabular Impingement (n=12) and Legg-Calvé Perthes disease (n=3). Participants underwent 3D gait analysis and gait deviations were quantified using the Gait Deviation Index (GDI) and Gait Profile Score (GPS). Temporo-spatial parameters were also calculated. Self-reported pain/function and activity level were assessed via the Harris Hip Score (HHS) and UCLA Activity Scale (UCLA). Participants wore a StepWatch Activity Monitor in their community and the Intensity/Duration of ambulatory bouts were analyzed. Spearman correlation coefficients were run to assess the following relationships: in-lab walking measures, self-reported function/activity vs.community ambulatory activity. There were no statistically significant correlations between HHS, UCLA or temporospatial parameters with ambulatory activity (p>0.05). Worsening gait deviations (GDI/GPS scores) correlated with daily total ambulatory time (ρ=0.284/-0.284, p<0.05), time spent in Short duration ambulatory bouts (ρ=-0.321/0.321, p<0.05) and the amount of time in Long duration ambulatory bouts (ρ=0.366/-0.366, p<0.05). The amount of time spent in Easy intensity/Short duration and Easy intensity/Long duration ambulatory bouts did have a weak correlation with the GDI and GPS (p<0.05). In HPS patients after long-term follow up, ambulatory activity in the community did not correlate with patient reported outcomes but there was a weak correlation with the presence of gait deviations. Incorporating wearable sensors to assess community ambulatory bout intensity/duration, provides additional quantifiable measures into the overall function of patients following HPS • Harris Hip Score did not correlate with community ambulatory activity. • Temporal spatial parameters did not correlate with community ambulatory activity. • Overall walking mechanics weakly correlate with community ambulatory activity. • Wearable sensors provide information on community ambulatory intensity/duration. [ABSTRACT FROM AUTHOR]

Purpose: Habitual strength and power-demanding activities of daily life may support the maintenance of adequate lower extremity functioning with aging, but this has been sparingly explored. Hence, we examined whether the characteristics of free-living sit-to-stand (STS) transitions predict a decline in lower extremity functioning over a 4-yr follow-up. Methods: A total of 340 community-dwelling older adults (60% women; age 75, 80, or 85 yr) participated in this prospective cohort study. At baseline, a thigh-worn accelerometer was used continuously (3-7 d) to monitor the number and intensity of free-living STS transitions. A decline in lower extremity functioning was defined as a drop of ≥2 points in the Short Physical Performance Battery (SPPB) from baseline to follow-up. Maximal isometric knee extension strength was measured in the laboratory. Results: Eighty-five participants (75%women) declined in SPPB over 4 yr. After adjusting for age, sex, and baseline SPPB points, higher free-living peak STS angular velocity (odds ratio (OR), 0.70; 95% confidence interval (CI), 0.52-0.92, per 20°⋅s-1 increase) protected against a future decline. When adjusting the model for maximal isometric knee extension strength, the statistical significance was attenuated (OR = 0.72; 95% CI = 0.54-0.96, per 20°⋅s-1 increase). Conclusions: Performing STS transitions at higher velocities in the free-living environment can prevent a future decline in lower extremity function. This indicates that changes in daily STS behavior may be useful in the early identification of functional loss. Free-living peak STS angular velocity may be a factor underlying the longitudinal association of lower extremity strength and performance. [ABSTRACT FROM AUTHOR]

Wedgefishes have recently been recognised as one of the most imperilled marine fish families worldwide. However, many knowledge gaps about their biology and ecology hinder conservation efforts. Here we used a combination of acoustic telemetry and acceleration datalogger technology to gain fundamental insights into the fine-scale behaviour, habitat use, size of activity spaces, and residency of adult female bottlenose wedgefish (Rhynchobatus australiae) in the Ningaloo region of northwestern Australia. Acoustic tracking data over one year demonstrated that female bottlenose wedgefish continuously resided in a relatively small area of a productive coral reef lagoon. Acceleration data revealed that bottlenose wedgefish were nocturnal, with time of day having a greater influence on activity than tidal patterns. Bottlenose wedgefish also increased activity with seasonally increasing temperatures. We identified several discrete behavioural signatures in the acceleration data, inferred to correspond to chafing, settling/burying behaviour, foraging behaviour, and escape behaviour, based on their kinematics. Further observations are required to confirm these behaviours with certainty. Additionally, according to datalogger and acoustic data, tagged bottlenose wedgefish rarely inhabited areas greater than 2 m deep. Together, these first insights into behaviour and habitat use of adult female bottlenose wedgefish highlight the importance of nearshore habitats for this species and indicate that they may be highly resident to specific areas. Our findings provide important insight into the conservation of bottlenose wedgefish in northwestern Australia, including potential effectiveness of protected areas and interactions with specific anthropogenic threats such as shoreline development and recreational beach fishing. [ABSTRACT FROM AUTHOR]

Studies on spontaneous rupture mechanisms are limited because using existing experimental apparatuses is difficult to accurately observe parameters related to the rupture (e.g., slip history and frictional properties of fault). This study proposes multiple diagnostic systems combined with a biaxial loading device to study the rupture mechanism. Two isosceles right triangle PMMA plates are held together to form a fault model. The spontaneous rupture is achieved under various friction angles through different combinations of the x-axis and y-axis loading forces. A digital image correlation (DIC) method is systematically used to measure displacement and stress fields along the fault during rupture, thus accurately quantifying rupture velocity and stress distribution. Accelerometers are used to measure seismic waves. In this experiment, the supershear rupture can be detected, and the potential energy and the energy consumed are discussed. Typical results prove the feasibility and flexibility of the proposed system. [ABSTRACT FROM AUTHOR]

The prevalence of sleep-related issues among older adults is a significant concern, with half of the older population reporting these problems. Consequently, strategies to improve sleep are needed for this population. This study aims to assess the effects of a health educational program on sleep behaviour among pre-frail or frail older adults residing in the community and to explore possible associations with frailty. This randomised controlled trial (NCT05610605) included a total of 197 community-dwelling older adults with frailty/pre-frailty, divided into control (n = 88) and educational (n = 109) groups, were assessed at baseline, after the 6-month educational program (6 months), and 6 months after the intervention (12 months). The intervention comprised four group sessions and six follow-up phone calls, focusing on frailty, physical activity, dietary habits, and cognitive training. Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI) and wrist-worn accelerometry. At 6 months, a significant time-by-group interaction was found for self-reported [β = −0.449, 95%CI (−0.844, −0.053), p = 0.026] and accelerometer-measured [β = 0.505, 95%CI (0.085, 0.926), p = 0.019] sleep efficiency, showing improved sleep efficiency in the intervention group vs. controls. A significant time-by-group interaction at 6 months was noted for sleep awakenings [β = −0.402, 95%CI (−0.825, −0.020), p = 0.047]. The educational program led to a significant decrease in awakenings, while the control group experienced an increase. The change in the number of awakenings (Rs = 0.183, p = 0.020) at 6 months was significantly associated with changes in frailty. Moreover, a significant time-by-group interaction was reported at the 12-month assessment [β = −0.449, 95%CI (−0.844, −0.053), p = 0.026] for self-reported sleep quality, indicating better results in the intervention group compared to controls. The educational program improved sleep quality and sleep efficiency while reducing the number of awakenings per night among community-dwelling frail older adults, offering a practical approach to addressing sleep-related challenges in this demographic. • A health educational program improved sleep quality among older adults with frailty. • Participants who adhered to their usual healthcare experienced a decrease in sleep quality. • Interventions for frailty, activity, diet, and cognition can boost sleep quality in frail adults. [ABSTRACT FROM AUTHOR]

• There is a growing implementation of device-based population surveillance of physical activity and sedentary behavior in the Nordic countries. • So far, none of the countries have collected device-based data on pre-schoolers or older adults in a national sample. • Steps per day and mean level of moderate-to-high intensity physical activity are similar across the countries and above the current recommendations for health benefits. • The amount of sedentary time is high in all Nordic countries. • Type of device, placement, and data procedures vary both within and between the Nordic countries, and more standardized data collection is needed for better comparisons. The purpose of this scoping review was to summarize and describe the methodology and results from population-based studies of physical activity and sedentary time measured with devices in the Nordic countries (Denmark, Finland, Iceland, Norway, and Sweden) and published in 2000 or later. A systematic search was carried out in PubMed and Web of Science in June 2023 using predefined search terms. Fourteen unique research projects or surveillance studies were identified. Additionally, 2 surveillance studies published by national agencies were included, resulting in a total of 16 studies for inclusion. National surveillance systems exist in Finland and Norway, with regular survey waves in school-aged children/adolescents and adults. In Denmark, recent nationally representative data have been collected in school children only. So far, Sweden has no regular national surveillance system using device-based data collection. No studies were found from Iceland. The first study was conducted in 2001 and the most recent in 2022, with most data collected from 2016 to date. Five studies included children/adolescents 6–18 years, no study included preschoolers. In total 11 studies included adults, of which 8 also covered older adults. No study focused specifically on older adults. The analytical sample size ranged from 205 to 27,890. Detailed methodology is presented, such as information on sampling strategy, device type and placement, wear protocols, and physical activity classification schemes. Levels of physical activity and sedentary time in children/adolescents, adults, and older adults across the Nordic countries are presented. A growing implementation of device-based population surveillance of physical activity and sedentary behavior in the Nordic countries has been identified. The variety of devices, placement, and data procedures both within and between the Nordic countries highlights the challenges when it comes to comparing study outcomes as well as the need for more standardized data collection. [Display omitted] [ABSTRACT FROM AUTHOR]

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We investigated the longitudinal associations between sports participation patterns in youth and physical activity (PA) in adulthood. PA was self-reported triannually between ages 9–18 (n = 2550, 52% females) and measured by accelerometers in mid-adulthood (n = 1002, 61% females, aged 48 ± 4 years). Three latent classes of youth sports participation emerged for both genders: 1) “organized sports” (persistent high PA with regular sports club activities), 2) “unorganized sports” (persistent high PA without sports club activities and 3) “low activity” (low PA with decreasing sports involvement). These groups comprised 29%, 34% and 37% of males, and 23%, 27% and 50% of females, respectively. Youth “organized sports” was associated with higher adult PA in both males (+1166 steps/day, p = 0.012) and females (+15 min/day moderate-to-vigorous PA [MVPA], +1064 steps/day, +1066 leisure-time steps/day; p ≤ 0.005) compared to “low activity”. In males, youth “organized sports” was associated with higher adult PA (+1103 steps/day, −26 min/day sedentary time and +133 counts/minute higher total PA, p ≤ 0.039) compared to “unorganized sports”. In females, “unorganized sports” in youth was related to higher adult PA (+10 min/day MVPA, p = 0.034) when compared to “low activity”. Sustained participation in youth organized sports, and for females, also in unorganized sports, is positively linked with adult PA. [ABSTRACT FROM AUTHOR]

Background: Physical inactivity and subsequent muscle atrophy are highly prevalent in neurocritical care and are recognized as key mechanisms underlying intensive care unit acquired weakness (ICUAW). The lack of quantifiable biomarkers for inactivity complicates the assessment of its relative importance compared to other conditions under the syndromic diagnosis of ICUAW. We hypothesize that active movement, as opposed to passive movement without active patient participation, can serve as a valid proxy for activity and may help predict muscle atrophy. To test this hypothesis, we utilized non-invasive, body-fixed accelerometers to compute measures of active movement and subsequently developed a machine learning model to predict muscle atrophy. Methods: This study was conducted as a single-center, prospective, observational cohort study as part of the MINCE registry (metabolism and nutrition in neurointensive care, DRKS-ID: DRKS00031472). Atrophy of rectus femoris muscle (RFM) relative to baseline (day 0) was evaluated at days 3, 7 and 10 after intensive care unit (ICU) admission and served as the dependent variable in a generalized linear mixed model with Least Absolute Shrinkage and Selection Operator regularization and nested-cross validation. Results: Out of 407 patients screened, 53 patients (age: 59.2 years (SD 15.9), 31 (58.5%) male) with a total of 91 available accelerometer datasets were enrolled. RFM thickness changed − 19.5% (SD 12.0) by day 10. Out of 12 demographic, clinical, nutritional and accelerometer-derived variables, baseline RFM muscle mass (beta − 5.1, 95% CI − 7.9 to − 3.8) and proportion of active movement (% activity) (beta 1.6, 95% CI 0.1 to 4.9) were selected as significant predictors of muscle atrophy. Including movement features into the prediction model substantially improved performance on an unseen test data set (including movement features: R2 = 79%; excluding movement features: R2 = 55%). Conclusion: Active movement, as measured with thigh-fixed accelerometers, is a key risk factor for muscle atrophy in neurocritical care patients. Quantifiable biomarkers reflecting the level of activity can support more precise phenotyping of ICUAW and may direct tailored interventions to support activity in the ICU. Studies addressing the external validity of these findings beyond the neurointensive care unit are warranted. Trial registration: DRKS00031472, retrospectively registered on 13.03.2023. [ABSTRACT FROM AUTHOR]

AbstractPurposeMaterials and methodsResultsConclusionsThis study aimed to investigate the relationship between cardiometabolic disease risk and time spent in device-measured activity behaviours in a cohort of people with advanced osteoarthritis (OA) awaiting joint replacement surgery.Cardiometabolic risk biomarkers were assessed in people with OA (n = 96; hip n = 38, knee n = 58; mean (SD) age = 64.3 (9.8) years; 71% female). Physical activity (PA) and sedentary behaviour (SB) were measured by accelerometer over seven days (24 h/day).There were similar patterns of PA and SB between the hip and knee OA participants except for total number of steps (hip = 3365 (2926) vs knee 4344 (2836) steps/day; p = 0.018) and total stepping time (hip = 50.8 (38.2) vs knee = 67.2 (38.5) min/day; p = 0.005). Each additional cardiometabolic risk factor accumulated was associated with a 26.3 min/day increase in sedentary behaviour (p = 0.032; 95% CI: 2.3, 50.2), a 26.3 min/day decrease in upright time (p = 0.032; −50.2, –2.3) and a 23.6 min/day decrease in standing time (p = 0.032; −45.1, –2.1).In people with hip or knee OA, increased cardiometabolic disease risk was associated with more sitting and less upright and standing time. Findings support targeting reductions in sedentary behaviour for improvements in cardiometabolic health in people with osteoarthritis.IMPLICATIONS FOR REHABILITATIONKnee and hip osteoarthritis is a condition which is associated with an increased risk of cardiometabolic disease but also due to the low levels of physical activity and high levels of sedentary behaviour.Offsetting sedentary behaviour with light physical activity offers a feasible interventional target to reduce the risk of cardiometabolic disease in people with hip and knee osteoarthritis.Knee and hip osteoarthritis is a condition which is associated with an increased risk of cardiometabolic disease but also due to the low levels of physical activity and high levels of sedentary behaviour.Offsetting sedentary behaviour with light physical activity offers a feasible interventional target to reduce the risk of cardiometabolic disease in people with hip and knee osteoarthritis. [ABSTRACT FROM AUTHOR]

Self-reported physical activity questionnaires (e.g., International Physical Activity Questionnaire, IPAQ) are a cost-effective, time-saving, and accessible method to assess sedentary behaviour and physical activity. There are conflicting findings regarding the validity of self-reported questionnaires in comparison to accelerometer-measured data in a free-living environment. This study aimed to investigate the concurrent validity between self-reported Arabic–English IPAQ short form (IPAQ-SF) and Fibion (Fibion Inc., Jyväskylä, Finland) accelerometer-measured sedentary and physical activity time among young adults. One hundred and one young healthy adults (mean age 20.8 ± 2.4 years) filled in the IPAQ short form (IPAQ-SF) and wore the Fibion device on the anterior thigh for ≥ 600 min per day for 4–7 days. Concurrent validity between the IPAQ-SF and Fibion accelerometer for sitting, walking, moderate activity, and vigorous activity time was assessed using the Spearman correlation coefficient (ρ ) and Bland–Altman plots. Significant weak associations between IPAQ-SF and Fibion measurements were found for total activity time (ρ = 0.4; P < 0.001) and for the duration of walking (ρ = 0.3; P = 0.01), moderate (ρ = 0.2; P = 0.02), and vigorous-intensity activities (ρ = 0.4; P < 0.001). However, ρ was not significant (ρ = − 0.2; P = 0.09) for sitting time. In addition, all the plots of the measured variables showed a proportional bias. A low association and agreement were found between self-reported IPAQ-SF scores and Fibion accelerometer measurements among young adults in the UAE. Adult sedentary and physical activity measurements should be obtained objectively with accelerometers rather than being limited to self-reported measures. [ABSTRACT FROM AUTHOR]

The utilization of inertial measurement units as wearable sensors is proliferating across various domains, such as health care, sports, and rehabilitation. This expansion has produced a market of devices tailored to accommodate very specific ranges of operational demands. Simultaneously, this growth is creating opportunities for the development of a new class of devices more oriented towards general-purpose use and capable of capturing both high-frequency signals for short-term, event-driven motion analysis and low-frequency signals for extended monitoring. For such a design, which combines flexibility and low cost, a rigorous evaluation of the device in terms of deviation, noise levels, and precision is essential. This evaluation is crucial for identifying potential improvements and refining the design accordingly, yet it is rarely addressed in the literature. This paper presents the development process of such a device. The results of the design process demonstrate acceptable performance in optimizing energy consumption and storage capacity while highlighting the most critical optimizations needed to advance the device towards the goal of a smart, general-purpose unit for human motion monitoring. [ABSTRACT FROM AUTHOR]

Background: Seniors wearing a passive hip exoskeleton (Exo) show increased walking speed and step length but reduced cadence. We assessed the test–retest reliability of seniors' gait characteristics with Exo. Methods: Twenty seniors walked with and without Exo (noExo) on a 10 m indoor track over two sessions separated by one week. Speed, step length, cadence and step time variability were extracted from one inertial measurement unit (IMU) placed over the L5 vertebra. Relative and absolute reliability were assessed using the intraclass correlation coefficient (ICC), standard error of measurement (SEM) and minimal detectable change (MDC). Results: The relative reliability of speed, step length, cadence and step time variability ranged from "almost perfect to substantial" for Exo and noExo with ICC values between 0.75 and 0.87 and 0.60 and 0.92, respectively. The SEM and MDC values for speed, step length cadence and step time variability during Exo and noExo were <0.002 and <0.006 m/s, <0.002 and <0.005 m, <0.30 and <0.83 steps/min and <0.38 s and <1.06 s, respectively. Conclusions: The high test–retest reliability of speed, step length and cadence estimated from IMU suggest a robust extraction of spatiotemporal gait characteristics during exoskeleton use. These findings indicate that IMUs can be used to assess the effects of wearing an exoskeleton on seniors, thus offering the possibility of conducting longitudinal studies. [ABSTRACT FROM AUTHOR]

Walking is crucial for independence and quality of life. This study leverages wrist-worn sensor data from UK Biobank participants to establish normative daily-life walking data, stratified by age and sex, to provide benchmarks for research and clinical practice. The Watch Walk digital biomarkers were developed, validated, and applied to 92,022 participants aged 45–79 who wore a wrist sensor for at least three days. Normative data were collected for daily-life walking speed, step-time variability, step count, and 17 other gait and sleep biomarkers. Test–retest reliability was calculated, and associations with sex, age, self-reported walking pace, and mobility problems were examined. Population mean maximal and usual walking speeds were 1.49 and 1.15 m/s, respectively. The daily step count was 7749 steps, and step regularity was 65%. Women walked more regularly but slower than men. Walking speed, step count, longest walk duration, and step regularity decreased with age. Walking speed is associated with sex, age, self-reported pace, and mobility problems. Test–retest reliability was good to excellent (ICC ≥ 0.80). This study provides large-scale normative data and benchmarks for wrist-sensor-derived digital gait and sleep biomarkers from real-world data for future research and clinical applications. [ABSTRACT FROM AUTHOR]

The uncertainty of hand gestures, the variability of gestures across subjects, and the high cost of collecting a large amount of annotated data lead to a great challenge to the robust recognition of gestures, and thus it remains quite crucial to capture the informative features of hand movements and to mitigate inter-subject variations. To this end, we propose a gesture recognition model that uses two different types of sensors and optimizes the feature space towards enhanced accuracy and better generalization. Specifically, we use an accelerometer and a surface electromyography sensor to capture kinematic and physiological signals of hand movements. We use a sliding window to divide the streaming sensor data and then extract time-domain and frequency-domain features from each segment to return feature vectors. Afterwards, the feature space is optimized with a feature selector and a gesture recognizer is optimized. To handle the case where no labeled training data are available for a new user, we apply the transfer learning technique to reuse the cross-subject knowledge. Finally, extensive comparative experiments concerning different classification models, different sensors, and different types of features are conducted. Results show that the joint use of kinematic and physiological sensors generally outperforms the use of single sensor, indicating the synthetic effect of different sensors, and that the use of transfer learning helps improve the cross-subject recognition accuracy. In addition, we quantitatively investigate the impact of null gesture on a gesture recognizer and results indicate that null gesture would lower its accuracy, enlightening related studies to consider it. [ABSTRACT FROM AUTHOR]

Background: We aimed to explore the association of sleep duration with depressive symptoms among rural-dwelling older adults in China, and to estimate the impact of substituting sleep with sedentary behavior (SB) and physical activity (PA) on the association with depressive symptoms. Methods: This population-based cross-sectional study included 2001 rural-dwelling older adults (age ≥ 60 years, 59.2% female). Sleep duration was assessed using the Pittsburgh Sleep Quality Index. We used accelerometers to assess SB and PA, and the 15-item Geriatric Depression Scale to assess depressive symptoms. Data were analyzed using restricted cubic splines, compositional logistic regression, and isotemporal substitution models. Results: Restricted cubic spline curves showed a U-shaped association between daily sleep duration and the likelihood of depressive symptoms (P-nonlinear < 0.001). Among older adults with sleep duration < 7 h/day, reallocating 60 min/day spent on SB and PA to sleep were associated with multivariable-adjusted odds ratio (OR) of 0.81 (95% confidence interval [CI] = 0.78–0.84) and 0.79 (0.76–0.82), respectively, for depressive symptoms. Among older adults with sleep duration ≥ 7 h/day, reallocating 60 min/day spent in sleep to SB and PA, and reallocating 60 min/day spent on SB to PA were associated with multivariable-adjusted OR of 0.78 (0.74–0.84), 0.73 (0.69–0.78), and 0.94 (0.92–0.96), respectively, for depressive symptoms. Conclusions: Our study reveals a U-shaped association of sleep duration with depressive symptoms in rural older adults and further shows that replacing SB and PA with sleep or vice versa is associated with reduced likelihoods of depressive symptoms depending on sleep duration. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. The use of sensor-based measures of rumination time as a parameter for early disease detection has received a lot of attention in scientific research. This study aimed to assess the accuracy of health alerts triggered by a sensor-based accelerometer system within 2 different management strategies on a commercial dairy farm. Multiparous Holstein cows were enrolled during the dry-off period and randomly allocated to conventional (CON) or sensor-based (SEN) management groups at calving. All cows were monitored for disorders for a minimum of 10 DIM following standardized operating procedures (SOP). The CON group (n = 199) followed an established monitoring protocol on the farm. The health alerts of this group were not available during the study but were later included in the analysis. The SEN group (n = 197) was only investigated when the sensor system triggered a health alert, and a more intensive monitoring approach was implemented according to the SOP. To analyze the efficiency of the health alerts in detecting disorders, the sensitivity (SE) and specificity (SP) of health alerts were determined for the CON group. In addition, all cows were divided into 3 subgroups based on their health status and the status of the health alerts in order to retrospectively compare the course of rumination time. Most health alerts (87%, n = 217) occurred on DIM 1. For the confirmation of diagnoses, health alerts showed SE and SP levels of 71% and 47% for CON cows. In SEN cows, SE of 71% and 75% and SP of 48% and 43% were found for the detection of ketosis and hypocalcemia, respectively. The rumination time of the subgroups was affected by DIM and the interaction between DIM and the status of health alert and health condition. [ABSTRACT FROM AUTHOR]

Purpose: Mental health is critical for a healthy pregnancy, yet few studies have evaluated its associations with best practice, objectively measured moderate- to vigorous-intensity physical activity (MVPA) or sedentary behavior (SED). This study evaluated associations of MVPA and SED with mental health across pregnancy. Materials and Methods: Two cohort studies (total n = 125, mean [standard deviation] 31 [5] years, and 14.4% Black) measured MVPA (waist-worn ActiGraph GT3X) and SED (thigh-mounted activPAL) as well as self-reported depressive symptoms and mood disturbance in each trimester. Associations of group-based trajectories of MVPA and SED with depressive symptoms and mood disturbance were analyzed using regression analyses, both overall and by trimester. Results: Overall, the medium versus low trajectory of MVPA was associated with lower levels of depressive symptoms (B = −1.82, 95% confidence interval [CI] −2.97 to −0.68). In the second trimester, women in either the medium or high MVPA trajectories had lower levels of depressive symptoms compared with women in the low MVPA trajectory (B = −8.73, 95% CI −15.74 to −1.71; and B = −2.18, 95% CI −3.80 to −0.56). SED trajectories were not associated with depressive symptoms. Higher trajectories of MVPA and lower trajectories of SED were associated with lower total mood disturbance, with significant associations in the second trimester for MVPA and the first and second trimesters for SED. Higher MVPA trajectories were associated with higher tension, fatigue, and confusion subscales, while higher SED trajectories were associated with higher anger and fatigue and lower esteem and vigor subscales. Conclusions: MVPA and SED levels appear to affect mental health during pregnancy, although larger prospective studies are warranted. Clinical Trail Registration Number: NCT03084302. [ABSTRACT FROM AUTHOR]

Motor neuron disease (MND) is a rapidly progressive condition traditionally assessed using a questionnaire to evaluate physical function, the revised amyotrophic lateral sclerosis functional rating scale (ALSFRS-R). Its use can be associated with poor sensitivity in detecting subtle changes over time and there is an urgent need for more sensitive and specific outcome measures. The ActiGraph GT9X is a wearable device containing multiple sensors that can be used to provide metrics that represent physical activity. The primary aim of this study was to investigate the initial suitability and acceptability of limb-worn wearable devices to group of people with MND in Scotland. A secondary aim was to explore the preliminary associations between the accelerometer sensor data within the ActiGraph GT9X and established measures of physical function. 10 participants with MND completed a 12-week schedule of assessments including fortnightly study visits, both in-person and over videoconferencing software. Participants wore the device on their right wrist and right ankle for a series of movements, during a 6-min walking test and for a period of 24-h wear, including overnight. Participants also completed an ALSFRS-R and questionnaires on their experience with the devices. 80% of the participants found wearing these devices to be a positive experience and no one reported interference with daily living or added burden. However, 30% of the participants experienced technical issues with their devices. Data from the wearable devices correlated with established measures of physical function. [ABSTRACT FROM AUTHOR]

Introduction: Physical activity is known to positively influence cognitive performance. For adults with mild cognitive impairment (MCI), the relationship between physical activity levels and cognitive performance is unknown. This cross‐sectional study aimed to determine if cognitive performance [as measured by the Montreal Cognitive Assessment (MoCA)] of people living in the community with MCI is associated with their physical activity levels or sedentary behaviour. Methods: ActivPAL™ accelerometers were used to objectively measure physical activity and sedentary behaviour for seven full days. Cognitive performance was measured using the MoCA. Consumer and Community Involvement: No involvement other than as research participants Results: Eighty‐two participants from the Balance on the Brain randomised controlled trial were included. Most participants were retired (88%), with 33 (40%) reporting a fall in the last year. The median MoCA score was 24 (IQR 22–26). Participants achieved a mean of 6296 (±2420) steps per day and were sedentary for 10.6 (±2) hours per day. The only physical activity outcomes that had a fair, positive correlation were moderate‐ to vigorous‐intensity physical activity measures of total stepping time and total number of steps (with a cadence of ≥100 steps/min) with the orientation MoCA domain score (r(82) = 0.36, p ≤ 0.001 and r(82) = 0.37, p ≤ 0.001, respectively). Higher total sedentary time had a weak, positive correlation with better visuospatial/executive performance (r(82) = 0.23, p = 0.041). The orientation outcomes remained significant when analysed in an adjusted logistic regression model. Conclusion: This study found that performance in the MoCA orientation domain had a fair‐positive correlation with moderate‐intensity physical activity (i.e., stepping time and step count with a cadence of ≥100 steps/min) as measured by a thigh‐worn accelerometer for community‐dwelling older adults with MCI. When considering the relationship between cognitive domains and sedentary behaviour, consideration may be needed regarding whether cognitive enhancing activities (such as crosswords and other brain games) are being performed, which may confound this relationship. Further investigation is required to confirm these results. PLAIN LANGUAGE SUMMARY: Physical activity is good for people of all ages. Regular moderate to vigorous physical activity is known to help memory and thinking skills. Moderate physical activity includes brisk walking, lawn mowing and water aerobics. Vigorous physical activity includes running, fast‐paced swimming, cycling and brisk walking up hills and steps. For adults with mild cognitive impairment (i.e., a person who is starting to have trouble with their memory, but the difficulty is not yet advanced enough to be diagnosed as dementia), we did not know how their level of engagement with physical activity may be related to their memory and thinking skills. A strength of this study is that we had a large population of 82 people living in the community with mild cognitive impairment to review. We found that people who completed more moderate to vigorous physical activity had better skills in remembering the current day, date and place (orientation). [ABSTRACT FROM AUTHOR]

Cadence thresholds have been widely used to categorize physical activity intensity in health-related research. We examined the convergent validity of two cadence-based intensity classification approaches against a machine-learning-based intensity schema in 84,315 participants (≥ 40 years) with wrist-worn accelerometers. Validity study. Both cadence-based methods (one-level cadence, two-level cadence) calculated intensity-specific time based on cadence-thresholds while the two-level cadence identified stepping behaviors first. We used an overlapping plot, mean absolute error, and Spearman's correlation coefficient to examine agreements between the cadence-based and machine-learning methods. We also evaluated agreements between methods based on practically-important-difference (moderate-to-vigorous-physical activity: ± 20 min/day, moderate-physical activity: ± 15, vigorous-physical activity: ± 2.5, light-physical activity: ± 30). The group-level (median) minutes of moderate-to-vigorous- and moderate-physical activity estimated by one-level cadence were within the range of practically-important-difference compared to the machine-learning method (bias of median: moderate-to-vigorous-physical activity, − 3.5, interquartile range [− 15.8, 12.2]; moderate-physical activity, − 6.0 [− 17.2, 4.1]). The group-level vigorous- and light-physical activity minutes derived by two-level cadence were within practically-important-difference range (vigorous-physical activity: − 0.9 [− 3.1, 0.5]; light-physical activity, − 1.3 [− 28.2, 28.9]). The individual-level differences between the cadence-based and machine learning methods were high across intensities (e.g., moderate-to-vigorous-physical activity: mean absolute error [one-level cadence: 24.2 min/day; two-level cadence: 26.2]), with the proportion of participants within the practically-important-difference ranging from 8.4 % to 61.6 %. One-level cadence showed acceptable group-level estimates of moderate-to-vigorous and moderate-physical activity while two-level cadence showed acceptable group-level estimates of vigorous- and light-physical activity. The cadence-based methods might not be appropriate for individual-level intensity-specific time estimation. [ABSTRACT FROM AUTHOR]

Individuals with chronic pain due to knee osteoarthritis (OA) are insufficiently physically active, and alterations of facilitatory and inhibitory nociceptive signaling are common in this population. Our objective was to examine the association of these alterations in nociceptive signaling with objective accelerometer-based measures of physical activity in a large observational cohort. We used data from the Multicenter Osteoarthritis Study. Measures of peripheral and central pain sensitivity included pressure pain threshold at the knee and mechanical temporal summation at the wrist, respectively. The presence of descending pain inhibition was assessed by conditioned pain modulation (CPM). Physical activity was quantitatively assessed over 7 days using a lower back-worn activity monitor. Summary metrics included steps/day, activity intensity, and sedentary time. Linear regression analyses were used to evaluate the association of pain sensitivity and the presence of descending pain inhibition with physical activity measures. Data from 1873 participants was analyzed (55.9% female, age = 62.8 ± 10.0 years). People having greater peripheral and central sensitivity showed lower step counts. CPM was not significantly related to any of the physical activity measures, and none of the exposures were significantly related to sedentary time. In this cohort, greater peripheral and central sensitivity were associated with reduced levels of objectively-assessed daily step counts. Further research may investigate ways to modify or treat heightened pain sensitivity as a means to increase physical activity in older adults with knee OA. [ABSTRACT FROM AUTHOR]

Energy deficiency (ED) and psychological stress affect athlete health. Given the emerging capabilities of wearable technology, the purpose of this study was to explore associations of wearable technology metrics in relation to lab-based measures of ED and psychological stress. We investigated the associations between (a) wearable-derived heart rate variability (HRV), resting heart rate (RHR), exercise strain and recovery, and (b) laboratory-derived measures of metabolism (resting metabolic rate (RMR), total triiodothyronine (TT3)) and a validated stress assessment (Recovery-Stress Questionnaire for Athletes (RESTQ)-52 items) in NCAA swimmers (n = 23, 10 male, 13 female) during heavy training. Swimmers were grouped by degree of metabolic adaption to ED using the ratio of actual-to-predicted RMR (utilizing the validated cutoff of <0.94) and by sex. Metabolically suppressed swimmers had lower HRV (81 ± 27 ms vs.110 ± 35 ms, p = 0.04). HRV correlated positively with RMR (kcal·kg LBM−1·day−1; where LBM stands for lean body mass) (r = 0.45; p = 0.03). HRV was negatively correlated with sport-specific (r = −0.46; p = 0.03) and total stress (r = −0.46; p = 0.03). In males, HRV correlated negatively with general stress (r = −0.72; p = 0.02) and total stress (r = −0.74, p = 0.01) (RESTQ). Additionally, in males only, the strain was correlated positively with RESTQ recovery–stress balance (r = 0.69; p = 0.03), and negatively correlated with general stress (r = −0.81, p = 0.01), and sport stress (r = −0.89, p < 0.01). No correlations between HRV, RHR, strain or recovery, and stress variables were observed in females. Associations between wearable technology measures of HRV, RHR, strain, and recovery with validated measures of ED and psychological stress should continue to be explored with a focus on underlying mechanisms and moderating influences of biological sex. [ABSTRACT FROM AUTHOR]

Today, the common solutions for underwater source angle detection require manned vessels and towed arrays, which are associated with high costs, risks, and deployment difficulties. An alternative solution for such applications is represented by acoustic vector sensors (AVSs), which are compact, lightweight and moderate in cost, and which have promising performance in terms of the bearing discrimination in two or three dimensions. One of the most popular devices for passive monitoring in underwater surveillance systems that employ AVSs is the directional frequency analysis and recording (DIFAR) sonobuoy. In this paper, direct data-processing (DDP) algorithms are implemented to calculate the azimuth angle of underwater acoustic sources by using short-time Fourier transform (STFT) via the arctan method instead of using fast Fourier transform (FFT). These algorithms for bearing estimation use the 'Azigram' to plot the estimated bearing of a source. It is demonstrated that by knowing the active sound intensity of the sound field and applying the inverse tangent to its real part, this matrix can be obtained. Announcing the time and frequency of the source simultaneously is one of the main advantages of this method, enabling the detection of multiple sources concurrently. DDP can also provide more details about sources' characteristics, such as the frequency of the source and the time of the source's presence. [ABSTRACT FROM AUTHOR]

Background: The more accurate we can assess human physical behaviour in free-living conditions the better we can understand its relationship with health and wellbeing. Thigh-worn accelerometry can be used to identify basic activity types as well as different postures with high accuracy. User-friendly software without the need for specialized programming may support the adoption of this method. This study aims to evaluate the classification accuracy of two novel no-code classification methods, namely SENS motion and ActiPASS. Methods: A sample of 38 healthy adults (30.8 ± 9.6 years; 53% female) wore the SENS motion accelerometer (12.5 Hz; ±4 g) on their thigh during various physical activities. Participants completed standardized activities with varying intensities in the laboratory. Activities included walking, running, cycling, sitting, standing, and lying down. Subsequently, participants performed unrestricted free-living activities outside of the laboratory while being video-recorded with a chest-mounted camera. Videos were annotated using a predefined labelling scheme and annotations served as a reference for the free-living condition. Classification output from the SENS motion software and ActiPASS software was compared to reference labels. Results: A total of 63.6 h of activity data were analysed. We observed a high level of agreement between the two classification algorithms and their respective references in both conditions. In the free-living condition, Cohen's kappa coefficients were 0.86 for SENS and 0.92 for ActiPASS. The mean balanced accuracy ranged from 0.81 (cycling) to 0.99 (running) for SENS and from 0.92 (walking) to 0.99 (sedentary) for ActiPASS across all activity types. Conclusions: The study shows that two available no-code classification methods can be used to accurately identify basic physical activity types and postures. Our results highlight the accuracy of both methods based on relatively low sampling frequency data. The classification methods showed differences in performance, with lower sensitivity observed in free-living cycling (SENS) and slow treadmill walking (ActiPASS). Both methods use different sets of activity classes with varying definitions, which may explain the observed differences. Our results support the use of the SENS motion system and both no-code classification methods. [ABSTRACT FROM AUTHOR]

Background: There are now many different types of activity trackers, including pedometers and accelerometers, to estimate step counts per day. Previous research has extensively examined step-count measurements using activity trackers across various settings while simultaneously wearing different devices.; however, older adults frequently display distinct walking patterns and gait speeds compared to younger adults. This study aimed to compare the step-count between older and younger adults by having them simultaneously wear seven different activity trackers in free-living experiments. Methods: This study included 35 younger adults (21–43 yrs) and 57 physically independent older adults (65–91 yrs). All participants simultaneously wore one pedometer and six activity trackers: ActiGraph GT3X + Wrist and Hip, Omron Active Style Pro HJA-350IT, Panasonic Actimarker, TANITA EZ-064, Yamasa TH-300, and Yamasa AS-200 for seven days. A regression equation was also used to assess inter-device compatibility. Results: When comparing wrist-worn ActiGraph to the six hip-worn activity trackers, the wrist-worn ActiGraph consistently recorded step counts over 4,000 steps higher than hip-worn activity trackers in both groups (range, 3000–5000 steps). Moreover, when comparing the ActiGraph worn on the wrist to that worn on the hip, the proportion was higher among older adults compared to younger ones (younger: 131%, older: 180%). The Actimarker recorded the highest average step counts among six hip-worn devices, with 8,569 ± 4,881 overall, 9,624 ± 5,177 for younger adults, and 7,890 ± 4,562 for older adults. The difference between the hip-worn ActiGraph and Active Style Pro was just about 70 steps/day overall. The correlation among all devices demonstrated a very high consistency, except for the wrist-worn ActiGraph (r = 0.874–0.978). Conclusions: Step counts recorded from seven selected consumer-based and research-grade activity trackers and one pedometer, except for the wrist-worn ActiGraph. showed a variation of approximately 1700 steps (range, 1265–2275 steps) steps for both groups, yet maintained a high correlation with each other. These findings will be valuable for researchers and clinicians as they compare step counts across different studies or representative surveys conducted globally. [ABSTRACT FROM AUTHOR]

Purpose: Devices such as the StepWatch Activity Monitor (SAM) have been available for 20 years and have been shown to accurately measure ambulatory activity. This study aimed to evaluate the agreement among the three generations of the StepWatch Activity Monitor (SW3, SW4, and SW5) with respect to stride count. Methods: A total of 36 participants (age range, 6-55 years) participated in this institutional review board-approved study. The participants concurrently wore three different SAM model devices on the same leg and performed a 6-min walk test (6MWT). A research staff member of the laboratory manually counted the number of strides for the first 2 min of the test (2MWT). Agreement among the device models was evaluated by calculating ANOVAs and interclass correlation coefficients (ICCs) and creating Bland-Altman plots. Results: There was no significant difference among the model versions during the 6MWT and 2MWT (p > 0.05). The ICC for the total stride count was 0.993 (95% CI = 0.988-0.996) during the 2MWT and 0.992 (95% CI = 0.986-0.996) during the 6MWT. There was a near-perfect agreement (ICC = 0.990) of each model version to the manually counted strides during the 2MWT. The systematic bias of all three SAM model versions was <1 step. Conclusions: The results from the present study demonstrate that the stride counts among all three devices are comparable and relative to the manual stride count. All three SAM model versions had an ICC of >0.90. Researchers can safely incorporate historical data from previous SAM model versions with newer data collected with the latest SAM model version. [ABSTRACT FROM AUTHOR]

Classifying behavior by tracking acceleration has received increased interest lately. Here, we evaluated the performance of three commercial activity trackers in differentiating seven dog behaviors. Adult companion dogs (N = 70) performed still (lying, sitting, standing) and dynamic (walking, sniffing, trotting, playing) tasks, while wearing ActiGraph GT9X Link, Kaunila and FitBark devices placed on the neck collar and ActiGraph GT9X Link placed on the back. Each task was performed for 3 min within a session and repeated in two sessions; the behaviors were confirmed from video recordings. Activity scores of devices were calculated as median values for behavioral differentiation, and as minute-based values for inter-device correlations and cutoff analysis. Measurements of all devices correlated with each other, and median activity scores of all devices − unaffected by dog age, weight or sex − differentiated the still from dynamic behaviors. Dynamic behaviors were also differentiated from each other, with exception of walking vs. sniffing by back-placed ActiGraph GT9X and Kaunila. The definition of cutoffs between behaviors varied from moderate to high accuracy; defined cutoffs for standing and walking were the least accurate. The classification performance of the cutoffs had an accuracy of 80% in all the devices; thus, they performed reasonably well in classifying these behaviors. [ABSTRACT FROM AUTHOR]

Purpose: The goals of this study were: (1) to assess students’ physical activity levels in a sport education season according to the phases of the model, adjusted for body mass index; (2) to uncover the contribution of the phases of the model to the World Health Organization recommendations; (3) to understand its impact in students with overweight/obesity. Methods: A total of 42 primary education students with a mean age of 10.68 ± 0.69 years, enrolled in one year 5 (10–11 years) and one year 6 (11–12 years). Physical Education lessons of the same school agreed to participate. Students in both groups experienced a 14-lesson learning unit in team handball through the sport education. Each participant wore an ActiGraph GT3X accelerometer to measure physical activity levels, counts/minute and steps counted. Cole and Lobstein cutoff points were used to determine groups based on body mass index (thin, normal-weight, overweight/obese). Results: The results showed that the overweight/obese group achieved scores for moderate to vigorous physical activity and sedentary time that were not significantly different from the normal-weight group. According to the phases, the preseason was the most active and contributed most to the World Health Organization recommendations in all groups. Conclusions: Sport education could be considered an appropriate pedagogical framework to help overweight/obese children achieve moderate-to-vigorous physical activity and sedentary scores similar to their normal-weight classmates. Key elements of sport education include performing roles, learning work routines, and having the autonomy to choose games/tasks, all of which promote physical activity amongst all students. [ABSTRACT FROM AUTHOR]

Introduction: Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder where hyperactivity often manifests as fidgeting, a non-goal-directed motoric action. Many studies demonstrate fidgeting varies under different conditions as a self-regulating mechanism for attention and alertness during cognitively demanding tasks. Fidgeting has also been associated with reaction time variability. However, a lack of standard variables to define and quantify fidgeting can lead to discrepancies in data and interpretability issues across studies. Furthermore, little is known about fidgeting in adults with ADHD compared to youth. This study aims to design a framework to quantify meaningful fidgeting variables and to apply them to test the relation between fidgeting and performance on a cognitive task, the Flanker, in adults with ADHD. Method: Our study included 70 adult participants diagnosed with ADHD, aged 18-50 years (30.5 ± 7.2 years). Screening included a structured clinical interview, childhood, current self and current observer ratings of ADHD symptoms. Actigraphy devices were attached to the left wrist and right ankle during completion of a cognitive control, attention task (the Flanker). Laboratory testing was subsequently completed on a single day. The relation between task performance, reaction time variability and fidgeting was examined. Results and Discussion: Our analysis revealed increased fidgeting during correct trials as defined by our new variables, consistent with previous observations. Furthermore, differences in fidgeting were observed between early and later trials while the percentage of correct trials were not significantly different. This suggests a relation between the role of fidgeting and sustaining attention. Participants with low reaction time variability, that is, those with more consistent reaction times, fidgeted more during later trials. This observation supports the theory that fidgeting aids arousal and improves sustained attention. Finally, a correlation analysis using ADHD-symptom rating scales validated the relevance of the fidget variables in relation to ADHD symptom severity. These findings suggest fidgeting may be a compensatory mechanism that aids in sustained attention for those with ADHD, although alternative explanations exist. Conclusion: Our study suggests that fidgeting may aid in sustained attention during the attention-demanding, cognitive control processes for adults with ADHD, with more fidgeting observed during correct trials and among participants with lower reaction time variability. Furthermore, the newly defined fidget variables were validated through a significant correlation with ADHD rating scales. By sharing our implementation of fidget variables, we hope to standardize and encourage further quantitative research into the role of fidgeting in ADHD. [ABSTRACT FROM AUTHOR]

Introduction: Physical activity, sedentary behavior, and sleep, that is, 24-h movement behaviors, often change in the transition from work to retirement, which may affect cardiometabolic health. This study investigates the longitudinal associations between changes in 24-h movement behaviors and cardiometabolic biomarkers during the retirement transition. Methods: Retiring public sector workers (n = 212; mean (SD) age, 63.5 (1.1) yr) from the Finnish Retirement and Aging study used a thigh-worn Axivity accelerometer and filled out a diary to obtain data on daily time spent in sedentary behavior (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA), and sleep before and after retirement (1 yr in-between). Cardiometabolic biomarkers, including LDL-cholesterol, HDL-cholesterol, total/HDL-cholesterol ratio, triglycerides, C-reactive protein, fasting glucose, and insulin, were measured. Associations between changes in 24-h movement behaviors and cardiometabolic biomarkers were analyzed using compositional robust regression and isotemporal substitution analysis. Results: Increasing LPA in relation to remaining behaviors was associated with an increase in HDL-cholesterol and decrease in total/HDL-cholesterol ratio ( P < 0.05 for both). For instance, reallocation of 30 min from sleep/SED to LPA was associated with an increase in HDL-cholesterol by 0.02 mmol⋅L-1. Moreover, increasing MVPA in relation to remaining behaviors was associated with a decrease in triglycerides ( P = 0.02). Reallocation of 30 min from SED/sleep to MVPA was associated with 0.07-0.08 mmol⋅L-1 decrease in triglycerides. Findings related to LDL-cholesterol, C-reactive protein, fasting glucose, and insulin were less conclusive. Conclusions: During the transition from work to retirement, increasing physical activity at the expense of passive behaviors was associated with a better lipid profile. Our findings suggest that life transitions like retirement could be utilized more as an optimal time window for promoting physical activity and health. [ABSTRACT FROM AUTHOR]